Four methods for generation of turbulent phase screens: comparison. (arXiv:1911.09185v1 [eess.IV])

We introduce a new method for generation of the phase screen samples with arbitrary spatial spectrum: Sparse Spectrum with uniform wave vectors (SU). Similar to the known Sparse Spectrum (SS) technique, it uses trigonometric series with random discrete support on the wave vector plane, but, unlike the SS technique, the random wave vectors are uniformly…

Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control. (arXiv:1911.09214v1 [math.OC])

Today’s fast linear algebra and numerical optimization tools have pushed the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems. The field of hybrid MPC has demonstrated that exact optimal control law can be computed, e.g., by mixed-integer programming (MIP) under piecewise-affine (PWA) system models. Despite the…

REVAMP$^2$T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking. (arXiv:1911.09217v1 [cs.CV])

This article presents REVAMP$^2$T, Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking, as an integrated end-to-end IoT system for privacy-built-in decentralized situational awareness. REVAMP$^2$T presents novel algorithmic and system constructs to push deep learning and video analytics next to IoT devices (i.e. video cameras). On the algorithm side, REVAMP$^2$T proposes a unified integrated computer…

Iterative Peptide Modeling With Active Learning And Meta-Learning. (arXiv:1911.09103v1 [q-bio.BM])

Often the development of novel materials is not amenable to high-throughput or purely computational screening methods. Instead, materials must be synthesized one at a time in a process that does not generate significant amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both…

On Universal Features for High-Dimensional Learning and Inference. (arXiv:1911.09105v1 [cs.LG])

We consider the problem of identifying universal low-dimensional features from high-dimensional data for inference tasks in settings involving learning. For such problems, we introduce natural notions of universality and we show a local equivalence among them. Our analysis is naturally expressed via information geometry, and represents a conceptually and computationally useful analysis. The development reveals…

OmniFold: A Method to Simultaneously Unfold All Observables. (arXiv:1911.09107v1 [hep-ph])

Collider data must be corrected for detector effects (“unfolded”) to be compared with theoretical calculations and measurements from other experiments. Unfolding is traditionally done for individual, binned observables without including all information relevant for characterizing the detector response. We introduce OmniFold, an unfolding method that iteratively reweights a simulated dataset, using machine learning to capitalize…

OmniFold: A Method to Simultaneously Unfold All Observables. (arXiv:1911.09107v1 [hep-ph])

Collider data must be corrected for detector effects (“unfolded”) to be compared with theoretical calculations and measurements from other experiments. Unfolding is traditionally done for individual, binned observables without including all information relevant for characterizing the detector response. We introduce OmniFold, an unfolding method that iteratively reweights a simulated dataset, using machine learning to capitalize…

Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations. (arXiv:1911.09100v1 [math.OC])

Continuous influence maximization (CIM) generalizes the original influence maximization by incorporating general marketing strategies: a marketing strategy mix is a vector $\boldsymbol x = (x_1,\dots,x_d)$ such that for each node $v$ in a social network, $v$ could be activated as a seed of diffusion with probability $h_v(\boldsymbol x)$, where $h_v$ is a strategy activation function…

Automatic Differentiable Monte Carlo: Theory and Application. (arXiv:1911.09117v1 [physics.comp-ph])

Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored. Here we present the general theory enabling infinite-order automatic differentiation on expectations computed by Monte Carlo with unnormalized probability distributions, which we call “automatic differentiable Monte…