The brain continuously processes information. The physical structure of the brain (its ‘hardware’) shapes this information processing and vice versa: the computations needed for information processing (the ‘software’) are adapted to the physical structure of the hardware. Moreover, both the hardware and the software are flexible: we change the way we sense the world by actively changing the physical properties of our brain to adapt to the task at hand. In this lecture, I will discuss what we already know about these mutual flexible interactions, and what the current open questions are.
Lecturer
Fleur Zeldenrust
Dr Fleur Zeldenrust studied physics and neuroscience at the University of Amsterdam, where she also did her PhD in computational neuroscience. She was a postdoctoral fellow at the École Normale Supérieure in Paris, after which she returned to the Netherlands to set up a track in computational neuroscience in the ‘Psychobiology’ BSc program at the University of Amsterdam. A Veni and a Mohrmann grant (2015) allowed her to set up her own research group called ‘Biophysics of Neural Computation’ at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, where she recently got tenured. She also recently became a member of ‘De Jonge Akademie’.
Affiliation: Donders Institute for Brain, Cognition and Behaviour, Radboud University Homepage:https://fleurzeldenrust.nl/
Many people are unsure of how to proceed with their career. No matter whether you are a student wondering what comes after your degree or an experienced professional considering new opportunities, this fishbowl discussion is supposed to provide inside views of different career options.
In the fishbowl, all panelists and the moderator are together on stage, with a couple of additional chairs. Participants who want to ask a question or give their own perspectives are welcome to take a seat on stage for some time and leave it then to somebody else.
We have a great panel of people with roots in academic research, who have chosen different career paths inside and outside of academia:
Lecturers
Enrico Fucci
Enrico Fucci holds a PhD in Neuroscience from UCBL Lyon 1, France. Implementing multidisciplinary approaches from neuroimaging, experimental psychology and neurophenomenology, his research aims to create bridges between Western science and contemplative traditions on topics such as social cognition, emotion regulation and perceptual learning. He is currently a researcher and board member of the Institute for Globally Distributed Open Research and Education (IGDORE), an independent research institute dedicated to improving the quality of science and quality of life for scientists and their families, with a strong emphasis on location-independent work and open and reproducible research. Check Enrico’s academic publications here.
Lydia Nemec
Lydia Nemec is a theoretical physicist with research focus at the interface between theoretical physics, computer science and chemistry. After her postdoc at the TU Munich, her career started as a Data Scientist in the Rail Industry at Knorr-Bremse. Today, she is the head of the ZEISS Data Science team – the ZEISS AI Accelerator.
Janina Radny
Janina Radny had a quite flexible career path. She started in forest ecology, made a detour into event management, and then took the step into science management at the Bernstein Network Computational Neuroscience. She’ll be happy to share how to upcycle experiences from former lifes to create new ideas.
Moritz Tenorth
Moritz Tenorth currently is CTO at Magazino. After spending several years in robotics research, he previously worked as robotics consultant for Siemens Novel Businesses. During his research at TU München, the CMU Robotics Institute in Pittsburgh, the ATR in Kyoto, and the University of Bremen, he investigated how autonomous manipulation robots can be equipped with Artificial Intelligence methods, in particular knowledge representation and reasoning capabilities. He published more than 50 articles and conference papers in robotics and AI. Moritz Tenorth obtained a diploma in Electrical Engineering from RWTH Aachen University and a PhD in Computer Science from TU Munich.
Bastian Epp
Bastian Epp: I studied a (at the time) novel study programme merging engineering and physics which lead to a doctoral degree in physics. I was so lucky that I was affiliated with an interdisciplinary graduate school, bringing together people from physics, biology, computer science and psychology. This formed the starting point to be able to follow my excitement of all aspects of nature and to share this excitement with other people. In science I explore the sense of hearing – in the overlap between physics, engineering and Ibiology. In my teaching activities, I enjoy to experience the moment where someone suddenly gains a deep understanding of some aspect of science.
