Contextual Realization of the Universal Quantum Cloning Machine and of the Universal-NOT gate by Quantum Injected Optical Parametric Amplification

A simultaneous, contextual experimental demonstration of the two processes of cloning an input qubit and of flipping it into the orthogonal qubit is reported. The adopted experimental apparatus, a Quantum-Injected Optical Parametric Amplifier (QIOPA) is transformed simultaneously into a Universal Optimal Quantum Cloning Machine (UOQCM) and into a Universal NOT quantum-information gate. The two processes, indeed forbidden in their exact form for fundamental quantum limitations, will be found to be universal and optimal, i.e. the measured fidelity of both processes F<1 will be found close to the limit values evaluated by quantum theory. A contextual theoretical and experimental investigation of these processes, which may represent the basic difference between the classical and the quantum worlds, can reveal in a unifying manner the detailed structure of quantum information. It may also enlighten the yet little explored interconnections of fundamental axiomatic properties within the deep structure of quantum mechanics. PACS numbers: 03.67.-a, 03.65.Ta, 03.65.UdComment: 27 pages, 7 figure

GUIDER: a GUI for semiautomatic, physiologically driven EEG feature selection for a rehabilitation BCI

GUIDER is a graphical user interface developed in MATLAB software environment to identify electroencephalography (EEG)-based brain computer interface (BCI) control features for a rehabilitation application (i.e. post-stroke motor imagery training). In this context, GUIDER aims to combine physiological and machine learning approaches. Indeed, GUIDER allows therapists to set parameters and constraints according to the rehabilitation principles (e.g. affected hemisphere, sensorimotor relevant frequencies) and foresees an automatic method to select the features among the defined subset. As a proof of concept, we compared offline performances between manual, just based on operator’s expertise and experience, and GUIDER semiautomatic features selection on BCI data collected from stroke patients during BCI-supported motor imagery training. Preliminary results suggest that this semiautomatic approach could be successfully applied to support the human selection reducing operator dependent variability in view of future multi-centric clinical trials

Optimizing single-photon-source heralding efficiency at 1550 nm using periodically poled lithium niobate

We explore the feasibility of using high conversion-efficiency periodically-poled crystals to produce photon pairs for photon-counting detector calibrations at 1550 nm. The goal is the development of an appropriate parametric down-conversion (PDC) source at telecom wavelengths meeting the requirements of high-efficiency pair production and collection in single spectral and spatial modes (single-mode fibers). We propose a protocol to optimize the photon collection, noise levels and the uncertainty evaluation. This study ties together the results of our efforts to model the single-mode heralding efficiency of a two-photon PDC source and to estimate the heralding uncertainty of such a source.Comment: 14 pages, 2 tables and 3 figures, final version accepted by Metrologi

Avalanche Photo-Detection for High Data Rate Applications

Avalanche photo detection is commonly used in applications which require single photon sensitivity. We examine the limits of using avalanche photo diodes (APD) for characterising photon statistics at high data rates. To identify the regime of linear APD operation we employ a ps-pulsed diode laser with variable repetition rates between 0.5MHz and 80MHz. We modify the mean optical power of the coherent pulses by applying different levels of well-calibrated attenuation. The linearity at high repetition rates is limited by the APD dead time and a non-linear response arises at higher photon-numbers due to multiphoton events. Assuming Poissonian input light statistics we ascertain the effective mean photon-number of the incident light with high accuracy. Time multiplexed detectors (TMD) allow to accomplish photon- number resolution by photon chopping. This detection setup extends the linear response function to higher photon-numbers and statistical methods may be used to compensate for non-linearity. We investigated this effect, compare it to the single APD case and show the validity of the convolution treatment in the TMD data analysis.Comment: 16 pages, 5 figure

Has VZV epidemiology changed in Italy? Results of a seroprevalence study

The aim of the study was to evaluate if and how varicella prevalence has changed in Italy. In particular a seroprevalence study was performed, comparing it to similar surveys conducted in pre-immunization era. During 2013–2014, sera obtained from blood samples taken for diagnostic purposes or routine investigations were collected in collaboration with at least one laboratory/center for each region, following the approval of the Ethics Committee. Data were stratified by sex and age. All samples were processed in a national reference laboratory by an immunoassay with high sensitivity and specificity. Statutory notifications, national hospital discharge database and mortality data related to VZV infection were analyzed as well. A total of 3707 sera were collected and tested. In the studied period both incidence and hospitalization rates decreased and about 5 deaths per year have been registered. The seroprevalence decreased in the first year of life in subjects passively protected by their mother, followed by an increase in the following age classes. The overall antibody prevalence was 84%. The comparison with surveys conducted with the same methodology in 1996–1997 and 2003–2004 showed significant differences in age groups 1–19&nbsp;y. The study confirms that in Italy VZV infection typically occurs in children. The impact of varicella on Italian population is changing. The comparison between studies performed in different periods shows a significant increase of seropositivity in age class 1–4&nbsp;years, expression of vaccine interventions already adopted in some regions

Implementing a GIS-Based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint: Part II, an Inductive Approach.

