The Mediating Effect of Commitment on Customer Loyalty in eBrokerage: An Enhanced Investment Model

Customers could switch service provider easily because of lower searching cost and identical service in online environment. Most marketing research for customer loyalty emphasizes the effect of satisfaction and switching barrier, derived from investment model. However, how satisfaction and switching barrier influence customer loyalty has been less conclusive. The possible reason is neglect of commitment. We inject the concept of commitment in relationship marketing, which consists of affective and continuous commitment, into investment model for enhancing mediating role of commitment. Empirical results gathered from online survey in virtual financial communities show that commitment is the essential mediator in cultivating customer loyalty. Besides, satisfaction and switching barrier influence loyalty by different component of commitment, affective continuous commitment respectively. Affective commitment is more important than continuous commitment. E-brokerage should pay attention to earning customers’ commitment for retaining customers

Flowtable-Free Routing for Data Center Networks: A Software-Defined Approach

The paradigm shift toward SDN has exhibited the following trends: (1) relying on a centralized and more powerful controller to make intelligent decisions, and (2) allowing a set of relatively dumb switches to route packets. Therefore, efficiently looking up the flowtables in forwarding switches to guarantee low latency becomes a critical issue. In this paper, following the similar paradigm, we propose a new routing scheme called KeySet which is flowtable-free and enables constant-time switching at the forwarding switches. Instead of looking up long flowtables, KeySet relies on a residual system to quickly calculate routing paths. A switch only needs to do simple modular arithmetics to obtain a packet's forwarding output port. Moreover, KeySet has a nice fault- tolerant capability because in many cases the controller does not need to update flowtables at switches when a failure occurs. We validate KeySet through extensive simulations by using general as well as Facebook fat-tree topologies. The results show that the KeySet outperforms the KeyFlow scheme [1] by at least 25% in terms of the length of the forwarding label. Moreover, we show that KeySet is very efficient when applied to fat-trees

Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models

Indoor Depth Completion with Boundary Consistency and Self-Attention

Depth estimation features are helpful for 3D recognition. Commodity-grade depth cameras are able to capture depth and color image in real-time. However, glossy, transparent or distant surface cannot be scanned properly by the sensor. As a result, enhancement and restoration from sensing depth is an important task. Depth completion aims at filling the holes that sensors fail to detect, which is still a complex task for machine to learn. Traditional hand-tuned methods have reached their limits, while neural network based methods tend to copy and interpolate the output from surrounding depth values. This leads to blurred boundaries, and structures of the depth map are lost. Consequently, our main work is to design an end-to-end network improving completion depth maps while maintaining edge clarity. We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced. In addition, we propose boundary consistency concept to enhance the depth map quality and structure. Experimental results validate the effectiveness of our self-attention and boundary consistency schema, which outperforms previous state-of-the-art depth completion work on Matterport3D dataset. Our code is publicly available at https://github.com/patrickwu2/Depth-CompletionComment: Accepted by ICCVW (RLQ) 201

Analysis of Agreement on Traditional Chinese Medical Diagnostics for Many Practitioners

In Traditional Chinese Medicine (TCM) diagnostics, it is an important issue to study the degree of agreement among several distinct practitioners. In order to study the reliability of TCM diagnostics, we have to design an experiment to simultaneously deal with both of the cases when the data is ordinal and when there are many TCM practitioners. In this study, we consider a reliability measure called “Krippendorff's alpha” to investigate the agreement of tongue diagnostics in TCM. Besides, since it is not easy to obtain a large data set with patients rated simultaneously by many TCM practitioners, we use the renowned “bootstrapping” to obtain a 95% confidence interval for the Krippendorff's alpha. The estimated Krippendorff's alpha for the agreement among ten physicians that discerned fifteen randomly chosen patients is 0.7343, and the 95% bootstrapping confidence interval for the true alpha coefficient is [0.6570, 0.7349]. The data was collected and analyzed at the Department of Traditional Chinese Medicine, Changhua Christian Hospital (CCH) in Taiwan

