160,594 research outputs found
Assessing cognitive dysfunction in Parkinson's disease: An online tool to detect visuo-perceptual deficits.
BackgroundPeople with Parkinson's disease (PD) who develop visuo-perceptual deficits are at higher risk of dementia, but we lack tests that detect subtle visuo-perceptual deficits and can be performed by untrained personnel. Hallucinations are associated with cognitive impairment and typically involve perception of complex objects. Changes in object perception may therefore be a sensitive marker of visuo-perceptual deficits in PD.ObjectiveWe developed an online platform to test visuo-perceptual function. We hypothesised that (1) visuo-perceptual deficits in PD could be detected using online tests, (2) object perception would be preferentially affected, and (3) these deficits would be caused by changes in perception rather than response bias.MethodsWe assessed 91 people with PD and 275 controls. Performance was compared using classical frequentist statistics. We then fitted a hierarchical Bayesian signal detection theory model to a subset of tasks.ResultsPeople with PD were worse than controls at object recognition, showing no deficits in other visuo-perceptual tests. Specifically, they were worse at identifying skewed images (P < .0001); at detecting hidden objects (P = .0039); at identifying objects in peripheral vision (P < .0001); and at detecting biological motion (P = .0065). In contrast, people with PD were not worse at mental rotation or subjective size perception. Using signal detection modelling, we found this effect was driven by change in perceptual sensitivity rather than response bias.ConclusionsOnline tests can detect visuo-perceptual deficits in people with PD, with object recognition particularly affected. Ultimately, visuo-perceptual tests may be developed to identify at-risk patients for clinical trials to slow PD dementia. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society
Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels
One important assumption underlying common classification models is the
stationarity of the data. However, in real-world streaming applications, the
data concept indicated by the joint distribution of feature and label is not
stationary but drifting over time. Concept drift detection aims to detect such
drifts and adapt the model so as to mitigate any deterioration in the model's
predictive performance. Unfortunately, most existing concept drift detection
methods rely on a strong and over-optimistic condition that the true labels are
available immediately for all already classified instances. In this paper, a
novel Hierarchical Hypothesis Testing framework with Request-and-Reverify
strategy is developed to detect concept drifts by requesting labels only when
necessary. Two methods, namely Hierarchical Hypothesis Testing with
Classification Uncertainty (HHT-CU) and Hierarchical Hypothesis Testing with
Attribute-wise "Goodness-of-fit" (HHT-AG), are proposed respectively under the
novel framework. In experiments with benchmark datasets, our methods
demonstrate overwhelming advantages over state-of-the-art unsupervised drift
detectors. More importantly, our methods even outperform DDM (the widely used
supervised drift detector) when we use significantly fewer labels.Comment: Published as a conference paper at IJCAI 201
Detecting change points in the large-scale structure of evolving networks
Interactions among people or objects are often dynamic in nature and can be
represented as a sequence of networks, each providing a snapshot of the
interactions over a brief period of time. An important task in analyzing such
evolving networks is change-point detection, in which we both identify the
times at which the large-scale pattern of interactions changes fundamentally
and quantify how large and what kind of change occurred. Here, we formalize for
the first time the network change-point detection problem within an online
probabilistic learning framework and introduce a method that can reliably solve
it. This method combines a generalized hierarchical random graph model with a
Bayesian hypothesis test to quantitatively determine if, when, and precisely
how a change point has occurred. We analyze the detectability of our method
using synthetic data with known change points of different types and
magnitudes, and show that this method is more accurate than several previously
used alternatives. Applied to two high-resolution evolving social networks,
this method identifies a sequence of change points that align with known
external "shocks" to these networks
A consensus based network intrusion detection system
Network intrusion detection is the process of identifying malicious behaviors
that target a network and its resources. Current systems implementing intrusion
detection processes observe traffic at several data collecting points in the
network but analysis is often centralized or partly centralized. These systems
are not scalable and suffer from the single point of failure, i.e. attackers
only need to target the central node to compromise the whole system. This paper
proposes an anomaly-based fully distributed network intrusion detection system
where analysis is run at each data collecting point using a naive Bayes
classifier. Probability values computed by each classifier are shared among
nodes using an iterative average consensus protocol. The final analysis is
performed redundantly and in parallel at the level of each data collecting
point, thus avoiding the single point of failure issue. We run simulations
focusing on DDoS attacks with several network configurations, comparing the
accuracy of our fully distributed system with a hierarchical one. We also
analyze communication costs and convergence speed during consensus phases.Comment: Presented at THE 5TH INTERNATIONAL CONFERENCE ON IT CONVERGENCE AND
SECURITY 2015 IN KUALA LUMPUR, MALAYSI
Searching for Gravitational Waves from the Inspiral of Precessing Binary Systems: New Hierarchical Scheme using "Spiky" Templates
In a recent investigation of the effects of precession on the anticipated
detection of gravitational-wave inspiral signals from compact object binaries
with moderate total masses, we found that (i) if precession is ignored, the
inspiral detection rate can decrease by almost a factor of 10, and (ii)
previously proposed ``mimic'' templates cannot improve the detection rate
significantly (by more than a factor of 2). In this paper we propose a new
family of templates that can improve the detection rate by factors of 5--6 in
cases where precession is most important. Our proposed method for these new
``mimic'' templates involves a hierarchical scheme of efficient, two-parameter
template searches that can account for a sequence of spikes that appear in the
residual inspiral phase, after one corrects for the any oscillatory
modification in the phase. We present our results for two cases of compact
object masses (10 and 1.4 solar masses and 7 and 3 solar masses) as a function
of spin properties. Although further work is needed to fully assess the
computational efficiency of this newly proposed template family, we conclude
that these ``spiky templates'' are good candidates for a family of precession
templates used in realistic searches, that can improve detection rates of
inspiral events.Comment: 17 pages, 22 figures, version accepted by PRD. Minor revision
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