136,116 research outputs found
An Optimization Theoretical Framework for Resource Allocation over Wireless Networks
With the advancement of wireless technologies, wireless networking has become ubiquitous owing to the great demand of pervasive mobile applications. Some fundamental challenges exist for the next generation wireless network design such as time varying nature of wireless channels, co-channel interferences, provisioning of heterogeneous type of services, etc. So how to overcome these difficulties and improve the system performance have become an important research topic.
Dynamic resource allocation is a general strategy to control the interferences and enhance the performance of wireless networks. The basic idea behind dynamic resource allocation is to utilize the channel more efficiently by sharing the spectrum and reducing
interference through optimizing parameters such as the
transmitting power, symbol transmission rate, modulation scheme, coding scheme, bandwidth, etc. Moreover, the network performance can be further improved by introducing diversity, such as
multiuser, time, frequency, and space diversity. In addition, cross layer approach for resource allocation can provide advantages such as low overhead, more efficiency, and direct end-to-end QoS provision.
The designers for next generation wireless networks face the common problem of how to optimize the system objective under the user Quality of Service (QoS) constraint. There is a need of unified but general optimization framework for resource allocation
to allow taking into account a diverse set of objective functions with various QoS requirements, while considering all kinds of diversity and cross layer approach. We propose an optimization
theoretical framework for resource allocation and apply these ideas to different network situations such as:
1.Centralized resource allocation with fairness constraint
2.Distributed resource allocation using game theory
3.OFDMA resource allocation
4.Cross layer approach
On the whole, we develop a universal view of the whole wireless networks from multiple dimensions: time, frequency, space, user, and layers. We develop some schemes to fully utilize the resources. The success of the proposed research will significantly
improve the way how to design and analyze resource allocation over wireless networks. In addition, the cross-layer optimization nature of the problem provides an innovative insight into vertical
integration of wireless networks
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model
Multilingual models for Automatic Speech Recognition (ASR) are attractive as
they have been shown to benefit from more training data, and better lend
themselves to adaptation to under-resourced languages. However, initialisation
from monolingual context-dependent models leads to an explosion of
context-dependent states. Connectionist Temporal Classification (CTC) is a
potential solution to this as it performs well with monophone labels.
We investigate multilingual CTC in the context of adaptation and
regularisation techniques that have been shown to be beneficial in more
conventional contexts. The multilingual model is trained to model a universal
International Phonetic Alphabet (IPA)-based phone set using the CTC loss
function. Learning Hidden Unit Contribution (LHUC) is investigated to perform
language adaptive training. In addition, dropout during cross-lingual
adaptation is also studied and tested in order to mitigate the overfitting
problem.
Experiments show that the performance of the universal phoneme-based CTC
system can be improved by applying LHUC and it is extensible to new phonemes
during cross-lingual adaptation. Updating all the parameters shows consistent
improvement on limited data. Applying dropout during adaptation can further
improve the system and achieve competitive performance with Deep Neural Network
/ Hidden Markov Model (DNN/HMM) systems on limited data
On the equivalence between graph isomorphism testing and function approximation with GNNs
Graph neural networks (GNNs) have achieved lots of success on
graph-structured data. In the light of this, there has been increasing interest
in studying their representation power. One line of work focuses on the
universal approximation of permutation-invariant functions by certain classes
of GNNs, and another demonstrates the limitation of GNNs via graph isomorphism
tests.
Our work connects these two perspectives and proves their equivalence. We
further develop a framework of the representation power of GNNs with the
language of sigma-algebra, which incorporates both viewpoints. Using this
framework, we compare the expressive power of different classes of GNNs as well
as other methods on graphs. In particular, we prove that order-2 Graph
G-invariant networks fail to distinguish non-isomorphic regular graphs with the
same degree. We then extend them to a new architecture, Ring-GNNs, which
succeeds on distinguishing these graphs and provides improvements on real-world
social network datasets
NAG: Network for Adversary Generation
Adversarial perturbations can pose a serious threat for deploying machine
learning systems. Recent works have shown existence of image-agnostic
perturbations that can fool classifiers over most natural images. Existing
methods present optimization approaches that solve for a fooling objective with
an imperceptibility constraint to craft the perturbations. However, for a given
classifier, they generate one perturbation at a time, which is a single
instance from the manifold of adversarial perturbations. Also, in order to
build robust models, it is essential to explore the manifold of adversarial
perturbations. In this paper, we propose for the first time, a generative
approach to model the distribution of adversarial perturbations. The
architecture of the proposed model is inspired from that of GANs and is trained
using fooling and diversity objectives. Our trained generator network attempts
to capture the distribution of adversarial perturbations for a given classifier
and readily generates a wide variety of such perturbations. Our experimental
evaluation demonstrates that perturbations crafted by our model (i) achieve
state-of-the-art fooling rates, (ii) exhibit wide variety and (iii) deliver
excellent cross model generalizability. Our work can be deemed as an important
step in the process of inferring about the complex manifolds of adversarial
perturbations.Comment: CVPR 201
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