Ranking Enhanced Dialogue Generation

How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., recurrent neural networks, attention mechanisms, and hierarchical structures) to model the history. However, a recent empirical study by Sankar et al. has shown that these architectures lack the ability of understanding and modeling the dynamics of the dialogue history. For example, the widely used architectures are insensitive to perturbations of the dialogue history, such as words shuffling, utterances missing, and utterances reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper. Despite the traditional representation encoder and response generation modules, an additional ranking module is introduced to model the ranking relation between the former utterance and consecutive utterances. Specifically, the former utterance and consecutive utterances are treated as query and corresponding documents, and both local and global ranking losses are designed in the learning process. In this way, the dynamics in the dialogue history can be explicitly captured. To evaluate our proposed models, we conduct extensive experiments on three public datasets, i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models produce better responses in terms of both quantitative measures and human judgments, as compared with the state-of-the-art dialogue generation models. Furthermore, we give some detailed experimental analysis to show where and how the improvements come from.Comment: Accepted at CIKM 202

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.Comment: Accepted by The Web Conference 2020 (WWW 2020) as full paper (oral presentation

Investigating the impact of financial concerns on symptoms of depression in UK healthcare workers: data from the UK-REACH nationwide cohort study.

Exploration of the association between financial concerns and depression in UK healthcare workers (HCWs) is paramount given the current 'cost of living crisis', ongoing strike action and recruitment/retention problems in the National Health Service. To assess the impact of financial concerns on the risk of depression in HCWs, how these concerns have changed over time and what factors might predict financial concerns. We used longitudinal survey data from a UK-wide cohort of HCWs to determine whether financial concerns at baseline (December 2020 to March 2021) were associated with depression (measured with the Public Health Questionnaire-2) at follow-up (June to October 2022). We used logistic regression to examine the association between financial concerns and depression, and ordinal logistic regression to establish predictors of developing financial concerns. A total of 3521 HCWs were included. Those concerned about their financial situation at baseline had higher odds of developing depressive symptoms at follow-up. Financial concerns increased in 43.8% of HCWs and decreased in 9%. Those in nursing, midwifery and other nursing roles had over twice the odds of developing financial concerns compared with those in medical roles. Financial concerns are increasing in prevalence and predict the later development of depressive symptoms in UK HCWs. Those in nursing, midwifery and other allied nursing roles may have been disproportionately affected. Our results are concerning given the potential effects on sickness absence and staff retention. Policy makers should act to alleviate financial concerns to reduce the impact this may have on a discontent workforce plagued by understaffing

Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning

According to the World Health Organization(WHO), it is estimated that approximately 1.3 billion people live with some forms of vision impairment globally, of whom 36 million are blind. Due to their disability, engaging these minority into the society is a challenging problem. The recent rise of smart mobile phones provides a new solution by enabling blind users' convenient access to the information and service for understanding the world. Users with vision impairment can adopt the screen reader embedded in the mobile operating systems to read the content of each screen within the app, and use gestures to interact with the phone. However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app. Unfortunately, more than 77% apps have issues of missing labels, according to our analysis of 10,408 Android apps. Most of these issues are caused by developers' lack of awareness and knowledge in considering the minority. And even if developers want to add the labels to UI components, they may not come up with concise and clear description as most of them are of no visual issues. To overcome these challenges, we develop a deep-learning based model, called LabelDroid, to automatically predict the labels of image-based buttons by learning from large-scale commercial apps in Google Play. The experimental results show that our model can make accurate predictions and the generated labels are of higher quality than that from real Android developers.Comment: Accepted to 42nd International Conference on Software Engineerin

False-negative RT-PCR for COVID-19 and a diagnostic risk score: a retrospective cohort study among patients admitted to hospital

OBJECTIVE: To describe the characteristics and outcomes of patients with a clinical diagnosis of COVID-19 and false-negative SARS-CoV-2 reverse transcription-PCR (RT-PCR), and develop and internally validate a diagnostic risk score to predict risk of COVID-19 (including RT-PCR-negative COVID-19) among medical admissions. DESIGN: Retrospective cohort study. SETTING: Two hospitals within an acute NHS Trust in London, UK. PARTICIPANTS: All patients admitted to medical wards between 2 March and 3 May 2020. OUTCOMES: Main outcomes were diagnosis of COVID-19, SARS-CoV-2 RT-PCR results, sensitivity of SARS-CoV-2 RT-PCR and mortality during hospital admission. For the diagnostic risk score, we report discrimination, calibration and diagnostic accuracy of the model and simplified risk score and internal validation. RESULTS: 4008 patients were admitted between 2 March and 3 May 2020. 1792 patients (44.8%) were diagnosed with COVID-19, of whom 1391 were SARS-CoV-2 RT-PCR positive and 283 had only negative RT-PCRs. Compared with a clinical reference standard, sensitivity of RT-PCR in hospital patients was 83.1% (95% CI 81.2%-84.8%). Broadly, patients with false-negative RT-PCR COVID-19 and those confirmed by positive PCR had similar demographic and clinical characteristics but lower risk of intensive care unit admission and lower in-hospital mortality (adjusted OR 0.41, 95% CI 0.27-0.61). A simple diagnostic risk score comprising of age, sex, ethnicity, cough, fever or shortness of breath, National Early Warning Score 2, C reactive protein and chest radiograph appearance had moderate discrimination (area under the receiver-operator curve 0.83, 95% CI 0.82 to 0.85), good calibration and was internally validated. CONCLUSION: RT-PCR-negative COVID-19 is common and is associated with lower mortality despite similar presentation. Diagnostic risk scores could potentially help triage patients requiring admission but need external validation

