BEYOND INSTITUTION-BASED TRUST: BUILDING EFFECTIVE ONLINE MARKETPLACES WITH SOCIAL MECHANISMS

Researchers have devoted considerable efforts to design effective online marketplaces, especially with respect to the institutional structures believed to establish buyer trust in the community of sellers. Comparatively speaking, the effectiveness of social mechanisms, although practically evidenced as important, has received much less attention in e-commerce research. In the current study we explore the contribution of social mechanisms–specifically IT-enabled instant messaging, the message box, online community and informal coalition programme–to effective online marketplaces. We propose that these mechanisms facilitate social relationships and trust building processes during transactions, in addition to the existing institutional structures. When consumerto- consumer (C2C) transactions are considered risky, the buyer-seller social relationship is more critical for buyers when forming their transaction intentions. The research model is largely supported by a pilot study of 104 buyers of TaoBao.com, China’s C2C leading marketplace. We discuss the findings, implications, and our preparations for a large-scale study

Fuzzy rule based multiwavelet ECG signal denoising

Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are corrupted by additive white Gaussian noises. In this paper, some fuzzy rules are formulated for integrating different multiwavelets, pre- and post-filters together so that expert knowledge on employing different multiwavelets, pre- and post-filters at different noise levels on denoising performances is exploited. When an ECG signal is received, the noise level is first estimated. Then, based on the estimated noise level and our proposed fuzzy rules, different multiwavelets, pre- and post-filters are integrated together. A hard thresholding is applied on the multiwavelet coefficients. According to extensive numerical computer simulations, our proposed fuzzy rule based multiwavelet denoising algorithm outperforms traditional multiwavelet denoising algorithms by 30%

Autonomous response of a third-order digital filter with two’s complement arithmetic realized in parallel form

This paper investigates the output and state trajectories of a third-order digital filter with two’s complement arithmetic realized in parallel form. Although the output of the third-order digital filter seems to behave randomly, some regular patterns can be displayed on the plot of versus , where those regular patterns are similar to the second-order case. When the first-order subsystem is operated at the marginally stable points, the output of the third-order system is still mainly dependent on the behaviors of the corresponding second-order digital filter, even though overflow occurs. Explicit equations relating the trajectories of the system to the filter parameters and the initial conditions provide further insights into the behaviors of the system

Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer

Video-based human pose transfer is a video-to-video generation task that animates a plain source human image based on a series of target human poses. Considering the difficulties in transferring highly structural patterns on the garments and discontinuous poses, existing methods often generate unsatisfactory results such as distorted textures and flickering artifacts. To address these issues, we propose a novel Deformable Motion Modulation (DMM) that utilizes geometric kernel offset with adaptive weight modulation to simultaneously perform feature alignment and style transfer. Different from normal style modulation used in style transfer, the proposed modulation mechanism adaptively reconstructs smoothed frames from style codes according to the object shape through an irregular receptive field of view. To enhance the spatio-temporal consistency, we leverage bidirectional propagation to extract the hidden motion information from a warped image sequence generated by noisy poses. The proposed feature propagation significantly enhances the motion prediction ability by forward and backward propagation. Both quantitative and qualitative experimental results demonstrate superiority over the state-of-the-arts in terms of image fidelity and visual continuity. The source code is publicly available at github.com/rocketappslab/bdmm.Comment: ICCV 202

High glucose represses β-klotho expression and impairs fibroblast growth factor 21 action in mouse pancreatic islets: involvement of peroxisome proliferator-activated receptor γ signaling

Circulating fibroblast growth factor 21 (FGF21) levels are elevated in diabetic subjects and correlate directly with abnormal glucose metabolism, while pharmacologically administered FGF21 can ameliorate hyperglycemia. The pancreatic islet is an FGF21 target, yet the actions of FGF21 in the islet under normal and diabetic conditions are not fully understood. This study investigated the effects of high glucose on islet FGF21 actions in a diabetic mouse model by investigating db/db mouse islet responses to exogenous FGF21, the direct effects of glucose on FGF21 signaling, and the involvement of peroxisome proliferator-activated receptor γ (PPARγ) in FGF21 pathway activation. Results showed that both adult db/db mouse islets and normal islets treated with high glucose ex vivo displayed reduced β-klotho expression, resistance to FGF21, and decreased PPARγ expression. Rosiglitazone, an antidiabetic PPARγ ligand, ameliorated these effects. Our data indicate that hyperglycemia in type 2 diabetes mellitus may lead to FGF21 resistance in pancreatic islets, probably through reduction of PPARγ expression, which provides a novel mechanism for glucose-mediated islet dysfunction

SVCNet: Scribble-based Video Colorization Network with Temporal Aggregation

In this paper, we propose a scribble-based video colorization network with temporal aggregation called SVCNet. It can colorize monochrome videos based on different user-given color scribbles. It addresses three common issues in the scribble-based video colorization area: colorization vividness, temporal consistency, and color bleeding. To improve the colorization quality and strengthen the temporal consistency, we adopt two sequential sub-networks in SVCNet for precise colorization and temporal smoothing, respectively. The first stage includes a pyramid feature encoder to incorporate color scribbles with a grayscale frame, and a semantic feature encoder to extract semantics. The second stage finetunes the output from the first stage by aggregating the information of neighboring colorized frames (as short-range connections) and the first colorized frame (as a long-range connection). To alleviate the color bleeding artifacts, we learn video colorization and segmentation simultaneously. Furthermore, we set the majority of operations on a fixed small image resolution and use a Super-resolution Module at the tail of SVCNet to recover original sizes. It allows the SVCNet to fit different image resolutions at the inference. Finally, we evaluate the proposed SVCNet on DAVIS and Videvo benchmarks. The experimental results demonstrate that SVCNet produces both higher-quality and more temporally consistent videos than other well-known video colorization approaches. The codes and models can be found at https://github.com/zhaoyuzhi/SVCNet.Comment: accepted by IEEE Transactions on Image Processing (TIP