Community evolution mining and analysis in social network

NASA Astrophysics Data System (ADS)

Liu, Hongtao; Tian, Yuan; Liu, Xueyan; Jian, Jie

2017-03-01

With the development of digital and network technology, various social platforms emerge. These social platforms have greatly facilitated access to information, attracting more and more users. They use these social platforms every day to work, study and communicate, so every moment social platforms are generating massive amounts of data. These data can often be modeled as complex networks, making large-scale social network analysis possible. In this paper, the existing evolution classification model of community has been improved based on community evolution relationship over time in dynamic social network, and the Evolution-Tree structure is proposed which can show the whole life cycle of the community more clearly. The comparative test result shows that the improved model can excavate the evolution relationship of the community well.

An analog neural hardware implementation using charge-injection multipliers and neutron-specific gain control.

PubMed

Massengill, L W; Mundie, D B

1992-01-01

A neural network IC based on a dynamic charge injection is described. The hardware design is space and power efficient, and achieves massive parallelism of analog inner products via charge-based multipliers and spatially distributed summing buses. Basic synaptic cells are constructed of exponential pulse-decay modulation (EPDM) dynamic injection multipliers operating sequentially on propagating signal vectors and locally stored analog weights. Individually adjustable gain controls on each neutron reduce the effects of limited weight dynamic range. A hardware simulator/trainer has been developed which incorporates the physical (nonideal) characteristics of actual circuit components into the training process, thus absorbing nonlinearities and parametric deviations into the macroscopic performance of the network. Results show that charge-based techniques may achieve a high degree of neural density and throughput using standard CMOS processes.

Characterizing and modeling the dynamics of activity and popularity.

PubMed

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

Characterizing and Modeling the Dynamics of Activity and Popularity

PubMed Central

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks. PMID:24586586

Efficient collective influence maximization in cascading processes with first-order transitions

PubMed Central

Pei, Sen; Teng, Xian; Shaman, Jeffrey; Morone, Flaviano; Makse, Hernán A.

2017-01-01

In many social and biological networks, the collective dynamics of the entire system can be shaped by a small set of influential units through a global cascading process, manifested by an abrupt first-order transition in dynamical behaviors. Despite its importance in applications, efficient identification of multiple influential spreaders in cascading processes still remains a challenging task for large-scale networks. Here we address this issue by exploring the collective influence in general threshold models of cascading process. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given the same number of seeds compared with other scalable heuristic approaches. PMID:28349988

Efficient collective influence maximization in cascading processes with first-order transitions

NASA Astrophysics Data System (ADS)

Pei, Sen; Teng, Xian; Shaman, Jeffrey; Morone, Flaviano; Makse, Hernán A.

2017-03-01

In many social and biological networks, the collective dynamics of the entire system can be shaped by a small set of influential units through a global cascading process, manifested by an abrupt first-order transition in dynamical behaviors. Despite its importance in applications, efficient identification of multiple influential spreaders in cascading processes still remains a challenging task for large-scale networks. Here we address this issue by exploring the collective influence in general threshold models of cascading process. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given the same number of seeds compared with other scalable heuristic approaches.

Parallel Algorithms for Switching Edges in Heterogeneous Graphs.

PubMed

Bhuiyan, Hasanuzzaman; Khan, Maleq; Chen, Jiangzhuo; Marathe, Madhav

2017-06-01

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors.

Parallel Algorithms for Switching Edges in Heterogeneous Graphs☆

PubMed Central

Khan, Maleq; Chen, Jiangzhuo; Marathe, Madhav

2017-01-01

An edge switch is an operation on a graph (or network) where two edges are selected randomly and one of their end vertices are swapped with each other. Edge switch operations have important applications in graph theory and network analysis, such as in generating random networks with a given degree sequence, modeling and analyzing dynamic networks, and in studying various dynamic phenomena over a network. The recent growth of real-world networks motivates the need for efficient parallel algorithms. The dependencies among successive edge switch operations and the requirement to keep the graph simple (i.e., no self-loops or parallel edges) as the edges are switched lead to significant challenges in designing a parallel algorithm. Addressing these challenges requires complex synchronization and communication among the processors leading to difficulties in achieving a good speedup by parallelization. In this paper, we present distributed memory parallel algorithms for switching edges in massive networks. These algorithms provide good speedup and scale well to a large number of processors. A harmonic mean speedup of 73.25 is achieved on eight different networks with 1024 processors. One of the steps in our edge switch algorithms requires the computation of multinomial random variables in parallel. This paper presents the first non-trivial parallel algorithm for the problem, achieving a speedup of 925 using 1024 processors. PMID:28757680

Digging deeper on "deep" learning: A computational ecology approach.

PubMed

Buscema, Massimo; Sacco, Pier Luigi

2017-01-01

We propose an alternative approach to "deep" learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform "new" artificial intelligence.

Efficient discovery of overlapping communities in massive networks

PubMed Central

Gopalan, Prem K.; Blei, David M.

2013-01-01

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks. PMID:23950224

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Coupled Triboelectric Nanogenerator Networks for Efficient Water Wave Energy Harvesting.

PubMed

Xu, Liang; Jiang, Tao; Lin, Pei; Shao, Jia Jia; He, Chuan; Zhong, Wei; Chen, Xiang Yu; Wang, Zhong Lin

2018-02-27

Water wave energy is a promising clean energy source, which is abundant but hard to scavenge economically. Triboelectric nanogenerator (TENG) networks provide an effective approach toward massive harvesting of water wave energy in oceans. In this work, a coupling design in TENG networks for such purposes is reported. The charge output of the rationally linked units is over 10 times of that without linkage. TENG networks of three different connecting methods are fabricated and show better performance for the ones with flexible connections. The network is based on an optimized ball-shell structured TENG unit with high responsivity to small agitations. The dynamic behavior of single and multiple TENG units is also investigated comprehensively to fully understand their performance in water. The study shows that a rational design on the linkage among the units could be an effective strategy for TENG clusters to operate collaboratively for reaching a higher performance.

Massively Open Online Course for Educators (MOOC-Ed) Network Dataset

ERIC Educational Resources Information Center

Kellogg, Shaun; Edelmann, Achim

2015-01-01

This paper presents the Massively Open Online Course for Educators (MOOC-Ed) network dataset. It entails information on two online communication networks resulting from two consecutive offerings of the MOOC called "The Digital Learning Transition in K-12 Schools" in spring and fall 2013. The courses were offered to educators from the USA…

Network Analysis of an Emergent Massively Collaborative Creation on Video Sharing Website

NASA Astrophysics Data System (ADS)

Hamasaki, Masahiro; Takeda, Hideaki; Nishimura, Takuichi

The Web technology enables numerous people to collaborate in creation. We designate it as massively collaborative creation via the Web. As an example of massively collaborative creation, we particularly examine video development on Nico Nico Douga, which is a video sharing website that is popular in Japan. We specifically examine videos on Hatsune Miku, a version of a singing synthesizer application software that has inspired not only song creation but also songwriting, illustration, and video editing. As described herein, creators of interact to create new contents through their social network. In this paper, we analyzed the process of developing thousands of videos based on creators' social networks and investigate relationships among creation activity and social networks. The social network reveals interesting features. Creators generate large and sparse social networks including some centralized communities, and such centralized community's members shared special tags. Different categories of creators have different roles in evolving the network, e.g., songwriters gather more links than other categories, implying that they are triggers to network evolution.

Dynamic Power Distribution System Management With a Locally Connected Communication Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall-Anese, Emiliano; Zhang, Kaiqing; Basar, Tamer

Coordinated optimization and control of distribution-level assets can enable a reliable and optimal integration of massive amount of distributed energy resources (DERs) and facilitate distribution system management (DSM). Accordingly, the objective is to coordinate the power injection at the DERs to maintain certain quantities across the network, e.g., voltage magnitude, line flows, or line losses, to be close to a desired profile. By and large, the performance of the DSM algorithms has been challenged by two factors: i) the possibly non-strongly connected communication network over DERs that hinders the coordination; ii) the dynamics of the real system caused by themore » DERs with heterogeneous capabilities, time-varying operating conditions, and real-time measurement mismatches. In this paper, we investigate the modeling and algorithm design and analysis with the consideration of these two factors. In particular, a game theoretic characterization is first proposed to account for a locally connected communication network over DERs, along with the analysis of the existence and uniqueness of the Nash equilibrium (NE) therein. To achieve the equilibrium in a distributed fashion, a projected-gradient-based asynchronous DSM algorithm is then advocated. The algorithm performance, including the convergence speed and the tracking error, is analytically guaranteed under the dynamic setting. Extensive numerical tests on both synthetic and realistic cases corroborate the analytical results derived.« less

Research in Parallel Algorithms and Software for Computational Aerosciences

NASA Technical Reports Server (NTRS)

Domel, Neal D.

1996-01-01

Phase I is complete for the development of a Computational Fluid Dynamics parallel code with automatic grid generation and adaptation for the Euler analysis of flow over complex geometries. SPLITFLOW, an unstructured Cartesian grid code developed at Lockheed Martin Tactical Aircraft Systems, has been modified for a distributed memory/massively parallel computing environment. The parallel code is operational on an SGI network, Cray J90 and C90 vector machines, SGI Power Challenge, and Cray T3D and IBM SP2 massively parallel machines. Parallel Virtual Machine (PVM) is the message passing protocol for portability to various architectures. A domain decomposition technique was developed which enforces dynamic load balancing to improve solution speed and memory requirements. A host/node algorithm distributes the tasks. The solver parallelizes very well, and scales with the number of processors. Partially parallelized and non-parallelized tasks consume most of the wall clock time in a very fine grain environment. Timing comparisons on a Cray C90 demonstrate that Parallel SPLITFLOW runs 2.4 times faster on 8 processors than its non-parallel counterpart autotasked over 8 processors.

Research in Parallel Algorithms and Software for Computational Aerosciences

NASA Technical Reports Server (NTRS)

Domel, Neal D.

1996-01-01

Phase 1 is complete for the development of a computational fluid dynamics CFD) parallel code with automatic grid generation and adaptation for the Euler analysis of flow over complex geometries. SPLITFLOW, an unstructured Cartesian grid code developed at Lockheed Martin Tactical Aircraft Systems, has been modified for a distributed memory/massively parallel computing environment. The parallel code is operational on an SGI network, Cray J90 and C90 vector machines, SGI Power Challenge, and Cray T3D and IBM SP2 massively parallel machines. Parallel Virtual Machine (PVM) is the message passing protocol for portability to various architectures. A domain decomposition technique was developed which enforces dynamic load balancing to improve solution speed and memory requirements. A host/node algorithm distributes the tasks. The solver parallelizes very well, and scales with the number of processors. Partially parallelized and non-parallelized tasks consume most of the wall clock time in a very fine grain environment. Timing comparisons on a Cray C90 demonstrate that Parallel SPLITFLOW runs 2.4 times faster on 8 processors than its non-parallel counterpart autotasked over 8 processors.

EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Dipanjan; Song, Shuaiwen

With the prevalence of the World Wide Web and social networks, there has been a growing interest in high performance analytics for constantly-evolving dynamic graphs. Modern GPUs provide massive AQ1 amount of parallelism for efficient graph processing, but the challenges remain due to their lack of support for the near real-time streaming nature of dynamic graphs. Specifically, due to the current high volume and velocity of graph data combined with the complexity of user queries, traditional processing methods by first storing the updates and then repeatedly running static graph analytics on a sequence of versions or snapshots are deemed undesirablemore » and computational infeasible on GPU. We present EvoGraph, a highly efficient and scalable GPU- based dynamic graph analytics framework.« less

R144: a very massive binary likely ejected from R136 through a binary-binary encounter

NASA Astrophysics Data System (ADS)

Oh, Seungkyung; Kroupa, Pavel; Banerjee, Sambaran

2014-02-01

R144 is a recently confirmed very massive, spectroscopic binary which appears isolated from the core of the massive young star cluster R136. The dynamical ejection hypothesis as an origin for its location is claimed improbable by Sana et al. due to its binary nature and high mass. We demonstrate here by means of direct N-body calculations that a very massive binary system can be readily dynamically ejected from an R136-like cluster, through a close encounter with a very massive system. One out of four N-body cluster models produces a dynamically ejected very massive binary system with a mass comparable to R144. The system has a system mass of ≈355 M⊙ and is located at 36.8 pc from the centre of its parent cluster, moving away from the cluster with a velocity of 57 km s-1 at 2 Myr as a result of a binary-binary interaction. This implies that R144 could have been ejected from R136 through a strong encounter with another massive binary or single star. In addition, we discuss all massive binaries and single stars which are ejected dynamically from their parent cluster in the N-body models.

Opinion dynamics on interacting networks: media competition and social influence.

PubMed

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-05-27

The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

Opinion dynamics on interacting networks: media competition and social influence

NASA Astrophysics Data System (ADS)

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-05-01

The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

FDI and Accommodation Using NN Based Techniques

NASA Astrophysics Data System (ADS)

Garcia, Ramon Ferreiro; de Miguel Catoira, Alberto; Sanz, Beatriz Ferreiro

Massive application of dynamic backpropagation neural networks is used on closed loop control FDI (fault detection and isolation) tasks. The process dynamics is mapped by means of a trained backpropagation NN to be applied on residual generation. Process supervision is then applied to discriminate faults on process sensors, and process plant parameters. A rule based expert system is used to implement the decision making task and the corresponding solution in terms of faults accommodation and/or reconfiguration. Results show an efficient and robust FDI system which could be used as the core of an SCADA or alternatively as a complement supervision tool operating in parallel with the SCADA when applied on a heat exchanger.

Assessing Routing Strategies for Cognitive Radio Sensor Networks

PubMed Central

Zubair, Suleiman; Fisal, Norsheila; Baguda, Yakubu S.; Saleem, Kashif

2013-01-01

Interest in the cognitive radio sensor network (CRSN) paradigm has gradually grown among researchers. This concept seeks to fuse the benefits of dynamic spectrum access into the sensor network, making it a potential player in the next generation (NextGen) network, which is characterized by ubiquity. Notwithstanding its massive potential, little research activity has been dedicated to the network layer. By contrast, we find recent research trends focusing on the physical layer, the link layer and the transport layers. The fact that the cross-layer approach is imperative, due to the resource-constrained nature of CRSNs, can make the design of unique solutions non-trivial in this respect. This paper seeks to explore possible design opportunities with wireless sensor networks (WSNs), cognitive radio ad-hoc networks (CRAHNs) and cross-layer considerations for implementing viable CRSN routing solutions. Additionally, a detailed performance evaluation of WSN routing strategies in a cognitive radio environment is performed to expose research gaps. With this work, we intend to lay a foundation for developing CRSN routing solutions and to establish a basis for future work in this area. PMID:24077319

A CyberCIEGE Traffic Analysis Extension for Teaching Network Security

DTIC Science & Technology

2011-12-01

Information Technology LAN Local Area Network MAADNET Military Academy Attack/Defense Network MAC Media Access Control MMORPG Massively...ready to launch its latest massively multiplayer online role-playing game ( MMORPG ) “SyberSIEGE”! The product is currently in the final stages of...achieve his goal, this approach will still allow Tina to meet her goals and avoid disruptions to existing operations, which is also what would have

An Effective Massive Sensor Network Data Access Scheme Based on Topology Control for the Internet of Things.

PubMed

Yi, Meng; Chen, Qingkui; Xiong, Neal N

2016-11-03

This paper considers the distributed access and control problem of massive wireless sensor networks' data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival of massive service access requests at a virtual data center, this paper designs a massive sensing data access and control mechanism to improve the access efficiency of service requests and makes full use of the available resources at the data access center for the Internet of things. Firstly, this paper proposes a synergistically distributed buffer access model, which separates the information of resource and location. Secondly, the paper divides the service access requests into multiple virtual groups based on their characteristics and locations using an optimized self-organizing feature map neural network. Furthermore, this paper designs an optimal scheduling algorithm of group migration based on the combination scheme between the artificial bee colony algorithm and chaos searching theory. Finally, the experimental results demonstrate that this mechanism outperforms the existing schemes in terms of enhancing the accessibility of service requests effectively, reducing network delay, and has higher load balancing capacity and higher resource utility rate.

The 2nd Symposium on the Frontiers of Massively Parallel Computations

NASA Technical Reports Server (NTRS)

Mills, Ronnie (Editor)

1988-01-01

Programming languages, computer graphics, neural networks, massively parallel computers, SIMD architecture, algorithms, digital terrain models, sort computation, simulation of charged particle transport on the massively parallel processor and image processing are among the topics discussed.

An ultrastructural analysis of platelets, erythrocytes, white blood cells, and fibrin network in systemic lupus erythematosus.

PubMed

Pretorius, Etheresia; du Plooy, Jenny; Soma, Prashilla; Gasparyan, Armen Yuri

2014-07-01

The study suggests that patients with systemic lupus erythematosus (SLE) present with distinct inflammatory ultrastructural changes such as platelets blebbing, generation of platelet-derived microparticles, spontaneous formation of massive fibrin network and fusion of the erythrocytes membranes. Lupoid platelets actively interact with other inflammatory cells, particularly with white blood cells (WBCs), and the massive fibrin network facilitates such an interaction. It is possible that the concerted actions of platelets, erythrocytes and WBC, caught in the inflammatory fibrin network, predispose to pro-thrombotic states in patients with SLE.

Bio-Inspired Neural Model for Learning Dynamic Models

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu; Suri, Ronald

2009-01-01

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Dynamic collapses of relativistic degenerate stellar cores and radiation pressure dominated stellar interiors

NASA Astrophysics Data System (ADS)

Shi, Chun-Hui; Lou, Yu-Qing

2018-04-01

We investigate and explore self-similar dynamic radial collapses of relativistic degenerate stellar cores (RDSCs) and radiation pressure dominated stellar interiors (RPDSIs) of spherical symmetry by invoking a conventional polytropic (CP) equation of state (EoS) with a constant polytropic index γ = 4 / 3 and by allowing free-fall non-zero RDSC or RPDSI surface mass density and pressure due to their sustained physical contact with the outer surrounding stellar envelopes also in contraction. Irrespective of the physical triggering mechanisms (including, e.g., photodissociation, electron-positron pair instability, general relativistic instability etc.) for initiating such a self-similar dynamically collapsing RDSC or RPDSI embedded within a massive star, a very massive star (VMS) or a supermassive star (SMS) in contraction and by comparing with the Schwarzschild radii associated with their corresponding RDSC/RPDSI masses, the emergence of central black holes in a wide mass range appears inevitable during such RDSC/RPDSI dynamic collapses inside massive stars, VMSs, and SMSs, respectively. Radial pulsations of progenitor cores or during a stellar core collapse may well leave imprints onto collapsing RDSCs/RPDSIs towards their self-similar dynamic evolution. Massive neutron stars may form during dynamic collapses of RDSC inside massive stars in contraction under proper conditions.

Opinion dynamics on interacting networks: media competition and social influence

PubMed Central

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-01-01

The inner dynamics of the multiple actors of the informations systems – i.e, T.V., newspapers, blogs, social network platforms, – play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist. PMID:24861995

An Adaptive QSE-reduced Nuclear Reaction Network for Silicon Burning

NASA Astrophysics Data System (ADS)

Parete-Koon, Suzanne; Hix, William Raphael; Thielemann, Friedrich-Karl

2010-02-01

The nuclei of the ``iron peak'' are formed late in the evolution of massive stars and during supernovae. Silicon burning during these events is responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. The large number of nuclei involved make accurate modeling of silicon burning computationally expensive. Examination of the physics of silicon burning reveals that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present an improvement on our hybrid equilibrium-network scheme that takes advantage of this quasi-equilibrium (QSE) to reduce the number of independent variables calculated. Because the membership and number of these groups vary as the temperature, density and electron faction change, achieving maximal efficiency requires dynamic adjustment of group number and membership. The resultant QSE-reduced network is up to 20 times faster than the full network it replaces without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multi-dimensional applications. )

Statistical physics of balance theory

PubMed Central

Belaza, Andres M.; Hoefman, Kevin; Bramson, Aaron; van den Heuvel, Milan; Schoors, Koen

2017-01-01

Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively capture the dynamics of the four types of triadic relationships in a network. Central to our model are the triads’ incidence rates and the idea that those can be modeled by assigning a specific triadic energy to each type of triadic relation. We emphasize the role of the degeneracy of the different triads and how it impacts the degree of frustration in the political network. In order to account for a persistent form of disorder in the formation of the triadic relationships, we introduce the systemic variable temperature. In order to learn about the dynamics and motives, we propose a generic Hamiltonian with three terms to model the triadic energies. One term is connected with a three-body interaction that captures balance theory. The other terms take into account the impact of heterogeneity and of negative edges in the triads. The validity of our model is tested on four datasets including the time series of triadic relationships for the standings between two classes of alliances in a massively multiplayer online game (MMOG). We also analyze real-world data for the relationships between the “agents” involved in the Syrian civil war, and in the relations between countries during the Cold War era. We find emerging properties in the triadic relationships in a political network, for example reflecting itself in a persistent hierarchy between the four triadic energies, and in the consistency of the extracted parameters from comparing the model Hamiltonian to the data. PMID:28846726

Statistical physics of balance theory.

PubMed

Belaza, Andres M; Hoefman, Kevin; Ryckebusch, Jan; Bramson, Aaron; van den Heuvel, Milan; Schoors, Koen

2017-01-01

Triadic relationships are accepted to play a key role in the dynamics of social and political networks. Building on insights gleaned from balance theory in social network studies and from Boltzmann-Gibbs statistical physics, we propose a model to quantitatively capture the dynamics of the four types of triadic relationships in a network. Central to our model are the triads' incidence rates and the idea that those can be modeled by assigning a specific triadic energy to each type of triadic relation. We emphasize the role of the degeneracy of the different triads and how it impacts the degree of frustration in the political network. In order to account for a persistent form of disorder in the formation of the triadic relationships, we introduce the systemic variable temperature. In order to learn about the dynamics and motives, we propose a generic Hamiltonian with three terms to model the triadic energies. One term is connected with a three-body interaction that captures balance theory. The other terms take into account the impact of heterogeneity and of negative edges in the triads. The validity of our model is tested on four datasets including the time series of triadic relationships for the standings between two classes of alliances in a massively multiplayer online game (MMOG). We also analyze real-world data for the relationships between the "agents" involved in the Syrian civil war, and in the relations between countries during the Cold War era. We find emerging properties in the triadic relationships in a political network, for example reflecting itself in a persistent hierarchy between the four triadic energies, and in the consistency of the extracted parameters from comparing the model Hamiltonian to the data.

Parameters affecting the resilience of scale-free networks to random failures.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran

2005-09-01

It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degreemore » of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.« less

Measuring Affective Benefits and Costs of Mediated Awareness: Development and Validation of the ABC-Questionnaire

NASA Astrophysics Data System (ADS)

Ijsselsteijn, Wijnand; van Baren, Joy; Markopoulos, Panos; Romero, Natalia; De Ruyter, Boris

The interactions and relationships we have with other people form an essential social network that supports us and adds meaning to our lives. This well-known fact is illustrated by the massive success of communication media such as e-mail, mobile telephony, and text messaging and the massive adoption of social networking applications such as Facebook and Twitter.

Dynamic Imbalance Would Counter Offcenter Thrust

NASA Technical Reports Server (NTRS)

Mccanna, Jason

1994-01-01

Dynamic imbalance generated by offcenter thrust on rotating body eliminated by shifting some of mass of body to generate opposing dynamic imbalance. Technique proposed originally for spacecraft including massive crew module connected via long, lightweight intermediate structure to massive engine module, such that artificial gravitation in crew module generated by rotating spacecraft around axis parallel to thrust generated by engine. Also applicable to dynamic balancing of rotating terrestrial equipment to which offcenter forces applied.

An Effective Massive Sensor Network Data Access Scheme Based on Topology Control for the Internet of Things

PubMed Central

Yi, Meng; Chen, Qingkui; Xiong, Neal N.

2016-01-01

This paper considers the distributed access and control problem of massive wireless sensor networks’ data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival of massive service access requests at a virtual data center, this paper designs a massive sensing data access and control mechanism to improve the access efficiency of service requests and makes full use of the available resources at the data access center for the Internet of things. Firstly, this paper proposes a synergistically distributed buffer access model, which separates the information of resource and location. Secondly, the paper divides the service access requests into multiple virtual groups based on their characteristics and locations using an optimized self-organizing feature map neural network. Furthermore, this paper designs an optimal scheduling algorithm of group migration based on the combination scheme between the artificial bee colony algorithm and chaos searching theory. Finally, the experimental results demonstrate that this mechanism outperforms the existing schemes in terms of enhancing the accessibility of service requests effectively, reducing network delay, and has higher load balancing capacity and higher resource utility rate. PMID:27827878

High-velocity runaway stars from three-body encounters

NASA Astrophysics Data System (ADS)

Gvaramadze, V. V.; Gualandris, A.; Portegies Zwart, S.

2010-01-01

We performed numerical simulations of dynamical encounters between hard, massive binaries and a very massive star (VMS; formed through runaway mergers of ordinary stars in the dense core of a young massive star cluster) to explore the hypothesis that this dynamical process could be responsible for the origin of high-velocity (≥ 200 - 400 km s-1) early or late B-type stars. We estimated the typical velocities produced in encounters between very tight massive binaries and VMSs (of mass of ≥ 200 M⊙) and found that about 3 - 4% of all encounters produce velocities ≥ 400 km s-1, while in about 2% of encounters the escapers attain velocities exceeding the Milky Ways's escape velocity. We therefore argue that the origin of high-velocity (≥ 200 - 400 km s-1) runaway stars and at least some so-called hypervelocity stars could be associated with dynamical encounters between the tightest massive binaries and VMSs formed in the cores of star clusters. We also simulated dynamical encounters between tight massive binaries and single ordinary 50 - 100 M⊙ stars. We found that from 1 to ≃ 4% of these encounters can produce runaway stars with velocities of ≥ 300 - 400 km s-1 (typical of the bound population of high-velocity halo B-type stars) and occasionally (in less than 1% of encounters) produce hypervelocity (≥ 700 km s-1) late B-type escapers.

mIoT Slice for 5G Systems: Design and Performance Evaluation

PubMed Central

Condoluci, Massimo; An, Xueli

2018-01-01

Network slicing is a key feature of the upcoming 5G networks allowing the design and deployment of customized communication systems to integrate services provided by vertical industries. In this context, massive Internet of Things (mIoT) is regarded as a compelling use case, both for its relevance from business perspective, and for the technical challenges it poses to network design. With their envisaged massive deployment of devices requiring sporadic connectivity and small data transmission, yet Quality of Service (QoS) constrained, mIoT services will need an ad-hoc end-to-end (E2E) slice, i.e., both access and core network with enhanced Control and User planes (CP/UP). After revising the key requirements of mIoT and identifying major shortcomings of previous generation networks, this paper presents and evaluates an E2E mIoT network slicing solution, featuring a new connectivity model overcoming the load limitations of legacy systems. Unique in its kind, this paper addresses mIoT requirements from an end-to-end perspective highlighting and solving, unlike most prior related work, the connectivity challenges posed to the core network. Results demonstrate that the proposed solution, reducing CP signaling and optimizing UP resource utilization, is a suitable candidate for next generation network standards to efficiently handle massive device deployment. PMID:29466311

mIoT Slice for 5G Systems: Design and Performance Evaluation.

PubMed

Trivisonno, Riccardo; Condoluci, Massimo; An, Xueli; Mahmoodi, Toktam

2018-02-21

Network slicing is a key feature of the upcoming 5G networks allowing the design and deployment of customized communication systems to integrate services provided by vertical industries. In this context, massive Internet of Things (mIoT) is regarded as a compelling use case, both for its relevance from business perspective, and for the technical challenges it poses to network design. With their envisaged massive deployment of devices requiring sporadic connectivity and small data transmission, yet Quality of Service (QoS) constrained, mIoT services will need an ad-hoc end-to-end (E2E) slice, i.e., both access and core network with enhanced Control and User planes (CP/UP). After revising the key requirements of mIoT and identifying major shortcomings of previous generation networks, this paper presents and evaluates an E2E mIoT network slicing solution, featuring a new connectivity model overcoming the load limitations of legacy systems. Unique in its kind, this paper addresses mIoT requirements from an end-to-end perspective highlighting and solving, unlike most prior related work, the connectivity challenges posed to the core network. Results demonstrate that the proposed solution, reducing CP signaling and optimizing UP resource utilization, is a suitable candidate for next generation network standards to efficiently handle massive device deployment.

Properties of on-line social systems

NASA Astrophysics Data System (ADS)

Grabowski, A.; Kruszewska, N.; Kosiński, R. A.

2008-11-01

We study properties of five different social systems: (i) internet society of friends consisting of over 106 people, (ii) social network consisting of 3 × 104 individuals, who interact in a large virtual world of Massive Multiplayer Online Role Playing Games (MMORPGs), (iii) over 106 users of music community website, (iv) over 5 × 106 users of gamers community server and (v) over 0.25 × 106 users of books admirer website. Individuals included in large social network form an Internet community and organize themselves in groups of different sizes. The destiny of those systems, as well as the method of creating of new connections, are different, however we found that the properties of these networks are very similar. We have found that the network components size distribution follow the power-law scaling form. In all five systems we have found interesting scaling laws concerning human dynamics. Our research has shown how long people are interested in a single task, how much time they devote to it and how fast they are making friends. It is surprising that the time evolution of an individual connectivity is very similar in each system.

SDN-Enabled Dynamic Feedback Control and Sensing in Agile Optical Networks

NASA Astrophysics Data System (ADS)

Lin, Likun

Fiber optic networks are no longer just pipelines for transporting data in the long haul backbone. Exponential growth in traffic in metro-regional areas has pushed higher capacity fiber toward the edge of the network, and highly dynamic patterns of heterogeneous traffic have emerged that are often bursty, severely stressing the historical "fat and dumb pipe" static optical network, which would need to be massively over-provisioned to deal with these loads. What is required is a more intelligent network with a span of control over the optical as well as electrical transport mechanisms which enables handling of service requests in a fast and efficient way that guarantees quality of service (QoS) while optimizing capacity efficiency. An "agile" optical network is a reconfigurable optical network comprised of high speed intelligent control system fed by real-time in situ network sensing. It provides fast response in the control and switching of optical signals in response to changing traffic demands and network conditions. This agile control of optical signals is enabled by pushing switching decisions downward in the network stack to the physical layer. Implementing such agility is challenging due to the response dynamics and interactions of signals in the physical layer. Control schemes must deal with issues such as dynamic power equalization, EDFA transients and cascaded noise effects, impairments due to self-phase modulation and dispersion, and channel-to-channel cross talk. If these issues are not properly predicted and mitigated, attempts at dynamic control can drive the optical network into an unstable state. In order to enable high speed actuation of signal modulators and switches, the network controller must be able to make decisions based on predictive models. In this thesis, we consider how to take advantage of Software Defined Networking (SDN) capabilities for network reconfiguration, combined with embedded models that access updates from deployed network monitoring sensors. In order to maintain signal quality while optimizing network resources, we find that it is essential to model and update estimates of the physical link impairments in real-time. In this thesis, we consider the key elements required to enable an agile optical network, with contributions as follows: • Control Framework: extended the SDN concept to include the optical transport network through extensions to the OpenFlow (OF) protocol. A unified SDN control plane is built to facilitate control and management capability across the electrical/packet-switched and optical/circuit-switched portions of the network seamlessly. The SDN control plane serves as a platform to abstract the resources of multilayer/multivendor networks. Through this platform, applications can dynamically request the network resources to meet their service requirements. • Use of In-situ Monitors: enabled real-time physical impairment sensing in the control plane using in-situ Optical Performance Monitoring (OPM) and bit error rate (BER) analyzers. OPM and BER values are used as quantitative indicators of the link status and are fed to the control plane through a high-speed data collection interface to form a closed-loop feedback system to enable adaptive resource allocation. • Predictive Network Model: used a network model embedded in the control layer to study the link status. The estimated results of network status is fed into the control decisions to precompute the network resources. The performance of the network model can be enhanced by the sensing results. • Real-Time Control Algorithms: investigated various dynamic resource allocation mechanisms supporting an agile optical network. Intelligent routing and wavelength switching for recovering from traffic impairments is achieved experimentally in the agile optical network within one second. A distance-adaptive spectrum allocation scheme to address transmission impairments caused by cascaded Wavelength Selective Switches (WSS) is proposed and evaluated for improving network spectral efficiency.

Towards overcoming the Monte Carlo sign problem with tensor networks

NASA Astrophysics Data System (ADS)

Bañuls, Mari Carmen; Cichy, Krzysztof; Ignacio Cirac, J.; Jansen, Karl; Kühn, Stefan; Saito, Hana

2017-03-01

The study of lattice gauge theories with Monte Carlo simulations is hindered by the infamous sign problem that appears under certain circumstances, in particular at non-zero chemical potential. So far, there is no universal method to overcome this problem. However, recent years brought a new class of non-perturbative Hamiltonian techniques named tensor networks, where the sign problem is absent. In previous work, we have demonstrated that this approach, in particular matrix product states in 1+1 dimensions, can be used to perform precise calculations in a lattice gauge theory, the massless and massive Schwinger model. We have computed the mass spectrum of this theory, its thermal properties and real-time dynamics. In this work, we review these results and we extend our calculations to the case of two flavours and non-zero chemical potential. We are able to reliably reproduce known analytical results for this model, thus demonstrating that tensor networks can tackle the sign problem of a lattice gauge theory at finite density.

5G small-cell networks leveraging optical technologies with mm-wave massive MIMO and MT-MAC protocols

NASA Astrophysics Data System (ADS)

Papaioannou, S.; Kalfas, G.; Vagionas, C.; Mitsolidou, C.; Maniotis, P.; Miliou, A.; Pleros, N.

2018-01-01

Analog optical fronthaul for 5G network architectures is currently being promoted as a bandwidth- and energy-efficient technology that can sustain the data-rate, latency and energy requirements of the emerging 5G era. This paper deals with a new optical fronthaul architecture that can effectively synergize optical transceiver, optical add/drop multiplexer and optical beamforming integrated photonics towards a DSP-assisted analog fronthaul for seamless and medium-transparent 5G small-cell networks. Its main application targets include dense and Hot-Spot Area networks, promoting the deployment of mmWave massive MIMO Remote Radio Heads (RRHs) that can offer wireless data-rates ranging from 25Gbps up to 400Gbps depending on the fronthaul technology employed. Small-cell access and resource allocation is ensured via a Medium-Transparent (MT-) MAC protocol that enables the transparent communication between the Central Office and the wireless end-users or the lamp-posts via roof-top-located V-band massive MIMO RRHs. The MTMAC is analysed in detail with simulation and analytical theoretical results being in good agreement and confirming its credentials to satisfy 5G network latency requirements by guaranteeing latency values lower than 1 ms for small- to midload conditions. Its extension towards supporting optical beamforming capabilities and mmWave massive MIMO antennas is discussed, while its performance is analysed for different fiber fronthaul link lengths and different optical channel capacities. Finally, different physical layer network architectures supporting the MT-MAC scheme are presented and adapted to different 5G use case scenarios, starting from PON-overlaid fronthaul solutions and gradually moving through Spatial Division Multiplexing up to Wavelength Division Multiplexing transport as the user density increases.

Revealing catastrophic failure of leaf networks under stress

PubMed Central

Brodribb, Timothy J.; Bienaimé, Diane; Marmottant, Philippe

2016-01-01

The intricate patterns of veins that adorn the leaves of land plants are among the most important networks in biology. Water flows in these leaf irrigation networks under tension and is vulnerable to embolism-forming cavitations, which cut off water supply, ultimately causing leaf death. Understanding the ways in which plants structure their vein supply network to protect against embolism-induced failure has enormous ecological and evolutionary implications, but until now there has been no way of observing dynamic failure in natural leaf networks. Here we use a new optical method that allows the initiation and spread of embolism bubbles in the leaf network to be visualized. Examining embolism-induced failure of architecturally diverse leaf networks, we found that conservative rules described the progression of hydraulic failure within veins. The most fundamental rule was that within an individual venation network, susceptibility to embolism always increased proportionally with the size of veins, and initial nucleation always occurred in the largest vein. Beyond this general framework, considerable diversity in the pattern of network failure was found between species, related to differences in vein network topology. The highest-risk network was found in a fern species, where single events caused massive disruption to leaf water supply, whereas safer networks in angiosperm leaves contained veins with composite properties, allowing a staged failure of water supply. These results reveal how the size structure of leaf venation is a critical determinant of the spread of embolism damage to leaves during drought. PMID:27071104

Revealing catastrophic failure of leaf networks under stress.

PubMed

Brodribb, Timothy J; Bienaimé, Diane; Marmottant, Philippe

2016-04-26

The intricate patterns of veins that adorn the leaves of land plants are among the most important networks in biology. Water flows in these leaf irrigation networks under tension and is vulnerable to embolism-forming cavitations, which cut off water supply, ultimately causing leaf death. Understanding the ways in which plants structure their vein supply network to protect against embolism-induced failure has enormous ecological and evolutionary implications, but until now there has been no way of observing dynamic failure in natural leaf networks. Here we use a new optical method that allows the initiation and spread of embolism bubbles in the leaf network to be visualized. Examining embolism-induced failure of architecturally diverse leaf networks, we found that conservative rules described the progression of hydraulic failure within veins. The most fundamental rule was that within an individual venation network, susceptibility to embolism always increased proportionally with the size of veins, and initial nucleation always occurred in the largest vein. Beyond this general framework, considerable diversity in the pattern of network failure was found between species, related to differences in vein network topology. The highest-risk network was found in a fern species, where single events caused massive disruption to leaf water supply, whereas safer networks in angiosperm leaves contained veins with composite properties, allowing a staged failure of water supply. These results reveal how the size structure of leaf venation is a critical determinant of the spread of embolism damage to leaves during drought.

The big data-big model (BDBM) challenges in ecological research

NASA Astrophysics Data System (ADS)

Luo, Y.

2015-12-01

The field of ecology has become a big-data science in the past decades due to development of new sensors used in numerous studies in the ecological community. Many sensor networks have been established to collect data. For example, satellites, such as Terra and OCO-2 among others, have collected data relevant on global carbon cycle. Thousands of field manipulative experiments have been conducted to examine feedback of terrestrial carbon cycle to global changes. Networks of observations, such as FLUXNET, have measured land processes. In particular, the implementation of the National Ecological Observatory Network (NEON), which is designed to network different kinds of sensors at many locations over the nation, will generate large volumes of ecological data every day. The raw data from sensors from those networks offer an unprecedented opportunity for accelerating advances in our knowledge of ecological processes, educating teachers and students, supporting decision-making, testing ecological theory, and forecasting changes in ecosystem services. Currently, ecologists do not have the infrastructure in place to synthesize massive yet heterogeneous data into resources for decision support. It is urgent to develop an ecological forecasting system that can make the best use of multiple sources of data to assess long-term biosphere change and anticipate future states of ecosystem services at regional and continental scales. Forecasting relies on big models that describe major processes that underlie complex system dynamics. Ecological system models, despite great simplification of the real systems, are still complex in order to address real-world problems. For example, Community Land Model (CLM) incorporates thousands of processes related to energy balance, hydrology, and biogeochemistry. Integration of massive data from multiple big data sources with complex models has to tackle Big Data-Big Model (BDBM) challenges. Those challenges include interoperability of multiple, heterogeneous data sets; intractability of structural complexity of big models; equifinality of model structure selection and parameter estimation; and computational demand of global optimization with Big Models.

The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

PubMed Central

Zylberberg, Ariel; Fernández Slezak, Diego; Roelfsema, Pieter R.; Dehaene, Stanislas; Sigman, Mariano

2010-01-01

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates. PMID:20442869

Reconstructing the regulatory circuit of cell fate determination in yeast mating response.

PubMed

Shao, Bin; Yuan, Haiyu; Zhang, Rongfei; Wang, Xuan; Zhang, Shuwen; Ouyang, Qi; Hao, Nan; Luo, Chunxiong

2017-07-01

Massive technological advances enabled high-throughput measurements of proteomic changes in biological processes. However, retrieving biological insights from large-scale protein dynamics data remains a challenging task. Here we used the mating differentiation in yeast Saccharomyces cerevisiae as a model and developed integrated experimental and computational approaches to analyze the proteomic dynamics during the process of cell fate determination. When exposed to a high dose of mating pheromone, the yeast cell undergoes growth arrest and forms a shmoo-like morphology; however, at intermediate doses, chemotropic elongated growth is initialized. To understand the gene regulatory networks that control this differentiation switch, we employed a high-throughput microfluidic imaging system that allows real-time and simultaneous measurements of cell growth and protein expression. Using kinetic modeling of protein dynamics, we classified the stimulus-dependent changes in protein abundance into two sources: global changes due to physiological alterations and gene-specific changes. A quantitative framework was proposed to decouple gene-specific regulatory modes from the growth-dependent global modulation of protein abundance. Based on the temporal patterns of gene-specific regulation, we established the network architectures underlying distinct cell fates using a reverse engineering method and uncovered the dose-dependent rewiring of gene regulatory network during mating differentiation. Furthermore, our results suggested a potential crosstalk between the pheromone response pathway and the target of rapamycin (TOR)-regulated ribosomal biogenesis pathway, which might underlie a cell differentiation switch in yeast mating response. In summary, our modeling approach addresses the distinct impacts of the global and gene-specific regulation on the control of protein dynamics and provides new insights into the mechanisms of cell fate determination. We anticipate that our integrated experimental and modeling strategies could be widely applicable to other biological systems.

The Portals 4.0 network programming interface.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barrett, Brian W.; Brightwell, Ronald Brian; Pedretti, Kevin

2012-11-01

This report presents a specification for the Portals 4.0 network programming interface. Portals 4.0 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4.0 is well suited to massively parallel processing and embedded systems. Portals 4.0 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandias Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4.0 is targeted to the next generationmore » of machines employing advanced network interface architectures that support enhanced offload capabilities.« less

Very massive runaway stars from three-body encounters

NASA Astrophysics Data System (ADS)

Gvaramadze, Vasilii V.; Gualandris, Alessia

2011-01-01

Very massive stars preferentially reside in the cores of their parent clusters and form binary or multiple systems. We study the role of tight very massive binaries in the origin of the field population of very massive stars. We performed numerical simulations of dynamical encounters between single (massive) stars and a very massive binary with parameters similar to those of the most massive known Galactic binaries, WR 20a and NGC 3603-A1. We found that these three-body encounters could be responsible for the origin of high peculiar velocities (≥70 km s-1) observed for some very massive (≥60-70 M⊙) runaway stars in the Milky Way and the Large Magellanic Cloud (e.g. λ Cep, BD+43°3654, Sk -67°22, BI 237, 30 Dor 016), which can hardly be explained within the framework of the binary-supernova scenario. The production of high-velocity massive stars via three-body encounters is accompanied by the recoil of the binary in the opposite direction to the ejected star. We show that the relative position of the very massive binary R145 and the runaway early B-type star Sk-69°206 on the sky is consistent with the possibility that both objects were ejected from the central cluster, R136, of the star-forming region 30 Doradus via the same dynamical event - a three-body encounter.

Fiber optic sensor for monitoring a density of road traffic

NASA Astrophysics Data System (ADS)

Nedoma, Jan; Fajkus, Marcel; Martinek, Radek; Mec, Pavel; Novak, Martin; Jargus, Jan; Vasinek, Vladimir

2017-10-01

Authors of this article have focused on the use of fiber-optic technology in the car traffic. The article describes the use of fiber-optic interferometer for the purpose of the dynamic calculation of traffic density and inclusion the vehicle into the traffic lane. The objective is to increase safety and traffic flow. Presented solution is characterized by the non-destructive character to the road - sensor no need built into the roadway. The sensor works with standard telecommunications fibers of the G.652 standard. Other hallmarks are immunity to electromagnetic interference (EMI) and passivity of concerning the power supply. The massive expansion of optical cables within telecommunication needs along roads offers the possibility of connecting to the existing telecommunications fiber-optic network without a converter. Information can be transmitted at distances of several km up to tens km by this fiber-optic network. Set of experimental measurements in real traffic flow verified the functionality of presented solution.

Competition among memes in a world with limited attention

NASA Astrophysics Data System (ADS)

Weng, L.; Flammini, A.; Vespignani, A.; Menczer, F.

2012-03-01

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

Competition among memes in a world with limited attention.

PubMed

Weng, L; Flammini, A; Vespignani, A; Menczer, F

2012-01-01

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

Competition among memes in a world with limited attention

PubMed Central

Weng, L.; Flammini, A.; Vespignani, A.; Menczer, F.

2012-01-01

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas. PMID:22461971

Study on Data Clustering and Intelligent Decision Algorithm of Indoor Localization

NASA Astrophysics Data System (ADS)

Liu, Zexi

2018-01-01

Indoor positioning technology enables the human beings to have the ability of positional perception in architectural space, and there is a shortage of single network coverage and the problem of location data redundancy. So this article puts forward the indoor positioning data clustering algorithm and intelligent decision-making research, design the basic ideas of multi-source indoor positioning technology, analyzes the fingerprint localization algorithm based on distance measurement, position and orientation of inertial device integration. By optimizing the clustering processing of massive indoor location data, the data normalization pretreatment, multi-dimensional controllable clustering center and multi-factor clustering are realized, and the redundancy of locating data is reduced. In addition, the path is proposed based on neural network inference and decision, design the sparse data input layer, the dynamic feedback hidden layer and output layer, low dimensional results improve the intelligent navigation path planning.

MPI implementation of PHOENICS: A general purpose computational fluid dynamics code

NASA Astrophysics Data System (ADS)

Simunovic, S.; Zacharia, T.; Baltas, N.; Spalding, D. B.

1995-03-01

PHOENICS is a suite of computational analysis programs that are used for simulation of fluid flow, heat transfer, and dynamical reaction processes. The parallel version of the solver EARTH for the Computational Fluid Dynamics (CFD) program PHOENICS has been implemented using Message Passing Interface (MPI) standard. Implementation of MPI version of PHOENICS makes this computational tool portable to a wide range of parallel machines and enables the use of high performance computing for large scale computational simulations. MPI libraries are available on several parallel architectures making the program usable across different architectures as well as on heterogeneous computer networks. The Intel Paragon NX and MPI versions of the program have been developed and tested on massively parallel supercomputers Intel Paragon XP/S 5, XP/S 35, and Kendall Square Research, and on the multiprocessor SGI Onyx computer at Oak Ridge National Laboratory. The preliminary testing results of the developed program have shown scalable performance for reasonably sized computational domains.

MPI implementation of PHOENICS: A general purpose computational fluid dynamics code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simunovic, S.; Zacharia, T.; Baltas, N.

1995-04-01

PHOENICS is a suite of computational analysis programs that are used for simulation of fluid flow, heat transfer, and dynamical reaction processes. The parallel version of the solver EARTH for the Computational Fluid Dynamics (CFD) program PHOENICS has been implemented using Message Passing Interface (MPI) standard. Implementation of MPI version of PHOENICS makes this computational tool portable to a wide range of parallel machines and enables the use of high performance computing for large scale computational simulations. MPI libraries are available on several parallel architectures making the program usable across different architectures as well as on heterogeneous computer networks. Themore » Intel Paragon NX and MPI versions of the program have been developed and tested on massively parallel supercomputers Intel Paragon XP/S 5, XP/S 35, and Kendall Square Research, and on the multiprocessor SGI Onyx computer at Oak Ridge National Laboratory. The preliminary testing results of the developed program have shown scalable performance for reasonably sized computational domains.« less

Detecting Emotional Contagion in Massive Social Networks

PubMed Central

Coviello, Lorenzo; Sohn, Yunkyu; Kramer, Adam D. I.; Marlow, Cameron; Franceschetti, Massimo; Christakis, Nicholas A.; Fowler, James H.

2014-01-01

Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony. PMID:24621792

Massively parallel first-principles simulation of electron dynamics in materials

DOE PAGES

Draeger, Erik W.; Andrade, Xavier; Gunnels, John A.; ...

