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An AI-based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Samad Amini Boran Hao Lifu Zhang Mengting Song Aman Gupta Cody Karjadi Vijaya B. Kolachalama Rhoda Au Ioannis Paschalidis Division of Systems Engineering Boston University Boston MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA

Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide-spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub-tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develo

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Electronic Impurity Doping of a 2D Hybrid Lead Iodide Perovskite by Bi and Sn

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PRX Energy 2023年第2期2卷 023010-023010页

作者： Haipeng Lu Gabrielle Koknat Yi Yao Ji Hao Xixi Qin Chuanxiao Xiao Ruyi Song Florian Merz Markus Rampp Sebastian Kokott Christian Carbogno Tianyang Li Glenn Teeter Matthias Scheffler Joseph J. Berry David B. Mitzi Jeffrey L. Blackburn Volker Blum Matthew C. Beard Material Chemical and Computational Science Directorate National Renewable Energy Laboratory Golden Colorado 80401 USA Department of Chemistry The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong SAR China Thomas Lord Department of Mechanical Engineering and Material Science Duke University Durham North Carolina 27708 USA Department of Chemistry Duke University Durham North Carolina 27708 USA Lenovo HPC Innovation Center Meitnerstr. 9 D-70563 Stuttgart Germany Max Planck Computing and Data Facility Giessenbachstrasse 2 D-85748 Garching Germany The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin 14195 Berlin Germany Renewable and Sustainable Energy Institute University of Colorado Boulder Boulder Colorado 80309 USA Department of Physics University of Colorado Boulder Boulder Colorado 80309 USA

Control over conductivity and carrier type (electrons and holes) defines semiconductors. A primary approach to target carrier concentrations involves introducing a small population of aliovalent impurity dopant atoms. In a combined synthetic and computational study, we assess impurity doping by introducing Bi and Sn into the prototype 2D Ruddlesden-Popper hybrid perovskite phenylethylammonium lead iodide (PEA2PbI4). Experimentally, we demonstrate that Bi and Sn can achieve n- and p-type doping, respectively, but the doping efficiency is low. Simulations show that Bi introduces a deep defect energy level (∼0.5 eV below the conduction band minimum) that contributes to the low doping efficiency, but, to reproduce the low doping efficiency observed experimentally, an acceptor level must also be present that limits n-type doping. Experiments find that Sn achieves p-dopant behavior and simulations suggest that this occurs through the additional oxidation of Sn defects. We also study how substitutional Bi incorporation can be controlled by tuning the electrochemical environment during synthesis. First-principles impurity doping simulations can be challenging; typical dopant concentrations constitute less than 0.01% of the atoms, necessitating large supercells, while a high level of theory is needed to capture the electronic levels. We demonstrate simulations of complex defect-containing unit cells that include up to 3383 atoms, employing spin-orbit coupled hybrid density functional theory. While p- and n-type behavior can be achieved with Sn and Bi, simulations and experiments provide concrete directions where future efforts must be focused to achieve higher doping efficiency.

关键词： Energy materials Solar energy

Ten quick tips for deep learning in biology

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

UNCOVER: A NIRSpec Census of Lensed Galaxies at z = 8.50-13.08 Probing a High AGN Fraction and Ionized Bubbles in the Shadow

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arXiv 2023年

作者： Fujimoto, Seiji Wang, Bingjie Weaver, John R. Kokorev, Vasily Atek, Hakim Bezanson, Rachel Labbe, Ivo Brammer, Gabriel Greene, Jenny E. Chemerynska, Iryna Dayal, Pratika de Graaff, Anna Furtak, Lukas J. Oesch, Pascal A. Setton, David J. Price, Sedona H. Miller, Tim B. Williams, Christina C. Whitaker, Katherine E. Zitrin, Adi Cutler, Sam E. Leja, Joel Pan, Richard Coe, Dan van Dokkum, Pieter Feldmann, Robert Fudamoto, Yoshinobu Goulding, Andy D. Khullar, Gourav Marchesini, Danilo Maseda, Michael Nanayakkara, Themiya Nelson, Erica J. Smit, Renske Stefanon, Mauro Weibel, Andrea Department of Astronomy The University of Texas at Austin AustinTX78712 United States Department of Astronomy & Astrophysics The Pennsylvania State University University ParkPA16802 United States Institute for Computational & Data Sciences The Pennsylvania State University University ParkPA16802 United States Institute for Gravitation and the Cosmos The Pennsylvania State University University ParkPA16802 United States Department of Astronomy University of Massachusetts AmherstMA01003 United States Kapteyn Astronomical Institute University of Groningen Groningen9700 AV Netherlands Institut d'Astrophysique de Paris CNRS Sorbonne Université 98bis Boulevard Arago Paris75014 France Department of Physics and Astronomy PITT PACC University of Pittsburgh PittsburghPA15260 United States Centre for Astrophysics and Supercomputing Swinburne University of Technology MelbourneVIC3122 Australia Niels Bohr Institute University of Copenhagen Jagtvej 128 København NDK-2200 Denmark Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States Max-Planck-Institut für Astronomie Königstuhl 17 HeidelbergD-69117 Germany Physics Department Ben-Gurion University of the Negev P.O. Box 653 Be'er-Sheva84105 Israel Department of Astronomy University of Geneva Chemin Pegasi 51 Versoix1290 Switzerland Department of Astronomy Yale University New HavenCT06511 United States Department of Physics and Astronomy Northwestern University 1800 Sherman Ave EvanstonIL60201 United States NSF's National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Avenue TucsonAZ85719 United States Steward Observatory University of Arizona 933 North Cherry Avenue TucsonAZ85721 United States Department of Physics and Astronomy Tufts University 574 Boston Ave. MedfordMA02155 United States 3700 San Martin Drive BaltimoreMD21218 United States United States Center for Astrophysical Sciences Department of Physics

