Finding Statistically Significant Interactions between Continuous Features

The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing. While recent progress has been made regarding this challenge for binary features, we here present the first solution for continuous features. We propose an algorithm which overcomes the combinatorial explosion of the search space of higher-order interactions by deriving a lower bound on the p-value for each interaction, which enables us to massively prune interactions that can never reach significance and to thereby gain more statistical power. In our experiments, our approach efficiently detects all significant interactions in a variety of synthetic and real-world datasets.Comment: 13 pages, 5 figures, 2 tables, accepted to the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019

Inconsistency in serial choice decision and motor reaction times dissociate in younger and older adults

Intraindividual variability (inconsistency) in reaction time (RT) latencies was investigated in a group of younger (M = 25.46 years) and older (M = 69.29 years) men. Both groups performed 300 trials in 2-, 4-, and 8-choice RT conditions where RTs for decision and motor components of the task were recorded separately. A dissociation was evident in that inconsistency was greater in older adults for decision RTs when task demands relating to the number of choices and fatigue arising from time-on-task were high. For younger persons, a weak trend toward greater inconsistency in motor RTs was evident. The results are consistent with accounts suggesting that inconsistency in neurobiological mechanisms increases with age, and that attentional lapses or fluctuations in executive control contribute to RT inconsistency

UK Alcohol Treatment trial: client-treatment matching effects

Aim To test a priori hypotheses concerning client–treatment matching in the treatment of alcohol problems and to evaluate the more general hypothesis that client–treatment matching adds to the overall effectiveness of treatment. Design Pragmatic, multi-centre, randomized controlled trial (the UK Alcohol Treatment Trial: UKATT) with open follow-up at 3 months after entry and blind follow-up at 12 months. Setting Five treatment centres, comprising seven treatment sites, including National Health Service (NHS), social services and joint NHS/non-statutory facilities. Treatments Motivational enhancement therapy and social behaviour and network therapy. Measurements Matching hypotheses were tested by examining interactions between client attributes and treatment types at both 3 and 12 months follow-up using the outcome variables of percentage days abstinent, drinks per drinking day and scores on the Alcohol Problems Questionnaire and Leeds Dependence Questionnaire. Findings None of five matching hypotheses was confirmed at either follow-up point on any outcome variable. Conclusion The findings strongly support the conclusion reached in Project MATCH in the United States that client–treatment matching, at least of the kind examined, is unlikely to result in substantial improvements to the effectiveness of treatment for alcohol problems. Possible reasons for this failure to support the general matching hypothesis are discussed, as are the implications of UKATT findings for the provision of treatment for alcohol problems in the United Kingdom

Development in the early years : Its importance for school performance and adult outcomes [Wider Benefits of Learning Research Report No. 20]

Early development of children’s intellectual, social and physical abilities has the potential to affect their long term achievement, beyond the initial introduction to the classroom, through their school lives and into adulthood. A greater understanding of the processes at work in these early years and their role in later success is therefore important to ensure that resources are appropriately targeted. Past research has shown that early cognitive attainment is strongly related to later academic success. But we are also interested in the benefit that children gain from arriving at school with particular personal characteristics and the relationship which these may have to cognitive development. We also seek to explore the role of development (as opposed to innate capability) in the pre-school years. Data from the 1970 British Cohort Study is used to examine the importance of early measures of children’s cognitive ability and behavioural development for their subsequent school and labour market achievement. Our results suggest that, of the various measures used in this study, the most powerful predictor of later academic and labour market success is the ability of children to copy basic designs. However, we do not ignore the influence of behavioural factors and highlight the particular importance of skills related to attention with respect to these outcomes. The results clearly show that early development of both cognitive and behavioural skills have a role in subsequent achievement. In this respect, we believe that the findings in this report add to the debate on the appropriate balance between cognitive and non-cognitive skills at different ages and for different groups of children. In particular, failure to place sufficient emphasis on cognitive development may run counter to the interests of children from low SES groups. We believe that pedagogy should continue to address ways in which cognitive and non-cognitive abilities can support one another and how the interactions between these different groups of skills can best be harnessed for different groups of children

Predicting invasive breast cancer versus DCIS in different age groups.

BackgroundIncreasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age.MethodsWe analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women ≥ 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC).ResultsThe models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group--mass margins, and in the younger group--mass size were positive predictors of invasive cancer.ConclusionsClinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age

Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data

Object manipulation actions represent an important share of the Activities of Daily Living (ADLs). In this work, we study how to enable service robots to use human multi-modal data to understand object manipulation actions, and how they can recognize such actions when humans perform them during human-robot collaboration tasks. The multi-modal data in this study consists of videos, hand motion data, applied forces as represented by the pressure patterns on the hand, and measurements of the bending of the fingers, collected as human subjects performed manipulation actions. We investigate two different approaches. In the first one, we show that multi-modal signal (motion, finger bending and hand pressure) generated by the action can be decomposed into a set of primitives that can be seen as its building blocks. These primitives are used to define 24 multi-modal primitive features. The primitive features can in turn be used as an abstract representation of the multi-modal signal and employed for action recognition. In the latter approach, the visual features are extracted from the data using a pre-trained image classification deep convolutional neural network. The visual features are subsequently used to train the classifier. We also investigate whether adding data from other modalities produces a statistically significant improvement in the classifier performance. We show that both approaches produce a comparable performance. This implies that image-based methods can successfully recognize human actions during human-robot collaboration. On the other hand, in order to provide training data for the robot so it can learn how to perform object manipulation actions, multi-modal data provides a better alternative

Predicting Human Cooperation

The Prisoner's Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner's Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner's Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner's Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner's Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The computational model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is extremely successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.Comment: Added references. New inline citation style. Added small portions of text. Re-compiled Rmarkdown file with updated ggplot2 so small aesthetic changes to plot