15,102 research outputs found
Coded Slotted ALOHA: A Graph-Based Method for Uncoordinated Multiple Access
In this paper, a random access scheme is introduced which relies on the
combination of packet erasure correcting codes and successive interference
cancellation (SIC). The scheme is named coded slotted ALOHA. A bipartite graph
representation of the SIC process, resembling iterative decoding of generalized
low-density parity-check codes over the erasure channel, is exploited to
optimize the selection probabilities of the component erasure correcting codes
via density evolution analysis. The capacity (in packets per slot) of the
scheme is then analyzed in the context of the collision channel without
feedback. Moreover, a capacity bound is developed and component code
distributions tightly approaching the bound are derived.Comment: The final version to appear in IEEE Trans. Inf. Theory. 18 pages, 10
figure
Quantum simulation of bosonic-fermionic non-interacting particles in disordered systems via quantum walk
We report on the theoretical analysis of bosonic and fermionic
non-interacting systems in a discrete two-particle quantum walk affected by
different kinds of disorder. We considered up to 100-step QWs with a spatial,
temporal and space-temporal disorder observing how the randomness and the
wavefunction symmetry non-trivially affect the final spatial probability
distribution, the transport properties and the Shannon entropy of the walkers.Comment: 13 pages, 10 figures. arXiv admin note: text overlap with
arXiv:1101.2638 by other author
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
Rising Wage Inequality, the Decline of Collective Bargaining, and the Gender Wage Gap
This paper investigates the increase in wage inequality, the decline in collective bargaining, and the development of the gender wage gap in West Germany between 2001 and 2006. Based on detailed linked employer-employee data, we show that wage inequality is rising strongly – driven not only by real wage increases at the top of the wage distribution, but also by real wage losses below the median. Coverage by collective wage bargaining plummets by 16.5 (19.1) percentage points for male (female) employees. Despite these changes, the gender wage gap remains almost constant, with some small gains for women at the bottom and at the top of the wage distribution. A sequential decomposition analysis using quantile regression shows that all workplace related effects (firm effects and bargaining effects) and coefficients for personal characteristics contribute strongly to the rise in wage inequality. Among these, the firm coefficients effect dominates, which is almost exclusively driven by wage differences within and between different industries. Labor demand or firm wage policy related effects contribute to an increase in the gender wage gap. Personal characteristics tend to reduce wage inequality for both, males and females, as well as the gender wage gap.sequential decomposition, wage distribution, gender wage gap, collective bargaining, quantile regression
Rising wage inequality, the decline of collective bargaining, and the gender wage gap
This paper investigates the increase in wage inequality, the decline in collective bargaining, and the development of the gender wage gap in West Germany between 2001 and 2006. Based on detailed linked employer-employee data, we show that wage inequality is rising strongly – driven not only by real wage increases at the top of the wage distribution, but also by real wage losses below the median. Coverage by collective wage bargaining plummets by 16.5 (19.1) percentage points for male (female) employees. Despite these changes, the gender wage gap remains almost constant, with some small gains for women at the bottom and at the top of the wage distribution. A sequential decomposition analysis using quantile regression shows that all workplace related effects (firm effects and bargaining effects) and coefficients for personal characteristics contribute strongly to the rise in wage inequality. Among these, the firm coefficients effect dominates, which is almost exclusively driven by wage differences within and between different industries. Labor demand or firm wage policy related effects contribute to an increase in the gender wage gap. Personal characteristics tend to reduce wage inequality for both, males and females, as well as the gender wage gap. --Distribution,Gender Wage Gap,Collective Bargaining,Quantile Regression,Sequential Decomposition
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs
Laplacian mixture models identify overlapping regions of influence in
unlabeled graph and network data in a scalable and computationally efficient
way, yielding useful low-dimensional representations. By combining Laplacian
eigenspace and finite mixture modeling methods, they provide probabilistic or
fuzzy dimensionality reductions or domain decompositions for a variety of input
data types, including mixture distributions, feature vectors, and graphs or
networks. Provable optimal recovery using the algorithm is analytically shown
for a nontrivial class of cluster graphs. Heuristic approximations for scalable
high-performance implementations are described and empirically tested.
Connections to PageRank and community detection in network analysis demonstrate
the wide applicability of this approach. The origins of fuzzy spectral methods,
beginning with generalized heat or diffusion equations in physics, are reviewed
and summarized. Comparisons to other dimensionality reduction and clustering
methods for challenging unsupervised machine learning problems are also
discussed.Comment: 13 figures, 35 reference
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