Conic Multi-Task Classification

Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL. However, a more general framework, referred to as Conic MTL, can be formulated by considering conic combinations of the objective functions instead; in this framework, Average MTL arises as a special case, when all combination coefficients equal 1. Although the advantage of Conic MTL over Average MTL has been shown experimentally in previous works, no theoretical justification has been provided to date. In this paper, we derive a generalization bound for the Conic MTL method, and demonstrate that the tightest bound is not necessarily achieved, when all combination coefficients equal 1; hence, Average MTL may not always be the optimal choice, and it is important to consider Conic MTL. As a byproduct of the generalization bound, it also theoretically explains the good experimental results of previous relevant works. Finally, we propose a new Conic MTL model, whose conic combination coefficients minimize the generalization bound, instead of choosing them heuristically as has been done in previous methods. The rationale and advantage of our model is demonstrated and verified via a series of experiments by comparing with several other methods.Comment: Accepted by European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)-201

Codevelopment Between Key Personality Traits and Alcohol Use Disorder From Adolescence Through Young Adulthood

ObjectivePersonality traits related to negative emotionality and low constraint are strong correlates of alcohol use disorder (AUD), but few studies have evaluated the prospective interplay between these traits and AUD symptoms from adolescence to young adulthood.MethodThe Minnesota Twin Family Study (N = 2,769) was used to examine the developmental interplay between AUD symptoms and three personality measures of constraint, negative emotionality, and aggressive undercontrol from ages 17 to 29.ResultsResults from random‐intercept, cross‐lagged panel models showed that low constraint and aggressive undercontrol predicted subsequent rank‐order increases in AUD symptoms from ages 17 to 24. AUD symptoms did not predict rank‐order change in these traits from ages 17 to 24. There was support for both cross‐effects from ages 24 to 29. Biometric analysis of the twin data showed genetic influences accounted for most of the phenotypic correlations over time.ConclusionResults are consistent with the notion that personality traits related to low constraint and aggressive undercontrol are important vulnerability/predisposition factors for the development of early adult AUD. In later young adulthood, there is more evidence for the simultaneous codevelopment of personality and AUD. Implications are addressed with attention to personality‐based risk assessments and targeted AUD prevention approaches.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142935/1/jopy12311.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142935/2/jopy12311_am.pd

In need of mediation: The relation between syntax and information structure

This paper defends the view that syntax does not directly interact with information structure. Rather, information structure affects prosody, and only the latter has an interface with syntax. We illustrate this point by discussing scrambling, focus preposing, and topicalization. The position entertained here implies that syntax is not very informative when one wants to narrow down the interpretation of terms such as “focus”, “topic”, etc

A Taxonomy of Explainable Bayesian Networks

Artificial Intelligence (AI), and in particular, the explainability thereof, has gained phenomenal attention over the last few years. Whilst we usually do not question the decision-making process of these systems in situations where only the outcome is of interest, we do however pay close attention when these systems are applied in areas where the decisions directly influence the lives of humans. It is especially noisy and uncertain observations close to the decision boundary which results in predictions which cannot necessarily be explained that may foster mistrust among end-users. This drew attention to AI methods for which the outcomes can be explained. Bayesian networks are probabilistic graphical models that can be used as a tool to manage uncertainty. The probabilistic framework of a Bayesian network allows for explainability in the model, reasoning and evidence. The use of these methods is mostly ad hoc and not as well organised as explainability methods in the wider AI research field. As such, we introduce a taxonomy of explainability in Bayesian networks. We extend the existing categorisation of explainability in the model, reasoning or evidence to include explanation of decisions. The explanations obtained from the explainability methods are illustrated by means of a simple medical diagnostic scenario. The taxonomy introduced in this paper has the potential not only to encourage end-users to efficiently communicate outcomes obtained, but also support their understanding of how and, more importantly, why certain predictions were made

More data, more problems: Strategically addressing data ethics and policy issues in LIS curricula and courses

Library and information science (LIS) schools are revising undergraduate and graduate curricula and individual courses to prepare students for data-centric careers, as well as to participate in a data-driven society. To meet these new challenges, programs are developing courses on, among other things, data curation, analytics, visualization, algorithm design, and artificial intelligence. While such changes reflect new workforce and society needs, it remains to be seen whether or not such efforts adequately address the very real and serious ethics and policy issues associated with related data practices (e.g., privacy, bias, fairness, and justice). The Information Ethics SIG and the Information Policy SIG have merged to present a panel on data ethics and policy issues in LIS education. In this session, two recent books on information ethics and information policy will be discussed to bring context to the panel, three papers will be presented, and the audience will have an opportunity to participate in a structured discussion. The papers will address three topics that explore the implications and concerns of living in a data-driven society: collaborative strategies for contributing to the data ethics education landscape, young adult information privacy concerns when using mobile devices, and artificial intelligence and social responsibility. The structured discussion will invite participation on issues raised by the papers, as well as implications for practice in LIS education

A Genetic Epidemiological Mega Analysis of Smoking Initiation in Adolescents

Introduction. Previous studies in adolescents were not adequately powered to accurately disentangle genetic and environmental influences on smoking initiation across adolescence. Methods. Mega-analysis of pooled genetically informative data on smoking initiation was performed, with structural equation modeling, to test equality of prevalence and correlations across cultural backgrounds, and to estimate the significance and effect size of genetic and environmental effects according to the classical twin study, in adolescent male and female twins from same-sex and opposite-sex twin pairs (N=19 313 pairs) between age 10 and 19, with 76 358 longitudinal assessments between 1983 and 2007, from 11 population-based twin samples from the US, Europe and Australia. Results. Although prevalences differed between samples, twin correlations did not, suggesting similar etiology of smoking initiation across developed countries. The estimate of additive genetic contributions to liability of smoking initiation increased from approximately 15% to 45% from age 13 to 19. Correspondingly, shared environmental factors accounted for a substantial proportion of variance in liability to smoking initiation at age 13 (70%) and gradually less by age 19 (40%). Conclusions. Both additive genetic and shared environmental factors significantly contribute to variance in smoking initiation throughout adolescence. The present study, the largest genetic epidemiological study on smoking initiation to date, found consistent results across 11 studies for the etiology of smoking initiation. Environmental factors, especially those shared by siblings in a family, primarily influence smoking initiation variance in early adolescence, while an increasing role of genetic factors is seen at later ages, which has important implications for prevention strategies. IMPLICATIONS: This is the first study to find evidence of genetic factors in liability to smoking initiation at ages as young as 12. It also shows the strongest evidence to date for decay of effects of the shared environment from early adolescence to young adulthood. We found remarkable consistency of twin correlations across studies reflecting similar etiology of liability to initiate smoking across different cultures and time periods. Thus familial factors strongly contribute to individual differences in who starts to smoke with a gradual increase in the impact of genetic factors and a corresponding decrease in that of the shared environment

Evaluation of receptor and chemical transport models for PM10 source apportionment

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models