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New "Developing Minds" global online lecture series; September 30: Pierre-Yves Oudeyer

by Jochen Triesch

What: New bi-monthly "Developing Minds" global online lecture series; inaugural lecture by Pierre-Yves Oudeyer, INRIA, Bordeaux Sud-Ouest, France on "Developmental AI: machines that learn like children and help children learn better" When: Thursday, September 30, 13:00 UTC Where: online via zoom; please register at: https://sites.google.com/view/developing-minds-series Background: In 1950, Alan Turing asked "Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's?" Today, over 70 years later, constructing a computer program that can learn like a child and that develops a human-like general intelligence is still considered a grand, if not the ultimate, challenge for artificial intelligence (AI). An interdisciplinary community of scientists from AI, Cognitive Science, Psychology, Engineering, and Neuroscience are tackling this grand challenge. In the Developing Minds global lecture series we showcase the progress being made. It is organized by the IEEE Technical Committee on Cognitive and Developmental Systems of the IEEE Computational Intelligence Society. The inaugural lecture by Pierre-Yves Oudeyer is entitled: "Developmental AI: machines that learn like children and help children learn better" Abstract: Current approaches to AI and machine learning are still fundamentally limited in comparison with the amazing learning capabilities of children. What is remarkable is not that some children become world champions in certains games or specialties: it is rather their autonomy, open-endedness, flexibility and efficiency at learning many everyday skills under strongly limited resources of time, computation and energy. And they do not need the intervention of an engineer for each new task (e.g. they do not need someone to provide a new task specific reward function or representation). I will present a research program, which I call Developmental AI, that studies models of open-ended development and learning. These models are used as tools to help us understand better how children learn, as well as to build machines that learn like children with applications in educational technologies, automated discovery, robotics and human-computer interaction. I will ground this research program into several fundamental ideas proposed by developmental psychologists: 1) the child is autotelic, setting its own goals and spontaneously exploring the world like a curious little scientist (e.g. Piaget, Berlyne); 2) intelligence develops in a social context, where language and culture are internalized to become cognitive tools (e.g. Vygostky and Bruner); 3) intelligence is embodied and develops through self-organization of the dynamical system formed by the brain-body-environment interactions (e.g. Thelen and Smith). I will show how, together with many colleagues and students, we have worked on operationalizing these ideas in computer and robotic models, and explain how this has enabled to advance child development understanding, open new possibilities for AI systems (including for recent language-guided intrinsically motivated Deep RL systems and for training more classical Deep RL systems to foster generalization), and led to applications now used by thousands of children in the world to help them learn (educational technologies). Speaker Bio: Dr. Pierre-Yves Oudeyer has been Research Director at Inria, France, and head of the Flowers lab (Inria, Univ. Bordeaux, Ensta ParisTech). Before, he has been a permanent researcher in Sony Computer Science Laboratory for 8 years (1999-2007). He studies models of open-ended development and learning, at the frontiers of AI and cognitive sciences. These models are used as tools to help us understand better how children learn, as well as to build machines that learn like children within the field of developmental artificial intelligence. He has been developing models of intrinsically motivated learning, pioneering curiosity-driven learning algorithms working in real world robots, and developed theoretical frameworks to understand better human curiosity and autonomous learning. He also studied mechanisms enabling machines and humans to discover, invent, learn and evolve language. He is also working on applications in educational technologies, automated discovery, video games, robotics and human-computer interaction. He received several prizes for his work in developmental AI and on the origins of language. In particular, he is laureate of the Inria-National Academy of Science young researcher prize in computer sciences, and of an ERC Starting Grant EXPLORERS. Finally, he is also working actively for the diffusion of science towards the general public, through the writing of popular science articles and participation to radio and TV programs as well as science exhibitions. Web: http://www.pyoudeyer.com and http://flowers.inria.fr Upcoming speakers: Linda B. Smith, Indiana University Joshua B. Tenenbaum, MIT Please visit https://sites.google.com/view/developing-minds-series/home for up-to-date information. -- Prof. Dr. Jochen Triesch Johanna Quandt Research Professor Frankfurt Institute for Advanced Studies http://fias.uni-frankfurt.de/~triesch/ Tel: +49 (0)69 798-47531 Fax: +49 (0)69 798-47611

