Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements. The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting concepts from spectral graph theory to allow these operations on irregular graphs. We demonstrate the potential of our method in the field of connectomics, where neuronal pathways or functional connections between brain regions are commonly modelled as graphs. In this problem, the definition of an appropriate graph similarity function is critical to unveil patterns of disruptions associated with certain brain disorders. Experimental results on the ABIDE dataset show that our method can learn a graph similarity metric tailored for a clinical application, improving the performance of a simple k-nn classifier by 11.9% compared to a traditional distance metric.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

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

Cold and Slow Molecular Beam

Employing a two-stage cryogenic buffer gas cell, we produce a cold, hydrodynamically extracted beam of calcium monohydride molecules with a near effusive velocity distribution. Beam dynamics, thermalization and slowing are studied using laser spectroscopy. The key to this hybrid, effusive-like beam source is a "slowing cell" placed immediately after a hydrodynamic, cryogenic source [Patterson et al., J. Chem. Phys., 2007, 126, 154307]. The resulting CaH beams are created in two regimes. One modestly boosted beam has a forward velocity of vf = 65 m/s, a narrow velocity spread, and a flux of 10^9 molecules per pulse. The other has the slowest forward velocity of vf = 40 m/s, a longitudinal temperature of 3.6 K, and a flux of 5x10^8 molecules per pulse

Franck-Condon Factors and Radiative Lifetime of the A^{2}\Pi_{1/2} - X^{2}\Sigma^{+} Transition of Ytterbium Monoflouride, YbF

The fluorescence spectrum resulting from laser excitation of the A^{2}\Pi_{1/2} - X^{2}\Sigma^{+} (0,0) band of ytterbium monofluoride, YbF, has been recorded and analyzed to determine the Franck-Condon factors. The measured values are compared with those predicted from Rydberg-Klein-Rees (RKR) potential energy curves. From the fluorescence decay curve the radiative lifetime of the A^{2}\Pi_{1/2} state is measured to be 28\pm2 ns, and the corresponding transition dipole moment is 4.39\pm0.16 D. The implications for laser cooling YbF are discussed.Comment: 5 pages, 5 figure

Laser cooling of a diatomic molecule

It has been roughly three decades since laser cooling techniques produced ultracold atoms, leading to rapid advances in a vast array of fields. Unfortunately laser cooling has not yet been extended to molecules because of their complex internal structure. However, this complexity makes molecules potentially useful for many applications. For example, heteronuclear molecules possess permanent electric dipole moments which lead to long-range, tunable, anisotropic dipole-dipole interactions. The combination of the dipole-dipole interaction and the precise control over molecular degrees of freedom possible at ultracold temperatures make ultracold molecules attractive candidates for use in quantum simulation of condensed matter systems and quantum computation. Also ultracold molecules may provide unique opportunities for studying chemical dynamics and for tests of fundamental symmetries. Here we experimentally demonstrate laser cooling of the molecule strontium monofluoride (SrF). Using an optical cycling scheme requiring only three lasers, we have observed both Sisyphus and Doppler cooling forces which have substantially reduced the transverse temperature of a SrF molecular beam. Currently the only technique for producing ultracold molecules is by binding together ultracold alkali atoms through Feshbach resonance or photoassociation. By contrast, different proposed applications for ultracold molecules require a variety of molecular energy-level structures. Our method provides a new route to ultracold temperatures for molecules. In particular it bridges the gap between ultracold temperatures and the ~1 K temperatures attainable with directly cooled molecules (e.g. cryogenic buffer gas cooling or decelerated supersonic beams). Ultimately our technique should enable the production of large samples of molecules at ultracold temperatures for species that are chemically distinct from bialkalis.Comment: 10 pages, 7 figure

Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction

There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis. Despite the success of convolutional neural networks in representation learning for imaging data, it is still a very challenging task. In this paper, we propose a generalizable framework that can automatically integrate imaging data with non-imaging data in populations for uncertainty-aware disease prediction. At its core is a learnable adaptive population graph with variational edges, which we mathematically prove that it is optimizable in conjunction with graph convolutional neural networks. To estimate the predictive uncertainty related to the graph topology, we propose the novel concept of Monte-Carlo edge dropout. Experimental results on four databases show that our method can consistently and significantly improve the diagnostic accuracy for Autism spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its generalizability in leveraging multimodal data for computer-aided diagnosis.Comment: Accepted to MICCAI 202

Learning Graph-Convolutional Representations for Point Cloud Denoising

Point clouds are an increasingly relevant data type but they are often corrupted by noise. We propose a deep neural network based on graph-convolutional layers that can elegantly deal with the permutation-invariance problem encountered by learning-based point cloud processing methods. The network is fully-convolutional and can build complex hierarchies of features by dynamically constructing neighborhood graphs from similarity among the high-dimensional feature representations of the points. When coupled with a loss promoting proximity to the ideal surface, the proposed approach significantly outperforms state-of-the-art methods on a variety of metrics. In particular, it is able to improve in terms of Chamfer measure and of quality of the surface normals that can be estimated from the denoised data. We also show that it is especially robust both at high noise levels and in presence of structured noise such as the one encountered in real LiDAR scans.Comment: European Conference on Computer Vision (ECCV) 202

The emergence of waves in random discrete systems

Essential criteria for the emergence of wave-like manifestations occurring in an entirely discrete system are identified using a simple model for the movement of particles through a network. The dynamics are entirely stochastic and memoryless involving a birth-death-migration process. The requirements are that the network should have at least three nodes, that migration should have a directional bias, and that the particle dynamics have a non-local dependence. Well defined bifurcations mark transitions between amorphous, wave-like and collapsed states with an intermittent regime between the latter two

Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.Comment: Accepted for the Shape in Medical Imaging (ShapeMI) workshop at MICCAI International Conference 202

The Buffer Gas Beam: An Intense, Cold, and Slow Source for Atoms and Molecules

Beams of atoms and molecules are stalwart tools for spectroscopy and studies of collisional processes. The supersonic expansion technique can create cold beams of many species of atoms and molecules. However, the resulting beam is typically moving at a speed of 300-600 m/s in the lab frame, and for a large class of species has insufficient flux (i.e. brightness) for important applications. In contrast, buffer gas beams can be a superior method in many cases, producing cold and relatively slow molecules in the lab frame with high brightness and great versatility. There are basic differences between supersonic and buffer gas cooled beams regarding particular technological advantages and constraints. At present, it is clear that not all of the possible variations on the buffer gas method have been studied. In this review, we will present a survey of the current state of the art in buffer gas beams, and explore some of the possible future directions that these new methods might take