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Local properties on the remainders of the topological groups
When does a topological group have a Hausdorff compactification with
a remainder belonging to a given class of spaces? In this paper, we mainly
improve some results of A.V. Arhangel'ski\v{\i} and C. Liu's. Let be a
non-locally compact topological group and be a compactification of .
The following facts are established: (1) If has a locally a
point-countable -metabase and -character of is
countable, then and are separable and metrizable; (2) If has locally a -base, then and are separable and
metrizable; (3) If has locally a quasi--diagonal,
then and are separable and metrizable. Finally, we give a partial
answer for a question, which was posed by C. Liu in \cite{LC}.Comment: 10pages (replace
Deep SimNets
We present a deep layered architecture that generalizes convolutional neural
networks (ConvNets). The architecture, called SimNets, is driven by two
operators: (i) a similarity function that generalizes inner-product, and (ii) a
log-mean-exp function called MEX that generalizes maximum and average. The two
operators applied in succession give rise to a standard neuron but in "feature
space". The feature spaces realized by SimNets depend on the choice of the
similarity operator. The simplest setting, which corresponds to a convolution,
realizes the feature space of the Exponential kernel, while other settings
realize feature spaces of more powerful kernels (Generalized Gaussian, which
includes as special cases RBF and Laplacian), or even dynamically learned
feature spaces (Generalized Multiple Kernel Learning). As a result, the SimNet
contains a higher abstraction level compared to a traditional ConvNet. We argue
that enhanced expressiveness is important when the networks are small due to
run-time constraints (such as those imposed by mobile applications). Empirical
evaluation validates the superior expressiveness of SimNets, showing a
significant gain in accuracy over ConvNets when computational resources at
run-time are limited. We also show that in large-scale settings, where
computational complexity is less of a concern, the additional capacity of
SimNets can be controlled with proper regularization, yielding accuracies
comparable to state of the art ConvNets
Satisfaction Equilibrium: A General Framework for QoS Provisioning in Self-Configuring Networks
This paper is concerned with the concept of equilibrium and quality of
service (QoS) provisioning in self-configuring wireless networks with
non-cooperative radio devices (RD). In contrast with the Nash equilibrium (NE),
where RDs are interested in selfishly maximizing its QoS, we present a concept
of equilibrium, named satisfaction equilibrium (SE), where RDs are interested
only in guaranteing a minimum QoS. We provide the conditions for the existence
and the uniqueness of the SE. Later, in order to provide an equilibrium
selection framework for the SE, we introduce the concept of effort or cost of
satisfaction, for instance, in terms of transmit power levels, constellation
sizes, etc. Using the idea of effort, the set of efficient SE (ESE) is defined.
At the ESE, transmitters satisfy their minimum QoS incurring in the lowest
effort. We prove that contrary to the (generalized) NE, at least one ESE always
exists whenever the network is able to simultaneously support the individual
QoS requests. Finally, we provide a fully decentralized algorithm to allow
self-configuring networks to converge to one of the SE relying only on local
information.Comment: Accepted for publication in Globecom 201
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