Finding Motif Sets in Time Series

Time-series motifs are representative subsequences that occur frequently in a time series; a motif set is the set of subsequences deemed to be instances of a given motif. We focus on finding motif sets. Our motivation is to detect motif sets in household electricity-usage profiles, representing repeated patterns of household usage. We propose three algorithms for finding motif sets. Two are greedy algorithms based on pairwise comparison, and the third uses a heuristic measure of set quality to find the motif set directly. We compare these algorithms on simulated datasets and on electricity-usage data. We show that Scan MK, the simplest way of using the best-matching pair to find motif sets, is less accurate on our synthetic data than Set Finder and Cluster MK, although the latter is very sensitive to parameter settings. We qualitatively analyse the outputs for the electricity-usage data and demonstrate that both Scan MK and Set Finder can discover useful motif sets in such data

A shapelet transform for time series classification

The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of vari-ables is often crucial in finding the best discriminating fea-tures. One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership. The original research in this field embedded the procedure of finding shapelets within a decision tree. We propose disconnecting the process of finding shapelets from the classification algorithm by proposing a shapelet trans-formation. We describe a means of extracting the k best shapelets from a data set in a single pass, and then use these shapelets to transform data by calculating the distances from a series to each shapelet. We demonstrate that transfor-mation into this new data space can improve classification accuracy, whilst retaining the explanatory power provided by shapelets

Human cloning in film: horror, ambivalence, hope

Fictional filmic representations of human cloning have shifted in relation to the 1997 announcement of the birth of Dolly the cloned sheep, and since therapeutic human cloning became a scientific practice in the early twentieth century. The operation and detail of these shifts can be seen through an analysis of the films The Island (2005) and Aeon Flux (2005). These films provide a site for the examination of how these changes in human cloning from fiction to practice, and from horror to hope, have been represented and imagined, and how these distinctions have operated visually in fiction, and in relation to genre

Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $k$ shapelets, which are used to produce a transformed dataset, where each of the $k$ features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve

Experimental Platform to Facilitate Novel Back Brace Development for the Improvement of Spine Stability

The spine or ‘back’ has many functions including supporting our body frame whilst facilitating movement, protecting the spinal cord and nerves and acting as a shock absorber. In certain instances, individuals may develop conditions that not only cause back pain but also may require additional support for the spine. Common movements such as twisting, standing and bending motions could exacerbate these conditions and intensify this pain. Back braces can be used in certain instances to constrain such motion as part of an individual’s therapy and have existed as both medical and retail products for a number of decades. Arguably, back brace designs have lacked the innovation expected in this time. Existing designs are often found to be heavy, overly rigid, indiscrete and largely uncomfortable. In order to facilitate the development of new designs of back braces capable of being optimised to constrain particular motions for specific therapies, a numerical and experimental design strategy has been devised, tested and proven for the first time. The strategy makes use of an experimental test rig in conjunction with finite element analysis simulations to investigate and quantify the effects of back braces on flexion, extension, lateral bending and torsional motions as experienced by the human trunk. This paper describes this strategy and demonstrates its effectiveness through the proposal and comparison of two novel back brace designs

The optical design of the six-meter CCAT-prime and Simons Observatory telescopes

A common optical design for a coma-corrected, 6-meter aperture, crossed-Dragone telescope has been adopted for the CCAT-prime telescope of CCAT Observatory, Inc., and for the Large Aperture Telescope of the Simons Observatory. Both are to be built in the high altitude Atacama Desert in Chile for submillimeter and millimeter wavelength observations, respectively. The design delivers a high throughput, relatively flat focal plane, with a field of view 7.8 degrees in diameter for 3 mm wavelengths, and the ability to illuminate >100k diffraction-limited beams for < 1 mm wavelengths. The optics consist of offset reflecting primary and secondary surfaces arranged in such a way as to satisfy the Mizuguchi-Dragone criterion, suppressing first-order astigmatism and maintaining high polarization purity. The surface shapes are perturbed from their standard conic forms in order to correct coma aberrations. We discuss the optical design, performance, and tolerancing sensitivity. More information about CCAT-prime can be found at ccatobservatory.org and about Simons Observatory at simonsobservatory.org.Comment: Event: SPIE Astronomical Telescopes + Instrumentation, 2018, Austin, Texas, USA; Proceedings Volume 10700, Ground-based and Airborne Telescopes VII; 1070041 (2018

Costs and effects of a 'healthy living' approach to community development in two deprived communities: findings from a mixed methods study

Background: Inequalities in health have proved resistant to 'top down' approaches. It is increasingly recognised that health promotion initiatives are unlikely to succeed without strong local involvement at all stages of the process and many programmes now use grass roots approaches. A healthy living approach to community development (HLA) was developed as an innovative response to local concerns about a lack of appropriate services in two deprived communities in Pembrokeshire, West Wales. We sought to assess feasibility, costs, benefits and working relationships of this HLA. Methods: The HLA intervention operated through existing community forums and focused on the whole community and its relationship with statutory and voluntary sectors. Local people were trained as community researchers and gathered views about local needs though resident interviews. Forums used interview results to write action plans, disseminated to commissioning organisations. The process was supported throughout through the project. The evaluation used a multi-method before and after study design including process and outcome formative and summative evaluation; data gathered through documentary evidence, diaries and reflective accounts, semi-structured interviews, focus groups and costing proformas. Main outcome measures were processes and timelines of implementation of HLA; self reported impact on communities and participants; community-agency processes of liaison; costs. Results: Communities were able to produce and disseminate action plans based on locally-identified needs. The process was slower than anticipated: few community changes had occurred but expectations were high. Community participants gained skills and confidence. Cross-sector partnership working developed. The process had credibility within service provider organisations but mechanisms for refocusing commissioning were patchy. Intervention costs averaged £58,304 per community per annum. Conclusions: The intervention was feasible and inexpensive, with indications of potential impact at individual, community and policy planning levels. However, it is a long term process which requires sustained investment and must be embedded in planning and service delivery processes.12 page(s

Visual Scan Paths and Recognition of Facial Identity in Autism Spectrum Disorder and Typical Development

Background: Previous research suggests that many individuals with autism spectrum disorder (ASD) have impaired facial identity recognition, and also exhibit abnormal visual scanning of faces. Here, two hypotheses accounting for an association between these observations were tested: i) better facial identity recognition is associated with increased gaze time on the Eye region; ii) better facial identity recognition is associated with increased eye-movements around the face. Methodology and Principal Findings: Eye-movements of 11 children with ASD and 11 age-matched typically developing (TD) controls were recorded whilst they viewed a series of faces, and then completed a two alternative forced-choice recognition memory test for the faces. Scores on the memory task were standardized according to age. In both groups, there was no evidence of an association between the proportion of time spent looking at the Eye region of faces and age-standardized recognition performance, thus the first hypothesis was rejected. However, the 'Dynamic Scanning Index' - which was incremented each time the participant saccaded into and out of one of the core-feature interest areas - was strongly asso ciated with age-standardized face recognition scores in both groups, even after controlling for various other potential predictors of performance. Conclusions and Significance: In support of the second hypothesis, results suggested that increased saccading between core-features was associated with more accurate face recognition ability, both in typical development and ASD. Causal directions of this relationship remain undetermined.10 page(s