Miracle-dz/UniFAR-FTUs huggingface dataset:
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation

Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples. Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue. In this paper, we propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation. Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.
Enhanced Speaker-Aware Multi-Party Multi-Turn Dialogue Comprehension

Multi-party multi-turn dialogue comprehension brings unprecedented challenges in handling complicated scenarios, as the co-occurrence of multiple speakers causes complexity and inconsistency. As a result of the multiple participation, the shift of speaker roles and crisscrossed discourse relations among utterances hinder reading comprehension. Motivated by this, we further integrate the enhancements of speaker-related features for dialogue comprehension performance. This work proposes a novel model with enhancement from both sides of speaker roles and speaker-aware relations. At the token level, we apply a speaker mask for attention, while at the discourse level, we utilize heterogeneous graph networks for comprehensive speaker-aware discourse clues. Experimental results show that our Enhanced Speaker-Aware method (ESA) helps achieve state-of-the-art performance on the Molweni dataset, as well as significant improvements on the FriendsQA dataset. We find that our method makes steady improvements on stronger backbones. Analysis shows that our model enhances the connections between utterances and their own speakers and captures the speaker-aware discourse relations. Discussions on data features and error cases are presented, and a visualized case is displayed. The findings reveal the importance of speaker-aware signals in dialogue comprehension.
Reionization with Simba: How Much Does Astrophysics Matter in Modeling Cosmic Reionization?

Traditional large-scale models of reionization usually employ simple deterministic relations between halo mass and luminosity to predict how reionization proceeds. We here examine the impact on modeling reionization of using more detailed models for the ionizing sources as identified within the 100 h −1 Mpc cosmological hydrodynamic simulation Simba, coupled with postprocessed radiative transfer. Comparing with simple (one-to-one) models, the main difference with using Simba sources is the scatter in the relation between dark matter halos and star formation, and hence ionizing emissivity. We find that, at the power spectrum level, the ionization morphology remains mostly unchanged, regardless of the variability in the number of sources or escape fraction. In particular, the power spectrum shape remains unaffected and its amplitude changes slightly by less than 5%–10%, throughout reionization, depending on the scale and neutral fraction. Our results show that simplified models of ionizing sources remain viable to efficiently model the structure of reionization on cosmological scales, although the precise progress of reionization requires accounting for the scatter induced by astrophysical effects.
Institutional mortality rate and cause of death at health facilities in Ghana between 2014 and 2018

Background The epidemiological transition, touted as occurring in Ghana, requires research that tracks the changing patterns of diseases in order to capture the trend and improve healthcare delivery. This study examines national trends in mortality rate and cause of death at health facilities in Ghana between 2014 and 2018. Methods Institutional mortality data and cause of death from 2014–2018 were sourced from the Ghana Health Service’s District Health Information Management System. The latter collates healthcare service data routinely from government and non-governmental health institutions in Ghana yearly. The institutional mortality rate was estimated using guidelines from the Ghana Health Service. Percent change in mortality was examined for 2014 and 2018. In addition, cause of death data were available for 2017 and 2018. The World Health Organisation’s 11th International Classification for Diseases (ICD-11) was used to group the cause of death. Results Institutional mortality decreased by 7% nationally over the study period. However, four out of ten regions (Greater Accra, Volta, Upper East, and Upper West) recorded increases in institutional mortality. The Upper East (17%) and Volta regions (13%) recorded the highest increase. Chronic non-communicable diseases (NCDs) were the leading cause of death in 2017 (25%) and 2018 (20%). This was followed by certain infectious and parasitic diseases (15% for both years) and respiratory infections (10% in 2017 and 13% in 2018). Among the NCDs, hypertension was the leading cause of death with 2,243 and 2,472 cases in 2017 and 2018. Other (non-ischemic) heart diseases and diabetes were the second and third leading NCDs. Septicaemia, tuberculosis and pneumonia were the predominant infectious diseases. Regional variations existed in the cause of death. NCDs showed more urban-region bias while infectious diseases presented more rural-region bias. Conclusions This study examined national trends in mortality rate and cause of death at health facilities in Ghana. Ghana recorded a decrease in institutional mortality throughout the study. NCDs and infections were the leading causes of death, giving a double-burden of diseases. There is a need to enhance efforts towards healthcare and health promotion programmes for NCDs and infectious diseases at facility and community levels as outlined in the 2020 National Health Policy of Ghana.
Association Between Pandemic Coronavirus Disease 2019 Public Health Measures and Reduction in Critical Care Utilization Across ICUs in Alberta, Canada

