2,819 research outputs found
Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems
A key challenge in multi-robot and multi-agent systems is generating
solutions that are robust to other self-interested or even adversarial parties
who actively try to prevent the agents from achieving their goals. The
practicality of existing works addressing this challenge is limited to only
small-scale synchronous decision-making scenarios or a single agent planning
its best response against a single adversary with fixed, procedurally
characterized strategies. In contrast this paper considers a more realistic
class of problems where a team of asynchronous agents with limited observation
and communication capabilities need to compete against multiple strategic
adversaries with changing strategies. This problem necessitates agents that can
coordinate to detect changes in adversary strategies and plan the best response
accordingly. Our approach first optimizes a set of stratagems that represent
these best responses. These optimized stratagems are then integrated into a
unified policy that can detect and respond when the adversaries change their
strategies. The near-optimality of the proposed framework is established
theoretically as well as demonstrated empirically in simulation and hardware
Accurate geometry reconstruction of vascular structures using implicit splines
3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy
The Glasgow raspberry pi cloud: a scale model for cloud computing infrastructures
Data Centers (DC) used to support Cloud services
often consist of tens of thousands of networked machines under a single roof. The significant capital outlay required to replicate such infrastructures constitutes a major obstacle to practical implementation and evaluation of research in this domain. Currently, most research into Cloud computing relies on either limited software simulation, or the use of a testbed environments
with a handful of machines. The recent introduction of the
Raspberry Pi, a low-cost, low-power single-board computer, has made the construction of a miniature Cloud DCs more affordable.
In this paper, we present the Glasgow Raspberry Pi Cloud
(PiCloud), a scale model of a DC composed of clusters of
Raspberry Pi devices. The PiCloud emulates every layer of a
Cloud stack, ranging from resource virtualisation to network
behaviour, providing a full-featured Cloud Computing research and educational environment
Robust eye tracking based on multiple corneal reflections for clinical applications
Postprint (published version
GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization
There are plenty of graph neural network (GNN) accelerators being proposed.
However, they highly rely on users' hardware expertise and are usually
optimized for one specific GNN model, making them challenging for practical use
. Therefore, in this work, we propose GNNBuilder, the first automated, generic,
end-to-end GNN accelerator generation framework. It features four advantages:
(1) GNNBuilder can automatically generate GNN accelerators for a wide range of
GNN models arbitrarily defined by users; (2) GNNBuilder takes standard PyTorch
programming interface, introducing zero overhead for algorithm developers; (3)
GNNBuilder supports end-to-end code generation, simulation, accelerator
optimization, and hardware deployment, realizing a push-button fashion for GNN
accelerator design; (4) GNNBuilder is equipped with accurate performance models
of its generated accelerator, enabling fast and flexible design space
exploration (DSE). In the experiments, first, we show that our accelerator
performance model has errors within for latency prediction and
for BRAM count prediction. Second, we show that our generated accelerators can
outperform CPU by and GPU by . This framework is
open-source, and the code is available at
https://anonymous.4open.science/r/gnn-builder-83B4/.Comment: 10 pages, 7 figures, 4 tables, 3 listing
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