Mapping the forms of meaning in small worlds

International audienceProx is a stochastic method to map the local and global structures of real-world complex networks which are called Small Worlds. Prox transforms a graph into a Markov chain, the states of which are the nodes of the graph in question. Particles wander from one node to another within the graph by following the graph " s edges. It is the dynamics of the particles " trajectories that map the structural properties of the graphs that are studied. Concrete examples are presented in a graph of synonyms to illustrate this approach

Refining personal and social presence in virtual meetings

Virtual worlds show promise for conducting meetings and conferences without the need for physical travel. Current experience suggests the major limitation to the more widespread adoption and acceptance of virtual conferences is the failure of existing environments to provide a sense of immersion and engagement, or of ‘being there’. These limitations are largely related to the appearance and control of avatars, and to the absence of means to convey non-verbal cues of facial expression and body language. This paper reports on a study involving the use of a mass-market motion sensor (Kinect™) and the mapping of participant action in the real world to avatar behaviour in the virtual world. This is coupled with full-motion video representation of participant’s faces on their avatars to resolve both identity and facial expression issues. The outcomes of a small-group trial meeting based on this technology show a very positive reaction from participants, and the potential for further exploration of these concepts

A qualitative enquiry into OpenStreetMap making

Based on a case study on the OpenStreetMap community, this paper provides a contextual and embodied understanding of the user-led, user-participatory and user-generated produsage phenomenon. It employs Grounded Theory, Social Worlds Theory, and qualitative methods to illuminate and explores the produsage processes of OpenStreetMap making, and how knowledge artefacts such as maps can be collectively and collaboratively produced by a community of people, who are situated in different places around the world but engaged with the same repertoire of mapping practices. The empirical data illustrate that OpenStreetMap itself acts as a boundary object that enables actors from different social worlds to co-produce the Map through interacting with each other and negotiating the meanings of mapping, the mapping data and the Map itself. The discourses also show that unlike traditional maps that black-box cartographic knowledge and offer a single dominant perspective of cities or places, OpenStreetMap is an embodied epistemic object that embraces different world views. The paper also explores how contributors build their identities as an OpenStreetMaper alongside some other identities they have. Understanding the identity-building process helps to understand mapping as an embodied activity with emotional, cognitive and social repertoires

Mapping cyberspace: visualising, analysing and exploring virtual worlds

In the past years, with the development of computer networks such as the Internet and world wide web (WWW), cyberspace has been increasingly studied by researchers in various disciplines such as computer sciences, sociology, geography, and cartography as well. Cyberspace is mainly rooted in two computer technologies: network and virtual reality. Cybermaps, as special maps for cyberspace, have been used as a tool for understanding various aspects of cyberspace. As recognised, cyberspace as a virtual space can be distinguished from the earth we live on in many ways. Because of these distinctions, mapping it implies a big challenge for cartographers with their long tradition of mapping things in clear ways. This paper, by comparing it to traditional maps, addresses various cybermap issues such as visualising, analysing and exploring cyberspace from different aspects

Indeterministic Handling of Uncertain Decisions in Duplicate Detection

In current research, duplicate detection is usually considered as a deterministic approach in which tuples are either declared as duplicates or not. However, most often it is not completely clear whether two tuples represent the same real-world entity or not. In deterministic approaches, however, this uncertainty is ignored, which in turn can lead to false decisions. In this paper, we present an indeterministic approach for handling uncertain decisions in a duplicate detection process by using a probabilistic target schema. Thus, instead of deciding between multiple possible worlds, all these worlds can be modeled in the resulting data. This approach minimizes the negative impacts of false decisions. Furthermore, the duplicate detection process becomes almost fully automatic and human effort can be reduced to a large extent. Unfortunately, a full-indeterministic approach is by definition too expensive (in time as well as in storage) and hence impractical. For that reason, we additionally introduce several semi-indeterministic methods for heuristically reducing the set of indeterministic handled decisions in a meaningful way

The Minimal Modal Interpretation of Quantum Theory

We introduce a realist, unextravagant interpretation of quantum theory that builds on the existing physical structure of the theory and allows experiments to have definite outcomes, but leaves the theory's basic dynamical content essentially intact. Much as classical systems have specific states that evolve along definite trajectories through configuration spaces, the traditional formulation of quantum theory asserts that closed quantum systems have specific states that evolve unitarily along definite trajectories through Hilbert spaces, and our interpretation extends this intuitive picture of states and Hilbert-space trajectories to the case of open quantum systems as well. We provide independent justification for the partial-trace operation for density matrices, reformulate wave-function collapse in terms of an underlying interpolating dynamics, derive the Born rule from deeper principles, resolve several open questions regarding ontological stability and dynamics, address a number of familiar no-go theorems, and argue that our interpretation is ultimately compatible with Lorentz invariance. Along the way, we also investigate a number of unexplored features of quantum theory, including an interesting geometrical structure---which we call subsystem space---that we believe merits further study. We include an appendix that briefly reviews the traditional Copenhagen interpretation and the measurement problem of quantum theory, as well as the instrumentalist approach and a collection of foundational theorems not otherwise discussed in the main text.Comment: 73 pages + references, 9 figures; cosmetic changes, added figure, updated references, generalized conditional probabilities with attendant changes to the sections on the EPR-Bohm thought experiment and Lorentz invariance; for a concise summary, see the companion letter at arXiv:1405.675

Humble primary intensions: fixing two-dimensional semantics

Certain problems with standard two-dimensional semantics are addressed and cases in which these problems arise explored. In such cases the primary intension cannot be univocally mapped in one and only one indexical world, thus standard two-dimensional semantics cannot efficiently address the problems presented. Subsequently, a modified model is presented which leads these problems to be averted in the replicated cases. This modified model admits primary intensions that are not univocally mapped. The conclusion discusses the advantages and disadvantages of the modified model and analyzes its possible consequences for the philosophy of mind

Value Iteration Networks on Multiple Levels of Abstraction

Learning-based methods are promising to plan robot motion without performing extensive search, which is needed by many non-learning approaches. Recently, Value Iteration Networks (VINs) received much interest since---in contrast to standard CNN-based architectures---they learn goal-directed behaviors which generalize well to unseen domains. However, VINs are restricted to small and low-dimensional domains, limiting their applicability to real-world planning problems. To address this issue, we propose to extend VINs to representations with multiple levels of abstraction. While the vicinity of the robot is represented in sufficient detail, the representation gets spatially coarser with increasing distance from the robot. The information loss caused by the decreasing resolution is compensated by increasing the number of features representing a cell. We show that our approach is capable of solving significantly larger 2D grid world planning tasks than the original VIN implementation. In contrast to a multiresolution coarse-to-fine VIN implementation which does not employ additional descriptive features, our approach is capable of solving challenging environments, which demonstrates that the proposed method learns to encode useful information in the additional features. As an application for solving real-world planning tasks, we successfully employ our method to plan omnidirectional driving for a search-and-rescue robot in cluttered terrain