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587 results on '"Oliveira, Arlindo"'

355. Focussed Structured Document Retrieval

Author

Kazai, Gabrialla, Lalmas, Mounia, Roelleke, Thomas, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Laender, Alberto H. F., editor, and Oliveira, Arlindo L., editor

Published
2002
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356. Java MARIAN: From an OPAC to a Modern Digital Library System

Author

Gonçalves, Marcos André, Mather, Paul, Wang, Jun, Zhou, Ye, Luo, Ming, Richardson, Ryan, Shen, Rao, Xu, Liang, Fox, Edward A., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Laender, Alberto H. F., editor, and Oliveira, Arlindo L., editor

Published
2002
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358. Web Structure, Dynamics and Page Quality

Author

Baeza-Yates, Ricardo, Saint-Jean, Felipe, Castillo, Carlos, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Laender, Alberto H. F., editor, and Oliveira, Arlindo L., editor

Published
2002
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360. On the Size of DASG for Multiple Texts

Author

Crochemore, Maxime, Troníček, Zdeněk, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Laender, Alberto H. F., editor, and Oliveira, Arlindo L., editor

Published
2002
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368. Stemming Galician Texts

Author

Brisaboa, Nieves R., Callón, Carlos, López, Juan-Ramón, Places, Ángeles S., Sanmartín, Goretti, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Laender, Alberto H. F., editor, and Oliveira, Arlindo L., editor

Published
2002
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369. Sorting by Prefix Transpositions

Author

Dias, Zanoni, Meidanis, João, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Laender, Alberto H. F., editor, and Oliveira, Arlindo L., editor

Published
2002
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378. Inferring Subclasses of Contextual Languages

Author

Emerald, J. D., Subramanian, K. G., Thomas, D. G., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, and Oliveira, Arlindo L., editor

Published
2000
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389. Probabilistic k-Testable Tree Languages

Author

Rico-Juan, Juan Ramón, Calera-Rubio, Jorge, Carrasco, Rafael C., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, and Oliveira, Arlindo L., editor

Published
2000
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398. Non-adjacent Digrams Improve Matching of Cross-Lingual Spelling Variants

Author

Kari Visala, Heikki Keskustalo, Ari Pirkola, Erkka Leppänen, Kalervo Järvelin, Nascimento, Mario A, De, Moura Edleno S, Oliveira, Arlindo L, and University of Tampere

Subjects
Matching (statistics), Information retrieval, business.industry, Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, String searching algorithm, Approximate string matching, computer.software_genre, Spelling, Index (publishing), Tietojenkäsittelyoppi ja informaatiotieteet - Computer and information sciences, Multilingualism, Edit distance, Artificial intelligence, business, computer, Word (computer architecture), Natural language processing
Abstract

Untranslatable query keys pose a problem in dictionary-based cross-language information retrieval (CLIR). One solution consists of using approximate string matching methods for finding the spelling variants of the source key among the target database index. In such a setting, it is important to select a matching method suited especially for CLIR. This paper focuses on comparing the effectiveness of several matching methods in a cross-lingual setting. Search words from five domains were expressed in six languages (French, Spanish, Italian, German, Swedish, and Finnish). The target data consisted of the index of an English full-text database. In this setting, we first established the best method among six baseline matching methods for each language pair. Secondly, we tested novel matching methods based on binary digrams formed of both adjacent and non-adjacent characters of words. The latter methods consistently outperformed all baseline methods.

Published
2003

399. Multiple Instance Learning for WSI: A comparative analysis of attention-based approaches.

Author

Afonso M, Bhawsar PMS, Saha M, Almeida JS, and Oliveira AL

Abstract

Whole slide images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology. However, they represent a particular challenge to artificial intelligence (AI)-based/AI-mediated analysis because pathology labeling is typically done at slide-level, instead of tile-level. It is not just that medical diagnostics is recorded at the specimen level, the detection of oncogene mutation is also experimentally obtained, and recorded by initiatives like The Cancer Genome Atlas (TCGA), at the slide level. This configures a dual challenge: (a) accurately predicting the overall cancer phenotype and (b) finding out what cellular morphologies are associated with it at the tile level. To better understand and address these challenges, two existing weakly supervised Multiple Instance Learning (MIL) approaches were explored and compared: Attention MIL (AMIL) and Additive MIL (AdMIL). These architectures were analyzed on tumor detection (a task where these models obtained good results previously) and TP53 mutation detection (a much less explored task). For tumor detection, we built a dataset from Lung Squamous Cell Carcinoma (TCGA-LUSC) slides, with 349 positive and 349 negative slides. The patches were extracted from 5× magnification. For TP53 mutation detection, we explored a dataset built from Invasive Breast Carcinoma (TCGA-BRCA) slides, with 347 positive and 347 negative slides. In this case, we explored three different magnification levels: 5×, 10×, and 20×. Our results show that a modified additive implementation of MIL matched the performance of reference implementation (AUC 0.96), and was only slightly outperformed by AMIL (AUC 0.97) on the tumor detection task. TP53 mutation was most sensitive to features at the higher applications where cellular morphology is resolved. More interestingly from the perspective of the molecular pathologist, we highlight the possible ability of these MIL architectures to identify distinct sensitivities to morphological features (through the detection of regions of interest, ROIs) at different amplification levels. This ability for models to obtain tile-level ROIs is very appealing to pathologists as it provides the possibility for these algorithms to be integrated in a digital staining application for analysis, facilitating the navigation through these high-dimensional images and the diagnostic process., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (© 2024 The Authors.)

Published
2024
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400. Inflammation and all-cause mortality in patients undergoing peritoneal dialysis.

Author

Oliveira Júnior WV, Giarola LTP, Ferreira LGR, Schettini IVG, Turani SD, Oliveira AR, Marinho MAS, Pinto SWL, Barros-Pinheiro M, Figueiredo RC, and Rios DRA

Subjects
Humans, Male, Female, Middle Aged, Prospective Studies, Aged, Kaplan-Meier Estimate, Enzyme-Linked Immunosorbent Assay, Kidney Failure, Chronic mortality, Kidney Failure, Chronic therapy, Kidney Failure, Chronic blood, Adult, Cytokines blood, Tumor Necrosis Factor-alpha blood, Tumor Necrosis Factor-alpha analysis, Chemokine CCL2 blood, Chemokine CCL2 analysis, Renal Dialysis mortality, Risk Factors, Interleukin-17 blood, Cause of Death, Flow Cytometry, Peritoneal Dialysis mortality, Biomarkers blood, Inflammation blood, Inflammation mortality
Abstract

Objective: This study aimed to evaluate inflammatory biomarkers in patients undergoing peritoneal dialysis and investigate their association with all-cause mortality or transfer to hemodialysis., Methods: This prospective cohort study included 43 patients undergoing peritoneal dialysis. Plasma levels of cytokines were measured using flow cytometry and capture enzyme-linked immunosorbent assay. Biomarkers were categorized based on their respective median values. Survival analysis was conducted using the Kaplan-Meier estimator, considering two outcomes: all-cause mortality and transfer to hemodialysis., Results: After adjusting for confounding factors, plasma levels above the median of the levels of CCL2 and plasma, as well as below the median of TNF-α, and the median of dialysate IL-17 levels, were associated with an increased risk of experiencing the specified outcomes after approximately 16 months of follow-up., Conclusion: These findings suggest that inflammatory biomarkers may be a valuable tool for predicting all-cause mortality and transfer to hemodialysis in patients undergoing peritoneal dialysis.

Published
2024
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