Let's Talk About It! Subjective and Objective Disclosures to Social Robots

This study aims to test the viability of using social robots for eliciting rich disclosures from humans to identify their needs and emotional states. Self-disclosure has been studied in the psychological literature in many ways, addressing both peoples' subjective perceptions of their disclosures, as well as objective disclosures evaluating these via direct observation and analysis of verbal and written output. Here we are interested in how people disclose (non-sensitive) personal information to robots, in an aim to further understand the differences between one's subjective perceptions of disclosure compared to evidence of disclosure from the shared content. An experimental design is suggested for evaluating disclosure to social robots compared to humans and conversational agents. Initial results suggest that while people perceive they disclose more to humans than to humanoid social robots or conversational agents, no actual observed differences in the content of the disclosure emerges between the three agents

Building Long-Term Human–Robot Relationships: Examining Disclosure, Perception and Well-Being Across Time

While interactions with social robots are novel and exciting for many people, one concern is the extent to which people’s behavioural and emotional engagement might be sustained across time, since during initial interactions with a robot, its novelty is especially salient. This challenge is particularly noteworthy when considering interactions designed to support people’s well-being, with limited evidence (or empirical exploration) of social robots’ capacity to support people’s emotional health over time. Accordingly, our aim here was to examine how long-term repeated interactions with a social robot affect people’s self-disclosure behaviour toward the robot, their perceptions of the robot, and how such sustained interactions influence factors related to well-being. We conducted a mediated long-term online experiment with participants conversing with thesocial robot Pepper 10 times over 5 weeks. We found that people self-disclose increasingly more to a social robot over time, and report the robot to be more social and competent over time. Participants’ moods also improved after talking to the robot, and across sessions, they found the robot’s responses increasingly comforting as well as reported feeling less lonely. Finally, our results emphasize that when the discussion frame was supposedly more emotional (in this case, framing questions in the context of the COVID-19 pandemic), participants reported feeling lonelier and more stressed. These results set the stage forsituating social robots as conversational partners and provide crucial evidence for their potential inclusion in interventions supporting people’s emotional health through encouraging self-disclosure

Factors predicting incidence of post-operative delirium in older people following hip fracture surgery: a systematic review and meta-analysis

Objective: Delirium is one of the most common complications following hip fracture surgery in older people. This study identified pre- and peri-operative factors associated with the development of post-operative delirium following hip fracture surgery. Methods: Published and unpublished literature were searched to identify all evidence reporting variables on patient characteristics, on-admission, intra-operative and post-operative management assessing incident delirium in older people following hip fracture surgery. Pooled odds ratio (OR) and mean difference (MD) of those who experienced delirium compared to those who did not were calculated for each variable. Evidence was assessed using the Downs and Black appraisal tool and interpreted using the GRADE approach. Results: 6704 people (2090 people with post-operative delirium) from 32 studies were analysed. There was moderate evidence of nearly a two-times greater probability of post-operative delirium for those aged 80 years and over (OR: 1.77; 95% CI: 1.09, 2.87), whether patients lived in a care institution pre-admission (OR: 2.65; 95% CI: 1.79, 3.92), and a six-times greater probability of developing post-operative delirium with a pre-admission diagnosis of dementia (OR: 6.07, 95% CI: 4.84, 7.62). There was no association with intra-operative variables and probability of delirium. Conclusion: Clinicians treating people with a hip fracture should be vigilant towards post-operative delirium if their patients are older, have pre-existing cognitive impairment and poorer overall general health. This is also the case for those who experience post-operative complications such as pneumonia or a urinary tract infection

Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels

Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains. Existing text-guided or style-transfer image-to-image approaches do not translate to datasets where additional information is provided as discrete classes. We introduce and implement a model which combines image-to-image and class-guided denoising diffusion probabilistic models. We train our model on a real-world dataset of microscopy images used for drug discovery, with and without incorporating metadata labels. By exploring the properties of image-to-image diffusion with relevant labels, we show that class-guided image-to-image diffusion can improve the meaningful content of the reconstructed images and outperform the unguided model in useful downstream tasks

What makes a robot social? A review of social robots from science fiction to a home or hospital near you

