Deep learning for understanding multilabel imbalanced Chest X-ray datasets

Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary or multiclass classification problems. However, in many real-life situations, such as chest X-rays, radiological signs of different diseases can appear at the same time. This gives rise to what is known as ”multilabel classification problems”. A disadvantage of these tasks is class imbalance, i.e. different labels do not have the same number of samples. The main contribution of this paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. This technique also includes probabilities and inter-model matching. The results of our system are promising, especially considering the number of labels used. Furthermore, the heatmaps match the expected areas, i.e. they mark the areas that an expert would use to make a decision.This work has been funded by Grant PLEC2021-007681 (XAI-DisInfodemics) and PID2020-117263GB-100 (FightDIS) funded by MCIN/AEI/ 10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union NextGenerationEU/PRTR”, by the research project CIVIC: Intelligent characterisation of the veracity of the information related to COVID-19, granted by BBVA FOUNDATION GRANTS FOR SCIENTIFIC RESEARCH TEAMS SARS-CoV-2 and COVID-19, by European Comission under IBERIFIER - Iberian Digital Media Research and Fact-Checking Hub (2020-EU-IA-0252), by “Convenio Plurianual with the Universidad Politécnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario”, and by Comunidad Autónoma de Madrid under S2018/TCS-4566 (CYNAMON) grant. M. Sánchez-Montañés has been supported by grants PID2021-127946OB-I00 and PID2021-122347NB-I00 (funded by MCIN/AEI/ 10.13039/501100011033 and ERDF - “A way of making Europe”) and Comunidad Autónoma de Madrid, Spain (S2017/BMD-3688 MULTI-TARGET&VIEW-CM grant). J. Del Ser thanks the financial support of the Spanish Centro para el Desarrollo Tecnológico Industrial (CDTI, Ministry of Science and Innovation) through the “Red Cervera” Programme (AI4ES project), as well as the support of the Basque Government (consolidated research group MATHMODE, ref. IT1456-22

Optimización inspirada en la naturaleza y en la biología: Lo bueno, lo malo, lo feo y lo esperanzador

Nowadays, optimization has become an important issue for industrial systems and product development. From an engineering perspective, optimization implies adjusting or fine-tuning system designs considering one or more performance factors. Unfortunately, for many complex problems there is no optimization technique that can achieve the optimum solution in a reasonable computation time. As a result, the optimization process is often done manually. In recent years a myriad of optimization techniques have appeared, all inspired by phenomena observed in nature, such as behavioral patterns in animals (such as the exploration and search for food, moving, hunting, …), physical and chemical processes [1]. These techniques, often referred to as nature- or bio-inspired optimization algorithms, allow users to optimize a problem without requiring special knowledge about it: they only need to be informed about the fitness function to be optimized, and the mechanisms by which new candidate solutions can be produced. Each algorithm defines how existing solutions can be combined and modified to create new ones in an intelligent way to search for the best solution. Although they cannot guarantee that the optimum solution will be eventually achieved, they can automatically yield good solutions in reasonable computation times. These features make bio-inspired optimization proposals a promising research area and a great alternative to optimize complex processes, as has been already showcased in many real-world problems. In this work we present nature- and bio-inspired optimization from a global perspective. We describe techniques falling in this area, their evolution, how they operate, and why they bridge an important gap not covered by previous optimization techniques. On a critical note, we also give a clear view of the current situation in the area, indicating the positive aspects and issues that should be urgently improved. Considering this critical view, we suggest promising trends that we believe will lead us to a brighter future in nature- and bio-inspired optimization, plenty of successful examples of their application to real-world engineering problems. The manuscript is structured as follows: Section 2 describes bio-inspired optimization and exposes the reasons and advantages that make this area interesting from the scientific and practical points of view (focusing on introducing what they are and why they are useful). In Section 3 we examine the exciting panorama of recent applications in which nature- and bio-inspired optimization has become a central technology (the good), the upsurge of novel metaphors for the design of new proposals that do not lead to innovative solutions (the bad), and poor methodological practices that draw misleading conclusions that must be avoided in this field (the ugly). Finally, Section 4 summarizes the paper and highlights what is next to be done in the area of bio-inspired optimization (the hopeful), especially for engineering applications

