Every day, global trends are reshaping society and the business landscape. Today's infographic from McKinsey Global Institute (MGI) presents a snapshot of 10 insights into how the world is changing, based on its research work from 2019. How did we get here, and where are we going? Globalization is making the world "shrink" every day, as humans and trade become increasingly connected. However, there are signs that point to a new phase of globalization that is leading to different outcomes than prior years.

Data imbalance is a major problem that affects several machine learning algorithms. Such problems are troublesome because most of the learning algorithms attempts to optimize a loss function based on error measures that do not take into account the data imbalance. Accordingly, the learning algorithm simply generates a trivial model that is biased toward predicting the most frequent class in the training data. Data augmentation techniques have been used to mitigate the data imbalance problem. However, in the case of histopathologic images (HIs), low-level as well as high-level data augmentation techniques still present performance issues when applied in the presence of inter-patient variability; whence the model tends to learn color representations, which are in fact related to the stain process. In this paper, we propose an approach capable of not only augmenting HIs database but also distributing the inter-patient variability by means of image blending using Gaussian-Laplacian pyramid. The proposed approach consists in finding the Gaussian pyramids of two images of different patients and finding the Laplacian pyramids thereof. Afterwards, the left half of one image and the right half of another are joined in each level of Laplacian pyramid, and from the joint pyramids, the original image is reconstructed. This composition, resulting from the blending process, combines stain variation of two patients, avoiding that color misleads the learning process. Experimental results on the BreakHis dataset have shown promising gains vis-\`a-vis the majority of traditional techniques presented in the literature.

Loss function optimization for neural networks has recently emerged as a new direction for metalearning, with Genetic Loss Optimization (GLO) providing a general approach for the discovery and optimization of such functions. GLO represents loss functions as trees that are evolved and further optimized using evolutionary strategies. However, searching in this space is difficult because most candidates are not valid loss functions. In this paper, a new technique, Multivariate Taylor expansion-based genetic loss-function optimization (TaylorGLO), is introduced to solve this problem. It represents functions using a novel parameterization based on Taylor expansions, making the search more effective. TaylorGLO is able to find new loss functions that outperform those found by GLO in many fewer generations, demonstrating that loss function optimization is a productive avenue for metalearning.

--One problem found when working with satellite images is the radiometric variations across the image and different images. Intending to improve remote sensing models for the classification of burnt areas, we set two objectives. The first is to understand the relationship between feature spaces and the predictive ability of the models, allowing us to explain the differences between learning and generalization when training and testing in different datasets. We find that training on datasets built from more than one image provides models that generalize better . These results are explained by visualizing the dispersion of values on the feature space. The second objective is to evolve hyper-features that improve the performance of different classifiers on a variety of test sets. We find the hyper-features to be beneficial, and obtain the best models with XGBoost, even if the hyper-features are optimized for a different method. Deforestation has serious implications on biodiversity, on rural communities that depend on forests for survival, and on greenhouse gas emissions that drive the global climate. The machine learning (ML) community can help by providing predictive models that, after learning from a small sample of an image, can automatically classify the whole image. Although previous ML work in forest monitoring has shown good results, the predictive models are often applied on the same location where they were learnt, i.e., the models are trained and tested in samples from the same dataset (e.g., [1]) or time series from the same area (e.g., [2]).

Recurrent correlation neural networks (RCNNs), introduced by Chiueh and Goodman as an improved version of the bipolar correlation-based Hopfield neural network, can be used to implement high-capacity associative memories. In this paper, we extend the bipolar RCNNs for processing hypercomplex-valued data. Precisely, we present the mathematical background for a broad class of hypercomplex-valued RCNNs. Then, we provide the necessary conditions which ensure that a hypercomplex-valued RCNN always settles at an equilibrium using either synchronous or asynchronous update modes. Examples with bipolar, complex, hyperbolic, quaternion, and octonion-valued RCNNs are given to illustrate the theoretical results. Finally, computational experiments confirm the potential application of hypercomplex-valued RCNNs as associative memories designed for the storage and recall of gray-scale images.

The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Facebook ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics. We noticed that not all political ads we detected were present in the Facebook Ad Library for political ads. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms.

