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Publications

Publications by Conceição Nunes Rocha

2010

Time Domain BRS Estimation: Least Squares versus Quantile Regression

Authors
Gouveia, S; Rocha, C; Rocha, AP; Silva, ME;

Publication
COMPUTING IN CARDIOLOGY 2010, VOL 37

Abstract
The BRS can be quantified as the slope between SBP and RR values identified in baroreflex events, estimated by ordinary least squares (OLS) minimization. Quantile regression (QR) is a more robust procedure than OLS and allows a more complete characterization of the data, by estimating conditional functions for different quantiles of interest. In this work, OLS and QR for BRS estimation are compared regarding slope estimates and dispersion. The EuroBaVar results indicate that OLS slope and QR slopes at different quantiles do not exhibit significant differences. Also, OLS and QR slopes require similar number of beats to achieve a given BRS precision in stationary recordings. Finally, BRS estimated with OLS exhibit relative dispersion lower than 10% and 5% when computed from stationary recordings of approximately 3 and 9 minutes length, respectively.

2016

PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese

Authors
Rocha, Conceicao; Jorge, Alipio; Sionara, Roberta; Brito, Paula; Pimenta, Carlos; Rezende, SolangeO.;

Publication
CoRR

Abstract

2023

Report on the 6th International Workshop on Narrative Extraction from Texts (Text2Story 2023) at ECIR 2023

Authors
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S; Litvak, M; Cordeiro, JP; Rocha, C; Sousa, H; Mansouri, B;

Publication
SIGIR Forum

Abstract

2024

Report on the 7th International Workshop on Narrative Extraction from Texts (Text2Story 2024) at ECIR 2024

Authors
Campos, R; Jorge, AM; Jatowt, A; Bhatia, S; Litvak, M; Cordeiro, JP; Rocha, C; Sousa, HO; Mansouri, B;

Publication
SIGIR Forum

Abstract

2014

Data Assimilation: Contribuições para a Modelação, Previsão e Controlo em Anestesia

Authors
Maria da Conceição de Oliveira Nunes Rocha;

Publication

Abstract

2025

Resilient Agent-Based Networks in the Automotive Industry

Authors
, A; Rocha, C; Campos, P;

Publication
Machine Learning Perspectives of Agent-Based Models

Abstract
The present work is inspired by the aftermarket companies of the automotive industry. The goal is to investigate how companies react to market change, by understanding the effect of a perturbation (such as a business cessation) on the rest of the companies that are interconnected through peer-to-peer relationships. An agent-based model has been developed that simulates a multilayer network involving different types of companies: suppliers, aftermarket companies; retailers and consumers. The effect of the cessation is measured by the resilience of the multilayer network after suffering the perturbation. The multilayer network is inspired in a business model of the automobile industry’s aftermarket and each type of company has some defined characteristics. The agent-based model produces the network dynamics due to the changes in its configuration throughout time. No learning mechanism is introduced in this work. We demonstrate that the number of links, the volume of sales and the total profit of a node in the network has an impact on its survival throughout time. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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