An approach to quality analysis, gap filling and homogeneity of monthly rainfall series

The aim of this work was to propose a method for the consistency of climatic series of monthly rainfall using a supervised and unsupervised approach. The methodology was applied for the series (1961-2010) of rainfall from weather stations located in the State of Rio de Janeiro (RJ) and in the borders with the states of São Paulo, Minas Gerais and Espírito Santo with the State of Rio de Janeiro. The data were submitted to quality analysis (physical and climatic limit and, space-time tendency) and gap filling, based on simple linear regression analysis, associated with the prediction band (p < 0.05 or 0.01), in addition to the Z-score (3, 4 or 5). Next, homogeneity analysis was applied to the continuous series, using the method of cumulative residuals. The coefficients of determination (r²) between the assessed series and the reference series were greater than 0.70 for gap filling both for the supervised and unsupervised approaches. In the analysis of data homogeneity, supervised and unsupervised approaches were effective in selecting homogeneous series, in which five out of the nine final stations were homogeneous (p > 0.9). In the other series, the homogeneity break points were identified and the simple linear regression method was applied for their homogenization. The proposed method was effective to consist of the rainfall series and allows the use of these data in climate studies.

Author Correction: Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Correcting for Background Noise Improves Phenotype Prediction from Human Gut Microbiome Data

The ability to predict human phenotypes accurately from metagenomic data is crucial for developing biomarkers and therapeutics for diseases. However, metagenomic data is commonly affected by technical or biological variables, unrelated to the phenotype of interest, such as sequencing protocol or host sex, which can greatly reduce or, when correlated to the phenotype of interest, inflate prediction accuracy. We perform a comparative analysis of the ability of different data transformations and existing supervised and unsupervised methods to correct microbiome data for background noise. We find that supervised methods are limited because they cannot account for unmeasured sources of variation. In addition, we observe that unsupervised approaches are often superior in addressing these issues, but existing methods developed for other 'omic data types, e.g., gene expression and methylation, are restricted by parametric assumptions unsuitable for microbiome data, which is typically compositional, highly skewed, and sparse. We show that application of the centered log-ratio transformation prior to correction with unsupervised approaches improves prediction accuracy for many phenotypes while simultaneously reducing variance due to unwanted sources of variation. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.

CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation

The interpretation of ego motion and scene change is a fundamental task for mobile robots. Optical flow information can be employed to estimate motion in the surroundings. Recently, unsupervised optical flow estimation has become a research hotspot. However, unsupervised approaches are often easy to be unreliable on partially occluded or texture-less regions. To deal with this problem, we propose CoT-AMFlow in this paper, an unsupervised optical flow estimation approach. In terms of the network architecture, we develop an adaptive modulation network that employs two novel module types, flow modulation modules (FMMs) and cost volume modulation modules (CMMs), to remove outliers in challenging regions. As for the training paradigm, we adopt a co-teaching strategy, where two networks simultaneously teach each other about challenging regions to further improve accuracy. Experimental results on the MPI Sintel, KITTI Flow and Middlebury Flow benchmarks demonstrate that our CoT-AMFlow outperforms all other state-of-the-art unsupervised approaches, while still running in real time. Our project page is available at https://sites.google.com/view/cot-amflow.

Author name disambiguation of bibliometric data: A comparison of several unsupervised approaches

Adequately disambiguating author names in bibliometric databases is a precondition for conducting reliable analyses at the author level. In the case of bibliometric studies that include many researchers, it is not possible to disambiguate each single researcher manually. Several approaches have been proposed for author name disambiguation, but there has not yet been a comparison of them under controlled conditions. In this study, we compare a set of unsupervised disambiguation approaches. Unsupervised approaches specify a model to assess the similarity of author mentions a priori instead of training a model with labeled data. To evaluate the approaches, we applied them to a set of author mentions annotated with a ResearcherID, this being an author identifier maintained by the researchers themselves. Apart from comparing the overall performance, we take a more detailed look at the role of the parametrization of the approaches and analyze the dependence of the results on the complexity of the disambiguation task. Furthermore, we examine which effects the differences in the set of metadata considered by the different approaches have on the disambiguation results. In the context of this study, the approach proposed by Caron and van Eck (2014) produced the best results.

Comparative Analysis of Supervised and Unsupervised Approaches Applied to Large-Scale “In The Wild” Face Verification

Deep learning-based feature extraction methods and transfer learning have become common approaches in the field of pattern recognition. Deep convolutional neural networks trained using tripled-based loss functions allow for the generation of face embeddings, which can be directly applied to face verification and clustering. Knowledge about the ground truth of face identities might improve the effectiveness of the final classification algorithm; however, it is also possible to use ground truth clusters previously discovered using an unsupervised approach. The aim of this paper is to evaluate the potential improvement of classification results of state-of-the-art supervised classification methods trained with and without ground truth knowledge. In this study, we use two sufficiently large data sets containing more than 200,000 “taken in the wild” images, each with various resolutions, visual quality, and face poses which, in our opinion, guarantee the statistical significance of the results. We examine several clustering and supervised pattern recognition algorithms and find that knowledge about the ground truth has a very small influence on the Fowlkes–Mallows score (FMS) of the classification algorithm. In the case of the classification algorithm that obtained the highest accuracy in our experiment, the FMS improved by only 5.3% (from 0.749 to 0.791) in the first data set and by 6.6% (from 0.652 to 0.718) in the second data set. Our results show that, beside highly secure systems in which face verification is a key component, face identities discovered by unsupervised approaches can be safely used for training supervised classifiers. We also found that the Silhouette Coefficient (SC) of unsupervised clustering is positively correlated with the Adjusted Rand Index, V-measure score, and Fowlkes–Mallows score and, so, we can use the SC as an indicator of clustering performance when the ground truth of face identities is not known. All of these conclusions are important findings for large-scale face verification problems. The reason for this is the fact that skipping the verification of people’s identities before supervised training saves a lot of time and resources.

CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation

The interpretation of ego motion and scene change is a fundamental task for mobile robots. Optical flow information can be employed to estimate motion in the surroundings. Recently, unsupervised optical flow estimation has become a research hotspot. However, unsupervised approaches are often easy to be unreliable on partially occluded or texture-less regions. To deal with this problem, we propose CoT-AMFlow in this paper, an unsupervised optical flow estimation approach. In terms of the network architecture, we develop an adaptive modulation network that employs two novel module types, flow modulation modules (FMMs) and cost volume modulation modules (CMMs), to remove outliers in challenging regions. As for the training paradigm, we adopt a co-teaching strategy, where two networks simultaneously teach each other about challenging regions to further improve accuracy. Experimental results on the MPI Sintel, KITTI Flow and Middlebury Flow benchmarks demonstrate that our CoT-AMFlow outperforms all other state-of-the-art unsupervised approaches, while still running in real time. Our project page is available at https://sites.google.com/view/cot-amflow.