More security usually means a higher trust from #customers, that will be more enclined to #shop online. Here is an example with Latin America: https://zd.net/2O5Drdi

Gaussian process (GP) priors are non-parametric generative models with appealing modelling properties for Bayesian inference: they can model non-linear relationships through noisy observations, have closed-form expressions for training and inference, and are governed by interpretable hyperparameters. However, GP models rely on Gaussianity, an assumption that does not hold in several real-world scenarios, e.g., when observations are bounded or have extreme-value dependencies, a natural phenomenon in physics, finance and social sciences. Although beyond-Gaussian stochastic processes have caught the attention of the GP community, a principled definition and rigorous treatment is still lacking. In this regard, we propose a methodology to construct stochastic processes, which include GPs, warped GPs, Student-t processes and several others under a single unified approach. We also provide formulas and algorithms for training and inference of the proposed models in the regression problem. Our approach is inspired by layers-based models, where each proposed layer changes a specific property over the generated stochastic process. That, in turn, allows us to push-forward a standard Gaussian white noise prior towards other more expressive stochastic processes, for which marginals and copulas need not be Gaussian, while retaining the appealing properties of GPs. We validate the proposed model through experiments with real-world data.

Hypercomplex-valued neural networks, including quaternion-valued neural networks, can treat multidimensional data as a single entity. In this paper, we present the quaternion-valued recurrent projection neural networks (QRPNNs). Briefly, QRPNNs are obtained by combining the non-local projection learning with the quaternion-valued recurrent correlation neural network (QRCNNs). We show that QRPNNs overcome the crosstalk problem of QRCNNs. Thus, they are appropriate to implement associative memories. Furthermore, computational experiments reveal that QRPNNs exhibit greater storage capacity and noise tolerance than their corresponding QRCNNs. Introduction The Hopfield neural network, developed in the early 1980s, is an important and widely-known recurrent neural network which can be used to implement associative memories [1, 2]. Successful applications of the Hopfield network include control [3, 4], computer vision and image processing [5, 6], classification [7, 8], and optimization [2, 9, 10]. Despite its many successful applications, the Hopfield network may suffer from a very low storage capacity when used to implement associative memories. Precisely, due to crosstalk between the stored items, the Hebbian learning adopted by Hopfield in his original work allows for the storage of approximately n/(2 ln n) items, where n denotes the length of the stored vectors [11]. For example, Personnaz et al. [12] as well as Kanter and Sompolinsky [13] proposed the projection rule to determine the synaptic weights of the Hopfield networks. The projection rule increases the storage capacity of the Hopfield network to n 1 items. Another simple but effective improvement on the storage capacity of the original Hopfield networks was achieved by Chiueh and Goodman's recurrent correlation neural networks (RCNNs) [14, 15]. Briefly, an RCNN is obtained by decomposing the Hopfield network with Hebbian learning into a two layer recurrent neural network.

In part \textit{I} we proposed a structure for a general Hypotheses Space $\mathcal{H}$, the Learning Space $\mathbb{L}(\mathcal{H})$, which can be employed to avoid \textit{overfitting} when estimating in a complex space with relative shortage of examples. Also, we presented the U-curve property, which can be taken advantage of in order to select a Hypotheses Space without exhaustively searching $\mathbb{L}(\mathcal{H})$. In this paper, we carry further our agenda, by showing the consistency of a model selection framework based on Learning Spaces, in which one selects from data the Hypotheses Space on which to learn. The method developed in this paper adds to the state-of-the-art in model selection, by extending Vapnik-Chervonenkis Theory to \textit{random} Hypotheses Spaces, i.e., Hypotheses Spaces learned from data. In this framework, one estimates a random subspace $\hat{\mathcal{M}} \in \mathbb{L}(\mathcal{H})$ which converges with probability one to a target Hypotheses Space $\mathcal{M}^{\star} \in \mathbb{L}(\mathcal{H})$ with desired properties. As the convergence implies asymptotic unbiased estimators, we have a consistent framework for model selection, showing that it is feasible to learn the Hypotheses Space from data. Furthermore, we show that the generalization errors of learning on $\hat{\mathcal{M}}$ are lesser than those we commit when learning on $\mathcal{H}$, so it is more efficient to learn on a subspace learned from data.