Estimation of dynamic networks for high-dimensional nonstationary time series. (arXiv:1911.06385v1 [math.ST])

This paper is concerned with the estimation of time-varying networks for high-dimensional nonstationary time series. Two types of dynamic behaviors are considered: structural breaks (i.e., abrupt change points) and smooth changes. To simultaneously handle these two types of time-varying features, a two-step approach is proposed: multiple change point locations are first identified based on comparing…

Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling. (arXiv:1911.06393v1 [cs.LG])

Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. However, efficiently modelling long-term dependencies in these sequences is still challenging. Although the receptive field of these models grows exponentially with the number of layers, computing the…

Mining News Events from Comparable News Corpora: A Multi-Attribute Proximity Network Modeling Approach. (arXiv:1911.06407v1 [cs.LG])

We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model differentiates itself from other approaches by modeling both the event correlation within each individual document as well as across the corpus. To facilitate this, we introduce the concept of a…

Give me (un)certainty — An exploration of parameters that affect segmentation uncertainty. (arXiv:1911.06357v1 [eess.IV])

Segmentation tasks in medical imaging are inherently ambiguous: the boundary of a target structure is oftentimes unclear due to image quality and biological factors. As such, predicted segmentations from deep learning algorithms are inherently ambiguous. Additionally, “ground truth” segmentations performed by human annotators are in fact weak labels that further increase the uncertainty of outputs…

Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs. (arXiv:1911.06363v1 [eess.SP])

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients’ behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering…

Cavity-induced backscattering in a two-dimensional photonic topological system. (arXiv:1911.06323v1 [cond-mat.mes-hall])

The discovery of robust transport via topological states in electronic, photonic and phononic materials has deepened our understanding of wave propagation in condensed matter with prospects for critical applications of engineered metamaterials in communications, sensing, and controlling the environment. Topological protection of transmission has been demonstrated in the face of bent paths and on-site randomness…

Numerical simulations of self-diffusiophoretic colloids at fluid interfaces. (arXiv:1911.06324v1 [cond-mat.soft])

The dynamics of active colloids is very sensitive to the presence of boundaries and interfaces which therefore can be used to control their motion. Here we analyze the dynamics of active colloids adsorbed at a fluid-fluid interface. By using a mesoscopic numerical approach which relies on an approximated numerical solution of the Navier-Stokes equation, we…

Shape optimisation of stirring rods in mixing binary fluids. (arXiv:1911.06351v1 [physics.flu-dyn])

Mixing is an omnipresent process in a wide-range of industrial applications, which supports scientific efforts to devise techniques for optimising mixing processes under time and energy constraints. In this endeavor, we present a computational framework based on nonlinear direct-adjoint looping for the enhancement of mixing efficiency in a binary fluid system. The governing equations consist…

Correcting for Model Changes in Statistical Post-Processing — An approach based on Response Theory. (arXiv:1911.06361v1 [physics.ao-ph])

For most statistical post-processing schemes used to correct weather forecasts, changes to the forecast model induce a considerable reforcasting effort. We present a new approach based on response theory to cope with slight model change. In this framework, the model change is seen as a perturbation of the original forecast model. The response theory allows…