We learn new words almost on a daily basis: as adults, a new element is introduced in our vocabulary every other day. With new words, we also learn about new objects and ideas - in most cases new words are not simply additional labels to be applied to familiar objects: they connote meanings that are unknown to the speaker of a language. However, when we experience, as adults, an unfamiliar word, typically its referent is not immediately available in the same context. How then can language, by itself, constitute such a reliable instrument for the acquisition of novel meanings? What do we exploit to induce new meanings on the basis of an unfamiliar sequence of sounds or graphical elements? BraveNewWord addresses these questions in an innovative multidisciplinary perspective, combining cutting-edge proposals from computational linguistics and empirical investigation techniques from experimental psychology and cognitive neuroscience. BraveNewWord posits three main sources for lexically-driven meaning acquisition: linguistic context, word structure, form-meaning mapping. The project advances a computational framework that models these mechanisms through data-driven, psychologically plausible distributional systems trained on examples of natural language usage. The quantitative characterizations and algorithmic definitions offered by these models constitute, in turn, the basis for BraveNewWord large-scale empirical investigation, involving both behavioral (reaction times, mouse-tracking trajectories, diachronic language changes) and neuroscience data (event-related potentials, neuroimaging). With its innovative perspective and advanced computational and empirical approach, BraveNewWord will constitute a non-incremental contribution to understanding how human speakers use new lexical information as a mean for enriching their semantic system, and provide a ground-breaking perspective on the cognitive processes relating language and thought.
Massive black hole binaries (MBHBs) are the most extreme, fascinating yet elusive astrophysical objects in the Universe. Establishing observationally their existence will be a milestone for contemporary astronomy, providing a fundamental missing piece in the puzzle of galaxy formation, piercing through the (hydro)dynamical physical processes shaping dense galactic nuclei from parsec scales down to the event horizon, and probing gravity in extreme conditions. We can both see and listen to MBHBs. Remarkably, besides arguably being among the brightest variable objects shining in the Cosmos, MBHBs are also the loudest gravitational wave (GW) sources in the Universe. As such, we shall take advantage of both the type of messengers – photons and gravitons – they are sending to us, which can now be probed by all-sky time-domain surveys and radio pulsar timing arrays (PTAs) respectively. B MASSIVE leverages on a unique comprehensive approach combining theoretical astrophysics, radio and gravitational-wave astronomy and time-domain surveys, with state of the art data analysis techniques to: i) observationally prove the existence of MBHBs, ii) understand and constrain their astrophysics and dynamics, iii) enable and bring closer in time the direct detection of GWs with PTA. As European PTA (EPTA) executive committee member and former I International PTA (IPTA) chair, I am a driving force in the development of pulsar timing science world-wide, and the project will build on the profound knowledge, broad vision and wide collaboration network that established me as a world leader in the field of MBHB and GW astrophysics. B MASSIVE is extremely timely; a pulsar timing data set of unprecedented quality is being assembled by EPTA/IPTA, and Time-Domain astronomy surveys are at their dawn. In the long term, B MASSIVE will be a fundamental milestone establishing European leadership in the cutting-edge field of MBHB astrophysics in the era of LSST, SKA and LISA.
Our cosmological model predicts that most of the matter in the universe is distributed in a network of filaments - the Cosmic Web - in which galaxies form and evolve. Because most of this material is too diffuse to form stars, its direct imaging has remained elusive for several decades leaving fundamental questions still open, including: what are the morphological and kinematical properties of the Cosmic Web on both small (kpc) and large (Mpc) scales? How do galaxies get their gas from the Cosmic Web? In this programme, I will tackle these questions with an innovative method and technology that allows us to directly detect in emission the gaseous Cosmic Web before the peak of galaxy formation, when the universe is less than 3 billion years old: using bright quasars and galaxies as “cosmic flashlights” to make the gas “fluorescently” glow. Although challenging, detecting such emission is possible: I have recently demonstrated that some parts of the Cosmic Web illuminated by bright quasars can be detected in both hydrogen Lyman-alpha and H-alpha emission. These pilot studies and new instruments such as VLT/MUSE and the James Webb Space Telescope (JWST; available from 2021) are the ideal stepping stones for a revolution in the field, the main goals of this programme: 1) direct imaging of the average Cosmic Web extending on cosmological scales (tens of Mpc) in the young universe, away from quasars; 2) revealing the small-scale distribution (below one kpc) of gas within Cosmic Web filaments. For this aim, I will use the deepest available observations to date, including a 160-hours deep integration that is being obtained through our MUSE Guaranteed Time of Observations, and future ground-based Adaptive-Optics and JWST infrared H-alpha observations. These datasets will be combined with new data analysis methods and numerical models that will be specifically developed in this programme opening up a completely new window to study cosmic structure and galaxy formation.
Verifying the correctness of software systems requires extensive and expensive testing sessions. While there are tools and methodologies to efficiently address unit and integration testing, system testing is still largely the result of manual effort. Testing software applications at the system level requires executing the applications through their interfaces to verify the correctness of their functionalities and stimulate all their layers and components. Automating just part of this process can dramatically improve the effectiveness of verification activities and significantly reduce development costs, relevantly alleviating developers from their verification effort. This project addresses the development of a pre-commercial tool that has the unique capability of efficiently and automatically generating semantically-relevant system test cases equipped with functional oracles. This capability derives from the AUGUSTO technique, which is an outcome of the Learn ERC project. The idea behind Augusto is to exploit the common-sense knowledge, that is, the background knowledge that every computer user has and that normally lets her/him use software applications without the need of accessing any documentation or manual. Once this knowledge is represented abstractly and then embedded in AUGUSTO, the technique can automatically adapt its definition to the software under test every time a program is tested. This development work will be performed jointly with A company that produces and markets testing tools.