Scalable Population Synthesis with Deep Generative Modeling

Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models. In this paper, a new methodological framework for how to 'grow' pools of micro-agents is presented. The model framework adopts a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE). Compared to the previous population synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs sampling and traditional generative models such as Bayesian Networks or Hidden Markov Models, the proposed method allows fitting the full joint distribution for high dimensions. The proposed methodology is compared with a conventional Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary. It is shown that, while these two methods outperform the VAE in the low-dimensional case, they both suffer from scalability issues when the number of modeled attributes increases. It is also shown that the Gibbs sampler essentially replicates the agents from the original sample when the required conditional distributions are estimated as frequency tables. In contrast, the VAE allows addressing the problem of sampling zeros by generating agents that are virtually different from those in the original data but have similar statistical properties. The presented approach can support agent-based modeling at all levels by enabling richer synthetic populations with smaller zones and more detailed individual characteristics.Comment: 27 pages, 15 figures, 4 table

Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package

This paper describes the R package crqa to perform cross-recurrence quantification analysis of two time series of either a categorical or continuous nature. Streams of behavioral information, from eye movements to linguistic elements, unfold over time. When two people interact, such as in conversation, they often adapt to each other, leading these behavioral levels to exhibit recurrent states. In dialogue, for example, interlocutors adapt to each other by exchanging interactive cues: smiles, nods, gestures, choice of words, and so on. In order for us to capture closely the goings-on of dynamic interaction, and uncover the extent of coupling between two individuals, we need to quantify how much recurrence is taking place at these levels. Methods available in crqa would allow researchers in cognitive science to pose such questions as how much are two people recurrent at some level of analysis, what is the characteristic lag time for one person to maximally match another, or whether one person is leading another. First, we set the theoretical ground to understand the difference between 'correlation' and 'co-visitation' when comparing two time series, using an aggregative or cross-recurrence approach. Then, we describe more formally the principles of cross-recurrence, and show with the current package how to carry out analyses applying them. We end the paper by comparing computational efficiency, and results' consistency, of crqa R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect comparability between the two libraries on both levels

Interaction of numerosity and time in prefrontal and parietal cortex

It has been proposed that numerical and temporal information are processed by partially overlapping magnitude systems. Interactions across different magnitude domains could occur both at the level of perception and decision-making. However, their neural correlates have been elusive. Here, using functional magnetic resonance imaging in humans, we show that the right intraparietal cortex (IPC) and inferior frontal gyrus (IFG) are jointly activated by duration and numerosity discrimination tasks, with a congruency effect in the right IFG. To determine whether the IPC and the IFG are involved in response conflict (or facilitation) or modulation of subjective passage of time by numerical information, we examined their functional roles using transcranial magnetic stimulation (TMS) and two different numerosity-time interaction tasks: duration discrimination and time reproduction tasks. Our results show that TMS of the right IFG impairs categorical duration discrimination, whereas that of the right IPC modulates the degree of influence of numerosity on time perception and impairs precise time estimation. These results indicate that the right IFG is specifically involved at the categorical decision stage, whereas bleeding of numerosity information on perception of time occurs within the IPC. Together, our findings suggest a two-stage model of numerosity-time interactions whereby the interaction at the perceptual level occurs within the parietal region and the interaction at categorical decisions takes place in the prefrontal cortex