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

Fine Particulates, Preterm Birth, and Membrane Rupture in Rochester, NY

Background: It remains unclear whether fine particulate (PM2.5) exposure affects risk of preterm birth and prelabor rupture of membranes. Unmeasured, poorly measured, and undiscovered individual-level confounders might have introduced bias into past studies that relied on between-women comparisons. Methods: This was a longitudinal study of preterm birth and prelabor rupture of membranes in Rochester, NY, 2004–2012 (N = 3,264 women, N = 7,121 singleton births). We used conditional logistic regression to match pregnancies to the same woman and estimate the odds of each outcome associated with average PM2.5 concentrations during each trimester and whole pregnancy. Results: For preterm birth, adjusted odds ratios (95% confidence interval) for 1 µg/m3 increase in PM2.5 in the first trimester, second trimester, third trimester, and whole pregnancy were 1.11 (1.04, 1.18), 1.09 (1.02, 1.16), 1.06 (1.00, 1.13), and 1.17 (1.07, 1.28), respectively. For prelabor rupture of membranes, corresponding odds ratios were 1.00 (0.97, 1.04), 0.99 (0.96, 1.02), 0.99 (0.96, 1.03), and 0.99 (0.94, 1.04), respectively. Conclusion: Risk of preterm birth was greater for pregnancies with elevated PM2.5 exposure than other pregnancies to the same women at lower exposure. We did not observe an association between PM2.5 concentrations and prelabor rupture of membranes

Prediction of rare feature combinations in population synthesis: Application of deep generative modelling

In population synthesis applications, when considering populations with many attributes, a fundamental problem is the estimation of rare combinations of feature attributes. Unsurprisingly, it is notably more difficult to reliably representthe sparser regions of such multivariate distributions and in particular combinations of attributes which are absent from the original sample. In the literature this is commonly known as sampling zeros for which no systematic solution has been proposed so far. In this paper, two machine learning algorithms, from the family of deep generative models,are proposed for the problem of population synthesis and with particular attention to the problem of sampling zeros. Specifically, we introduce the Wasserstein Generative Adversarial Network (WGAN) and the Variational Autoencoder(VAE), and adapt these algorithms for a large-scale population synthesis application. The models are implemented on a Danish travel survey with a feature-space of more than 60 variables. The models are validated in a cross-validation scheme and a set of new metrics for the evaluation of the sampling-zero problem is proposed. Results show how these models are able to recover sampling zeros while keeping the estimation of truly impossible combinations, the structural zeros, at a comparatively low level. Particularly, for a low dimensional experiment, the VAE, the marginal sampler and the fully random sampler generate 5%, 21% and 26%, respectively, more structural zeros per sampling zero generated by the WGAN, while for a high dimensional case, these figures escalate to 44%, 2217% and 170440%, respectively. This research directly supports the development of agent-based systems and in particular cases where detailed socio-economic or geographical representations are required

Context-Aware Sensing and Implicit Ground Truth Collection: Building a Foundation for Event Triggered Surveys on Autonomous Shuttles: Artikel

The LINC project aims to study interactions between passengers and autonomous vehicles in natural settings at the campus of Technical University of Denmark. To leverage the potential of IoT components in smartphone-based surveying, a system to identify specific spatial, temporal and occupancy contexts relevant for passengers’ experience was proposed as a central data collection strategy in the LINC project. Based on predefined contextual triggers specific questionnaires can be distributed to affected passengers. This work focuses on the data-based discrimination between two fundamental contexts for LINC passengers: be-in and be-out (BIBO) of the vehicle. We present empirical evidence that Bluetooth-low-energy beacons (BLE) have the potential for BIBO independent classification. We compare BLE with other smartphone onboard sensors, such as the global positioning system (GPS) and the accelerometer through: (i) random-forest (RF); (ii) multi-layer perceptron (MLP); and (iii) smartphone native off-the-shelve classifiers. We also perform a sensitivity analysis regarding the impact that faulty BIBO ground-truth has on the performance of the supervised classifiers (i) and (ii). Results show that BLE and GPS could allow reciprocal validation for BIBO passengers’ status. This potential might lift passengers from providing any further validation. We describe the smartphone-sensing platform deployed to gather the dataset used in this work, which involves passengers and autonomous vehicles in a realistic setting

Term birth weight and ambient air pollutant concentrations during pregnancy, among women living in Monroe County, New York

