A Crowdsourcing Mode of Tourism Customization Based on Sharing Economy

China’s latest innovations of Internet Economy are partly reflected in video living broadcast, shared bicycles etc. In recent years, tourism industry in China obtains rapid development by utilizing Internet. However, it is still difficult to meet the growing tourist demands. In order to solve this problem, in this paper, we put forward a Tourism Crowdsourcing Model (TCM), which utilizes the public creativity to meet the increasing demands for personalized tourism. Firstly, the main problems of the tourism industry are analyzed. Secondly, the pattern of TCM is elaborated, and a matching algorithm between the tourist requirements and the workers’ abilities is well designed to find the qualified service providers efficiently and accurately. Finally, an example is given to verify the feasibility and effectiveness of the TCM based on shared economy. The results shows that TCM has some significant advantages to satisfy the tourism personalized needs by motivating the public to participate in the tourism industry initiatively

ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation

The performance of a semantic segmentation model for remote sensing (RS) images pretrained on an annotated dataset would greatly decrease when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods are facing two problems when performing RS images translation: 1) ignoring the scale discrepancy between two RS datasets which greatly affects the accuracy performance of scale-invariant objects, 2) ignoring the characteristic of real-to-real translation of RS images which brings an unstable factor for the training of the models. In this paper, ResiDualGAN is proposed for RS images translation, where an in-network resizer module is used for addressing the scale discrepancy of RS datasets, and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks. Combined with an output space adaptation method, the proposed method greatly improves the accuracy performance on common benchmarks, which demonstrates the superiority and reliability of ResiDuanGAN. At the end of the paper, a thorough discussion is also conducted to give a reasonable explanation for the improvement of ResiDualGAN. Our source code is available at https://github.com/miemieyanga/ResiDualGAN-DRDG

Learning Part Segmentation from Synthetic Animals

Semantic part segmentation provides an intricate and interpretable understanding of an object, thereby benefiting numerous downstream tasks. However, the need for exhaustive annotations impedes its usage across diverse object types. This paper focuses on learning part segmentation from synthetic animals, leveraging the Skinned Multi-Animal Linear (SMAL) models to scale up existing synthetic data generated by computer-aided design (CAD) animal models. Compared to CAD models, SMAL models generate data with a wider range of poses observed in real-world scenarios. As a result, our first contribution is to construct a synthetic animal dataset of tigers and horses with more pose diversity, termed Synthetic Animal Parts (SAP). We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution. Concretely, we examine three Syn-to-Real adaptation methods but observe relative performance drop due to the innate difference between the two tasks. To address this, we propose a simple yet effective method called Class-Balanced Fourier Data Mixing (CB-FDM). Fourier Data Mixing aligns the spectral amplitudes of synthetic images with real images, thereby making the mixed images have more similar frequency content to real images. We further use Class-Balanced Pseudo-Label Re-Weighting to alleviate the imbalanced class distribution. We demonstrate the efficacy of CB-FDM on SynRealPart over previous methods with significant performance improvements. Remarkably, our third contribution is to reveal that the learned parts from synthetic tiger and horse are transferable across all quadrupeds in PartImageNet, further underscoring the utility and potential applications of animal part segmentation

Active beam steering enabled by photonic crystal surface emitting laser

Emitting light towards on-demand directions is important for various optoelectronic applications, such as optical communication, displaying, and ranging. However, almost all existing directional emitters are assemblies of passive optical antennae and external light sources, which are usually bulky, fragile, and with unendurable loss of light power. Here we theoretically propose and experimentally demonstrate a new conceptual design of directional emitter, by using a single surface-emitting laser source itself to achieve dynamically controlled beam steering. The laser is built on photonic crystals that operates near the band edges in the continuum. By shrinking laser sizes into tens-of-wavelength, the optical modes quantize in three-dimensional momentum space, and each of them directionally radiates towards the far-field. Further utilizing the luminescence spectrum shifting effect under current injection, we consecutively select a sequence of modes into lasing action and show the laser maintaining in single mode operation with linewidths at a minimum of $1.8$ MHz and emitting power of $\sim$ ten milliwatts, and we demonstrate fast beam steering across a range of $3.2^\circ \times 4^\circ$ in a time scale of $500$ nanoseconds. Our work proposes a novel method for on-chip active beam steering, which could pave the way for the development of automotive, industrial, and robotic applications.Comment: 23 pages, 5 figure

Study on human-SRL synchronized walking based on coupled impedance

IntroductionSupernumerary robotic limbs (SRL) is a novel category of wearable robotics. Unlike prostheses (compensation for human limbs) and exoskeletons (augmentation of human limbs), SRL focuses on expanding human limbs and enhancing human activities, perception, and operation through the mutual collaboration of mechanical limbs and human limbs. The SRL of lower limbs are attached to the human waist, synchronized with the human walking in the forward direction, and can carry weight independently in the vertical direction.MethodsIn order to enhance the synchronization performance of the human-machine system during walking and minimize interference with human gait, it is essential to investigate the coupling dynamics within the human-SRL system. To facilitate our research, this paper focuses on relatively ideal working conditions: level road surfaces, no additional weight-bearing on the SRL, and humans walking in a straight line without any turns. We build upon the passive dynamic walking theory and utilize the human-SRL system model established by MIT to develop a coupling system model. Through numerical simulations, we identify the optimal values for the stiffness and damping coefficients of the human-machine connection. Additionally, we have designed the wheel-legged SRL structure and constructed the SRL control system for experimental validation.ResultsIt is found that a better synchronization of the human-machine walking process can be achieved by configuring suitable spring and damping units in the human-machine connection part.DiscussionIn this study, we explored the concept of SRL and its potential benefits for enhancing human motion, conducting simulations and experiments based on the coupled dynamics of human-SRL systems. The results indicate that by equipping the human-machine connection component with suitable spring and damping units, synchronization during the walking process can be improved

Arteriovenous fistulas in the craniocervical junction region: With vs. without spinal arterial feeders

ObjectiveArteriovenous fistulas (AVFs) in the craniocervical junction (CCJ) region are a rare occurrence with special clinical manifestations. This study retrospectively reviewed patients with CCJ AVFs treated at our neurosurgical center, aiming to enhance the understanding of CCJ AVFs.MethodsA total of 113 patients with CCJ AVFs treated at our neurosurgical center between January 2013 and December 2020 were enrolled. They were grouped as patients with CCJ AVFs with spinal arterial feeders (n = 20) and patients with CCJ AVF without spinal arterial feeders (n = 93). Clinical presentation, angiographic characteristics, intraoperative findings, and treatment outcomes were analyzed.ResultsThe patients’ median age was 55 years (IQR 47.5–62 years). The proportion of males in the group without spinal arterial feeders was significantly higher (p = 0.001). Subarachnoid hemorrhage (SAH) was the most common clinical presentation, especially in the group with spinal arterial feeders (p < 0.001). There were significant differences in AVF type, fistula location, and direction of the venous drainage between the two groups (p < 0.001). Intervention embolization combined with microsurgery was more common in treating AVFs with spinal arterial feeders (p = 0.006). Spinal arterial feeders did not affect the outcome (p = 0.275).ConclusionsSAH was the most common presentation of CCJ AVFs in this study. Microsurgery and interventional embolization were optional treatment strategies. The angioarchitecture of CCJ AVFs was essential for selecting treatment strategies

SAMAug: Point Prompt Augmentation for Segment Anything Model

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu