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BioTag: Robust RFIDbased Continuous User Verification Using Physiological Features from RespirationFree, publiclyaccessible full text available October 17, 2023

Free, publiclyaccessible full text available September 26, 2023

The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energybased model, where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an “analysis by synthesis” scheme, which iterates (1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via backpropagation through time, and (2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a convenient approximation of the above learning method, where we replace the sampling in the synthesis step by optimization. Moreover, to make the sampling or optimization more efficient, we propose to train the energybased model simultaneously with a topdown trajectory generator via cooperative learning, where the trajectory generator ismore »Free, publiclyaccessible full text available January 1, 2023

Free, publiclyaccessible full text available October 4, 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 a belief vector which is used to make a path plan, which then in turn helps to predict a longrange trajectory. The effectiveness of LBEBM and the twostep approach are supported by strong empirical results. Our model is able to make accurate, multimodal, and social compliant trajectory predictions and improves over prior stateofthearts performance on the Stanford Drone trajectory prediction benchmark by 10:9% and on the ETHUCY benchmark by 27:6%.

We combine equation of state of dense matter up to twice nuclear saturation density (nsat = 0.16 fm−3 ) obtained using chiral effective field theory (χEFT), and recent observations of neutron stars to gain insights about the highdensity matter encountered in their cores. A key element in our study is the recent Bayesian analysis of correlated EFT truncation errors based on orderbyorder calculations up to nexttonexttonexttoleading order in the χEFT expansion. We refine the bounds on the maximum mass imposed by causality at high densities, and provide stringent limits on the maximum and minimum radii of ∼ 1.4 M and ∼ 2.0 M stars. Including χEFT predictions from nsat to 2 nsat reduces the permitted ranges of the radius of a 1.4 M star, R1.4, by ∼ 3.5 km. If observations indicate R1.4 < 11.2 km, our study implies that either the squared speed of sound c 2 s > 1/2 for densities above 2 nsat, or that χEFT breaks down below 2 nsat. We also comment on the nature of the secondary compact object in GW190814 with mass ' 2.6 M , and discuss the implications of massive neutron stars > 2.1 M (2.6 M ) in future radiomore »

This paper proposes a new metalearning method – named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from short event sequence data along with a relational network. Specifically, we propose a hierarchical Bayesian mixture Hawkes process model, which naturally incorporates the relational information among sequences into point process modeling. Compared with existing methods, our model can capture the underlying mixedcommunity patterns of the relational network, which simultaneously encourages knowledge sharing among sequences and facilitates adaptive learning for each individual sequence. We further propose an efficient stochastic variational meta expectation maximization algorithm that can scale to large problems. Numerical experiments on both synthetic and real data show that HARMLESS outperforms existing methods in terms of predicting the future events.