@article{4, abstract = {We present a data-driven technique to instantly predict how fluid flows around various three-dimensional objects. Such simulation is useful for computational fabrication and engineering, but is usually computationally expensive since it requires solving the Navier-Stokes equation for many time steps. To accelerate the process, we propose a machine learning framework which predicts aerodynamic forces and velocity and pressure fields given a threedimensional shape input. Handling detailed free-form three-dimensional shapes in a data-driven framework is challenging because machine learning approaches usually require a consistent parametrization of input and output. We present a novel PolyCube maps-based parametrization that can be computed for three-dimensional shapes at interactive rates. This allows us to efficiently learn the nonlinear response of the flow using a Gaussian process regression. We demonstrate the effectiveness of our approach for the interactive design and optimization of a car body.}, author = {Umetani, Nobuyuki and Bickel, Bernd}, journal = {ACM Trans. Graph.}, number = {4}, publisher = {ACM}, title = {{Learning three-dimensional flow for interactive aerodynamic design}}, doi = {10.1145/3197517.3201325}, volume = {37}, year = {2018}, } @inproceedings{183, abstract = {Fault-localization is considered to be a very tedious and time-consuming activity in the design of complex Cyber-Physical Systems (CPS). This laborious task essentially requires expert knowledge of the system in order to discover the cause of the fault. In this context, we propose a new procedure that AIDS designers in debugging Simulink/Stateflow hybrid system models, guided by Signal Temporal Logic (STL) specifications. The proposed method relies on three main ingredients: (1) a monitoring and a trace diagnostics procedure that checks whether a tested behavior satisfies or violates an STL specification, localizes time segments and interfaces variables contributing to the property violations; (2) a slicing procedure that maps these observable behavior segments to the internal states and transitions of the Simulink model; and (3) a spectrum-based fault-localization method that combines the previous analysis from multiple tests to identify the internal states and/or transitions that are the most likely to explain the fault. We demonstrate the applicability of our approach on two Simulink models from the automotive and the avionics domain.}, author = {Bartocci, Ezio and Ferrere, Thomas and Manjunath, Niveditha and Nickovic, Dejan}, location = {Porto, Portugal}, pages = {197 -- 206}, publisher = {Association for Computing Machinery, Inc}, title = {{Localizing faults in simulink/stateflow models with STL}}, doi = {10.1145/3178126.3178131}, year = {2018}, } @article{566, abstract = {We consider large random matrices X with centered, independent entries which have comparable but not necessarily identical variances. Girko's circular law asserts that the spectrum is supported in a disk and in case of identical variances, the limiting density is uniform. In this special case, the local circular law by Bourgade et. al. [11,12] shows that the empirical density converges even locally on scales slightly above the typical eigenvalue spacing. In the general case, the limiting density is typically inhomogeneous and it is obtained via solving a system of deterministic equations. Our main result is the local inhomogeneous circular law in the bulk spectrum on the optimal scale for a general variance profile of the entries of X. }, author = {Alt, Johannes and Erdös, László and Krüger, Torben H}, journal = {Annals Applied Probability }, number = {1}, pages = {148--203}, publisher = {Institute of Mathematical Statistics}, title = {{Local inhomogeneous circular law}}, doi = {10.1214/17-AAP1302}, volume = {28}, year = {2018}, } @article{106, abstract = {The goal of this article is to introduce the reader to the theory of intrinsic geometry of convex surfaces. We illustrate the power of the tools by proving a theorem on convex surfaces containing an arbitrarily long closed simple geodesic. Let us remind ourselves that a curve in a surface is called geodesic if every sufficiently short arc of the curve is length minimizing; if, in addition, it has no self-intersections, we call it simple geodesic. A tetrahedron with equal opposite edges is called isosceles. The axiomatic method of Alexandrov geometry allows us to work with the metrics of convex surfaces directly, without approximating it first by a smooth or polyhedral metric. Such approximations destroy the closed geodesics on the surface; therefore it is difficult (if at all possible) to apply approximations in the proof of our theorem. On the other hand, a proof in the smooth or polyhedral case usually admits a translation into Alexandrov’s language; such translation makes the result more general. In fact, our proof resembles a translation of the proof given by Protasov. Note that the main theorem implies in particular that a smooth convex surface does not have arbitrarily long simple closed geodesics. However we do not know a proof of this corollary that is essentially simpler than the one presented below.}, author = {Akopyan, Arseniy and Petrunin, Anton}, journal = {Mathematical Intelligencer}, number = {3}, pages = {26 -- 31}, publisher = {Springer}, title = {{Long geodesics on convex surfaces}}, doi = {10.1007/s00283-018-9795-5}, volume = {40}, year = {2018}, } @misc{9810, author = {Chaudhry, Waqas and Pleska, Maros and Shah, Nilang and Weiss, Howard and Mccall, Ingrid and Meyer, Justin and Gupta, Animesh and Guet, Calin C and Levin, Bruce}, publisher = {Public Library of Science}, title = {{Numerical data used in figures}}, doi = {10.1371/journal.pbio.2005971.s008}, year = {2018}, }