10,446 research outputs found
Analysis of loss networks with routing
This paper analyzes stochastic networks consisting of finite capacity nodes
with different classes of requests which move according to some routing policy.
The Markov processes describing these networks do not, in general, have
reversibility properties, so the explicit expression of their invariant
distribution is not known. Kelly's limiting regime is considered: the arrival
rates of calls as well as the capacities of the nodes are proportional to a
factor going to infinity. It is proved that, in limit, the associated rescaled
Markov process converges to a deterministic dynamical system with a unique
equilibrium point characterized by a nonstandard fixed point equation.Comment: Published at http://dx.doi.org/10.1214/105051606000000466 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
Using data network metrics, graphics, and topology to explore network characteristics
Yehuda Vardi introduced the term network tomography and was the first to
propose and study how statistical inverse methods could be adapted to attack
important network problems (Vardi, 1996). More recently, in one of his final
papers, Vardi proposed notions of metrics on networks to define and measure
distances between a network's links, its paths, and also between different
networks (Vardi, 2004). In this paper, we apply Vardi's general approach for
network metrics to a real data network by using data obtained from special data
network tools and testing procedures presented here. We illustrate how the
metrics help explicate interesting features of the traffic characteristics on
the network. We also adapt the metrics in order to condition on traffic passing
through a portion of the network, such as a router or pair of routers, and show
further how this approach helps to discover and explain interesting network
characteristics.Comment: Published at http://dx.doi.org/10.1214/074921707000000058 in the IMS
Lecture Notes Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
Product Return Handling
In this article we focus on product return handling and warehousingissues. In some businesses return rates can be well over 20% andreturns can be especially costly when not handled properly. In spiteof this, many managers have handled returns extemporarily. The factthat quantitative methods barely exist to support return handlingdecisions adds to this. In this article we bridge those issues by 1)going over the key decisions related with return handling; 2)identifying quantitative models to support those decisions.Furthermore, we provide insights on directions for future research.reverse logistics;decision-making;quantitative models;retailing and warehousing
Virtual Geodemographics: Repositioning Area Classification for Online and Offline Spaces
Computer mediated communication and the Internet has fundamentally changed how consumers and producers connect and interact across both real space, and has also opened up new opportunities in virtual spaces. This paper describes how technologies capable of locating and sorting networked communities of geographically disparate individuals within virtual communities present a sea change in the conception, representation and analysis of socioeconomic distributions through geodemographic analysis. We argue that through virtual communities, social networks between individuals may subsume the role of neighbourhood areas as the most appropriate units of analysis, and as such, geodemographics needs to be repositioned in order to accommodate social similarities in virtual, as well as geographical, space. We end the paper by proposing a new model for geodemographics which spans both real and virtual geographies
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight
quantization as a means to increase the resource- and power-efficiency of
hardware devices. As full on-chip weight storage is necessary to avoid the high
energy cost of off-chip memory accesses, memory reduction requirements for
weight storage pushed toward the use of binary weights, which were demonstrated
to have a limited accuracy reduction on many applications when
quantization-aware training techniques are used. In parallel, spiking neural
network (SNN) architectures are explored to further reduce power when
processing sparse event-based data streams, while on-chip spike-based online
learning appears as a key feature for applications constrained in power and
resources during the training phase. However, designing power- and
area-efficient spiking neural networks still requires the development of
specific techniques in order to leverage on-chip online learning on binary
weights without compromising the synapse density. In this work, we demonstrate
MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a
stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning
rule and a hierarchical routing fabric for large-scale chip interconnection.
The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF)
neurons and more than two million plastic synapses for an active silicon area
of 2.86mm in 65nm CMOS, achieving a high density of 738k synapses/mm.
MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy
tradeoff on the MNIST classification task compared to previously-proposed SNNs,
while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE
Transactions on Biomedical Circuits and Systems journal (2019), the
fully-edited paper is available at
https://ieeexplore.ieee.org/document/876400
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