Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models

The approaches taken to describe and develop spatial discretisations of the domains required for geophysical simulation models are commonly ad hoc, model or application specific and under-documented. This is particularly acute for simulation models that are flexible in their use of multi-scale, anisotropic, fully unstructured meshes where a relatively large number of heterogeneous parameters are required to constrain their full description. As a consequence, it can be difficult to reproduce simulations, ensure a provenance in model data handling and initialisation, and a challenge to conduct model intercomparisons rigorously. This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions, that enables full accounts of provenance, sharing and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed, that is automated, robust and repeatable, quick-to-draft, rigorously verified and consistent to the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics.Comment: 18 pages, 10 figures, 1 table. Submitted for publication and under revie

A design representation model for high-level synthesis

Design tools share and exchange various types of information pertaining to the design. The identification of a uniform design representation to capture this information is essential for the development of a successful design environment. We have done an extensive study on the representation needs of existing database tools in the UCI CADLAB; examples of which are graph compilers for high-level hardware specifications, state schedulers, hardware allocators, and microarchitecture optimizers. The result of this study is the development of a design representation model that will serve as a common internal representation (DDM) for all system and behavioral synthesis tools. DDM thus builds the foundation for a CAD Framework in which design tools can communicate via operating on this common representation. The design information is composed of three separate graph models: the conceptual model, the behavioral model and the structural model. The conceptual model (represented by a Design Entity Graph) captures the overall organization of the design information, such as, versions and configurations. The behavioral model (represented by an Augmented Control/Data Flow Graph) describes the design behavior. The structural model (represented by an Annotated Component Graph) captures the hierarchical data path structure and its geometric information. In this paper, we define the last two graph models. They both capture the actual design data of the application domain. Since VHDL has gained increasing popularity as hardware description language for synthesis, we give numerous examples throughout this report that show how the proposed design representation model can be used to represent VHDL specifications

Hierarchical Attention Network for Action Segmentation

The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture frame-level salient aspects through attention but they lack the capacity to effectively map the temporal relationships in between the frames as they only capture a limited span of temporal dependencies. To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time, thus improving the overall segmentation performance. The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales, to form embeddings at frame level and segment level, and perform fine-grained action segmentation. This generates a simple, lightweight, yet extremely effective architecture for segmenting continuous video streams and has multiple application domains. We evaluate our system on multiple challenging public benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech Egocentric datasets, and achieves state-of-the-art performance. The evaluated datasets encompass numerous video capture settings which are inclusive of static overhead camera views and dynamic, ego-centric head-mounted camera views, demonstrating the direct applicability of the proposed framework in a variety of settings.Comment: Published in Pattern Recognition Letter