Measuring the complex orbital angular momentum spectrum and spatial mode decomposition of structured light beams

Light beams carrying orbital angular momentum are key resources in modern photonics. In many applications, the ability of measuring the complex spectrum of structured light beams in terms of these fundamental modes is crucial. Here we propose and experimentally validate a simple method that achieves this goal by digital analysis of the interference pattern formed by the light beam and a reference field. Our approach allows one to characterize the beam radial distribution also, hence retrieving the entire information contained in the optical field. Setup simplicity and reduced number of measurements could make this approach practical and convenient for the characterization of structured light fields.Comment: 8 pages (including Methods and References), 6 figure

Photon energy lifter

We propose a time-dependent photonic structure, in which the carrier frequency of an optical pulse is shifted without changing its shape. The efficiency of the device takes advantage of slow group velocities of light attainable in periodic photonic structures. The frequency shifting effect is quantitatively studied by means of Finite Difference Time Domain simulations for realistic systems with optical parameters of conventional silicon technology.Comment: 4 pages 5 figure

Stellar 3-D kinematics in the Draco dwarf spheroidal galaxy

Aims. We present the first three-dimensional internal motions for individual stars in the Draco dwarf spheroidal galaxy. Methods. By combining first-epoch $Hubble$ $Space$ $Telescope$ observations and second-epoch $Gaia$ Data Release 2 positions, we measured the proper motions of $149$ sources in the direction of Draco. We determined the line-of-sight velocities for a sub-sample of $81$ red giant branch stars using medium resolution spectra acquired with the DEIMOS spectrograph at the Keck II telescope. Altogether, this resulted in a final sample of $45$ Draco members with high-precision and accurate 3D motions, which we present as a table in this paper. Results. Based on this high-quality dataset, we determined the velocity dispersions at a projected distance of $\sim120$ pc from the centre of Draco to be $\sigma_{R} =11.0^{+2.1}_{-1.5}$ km/s, $\sigma_{T}=9.9^{+2.3}_{-3.1}$ km/s and $\sigma_{LOS}=9.0^{+1.1}_{-1.1}$ km/s in the projected radial, tangential, and line-of-sight directions. This results in a velocity anisotropy $\beta=0.25^{+0.47}_{-1.38}$ at $r \gtrsim120$ pc. Tighter constraints may be obtained using the spherical Jeans equations and assuming constant anisotropy and Navarro-Frenk-White (NFW) mass profiles, also based on the assumption that the 3D velocity dispersion should be lower than $\approx 1/3$ of the escape velocity of the system. In this case, we constrain the maximum circular velocity $V_{max}$ of Draco to be in the range of $10.2-17.0$ km/s. The corresponding mass range is in good agreement with previous estimates based on line-of-sight velocities only. Conclusions. Our Jeans modelling supports the case for a cuspy dark matter profile in this galaxy. Firmer conclusions may be drawn by applying more sophisticated models to this dataset and with new datasets from upcoming $Gaia$ releases.Comment: 12 pages, 15 figures, 3 tables. Accepted for publication by A&

Quantum-inspired Machine Learning on high-energy physics data

Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big data problem in high energy physics: the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from proton-proton collisions in the LHCb experiment, and how to interpret the classification results. We exploit the Tensor Network approach to select important features and adapt the network geometry based on information acquired in the learning process. Finally, we show how to adapt the tree tensor network to achieve optimal precision or fast response in time without the need of repeating the learning process. These results pave the way to the implementation of high-frequency real-time applications, a key ingredient needed among others for current and future LHCb event classification able to trigger events at the tens of MHz scale.Comment: 13 pages, 4 figure

Variable Definition and Independent Components

In the causal modelling literature, it is well known that “ill-defined” variables may give rise to “ambiguous manipulations” (Spirtes and Scheines, 2004). Here, we illustrate how ill-defined variables may also induce mistakes in causal inference when standard causal search methods are applied (Spirtes et al., 2000; Pearl, 2009). To address the problem, we introduce a representation framework, which exploits an independent component representation of the data, and demonstrate its potential for detecting ill-defined variables and avoiding mistaken causal inferences

Sentiment Analysis for Performance Evaluation of Maintenance in Healthcare

This paper presents a framework which makes use of Sentiment Analysis techniques for retrieving Real World Data (RWD) starting from scheduled and corrective maintenance data. The scope of the analysis is to automatically extract features from maintenance work orders, in order to calculate Key Performance Indicators of maintenance operations on medical devices, for Health Technologies Assessment purposes. Data are extracted from Computerized Maintenance Management System reports of healthcare facilities

Erase and Rewind: Manual Correction of NLP Output through a Web Interface

In this paper, we present Tintful, an NLP annotation software that can be used both to manually annotate texts and to fix mistakes in NLP pipelines, such as Stanford CoreNLP. Using a paradigm similar to wiki-like systems, a user who notices some wrong annotation can easily fix it and submit the resulting (and right) entry back to the tool developers. Moreover, Tintful can be used to easily annotate data from scratch. The input documents do not need to be in a particular format: starting from the plain text, the sentences are first annotated with CoreNLP, then the user can edit the annotations and submit everything back through a user-friendly interface