The recent discovery of three off-nuclear tidal disruption events (EP240222a, AT2024tvd, and AT2025abcr) - following the first such source, 3XMM J2150$-$05 - reveals a small but robust population of off-nuclear, or `wandering', black holes (WBHs) with masses $M_\bullet > 10^4 M_\odot$. Two demographic trends are already apparent: (i) all events occur in massive, early-type parent galaxies with stellar masses $10.8 \lesssim \log_{10}(M_\star/M_\odot) \lesssim 11.1$; and (ii) events at larger halo-centric radii ($R_{\rm TDE}/R_{200}$) are associated with dwarf satellites ($M_\star \sim 10^7 M_\odot$), while those closer to halo centers lack detected stellar counterparts. Using results from the \texttt{ROMULUS} cosmological simulation, we show that both trends naturally arise from hierarchical galaxy formation. By combining the simulation with empirical constraints on the local galaxy population, we compute the volumetric density of WBHs, $φ_{\rm WBH}(M_\star)$, finding that it peaks at $\log_{10}(M_\star/M_\odot)=11.10^{+0.05}_{-0.10}$ and that more than half of all WBHs in the local Universe reside in galaxies with $10.7 \lesssim \log_{10}(M_\star/M_\odot) \lesssim 11.2$, explaining (i) and predicting its persistence as the sample grows. We further show that ii), i.e., the observed link between detection of stellar counterparts and $R_{\rm TDE}/R_{200}$, is also expected from tidal stripping. These results demonstrate that off-nuclear TDEs are powered by the population of WBHs long predicted by cosmological simulations.
Objectives
There is a lack of recent evidence on the association between Welsh language ability and employment outcomes. This paper addresses this gap by simultaneously examining the interplay between Welsh language ability and patterns of migration among recent cohorts of Welsh graduates and how these in turn influence employment outcomes.
Methods
Based upon Higher Education Statistics Agency data, we examine the university locations of six cohorts of Welsh domiciled Year 11 pupils (covering the period 2013-2018) who choose to enter Higher Education (HE). Using data from the 2011 Census and records related to compulsory and post-compulsory education, the characteristics of HE students who remain in Wales are compared to those who leave. Data from the Destinations of Leavers from Higher Education (DHLE) survey is used to examine migratory patterns following HE, how Welsh language influences graduate migration and how these in turn relate to employment outcomes.
Results
The existence of a ‘brain drain’ of graduate labour from Wales has become a focus of debate. Any region’s ability to generate, retain and attract graduate workers is critically linked to the relative employment opportunities that are available compared to elsewhere. The analysis confirms that those students who speak Welsh are more likely to remain in Wales for the purpose of attending university. In the context of the relatively poor economic performance of Wales, those students who migrate from Wales exhibit improved economic outcomes. Additional analysis will examine whether the ability to speak Welsh has a positive impact upon employment outcomes and whether there is separate and additional effect of receiving tuition through the medium of Welsh among those who choose to study in Wales.
Conclusion
The analysis emphasises the link between economic and language policy, as described within the Welsh Government’s national strategy Prosperity for All: Good quality jobs (in) attractive places…to live…(are required) to provide people with a reason to remain or return to work and live in communities where the Welsh language thrives.
Introduction
We previously investigated the frequency of screening for celiac disease (CD) based on tissue transglutaminase antibody testing (tTG-IgA) and developed the first incident cohort of celiac autoimmunity in Canada.
Methods
Administrative data sources used in this study were population-based and covered the entire province of Alberta (~4.3M residents during study period). Various approaches to querying data were employed within a diverse team of health information managers, data analysts, clinical scientists, and gastroenterologists. This process involved a broad search for CD screening tests, thorough data inspection/cleaning, and numerous discussions to determine potential explanations for the findings.
Results
Approximately ~950,000 records for tTG-IgA were first identified. Records were then excluded due to missing/invalid patient identifiers or test results (0.8%), non-Alberta residency (0.4%), and duplicate records (1.1%). A final dataset included ~920,000 tTG-IgA tests on ~680,000 unique patients, which was also validated through a separate query performed by an analyst external to the study team. A conservative approach to excluding as many potential prevalent cases of CD was applied given a robust algorithm for CD has not yet been established in Canada. The final rate of celiac autoimmunity (34 per 100,000) offered further face validity based on prior estimates of diagnosed CD in Alberta and other countries reporting on celiac autoimmunity.