Sept. 19, 2021

CFP - Annual Conference of Cognitive Science (ACCS8) - Jan 20--22, 2022, India

by Shyam Diwakar

Dear All, As the local organizer, it is our pleasure to invite you to participate at ACCS8. Here's more details. ACCS8: Call for contributions 8th Annual Conference of Cognitive Science (ACCS8) Amrita University, India – 20 – 22 January 2022 https://www.amrita.edu/accs8 The 8th edition of ACCS, to be hosted by Amrita Vishwa Vidyapeetham (Amrita University), Kollam, India will be held virtually. List of our invited speakers, TPC and other info will soon be made available at https://www.amrita.edu/event/accs8 CONFERENCE TOPICS Like in our previous years, the ACCS8 conference is open to all topics with Cognitive Science as a discipline, and in various areas of study, including Neuroscience, Artificial Intelligence, Linguistics, Skilling, Medicine, Anthropology, Psychology, Philosophy, and Education. SUBMISSION GUIDELINES We welcome submissions from all areas of cognitive science and from anyone worldwide. For the 2022 online event, we are accepting only abstracts containing the salient details of your study. Please copy and paste the title and abstract into the submission form. Contribution size limit for the abstract is 1000 words. We are not accepting any paper-length submissions this year, so do include all relevant details in the abstract itself. A small number of abstracts will be selected for oral presentations/talks. Submission link: https://easychair.org/conferences/?conf=accs8 For CFP: https://easychair.org/cfp/accs8 IMPORTANT DATES Abstract registration deadline November 25, 2021 Submission deadline November 25, 2021 BEST PAPER AWARDS We are planning some best poster and oral presentations. Please stay tuned. CONTACT All questions about submissions should be emailed to accs8conference(a)gmail.com<mailto:accs8conference@gmail.com> Previous ACCS events - https://www.amrita.edu/event/accs8/about With best regards, Shyam Diwakar -- Prof. Shyam Diwakar, Ph.D. Director - Amrita Mind Brain Center Faculty Fellow - Amrita Center for International Programs Amrita Vishwa Vidyapeetham (Amrita University) Amritapuri, Clappana P.O. Kollam, India. Pin: 690525 Ph:+91-476-2803116 Fax:+91-476-2899722 http://amrita.edu/mindbrain [https://docs.google.com/uc?export=download&id=1_nOxaCXob6wVcuQyYI5KVk1PhMgi…] [https://intranet.cb.amrita.edu/sig/RankingLogo.png] Disclaimer : The information transmitted in this email, including attachments, is intended only for the person(s) or entity to which it is addressed and may contain confidential and/or privileged material. Any review, retransmission, dissemination or other use of, or taking of any action in reliance upon this information by persons or entities other than the intended recipient is prohibited. Any views expressed in any message are those of the individual sender and may not necessarily reflect the views of Amrita Vishwa Vidyapeetham. If you received this in error, please contact the sender and destroy any copies of this information.

