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

Behavioral Impediments to Valuing Annuities: Complexity and Choice Bracketing

This paper examines two behavioral factors that diminish people’s ability to value a lifetime income stream or annuity, drawing on a survey of about 4,000 adults in a U.S. nationally representative sample. By experimentally varying the degree of complexity, we provide the first causal evidence that increasing the complexity of the annuity choice reduces respondents’ ability to value the annuity, measured by the difference between the sell and buy values people assign to the annuity. We also find that people’s ability to value an annuity increases when we experimentally induce them to think jointly about the annuitization decision as well as how quickly or slowly to spend down assets in retirement. Accordingly, we conclude that narrow choice bracketing is an impediment to annuitization, yet this impediment can be mitigated with a relatively straightforward intervention

Laser-induced breakdown spectroscopy: a tool for real-time, in vitro and in vivo identification of carious teeth

BACKGROUND: Laser Induced Breakdown Spectroscopy (LIBS) can be used to measure trace element concentrations in solids, liquids and gases, with spatial resolution and absolute quantifaction being feasible, down to parts-per-million concentration levels. Some applications of LIBS do not necessarily require exact, quantitative measurements. These include applications in dentistry, which are of a more "identify-and-sort" nature – e.g. identification of teeth affected by caries. METHODS: A one-fibre light delivery / collection assembly for LIBS analysis was used, which in principle lends itself for routine in vitro / in vivo applications in a dental practice. A number of evaluation algorithms for LIBS data can be used to assess the similarity of a spectrum, measured at specific sample locations, with a training set of reference spectra. Here, the description has been restricted to one pattern recognition algorithm, namely the so-called Mahalanobis Distance method. RESULTS: The plasma created when the laser pulse ablates the sample (in vitro / in vivo), was spectrally analysed. We demonstrated that, using the Mahalanobis Distance pattern recognition algorithm, we could unambiguously determine the identity of an "unknown" tooth sample in real time. Based on single spectra obtained from the sample, the transition from caries-affected to healthy tooth material could be distinguished, with high spatial resolution. CONCLUSIONS: The combination of LIBS and pattern recognition algorithms provides a potentially useful tool for dentists for fast material identification problems, such as for example the precise control of the laser drilling / cleaning process

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

Machine Learning for Health: Algorithm Auditing & Quality Control

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing

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

Carotenoid Distribution in Living Cells of Haematococcus pluvialis (Chlorophyceae)

Haematococcus pluvialis is a freshwater unicellular green microalga belonging to the class Chlorophyceae and is of commercial interest for its ability to accumulate massive amounts of the red ketocarotenoid astaxanthin (3,3′-dihydroxy-β,β-carotene-4,4′-dione). Using confocal Raman microscopy and multivariate analysis, we demonstrate the ability to spectrally resolve resonance–enhanced Raman signatures associated with astaxanthin and β-carotene along with chlorophyll fluorescence. By mathematically isolating these spectral signatures, in turn, it is possible to locate these species independent of each other in living cells of H. pluvialis in various stages of the life cycle. Chlorophyll emission was found only in the chloroplast whereas astaxanthin was identified within globular and punctate regions of the cytoplasmic space. Moreover, we found evidence for β-carotene to be co-located with both the chloroplast and astaxanthin in the cytosol. These observations imply that β-carotene is a precursor for astaxanthin and the synthesis of astaxanthin occurs outside the chloroplast. Our work demonstrates the broad utility of confocal Raman microscopy to resolve spectral signatures of highly similar chromophores in living cells