Conclusion
When developing a novel case definition or investigating unfamiliar outcomes using routinely collected data, collaboration across several disciplines is highly recommended. Certain stages of the project may require additional scrutiny and discussion to ensure findings are valid and reliable.
Abstract This paper contains the reflections of the author on the state of family history within the constellation of History and the Social Sciences. The first part of the paper presents a brief outline of how the discipline was founded and the elements in play that contributed to its enormous initial success, especially visible during the last three decades of the twentieth century. In recent years, however, there is some indication that research output on family history has been in decline and, more important, appears to have lost a significant part of the luster it once had. In the second part of this paper the author looks at the importance of promoting a past–present dialogue on the family and the way both historians and social scientists understand it. Ways of strengthening interactions between family history and family studies are discussed, as is the crucial importance for the discipline of the data revolution currently underway that for the first time offers massive information about families around the world over the past six decades. The author argues that both historians and social scientists have much to say about family life during this relatively recent past, albeit from very different perspectives. Studying family change constitutes a key challenge for the field. We discuss different ways of approaching this issue in the recent past, as well as the advisability of looking at the concept of family systems more closely. In societies or regions where earlier more historical data exist (normally in the more developed world), it may also be possible to link existing historical results to those from the census microdata era in order to provide a new, long-term perspective on family life spanning two centuries or even more. For social scientists and family historians alike, understanding the key dimensions of change and their implications for society constitute a crucial challenge for the discipline.
Reconstructing the properties of the astrophysical population of binary compact objects in the universe is a key science goal of gravitational wave detectors. This goal is hindered by the finite strain, frequency sensitivity and observing time of current and future detectors. This implies that we can in general observe only a selected subset of the underlying population, with limited event statistics, and also nontrivial observational uncertainties in the parameters of each event. In this work, we will focus on observations of massive black hole binaries with the Laser Interferometer Space Antenna (LISA). If these black holes grow from population III star remnants (``light seeds''), a significant fraction of the binary population at low masses and high redshift will be beyond LISA's observational reach; thus, selection effects have to be accounted for when reconstructing the underlying population. Here we propose an iterative, kernel density estimation (KDE)-based non-parametric method, in order to tackle these statistical challenges in reconstructing the astrophysical population distribution from a finite number of observed signals over total mass and redshift. We test the method against a set of simulated LISA observations in a light seed formation scenario. We find that our approach is successful at reconstructing the underlying astrophysical distribution in mass and redshift, except in parameter regions where zero or order(1) signals are observed.
Andrea Iglesias-Ramas, Samuele Pio Lipani, Rosalind J. Allen
Population genetics lies at the heart of evolutionary theory. This topic forms part of many biological science curricula but is rarely taught to physics students. Since physicists are becoming increasingly interested in biological evolution, we aim to provide a brief introduction to population genetics, written for physicists. We start with two background chapters: chapter 1 provides a brief historical introduction to the topic, while chapter 2 provides some essential biological background. We begin our main content with chapter 3 which discusses the key concepts behind Darwinian natural selection and Mendelian inheritance. Chapter 4 covers the basics of how variation is maintained in populations, while chapter 5 discusses mutation and selection. In chapter 6 we discuss stochastic effects in population genetics using the Wright-Fisher model as our example, and finally we offer concluding thoughts and references to excellent textbooks in chapter 7.