Lecturer:Suzanne Dikker Fields: neuroscience, social psychology
Content
When we feel like we’re ‘on the same wavelength’ with another person, are our brainwaves literally ‘in sync’? Methodological innovations now make it possible to study the human brain during naturalistic social events. We will discuss examples from both within and outside the laboratory to explore how our brains and bodies adapt to others and to our environment during dynamic face-to-face social interactions, and how such flexibility may help facilitate successful communication and increase social connectedness.
Literature
Hoehl, S., Fairhurst, M., & Schirmer, A. (2021). Interactional synchrony: signals, mechanisms and benefits. Social Cognitive and Affective Neuroscience, 16(1-2), 5-18.
Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S., & Keysers, C. (2012). Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends in cognitive sciences, 16(2), 114-121.
Koole, S. L., & Tschacher, W. (2016). Synchrony in psychotherapy: A review and an integrative framework for the therapeutic alliance. Frontiers in psychology, 7, 862.
Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J.J., Ding, M. and Poeppel, D., 2017. Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Current biology, 27(9), 375-1380.
Lecturer
Dr. Suzanne Dikker
Suzanne Dikker’s work merges cognitive neuroscience, performance art and education. She uses a ‘crowdsourcing’ neuroscience approach to bring human brain and behavior research out of the lab, into real-world, everyday situations, with the goal to characterize the brain basis of dynamic human social communication. As a senior research scientist at the Max Planck — NYU Center for Language, Music and Emotion (CLaME), affiliate research scientist at the Department of Clinical Psychology at VU Amsterdam, and member of the art/science collective OOSTRIK + DIKKER, Suzanne leads various research projects, including MindHive, a citizen science platform that supports community-based initiatives and student-teacher-scientist partnerships for human brain and behavior research.
Affiliation: NYU-Max Planck Center for Language, Music and Emotion Homepage:www.suzannedikker.net
Lecturer:Tarek R. Besold Fields: Artificial Intelligence/Machine Learning
Content
Trust and technology don’t always go hand in hand–be it because of scepticism when technological advances change familiar structures of everyday life, or because of actual (ab)uses of technology that infringe upon the rights of individuals or of society as a whole. With the widespread adoption of (currently mostly ML-driven) AI solutions in different domains of professional and private life, the concept of “trustworthy AI” has gained quite some popularity with general audiences, as well as with regulators, system producers/developers, and researchers.
In this talk we will have a look at some of the key questions relating to “trustworthy AI”, such as: (1) Why is trust (or the lack thereof) an actual issue in the context of AI (and especially ML) systems? (2) What are the corresponding theoretical and/or practical challenges? (3) What are some regulatory tools and processes that can be deployed to govern AI and assure trustworthiness (to some degree)? (4) What does all of this mean for the AI ecosystem and the different market participants?
Lecturer
Dr. Tarek R. Besold
Dr. Tarek R. Besold is Head of Strategic AI at DEKRA DIGITAL. Before taking up his role in the management team of the DEKRA AI Hub, which was founded in 2020, he held various positions as CTO at a Berlin deep-tech start-up, as Chief Science Officer of Telefonica’s Digital Health Moonshot in Barcelona, and as Lecturer/Assistant Professor in Data Science at City, University of London. Tarek completed his PhD in 2014 at the Institute for Cognitive Science in Osnabrück on topics at the interface between cognition and AI. He is chairman of the DIN standardization committee for Artificial Intelligence (NA 043-01-42 AA) and a member of the AI expert advisory board of Microsoft Germany.
