9,252 research outputs found
Electromagnetic modelling of a monolithic pulse reshaper based on a photonic crystal waveguide integrated with a SOA
Machine learning reduced workload with minimal risk of missing studies: development and evaluation of an RCT classifier for Cochrane Reviews
BACKGROUND:
To describe the development, calibration and evaluation of a machine learning classifier designed to
reduce study identification workload in Cochrane for producing systematic reviews.
METHODS:
A machine learning classifier for retrieving RCTs was developed (the ‘Cochrane RCT Classifier’), with
the algorithm trained using a dataset of title-abstract records from Embase, manually labelled by the
Cochrane Crowd. The classifier was then calibrated using a further dataset of similar records
manually labelled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the
calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had
abstracts of sufficient length to allow machine classification.
RESULTS:
The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A
classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs) and
our bootstrap validation found the classifier had recall of 0.99 (95% CI 0.98 to 0.99) and precision of
0.08 (95% CI 0.06 to 0.12) in this dataset. The final, calibrated RCT classifier correctly retrieved
43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records
were more likely to be missed than those more recently published.
CONCLUSIONS:
The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane reviews,
with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the
Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency
of the study identification processes that support systematic review production
Towards a novel biologically-inspired cloud elasticity framework
With the widespread use of the Internet, the popularity of web applications has
significantly increased. Such applications are subject to unpredictable workload
conditions that vary from time to time. For example, an e-commerce website may
face higher workloads than normal during festivals or promotional schemes. Such
applications are critical and performance related issues, or service disruption can
result in financial losses. Cloud computing with its attractive feature of dynamic
resource provisioning (elasticity) is a perfect match to host such applications.
The rapid growth in the usage of cloud computing model, as well as the rise in
complexity of the web applications poses new challenges regarding the effective
monitoring and management of the underlying cloud computational resources.
This thesis investigates the state-of-the-art elastic methods including the models
and techniques for the dynamic management and provisioning of cloud resources
from a service provider perspective.
An elastic controller is responsible to determine the optimal number of cloud resources,
required at a particular time to achieve the desired performance demands.
Researchers and practitioners have proposed many elastic controllers using versatile
techniques ranging from simple if-then-else based rules to sophisticated
optimisation, control theory and machine learning based methods. However,
despite an extensive range of existing elasticity research, the aim of implementing
an efficient scaling technique that satisfies the actual demands is still a challenge
to achieve. There exist many issues that have not received much attention from
a holistic point of view. Some of these issues include: 1) the lack of adaptability
and static scaling behaviour whilst considering completely fixed approaches; 2)
the burden of additional computational overhead, the inability to cope with the
sudden changes in the workload behaviour and the preference of adaptability
over reliability at runtime whilst considering the fully dynamic approaches; and 3)
the lack of considering uncertainty aspects while designing auto-scaling solutions.
This thesis seeks solutions to address these issues altogether using an integrated
approach. Moreover, this thesis aims at the provision of qualitative elasticity rules.
This thesis proposes a novel biologically-inspired switched feedback control
methodology to address the horizontal elasticity problem. The switched methodology
utilises multiple controllers simultaneously, whereas the selection of a
suitable controller is realised using an intelligent switching mechanism. Each
controller itself depicts a different elasticity policy that can be designed using the
principles of fixed gain feedback controller approach. The switching mechanism
is implemented using a fuzzy system that determines a suitable controller/-
policy at runtime based on the current behaviour of the system. Furthermore,
to improve the possibility of bumpless transitions and to avoid the oscillatory
behaviour, which is a problem commonly associated with switching based control
methodologies, this thesis proposes an alternative soft switching approach. This
soft switching approach incorporates a biologically-inspired Basal Ganglia based
computational model of action selection.
In addition, this thesis formulates the problem of designing the membership functions
of the switching mechanism as a multi-objective optimisation problem. The
key purpose behind this formulation is to obtain the near optimal (or to fine tune)
parameter settings for the membership functions of the fuzzy control system in
the absence of domain experts’ knowledge. This problem is addressed by using
two different techniques including the commonly used Genetic Algorithm and
an alternative less known economic approach called the Taguchi method. Lastly,
we identify seven different kinds of real workload patterns, each of which reflects
a different set of applications. Six real and one synthetic HTTP traces, one for
each pattern, are further identified and utilised to evaluate the performance of
the proposed methods against the state-of-the-art approaches
A comparison of national cancer registry and direct follow-up in the ascertainment of ovarian cancer
Paroxysmal sympathetic hyperactivity in brainstem-compressing huge benign tumors: clinical experiences and literature review
Mitochondrially targeted ZFNs for selective degradation of pathogenic mitochondrial genomes bearing large‐scale deletions or point mutations
We designed and engineered mitochondrially targeted obligate heterodimeric zinc finger nucleases (mtZFNs) for site‐specific elimination of pathogenic human mitochondrial DNA (mtDNA). We used mtZFNs to target and cleave mtDNA harbouring the m.8993T>G point mutation associated with neuropathy, ataxia, retinitis pigmentosa (NARP) and the “common deletion” (CD), a 4977‐bp repeat‐flanked deletion associated with adult‐onset chronic progressive external ophthalmoplegia and, less frequently, Kearns‐Sayre and Pearson's marrow pancreas syndromes. Expression of mtZFNs led to a reduction in mutant mtDNA haplotype load, and subsequent repopulation of wild‐type mtDNA restored mitochondrial respiratory function in a CD cybrid cell model. This study constitutes proof‐of‐principle that, through heteroplasmy manipulation, delivery of site‐specific nuclease activity to mitochondria can alleviate a severe biochemical phenotype in primary mitochondrial disease arising from deleted mtDNA species
An adaptive technique for content-based image retrieval
We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search
On the Schoenberg Transformations in Data Analysis: Theory and Illustrations
The class of Schoenberg transformations, embedding Euclidean distances into
higher dimensional Euclidean spaces, is presented, and derived from theorems on
positive definite and conditionally negative definite matrices. Original
results on the arc lengths, angles and curvature of the transformations are
proposed, and visualized on artificial data sets by classical multidimensional
scaling. A simple distance-based discriminant algorithm illustrates the theory,
intimately connected to the Gaussian kernels of Machine Learning
Reaction Time and Mortality from the Major Causes of Death:The NHANES-III Study
Studies examining the relation of information processing speed, as measured by reaction time, with mortality are scarce. We explored these associations in a representative sample of the US population
METHODS OF ASTM G16 AND CONFLICTS IN CORROSION TEST DATA: CASE STUDY OF NANO2 EFFECTIVENESS ON STEEL-REBAR CORROSION
In this paper, applications of the methods of ASTM G16 for addressing inherent conflicts in laboratory measurements of
corrosion test data were studied, using the inhibiting effect of NaNO2 on the corrosion of concrete steel-rebar for the case
study. For this, electrochemical monitoring techniques were employed for studying effectiveness of different
concentrations of NaNO2 admixture in replicated concrete samples immersed in NaCl and in H2SO4 media for an
experimental period of sixty-eight days. The corrosion test data from this experimental setup were subjected to the
probability density fittings of the Normal and the Weibull functions as well as to significance testing methods of ASTM
G16-99 R04 specifications. Results identified 10g (0.1208M) NaNO2 admixture with optimal inhibition efficiency
model, η = 88.38±4.62%, in the saline/marine simulating environment and the 8 g (0.0966M) NaNO2 admixture with
optimum effectiveness, η = 13.51±83.48%, in the acidic environment. The techniques of ASTM G16 adequately
identified and addressed conflicting effectiveness from the test data of NaNO2 admixtures in the studied test
environments
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