Alexander W. Criswell, Sharan Banagiri, Vera Delfavero
et al.
Population inference in gravitational-wave astronomy allows us to connect individual detections to the astrophysics of compact objects and their environments. Current approaches employed for population inference with LIGO-Virgo-KAGRA data approximate evaluation of the hierarchical population likelihood via post-processing of individual-event posteriors. However, the case of the Laser Interferometer Space Antenna (LISA) will be more complex for two main reasons: the transdimensional "global fit" approach to LISA data analysis which models all signals and noise simultaneously, and the presence of both individually-resolved signals and the unresolved stochastic ``Galactic foreground" arising from the Galactic binary population, which induces a circular dependence between the resolved and unresolved systems and our ability to detect the former. These challenges are not without opportunity; LISA's data will contain every mHz compact binary in the Milky Way -- either individually or within the Galactic foreground -- with great potential for Galactic and stellar astrophysics. We therefore propose an alternative approach: direct evaluation of the full hierarchical population likelihood within the LISA global fit. We develop a statistical formalism for joint inference of individually-resolved gravitational-wave sources, an unresolved stochastic foreground, and a shared, underlying astrophysical population, present PELARGIR, a prototype GPU-accelerated population inference module for the LISA global fit, demonstrate the formalism and PELARGIR via a toy model analysis, and lay out a roadmap towards an astrophysically-motivated LISA global fit with embedded population inference. While we apply the formalism here to the population of LISA Galactic binaries, it is applicable across the gravitational-wave spectrum with use cases in pulsar timing and next-generation terrestrial observatories.
Alice Pearsons, Eleanor Mitchell, Euan Shields
et al.
Objectives
Nursing is facing substantial workforce challenges. Applications to study nursing have declined by 25% in Scotland, while 900 vacancies remain unfilled. The NHS Long Term Workforce Plan aims to grow the UK nursing workforce from 350,000 to 550,000 nurses by 2036/37. However, little is known about nursing students’ progression into practice.
Methods
This study proposes using the Scottish Longitudinal Outcomes database (LEO) to establish the early career trajectories of nursing students graduating from Scottish Universities. This new data linkage brings together data from Scottish universities (Higher Education Statistics Agency) from academic year 2003/04 to 2018/19 and employment data drawn from HMRC. Taken together, this will provide analysis of the early career trajectories of newly qualified nurses in Scotland. Findings will represent the first time that the Scottish LEO has been used for projects outside the Scottish Government.
Results
Descriptive statistics will ascertain the proportions of nursing students who entered into a career in healthcare using SIC codes. The extent to which first year graduates do not achieve a clinical post will be reported, other occupations entered, and the extent to which no employment is achieved. For those who do not achieve employment straightaway, or who go into positions outside of health, the length of time to first nursing position will be assessed. Results of survival models will be reported to assess how long it takes to achieve a clinical position from graduation. Models will explore the extent to which entry to clinical roles is influenced by socio-demographic characteristics and the extent to which the situation has changed over time.
Conclusion
To our knowledge, this study will provide the first insights into the implications of early career nurses securing employment within nursing. Given declines in recent programme entries and costs of training, these findings will hold considerable policy potential.
Recent public attitudinal studies have shown that the public want to know of the benefits of administrative data research. However, a common challenge is translating this information in an engaging and meaningful way. To bridge this gap, ADR UK hosted a summer intern to translate some research for the public.
Drawing from her own perspectives and skill, the intern will create a bespoke translation of administrative data research and accompanying communications campaign over the course of 12 weeks. The intern will work closely with relevant ADR UK researchers and gain feedback from a public panel to ensure the translation is both accurate and engaging.
The publication will be available in early September 2025. It will not only add to a suite of publicly-accessible outputs demonstrating the value of administrative data research, but the internship will demonstrate an example of how to meaningfully include young people in administrative data research.
The publication and accompanying communications campaign will launch later this year.
Objectives
To assess the distance nurses in England and Wales lived from their place of work at point of the 2021 census. This will assess the degree to which their geographical proximity to work may have implications for their own wellbeing and that of their patients.
