140,865 research outputs found
Meeting in a Polygon by Anonymous Oblivious Robots
The Meeting problem for searchers in a polygon (possibly with
holes) consists in making the searchers move within , according to a
distributed algorithm, in such a way that at least two of them eventually come
to see each other, regardless of their initial positions. The polygon is
initially unknown to the searchers, and its edges obstruct both movement and
vision. Depending on the shape of , we minimize the number of searchers
for which the Meeting problem is solvable. Specifically, if has a
rotational symmetry of order (where corresponds to no
rotational symmetry), we prove that searchers are sufficient, and
the bound is tight. Furthermore, we give an improved algorithm that optimally
solves the Meeting problem with searchers in all polygons whose
barycenter is not in a hole (which includes the polygons with no holes). Our
algorithms can be implemented in a variety of standard models of mobile robots
operating in Look-Compute-Move cycles. For instance, if the searchers have
memory but are anonymous, asynchronous, and have no agreement on a coordinate
system or a notion of clockwise direction, then our algorithms work even if the
initial memory contents of the searchers are arbitrary and possibly misleading.
Moreover, oblivious searchers can execute our algorithms as well, encoding
information by carefully positioning themselves within the polygon. This code
is computable with basic arithmetic operations, and each searcher can
geometrically construct its own destination point at each cycle using only a
compass. We stress that such memoryless searchers may be located anywhere in
the polygon when the execution begins, and hence the information they initially
encode is arbitrary. Our algorithms use a self-stabilizing map construction
subroutine which is of independent interest.Comment: 37 pages, 9 figure
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
An aesthetics of touch: investigating the language of design relating to form
How well can designers communicate qualities of touch?
This paper presents evidence that they have some capability to do so, much of which appears to have been learned, but at present make limited use of such language. Interviews with graduate designer-makers suggest that they are aware of and value the importance of touch and materiality in their work, but lack a vocabulary to fully relate to their detailed explanations of other aspects such as their intent or selection of materials. We believe that more attention should be paid to the verbal dialogue that happens in the design process, particularly as other researchers show that even making-based learning also has a strong verbal element to it. However, verbal language alone does not appear to be adequate for a comprehensive language of touch. Graduate designers-makers’ descriptive practices combined non-verbal manipulation within verbal accounts. We thus argue that haptic vocabularies do not simply describe material qualities, but rather are situated competences that physically demonstrate the presence of haptic qualities. Such competencies are more important than groups of verbal vocabularies in isolation. Design support for developing and extending haptic competences must take this wide range of considerations into account to comprehensively improve designers’ capabilities
Real Virtuality: A Code of Ethical Conduct. Recommendations for Good Scientific Practice and the Consumers of VR-Technology
The goal of this article is to present a first list of ethical concerns that may arise from research and personal use of virtual reality (VR) and related technology, and to offer concrete recommendations for minimizing those risks. Many of the recommendations call for focused research initiatives. In the first part of the article, we discuss the relevant evidence from psychology that motivates our concerns. In Section “Plasticity in the Human Mind,” we cover some of the main results suggesting that one’s environment can influence one’s psychological states, as well as recent work on inducing illusions of embodiment. Then, in Section “Illusions of Embodiment and Their Lasting Effect,” we go on to discuss recent evidence indicating that immersion in VR can have psychological effects that last after leaving the virtual environment. In the second part of the article, we turn to the risks and recommendations. We begin, in Section “The Research Ethics of VR,” with the research ethics of VR, covering six main topics: the limits of experimental environments, informed consent, clinical risks, dual-use, online research, and a general point about the limitations of a code of conduct for research. Then, in Section “Risks for Individuals and Society,” we turn to the risks of VR for the general public, covering four main topics: long-term immersion, neglect of the social and physical environment, risky content, and privacy. We offer concrete recommendations for each of these 10 topics, summarized in Table 1
Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
In the spectrum of vision-based autonomous driving, vanilla end-to-end models
are not interpretable and suboptimal in performance, while mediated perception
models require additional intermediate representations such as segmentation
masks or detection bounding boxes, whose annotation can be prohibitively
expensive as we move to a larger scale. More critically, all prior works fail
to deal with the notorious domain shift if we were to merge data collected from
different sources, which greatly hinders the model generalization ability. In
this work, we address the above limitations by taking advantage of virtual data
collected from driving simulators, and present DU-drive, an unsupervised
real-to-virtual domain unification framework for end-to-end autonomous driving.
It first transforms real driving data to its less complex counterpart in the
virtual domain and then predicts vehicle control commands from the generated
virtual image. Our framework has three unique advantages: 1) it maps driving
data collected from a variety of source distributions into a unified domain,
effectively eliminating domain shift; 2) the learned virtual representation is
simpler than the input real image and closer in form to the "minimum sufficient
statistic" for the prediction task, which relieves the burden of the
compression phase while optimizing the information bottleneck tradeoff and
leads to superior prediction performance; 3) it takes advantage of annotated
virtual data which is unlimited and free to obtain. Extensive experiments on
two public driving datasets and two driving simulators demonstrate the
performance superiority and interpretive capability of DU-drive
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