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340 results on '"Pereira, Francisco C."'

51. The Geography of Taste: Analyzing Cell-Phone Mobility and Social Events

Author

Calabrese, Francesco, Pereira, Francisco C., Di Lorenzo, Giusy, Liu, Liang, Ratti, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Floréen, Patrik, editor, Krüger, Antonio, editor, and Spasojevic, Mirjana, editor

Published
2010
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52. Place Enrichment by Mining the Web

Author

Alves, Ana O., Pereira, Francisco C., Biderman, Assaf, Ratti, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Tscheligi, Manfred, editor, de Ruyter, Boris, editor, Markopoulus, Panos, editor, Wichert, Reiner, editor, Mirlacher, Thomas, editor, Meschterjakov, Alexander, editor, and Reitberger, Wolfgang, editor

Published
2009
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53. Semantics of Place: Ontology Enrichment

Author

Antunes, Bruno, Alves, Ana, Pereira, Francisco C., Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Geffner, Hector, editor, Prada, Rui, editor, Machado Alexandre, Isabel, editor, and David, Nuno, editor

Published
2008
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59. Using CBR for Semantic Analysis of Software Specifications

Author

Seco, Nuno, Gomes, Paulo, Pereira, Francisco C., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Funk, Peter, editor, and González Calero, Pedro A., editor

Published
2004
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60. REBUILDER: A CBR Approach to Knowledge Management in Software Design

Author

Gomes, Paulo, Pereira, Francisco C., Paiva, Paulo, Seco, Nuno, Carreiro, Paulo, Ferreira, José L., Bento, Carlos, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Melnik, Grigori, editor, and Holz, Harald, editor

Published
2004
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65. Using CBR for Automation of Software Design Patterns

Author

Gomes, Paulo, Pereira, Francisco C., Paiva, Paulo, Seco, Nuno, Carreiro, Paulo, Ferreira, José L., Bento, Carlos, Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Craw, Susan, editor, and Preece, Alun, editor

Published
2002
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66. Experiments on Case-Based Retrieval of Software Designs

Author

Gomes, Paulo, Pereira, Francisco C., Paiva, Paulo, Seco, Nuno, Carreiro, Paulo, Ferreira, José L., Bento, Carlos, Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Craw, Susan, editor, and Preece, Alun, editor

Published
2002
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67. Combining Case-Based Reasoning and Analogical Reasoning in Software Design

Author

Gomes, Paulo, Pereira, Francisco C., Seco, Nuno, Paiva, Paulo, Carreiro, Paulo, Ferreira, José L., Bento, Carlos, Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, O’Neill, Michael, editor, Sutcliffe, Richard F. E., editor, Ryan, Conor, editor, Eaton, Malachy, editor, and Griffith, Niall J. L., editor

Published
2002
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69. A visão dos recursos humanos ao trabalho análogo à escravidão no meio empresarial.

Author

FRAGA, Leilane R., MATHEUS, Pollyana S., NASCIMENTO, Deyvison R., and PEREIRA, Francisco C.

Subjects
INDUSTRIAL relations, SLAVE labor, SLAVERY, DIGNITY
Abstract

Copyright of Revista Espacios is the property of Talleres de Impresos Oma and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023
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70. Darwinci: Creating Bridges to Creativity

Author

Pereira, Francisco C., Machado, Penousal, Cardoso, Amílcar, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, and de Oliveira, Flávio Moreira, editor

Published
1998
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72. Modeling Censored Mobility Demand Through Censored Quantile Regression Neural Networks

Author

Huttel, Frederik Boe, Peled, Inon, Rodrigues, Filipe, Pereira, Francisco C., Huttel, Frederik Boe, Peled, Inon, Rodrigues, Filipe, and Pereira, Francisco C.

Abstract

Shared mobility services require accurate demand models for effective service planning. On the one hand, modeling the full probability distribution of demand is advantageous because the entire uncertainty structure preserves valuable information for decision-making. On the other hand, demand is often observed through the usage of the service itself, so that the observations are censored, as they are inherently limited by available supply. Since the 1980s, various works on Censored Quantile Regression models have performed well under such conditions. Further, in the last two decades, several papers have proposed to implement these models flexibly through Neural Networks. However, the models in current works estimate the quantiles individually, thus incurring a computational overhead and ignoring valuable relationships between the quantiles. We address this gap by extending current Censored Quantile Regression models to learn multiple quantiles at once and apply these to synthetic baseline datasets and datasets from two shared mobility providers in the Copenhagen metropolitan area in Denmark. The results show that our extended models yield fewer quantile crossings and less computational overhead without compromising model performance.

