Library anxiety among international graduate students

This pilot study investigated the level of library anxiety among 15 international graduate students in the United States, using a modified version of Bostick’s(1992) Library Anxiety Scale (LAS) with a proposed Language & Culture Barriers sub-scale. Findings from the pilot study revealed that mechanical barriers were the smallest source of library anxiety, and affective and staff barriers were the greatest sources of library anxiety.Posted presented in 2012 at the American Society for Information Science & Technology 75th Annual Meeting Proceedings

Spillover Effects of FDI in China: From the Perspective of Technology Gaps

On January 1, 2008, the Chinese government partly reduced the privileges enjoyed by FDI firms. This policy change again put the effects of FDI into public focus. Using Chinese industry-level panel data, this paper analyzes the spillover effects of FDI from the perspective of technology gaps (GAP). Unlike most previous studies that only analyze two or three levels of GAP, we instead treat it as a continuous explanatory variable. Also, we propose a more accurate measure for GAP. To overcome the difficulty of measuring spillover effects, we transform the spillover regression into the output regression. The method of DEA and a new set of instrumental variables are also employed to solve the problems of misspecification and endogeneity. We find that the spillover effects are negative and have a U-shaped relationship with GAP. These results are robust to various model specifications and estimation methods. We interpret our seemingly counter-intuitive findings as follows: the overall spillover effect can be divided into three components--the increasing "learning-room effect," the decreasing "learning-ability effect" and the "crowding-out effect," which is uncorrelated with GAP. Mainly because of the strict controls that FDI firms place on their core technologies, the "brain drain" from domestic firms to FDI firms, and the GDP-oriented behaviors of Chinese municipal officials, the negative "crowding-out effect" dominates the other two positive effects, and as GAP decreases from a small initial value, the increasing "learning-ability effect" dominates the decreasing "learning-room effect" (and vice versa). Consequently, a policy of reducing the privileges of FDI firms in industries with middle-sized technology gaps is suggested

Spillover Effects of FDI in China: From the Perspective of Technology Gaps

On January 1, 2008, the Chinese government partly reduced the privileges enjoyed by FDI firms. This policy change again put the effects of FDI into public focus. Using Chinese industry-level panel data, this paper analyzes the spillover effects of FDI from the perspective of technology gaps (GAP). Unlike most previous studies that only analyze two or three levels of GAP, we instead treat it as a continuous explanatory variable. Also, we propose a more accurate measure for GAP. To overcome the difficulty of measuring spillover effects, we transform the spillover regression into the output regression. The method of DEA and a new set of instrumental variables are also employed to solve the problems of misspecification and endogeneity. We find that the spillover effects are negative and have a U-shaped relationship with GAP. These results are robust to various model specifications and estimation methods. We interpret our seemingly counter-intuitive findings as follows: the overall spillover effect can be divided into three components--the increasing "learning-room effect," the decreasing "learning-ability effect" and the "crowding-out effect," which is uncorrelated with GAP. Mainly because of the strict controls that FDI firms place on their core technologies, the "brain drain" from domestic firms to FDI firms, and the GDP-oriented behaviors of Chinese municipal officials, the negative "crowding-out effect" dominates the other two positive effects, and as GAP decreases from a small initial value, the increasing "learning-ability effect" dominates the decreasing "learning-room effect" (and vice versa). Consequently, a policy of reducing the privileges of FDI firms in industries with middle-sized technology gaps is suggested

Remodeling and Estimation for Sparse Partially Linear Regression Models

When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains significant variables. But it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the first stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. The simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction

Remodeling and Estimation for Sparse Partially Linear Regression Models

When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains signi�cant variables. �ut it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the �rst stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. e simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction

AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy

Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to support context-awareness for next-generation intelligent surgical systems. To achieve accurate perception and automatic manipulation during the procedure, learning based technique is a promising way, which enables advanced image analysis and scene understanding in recent years. However, learning such models highly relies on large-scale, high-quality, and multi-task labelled data. This is currently a bottleneck for the topic, as available public dataset is still extremely limited in the field of CAI. In this paper, we present and release the first integrated dataset (named AutoLaparo) with multiple image-based perception tasks to facilitate learning-based automation in hysterectomy surgery. Our AutoLaparo dataset is developed based on full-length videos of entire hysterectomy procedures. Specifically, three different yet highly correlated tasks are formulated in the dataset, including surgical workflow recognition, laparoscope motion prediction, and instrument and key anatomy segmentation. In addition, we provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset. The dataset is available at https://autolaparo.github.io.Comment: Accepted at MICCAI 202