Contemporary discussions in philosophy and cognitive science analyze experience by seeking to understand how it is possible for a conscious subject to get in touch, know, and operate within a world outside of it. The notions of ‘consciousness,’ ‘mind,’ and ‘external world’ take center stage in these approaches and a spectrum of different theoretical options is available to flesh out the relation between these concepts. For realist accounts, consciousness and world are two relatively independent (albeit related) domains of reality, while for idealist accounts, the world is just a projection of conscious activity itself. More recently, a new enactivist account suggested that both conscious experience and the world are co-originated together in their mutual interplay.This short course explores the way in which early Buddhist philosophy navigated between the extremes of realism and idealism, by arriving at an understanding of experience that might fit the ‘enactivist’ approach. In particular, we will investigate how what is called ‘consciousness’ in today’s Western discussions is best understood in Buddhist thought in terms of ‘contact’ (Pali phassa), namely, the complex activity of discerning and parsing contents of experience which gives rise to subjective experience. For early Buddhist thought, conscious experience requires a basis in something that is different and outside the sentient subject itself and that conditions the way in which consciousness works (hence strict idealism is rejected). However, the contents of conscious experience are not representations of what is in the ‘external world,’ but rather constructions conditioned by meaning (perception), conative drives, and feelings (hence strict realism is also rejected). By underscoring this point, Buddhist texts draw attention to the way in which experience is not only constantly shaped by various factors and conditions, but also how a disciplined practice can allow one to steer this process at will and thus shape their own experience in specific ways, which might be conducive to the achievement of the soteriological goals that are central in early Buddhism (freedom from craving, and peace).
Lecturer
Dr. Andrea Sangiacomo
Andrea Sangiacomo is Associate Professor of Philosophy at the Faculty of Philosophy at the University of Groningen, where he currently teaches global hermeneutics and ancient Buddhist philosophy. His research interests include Western early modern philosophy and science, soteriological conceptions of selfhood in a cross-cultural perspective, and ancient Buddhist thought and practice.
Machine learning (ML) and artificial intelligence (AI) services are having a growing impact on the way we live and work. The most prominent goal of contemporary AI is to support human decision making and action with intelligent services. Widely available ML and AI tools are increasingly enabling the design and development of automated processes that provide (potentially) deep integration of complex information, often with the capacity to respond autonomously, mimicking aspects of human cognition and behavior. However – even questionable marketing aside – the term “artificial intelligence” alone is prone to generating misunderstandings and bloated expectations, leading to bad user experiences or worse. In this context, the course will explore flexibility in human-AI interaction with a view of both the potential upsides and pitfalls. The talk for this course will introduce the foundations of critical and responsible design, development, and evaluation of AI technologies with a focus on human-AI-interaction. It aims to provide participants with an intuition towards utilizing – and critically evaluating the impact of – human-AI interaction concepts and technologies. The workshop elements will scaffold further critical discussion along hands-on ML/AI use-cases.
Literature
Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html
Confalonieri, R., Coba, L., Wagner, B., & Besold, T. R. (2021). A historical perspective of explainable Artificial Intelligence. WIREs Data Mining and Knowledge Discovery, 11(1), e1391. https://doi.org/10.1002/widm.1391
Dauvergne, P. (2020). AI in the Wild: Sustainability in the Age of Artificial Intelligence. The MIT Press.
Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer Nature.
Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.
Hassenzahl, M., Borchers, J., Boll, S., Pütten, A. R. der, & Wulf, V. (2021). Otherware: How to best interact with autonomous systems. Interactions, 28(1), 54–57. https://doi.org/10.1145/3436942
Le, H. V., Mayer, S., & Henze, N. (2021). Deep learning for human-computer interaction. Interactions, 28(1), 78–82. https://doi.org/10.1145/3436958
Mattu, J. A., Jeff Larson,Lauren Kirchner,Surya. (n.d.). Machine Bias. ProPublica. Retrieved 7 November 2021, from https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
O’Neil, C. (2017). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Reprint edition). Crown.