OBJECTIVES: The coronavirus disease 2019 pandemic has disrupted critical care services across the world. In anticipation of surges in the need for critical care services, governments implemented “lockdown” measures to preserve and create added critical care capacity. Herein, we describe the impact of lockdown measures on the utilization of critical care services and patient outcomes compared with nonlockdown epochs in a large integrated health region. DESIGN: This was a population-based retrospective cohort study. SETTING: Seventeen adult ICUs across 14 acute care hospitals in Alberta, Canada. PATIENTS: All adult (age ≥ 15 yr) patients admitted to any study ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The main exposure was ICU admission during “lockdown” occurring between March 16, 2020, and June 30, 2020. This period was compared with two nonpandemic control periods: “year prior” (March 16, 2019, to June 30, 2019) and “pre lockdown” immediately prior (November 30, 2019, to March 15, 2020). The primary outcome was the number of ICU admissions. Secondary outcomes included the following: daily measures of ICU utilization, ICU duration of stay, avoidable delay in ICU discharge, and occupancy; and patient outcomes. Mixed multilevel negative binomial regression and interrupted time series regression were used to compare rates of ICU admissions between periods. Multivariable regressions were used to compare patient outcomes between periods. During the lockdown, there were 3,649 ICU admissions (34.1 [8.0] ICU admissions/d), compared with 4,125 (38.6 [9.3]) during the prelockdown period and 3,919 (36.6 [8.7]) during the year prior. Mean bed occupancy declined significantly during the lockdown compared with the nonpandemic periods (78.7%, 95.9%, and 96.4%; p < 0.001). Avoidable ICU discharge delay also decreased significantly (42.0%, 53.2%, and 58.3%; p < 0.001). During the lockdown, patients were younger, had fewer comorbid diseases, had higher acuity, and were more likely to be medical admissions compared with the nonpandemic periods. Adjusted ICU and hospital mortality and ICU and hospital lengths of stay were significantly lower during the lockdown compared with nonpandemic periods. CONCLUSIONS: The coronavirus disease 2019 lockdown resulted in substantial changes to ICU utilization, including a reduction in admissions, occupancy, patient lengths of stay, and mortality.
Modified Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours

One of the major reasons for vehicle accidents is distracted driving. Therefore, detecting distracted driving behaviours is of paramount importance to reduce millions of deaths and injuries occurring worldwide. It is given that majority of the time, drivers are driving normally. Distracted or anomalous driving behaviours are deviations from the ’normal’ driving that need to be identiﬁed correctly to alert the driver. However, these driving behaviours do not comprise of one spe-ciﬁc type of driving style and their distribution can be different during training and testing phases of a classiﬁer. We formulate this problem as a supervised contrastive learning approach to learn a visual representation to detect normal, and seen and unseen anomalous driving behaviours. We made a change to the standard contrastive loss function to adjust the similarity of negative pairs to aid the optimization. Nor-mally, in a (self) supervised contrastive framework, the architecture contains encoding layers followed by a projection head. The projection head layers are omitted during testing phase as the encoding layers are considered to contain general visual representative information. However, we assert that for supervised contrastive learning task, including projection head will be beneﬁcial. We showed our results on a Driver Anomaly Detection dataset that contains 783 minutes of video recordings of normal and anomalous driving behaviours of 31 drivers from various from top and front cameras (both depth and infrared). We also performed an extra step of ﬁne tuning the labels in this dataset. Out of 9 video modalities combinations, our modiﬁed contrastive approach improved the ROC AUC on 7 in comparison to the baseline models (from 3 . 12% to 8 . 91% for different modalities); the remaining two models also had manual labelling. We performed statistical tests that showed evidence that our modi-ﬁcations perform better than the baseline contrastive learning setup. Finally, the results showed that the fusion of depth and infrared modalities from top and front view achieved the best AUC ROC of 0 . 9738 and AUC PR of 0 . 9772 .
The AUA/SUFU Guideline on Adult Neurogenic Lower Urinary Tract Dysfunction: Treatment and Follow-up

Purpose: The clinician treating patients with neurogenic lower urinary tract dysfunction (NLUTD) needs to balance a variety of factors when making treatment decisions. In addition to the patient's urologic symptoms and urodynamic findings, other issues that may influence management options of the lower urinary tract include cognition, hand function, type of neurologic disease, mobility, bowel function/management, and social and caregiver support. This Guideline allows the clinician to understand the options available to treat patients, understand the findings that can be seen in NLUTD, and appreciate which options are best for each individual patient. This allows for decisions to be made with the patient, in a shared decision-making manner, such that the patient's quality of life can be optimized with respect to their bladder management. Materials and Methods: A comprehensive search for studies assessing patients undergoing evaluation, surveillance, management, or follow-up for NLUTD was conducted from January 2001 through October 2017 and was rerun in February 2021 to capture newer literature. The primary search returned 20,496 unique citations. Following a title and abstract screen, full texts were obtained for 3,036 studies. During full-text review, studies were primarily excluded for not meeting the PICO criteria. One hundred eight-four primary literature studies met the inclusion criteria and were included in the evidence base. Results: This guideline was developed to inform clinicians on the proper evaluation, diagnosis, and risk stratification of adult patients with NLUTD and the non-surgical and surgical treatment options available. Additional statements on urinary tract infection and autonomic dysreflexia were developed to guide the clinician. Conclusions: NLUTD patients may undergo non-surgical and surgical treatment options depending on their level of risk, symptoms, and urodynamic findings. Appropriate follow-up, primarily based on their risk stratification, must be maintained after treatment.
Competence-based Curriculum Learning for Multilingual Machine Translation