2017-08-01

Here we present a highly scalable, parallel implementation of first-principles electron dynamics coupled with molecular dynamics (MD). By using optimized kernels, network topology aware communication, and by fully distributing all terms in the time-dependent Kohn–Sham equation, we demonstrate unprecedented time to solution for disordered aluminum systems of 2000 atoms (22,000 electrons) and 5400 atoms (59,400 electrons), with wall clock time as low as 7.5 s per MD time step. Despite a significant amount of non-local communication required in every iteration, we achieved excellent strong scaling and sustained performance on the Sequoia Blue Gene/Q supercomputer at LLNL. We obtained up tomore » 59% of the theoretical sustained peak performance on 16,384 nodes and performance of 8.75 Petaflop/s (43% of theoretical peak) on the full 98,304 node machine (1,572,864 cores). Lastly, scalable explicit electron dynamics allows for the study of phenomena beyond the reach of standard first-principles MD, in particular, materials subject to strong or rapid perturbations, such as pulsed electromagnetic radiation, particle irradiation, or strong electric currents.« less

Massively parallel first-principles simulation of electron dynamics in materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Draeger, Erik W.; Andrade, Xavier; Gunnels, John A.

Here we present a highly scalable, parallel implementation of first-principles electron dynamics coupled with molecular dynamics (MD). By using optimized kernels, network topology aware communication, and by fully distributing all terms in the time-dependent Kohn–Sham equation, we demonstrate unprecedented time to solution for disordered aluminum systems of 2000 atoms (22,000 electrons) and 5400 atoms (59,400 electrons), with wall clock time as low as 7.5 s per MD time step. Despite a significant amount of non-local communication required in every iteration, we achieved excellent strong scaling and sustained performance on the Sequoia Blue Gene/Q supercomputer at LLNL. We obtained up tomore » 59% of the theoretical sustained peak performance on 16,384 nodes and performance of 8.75 Petaflop/s (43% of theoretical peak) on the full 98,304 node machine (1,572,864 cores). Lastly, scalable explicit electron dynamics allows for the study of phenomena beyond the reach of standard first-principles MD, in particular, materials subject to strong or rapid perturbations, such as pulsed electromagnetic radiation, particle irradiation, or strong electric currents.« less

Performance Evaluation of Counter-Based Dynamic Load Balancing Schemes for Massive Contingency Analysis with Different Computing Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yousu; Huang, Zhenyu; Chavarría-Miranda, Daniel

Contingency analysis is a key function in the Energy Management System (EMS) to assess the impact of various combinations of power system component failures based on state estimation. Contingency analysis is also extensively used in power market operation for feasibility test of market solutions. High performance computing holds the promise of faster analysis of more contingency cases for the purpose of safe and reliable operation of today’s power grids with less operating margin and more intermittent renewable energy sources. This paper evaluates the performance of counter-based dynamic load balancing schemes for massive contingency analysis under different computing environments. Insights frommore » the performance evaluation can be used as guidance for users to select suitable schemes in the application of massive contingency analysis. Case studies, as well as MATLAB simulations, of massive contingency cases using the Western Electricity Coordinating Council power grid model are presented to illustrate the application of high performance computing with counter-based dynamic load balancing schemes.« less

Graph Theory-Based Technique for Isolating Corrupted Boundary Conditions in Continental-Scale River Network Hydrodynamic Simulation

NASA Astrophysics Data System (ADS)

Yu, C. W.; Hodges, B. R.; Liu, F.

2017-12-01

Development of continental-scale river network models creates challenges where the massive amount of boundary condition data encounters the sensitivity of a dynamic nu- merical model. The topographic data sets used to define the river channel characteristics may include either corrupt data or complex configurations that cause instabilities in a numerical solution of the Saint-Venant equations. For local-scale river models (e.g. HEC- RAS), modelers typically rely on past experience to make ad hoc boundary condition adjustments that ensure a stable solution - the proof of the adjustment is merely the sta- bility of the solution. To date, there do not exist any formal methodologies or automated procedures for a priori detecting/fixing boundary conditions that cause instabilities in a dynamic model. Formal methodologies for data screening and adjustment are a critical need for simulations with a large number of river reaches that draw their boundary con- dition data from a wide variety of sources. At the continental scale, we simply cannot assume that we will have access to river-channel cross-section data that has been ade- quately analyzed and processed. Herein, we argue that problematic boundary condition data for unsteady dynamic modeling can be identified through numerical modeling with the steady-state Saint-Venant equations. The fragility of numerical stability increases with the complexity of branching in river network system and instabilities (even in an unsteady solution) are typically triggered by the nonlinear advection term in Saint-Venant equations. It follows that the behavior of the simpler steady-state equations (which retain the nonlin- ear term) can be used to screen the boundary condition data for problematic regions. In this research, we propose a graph-theory based method to isolate the location of corrupted boundary condition data in a continental-scale river network and demonstrate its utility with a network of O(10^4) elements. Acknowledgement: This research is supported by the National Science Foundation un- der grant number CCF-1331610.

Biology-Inspired Distributed Consensus in Massively-Deployed Sensor Networks

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

2005-01-01

Promises of ubiquitous control of the physical environment by large-scale wireless sensor networks open avenues for new applications that are expected to redefine the way we live and work. Most of recent research has concentrated on developing techniques for performing relatively simple tasks in small-scale sensor networks assuming some form of centralized control. The main contribution of this work is to propose a new way of looking at large-scale sensor networks, motivated by lessons learned from the way biological ecosystems are organized. Indeed, we believe that techniques used in small-scale sensor networks are not likely to scale to large networks; that such large-scale networks must be viewed as an ecosystem in which the sensors/effectors are organisms whose autonomous actions, based on local information, combine in a communal way to produce global results. As an example of a useful function, we demonstrate that fully distributed consensus can be attained in a scalable fashion in massively deployed sensor networks where individual motes operate based on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects.

The portals 4.0.1 network programming interface.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barrett, Brian W.; Brightwell, Ronald Brian; Pedretti, Kevin

2013-04-01

This report presents a specification for the Portals 4.0 network programming interface. Portals 4.0 is intended to allow scalable, high-performance network communication between nodes of a parallel computing system. Portals 4.0 is well suited to massively parallel processing and embedded systems. Portals 4.0 represents an adaption of the data movement layer developed for massively parallel processing platforms, such as the 4500-node Intel TeraFLOPS machine. Sandias Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4.0 is targeted to the next generationmore » of machines employing advanced network interface architectures that support enhanced offload capabilities. 3« less

A Methodology to Develop Entrepreneurial Networks: The Tech Ecosystem of Six African Cities

DTIC Science & Technology

2014-11-01

Information Center. Greve, A. and Salaff, J. W. (2003), Social Networks and Entrepreneurship . Entrepreneurship Theory and Practice, 28: 1–22. doi...methodology enables us to accurately measure social capital and circumvents the massive effort of mapping an individual’s social network before...locating the social resources in it. 15. SUBJECT TERMS Network Analysis, Economic Networks, Network Topology, Network Classification 16. SECURITY

Gauge symmetry and constraints structure for topologically massive AdS gravity: a symplectic viewpoint

NASA Astrophysics Data System (ADS)

Rodríguez-Tzompantzi, Omar; Escalante, Alberto

2018-05-01

By applying the Faddeev-Jackiw symplectic approach we systematically show that both the local gauge symmetry and the constraint structure of topologically massive gravity with a cosmological constant Λ , elegantly encoded in the zero-modes of the symplectic matrix, can be identified. Thereafter, via a suitable partial gauge-fixing procedure, the time gauge, we calculate the quantization bracket structure (generalized Faddeev-Jackiw brackets) for the dynamic variables and confirm that the number of physical degrees of freedom is one. This approach provides an alternative to explore the dynamical content of massive gravity models.

An overview of structurally complex network-based modeling of public opinion in the “We the Media” era

NASA Astrophysics Data System (ADS)

Wang, Guanghui; Wang, Yufei; Liu, Yijun; Chi, Yuxue

2018-05-01

As the transmission of public opinion on the Internet in the “We the Media” era tends to be supraterritorial, concealed and complex, the traditional “point-to-surface” transmission of information has been transformed into “point-to-point” reciprocal transmission. A foundation for studies of the evolution of public opinion and its transmission on the Internet in the “We the Media” era can be laid by converting the massive amounts of fragmented information on public opinion that exists on “We the Media” platforms into structurally complex networks of information. This paper describes studies of structurally complex network-based modeling of public opinion on the Internet in the “We the Media” era from the perspective of the development and evolution of complex networks. The progress that has been made in research projects relevant to the structural modeling of public opinion on the Internet is comprehensively summarized. The review considers aspects such as regular grid-based modeling of the rules that describe the propagation of public opinion on the Internet in the “We the Media” era, social network modeling, dynamic network modeling, and supernetwork modeling. Moreover, an outlook for future studies that address complex network-based modeling of public opinion on the Internet is put forward as a summary from the perspective of modeling conducted using the techniques mentioned above.

cellGPU: Massively parallel simulations of dynamic vertex models

NASA Astrophysics Data System (ADS)

Sussman, Daniel M.

2017-10-01

Vertex models represent confluent tissue by polygonal or polyhedral tilings of space, with the individual cells interacting via force laws that depend on both the geometry of the cells and the topology of the tessellation. This dependence on the connectivity of the cellular network introduces several complications to performing molecular-dynamics-like simulations of vertex models, and in particular makes parallelizing the simulations difficult. cellGPU addresses this difficulty and lays the foundation for massively parallelized, GPU-based simulations of these models. This article discusses its implementation for a pair of two-dimensional models, and compares the typical performance that can be expected between running cellGPU entirely on the CPU versus its performance when running on a range of commercial and server-grade graphics cards. By implementing the calculation of topological changes and forces on cells in a highly parallelizable fashion, cellGPU enables researchers to simulate time- and length-scales previously inaccessible via existing single-threaded CPU implementations. Program Files doi:http://dx.doi.org/10.17632/6j2cj29t3r.1 Licensing provisions: MIT Programming language: CUDA/C++ Nature of problem: Simulations of off-lattice "vertex models" of cells, in which the interaction forces depend on both the geometry and the topology of the cellular aggregate. Solution method: Highly parallelized GPU-accelerated dynamical simulations in which the force calculations and the topological features can be handled on either the CPU or GPU. Additional comments: The code is hosted at https://gitlab.com/dmsussman/cellGPU, with documentation additionally maintained at http://dmsussman.gitlab.io/cellGPUdocumentation

Role of Social Media and the Internet in Education.

PubMed

Vervaart, Peter

2012-07-01

Since the advent of the Internet, and in particular the development of the interactive version of the web, Web 2.0, use of Social Media has developed into a major strategy for businesses and organizations such as the IFCC to use for the purposes of Public Relations and Education. The early Internet 'Web 1.0' was a largely static environment which did not allow interaction between organizations and their customers and/or members and as such was mainly used as an information repository rather than a dynamic environment for the exchange of ideas and active marketing and education. Since the development of Web 2.0 we have seen a massive increase in web based traffic which could be loosely called 'social networking' which initially was mainly networking between individuals but more recently has developed into a major marketing resource allowing networking between organizations and individuals on the web. It follows then that by developing a Social Media presence on platforms such as Facebook, LinkedIn, Twitter and other social media sites organizations can use this networking for the purposes of marketing, public relations, and in the case of IFCC, education of members and other interested individuals across the globe.

UAV Trajectory Modeling Using Neural Networks

NASA Technical Reports Server (NTRS)

Xue, Min

2017-01-01

Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

Streaming parallel GPU acceleration of large-scale filter-based spiking neural networks.

PubMed

Slażyński, Leszek; Bohte, Sander

2012-01-01

The arrival of graphics processing (GPU) cards suitable for massively parallel computing promises affordable large-scale neural network simulation previously only available at supercomputing facilities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of magnitude, the challenge is to develop fine-grained parallel algorithms to fully exploit the particulars of GPUs. Computation in a neural network is inherently parallel and thus a natural match for GPU architectures: given inputs, the internal state for each neuron can be updated in parallel. We show that for filter-based spiking neurons, like the Spike Response Model, the additive nature of membrane potential dynamics enables additional update parallelism. This also reduces the accumulation of numerical errors when using single precision computation, the native precision of GPUs. We further show that optimizing simulation algorithms and data structures to the GPU's architecture has a large pay-off: for example, matching iterative neural updating to the memory architecture of the GPU speeds up this simulation step by a factor of three to five. With such optimizations, we can simulate in better-than-realtime plausible spiking neural networks of up to 50 000 neurons, processing over 35 million spiking events per second.

Constructing Neuronal Network Models in Massively Parallel Environments.

PubMed

Ippen, Tammo; Eppler, Jochen M; Plesser, Hans E; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

Constructing Neuronal Network Models in Massively Parallel Environments

PubMed Central

Ippen, Tammo; Eppler, Jochen M.; Plesser, Hans E.; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers. PMID:28559808

Fitness landscape transformation through a single amino acid change in the rho terminator.

PubMed

Freddolino, Peter L; Goodarzi, Hani; Tavazoie, Saeed

2012-05-01

Regulatory networks allow organisms to match adaptive behavior to the complex and dynamic contingencies of their native habitats. Upon a sudden transition to a novel environment, the mismatch between the native behavior and the new niche provides selective pressure for adaptive evolution through mutations in elements that control gene expression. In the case of core components of cellular regulation and metabolism, with broad control over diverse biological processes, such mutations may have substantial pleiotropic consequences. Through extensive phenotypic analyses, we have characterized the systems-level consequences of one such mutation (rho*) in the global transcriptional terminator Rho of Escherichia coli. We find that a single amino acid change in Rho results in a massive change in the fitness landscape of the cell, with widely discrepant fitness consequences of identical single locus perturbations in rho* versus rho(WT) backgrounds. Our observations reveal the extent to which a single regulatory mutation can transform the entire fitness landscape of the cell, causing a massive change in the interpretation of individual mutations and altering the evolutionary trajectories which may be accessible to a bacterial population.

Improving Performance and Predictability of Storage Arrays

ERIC Educational Resources Information Center

Altiparmak, Nihat

2013-01-01

Massive amount of data is generated everyday through sensors, Internet transactions, social networks, video, and all other digital sources available. Many organizations store this data to enable breakthrough discoveries and innovation in science, engineering, medicine, and commerce. Such massive scale of data poses new research problems called big…

Bidirectional multi-optical line terminals incorporated converged WSN-PON network using M/M/1 queuing

NASA Astrophysics Data System (ADS)

Kumar, Love; Sharma, Vishal; Singh, Amarpal

2017-12-01

Wireless Sensor Networks (WSNs) have an assortment of application areas, for instance, civil, military, and video surveillance with restricted power resources and transmission link. To accommodate the massive traffic load in hefty sensor networks is another key issue. Subsequently, there is a necessity to backhaul the sensed information of such networks and prolong the transmission link to access the distinct receivers. Passive Optical Network (PON), a next-generation access technology, comes out as a suitable candidate for the convergence of the sensed data to the core system. The earlier demonstrated work with single-OLT-PON introduces an overloaded buffer akin to video surveillance scenarios. In this paper, to combine the bandwidth potential of PONs with the mobility capability of WSNs, the viability for the convergence of PONs and WSNs incorporating multi-optical line terminals is demonstrated to handle the overloaded OLTs. The existing M/M/1 queue theory with interleaving polling with adaptive cycle time as dynamic bandwidth algorithm is used to shun the probability of packets clash. Further, the proposed multi-sink WSN and multi-OLT PON converged structure is investigated in bidirectional mode analytically and through computer simulations. The observations establish the proposed structure competent to accommodate the colossal data traffic through less time consumption.

Pre-supernova models for massive stars produced with large nuclear reaction network by MESA

NASA Astrophysics Data System (ADS)

Park, Byeongchan; Kwak, Kyujin

2018-04-01

Core-collapsed Supernova (CCSN) is one of violent phenomena in the universe. CCSN generates heavy elements and leaves a neutron star behind. It has been known that the physical properties of CCSN depend on those of pre-supernova such as mass, metallicities including distribution of elements, and the density and temperature profile which are obtained from the stellar evolution calculation. In particular, the production of heavy elements in CCSN is sensitive to the abundance profiles in the pre-supernova models. In this study, we evolve a massive main sequence star with 15Msun and solar metallicity to the pre-supernova stage by using two different networks, small and large. The large nuclear reaction network includes more than four times isotopes than the small network. Our calculations were done by MESA (Modules for Experiments in Stellar Astrophysics) which allowed us to use the large network containing about a hundred isotopes. We compare the results obtained with two networks.

Massive black hole and gas dynamics in galaxy nuclei mergers - I. Numerical implementation

NASA Astrophysics Data System (ADS)

Lupi, Alessandro; Haardt, Francesco; Dotti, Massimo

2015-01-01

Numerical effects are known to plague adaptive mesh refinement (AMR) codes when treating massive particles, e.g. representing massive black holes (MBHs). In an evolving background, they can experience strong, spurious perturbations and then follow unphysical orbits. We study by means of numerical simulations the dynamical evolution of a pair MBHs in the rapidly and violently evolving gaseous and stellar background that follows a galaxy major merger. We confirm that spurious numerical effects alter the MBH orbits in AMR simulations, and show that numerical issues are ultimately due to a drop in the spatial resolution during the simulation, drastically reducing the accuracy in the gravitational force computation. We therefore propose a new refinement criterion suited for massive particles, able to solve in a fast and precise way for their orbits in highly dynamical backgrounds. The new refinement criterion we designed enforces the region around each massive particle to remain at the maximum resolution allowed, independently upon the local gas density. Such maximally resolved regions then follow the MBHs along their orbits, and effectively avoids all spurious effects caused by resolution changes. Our suite of high-resolution, AMR hydrodynamic simulations, including different prescriptions for the sub-grid gas physics, shows that the new refinement implementation has the advantage of not altering the physical evolution of the MBHs, accounting for all the non-trivial physical processes taking place in violent dynamical scenarios, such as the final stages of a galaxy major merger.

UAV Trajectory Modeling Using Neural Networks

NASA Technical Reports Server (NTRS)

Xue, Min

2017-01-01

Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural network should be able to predict the vehicle's future states at next time step. A complete 4-D trajectory are then generated step by step using the trained neural network. Experiments in this work show that the neural network can approximate the sUAV's model and predict the trajectory accurately.

Inconsistency of topologically massive hypergravity

NASA Technical Reports Server (NTRS)

Aragone, C.; Deser, S.

1985-01-01

The coupled topologically massive spin-5/2 gravity system in D = 3 dimensions whose kinematics represents dynamical propagating gauge invariant massive spin-5/2 and spin-2 excitations, is shown to be inconsistent, or equivalently, not locally hypersymmetric. In contrast to D = 4, the local constraints on the system arising from failure of the fermionic Bianchi identities do not involve the 'highest spin' components of the field, but rather the auxiliary spinor required to construct a consistent massive model.

Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation

PubMed Central

Li, Wenyuan; Liu, Chun-Chi; Zhang, Tong; Li, Haifeng; Waterman, Michael S.; Zhou, Xianghong Jasmine

2011-01-01

The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks. PMID:21698123

Managing Network Partitions in Structured P2P Networks

NASA Astrophysics Data System (ADS)

Shafaat, Tallat M.; Ghodsi, Ali; Haridi, Seif

Structured overlay networks form a major class of peer-to-peer systems, which are touted for their abilities to scale, tolerate failures, and self-manage. Any long-lived Internet-scale distributed system is destined to face network partitions. Consequently, the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems. This makes it a crucial requirement for building any structured peer-to-peer systems to be resilient to network partitions. Although the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems, it has hardly been studied in the context of structured peer-to-peer systems. Structured overlays have mainly been studied under churn (frequent joins/failures), which as a side effect solves the problem of network partitions, as it is similar to massive node failures. Yet, the crucial aspect of network mergers has been ignored. In fact, it has been claimed that ring-based structured overlay networks, which constitute the majority of the structured overlays, are intrinsically ill-suited for merging rings. In this chapter, we motivate the problem of network partitions and mergers in structured overlays. We discuss how a structured overlay can automatically detect a network partition and merger. We present an algorithm for merging multiple similar ring-based overlays when the underlying network merges. We examine the solution in dynamic conditions, showing how our solution is resilient to churn during the merger, something widely believed to be difficult or impossible. We evaluate the algorithm for various scenarios and show that even when falsely detecting a merger, the algorithm quickly terminates and does not clutter the network with many messages. The algorithm is flexible as the tradeoff between message complexity and time complexity can be adjusted by a parameter.

Semi-Classical Models for Virtual Antiparticle Pairs

NASA Technical Reports Server (NTRS)

Batchelor, David; Zukor, Dorothy (Technical Monitor)

2001-01-01

Virtual particle-antiparticle pairs of massive elementary particle& are predicted in Quantum Field Theory (QFT) to appear from the vacuum and annihilate each other again within their Heisenberg lifetimes h/4mc(exp 2). In this work, semiclassical models of this process - for the cases of massive leptons, quarks, and the massive weak bosons W and Z - are constructed. It is shown that the dynamical lifetime of the particle- antiparticle system in each case equals the Heisenberg lifetime to good approximation, and obeys appropriate quantization conditions on the field fluctuation action. In other words, the dynamical lifetime of the semiclassical model agrees with QED and QCD to good approximation. But the formula for the dynamical lifetime in each model includes the force strength coupling constant (e in the lepton case, alpha(sup s) (q(exp 2)) in the quark cases), while the Heisenberg lifetime formula does not. Observing the agreement of the Heisenberg and dynamical lifetimes, we may derive the QED and QCD coupling constants in terms of h, c, and numerical factors only.

On the origin of the hypervelocity runaway star HD 271791

NASA Astrophysics Data System (ADS)

Gvaramadze, V. V.

2010-01-01

We discuss the origin of the early-B-type runaway star HD 271791 and show that its extremely high velocity (≃530 - 920km s-1) cannot be explained within the framework of the binary-supernova ejection scenario. Instead, we suggest that HD 271791 attained its peculiar velocity in the course of a strong dynamical encounter between two hard, massive binaries or through an exchange encounter between a hard, massive binary and a very massive star, formed through runaway mergers of ordinary massive stars in the dense core of a young massive star cluster.

Leadership in MMOGs: A Field of Research on Virtual Teams

ERIC Educational Resources Information Center

Mysirlaki, Sofia; Paraskeva, Fotini

2012-01-01

As our need for collaboration constantly grows, new tools have emerged to connect us in social networks, supporting the development of online communities, such as online games and virtual worlds. MMOGs (Massively Multiplayer Online Games) and MMORPGs (Massively Multiplayer Online Role-Playing Games) are complex systems, in which players are…

The WoW Factor

ERIC Educational Resources Information Center

Demski, Jennifer

2009-01-01

For a growing group of educators, the online role-playing game World of Warcraft, Blizzard Entertainment's massively popular massively multiplayer online role-playing game (MMORPG), is a place to go to relax, network, and discover potential learning strategies-- and slay a few monsters if they get in the way. The online role-playing game World of…

Local election: does bureaucracy become one of main political power?

NASA Astrophysics Data System (ADS)

Amin, Muryanto; Musthafa Sembiring, Walid

2018-03-01

This writing aims to analyze the emergence of bureaucracy as one of political power in local level after the local election is held in Indonesia. Due to information authorization, media network, and stable structure, the bureaucracy soon transforms into political power which can compete with the other political power at the local level. In Medan local election in 2010 and 2015 has evidently proven the power of bureaucracy network in winning the bureaucrat-background candidates. As methods of the research, the researcher held a Focus-Group Discussion (FGD) and had an in-depth interview with ten bureaucracy elites in Medan and local political elites. The observation and Focus-Group Discussion (FGD) are analyzed using qualitative analysis technique typology. The result states that the bureaucracy network in Medan has been used in a massive way as the political power of winning. The structure of bureaucracy – from the top to the low – is involved in the winning. The most governmental programs were applied to attract the mass’ sympathy toward the candidates. The bureaucratic proximity to media network is also used to do a campaign in a massive way. The conclusion of the research is that bureaucracy emerges as a new, massive, effective local political power in the local election.

A sweep algorithm for massively parallel simulation of circuit-switched networks

NASA Technical Reports Server (NTRS)

Gaujal, Bruno; Greenberg, Albert G.; Nicol, David M.

1992-01-01

A new massively parallel algorithm is presented for simulating large asymmetric circuit-switched networks, controlled by a randomized-routing policy that includes trunk-reservation. A single instruction multiple data (SIMD) implementation is described, and corresponding experiments on a 16384 processor MasPar parallel computer are reported. A multiple instruction multiple data (MIMD) implementation is also described, and corresponding experiments on an Intel IPSC/860 parallel computer, using 16 processors, are reported. By exploiting parallelism, our algorithm increases the possible execution rate of such complex simulations by as much as an order of magnitude.

Massive Social Network Analysis: Mining Twitter for Social Good

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ediger, David; Jiang, Karl; Riedy, Edward J.

Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software and hardware. We present GraphCT, a Graph Characterization Tooklit for massive graphs representing social network data. On a 128-processor Cray XMT, GraphCT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes. We use GraphCT to analyze public data from Twitter, a microblogging network. Twitter's message connections appear primarily tree-structured as a news dissemination system.more » Within the public data, however, are clusters of conversations. Using GraphCT, we can rank actors within these conversations and help analysts focus attention on a much smaller data subset.« less

Analysis of context dependence in social interaction networks of a massively multiplayer online role-playing game.

PubMed

Son, Seokshin; Kang, Ah Reum; Kim, Hyun-chul; Kwon, Taekyoung; Park, Juyong; Kim, Huy Kang

2012-01-01

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

The Formation and Gravitational-wave Detection of Massive Stellar Black Hole Binaries

NASA Astrophysics Data System (ADS)

Belczynski, Krzysztof; Buonanno, Alessandra; Cantiello, Matteo; Fryer, Chris L.; Holz, Daniel E.; Mandel, Ilya; Miller, M. Coleman; Walczak, Marek

2014-07-01

If binaries consisting of two ~100 M ⊙ black holes exist, they would serve as extraordinarily powerful gravitational-wave sources, detectable to redshifts of z ~ 2 with the advanced LIGO/Virgo ground-based detectors. Large uncertainties about the evolution of massive stars preclude definitive rate predictions for mergers of these massive black holes. We show that rates as high as hundreds of detections per year, or as low as no detections whatsoever, are both possible. It was thought that the only way to produce these massive binaries was via dynamical interactions in dense stellar systems. This view has been challenged by the recent discovery of several >~ 150 M ⊙ stars in the R136 region of the Large Magellanic Cloud. Current models predict that when stars of this mass leave the main sequence, their expansion is insufficient to allow common envelope evolution to efficiently reduce the orbital separation. The resulting black hole-black hole binary remains too wide to be able to coalesce within a Hubble time. If this assessment is correct, isolated very massive binaries do not evolve to be gravitational-wave sources. However, other formation channels exist. For example, the high multiplicity of massive stars, and their common formation in relatively dense stellar associations, opens up dynamical channels for massive black hole mergers (e.g., via Kozai cycles or repeated binary-single interactions). We identify key physical factors that shape the population of very massive black hole-black hole binaries. Advanced gravitational-wave detectors will provide important constraints on the formation and evolution of very massive stars.

Energy-efficient STDP-based learning circuits with memristor synapses

NASA Astrophysics Data System (ADS)

Wu, Xinyu; Saxena, Vishal; Campbell, Kristy A.

2014-05-01

It is now accepted that the traditional von Neumann architecture, with processor and memory separation, is ill suited to process parallel data streams which a mammalian brain can efficiently handle. Moreover, researchers now envision computing architectures which enable cognitive processing of massive amounts of data by identifying spatio-temporal relationships in real-time and solving complex pattern recognition problems. Memristor cross-point arrays, integrated with standard CMOS technology, are expected to result in massively parallel and low-power Neuromorphic computing architectures. Recently, significant progress has been made in spiking neural networks (SNN) which emulate data processing in the cortical brain. These architectures comprise of a dense network of neurons and the synapses formed between the axons and dendrites. Further, unsupervised or supervised competitive learning schemes are being investigated for global training of the network. In contrast to a software implementation, hardware realization of these networks requires massive circuit overhead for addressing and individually updating network weights. Instead, we employ bio-inspired learning rules such as the spike-timing-dependent plasticity (STDP) to efficiently update the network weights locally. To realize SNNs on a chip, we propose to use densely integrating mixed-signal integrate-andfire neurons (IFNs) and cross-point arrays of memristors in back-end-of-the-line (BEOL) of CMOS chips. Novel IFN circuits have been designed to drive memristive synapses in parallel while maintaining overall power efficiency (<1 pJ/spike/synapse), even at spike rate greater than 10 MHz. We present circuit design details and simulation results of the IFN with memristor synapses, its response to incoming spike trains and STDP learning characterization.

Humanitarian Information Management Network Effectiveness: An Analysis at the Organizational and Network Levels

ERIC Educational Resources Information Center

Ngamassi Tchouakeu, Louis-Marie

2011-01-01

Massive international response to humanitarian crises such as the South Asian Tsunami in 2004, the Hurricane Katrina in 2005 and the Haiti earthquake in 2010 highlights the importance of humanitarian inter-organizational collaboration networks, especially in information management and exchange. Despite more than a decade old call for more research…

Connections in wood and foliage

Treesearch

Kevin T. Smith

2009-01-01

Trees are networked systems that capture energy, move massive amounts of water and material, and provide the setting for human society and for the lives of many associated organisms. Tree survival depends on making and breaking the right connections within these networks.

Method and apparatus for routing data in an inter-nodal communications lattice of a massively parallel computer system by semi-randomly varying routing policies for different packets

DOEpatents

Archer, Charles Jens; Musselman, Roy Glenn; Peters, Amanda; Pinnow, Kurt Walter; Swartz, Brent Allen; Wallenfelt, Brian Paul

2010-11-23

A massively parallel computer system contains an inter-nodal communications network of node-to-node links. Nodes vary a choice of routing policy for routing data in the network in a semi-random manner, so that similarly situated packets are not always routed along the same path. Semi-random variation of the routing policy tends to avoid certain local hot spots of network activity, which might otherwise arise using more consistent routing determinations. Preferably, the originating node chooses a routing policy for a packet, and all intermediate nodes in the path route the packet according to that policy. Policies may be rotated on a round-robin basis, selected by generating a random number, or otherwise varied.

Optimized scalable network switch

DOEpatents

Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Takken, Todd E [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2007-12-04

In a massively parallel computing system having a plurality of nodes configured in m multi-dimensions, each node including a computing device, a method for routing packets towards their destination nodes is provided which includes generating at least one of a 2m plurality of compact bit vectors containing information derived from downstream nodes. A multilevel arbitration process in which downstream information stored in the compact vectors, such as link status information and fullness of downstream buffers, is used to determine a preferred direction and virtual channel for packet transmission. Preferred direction ranges are encoded and virtual channels are selected by examining the plurality of compact bit vectors. This dynamic routing method eliminates the necessity of routing tables, thus enhancing scalability of the switch.

Optimized scalable network switch

DOEpatents

Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.

2010-02-23

In a massively parallel computing system having a plurality of nodes configured in m multi-dimensions, each node including a computing device, a method for routing packets towards their destination nodes is provided which includes generating at least one of a 2m plurality of compact bit vectors containing information derived from downstream nodes. A multilevel arbitration process in which downstream information stored in the compact vectors, such as link status information and fullness of downstream buffers, is used to determine a preferred direction and virtual channel for packet transmission. Preferred direction ranges are encoded and virtual channels are selected by examining the plurality of compact bit vectors. This dynamic routing method eliminates the necessity of routing tables, thus enhancing scalability of the switch.

Research in Computational Astrobiology

NASA Technical Reports Server (NTRS)

Chaban, Galina; Jaffe, Richard; Liang, Shoudan; New, Michael H.; Pohorille, Andrew; Wilson, Michael A.

2002-01-01

We present results from several projects in the new field of computational astrobiology, which is devoted to advancing our understanding of the origin, evolution and distribution of life in the Universe using theoretical and computational tools. We have developed a procedure for calculating long-range effects in molecular dynamics using a plane wave expansion of the electrostatic potential. This method is expected to be highly efficient for simulating biological systems on massively parallel supercomputers. We have perform genomics analysis on a family of actin binding proteins. We have performed quantum mechanical calculations on carbon nanotubes and nucleic acids, which simulations will allow us to investigate possible sources of organic material on the early earth. Finally, we have developed a model of protobiological chemistry using neural networks.

Network gateway security method for enterprise Grid: a literature review

NASA Astrophysics Data System (ADS)

Sujarwo, A.; Tan, J.

2017-03-01

The computational Grid has brought big computational resources closer to scientists. It enables people to do a large computational job anytime and anywhere without any physical border anymore. However, the massive and spread of computer participants either as user or computational provider arise problems in security. The challenge is on how the security system, especially the one which filters data in the gateway could works in flexibility depends on the registered Grid participants. This paper surveys what people have done to approach this challenge, in order to find the better and new method for enterprise Grid. The findings of this paper is the dynamically controlled enterprise firewall to secure the Grid resources from unwanted connections with a new firewall controlling method and components.

Learning Specialised Vocabulary through Facebook in a Massive Open Online Course

ERIC Educational Resources Information Center

Ventura, Patricia; Martín-Monje, Elena

2016-01-01

This paper explores how the incorporation of a social network such as Facebook can enhance the acquisition of specialised vocabulary in the context of a Massive Open Online Course (MOOC). Such initiative took place in the second edition of the MOOC Professional English, the first ever English for Specific Purposes (ESP) MOOC to be launched in…

"Elven Elder LVL59 LFP/RB. Please PM Me": Immersion, Collaborative Tasks and Problem-Solving in Massively Multiplayer Online Games

ERIC Educational Resources Information Center

Voulgari, Iro; Komis, Vassilis

2010-01-01

Although there is strong evidence that massively multiplayer online games (MMOGs) constitute environments of social interactions and effective learning, we currently lack the tools for investigating the effectiveness of the social networks emerging as well as the cognitive aspects and knowledge acquisition such environments involve. Within this…

Efficient massively parallel simulation of dynamic channel assignment schemes for wireless cellular communications

NASA Technical Reports Server (NTRS)

Greenberg, Albert G.; Lubachevsky, Boris D.; Nicol, David M.; Wright, Paul E.

1994-01-01

Fast, efficient parallel algorithms are presented for discrete event simulations of dynamic channel assignment schemes for wireless cellular communication networks. The driving events are call arrivals and departures, in continuous time, to cells geographically distributed across the service area. A dynamic channel assignment scheme decides which call arrivals to accept, and which channels to allocate to the accepted calls, attempting to minimize call blocking while ensuring co-channel interference is tolerably low. Specifically, the scheme ensures that the same channel is used concurrently at different cells only if the pairwise distances between those cells are sufficiently large. Much of the complexity of the system comes from ensuring this separation. The network is modeled as a system of interacting continuous time automata, each corresponding to a cell. To simulate the model, conservative methods are used; i.e., methods in which no errors occur in the course of the simulation and so no rollback or relaxation is needed. Implemented on a 16K processor MasPar MP-1, an elegant and simple technique provides speedups of about 15 times over an optimized serial simulation running on a high speed workstation. A drawback of this technique, typical of conservative methods, is that processor utilization is rather low. To overcome this, new methods were developed that exploit slackness in event dependencies over short intervals of time, thereby raising the utilization to above 50 percent and the speedup over the optimized serial code to about 120 times.

Simplified Antenna Group Determination of RS Overhead Reduced Massive MIMO for Wireless Sensor Networks.

PubMed

Lee, Byung Moo

2017-12-29

Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices.

Simplified Antenna Group Determination of RS Overhead Reduced Massive MIMO for Wireless Sensor Networks

PubMed Central

2017-01-01

Massive multiple-input multiple-output (MIMO) systems can be applied to support numerous internet of things (IoT) devices using its excessive amount of transmitter (TX) antennas. However, one of the big obstacles for the realization of the massive MIMO system is the overhead of reference signal (RS), because the number of RS is proportional to the number of TX antennas and/or related user equipments (UEs). It has been already reported that antenna group-based RS overhead reduction can be very effective to the efficient operation of massive MIMO, but the method of deciding the number of antennas needed in each group is at question. In this paper, we propose a simplified determination scheme of the number of antennas needed in each group for RS overhead reduced massive MIMO to support many IoT devices. Supporting many distributed IoT devices is a framework to configure wireless sensor networks. Our contribution can be divided into two parts. First, we derive simple closed-form approximations of the achievable spectral efficiency (SE) by using zero-forcing (ZF) and matched filtering (MF) precoding for the RS overhead reduced massive MIMO systems with channel estimation error. The closed-form approximations include a channel error factor that can be adjusted according to the method of the channel estimation. Second, based on the closed-form approximation, we present an efficient algorithm determining the number of antennas needed in each group for the group-based RS overhead reduction scheme. The algorithm depends on the exact inverse functions of the derived closed-form approximations of SE. It is verified with theoretical analysis and simulation that the proposed algorithm works well, and thus can be used as an important tool for massive MIMO systems to support many distributed IoT devices. PMID:29286339

RAMA: A file system for massively parallel computers

NASA Technical Reports Server (NTRS)

Miller, Ethan L.; Katz, Randy H.

1993-01-01

This paper describes a file system design for massively parallel computers which makes very efficient use of a few disks per processor. This overcomes the traditional I/O bottleneck of massively parallel machines by storing the data on disks within the high-speed interconnection network. In addition, the file system, called RAMA, requires little inter-node synchronization, removing another common bottleneck in parallel processor file systems. Support for a large tertiary storage system can easily be integrated in lo the file system; in fact, RAMA runs most efficiently when tertiary storage is used.

Three-dimensional massive gravity and the bigravity black hole

NASA Astrophysics Data System (ADS)

Bañados, Máximo; Theisen, Stefan

2009-11-01

We study three-dimensional massive gravity formulated as a theory with two dynamical metrics, like the f-g theories of Isham-Salam and Strathdee. The action is parity preserving and has no higher derivative terms. The spectrum contains a single massive graviton. This theory has several features discussed recently in TMG and NMG. We find warped black holes, a critical point, and generalized Brown-Henneaux boundary conditions.

Black Hole Universe Model for Explaining GRBs, X-Ray Flares, and Quasars as Emissions of Dynamic Star-like, Massive, and Supermassive Black Holes

NASA Astrophysics Data System (ADS)

Zhang, Tianxi

2014-01-01

Slightly modifying the standard big bang theory, the author has recently developed a new cosmological model called black hole universe, which is consistent with Mach’s principle, governed by Einstein’s general theory of relativity, and able to explain all observations of the universe. Previous studies accounted for the origin, structure, evolution, expansion, cosmic microwave background radiation, and acceleration of the black hole universe, which grew from a star-like black hole with several solar masses through a supermassive black hole with billions of solar masses to the present state with hundred billion-trillions of solar masses by accreting ambient matter and merging with other black holes. This study investigates the emissions of dynamic black holes according to the black hole universe model and provides a self-consistent explanation for the observations of gamma ray bursts (GRBs), X-ray flares, and quasars as emissions of dynamic star-like, massive, and supermassive black holes. It is shown that a black hole, when it accretes its ambient matter or merges with other black holes, becomes dynamic. Since the event horizon of a dynamic black hole is broken, the inside hot (or high-frequency) blackbody radiation leaks out. The leakage of the inside hot blackbody radiation leads to a GRB if it is a star-like black hole, an X-ray flare if it is a massive black hole like the one at the center of the Milky Way, or a quasar if it is a supermassive black hole like an active galactic nucleus (AGN). The energy spectra and amount of emissions produced by the dynamic star-like, massive, and supermassive black holes can be consistent with the measurements of GRBs, X-ray flares, and quasars.

SiGN-SSM: open source parallel software for estimating gene networks with state space models.

PubMed

Tamada, Yoshinori; Yamaguchi, Rui; Imoto, Seiya; Hirose, Osamu; Yoshida, Ryo; Nagasaki, Masao; Miyano, Satoru

2011-04-15

SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles. SiGN-SSM implements a novel parameter constraint effective to stabilize the estimated models. Also, by using a supercomputer, it is able to determine the gene network structure by a statistical permutation test in a practical time. SiGN-SSM is applicable not only to analyzing temporal regulatory dependencies between genes, but also to extracting the differentially regulated genes from time series expression profiles. SiGN-SSM is distributed under GNU Affero General Public Licence (GNU AGPL) version 3 and can be downloaded at http://sign.hgc.jp/signssm/. The pre-compiled binaries for some architectures are available in addition to the source code. The pre-installed binaries are also available on the Human Genome Center supercomputer system. The online manual and the supplementary information of SiGN-SSM is available on our web site. tamada@ims.u-tokyo.ac.jp.

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

PubMed

Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

2015-08-01

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

PubMed Central

Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

2015-01-01

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is “non-intrusive” and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design. PMID:26317784

Role of Social Media and the Internet in Education

PubMed Central

2012-01-01

Since the advent of the Internet, and in particular the development of the interactive version of the web, Web 2.0, use of Social Media has developed into a major strategy for businesses and organizations such as the IFCC to use for the purposes of Public Relations and Education. The early Internet ‘Web 1.0’ was a largely static environment which did not allow interaction between organizations and their customers and/or members and as such was mainly used as an information repository rather than a dynamic environment for the exchange of ideas and active marketing and education. Since the development of Web 2.0 we have seen a massive increase in web based traffic which could be loosely called ‘social networking’ which initially was mainly networking between individuals but more recently has developed into a major marketing resource allowing networking between organizations and individuals on the web. It follows then that by developing a Social Media presence on platforms such as Facebook, LinkedIn, Twitter and other social media sites organizations can use this networking for the purposes of marketing, public relations, and in the case of IFCC, education of members and other interested individuals across the globe. PMID:27683408

On the dual equivalence of the self-dual and topologically massive /B∧F models coupled to dynamical fermionic matter

NASA Astrophysics Data System (ADS)

Menezes, R.; Nascimento, J. R. S.; Ribeiro, R. F.; Wotzasek, C.

2002-06-01

We study the equivalence between the /B∧F self-dual (SDB∧F) and the /B∧F topologically massive (TMB∧F) models including the coupling to dynamical, U(1) charged fermionic matter. This is done through an iterative procedure of gauge embedding that produces the dual mapping. In the interactive cases, the minimal coupling adopted for both vector and tensor fields in the self-dual representation is transformed into a non-minimal magnetic like coupling in the topologically massive representation but with the currents swapped. It is known that to establish this equivalence a current-current interaction term is needed to render the matter sector unchanged. We show that both terms arise naturally from the embedding procedure.

Method and apparatus for routing data in an inter-nodal communications lattice of a massively parallel computer system by employing bandwidth shells at areas of overutilization

DOEpatents

Archer, Charles Jens; Musselman, Roy Glenn; Peters, Amanda; Pinnow, Kurt Walter; Swartz, Brent Allen; Wallenfelt, Brian Paul

2010-04-27

A massively parallel computer system contains an inter-nodal communications network of node-to-node links. An automated routing strategy routes packets through one or more intermediate nodes of the network to reach a final destination. The default routing strategy is altered responsive to detection of overutilization of a particular path of one or more links, and at least some traffic is re-routed by distributing the traffic among multiple paths (which may include the default path). An alternative path may require a greater number of link traversals to reach the destination node.

Empirical confirmation of creative destruction from world trade data.

PubMed

Klimek, Peter; Hausmann, Ricardo; Thurner, Stefan

2012-01-01

We show that world trade network datasets contain empirical evidence that the dynamics of innovation in the world economy indeed follows the concept of creative destruction, as proposed by J.A. Schumpeter more than half a century ago. National economies can be viewed as complex, evolving systems, driven by a stream of appearance and disappearance of goods and services. Products appear in bursts of creative cascades. We find that products systematically tend to co-appear, and that product appearances lead to massive disappearance events of existing products in the following years. The opposite-disappearances followed by periods of appearances-is not observed. This is an empirical validation of the dominance of cascading competitive replacement events on the scale of national economies, i.e., creative destruction. We find a tendency that more complex products drive out less complex ones, i.e., progress has a direction. Finally we show that the growth trajectory of a country's product output diversity can be understood by a recently proposed evolutionary model of Schumpeterian economic dynamics.

Detection of single ion channel activity with carbon nanotubes

NASA Astrophysics Data System (ADS)

Zhou, Weiwei; Wang, Yung Yu; Lim, Tae-Sun; Pham, Ted; Jain, Dheeraj; Burke, Peter J.

2015-03-01

Many processes in life are based on ion currents and membrane voltages controlled by a sophisticated and diverse family of membrane proteins (ion channels), which are comparable in size to the most advanced nanoelectronic components currently under development. Here we demonstrate an electrical assay of individual ion channel activity by measuring the dynamic opening and closing of the ion channel nanopores using single-walled carbon nanotubes (SWNTs). Two canonical dynamic ion channels (gramicidin A (gA) and alamethicin) and one static biological nanopore (α-hemolysin (α-HL)) were successfully incorporated into supported lipid bilayers (SLBs, an artificial cell membrane), which in turn were interfaced to the carbon nanotubes through a variety of polymer-cushion surface functionalization schemes. The ion channel current directly charges the quantum capacitance of a single nanotube in a network of purified semiconducting nanotubes. This work forms the foundation for a scalable, massively parallel architecture of 1d nanoelectronic devices interrogating electrophysiology at the single ion channel level.

BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

PubMed

Zhao, Ming; Rattanatamrong, Prapaporn; DiGiovanna, Jack; Mahmoudi, Babak; Figueiredo, Renato J; Sanchez, Justin C; Príncipe, José C; Fortes, José A B

2008-01-01

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

Empirical Confirmation of Creative Destruction from World Trade Data

PubMed Central

Klimek, Peter; Hausmann, Ricardo; Thurner, Stefan

2012-01-01

We show that world trade network datasets contain empirical evidence that the dynamics of innovation in the world economy indeed follows the concept of creative destruction, as proposed by J.A. Schumpeter more than half a century ago. National economies can be viewed as complex, evolving systems, driven by a stream of appearance and disappearance of goods and services. Products appear in bursts of creative cascades. We find that products systematically tend to co-appear, and that product appearances lead to massive disappearance events of existing products in the following years. The opposite–disappearances followed by periods of appearances–is not observed. This is an empirical validation of the dominance of cascading competitive replacement events on the scale of national economies, i.e., creative destruction. We find a tendency that more complex products drive out less complex ones, i.e., progress has a direction. Finally we show that the growth trajectory of a country’s product output diversity can be understood by a recently proposed evolutionary model of Schumpeterian economic dynamics. PMID:22719989

Examining the Presence of Social Media on University Web Sites

ERIC Educational Resources Information Center

Greenwood, Grant

2012-01-01

Over the past few years, social networking has exploded into a massive medium that has captured the attention of a large portion of the American population. The ever-growing social networking site(s) (SNS) movement has filled a networking gap and thus, has presented higher education institutions with unique opportunities (Reid 2009) to further…

The formation and gravitational-wave detection of massive stellar black hole binaries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Belczynski, Krzysztof; Walczak, Marek; Buonanno, Alessandra

2014-07-10

If binaries consisting of two ∼100 M{sub ☉} black holes exist, they would serve as extraordinarily powerful gravitational-wave sources, detectable to redshifts of z ∼ 2 with the advanced LIGO/Virgo ground-based detectors. Large uncertainties about the evolution of massive stars preclude definitive rate predictions for mergers of these massive black holes. We show that rates as high as hundreds of detections per year, or as low as no detections whatsoever, are both possible. It was thought that the only way to produce these massive binaries was via dynamical interactions in dense stellar systems. This view has been challenged by themore » recent discovery of several ≳ 150 M{sub ☉} stars in the R136 region of the Large Magellanic Cloud. Current models predict that when stars of this mass leave the main sequence, their expansion is insufficient to allow common envelope evolution to efficiently reduce the orbital separation. The resulting black hole-black hole binary remains too wide to be able to coalesce within a Hubble time. If this assessment is correct, isolated very massive binaries do not evolve to be gravitational-wave sources. However, other formation channels exist. For example, the high multiplicity of massive stars, and their common formation in relatively dense stellar associations, opens up dynamical channels for massive black hole mergers (e.g., via Kozai cycles or repeated binary-single interactions). We identify key physical factors that shape the population of very massive black hole-black hole binaries. Advanced gravitational-wave detectors will provide important constraints on the formation and evolution of very massive stars.« less

First Responder Weapons of Mass Destruction Training Using Massively Multiplayer On-Line Gaming

DTIC Science & Technology

2004-06-01

Training Using Massively Multiplayer On-Line Gaming 6. AUTHOR(S) Thomas J. Richardson 5. FUNDING NUMBERS 7. PERFORMING ORGANIZATION NAME(S) AND...ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME...37 1. Transitioning from Hierarchical to Networked Control................37 2. Compliance with Government Performance and Results Act

A study on the achievable data rate in massive MIMO system

NASA Astrophysics Data System (ADS)

Salh, Adeeb; Audah, Lukman; Shah, Nor Shahida M.; Hamzah, Shipun A.

