8,408 research outputs found
A methodology for neural spatial interaction modelling
This paper presents a methodology for neural spatial interaction modelling. Particular emphasis is laid on design, estimation and performance issues in both cases, unconstrained and singly constrained spatial interaction. Families of classical neural network models, but also less classical ones such as product unit neural network models are considered. Some novel classes of product unit and summation unit models are presented for the case of origin or destination constrained spatial interaction flows. The models are based on a modular connectionist architecture that may be viewed as a linked collection of functionally independent neural modules with identical feedforward topologies, operating under supervised learning algorithms. Parameter estimation is viewed as Maximum Likelihood (ML) learning. The nonconvex nature of the loss function makes the Alopex procedure, a global search procedure, an attractive and appropriate optimising scheme for ML learning. A benchmark comparison against the classical gravity models illustrates the superiority of both, the unconstrained and the origin constrained, neural network model versions in terms of generalization performance measured by Kullback and Leibler`s information criterion. Hereby, the authors make use of the bootstrapping pairs approach to overcome the largely neglected problem of sensitivity to the specific splitting of the data into training, internal validation and testing data sets, and to get a better statistical picture of prediction variability of the models. Keywords: Neural spatial interaction models, origin constrained or destination constrained spatial interaction, product unit network, Alopex procedure, boostrapping, benchmark performance tests.
A Numerical Approach to Stability of Multiclass Queueing Networks
The Multi-class Queueing Network (McQN) arises as a natural multi-class
extension of the traditional (single-class) Jackson network. In a single-class
network subcriticality (i.e. subunitary nominal workload at every station)
entails stability, but this is no longer sufficient when jobs/customers of
different classes (i.e. with different service requirements and/or routing
scheme) visit the same server; therefore, analytical conditions for stability
of McQNs are lacking, in general. In this note we design a numerical
(simulation-based) method for determining the stability region of a McQN, in
terms of arrival rate(s). Our method exploits certain (stochastic) monotonicity
properties enjoyed by the associated Markovian queue-configuration process.
Stochastic monotonicity is a quite common feature of queueing models and can be
easily established in the single-class framework (Jackson networks); recently,
also for a wide class of McQNs, including first-come-first-serve (FCFS)
networks, monotonicity properties have been established. Here, we provide a
minimal set of conditions under which the method performs correctly.
Eventually, we illustrate the use of our numerical method by presenting a set
of numerical experiments, covering both single and multi-class networks
MIMO Networks: the Effects of Interference
Multiple-input/multiple-output (MIMO) systems promise enormous capacity
increase and are being considered as one of the key technologies for future
wireless networks. However, the decrease in capacity due to the presence of
interferers in MIMO networks is not well understood. In this paper, we develop
an analytical framework to characterize the capacity of MIMO communication
systems in the presence of multiple MIMO co-channel interferers and noise. We
consider the situation in which transmitters have no information about the
channel and all links undergo Rayleigh fading. We first generalize the known
determinant representation of hypergeometric functions with matrix arguments to
the case when the argument matrices have eigenvalues of arbitrary multiplicity.
This enables the derivation of the distribution of the eigenvalues of Gaussian
quadratic forms and Wishart matrices with arbitrary correlation, with
application to both single user and multiuser MIMO systems. In particular, we
derive the ergodic mutual information for MIMO systems in the presence of
multiple MIMO interferers. Our analysis is valid for any number of interferers,
each with arbitrary number of antennas having possibly unequal power levels.
This framework, therefore, accommodates the study of distributed MIMO systems
and accounts for different positions of the MIMO interferers.Comment: Submitted to IEEE Trans. on Info. Theor
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
This article provides a unifying Bayesian network view on various approaches
for acoustic model adaptation, missing feature, and uncertainty decoding that
are well-known in the literature of robust automatic speech recognition. The
representatives of these classes can often be deduced from a Bayesian network
that extends the conventional hidden Markov models used in speech recognition.
These extensions, in turn, can in many cases be motivated from an underlying
observation model that relates clean and distorted feature vectors. By
converting the observation models into a Bayesian network representation, we
formulate the corresponding compensation rules leading to a unified view on
known derivations as well as to new formulations for certain approaches. The
generic Bayesian perspective provided in this contribution thus highlights
structural differences and similarities between the analyzed approaches
Multiplexing regulated traffic streams: design and performance
The main network solutions for supporting QoS rely on traf- fic policing (conditioning, shaping). In particular, for IP networks the IETF has developed Intserv (individual flows regulated) and Diffserv (only ag- gregates regulated). The regulator proposed could be based on the (dual) leaky-bucket mechanism. This explains the interest in network element per- formance (loss, delay) for leaky-bucket regulated traffic. This paper describes a novel approach to the above problem. Explicitly using the correlation structure of the sources’ traffic, we derive approxi- mations for both small and large buffers. Importantly, for small (large) buffers the short-term (long-term) correlations are dominant. The large buffer result decomposes the traffic stream in a stream of constant rate and a periodic impulse stream, allowing direct application of the Brownian bridge approximation. Combining the small and large buffer results by a concave majorization, we propose a simple, fast and accurate technique to statistically multiplex homogeneous regulated sources. To address heterogeneous inputs, we present similarly efficient tech- niques to evaluate the performance of multiple classes of traffic, each with distinct characteristics and QoS requirements. These techniques, applica- ble under more general conditions, are based on optimal resource (band- width and buffer) partitioning. They can also be directly applied to set GPS (Generalized Processor Sharing) weights and buffer thresholds in a shared resource system
FFT-Based Deep Learning Deployment in Embedded Systems
Deep learning has delivered its powerfulness in many application domains,
especially in image and speech recognition. As the backbone of deep learning,
deep neural networks (DNNs) consist of multiple layers of various types with
hundreds to thousands of neurons. Embedded platforms are now becoming essential
for deep learning deployment due to their portability, versatility, and energy
efficiency. The large model size of DNNs, while providing excellent accuracy,
also burdens the embedded platforms with intensive computation and storage.
Researchers have investigated on reducing DNN model size with negligible
accuracy loss. This work proposes a Fast Fourier Transform (FFT)-based DNN
training and inference model suitable for embedded platforms with reduced
asymptotic complexity of both computation and storage, making our approach
distinguished from existing approaches. We develop the training and inference
algorithms based on FFT as the computing kernel and deploy the FFT-based
inference model on embedded platforms achieving extraordinary processing speed.Comment: Design, Automation, and Test in Europe (DATE) For source code, please
contact Mahdi Nazemi at <[email protected]
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