As a popular dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in image classification. However, the NMF does not consider discriminant information from the ...data themselves. In addition, most NMF-based methods use the Euclidean distance as a metric, which is sensitive to noise or outliers in data. To solve these problems, in this paper, we introduce structural incoherence and low-rank to NMF and propose a novel nonnegative factorization method, called structurally incoherent low-rank NMF (SILR-NMF), in which we jointly consider structural incoherence and low-rank properties of data for image classification. For the corrupted data, we use the L 1 norm as a constraint to ensure the noise is sparse. SILR-NMF learns a clean data matrix from the noisy data by low-rank learning. As a result, the SILR-NMF can capture the global structure information of the data, which is more robust than local information to noise. By introducing the structural incoherence of the learned clean data, SILR-NMF ensures the clean data points from different classes are as independent as possible. To verify the performance of the proposed method, extensive experiments are conducted on six image databases. The experimental results demonstrate that our proposed method has substantial gain over existing NMF approaches.
Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount ...of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks-from regional content delivery network (CDN) servers to peer CDN servers (e.g., smartrouters in users' homes)-to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world data sets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over two weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand the performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching, including least recently used (LRU) and least frequently used (LFU), in terms of supporting mobile video requests. We reveal that content, location, and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30% compared with LRU/LFU.
Exponential growth of information generated by online social networks demands effective and scalable recommender systems to give useful results. Traditional techniques become unqualified because they ...ignore social relation data; existing social recommendation approaches consider social network structure, but social contextual information has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users' motivation of social behaviors into social recommendation. In this paper, we investigate the social recommendation problem on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online behavior prediction. Then we propose a novel probabilistic matrix factorization method to fuse them in latent space. We further provide a scalable algorithm which can incrementally process the large scale data. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network data sets. The empirical results and analysis on these two large data sets demonstrate that our method significantly outperforms the existing approaches.approaches.
The problem of current sharing controller for parallel dc-dc buck converter system is investigated in this paper. Specifically, to achieve the goal of sharing control and improve the systems dynamic ...performance, a new finite-time current sharing control algorithm is designed and employed. Under the proposed control algorithm, rigorous proofs show that not only the output voltage of the converter system can reach the desired reference voltage in a finite time, but also the objective of current sharing can be achieved within almost the same time. In addition, when the external load is time-varying and unknown, a finite-time load estimator is given to handle the load variation, and an adaptive finite-time current sharing control algorithm is subsequently developed. Experimental results are presented to verify the effectiveness of the proposed method, and to show its advantages over some traditional control algorithms. It is shown that a faster convergence rate and a better load disturbance rejection performance can be yielded by the present algorithm. Finally, it is shown that the main results can be extended to the case of n parallel dc-dc buck converters.
Accumulating evidence has shown that mesenchymal stem cell (MSC)‐derived exosomes (exo) mediate cardiac repair following myocardial infarction (MI). Macrophage migration inhibitory factor (MIF), a ...proinflammatory cytokine, plays a critical role in regulating cell homeostasis. This study aimed to investigate the cardioprotective effects of exo secreted from bone marrow‐MSCs (BM‐MSCs) overexpressing MIF in a rat model of MI. MIF plasmid was transducted in BM‐MSCs. Exo were isolated from the supernatants of BM‐MSCs and MIF‐BM‐MSCs, respectively. The morphology of mitochondria in neonatal mice cardiomyocytes (NRCMs) was determined by MitoTracker staining. The apoptosis of NRCMs was examined by deoxynucleotidyl transferase‐mediated dUTP nick end‐labeling. BM‐MSC‐exo and MIF‐BM‐MSC‐exo were intramuscularly injected into the peri‐infarct region in a rat model of MI. The heart function of rats was assessed by echocardiography. The expression of MIF was greatly enhanced in MIF‐BM‐MSCs compared with BM‐MSCs. Both BM‐MSC‐exo and MIF‐BM‐MSC‐exo expressed CD63 and CD81. NRCMs treated with MIF‐BM‐MSC‐exo exhibited less mitochondrial fragmentation and cell apoptosis under hypoxia/serum deprivation (H/SD) challenge than those treated with BM‐MSC‐exo via activating adenosine 5′‐monophosphate‐activated protein kinase signaling. Moreover, these effects were partially abrogated by Compound C. Injection of BM‐MSC‐exo or MIF‐BM‐MSC‐exo greatly restored heart function in a rat model of MI. Compared with BM‐MSC‐exo, injection of MIF‐BM‐MSC‐exo was associated with enhanced heart function, reduced heart remodeling, less cardiomyocyte mitochondrial fragmentation, reactive oxygen species generation, and apoptosis. Our study reveals a new mechanism of MIF‐BM‐MSC‐exo‐based therapy for MI and provides a novel strategy for cardiovascular disease treatment.
