5,326 research outputs found
Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device
A Hidden Markov Model (HMM) modified to work in combination with a Fuzzy System is utilised to determine the current behavioural state of the user from information obtained with specialised hardware. Due to the high dimensionality and not-linearly-separable nature of the Fuzzy System and the sensor data obtained with the hardware which informs the state decision, a new method is devised to update the HMM and replace the initial Fuzzy System such that subsequent state decisions are based on the most recent information. The resultant system first reduces the dimensionality of the original information by using a manifold representation in the high dimension which is unfolded in the lower dimension. The data is then linearly separable in the lower dimension where a simple linear classifier, such as the perceptron used here, is applied to determine the probability of the observations belonging to a state. Experiments using the new system verify its applicability in a real scenario
Dimension reduction for linear separation with curvilinear distances
Any high dimensional data in its original raw form may contain obviously classifiable clusters which are difficult to identify given the high-dimension representation. In reducing the dimensions it may be possible to perform a simple classification technique to extract this cluster information whilst retaining the overall topology of the data set. The supervised method presented here takes a high dimension data set consisting of multiple clusters and employs curvilinear distance as a relation between points, projecting in a lower dimension according to this relationship. This representation allows for linear separation of the non-separable high dimensional cluster data and the classification to a cluster of any successive unseen data point extracted from the same higher dimension
Comparative analysis of imaging configurations and objectives for Fourier microscopy
Fourier microscopy is becoming an increasingly important tool for the
analysis of optical nanostructures and quantum emitters. However, achieving
quantitative Fourier space measurements requires a thorough understanding of
the impact of aberrations introduced by optical microscopes, which have been
optimized for conventional real-space imaging. Here, we present a detailed
framework for analyzing the performance of microscope objectives for several
common Fourier imaging configurations. To this end, we model objectives from
Nikon, Olympus, and Zeiss using parameters that were inferred from patent
literature and confirmed, where possible, by physical disassembly. We then
examine the aberrations most relevant to Fourier microscopy, including the
alignment tolerances of apodization factors for different objective classes,
the effect of magnification on the modulation transfer function, and
vignetting-induced reductions of the effective numerical aperture for
wide-field measurements. Based on this analysis, we identify an optimal
objective class and imaging configuration for Fourier microscopy. In addition,
as a resource for future studies, the Zemax files for the objectives and setups
used in this analysis have been made publicly available.Comment: For related figshare fileset with complete Zemax models of microscope
objectives, tube lenses, and Fourier imaging configurations, see Ref. [41]
(available at http://dx.doi.org/10.6084/m9.figshare.1481270
Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection
Linguistically diverse datasets are critical for training and evaluating
robust machine learning systems, but data collection is a costly process that
often requires experts. Crowdsourcing the process of paraphrase generation is
an effective means of expanding natural language datasets, but there has been
limited analysis of the trade-offs that arise when designing tasks. In this
paper, we present the first systematic study of the key factors in
crowdsourcing paraphrase collection. We consider variations in instructions,
incentives, data domains, and workflows. We manually analyzed paraphrases for
correctness, grammaticality, and linguistic diversity. Our observations provide
new insight into the trade-offs between accuracy and diversity in crowd
responses that arise as a result of task design, providing guidance for future
paraphrase generation procedures.Comment: Published at ACL 201
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