3,547 research outputs found
A Bayesian Poisson-Gaussian Process Model for Popularity Learning in Edge-Caching Networks
Edge-caching is recognized as an efficient technique for future cellular
networks to improve network capacity and user-perceived quality of experience.
To enhance the performance of caching systems, designing an accurate content
request prediction algorithm plays an important role. In this paper, we develop
a flexible model, a Poisson regressor based on a Gaussian process, for the
content request distribution.
The first important advantage of the proposed model is that it encourages the
already existing or seen contents with similar features to be correlated in the
feature space and therefore it acts as a regularizer for the estimation.
Second, it allows to predict the popularities of newly-added or unseen contents
whose statistical data is not available in advance. In order to learn the model
parameters, which yield the Poisson arrival rates or alternatively the content
\textit{popularities}, we invoke the Bayesian approach which is robust against
over-fitting.
However, the resulting posterior distribution is analytically intractable to
compute. To tackle this, we apply a Markov Chain Monte Carlo (MCMC) method to
approximate this distribution which is also asymptotically exact. Nevertheless,
the MCMC is computationally demanding especially when the number of contents is
large. Thus, we employ the Variational Bayes (VB) method as an alternative low
complexity solution. More specifically, the VB method addresses the
approximation of the posterior distribution through an optimization problem.
Subsequently, we present a fast block-coordinate descent algorithm to solve
this optimization problem. Finally, extensive simulation results both on
synthetic and real-world datasets are provided to show the accuracy of our
prediction algorithm and the cache hit ratio (CHR) gain compared to existing
methods from the literature
Development of an efficient hybrid GA-PSO approach applicable for well placement optimization
When it comes to the economic efficiency of oil and gas field development, finding the optimum well locations that augment an economical cost function like net present value (NPV) is of paramount importance. Well location optimization has long been a challenging problem due to the heterogeneous nature of hydrocarbon reservoirs, economic criteria, and technical uncertainties. These complexities lead to an enormous number of possible solutions that must be evaluated using an evaluation function (e.g. a simulator). This makes it necessary to develop a powerful optimization algorithm into which a fast function evaluation tool is incorporated. The present study describes the application of a combination of the genetic algorithm (GA) and the particle swarm optimization (PSO) into a hybrid GA-PSO algorithm that is implemented in a streamline simulator to determine optimal locations for production and injection wells across heterogeneous reservoir models. Performance of the hybrid GA-PSO algorithm is then compared to that of the PSO and the GA separately. The results confirm that compared to conventional methods, the recommended method provides a fast and well-defined approach for production optimization complications.Cited as: Yazdanpanah, A., Rezaei, A., Mahdiyar, H., Kalantariasl, A. Development of an efficient hybrid GA-PSO approach applicable for well. Advances in Geo-Energy Research, 2019, 3(4): 365-374, doi: 10.26804/ager.2019.04.0
Practical issues for the implementation of survivability and recovery techniques in optical networks
Many-core and heterogeneous architectures: programming models and compilation toolchains
1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInopartially_openembargoed_20211002Barchi, Francesc
MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
Placement is an essential task in modern chip design, aiming at placing
millions of circuit modules on a 2D chip canvas. Unlike the human-centric
solution, which requires months of intense effort by hardware engineers to
produce a layout to minimize delay and energy consumption, deep reinforcement
learning has become an emerging autonomous tool. However, the learning-centric
method is still in its early stage, impeded by a massive design space of size
ten to the order of a few thousand. This work presents MaskPlace to
automatically generate a valid chip layout design within a few hours, whose
performance can be superior or comparable to recent advanced approaches. It has
several appealing benefits that prior arts do not have. Firstly, MaskPlace
recasts placement as a problem of learning pixel-level visual representation to
comprehensively describe millions of modules on a chip, enabling placement in a
high-resolution canvas and a large action space. It outperforms recent methods
that represent a chip as a hypergraph. Secondly, it enables training the policy
network by an intuitive reward function with dense reward, rather than a
complicated reward function with sparse reward from previous methods. Thirdly,
extensive experiments on many public benchmarks show that MaskPlace outperforms
existing RL approaches in all key performance metrics, including wirelength,
congestion, and density. For example, it achieves 60%-90% wirelength reduction
and guarantees zero overlaps. We believe MaskPlace can improve AI-assisted chip
layout design. The deliverables are released at
https://laiyao1.github.io/maskplace
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