Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes

Approximate Bayesian Computation (ABC) methods have gained in popularity over the last decade because they expand the horizon of Bayesian parameter inference methods to the range of models for which an analytical formula for the likelihood function might be difficult, or even impossible, to establish. The majority of the ABC methods rely on the choice of a set of summary statistics to reduce the dimension of the data. However, as has been noted in the ABC literature, the lack of convergence guarantees induced by the absence of a vector of sufficient summary statistics that assures intermodel sufficiency over the set of competing models hinders the use of the usual ABC methods when applied to Bayesian model selection or assessment. In this paper, we present a novel ABC model selection procedure for dynamical systems based on a recently introduced multilevel Markov chain Monte Carlo method, self-regulating ABC-SubSim, and a hierarchical state-space formulation of dynamic models. We show that this formulation makes it possible to independently approximate the model evidence required for assessing the posterior probability of each of the competing models. We also show that ABC-SubSim not only provides an estimate of the model evidence as a simple by-product but also gives the posterior probability of each model as a function of the tolerance level, which allows the ABC model choices made in previous studies to be understood. We illustrate the performance of the proposed framework for ABC model updating and model class selection by applying it to two problems in Bayesian system identification: a single-degree-of-freedom bilinear hysteretic oscillator and a three-story shear building with Masing hysteresis, both of which are subject to a seismic excitation

Analysis of Speech-to-Text Algorithms in Recognizing Down Syndrome Conversations

Introduction: Speech-to-text technology has become key in supporting technologies such as voice assistants (e.g., Alexa, Siri). Unfortunately, some individuals with speech differences, such as accents, female voices, children, or individuals with disabilities such as Down Syndrome, are not well recognized, creating issues in inclusivity. The first step toward making it more inclusive is to figure out where the errors or weaknesses are in speech-to-text algorithms (YouTube, IBM, Zoom, and Azure) in recognizing dialogs from diverse populations. Methods: We analyze 10 videos from the ‘Special Books by Special Kids’ YouTube channel. Videos include 15 people with Down Syndrome and 6 Neurotypicals. To compare how algorithms perform, we developed a python script to compute the word error rate, mismatch, insertion, and deletion. Results: Each algorithm did better for Neurotypicals than individuals with Down Syndrome by almost 40%. Overall, the most accurate algorithm was Azure for both Down Syndrome (46%) and Neurotypicals (87%). In general, all algorithms struggled the most with mismatching words, then deleting words, and the least common mistake was inserting words. Conclusion: Even though Azure is doing better than other algorithms, it still does not work well for Down Syndrome. To further understand the limitations and potential improvement of these algorithms, we propose a phonetic analysis to identify key sounds that prove difficult to detect in each algorithm. The end goal is to determine the best algorithm for analyzing speech from individuals with Down Syndrome and to ultimately provide an inclusive and more accurate algorithm. We are also planning to use estate of the art AI algorithms such as OpenAI and AssemblyAI. Acknowledgments: The first three authors equally contributed to this paper. We also thank Dr. Vivian Genaro Motti for her contributions to this research

Further Studies on the Transient Stability of Synchronous-Synchronous Rotary Frequency Converter Fed Railways with Low-Frequency ac High-Voltage Transmission

This paper continues the pursuit of getting a deeper understanding regarding the transient stability of low-frequency AC railway power systems operated at 16 2/3 Hz that are synchronously connected to the public grid. Here, the focus is set on such grids with a low-frequency AC high-voltage transmission line subject to a fault. The study here is limited to railways being fed by different distributions of Rotary Frequency Converter (RFC) types. Both auto transformer (AT) and booster transformer (BT) catenaries are considered. No mixed model configurations in the converter stations (CSs) are considered in this study. Therefore, only interactions between RFCs in different CSs and between RFCs, the fault, and the load can take place in this study. The RFC dynamic models are essentially two Anderson-Fouad models of synchronous machines coupled mechanically by their rotors being connected to the same mechanical shaft. Besides the new cases studied, also a new voltage-dependent active power load model is presented and used in this study

Programmable models of growth and mutation of cancer-cell populations

In this paper we propose a systematic approach to construct mathematical models describing populations of cancer-cells at different stages of disease development. The methodology we propose is based on stochastic Concurrent Constraint Programming, a flexible stochastic modelling language. The methodology is tested on (and partially motivated by) the study of prostate cancer. In particular, we prove how our method is suitable to systematically reconstruct different mathematical models of prostate cancer growth - together with interactions with different kinds of hormone therapy - at different levels of refinement.Comment: In Proceedings CompMod 2011, arXiv:1109.104

Excited State Dynamics of Bistridentate and Trisbidentate RuII Complexes of Quinoline-Pyrazole Ligands

Three homoleptic ruthenium(II) complexes, [Ru(Q3PzH)3]2+, [Ru(Q1Pz)3]2+, and [Ru(DQPz)2]2+, based on the quinoline-pyrazole ligands, Q3PzH (8-(3-pyrazole)-quinoline), Q1Pz (8-(1-pyrazole)-quinoline), and DQPz (bis(quinolinyl)-1,3-pyrazole), have been spectroscopically and theoretically investigated. Spectral component analysis, transient absorption spectroscopy, density functional theory calculations, and ligand exchange reactions with different chlorination agents reveal that the excited state dynamics for Ru(II) complexes with these biheteroaromatic ligands differ significantly from that of traditional polypyridyl complexes. Despite the high energy and low reorganization energy of the excited state, nonradiative decay dominates even at liquid nitrogen temperatures, where triplet metal-to-ligand-charge-transfer emission quantum yields range from 0.7 to 3.8%, and microsecond excited state lifetimes are observed. In contrast to traditional polypyridyl complexes where ligand exchange is facilitated by expansion of the metal-ligand bonds to stabilize a metal-centered state, photoinduced ligand exchange occurs in the bidentate complexes despite no substantial MC state population, while the tridentate complex is extremely photostable despite an activated decay route, highlighting the versatile photochemistry of nonpolypyridine ligands.\ua0\ua9 2019 American Chemical Society

Approximate Bayesian Computation by Subset Simulation for model selection in dynamical systems

Approximate Bayesian Computation (ABC) methods are originally conceived to expand the horizon of Bayesian inference methods to the range of models for which only forward simulation is available. However, there are well-known limitations of the ABC approach to the Bayesian model selection problem, mainly due to lack of a sufficient summary statistics that work across models. In this paper, we show that formulating the standard ABC posterior distribution as the exact posterior PDF for a hierarchical state-space model class allows us to independently estimate the evidence for each alternative candidate model. We also show that the model evidence is a simple by-product of the ABC-SubSim algorithm. The validity of the proposed approach to ABC model selection is illustrated using simulated data from a three-story shear building with Masing hysteresis

Cooling process for inelastic Boltzmann equations for hard spheres, Part II: Self-similar solutions and tail behavior

We consider the spatially homogeneous Boltzmann equation for inelastic hard spheres, in the framework of so-called constant normal restitution coefficients. We prove the existence of self-similar solutions, and we give pointwise estimates on their tail. We also give general estimates on the tail and the regularity of generic solutions. In particular we prove Haff 's law on the rate of decay of temperature, as well as the algebraic decay of singularities. The proofs are based on the regularity study of a rescaled problem, with the help of the regularity properties of the gain part of the Boltzmann collision integral, well-known in the elastic case, and which are extended here in the context of granular gases.Comment: 41 page