11,659 research outputs found
Exponential-family Random Network Models
Random graphs, where the connections between nodes are considered random
variables, have wide applicability in the social sciences. Exponential-family
Random Graph Models (ERGM) have shown themselves to be a useful class of models
for representing com- plex social phenomena. We generalize ERGM by also
modeling nodal attributes as random variates, thus creating a random model of
the full network, which we call Exponential-family Random Network Models
(ERNM). We demonstrate how this framework allows a new formu- lation for
logistic regression in network data. We develop likelihood-based inference for
the model and an MCMC algorithm to implement it. This new model formulation is
used to analyze a peer social network from the National Lon- gitudinal Study of
Adolescent Health. We model the relationship between substance use and
friendship relations, and show how the results differ from the standard use of
logistic regression on network data
Causal connectivity of evolved neural networks during behavior
To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics
Millimeter Wave Cellular Networks: A MAC Layer Perspective
The millimeter wave (mmWave) frequency band is seen as a key enabler of
multi-gigabit wireless access in future cellular networks. In order to overcome
the propagation challenges, mmWave systems use a large number of antenna
elements both at the base station and at the user equipment, which lead to high
directivity gains, fully-directional communications, and possible noise-limited
operations. The fundamental differences between mmWave networks and traditional
ones challenge the classical design constraints, objectives, and available
degrees of freedom. This paper addresses the implications that highly
directional communication has on the design of an efficient medium access
control (MAC) layer. The paper discusses key MAC layer issues, such as
synchronization, random access, handover, channelization, interference
management, scheduling, and association. The paper provides an integrated view
on MAC layer issues for cellular networks, identifies new challenges and
tradeoffs, and provides novel insights and solution approaches.Comment: 21 pages, 9 figures, 2 tables, to appear in IEEE Transactions on
Communication
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