31,558 research outputs found
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
Human bony labyrinth is an indicator of population history and dispersal from Africa.
The dispersal of modern humans from Africa is now well documented with genetic data that track population history, as well as gene flow between populations. Phenetic skeletal data, such as cranial and pelvic morphologies, also exhibit a dispersal-from-Africa signal, which, however, tends to be blurred by the effects of local adaptation and in vivo phenotypic plasticity, and that is often deteriorated by postmortem damage to skeletal remains. These complexities raise the question of which skeletal structures most effectively track neutral population history. The cavity system of the inner ear (the so-called bony labyrinth) is a good candidate structure for such analyses. It is already fully formed by birth, which minimizes postnatal phenotypic plasticity, and it is generally well preserved in archaeological samples. Here we use morphometric data of the bony labyrinth to show that it is a surprisingly good marker of the global dispersal of modern humans from Africa. Labyrinthine morphology tracks genetic distances and geography in accordance with an isolation-by-distance model with dispersal from Africa. Our data further indicate that the neutral-like pattern of variation is compatible with stabilizing selection on labyrinth morphology. Given the increasingly important role of the petrous bone for ancient DNA recovery from archaeological specimens, we encourage researchers to acquire 3D morphological data of the inner ear structures before any invasive sampling. Such data will constitute an important archive of phenotypic variation in present and past populations, and will permit individual-based genotype-phenotype comparisons
Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization
Statistical traffic data analysis is a hot topic in traffic management and
control. In this field, current research progresses focus on analyzing traffic
flows of individual links or local regions in a transportation network. Less
attention are paid to the global view of traffic states over the entire
network, which is important for modeling large-scale traffic scenes. Our aim is
precisely to propose a new methodology for extracting spatio-temporal traffic
patterns, ultimately for modeling large-scale traffic dynamics, and long-term
traffic forecasting. We attack this issue by utilizing Locality-Preserving
Non-negative Matrix Factorization (LPNMF) to derive low-dimensional
representation of network-level traffic states. Clustering is performed on the
compact LPNMF projections to unveil typical spatial patterns and temporal
dynamics of network-level traffic states. We have tested the proposed method on
simulated traffic data generated for a large-scale road network, and reported
experimental results validate the ability of our approach for extracting
meaningful large-scale space-time traffic patterns. Furthermore, the derived
clustering results provide an intuitive understanding of spatial-temporal
characteristics of traffic flows in the large-scale network, and a basis for
potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013
Testing for the Monotone Likelihood Ratio Assumption
Monotonicity of the likelihood ratio for conditioned densities is a common technical assumption in economic models. But we have found no empirical tests for its plausibility. This paper develops such a test based on the theory of order-restricted inference, which is robust with respect to the correlation structure of the distributions being compared. We apply the test to study the technology revealed by agricultural production experiments. For the data under scrutiny, the results support the assumption of the monotone likelihood ratio. In a second application, we find some support for the assumption of affiliation among bids cast in a multiple-round Vickrey auction for a consumption good. Keywords: affiliation, auction, likelihood ratio, order-restricted inference, stochastic order.
Toward optimal implementation of cancer prevention and control programs in public health: A study protocol on mis-implementation
Abstract Background Much of the cancer burden in the USA is preventable, through application of existing knowledge. State-level funders and public health practitioners are in ideal positions to affect programs and policies related to cancer control. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Greater attention to mis-implementation should lead to use of effective interventions and more efficient expenditure of resources, which in the long term, will lead to more positive cancer outcomes. Methods This is a three-phase study that takes a comprehensive approach, leading to the elucidation of tactics for addressing mis-implementation. Phase 1: We assess the extent to which mis-implementation is occurring among state cancer control programs in public health. This initial phase will involve a survey of 800 practitioners representing all states. The programs represented will span the full continuum of cancer control, from primary prevention to survivorship. Phase 2: Using data from phase 1 to identify organizations in which mis-implementation is particularly high or low, the team will conduct eight comparative case studies to get a richer understanding of mis-implementation and to understand contextual differences. These case studies will highlight lessons learned about mis-implementation and identify hypothesized drivers. Phase 3: Agent-based modeling will be used to identify dynamic interactions between individual capacity, organizational capacity, use of evidence, funding, and external factors driving mis-implementation. The team will then translate and disseminate findings from phases 1 to 3 to practitioners and practice-related stakeholders to support the reduction of mis-implementation. Discussion This study is innovative and significant because it will (1) be the first to refine and further develop reliable and valid measures of mis-implementation of public health programs; (2) bring together a strong, transdisciplinary team with significant expertise in practice-based research; (3) use agent-based modeling to address cancer control implementation; and (4) use a participatory, evidence-based, stakeholder-driven approach that will identify key leverage points for addressing mis-implementation among state public health programs. This research is expected to provide replicable computational simulation models that can identify leverage points and public health system dynamics to reduce mis-implementation in cancer control and may be of interest to other health areas
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