We present JWST NIRSpec prism spectroscopy of lensed galaxies at z 9 found behind the massive galaxy cluster Abell 2744 in the UNCOVER Cycle 1 Treasury Program. We confirm the redshift via emission lines and/or the Lyα break for ten galaxies at z = 8.50-13.08 down to MUV = −17.3. We achieve a 100% confirmation rate for z > 9 candidates reported in Atek et al. (2023b). Using six sources with multiple line detections, we find that offsets in redshift estimates between the lines and the Lyα break alone can be ±0.2, raising caution in designing future follow-up spectroscopy for the break-only sources. With spec-z confirmed sources in UNCOVER and literature, we derive lower limits on the rest-frame ultraviolet (UV) luminosity function (LF) at z 9-12 and find that these lower limits agree with recent photometric measurements. We identify at least two unambiguous and several possible active galactic nucleus (AGN) systems based on X-ray, broad Hβ, high ionization lines (e.g., N iv] λ1487, C iv λ1549), and excess in UVLF. This requires the AGN LFs at z 9-10 to be comparable or even higher than the X-ray AGN LF estimated at z ∼ 6 and suggests a plausible cause of the high abundance of z > 9 galaxies claimed in the recent photometric measurements is AGNs. One UV-luminous source is confirmed at the same redshift as a broad-line AGN at z = 8.50 with a physical separation of 380 kpc in the source plane. These two sources show the emission blueward of Lyα, indicating a giant ionized bubble enclosing them with a radius of 7.69 ± 0.18 pMpc. Our results imply that AGNs have a non-negligible contribution to cosmic reionization. © 2023, CC BY.

关键词： Cosmology

Lmlfm: Longitudinal multi-level factorization machine

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arXiv 2019年

作者： Liang, Junjie Xu, Dongkuan Sun, Yiwei Honavar, Vasant Artificial Intelligence Research Laboratory Pennsylvania State University College of Information Sciences and Technology Pennsylvania State University Department of Computer Science and Engineering Pennsylvania State University Institute of Computational and Data Sciences Pennsylvania State University

We consider the problem of learning predictive models from longitudinal data, consisting of irregularly repeated, sparse observations from a set of individuals over time. Such data often exhibit longitudinal correlation (LC) (correlations among observations for each individual over time), cluster correlation (CC) (correlations among individuals that have similar characteristics), or both. These correlations are often accounted for using mixed effects models that include fixed effects and random effects, where the fixed effects capture the regression parameters that are shared by all individuals, whereas random effects capture those parameters that vary across individuals. However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables. We propose Longitudinal Multi-Level Factorization Machine (LMLFM), to the best of our knowledge, the first model to address these challenges in learning predictive models from longitudinal data. We establish the convergence properties, and analyze the computational complexity, of LMLFM. We present results of experiments with both simulated and real-world longitudinal data which show that LMLFM outperforms the state-of-the-art methods in terms of predictive accuracy, variable selection ability, and scalability to data with large number of variables. The code and supplemental material is available at https://***/junjieliang672/LMLFM. Copyright © 2019, The Authors. All rights reserved.

关键词： Random processes

An Early Stage Researcher's Primer on Systems Medicine Terminology

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Network and Systems Medicine

