22,827 research outputs found
Does the motor system need intermittent control?
Explanation of motor control is dominated by continuous neurophysiological pathways (e.g. trans-cortical, spinal) and the continuous control paradigm. Using new theoretical development, methodology and evidence, we propose intermittent control, which incorporates a serial ballistic process within the main feedback loop, provides a more general and more accurate paradigm necessary to explain attributes highly advantageous for competitive survival and performance
From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest
A central question in ecology is how to link processes that occur over
different scales. The daily interactions of individual organisms ultimately
determine community dynamics, population fluctuations and the functioning
of entire ecosystems. Observations of these multiscale ecological
processes are constrained by various technological, biological or logistical
issues, and there are often vast discrepancies between the scale at which
observation is possible and the scale of the question of interest. Animal
movement is characterized by processes that act over multiple spatial and
temporal scales. Second-by-second decisions accumulate to produce
annual movement patterns. Individuals influence, and are influenced by,
collective movement decisions, which then govern the spatial distribution
of populations and the connectivity of meta-populations. While the
field of movement ecology is experiencing unprecedented growth in the
availability of movement data, there remain challenges in integrating
observations with questions of ecological interest. In this article, we present
the major challenges of addressing these issues within the context of the
Serengeti wildebeest migration, a keystone ecological phenomena that
crosses multiple scales of space, time and biological complexity.
This article is part of the theme issue ’Collective movement ecology’
Fundamental structures of dynamic social networks
Social systems are in a constant state of flux with dynamics spanning from
minute-by-minute changes to patterns present on the timescale of years.
Accurate models of social dynamics are important for understanding spreading of
influence or diseases, formation of friendships, and the productivity of teams.
While there has been much progress on understanding complex networks over the
past decade, little is known about the regularities governing the
micro-dynamics of social networks. Here we explore the dynamic social network
of a densely-connected population of approximately 1000 individuals and their
interactions in the network of real-world person-to-person proximity measured
via Bluetooth, as well as their telecommunication networks, online social media
contacts, geo-location, and demographic data. These high-resolution data allow
us to observe social groups directly, rendering community detection
unnecessary. Starting from 5-minute time slices we uncover dynamic social
structures expressed on multiple timescales. On the hourly timescale, we find
that gatherings are fluid, with members coming and going, but organized via a
stable core of individuals. Each core represents a social context. Cores
exhibit a pattern of recurring meetings across weeks and months, each with
varying degrees of regularity. Taken together, these findings provide a
powerful simplification of the social network, where cores represent
fundamental structures expressed with strong temporal and spatial regularity.
Using this framework, we explore the complex interplay between social and
geospatial behavior, documenting how the formation of cores are preceded by
coordination behavior in the communication networks, and demonstrating that
social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39
pages, 34 figure
Robot control based on qualitative representation of human trajectories
A major challenge for future social robots is the high-level interpretation of human motion, and the consequent generation of appropriate robot actions. This paper describes some fundamental steps towards the real-time implementation of a system that allows a mobile robot to transform quantitative information about human trajectories (i.e. coordinates and speed) into qualitative concepts, and from these to generate appropriate control commands. The problem is formulated using a simple version of qualitative trajectory calculus, then solved using an inference engine based on fuzzy temporal logic and situation graph trees. Preliminary results are discussed and future directions of the current research are drawn
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