Increased ambient air pollutant concentrations during pregnancy have been associated with reduced birth weight, but the etiologically relevant pregnancy time window(s) is/are unclear. In 76,500 singleton births in Monroe County, NY (2005–2016), who were 37–42 gestational weeks at delivery, we used generalized linear models to regress term birth weight against mean gestational month pollutant concentrations, adjusting for mean temperature, and maternal, infant, and medical service use characteristics. Overall, there were no clear patterns of term birth weight change associated with increased concentrations of any pollutant across gestational months. However, among Hispanic women only, increases in all pollutants, except O3, in multiple gestational months, were associated with decreased term birth weight. Each 3.25 µg/m3 increase in PM2.5 concentration in the 6th gestational month was associated with a −20.4 g (95% CI = −34.0, −6.8) reduction in term birth weight among Hispanic women, but a 4.1 g (95% CI = −2.5, 10.8) increase among non-Hispanic mothers (p for interaction < 0.001). Although ambient air pollutant concentrations during pregnancy were not associated with reduced term birth weight among women of all ethnicities living in Monroe County, this observed association in Hispanic mothers may be a result of less exposure misclassification and bias (due to closer residential proximity to the monitoring site)

On the use of parataxonomy in biodiversity monitoring: a case study on wild flora

International audienceMonitoring programs that assess species-richness and turnover are now regarded as essential to document biodiversity loss worldwide. Implementation of such programs is impeded by a general decrease in the number of skilled naturalists. Here we studied how morphotypes, instead of species, might be used by unskilled participants (referred to as “volunteers”) to survey common plant communities. Our main questions were: (1) Can morphotypes be used as a robust estimator of species-richness (alpha-diversity) and assemblage turnover (Beta-diversity)? and (2) What is the robustness (reproducibility and repeatability) of such methods? Double inventories were performed on 150 plots in arable Weld margins, one by a non-expert using morphotypes, the other by a taxonomist using species. To test the robustness of morphotype identiWcation among participants, 20 additional plots were surveyed by eight volunteers using the same protocol. We showed that (1) the number of morphotypes identiWed by unskilled volunteers in a plot was always strongly correlated with species-richness. (2) Morphotypes were sensitive to diVerences among habitats but were less accurate than species to detect these diVerences. (3) Morphotype identiWcation varied signiWcantly within and between volunteers. Due to this lack of repeatability and reproducibility, parataxonomy cannot be considered a good surrogate for taxonomy. Nevertheless, assuming that morphotypes are identiWed with standardized methods, and that results are used only to evaluate gross species-richness but not species turnover, parataxonomy might be a valuable tool for rapid biodiversity assessment of common wild flora

Pathological role of interleukin 17 in mice subjected to repeated BCG vaccination after infection with Mycobacterium tuberculosis

Infection usually leads to the development of acquired immune responses associated with clearance or control of the infecting organism. However, if not adequately regulated, immune-mediated pathology can result. Tuberculosis is a worldwide threat, and development of an effective vaccine requires that the protective immune response to Mycobacterium tuberculosis (Mtb) be dissected from the pathological immune response. This distinction is particularly important if new vaccines are to be delivered to Mtb-exposed individuals, as repeated antigenic exposure can lead to pathological complications. Using a model wherein mice are vaccinated with bacille Calmette-Guérin after Mtb infection, we show that repeated vaccination results in increased IL-17, tumor necrosis factor, IL-6, and MIP-2 expression, influx of granulocytes/neutrophils, and lung tissue damage. This pathological response is abrogated in mice deficient in the gene encoding IL-23p19 or in the presence of IL-17–blocking antibody. This finding that repeated exposure to mycobacterial antigen promotes enhanced IL-17–dependent pathological consequences has important implications for the design of effective vaccines against Mtb

Bloodborne Viral Hepatitis Infections among Drug Users: The Role of Vaccination

Drug use is a prevalent world-wide phenomenon and hepatitis virus infections are traditionally a major health problem among drug users (DUs). HBV and HCV, and to a lesser extent HAV, are easily transmitted through exposure to infected blood and body fluids. Viral hepatitis is not inevitable for DUs. Licensed vaccines are available for hepatitis A and hepatitis B. The purpose of this overview is to show some epidemiological data about HBV and the other blood-borne viral hepatitis among DUs and to summarize and discuss use of hepatitis vaccinations in this population. Successful vaccination campaigns among DUs are feasible and well described. We try to focus on the most significant results achieved in successful vaccination programs as reported in scientific literature. Vaccination campaigns among DUs represent a highly effective form of health education and they are cost-saving