2017-09-01

The achievable high data rates depend on the ability of massive multi-input-multi-output (MIMO) for the fifth-generation (5G) cellular networks, where the massive MIMO systems can support very high energy and spectral efficiencies. A major challenge in mobile broadband networks is how to support the throughput in the future 5G, where the highlight of 5G expected to provide high speed internet for every user. The performance massive MIMO system increase with linear minimum mean square error (MMSE), zero forcing (ZF) and maximum ratio transmission (MRT) when the number of antennas increases to infinity, by deriving the closed-form approximation for achievable data rate expressions. Meanwhile, the high signal-to-noise ratio (SNR) can be mitigated by using MMSE, ZF and MRT, which are used to suppress the inter-cell interference signals between neighboring cells. The achievable sum rate for MMSE is improved based on the distributed users inside cell, mitigated the inter-cell interference caused when send the same signal by other cells. By contrast, MMSE is better than ZF in perfect channel state information (CSI) for approximately 20% of the achievable sum rate.

Reconfigurable optical interconnection network for multimode optical fiber sensor arrays

NASA Technical Reports Server (NTRS)

Chen, R. T.; Robinson, D.; Lu, H.; Wang, M. R.; Jannson, T.; Baumbick, R.

1992-01-01

A single-source, single-detector architecture has been developed to implement a reconfigurable optical interconnection network multimode optical fiber sensor arrays. The network was realized by integrating LiNbO3 electrooptic (EO) gratings working at the Raman Na regime and a massive fan-out waveguide hologram (WH) working at the Bragg regime onto a multimode glass waveguide. The glass waveguide utilized the whole substrate as a guiding medium. A 1-to-59 massive waveguide fan-out was demonstrated using a WH operating at 514 nm. Measured diffraction efficiency of 59 percent was experimentally confirmed. Reconfigurability of the interconnection was carried out by generating an EO grating through an externally applied electric field. Unlike conventional single-mode integrated optical devices, the guided mode demonstrated has an azimuthal symmetry in mode profile which is the same as that of a fiber mode.

Ocean Networks Canada: Live Sensing of a Dynamic Ocean System

NASA Astrophysics Data System (ADS)

Heesemann, Martin; Juniper, Kim; Hoeberechts, Maia; Matabos, Marjolaine; Mihaly, Steven; Scherwath, Martin; Dewey, Richard

2013-04-01

Ocean Networks Canada operates two advanced cabled networks on the west coast of British Columbia. VENUS, the coastal network consisting of two cabled arrays with four Nodes reaching an isolated fjord (Saanich Inlet) and a busy shipping corridor near Vancouver (the Strait of Georgia) went into operation in February 2006. NEPTUNE Canada is the first operational deep-sea regional cabled ocean observatory worldwide. Since the first data began streaming to the public in 2009, instruments on the five active nodes along the 800 km cable loop have gathered a time-series documenting three years in the northeastern Pacific. Observations cover the northern Juan de Fuca tectonic plate from ridge to trench and the continental shelf and slope off Vancouver Island. The cabled systems provide power and high bandwidth communications to a wide range of oceanographic instrument systems which measure the physical, chemical, geological, and biological conditions of the dynamic earth-ocean system. Over the years significant challenges have been overcome and currently we have more than 100 instruments with hundreds of sensors reporting data in real-time. Salient successes are the first open-ocean seafloor to sea-surface vertical profiling system, three years of operation of Wally—a seafloor crawler that explores a hydrate mound, and a proven resilient cable design that can recover from trawler hits and major equipment meltdown with minimal loss of data. A network wide array of bottom mounted pressure recorders and seismometers recorded the passage of three major tsunamis, numerous earthquakes and frequent whale calls. At the Endeavour segment of the Juan de Fuca ridge high temperature and diffuse vent fluids were monitored and sampled using novel equipment, including high resolution active acoustics instrumentation to study plume dynamics at a massive sulfide hydrothermal vent. Also, four deep sea cabled moorings (300 m high) were placed in the precipitous bathymetry of the 2200 m deep axial valley. Close to shore, a three-dimensional imaging system monitors the growth of a sponge complex on the 20 m deep Folger pinnacle in the wave zone offshore Vancouver Island. Instruments monitoring the delta and estuarine dynamics of the Fraser River that empties into the eastern edge of the Strait of Georgia complete the picture of this northeast Pacific dynamic ocean system from an active spreading ridge, down to the abyss, along the hydrate-rich slope, and up to the coast. While the installation of the first phase of experiments is nearing completion, the cabled networks still provide ample opportunity for expansion and scientists from all over the world are invited to join our community and advance science by using the data that is publicly available at http://www.oceannetworks.ca/.

Neutron stars structure in the context of massive gravity

NASA Astrophysics Data System (ADS)

Hendi, S. H.; Bordbar, G. H.; Eslam Panah, B.; Panahiyan, S.

2017-07-01

Motivated by the recent interests in spin-2 massive gravitons, we study the structure of neutron star in the context of massive gravity. The modifications of TOV equation in the presence of massive gravity are explored in 4 and higher dimensions. Next, by considering the modern equation of state for the neutron star matter (which is extracted by the lowest order constrained variational (LOCV) method with the AV18 potential), different physical properties of the neutron star (such as Le Chatelier's principle, stability and energy conditions) are investigated. It is shown that consideration of the massive gravity has specific contributions into the structure of neutron star and introduces new prescriptions for the massive astrophysical objects. The mass-radius relation is examined and the effects of massive gravity on the Schwarzschild radius, average density, compactness, gravitational redshift and dynamical stability are studied. Finally, a relation between mass and radius of neutron star versus the Planck mass is extracted.

Mastering Mobile Security

ERIC Educational Resources Information Center

Panettieri, Joseph C.

2007-01-01

Without proper security, mobile devices are easy targets for worms, viruses, and so-called robot ("bot") networks. Hackers increasingly use bot networks to launch massive attacks against eCommerce websites--potentially targeting one's online tuition payment or fundraising/financial development systems. How can one defend his mobile systems against…

Analysis of Context Dependence in Social Interaction Networks of a Massively Multiplayer Online Role-Playing Game

PubMed Central

Son, Seokshin; Kang, Ah Reum; Kim, Hyun-chul; Kwon, Taekyoung; Park, Juyong; Kim, Huy Kang

2012-01-01

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior. PMID:22496771

Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.

Energy Efficiency Challenges of 5G Small Cell Networks.

PubMed

Ge, Xiaohu; Yang, Jing; Gharavi, Hamid; Sun, Yang

2017-05-01

The deployment of a large number of small cells poses new challenges to energy efficiency, which has often been ignored in fifth generation (5G) cellular networks. While massive multiple-input multiple outputs (MIMO) will reduce the transmission power at the expense of higher computational cost, the question remains as to which computation or transmission power is more important in the energy efficiency of 5G small cell networks. Thus, the main objective in this paper is to investigate the computation power based on the Landauer principle. Simulation results reveal that more than 50% of the energy is consumed by the computation power at 5G small cell base stations (BSs). Moreover, the computation power of 5G small cell BS can approach 800 watt when the massive MIMO (e.g., 128 antennas) is deployed to transmit high volume traffic. This clearly indicates that computation power optimization can play a major role in the energy efficiency of small cell networks.

Energy Efficiency Challenges of 5G Small Cell Networks

PubMed Central

Ge, Xiaohu; Yang, Jing; Gharavi, Hamid; Sun, Yang

2017-01-01

The deployment of a large number of small cells poses new challenges to energy efficiency, which has often been ignored in fifth generation (5G) cellular networks. While massive multiple-input multiple outputs (MIMO) will reduce the transmission power at the expense of higher computational cost, the question remains as to which computation or transmission power is more important in the energy efficiency of 5G small cell networks. Thus, the main objective in this paper is to investigate the computation power based on the Landauer principle. Simulation results reveal that more than 50% of the energy is consumed by the computation power at 5G small cell base stations (BSs). Moreover, the computation power of 5G small cell BS can approach 800 watt when the massive MIMO (e.g., 128 antennas) is deployed to transmit high volume traffic. This clearly indicates that computation power optimization can play a major role in the energy efficiency of small cell networks. PMID:28757670

Fluid/gravity correspondence for massive gravity

NASA Astrophysics Data System (ADS)

Pan, Wen-Jian; Huang, Yong-Chang

2016-11-01

In this paper, we investigate the fluid/gravity correspondence in the framework of massive Einstein gravity. Treating the gravitational mass terms as an effective energy-momentum tensor and utilizing the Petrov-like boundary condition on a timelike hypersurface, we find that the perturbation effects of massive gravity in bulk can be completely governed by the incompressible Navier-Stokes equation living on the cutoff surface under the near horizon and nonrelativistic limits. Furthermore, we have concisely computed the ratio of dynamical viscosity to entropy density for two massive Einstein gravity theories, and found that they still saturate the Kovtun-Son-Starinets (KSS) bound.

Joint Services Electronics Program

DTIC Science & Technology

1992-03-05

Packaging Considerations M. T. Raghunath (Professor Abhiram Ranade) A central issue in massively parallel computation is the design of the interconnection...programs on promising network architectures. Publications: [1] M. T. Raghunath and A. G. Ranade, A Simulation-Based Compari- son of Interconnection Networks...more difficult analog function approximation task. Network Design Issues for Fast Global Communication Professor A. Ranade with M.T. Raghunath A

Secular resonances with massive asteroids and their impact on the dynamics of small bodies

NASA Astrophysics Data System (ADS)

Tsirvoulis, Georgios; Novaković, Bojan; Djošović, Valdimir

2015-08-01

The quest for understanding the dynamical structure of the main belt has been a long-lasting endeavor. From the discovery of the Kirkwood gaps and the Hirayama families, to the more recent advances in secular perturbation theory, the refinement of the proper elements and the discovery of the three-body mean-motion resonances, only to name a few, the progress has been immense. Dynamical models coupled with the outbursts in computational power and observations have greatly improved our knowledge of the dynamical evolution of the small bodies in the Solar System.While our set of tools for studying the dynamical porperties of the main belt is believed to be sufficiently complete, our assumptions on how to use them seem to have hindered this effort.The concensus has been that, judging by their mass, only the planets, especially the giant ones, can act as efficient perturbers of the orbits of asteroids. Thus a lot of studies have been made on the locations and effects of secular resonances with the giant planets in different parts of the main belt, explaining among other things the presence of gaps in the distribution of asteroids, strange shapes of some asteroid families and transport mechanisms of asteroids to the near-Earth region.Our work is motivated by the first discovery that a secular resonance with the most massive asteroid, Ceres, is the dominant dynamical mechanism responsible for the post-impact evolution of the Hoffmeister family members. Thus the concensus is wrong. Knowing now, that secular resonances with massive asteroids can be effective on asteroid dynamics, we set out to construct a dynamical map of these resonances across the main belt.Our study is focused on the linear and degree four non-linear secular resonances with the two most massive asteroids (1) Ceres and (4) Vesta. First we determine the locations of these secular resonances in the proper elements space, acquiring an understanding of the potentially affected regions, and then we perform numerical simulations to investigate the importance of each secular resonance on the dynamical evolution of asteroid orbits in the different parts of the main belt.

A Pedagogy of Abundance or a Pedagogy to Support Human Beings? Participant Support on Massive Open Online Courses

ERIC Educational Resources Information Center

Kop, Rita; Fournier, Helene; Mak, John Sui Fai

2011-01-01

This paper examines how emergent technologies could influence the design of learning environments. It will pay particular attention to the roles of educators and learners in creating networked learning experiences on massive open online courses (MOOCs). The research shows that it is possible to move from a pedagogy of abundance to a pedagogy that…

Medical applications for high-performance computers in SKIF-GRID network.

PubMed

Zhuchkov, Alexey; Tverdokhlebov, Nikolay

2009-01-01

The paper presents a set of software services for massive mammography image processing by using high-performance parallel computers of SKIF-family which are linked into a service-oriented grid-network. An experience of a prototype system implementation in two medical institutions is also described.

Extracting functionally feedforward networks from a population of spiking neurons

PubMed Central

Vincent, Kathleen; Tauskela, Joseph S.; Thivierge, Jean-Philippe

2012-01-01

Neuronal avalanches are a ubiquitous form of activity characterized by spontaneous bursts whose size distribution follows a power-law. Recent theoretical models have replicated power-law avalanches by assuming the presence of functionally feedforward connections (FFCs) in the underlying dynamics of the system. Accordingly, avalanches are generated by a feedforward chain of activation that persists despite being embedded in a larger, massively recurrent circuit. However, it is unclear to what extent networks of living neurons that exhibit power-law avalanches rely on FFCs. Here, we employed a computational approach to reconstruct the functional connectivity of cultured cortical neurons plated on multielectrode arrays (MEAs) and investigated whether pharmacologically induced alterations in avalanche dynamics are accompanied by changes in FFCs. This approach begins by extracting a functional network of directed links between pairs of neurons, and then evaluates the strength of FFCs using Schur decomposition. In a first step, we examined the ability of this approach to extract FFCs from simulated spiking neurons. The strength of FFCs obtained in strictly feedforward networks diminished monotonically as links were gradually rewired at random. Next, we estimated the FFCs of spontaneously active cortical neuron cultures in the presence of either a control medium, a GABAA receptor antagonist (PTX), or an AMPA receptor antagonist combined with an NMDA receptor antagonist (APV/DNQX). The distribution of avalanche sizes in these cultures was modulated by this pharmacology, with a shallower power-law under PTX (due to the prominence of larger avalanches) and a steeper power-law under APV/DNQX (due to avalanches recruiting fewer neurons) relative to control cultures. The strength of FFCs increased in networks after application of PTX, consistent with an amplification of feedforward activity during avalanches. Conversely, FFCs decreased after application of APV/DNQX, consistent with fading feedforward activation. The observed alterations in FFCs provide experimental support for recent theoretical work linking power-law avalanches to the feedforward organization of functional connections in local neuronal circuits. PMID:23091458

Extracting functionally feedforward networks from a population of spiking neurons.

PubMed

Vincent, Kathleen; Tauskela, Joseph S; Thivierge, Jean-Philippe

2012-01-01

Neuronal avalanches are a ubiquitous form of activity characterized by spontaneous bursts whose size distribution follows a power-law. Recent theoretical models have replicated power-law avalanches by assuming the presence of functionally feedforward connections (FFCs) in the underlying dynamics of the system. Accordingly, avalanches are generated by a feedforward chain of activation that persists despite being embedded in a larger, massively recurrent circuit. However, it is unclear to what extent networks of living neurons that exhibit power-law avalanches rely on FFCs. Here, we employed a computational approach to reconstruct the functional connectivity of cultured cortical neurons plated on multielectrode arrays (MEAs) and investigated whether pharmacologically induced alterations in avalanche dynamics are accompanied by changes in FFCs. This approach begins by extracting a functional network of directed links between pairs of neurons, and then evaluates the strength of FFCs using Schur decomposition. In a first step, we examined the ability of this approach to extract FFCs from simulated spiking neurons. The strength of FFCs obtained in strictly feedforward networks diminished monotonically as links were gradually rewired at random. Next, we estimated the FFCs of spontaneously active cortical neuron cultures in the presence of either a control medium, a GABA(A) receptor antagonist (PTX), or an AMPA receptor antagonist combined with an NMDA receptor antagonist (APV/DNQX). The distribution of avalanche sizes in these cultures was modulated by this pharmacology, with a shallower power-law under PTX (due to the prominence of larger avalanches) and a steeper power-law under APV/DNQX (due to avalanches recruiting fewer neurons) relative to control cultures. The strength of FFCs increased in networks after application of PTX, consistent with an amplification of feedforward activity during avalanches. Conversely, FFCs decreased after application of APV/DNQX, consistent with fading feedforward activation. The observed alterations in FFCs provide experimental support for recent theoretical work linking power-law avalanches to the feedforward organization of functional connections in local neuronal circuits.

Condensate of massive graviton and dark matter

NASA Astrophysics Data System (ADS)

Aoki, Katsuki; Maeda, Kei-ichi

2018-02-01

We study coherently oscillating massive gravitons in the ghost-free bigravity theory. This coherent field can be interpreted as a condensate of the massive gravitons. We first define the effective energy-momentum tensor of the coherent massive gravitons in a curved spacetime. We then study the background dynamics of the Universe and the cosmic structure formation including the effects of the coherent massive gravitons. We find that the condensate of the massive graviton behaves as a dark matter component of the Universe. From the geometrical point of view the condensate is regarded as a spacetime anisotropy. Hence, in our scenario, dark matter is originated from the tiny deformation of the spacetime. We also discuss a production of the spacetime anisotropy and find that the extragalactic magnetic field of a primordial origin can yield a sufficient amount for dark matter.

Large-scale molecular dynamics simulation of DNA: implementation and validation of the AMBER98 force field in LAMMPS.

PubMed

Grindon, Christina; Harris, Sarah; Evans, Tom; Novik, Keir; Coveney, Peter; Laughton, Charles

2004-07-15

Molecular modelling played a central role in the discovery of the structure of DNA by Watson and Crick. Today, such modelling is done on computers: the more powerful these computers are, the more detailed and extensive can be the study of the dynamics of such biological macromolecules. To fully harness the power of modern massively parallel computers, however, we need to develop and deploy algorithms which can exploit the structure of such hardware. The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is a scalable molecular dynamics code including long-range Coulomb interactions, which has been specifically designed to function efficiently on parallel platforms. Here we describe the implementation of the AMBER98 force field in LAMMPS and its validation for molecular dynamics investigations of DNA structure and flexibility against the benchmark of results obtained with the long-established code AMBER6 (Assisted Model Building with Energy Refinement, version 6). Extended molecular dynamics simulations on the hydrated DNA dodecamer d(CTTTTGCAAAAG)(2), which has previously been the subject of extensive dynamical analysis using AMBER6, show that it is possible to obtain excellent agreement in terms of static, dynamic and thermodynamic parameters between AMBER6 and LAMMPS. In comparison with AMBER6, LAMMPS shows greatly improved scalability in massively parallel environments, opening up the possibility of efficient simulations of order-of-magnitude larger systems and/or for order-of-magnitude greater simulation times.

Stellar-mass black holes in young massive and open stellar clusters and their role in gravitational-wave generation - II

NASA Astrophysics Data System (ADS)

Banerjee, Sambaran

2018-01-01

The study of stellar-remnant black holes (BH) in dense stellar clusters is now in the spotlight, especially due to their intrinsic ability to form binary black holes (BBH) through dynamical encounters, which potentially coalesce via gravitational-wave (GW) radiation. In this work, which is a continuation from a recent study (Paper I), additional models of compact stellar clusters with initial masses ≲ 105 M⊙ and also those with small fractions of primordial binaries (≲ 10 per cent) are evolved for long term, applying the direct N-body approach, assuming state-of-the-art stellar-wind and remnant-formation prescriptions. That way, a substantially broader range of computed models than that in Paper I is achieved. As in Paper I, the general-relativistic BBH mergers continue to be mostly mediated by triples that are bound to the clusters rather than happen among the ejected BBHs. In fact, the number of such in situ BBH mergers, per cluster, tends to increase significantly with the introduction of a small population of primordial binaries. Despite the presence of massive primordial binaries, the merging BBHs, especially the in situ ones, are found to be exclusively dynamically assembled and hence would be spin-orbit misaligned. The BBHs typically traverse through both the LISA's and the LIGO's detection bands, being audible to both instruments. The 'dynamical heating' of the BHs keeps the electron-capture-supernova (ECS) neutron stars (NS) from effectively mass segregating and participating in exchange interactions; the dynamically active BHs would also exchange into any NS binary within ≲1 Gyr. Such young massive and open clusters have the potential to contribute to the dynamical BBH merger detection rate to a similar extent as their more massive globular-cluster counterparts.

Modelling the chemistry of a gravitationally unstable protoplanetary disc

NASA Astrophysics Data System (ADS)

Ilee, J. D.; Boley, A. C.; Caselli, P.; Durisen, R. H.; Hartquist, T. W.; Rawlings, J. M. C.

2011-05-01

Until now, axisymmetric, α-disc simulations have been adopted to describe the dynamics used in the construction of chemical models of protoplanetary discs. While this approach is reasonable for many discs, it is not appropriate for young, massive discs in which self-gravity is important. Spiral waves and shocks cause significant temperature and density variations which affect the chemistry. We have used a dynamical model of solar mass star surrounded by a massive (0.39 M⊙), self-gravitating disc to model the chemistry of one of these objects.

Analysis of multigrid methods on massively parallel computers: Architectural implications

NASA Technical Reports Server (NTRS)

Matheson, Lesley R.; Tarjan, Robert E.

1993-01-01

We study the potential performance of multigrid algorithms running on massively parallel computers with the intent of discovering whether presently envisioned machines will provide an efficient platform for such algorithms. We consider the domain parallel version of the standard V cycle algorithm on model problems, discretized using finite difference techniques in two and three dimensions on block structured grids of size 10(exp 6) and 10(exp 9), respectively. Our models of parallel computation were developed to reflect the computing characteristics of the current generation of massively parallel multicomputers. These models are based on an interconnection network of 256 to 16,384 message passing, 'workstation size' processors executing in an SPMD mode. The first model accomplishes interprocessor communications through a multistage permutation network. The communication cost is a logarithmic function which is similar to the costs in a variety of different topologies. The second model allows single stage communication costs only. Both models were designed with information provided by machine developers and utilize implementation derived parameters. With the medium grain parallelism of the current generation and the high fixed cost of an interprocessor communication, our analysis suggests an efficient implementation requires the machine to support the efficient transmission of long messages, (up to 1000 words) or the high initiation cost of a communication must be significantly reduced through an alternative optimization technique. Furthermore, with variable length message capability, our analysis suggests the low diameter multistage networks provide little or no advantage over a simple single stage communications network.

A Parallel and Incremental Approach for Data-Intensive Learning of Bayesian Networks.

PubMed

Yue, Kun; Fang, Qiyu; Wang, Xiaoling; Li, Jin; Liu, Weiyi

2015-12-01

Bayesian network (BN) has been adopted as the underlying model for representing and inferring uncertain knowledge. As the basis of realistic applications centered on probabilistic inferences, learning a BN from data is a critical subject of machine learning, artificial intelligence, and big data paradigms. Currently, it is necessary to extend the classical methods for learning BNs with respect to data-intensive computing or in cloud environments. In this paper, we propose a parallel and incremental approach for data-intensive learning of BNs from massive, distributed, and dynamically changing data by extending the classical scoring and search algorithm and using MapReduce. First, we adopt the minimum description length as the scoring metric and give the two-pass MapReduce-based algorithms for computing the required marginal probabilities and scoring the candidate graphical model from sample data. Then, we give the corresponding strategy for extending the classical hill-climbing algorithm to obtain the optimal structure, as well as that for storing a BN by pairs. Further, in view of the dynamic characteristics of the changing data, we give the concept of influence degree to measure the coincidence of the current BN with new data, and then propose the corresponding two-pass MapReduce-based algorithms for BNs incremental learning. Experimental results show the efficiency, scalability, and effectiveness of our methods.

ls1 mardyn: The Massively Parallel Molecular Dynamics Code for Large Systems.

PubMed

Niethammer, Christoph; Becker, Stefan; Bernreuther, Martin; Buchholz, Martin; Eckhardt, Wolfgang; Heinecke, Alexander; Werth, Stephan; Bungartz, Hans-Joachim; Glass, Colin W; Hasse, Hans; Vrabec, Jadran; Horsch, Martin

2014-10-14

The molecular dynamics simulation code ls1 mardyn is presented. It is a highly scalable code, optimized for massively parallel execution on supercomputing architectures and currently holds the world record for the largest molecular simulation with over four trillion particles. It enables the application of pair potentials to length and time scales that were previously out of scope for molecular dynamics simulation. With an efficient dynamic load balancing scheme, it delivers high scalability even for challenging heterogeneous configurations. Presently, multicenter rigid potential models based on Lennard-Jones sites, point charges, and higher-order polarities are supported. Due to its modular design, ls1 mardyn can be extended to new physical models, methods, and algorithms, allowing future users to tailor it to suit their respective needs. Possible applications include scenarios with complex geometries, such as fluids at interfaces, as well as nonequilibrium molecular dynamics simulation of heat and mass transfer.

On delay adjustment for dynamic load balancing in distributed virtual environments.

PubMed

Deng, Yunhua; Lau, Rynson W H

2012-04-01

Distributed virtual environments (DVEs) are becoming very popular in recent years, due to the rapid growing of applications, such as massive multiplayer online games (MMOGs). As the number of concurrent users increases, scalability becomes one of the major challenges in designing an interactive DVE system. One solution to address this scalability problem is to adopt a multi-server architecture. While some methods focus on the quality of partitioning the load among the servers, others focus on the efficiency of the partitioning process itself. However, all these methods neglect the effect of network delay among the servers on the accuracy of the load balancing solutions. As we show in this paper, the change in the load of the servers due to network delay would affect the performance of the load balancing algorithm. In this work, we conduct a formal analysis of this problem and discuss two efficient delay adjustment schemes to address the problem. Our experimental results show that our proposed schemes can significantly improve the performance of the load balancing algorithm with neglectable computation overhead.

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

PubMed Central

Osswald, Marc; Ieng, Sio-Hoi; Benosman, Ryad; Indiveri, Giacomo

2017-01-01

Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain. Here we propose a radically novel model that solves the stereo-correspondence problem with a spiking neural network that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings. We demonstrate its features with a prototype neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological stereo vision processing systems. PMID:28079187

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems.

PubMed

Osswald, Marc; Ieng, Sio-Hoi; Benosman, Ryad; Indiveri, Giacomo

2017-01-12

Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain. Here we propose a radically novel model that solves the stereo-correspondence problem with a spiking neural network that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings. We demonstrate its features with a prototype neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological stereo vision processing systems.

A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems

NASA Astrophysics Data System (ADS)

Osswald, Marc; Ieng, Sio-Hoi; Benosman, Ryad; Indiveri, Giacomo

2017-01-01

Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer a possible solution to this problem, with the use of a new class of event-based vision sensors and neural processing devices inspired by the organizing principles of the brain. Here we propose a radically novel model that solves the stereo-correspondence problem with a spiking neural network that can be directly implemented with massively parallel, compact, low-latency and low-power neuromorphic engineering devices. We validate the model with experimental results, highlighting features that are in agreement with both computational neuroscience stereo vision theories and experimental findings. We demonstrate its features with a prototype neuromorphic hardware system and provide testable predictions on the role of spike-based representations and temporal dynamics in biological stereo vision processing systems.

Photonic reservoir computing: a new approach to optical information processing

NASA Astrophysics Data System (ADS)

Vandoorne, Kristof; Fiers, Martin; Verstraeten, David; Schrauwen, Benjamin; Dambre, Joni; Bienstman, Peter

2010-06-01

Despite ever increasing computational power, recognition and classification problems remain challenging to solve. Recently, advances have been made by the introduction of the new concept of reservoir computing. This is a methodology coming from the field of machine learning and neural networks that has been successfully used in several pattern classification problems, like speech and image recognition. Thus far, most implementations have been in software, limiting their speed and power efficiency. Photonics could be an excellent platform for a hardware implementation of this concept because of its inherent parallelism and unique nonlinear behaviour. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed. We propose using a network of coupled Semiconductor Optical Amplifiers (SOA) and show in simulation that it could be used as a reservoir by comparing it to conventional software implementations using a benchmark speech recognition task. In spite of the differences with classical reservoir models, the performance of our photonic reservoir is comparable to that of conventional implementations and sometimes slightly better. As our implementation uses coherent light for information processing, we find that phase tuning is crucial to obtain high performance. In parallel we investigate the use of a network of photonic crystal cavities. The coupled mode theory (CMT) is used to investigate these resonators. A new framework is designed to model networks of resonators and SOAs. The same network topologies are used, but feedback is added to control the internal dynamics of the system. By adjusting the readout weights of the network in a controlled manner, we can generate arbitrary periodic patterns.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hendi, S.H.; Bordbar, G.H.; Panah, B. Eslam

Motivated by the recent interests in spin−2 massive gravitons, we study the structure of neutron star in the context of massive gravity. The modifications of TOV equation in the presence of massive gravity are explored in 4 and higher dimensions. Next, by considering the modern equation of state for the neutron star matter (which is extracted by the lowest order constrained variational (LOCV) method with the AV18 potential), different physical properties of the neutron star (such as Le Chatelier's principle, stability and energy conditions) are investigated. It is shown that consideration of the massive gravity has specific contributions into themore » structure of neutron star and introduces new prescriptions for the massive astrophysical objects. The mass-radius relation is examined and the effects of massive gravity on the Schwarzschild radius, average density, compactness, gravitational redshift and dynamical stability are studied. Finally, a relation between mass and radius of neutron star versus the Planck mass is extracted.« less

Inflation with a massive vector field nonminimally coupled to gravity

NASA Astrophysics Data System (ADS)

Páramos, J.

2018-01-01

The possibility that inflation is driven by a massive vector field with SO(3) global symmetry nonminimally coupled to gravity is presented. Through an appropriate Ansatz for the vector field, the behaviour of the equations of motion is studied through the ensuing dynamical system, focusing on the characterisation of the ensuing fixed points.

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

PubMed Central

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs). PMID:29662297

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

PubMed

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).

Biology Inspired Approach for Communal Behavior in Sensor Networks

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

2006-01-01

Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.

Real-time electron dynamics for massively parallel excited-state simulations

NASA Astrophysics Data System (ADS)

Andrade, Xavier

The simulation of the real-time dynamics of electrons, based on time dependent density functional theory (TDDFT), is a powerful approach to study electronic excited states in molecular and crystalline systems. What makes the method attractive is its flexibility to simulate different kinds of phenomena beyond the linear-response regime, including strongly-perturbed electronic systems and non-adiabatic electron-ion dynamics. Electron-dynamics simulations are also attractive from a computational point of view. They can run efficiently on massively parallel architectures due to the low communication requirements. Our implementations of electron dynamics, based on the codes Octopus (real-space) and Qball (plane-waves), allow us to simulate systems composed of thousands of atoms and to obtain good parallel scaling up to 1.6 million processor cores. Due to the versatility of real-time electron dynamics and its parallel performance, we expect it to become the method of choice to apply the capabilities of exascale supercomputers for the simulation of electronic excited states.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barrett, Brian; Brightwell, Ronald B.; Grant, Ryan

This report presents a specification for the Portals 4 networ k programming interface. Portals 4 is intended to allow scalable, high-performance network communication betwee n nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded syste ms. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platfor ms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is tarmore » geted to the next generation of machines employing advanced network interface architectures that support enh anced offload capabilities.« less

Method and apparatus for routing data in an inter-nodal communications lattice of a massively parallel computer system by routing through transporter nodes

DOEpatents

Archer, Charles Jens; Musselman, Roy Glenn; Peters, Amanda; Pinnow, Kurt Walter; Swartz, Brent Allen; Wallenfelt, Brian Paul

2010-11-16

A massively parallel computer system contains an inter-nodal communications network of node-to-node links. An automated routing strategy routes packets through one or more intermediate nodes of the network to reach a destination. Some packets are constrained to be routed through respective designated transporter nodes, the automated routing strategy determining a path from a respective source node to a respective transporter node, and from a respective transporter node to a respective destination node. Preferably, the source node chooses a routing policy from among multiple possible choices, and that policy is followed by all intermediate nodes. The use of transporter nodes allows greater flexibility in routing.

Efficiently modeling neural networks on massively parallel computers

NASA Technical Reports Server (NTRS)

Farber, Robert M.

1993-01-01

Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applying neural network simulations to real world problems generally involves large amounts of data and massive amounts of computation. To efficiently handle the computational requirements of large problems, we have implemented at Los Alamos a highly efficient neural network compiler for serial computers, vector computers, vector parallel computers, and fine grain SIMD computers such as the CM-2 connection machine. This paper describes the mapping used by the compiler to implement feed-forward backpropagation neural networks for a SIMD (Single Instruction Multiple Data) architecture parallel computer. Thinking Machines Corporation has benchmarked our code at 1.3 billion interconnects per second (approximately 3 gigaflops) on a 64,000 processor CM-2 connection machine (Singer 1990). This mapping is applicable to other SIMD computers and can be implemented on MIMD computers such as the CM-5 connection machine. Our mapping has virtually no communications overhead with the exception of the communications required for a global summation across the processors (which has a sub-linear runtime growth on the order of O(log(number of processors)). We can efficiently model very large neural networks which have many neurons and interconnects and our mapping can extend to arbitrarily large networks (within memory limitations) by merging the memory space of separate processors with fast adjacent processor interprocessor communications. This paper will consider the simulation of only feed forward neural network although this method is extendable to recurrent networks.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mercier, C.W.

The Network File System (NFS) will be the user interface to a High-Performance Data System (HPDS) being developed at Los Alamos National Laboratory (LANL). HPDS will manage high-capacity, high-performance storage systems connected directly to a high-speed network from distributed workstations. NFS will be modified to maximize performance and to manage massive amounts of data. 6 refs., 3 figs.

Mining and Modeling Real-World Networks: Patterns, Anomalies, and Tools

ERIC Educational Resources Information Center

Akoglu, Leman

2012-01-01

Large real-world graph (a.k.a network, relational) data are omnipresent, in online media, businesses, science, and the government. Analysis of these massive graphs is crucial, in order to extract descriptive and predictive knowledge with many commercial, medical, and environmental applications. In addition to its general structure, knowing what…

Multichannel microformulators for massively parallel machine learning and automated design of biological experiments

NASA Astrophysics Data System (ADS)

Wikswo, John; Kolli, Aditya; Shankaran, Harish; Wagoner, Matthew; Mettetal, Jerome; Reiserer, Ronald; Gerken, Gregory; Britt, Clayton; Schaffer, David

Genetic, proteomic, and metabolic networks describing biological signaling can have 102 to 103 nodes. Transcriptomics and mass spectrometry can quantify 104 different dynamical experimental variables recorded from in vitro experiments with a time resolution approaching 1 s. It is difficult to infer metabolic and signaling models from such massive data sets, and it is unlikely that causality can be determined simply from observed temporal correlations. There is a need to design and apply specific system perturbations, which will be difficult to perform manually with 10 to 102 externally controlled variables. Machine learning and optimal experimental design can select an experiment that best discriminates between multiple conflicting models, but a remaining problem is to control in real time multiple variables in the form of concentrations of growth factors, toxins, nutrients and other signaling molecules. With time-division multiplexing, a microfluidic MicroFormulator (μF) can create in real time complex mixtures of reagents in volumes suitable for biological experiments. Initial 96-channel μF implementations control the exposure profile of cells in a 96-well plate to different temporal profiles of drugs; future experiments will include challenge compounds. Funded in part by AstraZeneca, NIH/NCATS HHSN271201600009C and UH3TR000491, and VIIBRE.

Sphingosine and Sphingosine Kinase 1 Involvement in Endocytic Membrane Trafficking*

PubMed Central

Lima, Santiago; Milstien, Sheldon; Spiegel, Sarah

2017-01-01

The balance between cholesterol and sphingolipids within the plasma membrane has long been implicated in endocytic membrane trafficking. However, in contrast to cholesterol functions, little is still known about the roles of sphingolipids and their metabolites. Perturbing the cholesterol/sphingomyelin balance was shown to induce narrow tubular plasma membrane invaginations enriched with sphingosine kinase 1 (SphK1), the enzyme that converts the bioactive sphingolipid metabolite sphingosine to sphingosine-1-phosphate, and suggested a role for sphingosine phosphorylation in endocytic membrane trafficking. Here we show that sphingosine and sphingosine-like SphK1 inhibitors induced rapid and massive formation of vesicles in diverse cell types that accumulated as dilated late endosomes. However, much smaller vesicles were formed in SphK1-deficient cells. Moreover, inhibition or deletion of SphK1 prolonged the lifetime of sphingosine-induced vesicles. Perturbing the plasma membrane cholesterol/sphingomyelin balance abrogated vesicle formation. This massive endosomal influx was accompanied by dramatic recruitment of the intracellular SphK1 and Bin/Amphiphysin/Rvs domain-containing proteins endophilin-A2 and endophilin-B1 to enlarged endosomes and formation of highly dynamic filamentous networks containing endophilin-B1 and SphK1. Together, our results highlight the importance of sphingosine and its conversion to sphingosine-1-phosphate by SphK1 in endocytic membrane trafficking. PMID:28049734

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers

PubMed Central

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier

2017-01-01

Background The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. Objective MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. Methods MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. Results MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user’s specific interests and provides an efficient way to share information with collaborators. Furthermore, the user’s behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. Conclusions We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. PMID:28623182

Initial conditions of formation of starburst clusters: constraints from stellar dynamics

NASA Astrophysics Data System (ADS)

Banerjee, Sambaran

2017-03-01

How starburst clusters form out of molecular clouds is still an open question. In this article, I highlight some of the key constraints in this regard, that one can get from the dynamical evolutionary properties of dense stellar systems. I particularly focus on secular expansion of massive star clusters and hierarchical merging of sub-clusters, and discuss their implications vis-á-vis the observed properties of young massive clusters. The analysis suggests that residual gas expulsion is necessary for shaping these clusters as we see them today, irrespective of their monolithic or hierarchical mode of formation.

Hyperfast pulsars as the remnants of massive stars ejected from young star clusters

NASA Astrophysics Data System (ADS)

Gvaramadze, Vasilii V.; Gualandris, Alessia; Portegies Zwart, Simon

2008-04-01

Recent proper motion and parallax measurements for the pulsar PSR B1508+55 indicate a transverse velocity of ~1100kms-1, which exceeds earlier measurements for any neutron star. The spin-down characteristics of PSR B1508+55 are typical for a non-recycled pulsar, which implies that the velocity of the pulsar cannot have originated from the second supernova disruption of a massive binary system. The high velocity of PSR B1508+55 can be accounted for by assuming that it received a kick at birth or that the neutron star was accelerated after its formation in the supernova explosion. We propose an explanation for the origin of hyperfast neutron stars based on the hypothesis that they could be the remnants of a symmetric supernova explosion of a high-velocity massive star which attained its peculiar velocity (similar to that of the pulsar) in the course of a strong dynamical three- or four-body encounter in the core of dense young star cluster. To check this hypothesis, we investigated three dynamical processes involving close encounters between: (i) two hard massive binaries, (ii) a hard binary and an intermediate-mass black hole (IMBH) and (iii) a single stars and a hard binary IMBH. We find that main-sequence O-type stars cannot be ejected from young massive star clusters with peculiar velocities high enough to explain the origin of hyperfast neutron stars, but lower mass main-sequence stars or the stripped helium cores of massive stars could be accelerated to hypervelocities. Our explanation for the origin of hyperfast pulsars requires a very dense stellar environment of the order of 106- 107starspc-3. Although such high densities may exist during the core collapse of young massive star clusters, we caution that they have never been observed.

Development of massive multilevel molecular dynamics simulation program, Platypus (PLATform for dYnamic Protein Unified Simulation), for the elucidation of protein functions.

PubMed

Takano, Yu; Nakata, Kazuto; Yonezawa, Yasushige; Nakamura, Haruki

2016-05-05

A massively parallel program for quantum mechanical-molecular mechanical (QM/MM) molecular dynamics simulation, called Platypus (PLATform for dYnamic Protein Unified Simulation), was developed to elucidate protein functions. The speedup and the parallelization ratio of Platypus in the QM and QM/MM calculations were assessed for a bacteriochlorophyll dimer in the photosynthetic reaction center (DIMER) on the K computer, a massively parallel computer achieving 10 PetaFLOPs with 705,024 cores. Platypus exhibited the increase in speedup up to 20,000 core processors at the HF/cc-pVDZ and B3LYP/cc-pVDZ, and up to 10,000 core processors by the CASCI(16,16)/6-31G** calculations. We also performed excited QM/MM-MD simulations on the chromophore of Sirius (SIRIUS) in water. Sirius is a pH-insensitive and photo-stable ultramarine fluorescent protein. Platypus accelerated on-the-fly excited-state QM/MM-MD simulations for SIRIUS in water, using over 4000 core processors. In addition, it also succeeded in 50-ps (200,000-step) on-the-fly excited-state QM/MM-MD simulations for the SIRIUS in water. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Explosive Disintegration of a Massive Young Stellar System in Orion

NASA Astrophysics Data System (ADS)

Zapata, Luis A.; Schmid-Burgk, Johannes; Ho, Paul T. P.; Rodríguez, Luis F.; Menten, Karl M.

2009-10-01

Young massive stars in the center of crowded star clusters are expected to undergo close dynamical encounters that could lead to energetic, explosive events. However, there has so far never been clear observational evidence of such a remarkable phenomenon. We here report new interferometric observations that indicate the well-known enigmatic wide-angle outflow located in the Orion BN/KL star-forming region to have been produced by such a violent explosion during the disruption of a massive young stellar system, and that this was caused by a close dynamical interaction about 500 years ago. This outflow thus belongs to a totally different family of molecular flows that is not related to the classical bipolar flows that are generated by stars during their formation process. Our molecular data allow us to create a three-dimensional view of the debris flow and to link this directly to the well-known Orion H2 "fingers" farther out.

The Origin of IRS 16: Dynamically Driven In-Spiral of a Dense Star Cluster to the Galactic Center?

NASA Astrophysics Data System (ADS)

Portegies Zwart, Simon F.; McMillan, Stephen L. W.; Gerhard, Ortwin

2003-08-01

We use direct N-body simulations to study the in-spiral and internal evolution of dense star clusters near the Galactic center. These clusters sink toward the center owing to dynamical friction with the stellar background and may go into core collapse before being disrupted by the Galactic tidal field. If a cluster reaches core collapse before disruption, its dense core, which has become rich in massive stars, survives to reach close to the Galactic center. When it eventually dissolves, the cluster deposits a disproportionate number of massive stars in the innermost parsec of the Galactic nucleus. Comparing the spatial distribution and kinematics of the massive stars with observations of IRS 16, a group of young He I stars near the Galactic center, we argue that this association may have formed in this way.

Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges.

PubMed

Chen, Yuanfang; Lee, Gyu Myoung; Shu, Lei; Crespi, Noel

2016-02-06

The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well.

Industrial Internet of Things-Based Collaborative Sensing Intelligence: Framework and Research Challenges

PubMed Central

Chen, Yuanfang; Lee, Gyu Myoung; Shu, Lei; Crespi, Noel

2016-01-01

The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of the Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a collaborative sensing intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed, as well. PMID:26861345

Dynamical age differences among coeval star clusters as revealed by blue stragglers.

PubMed

Ferraro, F R; Lanzoni, B; Dalessandro, E; Beccari, G; Pasquato, M; Miocchi, P; Rood, R T; Sigurdsson, S; Sills, A; Vesperini, E; Mapelli, M; Contreras, R; Sanna, N; Mucciarelli, A

2012-12-20

Globular star clusters that formed at the same cosmic time may have evolved rather differently from the dynamical point of view (because that evolution depends on the internal environment) through a variety of processes that tend progressively to segregate stars more massive than the average towards the cluster centre. Therefore clusters with the same chronological age may have reached quite different stages of their dynamical history (that is, they may have different 'dynamical ages'). Blue straggler stars have masses greater than those at the turn-off point on the main sequence and therefore must be the result of either a collision or a mass-transfer event. Because they are among the most massive and luminous objects in old clusters, they can be used as test particles with which to probe dynamical evolution. Here we report that globular clusters can be grouped into a few distinct families on the basis of the radial distribution of blue stragglers. This grouping corresponds well to an effective ranking of the dynamical stage reached by stellar systems, thereby permitting a direct measure of the cluster dynamical age purely from observed properties.

Using algebra for massively parallel processor design and utilization

NASA Technical Reports Server (NTRS)

Campbell, Lowell; Fellows, Michael R.

1990-01-01

This paper summarizes the author's advances in the design of dense processor networks. Within is reported a collection of recent constructions of dense symmetric networks that provide the largest know values for the number of nodes that can be placed in a network of a given degree and diameter. The constructions are in the range of current potential engineering significance and are based on groups of automorphisms of finite-dimensional vector spaces.

A Faster Parallel Algorithm and Efficient Multithreaded Implementations for Evaluating Betweenness Centrality on Massive Datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madduri, Kamesh; Ediger, David; Jiang, Karl

2009-02-15

We present a new lock-free parallel algorithm for computing betweenness centralityof massive small-world networks. With minor changes to the data structures, ouralgorithm also achieves better spatial cache locality compared to previous approaches. Betweenness centrality is a key algorithm kernel in HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging high-performance computing architectures for graph-theoretic computations. We design optimized implementations of betweenness centrality and the SSCA#2 benchmark for two hardware multithreaded systems: a Cray XMT system with the Threadstorm processor, and a single-socket Sun multicore server with the UltraSPARC T2 processor. For a small-world network of 134 millionmore » vertices and 1.073 billion edges, the 16-processor XMT system and the 8-core Sun Fire T5120 server achieve TEPS scores (an algorithmic performance count for the SSCA#2 benchmark) of 160 million and 90 million respectively, which corresponds to more than a 2X performance improvement over the previous parallel implementations. To better characterize the performance of these multithreaded systems, we correlate the SSCA#2 performance results with data from the memory-intensive STREAM and RandomAccess benchmarks. Finally, we demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for a large-scale IMDb movie-actor network.« less

A Faster Parallel Algorithm and Efficient Multithreaded Implementations for Evaluating Betweenness Centrality on Massive Datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madduri, Kamesh; Ediger, David; Jiang, Karl

2009-05-29

We present a new lock-free parallel algorithm for computing betweenness centrality of massive small-world networks. With minor changes to the data structures, our algorithm also achieves better spatial cache locality compared to previous approaches. Betweenness centrality is a key algorithm kernel in the HPCS SSCA#2 Graph Analysis benchmark, which has been extensively used to evaluate the performance of emerging high-performance computing architectures for graph-theoretic computations. We design optimized implementations of betweenness centrality and the SSCA#2 benchmark for two hardware multithreaded systems: a Cray XMT system with the ThreadStorm processor, and a single-socket Sun multicore server with the UltraSparc T2 processor.more » For a small-world network of 134 million vertices and 1.073 billion edges, the 16-processor XMT system and the 8-core Sun Fire T5120 server achieve TEPS scores (an algorithmic performance count for the SSCA#2 benchmark) of 160 million and 90 million respectively, which corresponds to more than a 2X performance improvement over the previous parallel implementations. To better characterize the performance of these multithreaded systems, we correlate the SSCA#2 performance results with data from the memory-intensive STREAM and RandomAccess benchmarks. Finally, we demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for a large-scale IMDb movie-actor network.« less

Computer Sciences and Data Systems, volume 2

NASA Technical Reports Server (NTRS)

1987-01-01

Topics addressed include: data storage; information network architecture; VHSIC technology; fiber optics; laser applications; distributed processing; spaceborne optical disk controller; massively parallel processors; and advanced digital SAR processors.

Quantitative analysis of rib movement based on dynamic chest bone images: preliminary results

NASA Astrophysics Data System (ADS)

Tanaka, R.; Sanada, S.; Oda, M.; Mitsutaka, M.; Suzuki, K.; Sakuta, K.; Kawashima, H.