Pfau, J., Smeddinck, J. D., & Malaka, R. (2020). The Case for Usable AI: What Industry Professionals Make of Academic AI in Video Games. In Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play (pp. 330–334). Association for Computing Machinery. https://doi.org/10.1145/3383668.3419905
Swartz, L. (2003). Why People Hate the Paperclip: Labels, Appearance, Behavior, and Social Responses to User Interface Agents. https://doi.org/10.13140/RG.2.1.2508.1047
Thieme, A., Cutrell, E., Morrison, C., Taylor, A., & Sellen, A. (2020). Interpretability as a dynamic of human-AI interaction. Interactions, 27(5), 40–45. https://doi.org/10.1145/3411286
Veale, M., Binns, R., & Edwards, L. (2018). Algorithms that remember: Model inversion attacks and data protection law. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180083. https://doi.org/10.1098/rsta.2018.0083
Qian Yang, Aaron Steinfeld, Carolyn Rosé, & John Zimmerman. (2020). Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3313831.3376301
Zimmerman, J., Oh, C., Yildirim, N., Kass, A., Tung, T., & Forlizzi, J. (2020). UX designers pushing AI in the enterprise: A case for adaptive UIs. Interactions, 28(1), 72–77. https://doi.org/10.1145/3436954
Lecturer
Jan Smeddinck
Jan Smeddinck is currently a Principal Investigator at – and the Co-Director of – the Ludwig Boltzmann Institute for Digital Health and Prevention (LBI-DHP) in Salzburg, Austria. For the LBI-DHP, he leads research programme lines on digital technologies and data analytics. Prior to this appointment he was a Lecturer (Assistant Professor) in Digital Health at Open Lab and the School of Computing at Newcastle University in the UK. He also spent one year as a postdoc visiting research scholar at the International Computer Science Institute (ICSI) in Berkeley and retains an association with his PhD alma mater, the TZI Digital Media Lab at the University of Bremen in Germany. Building on his background in interaction design, serious games, web technologies, human computation, machine learning, and visual effects, he has found a home in the research field of human-computer interaction (HCI) research with a focus on digital health.
Affiliation: Ludwig Boltzmann Institute for Digital Health and Prevention Homepage:https://smeddinck.com/
Lecturer:Udo Ernst Fields: Neurobiology / Robotics
Content
The visual system of higher mammals is a complex neural machinery which efficiently solves sophisticated computational problems on a massively parallel stream of information originating in dynamic environments. This is only possible by being highly flexible, i.e. by adapting visual processing to sensory, behavioral, and cognitive contexts. Flexibility also makes our visual system (still) superior to computer vision, in which state-of-the-art deep convolutional networks may perform near error-free object recognition, but fail to adapt to novel situations or break down under adversarial attacks.
In my presentation, I will discuss different examples of flexibility in the visual system in the context of three major principles: configuration, coordination and control. Configuration adapts circuits and networks to current behavioural needs, optimizing their function towards specific tasks or for performing specific computations more efficiently. The interplay between computational units is organized by coordination principles towards common goals, leading to interaction of multiple ‘players’ such as different visual areas in the brain, or and to dynamical network changes on multiple time scales. Both configuration and coordination needs control units to monitor and signal changes in the external and/or internal situation, and to initiate appropriate reaction mechanisms.
We will argue that it is necessary to combine different methodological approaches for understanding flexibility in vision: for example, electrophysiological studies to reveal mechanisms of flexibility, psychophysical investigations to characterize the impact of neural flexibility on function, and theoretical work to provide unifying frameworks and explanations for dynamics, mechanisms and function of flexibility.
The aim of our workshop is to implement different principles of flexibility in a computer simulation, and make them work together. Participants will team up in small groups which will first focus on one particular, simple aspect of flexibility, i.e. adapting to the ambient light, focusing attention on particular visual features, detecting rapid changes in the environment. Our goal is to realize flexibility with appropriate neural mechanisms, for better understanding how the brain might solve a corresponding task. In a second step, different groups will put their solutions together and try to ‘coordinate’ them, i.e. to combine flexible processing on multiple levels in a meaningful manner.
For testing your ideas, we will use our webcams or short movie sequences and investigate how well flexible neural processing works under different conditions – maybe you can even mount a webcam to your head, close your eyes, and try for yourself if your artificial visual system can direct you safely towards the coffee machine in your home office, hereby avoiding all obstacles… :-))))
Let’s see what complex and unexpected behaviours will emerge, and let’s be flexible! For participating in our workshop, you only need some knowledge in programming, preferrably in Python. In our course repository you will find more information about program packages required for Python, installation guides, literature and other ressources. We suggest you perform installation of an appropriate Python package and editor prior to the course, and familiarize yourself with the most important features of these tools.
