2,912 research outputs found
VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting
Information spread in social media depends on a number of factors, including
how the site displays information, how users navigate it to find items of
interest, users' tastes, and the `virality' of information, i.e., its
propensity to be adopted, or retweeted, upon exposure. Probabilistic models can
learn users' tastes from the history of their item adoptions and recommend new
items to users. However, current models ignore cognitive biases that are known
to affect behavior. Specifically, people pay more attention to items at the top
of a list than those in lower positions. As a consequence, items near the top
of a user's social media stream have higher visibility, and are more likely to
be seen and adopted, than those appearing below. Another bias is due to the
item's fitness: some items have a high propensity to spread upon exposure
regardless of the interests of adopting users. We propose a probabilistic model
that incorporates human cognitive biases and personal relevance in the
generative model of information spread. We use the model to predict how
messages containing URLs spread on Twitter. Our work shows that models of user
behavior that account for cognitive factors can better describe and predict
user behavior in social media.Comment: SBP 201
Search in Power-Law Networks
Many communication and social networks have power-law link distributions,
containing a few nodes which have a very high degree and many with low degree.
The high connectivity nodes play the important role of hubs in communication
and networking, a fact which can be exploited when designing efficient search
algorithms. We introduce a number of local search strategies which utilize high
degree nodes in power-law graphs and which have costs which scale sub-linearly
with the size of the graph. We also demonstrate the utility of these strategies
on the Gnutella peer-to-peer network.Comment: 17 pages, 14 figure
Effect of the accelerating growth of communications networks on their structure
Motivated by data on the evolution of the Internet and World Wide Web we
consider scenarios of self-organization of the nonlinearly growing networks
into free-scale structures. We find that the accelerating growth of the
networks establishes their structure. For the growing networks with
preferential linking and increasing density of links, two scenarios are
possible. In one of them, the value of the exponent of the
connectivity distribution is between 3/2 and 2. In the other, and
the distribution is necessarily non-stationary.Comment: 4 pages revtex, 3 figure
Novel Si(1-x)Ge(x)/Si heterojunction internal photoemission long wavelength infrared detectors
There is a major need for long-wavelength-infrared (LWIR) detector arrays in the range of 8 to 16 microns which operate with close-cycle cryocoolers above 65 K. In addition, it would be very attractive to have Si-based infrared (IR) detectors that can be easily integrated with Si readout circuitry and have good pixel-to-pixel uniformity, which is critical for focal plane array (FPA) applications. Here, researchers report a novel Si(1-x)Ge(x)/Si heterojunction internal photoemission (HIP) detector approach with a tailorable long wavelength infrared cutoff wavelength, based on internal photoemission over the Si(1-x)Ge(x)/Si heterojunction. The HIP detectors were grown by molecular beam epitaxy (MBE), which allows one to optimize the device structure with precise control of doping profiles, layer thickness and composition. The feasibility of a novel Si(1-x)Ge(x)/Si HIP detector has been demonstrated with tailorable cutoff wavelength in the LWIR region. Photoresponse at wavelengths 2 to 10 microns are obtained with quantum efficiency (QE) above approx. 1 percent in these non-optimized device structures. It should be possible to significantly improve the QE of the HIP detectors by optimizing the thickness, composition, and doping concentration of the Si(1-x)Ge(x) layers and by configuring the detector for maximum absorption such as the use of a cavity structure. With optimization of the QE and by matching the barrier energy to the desired wavelength cutoff to minimize the thermionic current, researchers predict near background limited performance in the LWIR region with operating temperatures above 65K. Finally, with mature Si processing, the relatively simple device structure offers potential for low-cost producible arrays with excellent uniformity
Economics-Based Optimization of Unstable Flows
As an example for the optimization of unstable flows, we present an
economics-based method for deciding the optimal rates at which vehicles are
allowed to enter a highway. It exploits the naturally occuring fluctuations of
traffic flow and is flexible enough to adapt in real time to the transient flow
characteristics of road traffic. Simulations based on realistic parameter
values show that this strategy is feasible for naturally occurring traffic, and
that even far from optimality, injection policies can improve traffic flow.
Moreover, the same method can be applied to the optimization of flows of gases
and granular media.Comment: Revised version of ``Optimizing Traffic Flow'' (cond-mat/9809397).
For related work see http://www.parc.xerox.com/dynamics/ and
http://www.theo2.physik.uni-stuttgart.de/helbing.htm
Intermittent exploration on a scale-free network
We study an intermittent random walk on a random network of scale-free degree
distribution. The walk is a combination of simple random walks of duration
and random long-range jumps. While the time the walker needs to cover all
the nodes increases with , the corresponding time for the edges displays a
non monotonic behavior with a minimum for some nontrivial value of . This
is a heterogeneity-induced effect that is not observed in homogeneous
small-world networks. The optimal increases with the degree of
assortativity in the network. Depending on the nature of degree correlations
and the elapsed time the walker finds an over/under-estimate of the degree
distribution exponent.Comment: 12 pages, 3 figures, 1 table, published versio
Quantum Portfolios
Quantum computation holds promise for the solution of many intractable
problems. However, since many quantum algorithms are stochastic in nature they
can only find the solution of hard problems probabilistically. Thus the
efficiency of the algorithms has to be characterized both by the expected time
to completion {\it and} the associated variance. In order to minimize both the
running time and its uncertainty, we show that portfolios of quantum algorithms
analogous to those of finance can outperform single algorithms when applied to
the NP-complete problems such as 3-SAT.Comment: revision includes additional data and corrects minor typo
A Statistical Measure of Complexity
A measure of complexity based on a probabilistic description of physical
systems is proposed. This measure incorporates the main features of the
intuitive notion of such a magnitude. It can be applied to many physical
situations and to different descriptions of a given system. Moreover, the
calculation of its value does not require a considerable computational effort
in many cases of physical interest.Comment: 8 pages, 0 figure
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