40,882 research outputs found
Essential metals at the host-pathogen interface : nutritional immunity and micronutrient assimilation by human fungal pathogens
AC and DW are supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 102549/Z/13/Z).Peer reviewedPublisher PD
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Multi-Entity Dependence Learning (MEDL) explores conditional correlations
among multiple entities. The availability of rich contextual information
requires a nimble learning scheme that tightly integrates with deep neural
networks and has the ability to capture correlation structures among
exponentially many outcomes. We propose MEDL_CVAE, which encodes a conditional
multivariate distribution as a generating process. As a result, the variational
lower bound of the joint likelihood can be optimized via a conditional
variational auto-encoder and trained end-to-end on GPUs. Our MEDL_CVAE was
motivated by two real-world applications in computational sustainability: one
studies the spatial correlation among multiple bird species using the eBird
data and the other models multi-dimensional landscape composition and human
footprint in the Amazon rainforest with satellite images. We show that
MEDL_CVAE captures rich dependency structures, scales better than previous
methods, and further improves on the joint likelihood taking advantage of very
large datasets that are beyond the capacity of previous methods.Comment: The first two authors contribute equall
Controlling Reversibility in Reversing Petri Nets with Application to Wireless Communications
Petri nets are a formalism for modelling and reasoning about the behaviour of
distributed systems. Recently, a reversible approach to Petri nets, Reversing
Petri Nets (RPN), has been proposed, allowing transitions to be reversed
spontaneously in or out of causal order. In this work we propose an approach
for controlling the reversal of actions of an RPN, by associating transitions
with conditions whose satisfaction/violation allows the execution of
transitions in the forward/reversed direction, respectively. We illustrate the
framework with a model of a novel, distributed algorithm for antenna selection
in distributed antenna arrays.Comment: RC 201
An Automated Social Graph De-anonymization Technique
We present a generic and automated approach to re-identifying nodes in
anonymized social networks which enables novel anonymization techniques to be
quickly evaluated. It uses machine learning (decision forests) to matching
pairs of nodes in disparate anonymized sub-graphs. The technique uncovers
artefacts and invariants of any black-box anonymization scheme from a small set
of examples. Despite a high degree of automation, classification succeeds with
significant true positive rates even when small false positive rates are
sought. Our evaluation uses publicly available real world datasets to study the
performance of our approach against real-world anonymization strategies, namely
the schemes used to protect datasets of The Data for Development (D4D)
Challenge. We show that the technique is effective even when only small numbers
of samples are used for training. Further, since it detects weaknesses in the
black-box anonymization scheme it can re-identify nodes in one social network
when trained on another.Comment: 12 page
- …