On this page you will also find information on how to contact us by e-mail if you have questions in advance.
Let’s see what complex and unexpected behaviours will emerge, and let’s be flexible! For participating in our workshop, you only need some knowledge in programming, preferrably in Python. Please bring a laptop; we will inform you in advance which program packages you would have to install prior to the course. This course is configured to take place on-site, but we will try to be flexible and activate our control circuits for coordinating with one (small) external group of participants if necessary…
Lecturer
Dr. Udo Ernst
Dr. Udo Ernst studied Physics in Frankfurt and received his PhD in 1999 at the Max-Planck-Institute for Dynamics and Self-Organization in Göttingen. Since 2000, he is working at the Institute for Theoretical Physics at the University of Bremen, with interim research stays at University of Tsukuba (Japan), the Weizmann Institute (Israel), and Ecole Normale Superieure (France). Having received the Bernstein Award in Computational Neuroscience in 2010, Dr. Ernst is now leading the Computational Neurophysics Lab in Bremen. Research interests revolve around understanding collective dynamics in neural systems using data analysis, mathematical analysis, modelling and simulation; with particular interest in feature integration, criticality, and flexible information processing in the visual system.
Maik Schünemann
Maik Schünemann is a PhD-student at the Computational Neurophysics Lab in Bremen. He joined the Lab after completing masters studies in Mathematics, with a focus on dynamical systems and random processes, and Neurosciences, with a focus on Computational Neurosciences. His research focuses on how attention establishes flexible and selective information processing in the visual system. In addition, he participated both as student and tutor in the G-Node Advanced Neural Data Analysis Course.
Lecturer:Bettina Bläsing Fields: Cognitive movement science / practical course
Content
Improvisation (from latin improvidere: not foreseeing) is a highly sophisticated human activity that draws on different forms of memory and cognitive meta-skills, increasing the individual’s flexibility and supporting adaptation under uncertain conditions. For the human mind, improvisation can also be means of exploring and expanding the options to interact with the world, and a source of enjoyment and stimulation. In dance, improvisation is used for different purposes: as a choreographic tool, to inspire novel ideas in composition; in contemporary dance training, to support dancers’ movement experience and bodily creativity; or as artistic practice per se, in live improvisation performance. Dance and movement improvisation offer a multitude of tools and techniques that help to discover new ways of moving, interacting and communicating through the body. In this course we will use a range of these tools to explore and create. We will encounter unexpected tasks and problems, set and break rules, try to escape habits and enjoy wandering astray, making our way through our own danced stories. Starting from movement and dance improvisation tasks, we will enter other areas of life, including the academic, and watch out for novel ways of approaching old problems, embracing the unforeseeable.
Lecturer
Dr. Bläsing
Bettina Bläsing works as a lecturer in rehabilitation science at the Technical University Dortmund. She studied in Bielefeld, Münster and Edinburgh and received her doctorate in biology from Bielefeld University in 2004. As a postdoc, she worked at the Max Planck Institute for Evolutionary Anthropology and the Institute for Psychology at Leipzig University, as well as in the “Cognitive Interaction Technology” cluster of excellence and in the “Neurocognition and Movement” working group at Bielefeld University. In 2019 she received the venia legendi in sports science for her habilitation on memory, learning and expertise in dance. Her current focus in research and teaching includes memory processes, improvisation and multimodal perception of body and movement in (inclusive) dance.
The talk will present central theoretical concepts of (mindfulness) meditation as well as empirical findings regarding meditation-induced neural plasticity and effects on cognition, affect and the sense of self. These findings will be integrated by discussing potential computational mechanisms within the active inference framework. Finally, in line with the neurophenomenological reserach program, it will be explored how meditation can enrich our understanding of the mind not only as an object of study, but also as a tool of investigation.
