Endogenous space in the Net era

Libre Software communities are among the most interesting and advanced socio-economic laboratories on the Net. In terms of directions of Regional Science research, this paper addresses a simple question: “Is the socio-economics of digital nets out of scope for Regional Science, or might the latter expand to a cybergeography of digitally enhanced territories ?” As for most simple questions, answers are neither so obvious nor easy. The authors start drafting one in a positive sense, focussing upon a file rouge running across the paper: endogenous spaces woven by socio-economic processes. The drafted answer declines on an Evolutionary Location Theory formulation, together with two computational modelling views. Keywords: Complex networks, Computational modelling, Economics of Internet, Endogenous spaces, Evolutionary location theory, Free or Libre Software, Path dependence, Positionality.

ASIME 2018 White Paper. In-Space Utilisation of Asteroids: Asteroid Composition -- Answers to Questions from the Asteroid Miners

In keeping with the Luxembourg government's initiative to support the future use of space resources, ASIME 2018 was held in Belval, Luxembourg on April 16-17, 2018. The goal of ASIME 2018: Asteroid Intersections with Mine Engineering, was to focus on asteroid composition for advancing the asteroid in-space resource utilisation domain. What do we know about asteroid composition from remote-sensing observations? What are the potential caveats in the interpretation of Earth-based spectral observations? What are the next steps to improve our knowledge on asteroid composition by means of ground-based and space-based observations and asteroid rendez-vous and sample return missions? How can asteroid mining companies use this knowledge? ASIME 2018 was a two-day workshop of almost 70 scientists and engineers in the context of the engineering needs of space missions with in-space asteroid utilisation. The 21 Questions from the asteroid mining companies were sorted into the four asteroid science themes: 1) Potential Targets, 2) Asteroid-Meteorite Links, 3) In-Situ Measurements and 4) Laboratory Measurements. The Answers to those Questions were provided by the scientists with their conference presentations and collected by A. Graps or edited directly into an open-access collaborative Google document or inserted by A. Graps using additional reference materials. During the ASIME 2018, first day and second day Wrap-Ups, the answers to the questions were discussed further. New readers to the asteroid mining topic may find the Conversation boxes and the Mission Design discussions especially interesting.Comment: Outcome from the ASIME 2018: Asteroid Intersections with Mine Engineering, Luxembourg. April 16-17, 2018. 65 Pages. arXiv admin note: substantial text overlap with arXiv:1612.0070

General Jurisprudence, Empirical Legal Theory, Epistemic Fruit, and the Ontology of ‘Law’: Scope, Scepticism, Demarcation, Artefacts, Hermeneutic Concepts, Normativity and Natural Kinds

Positivist and natural law theories are interested in answers to different questions, and are mostly compatible. But positivists and empirical legal theorists (ELTs) each claim to offer a genuinely descriptive account of law, and a better position from which to criticize real world legal institutions. They deploy different heuristics, primarily in the guise of points of view (internal vs external), which betray differing commitments to offering a descriptive account of law, and to the separation thesis. They also have different ideas about the nature of the object in question and consequently how it might be known. For positivists law is artificial and dependent on the existence of states (though states are left untheorized). For ELTs law is a naturally occurring social phenomenon (analogous to language) discernible by structures and functions. These different ontologies suggest different epistemic standards (normal vs special) for knowing the object. This paper clarifies key points of dispute between these two camps, and argues that on several fronts ELT offers up a more plausible and useful theory of law. A recent paper by Brian Leiter, “The Demarcation Problem in Jurisprudence,” and some of his earlier work on naturalizing jurisprudence, help to frame the discussion

General Jurisprudence, Empirical Legal Theory, Epistemic Fruit, and the Ontology of ‘Law’: Scope, Scepticism, Demarcation, Artefacts, Hermeneutic Concepts, Normativity and Natural Kinds

Positivist and natural law theories are interested in answers to different questions, and are mostly compatible. But positivists and empirical legal theorists (ELTs) each claim to offer a genuinely descriptive account of law, and a better position from which to criticize real world legal institutions. They deploy different heuristics, primarily in the guise of points of view (internal vs external), which betray differing commitments to offering a descriptive account of law, and to the separation thesis. They also have different ideas about the nature of the object in question and consequently how it might be known. For positivists law is artificial and dependent on the existence of states (though states are left untheorized). For ELTs law is a naturally occurring social phenomenon (analogous to language) discernible by structures and functions. These different ontologies suggest different epistemic standards (normal vs special) for knowing the object. This paper clarifies key points of dispute between these two camps, and argues that on several fronts ELT offers up a more plausible and useful theory of law. A recent paper by Brian Leiter, “The Demarcation Problem in Jurisprudence,” and some of his earlier work on naturalizing jurisprudence, help to frame the discussion

