983 research outputs found
Complementary monostable circuits achieve low power drain and high reliability
Two-transistor multivibrator has minimum power dissipation and maximum reliability. It minimizes the use of components that are subject to environmental changes or other unpredictable behavior
Variable voltage supply uses Zener dioxide as reference
Using a zener diode as the reference element, a simple transistorized circuit provides a stable variable reference voltage
Activity ageing in growing networks
We present a model for growing information networks where the ageing of a
node depends on the time at which it entered the network and on the last time
it was cited. The model is shown to undergo a transition from a small-world to
large-world network. The degree distribution may exhibit very different shapes
depending on the model parameters, e.g. delta-peaked, exponential or power-law
tailed distributions.Comment: 9 pages, 2 figure
Experiments on Dynamic Parallel Magnetism in Superfluid 3He
Observations are reported of the ringing of parallel magnetization in superfluid 3He when an incremental magnetic field parallel to a steady field is suddenly turned off
Log-Networks
We introduce a growing network model in which a new node attaches to a
randomly-selected node, as well as to all ancestors of the target node. This
mechanism produces a sparse, ultra-small network where the average node degree
grows logarithmically with network size while the network diameter equals 2. We
determine basic geometrical network properties, such as the size dependence of
the number of links and the in- and out-degree distributions. We also compare
our predictions with real networks where the node degree also grows slowly with
time -- the Internet and the citation network of all Physical Review papers.Comment: 7 pages, 6 figures, 2-column revtex4 format. Version 2: minor changes
in response to referee comments and to another proofreading; final version
for PR
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search
We present a framework for quantifying and mitigating algorithmic bias in
mechanisms designed for ranking individuals, typically used as part of
web-scale search and recommendation systems. We first propose complementary
measures to quantify bias with respect to protected attributes such as gender
and age. We then present algorithms for computing fairness-aware re-ranking of
results. For a given search or recommendation task, our algorithms seek to
achieve a desired distribution of top ranked results with respect to one or
more protected attributes. We show that such a framework can be tailored to
achieve fairness criteria such as equality of opportunity and demographic
parity depending on the choice of the desired distribution. We evaluate the
proposed algorithms via extensive simulations over different parameter choices,
and study the effect of fairness-aware ranking on both bias and utility
measures. We finally present the online A/B testing results from applying our
framework towards representative ranking in LinkedIn Talent Search, and discuss
the lessons learned in practice. Our approach resulted in tremendous
improvement in the fairness metrics (nearly three fold increase in the number
of search queries with representative results) without affecting the business
metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users
worldwide. Ours is the first large-scale deployed framework for ensuring
fairness in the hiring domain, with the potential positive impact for more than
630M LinkedIn members.Comment: This paper has been accepted for publication at ACM KDD 201
Identifying communities by influence dynamics in social networks
Communities are not static; they evolve, split and merge, appear and
disappear, i.e. they are product of dynamical processes that govern the
evolution of the network. A good algorithm for community detection should not
only quantify the topology of the network, but incorporate the dynamical
processes that take place on the network. We present a novel algorithm for
community detection that combines network structure with processes that support
creation and/or evolution of communities. The algorithm does not embrace the
universal approach but instead tries to focus on social networks and model
dynamic social interactions that occur on those networks. It identifies
leaders, and communities that form around those leaders. It naturally supports
overlapping communities by associating each node with a membership vector that
describes node's involvement in each community. This way, in addition to
overlapping communities, we can identify nodes that are good followers to their
leader, and also nodes with no clear community involvement that serve as a
proxy between several communities and are equally as important. We run the
algorithm for several real social networks which we believe represent a good
fraction of the wide body of social networks and discuss the results including
other possible applications.Comment: 10 pages, 6 figure
Path finding strategies in scale-free networks
We numerically investigate the scale-free network model of Barab{\'a}si and
Albert [A. L. Barab{\'a}si and R. Albert, Science {\bf 286}, 509 (1999)]
through the use of various path finding strategies. In real networks, global
network information is not accessible to each vertex, and the actual path
connecting two vertices can sometimes be much longer than the shortest one. A
generalized diameter depending on the actual path finding strategy is
introduced, and a simple strategy, which utilizes only local information on the
connectivity, is suggested and shown to yield small-world behavior: the
diameter of the network increases logarithmically with the network size
, the same as is found with global strategy. If paths are sought at random,
is found.Comment: 4 pages, final for
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
Small world yields the most effective information spreading
Spreading dynamics of information and diseases are usually analyzed by using
a unified framework and analogous models. In this paper, we propose a model to
emphasize the essential difference between information spreading and epidemic
spreading, where the memory effects, the social reinforcement and the
non-redundancy of contacts are taken into account. Under certain conditions,
the information spreads faster and broader in regular networks than in random
networks, which to some extent supports the recent experimental observation of
spreading in online society [D. Centola, Science {\bf 329}, 1194 (2010)]. At
the same time, simulation result indicates that the random networks tend to be
favorable for effective spreading when the network size increases. This
challenges the validity of the above-mentioned experiment for large-scale
systems. More significantly, we show that the spreading effectiveness can be
sharply enhanced by introducing a little randomness into the regular structure,
namely the small-world networks yield the most effective information spreading.
Our work provides insights to the understanding of the role of local clustering
in information spreading.Comment: 6 pages, 7 figures, accepted by New J. Phy
- …