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CyberPhysical Systems (CPS) are important components of critical infrastructure and must operate with high levels of reliability and security. We propose a conceptual approach to securing CPSs: the CyberPhysical Immune System (CPIS), a collection of hardware and software elements deployed on top of a conventional CPS. Inspired by its biological counterpart, the CPIS comprises an independent network of distributed computing units that collects data from the conventional CPS, utilizes datadriven techniques to identify threats, adapts to the changing environment, alerts the user of any threats or anomalies, and deploys threatmitigation strategies.Free, publiclyaccessible full text available October 15, 2022

We propose to learn energybased model (EBM) in the latent space of a generator model, so that the EBM serves as a prior model that stands on the topdown networkofthegeneratormodel. BoththelatentspaceEBMandthetopdown network can be learned jointly by maximum likelihood, which involves shortrun MCMC sampling from both the prior and posterior distributions of the latent vector. Due to the low dimensionality of the latent space and the expressiveness of the topdown network, a simple EBM in latent space can capture regularities in the data effectively, and MCMC sampling in latent space is efficient and mixes well. We show that the learnedmore »Free, publiclyaccessible full text available July 1, 2022

Human trajectory prediction is critical for autonomous platforms like selfdriving cars or social robots. We present a latent belief energybased model (LBEBM) for diverse human trajectory forecast. LBEBM is a probabilistic model with cost function defined in the latent space to account for the movement history and social context. The low dimensionality of the latent space and the high expressivity of the EBM make it easy for the model to capture the multimodality of pedestrian trajectory distributions. LBEBM is learned from expert demonstrations (i.e., human trajectories) projected into the latent space. Sampling from or optimizing the learned LBEBM yields amore »Free, publiclyaccessible full text available April 1, 2022

Latent variable models for text, when trained successfully, accurately model the data distribution and capture global semantic and syntactic features of sentences. The prominent approach to train such models is variational autoencoders (VAE). It is nevertheless challenging to train and often results in a trivial local optimum where the latent variable is ignored and its posterior collapses into the prior, an issue known as posterior collapse. Various techniques have been proposed to mitigate this issue. Most of them focus on improving the inference model to yield latent codes of higher quality. The present work proposes a short run dynamics formore »

This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in topdown architectures. Such models have high expressivity and allow for learning hierarchical representations. Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference typically requires Markov chain Monte Caro (MCMC) that can be time consuming. In this paper, we propose to use noise initialized nonpersistent short run MCMC, such as nite step Langevin dynamics initialized from the prior distribution of the latent variables, asmore »

Abstract We present our current best estimate of the plausible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitationalwave detectors over the next several years, with the intention of providing information to facilitate planning for multimessenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitationalwave signals for the third (O3), fourth (O4) and fifth observing (O5) runs, including the planned upgrades of the Advanced LIGO and Advanced Virgo detectors. We study the capability of the network to determine the sky location of the source for gravitationalwave signals from the inspiral of binary systemsmore »