Why do we need longitudinal survey data?

Information from longitudinal surveys transforms snapshots of a given moment into something with a time dimension. It illuminates patterns of events within an individual’s life and records mobility and immobility between older and younger generations. It can track the different pathways of men and women and people of diverse socio-economic background through the life course. It can join up data on aspects of a person’s life, health, education, family, and employment and show how these domains affect one another. It is ideal for bridging the different silos of policies that affect people’s lives

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation

There exist features that are related to the label in the same way across different settings for that task; these are semantic features or semantics. Features with varying relationships to the label are nuisances. For example, in detecting cows from natural images, the shape of the head is a semantic and because images of cows often have grass backgrounds but only in certain settings, the background is a nuisance. Relationships between a nuisance and the label are unstable across settings and, consequently, models that exploit nuisance-label relationships face performance degradation when these relationships change. Direct knowledge of a nuisance helps build models that are robust to such changes, but knowledge of a nuisance requires extra annotations beyond the label and the covariates. In this paper, we develop an alternative way to produce robust models by data augmentation. These data augmentations corrupt semantic information to produce models that identify and adjust for where nuisances drive predictions. We study semantic corruptions in powering different robust-modeling methods for multiple out-of distribution (OOD) tasks like classifying waterbirds, natural language inference, and detecting Cardiomegaly in chest X-rays

Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples

Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity. Recent studies have shown that large language models (LLMs) possess some abstract deductive reasoning ability given chain-of-thought prompts. However, they have primarily been tested on proofs using modus ponens or of a specific size, and from the same distribution as the in-context examples. To measure the general deductive reasoning ability of LLMs, we test on a broad set of deduction rules and measure their ability to generalize to more complex proofs from simpler demonstrations from multiple angles: depth-, width-, and compositional generalization. To facilitate systematic exploration, we construct a new synthetic and programmable reasoning dataset that enables control over deduction rules and proof complexity. Our experiments on four LLMs of various sizes and training objectives show that they are able to generalize to longer and compositional proofs. However, they require explicit demonstrations to produce hypothetical subproofs, specifically in proof by cases and proof by contradiction

The UK Millennium Cohort: the making of a multipurpose resource for social science and policy

This paper gives an account of the origins, objectives and structure of the Millennium Cohort Study (MCS) – some 19,000 individuals born in the UK in 2000-2001 – and its use in a wide range of research on many aspects of their lives in childhood years. We highlight some of the mass of output on the first five surveys to age 11 in 2012. Topics discussed are social inequalities in child development; comparisons with other cohorts; areas not well covered by previous national cohorts: season of birth, fathers, ethnicity and childcare; parental behaviour; intergenerational links; social ecology and differences between and within UK countries. We also discuss the challenges faced by the National Evaluation of Sure Start (NESS) in drawing controls from the MCS. As the cohort marches to its seventh survey in 2018, and beyond, the potential for research across life course domains will only continue to grow

Applications of cognitive radio networks:recent advances and future directions

International audienc

Moving to a better place? The outcomes of residential mobility among families with young children in the Millennium Cohort Study

This paper assesses how far residential moves can result in improvement or deterioration of the housing and neighbourhood circumstances for families with young children. It uses data from the UK Millennium Cohort Study concentrating on the time between infancy and age five, 2001 to 2006. First we ask which families moved home and in what circumstances. We then examine how moving changed several aspects of housing: space standards, damp problems, and tenure. We show that the majority of moves resulted in improvements to housing conditions, especially in reducing overcrowding. We also consider neighbourhood circumstances, proxied by a measure of local poverty at small area level. Movers generally ended up in neighbourhoods with lower levels of poverty, or no worse, but almost one fifth of moves were downward or remained in the 30 percent poorest areas. We ask whether locating in an area with more local poverty may help achieve a larger home. There is evidence of such a trade-off – one in five families moved to a larger home which was either in a poorer area than before or remained in the 30 percent poorest areas. We conclude by showing how the path of upward housing mobility, while numerically dominant, was far less common among families with relatively low resources and/or whose moves were attendant on partnership changes. For them, moves often result in smaller homes in poorer areas