1,192,821 research outputs found
Portfolio selection using neural networks
In this paper we apply a heuristic method based on artificial neural networks
in order to trace out the efficient frontier associated to the portfolio
selection problem. We consider a generalization of the standard Markowitz
mean-variance model which includes cardinality and bounding constraints. These
constraints ensure the investment in a given number of different assets and
limit the amount of capital to be invested in each asset. We present some
experimental results obtained with the neural network heuristic and we compare
them to those obtained with three previous heuristic methods.Comment: 12 pages; submitted to "Computers & Operations Research
Seed selection for information cascade in multilayer networks
Information spreading is an interesting field in the domain of online social
media. In this work, we are investigating how well different seed selection
strategies affect the spreading processes simulated using independent cascade
model on eighteen multilayer social networks. Fifteen networks are built based
on the user interaction data extracted from Facebook public pages and tree of
them are multilayer networks downloaded from public repository (two of them
being Twitter networks). The results indicate that various state of the art
seed selection strategies for single-layer networks like K-Shell or VoteRank do
not perform so well on multilayer networks and are outperformed by Degree
Centrality
Self-selection patterns in Mexico-U.S. migration: the role of migration networks
This paper examines the role of migration networks in determining self-selection
patterns of Mexico-U.S. migration. We first present a simple theoretical framework
showing how such networks impact on migration incentives at different education
levels and, consequently, how they are likely to affect the expected skill composition
of migration. Using survey data from Mexico, we then show that the probability of
migration is increasing with education in communities with low migrant networks,
but decreasing with education in communities with high migrant networks. This is
consistent with positive self-selection of migrants being driven by high migration
costs, as advocated by Chiquiar and Hanson (2005), and with negative self-selection
of migrants being driven by lower returns to education in the U.S. than in Mexico, as
advocated by Borjas (1987)
Path Selection for Quantum Repeater Networks
Quantum networks will support long-distance quantum key distribution (QKD)
and distributed quantum computation, and are an active area of both
experimental and theoretical research. Here, we present an analysis of
topologically complex networks of quantum repeaters composed of heterogeneous
links. Quantum networks have fundamental behavioral differences from classical
networks; the delicacy of quantum states makes a practical path selection
algorithm imperative, but classical notions of resource utilization are not
directly applicable, rendering known path selection mechanisms inadequate. To
adapt Dijkstra's algorithm for quantum repeater networks that generate
entangled Bell pairs, we quantify the key differences and define a link cost
metric, seconds per Bell pair of a particular fidelity, where a single Bell
pair is the resource consumed to perform one quantum teleportation. Simulations
that include both the physical interactions and the extensive classical
messaging confirm that Dijkstra's algorithm works well in a quantum context.
Simulating about three hundred heterogeneous paths, comparing our path cost and
the total work along the path gives a coefficient of determination of 0.88 or
better.Comment: 12 pages, 8 figure
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