Establishing and Developing B2B Partnerships in Healthcare Services Industry: A case study of a Swedish healthcare provider

This study’s purpose is to find out in what ways do healthcare providers in Sweden establish their B2B partnerships. This study seeks understanding of the practical deployment of relationship marketing as a process. To fulfill this purpose, in this study two theories are used – network theory and commitment-trust theory. It follows the qualitative research analytical design and the main tool for gathering empirical data are semi-structured interviews conducted with the representatives of Skåne Care, Swecare and the University Hospital in Lund and Malmö. The object of this study is Skåne Care and its B2B partnerships with the aforementioned healthcare organizations operating in the Swedish healthcare market. The purpose of this research was accomplished and led to new findings on the importance of personal encounters in the beginning of the relationship establishment and lack of power plays in the relationships under study. These findings were followed by a couple of theoretical and practical implications

Support Neighbor Loss for Person Re-Identification

Person re-identification (re-ID) has recently been tremendously boosted due to the advancement of deep convolutional neural networks (CNN). The majority of deep re-ID methods focus on designing new CNN architectures, while less attention is paid on investigating the loss functions. Verification loss and identification loss are two types of losses widely used to train various deep re-ID models, both of which however have limitations. Verification loss guides the networks to generate feature embeddings of which the intra-class variance is decreased while the inter-class ones is enlarged. However, training networks with verification loss tends to be of slow convergence and unstable performance when the number of training samples is large. On the other hand, identification loss has good separating and scalable property. But its neglect to explicitly reduce the intra-class variance limits its performance on re-ID, because the same person may have significant appearance disparity across different camera views. To avoid the limitations of the two types of losses, we propose a new loss, called support neighbor (SN) loss. Rather than being derived from data sample pairs or triplets, SN loss is calculated based on the positive and negative support neighbor sets of each anchor sample, which contain more valuable contextual information and neighborhood structure that are beneficial for more stable performance. To ensure scalability and separability, a softmax-like function is formulated to push apart the positive and negative support sets. To reduce intra-class variance, the distance between the anchor's nearest positive neighbor and furthest positive sample is penalized. Integrating SN loss on top of Resnet50, superior re-ID results to the state-of-the-art ones are obtained on several widely used datasets.Comment: Accepted by ACM Multimedia (ACM MM) 201

The Predictive Role of Materialistic Values on Learning Burnout by Pre-service Teachers: A Parallel Channel Model

This study set out to explore the relationship between materialistic values (MVS), ontological security threat (OST), gratitude, and learning burnout (LB) among pre-service teachers enrolled in the Free Teacher Education program in China. MVS, adolescent student burnout, gratitude, and OST questionnaires were administered to 801 pre-service teachers. Data processing was conducted using IBM SPSS 26.0 and AMOS 24.0. The SPSS macro program Model 4 was used to identify mediating mechanisms. Study findings were as follows: (1) MVS was positively correlated with both OST and LB, but negatively correlated with gratitude. (2) OST was positively correlated with LB, while gratitude was negatively correlated with LB. (3) The impact of MVS on pre-service teachers' LB was simultaneously mediated by OST and gratitude. MVS not only directly predicts pre-service teachers' LB, but also influences LB through the independent mediators of OST and gratitude

Spatial-Spectral Transformer for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI. In this paper, we propose a Spatial-Spectral Transformer (SST) to alleviate this problem. To fully explore intrinsic similarity characteristics in both spatial dimension and spectral dimension, we conduct non-local spatial self-attention and global spectral self-attention with Transformer architecture. The window-based spatial self-attention focuses on the spatial similarity beyond the neighboring region. While, spectral self-attention exploits the long-range dependencies between highly correlative bands. Experimental results show that our proposed method outperforms the state-of-the-art HSI denoising methods in quantitative quality and visual results

Binarized Low-light Raw Video Enhancement

Recently, deep neural networks have achieved excellent performance on low-light raw video enhancement. However, they often come with high computational complexity and large memory costs, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-light raw video enhancement. Nevertheless, there are two main issues with binarizing video enhancement models. One is how to fuse the temporal information to improve low-light denoising without complex modules. The other is how to narrow the performance gap between binary convolutions with the full precision ones. To address the first issue, we introduce a spatial-temporal shift operation, which is easy-to-binarize and effective. The temporal shift efficiently aggregates the features of neighbor frames and the spatial shift handles the misalignment caused by the large motion in videos. For the second issue, we present a distribution-aware binary convolution, which captures the distribution characteristics of real-valued input and incorporates them into plain binary convolutions to alleviate the degradation in performance. Extensive quantitative and qualitative experiments have shown our high-efficiency binarized low-light raw video enhancement method can attain a promising performance.Comment: Accepted at CVPR 202

Lightweight Image Super-Resolution with Information Multi-distillation Network

In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive results. Thanks to the powerful representation capabilities of the deep networks, numerous previous ways can learn the complex non-linear mapping between low-resolution (LR) image patches and their high-resolution (HR) versions. However, excessive convolutions will limit the application of super-resolution technology in low computing power devices. Besides, super-resolution of any arbitrary scale factor is a critical issue in practical applications, which has not been well solved in the previous approaches. To address these issues, we propose a lightweight information multi-distillation network (IMDN) by constructing the cascaded information multi-distillation blocks (IMDB), which contains distillation and selective fusion parts. Specifically, the distillation module extracts hierarchical features step-by-step, and fusion module aggregates them according to the importance of candidate features, which is evaluated by the proposed contrast-aware channel attention mechanism. To process real images with any sizes, we develop an adaptive cropping strategy (ACS) to super-resolve block-wise image patches using the same well-trained model. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, memory footprint, and inference time. Code is available at \url{https://github.com/Zheng222/IMDN}.Comment: To be appear in ACM Multimedia 2019, https://github.com/Zheng222/IMD