Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice / Вісник Національного технічного університету "ХПІ". Серія: Проблеми удосконалювання електричних машин і апаратів. Теорія і практика
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Engineering Conversation: Understanding the control requirements of language production in monologue and dialogue.
No full textBoth artificial and biological systems are faced with the challenge of noisy and uncertain estimation of the state of the world, in contexts where feedback is often delayed. This challenge also applies to the processes of language production and comprehension, both when they take place in isolation (e.g., in monologue or solo reading) and when they are combined as is the case in dialogue. Crucially, we argue, dialogue brings with it some unique challenges. In this paper, we describe three such challenges within the general framework of control theory, drawing analogies to mechanical and biological systems where possible: (1) the need to distinguish between self- and other-generated utterances; (2) the need to adjust the amount of advance planning (i.e., the degree to which planning precedes articulation) flexibly to achieve timely turn-taking; (3) the need to track changing conversational goals. We show that message-to-sound models of language production (i.e., those the cover the whole process from message generation to articulation) tend to implement fairly simple control architectures. However, we argue that more sophisticated control architectures are necessary to build language production models that can account for both monologue and dialogue
Venture Capital and Startup Agglomeration
No full textThe paper studies venture capital’s (VC) role in the geographic clustering of high-growth startups. We exploit a rule change that disproportionately impacted U.S. regions that historically lacked VC financing via a restriction of banks to invest in the asset class. A one-standard-deviation increase in VCs’ exposure to the rule led to a 20% decline in fund size and a 10% decrease in the likelihood of raising a follow-on fund. Startups were not wholly cushioned: financing and valuations declined. Startups also moved out of impacted states after the rule change, likely exacerbating existing geographic disparity in entrepreneurship
Coordinated Data Analysis: Knowledge Accumulation in Lifespan Developmental Psychology
No full textCoordinated analysis is a powerful form of integrative analysis, and is well suited in its capacity to promote cumulative scientific knowledge, particularly in subfields of psychology that focus on the processes of lifespan development and aging. Coordinated analysis uses raw data from individual studies to create similar hypothesis tests for a given research question across multiple datasets, thereby making it less vulnerable to common criticisms of meta-analysis such as file drawer effects or publication bias. Coordinated analysis can sometimes use random effects meta-analysis to summarize results, which does not assume a single true effect size for a given statistical test. By fitting parallel models in separate datasets, coordinated analysis preserves the heterogeneity among studies, and provides a window into the generalizability and external validity of a set of results. The current paper achieves three goals: First, it describes the phases of a coordinated analysis so that interested researchers can more easily adopt these methods in their labs. Second, it discusses the importance of coordinated analysis within the context of the credibility revolution in psychology. Third, it encourages the use of existing data networks and repositories for conducting coordinated analysis, in order to enhance accessibility and inclusivity. Subfields of research that require time- or resource- intensive data collection, such as longitudinal aging research, would benefit by adopting these methods
Housing Market Appreciation and the White-Black Wealth Gap
No full textReal house prices in the United States have risen by 55% over the last four decades, driving substantial wealth benefits to homeowners. However, research has not explored how this rise in house prices has affected White-Black wealth gaps, or the mechanisms that may underlie this relationship. Using geocoded longitudinal household-level wealth data from the Panel Study of Income Dynamics and tract-level house price index data, I estimate that housing market appreciation between 1984-2021 explains 70% of the increase in the median White-Black wealth gap over this period. I find that most of this effect is due to White-Black gaps in homeownership, while White-Black gaps in house values playing a smaller role. In contrast to recent findings about racialized housing markets, I do not find that gaps in neighborhood house price appreciation between White and Black homeowners contributed to White-Black wealth gaps in the 2000s and 2010s. These results highlight the importance of cumulative advantage processes in driving wealth inequalities, and demonstrate how the legacies of institutional racism contribute to contemporary racial wealth gaps
Mindfulness-based programmes for mental health promotion in adults in non-clinical settings: protocol of an individual participant data meta-analysis of randomised controlled trials
No full textIntroduction
With mental ill health listed as a top cause of global disease burden, there is an urgent need to prioritise mental health promotion programmes. Mindfulness–based programmes (MBPs) are being widely implemented to reduce stress in non-clinical settings. In a recent aggregate-level meta-analysis we found that, compared with no intervention, these MBPs reduce average psychological distress. However, heterogeneity between studies impedes generalisation of effects across every setting. Study-level moderators were insufficient to reduce heterogeneity; studying individual–level moderators is warranted. This requires individual participant data (IPD) and larger samples than those found in existing individual trials.
Methods and analysis
We propose an IPD meta–analysis. Our primary aim is to see if, and how, baseline psychological distress, gender, age, education, and dispositional mindfulness moderate the effect of MBPs on distress. We will search 13 databases for good-quality randomised controlled trials (RCTs) comparing in–person, expert–defined MBPs in non-clinical settings with passive controls. Two researchers will independently select, extract, and appraise trials using the revised Cochrane Risk–of–Bias Tool (RoB2). Anonymised IPD of eligible trials will be sought from authors, who will be invited to collaborate.
