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In this paper, we present a majorized semismooth Newton-CG augmented Lagrangian method, called SDPNAL+, for semidefinite programming (SDP) with partial or full nonnegative constraints on the matrix variable. SDPNAL+ is a much enha...
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In this paper, we present a majorized semismooth Newton-CG augmented Lagrangian method, called SDPNAL+, for semidefinite programming (SDP) with partial or full nonnegative constraints on the matrix variable. SDPNAL+ is a much enhanced version of SDPNAL introduced by Zhao et al. (SIAM J Optim 20:1737–1765, 2010) for solving generic SDPs. SDPNAL works very efficiently for nondegenerate SDPs but may encounter numerical difficulty for degenerate ones. Here we tackle this numerical difficulty by employing a majorized semismooth Newton- CG augmented Lagrangian method coupled with a convergent 3-block alternating direction method of multipliers introduced recently by Sun et al. (SIAM J Optim, to appear). Numerical results for various large scale SDPs with or without nonnegative constraints show that the proposed method is not only fast but also robust in obtaining accurate solutions. It outperforms, by a significant margin, two other competitive publicly available first order methods based codes: (1) an alternating direction method of multipliers based solver called SDPAD by Wen et al. (Math Program Comput 2:203– 230, 2010) and (2) a two-easy-block-decomposition hybrid proximal extragradient method called 2EBD-HPE by Monteiro et al. (Math Program Comput 1–48, 2014). In contrast to these two codes, we are able to solve all the 95 difficult SDP problems arising from the relaxations of quadratic assignment problems tested in SDPNAL to an accuracy of 10?6 efficiently, while SDPAD and 2EBD-HPE successfully solve 30 and 16 problems, respectively. In addition, SDPNAL+ appears to be the only viable method currently available to solve large scale SDPs arising from rank-1 tensor approximation problems constructed by Nie and Wang (SIAM J Matrix Anal Appl 35:1155–1179, 2014). The largest rank-1 tensor approximation problem we solved (in about 14.5 h) is nonsym(21,4), in which its resulting SDP problem has matrix dimension n = 9261 and the number of equality constraints m = 12,326,390.
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Let $L:=-\Delta+V$ be a Schrodinger operator on $\mathbb{R}^n$ with $n\ge3$ and $V\ge0$ satisfying $\Delta^{-1} V\in L^\infty(\mathbb{R}^n)$. Assume that $\varphi \mathbb{R}^n\times[0,\infty)\to[0,\infty)$ is a function such that ...
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Let $L:=-\Delta+V$ be a Schrodinger operator on $\mathbb{R}^n$ with $n\ge3$ and $V\ge0$ satisfying $\Delta^{-1} V\in L^\infty(\mathbb{R}^n)$. Assume that $\varphi \mathbb{R}^n\times[0,\infty)\to[0,\infty)$ is a function such that $\varphi(x,\cdot)$ is an Orlicz function, $\varphi(\cdot,t)\in{\mathbb A}_{\infty}(\mathbb{R}^n)$ (the class of uniformly Muckenhoupt weights). Let $w$ be an $L$-harmonic function on $\mathbb{R}^n$ with $0<C_1\le w\le C_2$, where $C_1$ and $C_2$ are positive constants. In this article, the author proves that the mapping $H_{\varphi,L}(\mathbb{R}^n)\ni f\mapsto wf\in H_\varphi(\mathbb{R}^n)$ is an isomorphism from the Musielak-Orlicz-Hardy space associated with $L$, $H_{\varphi,L}(\mathbb{R}^n)$, to the Musielak-Orlicz-Hardy space $H_{\varphi}(\mathbb{R}^n)$ under some assumptions on $\varphi$. As applications, the author further obtains the atomic and molecular characterizations of the space $H_{\varphi,L}(\mathbb{R}^n)$ associated with $w$, and proves that the operator $(-\Delta)^{-1/2}L^{1/2}$ is an isomorphism of the spaces $H_{\varphi,L}(\mathbb{R}^n)$ and $H_{\varphi}(\mathbb{R}^n)$. All these results are new even when $\varphi(x,t):=t^p$, for all $x\in\mathbb{R}^n$ and $t\in[0,\infty)$, with $p\in(n/{(n+\mu_0)},1)$ and some $\mu_0\in(0,1]$.
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In the application layer multicast (ALM), clustering nearby nodes can effectively improve the multicast performance. However, it is difficult for the ALM solution to quickly and accurately position the newcomer, because group memb...
