Detecting total hip replacement prosthesis design on preoperative radiographs using deep convolutional neural network. (arXiv:1911.12387v1 [eess.IV])

Identifying the design of a failed implant is a key step in preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss,…

Simultaneous Segmentation and Relaxometry for MRI through Multitask Learning. (arXiv:1911.12389v1 [physics.med-ph])

Purpose: This study demonstrated an MR signal multitask learning method for 3D simultaneous segmentation and relaxometry of human brain tissues. Materials and Methods: A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multi-contrast brain images. The deep neural network contained 3 residual blocks, and each block had 8 fully connected…

Goodness-of-fit test for the bivariate Hermite distribution. (arXiv:1911.12400v1 [math.ST])

This paper studies the goodness of fit test for the bivariate Hermite distribution. Specifically, we propose and study a Cram\’er-von Mises-type test based on the empirical probability generation function. The bootstrap can be used to consistently estimate the null distribution of the test statistics. A simulation study investigates the goodness of the bootstrap approach for…

Modelling dependence within and across run-off triangles for claims reserving. (arXiv:1911.12405v1 [stat.AP])

We propose a stochastic model for claims reserving that captures dependence along development years within a single triangle. This dependence is of autoregressive form of order $p$ and is achieved through the use of latent variables. We carry out bayesian inference on model parameters and borrow strength across several triangles, coming from different lines of…

Model-Aware Deep Architectures for One-Bit Compressive Variational Autoencoding. (arXiv:1911.12410v1 [eess.SP])

Parameterized mathematical models play a central role in understanding and design of complex information systems. However, they often cannot take into account the intricate interactions innate to such systems. On the contrary, purely data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of interpretability. In…

Conditional Hierarchical Bayesian Tucker Decomposition. (arXiv:1911.12426v1 [cs.LG])

Our research focuses on studying and developing methods for reducing the dimensionality of large datasets, common in biomedical applications. A major problem when learning information about patients based on genetic sequencing data is that there are often more feature variables (genetic data) than observations (patients). This makes direct supervised learning difficult. One way of reducing…

Causal inference of hazard ratio based on propensity score matching. (arXiv:1911.12430v1 [stat.ME])

Propensity score matching is commonly used to draw causal inference from observational survival data. However, there is no gold standard approach to analyze survival data after propensity score matching, and variance estimation after matching is open to debate. We derive the statistical properties of the propensity score matching estimator of the marginal causal hazard ratio…

AR-Net: A simple Auto-Regressive Neural Network for time-series. (arXiv:1911.12436v1 [cs.LG])

In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks. We focus on time-series with long-range dependencies, needed for monitoring fine granularity data (e.g. minutes, seconds, milliseconds), prevalent in operational use-cases. Traditional models, such as auto-regression fitted with least squares (Classic-AR) can model…

PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds. (arXiv:1911.12408v1 [cs.CV])

We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion without a prohibitive search space. We introduce novel cost volume, upsampling, and warping layers…

PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition. (arXiv:1911.12409v1 [cs.CV])

We propose a novel system for unsupervised skeleton-based action recognition. Given inputs of body keypoints sequences obtained during various movements, our system associates the sequences with actions. Our system is based on an encoder-decoder recurrent neural network, where the encoder learns a separable feature representation within its hidden states formed by training the model to…