Methods
Analyzing census microdata, the dataset used represents a 5% sample of the population of England and Wales. The key outcome of interest was straight-line distance from home to place of work. This will be used to assess the extent to which nurses, who will largely be working shifts, are able to live within reasonable proximity to the hospital, clinic or practice where they are based. Descriptive statistics will be calculated to assess the extent to which socio-demographic characteristics are associated with longer commutes. A matched comparison will be used to assess the extent to which nurses travel compares to the general population.
Results
Findings will be presented describing distance resided from workplace for nurses and for the matched comparison group and the extent to which being employed in nursing is associated with disproportionate commuter burdens. This is especially important given that most nurses work shifts with potentially fewer travel options. Tables will be presented that describe the socio-demographic composition of both, and the extent to which characteristics are associated with distances travelled. Maps will highlight the extent of geographical variation. Statistical models will then be presented that explore commutes and drivers associated with home-work proximity. These will ascertain the main drivers of workplace commuting distance for both the nursing workforce and matched counterparts from the general working population.
Conclusion
Findings will be discussed with focus on policy relevance, specifically implications for cost of living, quality of life, and potential implications for nurses’ own health, their ability to care, and workforce retention. These are key concerns regarding local and national housing policies.
Modern machine learning has enabled parameter inference from event-level data without the need to first summarize all events with a histogram. All of these unbinned inference methods make use of the fact that the events are statistically independent so that the log likelihood is a sum over events. However, this assumption is not valid for unbinned inference on unfolded data, where the deconvolution process induces a correlation between events. We explore the impact of event correlations on downstream inference tasks in the context of the OmniFold unbinned unfolding method. We find that uncertainties may be significantly underestimated when event correlations are excluded from uncertainty quantification.
Alex Bocchieri, John Mamish, David Appleyard
et al.
Scintillators are transparent materials that interact with high-energy particles and emit visible light as a result. They are used in state of the art methods of measuring high-energy particles and radiation sources. Most existing methods use fast single-pixel detectors to detect and time scintillation events. Cameras provide spatial resolution but can only capture an average over many events, making it difficult to image the events associated with an individual particle. Emerging single-photon avalanche diode cameras combine speed and spatial resolution to enable capturing images of individual events. This allows us to use machine vision techniques to analyze events, enabling new types of detectors. The main challenge is the very low brightness of the events. Techniques have to work with a very limited number of photons. We propose a kaleidoscopic scintillator to increase light collection in a single-photon camera while preserving the event's spatial information. The kaleidoscopic geometry creates mirror reflections of the event in known locations for a given event location that are captured by the camera. We introduce theory for imaging an event in a kaleidoscopic scintillator and an algorithm to estimate the event's 3D position. We find that the kaleidoscopic scintillator design provides sufficient light collection to perform high-resolution event measurements for advanced radiation imaging techniques using a commercial CMOS single-photon camera.
It takes time, energy, and goodwill to start a data partnership. The corollary to this truism is that maintaining and expanding such partnerships will demand as much, if not more, of these resources. Learn about the strategies we are using to expand our ability to produce rigorous, policy-relevant evidence to combat poverty and inequality while staying committed to addressing the needs of our long-term partners.
Developed by the University of Wisconsin-Madison’s Institute for Research on Poverty (IRP), the Wisconsin Administrative Data Core (WADC) evolved from a series of large-scale evaluation projects conducted for the state starting in the 1980s. This data enterprise, built by linking administrative data from multiple agencies, allows for cross-program analyses that are otherwise impossible.
Increased turnover since the pandemic coupled with more formalized data governance structures has highlighted the importance of maintaining good relationships and providing value back to partners who devote scarce bandwidth to sharing data. At the same time, we are adding new agency partners and working to increase usage of the WADC. These changes multiply the complexity involved in coordinating the data enterprise. We are adapting to this new world by focusing on relationships: creating formal structures that deepen the network of direct relationships among agency staff and researchers, taking advantage of increased turnover by hiring staff who have worked in state agencies, and seeking agency partners who want to be active partners versus data providers. More solid relationships will yield more policy-relevant research that agencies can use to better serve the state.
