Privacy in Cross-border Digital Currency. A Transatlantic Approach

This paper is one of four publications launched at the inaugural Frankfurt Forum on US-European GeoEconomics held in Germany from September 27 – 29, 2022. Co-hosted by the Atlantic Council GeoEconomics Center and Atlantik-Brücke, the Frankfurt Forum anchors critical work on transatlantic economic cooperation. The war in Ukraine, and the G7 response, reminded the world of the impact of transatlantic coordination. As part of the Frankfurt Forum, this new research aims to advance transatlantic dialogue from crisis response to addressing the key economic issues that will underpin the US-EU partnership over the next decade. The goal of the Frankfurt Forum is to deliver a blueprint for cooperation in four key areas: digital currencies, monetary policy, international trade, and economic statecraft

The NASA computer science research program plan

A taxonomy of computer science is included, one state of the art of each of the major computer science categories is summarized. A functional breakdown of NASA programs under Aeronautics R and D, space R and T, and institutional support is also included. These areas were assessed against the computer science categories. Concurrent processing, highly reliable computing, and information management are identified

Multi-GPU design and performance evaluation of homomorphic encryption on GPU clusters

We present a multi-GPU design, implementation and performance evaluation of the Halevi-Polyakov-Shoup (HPS) variant of the Fan-Vercauteren (FV) levelled Fully Homomorphic Encryption (FHE) scheme. Our design follows a data parallelism approach and uses partitioning methods to distribute the workload in FV primitives evenly across available GPUs. The design is put to address space and runtime requirements of FHE computations. It is also suitable for distributed-memory architectures, and includes efficient GPU-to-GPU data exchange protocols. Moreover, it is user-friendly as user intervention is not required for task decomposition, scheduling or load balancing. We implement and evaluate the performance of our design on two homogeneous and heterogeneous NVIDIA GPU clusters: K80, and a customized P100. We also provide a comparison with a recent shared-memory-based multi-core CPU implementation using two homomorphic circuits as workloads: vector addition and multiplication. Moreover, we use our multi-GPU Levelled-FHE to implement the inference circuit of two Convolutional Neural Networks (CNNs) to perform homomorphically image classification on encrypted images from the MNIST and CIFAR - 10 datasets. Our implementation provides 1 to 3 orders of magnitude speedup compared with the CPU implementation on vector operations. In terms of scalability, our design shows reasonable scalability curves when the GPUs are fully connected.This work is supported by A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Programmtic Programme (Award A19E3b0099).Peer ReviewedPostprint (author's final draft

On the usage of GRECOSAR, an orbital polarimetric SAR simulator of complex targets, to vessel classification studies

This paper presents a synthetic aperture radar (SAR) simulator that is able to generate polarimetric SAR (POLSAR) and polarimetric inverse SAR data of complex targets. It solves the electromagnetic problem via high-frequency approximations, such as physical optics and the physical theory of diffraction, with notable computational efficiency. In principle, any orbital monostatic sensor working at any band, resolution, and operating mode can be modeled. To make simulations more realistic, the target’s bearing and speed are considered, and for the particular case of vessels, even the translational and rotational movements induced by the sea state. All these capabilities make the simulator a powerful tool for supplying large amounts of data with precise scenario information and for testing future sensor configurations. In this paper, the usefulness of the simulator on vessel classification studies is assessed. Several simulated polarimetric images are presented to analyze the potentialities of coherent target decompositions for classifying complex geometries, thus basing an operational algorithm. The limitations highlighted by the results suggest that other approaches, like POLSAR interferometry, should be explored.Peer Reviewe

On the usage of GRECOSAR: an orbital polarimetric SAR simulator of complex targets for vessel classification studies

This paper presents a synthetic aperture radar (SAR) simulator that is able to generate polarimetric SAR (POLSAR) and polarimetric inverse SAR data of complex targets. It solves the electromagnetic problem via high-frequency approximations, such as physical optics and the physical theory of diffraction, with notable computational efficiency. In principle, any orbital monostatic sensor working at any band, resolution, and operating mode can be modeled. To make simulations more realistic, the target’s bearing and speed are considered, and for the particular case of vessels, even the translational and rotational movements induced by the sea state. All these capabilities make the simulator a powerful tool for supplying large amounts of data with precise scenario information and for testing future sensor configurations. In this paper, the usefulness of the simulator on vessel classification studies is assessed. Several simulated polarimetric images are presented to analyze the potentialities of coherent target decompositions for classifying complex geometries, thus basing an operational algorithm. The limitations highlighted by the results suggest that other approaches, like POLSAR interferometry, should be explored.Peer Reviewe

Proceedings Work-In-Progress Session of the 13th Real-Time and Embedded Technology and Applications Symposium

The Work-In-Progress session of the 13th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS\u2707) presents papers describing contributions both to state of the art and state of the practice in the broad field of real-time and embedded systems. The 17 accepted papers were selected from 19 submissions. This proceedings is also available as Washington University in St. Louis Technical Report WUCSE-2007-17, at http://www.cse.seas.wustl.edu/Research/FileDownload.asp?733. Special thanks go to the General Chairs – Steve Goddard and Steve Liu and Program Chairs - Scott Brandt and Frank Mueller for their support and guidance

Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

Fusion of Interferometric SAR and Photogrammetric Elevation Data

Neurology & clinical neurophysiolog