Th e phenomenon of social network services (SNSs), investigated by numerous scientifi c disciplines, can hardly be overestimated in today's globalised world. However, there is a lack of studies focused on the spatial aspects of SNS development and operation. Th is article deals with peculiarities of the current distribution and main spatial tendencies of the SNS Facebook’s development in Central and Eastern Europe on the examples of Poland, Ukraine and Belarus. The spatial distribution of Facebook has the following patterns in the studied area: a relationship between the type of development of the country and the level of Facebook penetration (i.e., the “Facebook Divide”); an increase in number of users and penetration rate in developed areas (capital regions, major socio-economic
centres); a progressive increase in the share of users from east to west; a large concentration of users in capitals and regional centres; the fi lter function of the state border
Константин Игоревич Казенин, Екатерина Сергеевна Митрофанова
Статья посвящена особенностям рождаемости на постсоветском Северном Кавказе и их возможным причинам. На примере Дагестана и республик Северо-Западного Кавказа рассматриваются три характеристики рождаемости: более высокая по сравнению с Россией в целом доля третьих и последующих детей среди всех родившихся, более короткий интервал между первым и вторым деторождениями и наличие межэтнических различий по среднему числу рожденных детей. Исследуется вопрос, в какой мере эти характеристики постсоветской рождаемости имеют исторические корни. Для этого на основе данных микропереписи населения России 1994 г. с помощью дескриптивных методов, а также с помощью моделей пропорциональных рисков изучается рождаемость когорт женщин
1920-1970-х годов рождения, относящихся к коренным этносам данных регионов. Показано, что относительно высокая доля третьих и последующих детей, а также наличие межэтнических различий, наблюдаемых в постсоветское время, присущи и современным, и «советским» когортам. Короткий интервал между первыми и вторыми рождениями, напротив, скорее является «инновацией» постсоветского времени, особенно в Дагестане. Обсуждаются возможные пути объяснения полученных результатов.
Document-level multi-event extraction aims to extract the structural information from a given document automatically. Most recent approaches usually involve two steps: (1) modeling entity interactions; (2) decoding entity interactions into events. However, such approaches ignore a global view of inter-dependency of multiple events. Moreover, an event is decoded by iteratively merging its related entities as arguments, which might suffer from error propagation and is computationally inefficient. In this paper, we propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization. The event proxy nodes, representing pseudo-events, are able to build connections with other event proxy nodes, essentially capturing global information. The Hausdorff distance makes it possible to compare the similarity between the set of predicted events and the set of ground-truth events. By directly minimizing Hausdorff distance, the model is trained towards the global optimum directly, which improves performance and reduces training time. Experimental results show that our model outperforms previous state-of-the-art method in F1-score on two datasets with only a fraction of training time.
Eva Sextl, Rolf-Peter Kudritzki, Andreas Burkert
et al.
We analyze TYPHOON long slit absorption line spectra of the starburst barred spiral galaxy NGC 1365 obtained with the Progressive Integral Step Method covering an area of 15 square kpc. Applying a population synthesis technique, we determine the spatial distribution of ages and metallicity of the young and old stellar population together with star formation rates, reddening, extinction and the ratio R$_V$ of extinction to reddening. We detect a clear indication of inside-out growth of the stellar disk beyond 3 kpc characterized by an outward increasing luminosity fraction of the young stellar population, a decreasing average age and a history of mass growth, which was finished 2 Gyrs later in the outermost disk. The metallicity of the young stellar population is clearly super solar but decreases towards larger galactocentric radii with a gradient of -0.02 dex/kpc. On the other hand, the metal content of the old population does not show a gradient and stays constant at a level roughly 0.4 dex lower than that of the young population. In the center of NGC 1365 we find a confined region where the metallicity of the young population drops dramatically and becomes lower than that of the old population. We attribute this to infall of metal poor gas and, additionally, to interrupted chemical evolution where star formation is stopped by AGN and supernova feedback and then after several Gyrs resumes with gas ejected by stellar winds from earlier generations of stars. We provide a simple model calculation as support for the latter.
Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi-view images, its performance is often deteriorated by dense occlusions and extreme lighting conditions. To address the problem, this paper presents an Event-based SAI (E-SAI) method by relying on the asynchronous events with extremely low latency and high dynamic range acquired by an event camera. Specifically, the collected events are first refocused by a Refocus-Net module to align in-focus events while scattering out off-focus ones. Following that, a hybrid network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs) is proposed to encode the spatio-temporal information from the refocused events and reconstruct a visual image of the occluded targets. Extensive experiments demonstrate that our proposed E-SAI method can achieve remarkable performance in dealing with very dense occlusions and extreme lighting conditions and produce high-quality images from pure events. Codes and datasets are available at https://dvs-whu.cn/projects/esai/.