Sept. 18, 2021

(Call for papers) Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems

by Andrea Soltoggio

Dear All, This is a reminder of the upcoming deadline (30th of September) to submit to the Research Topic “Adaptive Machines: Leveraging Neuroscience for Lifelong Learning Systems”, co-hosted by Frontiers in Neuroscience, Robotics and AI, Big Data, Artificial Intelligence, Neurorobotics and Computational Neuroscience. Link: https://www.frontiersin.org/research-topics/20053/adaptive-machines-leverag… Description: Artificial intelligence has always sought inspiration in the brain. Artificial neural networks (ANNs), in particular, were modeled after their biological counterparts (biological neural networks, BNNs). However, state-of-the-art deep networks are drastically different from BNNs. Their architectures (at both the single-neuron and macro levels), learning algorithms, and failure conditions share little in common with brain circuit dynamics. In particular, state-of-the-art deep learning is optimized for single, well-defined, static tasks. Deep networks struggle to learn multiple or evolving tasks over time (i.e., continual learning) or to adjust their processing based on environmental conditions. They are also less modular than BNNs, and thus less energy efficient, and utilize little-to-no feedback signals. We believe that deep networks suffer from these limitations because their modeling of brain dynamics is too superficial. Modeling more sophisticated neural mechanisms is therefore key for deep networks to achieve continual or lifelong learning and to cope with open-ended, dynamic environments. The goal of this Research Topic is to publish novel models and/or algorithms that expand the capabilities of deep learning (e.g., achieve better continual learning) by incorporating additional properties of BNNs. We are also interested in neuroscience research that elucidates mechanisms that can be leveraged by the AI community. Neuroscience has uncovered a wide range of learning and regulatory mechanisms, at scales ranging from single molecules to the entire nervous system, that help the brain learn, remember, and adapt. These include replay (systems-level consolidation), neuromodulation, neurogenesis, neuroevolution, attention, homeostatic plasticity, and synaptic consolidation. There exist some deep learning techniques motivated by these mechanisms (e.g., experience replay or attention networks), but they have rarely been used for lifelong learning. As such, our focus will be on approaches that leverage brain-like principles---ideally biologically well-grounded---to solve problems currently beyond the capabilities of the state of the art. We will also welcome neuroscience research that helps to elucidate the brain’s mechanisms, in the hope that they can be used by the next generation of machine learning models. The scope of this Research Topic covers: (1) biologically inspired techniques in deep learning (2) neuroscience research on how the brain facilitates learning and adapts to different contexts We seek to address continual or lifelong learning, transfer learning, and other ML scenarios that go beyond the traditional, train-then-test learning paradigm. Since machine learning is an experimental field, a paper must include experimental results to be accepted; however, we encourage authors to include theoretical analyses of their methods. We aim to collect the following types of manuscripts: Original Research and Brief Research Reports. Since the goal of this Research Topic is to publish highly novel, experimentally grounded work, we are not interested in Reviews (Systematic, Policy and Practice, etc.), General Commentaries, or Opinions. In addition, since the editorial board does not include medical experts, we cannot accept Clinical Trials or Study Protocols. The Research Topic is edited by Rolando Jose Estrada, Georgia State University, USA Andrea Soltoggio, Loughborough University, UK Praveen K. Pilly, HRL Laboratories, USA Vincenzo Lomonaco, University of Pisa, Italy, Davide Maltoni, University of Bologna, Italy. Please consider submitting your original research to this Research Topic. Do not hesitate to get in touch if you wish to discuss your contribution. Best wishes, Andrea -- Dr. Andrea Soltoggio (he/him or they/them) Senior Lecturer (Associate Professor) in Artificial Intelligence Department of Computer Science, School of Science & Intelligent Automation Centre https://www.intelligent-automation.org.uk/about-us/centre-staff & Centre for Information Management https://www.lboro.ac.uk/departments/sbe/cim/ Haslegrave Building, N.2.03 Loughborough University LE11 3TU, UK Email: a.soltoggio(a)lboro.ac.uk<mailto:a.soltoggio@lboro.ac.uk> Web: http://www.lboro.ac.uk/departments/compsci/staff/dr-andrea-soltoggio.html

Sept. 17, 2021

ISRC Computational Neuroscience, Neurotechnology and Neuro-inspired AI Autumn School (ISRC-CN3)

by Wong-Lin, Kongfatt

Dear All, We are excited to announce that our ISRC Computational Neuroscience, Neurotechnology and Neuro-inspired AI Autumn School (ISRC-CN3) is now live and open for registration at the following link: https://store.ulster.ac.uk/product-catalogue/faculty-of-computing-engineeri… This Autumn School (from 25th October to 29th October), which will be conducted in a hybrid format, is organised by our Intelligent Systems Research Centre (ISRC) at Ulster University, Magee campus. More details can be found at the website: https://sites.google.com/view/isrc-cn3/home<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsites.goo…> If you have any further queries about this event, please contact Dr. KongFatt Wong-Lin (k.wong-lin(a)ulster.ac.uk) This email and any attachments are confidential and intended solely for the use of the addressee and may contain information which is covered by legal, professional or other privilege. If you have received this email in error please notify the system manager at postmaster(a)ulster.ac.uk and delete this email immediately. Any views or opinions expressed are solely those of the author and do not necessarily represent those of Ulster University. The University's computer systems may be monitored and communications carried out on them may be recorded to secure the effective operation of the system and for other lawful purposes. Ulster University does not guarantee that this email or any attachments are free from viruses or 100% secure. Unless expressly stated in the body of a separate attachment, the text of email is not intended to form a binding contract. Correspondence to and from the University may be subject to requests for disclosure by 3rd parties under relevant legislation. The Ulster University was founded by Royal Charter in 1984 and is registered with company number RC000726 and VAT registered number GB672390524.The primary contact address for Ulster University in Northern Ireland is Cromore Road, Coleraine, Co. Londonderry BT52 1SA