Purpose of Review: We provide an outlook on the definitions, laboratory research, and applications of social robots, with an aim to understand what makes a robot social—in the eyes of science and the general public. Recent Findings: Social robots demonstrate their potential when deployed within contexts appropriate to their form and functions. Some examples include companions for the elderly and cognitively impaired individuals, robots within educational settings, and as tools to support cognitive and behavioural change interventions. Summary: Science fiction has inspired us to conceive of a future with autonomous robots helping with every aspect of our daily lives, although the robots we are familiar with through film and literature remain a vision of the distant future. While there are still miles to go before robots become a regular feature within our social spaces, rapid progress in social robotics research, aided by the social sciences, is helping to move us closer to this reality

Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels

We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content fluorescent images of cells. Our model is based on a knowledge distillation approach with a vision transformer backbone (DINO), and we use this as a benchmark model for our study. Using WS-DINO, we fine-tuned with weak label information available in high-content microscopy screens (treatment and compound), and achieve state-of-the-art performance in not-same-compound mechanism of action prediction on the BBBC021 dataset (98%), and not-same-compound-and-batch performance (96%) using the compound as the weak label. Our method bypasses single cell cropping as a pre-processing step, and using self-attention maps we show that the model learns structurally meaningful phenotypic profiles

Social robots for health psychology: a new frontier for improving human health and well-being

No abstract available

Estimating the potential of beekeeping to alleviate household poverty in rural Uganda

<div><p>Robust evidence underpinning the role of beekeeping in poverty alleviation is currently lacking. This study estimated the production potential for beekeepers in Northern Uganda by quantifying current production assets (equipment and knowledge) and impact on rural income streams range of proposed interventions. Intervention scenarios evaluated the economic benefits to be derived from different hive types combined with year-round provision of a nectar source (<i>Calliandra calothyrsus</i>) planted at varying density. Findings show that the type and number of beehive combinations used influenced the amount of revenue streams generated by the beekeepers. Addition of 20 log hives increased incomes 10 times, 20 KTBs increased revenues 16 times and Langstroth 18 times. Adding <i>Calliandra</i> trees as a forage source to the baseline scenario yielded revenues up to 17.6 times higher than the baseline. Implying that good management plus the introduction of a reliable nectar source, to off-set dry season challenges (absconding), could improve beekeeping productivity in Northern Uganda. Further research is required to validate <i>in situ</i> the impact of modelled scenarios on both honey yield and other ecosystem service benefits.</p></div

Environmental contaminants of honeybee products in Uganda detected using LC-MS/MS and GC-ECD

Pollinator services and the development of beekeeping as a poverty alleviating tool have gained considerable focus in recent years in sub-Saharan Africa. An improved understanding of the pervasive environmental extent of agro-chemical contaminants is critical to the success of beekeeping development and the production of clean hive products. This study developed and validated a multi-residue method for screening 36 pesticides in honeybees, honey and beeswax using LC-MS/MS and GC-ECD. Of the 36 screened pesticides, 20 were detected. The highest frequencies occurred in beeswax and in samples from apiaries located in the proximity of citrus and tobacco farms. Fungicides were the most prevalent chemical class. Detected insecticides included neonicotinoids, organophosphates, carbamates, organophosphorus, tetrazines and diacylhydrazines. All detected pesticide levels were below maximum residue limits (according to EU regulations) and the lethal doses known for honeybees. However, future risk assessment is needed to determine the health effects on the African genotype of honeybees by these pesticide classes and combinations of these. In conclusion, our data present a significant challenge to the burgeoning organic honey sector in Uganda, but to achieve this, there is an urgent need to regulate the contact routes of pesticides into the beehive products. Interestingly, the "zero" detection rate of pesticides in the Mid-Northern zone is a significant indicator of the large potential to promote Ugandan organic honey for the export market

Informal Caregivers Disclose Increasingly More to a Social Robot Over Time

Informal caregivers often struggle in managing to cope with both the stress and the practical demands of the caregiving situation. It has been suggested that digital solutions might be useful to monitor caregivers’ health and well-being, by providing early intervention and support. Given the importance of self-disclosure for psychological health, here we aimed to investigate the potential of employing a social robot for eliciting self-disclosure among informal caregivers over time. We conducted a longitudinal experiment across a five-week period, measuring participants’ disclosure duration (in seconds) and length (in number of words). Our preliminary results show a positive trend where informal caregivers speak for a longer time and share more information in their disclosures to a social robot across the five-week period. These results provide useful evidence supporting the potential deployment of social robots as intervention tools to help provide support for individuals suffering from stress and experiencing challenging life situations