The spatially resolved stellar population and ionized gas properties in the merger LIRG NGC 2623

We report on a detailed study of the stellar populations and ionized gas properties in the merger LIRG NGC 2623, analyzing optical integral field spectroscopy from the CALIFA survey and PMAS LArr, multiwavelength HST imaging, and OSIRIS narrow band Hα and [NII]λ6584 imaging. The spectra were processed with the starlight full spectral fitting code, and the results are compared with those for two early-stage merger LIRGs (IC 1623 W and NGC 6090), together with CALIFA Sbc/Sc galaxies. We find that NGC 2623 went through two periods of increased star formation (SF), a first and widespread episode, traced by intermediate-age stellar populations ISP (140 Myr–1.4 Gyr), and a second one, traced by young stellar populations YSP (<140 Myr), which is concentrated in the central regions (<1.4 kpc). Our results are in agreement with the epochs of the first peri-center passage (~200 Myr ago) and coalescence (<100 Myr ago) predicted by dynamical models, and with high-resolution merger simulations in the literature, consistent with NGC 2623 representing an evolved version of the early-stage mergers. Most ionized gas is concentrated within <2.8 kpc, where LINER-like ionization and high-velocity dispersion (~220 km s-1) are found, consistent with the previously reported outflow. As revealed by the highest-resolution OSIRIS and HST data, a collection of HII regions is also present in the plane of the galaxy, which explains the mixture of ionization mechanisms in this system. It is unlikely that the outflow in NGC 2623 will escape from the galaxy, given the low SFR intensity (~0.5 M⊙ yr-1 kpc-2), the fact that the outflow rate is three times lower than the current SFR, and the escape velocity in the central areas is higher than the outflow velocity

VLT-SINFONI integral field spectroscopy of low-z luminous and ultraluminous infrared galaxies

We present a 2D study of the internal extinction on (sub)kiloparsec scales of a sample of local (z < 0.1) LIRGs (10) and ULIRGs (7), based on near-infrared Paα, Brδ, and Brγ line ratios, obtained with VLT-SINFONI integral-field spectroscopy (IFS). The 2D extinction (AV) distributions of the objects, map regions of ~ 3 × 3 kpc (LIRGs) and ~ 12 × 12 kpc (ULIRGs), with average angular resolutions (FWHM) of ~0.2 kpc and ~0.9 kpc, respectively. The individual AV galaxy distributions indicate a very clumpy dust structure already on sub-kiloparsec scales, with values (per spaxel) ranging from AV ~ 1 to 20 mag in LIRGs, and from AV ~ 2 to 15 mag in ULIRGs. As a class, the median values of the distributions are AV = 5.3 mag and AV = 6.5 mag for the LIRG and ULIRG subsamples, respectively. In ~70% of the objects, the extinction peaks at the nucleus with values ranging from AV ~ 3 to 17 mag. Within each galaxy, the AV radial profile shows a mild decrement in LIRGs within the inner 2 kpc radius, while the same radial variation is not detected in ULIRGs, likely because of the lower linear scale resolution of the observations at the distance of ULIRGs. We evaluated the effects of the galaxy distance in the measurements of the extinction as a function of the linear scale (in kpc) of the spaxel (i.e. due to the limited angular resolution of the observations). If the distribution of the gas/dust and star-forming regions in local LIRGs (63 Mpc, 40 pc/spaxel on average) is the same for galaxies at greater distances, the observed median AV values based on emission line ratios would be a factor ~0.8 lower at the average distance of our ULIRG sample (328 Mpc, 0.2 kpc/spaxel), and a factor ~0.67 for galaxies located at distances of more than 800 Mpc (0.4 kpc/spaxel). This distance effect would have implications for deriving the intrinsic extinction in high-z star-forming galaxies and for subsequent properties such as star formation rate, star formation surface density, and KS- law, based on Hα line fluxes. If local LIRGs are analogues of the main-sequence star-forming galaxies at cosmological distances, the extinction values (AV) derived from the observed emission lines in these high-z sources would need to be increased by a factor 1.4 on average