Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regionsof- interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability

Introducing coherent time control to cavity magnon-polariton modes

By connecting light to magnetism, cavity magnon-polaritons (CMPs) can link quantum computation to spintronics. Consequently, CMP-based information processing devices have emerged over the last years, but have almost exclusively been investigated with single-tone spectroscopy. However, universal computing applications will require a dynamic and on-demand control of the CMP within nanoseconds. Here, we perform fast manipulations of the different CMP modes with independent but coherent pulses to the cavity and magnon system. We change the state of the CMP from the energy exchanging beat mode to its normal modes and further demonstrate two fundamental examples of coherent manipulation. We first evidence dynamic control over the appearance of magnon-Rabi oscillations, i.e., energy exchange, and second, energy extraction by applying an anti-phase drive to the magnon. Our results show a promising approach to control building blocks valuable for a quantum internet and pave the way for future magnon-based quantum computing research

Carbohydrate and protein contents of grain dusts in relation to dust morphology.

Grain dusts contain a variety of materials which are potentially hazardous to the health of workers in the grain industry. Because the characterization of grain dusts is incomplete, we are defining the botanical, chemical, and microbial contents of several grain dusts collected from grain elevators in the Duluth-Superior regions of the U.S. Here, we report certain of the carbohydrate and protein contents of dusts in relation to dust morphology. Examination of the gross morphologies of the dusts revealed that, except for corn, each dust contained either husk or pericarp (seed coat in the case of flax) fragments in addition to respirable particles. When viewed with the light microscope, the fragments appeared as elongated, pointed structures. The possibility that certain of the fragments within corn, settled, and spring wheat were derived from cell walls was suggested by the detection of pentoses following colorimetric assay of neutralized 2 N trifluoroacetic acid hydrolyzates of these dusts. The presence of pentoses together with the occurrence of proteins within water washings of grain dusts suggests that glycoproteins may be present within the dusts. With scanning electron microscopy, each dust was found to consist of a distinct assortment of particles in addition to respirable particles. Small husk fragments and "trichome-like" objects were common to all but corn dust

Shift-Symmetric Configurations in Two-Dimensional Cellular Automata: Irreversibility, Insolvability, and Enumeration

The search for symmetry as an unusual yet profoundly appealing phenomenon, and the origin of regular, repeating configuration patterns have long been a central focus of complexity science and physics. To better grasp and understand symmetry of configurations in decentralized toroidal architectures, we employ group-theoretic methods, which allow us to identify and enumerate these inputs, and argue about irreversible system behaviors with undesired effects on many computational problems. The concept of so-called configuration shift-symmetry is applied to two-dimensional cellular automata as an ideal model of computation. Regardless of the transition function, the results show the universal insolvability of crucial distributed tasks, such as leader election, pattern recognition, hashing, and encryption. By using compact enumeration formulas and bounding the number of shift-symmetric configurations for a given lattice size, we efficiently calculate the probability of a configuration being shift-symmetric for a uniform or density-uniform distribution. Further, we devise an algorithm detecting the presence of shift-symmetry in a configuration. Given the resource constraints, the enumeration and probability formulas can directly help to lower the minimal expected error and provide recommendations for system's size and initialization. Besides cellular automata, the shift-symmetry analysis can be used to study the non-linear behavior in various synchronous rule-based systems that include inference engines, Boolean networks, neural networks, and systolic arrays.Comment: 22 pages, 9 figures, 2 appendice

Impact of foregrounds on Hi intensity mapping cross-correlations with optical surveys

The future of precision cosmology could benefit from cross-correlations between intensity maps of unresolved neutral hydrogen (HI) and more conventional optical galaxy surveys. A major challenge that needs to be overcome is removing the 21cm foreground emission that contaminates the cosmological HI signal. Using N-body simulations we simulate HI intensity maps and optical catalogues which share the same underlying cosmology. Adding simulated foreground contamination and using state-of-the-art reconstruction techniques we investigate the impacts that 21cm foregrounds and other systematics have on these cross-correlations. We find that the impact a FASTICA 21cm foreground clean has on the cross-correlations with spectroscopic optical surveys with well-constrained redshifts is minimal. However, problems arise when photometric surveys are considered: we find that a redshift uncertainty {\sigma}_z {\geq} 0.04 causes significant degradation in the cross power spectrum signal. We diagnose the main root of these problems, which relates to arbitrary amplitude changes along the line-of-sight in the intensity maps caused by the foreground clean and suggest solutions which should be applicable to real data. These solutions involve a reconstruction of the line-of-sight temperature means using the available overlapping optical data along with an artificial extension to the HI data through redshift to address edge effects. We then put these solutions through a further test in a mock experiment that uses a clustering-based redshift estimation technique to constrain the photometric redshifts of the optical sample. We find that with our suggested reconstruction, cross-correlations can be utilized to make an accurate prediction of the optical redshift distribution.Comment: Version 2 - accepted for publication on 5th July 2019 in Monthly Notices of the Royal Astronomical Society Main Journa

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localized) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.Comment: Presented in MICCAI 2017 workshop, DLMIA 2017 (Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support