Published
2022

76. Electrochemical reduction and cathodic stripping voltammetric determination of clotrimazole

Author

Pereira Francisco C., Stradiotto Nelson R., and Zanoni Maria Valnice B.

Subjects
clotrimazole, cyclic voltammetry, differential pulse polarography, cathodic stripping voltammetry, Chemistry, QD1-999
Abstract

The reduction of clotrimazole at a mercury electrode, in phosphate buffer, pH>6, involves a reversible one-electron process. The electrochemical process presents a large contribution from adsorption effects. For the differential pulse polarographic determination the addition of Triton X-100 is recommended. Clotrimazole can be determined by cathodic stripping voltammetry at 50 ng mL-1 level when pre-accumulated for 3 min at an accumulation potential of -0.20 V. The proposed method is applied successfully for the determination of clotrimazole in a commercial formulation.

Published
2001

78. On the quality requirements of demand prediction for dynamic public transport

Author

Peled, Inon (author), Lee, Kelvin (author), Jiang, Yu (author), Dauwels, J.H.G. (author), Pereira, Francisco C. (author), Peled, Inon (author), Lee, Kelvin (author), Jiang, Yu (author), Dauwels, J.H.G. (author), and Pereira, Francisco C. (author)

Abstract

As Public Transport (PT) becomes more dynamic and demand-responsive, it increasingly depends on predictions of transport demand. But how accurate need such predictions be for effective PT operation? We address this question through an experimental case study of PT trips in Metropolitan Copenhagen, Denmark, which we conduct independently of any specific prediction models. First, we simulate errors in demand prediction through unbiased noise distributions that vary considerably in shape. Using the noisy predictions, we then simulate and optimize demand-responsive PT fleets via a linear programming formulation and measure their performance. Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors. In particular, the optimized performance can improve under non-Gaussian vs. Gaussian noise. We also find that dynamic routing could reduce trip time by at least 23% vs. static routing. This reduction is estimated at 809,000 €/year in terms of Value of Travel Time Savings for the case study., Circuits and Systems

Published
2021
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79. cAMP and Schwann cells promote axonal growth and functional recovery after spinal cord injury

Author

Pearse, Damien D, Pereira, Francisco C, Marcillo, Alexander E, Bates, Margaret L, Berrocal, Yerko A, Filbin, Marie T, and Bunge, Mary Bartlett

Abstract

Central neurons regenerate axons if a permissive environment is provided; after spinal cord injury, however, inhibitory molecules are present that make the local environment nonpermissive. A promising new strategy for inducing neurons to overcome inhibitory signals is to activate cAMP signaling. Here we show thatcAMP levels fall in the rostral spinal cord, sensorimotor cortex and brainstem after spinal cord contusion. Inhibition of cAMP hydrolysis by the phosphodiesterase IV inhibitor rolipram prevents this decrease and when combined with Schwann cell grafts promotes significant supraspinal and proprioceptive axon sparing and myelination. Furthermore, combining rolipram with an injection of db-cAMP near the graft not only prevents the drop in cAMP levels but increases them above those in uninjured controls. This further enhances axonal sparing and myelination, promotes growth of serotonergic fibers into and beyond grafts, and significantly improves locomotion. These findings show that cAMP levels are key for protection, growth and myelination of injured CNS axons in vivo and recovery of function., Author(s): Damien D Pearse (corresponding author) [1]; Francisco C Pereira [2]; Alexander E Marcillo [1]; Margaret L Bates [1]; Yerko A Berrocal [1]; Marie T Filbin [3]; Mary Bartlett Bunge [...]

Published
2004
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89. QTIP: Quick simulation-based adaptation of traffic model per incident parameters.

Author

Peled, Inon, Kamalakar, Raghuveer, Azevedo, Carlos Lima, and Pereira, Francisco C.

