14,741 research outputs found

    Design Ltd.: Renovated Myths for the Development of Socially Embedded Technologies

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    This paper argues that traditional and mainstream mythologies, which have been continually told within the Information Technology domain among designers and advocators of conceptual modelling since the 1960s in different fields of computing sciences, could now be renovated or substituted in the mould of more recent discourses about performativity, complexity and end-user creativity that have been constructed across different fields in the meanwhile. In the paper, it is submitted that these discourses could motivate IT professionals in undertaking alternative approaches toward the co-construction of socio-technical systems, i.e., social settings where humans cooperate to reach common goals by means of mediating computational tools. The authors advocate further discussion about and consolidation of some concepts in design research, design practice and more generally Information Technology (IT) development, like those of: task-artifact entanglement, universatility (sic) of End-User Development (EUD) environments, bricolant/bricoleur end-user, logic of bricolage, maieuta-designers (sic), and laissez-faire method to socio-technical construction. Points backing these and similar concepts are made to promote further discussion on the need to rethink the main assumptions underlying IT design and development some fifty years later the coming of age of software and modern IT in the organizational domain.Comment: This is the peer-unreviewed of a manuscript that is to appear in D. Randall, K. Schmidt, & V. Wulf (Eds.), Designing Socially Embedded Technologies: A European Challenge (2013, forthcoming) with the title "Building Socially Embedded Technologies: Implications on Design" within an EUSSET editorial initiative (www.eusset.eu/

    Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

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    Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

    Review of single vector boson production in pp collisions at s=7\sqrt{s} = 7 TeV

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    This review summarises the main results on the production of single vector bosons in the Standard Model, both inclusively and in association with light and heavy flavour jets, at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of 7 TeV. The general purpose detectors at this collider, ATLAS and CMS, each recorded an integrated luminosity of ≈40 pb−1\approx 40\,{\rm pb^{-1}} and 5 fb−15\,{\rm fb^{-1}} in the years 2010 and 2011, respectively. The corresponding data offer the unique possibility to precisely study the properties of the production of heavy vector bosons in a new energy regime. The accurate understanding of the Standard Model is not only crucial for searches of unknown particles and phenomena but also to test predictions of perturbative Quantum-Chromo-Dynamics calculations and for precision measurements of observables in the electroweak sector. Results from a variety of measurements in which single W or Z bosons are identified are reviewed. Special emphasis in this review is given to interpretations of the experimental results in the context of state-of-the-art predictions.Comment: 60 pages, 64 figures, For Eur. Phys. J.

    Dynamical system analysis and forecasting of deformation produced by an earthquake fault

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    We present a method of constructing low-dimensional nonlinear models describing the main dynamical features of a discrete 2D cellular fault zone, with many degrees of freedom, embedded in a 3D elastic solid. A given fault system is characterized by a set of parameters that describe the dynamics, rheology, property disorder, and fault geometry. Depending on the location in the system parameter space we show that the coarse dynamics of the fault can be confined to an attractor whose dimension is significantly smaller than the space in which the dynamics takes place. Our strategy of system reduction is to search for a few coherent structures that dominate the dynamics and to capture the interaction between these coherent structures. The identification of the basic interacting structures is obtained by applying the Proper Orthogonal Decomposition (POD) to the surface deformations fields that accompany strike-slip faulting accumulated over equal time intervals. We use a feed-forward artificial neural network (ANN) architecture for the identification of the system dynamics projected onto the subspace (model space) spanned by the most energetic coherent structures. The ANN is trained using a standard back-propagation algorithm to predict (map) the values of the observed model state at a future time given the observed model state at the present time. This ANN provides an approximate, large scale, dynamical model for the fault.Comment: 30 pages, 12 figure

    Morphology of three-body quantum states from machine learning

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    The relative motion of three impenetrable particles on a ring, in our case two identical fermions and one impurity, is isomorphic to a triangular quantum billiard. Depending on the ratio κ of the impurity and fermion masses, the billiards can be integrable or non-integrable (also referred to in the main text as chaotic). To set the stage, we first investigate the energy level distributions of the billiards as a function of 1/κ ∈ [0, 1] and find no evidence of integrable cases beyond the limiting values 1/κ = 1 and 1/κ = 0. Then, we use machine learning tools to analyze properties of probability distributions of individual quantum states. We find that convolutional neural networks can correctly classify integrable and non-integrable states. The decisive features of the wave functions are the normalization and a large number of zero elements, corresponding to the existence of a nodal line. The network achieves typical accuracies of 97%, suggesting that machine learning tools can be used to analyze and classify the morphology of probability densities obtained in theory or experiment

    Towards hybrid primary intersubjectivity: a neural robotics library for human science

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    Human-robot interaction is becoming an interesting area of research in cognitive science, notably, for the study of social cognition. Interaction theorists consider primary intersubjectivity a non-mentalist, pre-theoretical, non-conceptual sort of processes that ground a certain level of communication and understanding, and provide support to higher-level cognitive skills. We argue this sort of low level cognitive interaction, where control is shared in dyadic encounters, is susceptible of study with neural robots. Hence, in this work we pursue three main objectives. Firstly, from the concept of active inference we study primary intersubjectivity as a second person perspective experience characterized by predictive engagement, where perception, cognition, and action are accounted for an hermeneutic circle in dyadic interaction. Secondly, we propose an open-source methodology named \textit{neural robotics library} (NRL) for experimental human-robot interaction, and a demonstration program for interacting in real-time with a virtual Cartesian robot (VCBot). Lastly, through a study case, we discuss some ways human-robot (hybrid) intersubjectivity can contribute to human science research, such as to the fields of developmental psychology, educational technology, and cognitive rehabilitation
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