Twitter is recognized as a crucial platform for the dissemination and gathering of Cyber Threat Intelligence (CTI). Its capability to provide real-time, actionable intelligence makes it an indispensable tool for detecting security events, helping security professionals cope with ever-growing threats. However, the large volume of tweets and inherent noises of human-crafted tweets pose significant challenges in accurately identifying security events. While many studies tried to filter out event-related tweets based on keywords, they are not effective due to their limitation in understanding the semantics of tweets. Another challenge in security event detection from Twitter is the comprehensive coverage of security events. Previous studies emphasized the importance of early detection of security events, but they overlooked the importance of event coverage. To cope with these challenges, in our study, we introduce a novel event attribution-centric tweet embedding method to enable the high precision and coverage of events. Our experiment result shows that the proposed method outperforms existing text and graph-based tweet embedding methods in identifying security events. Leveraging this novel embedding approach, we have developed and implemented a framework, Tweezers, that is applicable to security event detection from Twitter for CTI gathering. This framework has demonstrated its effectiveness, detecting twice as many events compared to established baselines. Additionally, we have showcased two applications, built on Tweezers for the integration and inspection of security events, i.e., security event trend analysis and informative security user identification.
Tommi Härkänen, Sangita Kulathinal, Arya Panthalanickal Vijayakumar
Routinely collected nation-wide registers contain socio-economic and health-related information from a large number of individuals. However, important information on lifestyle, biological and other risk factors is available at most for small samples of the population through surveys. A majority of health surveys lack detailed medical information necessary for assessing the disease burden. Hence, traditionally data from the registers and the surveys are combined to have necessary information for the survey sample. Our idea is to base analyses on a combined sample obtained by adding a (large) sample of individuals from the population to the survey sample. The main objective is to assess the bias and gain in efficiency of such combined analyses with a binary or time-to-event outcome. We employ (i) the complete-case analysis (CCA) using the respondents of the survey, (ii) analysis of the full survey sample with both unit- and item-nonresponse under the missing at random (MAR) assumption and (iii) analysis of the combined sample under mixed type of missing data mechanism. We handle the missing data using multiple imputation (MI)-based analysis in (ii) and (iii). We utilize simulated as well as empirical data on ischemic heart disease obtained from the Finnish population. Our results suggested that the MI methods improved the efficiency of the estimates when we used the combined data for a binary outcome, but in the case of a time-to-event outcome the CCA was at least as good as the MI using the larger datasets, in terms of the the mean absolute and squared errors. Increasing the participation in the surveys and having good statistical methods for handling missing covariate data when the outcome is time-to-event would be needed for implementation of the proposed ideas.
Events are essential components of speech and texts, describing the changes in the state of entities. The event extraction task aims to identify and classify events and find their participants according to event schemas. Manually predefined event schemas have limited coverage and are hard to migrate across domains. Therefore, the researchers propose Liberal Event Extraction (LEE), which aims to extract events and discover event schemas simultaneously. However, existing LEE models rely heavily on external language knowledge bases and require the manual development of numerous rules for noise removal and knowledge alignment, which is complex and laborious. To this end, we propose a Prompt-based Graph Model for Liberal Event Extraction (PGLEE). Specifically, we use a prompt-based model to obtain candidate triggers and arguments, and then build heterogeneous event graphs to encode the structures within and between events. Experimental results prove that our approach achieves excellent performance with or without predefined event schemas, while the automatically detected event schemas are proven high quality.
Event cameras are a type of novel neuromorphic sen-sor that has been gaining increasing attention. Existing event-based backbones mainly rely on image-based designs to extract spatial information within the image transformed from events, overlooking important event properties like time and polarity. To address this issue, we propose a novel Group-based vision Transformer backbone for Event-based vision, called Group Event Transformer (GET), which de-couples temporal-polarity information from spatial infor-mation throughout the feature extraction process. Specifi-cally, we first propose a new event representation for GET, named Group Token, which groups asynchronous events based on their timestamps and polarities. Then, GET ap-plies the Event Dual Self-Attention block, and Group Token Aggregation module to facilitate effective feature commu-nication and integration in both the spatial and temporal-polarity domains. After that, GET can be integrated with different downstream tasks by connecting it with vari-ous heads. We evaluate our method on four event-based classification datasets (Cifar10-DVS, N-MNIST, N-CARS, and DVS128Gesture) and two event-based object detection datasets (1Mpx and Gen1), and the results demonstrate that GET outperforms other state-of-the-art methods. The code is available at https://github.com/Peterande/GET-Group-Event-Transformer.
