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1. Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems

   https://doi.org/10.1109/TCBB.2018.2880234  

   Bahadorinejad, Arghavan; Imani, Mahdi; Braga-Neto, Ulisses (November 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

   We propose a novel methodology for fault detection and diagnosis in partially-observed Boolean dynamical systems (POBDS). These are stochastic, highly nonlinear, and derivative- less systems, rendering difficult the application of classical fault detection and diagnosis methods. The methodology comprises two main approaches. The first addresses the case when the normal mode of operation is known but not the fault modes. It applies an innovations filter (IF) to detect deviations from the nominal normal mode of operation. The second approach is applicable when the set of possible fault models is finite and known, in which case we employ a multiple model adaptive estimation (MMAE) approach based on a likelihood-ratio (LR) statistic. Unknown system parameters are estimated by an adaptive expectation- maximization (EM) algorithm. Particle filtering techniques are used to reduce the computational complexity in the case of systems with large state-spaces. The efficacy of the proposed methodology is demonstrated by numerical experiments with a large gene regulatory network (GRN) with stuck-at faults observed through a single noisy time series of RNA-seq gene expression measurements. 

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2. PROBABILISTIC PCA FOR HETEROSCEDASTIC DATA

   Hong, David; Balzano, Laura; Fessler, Jeffrey A (January 2019, IEEE CAMSAP 2019)

   Principal Component Analysis (PCA) is a standard dimensionality reduction technique, but it treats all samples uniformly, making it suboptimal for heterogeneous data that are increasingly common in modern settings. This paper proposes a PCA variant for samples with heterogeneous noise levels, i.e., heteroscedastic noise, that naturally arise when some of the data come from higher quality sources than others. The technique handles heteroscedasticity by incorporating it in the statistical model of a probabilistic PCA. The resulting optimization problem is an interesting nonconvex problem related to but not seemingly solved by singular value decomposition, and this paper derives an expectation maximization (EM) algorithm. Numerical experiments illustrate the benefits of using the proposed method to combine samples with heteroscedastic noise in a single analysis, as well as benefits of careful initialization for the EM algorithm. Index Terms— Principal component analysis, heterogeneous data, maximum likelihood estimation, latent factors 

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3. Sequential Monte Carlo with model tempering

   https://doi.org/10.1515/snde-2022-0103  

   Mlikota, Marko; Schorfheide, Frank (May 2023, Studies in Nonlinear Dynamics & Econometrics)

   Abstract Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and two nonlinear dynamic stochastic general equilibrium models. The runtime reductions we obtain range from 27 % to 88 %. 

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4. A Converse Result on Convergence Time for Opportunistic Wireless Scheduling

   Neely, Michael J. (January 2020, IEEE INFOCOM---International Conference on Computer Communications)

   This paper proves an impossibility result for stochastic network utility maximization for multi-user wireless systems, including multi-access and broadcast systems. Every time slot an access point observes the current channel states and opportunistically selects a vector of transmission rates. Channel state vectors are assumed to be independent and identically distributed with an unknown probability distribution. The goal is to learn to make decisions over time that maximize a concave utility function of the running time average transmission rate of each user. Recently it was shown that a stochastic Frank-Wolfe algorithm converges to utility-optimality with an error of O(log(T)/T ), where T is the time the algorithm has been running. An existing O(1/T ) converse is known. The current paper improves the converse to O(log(T)/T), which matches the known achievability result. The proof uses a reduction from the opportunistic scheduling problem to a Bernoulli estimation problem. Along the way, it refines a result on Bernoulli estimation. 

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5. Network Diffusions via Neural Mean-Field Dynamics

   He, Shushan; Zha, Hongyuan; Ye, Xiaojing (December 2020, Advances in neural information processing systems)

   Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M. F.; Lin, H. (Ed.)

   We propose a novel learning framework based on neural mean-field dynamics for inference and estimation problems of diffusion on networks. Our new framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities, which renders a delay differential equation with memory integral approximated by learnable time convolution operators, resulting in a highly structured and interpretable RNN. Directly using cascade data, our framework can jointly learn the structure of the diffusion network and the evolution of infection probabilities, which are cornerstone to important downstream applications such as influence maximization. Connections between parameter learning and optimal control are also established. Empirical study shows that our approach is versatile and robust to variations of the underlying diffusion network models, and significantly outperform existing approaches in accuracy and efficiency on both synthetic and real-world data. 

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