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Institution

INESC-ID

NonprofitLisbon, Portugal
About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.


Papers
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Book ChapterDOI
20 Mar 2010
TL;DR: This work defines an encoding of parallel recovery into static recovery enjoying nice compositionality properties, showing that the two approaches have the same expressive power.
Abstract: Modern software systems have frequently to face unexpected events, reacting so to reach a consistent state. In the field of concurrent and mobile systems (e.g., for web services) the problem is usually tackled using long running transactions and compensations: activities programmed to recover partial executions of long running transactions. We compare the expressive power of different approaches to the specification of those compensations. We consider (i) static recovery, where the compensation is statically defined together with the transaction, (ii) parallel recovery, where the compensation is dynamically built as parallel composition of compensation elements and (iii) general dynamic recovery, where more refined ways of composing compensation elements are provided. We define an encoding of parallel recovery into static recovery enjoying nice compositionality properties, showing that the two approaches have the same expressive power. We also show that no such encoding of general dynamic recovery into static recovery is possible, i.e. general dynamic recovery is strictly more expressive.

46 citations

Journal ArticleDOI
01 May 2014-Energy
TL;DR: In this article, the optimal weekly scheduling of a pumped storage hydro (PSH) unit in a price-taker and price-maker scenario was investigated in a liberalized electricity market under a pricemaker context.

45 citations

Book ChapterDOI
03 Oct 2005
TL;DR: This work proposes an algorithm that finds and reports all relevant biclusters in time linear on the size of the data matrix by manipulating a discretized version of the matrix and by using string processing techniques based on suffix trees.
Abstract: Several non-supervised machine learning methods have been used in the analysis of gene expression data obtained from microarray experiments Recently, biclustering, a non-supervised approach that performs simultaneous clustering on the row and column dimensions of the data matrix, has been shown to be remarkably effective in a variety of applications The goal of biclustering is to find subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated behaviors In the most common settings, biclustering is an NP-complete problem, and heuristic approaches are used to obtain sub-optimal solutions using reasonable computational resources In this work, we examine a particular setting of the problem, where we are concerned with finding biclusters in time series expression data In this context, we are interested in finding biclusters with consecutive columns For this particular version of the problem, we propose an algorithm that finds and reports all relevant biclusters in time linear on the size of the data matrix This complexity is obtained by manipulating a discretized version of the matrix and by using string processing techniques based on suffix trees We report results in both synthetic and real data that show the effectiveness of the approach

45 citations

Journal ArticleDOI
TL;DR: The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies, suggesting that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Abstract: The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

45 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered the impact of the losses in the conductors associated with the efficiency of the equipment, allowing better use of the available energy, and proposed an investment analysis of efficiency and sustainable street lighting via simulation and experimental results.

45 citations


Authors

Showing all 967 results

NameH-indexPapersCitations
João Carvalho126127877017
Jaime G. Carbonell7249631267
Chris Dyer7124032739
Joao P. S. Catalao68103919348
Muhammad Bilal6372014720
Alan W. Black6141319215
João Paulo Teixeira6063619663
Bhiksha Raj5135913064
Joao Marques-Silva482899374
Paulo Flores483217617
Ana Paiva474729626
Miadreza Shafie-khah474508086
Susana Cardoso444007068
Mark J. Bentum422268347
Joaquim Jorge412906366
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202311
202252
202196
2020131
2019133
2018126