Adaptive, Block-Structured Multigrid on Local Memory Machines

A language is considered in which the reader can express such properties of block-structured programs with recursive functions as correctness and partial correctness. The semantics of this language is fully described by a set of schemes of axioms and inference rules. The completeness theorem and the soundness theorem for this axiomatization are proved.

Download Full-text

Multimodal emotion recognition with hierarchical memory networks

Intelligent Data Analysis ◽

10.3233/ida-205183 ◽

2021 ◽

Vol 25 (4) ◽

pp. 1031-1045

Author(s):

Helang Lai ◽

Keke Wu ◽

Lingli Li

Keyword(s):

Emotion Recognition ◽

The Self ◽

Global Memory ◽

Human Computer Interactions ◽

Accuracy Improvement ◽

Local Memory ◽

Hierarchical Memory ◽

Multimodal Emotion Recognition ◽

Novel Model ◽

Computer Interactions

Emotion recognition in conversations is crucial as there is an urgent need to improve the overall experience of human-computer interactions. A promising improvement in this field is to develop a model that can effectively extract adequate contexts of a test utterance. We introduce a novel model, termed hierarchical memory networks (HMN), to address the issues of recognizing utterance level emotions. HMN divides the contexts into different aspects and employs different step lengths to represent the weights of these aspects. To model the self dependencies, HMN takes independent local memory networks to model these aspects. Further, to capture the interpersonal dependencies, HMN employs global memory networks to integrate the local outputs into global storages. Such storages can generate contextual summaries and help to find the emotional dependent utterance that is most relevant to the test utterance. With an attention-based multi-hops scheme, these storages are then merged with the test utterance using an addition operation in the iterations. Experiments on the IEMOCAP dataset show our model outperforms the compared methods with accuracy improvement.

Download Full-text

AMReX: Block-structured adaptive mesh refinement for multiphysics applications

The International Journal of High Performance Computing Applications ◽

10.1177/10943420211022811 ◽

2021 ◽

pp. 109434202110228

Author(s):

Weiqun Zhang ◽

Andrew Myers ◽

Kevin Gott ◽

Ann Almgren ◽

John Bell

Keyword(s):

Adaptive Mesh Refinement ◽

Mesh Refinement ◽

Computational Cost ◽

Adaptive Mesh ◽

Uniform Mesh ◽

Physical Processes ◽

Structured Adaptive Mesh Refinement ◽

Core Elements ◽

Accelerator Design ◽

Block Structured

Block-structured adaptive mesh refinement (AMR) provides the basis for the temporal and spatial discretization strategy for a number of Exascale Computing Project applications in the areas of accelerator design, additive manufacturing, astrophysics, combustion, cosmology, multiphase flow, and wind plant modeling. AMReX is a software framework that provides a unified infrastructure with the functionality needed for these and other AMR applications to be able to effectively and efficiently utilize machines from laptops to exascale architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multiphysics algorithms. AMReX supports algorithms that solve systems of partial differential equations in simple or complex geometries and those that use particles and/or particle–mesh operations to represent component physical processes. In this article, we will discuss the core elements of the AMReX framework such as data containers and iterators as well as several specialized operations to meet the needs of the application projects. In addition, we will highlight the strategy that the AMReX team is pursuing to achieve highly performant code across a range of accelerator-based architectures for a variety of different applications.

Download Full-text

Displaced Art and the Reconstruction of Memory: Ukrainian Artists from Crimea and Donbas

Open Cultural Studies ◽

10.1515/culture-2018-0063 ◽

2018 ◽

Vol 2 (1) ◽

pp. 700-709

Author(s):

Iuliia Lashchuk

Keyword(s):

Cultural Context ◽

Main Idea ◽

Personal History ◽

Local Memory ◽

Multicultural Identity ◽

Left Behind ◽

The People

Abstract After the occupation of Crimea and the conflict in Eastern Ukraine, many people were forced to leave their homes and look for a new place to live. The cultural context, memories, narratives, including the scarcely built identity of artificially made sites like those from Donbas (Donetsk and Luhansk regions) and the multicultural identity of Crimea, were all destroyed and left behind. Among the people who left their roots and moved away were many artists, who naturally fell into two groups-the ones who wanted to remember and the ones who wanted to forget. The aim of this paper is to analyse the ways in which the local memory of those lost places is represented in the works of Ukrainian artists from the conflict territories, who were forced to change their dwelling- place. The main idea is to show how losing the memory of places, objects, sounds, etc. affects the continuity of personal history.

Download Full-text