Health and illness beliefs in adults with tuberculosis infection during the COVID-19 pandemic in the UK

BACKGROUND: COVID-19 disrupted the TB prevention programme in the UK, especially for TB infection (TBI) care. We explore whether experience of the COVID-19 pandemic impacted on patients' perceptions of TBI and its treatment. METHODS: Semi-structured interviews were conducted as part of the Research to Improve Detection and Treatment of TBI (RID-TB) programme, exploring perceptual and practical barriers to TBI treatment. Nineteen people diagnosed with TBI were interviewed between August 2020 and April 2021. Recordings were transcribed and analysed using a constant comparative approach, allowing for a dynamic and iterative exploration of themes. Themes are organised using the Perceptions and Practicalities Approach. FINDINGS: Some participants perceived TBI as a risk factor for increased susceptibility to COVID-19, while some thought that treatment for TBI might protect against COVID-19 or mitigate its effects. Adaptations to TB services (e.g., remote follow-up) and integrated practices during the COVID-19 restrictions (e.g., medication being posted) addressed some practical barriers to TBI treatment. However, we identified beliefs about TBI and COVID-19 that are likely to act as barriers to engagement with TBI treatment, including: interpreting service delays as an indication of TBI not being serious enough for treatment and concerns about contracting COVID-19 in TB clinics. INTERPRETATION: COVID-19 and TBI service delays influence people's perceptions and practical barriers to TBI treatment adherence. Failure to address these beliefs may lead to people's concerns about their treatment not being fully addressed. Utilised service adaptations like remote consultations to address practical barriers may be relevant beyond COVID-19

Coverage, completion and outcomes of COVID-19 risk assessments in a multi-ethnic nationwide cohort of UK healthcare workers: A cross-sectional analysis from the UK-REACH Study

Introduction There are limited data on the outcomes of COVID-19 risk assessment in healthcare workers (HCWs) or the association of ethnicity, other sociodemographic and occupational factors with risk assessment outcomes. Methods We used questionnaire data from UK-REACH (UK Research study into Ethnicity And COVID-19 outcomes in Healthcare workers), an ethnically diverse, nationwide cohort of UK HCWs. We derived four binary outcomes: (1) offered a risk assessment; (2) completed a risk assessment; (3) working practices changed as a result of the risk assessment; (4) wanted changes to working practices after risk assessment but working practices did not change. We examined the association of ethnicity, other sociodemographic/occupational factors and actual/perceived COVID-19 risk variables on our outcomes using multivariable logistic regression. Results 8649 HCWs were included in total. HCWs from ethnic minority groups were more likely to report being offered a risk assessment than white HCWs, and those from Asian and black ethnic groups were more likely to report having completed an assessment if offered. Ethnic minority HCWs had lower odds of reporting having their work change as a result of risk assessment. Those from Asian and black ethnic groups were more likely to report no changes to their working practices despite wanting them. Previous SARS-CoV-2 infection was associated with lower odds of being offered a risk assessment and having adjustments made to working practices. Discussion We found differences in risk assessment outcomes by ethnicity, other sociodemographic/occupational factors and actual/perceived COVID-19 risk factors. These findings are concerning and warrant further research using actual (rather than reported) risk assessment outcomes in an unselected cohort

Systemic Antitumor Immune Response of Doped Yttria Nanoscintillators Under Low-Dose X-Ray Irradiation

Inadequate light penetration in tissues restricts photodynamic therapy to treating only superficial tumors. To enable x-ray-excited photodynamic therapy (XPDT) that targets deep-seated tumors, we synthesized a nanoscintillator-photosensitizer complex containing 5% Eu-doped Y2O3 fluorescing at 611 nanometers and decorated with SiO2 containing the scintillation-coupled photosensitizer methylene blue and a polyethylene glycol coating [PEGylated Y2O3:Eu@SiO2-methylene blue (pYSM)]. When irradiated, pYSMs generate singlet oxygen species in vitro, causing cytotoxicity with hallmarks of immunogenic cell death (calreticulin translocation to the cell membrane). Intravenously administered pYSMs home passively to pancreatic tumor xenografts and, upon 10 gray irradiation, cause significant tumor regression (P \u3c 0.01). On combining XPDT with anti-PD1 immunotherapy, a distant nonirradiated tumor also regresses via an increase in intratumoral activated CD8+ cytotoxic T cells. Collectively, we advance a systemically delivered XPDT strategy that mediates an antitumor effect in both irradiated and nonirradiated (abscopal) tumors when coupled with immunotherapy, converting an immunologically cold tumor to an immunologically hot tumor