Toward Image-Guided Automated Suture Grasping Under Complex Environments: A Learning-Enabled and Optimization-Based Holistic Framework

To realize a higher-level autonomy of surgical knot tying in minimally invasive surgery (MIS), automated suture grasping, which bridges the suture stitching and looping procedures, is an important yet challenging task needs to be achieved. This paper presents a holistic framework with image-guided and automation techniques to robotize this operation even under complex environments. The whole task is initialized by suture segmentation, in which we propose a novel semi-supervised learning architecture featured with a suture-aware loss to pertinently learn its slender information using both annotated and unannotated data. With successful segmentation in stereo-camera, we develop a Sampling-based Sliding Pairing (SSP) algorithm to online optimize the suture's 3D shape. By jointly studying the robotic configuration and the suture's spatial characteristics, a target function is introduced to find the optimal grasping pose of the surgical tool with Remote Center of Motion (RCM) constraints. To compensate for inherent errors and practical uncertainties, a unified grasping strategy with a novel vision-based mechanism is introduced to autonomously accomplish this grasping task. Our framework is extensively evaluated from learning-based segmentation, 3D reconstruction, and image-guided grasping on the da Vinci Research Kit (dVRK) platform, where we achieve high performances and successful rates in perceptions and robotic manipulations. These results prove the feasibility of our approach in automating the suture grasping task, and this work fills the gap between automated surgical stitching and looping, stepping towards a higher-level of task autonomy in surgical knot tying

Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery

Objective: The computation of anatomical information and laparoscope position is a fundamental block of surgical navigation in Minimally Invasive Surgery (MIS). Recovering a dense 3D structure of surgical scene using visual cues remains a challenge, and the online laparoscopic tracking primarily relies on external sensors, which increases system complexity. Methods: Here, we propose a learning-driven framework, in which an image-guided laparoscopic localization with 3D reconstructions of complex anatomical structures is obtained. To reconstruct the 3D structure of the whole surgical environment, we first fine-tune a learning-based stereoscopic depth perception method, which is robust to the texture-less and variant soft tissues, for depth estimation. Then, we develop a dense visual reconstruction algorithm to represent the scene by surfels, estimate the laparoscope poses and fuse the depth maps into a unified reference coordinate for tissue reconstruction. To estimate poses of new laparoscope views, we achieve a coarse-to-fine localization method, which incorporates our reconstructed 3D model. Results: We evaluate the reconstruction method and the localization module on three datasets, namely, the stereo correspondence and reconstruction of endoscopic data (SCARED), the ex-vivo phantom and tissue data collected with Universal Robot (UR) and Karl Storz Laparoscope, and the in-vivo DaVinci robotic surgery dataset, where the reconstructed 3D structures have rich details of surface texture with an accuracy error under 1.71 mm and the localization module can accurately track the laparoscope with only images as input. Conclusions: Experimental results demonstrate the superior performance of the proposed method in 3D anatomy reconstruction and laparoscopic localization. Significance: The proposed framework can be potentially extended to the current surgical navigation system

Precisely visit the performance modulation of functionalized separator in Li-S batteries via consecutive multiscale analysis

Despite progress of functionalized separator in preventing the shuttle effect and promoting the sulfur utilization, the precise and non-destructive investigation of structure-function-performance associativity remains limited so far in Li-S batteries. Here, we build consecutive multiscale analysis via combining X-ray absorption fine structure (XAFS) and X-ray computational tomography (CT) techniques to precisely visit the structure-function-performance relationship. XAFS measurement offers the atomic scale changes in the chemical structure and environment. Moreover, a non-destructive technique of X-ray CT proves the functionalized separator role for microscopic scale, which is powerful chaining to bridge the chemical structures of the materials with the overall performance modulation of cells. Benefiting from this consecutive multiscale analysis, we report that the uniform doping of Sr2+ into the perovskite LaMnO3-δ material changes the Mn oxidation states and conductivity (chemical structure), leading to effective lithium polysulfide trapping and accelerated sulfur redox (separator function), and resulting in outstanding cell performance