Brian Kelley

Matthias Mayr, Alexander Heinlein, Christian Glusa et al.

Trilinos is a community-developed, open-source software framework that facilitates building large-scale, complex, multiscale, multiphysics simulation code bases for scientific and engineering problems. Since the Trilinos framework has undergone substantial changes to support new applications and new hardware architectures, this document is an update to ``An Overview of the Trilinos project'' by Heroux et al. (ACM Transactions on Mathematical Software, 31(3):397-423, 2005). It describes the design of Trilinos, introduces its new organization in product areas, and highlights established and new features available in Trilinos. Particular focus is put on the modernized software stack based on the Kokkos ecosystem to deliver performance portability across heterogeneous hardware architectures. This paper also outlines the organization of the Trilinos community and the contribution model to help onboard interested users and contributors.

Kevin Yang, Kyle Swanson, Wengong Jin et al.

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial datasets spanning a wide variety of chemical endpoints. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows.

Nima Namvar, Walid Saad, Niloofar Bahadori et al.

Due to its scale and largely interconnected nature, the Internet of Things (IoT) will be vulnerable to a number of security threats that range from physical layer attacks to network layer attacks. In this paper, a novel anti-jamming strategy for OFDM-based IoT systems is proposed which enables an IoT controller to protect the IoT devices against a malicious radio jammer. The interaction between the controller node and the jammer is modeled as a Colonel Blotto game with continuous and asymmetric resources in which the IoT controller, acting as defender, seeks to thwart the jamming attack by distributing its power among the subcarries in a smart way to decrease the aggregate bit error rate (BER) caused by the jammer. The jammer, on the other hand, aims at disrupting the system performance by allocating jamming power to different frequency bands. To solve the game, an evolutionary algorithm is proposed which can find a mixed-strategy Nash equilibrium of the Blotto game. Simulation results show that the proposed algorithm enables the IoT controller to maintain the BER above an acceptable threshold, thereby preserving the IoT network performance in the presence of malicious jamming.