Synapse: Interactive Guidance by Demonstration with Trial-and-Error Support for Older Adults to Use Smartphone Apps

As smartphones are widely adopted, mobile applications (apps) are emerging to provide critical services such as food delivery and telemedicine. While bring convenience to everyday life, this trend may create barriers for older adults who tend to be less tech-savvy than young people. In-person or screen sharing support is helpful but limited by the help-givers' availability. Video tutorials can be useful but require users to switch contexts between watching the tutorial and performing the corresponding actions in the app, which is cumbersome to do on a mobile phone. Although interactive tutorials have been shown to be promising, none was designed for older adults. Furthermore, the trial-and-error approach has been shown to be beneficial for older adults, but they often lack support to use the approach. Inspired by both interactive tutorials and trial-and-error approach, we designed an app-independent mobile service, \textit{Synapse}, for help-givers to create a multimodal interactive tutorial on a smartphone and for help-receivers (e.g., older adults) to receive interactive guidance with trial-and-error support when they work on the same task. We conducted a user study with 18 older adults who were 60 and over. Our quantitative and qualitative results show that Synapse provided better support than the traditional video approach and enabled participants to feel more confident and motivated. Lastly, we present further design considerations to better support older adults with trial-and-error on smartphones

DisLoc: A Convex Partitioning Based Approach for Distributed 3-D Localization in Wireless Sensor Networks

Accurate localization in wireless sensor networks (WSNs) is fundamental to many applications, such as geographic routing and position-aware data processing. This, however, is challenging in large scale 3-D WSNs due to the irregular topology, such as holes in the path, of the network. The irregular topology may cause overestimated Euclidean distance between nodes as the communication path is bent and accordingly introduces severe errors in 3-D WSN localization. As an effort towards the issue, this paper develops a distributed algorithm to achieve accurate 3-D WSN localization. Our proposal is composed of two steps, segmentation and joint localization. In specific, the entire network is first divided into several subnetworks by applying the approximate convex partitioning. A spatial convex node recognition mechanism is developed to assist the network segmentation, which relies on the connectivity information only. After that, each subnetwork is accurately localized by using the multidimensional scaling-based algorithm. The proposed localization algorithm also applies a new 3-D coordinate transformation algorithm, which helps reduce the errors introduced by coordinate integration between subnetworks and improve the localization accuracy. Using extensive simulations, we show that our proposal can effectively segment a complex 3-D sensor network and significantly improve the localization rate in comparison with existing solutions

Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution

Hyperspectral image has become increasingly crucial due to its abundant spectral information. However, It has poor spatial resolution with the limitation of the current imaging mechanism. Nowadays, many convolutional neural networks have been proposed for the hyperspectral image super-resolution problem. However, convolutional neural network (CNN) based methods only consider the local information instead of the global one with the limited kernel size of receptive field in the convolution operation. In this paper, we design a network based on the transformer for fusing the low-resolution hyperspectral images and high-resolution multispectral images to obtain the high-resolution hyperspectral images. Thanks to the representing ability of the transformer, our approach is able to explore the intrinsic relationships of features globally. Furthermore, considering the LR-HSIs hold the main spectral structure, the network focuses on the spatial detail estimation releasing from the burden of reconstructing the whole data. It reduces the mapping space of the proposed network, which enhances the final performance. Various experiments and quality indexes show our approach's superiority compared with other state-of-the-art methods

Effect of intense pulsed-light therapy on hair regrowth in C57BL/6J mice mediated by WNT/β-catenin signaling pathway

Purpose: To evaluate the effect of low-fluence intense pulsed light (IPL) on hair  growth in C57BL/6 mice, and to explore the potential molecular mechanisms of IPL actions on hair growth.Methods: After low-fluence IPL irradiation was applied to depilated dorsal skin of C57BL/6 mice in the telogen, or resting hair cycle phase, tissue samples were obtained and used for histopathological analysis. Hair growth was analyzed by measuring hair length. In addition, protein expression levels of WNT3A and β-catenin were assayed by western blot.Results: Low-fluence IPL irradiation promoted hair growth by inducing the anagen, or growth, phase in telogenic C57BL/6J mice. In particular, hair growth analysis suggested that application of low-fluence IPL induced an earlier transition from telogen to anagen phase and prolonged the duration of anagen phase compared to the control group (p < 0.05). Moreover, western blotting assay revealed that WNT3A and β-catenin protein levels were up-regulated compared to the control group (p < 0.05).Conclusion: These findings suggest that low-fluence IPL irradiation may be effective for promoting hair regrowth via activation of the WNT/β-catenin pathway, and may, therefore, be a potential novel therapeutic treatment to stimulate hair regrowth.Keywords: Intense pulsed light, Hair follicles, Hair growth, WNT3a/β-catenin  pathwa