2014-03-01

Rib movement during respiration is one of the diagnostic criteria in pulmonary impairments. In general, the rib movement is assessed in fluoroscopy. However, the shadows of lung vessels and bronchi overlapping ribs prevent accurate quantitative analysis of rib movement. Recently, an image-processing technique for separating bones from soft tissue in static chest radiographs, called "bone suppression technique", has been developed. Our purpose in this study was to evaluate the usefulness of dynamic bone images created by the bone suppression technique in quantitative analysis of rib movement. Dynamic chest radiographs of 10 patients were obtained using a dynamic flat-panel detector (FPD). Bone suppression technique based on a massive-training artificial neural network (MTANN) was applied to the dynamic chest images to create bone images. Velocity vectors were measured in local areas on the dynamic bone images, which formed a map. The velocity maps obtained with bone and original images for scoliosis and normal cases were compared to assess the advantages of bone images. With dynamic bone images, we were able to quantify and distinguish movements of ribs from those of other lung structures accurately. Limited rib movements of scoliosis patients appeared as reduced rib velocity vectors. Vector maps in all normal cases exhibited left-right symmetric distributions, whereas those in abnormal cases showed nonuniform distributions. In conclusion, dynamic bone images were useful for accurate quantitative analysis of rib movements: Limited rib movements were indicated as a reduction of rib movement and left-right asymmetric distribution on vector maps. Thus, dynamic bone images can be a new diagnostic tool for quantitative analysis of rib movements without additional radiation dose.

Quantity is nothing without quality: automated QA/QC for streaming sensor networks

Treesearch

John L. Campbell; Lindsey E. Rustad; John H. Porter; Jeffrey R. Taylor; Ethan W. Dereszynski; James B. Shanley; Corinna Gries; Donald L. Henshaw; Mary E. Martin; Wade. M. Sheldon; Emery R. Boose

2013-01-01

Sensor networks are revolutionizing environmental monitoring by producing massive quantities of data that are being made publically available in near real time. These data streams pose a challenge for ecologists because traditional approaches to quality assurance and quality control are no longer practical when confronted with the size of these data sets and the...

Roles of Course Facilitators, Learners, and Technology in the Flow of Information of a cMOOC

ERIC Educational Resources Information Center

Skrypnyk, Oleksandra; Joksimovic, Srec´ko; Kovanovic, Vitomir; Gas?evic, Dragan; Dawson, Shane

2015-01-01

Distributed Massive Open Online Courses (MOOCs) are based on the premise that online learning occurs through a network of interconnected learners. The teachers' role in distributed courses extends to forming such a network by facilitating communication that connects learners and their separate personal learning environments scattered around the…

Dynamic Three-Dimensional Shoulder Mri during Active Motion for Investigation of Rotator Cuff Diseases.

PubMed

Tempelaere, Christine; Pierrart, Jérome; Lefèvre-Colau, Marie-Martine; Vuillemin, Valérie; Cuénod, Charles-André; Hansen, Ulrich; Mir, Olivier; Skalli, Wafa; Gregory, Thomas

2016-01-01

MRI is the standard methodology in diagnosis of rotator cuff diseases. However, many patients continue to have pain despite treatment, and MRI of a static unloaded shoulder seems insufficient for best diagnosis and treatment. This study evaluated if Dynamic MRI provides novel kinematic data that can be used to improve the understanding, diagnosis and best treatment of rotator cuff diseases. Dynamic MRI provided real-time 3D image series and was used to measure changes in the width of subacromial space, superior-inferior translation and anterior-posterior translation of the humeral head relative to the glenoid during active abduction. These measures were investigated for consistency with the rotator cuff diseases classifications from standard MRI. The study included: 4 shoulders with massive rotator cuff tears, 5 shoulders with an isolated full-thickness supraspinatus tear, 5 shoulders with tendinopathy and 6 normal shoulders. A change in the width of subacromial space greater than 4mm differentiated between rotator cuff diseases with tendon tears (massive cuff tears and supraspinatus tear) and without tears (tendinopathy) (p = 0.012). The range of the superior-inferior translation was higher in the massive cuff tears group (6.4mm) than in normals (3.4mm) (p = 0.02). The range of the anterior-posterior translation was higher in the massive cuff tears (9.2 mm) and supraspinatus tear (9.3 mm) shoulders compared to normals (3.5mm) and tendinopathy (4.8mm) shoulders (p = 0.05). The Dynamic MRI enabled a novel measure; 'Looseness', i.e. the translation of the humeral head on the glenoid during an abduction cycle. Looseness was better able at differentiating different forms of rotator cuff disease than a simple static measure of relative glenohumeral position.

Dynamic Three-Dimensional Shoulder Mri during Active Motion for Investigation of Rotator Cuff Diseases

PubMed Central

Tempelaere, Christine; Pierrart, Jérome; Lefèvre-Colau, Marie-Martine; Vuillemin, Valérie; Cuénod, Charles-André; Hansen, Ulrich; Mir, Olivier; Skalli, Wafa; Gregory, Thomas

2016-01-01

Background MRI is the standard methodology in diagnosis of rotator cuff diseases. However, many patients continue to have pain despite treatment, and MRI of a static unloaded shoulder seems insufficient for best diagnosis and treatment. This study evaluated if Dynamic MRI provides novel kinematic data that can be used to improve the understanding, diagnosis and best treatment of rotator cuff diseases. Methods Dynamic MRI provided real-time 3D image series and was used to measure changes in the width of subacromial space, superior-inferior translation and anterior-posterior translation of the humeral head relative to the glenoid during active abduction. These measures were investigated for consistency with the rotator cuff diseases classifications from standard MRI. Results The study included: 4 shoulders with massive rotator cuff tears, 5 shoulders with an isolated full-thickness supraspinatus tear, 5 shoulders with tendinopathy and 6 normal shoulders. A change in the width of subacromial space greater than 4mm differentiated between rotator cuff diseases with tendon tears (massive cuff tears and supraspinatus tear) and without tears (tendinopathy) (p = 0.012). The range of the superior-inferior translation was higher in the massive cuff tears group (6.4mm) than in normals (3.4mm) (p = 0.02). The range of the anterior-posterior translation was higher in the massive cuff tears (9.2 mm) and supraspinatus tear (9.3 mm) shoulders compared to normals (3.5mm) and tendinopathy (4.8mm) shoulders (p = 0.05). Conclusion The Dynamic MRI enabled a novel measure; ‘Looseness’, i.e. the translation of the humeral head on the glenoid during an abduction cycle. Looseness was better able at differentiating different forms of rotator cuff disease than a simple static measure of relative glenohumeral position. PMID:27434235

OAM-labeled free-space optical flow routing.

PubMed

Gao, Shecheng; Lei, Ting; Li, Yangjin; Yuan, Yangsheng; Xie, Zhenwei; Li, Zhaohui; Yuan, Xiaocong

2016-09-19

Space-division multiplexing allows unprecedented scaling of bandwidth density for optical communication. Routing spatial channels among transmission ports is critical for future scalable optical network, however, there is still no characteristic parameter to label the overlapped optical carriers. Here we propose a free-space optical flow routing (OFR) scheme by using optical orbital angular moment (OAM) states to label optical flows and simultaneously steer each flow according to their OAM states. With an OAM multiplexer and a reconfigurable OAM demultiplexer, massive individual optical flows can be routed to the demanded optical ports. In the routing process, the OAM beams act as data carriers at the same time their topological charges act as each carrier's labels. Using this scheme, we experimentally demonstrate switching, multicasting and filtering network functions by simultaneously steer 10 input optical flows on demand to 10 output ports. The demonstration of data-carrying OFR with nonreturn-to-zero signals shows that this process enables synchronous processing of massive spatial channels and flexible optical network.

Adaptive dynamical networks

NASA Astrophysics Data System (ADS)

Maslennikov, O. V.; Nekorkin, V. I.

2017-10-01

Dynamical networks are systems of active elements (nodes) interacting with each other through links. Examples are power grids, neural structures, coupled chemical oscillators, and communications networks, all of which are characterized by a networked structure and intrinsic dynamics of their interacting components. If the coupling structure of a dynamical network can change over time due to nodal dynamics, then such a system is called an adaptive dynamical network. The term ‘adaptive’ implies that the coupling topology can be rewired; the term ‘dynamical’ implies the presence of internal node and link dynamics. The main results of research on adaptive dynamical networks are reviewed. Key notions and definitions of the theory of complex networks are given, and major collective effects that emerge in adaptive dynamical networks are described.

Rise of the First Super-Massive Stars

NASA Astrophysics Data System (ADS)

Regan, John A.; Downes, Turlough P.

2018-05-01

We use high resolution adaptive mesh refinement simulations to model the formation of massive metal-free stars in the early Universe. By applying Lyman-Werner (LW) backgrounds of 100 J21 and 1000 J21 respectively we construct environments conducive to the formation of massive stars. We find that only in the case of the higher LW backgrounds are super-critical accretion rates realised that are necessary for super-massive star (SMS) formation. Mild fragmentation is observed for both backgrounds. Violent dynamical interactions between the stars that form in the more massive halo formed (1000 J21 background) results in the eventual expulsion of the two most massive stars from the halo. In the smaller mass halo (100 J21 background) mergers of stars occur before any multibody interactions and a single massive Pop III star is left at the centre of the halo at the end of our simulation. Feedback from the very massive Pop III stars is not effective in generating a large HII region with ionising photons absorbed within a few thousand AU of the star. In all cases a massive black hole seed is the expected final fate of the most massive objects. The seed of the massive Pop III star which remained at the centre of the less massive halo experiences steady accretion rates of almost 10-2M_{⊙}/yr and if these rates continue could potentially experience super-Eddington accretion rates in the immediate aftermath of collapsing into a black hole.

Merlin - Massively parallel heterogeneous computing

NASA Technical Reports Server (NTRS)

Wittie, Larry; Maples, Creve

1989-01-01

Hardware and software for Merlin, a new kind of massively parallel computing system, are described. Eight computers are linked as a 300-MIPS prototype to develop system software for a larger Merlin network with 16 to 64 nodes, totaling 600 to 3000 MIPS. These working prototypes help refine a mapped reflective memory technique that offers a new, very general way of linking many types of computer to form supercomputers. Processors share data selectively and rapidly on a word-by-word basis. Fast firmware virtual circuits are reconfigured to match topological needs of individual application programs. Merlin's low-latency memory-sharing interfaces solve many problems in the design of high-performance computing systems. The Merlin prototypes are intended to run parallel programs for scientific applications and to determine hardware and software needs for a future Teraflops Merlin network.

The Rb problem in massive AGB stars.

NASA Astrophysics Data System (ADS)

Pérez-Mesa, V.; García-Hernández, D. A.; Zamora, O.; Plez, B.; Manchado, A.; Karakas, A. I.; Lugaro, M.

2017-03-01

The asymptotic giant branch (AGB) is formed by low- and intermediate-mass stars (0.8 M_{⊙} < M < 8 M_{⊙}) in their last nuclear-burning phase, when they develop thermal pulses (TP) and suffer extreme mass loss. AGB stars are the main contributor to the enrichment of the interstellar medium (ISM) and thus to the chemical evolution of galaxies. In particular, the more massive AGB stars (M > 4 M_{⊙}) are expected to produce light (e.g., Li, N) and heavy neutron-rich s-process elements (such as Rb, Zr, Ba, Y, etc.), which are not formed in lower mass AGB stars and Supernova explosions. Classical chemical analyses using hydrostatic atmospheres revealed strong Rb overabundances and high [Rb/Zr] ratios in massive AGB stars of our Galaxy and the Magellanic Clouds (MC), confirming for the first time that the ^{22}Ne neutron source dominates the production of s-process elements in these stars. The extremely high Rb abundances and [Rb/Zr] ratios observed in the most massive stars (specially in the low-metallicity MC stars) uncovered a Rb problem; such extreme Rb and [Rb/Zr] values are not predicted by the s-process AGB models, suggesting fundamental problems in our present understanding of their atmospheres. We present more realistic dynamical model atmospheres that consider a gaseous circumstellar envelope with a radial wind and we re-derive the Rb (and Zr) abundances in massive Galactic AGB stars. The new Rb abundances and [Rb/Zr] ratios derived with these dynamical models significantly resolve the problem of the mismatch between the observations and the theoretical predictions of the more massive AGB stars.

Physical approach to quantum networks with massive particles

NASA Astrophysics Data System (ADS)

Andersen, Molte Emil Strange; Zinner, Nikolaj Thomas

2018-04-01

Assembling large-scale quantum networks is a key goal of modern physics research with applications in quantum information and computation. Quantum wires and waveguides in which massive particles propagate in tailored confinement is one promising platform for realizing a quantum network. In the literature, such networks are often treated as quantum graphs, that is, the wave functions are taken to live on graphs of one-dimensional edges meeting in vertices. Hitherto, it has been unclear what boundary conditions on the vertices produce the physical states one finds in nature. This paper treats a quantum network from a physical approach, explicitly finds the physical eigenstates and compares them to the quantum-graph description. The basic building block of a quantum network is an X-shaped potential well made by crossing two quantum wires, and we consider a massive particle in such an X well. The system is analyzed using a variational method based on an expansion into modes with fast convergence and it provides a very clear intuition for the physics of the problem. The particle is found to have a ground state that is exponentially localized to the center of the X well, and the other symmetric solutions are formed so to be orthogonal to the ground state. This is in contrast to the predictions of the conventionally used so-called Kirchoff boundary conditions in quantum graph theory that predict a different sequence of symmetric solutions that cannot be physically realized. Numerical methods have previously been the only source of information on the ground-state wave function and our results provide a different perspective with strong analytical insights. The ground-state wave function has a spatial profile that looks very similar to the shape of a solitonic solution to a nonlinear Schrödinger equation, enabling an analytical prediction of the wave number. When combining multiple X wells into a network or grid, each site supports a solitonlike localized state. These localized solutions only couple to each other and are able to jump from one site to another as if they were trapped in a discrete lattice.

Massive collisions in debris disks: possible application to the beta Pic disc

NASA Astrophysics Data System (ADS)

Kral, Q.; Thébault, P.; Augereau, J.-C.; Boccaletti, A.; Charnoz, S.

2014-09-01

The new LIDT-DD code has been used to study massive collisions in debris discs. This new hybrid model is a fully self-consistent code coupling dynamics and collisions to study debris discs (Kral et al. 2013). It models the full complexity of debris discs' physics such as high velocity collisions, radiation-pressure affected orbits, wide range of grains' dynamical behaviour, etc. LIDT-DD can be used on many possible applications. Our first test case concerns the violent breakup of a massive planetesimal such as the ones happening during the late stages of planetary formation or with the biggest bodies in debris belts. We investigate the duration, magnitude and spatial structure of the signature left by such a violent event, as well as its observational detectability. We find that the breakup of a Ceres-sized body creates an asymmetric dust disc that is homogenized, by the coupled action of collisions and dynamics. The luminosity excess in the breakup's aftermath should be detectable by mid-IR photometry, from a 30 pc distance. As for the asymmetric structures, we derive synthetic images for the SPHERE/VLT and MIRI/JWST instruments, showing that they should be clearly visible and resolved from a 10 pc distance. We explain the observational signature of such impacts and give scaling laws to extrapolate our results to different configurations. These first results confirm that our code can be used to study the massive collision scenario to explain some asymmetries in the Beta-Pic disc.

Designing After-School Learning Using the Massively Multiplayer Online Role-Playing Game

ERIC Educational Resources Information Center

King, Elizabeth M.

2015-01-01

Digital games have become popular for engaging students in a range of learning goals, both in the classroom and the after-school space. In this article, I discuss a specific genre of video game, the massively multiplayer online role-playing game (MMO), which has been identified as a dynamic environment for encountering 21st-century workplace…

Interactive Scripting for Analysis and Visualization of Arbitrarily Large, Disparately Located Climate Data Ensembles Using a Progressive Runtime Server

NASA Astrophysics Data System (ADS)

Christensen, C.; Summa, B.; Scorzelli, G.; Lee, J. W.; Venkat, A.; Bremer, P. T.; Pascucci, V.

2017-12-01

Massive datasets are becoming more common due to increasingly detailed simulations and higher resolution acquisition devices. Yet accessing and processing these huge data collections for scientific analysis is still a significant challenge. Solutions that rely on extensive data transfers are increasingly untenable and often impossible due to lack of sufficient storage at the client side as well as insufficient bandwidth to conduct such large transfers, that in some cases could entail petabytes of data. Large-scale remote computing resources can be useful, but utilizing such systems typically entails some form of offline batch processing with long delays, data replications, and substantial cost for any mistakes. Both types of workflows can severely limit the flexible exploration and rapid evaluation of new hypotheses that are crucial to the scientific process and thereby impede scientific discovery. In order to facilitate interactivity in both analysis and visualization of these massive data ensembles, we introduce a dynamic runtime system suitable for progressive computation and interactive visualization of arbitrarily large, disparately located spatiotemporal datasets. Our system includes an embedded domain-specific language (EDSL) that allows users to express a wide range of data analysis operations in a simple and abstract manner. The underlying runtime system transparently resolves issues such as remote data access and resampling while at the same time maintaining interactivity through progressive and interruptible processing. Computations involving large amounts of data can be performed remotely in an incremental fashion that dramatically reduces data movement, while the client receives updates progressively thereby remaining robust to fluctuating network latency or limited bandwidth. This system facilitates interactive, incremental analysis and visualization of massive remote datasets up to petabytes in size. Our system is now available for general use in the community through both docker and anaconda.

Dynamic enhanced computed tomography imaging findings of an inflammatory fibroid polyp with massive fibrosis in the stomach

PubMed Central

Shim, Eun Jung; Ahn, Sung Eun; Lee, Dong Ho; Park, Seong Jin; Kim, Youn Wha

2017-01-01

Inflammatory fibroid polyp (IFP) is a rare benign lesion of the gastrointestinal tract. We report a case of computed tomography (CT) imaging finding of a gastric IFP with massive fibrosis. CT scans showed thickening of submucosal layer with overlying mucosal hyperenhancement in the gastric antrum. The submucosal layer showed increased enhancement on delayed phase imaging. An antrectomy with gastroduodenostomy was performed because gastric cancer was suspected, particularly signet ring cell carcinoma. The histopathological diagnosis was an IFP with massive fibrosis. The authors suggest that when the submucosal layer of the gastric wall is markedly thickened with delayed enhancement and preservation of the mucosal layer, an IFP with massive fibrosis should be considered in the differential diagnosis. PMID:28373777

Managed traffic evacuation using distributed sensor processing

NASA Astrophysics Data System (ADS)

Ramuhalli, Pradeep; Biswas, Subir

2005-05-01

This paper presents an integrated sensor network and distributed event processing architecture for managed in-building traffic evacuation during natural and human-caused disasters, including earthquakes, fire and biological/chemical terrorist attacks. The proposed wireless sensor network protocols and distributed event processing mechanisms offer a new distributed paradigm for improving reliability in building evacuation and disaster management. The networking component of the system is constructed using distributed wireless sensors for measuring environmental parameters such as temperature, humidity, and detecting unusual events such as smoke, structural failures, vibration, biological/chemical or nuclear agents. Distributed event processing algorithms will be executed by these sensor nodes to detect the propagation pattern of the disaster and to measure the concentration and activity of human traffic in different parts of the building. Based on this information, dynamic evacuation decisions are taken for maximizing the evacuation speed and minimizing unwanted incidents such as human exposure to harmful agents and stampedes near exits. A set of audio-visual indicators and actuators are used for aiding the automated evacuation process. In this paper we develop integrated protocols, algorithms and their simulation models for the proposed sensor networking and the distributed event processing framework. Also, efficient harnessing of the individually low, but collectively massive, processing abilities of the sensor nodes is a powerful concept behind our proposed distributed event processing algorithms. Results obtained through simulation in this paper are used for a detailed characterization of the proposed evacuation management system and its associated algorithmic components.

Interevent time distributions of human multi-level activity in a virtual world

NASA Astrophysics Data System (ADS)

Mryglod, O.; Fuchs, B.; Szell, M.; Holovatch, Yu.; Thurner, S.

2015-02-01

Studying human behavior in virtual environments provides extraordinary opportunities for a quantitative analysis of social phenomena with levels of accuracy that approach those of the natural sciences. In this paper we use records of player activities in the massive multiplayer online game Pardus over 1238 consecutive days, and analyze dynamical features of sequences of actions of players. We build on previous work where temporal structures of human actions of the same type were quantified, and provide an empirical understanding of human actions of different types. This study of multi-level human activity can be seen as a dynamic counterpart of static multiplex network analysis. We show that the interevent time distributions of actions in the Pardus universe follow highly non-trivial distribution functions, from which we extract action-type specific characteristic 'decay constants'. We discuss characteristic features of interevent time distributions, including periodic patterns on different time scales, bursty dynamics, and various functional forms on different time scales. We comment on gender differences of players in emotional actions, and find that while males and females act similarly when performing some positive actions, females are slightly faster for negative actions. We also observe effects on the age of players: more experienced players are generally faster in making decisions about engaging in and terminating enmity and friendship, respectively.

A comparison between elastic network interpolation and MD simulation of 16S ribosomal RNA.

PubMed

Kim, Moon K; Li, Wen; Shapiro, Bruce A; Chirikjian, Gregory S

2003-12-01

In this paper a coarse-grained method called elastic network interpolation (ENI) is used to generate feasible transition pathways between two given conformations of the core central domain of 16S Ribosomal RNA (16S rRNA). The two given conformations are the extremes generated by a molecular dynamics (MD) simulation, which differ from each other by 10A in root-mean-square deviation (RMSD). It takes only several hours to build an ENI pathway on a 1.5GHz Pentium with 512 MB memory, while the MD takes several weeks on high-performance multi-processor servers such as the SGI ORIGIN 2000/2100. It is shown that multiple ENI pathways capture the essential anharmonic motions of millions of timesteps in a particular MD simulation. A coarse-grained normal mode analysis (NMA) is performed on each intermediate ENI conformation, and the lowest 1% of the normal modes (representing about 40 degrees of freedom (DOF)) are used to parameterize fluctuations. This combined ENI/NMA method captures all intermediate conformations in the MD run with 1.5A RMSD on average. In addition, if we restrict attention to the time interval of the MD run between the two extreme conformations, the RMSD between the closest ENI/NMA pathway and the MD results is about 1A. These results may serve as a paradigm for reduced-DOF dynamic simulations of large biological macromolecules as well as a method for the reduced-parameter interpretation of massive amounts of MD data.

Spintronic Nanodevices for Bioinspired Computing

PubMed Central

Grollier, Julie; Querlioz, Damien; Stiles, Mark D.

2016-01-01

Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for biomedical prosthesis. However, one of the major challenges of fabricating bioinspired hardware is building ultra-high-density networks out of complex processing units interlinked by tunable connections. Nanometer-scale devices exploiting spin electronics (or spintronics) can be a key technology in this context. In particular, magnetic tunnel junctions (MTJs) are well suited for this purpose because of their multiple tunable functionalities. One such functionality, non-volatile memory, can provide massive embedded memory in unconventional circuits, thus escaping the von-Neumann bottleneck arising when memory and processors are located separately. Other features of spintronic devices that could be beneficial for bioinspired computing include tunable fast nonlinear dynamics, controlled stochasticity, and the ability of single devices to change functions in different operating conditions. Large networks of interacting spintronic nanodevices can have their interactions tuned to induce complex dynamics such as synchronization, chaos, soliton diffusion, phase transitions, criticality, and convergence to multiple metastable states. A number of groups have recently proposed bioinspired architectures that include one or several types of spintronic nanodevices. In this paper, we show how spintronics can be used for bioinspired computing. We review the different approaches that have been proposed, the recent advances in this direction, and the challenges toward fully integrated spintronics complementary metal–oxide–semiconductor (CMOS) bioinspired hardware. PMID:27881881

Exploring the Plausibility of a National Multi-Agency Communications System for the Homeland Security Community: A Southeast Ohio Half-Duplex Voice Over IP Case Study

DTIC Science & Technology

2009-03-01

modeled after the use by computer gamers in MMORPGs (massively multiuser online role-playing games). This is a good example of engaging the larger...during grant evaluations. There are more and more peer reviewed journal articles being written on MMORPGs , but this work is mainly geared towards...of the massively multiuser online role-playing games ( MMORPGs ). Most of the literature is about leadership and social networking. There is

Test and Evaluation of the Malicious Activity Simulation Tool (MAST) in a Local Area Network (LAN) Running the Common PC Operating System Environment (COMPOSE)

DTIC Science & Technology

2013-09-01

Malicious Activity Simulation Tool MMORPG Massively Multiplayer Online Role-Playing Game MMS Mission Management Server MOA Memorandum of Agreement MS...conferencing, and massively multiplayer online role- playing games (MMORPG). During all of these Internet-based exchanges and transactions, the Internet user...In its 2011 Internet Crime Report, the Internet Crime Complaint Center (IC3) stated there were more than 300,000 complaints of online criminal

REVIEWS OF TOPICAL PROBLEMS: Birth and life of massive black holes

NASA Astrophysics Data System (ADS)

Dokuchaev, V. I.

1991-06-01

The problems of massive black holes in galactic nuclei of different types are reviewed. The dynamical evolution of compact star systems ends naturally in a gigantic concentrated mass of gas, containing an admixture of surviving stars, that unavoidably collapses into a black hole. The subsequent joint evolution of the remnant star system with a massive black hole at the center leads either to the phenomenon of a bright central source in the nuclei of active galaxies and quasars or to the opposite case of a "dead" frozen black hole in the nucleus of a normal galaxy.

A Fast Algorithm for Massively Parallel, Long-Term, Simulation of Complex Molecular Dynamics Systems

NASA Technical Reports Server (NTRS)

Jaramillo-Botero, Andres; Goddard, William A, III; Fijany, Amir

1997-01-01

The advances in theory and computing technology over the last decade have led to enormous progress in applying atomistic molecular dynamics (MD) methods to the characterization, prediction, and design of chemical, biological, and material systems,.

Working in a Text Mine; Is Access about to Go down?

ERIC Educational Resources Information Center

Emery, Jill

2008-01-01

The age of networked research and networked data analysis is upon us. "Wired Magazine" proclaims on the cover of their July 2008 issue: "The End of Science. The quest for knowledge used to begin with grand theories. Now it begins with massive amounts of data. Welcome to the Petabyte Age." Computing technology is sufficiently complex at this point…

PuLP/XtraPuLP : Partitioning Tools for Extreme-Scale Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slota, George M; Rajamanickam, Sivasankaran; Madduri, Kamesh

2017-09-21

PuLP/XtraPulp is software for partitioning graphs from several real-world problems. Graphs occur in several places in real world from road networks, social networks and scientific simulations. For efficient parallel processing these graphs have to be partitioned (split) with respect to metrics such as computation and communication costs. Our software allows such partitioning for massive graphs.

Experimental evidence of massive-scale emotional contagion through social networks

PubMed Central

Kramer, Adam D. I.; Guillory, Jamie E.; Hancock, Jeffrey T.

2014-01-01

Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people. PMID:24889601

Experimental evidence of massive-scale emotional contagion through social networks.

PubMed

Kramer, Adam D I; Guillory, Jamie E; Hancock, Jeffrey T

2014-06-17

Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.

Massive Scale Cyber Traffic Analysis: A Driver for Graph Database Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff A.; Choudhury, S.; Haglin, David J.

2013-06-19

We describe the significance and prominence of network traffic analysis (TA) as a graph- and network-theoretical domain for advancing research in graph database systems. TA involves observing and analyzing the connections between clients, servers, hosts, and actors within IP networks, both at particular times and as extended over times. Towards that end, NetFlow (or more generically, IPFLOW) data are available from routers and servers which summarize coherent groups of IP packets flowing through the network. IPFLOW databases are routinely interrogated statistically and visualized for suspicious patterns. But the ability to cast IPFLOW data as a massive graph and query itmore » interactively, in order to e.g.\\ identify connectivity patterns, is less well advanced, due to a number of factors including scaling, and their hybrid nature combining graph connectivity and quantitative attributes. In this paper, we outline requirements and opportunities for graph-structured IPFLOW analytics based on our experience with real IPFLOW databases. Specifically, we describe real use cases from the security domain, cast them as graph patterns, show how to express them in two graph-oriented query languages SPARQL and Datalog, and use these examples to motivate a new class of "hybrid" graph-relational systems.« less

Strategy for a Sustained Quality Delivery Mode of ODL Programmes for Massive Enrollments and E-Learning: The Case for Zimbabwe Open University

ERIC Educational Resources Information Center

Kabanda, Gabriel

2014-01-01

The market dynamics in distance education has precipitated phenomenal growth opportunities in enrollments and e-learning. The purpose of the paper was to develop a strategy for sustained quality delivery mode of distance education progammes that precipitate massive enrollments and e-learning in an open and distance learning (ODL) institution using…

Search for OB stars running away from young star clusters. II. The NGC 6357 star-forming region

NASA Astrophysics Data System (ADS)

Gvaramadze, V. V.; Kniazev, A. Y.; Kroupa, P.; Oh, S.

2011-11-01

Dynamical few-body encounters in the dense cores of young massive star clusters are responsible for the loss of a significant fraction of their massive stellar content. Some of the escaping (runaway) stars move through the ambient medium supersonically and can be revealed via detection of their bow shocks (visible in the infrared, optical or radio). In this paper, which is the second of a series of papers devoted to the search for OB stars running away from young ( ≲ several Myr) Galactic clusters and OB associations, we present the results of the search for bow shocks around the star-forming region NGC 6357. Using the archival data of the Midcourse Space Experiment (MSX) satellite and the Spitzer Space Telescope, and the preliminary data release of the Wide-Field Infrared Survey Explorer (WISE), we discovered seven bow shocks, whose geometry is consistent with the possibility that they are generated by stars expelled from the young (~1-2 Myr) star clusters, Pismis 24 and AH03 J1725-34.4, associated with NGC 6357. Two of the seven bow shocks are driven by the already known OB stars, HD 319881 and [N78] 34. Follow-up spectroscopy of three other bow-shock-producing stars showed that they are massive (O-type) stars as well, while the 2MASS photometry of the remaining two stars suggests that they could be B0 V stars, provided that both are located at the same distance as NGC 6357. Detection of numerous massive stars ejected from the very young clusters is consistent with the theoretical expectation that star clusters can effectively lose massive stars at the very beginning of their dynamical evolution (long before the second mechanism for production of runaway stars, based on a supernova explosion in a massive tight binary system, begins to operate) and lends strong support to the idea that probably all field OB stars have been dynamically ejected from their birth clusters. A by-product of our search for bow shocks around NGC 6357 is the detection of three circular shells typical of luminous blue variable and late WN-type Wolf-Rayet stars.

Robustness of Oscillatory Behavior in Correlated Networks

PubMed Central

Sasai, Takeyuki; Morino, Kai; Tanaka, Gouhei; Almendral, Juan A.; Aihara, Kazuyuki

2015-01-01

Understanding network robustness against failures of network units is useful for preventing large-scale breakdowns and damages in real-world networked systems. The tolerance of networked systems whose functions are maintained by collective dynamical behavior of the network units has recently been analyzed in the framework called dynamical robustness of complex networks. The effect of network structure on the dynamical robustness has been examined with various types of network topology, but the role of network assortativity, or degree–degree correlations, is still unclear. Here we study the dynamical robustness of correlated (assortative and disassortative) networks consisting of diffusively coupled oscillators. Numerical analyses for the correlated networks with Poisson and power-law degree distributions show that network assortativity enhances the dynamical robustness of the oscillator networks but the impact of network disassortativity depends on the detailed network connectivity. Furthermore, we theoretically analyze the dynamical robustness of correlated bimodal networks with two-peak degree distributions and show the positive impact of the network assortativity. PMID:25894574

Adaptive parallel logic networks

NASA Technical Reports Server (NTRS)

Martinez, Tony R.; Vidal, Jacques J.

1988-01-01

Adaptive, self-organizing concurrent systems (ASOCS) that combine self-organization with massive parallelism for such applications as adaptive logic devices, robotics, process control, and system malfunction management, are presently discussed. In ASOCS, an adaptive network composed of many simple computing elements operating in combinational and asynchronous fashion is used and problems are specified by presenting if-then rules to the system in the form of Boolean conjunctions. During data processing, which is a different operational phase from adaptation, the network acts as a parallel hardware circuit.

Coupling human mobility and social ties.

PubMed

Toole, Jameson L; Herrera-Yaqüe, Carlos; Schneider, Christian M; González, Marta C

2015-04-06

Studies using massive, passively collected data from communication technologies have revealed many ubiquitous aspects of social networks, helping us understand and model social media, information diffusion and organizational dynamics. More recently, these data have come tagged with geographical information, enabling studies of human mobility patterns and the science of cities. We combine these two pursuits and uncover reproducible mobility patterns among social contacts. First, we introduce measures of mobility similarity and predictability and measure them for populations of users in three large urban areas. We find individuals' visitations patterns are far more similar to and predictable by social contacts than strangers and that these measures are positively correlated with tie strength. Unsupervised clustering of hourly variations in mobility similarity identifies three categories of social ties and suggests geography is an important feature to contextualize social relationships. We find that the composition of a user's ego network in terms of the type of contacts they keep is correlated with mobility behaviour. Finally, we extend a popular mobility model to include movement choices based on social contacts and compare its ability to reproduce empirical measurements with two additional models of mobility. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

The production of information in the attention economy

PubMed Central

Ciampaglia, Giovanni Luca; Flammini, Alessandro; Menczer, Filippo

2015-01-01

Online traces of human activity offer novel opportunities to study the dynamics of complex knowledge exchange networks, in particular how emergent patterns of collective attention determine what new information is generated and consumed. Can we measure the relationship between demand and supply for new information about a topic? We propose a normalization method to compare attention bursts statistics across topics with heterogeneous distribution of attention. Through analysis of a massive dataset on traffic to Wikipedia, we find that the production of new knowledge is associated to significant shifts of collective attention, which we take as proxy for its demand. This is consistent with a scenario in which allocation of attention toward a topic stimulates the demand for information about it, and in turn the supply of further novel information. However, attention spikes only for a limited time span, during which new content has higher chances of receiving traffic, compared to content created later or earlier on. Our attempt to quantify demand and supply of information, and our finding about their temporal ordering, may lead to the development of the fundamental laws of the attention economy, and to a better understanding of social exchange of knowledge information networks. PMID:25989177

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things

PubMed Central

Akan, Ozgur B.

2018-01-01

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST). PMID:29538405

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things.

PubMed

Aktas, Metin; Kuscu, Murat; Dinc, Ergin; Akan, Ozgur B

2018-01-01

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).

The Anglo-Australian Planet Search. XXV. A Candidate Massive Saturn Analog Orbiting HD 30177

NASA Astrophysics Data System (ADS)

Wittenmyer, Robert A.; Horner, Jonathan; Mengel, M. W.; Butler, R. P.; Wright, D. J.; Tinney, C. G.; Carter, B. D.; Jones, H. R. A.; Anglada-Escudé, G.; Bailey, J.; O'Toole, Simon J.

2017-04-01

We report the discovery of a second long-period giant planet orbiting HD 30177, a star previously known to host a massive Jupiter analog (HD 30177b: a = 3.8 ± 0.1 au, m sin I = 9.7 ± 0.5 M Jup). HD 30177c can be regarded as a massive Saturn analog in this system, with a = 9.9 ± 1.0 au and m sin I = 7.6 ± 3.1 M Jup. The formal best-fit solution slightly favors a closer-in planet at a ˜ 7 au, but detailed n-body dynamical simulations show that configuration to be unstable. A shallow local minimum of longer period, lower eccentricity solutions was found to be dynamically stable, and hence we adopt the longer period in this work. The proposed ˜32 year orbit remains incomplete; further monitoring of this and other stars is necessary to reveal the population of distant gas giant planets with orbital separations a ˜ 10 au, analogous to that of Saturn.

Cosmology in massive gravity with effective composite metric

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heisenberg, Lavinia; Refregier, Alexandre, E-mail: lavinia.heisenberg@eth-its.ethz.ch, E-mail: alexandre.refregier@phys.ethz.ch

This paper is dedicated to scrutinizing the cosmology in massive gravity. A matter field of the dark sector is coupled to an effective composite metric while a standard matter field couples to the dynamical metric in the usual way. For this purpose, we study the dynamical system of cosmological solutions by using phase analysis, which provides an overview of the class of cosmological solutions in this setup. This also permits us to study the critical points of the cosmological equations together with their stability. We show the presence of stable attractor de Sitter critical points relevant to the late-time cosmicmore » acceleration. Furthermore, we study the tensor, vector and scalar perturbations in the presence of standard matter fields and obtain the conditions for the absence of ghost and gradient instabilities. Hence, massive gravity in the presence of the effective composite metric can accommodate interesting dark energy phenomenology, that can be observationally distinguished from the standard model according to the expansion history and cosmic growth.« less

Resolving Supercritical Orion Cores

NASA Astrophysics Data System (ADS)

Li, Di; Chapman, N.; Goldsmith, P.; Velusamy, T.

2009-01-01

The theoretical framework for high mass star formation (HMSF) is unclear. Observations reveal a seeming dichotomy between high- and low-mass star formation, with HMSF occurring only in Giant Molecular Clouds (GMC), mostly in clusters, and with higher star formation efficiencies than low-mass star formation. One crucial constraint to any theoretical model is the dynamical state of massive cores, in particular, whether a massive core is in supercritical collapse. Based on the mass-size relation of dust emission, we select likely unstable targets from a sample of massive cores (Li et al. 2007 ApJ 655, 351) in the nearest GMC, Orion. We have obtained N2H+ (1-0) maps using CARMA with resolution ( 2.5", 0.006 pc) significantly better than existing observations. We present observational and modeling results for ORI22. By revealing the dynamic structure down to Jeans scale, CARMA data confirms the dominance of gravity over turbulence in this cores. This work was performed by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Massive star winds interacting with magnetic fields on various scales

NASA Astrophysics Data System (ADS)

David-Uraz, A.; Petit, V.; Erba, C.; Fullerton, A.; Walborn, N.; MacInnis, R.

2018-01-01

One of the defining processes which govern massive star evolution is their continuous mass loss via dense, supersonic line-driven winds. In the case of those OB stars which also host a surface magnetic field, the interaction between that field and the ionized outflow leads to complex circumstellar structures known as magnetospheres. In this contribution, we review recent developments in the field of massive star magnetospheres, including current efforts to characterize the largest magnetosphere surrounding an O star: that of NGC 1624-2. We also discuss the potential of the "analytic dynamical magnetosphere" (ADM) model to interpret multi-wavelength observations. Finally, we examine the possible effects of — heretofore undetected — small-scale magnetic fields on massive star winds and compare their hypothetical consequences to existing, unexplained observations.

Investigation of Non-Linear Dynamics of the Rock Massive,Using Seismological Catalogue data and Induction Electromagnetic Monitoring Data in a Rock Burst Mine.

NASA Astrophysics Data System (ADS)

Hachay, O. A.; Khachay, O. Y.; Klimko, V. K.; Shipeev, O. V.

2012-04-01

Geological medium is an open dynamical system, which is influenced on different scales by natural and man-made impacts, which change the medium state and lead as a result to a complicated many ranked hierarchic evolution. That is the subject of geo synergetics. Paradigm of physical mesomechanics, which was advanced by academician Panin V.E. and his scientific school, which includes the synergetic approach is a constructive method for research and changing the state of heterogenic materials [1]. That result had been obtained on specimens of different materials. In our results of research of no stationary geological medium in a frame of natural experiments in real rock massifs, which are under high man-made influence it was shown, that the state dynamics can be revealed with use synergetics in hierarchic medium. Active and passive geophysical monitoring plays a very important role for research of the state of dynamical geological systems. It can be achieved by use electromagnetic and seismic fields. Our experience of that research showed the changing of the system state reveals on the space scales and times in the parameters, which are linked with the peculiarities of the medium of the second or higher ranks [2-5]. Results of seismological and electromagnetic information showed the mutual additional information on different space-time levels of rock massive state, which are energetic influenced by explosions, used in mining technology. It is revealed a change of nonlinearity degree in time of the massive state by active influence on it. The description of massive movement in a frame of linear dynamical system does not satisfy the practical situation. The received results are of great significance because for the first time we could find the coincidences with the mathematical theory of open systems and experimental natural results with very complicated structure. On that base we developed a new processing method for the seismological information which can be used in real time for estimation of the disaster degree changing in mine massive. The work was supported by the grant RFBR 10-05-00013. 1. Panin,V.E. et all. 1995. Physical mesomechanics and computer construction of materials . Novosibirsk.: Nauka, SIFR. V.1 pp. 350. 2. Hachay, O.A. 2006. "The problem of the research of redistribution of stress and phase states of massive between high man-made influences," Mining information and analytic bulletin, 5:109-115. 3. Hachay, O.A. and Khachay, O.Yu. 2008. "Theoretical approaches for system of geophysical state control validation of geological medium by man-made influence," Mining information and analytic bulletin 1:161-169. 4. Hachay, O.A., and Khachay, O.Yu. 2009. "Results of electromagnetic and seismic monitoring of the state of rock massive by use the approach of the open dynamical systems,"presented at the EGU2009 - EGU General Assembly 2009, session: Thermo- hydro- mechanical coupling in stressed rock, 19 April 19 - 24 April 2009. 5. Hachay, O.A. "Synergetic events in geological medium and nonlinear features of wave propagation," presented at the EGU2009 - EGU General Assembly 2009, session: Solid Earth geocomplexity: surface processes, morphology and natural resources over wide ranges of scale, 19 April 19 - 24 April 2009.

Dynamic Load Predictions for Launchers Using Extra-Large Eddy Simulations X-Les

NASA Astrophysics Data System (ADS)

Maseland, J. E. J.; Soemarwoto, B. I.; Kok, J. C.

2005-02-01

Flow-induced unsteady loads can have a strong impact on performance and flight characteristics of aerospace vehicles and therefore play a crucial role in their design and operation. Complementary to costly flight tests and delicate wind-tunnel experiments, unsteady loads can be calculated using time-accurate Computational Fluid Dynamics. A capability to accurately predict the dynamic loads on aerospace structures at flight Reynolds numbers can be of great value for the design and analysis of aerospace vehicles. Advanced space launchers are subject to dynamic loads in the base region during the ascent to space. In particular the engine and nozzle experience aerodynamic pressure fluctuations resulting from massive flow separations. Understanding these phenomena is essential for performance enhancements for future launchers which operate a larger nozzle. A new hybrid RANS-LES turbulence modelling approach termed eXtra-Large Eddy Simulations (X-LES) holds the promise to capture the flow structures associated with massive separations and enables the prediction of the broad-band spectrum of dynamic loads. This type of method has become a focal point, reducing the cost of full LES, driven by the demand for their applicability in an industrial environment. The industrial feasibility of X-LES simulations is demonstrated by computing the unsteady aerodynamic loads on the main-engine nozzle of a generic space launcher configuration. The potential to calculate the dynamic loads is qualitatively assessed for transonic flow conditions in a comparison to wind-tunnel experiments. In terms of turn-around-times, X-LES computations are already feasible within the time-frames of the development process to support the structural design. Key words: massive separated flows; buffet loads; nozzle vibrations; space launchers; time-accurate CFD; composite RANS-LES formulation.

Collective network for computer structures

DOEpatents

Blumrich, Matthias A; Coteus, Paul W; Chen, Dong; Gara, Alan; Giampapa, Mark E; Heidelberger, Philip; Hoenicke, Dirk; Takken, Todd E; Steinmacher-Burow, Burkhard D; Vranas, Pavlos M

2014-01-07

A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to the needs of a processing algorithm.

Collective network for computer structures

DOEpatents

Blumrich, Matthias A [Ridgefield, CT; Coteus, Paul W [Yorktown Heights, NY; Chen, Dong [Croton On Hudson, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk [Ossining, NY; Takken, Todd E [Brewster, NY; Steinmacher-Burow, Burkhard D [Wernau, DE; Vranas, Pavlos M [Bedford Hills, NY

2011-08-16

A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

A hierarchical pyramid method for managing large-scale high-resolution drainage networks extracted from DEM

NASA Astrophysics Data System (ADS)

Bai, Rui; Tiejian, Li; Huang, Yuefei; Jiaye, Li; Wang, Guangqian; Yin, Dongqin

2015-12-01

The increasing resolution of Digital Elevation Models (DEMs) and the development of drainage network extraction algorithms make it possible to develop high-resolution drainage networks for large river basins. These vector networks contain massive numbers of river reaches with associated geographical features, including topological connections and topographical parameters. These features create challenges for efficient map display and data management. Of particular interest are the requirements of data management for multi-scale hydrological simulations using multi-resolution river networks. In this paper, a hierarchical pyramid method is proposed, which generates coarsened vector drainage networks from the originals iteratively. The method is based on the Horton-Strahler's (H-S) order schema. At each coarsening step, the river reaches with the lowest H-S order are pruned, and their related sub-basins are merged. At the same time, the topological connections and topographical parameters of each coarsened drainage network are inherited from the former level using formulas that are presented in this study. The method was applied to the original drainage networks of a watershed in the Huangfuchuan River basin extracted from a 1-m-resolution airborne LiDAR DEM and applied to the full Yangtze River basin in China, which was extracted from a 30-m-resolution ASTER GDEM. In addition, a map-display and parameter-query web service was published for the Mississippi River basin, and its data were extracted from the 30-m-resolution ASTER GDEM. The results presented in this study indicate that the developed method can effectively manage and display massive amounts of drainage network data and can facilitate multi-scale hydrological simulations.

The Dynamics of Massive Starless Cores with ALMA

NASA Astrophysics Data System (ADS)

Tan, Jonathan C.; Kong, Shuo; Butler, Michael J.; Caselli, Paola; Fontani, Francesco

2013-12-01

How do stars that are more massive than the Sun form, and thus how is the stellar initial mass function (IMF) established? Such intermediate- and high-mass stars may be born from relatively massive pre-stellar gas cores, which are more massive than the thermal Jeans mass. The turbulent core accretion model invokes such cores as being in approximate virial equilibrium and in approximate pressure equilibrium with their surrounding clump medium. Their internal pressure is provided by a combination of turbulence and magnetic fields. Alternatively, the competitive accretion model requires strongly sub-virial initial conditions that then lead to extensive fragmentation to the thermal Jeans scale, with intermediate- and high-mass stars later forming by competitive Bondi-Hoyle accretion. To test these models, we have identified four prime examples of massive (~100 M ⊙) clumps from mid-infrared extinction mapping of infrared dark clouds. Fontani et al. found high deuteration fractions of N2H+ in these objects, which are consistent with them being starless. Here we present ALMA observations of these four clumps that probe the N2D+ (3-2) line at 2.''3 resolution. We find six N2D+ cores and determine their dynamical state. Their observed velocity dispersions and sizes are broadly consistent with the predictions of the turbulent core model of self-gravitating, magnetized (with Alfvén Mach number mA ~ 1) and virialized cores that are bounded by the high pressures of their surrounding clumps. However, in the most massive cores, with masses up to ~60 M ⊙, our results suggest that moderately enhanced magnetic fields (so that mA ~= 0.3) may be needed for the structures to be in virial and pressure equilibrium. Magnetically regulated core formation may thus be important in controlling the formation of massive cores, inhibiting their fragmentation, and thus helping to establish the stellar IMF.

Dynamical Models for High-Energy Emission from Massive Stars

NASA Astrophysics Data System (ADS)

Owocki, Stanley %FAA(University of Delaware)

Massive stars are prominent sources of X-rays and gamma-rays detected by both targeted and survey observations from orbiting telescopes like Chandra, XMM/Newton, RXTE, and Fermi. Such high-energy emissions represent key probes of the dynamics of massive-star mass loss, and their penetration through many magnitudes of visible interstellar extinction makes them effective beacons of massive stars in distant reaches of the Galaxy, and in young, active star-forming regions. The project proposed here will develop a comprehensive theoretical framework for interpreting both surveys and targeted observations of high-energy emission from massive stars. It will build on our team's extensive experience in both theoretical models and observational analyses for three key types of emission mechanisms in the stellar wind outflows of these stars, namely: 1) Embedded Wind Shocks (EWS) arising from internal instabilities in the wind driving; 2) shocks in Colliding Wind Binary (CWB) systems; and 3) High-Mass X-ray Binaries (HMXB) systems with interaction between massive-star wind with a compact companion (neutron star or black hole). Taking advantage of commonalities in the treatment of radiative driving, hydrodynamics, shock heating and cooling, and radiation transport, we will develop radiation hydrodynamical models for the key observational signatures like energy distribution, emission line spectrum, and variability, with an emphasis on how these can be used in affiliated analyses of both surveys like the recent Chandra mapping of the Carina association, and targeted observations of galactic X-ray and gamma-ray sources associated with each of the above specific model types. The promises of new clumping-insensitive diagnostics of mass loss rates, and the connection to mass transfer and binarity, all have broad relevance for understanding the origin, evolution, and fate of massive stars, in concert with elements of NASA's Strategic Subgoal 3D. Building on our team's expertise, the project emphasizes training of a new generation of students and post-doctoral researchers to model and analyze observations by current and future NASA X-ray and gamma-ray observatories.

