Spectral Dynamic Causal Modelling of Resting-State fMRI: Relating Effective Brain Connectivity in the Default Mode Network to Genetics. (arXiv:1901.09975v7 [q-bio.NC] UPDATED)

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We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects…

Inverse Reinforcement Learning with Missing Data. (arXiv:1911.06930v1 [cs.LG])

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We consider the problem of recovering an expert’s reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in the demonstrated trajectories. This issue of missing trajectory data or information occurs in many situations, e.g., GPS signals from vehicles moving on a road network are intermittent. In this paper, we…

Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare. (arXiv:1911.06935v1 [cs.LG])

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Common fairness definitions in machine learning focus on balancing notions of disparity and utility. In this work, we study fairness in the context of risk disparity among sub-populations. We are interested in learning models that minimize performance discrepancies across sensitive groups without causing unnecessary harm. This is relevant to high-stakes domains such as healthcare, where…

$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering. (arXiv:1911.06944v1 [cs.LG])

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Divide-and-conquer is a general strategy to deal with large scale problems. It is typically applied to generate ensemble instances, which potentially limits the problem size it can handle. Additionally, the data are often divided by random sampling which may be suboptimal. To address these concerns, we propose the $DC^2$ algorithm. Instead of ensemble instances, we…

Marginal and Interactive Feature Screening of Ultra-high Dimensional Feature Spaces with Multivariate Response. (arXiv:1911.06955v1 [stat.ME])

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When the number of features exponentially outnumbers the number of samples, feature screening plays a pivotal role in reducing the dimension of the feature space and developing models based on such data. While most extant feature screening approaches are only applicable to data having univariate response, we propose a new method (GenCorr) that admits a…

Learning Behavioral Representations from Wearable Sensors. (arXiv:1911.06959v1 [cs.LG])

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The ubiquity of mobile devices and wearable sensors offers unprecedented opportunities for continuous collection of multimodal physiological data. Such data enables temporal characterization of an individual’s behaviors, which can provide unique insights into her physical and psychological health. Understanding the relation between different behaviors/activities and personality traits such as stress or work performance can help…

Inductive Relation Prediction on Knowledge Graphs. (arXiv:1911.06962v1 [cs.LG])

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Inferring missing edges in multi-relational knowledge graphs is a fundamental task in statistical relational learning. However, previous work has largely focused on the transductive relation prediction problem, where missing edges must be predicted for a single, fixed graph. In contrast, many real-world situations require relation prediction on dynamic or previously unseen knowledge graphs (e.g., for…

Strengths of near-threshold optical Feshbach resonances. (arXiv:1911.07063v1 [physics.atom-ph])

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Optical Feshbach resonances allow one to control cold atomic scattering, produce ultracold molecules and study atomic interactions via photoassociation spectroscopy. Here we give practical analytic expressions for the strength parameter, the optical length, of Feshbach resonances due to near-threshold bound states of an excited molecular state dominated by either a resonant-dipole or van der Waals…

Multilayer plasmonic photonic structures embedding photochromic molecules or optical gain molecules. (arXiv:1911.07070v1 [physics.optics])

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We design photonic structures embedding different functional molecular systems of photochromic switching and lasing. We study the light absorption of two photochromic molecules and of 4,4′-bis[(N-carbazole)styryl]biphenyl (BSB-Cz) with density functional theory. For the photochromic diarylethene we derivate the refractive index with Kramers-Kronig relations and we design multilayer photonic structures alternating diarylethene with either poly vinyl…

Electric field assisted alignment of monoatomic carbon chains. (arXiv:1911.07071v1 [physics.atm-clus])

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We stabilize monoatomic carbon chains in water by attaching them to gold nanoparticles (NPs) by means of the laser ablation process. Resulting nanoobjects represent pairs of NPs connected by multiple straight carbon chains of several nanometer lengths. If NPs at the opposite ends of a chain differ in size, the structure acquires a dipole moment…