This article presents a novel approach to analyzing quasiperiodically driven dynamical systems. It presents a holistic data-driven framework for reconstructing such a system and gaining insights into its dynamical behavior. A quasiperiodically driven dynamics has two components: 1) the quasiperiodic driving source with generating frequencies and 2) the driven nonlinear dynamics. The driving dynamics are reconstructed by accurately computing a large number of Koopman eigenfrequencies, using techniques based on the theory of reproducing kernel Hilbert space (RKHS) and from ergodic theory. Unlike Fourier analysis and dynamic mode decomposition (DMD), the proposed framework can analyze systems/signals with nondominant or absent periodic components. The driven system is next reconstructed by continuing to use the RKHS framework. As a case study, we investigate data collected from a corridor of nine consecutive signalized traffic intersections. The proposed framework provides insights into generating frequencies and associated modes, accurately reconstructs the queue lengths at the signalized intersections, and makes stable long-term forecasts. The unique contributions of this article are spread across multiple domains. It includes the novel modeling framework for quasiperiodically driven systems, a computationally efficient approach able to handle a large amount of data, utilization of true Koopman eigenfrequencies, and an illustration of the proposed approach on a corridor of signalized traffic intersections.

Predicting short-term traffic volume is essential to improve transportation systems management and operations (TSM0) and the overall efficiency of traffic networks. The real-time data, collected from Internet of Things (loT) devices, can be used to predict traffic volume. More specifically, the Automated Traffic Signal Performance Measures (ATSPM) data contain high-fidelity traffic data at multiple intersections and can reveal the spatio-temporal patterns of traffic volume for each signal. In this study, we have developed a machine learning-based approach using the data collected from ATSPM sensors of a corridor in Orlando, FL to predict future hourly traffic. The hourly predictions are calculated based on the previous six hours volume seen at the selected intersections. Additional factors that play an important role in traffic fluctuations include peak hours, day of the week, holidays, among others. Multiple machine learning models are applied to the dataset to determine the model with the best performance. Random Forest, XGBoost, and LSTM models show the best performance in predicting hourly traffic volumes.

Geo-location data from the check-ins made in online social media offers us information, in new ways, to understand activity-location choices of a large number of people. However, one of the major challenges of using check-in data is that it has missing activities, since users share their activities voluntarily. In this paper, we present a probabilistic modeling approach to reconstruct user activity-location sequences from this incomplete activity participation information. Specifically, we answer the question of how to predict an individual's next activity, its duration and location given the incomplete trajectory data. The model describes the dynamics of individual activity participation behavior evolving over continuous time. A semi-Markov modeling approach is used to capture the stochastic processes involved in the activity generation mechanism. We present a particle-based Markov chain Monte Carlo sampler to run inference over the model. We further develop an expectation-maximization algorithm to learn the unknown parameters of the model from incomplete trajectory data. Finally, the method is applied to synthetically generated activity-location sequences and a data set of Foursquare check-ins of the users from New York City. Our experiments show that this method can successfully extract the true transition and duration distributions given the incomplete trajectory information. The proposed approach can help building many intelligent transportation applications using check-in data.

MOTIVATION: ADME SARfari is a freely available web resource that enables comparative analyses of drug-disposition genes. It does so by integrating a number of publicly available data sources, which have subsequently been used to build data mining services, predictive tools and visualizations for drug metabolism researchers. The data include the interactions of small molecules with ADME (absorption, distribution, metabolism and excretion) proteins responsible for the metabolism and transport of molecules; available pharmacokinetic (PK) data; protein sequences of ADME-related molecular targets for pre-clinical model species and human; alignments of the orthologues including information on known SNPs (Single Nucleotide Polymorphism) and information on the tissue distribution of these proteins. In addition, in silico models have been developed, which enable users to predict which ADME relevant protein targets a novel compound is likely to interact with.

We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome ("metabolic chokepoints") and their similarity to known "druggable" protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as "persistence." The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.