Formation of massive seed black holes via collisions and accretion

NASA Astrophysics Data System (ADS)

Boekholt, T. C. N.; Schleicher, D. R. G.; Fellhauer, M.; Klessen, R. S.; Reinoso, B.; Stutz, A. M.; Haemmerlé, L.

2018-05-01

Models aiming to explain the formation of massive black hole seeds, and in particular the direct collapse scenario, face substantial difficulties. These are rooted in rather ad hoc and fine-tuned initial conditions, such as the simultaneous requirements of extremely low metallicities and strong radiation backgrounds. Here, we explore a modification of such scenarios where a massive primordial star cluster is initially produced. Subsequent stellar collisions give rise to the formation of massive (104-105 M⊙) objects. Our calculations demonstrate that the interplay among stellar dynamics, gas accretion, and protostellar evolution is particularly relevant. Gas accretion on to the protostars enhances their radii, resulting in an enhanced collisional cross-section. We show that the fraction of collisions can increase from 0.1 to 1 per cent of the initial population to about 10 per cent when compared to gas-free models or models of protostellar clusters in the local Universe. We conclude that very massive objects can form in spite of initial fragmentation, making the first massive protostellar clusters viable candidate birth places for observed supermassive black holes.

Randomized Dynamic Mode Decomposition

NASA Astrophysics Data System (ADS)

Erichson, N. Benjamin; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01

The dynamic mode decomposition (DMD) is an equation-free, data-driven matrix decomposition that is capable of providing accurate reconstructions of spatio-temporal coherent structures arising in dynamical systems. We present randomized algorithms to compute the near-optimal low-rank dynamic mode decomposition for massive datasets. Randomized algorithms are simple, accurate and able to ease the computational challenges arising with `big data'. Moreover, randomized algorithms are amenable to modern parallel and distributed computing. The idea is to derive a smaller matrix from the high-dimensional input data matrix using randomness as a computational strategy. Then, the dynamic modes and eigenvalues are accurately learned from this smaller representation of the data, whereby the approximation quality can be controlled via oversampling and power iterations. Here, we present randomized DMD algorithms that are categorized by how many passes the algorithm takes through the data. Specifically, the single-pass randomized DMD does not require data to be stored for subsequent passes. Thus, it is possible to approximately decompose massive fluid flows (stored out of core memory, or not stored at all) using single-pass algorithms, which is infeasible with traditional DMD algorithms.

Exploring the Ability of a Coarse-grained Potential to Describe the Stress-strain Response of Glassy Polystyrene

DTIC Science & Technology

2012-10-01

using the open-source code Large-scale Atomic/Molecular Massively Parallel Simulator ( LAMMPS ) (http://lammps.sandia.gov) (23). The commercial...parameters are proprietary and cannot be ported to the LAMMPS 4 simulation code. In our molecular dynamics simulations at the atomistic resolution, we...IBI iterative Boltzmann inversion LAMMPS Large-scale Atomic/Molecular Massively Parallel Simulator MAPS Materials Processes and Simulations MS

Designing Next Generation Massively Multithreaded Architectures for Irregular Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumeo, Antonino; Secchi, Simone; Villa, Oreste

Irregular applications, such as data mining or graph-based computations, show unpredictable memory/network access patterns and control structures. Massively multi-threaded architectures with large node count, like the Cray XMT, have been shown to address their requirements better than commodity clusters. In this paper we present the approaches that we are currently pursuing to design future generations of these architectures. First, we introduce the Cray XMT and compare it to other multithreaded architectures. We then propose an evolution of the architecture, integrating multiple cores per node and next generation network interconnect. We advocate the use of hardware support for remote memory referencemore » aggregation to optimize network utilization. For this evaluation we developed a highly parallel, custom simulation infrastructure for multi-threaded systems. Our simulator executes unmodified XMT binaries with very large datasets, capturing effects due to contention and hot-spotting, while predicting execution times with greater than 90% accuracy. We also discuss the FPGA prototyping approach that we are employing to study efficient support for irregular applications in next generation manycore processors.« less

Hybrid function projective synchronization in complex dynamical networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, Qiang; Wang, Xing-yuan, E-mail: wangxy@dlut.edu.cn; Hu, Xiao-peng

2014-02-15

This paper investigates hybrid function projective synchronization in complex dynamical networks. When the complex dynamical networks could be synchronized up to an equilibrium or periodic orbit, a hybrid feedback controller is designed to realize the different component of vector of node could be synchronized up to different desired scaling function in complex dynamical networks with time delay. Hybrid function projective synchronization (HFPS) in complex dynamical networks with constant delay and HFPS in complex dynamical networks with time-varying coupling delay are researched, respectively. Finally, the numerical simulations show the effectiveness of theoretical analysis.

Contention Modeling for Multithreaded Distributed Shared Memory Machines: The Cray XMT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Secchi, Simone; Tumeo, Antonino; Villa, Oreste

Distributed Shared Memory (DSM) machines are a wide class of multi-processor computing systems where a large virtually-shared address space is mapped on a network of physically distributed memories. High memory latency and network contention are two of the main factors that limit performance scaling of such architectures. Modern high-performance computing DSM systems have evolved toward exploitation of massive hardware multi-threading and fine-grained memory hashing to tolerate irregular latencies, avoid network hot-spots and enable high scaling. In order to model the performance of such large-scale machines, parallel simulation has been proved to be a promising approach to achieve good accuracy inmore » reasonable times. One of the most critical factors in solving the simulation speed-accuracy trade-off is network modeling. The Cray XMT is a massively multi-threaded supercomputing architecture that belongs to the DSM class, since it implements a globally-shared address space abstraction on top of a physically distributed memory substrate. In this paper, we discuss the development of a contention-aware network model intended to be integrated in a full-system XMT simulator. We start by measuring the effects of network contention in a 128-processor XMT machine and then investigate the trade-off that exists between simulation accuracy and speed, by comparing three network models which operate at different levels of accuracy. The comparison and model validation is performed by executing a string-matching algorithm on the full-system simulator and on the XMT, using three datasets that generate noticeably different contention patterns.« less

Network cosmology.

PubMed

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

Network Cosmology

PubMed Central

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

Crystal MD: The massively parallel molecular dynamics software for metal with BCC structure

NASA Astrophysics Data System (ADS)

Hu, Changjun; Bai, He; He, Xinfu; Zhang, Boyao; Nie, Ningming; Wang, Xianmeng; Ren, Yingwen

2017-02-01

Material irradiation effect is one of the most important keys to use nuclear power. However, the lack of high-throughput irradiation facility and knowledge of evolution process, lead to little understanding of the addressed issues. With the help of high-performance computing, we could make a further understanding of micro-level-material. In this paper, a new data structure is proposed for the massively parallel simulation of the evolution of metal materials under irradiation environment. Based on the proposed data structure, we developed the new molecular dynamics software named Crystal MD. The simulation with Crystal MD achieved over 90% parallel efficiency in test cases, and it takes more than 25% less memory on multi-core clusters than LAMMPS and IMD, which are two popular molecular dynamics simulation software. Using Crystal MD, a two trillion particles simulation has been performed on Tianhe-2 cluster.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khan, Fazeel Mahmood; Holley-Bockelmann, Kelly; Berczik, Peter, E-mail: khan@ari.uni-heidelberg.de, E-mail: k.holley@vanderbilt.edu

Although supermassive black holes (SMBHs) correlate well with their host galaxies, there is an emerging view that outliers exist. Henize 2-10, NGC 4889, and NGC 1277 are examples of SMBHs at least an order of magnitude more massive than their host galaxy suggests. The dynamical effects of such ultramassive central black holes is unclear. Here, we perform direct N-body simulations of mergers of galactic nuclei where one black hole is ultramassive to study the evolution of the remnant and the black hole dynamics in this extreme regime. We find that the merger remnant is axisymmetric near the center, while near the largemore » SMBH influence radius, the galaxy is triaxial. The SMBH separation shrinks rapidly due to dynamical friction, and quickly forms a binary black hole; if we scale our model to the most massive estimate for the NGC 1277 black hole, for example, the timescale for the SMBH separation to shrink from nearly a kiloparsec to less than a parsec is roughly 10 Myr. By the time the SMBHs form a hard binary, gravitational wave emission dominates, and the black holes coalesce in a mere few Myr. Curiously, these extremely massive binaries appear to nearly bypass the three-body scattering evolutionary phase. Our study suggests that in this extreme case, SMBH coalescence is governed by dynamical friction followed nearly directly by gravitational wave emission, resulting in a rapid and efficient SMBH coalescence timescale. We discuss the implications for gravitational wave event rates and hypervelocity star production.« less

Dynamical Formation Signatures of Black Hole Binaries in the First Detected Mergers by LIGO

NASA Astrophysics Data System (ADS)

O'Leary, Ryan M.; Meiron, Yohai; Kocsis, Bence

2016-06-01

The dynamical formation of stellar-mass black hole-black hole binaries has long been a promising source of gravitational waves for the Laser Interferometer Gravitational-Wave Observatory (LIGO). Mass segregation, gravitational focusing, and multibody dynamical interactions naturally increase the interaction rate between the most massive black holes in dense stellar systems, eventually leading them to merge. We find that dynamical interactions, particularly three-body binary formation, enhance the merger rate of black hole binaries with total mass M tot roughly as \\propto {M}{{tot}}β , with β ≳ 4. We find that this relation holds mostly independently of the initial mass function, but the exact value depends on the degree of mass segregation. The detection rate of such massive black hole binaries is only further enhanced by LIGO’s greater sensitivity to massive black hole binaries with M tot ≲ 80 {M}⊙ . We find that for power-law BH mass functions dN/dM ∝ M -α with α ≤ 2, LIGO is most likely to detect black hole binaries with a mass twice that of the maximum initial black hole mass and a mass ratio near one. Repeated mergers of black holes inside the cluster result in about ˜5% of mergers being observed between two and three times the maximum initial black hole mass. Using these relations, one may be able to invert the observed distribution to the initial mass function with multiple detections of merging black hole binaries.

The architecture of tomorrow's massively parallel computer

NASA Technical Reports Server (NTRS)

Batcher, Ken

1987-01-01

Goodyear Aerospace delivered the Massively Parallel Processor (MPP) to NASA/Goddard in May 1983, over three years ago. Ever since then, Goodyear has tried to look in a forward direction. There is always some debate as to which way is forward when it comes to supercomputer architecture. Improvements to the MPP's massively parallel architecture are discussed in the areas of data I/O, memory capacity, connectivity, and indirect (or local) addressing. In I/O, transfer rates up to 640 megabytes per second can be achieved. There are devices that can supply the data and accept it at this rate. The memory capacity can be increased up to 128 megabytes in the ARU and over a gigabyte in the staging memory. For connectivity, there are several different kinds of multistage networks that should be considered.

Complexity growth in massive gravity theories, the effects of chirality, and more

NASA Astrophysics Data System (ADS)

Ghodrati, Mahdis

2017-11-01

To study the effect of parity violation on the rate of complexity growth, by using "complexity=action " conjecture, we find the complexity growth rates in different solutions of the chiral theory of topologically massive gravity (TMG) and parity-preserving theory of new massive gravity (NMG). Using the results, one can see that decreasing the parameter μ , which increases the effect of the Chern-Simons term and increases chirality, would increase the rate of growth of complexity. Also one can observe a stronger correlation between complexity growth and temperature rather than complexity growth and entropy. At the end we comment on the possible meaning of the deforming term of chiral Liouville action for the rate of complexity growth of warped conformal field theories in the tensor network renormalization picture.

Thought Leaders during Crises in Massive Social Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corley, Courtney D.; Farber, Robert M.; Reynolds, William

The vast amount of social media data that can be gathered from the internet coupled with workflows that utilize both commodity systems and massively parallel supercomputers, such as the Cray XMT, open new vistas for research to support health, defense, and national security. Computer technology now enables the analysis of graph structures containing more than 4 billion vertices joined by 34 billion edges along with metrics and massively parallel algorithms that exhibit near-linear scalability according to number of processors. The challenge lies in making this massive data and analysis comprehensible to an analyst and end-users that require actionable knowledge tomore » carry out their duties. Simply stated, we have developed language and content agnostic techniques to reduce large graphs built from vast media corpora into forms people can understand. Specifically, our tools and metrics act as a survey tool to identify thought leaders' -- those members that lead or reflect the thoughts and opinions of an online community, independent of the source language.« less

High-Mass Stars in the Centers of Young Dense Clusters: Mass Segregation, Binary Mergers and Gamma-Ray Bursts

NASA Astrophysics Data System (ADS)

Zinnecker, H.

We start by discussing dense, young star-clusters, particularly the 30 Doradus cluster with its core R136. The question of mass segregation and core collapse of the massive stars is addressed. Analytical estimates of relaxation times and collision times predict that the central N=10 subsystem of massive stars in the R136 core will evolve dynamically in such a way and fast enough (i.e. within their main-sequence lifetime of a few Myr) that a dominant massive binary system is formed whose orbit will shrink to a point where merging of the components appears inevitable. The merger product will be spinning rapidly, and we put forward the idea that this rare and very massive object might be the perfect precursor of a gamma-ray burst (collapsar).

The effect of gas dynamics on semi-analytic modelling of cluster galaxies

NASA Astrophysics Data System (ADS)

Saro, A.; De Lucia, G.; Dolag, K.; Borgani, S.

2008-12-01

We study the degree to which non-radiative gas dynamics affect the merger histories of haloes along with subsequent predictions from a semi-analytic model (SAM) of galaxy formation. To this aim, we use a sample of dark matter only and non-radiative smooth particle hydrodynamics (SPH) simulations of four massive clusters. The presence of gas-dynamical processes (e.g. ram pressure from the hot intra-cluster atmosphere) makes haloes more fragile in the runs which include gas. This results in a 25 per cent decrease in the total number of subhaloes at z = 0. The impact on the galaxy population predicted by SAMs is complicated by the presence of `orphan' galaxies, i.e. galaxies whose parent substructures are reduced below the resolution limit of the simulation. In the model employed in our study, these galaxies survive (unaffected by the tidal stripping process) for a residual merging time that is computed using a variation of the Chandrasekhar formula. Due to ram-pressure stripping, haloes in gas simulations tend to be less massive than their counterparts in the dark matter simulations. The resulting merging times for satellite galaxies are then longer in these simulations. On the other hand, the presence of gas influences the orbits of haloes making them on average more circular and therefore reducing the estimated merging times with respect to the dark matter only simulation. This effect is particularly significant for the most massive satellites and is (at least in part) responsible for the fact that brightest cluster galaxies in runs with gas have stellar masses which are about 25 per cent larger than those obtained from dark matter only simulations. Our results show that gas dynamics has only a marginal impact on the statistical properties of the galaxy population, but that its impact on the orbits and merging times of haloes strongly influences the assembly of the most massive galaxies.

Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics

NASA Astrophysics Data System (ADS)

Chen, Yu-Zhong; Lai, Ying-Cheng

2018-03-01

Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics.

PubMed

Chen, Yu-Zhong; Lai, Ying-Cheng

2018-03-01

Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

Massive Black-Hole Binary Mergers: Dynamics, Environments & Expected Detections

NASA Astrophysics Data System (ADS)

Kelley, Luke Zoltan

2018-05-01

This thesis studies the populations and dynamics of massive black-hole binaries and their mergers, and explores the implications for electromagnetic and gravitational-wave signals that will be detected in the near future. Massive black-holes (MBH) reside in the centers of galaxies, and when galaxies merge, their MBH interact and often pair together. We base our study on the populations of MBH and galaxies from the `Illustris' cosmological hydrodynamic simulations. The bulk of the binary merger dynamics, however, are unresolved in cosmological simulations. We implement a suite of comprehensive physical models for the merger process, like dynamical friction and gravitational wave emission, which are added in post-processing. Contrary to many previous studies, we find that the most massive binaries with near equal-mass companions are the most efficient at coalescing; though the process still typically takes gigayears.From the data produced by these MBH binary populations and their dynamics, we calculate the expected gravitational wave (GW) signals: both the stochastic, GW background of countless unresolved sources, and the GW foreground of individually resolvable binaries which resound above the noise. Ongoing experiments, called pulsar timing arrays, are sensitive to both of these types of signals. We find that, while the current lack of detections is unsurprising, both the background and foreground will plausibly be detected in the next decade. Unlike previous studies which have predicted the foreground to be significantly harder to detect than the background, we find their typical amplitudes are comparable.With traditional electromagnetic observations, there has also been a dearth of confirmed detections of MBH binary systems. We use our binaries, combined with models of emission from accreting MBH systems, to make predictions for the occurrence rate of systems observable using photometric, periodic-variability surveys. These variables should be detectable in current surveys, and indeed, we expect many candidates recently identified to be true binaries - though a significant fraction are likely false positives. Overall, this thesis finds the science of MBH binaries at an exciting cusp: just before incredible breakthroughs in observations, both electromagnetically and in the new age of gravitational wave astrophysics.

Emergent Adaptive Noise Reduction from Communal Cooperation of Sensor Grid

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Jones, Michael G.; Nark, Douglas M.; Lodding, Kenneth N.

2010-01-01

In the last decade, the realization of small, inexpensive, and powerful devices with sensors, computers, and wireless communication has promised the development of massive sized sensor networks with dense deployments over large areas capable of high fidelity situational assessments. However, most management models have been based on centralized control and research has concentrated on methods for passing data from sensor devices to the central controller. Most implementations have been small but, as it is not scalable, this methodology is insufficient for massive deployments. Here, a specific application of a large sensor network for adaptive noise reduction demonstrates a new paradigm where communities of sensor/computer devices assess local conditions and make local decisions from which emerges a global behaviour. This approach obviates many of the problems of centralized control as it is not prone to single point of failure and is more scalable, efficient, robust, and fault tolerant

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

1997-01-01

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added.

DSGRN: Examining the Dynamics of Families of Logical Models.

PubMed

Cummins, Bree; Gedeon, Tomas; Harker, Shaun; Mischaikow, Konstantin

2018-01-01

We present a computational tool DSGRN for exploring the dynamics of a network by computing summaries of the dynamics of switching models compatible with the network across all parameters. The network can arise directly from a biological problem, or indirectly as the interaction graph of a Boolean model. This tool computes a finite decomposition of parameter space such that for each region, the state transition graph that describes the coarse dynamical behavior of a network is the same. Each of these parameter regions corresponds to a different logical description of the network dynamics. The comparison of dynamics across parameters with experimental data allows the rejection of parameter regimes or entire networks as viable models for representing the underlying regulatory mechanisms. This in turn allows a search through the space of perturbations of a given network for networks that robustly fit the data. These are the first steps toward discovering a network that optimally matches the observed dynamics by searching through the space of networks.

DAWN: Dynamic Ad-hoc Wireless Network

DTIC Science & Technology

2016-06-19

DAWN: Dynamic Ad-hoc Wireless Network The DAWN (Dynamic Ad-hoc Wireless Networks) project is developing a general theory of complex and dynamic... wireless communication networks. To accomplish this, DAWN adopts a very different approach than those followed in the past and summarized above. DAWN... wireless communication networks. The members of DAWN investigated difference aspects of wireless mobile ad hoc networks (MANET). The views, opinions and/or

Who Networks? The Social Psychology of Virtual Communities

DTIC Science & Technology

2004-06-01

virtual life: the open side - characterized by communities of interest, civil society movements, virtual “states,” and 4 online gaming communities...network of people hailing from Sicily. Sometimes the offline/ online similari- ties mesh even more, as when a gaming society in a small Swedish town...Commercially owned and regulated graphics-based Massively Multi- Player Gaming Communities (EverQuest, The Matrix Online ®, etc.) • UseNET

Single Sided Messaging v. 0.6.6

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curry, Matthew Leon; Farmer, Matthew Shane; Hassani, Amin

Single-Sided Messaging (SSM) is a portable, multitransport networking library that enables applications to leverage potential one-sided capabilities of underlying network transports. It also provides desirable semantics that services for highperformance, massively parallel computers can leverage, such as an explicit cancel operation for pending transmissions, as well as enhanced matching semantics favoring large numbers of buffers attached to a single match entry. This release supports TCP/IP, shared memory, and Infiniband.

Massively-Parallel Architectures for Automatic Recognition of Visual Speech Signals

DTIC Science & Technology

1988-10-12

Secusrity Clamifieation, Nlassively-Parallel Architectures for Automa ic Recognitio of Visua, Speech Signals 12. PERSONAL AUTHOR(S) Terrence J...characteristics of speech from tJhe, visual speech signals. Neural networks have been trained on a database of vowels. The rqw images of faces , aligned and...images of faces , aligned and preprocessed, were used as input to these network which were trained to estimate the corresponding envelope of the

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

NASA Technical Reports Server (NTRS)

Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

1993-01-01

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

An Adaptive QSE-reduced Nuclear Reaction Network for Silicon Burning

NASA Astrophysics Data System (ADS)

Parete-Koon, Suzanne; Hix, W.; Thielemann, F.

2008-03-01

The nuclei of the "iron peak" are formed in massive stars shortly before core collapse and during their supernova outbursts as well as during thermonuclear supernovae. Complete and incomplete silicon burning during these events are responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. Because of the large number of nuclei involved, accurate modeling of silicon burning is computationally expensive. However, examination of the physics of silicon burning has revealed that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present an improvement on our hybrid equilibrium-network scheme which takes advantage of this quasi-equilibrium in order to reduce the number of independent variables calculated. Because the size and membership of these groups vary as the temperature, density and electron faction change, achieving maximal efficiency requires dynamic adjustment of group number and membership. Toward this end, we are implementing a scheme beginning with a single QSE (NSE) group at appropriately high temperature, then progressing through 2, 3 and 4 group stages (with successively more independent variables) as temperature declines. This combination allows accurate prediction of the nuclear abundance evolution, deleptonization and energy generation at a further reduced computational cost when compared to a conventional nuclear reaction network or our previous 3 fixed group QSE-reduced network. During silicon burning, the resultant QSE-reduced network is up to 20 times faster than the full network it replaces without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multi-dimensional applications. This work has been supported by the National Science Foundation, by the Department of Energy's Scientic Discovery through Advanced Computing Programs, and by the Joint Institute for Heavy Ion Research at ORNL.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

PubMed

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

2017-06-16

The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis Nevers, Laetitia Poidevin, Arnaud Kress, Raymond Ripp, Julie Dawn Thompson, Olivier Poch, Odile Lecompte. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.06.2017.

A Massive Star Census of the Starburst Cluster R136

NASA Astrophysics Data System (ADS)

Crowther, Paul

2011-10-01

We propose to carry out a comprehensive census of the most massive stars in the central parsec {4"} of the starburst cluster, R136, which powers the Tarantula Nebula in the LMC. R136 is both sufficiently massive that the upper mass function is richly populated and young enough that its most massive stars have yet to explode as supernovae. The identification of very massive stars in R136, up to 300 solar masses, raises general questions of star formation, binarity and feedback in young massive clusters. The proposed STIS spectral survey of 36 stars more massive than 50 solar masses within R136 is ground-breaking, of legacy value, and is specifically tailored to a} yield physical properties; b} detect the majority of binaries by splitting observations between Cycles 19 and 20; c} measure rotational velocities, relevant for predictions of rotational mixing; d} quantify mass-loss properties for very massive stars; e} determine surface compositions; f} measure radial velocities, relevant for runaway stars and cluster dynamics; g} quantify radiative and mechanical feedback. This census will enable the mass function of very massive stars to be measured for the first time, as a result of incomplete and inadequate spectroscopy to date. It will also perfectly complement our Tarantula Survey, a ground-based VLT Large Programme, by including the most massive stars that are inaccessible to ground-based visual spectroscopy due to severe crowding. These surveys, together with existing integrated UV and optical studies will enable 30 Doradus to serve as a bona-fide template for unresolved extragalactic starburst regions.

A Massive Star Census of the Starburst Cluster R136

NASA Astrophysics Data System (ADS)

Crowther, Paul

2012-10-01

We propose to carry out a comprehensive census of the most massive stars in the central parsec {4"} of the starburst cluster, R136, which powers the Tarantula Nebula in the LMC. R136 is both sufficiently massive that the upper mass function is richly populated and young enough that its most massive stars have yet to explode as supernovae. The identification of very massive stars in R136, up to 300 solar masses, raises general questions of star formation, binarity and feedback in young massive clusters. The proposed STIS spectral survey of 36 stars more massive than 50 solar masses within R136 is ground-breaking, of legacy value, and is specifically tailored to a} yield physical properties; b} detect the majority of binaries by splitting observations between Cycles 19 and 20; c} measure rotational velocities, relevant for predictions of rotational mixing; d} quantify mass-loss properties for very massive stars; e} determine surface compositions; f} measure radial velocities, relevant for runaway stars and cluster dynamics; g} quantify radiative and mechanical feedback. This census will enable the mass function of very massive stars to be measured for the first time, as a result of incomplete and inadequate spectroscopy to date. It will also perfectly complement our Tarantula Survey, a ground-based VLT Large Programme, by including the most massive stars that are inaccessible to ground-based visual spectroscopy due to severe crowding. These surveys, together with existing integrated UV and optical studies will enable 30 Doradus to serve as a bona-fide template for unresolved extragalactic starburst regions.

The 40 Gbps cascaded bit-interleaving PON

NASA Astrophysics Data System (ADS)

Vyncke, A.; Torfs, G.; Van Praet, C.; Verbeke, M.; Duque, A.; Suvakovic, D.; Chow, H. K.; Yin, X.

2015-12-01

In this paper, a 40 Gbps cascaded bit-interleaving passive optical network (CBI-PON) is proposed to achieve power reduction in the network. The massive number of devices in the access network makes that power consumption reduction in this part of the network has a major impact on the total network power consumption. Starting from the proven BiPON technology, an extension to this concept is proposed to introduce multiple levels of bit-interleaving. The paper discusses the CBI protocol in detail, as well as an ASIC implementation of the required custom CBI Repeater and End-ONT. From the measurements of this first 40 Gbps ASIC prototype, power consumption reduction estimates are presented.

Transcription factor assisted loading and enhancer dynamics dictate the hepatic fasting response

PubMed Central

Goldstein, Ido; Baek, Songjoon; Presman, Diego M.; Paakinaho, Ville; Swinstead, Erin E.; Hager, Gordon L.

2017-01-01

Fasting elicits transcriptional programs in hepatocytes leading to glucose and ketone production. This transcriptional program is regulated by many transcription factors (TFs). To understand how this complex network regulates the metabolic response to fasting, we aimed at isolating the enhancers and TFs dictating it. Measuring chromatin accessibility revealed that fasting massively reorganizes liver chromatin, exposing numerous fasting-induced enhancers. By utilizing computational methods in combination with dissecting enhancer features and TF cistromes, we implicated four key TFs regulating the fasting response: glucocorticoid receptor (GR), cAMP responsive element binding protein 1 (CREB1), peroxisome proliferator activated receptor alpha (PPARA), and CCAAT/enhancer binding protein beta (CEBPB). These TFs regulate fuel production by two distinctly operating modules, each controlling a separate metabolic pathway. The gluconeogenic module operates through assisted loading, whereby GR doubles the number of sites occupied by CREB1 as well as enhances CREB1 binding intensity and increases accessibility of CREB1 binding sites. Importantly, this GR-assisted CREB1 binding was enhancer-selective and did not affect all CREB1-bound enhancers. Single-molecule tracking revealed that GR increases the number and DNA residence time of a portion of chromatin-bound CREB1 molecules. These events collectively result in rapid synergistic gene expression and higher hepatic glucose production. Conversely, the ketogenic module operates via a GR-induced TF cascade, whereby PPARA levels are increased following GR activation, facilitating gradual enhancer maturation next to PPARA target genes and delayed ketogenic gene expression. Our findings reveal a complex network of enhancers and TFs that dynamically cooperate to restore homeostasis upon fasting. PMID:28031249

Dynamics of massive black holes as a possible candidate of Galactic dark matter

NASA Technical Reports Server (NTRS)

Xu, Guohong; Ostriker, Jeremiah P.

1994-01-01

If the dark halo of the Galaxy is comprised of massive black holes (MBHs), then those within approximately 1 kpc will spiral to the center, where they will interact with one another, forming binaries which contract, owing to further dynamical friction, and then possibly merge to become more massive objects by emission of gravitational radiation. If successive mergers would invariably lead, as has been proposed by various authors, to the formation of a very massive nucleus of 10(exp 8) solar mass, then the idea of MBHs as a dark matter candidate could be excluded on observational grounds, since the observed limit (or value) for a Galactic central black hole is approximately 10(exp 6.5) solar mass. But, if successive mergers are delayed or prevented by other processes, such as the gravitational slingshot or rocket effect of gravitational radiation, then a large mass accumulation will not occur. In order to resolve this issue, we perform detailed N-body simulations using a modfied Aarseth code to explore the dynamical behavior of the MBHs, and we find that for a 'best estimate' model of the Galaxy a runaway does not occur. The code treates the MBHs as subject to the primary gravitational forces of one another and to the smooth stellar distribution, as well as the secondary perturbations in their orbits due to another and to the smooth stellar distribution, as well as the secondary perturbations in their orbits due to dynamical friction and gravitational radiation. Instead of a runaway, three-body interactions between hard binaries and single MBHs eject massive objects before accumulation of more than a few units, so that typically the center will contain zero, one, or two MBHs. We study how the situation depends in detail on the mass per MBH, the rotation of the halo, the mass distribution within the Galaxy, and other parameters. A runaway will most sensitively depend on the ratio of initial (spheroid/halo) central mass densities and secondarily on the typical values for the mass per MBH, with the rough dividing line, using Galactic parameters, being M(sub BH) less than or = 10(exp 6.5) solar mass. Using parameters from Lacey & Ostriker (1985) and our most accurate model for Galaxy, no runaway occurs.

Google matrix analysis of directed networks

NASA Astrophysics Data System (ADS)

Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.

2015-10-01

In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.

Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output.

PubMed

Nochomovitz, Yigal D; Li, Hao

2006-03-14

Deciphering the design principles for regulatory networks is fundamental to an understanding of biological systems. We have explored the mapping from the space of network topologies to the space of dynamical phenotypes for small networks. Using exhaustive enumeration of a simple model of three- and four-node networks, we demonstrate that certain dynamical phenotypes can be generated by an atypically broad spectrum of network topologies. Such dynamical outputs are highly designable, much like certain protein structures can be designed by an unusually broad spectrum of sequences. The network topologies that encode a highly designable dynamical phenotype possess two classes of connections: a fully conserved core of dedicated connections that encodes the stable dynamical phenotype and a partially conserved set of variable connections that controls the transient dynamical flow. By comparing the topologies and dynamics of the three- and four-node network ensembles, we observe a large number of instances of the phenomenon of "mutational buffering," whereby addition of a fourth node suppresses phenotypic variation amongst a set of three-node networks.

Advanced Polymer Network Structures

DTIC Science & Technology

2016-02-01

double networks in a single step was identified from coarse-grained molecular dynamics simulations of polymer solvents bearing rigid side chains dissolved...in a polymer network. Coarse-grained molecular dynamics simulations also explored the mechanical behavior of traditional double networks and...DRI), polymer networks, polymer gels, molecular dynamics simulations , double networks 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

Dynamic Trust Management for Mobile Networks and Its Applications

ERIC Educational Resources Information Center

Bao, Fenye

2013-01-01

Trust management in mobile networks is challenging due to dynamically changing network environments and the lack of a centralized trusted authority. In this dissertation research, we "design" and "validate" a class of dynamic trust management protocols for mobile networks, and demonstrate the utility of dynamic trust management…

Massive Multi-Agent Systems Control

NASA Technical Reports Server (NTRS)

Campagne, Jean-Charles; Gardon, Alain; Collomb, Etienne; Nishida, Toyoaki

2004-01-01

In order to build massive multi-agent systems, considered as complex and dynamic systems, one needs a method to analyze and control the system. We suggest an approach using morphology to represent and control the state of large organizations composed of a great number of light software agents. Morphology is understood as representing the state of the multi-agent system as shapes in an abstract geometrical space, this notion is close to the notion of phase space in physics.

Non-thermal emission and dynamical state of massive galaxy clusters from CLASH sample

NASA Astrophysics Data System (ADS)

Pandey-Pommier, M.; Richard, J.; Combes, F.; Edge, A.; Guiderdoni, B.; Narasimha, D.; Bagchi, J.; Jacob, J.

2016-12-01

Massive galaxy clusters are the most violent large scale structures undergoing merger events in the Universe. Based upon their morphological properties in X-rays, they are classified as un-relaxed and relaxed clusters and often host (a fraction of them) different types of non-thermal radio emitting components, viz., 'haloes', 'mini-haloes', 'relics' and 'phoenix' within their Intra Cluster Medium (ICM). The radio haloes show steep (α = -1.2) and ultra steep (α < -1.5) spectral properties at low radio frequencies, giving important insights on the merger (pre or post) state of the cluster. Ultra steep spectrum radio halo emissions are rare and expected to be the dominating population to be discovered via LOFAR and SKA in the future. Further, the distribution of matter (morphological information), alignment of hot X-ray emitting gas from the ICM with the total mass (dark + baryonic matter) and the bright cluster galaxy (BCG) is generally used to study the dynamical state of the cluster. We present here a multi wavelength study on 14 massive clusters from the CLASH survey and show the correlation between the state of their merger in X-ray and spectral properties (1.4 GHz - 150 MHz) at radio wavelengths. Using the optical data we also discuss about the gas-mass alignment, in order to understand the interplay between dark and baryonic matter in massive galaxy clusters.

High Resolution Studies of Mass Loss from Massive Binary Stars

NASA Astrophysics Data System (ADS)

Corcoran, Michael F.; Gull, Theodore R.; Hamaguchi, Kenji; Richardson, Noel; Madura, Thomas; Post Russell, Christopher Michael; Teodoro, Mairan; Nichols, Joy S.; Moffat, Anthony F. J.; Shenar, Tomer; Pablo, Herbert

2017-01-01

Mass loss from hot luminous single and binary stars has a significant, perhaps decisive, effect on their evolution. The combination of X-ray observations of hot shocked gas embedded in the stellar winds and high-resolution optical/UV spectra of the cooler mass in the outflow provides unique ways to study the unstable process by which massive stars lose mass both through continuous stellar winds and rare, impulsive, large-scale mass ejections. The ability to obtain coordinated observations with the Hubble Space Telescope Imaging Spectrograph (HST/STIS) and the Chandra High-Energy Transmission Grating Spectrometer (HETGS) and other X-ray observatories has allowed, for the first time, studies of resolved line emisssion over the temperature range of 104- 108K, and has provided observations to confront numerical dynamical models in three dimensions. Such observations advance our knowledge of mass-loss asymmetries, spatial and temporal variabilities, and the fundamental underlying physics of the hot shocked outflow, providing more realistic constraints on the amount of mass lost by different luminous stars in a variety of evolutionary stages. We discuss the impact that these joint observational studies have had on our understanding of dynamical mass outflows from massive stars, with particular emphasis on two important massive binaries, Delta Ori Aa, a linchpin of the mass luminosity relation for upper HRD main sequence stars, and the supermassive colliding wind binary Eta Carinae.

Emergent universe with wormholes in massive gravity

NASA Astrophysics Data System (ADS)

Paul, B. C.; Majumdar, A. S.

2018-03-01

An emergent universe (EU) scenario is proposed to obtain a universe free from big-bang singularity. In this framework the present universe emerged from a static Einstein universe phase in the infinite past. A flat EU scenario is found to exist in Einstein’s gravity with a non-linear equation of state (EoS). It has been shown subsequently that a physically realistic EU model can be obtained considering cosmic fluid composed of interacting fluids with a non-linear equation of state. It results a viable cosmological model accommodating both early inflation and present accelerating phases. In the present paper, the origin of an initial static Einstein universe needed in the EU model is explored in a massive gravity theory which subsequently emerged to be a dynamically evolving universe. A new gravitational instanton solution in a flat universe is obtained in the massive gravity theory which is a dynamical wormhole that might play an important role in realizing the origin of the initial state of the emergent universe. The emergence of a Lorentzian universe from a Euclidean gravity is understood by a Wick rotation τ = i t . A universe with radiation at the beginning finally transits into the present observed universe with a non-linear EoS as the interactions among the fluids set in. Thus a viable flat EU scenario where the universe stretches back into time infinitely, with no big bang is permitted in a massive gravity.

Detailed analysis of routing protocols with different network limitations

NASA Astrophysics Data System (ADS)

Masood, Mohsin; Abuhelala, Mohamed; Glesk, Ivan

2016-12-01

In network communication field, routing protocols have got a significant role which are not only used in networks to handle the user data but also to monitor the different network environments. Dynamic routing protocols such as OSPF, EIGRP and RIP are used for forwarding user data to its destination by instantly detecting the dynamic changes across the network. The dynamic changes in the network can be in the form of topological changes, congestions, links failure etc. Therefore, it becomes a challenge to develop and implement dynamic routing protocols that fulfills the network requirements. Hence, each routing protocol has its own characteristics such as convergence activity, routing metric, routing table etc. and will perform differently in various network environments. This paper presents a comprehensive study of static and dynamic routing, along with dynamic routing protocols. Experiments that are conducted under various network limitations are presented using the OPNET tool. The performance of each of dynamic routing protocols are monitored and explained in the form of simulated results using network parameters. The results are analyzed, in order to provide a clear understanding of each protocol performance for the selection of the proper protocol for a given network environment.

NetMiner-an ensemble pipeline for building genome-wide and high-quality gene co-expression network using massive-scale RNA-seq samples.

PubMed

Yu, Hua; Jiao, Bingke; Lu, Lu; Wang, Pengfei; Chen, Shuangcheng; Liang, Chengzhi; Liu, Wei

2018-01-01

Accurately reconstructing gene co-expression network is of great importance for uncovering the genetic architecture underlying complex and various phenotypes. The recent availability of high-throughput RNA-seq sequencing has made genome-wide detecting and quantifying of the novel, rare and low-abundance transcripts practical. However, its potential merits in reconstructing gene co-expression network have still not been well explored. Using massive-scale RNA-seq samples, we have designed an ensemble pipeline, called NetMiner, for building genome-scale and high-quality Gene Co-expression Network (GCN) by integrating three frequently used inference algorithms. We constructed a RNA-seq-based GCN in one species of monocot rice. The quality of network obtained by our method was verified and evaluated by the curated gene functional association data sets, which obviously outperformed each single method. In addition, the powerful capability of network for associating genes with functions and agronomic traits was shown by enrichment analysis and case studies. In particular, we demonstrated the potential value of our proposed method to predict the biological roles of unknown protein-coding genes, long non-coding RNA (lncRNA) genes and circular RNA (circRNA) genes. Our results provided a valuable and highly reliable data source to select key candidate genes for subsequent experimental validation. To facilitate identification of novel genes regulating important biological processes and phenotypes in other plants or animals, we have published the source code of NetMiner, making it freely available at https://github.com/czllab/NetMiner.

Nonequilibrium parton dynamics in the strongly interacting QGP

NASA Astrophysics Data System (ADS)

Cassing, W.; Bratkovskaya, E. L.

2011-12-01

The dynamics of partons, hadrons and strings in relativistic nucleus-nucleus collisions is analyzed within the novel Parton-Hadron-String Dynamics (PHSD) transport approach, which is based on a dynamical quasiparticle model for partons (DQPM) matched to reproduce recent lattice-QCD results—including the partonic equation of state—in thermodynamic equilibrium. The transition from partonic to hadronic degrees of freedom is described by covariant transition rates for the fusion of quark-antiquark pairs or three quarks (antiquarks), respectively, obeying flavor current-conservation, color neutrality as well as energymomentum conservation. Since the dynamical quarks and antiquarks become very massive close to the phase transition, the formed resonant `pre-hadronic' color-dipole states ( q bar q or qqq) are of high invariant mass, too, and sequentially decay to the groundstate meson and baryon octets increasing the total entropy. When applying the PHSD approach to Pb + Pb colllisions at 158 A GeV we find a significant effect of the partonic phase on the production of multi-strange antibaryons due to a slightly enhanced s bar q pair production from massive time-like gluon decay and a larger formation of antibaryons in the hadronization process.

Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error.

PubMed

Mohsenizadeh, Daniel N; Dehghannasiri, Roozbeh; Dougherty, Edward R

2018-01-01

In systems biology, network models are often used to study interactions among cellular components, a salient aim being to develop drugs and therapeutic mechanisms to change the dynamical behavior of the network to avoid undesirable phenotypes. Owing to limited knowledge, model uncertainty is commonplace and network dynamics can be updated in different ways, thereby giving multiple dynamic trajectories, that is, dynamics uncertainty. In this manuscript, we propose an experimental design method that can effectively reduce the dynamics uncertainty and improve performance in an interaction-based network. Both dynamics uncertainty and experimental error are quantified with respect to the modeling objective, herein, therapeutic intervention. The aim of experimental design is to select among a set of candidate experiments the experiment whose outcome, when applied to the network model, maximally reduces the dynamics uncertainty pertinent to the intervention objective.

DICE/ColDICE: 6D collisionless phase space hydrodynamics using a lagrangian tesselation

NASA Astrophysics Data System (ADS)

Sousbie, Thierry

2018-01-01

DICE is a C++ template library designed to solve collisionless fluid dynamics in 6D phase space using massively parallel supercomputers via an hybrid OpenMP/MPI parallelization. ColDICE, based on DICE, implements a cosmological and physical VLASOV-POISSON solver for cold systems such as dark matter (CDM) dynamics.

Physical conditions, dynamics and mass distribution in the center of the galaxy

NASA Technical Reports Server (NTRS)

Genzel, R.; Townes, C. H.

1987-01-01

Investigations of the central 10 pc of the Galaxy, and conclusions on energetics, dynamics, and mass distribution derived from X and gamma ray measurements and from infrared and microwave studies, especially from spectroscopy, high resolution imaging, and interferometry are reviewed. Evidence for and against a massive black hole is analyzed.

Parallel computational fluid dynamics '91; Conference Proceedings, Stuttgart, Germany, Jun. 10-12, 1991

NASA Technical Reports Server (NTRS)

Reinsch, K. G. (Editor); Schmidt, W. (Editor); Ecer, A. (Editor); Haeuser, Jochem (Editor); Periaux, J. (Editor)

1992-01-01

A conference was held on parallel computational fluid dynamics and produced related papers. Topics discussed in these papers include: parallel implicit and explicit solvers for compressible flow, parallel computational techniques for Euler and Navier-Stokes equations, grid generation techniques for parallel computers, and aerodynamic simulation om massively parallel systems.

Neural dynamics in reconfigurable silicon.

PubMed

Basu, A; Ramakrishnan, S; Petre, C; Koziol, S; Brink, S; Hasler, P E

2010-10-01

A neuromorphic analog chip is presented that is capable of implementing massively parallel neural computations while retaining the programmability of digital systems. We show measurements from neurons with Hopf bifurcations and integrate and fire neurons, excitatory and inhibitory synapses, passive dendrite cables, coupled spiking neurons, and central pattern generators implemented on the chip. This chip provides a platform for not only simulating detailed neuron dynamics but also uses the same to interface with actual cells in applications such as a dynamic clamp. There are 28 computational analog blocks (CAB), each consisting of ion channels with tunable parameters, synapses, winner-take-all elements, current sources, transconductance amplifiers, and capacitors. There are four other CABs which have programmable bias generators. The programmability is achieved using floating gate transistors with on-chip programming control. The switch matrix for interconnecting the components in CABs also consists of floating-gate transistors. Emphasis is placed on replicating the detailed dynamics of computational neural models. Massive computational area efficiency is obtained by using the reconfigurable interconnect as synaptic weights, resulting in more than 50 000 possible 9-b accurate synapses in 9 mm(2).

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Online learning: the brave new world of massive open online courses and the role of the health librarian.

PubMed

Spring, Hannah

2016-03-01

In a wired, virtual and information rich society, MOOCs (Massive Open Online Courses) are leading us into a brave new world in which their key role is to support lifelong networked learning. This feature looks at the broad role of MOOCs and considers them within the context of health, and health librarianship. In particular, it provides examples of where health librarians have developed MOOCs and what opportunities there are in the future for health librarians to collaborate in the development and delivery of health MOOCs. H.S. © 2016 Health Libraries Group.

Galaxy And Mass Assembly (GAMA): A “No Smoking” Zone for Giant Elliptical Galaxies?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khosroshahi, Habib G.; Raouf, Mojtaba; Miraghaei, Halime

We study the radio emission of the most massive galaxies in a sample of dynamically relaxed and unrelaxed galaxy groups from the Galaxy and Mass Assembly survey. The dynamical state of the group is defined by the stellar dominance of the brightest group galaxy (BGG), e.g., the luminosity gap between the two most luminous members, and the offset between the position of the BGG and the luminosity centroid of the group. We find that the radio luminosity of the largest galaxy in the group strongly depends on its environment, such that the BGGs in dynamically young (evolving) groups are anmore » order of magnitude more luminous in the radio than those with a similar stellar mass but residing in dynamically old (relaxed) groups. This observation has been successfully reproduced by a newly developed semi-analytic model that allows us to explore the various causes of these findings. We find that the fraction of radio-loud BGGs in the observed dynamically young groups is ∼2 times that of the dynamically old groups. We discuss the implications of this observational constraint on the central galaxy properties in the context of galaxy mergers and the super massive black hole accretion rate.« less

Dynamical Stability of Imaged Planetary Systems in Formation: Application to HL Tau

NASA Astrophysics Data System (ADS)

Tamayo, D.; Triaud, A. H. M. J.; Menou, K.; Rein, H.

2015-06-01

A recent Atacama Large Millimeter/Submillimeter Array image revealed several concentric gaps in the protoplanetary disk surrounding the young star HL Tau. We consider the hypothesis that these gaps are carved by planets, and present a general framework for understanding the dynamical stability of such systems over typical disk lifetimes, providing estimates for the maximum planetary masses. We collect these easily evaluated constraints into a workflow that can help guide the design and interpretation of new observational campaigns and numerical simulations of gap opening in such systems. We argue that the locations of resonances should be significantly shifted in massive disks like HL Tau, and that theoretical uncertainties in the exact offset, together with observational errors, imply a large uncertainty in the dynamical state and stability in such disks. This presents an important barrier to using systems like HL Tau as a proxy for the initial conditions following planet formation. An important observational avenue to breaking this degeneracy is to search for eccentric gaps, which could implicate resonantly interacting planets. Unfortunately, massive disks like HL Tau should induce swift pericenter precession that would smear out any such eccentric features of planetary origin. This motivates pushing toward more typical, less massive disks. For a nominal non-resonant model of the HL Tau system with five planets, we find a maximum mass for the outer three bodies of approximately 2 Neptune masses. In a resonant configuration, these planets can reach at least the mass of Saturn. The inner two planets’ masses are unconstrained by dynamical stability arguments.

Massively Parallel Simulations of Diffusion in Dense Polymeric Structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faulon, Jean-Loup, Wilcox, R.T.

