Users’ Continued Usage of Online Healthcare Virtual Communities: An Empirical Investigation in the Context of HIV Support Communities

This study uses data from an online HIV/AIDS health support virtual community to examine whether users’ emotional states and the social support they receive influence their continued usage. We adopt grief theory to conceptualize the negative emotions that people living with HIV/AIDS could experience. Linguistic analysis is used to measure the emotional states of the users and the informational and emotional support that they receive. Results show that users showing a higher level of disbelief and yearning are more likely to leave the community while those with a high level of anger and depression are more likely to stay on. Users who receive more informational support are more likely to leave once they have obtained the information they sought, but those who receive more emotional support are more likely to stay on. The findings of this study can help us better understand users’ support seeking behavior in online support VCs

Optimization of Renormalization Group Flow

Renormalization group flow equations for scalar lambda Phi^4 are generated using three classes of smooth smearing functions. Numerical results for the critical exponent nu in three dimensions are calculated by means of a truncated series expansion of the blocked potential. We demonstrate how the convergence of nu as a function of the order of truncation can be improved through a fine tuning of the smoothness of the smearing functions.Comment: 23 pages, 7 figure

Triplet-based Deep Similarity Learning for Person Re-Identification

In recent years, person re-identification (re-id) catches great attention in both computer vision community and industry. In this paper, we propose a new framework for person re-identification with a triplet-based deep similarity learning using convolutional neural networks (CNNs). The network is trained with triplet input: two of them have the same class labels and the other one is different. It aims to learn the deep feature representation, with which the distance within the same class is decreased, while the distance between the different classes is increased as much as possible. Moreover, we trained the model jointly on six different datasets, which differs from common practice - one model is just trained on one dataset and tested also on the same one. However, the enormous number of possible triplet data among the large number of training samples makes the training impossible. To address this challenge, a double-sampling scheme is proposed to generate triplets of images as effective as possible. The proposed framework is evaluated on several benchmark datasets. The experimental results show that, our method is effective for the task of person re-identification and it is comparable or even outperforms the state-of-the-art methods.Comment: ICCV Workshops 201

World Market for Mergers and Acquisitions

Despite the fact that one-third of worldwide mergers involve firms from different countries, the vast majority of the academic literature on mergers studies domestic mergers. What little has been written about cross-border mergers has focused on public firms, usually from the United States. Yet, the vast majority of cross-border mergers involve private firms that are not from the United States. We provide an analysis of a sample of 56,978 cross-border mergers occurring between 1990 and 2007. We first characterize the patterns of who buys whom: Geography matters, with firms being much more likely to purchase firms in nearby countries than in countries far away. Purchasers are usually but not always from developed countries and they tend to purchase firms in countries with lower accounting standards. A significant factor in determining acquisition patterns is currency movements; firms tend to purchase firms from countries relative to which the currency of the acquirers country has appreciated. In addition, economy-wide factors reflected in the countrys stock market returns lead to acquisitions as well. Both the currency and stock market effect could suggest either misvaluation or wealth explanations. Our evidence is more consistent with the wealth explanation than the misvaluation explanation.Mergers; Currency movements; Market movements; Valuation

LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery

State-of-the-art object detection approaches such as Fast/Faster R-CNN, SSD, or YOLO have difficulties detecting dense, small targets with arbitrary orientation in large aerial images. The main reason is that using interpolation to align RoI features can result in a lack of accuracy or even loss of location information. We present the Local-aware Region Convolutional Neural Network (LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery. We enhance translation invariance to detect dense vehicles and address the boundary quantization issue amongst dense vehicles by aggregating the high-precision RoIs' features. Moreover, we resample high-level semantic pooled features, making them regain location information from the features of a shallower convolutional block. This strengthens the local feature invariance for the resampled features and enables detecting vehicles in an arbitrary orientation. The local feature invariance enhances the learning ability of the focal loss function, and the focal loss further helps to focus on the hard examples. Taken together, our method better addresses the challenges of aerial imagery. We evaluate our approach on several challenging datasets (VEDAI, DOTA), demonstrating a significant improvement over state-of-the-art methods. We demonstrate the good generalization ability of our approach on the DLR 3K dataset.Comment: 8 page

Target-Tailored Source-Transformation for Scene Graph Generation

Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performing works to date are based on exploiting the context surrounding objects or relations,e.g., by passing information among objects. In these approaches, to transform the representation of source objects is a critical process for extracting information for the use by target objects. In this work, we argue that a source object should give what tar-get object needs and give different objects different information rather than contributing common information to all targets. To achieve this goal, we propose a Target-TailoredSource-Transformation (TTST) method to efficiently propagate information among object proposals and relations. Particularly, for a source object proposal which will contribute information to other target objects, we transform the source object feature to the target object feature domain by simultaneously taking both the source and target into account. We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation. By doing so the target object is able to extract target-specific information from the source object and source relation accordingly to refine its representation. Our framework is validated on the Visual Genome bench-mark and demonstrated its state-of-the-art performance for the scene graph generation. The experimental results show that the performance of object detection and visual relation-ship detection are promoted mutually by our method