The primary outcome will be psychological distress measured using psychometrically-validated questionnaires at 1 to 6 months after programme completion. Pairwise random-effects two-stage IPD meta-analyses will be conducted. Moderator analyses will follow a “deft” approach. We will estimate subgroup-specific intervention effects. Secondary outcomes and sensitivity analyses are pre-specified. Multiple imputation strategies will be applied to missing data.
Ethics and dissemination
The findings will refine our knowledge on the effectiveness of MBPs and help improve the targeting of MBPs in non-clinical settings. They will be shared in accessible formats with a range of stakeholders. Public and professional stakeholders are being involved in the planning, conduct and dissemination of this project.
PROSPERO registration number CRD4202020011
The Your COVID-19 Risk Assessment Tool and the Accompanying Open Access Data and Materials Repositories
No full textIn March 2020, the Your COVID-19 Risk tool was developed in response to the global spread of SARS-CoV-2. The tool is an online resource based on key behavioural evidence-based risk factors related to contracting and spreading SARS-CoV-2. This article describes the development of the tool, the produced resources, the associated open repository, and initial results. This tool was developed by a multidisciplinary research team consisting of more than 150 international experts. This project leverages knowledge obtained in behavioural science, aiming to promote behaviour change by assessing risk and supporting individuals completing the assessment tool to protect themselves and others from infection. To enable iterative improvements of the tool, tool users can optionally answer questions about behavioural determinants. The data and results are openly shared to support governments and health agencies developing behaviour change interventions. Over 60 000 users in more than 150 countries have assessed their risk and provided data
Maternal depressive symptoms and early childhood temperament before and during the COVID-19 pandemic in the United Kingdom
No full textThe COVID-19 pandemic is an unexpected and major global event, with the potential to have many and varied impacts on child development. However, the implications of the pandemic for maternal depressive symptoms, early childhood temperament dimensions, and their associations, remain largely unknown. To investigate this, questionnaires were completed by mothers (N = 175) before and during the pandemic when their child was 10- and 16-months (Study 1), and by an extended group of mothers with young children (6 – 48 months; 66 additional mothers) during the first and second national lockdowns in the United Kingdom in 2020 (Study 2). Results indicated that whilst maternal pandemic-related stress decreased over the pandemic, the proportion of mothers feeling some level of pandemic-specific depression increased. Despite this, we did not observe an increase in the severity of global maternal depressive symptoms, or any negative impact of the pandemic on the development of temperament in infancy and early childhood
Uma métrica que aproxima os reais ao zero
No full textMostramos que existe uma métrica nos reais tal que qualquer número real está mais próximo do zero do que de qualquer outro real
Postnatal depression symptom trajectories across the COVID-19 pandemic: Evidence from the United Kingdom
No full textBackground: Postnatal maternal mental health is known to have declined in the early COVID-19 pandemic; however, less is known about how the wellbeing of these mothers progressed or whether risk remained elevated for those giving birth later in the pandemic. Here we explore the continuing trajectory of postnatal depressive symptoms across two years of the pandemic in the UK.
Methods: We report descriptive statistics from a six-wave longitudinal online survey, tracking a cohort of 569 mothers giving birth between November 2019 and December 2020 until April 2022, and a subsequent cohort of 70 mothers giving birth in 2022.
Results: The percentage of participants meeting the ≥11 Edinburgh Postnatal Depression Scale cut-off for postnatal depression was high early in the pandemic. While declining as social distancing restrictions eased, rates remained above pre-pandemic levels in April 2022: 47.5% (May-June 2020), 32.8% (July 2020), 51.3% (October-December 2020), 54.0% (February 2021), 38.2% (September 2021), 35.1% (April 2022). Those with greater symptom severity early in the pandemic showed a tendency to remain in depressive range. Symptoms were higher, and the decline in symptoms overtime attenuated, in those experiencing financial difficulty, while only minimal differences were apparent between mothers dependent on number of children. Of mothers giving birth in 2022, 44.3% scored ≥11.
Conclusions: The percentage of mothers meeting EPDS diagnostic criteria remained around 50% throughout periods of social distancing restrictions, and was slow to decline after restrictions eased. Evidence is suggestive of mothers giving birth in 2022 also experiencing elevated risk of postnatal depressive symptoms compared to pre-pandemic
Robust Bayesian Meta-Analysis: Model-Averaging Across Complementary Publication Bias Adjustment Methods
No full textPublication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods’ performance to depend on the true data generating process – no method consistently outperforms the others across a wide range of conditions. To avoid the condition-dependent, all-or-none choice between competing methods we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models of the relationship between effect sizes and their standard errors. The resulting estimator weights the models with the support they receive from the existing research record. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of competing publication bias adjustment methods