Migration inhibitory factor (MIF)‐bone marrow (BM)‐mesenchymal stem cell (MSC)‐exosome (exo) attenuated hypoxia and serum deprivation‐induced cardiomyocyte injury via inhibition of mitochondrial fragmentation. Injection of MIF‐BM‐MSC‐exo restored heart function by inhibiting mitochondrial fragmentation and apoptosis in a rat model of myocardial infarction. MSC‐exo represents a novel strategy for cardioprotection and MIF engineering MSCs can augment their therapeutic efficacy in regenerative medicine.
Image representation learning is a fundamental problem in understanding semantics of images. However, traditional classification-based representation learning methods face the noisy and incomplete ...problem of the supervisory labels. In this paper, we propose a general knowledge base embedded image representation learning approach, which uses general knowledge graph, which is a multitype relational knowledge graph consisting of human commonsense beyond image space, as external semantic resource to capture the relations of concepts in image representation learning. A relational regularized regression CNN (R 3 CNN) model is designed to jointly optimize the image representation learning problem and knowledge graph embedding problem. In this manner, the learnt representation can capture not only labeled tags but also related concepts of images, which involves more precise and complete semantics. Comprehensive experiments are conducted to investigate the effectiveness and transferability of our approach in tag prediction task, zero-shot tag inference task, and content-based image retrieval task. The experimental results demonstrate that the proposed approach performs significantly better than the existing representation learning methods. Finally, observation of the learnt relations show that our approach can somehow refine the knowledge base to describe images and label the images with structured tags.
As large-scale multimodal data are ubiquitous in many real-world applications, learning multimodal representations for efficient retrieval is a fundamental problem. Most existing methods adopt ...shallow structures to perform multimodal representation learning. Due to a limitation of learning ability of shallow structures, they fail to capture the correlation of multiple modalities. Recently, multimodal deep learning was proposed and had proven its superiority in representing multimodal data due to its high nonlinearity. However, in order to learn compact and accurate representations, how to reduce the redundant information lying in the multimodal representations and incorporate different complexities of different modalities in the deep models is still an open problem. In order to address the aforementioned problem, in this paper we propose a hashing-based orthogonal deep model to learn accurate and compact multimodal representations. The method can better capture the intra-modality and inter-modality correlations to learn accurate representations. Meanwhile, in order to make the representations compact, the hashing-based model can generate compact hash codes and the proposed orthogonal structure can reduce the redundant information lying in the codes by imposing orthogonal regularizer on the weighting matrices. We also theoretically prove that, in this case, the learned codes are guaranteed to be approximately orthogonal. Moreover, considering the different characteristics of different modalities, effective representations can be attained with different number of layers for different modalities. Comprehensive experiments on three real-world datasets demonstrate a substantial gain of our method on retrieval tasks compared with existing algorithms.
Recommender systems can suffer from data sparsity and cold start issues. However, social networks, which enable users to build relationships and create different types of items, present an ...unprecedented opportunity to alleviate these issues. In this paper, we represent a social network as a star-structured hybrid graph centered on a social domain, which connects with other item domains. With this innovative representation, useful knowledge from an auxiliary domain can be transferred through the social domain to a target domain. Various factors of item transferability, including popularity and behavioral consistency, are determined. We propose a novel Hybrid Random Walk (HRW) method, which incorporates such factors, to select transferable items in auxiliary domains, bridge cross-domain knowledge with the social domain, and accurately predict user-item links in a target domain. Extensive experiments on a real social dataset demonstrate that HRW significantly outperforms existing approaches.
Group recommendation plays a significant role in today's social media systems, where users form social groups to receive multimedia content together and interact with each other, instead of consuming ...the online content individually. Limitations of traditional group recommendation approaches are as follows. First, they usually infer group members' preferences by their historical behaviors, failing to capture inactive users' preferences from the sparse historical data. Second, relationships between group members are not studied by these approaches, which fail to capture the inherent personality of members in a group. To address these issues, we propose a social-aware group recommendation framework that jointly utilizes both social relationships and social behaviors to not only infer a group's preference, but also model the tolerance and altruism characteristics of group members. Based on the observation that the following relationship in the online social network reflects common interests of users, we propose a group preference model based on external experts of group members. Furthermore, we model users' tolerance (willingness to receive content not preferred) and altruism (willingness to receive content preferred by friends). Finally, based on the group preference model, we design recommendation algorithms for users under different social contexts. Experimental results demonstrate the effectiveness of our approach, which significantly improves the recommendation accuracy against traditional approaches, especially in the cases of inactive group members.
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