Currently, multilingual machine translation is receiving more and more attention since it brings better performance for low resource languages (LRLs) and saves more space. However, existing multilingual machine translation models face a severe challenge: imbalance. As a result, the translation performance of different languages in multilingual translation models are quite different. We argue that this imbalance problem stems from the different learning competencies of different languages. Therefore, we focus on balancing the learning competencies of different languages and propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M. Specifically, we firstly define two competencies to help schedule the high resource languages (HRLs) and the low resource languages: 1) Self-evaluated Competence, evaluating how well the language itself has been learned; and 2) HRLs-evaluated Competence, evaluating whether an LRL is ready to be learned according to HRLs' Self-evaluated Competence. Based on the above competencies, we utilize the proposed CCL-M algorithm to gradually add new languages into the training set in a curriculum learning manner. Furthermore, we propose a novel competenceaware dynamic balancing sampling strategy for better selecting training samples in multilingual training. Experimental results show that our approach has achieved a steady and significant performance gain compared to the previous state-of-the-art approach on the TED talks dataset.
SensiX++: Bringing MLOps and Multi-tenant Model Serving to Sensory Edge Devices

We present SensiX++, a multi-tenant runtime for adaptive model execution with integrated MLOps on edge devices, e.g., a camera, a microphone, or IoT sensors. SensiX++ operates on two fundamental principles: highly modular componentisation to externalise data operations with clear abstractions and document-centric manifestation for system-wide orchestration. First, a data coordinator manages the lifecycle of sensors and serves models with correct data through automated transformations. Next, a resource-aware model server executes multiple models in isolation through model abstraction, pipeline automation, and feature sharing. An adaptive scheduler then orchestrates the best-effort executions of multiple models across heterogeneous accelerators, balancing latency and throughput. Finally, microservices with REST APIs serve synthesised model predictions, system statistics, and continuous deployment. Collectively, these components enable SensiX++ to serve multiple models efficiently with fine-grained control on edge devices while minimising data operation redundancy, managing data and device heterogeneity, and reducing resource contention. We benchmark SensiX++ with 10 different vision and acoustics models across various multi-tenant configurations on different edge accelerators (Jetson AGX and Coral TPU) designed for sensory devices. We report on the overall throughput and quantified benefits of various automation components of SensiX++ and demonstrate its efficacy in significantly reducing operational complexity and lowering the effort to deploy, upgrade, reconfigure, and serve embedded models on edge devices.
Gradual (In)Compatibility of Fairness Criteria

Impossibility results show that important fairness measures (independence, separation, sufficiency) cannot be satisfied at the same time under reasonable assumptions. This paper explores whether we can satisfy and/or improve these fairness measures simultaneously to a certain degree. We introduce information-theoretic formulations of the fairness measures and define degrees of fairness based on these formulations. The information-theoretic formulations suggest unexplored theoretical relations between the three fairness measures. In the experimental part, we use the information-theoretic expressions as regularizers to obtain fairness-regularized predictors for three standard datasets. Our experiments show that a) fairness regularization directly increases fairness measures, in line with existing work, and b) some fairness regularizations indirectly increase other fairness measures, as suggested by our theoretical findings. This establishes that it is possible to increase the degree to which some fairness measures are satisfied at the same time -- some fairness measures are gradually compatible.
Comparative Efficacy and Safety of Targeted Therapies for Chronic Thromboembolic Pulmonary Hypertension: A Systematic Review and Network Meta-Analysis