Literature
Berkovich-Ohana, A., Dor-Ziderman, Y., Trautwein, F.-M., Schweitzer, Y., Nave, O., Fulder, S., & Ataria, Y. (2020). The hitchhiker’s guide to neurophenomenology – The case of studying self boundaries with meditators. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01680
Dahl, C. J., Lutz, A., & Davidson, R. J. (2015). Reconstructing and deconstructing the self: Cognitive mechanisms in meditation practice. Trends in Cognitive Sciences, 19(9), 515–523. https://doi.org/10.1016/j.tics.2015.07.001
Laukkonen, R. E., & Slagter, H. A. (2021). From many to (n)one:Meditation and the plasticity of the predictive mind. Neuroscience & Biobehavioral Reviews, 128(June), 199–217. https://doi.org/10.1016/j.neubiorev.2021.06.021
Lecturer
Dr. Trautwein
Dr. Fynn-Mathis Trautwein investigates mental processes underlying attention, social cognition and the sense of self through the lens of meditation research. After studying psychology, he completed a PhD at the Max Planck Institute for Human Cognitive and Brain Sciences, where he was involved in a large-scale longitudinal mental training study. He then investigated neural mechanisms and phenomenological reports of deep meditative states at the University of Haifa. Currently he is a postdoc at the Department of Psychosomatic Medicine and Psychotherapy, Medical Center – University of Freiburg.
I would like to give a hands-on workshop on how to create a custom space in Gather Town. Afterwards you should have a good grasp of how the parts of a Gather Town space work and you should have built a custom space for yourself.
The workshop will start on Tuesday, 18:30 CEST in the lecture hall of the VIK space.
Please note: This will be an IK-internal event. If you have questions in that regard (or if you cannot make it), feel free to get in contact with me and we can discuss details.
What is it all about
Some people have asked me questions like:
— How difficult is it to create a map or space in Gather Town? — How long does it take to create a map like [cool map]? — Can I integrate [cool content] in Gather Town? — and so on
The workshop is my long-winded answer, such that you can answer those questions yourself afterwards. Based on your own experience. Because you built something. With hands. Your hands. It will be great.
What to expect
The workshop will probably have the following structure:
Part I: The basics 1) How a space works, the Map Maker, how to break things, good practises 2) Creating a map, method 1: Gather Town’s Map Maker Part II: Advanced methods 3) Creating a map, method 2: external tools, esp. Tiled Map Editor 4) Automating recurring tasks Part III: Do it yourself a) Hands-on: Build your own space! b) Questions and (hopefully) answers
Part II, which requires a bit more technical understanding, builds on Part I, which should be easy to follow along just like that. I intend to make it such that people can sit back and relax _or_ get their hands dirty, but are not forced to do both at the same time. Part III should really be about you creating universes and me shutting up and only talk when being asked questions. (Let’s see how that goes.)
Each Part is supposed to be as short as possible and between parts there will be short breaks. Not sure how to estimate times, but I’ll try to fit Part I and II into 25min each, give or take. Very specific needs and more complicated questions might be postponed and addressed in Part III.
Here’s the cool thing
Come for whatever Part matches your interest and drop out into working on your own space, when you’ve got enough of the talking.
Not interested in tech mumbo jumbo? Just listen in on Part I. You already worked with the Map Maker? Join for Part II. You wonder whether a certain idea could be realised for your specific classroom teaching needs? Shoot your questions at me in Part III.
For those interested I’m happy to build a “space hub” and connect it with all the spaces created on that evening. That’s cool because it invites people to explore and discover, without requesting links or sending emails. Showcasing work on the VIK’s Bunter Abend is an option, but details are to be discussed at the workshop.
Who’s talking
I’m not getting paid by Gather Town for advertisement (dang) and there might be smarter ways than how I do it to achieve the same results. Gather Town certainly is not the ultimate answer. However, I see great potential in this incarnation of virtual environments. I want to share the lessons I learned in the past months to empower people, if possible, such that you can use widely available, modern technology to improve e.g. social well-being or education, especially in the times we have. The stuff you need to get into a space is not rocket science, but imagination. (And your hands.)
I leave it to the reader to judge whether I’m qualified to talk about Gather Town space building. In my defence: I was not insignificantly involved in creating the space for the Virtual IK.
If you have any questions left, please le me know. A response will be be shorter than this email, most probably.
