64 research outputs found
Using Molecular Embeddings in QSAR Modeling: Does it Make a Difference?
With the consolidation of deep learning in drug discovery, several novel
algorithms for learning molecular representations have been proposed. Despite
the interest of the community in developing new methods for learning molecular
embeddings and their theoretical benefits, comparing molecular embeddings with
each other and with traditional representations is not straightforward, which
in turn hinders the process of choosing a suitable representation for QSAR
modeling. A reason behind this issue is the difficulty of conducting a fair and
thorough comparison of the different existing embedding approaches, which
requires numerous experiments on various datasets and training scenarios. To
close this gap, we reviewed the literature on methods for molecular embeddings
and reproduced three unsupervised and two supervised molecular embedding
techniques recently proposed in the literature. We compared these five methods
concerning their performance in QSAR scenarios using different classification
and regression datasets. We also compared these representations to traditional
molecular representations, namely molecular descriptors and fingerprints. As
opposed to the expected outcome, our experimental setup consisting of over
25,000 trained models and statistical tests revealed that the predictive
performance using molecular embeddings did not significantly surpass that of
traditional representations. While supervised embeddings yielded competitive
results compared to those using traditional molecular representations,
unsupervised embeddings tended to perform worse than traditional
representations. Our results highlight the need for conducting a careful
comparison and analysis of the different embedding techniques prior to using
them in drug design tasks, and motivate a discussion about the potential of
molecular embeddings in computer-aided drug design
Visual analysis of interactive document clustering streams
Interactive clustering techniques play a key role by putting the user in the clustering loop, allowing her to interact with document group abstractions instead of full-length documents. It allows users to focus on corpus exploration as an incremental task. To explore Information Discovery's incremental aspect, this article proposes a visual component to depict clustering membership changes throughout a clustering iteration loop in both static and dynamic data sets. The visual component is evaluated with an expert user and with an experiment with data streams
Assessing Causality Structures learned from Digital Text Media
In this paper we describe a framework to uncover potential causal relations between event mentions from streaming text of news media. This framework relies on a dataset of manually labeled events to train a recurrent neural network for event detection. It then creates a time series of event clusters, where clusters are based on BERT contextual word embedding representations of the identified events. Using these time series dataset, we assess four methods based on Granger causality for inferring causal relations. Granger causality is a statistical concept of causality that is based on forecasting. It states that a cause occurs before the effect, and the cause produces unique changes in the effect, so past values of the cause help predict future values of the effect. The four analyzed methods are the pairwise Granger test, VAR(1), BigVar and SiMoNe. The framework is applied to the New York Times dataset, which covers news for a period of 246 months. This preliminary analysis delivers important insights into the nature of each method, identifies differences and commonalities, and points out some of their strengths and weaknesses.Fil: Maisonnave, Mariano. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Ciencias e IngenierÃa de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación. Instituto de Ciencias e IngenierÃa de la Computación; ArgentinaFil: Delbianco, Fernando Andrés. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Matemática BahÃa Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática BahÃa Blanca; Argentina. Universidad Nacional del Sur. Departamento de EconomÃa; ArgentinaFil: Tohmé, Fernando Abel. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Matemática BahÃa Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática BahÃa Blanca; Argentina. Universidad Nacional del Sur. Departamento de EconomÃa; ArgentinaFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Ciencias e IngenierÃa de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación. Instituto de Ciencias e IngenierÃa de la Computación; ArgentinaFil: Milios, Evangelos E.. Dalhousie University. Faculty of Computer Science; CanadáDocEng '20: ACM Symposium on Document Engineering 2020New YorkEstados UnidosAssociation for Computing Machiner
A web platform for collaborative semi-automatic OCR Post-processing
