The impact of foreign trading information on emerging futures markets: a study of Taiwan's unique data set

Using a unique dataset from the Taiwan Futures Exchange, this paper investigates whether trading imbalances by foreign investors affect emerging Taiwan futures market in terms of returns and volatility. First, this evidence demonstrates a positive relation between contemporaneous futures returns and net purchases by foreign investors when other market factor effects are controlled. Second, this failure to detect price reversals is inconsistent with the price pressure hypothesis. Third, foreign investors do not exhibit positive feedback trading patterns. Fourth, a bi-directional Granger-causality relationship exists between futures volatility and foreign trading flows. As found for other stock or foreign exchange markets, our empirical results demonstrate that foreign trading flows do have impacts on the return and volatility of developing futures market, suggesting that trading by foreign investors may enhance the information flow of the local futures market.Foreign trading

Assessing the Stroke-Specific Quality of Life for Outcome Measurement in Stroke Rehabilitation: Minimal Detectable Change and Clinically Important Difference

<p>Abstract</p> <p>Background</p> <p>This study was conducted to establish the minimal detectable change (MDC) and clinically important differences (CIDs) of the physical category of the Stroke-Specific Quality of Life Scale in patients with stroke.</p> <p>Methods</p> <p>MDC and CIDs scores were calculated from the data of 74 participants enrolled in randomized controlled trials investigating the effects of two rehabilitation programs in patients with stroke. These participants received treatments for 3 weeks and underwent clinical assessment before and after treatment. To obtain test-retest reliability for calculating MDC, another 25 patients with chronic stroke were recruited. The MDC was calculated from the standard error of measurement (SEM) to indicate a real change with 95% confidence for individual patients (MDC₉₅). Distribution-based and anchor-based methods were adopted to triangulate the ranges of minimal CIDs. The percentage of scale width was calculated by dividing the MDC and CIDs by the total score range of each physical category. The percentage of patients exceeding MDC₉₅and minimal CIDs was also reported.</p> <p>Results</p> <p>The MDC₉₅of the mobility, self-care, and upper extremity (UE) function subscales were 5.9, 4.0, and 5.3 respectively. The minimal CID ranges for these 3 subscales were 1.5 to 2.4, 1.2 to 1.9, and 1.2 to 1.8. The percentage of patients exceeding MDC₉₅and minimal CIDs of the mobility, self-care, and UE function subscales were 9.5% to 28.4%, 6.8% to 28.4%, and 12.2% to 33.8%, respectively.</p> <p>Conclusions</p> <p>The change score of an individual patient has to reach 5.9, 4.0, and 5.3 on the 3 subscales to indicate a true change. The mean change scores of a group of patients with stroke on these subscales should reach the lower bound of CID ranges of 1.5 (6.3% scale width), 1.2 (6.0% scale width), and 1.2 (6.0% scale width) to be regarded as clinically important change. This information may facilitate interpretations of patient-reported outcomes after stroke rehabilitation. Future research is warranted to validate these findings.</p

MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction

We present Mesh Pre-Training (MPT), a new pre-training framework that leverages 3D mesh data such as MoCap data for human pose and mesh reconstruction from a single image. Existing work in 3D pose and mesh reconstruction typically requires image-mesh pairs as the training data, but the acquisition of 2D-to-3D annotations is difficult. In this paper, we explore how to leverage 3D mesh data such as MoCap data, that does not have RGB images, for pre-training. The key idea is that even though 3D mesh data cannot be used for end-to-end training due to a lack of the corresponding RGB images, it can be used to pre-train the mesh regression transformer subnetwork. We observe that such pre-training not only improves the accuracy of mesh reconstruction from a single image, but also enables zero-shot capability. We conduct mesh pre-training using 2 million meshes. Experimental results show that MPT advances the state-of-the-art results on Human3.6M and 3DPW datasets. We also show that MPT enables transformer models to have zero-shot capability of human mesh reconstruction from real images. In addition, we demonstrate the generalizability of MPT to 3D hand reconstruction, achieving state-of-the-art results on FreiHAND dataset