Comparative research: Team learning in higher education

Team learning is the process of aligning and developing the capacity of a team to create the results its members truly desire‟ (Senge, 1990, p 236). This emphasizes the significance of team learning as the fundamental learning units. Despite its importance, team learning among employees in higher education, especially among academics remains poorly understood. This research aims at shedding a light in the area which has recently been urged by the increasingly demanding requirements of interdisciplinary research and teaching in higher education around the world. Through a thorough literature review, a model of team learning has been built with a set of antecedents, two moderators, and the outcome of mental models. Hypotheses were formed, including team commitment, goal setting, development and training, organizational culture, and leadership are positively associated with team learning (antecedents), team learning is positively associated with knowledge sharing (outcome), and better communication systems, and learning environment provide better outcome of team learning (moderators). Thus, the study tested both mediating and Kaleidoscope Postgraduate Conference, Cambridge 2009 http://www.educatejournal.org/ 92 moderating relationships. The data were collected in a form of self-report questionnaires. The model was tested with the data collected from employees of two universities, one in the UK and the other in Vietnam. The findings revealed interesting information on the differences between two universities/two cultures, which is often the benefits of comparative research. The case in VN had more positive results than the case in the UK. There are not many differences between academic and non-academic employees, or between employees who work in science and non-science areas. The research could not avoid some limitations due to self-report questionnaires, though some actions were conducted to reduce research bias. In addition, it is really difficult to measure team performance in higher education, which should have been another outcome of team learning

Law and Development

This is a draft of a book to accompany a course on the sociology of law and law and development at Boston University

