Hierarchically branched diffusion models for efficient and interpretable multi-class conditional generation

Diffusion models have achieved justifiable popularity by attaining state-of-the-art performance in generating realistic objects, including when conditioning generation on labels. Current diffusion models are universally linear in nature, modeling diffusion identically for objects of all classes. For the multi-class conditional generation problem, we propose a novel, structurally unique framework of diffusion models which are hierarchically branched according to the inherent relationships between classes. In this work, we showcase several advantages of branched diffusion models. We demonstrate that branched models generate samples more efficiently, and are more easily extended to novel classes in a continual-learning setting. We also show that branched models enjoy a unique interpretability that offers insight into the modeled data distribution. Branched diffusion models represent an alternative paradigm to their traditional linear counterparts, and can have large impacts in how we use diffusion models for efficient generation, online learning, and scientific discovery

Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion

Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos. Despite their success, there has been limited research on how the underlying diffusion process and the final convergent prior can affect generative performance; this research has also been limited to continuous data types and a score-based diffusion framework. To fill this gap, we explore how different discrete diffusion kernels (which converge to different prior distributions) affect the performance of diffusion models for graphs. To this end, we developed a novel formulation of a family of discrete diffusion kernels which are easily adjustable to converge to different Bernoulli priors, and we study the effect of these different kernels on generative performance. We show that the quality of generated graphs is sensitive to the prior used, and that the optimal choice cannot be explained by obvious statistics or metrics, which challenges the intuitions which previous works have suggested

A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning

Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimensional representation. Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape. We demonstrate how this framework naturally captures key aspects of three-dimensionality that two-dimensional representations cannot and provides an inductive framework for scaffold hopping

Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter

Social Media provides a trove of information that, if aggregated and analysed appropriately can provide important statistical indicators to policy makers. In some situations these indicators are not available through other mechanisms. For example, given the ongoing COVID-19 outbreak, it is essential for governments to have access to reliable data on policy-adherence with regards to mask wearing, social distancing, and other hard-to-measure quantities. In this paper we investigate whether it is possible to obtain such data by aggregating information from images posted to social media. The paper presents VisualCit, a pipeline for image-based social sensing combining recent advances in image recognition technology with geocoding and crowdsourcing techniques. Our aim is to discover in which countries, and to what extent, people are following COVID-19 related policy directives. We compared the results with the indicators produced within the CovidDataHub behavior tracker initiative. Preliminary results shows that social media images can produce reliable indicators for policy makers.Comment: 10 pages, 9 figures, to be published in Proceedings of ICSE Software Engineering in Society, May 202

Oltre il Segno/OltreMare

La realizzazione di un volume contenente le incisioni scelte all’interno della Scuola di Grafica d’Arte dell’Accademia di Belle Arti di Palermo, coordinata dai Proff. Giovanni D’Alessandro e Riccardo Mazzarino rappresenta motivo di orgoglio e di soddisfazione per la nostra Istituzione che costruisce i percorsi didattici dei propri corsi a partire dall’esperienza laboratoriale. L’incisione grafica è tra le tecniche artistiche più antiche ma nel contempo più contemporanee. La gestualità intrinseca al segno, che si manifesta nella carta, svela universi della visione inaspettati.(Mario Zito - Direttore dell’Accademia di Belle Arti di Palermo) Il segno è il risultato di un gesto a volte deciso, a volte contorto, a volte leggero, i cui risultati spesso sono inattesi e sorprendenti. Il volume contiene esemplari di incisioni fortemente caratterizzanti della scuola di Grafica d’Arte che vanta all’interno del proprio corso di studi docenti-artisti che consapevoli della ricchezza del loro bagaglio esperienziale offrono agli studenti gli strumenti necessari per far sì che l’arte del saper fare artigianale, si trasformi in mera poetica artistica