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In the application layer multicast (ALM), clustering nearby nodes can effectively improve the multicast performance. However, it is difficult for the ALM solution to quickly and accurately position the newcomer, because group members have no direct knowledge of underlying network topology. Additionally, ALM delivery trees with different performances are built when group members join the group in different join sequences. To alleviate the above problems, this paper proposes a distance-heuristic tree building protocol (called DHTB). DHTB uses our proposed distance-constrained cluster model and close-member-first-receive (CF) rule. In the model, most nearby nodes are grouped into some distance-constrained clusters, with little cluster organization and maintenance overhead. The CF rule arranges or rearranges the locations of group members according to related distances, and effectively positions the newcomer with the help of on-demand landmarks. Both the distance-constrained cluster model and CF rule are distance-heuristic. Therefore DHTB can alleviate the join sequence problem, and build the ALM tree with desirable performance.
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A metal-free catalytic method for the synthesis of 2-deoxy glycosides and disaccharides has been developed using stable 2-deoxy glucosyl and galactosyl acetate donors. They could react with a variety of acceptors in the presence o...
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A metal-free catalytic method for the synthesis of 2-deoxy glycosides and disaccharides has been developed using stable 2-deoxy glucosyl and galactosyl acetate donors. They could react with a variety of acceptors in the presence of catalytic amount of TMSOTf at 0癈 to form glycosides, glycoconjugates, and disaccharides with excellent ?selectivity (> 19:1) and yields (up to 99%) in a short time (0.5 h). With this expedient method, several new compounds against human K562 and SMMC7721 cell lines were obtained and tested with in vitro antitumor bioactivities.
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In mobile ad hoc networks (MANET), nodes move freely and the distribution of access requests changes dynamically. Replica allocation in such a dynamic environment is a significant challenge. The communication cost has become a pro...
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In mobile ad hoc networks (MANET), nodes move freely and the distribution of access requests changes dynamically. Replica allocation in such a dynamic environment is a significant challenge. The communication cost has become a prominent factor influencing the performance of replica allocation in the MANET environment. In this paper, a dynamic adaptive replica allocation algorithm that can adapt to the nodes motion is proposed to minimize the communication cost of the object access. When changes occur in the access requests of the object or the network topology, each replica node collects access requests from its neighbors and makes decisions locally to expand the replica to neighbors or to relinquish the replica. This algorithm dynamically adjusts the replica allocation scheme towards a local optimal one. To reduce the oscillation of replica allocation, a statistical method based on history information is utilized to choose stable neighbors and to expand the replica to relatively stable nodes. Simulation results show that our algorithms efficiently reduce the communication cost of object access in MANET environment.
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The double-layered peer-to-peer (P2P) systems were introduced to reduce the network traffic in MANET. The peers in the systems are classified into super peers and sub-peers. Super peers manage their neighboring sub-peers. The netw...
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The double-layered peer-to-peer (P2P) systems were introduced to reduce the network traffic in MANET. The peers in the systems are classified into super peers and sub-peers. Super peers manage their neighboring sub-peers. The network communications in the systems are done mostly among super peers. In case when a pair of neighboring super peers is too far to communicate, one or two of their sub-peers bridges the super peers. However, the double-layered systems need to improve the reliability that guarantees communications among peers. In this paper, we propose a new double-layered P2P system in which super peers are selected based on their mobility. We also propose two reliability improvement schemes, the avoidance scheme and the role changing scheme. They are applied to the proposed system to enhance the reliability of the system. The proposed system is implemented in the dynamic mobile P2P environment where peers may join and leave the network dynamically and the number of peers varies. The various experiments are done with the Network Simulator-2 v2.33. The experimental results show that the proposed system with the two schemes improved the reliability over other double-layered systems in terms of the failure rate by up to 25 %, while increasing the network traffic marginally.
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The dual-target inhibitors tend to improve the response rate in treating tumors, comparing with the single-target inhibitors. Matrix metalloproteinase-2 (MMP-2) and histone deacetylase-6 (HDAC-6) are attractive targets for cancer ...
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The dual-target inhibitors tend to improve the response rate in treating tumors, comparing with the single-target inhibitors. Matrix metalloproteinase-2 (MMP-2) and histone deacetylase-6 (HDAC-6) are attractive targets for cancer therapy. In this study, the hierarchical virtual screening of dual MMP-2/HDAC-6 inhibitors from natural products is investigated. The pharmacophore model of MMP-2 inhibitors is built based on ligands, but the pharmacophore model of HDAC-6 inhibitors is built based on the experimental crystal structures of multiple receptorligand complexes. The reliability of these two pharmacophore models is validated subsequently. The hierarchical virtual screening, combining these two different pharmacophore models of MMP-2 and HDAC-6 inhibitors with molecular docking, is carried out to identify the dual MMP-2/HDAC-6 inhibitors from a database of natural products. The four potential dual MMP-2/HDAC-6 inhibitors of natural products, STOCK1 N-46177, STOCK1 N-52245, STOCK1 N-55477, and STOCK1 N-69706, are found. The studies of binding modes show that the screened four natural products can simultaneously well bind with the MMP-2 and HDAC-6 active sites by different kinds of interactions, to inhibit the MMP-2 and HDAC-6 activities. In addition, the ADMET properties of screened four natural products are assessed. These found dual MMP-2/HDAC-6 inhibitors of natural products could serve as the lead compounds for designing the new dual MMP-2/HDAC-6 inhibitors having higher biological activities by carrying out structural modifications and optimizations in the future studies. ?2018, ?2018 Informa UK Limited, trading as Taylor & Francis Group.
