Divided Loyalists Or Conditional Cooperators? Creating Consensus About Cooperation In Multiple Simultaneous Social Dilemmas

The current social dilemma literature lacks theoretical consensus regarding how individuals behave when facing multiple simultaneous social dilemmas. The divided-loyalty hypothesis, from organizational theory, predicts that cooperation will decline as individuals experience multiple social dilemmas with different compared to the same group members. The conditional-cooperation hypothesis, from behavioral economics, predicts that cooperation will increase as individuals experience multiple social dilemmas with different compared to the same group members. We employ a laboratory experiment to create consensus between these literatures and find support for the conditional-cooperation hypothesis. The positive effect of interacting with different group members comes from participants having an opportunity to shift their cooperative behavior from the less cooperative to the more cooperative group

Analyzing Neuroimaging Data Through Recurrent Deep Learning Models

The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI) data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates an fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DeepLight, we apply it to a large fMRI dataset of the Human Connectome Project. We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.BMBF, 01IS14013A, BBDC - Berliner Kompetenzzentrum für Big DataBMBF, 01IS18056A, TraMeExCo - Transparenter Begleiter für medizinische AnwendungDFG, EXC 2046, MATH+: Berlin Mathematics Research Cente

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

Enhanced transmission versus localization of a light pulse by a subwavelength metal slit: Can the pulse have both characteristics?

The existence of resonant enhanced transmission and collimation of light waves by subwavelength slits in metal films [for example, see T.W. Ebbesen et al., Nature (London) 391, 667 (1998) and H.J. Lezec et al., Science, 297, 820 (2002)] leads to the basic question: Can a light be enhanced and simultaneously localized in space and time by a subwavelength slit? To address this question, the spatial distribution of the energy flux of an ultrashort (femtosecond) wave-packet diffracted by a subwavelength (nanometer-size) slit was analyzed by using the conventional approach based on the Neerhoff and Mur solution of Maxwell's equations. The results show that a light can be enhanced by orders of magnitude and simultaneously localized in the near-field diffraction zone at the nm- and fs-scales. Possible applications in nanophotonics are discussed.Comment: 5 figure

A Treatise on Diversity in a Dominant Culture University

The authors examine progress in strengthening the Diversity agenda in a school of education within a private Christian university. This agenda is informed by external academic accrediting organizations and principles of social justice congruent with the historical roots of the university. Special emphasis is placed on the unique challenges of confronting how privilege manifests itself in seemingly homogeneous environments. The ultimate goal of the authors is to promote moving beyond cosmetic compliance with accreditation obligations towards a metabolized second order change reflecting internal paradigm shifts in which social justice is a central motivating factor in one’s vocation

Characterisation of solid particles emitted from diesel and petrol engines as a contribution to the determination of the origin of carbonaceous particles in urban aerosol

Solid particles emitted from diesel and petrol engines were studied using a scanning electron microscope fitted with an energy dispersive spectrometer. The soot emitted from different engines under different operating conditions differed in particle size, and the form and size of aggregates. Identification of the soot particles emitted from diesel or petrol engines in urban aerosol based on their size and morphology was found to be impossible

Towards the interpretability of deep learning models for human neuroimaging

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear.We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as lesions, iron accumulations and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular riskfactors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood