In this paper we introduce MOMEMTO (MOre MEMory Than Others) a new set of kernel mechanisms that allow users to have full control of the distributed shared memory on a cluster of personal computers. In contrast to man...
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In this paper we introduce MOMEMTO (MOre MEMory Than Others) a new set of kernel mechanisms that allow users to have full control of the distributed shared memory on a cluster of personal computers. In contrast to many existing software DSM systems, MOMEMTO supports efficiently and flexibly global shared-memory allowing applications to address larger memory space than that available in a single node. MOMEMTO has been implemented in the Linux 2.4 kernel and preliminary performance results show that MOMEMTO has low memory management and communication overheads and that it can indeed perform very well for large memory configurations.
The paper presents a comparison between two unsupervised neural network models: (i) the well-known fuzzy ART, and (ii) AUTOWISARD, a new unsupervised version of the classic WISARD weightless neural network model. It i...
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The paper presents a comparison between two unsupervised neural network models: (i) the well-known fuzzy ART, and (ii) AUTOWISARD, a new unsupervised version of the classic WISARD weightless neural network model. It is shown that AUTOWISARD is simple, fast and stable, whilst keeping compatibility with the original WISARD architecture. Experimental test results over binary patterns benchmarks have shown that, although both unsupervised learning models are remarkably simple, AUTOWISARD consistently exhibits better classification skills than fuzzy ART. It is also shown that such superiority happens thanks to AU-TOWISARD's richer internal representation of the trained patterns and the training methods employed by the algorithm, such as the learning window and partial training strategies.
In this paper we introduce a scalable Video-on-Demand (VoD) system called GloVE (Global Video Environment) in which active clients cooperate to create a shareable video cache that is used as the primary source of vide...
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In this paper we introduce a scalable Video-on-Demand (VoD) system called GloVE (Global Video Environment) in which active clients cooperate to create a shareable video cache that is used as the primary source of video content for subsequent client requests. In this way, GloVE server's bandwidth does not limit the number of simultaneous clients that can watch a video since once its content is in the cooperative video cache (CVC) it can be directly transmitted from the cache rather than the VoD server Also, GloVE follows the peer-to-peer approach, allowing the use of low-cost PCs as video servers. In addition, GloVE supports video servers without multicast capability and videos in any stored format. We analyze preliminary performance results of GloVE implemented in a PC server using a Fast Ethernet interconnect and small video buffers at the clients. Our results confirm that while the GloVE-based server uses only a single video channel to deliver a highly popular video simultaneously to N clients, conventional VoD servers require as much as N times more channels.
We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to...
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We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
This work in traducest wo new unsupervised learning algorithms based on the WISARD weightless neural classifier model. The first one, the standard AUTOWISARD model, is able to perform fast one-shot, learning of unsort...
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We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict nume...
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ISBN:
(纸本)0780370449
We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict numerical values. We consider three versions of the FBP, each one with a different dependence among the input data: independence, first-order and second-order dependence. For verifying the efficiency of the FBP's prediction, we compare it with two fuzzy systems and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor i...
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Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor in which an elastic ring is attracted by points in a bi-dimensional space while their internal elastic forces try to shun the elastic expansion. The different weights associated with these two kinds of forces lead the elastic to a gradual expansion in the direction of the bi-dimensional points. In this method, the elastic net algorithm is employed with the help of a heuristic framework that improves its performance for application in the n-dimensional space of cluster analysis. Tests were made with two types of data sets: (1) simulated data sets with up to 1000 points randomly generated in groups linearly separable with up to dimension 10 and (2) the Fisher Iris Plant database, a well-known database referred to in the pattern recognition literature. The advantages of the method presented are its simplicity, its fast and stable convergence, beyond efficiency in cluster analysis.
Developing transformative pathways for industry's compliance with international climate targets requires model-based insights into how supply- and demand-side measures affect industry, material cycles, global...
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Developing transformative pathways for industry's compliance with international climate targets requires model-based insights into how supply- and demand-side measures affect industry, material cycles, global supply chains, socioeconomic activities, and service provisioning that support societal well-being. We review the recent literature modeling the industrial system in low energy and material demand futures, which mitigates environmental impacts without relying on risky future negative emissions and technological fixes. We identify 77 innovative studies drawing on nine distinct industry modeling traditions. We critically assess system definitions and scopes, biophysical and thermodynamic consistency, granularity and heterogeneity, and operationalization of demand and service provisioning. We find that combined supply- and demand-side measures could reduce current economy-wide material use by 56%, energy use by 40% to 60%, and greenhouse gas emissions by 70% to net zero. We call for strengthened interdisciplinary collaborations between industry modeling traditions and demand-side research to produce more insightful scenarios, and we discuss challenges and recommendations for this emerging field.
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