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

TOI-2005b: An Eccentric Warm Jupiter in Spin-Orbit Alignment

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arXiv 2025年

作者： Bieryla, Allyson Dong, Jiayin Zhou, George Eastman, Jason D. Mayorga, L.C. Latham, David W. Carter, Brad Huang, Chelsea X. Quinn, Samuel N. Collins, Karen A. Abe, Lyu Beletsky, Yuri Brahm, Rafael Colón, Knicole D. Essack, Zahra Guillot, Tristan Henning, Thomas Hobson, Melissa J. Horne, Keith Jenkins, Jon M. Jones, Matías I. Jordán, Andrés Osip, David Ricker, George R. Rodriguez, Joseph E. Schulte, Jack Schwarz, Richard P. Seager, Sara Shporer, Avi Suarez, Olga Tan, Thiam-Guan Ting, Eric B. Triaud, Amaury Vanderburg, Andrew Villaseñor, Jesus Noel Vowell, Noah Watkins, Cristilyn N. Winn, Joshua N. Ziegler, Carl Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States University of Southern Queensland Centre for Astrophysics West Street ToowoombaQLD4350 Australia Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd LaurelMD20723 United States Observatoire de la Côte d’Azur UniCA Laboratoire Lagrange CNRS UMR 7293 CS 34229 06304 Nice cedex 4 France Las Campanas Observatory Carnegie Institution for Science Colina El Pino Casilla La Serena601 Chile Facultad de Ingeniería y Ciencias Universidad Adolfo Ibáñez Av. Diagonal las Torres 2640 Santiago Peñalolén Chile Millennium Institute for Astrophysics Chile Data Observatory Foundation Chile GreenbeltMD20771 United States Department of Physics and Astronomy The University of New Mexico 210 Yale Blvd NE AlbuquerqueNM87106 United States Max-Planck-Institut für Astronomie Königstuhl 17 Heidelberg69117 Germany SUPA Physics and Astronomy University of St. Andrews Fife ScotlandKY16 9SS United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States European Southern Observatory Alonso de Córdova 3107 Vitacura Casilla Santiago19001 Chile Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Aeronautics and Astronautics MIT 77 Massachusetts Avenue CambridgeMA02139 United States Observatoire de la Côte d’Azur UniCA CNRS CS 34229 Nice06304 cedex 4

We report the discovery and characterization of TOI-2005 b, a warm Jupiter on an eccentric (e ∼ 0.59), 17.3-day orbit around a Vmag = 9.867 rapidly rotating F-star. The object was detected as a candidate by TESS and the planetary nature of TOI-2005 b was then confirmed via a series of ground-based photometric, spectroscopic, and diffraction-limited imaging observations. The planet was found to reside in a low sky-projected stellar obliquity orbit (λ =4.8+2−235 degrees) via a transit spectroscopic observation using the Magellan MIKE spectrograph. TOI-2005 b is one of a few planets known to have a low-obliquity, high-eccentricity orbit, which may be the result of high-eccentricity coplanar migration. The planet has a periastron equilibrium temperature of ∼ 2100 K, similar to some highly irradiated hot Jupiters where atomic metal species have been detected in transmission spectroscopy, and varies by almost 1000 K during its orbit. Future observations of the atmosphere of TOI-2005b can inform us about its radiative timescales thanks to the rapid heating and cooling of the planet. © 2025, CC BY.

关键词： Photometry

Study and Trend Prediction of Covid-19 cases in India using Deep Learning Techniques

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Journal of Physics: Conference Series 2021年第1期1950卷

作者： V K Shrivastava Ajay Kumar A Shrivastava A Tiwari K Thiru R Batra Department of Big Data Analytics Adani Institute of Digital Technology Management Gandhinagar Gujrat 382423 India. Department of Mechanical Engineering SGT University Gurugram Haryana 122505 India. Department of Electronics and Tele Communication Engineering IIIT Bhubaneswar Odisha 751003 India. Department of Information Technology Madhav Institute of Technology and Science Gwalior 474005 India. United World School of Computational Intelligence Karnavati University Gandhinagar Gujrat 383422 India. Reenu Batra Department of Computer Science and Engineering SGT University Gurugram Haryana 122505 India.

The novel coronavirus or officially known as SARS-CoV 2 (Severe Acute Respiratory Syndrome Coronavirus 2) has caused a severe pandemic over the world affecting not only the economy of the countries but also the lifestyle of the people worldwide. As on 31.12.2020, Covid-19 (coronavirus disease) has infecting more than 10266674 people and causing about 148738 deaths in India. It has been seen through various statistics of various countries that the number of Covid-19 cases grows exponentially as the number of test increases then after some period, the rate of new cases decreases. In this research paper, researchers have created deep learning-based model to predict the curve of the new Covid-19 cases vs the total number of tests conducted in India. There is still lockdown in some part of the country while some states have partially relaxed the rules and some states totally lifted the lockdown. Predicting the number of new cases and their trend can help in deciding what is the optimal time to release the lockdown. It will also help in determining when the coronavirus will loosen its grip from India.

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Corrigendum to “Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation” Information Fusion Volume 69 (2020) 103-127

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Information Fusion 2021年 75卷 101-101页

作者： Iván Palomares James Neve Carlos Porcel Luiz Pizzato Ido Guy Enrique Herrera-Viedma Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI) University of Granada Spain Intelligent Systems Laboratory University of Bristol Bristol United Kingdom AI Labs Commonwealth Bank of Australia Sydney Australia eBay Research Netanya Israel Department of Electrical and Computer Engineering from the Faculty of Engineering of the King Abdulaziz University Jeddah 21589 Saudi Arabia