1997-11-01

An original computational technique to generate close-to-equilibrium dense polymeric structures is proposed. Diffusion of small gases are studied on the equilibrated structures using massively parallel molecular dynamics simulations running on the Intel Teraflops (9216 Pentium Pro processors) and Intel Paragon(1840 processors). Compared to the current state-of-the-art equilibration methods this new technique appears to be faster by some orders of magnitude.The main advantage of the technique is that one can circumvent the bottlenecks in configuration space that inhibit relaxation in molecular dynamics simulations. The technique is based on the fact that tetravalent atoms (such as carbon and silicon) fit in themore » center of a regular tetrahedron and that regular tetrahedrons can be used to mesh the three-dimensional space. Thus, the problem of polymer equilibration described by continuous equations in molecular dynamics is reduced to a discrete problem where solutions are approximated by simple algorithms. Practical modeling applications include the constructing of butyl rubber and ethylene-propylene-dimer-monomer (EPDM) models for oxygen and water diffusion calculations. Butyl and EPDM are used in O-ring systems and serve as sealing joints in many manufactured objects. Diffusion coefficients of small gases have been measured experimentally on both polymeric systems, and in general the diffusion coefficients in EPDM are an order of magnitude larger than in butyl. In order to better understand the diffusion phenomena, 10, 000 atoms models were generated and equilibrated for butyl and EPDM. The models were submitted to a massively parallel molecular dynamics simulation to monitor the trajectories of the diffusing species.« less

Ultrascalable petaflop parallel supercomputer

DOEpatents

Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Chiu, George [Cross River, NY; Cipolla, Thomas M [Katonah, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Hall, Shawn [Pleasantville, NY; Haring, Rudolf A [Cortlandt Manor, NY; Heidelberger, Philip [Cortlandt Manor, NY; Kopcsay, Gerard V [Yorktown Heights, NY; Ohmacht, Martin [Yorktown Heights, NY; Salapura, Valentina [Chappaqua, NY; Sugavanam, Krishnan [Mahopac, NY; Takken, Todd [Brewster, NY

2010-07-20

A massively parallel supercomputer of petaOPS-scale includes node architectures based upon System-On-a-Chip technology, where each processing node comprises a single Application Specific Integrated Circuit (ASIC) having up to four processing elements. The ASIC nodes are interconnected by multiple independent networks that optimally maximize the throughput of packet communications between nodes with minimal latency. The multiple networks may include three high-speed networks for parallel algorithm message passing including a Torus, collective network, and a Global Asynchronous network that provides global barrier and notification functions. These multiple independent networks may be collaboratively or independently utilized according to the needs or phases of an algorithm for optimizing algorithm processing performance. The use of a DMA engine is provided to facilitate message passing among the nodes without the expenditure of processing resources at the node.

Forming spectroscopic massive protobinaries by disc fragmentation

NASA Astrophysics Data System (ADS)

Meyer, D. M.-A.; Kuiper, R.; Kley, W.; Johnston, K. G.; Vorobyov, E.

2018-01-01

The surroundings of massive protostars constitute an accretion disc which has numerically been shown to be subject to fragmentation and responsible for luminous accretion-driven outbursts. Moreover, it is suspected to produce close binary companions which will later strongly influence the star's future evolution in the Hertzsprung-Russel diagram. We present three-dimensional gravitation-radiation-hydrodynamic numerical simulations of 100 M⊙ pre-stellar cores. We find that accretion discs of young massive stars violently fragment without preventing the (highly variable) accretion of gaseous clumps on to the protostars. While acquiring the characteristics of a nascent low-mass companion, some disc fragments migrate on to the central massive protostar with dynamical properties showing that its final Keplerian orbit is close enough to constitute a close massive protobinary system, having a young high- and a low-mass components. We conclude on the viability of the disc fragmentation channel for the formation of such short-period binaries, and that both processes - close massive binary formation and accretion bursts - may happen at the same time. FU-Orionis-type bursts, such as observed in the young high-mass star S255IR-NIRS3, may not only indicate ongoing disc fragmentation, but also be considered as a tracer for the formation of close massive binaries - progenitors of the subsequent massive spectroscopic binaries - once the high-mass component of the system will enter the main-sequence phase of its evolution. Finally, we investigate the Atacama Large (sub-)Millimeter Array observability of the disc fragments.

Runaway Massive Stars from R136: VFTS 682 is Very Likely a "Slow Runaway"

NASA Astrophysics Data System (ADS)

Banerjee, Sambaran; Kroupa, Pavel; Oh, Seungkyung

2012-02-01

We conduct a theoretical study on the ejection of runaway massive stars from R136—the central massive, starburst cluster in the 30 Doradus complex of the Large Magellanic Cloud. Specifically, we investigate the possibility of the very massive star (VMS) VFTS 682 being a runaway member of R136. Recent observations of the above VMS, by virtue of its isolated location and its moderate peculiar motion, have raised the fundamental question of whether isolated massive star formation is indeed possible. We perform the first realistic N-body computations of fully mass-segregated R136-type star clusters in which all the massive stars are in primordial binary systems. These calculations confirm that the dynamical ejection of a VMS from an R136-like cluster, with kinematic properties similar to those of VFTS 682, is common. Hence, the conjecture of isolated massive star formation is unnecessary to account for this VMS. Our results are also quite consistent with the ejection of 30 Dor 016, another suspected runaway VMS from R136. We further note that during the clusters' evolution, mergers of massive binaries produce a few single stars per cluster with masses significantly exceeding the canonical upper limit of 150 M ⊙. The observations of such single super-canonical stars in R136, therefore, do not imply an initial mass function with an upper limit greatly exceeding the accepted canonical 150 M ⊙ limit, as has been suggested recently, and they are consistent with the canonical upper limit.

Compressive Network Analysis

PubMed Central

Jiang, Xiaoye; Yao, Yuan; Liu, Han; Guibas, Leonidas

2014-01-01

Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets. PMID:25620806

Massively parallel processor networks with optical express channels

DOEpatents

Deri, R.J.; Brooks, E.D. III; Haigh, R.E.; DeGroot, A.J.

1999-08-24

An optical method for separating and routing local and express channel data comprises interconnecting the nodes in a network with fiber optic cables. A single fiber optic cable carries both express channel traffic and local channel traffic, e.g., in a massively parallel processor (MPP) network. Express channel traffic is placed on, or filtered from, the fiber optic cable at a light frequency or a color different from that of the local channel traffic. The express channel traffic is thus placed on a light carrier that skips over the local intermediate nodes one-by-one by reflecting off of selective mirrors placed at each local node. The local-channel-traffic light carriers pass through the selective mirrors and are not reflected. A single fiber optic cable can thus be threaded throughout a three-dimensional matrix of nodes with the x,y,z directions of propagation encoded by the color of the respective light carriers for both local and express channel traffic. Thus frequency division multiple access is used to hierarchically separate the local and express channels to eliminate the bucket brigade latencies that would otherwise result if the express traffic had to hop between every local node to reach its ultimate destination. 3 figs.

Massively parallel processor networks with optical express channels

DOEpatents

Deri, Robert J.; Brooks, III, Eugene D.; Haigh, Ronald E.; DeGroot, Anthony J.

1999-01-01

An optical method for separating and routing local and express channel data comprises interconnecting the nodes in a network with fiber optic cables. A single fiber optic cable carries both express channel traffic and local channel traffic, e.g., in a massively parallel processor (MPP) network. Express channel traffic is placed on, or filtered from, the fiber optic cable at a light frequency or a color different from that of the local channel traffic. The express channel traffic is thus placed on a light carrier that skips over the local intermediate nodes one-by-one by reflecting off of selective mirrors placed at each local node. The local-channel-traffic light carriers pass through the selective mirrors and are not reflected. A single fiber optic cable can thus be threaded throughout a three-dimensional matrix of nodes with the x,y,z directions of propagation encoded by the color of the respective light carriers for both local and express channel traffic. Thus frequency division multiple access is used to hierarchically separate the local and express channels to eliminate the bucket brigade latencies that would otherwise result if the express traffic had to hop between every local node to reach its ultimate destination.

Toward Petascale Biologically Plausible Neural Networks

NASA Astrophysics Data System (ADS)

Long, Lyle

This talk will describe an approach to achieving petascale neural networks. Artificial intelligence has been oversold for many decades. Computers in the beginning could only do about 16,000 operations per second. Computer processing power, however, has been doubling every two years thanks to Moore's law, and growing even faster due to massively parallel architectures. Finally, 60 years after the first AI conference we have computers on the order of the performance of the human brain (1016 operations per second). The main issues now are algorithms, software, and learning. We have excellent models of neurons, such as the Hodgkin-Huxley model, but we do not know how the human neurons are wired together. With careful attention to efficient parallel computing, event-driven programming, table lookups, and memory minimization massive scale simulations can be performed. The code that will be described was written in C + + and uses the Message Passing Interface (MPI). It uses the full Hodgkin-Huxley neuron model, not a simplified model. It also allows arbitrary network structures (deep, recurrent, convolutional, all-to-all, etc.). The code is scalable, and has, so far, been tested on up to 2,048 processor cores using 107 neurons and 109 synapses.

Genetic algorithm based task reordering to improve the performance of batch scheduled massively parallel scientific applications

DOE PAGES

Sankaran, Ramanan; Angel, Jordan; Brown, W. Michael

2015-04-08

The growth in size of networked high performance computers along with novel accelerator-based node architectures has further emphasized the importance of communication efficiency in high performance computing. The world's largest high performance computers are usually operated as shared user facilities due to the costs of acquisition and operation. Applications are scheduled for execution in a shared environment and are placed on nodes that are not necessarily contiguous on the interconnect. Furthermore, the placement of tasks on the nodes allocated by the scheduler is sub-optimal, leading to performance loss and variability. Here, we investigate the impact of task placement on themore » performance of two massively parallel application codes on the Titan supercomputer, a turbulent combustion flow solver (S3D) and a molecular dynamics code (LAMMPS). Benchmark studies show a significant deviation from ideal weak scaling and variability in performance. The inter-task communication distance was determined to be one of the significant contributors to the performance degradation and variability. A genetic algorithm-based parallel optimization technique was used to optimize the task ordering. This technique provides an improved placement of the tasks on the nodes, taking into account the application's communication topology and the system interconnect topology. As a result, application benchmarks after task reordering through genetic algorithm show a significant improvement in performance and reduction in variability, therefore enabling the applications to achieve better time to solution and scalability on Titan during production.« less

Geomorphic effects of large debris flows and flash floods, northern Venezuela, 1999

USGS Publications Warehouse

Larsen, M.C.; Wieczorek, G.F.

2006-01-01

A rare, high-magnitude storm in northern Venezuela in December 1999 triggered debris flows and flash floods, and caused one of the worst natural disasters in the recorded history of the Americas. Some 15,000 people were killed. The debris flows and floods inundated coastal communities on alluvial fans at the mouths of a coastal mountain drainage network and destroyed property estimated at more than $2 billion. Landslides were abundant and widespread on steep slopes within areas underlain by schist and gneiss from near the coast to slightly over the crest of the mountain range. Some hillsides were entirely denuded by single or coalescing failures, which formed massive debris flows in river channels flowing out onto densely populated alluvial fans at the coast. The massive amount of sediment derived from 24 watersheds along 50 km of the coast during the storm and deposited on alluvial fans and beaches has been estimated at 15 to 20 million m3. Sediment yield for the 1999 storm from the approximately 200 km2 drainage area of watersheds upstream of the alluvial fans was as much as 100,000 m3/km2. Rapid economic development in this dynamic geomorphic environment close to the capital city of Caracas, in combination with a severe rain storm, resulted in the death of approximately 5% of the population (300,000 total prior to the storm) in the northern Venezuelan state of Vargas. ?? 2006 Gebru??der Borntraeger.

Dynamic vs. static social networks in models of parasite transmission: predicting Cryptosporidium spread in wild lemurs.

PubMed

Springer, Andrea; Kappeler, Peter M; Nunn, Charles L

2017-05-01

Social networks provide an established tool to implement heterogeneous contact structures in epidemiological models. Dynamic temporal changes in contact structure and ranging behaviour of wildlife may impact disease dynamics. A consensus has yet to emerge, however, concerning the conditions in which network dynamics impact model outcomes, as compared to static approximations that average contact rates over longer time periods. Furthermore, as many pathogens can be transmitted both environmentally and via close contact, it is important to investigate the relative influence of both transmission routes in real-world populations. Here, we use empirically derived networks from a population of wild primates, Verreaux's sifakas (Propithecus verreauxi), and simulated networks to investigate pathogen spread in dynamic vs. static social networks. First, we constructed a susceptible-exposed-infected-recovered model of Cryptosporidium spread in wild Verreaux's sifakas. We incorporated social and environmental transmission routes and parameterized the model for two different climatic seasons. Second, we used simulated networks and greater variation in epidemiological parameters to investigate the conditions in which dynamic networks produce larger outbreak sizes than static networks. We found that average outbreak size of Cryptosporidium infections in sifakas was larger when the disease was introduced in the dry season than in the wet season, driven by an increase in home range overlap towards the end of the dry season. Regardless of season, dynamic networks always produced larger average outbreak sizes than static networks. Larger outbreaks in dynamic models based on simulated networks occurred especially when the probability of transmission and recovery were low. Variation in tie strength in the dynamic networks also had a major impact on outbreak size, while network modularity had a weaker influence than epidemiological parameters that determine transmission and recovery. Our study adds to emerging evidence that dynamic networks can change predictions of disease dynamics, especially if the disease shows low transmissibility and a long infectious period, and when environmental conditions lead to enhanced between-group contact after an infectious agent has been introduced. © 2016 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Chase Qishi; Zhu, Michelle Mengxia

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models featuremore » diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.« less

Research on dynamic routing mechanisms in wireless sensor networks.

PubMed

Zhao, A Q; Weng, Y N; Lu, Y; Liu, C Y

2014-01-01

WirelessHART is the most widely applied standard in wireless sensor networks nowadays. However, it does not provide any dynamic routing mechanism, which is important for the reliability and robustness of the wireless network applications. In this paper, a collection tree protocol based, dynamic routing mechanism was proposed for WirelessHART network. The dynamic routing mechanism was evaluated through several simulation experiments in three aspects: time for generating the topology, link quality, and stability of network. Besides, the data transmission efficiency of this routing mechanism was analyzed. The simulation and evaluation results show that this mechanism can act as a dynamic routing mechanism for the TDMA-based wireless sensor network.

The Number Density of Quiescent Compact Galaxies at Intermediate Redshift

NASA Astrophysics Data System (ADS)

Damjanov, Ivana; Hwang, Ho Seong; Geller, Margaret J.; Chilingarian, Igor

2014-09-01

Massive compact systems at 0.2 < z < 0.6 are the missing link between the predominantly compact population of massive quiescent galaxies at high redshift and their analogs and relics in the local volume. The evolution in number density of these extreme objects over cosmic time is the crucial constraining factor for the models of massive galaxy assembly. We select a large sample of ~200 intermediate-redshift massive compacts from the Baryon Oscillation Spectroscopic Survey (BOSS) spectroscopy by identifying point-like Sloan Digital Sky Survey photometric sources with spectroscopic signatures of evolved redshifted galaxies. A subset of our targets have publicly available high-resolution ground-based images that we use to augment the dynamical and stellar population properties of these systems by their structural parameters. We confirm that all BOSS compact candidates are as compact as their high-redshift massive counterparts and less than half the size of similarly massive systems at z ~ 0. We use the completeness-corrected numbers of BOSS compacts to compute lower limits on their number densities in narrow redshift bins spanning the range of our sample. The abundance of extremely dense quiescent galaxies at 0.2 < z < 0.6 is in excellent agreement with the number densities of these systems at high redshift. Our lower limits support the models of massive galaxy assembly through a series of minor mergers over the redshift range 0 < z < 2.

Optical RRH working in an all-optical fronthaul network

NASA Astrophysics Data System (ADS)

Zakrzewski, Zbigniew

2017-12-01

The paper presents an example of an optical RRH (Remote Radio Head) design, which is equipped with photonic components for direct connection to an all-optical network. The features that can be fulfilled by an all-optical network are indicated to support future 5G mobile networks. The demand for optical bandwidth in fronthaul/midhaul distribution network links, working in D-RoF and A-RoF formats was performed. The increase in demand is due to the very large traffic generated by the Optical Massive-MIMO RRH/RRU will work in format of an Active-Distributed Antenna System (A-DAS). An exemplary next-generation mobile network that will utilize O-RRH and an all-optical backbone is presented. All components of presented network will work in the Centralized/Cloud Radio Access Network (C-RAN) architecture, which is achievable by control with the use of the OpenFlow (OF).

Honeycomb: Visual Analysis of Large Scale Social Networks

NASA Astrophysics Data System (ADS)

van Ham, Frank; Schulz, Hans-Jörg; Dimicco, Joan M.

The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation.

Social Networking—Another Breach In The Wall

NASA Astrophysics Data System (ADS)

Bamnote, Gajendra; Patil, Gajendra; Shejole, Amol

2010-11-01

With the increasing popularity of social networks like Facebook and MySpace, such sites have lately become the favourite destinations for spammers and attackers. Social networks have experienced complex social engineering attacks, massive spam and aggressive malware distribution in the recent past. This paper presents a practical case study of social engineering, malware distribution and phishing attacks against social networking sites that are identified over last few months. It is explained how private data of the users are exposed to attackers and how easily their privacy is compromised as a result of these attacks and their own careless behaviour.

Massive quiver matrix models for massive charged particles in AdS

DOE PAGES

Asplund, Curtis T.; Denef, Frederik; Dzienkowski, Eric

2016-01-11

Here, we present a new class of N = 4 supersymmetric quiver matrix models and argue that it describes the stringy low-energy dynamics of internally wrapped D-branes in four-dimensional anti-de Sitter (AdS) flux compactifications. The Lagrangians of these models differ from previously studied quiver matrix models by the presence of mass terms, associated with the AdS gravitational potential, as well as additional terms dictated by supersymmetry. These give rise to dynamical phenomena typically associated with the presence of fluxes, such as fuzzy membranes, internal cyclotron motion and the appearance of confining strings. We also show how these models can bemore » obtained by dimensional reduction of four-dimensional supersymmetric quiver gauge theories on a three-sphere.« less

Final Report: Sampling-Based Algorithms for Estimating Structure in Big Data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matulef, Kevin Michael

The purpose of this project was to develop sampling-based algorithms to discover hidden struc- ture in massive data sets. Inferring structure in large data sets is an increasingly common task in many critical national security applications. These data sets come from myriad sources, such as network traffic, sensor data, and data generated by large-scale simulations. They are often so large that traditional data mining techniques are time consuming or even infeasible. To address this problem, we focus on a class of algorithms that do not compute an exact answer, but instead use sampling to compute an approximate answer using fewermore » resources. The particular class of algorithms that we focus on are streaming algorithms , so called because they are designed to handle high-throughput streams of data. Streaming algorithms have only a small amount of working storage - much less than the size of the full data stream - so they must necessarily use sampling to approximate the correct answer. We present two results: * A streaming algorithm called HyperHeadTail , that estimates the degree distribution of a graph (i.e., the distribution of the number of connections for each node in a network). The degree distribution is a fundamental graph property, but prior work on estimating the degree distribution in a streaming setting was impractical for many real-world application. We improve upon prior work by developing an algorithm that can handle streams with repeated edges, and graph structures that evolve over time. * An algorithm for the task of maintaining a weighted subsample of items in a stream, when the items must be sampled according to their weight, and the weights are dynamically changing. To our knowledge, this is the first such algorithm designed for dynamically evolving weights. We expect it may be useful as a building block for other streaming algorithms on dynamic data sets.« less

Tachyon field non-minimally coupled to massive neutrino matter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahmad, Safia; Myrzakulov, Nurgissa; Myrzakulov, R., E-mail: safia@ctp-jamia.res.in, E-mail: nmyrzakulov@gmail.com, E-mail: rmyrzakulov@gmail.com

2016-07-01

In this paper, we consider rolling tachyon, with steep run-away type of potentials non-minimally coupled to massive neutrino matter. The coupling dynamically builds up at late times as neutrino matter turns non-relativistic. In case of scaling and string inspired potentials, we have shown that non-minimal coupling leads to minimum in the field potential. Given a suitable choice of model parameters, it is shown to give rise to late-time acceleration with the desired equation of state.

Reconstruction of network topology using status-time-series data

NASA Astrophysics Data System (ADS)

Pandey, Pradumn Kumar; Badarla, Venkataramana

2018-01-01

Uncovering the heterogeneous connection pattern of a networked system from the available status-time-series (STS) data of a dynamical process on the network is of great interest in network science and known as a reverse engineering problem. Dynamical processes on a network are affected by the structure of the network. The dependency between the diffusion dynamics and structure of the network can be utilized to retrieve the connection pattern from the diffusion data. Information of the network structure can help to devise the control of dynamics on the network. In this paper, we consider the problem of network reconstruction from the available status-time-series (STS) data using matrix analysis. The proposed method of network reconstruction from the STS data is tested successfully under susceptible-infected-susceptible (SIS) diffusion dynamics on real-world and computer-generated benchmark networks. High accuracy and efficiency of the proposed reconstruction procedure from the status-time-series data define the novelty of the method. Our proposed method outperforms compressed sensing theory (CST) based method of network reconstruction using STS data. Further, the same procedure of network reconstruction is applied to the weighted networks. The ordering of the edges in the weighted networks is identified with high accuracy.

Dynamic tubulation of mitochondria drives mitochondrial network formation.

PubMed

Wang, Chong; Du, Wanqing; Su, Qian Peter; Zhu, Mingli; Feng, Peiyuan; Li, Ying; Zhou, Yichen; Mi, Na; Zhu, Yueyao; Jiang, Dong; Zhang, Senyan; Zhang, Zerui; Sun, Yujie; Yu, Li

2015-10-01

Mitochondria form networks. Formation of mitochondrial networks is important for maintaining mitochondrial DNA integrity and interchanging mitochondrial material, whereas disruption of the mitochondrial network affects mitochondrial functions. According to the current view, mitochondrial networks are formed by fusion of individual mitochondria. Here, we report a new mechanism for formation of mitochondrial networks through KIF5B-mediated dynamic tubulation of mitochondria. We found that KIF5B pulls thin, highly dynamic tubules out of mitochondria. Fusion of these dynamic tubules, which is mediated by mitofusins, gives rise to the mitochondrial network. We further demonstrated that dynamic tubulation and fusion is sufficient for mitochondrial network formation, by reconstituting mitochondrial networks in vitro using purified fusion-competent mitochondria, recombinant KIF5B, and polymerized microtubules. Interestingly, KIF5B only controls network formation in the peripheral zone of the cell, indicating that the mitochondrial network is divided into subzones, which may be constructed by different mechanisms. Our data not only uncover an essential mechanism for mitochondrial network formation, but also reveal that different parts of the mitochondrial network are formed by different mechanisms.

Graph fibrations and symmetries of network dynamics

NASA Astrophysics Data System (ADS)

Nijholt, Eddie; Rink, Bob; Sanders, Jan

2016-11-01

Dynamical systems with a network structure can display remarkable phenomena such as synchronisation and anomalous synchrony breaking. A methodology for classifying patterns of synchrony in networks was developed by Golubitsky and Stewart. They showed that the robustly synchronous dynamics of a network is determined by its quotient networks. This result was recently reformulated by DeVille and Lerman, who pointed out that the reduction from a network to a quotient is an example of a graph fibration. The current paper exploits this observation and demonstrates the importance of self-fibrations of network graphs. Self-fibrations give rise to symmetries in the dynamics of a network. We show that every network admits a lift with a semigroup or semigroupoid of self-fibrations. The resulting symmetries impact the global dynamics of the network and can therefore be used to explain and predict generic scenarios for synchrony breaking. Also, when the network has a trivial symmetry groupoid, then every robust synchrony in the lift is determined by symmetry. We finish this paper with a discussion of networks with interior symmetries and nonhomogeneous networks.

Three pillars for achieving quantum mechanical molecular dynamics simulations of huge systems: Divide-and-conquer, density-functional tight-binding, and massively parallel computation.

PubMed

Nishizawa, Hiroaki; Nishimura, Yoshifumi; Kobayashi, Masato; Irle, Stephan; Nakai, Hiromi

2016-08-05

The linear-scaling divide-and-conquer (DC) quantum chemical methodology is applied to the density-functional tight-binding (DFTB) theory to develop a massively parallel program that achieves on-the-fly molecular reaction dynamics simulations of huge systems from scratch. The functions to perform large scale geometry optimization and molecular dynamics with DC-DFTB potential energy surface are implemented to the program called DC-DFTB-K. A novel interpolation-based algorithm is developed for parallelizing the determination of the Fermi level in the DC method. The performance of the DC-DFTB-K program is assessed using a laboratory computer and the K computer. Numerical tests show the high efficiency of the DC-DFTB-K program, a single-point energy gradient calculation of a one-million-atom system is completed within 60 s using 7290 nodes of the K computer. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Massive graviton dark matter with environment dependent mass: A natural explanation of the dark matter-baryon ratio

NASA Astrophysics Data System (ADS)

Aoki, Katsuki; Mukohyama, Shinji

2017-11-01

We propose a scenario that can naturally explain the observed dark matter-baryon ratio in the context of bimetric theory with a chameleon field. We introduce two additional gravitational degrees of freedom, the massive graviton and the chameleon field, corresponding to dark matter and dark energy, respectively. The chameleon field is assumed to be nonminimally coupled to dark matter, i.e., the massive graviton, through the graviton mass terms. We find that the dark matter-baryon ratio is dynamically adjusted to the observed value due to the energy transfer by the chameleon field. As a result, the model can explain the observed dark matter-baryon ratio independently from the initial abundance of them.

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

PubMed Central

Canova, Carlos; Denker, Michael; Gerstein, George; Helias, Moritz

2016-01-01

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity. PMID:27420734

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

NASA Astrophysics Data System (ADS)

Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

2016-05-01

Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

Asynchronous networks: modularization of dynamics theorem

NASA Astrophysics Data System (ADS)

Bick, Christian; Field, Michael

2017-02-01

Building on the first part of this paper, we develop the theory of functional asynchronous networks. We show that a large class of functional asynchronous networks can be (uniquely) represented as feedforward networks connecting events or dynamical modules. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network: the modularization of dynamics theorem. We give examples to illustrate the main results.

Dynamic reconfiguration of frontal brain networks during executive cognition in humans

PubMed Central

Braun, Urs; Schäfer, Axel; Walter, Henrik; Erk, Susanne; Romanczuk-Seiferth, Nina; Haddad, Leila; Schweiger, Janina I.; Grimm, Oliver; Heinz, Andreas; Tost, Heike; Meyer-Lindenberg, Andreas; Bassett, Danielle S.

2015-01-01

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of “dynamic network neuroscience” to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the “n-back” task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes “network flexibility,” employs transient and heterogeneous connectivity between frontal systems, which we refer to as “integration.” Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia. PMID:26324898

Self-organization of complex networks as a dynamical system

NASA Astrophysics Data System (ADS)

Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio

2015-01-01

To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.

Dynamic information routing in complex networks

PubMed Central

Kirst, Christoph; Timme, Marc; Battaglia, Demian

2016-01-01

Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

Self-organization of complex networks as a dynamical system.

PubMed

Aoki, Takaaki; Yawata, Koichiro; Aoyagi, Toshio

2015-01-01

To understand the dynamics of real-world networks, we investigate a mathematical model of the interplay between the dynamics of random walkers on a weighted network and the link weights driven by a resource carried by the walkers. Our numerical studies reveal that, under suitable conditions, the co-evolving dynamics lead to the emergence of stationary power-law distributions of the resource and link weights, while the resource quantity at each node ceaselessly changes with time. We analyze the network organization as a deterministic dynamical system and find that the system exhibits multistability, with numerous fixed points, limit cycles, and chaotic states. The chaotic behavior of the system leads to the continual changes in the microscopic network dynamics in the absence of any external random noises. We conclude that the intrinsic interplay between the states of the nodes and network reformation constitutes a major factor in the vicissitudes of real-world networks.

Dynamics of Intersubject Brain Networks during Anxious Anticipation

PubMed Central

Najafi, Mahshid; Kinnison, Joshua; Pessoa, Luiz

2017-01-01

How do large-scale brain networks reorganize during the waxing and waning of anxious anticipation? Here, threat was dynamically modulated during human functional MRI as two circles slowly meandered on the screen; if they touched, an unpleasant shock was delivered. We employed intersubject correlation analysis, which allowed the investigation of network-level functional connectivity across brains, and sought to determine how network connectivity changed during periods of approach (circles moving closer) and periods of retreat (circles moving apart). Analysis of positive connection weights revealed that dynamic threat altered connectivity within and between the salience, executive, and task-negative networks. For example, dynamic functional connectivity increased within the salience network during approach and decreased during retreat. The opposite pattern was found for the functional connectivity between the salience and task-negative networks: decreases during approach and increases during approach. Functional connections between subcortical regions and the salience network also changed dynamically during approach and retreat periods. Subcortical regions exhibiting such changes included the putative periaqueductal gray, putative habenula, and putative bed nucleus of the stria terminalis. Additional analysis of negative functional connections revealed dynamic changes, too. For example, negative weights within the salience network decreased during approach and increased during retreat, opposite what was found for positive weights. Together, our findings unraveled dynamic features of functional connectivity of large-scale networks and subcortical regions across participants while threat levels varied continuously, and demonstrate the potential of characterizing emotional processing at the level of dynamic networks. PMID:29209184

Minimal Increase Network Coding for Dynamic Networks.

PubMed

Zhang, Guoyin; Fan, Xu; Wu, Yanxia

2016-01-01

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery.

Minimal Increase Network Coding for Dynamic Networks

PubMed Central

Wu, Yanxia

2016-01-01

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery. PMID:26867211

Measuring the potential of individual airports for pandemic spread over the world airline network.

PubMed

Lawyer, Glenn

2016-02-09

Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of this early stage variation can be explained by appropriately characterizing the local network topology surrounding an outbreak's debut location. Based on a model of the WAN derived from public data, we measure for each airport the expected force of infection (AEF) which a pandemic originating at that airport would generate, assuming an epidemic process which transmits from airport to airport via scheduled commercial flights. We observe, for a subset of world airports, the minimum transmission rate at which a disease becomes pandemically competent at each airport. We also observe, for a larger subset, the time until a pandemically competent outbreak achieves pandemic status given its debut location. Observations are generated using a highly sophisticated metapopulation reaction-diffusion simulator under a disease model known to well replicate the 2009 influenza pandemic. The robustness of the AEF measure to model misspecification is examined by degrading the underlying model WAN. AEF powerfully explains pandemic risk, showing correlation of 0.90 to the transmission level needed to give a disease pandemic competence, and correlation of 0.85 to the delay until an outbreak becomes a pandemic. The AEF is robust to model misspecification. For 97 % of airports, removing 15 % of airports from the model changes their AEF metric by less than 1 %. Appropriately summarizing the size, shape, and diversity of an airport's local neighborhood in the WAN accurately explains much of the macro-level stochasticity in pandemic outcomes.

Global structure of magnetorotationally turbulent protoplanetary discs

NASA Astrophysics Data System (ADS)

Flaig, M.; Ruoff, Patrick; Kley, W.; Kissmann, R.

2012-03-01

The aim of this paper is to investigate the spatial structure of a protoplanetary disc whose dynamics is governed by magnetorotational turbulence. We perform a series of local three-dimensional chemoradiative magnetohydrodynamic simulations located at different radii of a disc which is twice as massive as the standard minimum mass solar nebula of Hayashi. The ionization state of the disc is calculated by including collisional ionization, stellar X-rays, cosmic rays and the decay of radionuclides as ionization sources, and by solving a simplified chemical network which includes the effect of the absorption of free charges by μm-sized dust grains. In the region where the ionization is too low to ensure good coupling between matter and magnetic fields, a non-turbulent central 'dead zone' forms, which ranges approximately from a distance of 2 to 4 au from the central star. The approach taken in this work allows for the first time to derive the global spatial structure of a protoplanetary disc from a set of physically realistic numerical simulations.

Major component analysis of dynamic networks of physiologic organ interactions

NASA Astrophysics Data System (ADS)

Liu, Kang K. L.; Bartsch, Ronny P.; Ma, Qianli D. Y.; Ivanov, Plamen Ch

2015-09-01

The human organism is a complex network of interconnected organ systems, where the behavior of one system affects the dynamics of other systems. Identifying and quantifying dynamical networks of diverse physiologic systems under varied conditions is a challenge due to the complexity in the output dynamics of the individual systems and the transient and nonlinear characteristics of their coupling. We introduce a novel computational method based on the concept of time delay stability and major component analysis to investigate how organ systems interact as a network to coordinate their functions. We analyze a large database of continuously recorded multi-channel physiologic signals from healthy young subjects during night-time sleep. We identify a network of dynamic interactions between key physiologic systems in the human organism. Further, we find that each physiologic state is characterized by a distinct network structure with different relative contribution from individual organ systems to the global network dynamics. Specifically, we observe a gradual decrease in the strength of coupling of heart and respiration to the rest of the network with transition from wake to deep sleep, and in contrast, an increased relative contribution to network dynamics from chin and leg muscle tone and eye movement, demonstrating a robust association between network topology and physiologic function.

Remix as Professional Learning: Educators' Iterative Literacy Practice in CLMOOC

ERIC Educational Resources Information Center

Smith, Anna; West-Puckett, Stephanie; Cantrill, Christina; Zamora, Mia

2016-01-01

The Connected Learning Massive Open Online Collaboration (CLMOOC) is an online professional development experience designed as an openly networked, production-centered, participatory learning collaboration for educators. Addressing the paucity of research that investigates learning processes in MOOC experiences, this paper examines the situated…

MMOGs: Beyond the Wildest Imagination

ERIC Educational Resources Information Center

Gratch, Jonathan; Kelly, Janet

2009-01-01

Historically, Massive Multiplayer Online Games (MMOGs) have been used for entertainment purposes, but that is rapidly changing. Advances in computer technology and an enhanced perception of functionality have allowed MMOGs to combine with other technologies, such as networking and web-based learning, to provide educators with opportunities to…

Modeling Endoplasmic Reticulum Network Maintenance in a Plant Cell.

PubMed

Lin, Congping; White, Rhiannon R; Sparkes, Imogen; Ashwin, Peter

2017-07-11

The endoplasmic reticulum (ER) in plant cells forms a highly dynamic network of complex geometry. ER network morphology and dynamics are influenced by a number of biophysical processes, including filament/tubule tension, viscous forces, Brownian diffusion, and interactions with many other organelles and cytoskeletal elements. Previous studies have indicated that ER networks can be thought of as constrained minimal-length networks acted on by a variety of forces that perturb and/or remodel the network. Here, we study two specific biophysical processes involved in remodeling. One is the dynamic relaxation process involving a combination of tubule tension and viscous forces. The other is the rapid creation of cross-connection tubules by direct or indirect interactions with cytoskeletal elements. These processes are able to remodel the ER network: the first reduces network length and complexity whereas the second increases both. Using live cell imaging of ER network dynamics in tobacco leaf epidermal cells, we examine these processes on ER network dynamics. Away from regions of cytoplasmic streaming, we suggest that the dynamic network structure is a balance between the two processes, and we build an integrative model of the two processes for network remodeling. This model produces quantitatively similar ER networks to those observed in experiments. We use the model to explore the effect of parameter variation on statistical properties of the ER network. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Seafloor massive sulfide deposits support unique megafaunal assemblages: Implications for seabed mining and conservation.

PubMed

Boschen, Rachel E; Rowden, Ashley A; Clark, Malcolm R; Pallentin, Arne; Gardner, Jonathan P A

2016-04-01

Mining of seafloor massive sulfides (SMS) is imminent, but the ecology of assemblages at SMS deposits is poorly known. Proposed conservation strategies include protected areas to preserve biodiversity at risk from mining impacts. Determining site suitability requires biological characterisation of the mine site and protected area(s). Video survey of a proposed mine site and protected area off New Zealand revealed unique megafaunal assemblages at the mine site. Significant relationships were identified between assemblage structure and environmental conditions, including hydrothermal features. Unique assemblages occurred at both active and inactive chimneys and are particularly at risk from mining-related impacts. The occurrence of unique assemblages at the mine site suggests that the proposed protected area is insufficient alone and should instead form part of a network. These results provide support for including hydrothermally active and inactive features within networks of protected areas and emphasise the need for quantitative survey data of proposed sites. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Resilience and Controllability of Dynamic Collective Behaviors

PubMed Central

Komareji, Mohammad; Bouffanais, Roland

2013-01-01

The network paradigm is used to gain insight into the structural root causes of the resilience of consensus in dynamic collective behaviors, and to analyze the controllability of the swarm dynamics. Here we devise the dynamic signaling network which is the information transfer channel underpinning the swarm dynamics of the directed interagent connectivity based on a topological neighborhood of interactions. The study of the connectedness of the swarm signaling network reveals the profound relationship between group size and number of interacting neighbors, which is found to be in good agreement with field observations on flock of starlings [Ballerini et al. (2008) Proc. Natl. Acad. Sci. USA, 105: 1232]. Using a dynamical model, we generate dynamic collective behaviors enabling us to uncover that the swarm signaling network is a homogeneous clustered small-world network, thus facilitating emergent outcomes if connectedness is maintained. Resilience of the emergent consensus is tested by introducing exogenous environmental noise, which ultimately stresses how deeply intertwined are the swarm dynamics in the physical and network spaces. The availability of the signaling network allows us to analytically establish for the first time the number of driver agents necessary to fully control the swarm dynamics. PMID:24358209

On the number of different dynamics in Boolean networks with deterministic update schedules.

PubMed

Aracena, J; Demongeot, J; Fanchon, E; Montalva, M

2013-04-01

Deterministic Boolean networks are a type of discrete dynamical systems widely used in the modeling of genetic networks. The dynamics of such systems is characterized by the local activation functions and the update schedule, i.e., the order in which the nodes are updated. In this paper, we address the problem of knowing the different dynamics of a Boolean network when the update schedule is changed. We begin by proving that the problem of the existence of a pair of update schedules with different dynamics is NP-complete. However, we show that certain structural properties of the interaction diagraph are sufficient for guaranteeing distinct dynamics of a network. In [1] the authors define equivalence classes which have the property that all the update schedules of a given class yield the same dynamics. In order to determine the dynamics associated to a network, we develop an algorithm to efficiently enumerate the above equivalence classes by selecting a representative update schedule for each class with a minimum number of blocks. Finally, we run this algorithm on the well known Arabidopsis thaliana network to determine the full spectrum of its different dynamics. Copyright © 2013 Elsevier Inc. All rights reserved.

Entropy of dynamical social networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

2012-02-01

Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Barry Y.

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patternsmore » are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.« less

Neural networks for vertical microcode compaction

NASA Astrophysics Data System (ADS)

Chu, Pong P.

1992-09-01

Neural networks provide an alternative way to solve complex optimization problems. Instead of performing a program of instructions sequentially as in a traditional computer, neural network model explores many competing hypotheses simultaneously using its massively parallel net. The paper shows how to use the neural network approach to perform vertical micro-code compaction for a micro-programmed control unit. The compaction procedure includes two basic steps. The first step determines the compatibility classes and the second step selects a minimal subset to cover the control signals. Since the selection process is an NP- complete problem, to find an optimal solution is impractical. In this study, we employ a customized neural network to obtain the minimal subset. We first formalize this problem, and then define an `energy function' and map it to a two-layer fully connected neural network. The modified network has two types of neurons and can always obtain a valid solution.

Systems Proteomics for Translational Network Medicine

PubMed Central

Arrell, D. Kent; Terzic, Andre

2012-01-01

Universal principles underlying network science, and their ever-increasing applications in biomedicine, underscore the unprecedented capacity of systems biology based strategies to synthesize and resolve massive high throughput generated datasets. Enabling previously unattainable comprehension of biological complexity, systems approaches have accelerated progress in elucidating disease prediction, progression, and outcome. Applied to the spectrum of states spanning health and disease, network proteomics establishes a collation, integration, and prioritization algorithm to guide mapping and decoding of proteome landscapes from large-scale raw data. Providing unparalleled deconvolution of protein lists into global interactomes, integrative systems proteomics enables objective, multi-modal interpretation at molecular, pathway, and network scales, merging individual molecular components, their plurality of interactions, and functional contributions for systems comprehension. As such, network systems approaches are increasingly exploited for objective interpretation of cardiovascular proteomics studies. Here, we highlight network systems proteomic analysis pipelines for integration and biological interpretation through protein cartography, ontological categorization, pathway and functional enrichment and complex network analysis. PMID:22896016

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity.

PubMed

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-11-08

The increasing number of recording electrodes enhances the capability of capturing the network's cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity.

DebtRank-transparency: Controlling systemic risk in financial networks

PubMed Central

Thurner, Stefan; Poledna, Sebastian

2013-01-01

Nodes in a financial network, such as banks, cannot assess the true risks associated with lending to other nodes in the network, unless they have full information on the riskiness of all other nodes. These risks can be estimated by using network metrics (as DebtRank) of the interbank liability network. With a simple agent based model we show that systemic risk in financial networks can be drastically reduced by increasing transparency, i.e. making the DebtRank of individual banks visible to others, and by imposing a rule, that reduces interbank borrowing from systemically risky nodes. This scheme does not reduce the efficiency of the financial network, but fosters a more homogeneous risk-distribution within the system in a self-organized critical way. The reduction of systemic risk is due to a massive reduction of cascading failures in the transparent system. A regulation-policy implementation of the proposed scheme is discussed. PMID:23712454

Propagation peculiarities of mean field massive gravity

DOE PAGES

Deser, S.; Waldron, A.; Zahariade, G.

2015-07-28

Massive gravity (mGR) describes a dynamical “metric” on a fiducial, background one. We investigate fluctuations of the dynamics about mGR solutions, that is about its “mean field theory”. Analyzing mean field massive gravity (m¯GR) propagation characteristics is not only equivalent to studying those of the full non-linear theory, but also in direct correspondence with earlier analyses of charged higher spin systems, the oldest example being the charged, massive spin 3/2 Rarita–Schwinger (RS) theory. The fiducial and mGR mean field background metrics in the m¯GR model correspond to the RS Minkowski metric and external EM field. The common implications in bothmore » systems are that hyperbolicity holds only in a weak background-mean-field limit, immediately ruling both theories out as fundamental theories; a situation in stark contrast with general relativity (GR) which is at least a consistent classical theory. Moreover, even though both m¯GR and RS theories can still in principle be considered as predictive effective models in the weak regime, their lower helicities then exhibit superluminal behavior: lower helicity gravitons are superluminal as compared to photons propagating on either the fiducial or background metric. Thus our approach has uncovered a novel, dispersive, “crystal-like” phenomenon of differing helicities having differing propagation speeds. As a result, this applies both to m¯GR and mGR, and is a peculiar feature that is also problematic for consistent coupling to matter.« less

Transcription factor assisted loading and enhancer dynamics dictate the hepatic fasting response.

PubMed

Goldstein, Ido; Baek, Songjoon; Presman, Diego M; Paakinaho, Ville; Swinstead, Erin E; Hager, Gordon L

2017-03-01

Fasting elicits transcriptional programs in hepatocytes leading to glucose and ketone production. This transcriptional program is regulated by many transcription factors (TFs). To understand how this complex network regulates the metabolic response to fasting, we aimed at isolating the enhancers and TFs dictating it. Measuring chromatin accessibility revealed that fasting massively reorganizes liver chromatin, exposing numerous fasting-induced enhancers. By utilizing computational methods in combination with dissecting enhancer features and TF cistromes, we implicated four key TFs regulating the fasting response: glucocorticoid receptor (GR), cAMP responsive element binding protein 1 (CREB1), peroxisome proliferator activated receptor alpha (PPARA), and CCAAT/enhancer binding protein beta (CEBPB). These TFs regulate fuel production by two distinctly operating modules, each controlling a separate metabolic pathway. The gluconeogenic module operates through assisted loading, whereby GR doubles the number of sites occupied by CREB1 as well as enhances CREB1 binding intensity and increases accessibility of CREB1 binding sites. Importantly, this GR-assisted CREB1 binding was enhancer-selective and did not affect all CREB1-bound enhancers. Single-molecule tracking revealed that GR increases the number and DNA residence time of a portion of chromatin-bound CREB1 molecules. These events collectively result in rapid synergistic gene expression and higher hepatic glucose production. Conversely, the ketogenic module operates via a GR-induced TF cascade, whereby PPARA levels are increased following GR activation, facilitating gradual enhancer maturation next to PPARA target genes and delayed ketogenic gene expression. Our findings reveal a complex network of enhancers and TFs that dynamically cooperate to restore homeostasis upon fasting. Published by Cold Spring Harbor Laboratory Press.

Vestibular lesion-induced developmental plasticity in spinal locomotor networks during Xenopus laevis metamorphosis.

PubMed

Beyeler, Anna; Rao, Guillaume; Ladepeche, Laurent; Jacques, André; Simmers, John; Le Ray, Didier

2013-01-01

During frog metamorphosis, the vestibular sensory system remains unchanged, while spinal motor networks undergo a massive restructuring associated with the transition from the larval to adult biomechanical system. We investigated in Xenopus laevis the impact of a pre- (tadpole stage) or post-metamorphosis (juvenile stage) unilateral labyrinthectomy (UL) on young adult swimming performance and underlying spinal locomotor circuitry. The acute disruptive effects on locomotion were similar in both tadpoles and juvenile frogs. However, animals that had metamorphosed with a preceding UL expressed restored swimming behavior at the juvenile stage, whereas animals lesioned after metamorphosis never recovered. Whilst kinematic and electrophysiological analyses of the propulsive system showed no significant differences in either juvenile group, a 3D biomechanical simulation suggested that an asymmetry in the dynamic control of posture during swimming could account for the behavioral restoration observed in animals that had been labyrinthectomized before metamorphosis. This hypothesis was subsequently supported by in vivo electromyography during free swimming and in vitro recordings from isolated brainstem/spinal cord preparations. Specifically, animals lesioned prior to metamorphosis at the larval stage exhibited an asymmetrical propulsion/posture coupling as a post-metamorphic young adult. This developmental alteration was accompanied by an ipsilesional decrease in propriospinal coordination that is normally established in strict left-right symmetry during metamorphosis in order to synchronize dorsal trunk muscle contractions with bilateral hindlimb extensions in the swimming adult. Our data thus suggest that a disequilibrium in descending vestibulospinal information during Xenopus metamorphosis leads to an altered assembly of adult spinal locomotor circuitry. This in turn enables an adaptive compensation for the dynamic postural asymmetry induced by the vestibular imbalance and the restoration of functionally-effective behavior.

BrainBrowser: distributed, web-based neurological data visualization.

PubMed

Sherif, Tarek; Kassis, Nicolas; Rousseau, Marc-Étienne; Adalat, Reza; Evans, Alan C

2014-01-01

Recent years have seen massive, distributed datasets become the norm in neuroimaging research, and the methodologies used to analyze them have, in response, become more collaborative and exploratory. Tools and infrastructure are continuously being developed and deployed to facilitate research in this context: grid computation platforms to process the data, distributed data stores to house and share them, high-speed networks to move them around and collaborative, often web-based, platforms to provide access to and sometimes manage the entire system. BrainBrowser is a lightweight, high-performance JavaScript visualization library built to provide easy-to-use, powerful, on-demand visualization of remote datasets in this new research environment. BrainBrowser leverages modern web technologies, such as WebGL, HTML5 and Web Workers, to visualize 3D surface and volumetric neuroimaging data in any modern web browser without requiring any browser plugins. It is thus trivial to integrate BrainBrowser into any web-based platform. BrainBrowser is simple enough to produce a basic web-based visualization in a few lines of code, while at the same time being robust enough to create full-featured visualization applications. BrainBrowser can dynamically load the data required for a given visualization, so no network bandwidth needs to be waisted on data that will not be used. BrainBrowser's integration into the standardized web platform also allows users to consider using 3D data visualization in novel ways, such as for data distribution, data sharing and dynamic online publications. BrainBrowser is already being used in two major online platforms, CBRAIN and LORIS, and has been used to make the 1TB MACACC dataset openly accessible.

BrainBrowser: distributed, web-based neurological data visualization

PubMed Central

Sherif, Tarek; Kassis, Nicolas; Rousseau, Marc-Étienne; Adalat, Reza; Evans, Alan C.