John Paige, Geir-Arne Fuglstad, Andrea Riebler
et al.
Spatial aggregation with respect to a population distribution involves estimating aggregate quantities for a population based on an observation of individuals in a subpopulation. In this context, a geostatistical workflow must account for three major sources of `aggregation error': aggregation weights, fine scale variation, and finite population variation. However, common practice is to treat the unknown population distribution as a known population density and ignore empirical variability in outcomes. We improve common practice by introducing a `sampling frame model' that allows aggregation models to account for the three sources of aggregation error simply and transparently. We compare the proposed and the traditional approach using two simulation studies that mimic neonatal mortality rate (NMR) data from the 2014 Kenya Demographic and Health Survey (KDHS2014). For the traditional approach, undercoverage/overcoverage depends arbitrarily on the aggregation grid resolution, while the new approach exhibits low sensitivity. The differences between the two aggregation approaches increase as the population of an area decreases. The differences are substantial at the second administrative level and finer, but also at the first administrative level for some population quantities. We find differences between the proposed and traditional approach are consistent with those we observe in an application to NMR data from the KDHS2014.
Event schemas encode knowledge of stereotypical structures of events and their connections. As events unfold, schemas are crucial to act as a scaffolding. Previous work on event schema induction focuses either on atomic events or linear temporal event sequences, ignoring the interplay between events via arguments and argument relations. We introduce a new concept of Temporal Complex Event Schema: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. In addition, we propose a Temporal Event Graph Model that predicts event instances following the temporal complex event schema. To build and evaluate such schemas, we release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and we have manually constructed gold-standard schemas. Intrinsic evaluations based on schema matching and instance graph perplexity, prove the superior quality of our probabilistic graph schema library compared to linear representations. Extrinsic evaluation on schema-guided future event prediction further demonstrates the predictive power of our event graph model, significantly outperforming human schemas and baselines by more than 23.8% on HITS@1.
Ilyash Olha, Yildirim Osman, Smoliar Liubov
et al.
This article introduces a step-by-step methodology for evaluating an enterprise’s investment attractiveness in the context of economic development, using appropriate valuation parameters at macro, meso and micro levels. A system of indicators of macro-level investment attractiveness has been formed based on the criteria of socio-economic and legal attractiveness and investment risks. The indicators for assessing investment attractiveness of the industry have been grouped by the criteria of: prospects of the industry, positioning of the enterprise in the industry market, and sectoral investment risks. The indicators of investment attractiveness have been systematised with the use of three-dimensional current and operational analysis, as well as the method of risk assessment, which helped to determine the area of reaction to risk zones of the enterprise’s investment potential. The research allowed us to assess the position of a company in the market and to predict the risks of investing in the chemical industry.
There were total of 2,100,000 new HIV infections worldwide and 1,500,000 deaths from AIDS recorded in 2013. The total HIV/AIDS cases in 2017 in Sidoarjo reached 476 cases and cumulatively reached 1,245 cases. HIV/AIDS is a well-known topic among teenagers. Teenagers are often associated with physical development in puberty phase which usually followed by sexual development. Furthermore, they also experience changes emotionally and physically which are projected in their behavior and attitude. These circumstances make teenagers prone to the risky behavior towards HIV/AIDS transmission. This study aims to analyze the role of "Paguyuban Peduli HIV/AIDS Sidoarjo" or PARPAS on teenagers' knowledge, attitude, and behavior towards HIV/AIDS prevention. This research is an observational analytic using cross-sectional research design. The population of the study is all students of SMAN 1 Taman and SMAN 1 Sidoarjo, 2,370 students in total. The sampling technique uses simple random sampling and the sample size is 100 students. The result shows that there is correlation between PARPAS role on knowledge and attitude of students' in Sidoarjo towards HIV/AIDS prevention. Nevertheless, there is no correlation between PARPAS role on students' behavior towards HIV/AIDS prevention. Suggestions concluded from the results are including early detection, attempt in joining organization related to HIV/AIDS awareness, and health education given to both students and parents.
