Elliptic partial differential equations (PDEs) are a major class of time-independent PDEs that play a key role in many scientific and engineering domains such as fluid dynamics, plasma physics, and solid mechanics. Recently, neural operators have emerged as a promising technique to solve elliptic PDEs more efficiently by directly mapping the input to solutions. However, existing networks typically neglect complex geometries and inhomogeneous boundary values present in the real world. Here we introduce Boundary-Embedded Neural Operators (BENO), a novel neural operator architecture that embeds the complex geometries and inhomogeneous boundary values into the solving of elliptic PDEs. Inspired by classical Green's function, BENO consists of two Graph Neural Networks (GNNs) for interior source term and boundary values, respectively. Furthermore, a Transformer encoder maps the global boundary geometry into a latent vector which influences each message passing layer of the GNNs. We test our model and strong baselines extensively in elliptic PDEs with complex boundary conditions. We show that all existing baseline methods fail to learn the solution operator. In contrast, our model, endowed with boundary-embedded architecture, outperforms state-of-the-art neural operators and strong baselines by an average of 60.96%.

Embedding a modern language runtime as a component in a larger software system is popular these days. Communication between these systems often requires keeping references to each others' objects. In this paper we present and discuss the problem of cross-component memory management where reference cycles across component boundaries may lead to memory leaks and premature reclamation of objects may lead to dangling cross-component references. We provide a generic algorithm for effective, efficient, and safe garbage collection over component boundaries, which we call cross-component tracing. We designed and implemented cross-component tracing in the Chrome web browser where the JavaScript virtual machine V8 is embedded into the rendering engine Blink. Cross-component tracing from V8's JavaScript heap to Blink's C++ heap improves garbage collection latency and eliminates long-standing memory leaks for real websites in Chrome. We show how cross-component tracing can help web developers to reason about reachability and retainment of objects spanning both V8 and Blink components based on Chrome's heap snapshot memory tool. Cross-component tracing was enabled by default for all websites in Chrome version 57 and is also deployed in other widely used software systems such as Opera, Cobalt, and Electron.

Practical parallel scientific applications with domain decompositions, such as finite element methods, require irregular domain decompositions of complicated-shaped objects. However, existing PGAS frameworks, such as Global Arrays and XcalableMP, have supported the productive description of exchanging ghost points only for regular domain decompositions. With these backgrounds, we propose, implement and evaluate Distributed Memory Interface (DMI), a global address space framework for irregular applications. DMI provides highly productive APIs called read-write-set for irregular domain decompositions and complicated orderings in practical scientific applications.

An improved scheme using vertical block allocation to embed data in industrial-quality monochrome analog pictures by modulo masking is investigated. The video signal on each scan line is sampled, and a data bit is inserted into a block of three pels by a scrambling of the luminance level of only one pel in the block. The performance of the system is compared to that of a conventional system. The number of data bits embedded in an image for the proposed system is about 1.3 times as large as that of the conventional system. In addition, the SNR (signal-to-noise ratio) of the recovered image in the proposed system is increased by about 3-4 dB.>