Sept. 17, 2021

Postdoctoral Associate in Neural Circuits of Learning and Memory

by Yuri Dabaghian

NIH-funded postdoctoral position is immediately available in the group of Dr. Yuri Dabaghian in the Department of Neurology at McGovern Medical School of the University of Texas, Houston. We are looking for scientists with research interests at the intersection of mathematics, physics, and neuroscience, in the general area of emergent phenomena and representations, network dynamics, topological data analyses. Some projects aim to explain electrophysiological data patterns, some are theoretically motivated. Specifically, we are interested in circuit mechanisms of learning and memory and their involvement in neurological disorders, notably Alzheimer's Disease. Successful candidates will develop data-driven hippocampal neuronal network models explaining spatial learning dynamics, develop and creatively apply tools for the data analysis including novel methods of brain waves (EEG) analyses, Topological Data Analysis of spiking data, etc. The Position requires strong background in quantitative disciplines (PhD and ongoing interest in computational or theoretical neuroscience, physics, mathematics, or related), plus willingness to do programming and to analyze experimental data. Previous experiences in data analyses are preferred but not required. All Appointments are initially for one year and are renewable for at least three years given satisfactory performance. The salary is competitive. The postdoc will be integrated into the large, vibrant neuroscience community of the Texas Medical Center and have the opportunity to participate in collaborations with groups at Baylor College of Medicine and Rice University. To apply, please send application materials including a detailed CV, a brief statement of research interests, and contact information of 3 references to Dr. Yuri Dabaghian at [Yuri.A.Dabaghian [at] uth.tmc.edu) The position is available immediately, until filled. For Additional information and informal inquiries, please contact Dr. Dabaghian. We Strive for a diverse and inclusive environment, and encourage applications from members of any identity.