2015-01-01

Recent years have seen massive, distributed datasets become the norm in neuroimaging research, and the methodologies used to analyze them have, in response, become more collaborative and exploratory. Tools and infrastructure are continuously being developed and deployed to facilitate research in this context: grid computation platforms to process the data, distributed data stores to house and share them, high-speed networks to move them around and collaborative, often web-based, platforms to provide access to and sometimes manage the entire system. BrainBrowser is a lightweight, high-performance JavaScript visualization library built to provide easy-to-use, powerful, on-demand visualization of remote datasets in this new research environment. BrainBrowser leverages modern web technologies, such as WebGL, HTML5 and Web Workers, to visualize 3D surface and volumetric neuroimaging data in any modern web browser without requiring any browser plugins. It is thus trivial to integrate BrainBrowser into any web-based platform. BrainBrowser is simple enough to produce a basic web-based visualization in a few lines of code, while at the same time being robust enough to create full-featured visualization applications. BrainBrowser can dynamically load the data required for a given visualization, so no network bandwidth needs to be waisted on data that will not be used. BrainBrowser's integration into the standardized web platform also allows users to consider using 3D data visualization in novel ways, such as for data distribution, data sharing and dynamic online publications. BrainBrowser is already being used in two major online platforms, CBRAIN and LORIS, and has been used to make the 1TB MACACC dataset openly accessible. PMID:25628562

A 30 kpc Chain of "Beads on a String" Star Formation between Two Merging Early Type Galaxies in the Core of a Strong-lensing Galaxy Cluster

NASA Astrophysics Data System (ADS)

Tremblay, Grant R.; Gladders, Michael D.; Baum, Stefi A.; O'Dea, Christopher P.; Bayliss, Matthew B.; Cooke, Kevin C.; Dahle, Håkon; Davis, Timothy A.; Florian, Michael; Rigby, Jane R.; Sharon, Keren; Soto, Emmaris; Wuyts, Eva

2014-08-01

New Hubble Space Telescope ultraviolet and optical imaging of the strong-lensing galaxy cluster SDSS J1531+3414 (z = 0.335) reveals two centrally dominant elliptical galaxies participating in an ongoing major merger. The interaction is at least somewhat rich in cool gas, as the merger is associated with a complex network of 19 massive superclusters of young stars (or small tidal dwarf galaxies) separated by ~1 kpc in projection from one another, combining to an estimated total star formation rate of ~5 M ⊙ yr-1. The resolved young stellar superclusters are threaded by narrow Hα, [O II], and blue excess filaments arranged in a network spanning ~27 kpc across the two merging galaxies. This morphology is strongly reminiscent of the well-known "beads on a string" mode of star formation observed on kiloparsec scales in the arms of spiral galaxies, resonance rings, and in tidal tails between interacting galaxies. Nevertheless, the arrangement of this star formation relative to the nuclei of the two galaxies is difficult to interpret in a dynamical sense, as no known "beads on a string" systems associated with kiloparsec-scale tidal interactions exhibit such lopsided morphology relative to the merger participants. In this Letter, we present the images and follow-up spectroscopy and discuss possible physical interpretations for the unique arrangement of the young stellar clusters. While we suggest that this morphology is likely to be dynamically short-lived, a more quantitative understanding awaits necessary multiwavelength follow-up, including optical integral field spectroscopy, ALMA submillimeter interferometry, and Chandra X-ray imaging.

A 30 kpc CHAIN OF ''BEADS ON A STRING'' STAR FORMATION BETWEEN TWO MERGING EARLY TYPE GALAXIES IN THE CORE OF A STRONG-LENSING GALAXY CLUSTER

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tremblay, Grant R.; Davis, Timothy A.; Gladders, Michael D.

2014-08-01

New Hubble Space Telescope ultraviolet and optical imaging of the strong-lensing galaxy cluster SDSS J1531+3414 (z = 0.335) reveals two centrally dominant elliptical galaxies participating in an ongoing major merger. The interaction is at least somewhat rich in cool gas, as the merger is associated with a complex network of 19 massive superclusters of young stars (or small tidal dwarf galaxies) separated by ∼1 kpc in projection from one another, combining to an estimated total star formation rate of ∼5 M {sub ☉} yr{sup –1}. The resolved young stellar superclusters are threaded by narrow Hα, [O II], and blue excess filaments arrangedmore » in a network spanning ∼27 kpc across the two merging galaxies. This morphology is strongly reminiscent of the well-known ''beads on a string'' mode of star formation observed on kiloparsec scales in the arms of spiral galaxies, resonance rings, and in tidal tails between interacting galaxies. Nevertheless, the arrangement of this star formation relative to the nuclei of the two galaxies is difficult to interpret in a dynamical sense, as no known ''beads on a string'' systems associated with kiloparsec-scale tidal interactions exhibit such lopsided morphology relative to the merger participants. In this Letter, we present the images and follow-up spectroscopy and discuss possible physical interpretations for the unique arrangement of the young stellar clusters. While we suggest that this morphology is likely to be dynamically short-lived, a more quantitative understanding awaits necessary multiwavelength follow-up, including optical integral field spectroscopy, ALMA submillimeter interferometry, and Chandra X-ray imaging.« less

Predicting the evolution of complex networks via similarity dynamics

NASA Astrophysics Data System (ADS)

Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping

2017-01-01

Almost all real-world networks are subject to constant evolution, and plenty of them have been investigated empirically to uncover the underlying evolution mechanism. However, the evolution prediction of dynamic networks still remains a challenging problem. The crux of this matter is to estimate the future network links of dynamic networks. This paper studies the evolution prediction of dynamic networks with link prediction paradigm. To estimate the likelihood of the existence of links more accurate, an effective and robust similarity index is presented by exploiting network structure adaptively. Moreover, most of the existing link prediction methods do not make a clear distinction between future links and missing links. In order to predict the future links, the networks are regarded as dynamic systems in this paper, and a similarity updating method, spatial-temporal position drift model, is developed to simulate the evolutionary dynamics of node similarity. Then the updated similarities are used as input information for the future links' likelihood estimation. Extensive experiments on real-world networks suggest that the proposed similarity index performs better than baseline methods and the position drift model performs well for evolution prediction in real-world evolving networks.

Alignment of dynamic networks.

PubMed

Vijayan, V; Critchlow, D; Milenkovic, T

2017-07-15

Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. http://nd.edu/∼cone/DynaMAGNA++/ . tmilenko@nd.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Alignment of dynamic networks

PubMed Central

Vijayan, V.; Critchlow, D.; Milenković, T.

2017-01-01

Abstract Motivation: Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems’ static network representations, as is currently done. Results: For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. Availability and implementation: http://nd.edu/∼cone/DynaMAGNA++/. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881980

Dynamics and stellar population of the Galactic Center (French Title: Étude de la cinématique et de la population stellaire du Centre Galactique)

NASA Astrophysics Data System (ADS)

Paumard, Thibaut

2003-09-01

The central parsec of the Galaxy has been observed using BEAR spectroimagery at high spectral resolution (up to 21 km/s) and medium spatial resolution (0.5"), in Bracket gamma (2.16 microns) and He I (2.06 microns), and high resolution imaging. These data were used to study the young, massive stars of the central parsec, and the structure and dynamics of ionized gas in Sgr A West. The stellar population has been separated into two groups: the IRS 16 complex of 6 LBVs, and at least 20 Wolf-Rayets. The IRS 13E complex has been identified as a cluster of at least 6 massive stars. All this is consistent with the young stars being born in a massive cluster a few tens of parsecs from the Galactic Centre. Providing a deep insight into the morphology of Sgr A West, our data allowed us to derive a kinematic model for the Northern Arm. Our results are in agreement with the idea that the Minispiral is made of ionisation fronts of wider neutral clouds, gravitationally stretched, coming from the CND.

The fate of scattered planets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bromley, Benjamin C.; Kenyon, Scott J., E-mail: bromley@physics.utah.edu, E-mail: skenyon@cfa.harvard.edu

2014-12-01

As gas giant planets evolve, they may scatter other planets far from their original orbits to produce hot Jupiters or rogue planets that are not gravitationally bound to any star. Here, we consider planets cast out to large orbital distances on eccentric, bound orbits through a gaseous disk. With simple numerical models, we show that super-Earths can interact with the gas through dynamical friction to settle in the remote outer regions of a planetary system. Outcomes depend on planet mass, the initial scattered orbit, and the evolution of the time-dependent disk. Efficient orbital damping by dynamical friction requires planets atmore » least as massive as the Earth. More massive, longer-lived disks damp eccentricities more efficiently than less massive, short-lived ones. Transition disks with an expanding inner cavity can circularize orbits at larger distances than disks that experience a global (homologous) decay in surface density. Thus, orbits of remote planets may reveal the evolutionary history of their primordial gas disks. A remote planet with an orbital distance ∼100 AU from the Sun is plausible and might explain correlations in the orbital parameters of several distant trans-Neptunian objects.« less

RUNAWAY MASSIVE STARS FROM R136: VFTS 682 IS VERY LIKELY A 'SLOW RUNAWAY'

DOE Office of Scientific and Technical Information (OSTI.GOV)

Banerjee, Sambaran; Kroupa, Pavel; Oh, Seungkyung, E-mail: sambaran@astro.uni-bonn.de, E-mail: pavel@astro.uni-bonn.de, E-mail: skoh@astro.uni-bonn.de

2012-02-10

We conduct a theoretical study on the ejection of runaway massive stars from R136-the central massive, starburst cluster in the 30 Doradus complex of the Large Magellanic Cloud. Specifically, we investigate the possibility of the very massive star (VMS) VFTS 682 being a runaway member of R136. Recent observations of the above VMS, by virtue of its isolated location and its moderate peculiar motion, have raised the fundamental question of whether isolated massive star formation is indeed possible. We perform the first realistic N-body computations of fully mass-segregated R136-type star clusters in which all the massive stars are in primordialmore » binary systems. These calculations confirm that the dynamical ejection of a VMS from an R136-like cluster, with kinematic properties similar to those of VFTS 682, is common. Hence, the conjecture of isolated massive star formation is unnecessary to account for this VMS. Our results are also quite consistent with the ejection of 30 Dor 016, another suspected runaway VMS from R136. We further note that during the clusters' evolution, mergers of massive binaries produce a few single stars per cluster with masses significantly exceeding the canonical upper limit of 150 M{sub Sun }. The observations of such single super-canonical stars in R136, therefore, do not imply an initial mass function with an upper limit greatly exceeding the accepted canonical 150 M{sub Sun} limit, as has been suggested recently, and they are consistent with the canonical upper limit.« less

Exploring Entrainment Patterns of Human Emotion in Social Media

PubMed Central

Luo, Chuan; Zhang, Zhu

2016-01-01

Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace. PMID:26953692

Exploring Entrainment Patterns of Human Emotion in Social Media.

PubMed

He, Saike; Zheng, Xiaolong; Zeng, Daniel; Luo, Chuan; Zhang, Zhu

2016-01-01

Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.

Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Viana, R. L.; Lopes, S. R.; Batista, A. M.

2014-09-01

According to Stevens' law the relationship between stimulus and response is a power-law within an interval called the dynamic range. The dynamic range of sensory organs is found to be larger than that of a single neuron, suggesting that the network structure plays a key role in the behavior of both the scaling exponent and the dynamic range of neuron assemblies. In order to verify computationally the relationships between stimulus and response for spiking neurons, we investigate small-world networks of neurons described by the Hodgkin-Huxley equations connected by chemical synapses. We found that the dynamic range increases with the network size, suggesting that the enhancement of the dynamic range observed in sensory organs, with respect to single neurons, is an emergent property of complex network dynamics.

Coevolution of dynamical states and interactions in dynamic networks

NASA Astrophysics Data System (ADS)

Zimmermann, Martín G.; Eguíluz, Víctor M.; San Miguel, Maxi

2004-06-01

We explore the coupled dynamics of the internal states of a set of interacting elements and the network of interactions among them. Interactions are modeled by a spatial game and the network of interaction links evolves adapting to the outcome of the game. As an example, we consider a model of cooperation in which the adaptation is shown to facilitate the formation of a hierarchical interaction network that sustains a highly cooperative stationary state. The resulting network has the characteristics of a small world network when a mechanism of local neighbor selection is introduced in the adaptive network dynamics. The highly connected nodes in the hierarchical structure of the network play a leading role in the stability of the network. Perturbations acting on the state of these special nodes trigger global avalanches leading to complete network reorganization.

Boolean dynamics of genetic regulatory networks inferred from microarray time series data

DOE PAGES

Martin, Shawn; Zhang, Zhaoduo; Martino, Anthony; ...

2007-01-31

Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this paper we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our methodmore » first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation–inhibition networks to match the discretized data. In conclusion, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics.« less

Mean-field equations for neuronal networks with arbitrary degree distributions.

PubMed

Nykamp, Duane Q; Friedman, Daniel; Shaker, Sammy; Shinn, Maxwell; Vella, Michael; Compte, Albert; Roxin, Alex

2017-04-01

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections that are made randomly and independently with a fixed probability (Erdös-Rényi network) (ER) or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of presynaptic and postsynaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

Mean-field equations for neuronal networks with arbitrary degree distributions

NASA Astrophysics Data System (ADS)

Nykamp, Duane Q.; Friedman, Daniel; Shaker, Sammy; Shinn, Maxwell; Vella, Michael; Compte, Albert; Roxin, Alex

2017-04-01

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections that are made randomly and independently with a fixed probability (Erdös-Rényi network) (ER) or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of presynaptic and postsynaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

Dynamic Evolution Model Based on Social Network Services

NASA Astrophysics Data System (ADS)

Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

2013-11-01

Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

An overview of LIGO and Virgo -- status and plans

NASA Astrophysics Data System (ADS)

Miller, John

2014-06-01

Interferometric gravitational-wave detectors, the most sensitive position meters ever operated, aim to detect the motion of massive bodies throughout the universe by pushing precision measurement to the standard quantum limit and beyond. A global network of these detectors is currently under construction, promising unprecedented sensitivity and the ability to determine the sky position of any detected signals. I will describe the current status and expected performance of this network with a focus on limiting noise sources and the techniques currently being developed to combat them.

Diminished neural network dynamics after moderate and severe traumatic brain injury.

PubMed

Gilbert, Nicholas; Bernier, Rachel A; Calhoun, Vincent D; Brenner, Einat; Grossner, Emily; Rajtmajer, Sarah M; Hillary, Frank G

2018-01-01

Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain's subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network "states" that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics.

Diminished neural network dynamics after moderate and severe traumatic brain injury

PubMed Central

Gilbert, Nicholas; Bernier, Rachel A.; Calhoun, Vincent D.; Brenner, Einat; Grossner, Emily; Rajtmajer, Sarah M.

2018-01-01

Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain’s subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network “states” that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics. PMID:29883447

A fully coupled method for massively parallel simulation of hydraulically driven fractures in 3-dimensions: FULLY COUPLED PARALLEL SIMULATION OF HYDRAULIC FRACTURES IN 3-D

DOE PAGES

Settgast, Randolph R.; Fu, Pengcheng; Walsh, Stuart D. C.; ...

2016-09-18

This study describes a fully coupled finite element/finite volume approach for simulating field-scale hydraulically driven fractures in three dimensions, using massively parallel computing platforms. The proposed method is capable of capturing realistic representations of local heterogeneities, layering and natural fracture networks in a reservoir. A detailed description of the numerical implementation is provided, along with numerical studies comparing the model with both analytical solutions and experimental results. The results demonstrate the effectiveness of the proposed method for modeling large-scale problems involving hydraulically driven fractures in three dimensions.

A fully coupled method for massively parallel simulation of hydraulically driven fractures in 3-dimensions: FULLY COUPLED PARALLEL SIMULATION OF HYDRAULIC FRACTURES IN 3-D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Settgast, Randolph R.; Fu, Pengcheng; Walsh, Stuart D. C.

This study describes a fully coupled finite element/finite volume approach for simulating field-scale hydraulically driven fractures in three dimensions, using massively parallel computing platforms. The proposed method is capable of capturing realistic representations of local heterogeneities, layering and natural fracture networks in a reservoir. A detailed description of the numerical implementation is provided, along with numerical studies comparing the model with both analytical solutions and experimental results. The results demonstrate the effectiveness of the proposed method for modeling large-scale problems involving hydraulically driven fractures in three dimensions.

Systems and methods for rapid processing and storage of data

DOEpatents

Stalzer, Mark A.

2017-01-24

Systems and methods of building massively parallel computing systems using low power computing complexes in accordance with embodiments of the invention are disclosed. A massively parallel computing system in accordance with one embodiment of the invention includes at least one Solid State Blade configured to communicate via a high performance network fabric. In addition, each Solid State Blade includes a processor configured to communicate with a plurality of low power computing complexes interconnected by a router, and each low power computing complex includes at least one general processing core, an accelerator, an I/O interface, and cache memory and is configured to communicate with non-volatile solid state memory.

Qualitative dynamics semantics for SBGN process description.

PubMed

Rougny, Adrien; Froidevaux, Christine; Calzone, Laurence; Paulevé, Loïc

2016-06-16

Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far. We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F. The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them.

Probing Massive Black Hole Populations and Their Environments with LISA

NASA Astrophysics Data System (ADS)

Katz, Michael; Larson, Shane

2018-01-01

With the adoption of the LISA Mission Proposal by the European Space Agency in response to its call for L3 mission concepts, gravitational wave measurements from space are on the horizon. With data from the Illustris large-scale cosmological simulation, we provide analysis of LISA detection rates accompanied by characterization of the merging Massive Black Holes (MBH) and their host galaxies. MBHs of total mass $\\sim10^6-10^9 M_\\odot$ are the main focus of this study. Using a precise treatment of the dynamical friction evolutionary process prior to gravitational wave emission, we evolve MBH simulation particle mergers from $\\sim$kpc scales until coalescence to achieve a merger distribution. Using the statistical basis of the Illustris output, we Monte-carlo synthesize many realizations of the merging massive black hole population across space and time. We use those realizations to build mock LISA detection catalogs to understand the impact of LISA mission configurations on our ability to probe massive black hole merger populations and their environments throughout the visible Universe.

Media and Innovation Spark a Revolution in Higher Education in Iran

ERIC Educational Resources Information Center

Cummings, John; Rashidpour, Ebrahim

1976-01-01

A massive growth of the economy has fueled a continuing crisis in providing adequately trained professionals for Iran. One answer has been the creation of the Free University of Iran. Local Centers will form a network modeled after the British Open University. (Author/BD)

Accountability Practices in Adult Education: Insights from Actor-Network Theory

ERIC Educational Resources Information Center

Fenwick, Tara

2010-01-01

Accountability mechanisms in adult education, their constitution and their effects, are of increasing concern in an era threatening massive reductions to resources for adult education activity. Such mechanisms are frequently portrayed as unassailably oppressive. However, alternative analyses have illuminated contradictions and ambiguities in the…

Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks.

PubMed

Zhang, Juping; Yang, Chan; Jin, Zhen; Li, Jia

2018-07-14

In this paper, the correlation coefficients between nodes in states are used as dynamic variables, and we construct SIR epidemic dynamic models with correlation coefficients by using the pair approximation method in static networks and dynamic networks, respectively. Considering the clustering coefficient of the network, we analytically investigate the existence and the local asymptotic stability of each equilibrium of these models and derive threshold values for the prevalence of diseases. Additionally, we obtain two equivalent epidemic thresholds in dynamic networks, which are compared with the results of the mean field equations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Numerical modeling of landslides and generated seismic waves: The Bingham Canyon Mine landslides

NASA Astrophysics Data System (ADS)

Miallot, H.; Mangeney, A.; Capdeville, Y.; Hibert, C.

2016-12-01

Landslides are important natural hazards and key erosion processes. They create long period surface waves that can be recorded by regional and global seismic networks. The seismic signals are generated by acceleration/deceleration of the mass sliding over the topography. They consist in a unique and powerful tool to detect, characterize and quantify the landslide dynamics. We investigate here the processes at work during the two massive landslides that struck the Bingham Canyon Mine on the 10th April 2013. We carry a combined analysis of the generated seismic signals and the landslide processes computed with a 3D modeling on a complex topography. Forces computed by broadband seismic waveform inversion are used to constrain the study and particularly the force-source and the bulk dynamic. The source time function are obtained by a 3D model (Shaltop) where rheological parameters can be adjusted. We first investigate the influence of the initial shape of the sliding mass which strongly affects the whole landslide dynamic. We also see that the initial shape of the source mass of the first landslide constrains pretty well the second landslide source mass. We then investigate the effect of a rheological parameter, the frictional angle, that strongly influences the resulted computed seismic source function. We test here numerous friction laws as the frictional Coulomb law and a velocity-weakening friction law. Our results show that the force waveform fitting the observed data is highly variable depending on these different choices.

High-fidelity simulation capability for virtual testing of seismic and acoustic sensors

NASA Astrophysics Data System (ADS)

Wilson, D. Keith; Moran, Mark L.; Ketcham, Stephen A.; Lacombe, James; Anderson, Thomas S.; Symons, Neill P.; Aldridge, David F.; Marlin, David H.; Collier, Sandra L.; Ostashev, Vladimir E.

2005-05-01

This paper describes development and application of a high-fidelity, seismic/acoustic simulation capability for battlefield sensors. The purpose is to provide simulated sensor data so realistic that they cannot be distinguished by experts from actual field data. This emerging capability provides rapid, low-cost trade studies of unattended ground sensor network configurations, data processing and fusion strategies, and signatures emitted by prototype vehicles. There are three essential components to the modeling: (1) detailed mechanical signature models for vehicles and walkers, (2) high-resolution characterization of the subsurface and atmospheric environments, and (3) state-of-the-art seismic/acoustic models for propagating moving-vehicle signatures through realistic, complex environments. With regard to the first of these components, dynamic models of wheeled and tracked vehicles have been developed to generate ground force inputs to seismic propagation models. Vehicle models range from simple, 2D representations to highly detailed, 3D representations of entire linked-track suspension systems. Similarly detailed models of acoustic emissions from vehicle engines are under development. The propagation calculations for both the seismics and acoustics are based on finite-difference, time-domain (FDTD) methodologies capable of handling complex environmental features such as heterogeneous geologies, urban structures, surface vegetation, and dynamic atmospheric turbulence. Any number of dynamic sources and virtual sensors may be incorporated into the FDTD model. The computational demands of 3D FDTD simulation over tactical distances require massively parallel computers. Several example calculations of seismic/acoustic wave propagation through complex atmospheric and terrain environments are shown.

Galaxy evolution. Isolated compact elliptical galaxies: stellar systems that ran away.

PubMed

Chilingarian, Igor; Zolotukhin, Ivan

2015-04-24

Compact elliptical galaxies form a rare class of stellar system (~30 presently known) characterized by high stellar densities and small sizes and often harboring metal-rich stars. They were thought to form through tidal stripping of massive progenitors, until two isolated objects were discovered where massive galaxies performing the stripping could not be identified. By mining astronomical survey data, we have now found 195 compact elliptical galaxies in all types of environment. They all share similar dynamical and stellar population properties. Dynamical analysis for nonisolated galaxies demonstrates the feasibility of their ejection from host clusters and groups by three-body encounters, which is in agreement with numerical simulations. Hence, isolated compact elliptical and isolated quiescent dwarf galaxies are tidally stripped systems that ran away from their hosts. Copyright © 2015, American Association for the Advancement of Science.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald.

PubMed

Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2014-02-28

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald

PubMed Central

Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2015-01-01

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations. PMID:26512230

Incorporating spatial constraint in co-activation pattern analysis to explore the dynamics of resting-state networks: An application to Parkinson's disease.

PubMed

Zhuang, Xiaowei; Walsh, Ryan R; Sreenivasan, Karthik; Yang, Zhengshi; Mishra, Virendra; Cordes, Dietmar

2018-05-15

The dynamics of the brain's intrinsic networks have been recently studied using co-activation pattern (CAP) analysis. The CAP method relies on few model assumptions and CAP-based measurements provide quantitative information of network temporal dynamics. One limitation of existing CAP-related methods is that the computed CAPs share considerable spatial overlap that may or may not be functionally distinct relative to specific network dynamics. To more accurately describe network dynamics with spatially distinct CAPs, and to compare network dynamics between different populations, a novel data-driven CAP group analysis method is proposed in this study. In the proposed method, a dominant-CAP (d-CAP) set is synthesized across CAPs from multiple clustering runs for each group with the constraint of low spatial similarities among d-CAPs. Alternating d-CAPs with less overlapping spatial patterns can better capture overall network dynamics. The number of d-CAPs, the temporal fraction and spatial consistency of each d-CAP, and the subject-specific switching probability among all d-CAPs are then calculated for each group and used to compare network dynamics between groups. The spatial dissimilarities among d-CAPs computed with the proposed method were first demonstrated using simulated data. High consistency between simulated ground-truth and computed d-CAPs was achieved, and detailed comparisons between the proposed method and existing CAP-based methods were conducted using simulated data. In an effort to physiologically validate the proposed technique and investigate network dynamics in a relevant brain network disorder, the proposed method was then applied to data from the Parkinson's Progression Markers Initiative (PPMI) database to compare the network dynamics in Parkinson's disease (PD) and normal control (NC) groups. Fewer d-CAPs, skewed distribution of temporal fractions of d-CAPs, and reduced switching probabilities among final d-CAPs were found in most networks in the PD group, as compared to the NC group. Furthermore, an overall negative association between switching probability among d-CAPs and disease severity was observed in most networks in the PD group as well. These results expand upon previous findings from in vivo electrophysiological recording studies in PD. Importantly, this novel analysis also demonstrates that changes in network dynamics can be measured using resting-state fMRI data from subjects with early stage PD. Copyright © 2018 Elsevier Inc. All rights reserved.

Dynamic load balancing of applications

DOEpatents

Wheat, Stephen R.

1997-01-01

An application-level method for dynamically maintaining global load balance on a parallel computer, particularly on massively parallel MIMD computers. Global load balancing is achieved by overlapping neighborhoods of processors, where each neighborhood performs local load balancing. The method supports a large class of finite element and finite difference based applications and provides an automatic element management system to which applications are easily integrated.

Player guild dynamics and evolution in massively multiplayer online games.

PubMed

Chen, Chien-Hsun; Sun, Chuen-Tsai; Hsieh, Jilung

2008-06-01

In the latest versions of massively multiplayer online games (MMOGs), developers have purposefully made guilds part of game environments. Guilds represent a powerful method for giving players a sense of online community, but there is little quantitative data on guild dynamics. To address this topic, we took advantage of a feature found in one of today's most popular MMOGs (World of Warcraft) to collect in-game data: user interfaces that players can modify and refine. In addition to collecting data on in-game player activities, we used this feature to observe and investigate how players join and leave guilds. Data were analyzed for the purpose of identifying factors that propel game-world guild dynamics and evolution. After collecting data for 641,805 avatars on 62 Taiwanese World of Warcraft game servers between February 10 and April 10, 2006, we created five guild type categories (small, large, elite, newbie, and unstable) that have different meanings in terms of in-game group dynamics. By viewing players as the most important resource affecting guild life cycles, it is possible to analyze game worlds as ecosystems consisting of evolving guilds and to study how guild life cycles reflect game world characteristics.

A general stochastic model for studying time evolution of transition networks

NASA Astrophysics Data System (ADS)

Zhan, Choujun; Tse, Chi K.; Small, Michael

2016-12-01

We consider a class of complex networks whose nodes assume one of several possible states at any time and may change their states from time to time. Such networks represent practical networks of rumor spreading, disease spreading, language evolution, and so on. Here, we derive a model describing the dynamics of this kind of network and a simulation algorithm for studying the network evolutionary behavior. This model, derived at a microscopic level, can reveal the transition dynamics of every node. A numerical simulation is taken as an ;experiment; or ;realization; of the model. We use this model to study the disease propagation dynamics in four different prototypical networks, namely, the regular nearest-neighbor (RN) network, the classical Erdös-Renyí (ER) random graph, the Watts-Strogátz small-world (SW) network, and the Barabási-Albert (BA) scalefree network. We find that the disease propagation dynamics in these four networks generally have different properties but they do share some common features. Furthermore, we utilize the transition network model to predict user growth in the Facebook network. Simulation shows that our model agrees with the historical data. The study can provide a useful tool for a more thorough understanding of the dynamics networks.

Revealing networks from dynamics: an introduction

NASA Astrophysics Data System (ADS)

Timme, Marc; Casadiego, Jose

2014-08-01

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks

PubMed Central

Miconi, Thomas

2017-01-01

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior. DOI: http://dx.doi.org/10.7554/eLife.20899.001 PMID:28230528

Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

PubMed

Miconi, Thomas

2017-02-23

Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.

Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators.

PubMed

Fon, Warren; Matheny, Matthew H; Li, Jarvis; Krayzman, Lev; Cross, Michael C; D'Souza, Raissa M; Crutchfield, James P; Roukes, Michael L

2017-10-11

Control of the global parameters of complex networks has been explored experimentally in a variety of contexts. Yet, the more difficult prospect of realizing arbitrary network architectures, especially analog physical networks that provide dynamical control of individual nodes and edges, has remained elusive. Given the vast hierarchy of time scales involved, it also proves challenging to measure a complex network's full internal dynamics. These span from the fastest nodal dynamics to very slow epochs over which emergent global phenomena, including network synchronization and the manifestation of exotic steady states, eventually emerge. Here, we demonstrate an experimental system that satisfies these requirements. It is based upon modular, fully controllable, nonlinear radio frequency nanomechanical oscillators, designed to form the nodes of complex dynamical networks with edges of arbitrary topology. The dynamics of these oscillators and their surrounding network are analog and continuous-valued and can be fully interrogated in real time. They comprise a piezoelectric nanomechanical membrane resonator, which serves as the frequency-determining element within an electrical feedback circuit. This embodiment permits network interconnections entirely within the electrical domain and provides unprecedented node and edge control over a vast region of parameter space. Continuous measurement of the instantaneous amplitudes and phases of every constituent oscillator node are enabled, yielding full and detailed network data without reliance upon statistical quantities. We demonstrate the operation of this platform through the real-time capture of the dynamics of a three-node ring network as it evolves from the uncoupled state to full synchronization.

New and Topologically Massive Gravity, from the Outside In

NASA Astrophysics Data System (ADS)

Cunliff, Colin

This thesis examines the asymptotically anti-de Sitter solutions of higher-derivative gravity in 2+1 dimensions, using a Fefferman-Graham-like approach that expands solutions from the boundary (at infinity) into the interior. First, solutions of topologically massive gravity (TMG) are analyzed for values of the mass parameter in the range mu ≥ 1. The traditional Fefferman-Graham expansion fails to capture the dynamics of TMG, and new terms in the asymptotic expansion are needed to include the massive graviton modes. The linearized modes of Carlip, Deser, Waldron and Wise map onto the non-Einstein solutions for all μ, with nonlinear corrections appearing at higher order in the expansion. A similar result is found for new massive gravity (NMG), where the asymptotic behavior of massive gravitons is found to depend on the coupling parameter m2. Additionally, new boundary conditions are discovered for a range of values -1 < 2m2 l2 < 1 at which non-Einstein modes decay more slowly than the rate required for Brown-Henneaux boundary conditions. The holographically renormalized stress tensor is computed for these modes, and the relevant counterterms are identified up to unphysical ambiguities.

Interactions in Massive Colliding Wind Binaries

NASA Technical Reports Server (NTRS)

Corcoran, M.

2012-01-01

The most massive stars (M> 60 Solar Mass) play crucial roles in altering the chemical and thermodynamic properties of their host galaxies. Stellar mass is the fundamental stellar parameter that determines their ancillary properties and which ultimately determines the fate of these stars and their influence on their galactic environs. Unfortunately, stellar mass becomes observationally and theoretically less well constrained as it increases. Theory becomes uncertain mostly because very massive stars are prone to strong, variable mass loss which is difficult to model. Observational constraints are uncertain too. Massive stars are rare, and massive binary stars (needed for dynamical determination of mass) are rarer still: and of these systems only a fraction have suitably high orbital inclinations for direct photometric and spectroscopic radial-velocity analysis. Even in the small number of cases in which a high-inclination binary near the upper mass limit can be identified, rotational broadening and contamination of spectral line features from thick circumstellar material (either natal clouds or produced by strong stellar wind driven mass loss from one or both of he stellar components) biases the analysis. In the wilds of the upper HR diagram, we're often left with indirect and circumstantial means of determining mass, a rather unsatisfactory state of affairs.

On the spatial distributions of dense cores in Orion B

NASA Astrophysics Data System (ADS)

Parker, Richard J.

2018-05-01

We quantify the spatial distributions of dense cores in three spatially distinct areas of the Orion B star-forming region. For L1622, NGC 2068/NGC 2071, and NGC 2023/NGC 2024, we measure the amount of spatial substructure using the Q-parameter and find all three regions to be spatially substructured (Q < 0.8). We quantify the amount of mass segregation using ΛMSR and find that the most massive cores are mildly mass segregated in NGC 2068/NGC 2071 (ΛMSR ˜ 2), and very mass segregated in NGC 2023/NGC 2024 (Λ _MSR = 28^{+13}_{-10} for the four most massive cores). Whereas the most massive cores in L1622 are not in areas of relatively high surface density, or deeper gravitational potentials, the massive cores in NGC 2068/NGC 2071 and NGC 2023/NGC 2024 are significantly so. Given the low density (10 cores pc-2) and spatial substructure of cores in Orion B, the mass segregation cannot be dynamical. Our results are also inconsistent with simulations in which the most massive stars form via competitive accretion, and instead hint that magnetic fields may be important in influencing the primordial spatial distributions of gas and stars in star-forming regions.

Extended Reissner-Nordström solutions sourced by dynamical torsion

NASA Astrophysics Data System (ADS)

Cembranos, Jose A. R.; Valcarcel, Jorge Gigante

2018-04-01

We find a new exact vacuum solution in the framework of the Poincaré Gauge field theory with massive torsion. In this model, torsion operates as an independent field and introduces corrections to the vacuum structure present in General Relativity. The new static and spherically symmetric configuration shows a Reissner-Nordström-like geometry characterized by a spin charge. It extends the known massless torsion solution to the massive case. The corresponding Reissner-Nordström-de Sitter solution is also compatible with a cosmological constant and additional U (1) gauge fields.

Hamiltonian of Mean Force and Dissipative Scalar Field Theory

NASA Astrophysics Data System (ADS)

Jafari, Marjan; Kheirandish, Fardin

2018-04-01

Quantum dynamics of a dissipative scalar field is investigated. Using the Hamiltonian of mean force, internal energy, free energy and entropy of a dissipative scalar field are obtained. It is shown that a dissipative massive scalar field can be considered as a free massive scalar field described by an effective mass and dispersion relation. Internal energy of the scalar field, as the subsystem, is found in the limit of low temperature and weak and strong couplings to an Ohimc heat bath. Correlation functions for thermal and coherent states are derived.

Noncommutative massive unquenched ABJM

NASA Astrophysics Data System (ADS)

Bea, Yago; Jokela, Niko; Pönni, Arttu; Ramallo, Alfonso V.

2018-05-01

In this paper, we study noncommutative massive unquenched Chern-Simons matter theory using its gravity dual. We construct this novel background by applying a TsT-transformation on the known parent commutative solution. We discuss several aspects of this solution to the Type IIA supergravity equations of motion and, amongst others, check that it preserves 𝒩 = 1 supersymmetry. We then turn our attention to applications and investigate how dynamical flavor degrees of freedom affect numerous observables of interest. Our framework can be regarded as a key step toward the construction of holographic quantum Hall states on a noncommutative plane.

Translation invariant time-dependent massive gravity: Hamiltonian analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mourad, Jihad; Steer, Danièle A.; Noui, Karim, E-mail: mourad@apc.univ-paris7.fr, E-mail: karim.noui@lmpt.univ-tours.fr, E-mail: steer@apc.univ-paris7.fr

2014-09-01

The canonical structure of the massive gravity in the first order moving frame formalism is studied. We work in the simplified context of translation invariant fields, with mass terms given by general non-derivative interactions, invariant under the diagonal Lorentz group, depending on the moving frame as well as a fixed reference frame. We prove that the only mass terms which give 5 propagating degrees of freedom are the dRGT mass terms, namely those which are linear in the lapse. We also complete the Hamiltonian analysis with the dynamical evolution of the system.

Synthesis of recurrent neural networks for dynamical system simulation.

PubMed

Trischler, Adam P; D'Eleuterio, Gabriele M T

2016-08-01

We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. Copyright © 2016 Elsevier Ltd. All rights reserved.

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

The Five Central Psychological Challenges Facing Effective Mobile Learning

ERIC Educational Resources Information Center

Terras, Melody M.; Ramsay, Judith

2012-01-01

Web 2.0 technology not only offers the opportunity of massively parallel interconnected networks that support the provision of information and communication anytime and anywhere but also offers immense opportunities for collaboration and sharing of user-generated content. This information-rich environment may support both formal and informal…

A Bootstrapped Approach to Multilingual Text Stream Parsing

ERIC Educational Resources Information Center

Londhe, Nikhil

2017-01-01

The ubiquitous hashtag has disruptively transformed how news stories are reported and shared across social media networks. Often, such text streams are massively multilingual with 50 different languages on an average and contain a combination of subjective user opinion, objective evolving information about the story and unrelated spam. This is in…

Associative Networks on a Massively Parallel Computer.

DTIC Science & Technology

1985-10-01

lgbt (as a group of numbers, in this case), but this only leads to sensible queries when a statistical function is applied: "What is the largest salary...34.*"* . •.,. 64 the siW~pe operations being used during ascend, each movement step costs the same as executing an operation

CNN Newsroom Classroom Guides. March 1-31, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of March, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: monkeys cloned in Oregon, Iran suffers massive earthquake, tornados affect…

75 FR 3206 - Mission Statement; Middle East Public Health Mission, June 5-10, 2010

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-20

... gradually aging population, and greater material wealth along with an upsurge in lifestyle diseases all.... Saudi Arabia's leaky water supply and wastewater pipeline network is also receiving massive investment... increases in production and exports of liquefied natural gas (LNG), oil, and petrochemicals products...

Studies on the population dynamics of a rumor-spreading model in online social networks

NASA Astrophysics Data System (ADS)

Dong, Suyalatu; Fan, Feng-Hua; Huang, Yong-Chang

2018-02-01

This paper sets up a rumor spreading model in online social networks based on the European fox rabies SIR model. The model considers the impact of changing number of online social network users, combines the transmission dynamics to set up a population dynamics of rumor spreading model in online social networks. Simulation is carried out on online social network, and results show that the new rumor spreading model is in accordance with the real propagation characteristics in online social networks.

Temporal node centrality in complex networks

NASA Astrophysics Data System (ADS)

Kim, Hyoungshick; Anderson, Ross

2012-02-01

Many networks are dynamic in that their topology changes rapidly—on the same time scale as the communications of interest between network nodes. Examples are the human contact networks involved in the transmission of disease, ad hoc radio networks between moving vehicles, and the transactions between principals in a market. While we have good models of static networks, so far these have been lacking for the dynamic case. In this paper we present a simple but powerful model, the time-ordered graph, which reduces a dynamic network to a static network with directed flows. This enables us to extend network properties such as vertex degree, closeness, and betweenness centrality metrics in a very natural way to the dynamic case. We then demonstrate how our model applies to a number of interesting edge cases, such as where the network connectivity depends on a small number of highly mobile vertices or edges, and show that our centrality definition allows us to track the evolution of connectivity. Finally we apply our model and techniques to two real-world dynamic graphs of human contact networks and then discuss the implication of temporal centrality metrics in the real world.

Creative-Dynamics Approach To Neural Intelligence

NASA Technical Reports Server (NTRS)

Zak, Michail A.

1992-01-01

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

Multistability, local pattern formation, and global collective firing in a small-world network of nonleaky integrate-and-fire neurons.

PubMed

Rothkegel, Alexander; Lehnertz, Klaus

2009-03-01

We investigate numerically the collective dynamical behavior of pulse-coupled nonleaky integrate-and-fire neurons that are arranged on a two-dimensional small-world network. To ensure ongoing activity, we impose a probability for spontaneous firing for each neuron. We study network dynamics evolving from different sets of initial conditions in dependence on coupling strength and rewiring probability. Besides a homogeneous equilibrium state for low coupling strength, we observe different local patterns including cyclic waves, spiral waves, and turbulentlike patterns, which-depending on network parameters-interfere with the global collective firing of the neurons. We attribute the various network dynamics to distinct regimes in the parameter space. For the same network parameters different network dynamics can be observed depending on the set of initial conditions only. Such a multistable behavior and the interplay between local pattern formation and global collective firing may be attributable to the spatiotemporal dynamics of biological networks.

Noise-induced relations between network connectivity and dynamics

NASA Astrophysics Data System (ADS)

Ching, Emily Sc

Many biological systems of interest can be represented as networks of many nodes that are interacting with one another. Often these systems are subject to external influence or noise. One of the central issues is to understand the relation between dynamics and the interaction pattern of the system or the connectivity structure of the network. In particular, a challenging problem is to infer the network connectivity structure from the dynamics. In this talk, we show that for stochastic dynamical systems subjected to noise, the presence of noise gives rise to mathematical relations between the network connectivity structure and quantities that can be calculated using solely the time-series measurements of the dynamics of the nodes. We present these relations for both undirected networks with bidirectional coupling and directed networks with directional coupling and discuss how such relations can be utilized to infer the network connectivity structure of the systems. Work supported by the Hong Kong Research Grants Council under Grant No. CUHK 14300914.

Active influence in dynamical models of structural balance in social networks

NASA Astrophysics Data System (ADS)

Summers, Tyler H.; Shames, Iman

2013-07-01

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of “friendliness levels” in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international-relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.

Conflict and convention in dynamic networks.

PubMed

Foley, Michael; Forber, Patrick; Smead, Rory; Riedl, Christoph

2018-03-01

An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models. © 2017 The Author(s).

A neural network architecture for implementation of expert systems for real time monitoring

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.

1991-01-01

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity

NASA Astrophysics Data System (ADS)

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-11-01

The increasing number of recording electrodes enhances the capability of capturing the network’s cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity.

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity

PubMed Central

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-01-01

The increasing number of recording electrodes enhances the capability of capturing the network’s cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity. PMID:27824075

Optical fiber cable and wiring techniques for fiber to the home (FTTH)

NASA Astrophysics Data System (ADS)

Takai, Hirofumi; Yamauchi, Osamu

2009-08-01

NTT group's new medium-term management strategy calls for 20 million optical subscribers by 2010, and NTT Laboratories is pushing forward to meet this goal. Before that date, an efficient optical access network must be constructed, and afterwards, when the era of mass optical communications finally arrives, the facilities and equipment supporting the network will have to be effectively operated and maintained. At NTT Access Network Service Systems Laboratories, we are developing various technologies to correspond to the massive deployment of optical broadband services. We are also developing various new technologies for efficiently operating optical access network systems that will continue to expand in the future, and to supply our customers with good services. This paper provides an overview of the new optical access network system technologies that are being developed at NTT Access Network Service Systems Laboratories to address these issues.

A theory for how sensorimotor skills are learned and retained in noisy and nonstationary neural circuits

PubMed Central

Ajemian, Robert; D’Ausilio, Alessandro; Moorman, Helene; Bizzi, Emilio

2013-01-01

During the process of skill learning, synaptic connections in our brains are modified to form motor memories of learned sensorimotor acts. The more plastic the adult brain is, the easier it is to learn new skills or adapt to neurological injury. However, if the brain is too plastic and the pattern of synaptic connectivity is constantly changing, new memories will overwrite old memories, and learning becomes unstable. This trade-off is known as the stability–plasticity dilemma. Here a theory of sensorimotor learning and memory is developed whereby synaptic strengths are perpetually fluctuating without causing instability in motor memory recall, as long as the underlying neural networks are sufficiently noisy and massively redundant. The theory implies two distinct stages of learning—preasymptotic and postasymptotic—because once the error drops to a level comparable to that of the noise-induced error, further error reduction requires altered network dynamics. A key behavioral prediction derived from this analysis is tested in a visuomotor adaptation experiment, and the resultant learning curves are modeled with a nonstationary neural network. Next, the theory is used to model two-photon microscopy data that show, in animals, high rates of dendritic spine turnover, even in the absence of overt behavioral learning. Finally, the theory predicts enhanced task selectivity in the responses of individual motor cortical neurons as the level of task expertise increases. From these considerations, a unique interpretation of sensorimotor memory is proposed—memories are defined not by fixed patterns of synaptic weights but, rather, by nonstationary synaptic patterns that fluctuate coherently. PMID:24324147

An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator

PubMed Central

Wang, Runchun M.; Thakur, Chetan S.; van Schaik, André

2018-01-01

This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks. PMID:29692702

An FPGA-Based Massively Parallel Neuromorphic Cortex Simulator.

PubMed

Wang, Runchun M; Thakur, Chetan S; van Schaik, André

2018-01-01

This paper presents a massively parallel and scalable neuromorphic cortex simulator designed for simulating large and structurally connected spiking neural networks, such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.

NetFlow Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corbet Jr., Thomas F; Beyeler, Walter E; Vanwestrienen, Dirk

NetFlow Dynamics is a web-accessible analysis environment for simulating dynamic flows of materials on model networks. Performing a simulation requires both the NetFlow Dynamics application and a network model which is a description of the structure of the nodes and edges of a network including the flow capacity of each edge and the storage capacity of each node, and the sources and sinks of the material flowing on the network. NetFlow Dynamics consists of databases for storing network models, algorithms to calculate flows on networks, and a GIS-based graphical interface for performing simulations and viewing simulation results. Simulated flows aremore » dynamic in the sense that flows on each edge of the network and inventories at each node change with time and can be out of equilibrium with boundary conditions. Any number of network models could be simulated using Net Flow Dynamics. To date, the models simulated have been models of petroleum infrastructure. The main model has been the National Transportation Fuels Model (NTFM), a network of U.S. oil fields, transmission pipelines, rail lines, refineries, tank farms, and distribution terminals. NetFlow Dynamics supports two different flow algorithms, the Gradient Flow algorithm and the Inventory Control algorithm, that were developed specifically for the NetFlow Dynamics application. The intent is to add additional algorithms in the future as needed. The ability to select from multiple algorithms is desirable because a single algorithm never covers all analysis needs. The current algorithms use a demand-driven capacity-constrained formulation which means that the algorithms strive to use all available capacity and stored inventory to meet desired flows to sinks, subject to the capacity constraints of each network component. The current flow algorithms are best suited for problems in which a material flows on a capacity-constrained network representing a supply chain in which the material supplied can be stored at each node of the network. In the petroleum models, the flowing materials are crude oil and refined products that can be stored at tank farms, refineries, or terminals (i.e. the nodes of the network). Examples of other network models that could be simulated are currency flowing in a financial network, agricultural products moving to market, or natural gas flowing on a pipeline network.« less

Complex networks repair strategies: Dynamic models

NASA Astrophysics Data System (ADS)

Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang

2017-09-01

Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree 〈 k 〉 and enhances network invulnerability.

Simple deterministic models and applications. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Yang, Hyun Mo

2015-12-01

Currently, discrete modellings are largely accepted due to the access to computers with huge storage capacity and high performance processors and easy implementation of algorithms, allowing to develop and simulate increasingly sophisticated models. Wang et al. [7] present a review of dynamics in complex networks, focusing on the interaction between disease dynamics and human behavioral and social dynamics. By doing an extensive review regarding to the human behavior responding to disease dynamics, the authors briefly describe the complex dynamics found in the literature: well-mixed populations networks, where spatial structure can be neglected, and other networks considering heterogeneity on spatially distributed populations. As controlling mechanisms are implemented, such as social distancing due 'social contagion', quarantine, non-pharmaceutical interventions and vaccination, adaptive behavior can occur in human population, which can be easily taken into account in the dynamics formulated by networked populations.

