36,215 research outputs found
Coherent Diffusion of Polaritons in Atomic Media
Coherent diffusion pertains to the motion of atomic dipoles experiencing
frequent collisions in vapor while maintaining their coherence. Recent
theoretical and experimental studies on the effect of coherent diffusion on key
Raman processes, namely Raman spectroscopy, slow polariton propagation, and
stored light, are reviewed in this Colloquium.Comment: Submitted to Review of Modern Physic
The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges
The Internet of Things (IoT) refers to a network of connected devices
collecting and exchanging data over the Internet. These things can be
artificial or natural, and interact as autonomous agents forming a complex
system. In turn, Business Process Management (BPM) was established to analyze,
discover, design, implement, execute, monitor and evolve collaborative business
processes within and across organizations. While the IoT and BPM have been
regarded as separate topics in research and practice, we strongly believe that
the management of IoT applications will strongly benefit from BPM concepts,
methods and technologies on the one hand; on the other one, the IoT poses
challenges that will require enhancements and extensions of the current
state-of-the-art in the BPM field. In this paper, we question to what extent
these two paradigms can be combined and we discuss the emerging challenges
Handwritten digit recognition by bio-inspired hierarchical networks
The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evidences, we propose a learning framework with a
strong biological plausibility that mimics prominent functions of cortical
circuitries. We developed the Inductive Conceptual Network (ICN), that is a
hierarchical bio-inspired network, able to learn invariant patterns by
Variable-order Markov Models implemented in its nodes. The outputs of the
top-most node of ICN hierarchy, representing the highest input generalization,
allow for automatic classification of inputs. We found that the ICN clusterized
MNIST images with an error of 5.73% and USPS images with an error of 12.56%
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