Charting nanocluster structures via convolutional neural networks

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large dataset of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and to distill a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold, in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences, such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables which has the potential to be used for exploration and enhanced sampling purposes.Comment: 28 pages, 13 figure

Learning to Prompt in the Classroom to Understand AI Limits: A pilot study

Artificial intelligence's progress holds great promise in assisting society in addressing pressing societal issues. In particular Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems allowing them to process an unprecedented amount of unstructured data. The consequent hype has also backfired, raising negative sentiment even after novel AI methods' surprising contributions. One of the causes, but also an important issue per se, is the rising and misleading feeling of being able to access and process any form of knowledge to solve problems in any domain with no effort or previous expertise in AI or problem domain, disregarding current LLMs limits, such as hallucinations and reasoning limits. Acknowledging AI fallibility is crucial to address the impact of dogmatic overconfidence in possibly erroneous suggestions generated by LLMs. At the same time, it can reduce fear and other negative attitudes toward AI. AI literacy interventions are necessary that allow the public to understand such LLM limits and learn how to use them in a more effective manner, i.e. learning to "prompt". With this aim, a pilot educational intervention was performed in a high school with 30 students. It involved (i) presenting high-level concepts about intelligence, AI, and LLM, (ii) an initial naive practice with ChatGPT in a non-trivial task, and finally (iii) applying currently-accepted prompting strategies. Encouraging preliminary results have been collected such as students reporting a) high appreciation of the activity, b) improved quality of the interaction with the LLM during the educational activity, c) decreased negative sentiments toward AI, d) increased understanding of limitations and specifically We aim to study factors that impact AI acceptance and to refine and repeat this activity in more controlled settings.Comment: Submitted to AIXIA 2023 22nd International Conference of the Italian Association for Artificial Intelligence 6 - 9 Nov, 2023, Rome, Ital

Samuele Telari, acordeón (Italia)

Samuele Telari es un «artista cuya abrumadora musicalidad va más allá de su instrumento» (Il Sole24Ore). En 2019 fue premiado en las Audiciones Internacionales de YCAT celebradas en el Wigmore Hall. Adicionalmente, ganó el primer puesto en los concursos de Arassate-Hiria (España) en 2018 y de Castelfidardo (Italia) en 2013. Ha colaborado con la mezzosoprano Ema Nikolovska, el Cuarteto Castalian y el Cuarteto Hermès. Telari ha grabado varios álbumes con el sello Delphian y, en varias ocasiones, se ha presentado en Europa. Entre sus actuaciones más destacadas se incluyen las realizadas en Società dei Concerti (Milán), Amici della Musica di Firenze, Cité de la musique et de la danse (Estrasburgo) y Berlin Philharmonie. Como solista con orquesta, estrenó el Concierto para acordeón y orquesta de Enrico Blatti junto con la Orquesta Estatal del Hermitage, en San Petersburgo; e interpretó el Concierto Opale de Richard Galliano con I Virtuosi Italiani. Sus compromisos durante la temporada 2020-2021 incluyen recitales en el Wigmore Hall, Snape Maltings, Festspiele Mecklenburg-Vorpommern, Festival de la Academia de Verbier, Schlern Music (Norte de Italia) y la serie Divertimento Ensemble Rondò (Milán). Samuele Telari nació en Spoleto (Italia) y es profesor en los conservatorios ‘Gesualdo da Venosa’ de Potenza, ‘Bruno Maderna’ de Cesena y ‘F. A. Bonporti’ de Trento. Video disponible del miércoles 3 de marzo al viernes 2 de abril de 2021 en el perfil de Facebook de la Sala de Conciertos de la Biblioteca Luis Ángel Arango y en el canal de YouTube de Banrepcultural

The Phantom Pain of Ghosting: Multi-Day Experiments Show Differential Reactions to Ghosting and Rejection

Research on ghosting, the practice of ending a relationship without explanation and ignoring attempts of contact, has yet to develop an experimental approach that is not based on imagination or retrospective recall. The present study combines a newly developed experimental paradigm to manipulate ghosting with a multi-day daily-diary methodology to investigate the psychological consequences of ghosting, comparing it to rejection and inclusion within the theoretical framework of social exclusion research

Intrinsic and apparent slip at gas-enriched liquid–liquid interfaces: a molecular dynamics study

