345 research outputs found
Graph-based real-time fault diagnostics
A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components
Graphic simualtion test bed for robotics applications in a workstation environment
Graphical simulation is a cost-effective solution for developing and testing robots and their control systems. The availability of various high-performance workstations makes these systems feasible. Simulation offers preliminary testing of systems before their actual realizations, and it provides a framework for developing new control and planning algorithms. On the other hand, these simulation systems have to have the capability of incorporating various knowledge-based system components, e.g., task planners, representation formalisms, etc. They also should have an appropriate user interface, which makes possible the creation and control of simulation models. ROBOSIM was developed jointly by MSFC and Vanderbilt University, first in a VAX environment. Recently, the system has been ported to an HP-9000 workstation equipped with an SRX graphics accelerator. The user interface of the system now contains a menu- and icon-based facility, as well as the original ROBOSIM language. The system is also coupled to a symbolic computing system based on Common Lisp, where knowledge-based functionalities are implemented. The knowledge-based layer uses various representation and reasoning facilities for programming and testing the control systems of robots
Bursty egocentric network evolution in Skype
In this study we analyze the dynamics of the contact list evolution of
millions of users of the Skype communication network. We find that egocentric
networks evolve heterogeneously in time as events of edge additions and
deletions of individuals are grouped in long bursty clusters, which are
separated by long inactive periods. We classify users by their link creation
dynamics and show that bursty peaks of contact additions are likely to appear
shortly after user account creation. We also study possible relations between
bursty contact addition activity and other user-initiated actions like free and
paid service adoption events. We show that bursts of contact additions are
associated with increases in activity and adoption - an observation that can
inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify and analyse a prominent example of adaptive system: robot swarms equipped with obstacle-avoidance self-assembly strategies. The analysis exploits the statistical model checker PVesta
Autotuning Algorithmic Choice for Input Sensitivity
Empirical autotuning is increasingly being used in many domains to achieve optimized performance in a variety of different execution environments. A daunting challenge faced by such autotuners is input sensitivity, where the best autotuned configuration may vary with different input sets. In this paper, we propose a two level solution that: first, clusters to find input sets that are similar in input feature space; then, uses an evolutionary autotuner to build an optimized program for each of these clusters; and, finally, builds an adaptive overhead aware classifier which assigns each input to a specific input optimized program. Our approach addresses the complex trade-off between using expensive features, to accurately characterize an input, and cheaper features, which can be computed with less overhead. Experimental results show that by adapting to different inputs one can obtain up to a 3x speedup over using a single configuration for all inputs
Multiscale Analysis of Spreading in a Large Communication Network
In temporal networks, both the topology of the underlying network and the
timings of interaction events can be crucial in determining how some dynamic
process mediated by the network unfolds. We have explored the limiting case of
the speed of spreading in the SI model, set up such that an event between an
infectious and susceptible individual always transmits the infection. The speed
of this process sets an upper bound for the speed of any dynamic process that
is mediated through the interaction events of the network. With the help of
temporal networks derived from large scale time-stamped data on mobile phone
calls, we extend earlier results that point out the slowing-down effects of
burstiness and temporal inhomogeneities. In such networks, links are not
permanently active, but dynamic processes are mediated by recurrent events
taking place on the links at specific points in time. We perform a multi-scale
analysis and pinpoint the importance of the timings of event sequences on
individual links, their correlations with neighboring sequences, and the
temporal pathways taken by the network-scale spreading process. This is
achieved by studying empirically and analytically different characteristic
relay times of links, relevant to the respective scales, and a set of temporal
reference models that allow for removing selected time-domain correlations one
by one
Effects of time window size and placement on the structure of aggregated networks
Complex networks are often constructed by aggregating empirical data over
time, such that a link represents the existence of interactions between the
endpoint nodes and the link weight represents the intensity of such
interactions within the aggregation time window. The resulting networks are
then often considered static. More often than not, the aggregation time window
is dictated by the availability of data, and the effects of its length on the
resulting networks are rarely considered. Here, we address this question by
studying the structural features of networks emerging from aggregating
empirical data over different time intervals, focussing on networks derived
from time-stamped, anonymized mobile telephone call records. Our results show
that short aggregation intervals yield networks where strong links associated
with dense clusters dominate; the seeds of such clusters or communities become
already visible for intervals of around one week. The degree and weight
distributions are seen to become stationary around a few days and a few weeks,
respectively. An aggregation interval of around 30 days results in the stablest
similar networks when consecutive windows are compared. For longer intervals,
the effects of weak or random links become increasingly stronger, and the
average degree of the network keeps growing even for intervals up to 180 days.
The placement of the time window is also seen to affect the outcome: for short
windows, different behavioural patterns play a role during weekends and
weekdays, and for longer windows it is seen that networks aggregated during
holiday periods are significantly different.Comment: 19 pages, 11 figure
Circadian pattern and burstiness in mobile phone communication
The temporal communication patterns of human individuals are known to be
inhomogeneous or bursty, which is reflected as the heavy tail behavior in the
inter-event time distribution. As the cause of such bursty behavior two main
mechanisms have been suggested: a) Inhomogeneities due to the circadian and
weekly activity patterns and b) inhomogeneities rooted in human task execution
behavior. Here we investigate the roles of these mechanisms by developing and
then applying systematic de-seasoning methods to remove the circadian and
weekly patterns from the time-series of mobile phone communication events of
individuals. We find that the heavy tails in the inter-event time distributions
remain robustly with respect to this procedure, which clearly indicates that
the human task execution based mechanism is a possible cause for the remaining
burstiness in temporal mobile phone communication patterns.Comment: 17 pages, 12 figure
Attention on Weak Ties in Social and Communication Networks
Granovetter's weak tie theory of social networks is built around two central
hypotheses. The first states that strong social ties carry the large majority
of interaction events; the second maintains that weak social ties, although
less active, are often relevant for the exchange of especially important
information (e.g., about potential new jobs in Granovetter's work). While
several empirical studies have provided support for the first hypothesis, the
second has been the object of far less scrutiny. A possible reason is that it
involves notions relative to the nature and importance of the information that
are hard to quantify and measure, especially in large scale studies. Here, we
search for empirical validation of both Granovetter's hypotheses. We find clear
empirical support for the first. We also provide empirical evidence and a
quantitative interpretation for the second. We show that attention, measured as
the fraction of interactions devoted to a particular social connection, is high
on weak ties --- possibly reflecting the postulated informational purposes of
such ties --- but also on very strong ties. Data from online social media and
mobile communication reveal network-dependent mixtures of these two effects on
the basis of a platform's typical usage. Our results establish a clear
relationships between attention, importance, and strength of social links, and
could lead to improved algorithms to prioritize social media content
Burstiness and tie activation strategies in time-varying social networks
The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes
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