Aron Szanto

Lars Lorch, Heiner Kremer, William Trouleau et al.

Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. However, most of the existing epidemiological models fail to capture this aspect by neither representing the sites visited by individuals explicitly nor characterizing disease transmission as a function of individual mobility patterns. In this work, we introduce a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other. Under our model, the number of infections caused by an infectious individual naturally emerges to be overdispersed. Using an efficient sampling algorithm, we demonstrate how to estimate the transmission rate of infectious individuals at the sites they visit and in their households using Bayesian optimization and longitudinal case data. Simulations using fine-grained and publicly available demographic data and site locations from Bern, Switzerland showcase the flexibility of our framework. To facilitate research and analyses of other cities and regions, we release an open-source implementation of our framework.

Nir Rosenfeld, Aron Szanto, David C. Parkes

Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media. But text can be strategically manipulated and accounts reopened under different aliases, suggesting that these approaches are inherently brittle. In this work, we investigate an alternative modality that is naturally robust: the pattern in which information propagates. Can the veracity of an unverified rumor spreading online be discerned solely on the basis of its pattern of diffusion through the social network? Using graph kernels to extract complex topological information from Twitter cascade structures, we train accurate predictive models that are blind to language, user identities, and time, demonstrating for the first time that such "sanitized" diffusion patterns are highly informative of veracity. Our results indicate that, with proper aggregation, the collective sharing pattern of the crowd may reveal powerful signals of rumor truth or falsehood, even in the early stages of propagation.

Aron Szanto, Timothy Tamm, Artidoro Pagnoni

WebAssembly seeks to provide an alternative to running large and untrusted binaries within web browsers by implementing a portable, performant, and secure bytecode format for native web computation. However, WebAssembly is largely unstudied from a security perspective. In this work, we build the first WebAssembly virtual machine that runs in native JavaScript, and implement a novel taint tracking system that allows a user to run untrusted WebAssembly code while monitoring the flow of sensitive data through the application. We also introduce indirect taint, a label that denotes the implicit flow of sensitive information between local variables. Through rigorous testing and validation, we show that our system is correct, secure, and relatively efficient, benefiting from the native performance of WebAssembly while retaining precise security guarantees of more mature software paradigms.

Gabriel Grand, Aron Szanto, Yoon Kim et al.

Visual question answering (VQA) models respond to open-ended natural language questions about images. While VQA is an increasingly popular area of research, it is unclear to what extent current VQA architectures learn key semantic distinctions between visually-similar images. To investigate this question, we explore a reformulation of the VQA task that challenges models to identify counterexamples: images that result in a different answer to the original question. We introduce two methods for evaluating existing VQA models against a supervised counterexample prediction task, VQA-CX. While our models surpass existing benchmarks on VQA-CX, we find that the multimodal representations learned by an existing state-of-the-art VQA model do not meaningfully contribute to performance on this task. These results call into question the assumption that successful performance on the VQA benchmark is indicative of general visual-semantic reasoning abilities.