Plastic pollution, largely perceived by the public as a major risk factor that strongly impacts sea life and preservation, has an even higher negative impact on terrestrial ecosystems. Indeed, quantitative data about plastic contamination on agricultural soils are progressively emerging in alarming ways. One of the main contributors to this pollution involves the mismanagement of agricultural plastic waste (APW), i.e., the residues from plastic material used to improve the productivity of agricultural crops, such as greenhouse covers, mulching films, irrigation pipes, etc. Wrong management of agricultural plastics during and after their working lives may pollute the agricultural soil and aquifers by releasing macro-, micro-, and nanoplastics, which could also enter into the human food chain. In this study, we aimed to develop a methodology for the spatial quantification of agricultural plastics to achieve sustainable post-consumer management. Through an inductive approach, based on statistical data from the agricultural census of the administrative areas of the Italian provinces, an agricultural plastic coefficient (APC) was proposed, implemented, and spatialized in a GIS environment, to produce a database of APW for each type of crop. The proposed methodology can be exported to other countries. It represents valuable support that could realize, in integration with other tools, an atlas of agricultural plastics, which may be a starting point to plan strategies and actions targeted to the reduction of the plastic footprint of agriculture

Implementing a GIS-based Digital Atlas of Agricultural Plastics to Reduce Their Environmental Footprint. Part I: A Deductive Approach.

The agricultural sector has benefitted over the last century from several factors that have led to an exponential increase in its productive efficiency. The increasing use of new materials, such as plastics, has been one of the most important factors, as they have allowed for increased production in a simpler and more economical way. Various polymer types are used in different phases of the agricultural production cycle, but when their use is incorrectly managed, it can lead to different environmental impacts. In this study, an applied and simplified methodology to manage agricultural plastics monitoring and planning is proposed. The techniques used are based on quantification through the use of different datasets (orthophotos and satellite images) of the areas covered by plastics used for crop protection. The study area chosen is a part of the Ionian Coast of Southern Italy, which includes the most important municipalities of the Basilicata Region for fruit and vegetable production. The use of geographical techniques and observation methodologies, developed in an open‐source GIS environment, enabled accurate location of about 2000 hectares of agricultural land covered by plastics, as well as identification of the areas most susceptible to the accumulation of plastic waste. The techniques and the model implemented, due to its simplicity of use and reliability, can be applied by different local authorities in order to realize an Atlas of agricultural plastics, which would be applied for continuous monitoring, thereby enabling the upscaling of future social and ecological impact assessments, identification of new policy impacts, market searches, etc

Synthetic Data Pretraining for Hyperspectral Image Super-Resolution

Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural designs. However, the use of self-supervised pretraining lags behind in several domains, such as hyperspectral images, due to data scarcity. This paper addresses the challenge of data scarcity in the development of methods for spatial super-resolution of hyperspectral images (HSI-SR). We show that state-of-the-art HSI-SR methods are severely bottlenecked by the small paired datasets that are publicly available, also leading to unreliable assessment of the architectural merits of the models. We propose to capitalize on the abundance of high resolution (HR) RGB images to develop a self-supervised pretraining approach that significantly improves the quality of HSI-SR models. In particular, we leverage advances in spectral reconstruction methods to create a vast dataset with high spatial resolution and plausible spectra from RGB images, to be used for pretraining HSI-SR methods. Experimental results, conducted across multiple datasets, report large gains for state-of-the-art HSI-SR methods when pretrained according to the proposed procedure, and also highlight the unreliability of ranking methods when training on small datasets

MSR32 COVID-19 Beds’ Occupancy and Hospital Complaints: A Predictive Model

Objectives COVID-19 pandemic limited the number of patients that could be promptly and adequately taken in charge. The proposed research aims at predicting the number of patients requiring any type of hospitalizations, considering not only patients affected by COVID-19, but also other severe viral diseases, including untreated chronic and frail patients, and also oncological ones, to estimate potential hospital lawsuits and complaints. Methods An unsupervised learning approach of artificial neural network’s called Self-Organizing Maps (SOM), grounding on the prediction of the existence of specific clusters and useful to predict hospital behavioral changes, has been designed to forecast the hospital beds’ occupancy, using pre and post COVID-19 time-series, and supporting the prompt prediction of litigations and potential lawsuits, so that hospital managers and public institutions could perform an impacts’ analysis to decide whether to invest resources to increase or allocate differentially hospital beds and humans capacity. Data came from the UK National Health Service (NHS) statistic and digital portals, concerning a 4-year time horizon, related to 2 pre and 2 post COVID-19 years. Results Clusters revealed two principal behaviors in selecting the resources allocation. In case of increase of non-COVID hospitalized patients, a reduction in the number of complaints (-55%) emerged. A higher number of complaints was registered (+17%) against a considerable reduction in the number of beds occupied (-26%). Based on the above, the management of hospital beds is a crucial factor which can influence the complaints trend. Conclusions The model could significantly support in the management of hospital capacity, helping decision-makers in taking rational decisions under conditions of uncertainty. In addition, this model is highly replicable also in the estimation of current hospital beds, healthcare professionals, equipment, and other resources, extremely scarce during emergency or pandemic crises, being able to be adapted for different local and national settings