Medical event prediction (MEP) is a fundamental task in the medical domain, which needs to predict medical events, including medications, diagnosis codes, laboratory tests, procedures, outcomes, and so on, according to historical medical records. The task is challenging as medical data is a type of complex time series data with heterogeneous and temporal irregular characteristics. Many machine learning methods that consider the two characteristics have been proposed for medical event prediction. However, most of them consider the two characteristics separately and ignore the correlations among different types of medical events, especially relations between historical medical events and target medical events. In this paper, we propose a novel neural network based on attention mechanism, called cross-event attention-based time-aware network (CATNet), for medical event prediction. It is a time-aware, event-aware and task-adaptive method with the following advantages: 1) modeling heterogeneous information and temporal information in a unified way and considering temporal irregular characteristics locally and globally respectively, 2) taking full advantage of correlations among different types of events via cross-event attention. Experiments on two public datasets (MIMIC-III and eICU) show CATNet can be adaptive with different MEP tasks and outperforms other state-of-the-art methods on various MEP tasks. The source code of CATNet will be released after this manuscript is accepted.
The aim of this paper is to describe a population model with transition. We analyze the spectral properties of the transition matrix considering both irreducible and reducible structures. We give physical interpretations of these properties to population dynamics.
Population-level societal events, such as civil unrest and crime, often have a significant impact on our daily life. Forecasting such events is of great importance for decision-making and resource allocation. Event prediction has traditionally been challenging due to the lack of knowledge regarding the true causes and underlying mechanisms of event occurrence. In recent years, research on event forecasting has made significant progress due to two main reasons: (1) the development of machine learning and deep learning algorithms and (2) the accessibility of public data such as social media, news sources, blogs, economic indicators, and other meta-data sources. The explosive growth of data and the remarkable advancement in software/hardware technologies have led to applications of deep learning techniques in societal event studies. This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions. We focus on two domains of societal events: \textit{civil unrest} and \textit{crime}. We first introduce how event forecasting problems are formulated as a machine learning prediction task. Then, we summarize data resources, traditional methods, and recent development of deep learning models for these problems. Finally, we discuss the challenges in societal event forecasting and put forward some promising directions for future research.
The aim of this paper is to critically review recent EU level debates on territorial impact assessment, which serves as a tool to improve the understanding of uneven territorial impacts of the EU sectoral policies. The paper also seeks to elicit (1) which European countries employ territorial impact assessment when designing various national policies and (2) how this tool is used in different governance environments. Particular attention is paid to the case of Czechia. The paper elaborates upon the state-of-the-art tools used on a national level and analyses the motivation of actors on regional and local levels to use such tools in their decision-making process.
Road traffic fatalities in Ecuador are 20.4 deaths per 100,000 people. Men are the most affected by traffic accidents: 4.2 times higher than women (33 vs. 7.8 deaths per 100,000 people, respectively). Traffic accidents show a decrease: from 22 deaths per 100,000 people in 2010 to 18 deaths per 100,000 people in 2016. The estimation of DALY by the life expectancy method used age weighting β = 0.04, r = 0.03, C = 0.1658. The average burden of disease is 141,430 DALY or 897 DALY per 100,000 people (95% CI 892-902). The cost of DALY, using the approach of human capital, is US$ 806.8 million equivalent to 0.89% of GDP, 81% caused by males and 19% by females. This percentage of GDP lost for road fatalities is equivalent as if each individual in Ecuador paid US$ 358 annually. The provinces with the largest population (Guayas, Pichincha, & Manabí) contribute with the 52% to the total population, 67% to the number of vehicles and 49% of total deaths due to traffic accidents. However, when we analyze deaths per number of people and number of vehicles, these provinces are not the most dangerous for dying in a traffic accident. Considering number of deaths per 100,000 people, the most dangerous provinces are Sucumbíos (33.5), Cotopaxi (32.0), Orellana (31.2), together, they constitute just the 5.9% of the population and 3.8% of the total vehicles, however, the average death rate of these three provinces is 1.58 times the national average (20.4 per 100,000 people). Considering the number of deaths per 100,000 vehicles, the most dangerous provinces are Napo (460), Imbabura (429) and Morona Santiago (400), together, they constitute just the 4.5% of the population and 1.9% of the total vehicles, however, the average death rate of these three provinces is 2.7 times the national average (156 per 100,000 vehicles).
Social Sciences, Demography. Population. Vital events