In this paper, slip at liquid–liquid interfaces is studied focusing on the ubiquitous case in which a third species (e.g. a gas) is present. Non-equilibrium molecular dynamics simulations demonstrate that the contaminant species accumulate at the liquid–liquid interface, enriching it and affecting momentum transfer in a non-trivial fashion. The Navier boundary condition is seen to apply at this interface, accounting for slip between the liquids. Opposite trends are observed for soluble and poorly soluble species, with the slip length decreasing with concentration in the first case and significantly increasing in the latter. Two regimes are found, one in which the liquid–liquid interface is altered by the third species but changes in slip length remain limited to molecular sizes (intrinsic slip). In the second regime, further accumulation of non-soluble gas at the interface gives rise to a gaseous layer replacing the liquid–liquid interface; in this case, the apparent slip lengths are one order of magnitude larger and grow linearly with the layer width as captured quantitatively by a simple three-fluids model. Overall, results show that the presence of a third species considerably enriches the slip phenomenology both calling for new experiments and opening the door to novel strategies to control liquid–liquid slip, e.g. in liquid infused surfaces

On the other side of ostracism: a systematic literature review of the cyberball overinclusion condition

AbstractCyberball, the paradigm developed by Kipling D. Williams and colleagues (2000) to study ostracism, initially counted three experimental conditions: inclusion, exclusion, and overinclusion. The least known of these conditions is overinclusion, a social interaction characterized by excessive social attention (rather than fairness or no attention). This review provides an overview of original empirical studies implementing the overinclusion condition since its development. Following the PRISMA 2020 criteria, studies were drawn from four electronic databases (PubMed, Springer, PsycINFO, Web of Science), and Google Scholar was screened as a web-based academic search engine. In all, 33 studies met the inclusion criteria. Included studies described overinclusion specificities compared with exclusion and inclusion conditions, its effects in paradigms other than Cyberball, brain correlates associated with overinclusion, and its impact on clinical populations. 26 studies compared the inclusion and overinclusion conditions. 20 revealed significant differences between the two conditions, and 13 observed better mood and higher psychological needs satisfaction associated with the overinclusion condition. Studies investigating neural correlates revealed dACC involvement, P3 reduction, and P2 increase during overinclusion, supporting the idea of an ameliorative effect induced by the over-exposition to social stimulation. Findings on clinical populations suggest that overinclusion may help detect the social functioning of patients with psychological impairment. Despite the heterogeneity of the studies, our results showed that overinclusion can be associated with ameliorative psychological functioning. However, implementing standard guidelines for overinclusion will help provide a more thorough investigation of the psychological consequences of receiving excess social attention

Investigating complex dynamics of inclusion and exclusion in the Cyberball game: a trial-by-trial computational approach

Objective. While the detrimental outcomes experienced by the victims of unambiguous ostracism have been extensively investigated, the reactions to asymmetrical behavioural repertoires of social inclusion and exclusion are not fully clarified yet. Here, we evaluated how individuals react to others in a Cyberball experiment based on the perception of others’ differential inclusionary behaviour toward them and whether this response pattern varied as a function of their prosocial attitudes. Method. We adopted two Cyberball conditions toward this aim: partial ostracism and partial over-inclusion. In these asymmetrical conditions, the behaviour of one of the co-players is programmed to interact inclusively in a balanced manner, while the second co-player is programmed to either exclude (i.e., almost never tossing the ball) or over-include (i.e., almost always tossing the ball) the participant in different task blocks. Results. Across two studies (n=45 and n=106), participants correctly perceived the degree of inclusionary behaviour conveyed by different co-players in each condition and reacted emotionally accordingly. Trial-by-trial analysis revealed that in partial ostracism prosocial participants reciprocated more the inclusive co-player, thereby excluding the ostracising co-player, while individualistic participants did not. Conversely, in the partial over-inclusion condition the participants, irrespective of one’s own prosocial attitudes, did not choose to ostracise a normally including player who maintained a balanced engagement in the game, over a player who displayed an over-inclusive attitude towards the participant. Conclusions. Taken together, our results suggest that prosocial individuals selectively tend to exclude ostracizing –but not fairly including– interaction partners, likely in the attempt to restore the “norm” of social inclusion

Charting Nanocluster Structures via Convolutional Neural Networks

: A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large data set of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and distills a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables that has the potential to be used for exploration and enhanced sampling purposes