Background There is significant controversy relating to whether chronic thromboembolic pulmonary hypertension (CTEPH) can be treated with pulmonary arterial hypertension- (PAH-) targeted therapies and which therapy is the optimal choice for patients. A large number of randomized controlled trials (RCTs) have compared PAH-targeted therapies with placebo or conventional therapies. In this study, we aimed to compare all of the PAH-targeted medications that are used to treat CTEPH and rank their efficacy by the application of network meta-analysis (NMA). Methods We searched PubMed, EMBASE, Web of Science, the Cochrane Central Register, https://clinicaltrials.gov, and who.int/trialsearch/, for relevant RCTs published up to January 2020. In addition to traditional meta-analysis, we also performed NMA in our systematic review, as deployed in a previous protocol (PROSPERO: CRD42020173765). Results Our study identified eight eligible RCTs that evaluated seven PAH-targeted therapies in 703 patients with CTEPH. NMA revealed that riociguat was ranked first as the most optimized therapy for ameliorating the 6-minute walk distance with a probability of 80.4%. Bosentan was significantly better than others with regard to reducing brain natriuretic peptide/N-terminal pro-B-type natriuretic peptide with a probability of 84.3%. Sildenafil was identified as the best drug in terms of improving the New York Heart Association/World Health Organization functional class with a probability of 87.3%. Treprostinil and macitentan were more beneficial than other drugs in reducing pulmonary vascular resistance and lowering the incidence of clinical worsening with probabilities of 86.2% and 79.2%, respectively. Conclusion Analysis revealed positive advantages for the use of PAH-targeted drugs in patients with CTEPH. Overall, treprostinil and riociguat were superior to all other PAH-targeted medications in most of the outcomes investigated.
Patient satisfaction towards health care services provided in Ethiopian health institutions: a systematic review and meta-analysis

The level of patient satisfaction is a direct or indirect measure of services delivered in healthcare institutions. Different primary studies in Ethiopia showed the proportion of satisfied patients toward health services. Patient satisfaction reflects a wide gap between the current experience and the expected services and pushes clients to go to farther located health care facilities and even to more expensive private health care facilities to find quality healthcare services. Inconsistent findings regarding the proportion of patients that are satisfied with the healthcare services in Ethiopia make generalizations difficult at the national level. We have accessed previous studies through an electronic web-based search strategy using PubMed, Cochrane Library, Google Scholar, Embase, and CINAHL and a combination of search terms. The quality of each included article was assessed using a modified version of the Newcastle-Ottawa Scale for cross-sectional studies. All statistical analyses were done using STATA version 14 software for windows, and meta-analysis was carried out using a random-effects method. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed for reporting results. Out of 188 records screened, 41 studies with 17 176 participants fulfilled the inclusion criteria and were included for proportion estimation. The pooled proportion of satisfied patients was 63.7%. Attending a health center (AOR = 2.68; 95% CI = 1.79, 2.85), being literate (AOR = 0.46; 95% CI = 0.28-0.64), being younger than 34 years old (AOR = 2.07; 95% CI = 1.28, 2.85), and being divorced (AOR = 0.58; 95% CI = 0.38, 0.88) were factors identified as being associated with patient satisfaction. The proportion of patient satisfaction in Ethiopia was high based on over 50% satisfaction scale. The Ministry of Health should give more emphasis to improve hospital health care services to further improve patient satisfaction.
Association between electronic nicotine delivery systems and electronic non-nicotine delivery systems with initiation of tobacco use in individuals aged < 20 years. A systematic review and meta-analysis

Background This systematic review described the association between electronic nicotine delivery systems and electronic non-nicotine delivery systems (ENDS/ENNDS) use among non-smoking children and adolescents aged <20 years with subsequent tobacco use. Methods We searched five electronic databases and the grey literature up to end of September 2020. Prospective longitudinal studies that described the association between ENDS/ENNDS use, and subsequent tobacco use in those aged < 20 years who were non-smokers at baseline were included. The Joanna Briggs Institute Critical Appraisal Checklist was used to assess risk of bias. Data were extracted by two reviewers and pooled using a random-effects meta-analysis. We generated unadjusted and adjusted risk ratios (ARRs) describing associations between ENDS/ENNDS and tobacco use. Findings A total of 36 publications met the eligibility criteria, of which 25 were included in the systematic review (23 in the meta-analysis) after exclusion of overlapping studies. Sixteen studies had high to moderate risk of bias. Ever users of ENDS/ENNDS had over three times the risk of ever cigarette use (ARR 3·01 (95% CI: 2·37, 3·82; p<0·001, I2: 82·3%), and current cigarette use had over two times the risk (ARR 2·56 (95% CI: 1·61, 4·07; p<0·001, I2: 77·3%) at follow up. Among current ENDS/ENNDS users, there was a significant association with ever (ARR 2·63 (95% CI: 1·94, 3·57; p<0·001, I2: 21·2%)), but not current cigarette use (ARR 1·88 (95% CI: 0·34, 10·30; p = 0·47, I2: 0%)) at follow up. For other tobacco use, ARR ranged between 1·55 (95% CI 1·07, 2·23) and 8·32 (95% CI: 1·20, 57·04) for waterpipe and pipes, respectively. Additionally, two studies examined the use of ENNDS (non-nicotine devices) and found a pooled adjusted RR of 2·56 (95% CI: 0·47, 13·94, p = 0.035). Conclusion There is an urgent need for policies that regulate the availability, accessibility, and marketing of ENDS/ENNDS to children and adolescents. Governments should also consider adopting policies to prevent ENDS/ENNDS uptake and use in children and adolescents, up to and including a ban for this group.