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While DNA methylation carries genetic signals and is instrumental in the evolution of organismal complexity, small RNAs (sRNAs), ~18–24 ribonucleotide (nt) sequences, are crucial mediators of methylation as well as gene silencing...
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While DNA methylation carries genetic signals and is instrumental in the evolution of organismal complexity, small RNAs (sRNAs), ~18–24 ribonucleotide (nt) sequences, are crucial mediators of methylation as well as gene silencing. However, scant study deals with sRNA evolution via featuring their expression dynamics coupled with species of different evolutionary time. Here we report an atlas of sRNAs and microRNAs (miRNAs, single-stranded sRNAs) produced over time at seed-set of two major spermatophytes represented by populations of Picea glauca and Arabidopsis thaliana with different seed-set duration. We applied diverse profiling methods to examine sRNA and miRNA features, including size distribution, sequence conservation and reproduction-specific regulation, as well as to predict their putative targets. The top 27 most abundant miRNAs were highly overlapped between the two species (e.g., miR166,?319 and?396), but in P. glauca, they were less abundant and significantly less correlated with seed-set phases. The most abundant sRNAs in libraries were deeply conserved miRNAs in the plant kingdom for Arabidopsis but long sRNAs (24-nt) for P. glauca. We also found significant difference in normalized expression between populations for population-specific sRNAs but not for lineage-specific ones. Moreover, lineage-specific sRNAs were enriched in the 21-nt size class. This pattern is consistent in both species and alludes to a specific type of sRNAs (e.g., miRNA, tasiRNA) being selected for. In addition, we deemed 24 and 9 sRNAs in P. glauca and Arabidopsis, respectively, as sRNA candidates targeting known adaptive genes. Temperature had significant influence on selected gene and miRNA expression at seed development in both species. This study increases our integrated understanding of sRNA evolution and its potential link to genomic architecture (e.g., sRNA derivation from genome and sRNA-mediated genomic events) and organismal complexity (e.g., association between different sRNA expression and their functionality).
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Latent Semantic Indexing (LSI) has been widely used in information retrieval due to its efficiency in solving the problems of polysemy and synonymy. However, LSI is notably a computationally intensive process because of the comput...
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Latent Semantic Indexing (LSI) has been widely used in information retrieval due to its efficiency in solving the problems of polysemy and synonymy. However, LSI is notably a computationally intensive process because of the computing complexities of singular value decomposition and filtering operations involved in the process. This paper presents MR-LSI, a MapReduce based distributed LSI algorithm for scalable information retrieval. The performance of MR-LSI is first evaluated in a small scale experimental cluster environment, and subsequently evaluated in large scale simulation environments. By partitioning the dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computing nodes, the overhead of the MR-LSI algorithm is reduced significantly while maintaining a high level of accuracy in retrieving documents of user interest. A genetic algorithm based load balancing scheme is designed to optimize the performance of MR-LSI in heterogeneous computing environments in which the computing nodes have varied resources.
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With the recent advanced computing, networking technologies and embedded systems, the computing paradigm has switched from mainframe and desktop computing to ubiquitous computing, one of whose visions is to provide intelligent, pe...
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With the recent advanced computing, networking technologies and embedded systems, the computing paradigm has switched from mainframe and desktop computing to ubiquitous computing, one of whose visions is to provide intelligent, personalized and comprehensive services to users. As a new paradigm, Active Services is proposed to generate such services by retrieving, adapting, and composing of existing service components to satisfy user requirements. As the popularity of this paradigm and hence the number of service components increases, how to efficiently retrieve components to maximally meet user requirements has become a fundamental and significant problem. However, traditional facet-based retrieval methods only simply list out all the results without any kind of ranking and do not lay any emphasis on the differences of importance on each facet value in user requirements, which makes it hard for user to quickly select suitable components from the resulting list. To solve the problems, this paper proposes a novel personalized facet-weight based ranking method for service component retrieval, which assigns a weight for each facet to distinguish the importance of the facets, and constructs a personalized model to automatically calculate facet-weights for users according to their histo -rical retrieval records of the facet values and the weight setting. We optimize the parameters of the personalized model, evaluate the performance of the proposed retrieval method, and compare with the traditional facet-based matching methods. The experimental results show promising results in terms of retrieval accuracy and execution time.
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