Abstract

Current data-driven traffic prediction models are usually trained with large datasets, e.g., several months of speeds and flows. Such models provide very good fit for ordinary road conditions, but often fail just when they are most needed: when traffic suffers a sudden and significant disruption, e.g., a road incident. In this work, we describe QTIP: a simulation-based framework for quasi-instantaneous adaptation of prediction models upon traffic disruption. In a nutshell, QTIP performs real-time simulations of the affected road for multiple scenarios, analyzes the results, and suggests a change to an ordinary prediction model accordingly. QTIP constructs the simulated scenarios per properties of the incident, as conveyed by real-time distress signals from In-Vehicle Monitor Systems, which are becoming increasingly prevalent worldwide. We experiment QTIP in a case study of a Danish motorway, and the results show that QTIP can improve traffic prediction in the first critical minutes of road incidents. [ABSTRACT FROM AUTHOR]

Published
2022
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90. Rethinking travel behavior modeling representations through embeddings

Author

Pereira, Francisco C.

Subjects
FOS: Economics and business, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Econometrics (econ.EM), Computation and Language (cs.CL), Machine Learning (cs.LG), Economics - Econometrics
Abstract

This paper introduces the concept of travel behavior embeddings, a method for re-representing discrete variables that are typically used in travel demand modeling, such as mode, trip purpose, education level, family type or occupation. This re-representation process essentially maps those variables into a latent space called the \emph{embedding space}. The benefit of this is that such spaces allow for richer nuances than the typical transformations used in categorical variables (e.g. dummy encoding, contrasted encoding, principal components analysis). While the usage of latent variable representations is not new per se in travel demand modeling, the idea presented here brings several innovations: it is an entirely data driven algorithm; it is informative and consistent, since the latent space can be visualized and interpreted based on distances between different categories; it preserves interpretability of coefficients, despite being based on Neural Network principles; and it is transferrable, in that embeddings learned from one dataset can be reused for other ones, as long as travel behavior keeps consistent between the datasets. The idea is strongly inspired on natural language processing techniques, namely the word2vec algorithm. Such algorithm is behind recent developments such as in automatic translation or next word prediction. Our method is demonstrated using a model choice model, and shows improvements of up to 60\% with respect to initial likelihood, and up to 20% with respect to likelihood of the corresponding traditional model (i.e. using dummy variables) in out-of-sample evaluation. We provide a new Python package, called PyTre (PYthon TRavel Embeddings), that others can straightforwardly use to replicate our results or improve their own models. Our experiments are themselves based on an open dataset (swissmetro).

Published
2020

97. Active learning metamodeling for policy analysis

Author

Antunes, Francisco, Soares Amorim, Marco Raul, Pereira, Francisco C., Ribeiro, Bernadete, and Publica

Abstract

Simulation approaches constitute a well-established tool to model, understand, and predict the behavior of transportation systems, and ultimately to assess the performance of transportation policies. Due to their multidimensionality nature, such systems are not often approachable through conventional analytic methods, making simulation modeling the only reliable tool of study. Nevertheless, simulation models can turn out to be computationally expensive when embedded with enough detail. An immediate answer to this shortcoming is the use of simulation metamodels that are designed to approximate the simulators' results. In this work, the authors propose a metamodeling approach based on active learning that seeks to improve the exploration process of the simulation input space and the associated output behavior. A Gaussian Process (GP) is used as a metamodel to approximate the function inherently defined by the simulation model itself. The GPs can nicely handle the uncertainty associated with their predictions, which eventually can be improved with active learning through simulation runs. This property provides a practical and efficient way to analyze the simulator's behavior and therefore, to assess the performance of policies regarding the underlying real-world systems and services. The authors illustrate the proposed methodology using an Emergency Medical Service (EMS) simulator. Two outputs are analyzed and compared, namely, the survival rate and response time averages. The medical emergency response time recommendation of 8 min is explored as well ist relation with the survival rate. The results show that this methodology can identify regions in the simulation input space that might affect the performance and success of medical policies with regards to emergency vehicle dispatching services.

Published
2019

100. Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Author

Rodrigues, Filipe, Henrickson, Kristian, Pereira, Francisco C., Rodrigues, Filipe, Henrickson, Kristian, and Pereira, Francisco C.

Abstract

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for a good deal of analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this paper, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using six months of crowdsourced traffic speed data or ``probe vehicle data'' for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.

Published
2019

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