Seller Organization and Percentage Fee Design in the Daily Deal Market

The prosperity of the daily deal business has attracted more sellers to participate in daily deal campaigns with offering discounted deals via online platforms like Groupon and Juhuasuan. This gives rise to a new challenge for online platforms on how to efficiently organize a limited number of sellers to conduct daily deal campaigns. Our paper makes the first attempt to understand how different seller organization formats can influence the firms’ equilibrium strategies and profits in the daily deal market. We focus on two prevalent seller organization formats. (1) The seller agglomeration strategy: the platform (e.g., Groupon) does not distinguish the sellers’ type in each round of the campaign. (2) The seller segmentation strategy: the platform (e.g., Juhuasuan) organizes sellers of the same type in each round. Comparing to the agglomeration strategy, we show that the segmentation strategy can eliminate internal information asymmetry among competing sellers and thus can improve the sellers’ pricing efficiency and facilitate the platform to charge a higher percentage fee. This uncovers the value of seller segmentation and theoretically explains why platforms should carefully segmentate sellers in daily deal campaigns, although considerable efforts are required to enroll sellers.

A deep learning framework for quantitative analysis of actin microridges

Microridges are evolutionarily conserved actin-rich protrusions present on the apical surface of the squamous epithelial cells. In zebrafish epidermal cells, microridges form self-evolving patterns due to the underlying actomyosin network dynamics. However, their morphological and dynamic characteristics have remained poorly understood owing to lack of automated segmentation methods. We achieved ~97% pixel-level accuracy with the deep learning microridge segmentation strategy enabling quantitative insights into their bio-physical-mechanical characteristics. From the segmented images, we estimated an effective microridge persistence length as ~0.61μm. We discovered the presence of mechanical fluctuations and found relatively greater stresses stored within patterns of yolk than flank, indicating distinct regulation of their actomyosin networks. Furthermore, spontaneous formations and positional fluctuations of actin clusters within microridge influenced pattern rearrangements over short length/time-scales. Our framework allows large-scale spatiotemporal analysis of microridges during epithelial development and probing of their responses to chemical and genetic perturbations to unravel the underlying patterning mechanisms.

Iterative deep learning for improved segmentation of endoscopic images

Iterative segmentation is a unique way to prune the segmentation maps initialized by faster inference techniques or even unsupervised traditional thresholding methods. We used our previous feedback attention-based method for this work and demonstrate that with optimal iterative procedure our method can reach competitive accuracies in endoscopic imaging. For this work, we have applied this segmentation strategy for polyps and instruments.

How service quality and perceived value affect behavioral intentions of ecolodge guests: The moderating effect of prior visit

Psychographic segmentation is popular within the tourism literature. It is useful in describing a prototypical customer, however psychological attributes are hard to detect at the individual level and by front-line staff. This paper tests the viability of prior visits (first-time vs. repeat visits) as a segmentation strategy, given this information is readily available to tourism operators. We test an interaction effect between prior visits, service quality, and perceived value using the ECOSERV model, a well-established model of ecotourism customer satisfaction. Using a sample of ecolodge guests, we demonstrate that a prior visit attenuates the relationship between perceived value and customer satisfaction. Among repeat guests, perceived value has less impact upon customer satisfaction and intentions to revisit or recommend an ecolodge. Conversely, service quality continues to predict satisfaction for both first-time and repeat guests. The data suggest attracting first-time guests requires appeals to the setting, features and price of an offering. Meanwhile, strategies to maximize repeat guests should emphasize non-monetary qualities of the experience.

MSBC-Net: Automatic Rectal Cancer Segmentation from MR Scans

<div>Accurate segmentation of rectal cancer and rectal wall based on high-resolution T2-weighted magnetic resonance imaging (MRI-HRT2) is the basis of rectal cancer staging. However, complex imaging background, highly characteristics variation and poor contrast hindered the research progress of the automatic rectal cancer segmentation. In this study, a multi-task learning network, namely mask segmentation with boundary constraints (MSBC-Net), is proposed to overcome these limitations and to obtain accurate segmentation results by locating and segmenting rectal cancer and rectal wall automatically. Specifically, at first, a region of interest (RoI)-based segmentation strategy is designed to enable end-to-end multi-task training, where a sparse object detection module is used to automatically localize and classify rectal cancer and rectal wall to mitigate the problem of background interference, and a mask and boundary segmentation block is used to finely segment the RoIs; second, a modulated deformable backbone is introduced to handle the variable features of rectal cancer, which effectively improves the detection performance of small objects and adaptability of the proposed model. Moreover, the boundary head is fused into the mask head to segment the ambiguous boundary of the target and constrain the mask head to obtain more refined segmentation results. In total, 592 annotated rectal cancer patients in MRI-HRT2 are enrolled, and the comprehensive results show that the proposed MSBC-Net outperforms state-of-the-art methods with a dice similarity coefficient (DSC) of 0.801 (95\% CI, 0.791-0.811), which can be well extended to other medical image segmentation tasks with high potential clinical applicability.</div>

MSBC-Net: Automatic Rectal Cancer Segmentation from MR Scans

<div>Accurate segmentation of rectal cancer and rectal wall based on high-resolution T2-weighted magnetic resonance imaging (MRI-HRT2) is the basis of rectal cancer staging. However, complex imaging background, highly characteristics variation and poor contrast hindered the research progress of the automatic rectal cancer segmentation. In this study, a multi-task learning network, namely mask segmentation with boundary constraints (MSBC-Net), is proposed to overcome these limitations and to obtain accurate segmentation results by locating and segmenting rectal cancer and rectal wall automatically. Specifically, at first, a region of interest (RoI)-based segmentation strategy is designed to enable end-to-end multi-task training, where a sparse object detection module is used to automatically localize and classify rectal cancer and rectal wall to mitigate the problem of background interference, and a mask and boundary segmentation block is used to finely segment the RoIs; second, a modulated deformable backbone is introduced to handle the variable features of rectal cancer, which effectively improves the detection performance of small objects and adaptability of the proposed model. Moreover, the boundary head is fused into the mask head to segment the ambiguous boundary of the target and constrain the mask head to obtain more refined segmentation results. In total, 592 annotated rectal cancer patients in MRI-HRT2 are enrolled, and the comprehensive results show that the proposed MSBC-Net outperforms state-of-the-art methods with a dice similarity coefficient (DSC) of 0.801 (95\% CI, 0.791-0.811), which can be well extended to other medical image segmentation tasks with high potential clinical applicability.</div>

Market Segmentation Analysis in Tourism: A Perspective Paper

No two tourists are the same. This insight stands at the core of market segmentation. Pursuing a segmentation strategy as a tourist destination or a tourism business means catering to the specific needs of certain types of tourists (market segments), rather than attempting to satisfy the needs of the entire tourist market by effectively targeting the average tourist. But which market segments should a tourist destination or business target? Market segmentation analysis helps answer this question. Market segmentation analysis is “the process of grouping consumers into naturally existing or artificially created segments of consumers who share similar product preferences or characteristics” (Dolnicar, Grün &amp; Leisch, 2018, p. 11).