Gendered language in recent short stories by Japanese women, and in English translation

This article analyses five recent Japanese short stories written by women, with female first person narrators, and the English translations of these stories. I examine how the writers interact with the culturally loaded concept of gendered language to develop characters and themes. The strategies used by translators to render gendered styles into English are also discussed: case-by-case creative solutions appear most effective. ‘Feminine’ and other gendered styles are used to index social identity, to highlight the difference between the social and inner self, and different styles are mixed together for impact. Gendered styles, therefore, are of central importance and translators wishing to adhere closely to the source text should pay close attention to them. All the narrators of the stories demonstrate an understanding of ‘social sanction and taboo’. Two accustom themselves to a socially acceptable future, another displays an uneasy attitude to language and convention, while others fall into stereotypes imposed on them or chastise themselves for inappropriate behaviour. The stories illustrate the way in which gendered language styles in Japanese can be manipulated, as both the writers and the characters they create deliberately use different styles for effect

Representing temporal dependencies in human activity recognition.

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs and consider the temporal dependencies that exist in binary ambient sensor data in order to produce case-based representations. These LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

Representing temporal dependencies in smart home activity recognition for health monitoring.

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared overtime. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs which consider the temporal dependencies present in the sensor data in order to produce richer representations and improved classification accuracy. The LSTM approaches are compared to the performance of a selection of base line classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

Monitoring health in smart homes using simple sensors.

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident's typical profile and to known 'risky' profiles to support evidence-based interventions. Human activity recognition to identify ADLs from sensor data is a key challenge, a windowbased representation is compared on four existing datasets. We find that windowing works well, giving consistent performance. We also introduce FITsense, which is building a Smart Home environment to specifically identify increased risk of falls to allow interventions before falls occurs

Hospital Episode Statistics and trends in ophthalmic surgery 1998 – 2004

BACKGROUND: Hospital episode statistics (HES) is a UK national database for the National Health Service (NHS), now available online. The purpose of this study was to observe trends in ophthalmic operations performed during the period from 1998 to 2004, using this data. METHODS: From the 'Main Operation' codes within the 'Free data' section of the HES website we analysed data in regard to 28 specific ophthalmic operations. These represented each sub speciality within ophthalmology. RESULTS: The figures show a change in the total number and proportions of operations performed for many of the procedures. For example, there was an increase in numbers of orbital decompressions, but a decrease in numbers of glaucoma filtering operations. Changing trends could be seen in different surgical areas such as the change in operations used for corneal grafting and in retinal surgery. CONCLUSION: The HES database represents an important, potentially useful source of information. There are imitations in interpretation of and validity of such data related to coding inconsistencies. We suggest the benefit of the data comes from observing trends rather than exact numbers. As other studies using this data have suggested, it is important that clinicians are involved in improving the quality of this data

Genetic testing and personalized ovarian cancer screening: a survey of public attitudes

Background Advances in genetic technologies are expected to make population-wide genetic testing feasible. This could provide a basis for risk stratified cancer screening; but acceptability in the target populations has not been explored. Methods We assessed attitudes to risk-stratified ovarian cancer (OC) screening based on prior genetic risk assessment using a survey design. Home-based interviews were carried out by the UK Office of National Statistics in a population-based sample of 1095 women aged 18–74. Demographic and personal correlates of attitudes to risk-stratified OC screening based on prior genetic risk assessment were determined using univariate analyses and adjusted logistic regression models. Results Full data on the key analytic questions were available for 829 respondents (mean age 46 years; 27 % ‘university educated’; 93 % ‘White’). Relatively few respondents felt they were at ‘higher’ or ‘much higher’ risk of OC than other women of their age group (7.4 %, n = 61). Most women (85 %) said they would ‘probably’ or ‘definitely’ take up OC genetic testing; which increased to 88 % if the test also informed about breast cancer risk. Almost all women (92 %) thought they would ‘probably’ or ‘definitely’ participate in risk-stratified OC screening. In multivariate logistic regression models, university level education was associated with lower anticipated uptake of genetic testing (p = 0.009), but with more positive attitudes toward risk-stratified screening (p <0.001). Perceived risk was not significantly associated with any of the outcome variables. Conclusions These findings give confidence in taking forward research on integration of novel genomic technologies into mainstream healthcare