93,593 research outputs found
Multi-resolution two-sample comparison through the divide-merge Markov tree
We introduce a probabilistic framework for two-sample comparison based on a
nonparametric process taking the form of a Markov model that transitions
between a "divide" and a "merge" state on a multi-resolution partition tree of
the sample space. Multi-scale two-sample comparison is achieved through
inferring the underlying state of the process along the partition tree. The
Markov design allows the process to incorporate spatial clustering of
differential structures, which is commonly observed in two-sample problems but
ignored by existing methods. Inference is carried out under the Bayesian
paradigm through recursive propagation algorithms. We demonstrate the work of
our method through simulated data and a real flow cytometry data set, and show
that it substantially outperforms other state-of-the-art two-sample tests in
several settings.Comment: Corrected typos. Added Software sectio
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Parallel Weighted Random Sampling
Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps both for shared-memory and distributed-memory machines. We give efficient, fast, and practicable algorithms for sampling single items, k items with/without replacement, permutations, subsets, and reservoirs. We also give improved sequential algorithms for alias table construction and for sampling with replacement. Experiments on shared-memory parallel machines with up to 158 threads show near linear speedups both for construction and queries
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