Quinary (Italy)

Single photon detectors based on superconducting nanostrips demonstrated high performance at 1550 nm, combining high efficiency and low noise. Arranged in arrays, these devices can implement photon-number resolution, expanding the range of applicability of superconducting detectors. A detector of this kind was used in our recent work to implement the annihilation operator and the characterization of quantum states in terms of their photon number distribution. The reconstructed distributions were consistent with the theoretical predictions, exhibiting fidelities higher than 99.9%. In this work, this procedure was repeated with each pixel singularly to define a detector fidelity matrix, evaluating the fidelity among all the possible pixel combinations. In this way, we introduce an additional tool that quantifies not only the quality of the reconstructed photon number distribution when compared to the theoretical expectation, but also the mutual fidelity when different pixels are chosen as independent, and then the fabrication uniformity.

The advancement of Superconducting Nanowire Single-Photon Detectors (SNSPDs) is enhancing and sustaining the validation of quantum technologies, particularly at telecom bands. In this work, a focus on the SNSPD properties and on their impact on some key applications is presented.

The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (HPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.

In large organizations the resources needed to solve challenging problems are typically dispersed over systems within and beyond the organization, and also in different media. However, there is still the need, in knowledge environments, for extraction methods able to combine evidence for a fact from across different media. In many cases the whole is more than the sum of its parts: only when considering the different media simultaneously can enough evidence be obtained to derive facts otherwise inaccessible to the knowledge worker via traditional methods that work on each single medium separately. In this paper, we present a cross-media knowledge extraction framework specifically designed to handle large volumes of documents composed of three types of media text, images and raw data and to exploit the evidence across the media. Our goal is to improve the quality and depth of automatically extracted knowledge.

A multilingual Internet-based employment advertisement system is described. Job ads are submitted as e-mail texts, analysed by an example-based pattern matcher and stored in language-independent schemas in an object-oriented database. Users can search the database in their own language and get customized summaries of the job ads. The query engine uses symbolic case-based reasoning techniques, while the generation module integrates canned text, templates, and grammar rules to produce texts and hypertexts in a simple way.