LrrA, a novel leucine-rich repeat protein involved in cytoskeleton remodeling, is required for multicellular morphogenesis in Dictyostelium discoideum

AbstractCell sorting by differential cell adhesion and movement is a fundamental process in multicellular morphogenesis. We have identified a Dictyostelium discoideum gene encoding a novel protein, LrrA, which composes almost entirely leucine-rich repeats (LRRs) including a putative leucine zipper motif. Transcription of lrrA appeared to be developmentally regulated with robust expression during vegetative growth and early development. lrrA null cells generated by homologous recombination aggregated to form loose mounds, but subsequent morphogenesis was blocked without formation of the apical tip. The cells adhered poorly to a substratum and did not form tight cell–cell agglomerates in suspension; in addition, they were unable to polarize and exhibit chemotactic movement in the submerged aggregation and Dunn chamber chemotaxis assays. Fluorescence-conjugated phalloidin staining revealed that both vegetative and aggregation competent lrrA− cells contained numerous F-actin-enriched microspikes around the periphery of cells. Quantitative analysis of the fluorescence-stained F-actin showed that lrrA− cells exhibited a dramatically increase in F-actin as compared to the wild-type cells. When developed together with wild-type cells, lrrA− cells were unable to move to the apical tip and sorted preferentially to the rear and lower cup regions. These results indicate that LrrA involves in cytoskeleton remodeling, which is needed for normal chemotactic aggregation and efficient cell sorting during multicellular morphogenesis, particularly in the formation of apical tip

Bacteremic pneumonia caused by Nocardia veterana in an HIV-infected patient

SummaryDisseminated Nocardia veterana infection has rarely been reported. We describe the first reported case of N. veterana bacteremic pneumonia in an HIV-infected patient. The isolate was confirmed by 16S rRNA sequencing analysis. The patient initially responded well to trimethoprim–sulfamethoxazole treatment (minimum inhibitory concentration 0.25μg/ml), but died of ventilator-associated pneumonia

Investigation and identification of protein γ-glutamyl carboxylation sites

<p>Abstract</p> <p>Background</p> <p>Carboxylation is a modification of glutamate (Glu) residues which occurs post-translation that is catalyzed by γ-glutamyl carboxylase in the lumen of the endoplasmic reticulum. Vitamin K is a critical co-factor in the post-translational conversion of Glu residues to γ-carboxyglutamate (Gla) residues. It has been shown that the process of carboxylation is involved in the blood clotting cascade, bone growth, and extraosseous calcification. However, studies in this field have been limited by the difficulty of experimentally studying substrate site specificity in γ-glutamyl carboxylation. <it>In silico</it> investigations have the potential for characterizing carboxylated sites before experiments are carried out.</p> <p>Results</p> <p>Because of the importance of γ-glutamyl carboxylation in biological mechanisms, this study investigates the substrate site specificity in carboxylation sites. It considers not only the composition of amino acids that surround carboxylation sites, but also the structural characteristics of these sites, including secondary structure and solvent-accessible surface area (ASA). The explored features are used to establish a predictive model for differentiating between carboxylation sites and non-carboxylation sites. A support vector machine (SVM) is employed to establish a predictive model with various features. A five-fold cross-validation evaluation reveals that the SVM model, trained with the combined features of positional weighted matrix (PWM), amino acid composition (AAC), and ASA, yields the highest accuracy (0.892). Furthermore, an independent testing set is constructed to evaluate whether the predictive model is over-fitted to the training set.</p> <p>Conclusions</p> <p>Independent testing data that did not undergo the cross-validation process shows that the proposed model can differentiate between carboxylation sites and non-carboxylation sites. This investigation is the first to study carboxylation sites and to develop a system for identifying them. The proposed method is a practical means of preliminary analysis and greatly diminishes the total number of potential carboxylation sites requiring further experimental confirmation.</p