DDSL: Efficient Subgraph Listing on Distributed and Dynamic Graphs

Subgraph listing is a fundamental problem in graph theory and has wide applications in areas like sociology, chemistry, and social networks. Modern graphs can usually be large-scale as well as highly dynamic, which challenges the efficiency of existing subgraph listing algorithms. Recent works have shown the benefits of partitioning and processing big graphs in a distributed system, however, there is only few work targets subgraph listing on dynamic graphs in a distributed environment. In this paper, we propose an efficient approach, called Distributed and Dynamic Subgraph Listing (DDSL), which can incrementally update the results instead of running from scratch. DDSL follows a general distributed join framework. In this framework, we use a Neighbor-Preserved storage for data graphs, which takes bounded extra space and supports dynamic updating. After that, we propose a comprehensive cost model to estimate the I/O cost of listing subgraphs. Then based on this cost model, we develop an algorithm to find the optimal join tree for a given pattern. To handle dynamic graphs, we propose an efficient left-deep join algorithm to incrementally update the join results. Extensive experiments are conducted on real-world datasets. The results show that DDSL outperforms existing methods in dealing with both static dynamic graphs in terms of the responding time

Towards learning inverse kinematics with a neural network based tracking controller

Learning an inverse kinematic model of a robot is a well studied subject. However, achieving this without information about the geometric characteristics of the robot is less investigated. In this work, a novel control approach is presented based on a recurrent neural network. Without any prior knowledge about the robot, this control strategy learns to control the iCub’s robot arm online by solving the inverse kinematic problem in its control region. Because of its exploration strategy the robot starts to learn by generating and observing random motor behavior. The modulation and generalization capabilities of this approach are investigated as well

Ecogeographical rules and the macroecology of food webs

AimHow do factors such as space, time, climate and other ecological drivers influence food web structure and dynamics? Collections of well‐studied food webs and replicate food webs from the same system that span biogeographical and ecological gradients now enable detailed, quantitative investigation of such questions and help integrate food web ecology and macroecology. Here, we integrate macroecology and food web ecology by focusing on how ecogeographical rules [the latitudinal diversity gradient (LDG), Bergmann’s rule, the island rule and Rapoport’s rule] are associated with the architecture of food webs.LocationGlobal.Time periodCurrent.Major taxa studiedAll taxa.MethodsWe discuss the implications of each ecogeographical rule for food webs, present predictions for how food web structure will vary with each rule, assess empirical support where available, and discuss how food webs may influence ecogeographical rules. Finally, we recommend systems and approaches for further advancing this research agenda.ResultsWe derived testable predictions for some ecogeographical rules (e.g. LDG, Rapoport’s rule), while for others (e.g., Bergmann’s and island rules) it is less clear how we would expect food webs to change over macroecological scales. Based on the LDG, we found weak support for both positive and negative relationships between food chain length and latitude and for increased generality and linkage density at higher latitudes. Based on Rapoport’s rule, we found support for the prediction that species turnover in food webs is inversely related to latitude.Main conclusionsThe macroecology of food webs goes beyond traditional approaches to biodiversity at macroecological scales by focusing on trophic interactions among species. The collection of food web data for different types of ecosystems across biogeographical gradients is key to advance this research agenda. Further, considering food web interactions as a selection pressure that drives or disrupts ecogeographical rules has the potential to address both mechanisms of and deviations from these macroecological relationships. For these reasons, further integration of macroecology and food webs will help ecologists better understand the assembly, maintenance and change of ecosystems across space and time.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151318/1/geb12925_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151318/2/geb12925.pd

The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees

Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma

Motion Pattern Encapsulation for Data-Driven Constraint-Based Motion Editing

The growth of motion capture systems have contributed to the proliferation of human motion database, mainly because human motion is important in many applications, ranging from games entertainment and films to sports and medicine. However, the captured motions normally attend specific needs. As an effort for adapting and reusing captured human motions in new tasks and environments and improving the animator’s work, we present and discuss a new data-driven constraint-based animation system for interactive human motion editing. This method offers the compelling advantage that it provides faster deformations and more natural-looking motion results compared to goal-directed constraint-based methods found in the literature

Reversal of MDR1-associated resistance to topotecan by PAK-200S, a new dihydropyridine analogue, in human cancer cell lines

Recent data suggest that expression of the membrane P170-glycoprotein (P-gp) may confer resistance to the topoisomerase- I-interactive agent topotecan. The present study describes the cellular effects of a new dihydropyridine analogue, PAK-200S, on P-gp-mediated resistance to topotecan in human breast and ovarian tumour cells. PAK-200S at a non-cytotoxic concentration of 2.0 μM completely reversed resistance to topotecan in P-gp-expressing MCF-7/adr (breast) and A2780/Dx5 (ovarian) tumour cells, respectively, with no effects on parental cells. Cellular pharmacokinetic studies by reversed-phase high-performance liquid chromatography analysis showed significantly lower cellular drug concentrations of the pharmacologically active closed-ring lactone of topotecan in multidrug-resistant cells than in parental cells. PAK-200S was effective in restoring the cellular lactone concentrations of topotecan in resistant MCF-7/adr cells to levels comparable to those obtained in parental cells. Furthermore, exposure of MCF-7/adr cells to topotecan in the presence of PAK-200S significantly increased the induction of protein-linked DNA breaks. PAK-200S did not alter nuclear topoisomerase I-mediated ex vivo pBR322 DNA plasmid unwinding activity and topoisomerase-I protein expression. These results suggest that reversal of P-gp-mediated resistance to topotecan by PAK-200S was related to the restoration of cellular drug concentrations of the active lactone form of topotecan rather than a direct effect on topoisomerase-I function. © 1999 Cancer Research Campaig