The spread of disinformation is a serious problem that impacts social structure and threatens democracies worldwide. Citizens increasingly rely on (dis)information available online, either somewhat passively, through social media feeds, or actively, by using search engines and specific websites. In both scenarios, algorithms filter and select displayed information according to the users’ past preferences. There is a real risk that algorithms might reinforce the user’s beliefs and create (dis)information bubbles, by offering less divergent views, or even directing them to low-credibility content. For these reasons, serious efforts have been made to identify and remove “fake-news” websites and minimize the spread of disinformation on social media, but we have not witnessed equivalent attempts to understand and curtail the role of search engines. FARE_AUDIT addresses this imbalance and offers an innovative tool to audit search engines that can be broadly used. It will help to 1) better understand how browsing history influences search engine results, particularly the likelihood of being directed to disinformation, 2) create a system that democracy-promoting institutions and concerned citizens can use to identify new disinformation, in near real-time, and 3) breach information bubbles by simulating how search results would be different if users had a different online profile. By relying on web-crawlers, our tool is privacy-protecting and does not require any real user data. Moreover, the proposed system anticipates the announced shift from cookie-tracking to fingerprinting and takes advantage of the expected small time overlap between both systems to learn from both and broaden its scope. Overall, we expect this novel tool to have a meaningful social impact by increasing public awareness of the role of search engines on disinformation spread, and by equipping organizations with a tool to detect and monitor disinformation, especially in political contexts.

Recent events, from the anti-vaccination movement, to Brexit and even to mob killings, have raised serious concerns about the influence of the so-called fake news (FN). False information is not new in human history, but the recent surge in online activity, coupled with poor digital literacy, consumer profiling, and large profits from ad revenues, created a perfect storm for the FN epidemic, with still unimaginable consequences. This challenge is interdisciplinary and requires academic research to guide current calls for action issued by academics, governmental and non-governmental agencies, and the social network platforms themselves. FARE will enrich current efforts, which mostly confront FN spreading from an applied perspective, by offering a theoretical framework that allows to make testable predictions. FARE argues that sharing of FN is a deviation from pure rationality and brings together 1) state of the art knowledge in behavioural psychology, to assess the role that cognitive biases play in susceptibility to FN, and 2) current models in network science and epidemiology, to test whether FN spread more like simple or complex contagions. Finally, fully recognizing that these novel big-data approaches carry great risks, FARE will develop a new strategy, mostly based on distributed computing, and guidelines to the ethical handling of human-related big-data. Together, FARE will offer a comprehensive model to ask questions such as: 1) What role(s) cognitive biases play in FN spreading? 2) How does network architecture affect FNs spread? 3) How do biases and position on networks build on each other to impact propagation? 4) What monitoring and mitigation interventions are likely to be more efficient? Moreover, the study of FN from such a conceptual perspective has the potential to profoundly increase our knowledge on human behaviour and information spread, beyond specific problems, with implications for communication (science, political), economics, and psychology.

QCD is the only sector of the Standard Model where the exploration of the first levels of complexity, built from fundamental interactions at the quantum level, is experimentally feasible. An outstanding example is the thermalised state of QCD matter formed when heavy atomic nuclei are smashed in particle colliders. Systematic experimental studies, carried out in the last two decades, overwhelmingly support the picture of a deconfined state of matter, which behaves as a nearly perfect fluid, formed in a very short time, less than 5 yoctoseconds. The mechanism that so efficiently brings the initial out-of-equilibrium state into a thermalised system is, however, largely unknown. Most surprisingly, LHC experiments have found that collisions of small systems, i.e. proton-proton or proton-lead, seem to indicate the presence of a tiny drop of this fluid in events with a large number of produced particles. These systems have sizes of 1 fm or less, or time-scales of less than 3 ys. To add to the puzzle, jet quenching, the modifications of jet properties due to interactions with the medium, has not been observed in these small systems, while jet quenching and thermalisation are expected to be controlled by the same dynamics. Present experimental tools have limited sensitivity to the actual process of thermalisation. To solve these long-standing questions we propose, as a completely novel strategy, using jet observables to directly access the first yoctoseconds of the collision. This strategy needs developments well beyond the state-of-the-art in three subjects: i) novel theoretical descriptions of the initial stages of the collision — the first 5 ys; ii) jet quenching theory for yoctosecond precision, with new techniques to couple the jet to the surrounding matter and novel parton shower evolution; and iii) jet quenching tools for the 2020’s, where completely novel jet observables will be devised with a focus on determining the initial stages of the collision.

Metastatic bone cancer is an incurable disease and one of the most complex cancers to treat. Due to the high dose, tumour imaging is currently performed at the beginning and end of standard particle radio-therapy (PRT), making personalised treatment difficult. The main goal of BoneOscopy is to develop a radically new technology to enable informed medical decisions by monitoring bone cancer on a daily basis during PRT. At the heart of BoneOscopy is the ability to detect prompt gamma (PGs) emitted by cancer during PRT and separate them from healthy tissue, unlocking the full potential of spectroscopic analysis without the need for additional dose. The development of a highly specialised detection and collimation system will enable accurate spectroscopic analysis of a very small volume or region within the cancer. As the number of PRT centres grows, we anticipate that within 10 years BoneOscopy will benefit all patients treated with proton and carbon ions. The objectives of BoneOscopy will be achieved by its interdisciplinary consortium, which brings together six partners from five European countries with key expertise in bioengineering and PRT (DKFZ), medical physics and engineering (CSIC), fast electronics for PRT (LIP), Monte Carlo simulations and clinical PRT experience (THM), turnkey software for high performance medical devices (Cosylab) and EU project management, communication and dissemination (accelCH). If achieved, the proposed science-to-technology breakthrough will have a transformative impact on current cancer treatment by providing a safe, personalised and quantitative measure of daily treatment efficacy, thereby contributing to the global fight against cancer. In summary, BoneOscopy will lead to a significant reduction in the health burden in Europe and worldwide, improved quality of life for patients, reduced costs for healthcare systems and improved sustainability of healthcare.

With the 2012 discovery of the Higgs boson at the Large Hadron Collider, LHC, the Standard Model of particle physics has been completed, emerging as a most successful description of matter at the smallest distance scales. But as is always the case, the observation of this particle has also heralded the dawn of a new era in the field: particle physics is now turning to the mysteries posed by the presence of dark matter in the universe, as well as the very existence of the Higgs. The upcoming run of the LHC at 13 TeV will probe possible answers to both issues, providing detailed measurements of the properties of the Higgs and extending significantly the sensitivity to new phenomena. Since the LHC is the only accelerator currently exploring the energy frontier, it is imperative that the analyses of the collected data use the most powerful possible techniques. In recent years several analyses have utilized multi-variate analysis techniques, obtaining higher sensitivity; yet there is ample room for further improvement. With our programme we will import and specialize the most powerful advanced statistical learning techniques to data analyses at the LHC, with the objective of maximizing the chance of new physics discoveries. We aim at creating a network of European institutions to foster the development and exploitation of Advanced Multi-Variate Analysis (AMVA) for New Physics searches. The network will offer extensive training in both physics and advanced analysis techniques to graduate students, focusing on providing them with the know-how and the experience to boost their career prospects in and outside academia. The network will develop ties with non-academic partners for the creation of interdisciplinary software tools, allowing a successful knowledge transfer in both directions. The network will study innovative techniques and identify their suitability to problems encountered in searches for new physics at the LHC and detailed studies of the Higgs boson sector.