Sept. 17, 2021

COSYNE 2022: Meeting announcement

by Tomas Hromadka

==================================================== Computational and Systems Neuroscience 2022 (Cosyne) MAIN MEETING 17 - 20 March 2022 Lisbon, Portugal WORKSHOPS 21 - 22 March 2022 Cascais, Portugal www.cosyne.org ==================================================== ---------------------------------------------------- MEETING ANNOUNCEMENT ---------------------------------------------------- The annual Cosyne meeting provides an inclusive forum for the exchange of empirical and theoretical approaches to problems in systems neuroscience, in order to understand how neural systems function. The MAIN MEETING is single-track. A set of invited talks is selected by the Executive Committee, and additional talks and posters are selected by the Program Committee, based on submitted abstracts. The WORKSHOPS feature in-depth discussion of current topics of interest, in a small group setting. All abstract submissions will be reviewed double blind. The deadline for Abstract submission will be 20 November 2021. Cosyne topics include but are not limited to: neural basis of behavior, sensory and motor systems, circuitry, learning, neural coding, natural scene statistics, dendritic computation, neural basis of persistent activity, nonlinear receptive field mapping, representations of time and sequence, reward systems, decision-making, synaptic plasticity, map formation and plasticity, population coding, attention, neuromodulation, and computation with spiking networks. We would like to foster increased participation from experimental groups as well as computational ones. Please circulate widely and encourage your students and postdocs to apply. IMPORTANT DATES Abstract submission opens: 15 October 2021 Travel grant submission opens: 01 November 2021 Abstract submission deadline: 20 November 2021 When preparing an abstract, authors should be aware that not all abstracts can be accepted for the meeting. Abstracts will be selected based on the clarity with which they convey the substance, significance, and originality of the work to be presented. ORGANIZING COMMITTEE General Chairs: Anne-Marie Oswald (U Pittsburgh) and Srdjan Ostojic (Ecole Normale Superieure Paris) Program Chairs: Laura Busse (LMU Munich) and Tim Vogels (IST Austria) Workshop Chairs: Anna Schapiro (U Penn) and Blake Richards (McGill) Tutorial Chair: Kanaka Rajan (Mount Sinai) DEIA Committee: Bianca Jones Martin (Columbia), Gabrielle Gutierrez (Columbia), and Stefano Recanatesi (U Washington) Undergraduate Travel Chairs: Angela Langdon (Princeton) and Bob Wilson (U Arizona) Fundraising Chair: Michael Long (NYU) Social Media Chair: Grace Lindsay (Columbia) Poster Design: Maja Bialon PROGRAM COMMITTEE Laura Busse (U Munich) Tim Vogels (IST Austria) Athena Akrami (UCL) Omri Barak (Technion) Brice Bathellier (Paris) Bing Brunton (U Washington) Yoram Burak (Hebrew University) SueYeon Chung (Columbia) Christine Constantinople (NYU) Victor de Lafuente (UNAM Mexico) Jan Drugowitsch (Harvard) Alexander Ecker (Göttingen) Tatiana Engel (Cold Spring Harbor) Annegret Falkner (Princeton) Kevin Franks (Duke) Jens Kremkow (Berlin) Andrew Leifer (Princeton) Sukbin Lim (Shanghai) Scott Linderman (Stanford) Emilie Mace (MPI Neurobiology) Mackenzie Mathis (EPFL Lausanne) Ida Momennejad (Microsoft) Jill O'Reilly (Oxford) Il Memming Park (Stony Brook) Adrien Peyrache (McGill Montréal) Yiota Porazi (FORCE) Nathalie Rochefort (Edinburgh) Christina Savin (NYU) Daniela Vallentin (MPI Ornithology) Brad Wyble (U Penn) EXECUTIVE COMMITTEE Stephanie Palmer (U Chicago) Zachary Mainen (Champalimaud) Alexandre Pouget (U Geneva) Anthony Zador (CSHL) CONTACT meeting [at] cosyne.org COSYNE MAILING LISTS Please consider adding yourself to Cosyne mailing lists (groups) to receive email updates with various Cosyne-related information and join in helpful discussions. See Cosyne.org -> Mailing lists for details.

Sept. 16, 2021

Online workshop "Towards multipurpose neural network models II: Model testing and model fitting"

by Anton Arkhipov

Dear Colleagues, Registration is now open for the online workshop "Towards multipurpose neural network models II: Model testing and model fitting" organized by Gaute Einevoll (NMBU/University of Oslo) and Anton Arkhipov (Allen Institute, Seattle) and supported by the European Institute for Theoretical Neuroscience (EITN). Please join us for three days of talks about cutting-edge science by fantastic speakers, as well as panel discussions. Dates: September 29 – October 1, 2021 Time: 8AM – 12:15PM US Pacific time / 5PM – 9:15PM Central European Time Daily. Registration: https://cnrs.zoom.us/webinar/register/WN_saMXfg31Roe4tqHbWtTdFg Please see the agenda below and more details at this web page: https://www.eitn.org/index.php/calendar-event/eventdetail/750/-/workshop-on… We hope to see you there! Wednesday, September 29, 2021 Gaute Einevoll (NMBU/U. Oslo) Introduction Jacob Macke (U. Tübingen) Keynote. Simulation-based inference: Bridging the gap between mechanistic models and machine learning. Aaron Milstein (Rutgers U.) Nested parallel simulation and multi-objective optimization of neuronal cell and circuit models Atle Rimehaug (U. Oslo) Enhancing model constraints by utilizing current source densities Kristin Tøndel (NMBU) Facilitating optimization using metamodelling Frances Skinner (Krembil Brain Institute, University Health Network, and University of Toronto) Clarity in model development and goals leads to model linkages and biological insights Panel debate – All participants of the day. Thursday, September 30, 2021 Anton Arkhipov (Allen Institute) Introduction James DiCarlo (MIT) Keynote. Reverse Engineering Visual Intelligence Sharon Crook (Arizona State U.) Testing the Data-driven Model Szabolcs Kali (Institute of Experimental Medicine, Budapest, Hungary) Systematic construction and evaluation of models of rodent hippocampal neurons Peter Jedlicka (U. Giessen) Building consistent and robust models of hippocampal granule cells and CA1 pyramidal cells Stefan Mihalas (Allen Institute) Computing with a mess: How nonstationary, heterogeneous and noisy components help the brain’s computational power Panel debate – All participants of the day. Friday, October 1, 2021 Markus Covert (Stanford) Special lecture. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation Kanaka Rajan (Mount Sinai) Keynote. Data-constrained neural network models of adaptive learning in the brain Julijana Gjorgjeva (Max Planck Institute and TUM) Biologically plausible learning in developing networks Carsen Stringer (Janelia) Rastermap: Extracting structure from high-dimensional neural data Arvind Kumar (KTH Stockholm) Structure and activity dynamics relationship in biological neuronal networks: Measurements and models Panel debate – All participants of the day.

