Virtual Reality in Rehabilitation

Non

A Novel Data Mining Methodology for Narrative Text Mining and Its Application in MSHA Accident, Injury and Illness Database

Mining is one of the most dangerous industries. Mine Safety and Health Administration (MSHA) maintains a database that records thousands of mining related accidents, injuries or illnesses every year with incident descriptions in narrative texts. How to uncover knowledge from these narrative texts is lacking. The goal of this study is to propose a new data mining methodology that incorporates or extends existing methods and is able to uncover useful information from massive amount of narrative texts in a streamline fashion. In our experimentation with data of 2008, we focus on 3 different types of common injuries and apply the new methodology to their narrative texts. Some interesting results are found that are worthy further investigations with the help of mining safety experts

The Modality System and the Emotional Appeals: An Interpersonal Interpretation of Roosevelt’s Speeches

This article takes Franklin D. Roosevelt’s four inaugural speeches as objects of study, and mainly uses the modality system in Halliday’s systemic functional grammar as theoretical framework. This paper, from a functional-stylistic perspective, tries to investigate the close relationship between the modality system and the interpersonal function, i.e. its emotional appeals to the audience, underlying those typical linguistic markers, hence to uncover Roosevelt’s unmatched linguistic competence and speaking techniques. Our study shows that Roosevelt prefers modalization to modulation. As for modulation, obligation covers 18.70% signaling the speaker’s degree of pressure on the audience to take positive action, and inclination appears frequently, covering 13.01%, and is mainly realized by finite modal operators or adjectives, showing Roosevelt’s willingness to do something for his country and people. Through these sparkling speeches, his wisdom and intelligence, capability and responsibility, prestige and power are fully demonstrated

High-precision Absolute Distance Measurement using Dual-Laser Frequency Scanned Interferometry Under Realistic Conditions

In this paper, we report on new high-precision absolute distance measurements performed with frequency scanned interferometry using a pair of single-mode optical fibers. Absolute distances were determined by counting the interference fringes produced while scanning the frequencies of the two chopped lasers. High-finesse Fabry-Perot interferometers were used to determine frequency changes during scanning. Dual lasers with oppositely scanning directions, combined with a multi-distance-measurement technique previously reported, were used to cancel drift errors and to suppress vibration effects and interference fringe uncertainties. Under realistic conditions, a precision about 0.2 microns was achieved for a distance of 0.41 meters.Comment: 14 pages, 5 figures, submitted to Applied Optic

When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide the first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods \footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.Comment: Noted our camera-ready version has changed the title. "When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks" as the v3 official paper title in IEEE Proceeding. Please use it in your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm

Precise and Automated Tomographic Reconstruction with a Limited Number of Projections

This thesis proposed a parameter-optimized iterative reconstruction method (optimized-CGTV) for tomographic reconstruction with limited projections subject to the minimization of the total variation (TV). The reconstruction problem is solved with a parameter optimization applying a discrete L-curve. The optimized-CGTV reconstruction method is incorporated into an automatic framework of parallel 3D reconstruction on a computer cluster to achieve a rapid reconstruction process