Estimation of the Distribution of Hourly Pay from Household Survey Data: The Use of Missing Data Methods to Handle Measurement Error

Measurement errors in survey data on hourly pay may lead to serious upward bias in low pay estimates. We consider how to correct for this bias when auxiliary accurately measured data are available for a subsample. An application to the UK Labour Force Survey is described. The use of fractional imputation, nearest neighbour imputation, predictive mean matching and propensity score weighting are considered. Properties of point estimators are compared both theoretically and by simulation. A fractional predictive mean matching imputation approach is advocated. It performs similarly to propensity score weighting, but displays slight advantages of robustness and efficiency.

It's Just My History Isn't It? Understanding smart journaling practices

Smart journals are both an emerging class of lifelogging applications and novel digital possessions, which are used to create and curate a personal record of one's life. Through an in-depth interview study of analogue and digital journaling practices, and by drawing on a wide range of research around 'technologies of memory', we address fundamental questions about how people manage and value digital records of the past. Appreciating journaling as deeply idiographic, we map a broad range of user practices and motivations and use this understanding to ground four design considerations: recognizing the motivation to account for one's life; supporting the authoring of a unique perspective and finding a place for passive tracking as a chronicle. Finally, we argue that smart journals signal a maturing orientation to issues of digital archiving

Ensemble Learning of Colorectal Cancer Survival Rates

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved.Comment: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013, pp. 82 - 86, 201

Ensemble learning of colorectal cancer survival rates

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved

An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates

This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient’s biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not

An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates

This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient’s biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not

Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to cluster this dataset and important subsets of it in an effort to characterize the data and validate existing standards for tumour classification. It is apparent from optimal clustering that existing tumour classification is largely unrelated to immunological factors within a patient and that there may be scope for re-evaluating treatment options and survival estimates based on a combination of tumour physiology and patient histochemistry.Comment: Federated Conference on Computer Science and Information Systems (FedCSIS), pp 187-191, 201

Transitions in digital personhood:Making sense of online activity in early retirement

We present findings from a qualitative study about how Internet use supports self-functioning following the life transition of retirement from work. This study recruited six recent retirees and included the deployment of OnLines, a design research artifact that logged and visualized key online services used by participants at home over four-weeks. The deployment was supported by pre- and post-deployment interviews. OnLines prompted participants’ reflection on their patterns of Internet use. Position Exchange Theory was used to understand retirees’ sense making from a lifespan perspective, informing the design of supportive online services. This paper delivers a three-fold contribution to the field of human-computer interaction, advancing a lifespan-oriented approach by conceptualizing the self as a dialogical phenomenon that develops over time, advancing the ageing discourse by reporting on retirees’ complex identities in the context of their life histories, and advancing discourse on research through design by developing OnLines to foster participant-researcher reflection informed by Self Psychology

Effect of sink layer thickness on damping in CoMnGe (5 nm) / Ag (6 nm) / NiFe (x nm) spin valves

Poster presented at Magnetism 4 – 5 April 2016, Sheffield.In spin valve structures the damping of a ferromagnetic layer driven at resonance can be modified by the transfer of spin angular momentum into a ‘sink’ ferromagnetic layer. This effect, known as spin pumping, is interface dominated and expected to increase with increasing sink layer thickness up to a saturation absorption depth, previously reported to be 1.2 nm regardless of the sink layer’s composition [1]. Using vector network analyser ferromagnetic resonance (VNA-FMR), we have studied the variation in damping as a function of sink layer thickness in a series of CoMnGe (5 nm) / Ag (6 nm) / NiFe (x nm) spin valves. These measurements show only small variations in the CoMnGe Gilbert damping parameter for x ≤ 1.8 nm, although damping is observed to increase at x = 0.3 and 0.6 nm. Element-resolved x-ray detected ferromagnetic resonance (XFMR) [2] measurements confirm spin transfer torque due to spin pumping as the origin of the damping for x = 1.5 and 1.8 nm, with both thicknesses having the same effective spin mixing conductance, supporting the findings of Ghosh et al [1]. For thicker sink layers the source and sink FMR fields are seen to coincide, hampering the identification of spin pumping. [1] A Ghosh, et al. Physical Review Letters 109, 127202 (2012) [2] M Marcham, et al. Physical Review B 87, 180403 (2013)We thank the Advanced Light Source for access to beamlines 4.0.2 and 6.3.1 (ALS-06433, ALS-07116). The Advanced Light Source is supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.We thank Diamond Light Source for access to beamlines I06 and I10 (SI8782, SI11585, SI13063) that contributed to the results presented here.This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/J018767/1]