Johannes Kepler University of Linz

About: Johannes Kepler University of Linz is a education organization based out in Linz, Oberösterreich, Austria. It is known for research contribution in the topics: Computer science & Thin film. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

Isogeometric analysis with geometrically continuous functions on two-patch geometries

Abstract: We study the linear space of C s -smooth isogeometric functions defined on a multi-patch domain? ? ? R 2 . We show that the construction of these functions is closely related to the concept of geometric continuity of surfaces, which has originated in geometric design. More precisely, the C s -smoothness of isogeometric functions is found to be equivalent to geometric smoothness of the same order ( G s -smoothness) of their graph surfaces. This motivates us to call them C s -smooth geometrically continuous isogeometric functions. We present a general framework to construct a basis and explore potential applications in isogeometric analysis. The space of C 1 -smooth geometrically continuous isogeometric functions on bilinearly parameterized two-patch domains is analyzed in more detail. Numerical experiments with bicubic and biquartic functions for performing L 2 approximation and for solving Poisson's equation and the biharmonic equation on two-patch geometries are presented and indicate optimal rates of convergence.

Partial Escape Analysis and Scalar Replacement for Java

Abstract: Escape Analysis allows a compiler to determine whether an object is accessible outside the allocating method or thread. This information is used to perform optimizations such as Scalar Replacement, Stack Allocation and Lock Elision, allowing modern dynamic compilers to remove some of the abstractions introduced by advanced programming models.The all-or-nothing approach taken by most Escape Analysis algorithms prevents all these optimizations as soon as there is one branch where the object escapes, no matter how unlikely this branch is at runtime.This paper presents a new, practical algorithm that performs control flow sensitive Partial Escape Analysis in a dynamic Java compiler. It allows Escape Analysis, Scalar Replacement and Lock Elision to be performed on individual branches. We implemented the algorithm on top of Graal, an open-source Java just-in-time compiler, and it performs well on a diverse set of benchmarks.In this paper, we evaluate the effect of Partial Escape Analysis on the DaCapo, ScalaDaCapo and SpecJBB2005 benchmarks, in terms of run-time, number and size of allocations and number of monitor operations. It performs particularly well in situations with additional levels of abstraction, such as code generated by the Scala compiler. It reduces the amount of allocated memory by up to 58.5%, and improves performance by up to 33%.

Information-Theoretic Bounds on Quantum Advantage in Machine Learning

Abstract: We study the performance of classical and quantum machine learning (ML) models in predicting outcomes of physical experiments. The experiments depend on an input parameter x and involve execution of a (possibly unknown) quantum process E. Our figure of merit is the number of runs of E required to achieve a desired prediction performance. We consider classical ML models that perform a measurement and record the classical outcome after each run of E, and quantum ML models that can access E coherently to acquire quantum data; the classical or quantum data are then used to predict the outcomes of future experiments. We prove that for any input distribution D(x), a classical ML model can provide accurate predictions on average by accessing E a number of times comparable to the optimal quantum ML model. In contrast, for achieving an accurate prediction on all inputs, we prove that the exponential quantum advantage is possible. For example, to predict the expectations of all Pauli observables in an n-qubit system ρ, classical ML models require 2^{Ω(n)} copies of ρ, but we present a quantum ML model using only O(n) copies. Our results clarify where the quantum advantage is possible and highlight the potential for classical ML models to address challenging quantum problems in physics and chemistry.

Graph networks for molecular design

Abstract: Deep learning methods applied to chemistry can be used to accelerate the discovery of new molecules. This work introduces GraphINVENT, a platform developed for graph-based molecular design using graph neural networks (GNNs). GraphINVENT uses a tiered deep neural network architecture to probabilistically generate new molecules a single bond at a time. All models implemented in GraphINVENT can quickly learn to build molecules resembling the training set molecules without any explicit programming of chemical rules. The models have been benchmarked using the MOSES distribution-based metrics, showing how GraphINVENT models compare well with state-of-the-art generative models. This work is one of the first thorough graph-based molecular design studies, and illustrates how GNN-based models are promising tools for molecular discovery.

Dynamic code evolution for Java

Abstract: Dynamic code evolution is a technique to update a program while it is running. In an object-oriented language such as Java, this can be seen as replacing a set of classes by new versions. We modified an existing high-performance virtual machine to allow arbitrary changes to the definition of loaded classes. Besides adding and deleting fields and methods, we also allow any kind of changes to the class and interface hierarchy. Our approach focuses on increasing developer productivity during debugging. Changes can be applied at any point a Java program can be suspended. The evaluation section shows that our modifications to the virtual machine have no negative performance impact on normal program execution. The fast in-place instance update algorithm ensures that the performance characteristics of a change are comparable with performing a full garbage collection run. Standard Java development environments are capable of using the code evolution features of our modified virtual machine, so no additional tools are required.