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Domain decomposition methods with fundamental solutions for Helmholtz problems with discontinuous source terms
Publication . Alves, Carlos J. S.; Martins, Nuno F. M.; Valtchev, Svilen S.
The direct application of the classical method of fundamental solutions (MFS) is restricted to homogeneous linear partial differential equations (PDEs). The use of fundamental solutions with different frequencies allowed the extension of the MFS to non-homogeneous PDEs, in particular, for Poisson or Helmholtz equations and for elastostatic or elastodynamic problems. This method has been called method of fundamental solutions for domains (MFS-D), but it faces an approximation problem when the non-homogeneous term presents discontinuities, because the fundamental solutions are analytic functions outside the source point set. In this paper we analyze two domain decomposition techniques for overcoming this approximation problem. The problem is set in the context of the modified Helmholtz equation, and we also establish the missing density results that justify both the MFS and the MFS-D approximations. Numerical results are presented comparing a direct and an iterative domain decomposition technique, with simulations in non-trivial domains.
Data warehousing in the context of a Bologna undergraduate degree
Publication . Ramos, José Vitor; Rui Oliveira
Facing the incessant growth of data that organizations have to deal with on a daily basis, decision support systems and data warehousing techniques assume a vital importance in supporting the decision making process. Taking this necessity in consideration, the Bologna process was an opportunity to introduce data warehousing competences in the
undergraduate degree of Informatics Engineering at Polytechnic Institute of Leiria (IPL). The paper focus some aspects related with this adaptation, the difficulties and challenges during the implementation process and the solutions adopted, towards an effective acquisition of competences by students.
Coding Tree Depth Estimation for Complexity Reduction of HEVC
Publication . Correa, G.; Assunção, P.; Agostini, L.; Cruz, L. A. D. S.
The emerging HEVC standard introduces a number of tools which increase compression efficiency in comparison to its predecessors at the cost of greater computational complexity. This paper proposes a complexity control method for HEVC encoders based on dynamic adjustment of the newly proposed coding tree structures. The method improves a previous solution by adopting a strategy that takes into consideration both spatial and temporal correlation in order to decide the maximum coding tree depth allowed for each coding tree block. Complexity control capability is increased in comparison to a previous work, while compression losses are decreased by 70%. Experimental results show that the encoder computational complexity can be downscaled to 60% with an average bit rate increase around 1.3% and a PSNR decrease under 0.07 dB.
ASA classification – What is the real impact of the introduction of the new clinical examples?
Publication . Godinho, Pedro; Gonçalves, Lúcia; Muendane, Paulo; Dixe, Maria dos Anjos; Valente, Elisabete
Aim
The American Society of Anesthesiologists scale is used worldwide for the assessment of the physical status of patients proposed for anaesthesia interventions. This study aims to assess the level of agreement of the last updated American Society of Anesthesiologists classification version, with the introduction of examples for each class, and search for variables that could promote inconsistency.
Methods
An online questionnaire was sent to anaesthesiology specialists and residents in Portugal, describing 10 fictitious clinical cases. Sociodemographic and labour data were also correlated.
Results/findings: A total of 243 anaesthesiology physicians participated. There was a high diversity in responses. Years of practice influence this diversity (P < 0.05).
Discussion and conclusions: The need for a universal scale for classification of patients proposed for anaesthesia is consensual. Despite the last update in 2014, the American Society of Anesthesiologists classification continues to present limitations regarding consistency and objectivity. Efforts should be made to reduce their interpersonal variability.
Customized crowds and active learning to improve classification
Publication . Costa, Joana; Silva, Catarina; Antunes, Mário; Ribeiro, Bernardete
Traditional classification algorithms can be limited in their performance when a specific user is targeted. User preferences, e.g. in recommendation systems, constitute a challenge for learning algorithms. Additionally, in recent years user’s interaction through crowdsourcing has drawn significant interest, although its use in learning settings is still underused.
In this work we focus on an active strategy that uses crowd-based non-expert information to appropriately tackle the problem of capturing the drift between user preferences in a recommendation system. The proposed method combines two main ideas: to apply active strategies for adaptation to each user; to implement crowdsourcing to avoid excessive user feedback. A similitude technique is put forward to optimize the choice of the more appropriate similitude-wise crowd, under the guidance of basic user feedback.
The proposed active learning framework allows non-experts classification performed by crowds to be used to define the user profile, mitigating the labeling effort normally requested to the user. The framework is designed to be generic and suitable to be applied to different scenarios, whilst customizable for each specific user. A case study on humor classification scenario is used to demonstrate experimentally that the approach can improve baseline active results.