Radar-on-Lidar: metric radar localization on prior lidar maps

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes

The Linearization of V(P)-doubling Constructions

When an item moves, it is usually pronounced once but in some cases, it is pronounced multiple times. So, a question is: What determines whether a moved item gets pronounced in only one of its positions or in multiple positions? This dissertation aims at providing an answer to this question by designing a linearization process that yields the correct phonetic realization of a moved item, with a focus on V(P) movement. In particular, this dissertation provides a detailed analysis of how V(P)-doubling cases are linearized and thus show how a V(P) ends up being pronounced multiple times. Regarding the proposed linearization process in this dissertation, following Kusmer (2019), I assume that the basic linearization process contains Candidates Generator G, which generates a set of precedence relations, and Constraints, which pick the right subset from G. As for the Constraints, I adapt the Totality Constraint and the Asymmetry Constraint from Kayne (1994), the Anti-reflexivity Constraint from Partee, ter Meulen and Wall (1990), and Language Specific Constraints from Wilder (1999) and Kusmer (2019). In addition, I propose an Ordering Deletion rule that gets rid of redundant precedence relations and a Set-to-String algorithm that turns a set of precedence relations into a string. Furthermore, I adopt the idea from Fox and Pesetsky (2005) that linearization of precedence relations is implemented in a cyclic way. Also, I employ the idea behind Nunes (2004)\u27s morphological reanalysis that a higher level node can be linearized instead of the nodes it contains given certain circumstances. Finally, I present the predictions made by the proposed linearization process, which can be evaluated against more data for future research

Examining the Impact of Design Features of Electronic Health Records Patient Portals on the Usability and Information Communication for Shared Decision Making

The use of the Electronic Health Records (EHR) patient portal has been shown to be effective in generating positive outcomes in patients’ healthcare, improving patient engagement and patient-provider communication. Government legislation also required proof of its meaningful use among patients by healthcare providers. Typical patient portals also include features such as health information and patient education materials. However, little research has examined the specific use of patient portals related to individuals with specific diseases such as inflammatory bowel diseases (IBDs). IBDs are life-long, not curable, chronic diseases that can impact the whole population. Individuals with IBDs may have higher needs to acquire health information from their EHR portals to properly self-manage their health conditions. The research aims of the present dissertation are to understand the online health information-seeking behaviors of a target group (IBDs) of patients, the use of EHR patient portals, and the impact of design features of EHR patient portals on the usability and information communication for shared decision making. Through this dissertation, I conducted four studies to address the above research aims. First, I identified how individuals with inflammatory bowel disease (IBD) used the internet for health information seeking, the factors impacting their use of the internet to obtain health information, and how they used the internet for health-related tasks. The purpose of this study is to get a general understanding of the online health information-seeking behaviors and to guide the study of health information presentation of EHR portals in the following research. Second, I examined what factors influenced an EHR patient portal user to believe that the portal is a valuable part of their health care. This part of the dissertation aimed to reveal the critical design factors that help design an EHR portal perceived as valuable in managing health. Third, I looked at how patients used EHR patient portals, what features of the portals facilitated their use and encouraged Shared Decision Making (SDM) and engagement in health management and what features acted as barriers to SDM and their engagement in health management. This part of my dissertation focused on a broad understanding of EHR portals usage by introducing more specific factors such as features of EHR portals. Fourth, I conducted an eye-tracking study to examine how information presentation methods and chatbots impact the use and effect of patient portals. This part of my dissertation built on the other studies within my dissertation and deepened the understanding of the influence of different EHR portal designs on their effectiveness and people’s willingness to participate in SDM. The results of this dissertation contribute to the literature of understanding the information-seeking behaviors of IBD patients and the use of portals, as well as the design considerations of how to make a suitable EHR portal to support the information-seeking needs of IBD patients. The results of this dissertation can be used to guide building proper patient education materials to support their health information needs of their specific health condition, especially for individuals with chronic diseases that require a certain amount of self-management. Meanwhile, examining artificial intelligence (AI) based chatbots use in EHR portals reveals a potential path of AI use in healthcare, such as information acquisition and patient education. Designing good usable EHR may also facilitate the process of informing patients of the advantages and disadvantages of treatment plans for their disease and, therefore, may increase their willingness to participate in SDM

LocNet: Global localization in 3D point clouds for mobile vehicles

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.Comment: 6 pages, IV 2018 accepte

Can Corporate Governance Variables Enhance the Prediction Power of Accounting-Based Financial Distress Prediction Models?

We integrated accounting, corporate governance, and macroeconomic variables to build up a binary logistic regression model for the prediction of financially distressed firms. Debt ratio and ROA are found to be the most explanatory accounting variables while the percentage of directors controlled by the largest shareholder (which measures negative entrenchment effect), management participation, and the percentage of shares pledged for loans by large shareholders are shown to have positive contribution to the probability of financial distress. For macroeconomic sensitivities, firms with higher sensitivities to the annualized growth rates of manufacturing production index and money supply (M2) are more vulnerable to financial distress. As to the issue of sampling technique, we find that oversampling of distressed firms is subject to the problem of choice-based sample bias pointed out by Zmijewski (1984). The classification accuracy is overstated consequently. We try to include as many healthy firms as possible in our sample instead of following the traditional 1: 1 or 1: 2 matching principle. The results show that the classification accuracy is mostly significantly improved in our integrated prediction model when the sample is closest to the actual population. For the trade-off between type I and type II errors in the predicted probability classification, we maximize the sum of classification accuracy for both groups of firms (the healthy and the distressed). It is found that an estimated probability of financial distress of 0.2000 represents the optimal cutoff point for predicting financial distress. Under such a cutoff scheme, our integrated model produces an in-sample classification accuracy of 80.7% for distressed firms and 93.2% for healthy firms. For out-sample prediction, 90% of the distressed firms and 85.4% healthy firms in 2001 are correctly identified using an integrated model built upon samples from 1998 to 2000.Corporate governance, Financial distress prediction model, Choice-based sample bias