Multiple Imputation and Quantile Regression Methods for Biomarker Data subject to Detection Limits

Biomarkers are increasingly used in biomedical studies to better understand the natural history and development of a disease, identify the patients at high-risk and guide the therapeutic strategies for intervention. However, the measurement of these markers is often limited by the sensitivity of the given assay, resulting in data that are censored either at the lower limit or upper limit of detection. Ignoring censoring issue in any analysis may lead to the biased results. For a regression analysis where multiple censored biomarkers are included as predictors, we develop multiple imputation methods based on Gibbs sampling approach. The simulation study shows that our method significantly reduces the estimation bias as compared to the other simple imputation methods when the correlation between markers is high or the censoring proportion is high. The likelihood based mean regression for repeatedly measured biomarkers often assume a multivariate normal distribution that may not hold for biomarker data even after transformations. We consider a robust alternative, median regression, for censored longitudinal data. We develop an estimating equation approach that can incorporate the serial correlations between repeated measurements. We conduct simulation studies to evaluate the proposed estimators and compare median regression model with the mixed models under various specifications of distributions and covariance structures. Missing data is a common problem with longitudinal study. Under the assumptions that the missing pattern is monotonic and the missingness may only depend on the observed data, we propose a weighted estimating equation approach for the censored quantile regression models. The contribution of each individual to the estimating equation is weighted by the inverse probability of dropout at the given occasion. The resultant regression estimators are consistent when the dropout process is correctly specified. The performance of our estimating procedure is evaluated via simulation study. We illustrate all the proposed methods using the biomarker data of the Genetic and Inflammatory Markers of Sepsis (GenIMS) study. Appropriate handling of censored data in biomarker analysis is of public health importance because it will improve the understanding of the biological mechanisms of the underlying disease and aid in the successful development of future effective treatments

FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks

In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent neural networks (RNNs); one is a speech-to-character RNN for acoustic modeling (AM) and the other is for character-level language modeling (LM). The system also employs a statistical word-level LM to improve the recognition accuracy. The results of the AM, the character-level LM, and the word-level LM are combined using a fairly simple N-best search algorithm instead of the hidden Markov model (HMM) based network. The RNNs are implemented using massively parallel processing elements (PEs) for low latency and high throughput. The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA. The proposed algorithm is implemented on a Xilinx XC7Z045, and the system can operate much faster than real-time.Comment: Accepted to SiPS 201

Necessity Feature Correspondence Estimation for Large-scale Global Place Recognition and Relocalization

Global place recognition and 3D relocalization are one of the most important components in the loop closing detection for 3D LiDAR Simultaneous Localization and Mapping (SLAM). In order to find the accurate global 6-DoF transform by feature matching approach, various end-to-end architectures have been proposed. However, existing methods do not consider the false correspondence of the features, thereby unnecessary features are also involved in global place recognition and relocalization. In this paper, we introduce a robust correspondence estimation method by removing unnecessary features and highlighting necessary features simultaneously. To focus on the necessary features and ignore the unnecessary ones, we use the geometric correlation between two scenes represented in the 3D LiDAR point clouds. We introduce the correspondence auxiliary loss that finds key correlations based on the point align algorithm and enables end-to-end training of the proposed networks with robust correspondence estimation. Since the ground with many plane patches acts as an outlier during correspondence estimation, we also propose a preprocessing step to consider negative correspondence by removing dominant plane patches. The evaluation results on the dynamic urban driving dataset, show that our proposed method can improve the performances of both global place recognition and relocalization tasks. We show that estimating the robust feature correspondence is one of the important factors in place recognition and relocalization

Just Flip: Flipped Observation Generation and Optimization for Neural Radiance Fields to Cover Unobserved View

With the advent of Neural Radiance Field (NeRF), representing 3D scenes through multiple observations has shown remarkable improvements in performance. Since this cutting-edge technique is able to obtain high-resolution renderings by interpolating dense 3D environments, various approaches have been proposed to apply NeRF for the spatial understanding of robot perception. However, previous works are challenging to represent unobserved scenes or views on the unexplored robot trajectory, as these works do not take into account 3D reconstruction without observation information. To overcome this problem, we propose a method to generate flipped observation in order to cover unexisting observation for unexplored robot trajectory. To achieve this, we propose a data augmentation method for 3D reconstruction using NeRF by flipping observed images, and estimating flipped camera 6DOF poses. Our technique exploits the property of objects being geometrically symmetric, making it simple but fast and powerful, thereby making it suitable for robotic applications where real-time performance is important. We demonstrate that our method significantly improves three representative perceptual quality measures on the NeRF synthetic dataset

Density functional calculations of the electronic structure and magnetic properties of the hydrocarbon K3picene superconductor near the metal-insulator transition

We have investigated the electronic structures and magnetic properties of of K3picene, which is a first hydrocarbon superconductor with high transition temperature T_c=18K. We have shown that the metal-insulator transition (MIT) is driven in K3picene by 5% volume enhancement with a formation of local magnetic moment. Active bands for superconductivity near the Fermi level E_F are found to have hybridized character of LUMO and LUMO+1 picene molecular orbitals. Fermi surfaces of K3picene manifest neither prominent nesting feature nor marked two-dimensional behavior. By estimating the ratio of the Coulomb interaction U and the band width W of the active bands near E_F, U/W, we have demonstrated that K3picene is located in the vicinity of the Mott transition.Comment: 5 pages, 5 figure