Digital Humanities researchers often make use of software that helps them in the task of finding non-trivial relationships among characters in historical text. Usually, the source texts that contain such information come from OCR acquired volumes, carrying high amounts of errors within them. This work explains the development of a web platform for the task of OCR post-processing and ground-truth generation. This platform employs machine learning to predict the correct texts accurately from OCR noisy strings. The method used for this task involves transformers for character-based denoising language models. An active learning workflow is proposed, as the users can feed their corrections to the platform, generating new annotated data for re-training the underlying machine learning correction models.Fil: Mechaca, Ana Lidia. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; ArgentinaFil: Marmanillo, Walter Gabriel. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; ArgentinaFil: Xamena, Eduardo. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - Salta. Instituto de Investigaciones en Ciencias Sociales y Humanidades. Universidad Nacional de Salta. Facultad de Humanidades. Instituto de Investigaciones en Ciencias Sociales y Humanidades; Argentina. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; ArgentinaFil: Ramirez Orta, Juan. Dalhousie University Halifax; CanadáFil: Maguitman, Ana Gabriela. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - BahÃa Blanca. Instituto de Ciencias e IngenierÃa de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e IngenierÃa de la Computación. Instituto de Ciencias e IngenierÃa de la Computación; ArgentinaFil: Milios, Evangelos E.. Dalhousie University Halifax; Canadá50º Jornada Argentina de Informática; Simposio Argentino de Ciencia de Datos y Grandes DatosCiudad Autónoma de Buenos AiresArgentinaSociedad Argentina de Investigación OperativaInstituto Nacional de TecnologÃa Agropecuari
A web platform for collaborative semi-automatic OCR post-processing
Digital Humanities researchers often make use of software that helps them in the task of finding non-trivial relationships among characters in historical text. Usually, the source texts that contain such information come from OCR acquired volumes, carrying high amounts of errors within them. This work explains the development of a web platform for the task of OCR post-processing and ground-truth generation. This platform employs machine learning to predict the correct texts accurately from OCR noisy strings. The method used for this task involves transformers for character-based denoising language models. An active learning workflow is proposed, as the users can feed their corrections to the platform, generating new annotated data for re-training the underlying machine learning correction models.Sociedad Argentina de Informática e Investigación Operativ
Digital Signal Processing
Contains an introduction and reports on fifteen research projects.U.S. Navy - Office of Naval Research (Contract N00O14-81-K-0742)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0266)National Science Foundation (Grant ECS80-07102)National Science Foundation (Grant ECS84-07285)Amoco Foundation FellowshipSanders Associates, Inc.Advanced Television Research ProgramM.I.T. Vinton Hayes FellowshipHertz Foundation Fellowshi
Digital Signal Processing
Contains an introduction and reports on fourteen research projects.National Science Foundation FellowshipNational Science Foundation (Grant ECS84-07285)U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)Sanders Associates, Inc.U.S. Air Force - Office of Scientific Research (Contract F19628-85-K-0028)Advanced Television Research ProgramAmoco Foundation FellowshipHertz Foundation Fellowshi
Digital Signal Processing
Contains introduction and reports on seventeen research projects.U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0266)National Science Foundation (Grant ECS80-07102)Bell Laboratories FellowshipAmoco Foundation FellowshipSchlumberger-Doll Research Center FellowshipSanders Associates, Inc.Toshiba Company FellowshipM.I.T. Vinton Hayes FellowshipHertz Foundation Fellowshi
Digital Signal Processing
Contains an introduction and reports on seventeen research projects.U.S. Navy - Office of Naval Research (Contract N00014-77-C-0266)Amoco Foundation FellowshipU.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)National Science Foundation (Grant ECS80-07102)U.S. Army Research Office (Contract DAAG29-81-K-0073)Hughes Aircraft Company FellowshipAmerican Edwards Labs. GrantWhitaker Health Sciences FundPfeiffer Foundation GrantSchlumberger-Doll Research Center FellowshipGovernment of Pakistan ScholarshipU.S. Navy - Office of Naval Research (Contract N00014-77-C-0196)National Science Foundation (Grant ECS79-15226)Hertz Foundation Fellowshi
Digital Signal Processing Group
Contains an introduction and reports on nineteen research projects.U.S. Navy - Office of Naval Research (Contract N00014-77-C-0266)U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)National Science Foundation (Grant ECS80-07102)Bell Laboratories FellowshipAmoco Foundation FellowshipU.S. Navy - Office of Naval Research (Contract N00014-77-C-0196)Schlumberger-Doll Research Center FellowshipToshiba Company FellowshipVinton Hayes FellowshipHertz Foundation Fellowshi
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