The objective of this contribution is to identify, on the basis of an empirical content analysis of documents, the results of fulfilling the 2030 Agenda in the Czech Republic and Slovakia in terms of their global responsibility for fulfilling development goals, by identifying indicators for each of the 17 goals of the 2030 Agenda. This was based on selective research in terms of the selected pilot indicator for each goal of Agenda 2030. These selected indicators were chosen because they best represented the social, economic and territorial problems of the countries surveyed. Their fulfilment is therefore the most important part of fulfilling the complex Sustainable Development Goals. Then a selective and complex relational comparison of the analysed countries in terms of their performance in implementation will be performed on the basis of data obtained using the SDG index. The outcome of the paper is a specification of the surveyed countries’ prospects for meeting all the examined aspects of the SDG by 2030 by an arithmetic expression of their potential to reach 100% in the surveyed indicators. This paper is part of the solution of Project VEGA no. 1/0302/18 “Smart Cities as a possibility for implementation of the concept of Sustainable Urban Development in the Slovak Republic”.
Elizabeth Wilson, Hanna Tervonen, David Currow
et al.
Introduction
Cancer deaths are a major contributor to premature mortality in people with mental health conditions. Some cancers occur more often in people with mental health conditions because of increased risk factors. However, most premature cancer mortality in people with mental health conditions arises from increased cancer case fatality rates due to health care related factors. While there is substantial evidence that a problem exists, further evidence is needed to support effective action and the translation of research findings into better policy, services and care.
Objectives and Approach
The NSW Mental Health Living Longer program involves population-wide data linkage that combines records from nine NSW data collections. Our collection includes over 120 million records for more than nine million people. This presentation focuses on the use of linked data to develop indicators to support reporting on premature breast and cervical cancer mortality for women living with mental illness. These indicators will be used to identify variation in care, assess areas for targeted intervention, and evaluate the effectiveness of research translation into more effective care.
Results
This work is ongoing and will be finalised by August 2020. We will use regression techniques to examine predictors of participation in breast and cervical cancer screening for women who use mental health services in NSW. These results will be used to assess geographical variation in risk-adjusted screening participation rates. We will also present methods and results for measuring incidence and stage at presentation, as well as 5 year survival for women who use mental health services in NSW.
Conclusion / Implications
If cancer survival is a key measure of the effectiveness of healthcare systems, then reduced survival in people with mental health problems reflects less effective health care. Improving screening and treatment services is likely to be the most important strategy for reducing the cancer mortality gap for women with mental illness.
El trabajo busca modelar la distribución de la tasa de incidencia acumulada de COVID-19 en los municipios de México a través del ajuste de tres modelos lineales generalizados (en competencia) con efectos espaciales y temporales y función de enlace Poisson. Se utilizaron datos de casos confirmados de COVID-19, reportados por la Secretaría de Salud de febrero a julio de 2020. Con el objetivo de reducir los costos computacionales asociados a la estimación de múltiples parámetros, con grandes cantidades de datos, se optó por utilizar la aproximación integrada anidada de Laplace en lenguaje R (R-INLA). Los modelos fueron evaluados a través del criterio de información Akaike (AIC), donde el mejor resultó ser el Modelo No Paramétrico de Interacción Espacio-Temporal. Los resultados permiten confirmar la presencia de importantes niveles de heterogeneidad en la distribución espacio-temporal de las tasas de incidencia de COVID-19 entre municipios de México.
Social Sciences, Demography. Population. Vital events
Jocelyn Nappa, Bruno Schoumaker, Albert Phongi
et al.
Kinshasa, a metropolis with a population of 10 million, has undergone major economic, social, and demographic transformations over recent decades. This article analyses changes in marriage practices in Kinshasa against a backdrop of worsening economic conditions and high unemployment. Data from the MAFE survey (Migration between Africa and Europe) conducted in Kinshasa in 2009 reveal the decline in first unions and in marriages, for men and women alike. Event history analyses show that economic hardship reduces the likelihood of marriage. The effects of economic factors are stronger for men than for women, and the difference in marriage likelihood between rich and poor men has widened over time. These findings can be explained in part by the rising cost of marriage, shouldered mainly by the groom and his family, and the growing difficulty of acquiring the necessary sums of money. In this context, consensual unions and non-marital births are becoming more frequent and are tending to replace formal marriage.