Sept. 14, 2021

PhD position in computational neuroscience/machine learning at UC Santa Barbara

by Michael Beyeler

The Bionic Vision Lab lab at the University of California, Santa Barbara (UCSB) invites applications for a fully funded PhD position to develop computational models of neural activity in mice navigating through real-world and virtual environments. The successful candidate will have a strong quantitative background that includes machine learning, programming experience, and a thorough interest in neuroscience. This is an opportunity to work on an impactful real-world problem within a highly interdisciplinary and collaborative environment. Interested applicants should send a CV and transcript (unofficial is fine) to Michael Beyeler (mbeyeler(a)ucsb.edu) Official applications will have to be submitted through UCSB Grad Admissions before the application deadline indicated below. All genders welcome. Application deadline: December 1, 2021 Start date: Fall 2022 UCSB is a dynamic research institution, particularly in physical sciences and engineering, and was recently rated one of the Top 5 public schools in the United States (US News & World Report). Neuroscience research is well established here, featuring >35 faculty members whose research interests span the range from molecular to theoretical neuroscience.

Sept. 14, 2021

Call for community feedback on the endorsement of Neurolucida XML as a standard

by Malin Sandström

Dear all, INCF is seeking community feedback and comments on the review of the MBF neuromorphological file format v 4.0, a format for digital reconstruction & modeling structure for microscopic anatomies, also known as “Neurolucida XML”, which has been submitted to INCF for endorsement as a community standard. If you have experience of using Neurolucida XML, we ask you to read the review report and comment based on your experiences, especially if you share or reuse data in the format, or work on models or tools that use the format. Link to the review: https://f1000research.com/documents/10-712 *Why endorse community standards? * INCF (incf.org) has implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. Our mission is to make neuroscience more open and FAIR, to ensure that research funds and efforts are well invested, and that neuroscientific findings are robust and replicable. Open community review is a very important step in the process. Quality community standards are necessary to make FAIR resources and processes work, but too many neuroscience communities lack robust standards or have competing incompatible standards. The rapid development of new techniques also means that there is a continuous need for new and updated standards, and that old standards need an active developer and user community keeping them up to date. By endorsing standards, INCF wants to make it easy to find the best, most reliable standard appropriate for your research, and ensure recognition for community members investing their time and effort in standards. The review criteria (https://incf.org/incf-standards-review-criteria-v20) include Openness; FAIRness; Design, Testing and Implementation; Governance; Adoption and use; Stability and support; Extensibility; Comparison (similarity to other standards). -- Malin Sandström, PhD Community Engagement Officer malin.sandstrom(a)incf.org ORCID: 0000-0002-8464-2494 International Neuroinformatics Coordinating Facility Karolinska Institutet Nobels väg 15 A SE-171 77 Stockholm Sweden http://www.incf.org

Sept. 14, 2021

workshop on Brain Criticality: The Past, the Present and the Future

by Claudius Gros

You are invited to attend the upcoming Bernstein workshop on Brain Criticality: The Past, the Present and the Future https://www.bernstein-network.de/en/bernstein-conference/2021-online/satell… Tuesday, September 21, 2021, 2pm CEST It is free, but you need to register with the Bernstein conference (see link above). Claudius Gros ### ### Prof. Dr. Claudius Gros ### http://itp.uni-frankfurt.de/~gros ### ### Complex and Adaptive Dynamical Systems, A Primer ### A graduate-level textbook, Springer (2008/10/13/15) ### ### Life for barren exoplanets: The Genesis project ### https://link.springer.com/article/10.1007/s10509-016-2911-0 ###

Sept. 14, 2021