Accurate long-term air temperature prediction with Machine Learning models and data reduction techniques

In this paper, three customised Artificial Intelligence (AI) frameworks, considering Deep Learning, Machine Learning (ML) algorithms and data reduction techniques, are proposed for a problem of long-term summer air temperature prediction. Specifically, the prediction of the average air temperature in the first and second August fortnights, using input data from previous months, at two different locations (Paris, France) and (Córdoba, Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the heatwave of 2003, which affected France and the Iberian Peninsula. Three different computational frameworks for air temperature prediction are proposed: a Convolutional Neural Network (CNN), with video-to-image translation, several ML approaches including Lasso regression, Decision Trees and Random Forest, and finally a CNN with pre-processing step using Recurrence Plots, which convert time series into images. Using these frameworks, a very good prediction skill has been obtained in both Paris and Córdoba regions, showing that the proposed approaches can be an excellent option for seasonal climate prediction problems.This research has been partially supported by the European Union, through H2020 Project “CLIMATE INTELLIGENCE Extreme events detection, attribution and adaptation design using machine learning (CLINT)”, Ref: 101003876-CLINT. This research has also been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN)

Persistence in complex systems

Persistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems' persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.This research has been partially supported by the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). This research has also been partially supported by Comunidad de Madrid, PROMINT-CM project (grant ref: P2018/EMT-4366). J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs (3KIA project, KK-2020/00049), as well as the consolidated research group MATHMODE (ref. T1294-19). GCV work is supported by the European Research Council (ERC) under the ERC-CoG-2014 SEDAL Consolidator grant (grant agreement 647423)

Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks

Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research directions for scene understanding pursued by related studies include person/vehicle detection and segmentation, their transition analysis, lane change, and turns detection, among many others Unfortunately, these tasks seem insufficient to completely develop fully-autonomous vehicles i.e. achieving level-5 autonomy, travelling just like human-controlled cars. This latter statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. With this motivation, this survey defines, analyzes, and reviews the current achievements of the scene understanding research area that mostly rely on computationally complex deep learning models. Furthermore, it covers the generic scene understanding pipeline, investigates the performance reported by the state-of-the-art, informs about the time complexity analysis of avant garde modeling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The survey also includes a comprehensive discussion on the available datasets, and the challenges that, even if lately confronted by researchers, still remain open to date. Finally, our work outlines future research directions to welcome researchers and practitioners to this exciting domain.This work was supported by the European Commission through European Union (EU) and Japan for Artificial Intelligence (AI) under Grant 957339

Heuristics and Hyper-Heuristics - Principles and Applications

In the last few years, the society is witnessing ever-growing levels of complexity in the optimization paradigms lying at the core of different applications and processes. This augmented complexity has motivated the adoption of heuristic methods as a means to balance the Pareto trade-off between computational efficiency and the quality of the produced solutions to the problem at hand. The momentum gained by heuristics in practical applications spans further towards hyper-heuristics, which allow constructing ensembles of simple heuristics to handle efficiently several problems of a single class. In this context, this short book compiles selected applications of heuristics and hyper-heuristics for combinatorial optimization problems, including scheduling and other assorted application scenarios

A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

This work was supported by grants from the Spanish Ministry of Science (TIN2016-8113-R, TIN2017-89517-P and TIN2017-83132-C2-2-R) and Universidad Politecnica de Madrid (PINV-18-XEOGHQ-19-4QTEBP) . Eneko Osaba and Javier Del Ser-would also like to thank the Basque Government for its funding support through the ELKARTEK and EMAITEK programs. Javier Del Ser-receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government.Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.Spanish Government TIN2016-8113-R TIN2017-89517-P TIN2017-83132-C2-2-RUniversidad Politecnica de Madrid PINV-18-XEOGHQ-19-4QTEBPBasque Government IT1294-19Department of Education of the Basque Governmen

Self Organized Networks

To develop an intelligent optimization algorithm to coordinate the frequency selection at the back end (for radio resource management purposes), in unmanaged, partially cooperative urban environments where not all the hotspots can be configured