Motion representation with spiking neural networks for grasping and manipulation

Die Natur bedient sich Millionen von Jahren der Evolution, um adaptive physikalische Systeme mit effizienten Steuerungsstrategien zu erzeugen. Im Gegensatz zur konventionellen Robotik plant der Mensch nicht einfach eine Bewegung und führt sie aus, sondern es gibt eine Kombination aus mehreren Regelkreisen, die zusammenarbeiten, um den Arm zu bewegen und ein Objekt mit der Hand zu greifen. Mit der Forschung an humanoiden und biologisch inspirierten Robotern werden komplexe kinematische Strukturen und komplizierte Aktor- und Sensorsysteme entwickelt. Diese Systeme sind schwierig zu steuern und zu programmieren, und die klassischen Methoden der Robotik können deren Stärken nicht immer optimal ausnutzen. Die neurowissenschaftliche Forschung hat große Fortschritte beim Verständnis der verschiedenen Gehirnregionen und ihrer entsprechenden Funktionen gemacht. Dennoch basieren die meisten Modelle auf groß angelegten Simulationen, die sich auf die Reproduktion der Konnektivität und der statistischen neuronalen Aktivität konzentrieren. Dies öffnet eine Lücke bei der Anwendung verschiedener Paradigmen, um Gehirnmechanismen und Lernprinzipien zu validieren und Funktionsmodelle zur Steuerung von Robotern zu entwickeln. Ein vielversprechendes Paradigma ist die ereignis-basierte Berechnung mit SNNs. SNNs fokussieren sich auf die biologischen Aspekte von Neuronen und replizieren deren Arbeitsweise. Sie sind für spike- basierte Kommunikation ausgelegt und ermöglichen die Erforschung von Mechanismen des Gehirns für das Lernen mittels neuronaler Plastizität. Spike-basierte Kommunikation nutzt hoch parallelisierten Hardware-Optimierungen mittels neuromorpher Chips, die einen geringen Energieverbrauch und schnelle lokale Operationen ermöglichen. In dieser Arbeit werden verschiedene SNNs zur Durchführung von Bewegungss- teuerung für Manipulations- und Greifaufgaben mit einem Roboterarm und einer anthropomorphen Hand vorgestellt. Diese basieren auf biologisch inspirierten funktionalen Modellen des menschlichen Gehirns. Ein Motor-Primitiv wird auf parametrische Weise mit einem Aktivierungsparameter und einer Abbildungsfunktion auf die Roboterkinematik übertragen. Die Topologie des SNNs spiegelt die kinematische Struktur des Roboters wider. Die Steuerung des Roboters erfolgt über das Joint Position Interface. Um komplexe Bewegungen und Verhaltensweisen modellieren zu können, werden die Primitive in verschiedenen Schichten einer Hierarchie angeordnet. Dies ermöglicht die Kombination und Parametrisierung der Primitiven und die Wiederverwendung von einfachen Primitiven für verschiedene Bewegungen. Es gibt verschiedene Aktivierungsmechanismen für den Parameter, der ein Motorprimitiv steuert — willkürliche, rhythmische und reflexartige. Außerdem bestehen verschiedene Möglichkeiten neue Motorprimitive entweder online oder offline zu lernen. Die Bewegung kann entweder als Funktion modelliert oder durch Imitation der menschlichen Ausführung gelernt werden. Die SNNs können in andere Steuerungssysteme integriert oder mit anderen SNNs kombiniert werden. Die Berechnung der inversen Kinematik oder die Validierung von Konfigurationen für die Planung ist nicht erforderlich, da der Motorprimitivraum nur durchführbare Bewegungen hat und keine ungültigen Konfigurationen enthält. Für die Evaluierung wurden folgende Szenarien betrachtet, das Zeigen auf verschiedene Ziele, das Verfolgen einer Trajektorie, das Ausführen von rhythmischen oder sich wiederholenden Bewegungen, das Ausführen von Reflexen und das Greifen von einfachen Objekten. Zusätzlich werden die Modelle des Arms und der Hand kombiniert und erweitert, um die mehrbeinige Fortbewegung als Anwendungsfall der Steuerungsarchitektur mit Motorprimitiven zu modellieren. Als Anwendungen für einen Arm (3 DoFs) wurden die Erzeugung von Zeigebewegungen und das perzeptionsgetriebene Erreichen von Zielen modelliert. Zur Erzeugung von Zeigebewegun- gen wurde ein Basisprimitiv, das auf den Mittelpunkt einer Ebene zeigt, offline mit vier Korrekturprimitiven kombiniert, die eine neue Trajektorie erzeugen. Für das wahrnehmungsgesteuerte Erreichen eines Ziels werden drei Primitive online kombiniert unter Verwendung eines Zielsignals. Als Anwendungen für eine Fünf-Finger-Hand (9 DoFs) wurden individuelle Finger-aktivierungen und Soft-Grasping mit nachgiebiger Steuerung modelliert. Die Greif- bewegungen werden mit Motor-Primitiven in einer Hierarchie modelliert, wobei die Finger-Primitive die Synergien zwischen den Gelenken und die Hand-Primitive die unterschiedlichen Affordanzen zur Koordination der Finger darstellen. Für jeden Finger werden zwei Reflexe hinzugefügt, zum Aktivieren oder Stoppen der Bewegung bei Kontakt und zum Aktivieren der nachgiebigen Steuerung. Dieser Ansatz bietet enorme Flexibilität, da Motorprimitive wiederverwendet, parametrisiert und auf unterschiedliche Weise kombiniert werden können. Neue Primitive können definiert oder gelernt werden. Ein wichtiger Aspekt dieser Arbeit ist, dass im Gegensatz zu Deep Learning und End-to-End-Lernmethoden, keine umfangreichen Datensätze benötigt werden, um neue Bewegungen zu lernen. Durch die Verwendung von Motorprimitiven kann der gleiche Modellierungsansatz für verschiedene Roboter verwendet werden, indem die Abbildung der Primitive auf die Roboterkinematik neu definiert wird. Die Experimente zeigen, dass durch Motor- primitive die Motorsteuerung für die Manipulation, das Greifen und die Lokomotion vereinfacht werden kann. SNNs für Robotikanwendungen ist immer noch ein Diskussionspunkt. Es gibt keinen State-of-the-Art-Lernalgorithmus, es gibt kein Framework ähnlich dem für Deep Learning, und die Parametrisierung von SNNs ist eine Kunst. Nichtsdestotrotz können Robotikanwendungen - wie Manipulation und Greifen - Benchmarks und realistische Szenarien liefern, um neurowissenschaftliche Modelle zu validieren. Außerdem kann die Robotik die Möglichkeiten der ereignis- basierten Berechnung mit SNNs und neuromorpher Hardware nutzen. Die physikalis- che Nachbildung eines biologischen Systems, das vollständig mit SNNs implementiert und auf echten Robotern evaluiert wurde, kann neue Erkenntnisse darüber liefern, wie der Mensch die Motorsteuerung und Sensorverarbeitung durchführt und wie diese in der Robotik angewendet werden können. Modellfreie Bewegungssteuerungen, inspiriert von den Mechanismen des menschlichen Gehirns, können die Programmierung von Robotern verbessern, indem sie die Steuerung adaptiver und flexibler machen

Identification of genes involved in <i>Citif1</i> promoter activation and endoderm differentiation

In tadpole larva of the ascidian Ciona intestinalis (one of the most basal chordate) the endoderm is a simple tissue comprising about 500 cells. The commitment of endoderm occurs very early; in a 64-cells stage embryo five blastomeres pairs are restricted to endodermal fate. Although three genes, that exhibit an endoderm specific expression, Ci-alkaline phosphates, Cs-lhx3 and Cititf1 have been cloned, it remains to be elucidated how the first step of endoderm specification take place. As first strategy, I cloned a Ciona SoxE gene; this was found not to be endodermally expressed. In a second strategy, I focused on Cititf-1, a gene homologous to mammalian Nkx2.1 and the earliest zygotic endoderm specific marker isolated from Ciona thus far. Although its temporal and spatial expression seems to be finely regulated, the factors involved in its activation and likely involved in endoderm specification are unknown. The minimal region responsible for endodermal expression of Cititf1, has previously been located to a genomic fragment of about 200 base pairs upstream to putative TATA box sequence (Fanelli A. et al., 2003 Developmental Biology. 263, 12-23). Here I describe two imperfect repeats of eleven nucleotides, named R1 and R2, within this region and investigate their involvement in the transcriptional activation of Cititf1 promoter. Biochemical analyses showed that both repeats bind factors from ascidian embryos in a sequence-specific manner. The R2, however, is more important for endoderm specific expression and is able to drive the endodermal expression of a reporter gene. The R1 repeat cooperates with R2 to enhance this expression. A yeast one-hybrid screening was performed, leading to identification of a putative DNA-binding factor that could be involved in the regulation of Cititf1 in endoderm

The Role of N2 as a Geo-Biosignature for the Detection and Characterization of Earth-like Habitats

Since the Archean, N2 has been a major atmospheric constituent in Earth's atmosphere. Nitrogen is an essential element in the building blocks of life, therefore the geobiological nitrogen cycle is a fundamental factor in the long term evolution of both Earth and Earth-like exoplanets. We discuss the development of the Earth's N2 atmosphere since the planet's formation and its relation with the geobiological cycle. Then we suggest atmospheric evolution scenarios and their possible interaction with life forms: firstly, for a stagnant-lid anoxic world, secondly for a tectonically active anoxic world, and thirdly for an oxidized tectonically active world. Furthermore, we discuss a possible demise of present Earth's biosphere and its effects on the atmosphere. Since life forms are the most efficient means for recycling deposited nitrogen back into the atmosphere nowadays, they sustain its surface partial pressure at high levels. Also, the simultaneous presence of significant N2 and O2 is chemically incompatible in an atmosphere over geological timescales. Thus, we argue that an N2-dominated atmosphere in combination with O2 on Earth-like planets within circumstellar habitable zones can be considered as a geo-biosignature. Terrestrial planets with such atmospheres will have an operating tectonic regime connected with an aerobe biosphere, whereas other scenarios in most cases end up with a CO2-dominated atmosphere. We conclude with implications for the search for life on Earth-like exoplanets inside the habitable zones of M to K-stars

Investigation of the Sense of Agency in Social Cognition, based on frameworks of Predictive Coding and Active Inference: A simulation study on multimodal imitative interaction

When agents interact socially with different intentions, conflicts are difficult to avoid. Although how agents can resolve such problems autonomously has not been determined, dynamic characteristics of agency may shed light on underlying mechanisms. The current study focused on the sense of agency (SoA), a specific aspect of agency referring to congruence between the agent's intention in acting and the outcome. Employing predictive coding and active inference as theoretical frameworks of perception and action generation, we hypothesize that regulation of complexity in the evidence lower bound of an agent's model should affect the strength of the agent's SoA and should have a critical impact on social interactions. We built a computational model of imitative interaction between a robot and a human via visuo-proprioceptive sensation with a variational Bayes recurrent neural network, and simulated the model in the form of pseudo-imitative interaction using recorded human body movement data. A key feature of the model is that each modality's complexity can be regulated differently with a hyperparameter assigned to each module. We first searched for an optimal setting that endows the model with appropriate coordination of multimodal sensation. This revealed that the vision module's complexity should be more tightly regulated than that of the proprioception module. Using the optimally trained model, we examined how changing the tightness of complexity regulation after training affects the strength of the SoA during interactions. The results showed that with looser regulation, an agent tends to act more egocentrically, without adapting to the other. In contrast, with tighter regulation, the agent tends to follow the other by adjusting its intention. We conclude that the tightness of complexity regulation crucially affects the strength of the SoA and the dynamics of interactions between agents.Comment: 23 pages, 8 figure