A geometrical approach to control and controllability of nonlinear dynamical networks

PubMed Central

Wang, Le-Zhi; Su, Ri-Qi; Huang, Zi-Gang; Wang, Xiao; Wang, Wen-Xu; Grebogi, Celso; Lai, Ying-Cheng

2016-01-01

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control. PMID:27076273

STAR CLUSTER FORMATION WITH STELLAR FEEDBACK AND LARGE-SCALE INFLOW

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzner, Christopher D.; Jumper, Peter H., E-mail: matzner@astro.utoronto.ca

2015-12-10

During star cluster formation, ongoing mass accretion is resisted by stellar feedback in the form of protostellar outflows from the low-mass stars and photo-ionization and radiation pressure feedback from the massive stars. We model the evolution of cluster-forming regions during a phase in which both accretion and feedback are present and use these models to investigate how star cluster formation might terminate. Protostellar outflows are the strongest form of feedback in low-mass regions, but these cannot stop cluster formation if matter continues to flow in. In more massive clusters, radiation pressure and photo-ionization rapidly clear the cluster-forming gas when itsmore » column density is too small. We assess the rates of dynamical mass ejection and of evaporation, while accounting for the important effect of dust opacity on photo-ionization. Our models are consistent with the census of protostellar outflows in NGC 1333 and Serpens South and with the dust temperatures observed in regions of massive star formation. Comparing observations of massive cluster-forming regions against our model parameter space, and against our expectations for accretion-driven evolution, we infer that massive-star feedback is a likely cause of gas disruption in regions with velocity dispersions less than a few kilometers per second, but that more massive and more turbulent regions are too strongly bound for stellar feedback to be disruptive.« less

Risk assessment by dynamic representation of vulnerability, exploitation, and impact

NASA Astrophysics Data System (ADS)

Cam, Hasan

2015-05-01

Assessing and quantifying cyber risk accurately in real-time is essential to providing security and mission assurance in any system and network. This paper presents a modeling and dynamic analysis approach to assessing cyber risk of a network in real-time by representing dynamically its vulnerabilities, exploitations, and impact using integrated Bayesian network and Markov models. Given the set of vulnerabilities detected by a vulnerability scanner in a network, this paper addresses how its risk can be assessed by estimating in real-time the exploit likelihood and impact of vulnerability exploitation on the network, based on real-time observations and measurements over the network. The dynamic representation of the network in terms of its vulnerabilities, sensor measurements, and observations is constructed dynamically using the integrated Bayesian network and Markov models. The transition rates of outgoing and incoming links of states in hidden Markov models are used in determining exploit likelihood and impact of attacks, whereas emission rates help quantify the attack states of vulnerabilities. Simulation results show the quantification and evolving risk scores over time for individual and aggregated vulnerabilities of a network.

Counting and classifying attractors in high dimensional dynamical systems.

PubMed

Bagley, R J; Glass, L

1996-12-07

Randomly connected Boolean networks have been used as mathematical models of neural, genetic, and immune systems. A key quantity of such networks is the number of basins of attraction in the state space. The number of basins of attraction changes as a function of the size of the network, its connectivity and its transition rules. In discrete networks, a simple count of the number of attractors does not reveal the combinatorial structure of the attractors. These points are illustrated in a reexamination of dynamics in a class of random Boolean networks considered previously by Kauffman. We also consider comparisons between dynamics in discrete networks and continuous analogues. A continuous analogue of a discrete network may have a different number of attractors for many different reasons. Some attractors in discrete networks may be associated with unstable dynamics, and several different attractors in a discrete network may be associated with a single attractor in the continuous case. Special problems in determining attractors in continuous systems arise when there is aperiodic dynamics associated with quasiperiodicity of deterministic chaos.

Representing perturbed dynamics in biological network models

NASA Astrophysics Data System (ADS)

Stoll, Gautier; Rougemont, Jacques; Naef, Felix

2007-07-01

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g., the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, we focus on how perturbations affect the set of fixed points and sizes of the basins of attraction. Our approach uses two analytical measures: the basin entropy H and the perturbation size Δ , a quantity that reflects the distance between the set of fixed points of the perturbed network and that of the unperturbed network. Applying our approach to the yeast-cell-cycle network introduced by Li [Proc. Natl. Acad. Sci. U.S.A. 101, 4781 (2004)] provides a low-dimensional and informative fingerprint of network behavior under large classes of perturbations. We identify interactions that are crucial for proper network function, and also pinpoint functionally redundant network connections. Selected perturbations exemplify the breadth of dynamical responses in this cell-cycle model.

Exploring complex networks.

PubMed

Strogatz, S H

2001-03-08

The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Estimating Topology of Discrete Dynamical Networks

NASA Astrophysics Data System (ADS)

Guo, Shu-Juan; Fu, Xin-Chu

2010-07-01

In this paper, by applying Lasalle's invariance principle and some results about the trace of a matrix, we propose a method for estimating the topological structure of a discrete dynamical network based on the dynamical evolution of the network. The network concerned can be directed or undirected, weighted or unweighted, and the local dynamics of each node can be nonidentical. The connections among the nodes can be all unknown or partially known. Finally, two examples, including a Hénon map and a central network, are illustrated to verify the theoretical results.

Globular Cluster Formation at High Density: A Model for Elemental Enrichment with Fast Recycling of Massive-star Debris

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elmegreen, Bruce G., E-mail: bge@us.ibm.com

The self-enrichment of massive star clusters by p -processed elements is shown to increase significantly with increasing gas density as a result of enhanced star formation rates and stellar scatterings compared to the lifetime of a massive star. Considering the type of cloud core where a globular cluster (GC) might have formed, we follow the evolution and enrichment of the gas and the time dependence of stellar mass. A key assumption is that interactions between massive stars are important at high density, including interactions between massive stars and massive-star binaries that can shred stellar envelopes. Massive-star interactions should also scattermore » low-mass stars out of the cluster. Reasonable agreement with the observations is obtained for a cloud-core mass of ∼4 × 10{sup 6} M {sub ⊙} and a density of ∼2 × 10{sup 6} cm{sup −3}. The results depend primarily on a few dimensionless parameters, including, most importantly, the ratio of the gas consumption time to the lifetime of a massive star, which has to be low, ∼10%, and the efficiency of scattering low-mass stars per unit dynamical time, which has to be relatively large, such as a few percent. Also for these conditions, the velocity dispersions of embedded GCs should be comparable to the high gas dispersions of galaxies at that time, so that stellar ejection by multistar interactions could cause low-mass stars to leave a dwarf galaxy host altogether. This could solve the problem of missing first-generation stars in the halos of Fornax and WLM.« less

Neural Networks for Rapid Design and Analysis

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.; Maghami, Peiman G.

1998-01-01

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Framework based on communicability and flow to analyze complex network dynamics

NASA Astrophysics Data System (ADS)

Gilson, M.; Kouvaris, N. E.; Deco, G.; Zamora-López, G.

2018-05-01

Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks.

PubMed

Onaga, Tomokatsu; Gleeson, James P; Masuda, Naoki

2017-09-08

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

NASA Astrophysics Data System (ADS)

Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki

2017-09-01

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Temporal networks

NASA Astrophysics Data System (ADS)

Holme, Petter; Saramäki, Jari

2012-10-01

A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names-temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered, but does not attempt to unify related terminology-rather, we want to make papers readable across disciplines.

Temporal efficiency evaluation and small-worldness characterization in temporal networks

PubMed Central

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-01-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314

Temporal efficiency evaluation and small-worldness characterization in temporal networks

NASA Astrophysics Data System (ADS)

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-09-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.

Spectrum of Lyapunov exponents of non-smooth dynamical systems of integrate-and-fire type.

PubMed

Zhou, Douglas; Sun, Yi; Rangan, Aaditya V; Cai, David

2010-04-01

We discuss how to characterize long-time dynamics of non-smooth dynamical systems, such as integrate-and-fire (I&F) like neuronal network, using Lyapunov exponents and present a stable numerical method for the accurate evaluation of the spectrum of Lyapunov exponents for this large class of dynamics. These dynamics contain (i) jump conditions as in the firing-reset dynamics and (ii) degeneracy such as in the refractory period in which voltage-like variables of the network collapse to a single constant value. Using the networks of linear I&F neurons, exponential I&F neurons, and I&F neurons with adaptive threshold, we illustrate our method and discuss the rich dynamics of these networks.

Dynamic load balancing of applications

DOEpatents

Wheat, S.R.

1997-05-13

An application-level method for dynamically maintaining global load balance on a parallel computer, particularly on massively parallel MIMD computers is disclosed. Global load balancing is achieved by overlapping neighborhoods of processors, where each neighborhood performs local load balancing. The method supports a large class of finite element and finite difference based applications and provides an automatic element management system to which applications are easily integrated. 13 figs.

Computational methods and software systems for dynamics and control of large space structures

NASA Technical Reports Server (NTRS)

Park, K. C.; Felippa, C. A.; Farhat, C.; Pramono, E.

1990-01-01

Two key areas of crucial importance to the computer-based simulation of large space structures are discussed. The first area involves multibody dynamics (MBD) of flexible space structures, with applications directed to deployment, construction, and maneuvering. The second area deals with advanced software systems, with emphasis on parallel processing. The latest research thrust in the second area involves massively parallel computers.

Hamiltonian dynamics for complex food webs

NASA Astrophysics Data System (ADS)

Kozlov, Vladimir; Vakulenko, Sergey; Wennergren, Uno

2016-03-01

We investigate stability and dynamics of large ecological networks by introducing classical methods of dynamical system theory from physics, including Hamiltonian and averaging methods. Our analysis exploits the topological structure of the network, namely the existence of strongly connected nodes (hubs) in the networks. We reveal new relations between topology, interaction structure, and network dynamics. We describe mechanisms of catastrophic phenomena leading to sharp changes of dynamics and hence completely altering the ecosystem. We also show how these phenomena depend on the structure of interaction between species. We can conclude that a Hamiltonian structure of biological interactions leads to stability and large biodiversity.

PARD3 dysfunction in conjunction with dynamic HIPPO signaling drives cortical enlargement with massive heterotopia.

PubMed

Liu, Wenying Angela; Chen, She; Li, Zhizhong; Lee, Choong Heon; Mirzaa, Ghayda; Dobyns, William B; Ross, M Elizabeth; Zhang, Jiangyang; Shi, Song-Hai

2018-06-01

Proper organization and orderly mitosis of radial glial progenitors (RGPs) drive the formation of a laminated mammalian cortex in the correct size. However, the molecular underpinnings of the intricate process remain largely unclear. Here we show that RGP behavior and cortical development are controlled by temporally distinct actions of partitioning-defective 3 (PARD3) in concert with dynamic HIPPO signaling. RGPs lacking PARD3 exhibit developmental stage-dependent abnormal switches in division mode, resulting in an initial overproduction of RGPs located largely outside the ventricular zone at the expense of deep-layer neurons. Ectopically localized RGPs subsequently undergo accelerated and excessive neurogenesis, leading to the formation of an enlarged cortex with massive heterotopia and increased seizure susceptibility. Simultaneous removal of HIPPO pathway effectors Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) suppresses cortical enlargement and heterotopia formation. These results define a dynamic regulatory program of mammalian cortical development and highlight a progenitor origin of megalencephaly with ribbon heterotopia and epilepsy. © 2018 Liu et al.; Published by Cold Spring Harbor Laboratory Press.

Characterizing system dynamics with a weighted and directed network constructed from time series data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Xiaoran, E-mail: sxr0806@gmail.com; School of Mathematics and Statistics, The University of Western Australia, Crawley WA 6009; Small, Michael, E-mail: michael.small@uwa.edu.au

In this work, we propose a novel method to transform a time series into a weighted and directed network. For a given time series, we first generate a set of segments via a sliding window, and then use a doubly symbolic scheme to characterize every windowed segment by combining absolute amplitude information with an ordinal pattern characterization. Based on this construction, a network can be directly constructed from the given time series: segments corresponding to different symbol-pairs are mapped to network nodes and the temporal succession between nodes is represented by directed links. With this conversion, dynamics underlying the timemore » series has been encoded into the network structure. We illustrate the potential of our networks with a well-studied dynamical model as a benchmark example. Results show that network measures for characterizing global properties can detect the dynamical transitions in the underlying system. Moreover, we employ a random walk algorithm to sample loops in our networks, and find that time series with different dynamics exhibits distinct cycle structure. That is, the relative prevalence of loops with different lengths can be used to identify the underlying dynamics.« less

Propagation, cascades, and agreement dynamics in complex communication and social networks

NASA Astrophysics Data System (ADS)

Lu, Qiming

Many modern and important technological, social, information and infrastructure systems can be viewed as complex systems with a large number of interacting components. Models of complex networks and dynamical interactions, as well as their applications are of fundamental interests in many aspects. Here, several stylized models of multiplex propagation and opinion dynamics are investigated on complex and empirical social networks. We first investigate cascade dynamics in threshold-controlled (multiplex) propagation on random geometric networks. We find that such local dynamics can serve as an efficient, robust, and reliable prototypical activation protocol in sensor networks in responding to various alarm scenarios. We also consider the same dynamics on a modified network by adding a few long-range communication links, resulting in a small-world network. We find that such construction can further enhance and optimize the speed of the network's response, while keeping energy consumption at a manageable level. We also investigate a prototypical agent-based model, the Naming Game, on two-dimensional random geometric networks. The Naming Game [A. Baronchelli et al., J. Stat. Mech.: Theory Exp. (2006) P06014.] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the Naming Games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially-embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case. When applying the model of Naming Game on empirical social networks, this stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.

Highly dynamic animal contact network and implications on disease transmission

PubMed Central

Chen, Shi; White, Brad J.; Sanderson, Michael W.; Amrine, David E.; Ilany, Amiyaal; Lanzas, Cristina

2014-01-01

Contact patterns among hosts are considered as one of the most critical factors contributing to unequal pathogen transmission. Consequently, networks have been widely applied in infectious disease modeling. However most studies assume static network structure due to lack of accurate observation and appropriate analytic tools. In this study we used high temporal and spatial resolution animal position data to construct a high-resolution contact network relevant to infectious disease transmission. The animal contact network aggregated at hourly level was highly variable and dynamic within and between days, for both network structure (network degree distribution) and individual rank of degree distribution in the network (degree order). We integrated network degree distribution and degree order heterogeneities with a commonly used contact-based, directly transmitted disease model to quantify the effect of these two sources of heterogeneity on the infectious disease dynamics. Four conditions were simulated based on the combination of these two heterogeneities. Simulation results indicated that disease dynamics and individual contribution to new infections varied substantially among these four conditions under both parameter settings. Changes in the contact network had a greater effect on disease dynamics for pathogens with smaller basic reproduction number (i.e. R0 < 2). PMID:24667241

Linking dynamics of the inhibitory network to the input structure

PubMed Central

Komarov, Maxim

2017-01-01

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network’s response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives. PMID:27650865

Dynamics of social balance on networks

NASA Astrophysics Data System (ADS)

Antal, T.; Krapivsky, P. L.; Redner, S.

2005-09-01

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad—a triangular loop with one or three unfriendly links—is reversed to make the triad balanced. With this dynamics, an infinite network undergoes a dynamic phase transition from a steady state to “paradise”—all links are friendly—as the propensity p for friendly links in an update event passes through 1/2 . A finite network always falls into a socially balanced absorbing state where no imbalanced triads remain. If the additional constraint that the number of imbalanced triads in the network not increase in an update is imposed, then the network quickly reaches a balanced final state.

Bellman Ford algorithm - in Routing Information Protocol (RIP)

NASA Astrophysics Data System (ADS)

Krianto Sulaiman, Oris; Mahmud Siregar, Amir; Nasution, Khairuddin; Haramaini, Tasliyah

2018-04-01

In a large scale network need a routing that can handle a lot number of users, one of the solutions to cope with large scale network is by using a routing protocol, There are 2 types of routing protocol that is static and dynamic, Static routing is manually route input based on network admin, while dynamic routing is automatically route input formed based on existing network. Dynamic routing is efficient used to network extensively because of the input of route automatic formed, Routing Information Protocol (RIP) is one of dynamic routing that uses the bellman-ford algorithm where this algorithm will search for the best path that traversed the network by leveraging the value of each link, so with the bellman-ford algorithm owned by RIP can optimize existing networks.

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community detection in complex networks.

Dynamic model of time-dependent complex networks.

PubMed

Hill, Scott A; Braha, Dan

2010-10-01

The characterization of the "most connected" nodes in static or slowly evolving complex networks has helped in understanding and predicting the behavior of social, biological, and technological networked systems, including their robustness against failures, vulnerability to deliberate attacks, and diffusion properties. However, recent empirical research of large dynamic networks (characterized by irregular connections that evolve rapidly) has demonstrated that there is little continuity in degree centrality of nodes over time, even when their degree distributions follow a power law. This unexpected dynamic centrality suggests that the connections in these systems are not driven by preferential attachment or other known mechanisms. We present an approach to explain real-world dynamic networks and qualitatively reproduce these dynamic centrality phenomena. This approach is based on a dynamic preferential attachment mechanism, which exhibits a sharp transition from a base pure random walk scheme.

Application of dynamic recurrent neural networks in nonlinear system identification

NASA Astrophysics Data System (ADS)

Du, Yun; Wu, Xueli; Sun, Huiqin; Zhang, Suying; Tian, Qiang

2006-11-01

An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.

Westerlund 1: monolithic formation of a starburst cluster

NASA Astrophysics Data System (ADS)

Negueruela, Ignacio; Clark, J. Simon; Ritchie, Ben; Goodwin, Simon

2015-08-01

Westerlund 1 is in all likelihood the most massive young cluster in the Milky Way, with a mass on the order of 105 Msol. We have been observing its massive star population for ten years, measuring radial velocity changes for a substantial fraction of its OB stars and evolved supergiants. The properties of the evolved population are entirely consisting with a single burst of star formation, in excellent agreement with the results of studies based on the lower-mass population.Here we will present two new studies of the cluster: 1) A direct measurement of its average radial velocity and velocity dispersion based on individual measurements for several dozen stars with constant radial velocity and 2) A search for massive stars in its immediate neighbourhood using multi-object spectroscopy.The results of these two studies show that Westerlund 1 is decidedly subvirial and has a systemic radial velocity significantly different from that of nearby gas, which was assumed to provide a dynamical distance by previous authors. Moreover, the dynamical distance is inconsistent with the properties of the high-mass stellar population. In addition, we find that the cluster is completely isolated, with hardly any massive star in its vicinity that could be associated in terms of distance modulus or radial velocity. The cluster halo does not extend much further than five parsec away from the centre. All these properties are very unusual among starburst clusters in the Local Universe, which tend to form in the context of large star-forming regions.Westerlund 1 is thus the best example we have of a starburst cluster formed monolithically.

A distance-limited sample of massive molecular outflows

NASA Astrophysics Data System (ADS)

Maud, L. T.; Moore, T. J. T.; Lumsden, S. L.; Mottram, J. C.; Urquhart, J. S.; Hoare, M. G.

2015-10-01

We have observed 99 mid-infrared-bright, massive young stellar objects and compact H II regions drawn from the Red MSX source survey in the J = 3-2 transition of 12CO and 13CO, using the James Clerk Maxwell Telescope. 89 targets are within 6 kpc of the Sun, covering a representative range of luminosities and core masses. These constitute a relatively unbiased sample of bipolar molecular outflows associated with massive star formation. Of these, 59, 17 and 13 sources (66, 19 and 15 per cent) are found to have outflows, show some evidence of outflow, and have no evidence of outflow, respectively. The time-dependent parameters of the high-velocity molecular flows are calculated using a spatially variable dynamic time-scale. The canonical correlations between the outflow parameters and source luminosity are recovered and shown to scale with those of low-mass sources. For coeval star formation, we find the scaling is consistent with all the protostars in an embedded cluster providing the outflow force, with massive stars up to ˜30 M⊙ generating outflows. Taken at face value, the results support the model of a scaled-up version of the accretion-related outflow-generation mechanism associated with discs and jets in low-mass objects with time-averaged accretion rates of ˜10-3 M⊙ yr-1 on to the cores. However, we also suggest an alternative model, in which the molecular outflow dynamics are dominated by the entrained mass and are unrelated to the details of the acceleration mechanism. We find no evidence that outflows contribute significantly to the turbulent kinetic energy of the surrounding dense cores.

Simulations of Fractal Star Cluster Formation. I. New Insights for Measuring Mass Segregation of Star Clusters with Substructure

NASA Astrophysics Data System (ADS)

Yu, Jincheng; Puzia, Thomas H.; Lin, Congping; Zhang, Yiwei

2017-05-01

We compare the existent methods, including the minimum spanning tree based method and the local stellar density based method, in measuring mass segregation of star clusters. We find that the minimum spanning tree method reflects more the compactness, which represents the global spatial distribution of massive stars, while the local stellar density method reflects more the crowdedness, which provides the local gravitational potential information. It is suggested to measure the local and the global mass segregation simultaneously. We also develop a hybrid method that takes both aspects into account. This hybrid method balances the local and the global mass segregation in the sense that the predominant one is either caused by dynamical evolution or purely accidental, especially when such information is unknown a priori. In addition, we test our prescriptions with numerical models and show the impact of binaries in estimating the mass segregation value. As an application, we use these methods on the Orion Nebula Cluster (ONC) observations and the Taurus cluster. We find that the ONC is significantly mass segregated down to the 20th most massive stars. In contrast, the massive stars of the Taurus cluster are sparsely distributed in many different subclusters, showing a low degree of compactness. The massive stars of Taurus are also found to be distributed in the high-density region of the subclusters, showing significant mass segregation at subcluster scales. Meanwhile, we also apply these methods to discuss the possible mechanisms of the dynamical evolution of the simulated substructured star clusters.

Simulations of Fractal Star Cluster Formation. I. New Insights for Measuring Mass Segregation of Star Clusters with Substructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Jincheng; Puzia, Thomas H.; Lin, Congping

2017-05-10

We compare the existent methods, including the minimum spanning tree based method and the local stellar density based method, in measuring mass segregation of star clusters. We find that the minimum spanning tree method reflects more the compactness, which represents the global spatial distribution of massive stars, while the local stellar density method reflects more the crowdedness, which provides the local gravitational potential information. It is suggested to measure the local and the global mass segregation simultaneously. We also develop a hybrid method that takes both aspects into account. This hybrid method balances the local and the global mass segregationmore » in the sense that the predominant one is either caused by dynamical evolution or purely accidental, especially when such information is unknown a priori. In addition, we test our prescriptions with numerical models and show the impact of binaries in estimating the mass segregation value. As an application, we use these methods on the Orion Nebula Cluster (ONC) observations and the Taurus cluster. We find that the ONC is significantly mass segregated down to the 20th most massive stars. In contrast, the massive stars of the Taurus cluster are sparsely distributed in many different subclusters, showing a low degree of compactness. The massive stars of Taurus are also found to be distributed in the high-density region of the subclusters, showing significant mass segregation at subcluster scales. Meanwhile, we also apply these methods to discuss the possible mechanisms of the dynamical evolution of the simulated substructured star clusters.« less

Recovery time after localized perturbations in complex dynamical networks

NASA Astrophysics Data System (ADS)

Mitra, Chiranjit; Kittel, Tim; Choudhary, Anshul; Kurths, Jürgen; Donner, Reik V.

2017-10-01

Maintaining the synchronous motion of dynamical systems interacting on complex networks is often critical to their functionality. However, real-world networked dynamical systems operating synchronously are prone to random perturbations driving the system to arbitrary states within the corresponding basin of attraction, thereby leading to epochs of desynchronized dynamics with a priori unknown durations. Thus, it is highly relevant to have an estimate of the duration of such transient phases before the system returns to synchrony, following a random perturbation to the dynamical state of any particular node of the network. We address this issue here by proposing the framework of single-node recovery time (SNRT) which provides an estimate of the relative time scales underlying the transient dynamics of the nodes of a network during its restoration to synchrony. We utilize this in differentiating the particularly slow nodes of the network from the relatively fast nodes, thus identifying the critical nodes which when perturbed lead to significantly enlarged recovery time of the system before resuming synchronized operation. Further, we reveal explicit relationships between the SNRT values of a network, and its global relaxation time when starting all the nodes from random initial conditions. Earlier work on relaxation time generally focused on investigating its dependence on macroscopic topological properties of the respective network. However, we employ the proposed concept for deducing microscopic relationships between topological features of nodes and their respective SNRT values. The framework of SNRT is further extended to a measure of resilience of the different nodes of a networked dynamical system. We demonstrate the potential of SNRT in networks of Rössler oscillators on paradigmatic topologies and a model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics illustrating the conceivable practical applicability of the proposed concept.

Models, Entropy and Information of Temporal Social Networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Márton; Bianconi, Ginestra

Temporal social networks are characterized by heterogeneous duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.

A network of networks.

PubMed

Iedema, Rick; Verma, Raj; Wutzke, Sonia; Lyons, Nigel; McCaughan, Brian

2017-04-10

Purpose To further our insight into the role of networks in health system reform, the purpose of this paper is to investigate how one agency, the NSW Agency for Clinical Innovation (ACI), and the multiple networks and enabling resources that it encompasses, govern, manage and extend the potential of networks for healthcare practice improvement. Design/methodology/approach This is a case study investigation which took place over ten months through the first author's participation in network activities and discussions with the agency's staff about their main objectives, challenges and achievements, and with selected services around the state of New South Wales to understand the agency's implementation and large system transformation activities. Findings The paper demonstrates that ACI accommodates multiple networks whose oversight structures, self-organisation and systems change approaches combined in dynamic ways, effectively yield a diversity of network governances. Further, ACI bears out a paradox of "centralised decentralisation", co-locating agents of innovation with networks of implementation and evaluation expertise. This arrangement strengthens and legitimates the role of the strategic hybrid - the healthcare professional in pursuit of change and improvement, and enhances their influence and impact on the wider system. Research limitations/implications While focussing the case study on one agency only, this study is unique as it highlights inter-network connections. Contributing to the literature on network governance, this paper identifies ACI as a "network of networks" through which resources, expectations and stakeholder dynamics are dynamically and flexibly mediated and enhanced. Practical implications The co-location of and dynamic interaction among clinical networks may create synergies among networks, nurture "strategic hybrids", and enhance the impact of network activities on health system reform. Social implications Network governance requires more from network members than participation in a single network, as it involves health service professionals and consumers in a multi-network dynamic. This dynamic requires deliberations and collaborations to be flexible, and it increasingly positions members as "strategic hybrids" - people who have moved on from singular taken-as-given stances and identities, towards hybrid positionings and flexible perspectives. Originality/value This paper is novel in that it identifies a critical feature of health service reform and large system transformation: network governance is empowered through the dynamic co-location of and collaboration among healthcare networks, particularly when complemented with "enabler" teams of people specialising in programme implementation and evaluation.

Traffic Flow Density Distribution Based on FEM

NASA Astrophysics Data System (ADS)

Ma, Jing; Cui, Jianming

In analysis of normal traffic flow, it usually uses the static or dynamic model to numerical analyze based on fluid mechanics. However, in such handling process, the problem of massive modeling and data handling exist, and the accuracy is not high. Finite Element Method (FEM) is a production which is developed from the combination of a modern mathematics, mathematics and computer technology, and it has been widely applied in various domain such as engineering. Based on existing theory of traffic flow, ITS and the development of FEM, a simulation theory of the FEM that solves the problems existing in traffic flow is put forward. Based on this theory, using the existing Finite Element Analysis (FEA) software, the traffic flow is simulated analyzed with fluid mechanics and the dynamics. Massive data processing problem of manually modeling and numerical analysis is solved, and the authenticity of simulation is enhanced.

Starburst clusters in the Galactic center

NASA Astrophysics Data System (ADS)

Habibi, Maryam

2014-09-01

The central region of the Galaxy is the most active site of star formation in the Milky Way, where massive stars have formed very recently and are still forming today. The rich population of massive stars in the Galactic center provide a unique opportunity to study massive stars in their birth environment and probe their initial mass function, which is the spectrum of stellar masses at their birth. The Arches cluster is the youngest among the three massive clusters in the Galactic center, providing a collection of high-mass stars and a very dense core which makes this cluster an excellent site to address questions about massive star formation, the stellar mass function and the dynamical evolution of massive clusters in the Galactic center. In this thesis, I perform an observational study of the Arches cluster using K_{s}-band imaging obtained with NAOS/CONICA at the VLT combined with Subaru/Cisco J-band data to gain a full understanding of the cluster mass distribution out to its tidal radius for the first time. Since the light from the Galactic center reaches us through the Galactic disc, the extinction correction is crucial when studying this region. I use a Bayesian method to construct a realistic extinction map of the cluster. It is shown in this study that the determination of the mass of the most massive star in the Arches cluster, which had been used in previous studies to establish an upper mass limit for the star formation process in the Milky Way, strongly depends on the assumed slope of the extinction law. Assuming the two regimes of widely used infrared extinction laws, I show that the difference can reach up to 30% for individually derived stellar masses and Δ A_{Ks}˜ 1 magnitude in acquired K_{s}-band extinction, while the present-day mass function slope changes by ˜ 0.17 dex. The present-day mass function slope derived assuming the more recent extinction law, which suggests a steeper wavelength dependence for the infrared extinction law, reveals an overpopulation of massive stars in the core (r<0.2 pc) with a flat slope of α_{Nishi}=-1.50 ±0.35 in comparison to the Salpeter slope of α=-2.3. The slope of the mass function increases to α_{Nishi}=-2.21 ±0.27 in the intermediate annulus (0.2

Dynamic Server-Based KML Code Generator Method for Level-of-Detail Traversal of Geospatial Data

NASA Technical Reports Server (NTRS)

Baxes, Gregory; Mixon, Brian; Linger, TIm

2013-01-01

Web-based geospatial client applications such as Google Earth and NASA World Wind must listen to data requests, access appropriate stored data, and compile a data response to the requesting client application. This process occurs repeatedly to support multiple client requests and application instances. Newer Web-based geospatial clients also provide user-interactive functionality that is dependent on fast and efficient server responses. With massively large datasets, server-client interaction can become severely impeded because the server must determine the best way to assemble data to meet the client applications request. In client applications such as Google Earth, the user interactively wanders through the data using visually guided panning and zooming actions. With these actions, the client application is continually issuing data requests to the server without knowledge of the server s data structure or extraction/assembly paradigm. A method for efficiently controlling the networked access of a Web-based geospatial browser to server-based datasets in particular, massively sized datasets has been developed. The method specifically uses the Keyhole Markup Language (KML), an Open Geospatial Consortium (OGS) standard used by Google Earth and other KML-compliant geospatial client applications. The innovation is based on establishing a dynamic cascading KML strategy that is initiated by a KML launch file provided by a data server host to a Google Earth or similar KMLcompliant geospatial client application user. Upon execution, the launch KML code issues a request for image data covering an initial geographic region. The server responds with the requested data along with subsequent dynamically generated KML code that directs the client application to make follow-on requests for higher level of detail (LOD) imagery to replace the initial imagery as the user navigates into the dataset. The approach provides an efficient data traversal path and mechanism that can be flexibly established for any dataset regardless of size or other characteristics. The method yields significant improvements in userinteractive geospatial client and data server interaction and associated network bandwidth requirements. The innovation uses a C- or PHP-code-like grammar that provides a high degree of processing flexibility. A set of language lexer and parser elements is provided that offers a complete language grammar for writing and executing language directives. A script is wrapped and passed to the geospatial data server by a client application as a component of a standard KML-compliant statement. The approach provides an efficient means for a geospatial client application to request server preprocessing of data prior to client delivery. Data is structured in a quadtree format. As the user zooms into the dataset, geographic regions are subdivided into four child regions. Conversely, as the user zooms out, four child regions collapse into a single, lower-LOD region. The approach provides an efficient data traversal path and mechanism that can be flexibly established for any dataset regardless of size or other characteristics.

Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators.

PubMed

Xu, Kesheng; Maidana, Jean Paul; Castro, Samy; Orio, Patricio

2018-05-30

Chaotic dynamics has been shown in the dynamics of neurons and neural networks, in experimental data and numerical simulations. Theoretical studies have proposed an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to network behaviour and whether the dynamical richness of neural networks is sensitive to the dynamics of isolated neurons, still remain open questions. We investigated synchronization transitions in heterogeneous neural networks of neurons connected by electrical coupling in a small world topology. The nodes in our model are oscillatory neurons that - when isolated - can exhibit either chaotic or non-chaotic behaviour, depending on conductance parameters. We found that the heterogeneity of firing rates and firing patterns make a greater contribution than chaos to the steepness of the synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in the transition to full synchrony. Moreover, macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as multi-stable behaviour, where the network dynamically switches between a number of different semi-synchronized, metastable states.

Visualizing Mobility of Public Transportation System.

PubMed

Zeng, Wei; Fu, Chi-Wing; Arisona, Stefan Müller; Erath, Alexander; Qu, Huamin

2014-12-01

Public transportation systems (PTSs) play an important role in modern cities, providing shared/massive transportation services that are essential for the general public. However, due to their increasing complexity, designing effective methods to visualize and explore PTS is highly challenging. Most existing techniques employ network visualization methods and focus on showing the network topology across stops while ignoring various mobility-related factors such as riding time, transfer time, waiting time, and round-the-clock patterns. This work aims to visualize and explore passenger mobility in a PTS with a family of analytical tasks based on inputs from transportation researchers. After exploring different design alternatives, we come up with an integrated solution with three visualization modules: isochrone map view for geographical information, isotime flow map view for effective temporal information comparison and manipulation, and OD-pair journey view for detailed visual analysis of mobility factors along routes between specific origin-destination pairs. The isotime flow map linearizes a flow map into a parallel isoline representation, maximizing the visualization of mobility information along the horizontal time axis while presenting clear and smooth pathways from origin to destinations. Moreover, we devise several interactive visual query methods for users to easily explore the dynamics of PTS mobility over space and time. Lastly, we also construct a PTS mobility model from millions of real passenger trajectories, and evaluate our visualization techniques with assorted case studies with the transportation researchers.

(abstract) Spacecraft Doppler Tracking with the Deep Space Network in the Search for Gravitational Waves

NASA Technical Reports Server (NTRS)

Asmar, Sami; Renzetti, Nicholas

1994-01-01

The Deep Space Network generates accurate radio science data observables for investigators who use radio links between spacecraft and the Earth to examine small changes in the phase and/or amplitude of the signal to study a wide variety of structures and phenomena in space. Several such studies are directed at aspects of the theory of general relativity such as gravitational redshift and gravitational waves. A gravitational wave is a propagating, polarized gravitational field, a ripple in the curvature of space-time. In Einstein's theory of general relativity, the waves are propagating solutions of the Einstein field equations. Their amplitudes are dimensionless strain amplitudes that change the fractional difference in distance between test masses and the rates at which separated clocks keep time. Predicted by all relativistic theories of gravity, they are extremely weak (the ratio of gravitational forces to electrical forces is about 10(sup -40)) and are generated at detectable levels only by astrophysical sources - very massive sources under violent dynamical conditions. The waves have never been detected but searches in the low-frequency band using Doppler tracking of many spacecraft have been conducted and others are being planned. Upper limits have been placed on the gravitational wave strength with the best sensitivities to date are for periodic waves being 7 x 10(sup -15).

Focal-Plane Sensing-Processing: A Power-Efficient Approach for the Implementation of Privacy-Aware Networked Visual Sensors

PubMed Central

Fernández-Berni, Jorge; Carmona-Galán, Ricardo; del Río, Rocío; Kleihorst, Richard; Philips, Wilfried; Rodríguez-Vázquez, Ángel

2014-01-01

The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects. PMID:25195849

Focal-plane sensing-processing: a power-efficient approach for the implementation of privacy-aware networked visual sensors.

PubMed

Fernández-Berni, Jorge; Carmona-Galán, Ricardo; del Río, Rocío; Kleihorst, Richard; Philips, Wilfried; Rodríguez-Vázquez, Ángel

2014-08-19

The capture, processing and distribution of visual information is one of the major challenges for the paradigm of the Internet of Things. Privacy emerges as a fundamental barrier to overcome. The idea of networked image sensors pervasively collecting data generates social rejection in the face of sensitive information being tampered by hackers or misused by legitimate users. Power consumption also constitutes a crucial aspect. Images contain a massive amount of data to be processed under strict timing requirements, demanding high-performance vision systems. In this paper, we describe a hardware-based strategy to concurrently address these two key issues. By conveying processing capabilities to the focal plane in addition to sensing, we can implement privacy protection measures just at the point where sensitive data are generated. Furthermore, such measures can be tailored for efficiently reducing the computational load of subsequent processing stages. As a proof of concept, a full-custom QVGA vision sensor chip is presented. It incorporates a mixed-signal focal-plane sensing-processing array providing programmable pixelation of multiple image regions in parallel. In addition to this functionality, the sensor exploits reconfigurability to implement other processing primitives, namely block-wise dynamic range adaptation, integral image computation and multi-resolution filtering. The proposed circuitry is also suitable to build a granular space, becoming the raw material for subsequent feature extraction and recognition of categorized objects.

Zoning Issues and Area of Interest Management in Massively Multiplayer Online Games

NASA Astrophysics Data System (ADS)

Ahmed, Dewan Tanvir; Shirmohammadi, Shervin

Game is a way of entertainment, a means for excitement, fun and socialization. Online games have achieved popularity due to increasing broadband adoption among consumers. Relatively cheap bandwidth Internet connections allow large number of players to play together. Since the introduction of Network Virtual Environment (NVE) in 1980s for military simulation, many interesting applications have been evolved over the past few decades. Massively multiplayer online (role-playing) game, MMOG or MMORPG, is a new genre of online games that has emerged with the introduction of Ultima since 1997. It is a kind of online computer game with the participation of hundreds of thousands of players in a virtual world.

Performance effects of irregular communications patterns on massively parallel multiprocessors

NASA Technical Reports Server (NTRS)

Saltz, Joel; Petiton, Serge; Berryman, Harry; Rifkin, Adam

1991-01-01

A detailed study of the performance effects of irregular communications patterns on the CM-2 was conducted. The communications capabilities of the CM-2 were characterized under a variety of controlled conditions. In the process of carrying out the performance evaluation, extensive use was made of a parameterized synthetic mesh. In addition, timings with unstructured meshes generated for aerodynamic codes and a set of sparse matrices with banded patterns on non-zeroes were performed. This benchmarking suite stresses the communications capabilities of the CM-2 in a range of different ways. Benchmark results demonstrate that it is possible to make effective use of much of the massive concurrency available in the communications network.

A massively parallel computational approach to coupled thermoelastic/porous gas flow problems

NASA Technical Reports Server (NTRS)

Shia, David; Mcmanus, Hugh L.

1995-01-01

A new computational scheme for coupled thermoelastic/porous gas flow problems is presented. Heat transfer, gas flow, and dynamic thermoelastic governing equations are expressed in fully explicit form, and solved on a massively parallel computer. The transpiration cooling problem is used as an example problem. The numerical solutions have been verified by comparison to available analytical solutions. Transient temperature, pressure, and stress distributions have been obtained. Small spatial oscillations in pressure and stress have been observed, which would be impractical to predict with previously available schemes. Comparisons between serial and massively parallel versions of the scheme have also been made. The results indicate that for small scale problems the serial and parallel versions use practically the same amount of CPU time. However, as the problem size increases the parallel version becomes more efficient than the serial version.

Dissolved Massive Metal-rich Globular Clusters Can Cause the Range of UV Upturn Strengths Found among Early-type Galaxies

NASA Astrophysics Data System (ADS)

Goudfrooij, Paul

2018-04-01

I discuss a scenario in which the ultraviolet (UV) upturn of giant early-type galaxies (ETGs) is primarily due to helium-rich stellar populations that formed in massive metal-rich globular clusters (GCs), which subsequently dissolved in the strong tidal field in the central regions of the massive host galaxy. These massive GCs are assumed to show UV upturns similar to those observed recently in M87, the central giant elliptical galaxy in the Virgo cluster of galaxies. Data taken from the literature reveal a strong correlation between the strength of the UV upturn and the specific frequency of metal-rich GCs in ETGs. Adopting a Schechter function parameterization of GC mass functions, simulations of long-term dynamical evolution of GC systems show that the observed correlation between UV upturn strength and GC specific frequency can be explained by variations in the characteristic truncation mass {{ \\mathcal M }}{{c}} such that {{ \\mathcal M }}{{c}} increases with ETG luminosity in a way that is consistent with observed GC luminosity functions in ETGs. These findings suggest that the nature of the UV upturn in ETGs and the variation of its strength among ETGs are causally related to that of helium-rich populations in massive GCs, rather than intrinsic properties of field stars in massive galactic spheroids. With this in mind, I predict that future studies will find that [N/Fe] decreases with increasing galactocentric radius in massive ETGs, and that such gradients have the largest amplitudes in ETGs with the strongest UV upturns.

Controllability of flow-conservation networks

NASA Astrophysics Data System (ADS)

Zhao, Chen; Zeng, An; Jiang, Rui; Yuan, Zhengzhong; Wang, Wen-Xu

2017-07-01

The ultimate goal of exploring complex networks is to control them. As such, controllability of complex networks has been intensively investigated. Despite recent advances in studying the impact of a network's topology on its controllability, a comprehensive understanding of the synergistic impact of network topology and dynamics on controllability is still lacking. Here, we explore the controllability of flow-conservation networks, trying to identify the minimal number of driver nodes that can guide the network to any desirable state. We develop a method to analyze the controllability on flow-conservation networks based on exact controllability theory, transforming the original analysis on adjacency matrix to Laplacian matrix. With this framework, we systematically investigate the impact of some key factors of networks, including link density, link directionality, and link polarity, on the controllability of these networks. We also obtain the analytical equations by investigating the network's structural properties approximatively and design the efficient tools. Finally, we consider some real networks with flow dynamics, finding that their controllability is significantly different from that predicted by only considering the topology. These findings deepen our understanding of network controllability with flow-conservation dynamics and provide a general framework to incorporate real dynamics in the analysis of network controllability.

Nonblocking Clos networks of multiple ROADM rings for mega data centers.

PubMed

Zhao, Li; Ye, Tong; Hu, Weisheng

2015-11-02

Optical networks have been introduced to meet the bandwidth requirement of mega data centers (DC). Most existing approaches are neither scalable to face the massive growth of DCs, nor contention-free enough to provide full bisection bandwidth. To solve this problem, we propose two symmetric network structures: ring-MEMS-ring (RMR) network and MEMS-ring-MEMS (MRM) network based on classical Clos theory. New strategies are introduced to overcome the additional wavelength constraints that did not exist in the traditional Clos network. Two structures that followed the strategies can enable high scalability and nonblocking property simultaneously. The one-to-one correspondence of the RMR and MRM structures to a Clos is verified and the nonblocking conditions are given along with the routing algorithms. Compared to a typical folded-Clos network, both structures are more readily scalable to future mega data centers with 51200 racks while reducing number of long cables significantly. We show that the MRM network is more cost-effective than the RMR network, since the MRM network does not need tunable lasers to achieve nonblocking routing.

Upper Limits on the Presence of Central Massive Black Holes in Two Ultra-compact Dwarf Galaxies in Centaurus A

NASA Astrophysics Data System (ADS)

Voggel, Karina T.; Seth, Anil C.; Neumayer, Nadine; Mieske, Steffen; Chilingarian, Igor; Ahn, Christopher; Baumgardt, Holger; Hilker, Michael; Nguyen, Dieu D.; Romanowsky, Aaron J.; Walsh, Jonelle L.; den Brok, Mark; Strader, Jay

2018-05-01

The recent discovery of massive black holes (BHs) in the centers of high-mass ultra-compact dwarf galaxies (UCDs) suggests that at least some are the stripped nuclear star clusters of dwarf galaxies. We present the first study that investigates whether such massive BHs, and therefore stripped nuclei, also exist in low-mass (M < 107 M ⊙) UCDs. We constrain the BH masses of two UCDs located in Centaurus A (UCD 320 and UCD 330) using Jeans modeling of the resolved stellar kinematics from adaptive optics data obtained with the SINFONI integral field spectrograph at the Very Large Telescope (VLT/SINFONI). No massive BHs are found in either UCD. We find a 3σ upper limit on the central BH mass in UCD 330 of M • < 1.0 × 105 M ⊙, which corresponds to 1.7% of the total mass. This excludes a high-mass fraction BH and would only allow low-mass BHs similar to those claimed to be detected in Local Group globular clusters. For UCD 320, poorer data quality results in a less constraining 3σ upper limit of M • < 1 × 106 M ⊙, which is equal to 37.7% of the total mass. The dynamical mass-to-light ratios of UCD 320 and UCD 330 are not inflated compared to predictions from stellar population models. The non-detection of BHs in these low-mass UCDs is consistent with the idea that elevated dynamical mass-to-light ratios do indicate the presence of a substantial BH. Although no massive BHs are detected, these systems could still be stripped nuclei. The strong rotation (v/σ of 0.3–0.4) in both UCDs and the two-component light profile in UCD 330 support the idea that these UCDs may be stripped nuclei of low-mass galaxies whose BH occupation fraction is not yet known.

BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling.

PubMed

Feng, Song; Ollivier, Julien F; Swain, Peter S; Soyer, Orkun S

2015-10-30

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Exploring the structure and function of temporal networks with dynamic graphlets

PubMed Central

Hulovatyy, Y.; Chen, H.; Milenković, T.

2015-01-01

Motivation: With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships. Results: We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging. Availability and implementation: http://www.nd.edu/∼cone/DG. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26072480

Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models

PubMed Central

Snijders, Tom A.B.; Steglich, Christian E.G.

2014-01-01

Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of generalized linear statistical models they aim to be realistic detailed representations of network dynamics in empirical data sets. Statistical parallels to micro-macro considerations can be found in the estimation of parameters determining local actor behavior from empirical data, and the assessment of goodness of fit from the correspondence with network-level descriptives. This article studies several network-level consequences of dynamic actor-based models applied to represent cross-sectional network data. Two examples illustrate how network-level characteristics can be obtained as emergent features implied by micro-specifications of actor-based models. PMID:25960578

Using Network Dynamical Influence to Drive Consensus

NASA Astrophysics Data System (ADS)

Punzo, Giuliano; Young, George F.; MacDonald, Malcolm; Leonard, Naomi E.

2016-05-01

Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.

Dynamic defense and network randomization for computer systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chavez, Adrian R.; Stout, William M. S.; Hamlet, Jason R.

The various technologies presented herein relate to determining a network attack is taking place, and further to adjust one or more network parameters such that the network becomes dynamically configured. A plurality of machine learning algorithms are configured to recognize an active attack pattern. Notification of the attack can be generated, and knowledge gained from the detected attack pattern can be utilized to improve the knowledge of the algorithms to detect a subsequent attack vector(s). Further, network settings and application communications can be dynamically randomized, wherein artificial diversity converts control systems into moving targets that help mitigate the early reconnaissancemore » stages of an attack. An attack(s) based upon a known static address(es) of a critical infrastructure network device(s) can be mitigated by the dynamic randomization. Network parameters that can be randomized include IP addresses, application port numbers, paths data packets navigate through the network, application randomization, etc.« less

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

PubMed

Liu, Lizhen; Sun, Xiaowu; Song, Wei; Du, Chao

2018-06-01

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.

PubMed

Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

2017-07-20

Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

Markov State Models of gene regulatory networks.

PubMed

Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L

2017-02-06

Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.

Disease dynamics in a dynamic social network

NASA Astrophysics Data System (ADS)

Christensen, Claire; Albert, István; Grenfell, Bryan; Albert, Réka

2010-07-01

We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.

Near real-time traffic routing

NASA Technical Reports Server (NTRS)

Yang, Chaowei (Inventor); Xie, Jibo (Inventor); Zhou, Bin (Inventor); Cao, Ying (Inventor)

2012-01-01

A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.

Demography and population dynamics of massive coral communities in adjacent high latitude regions (United Arab Emirates).

PubMed

Foster, Kristi A; Foster, Greg

2013-01-01

Individual massive coral colonies, primarily faviids and poritids, from three distinct assemblages within the southeastern Arabian Gulf and northwestern Gulf of Oman (United Arab Emirates) were studied from 2006-2009. Annual photographic censuses of approximately 2000 colonies were used to describe the demographics (size class frequencies, abundance, area cover) and population dynamics under "normal" environmental conditions. Size class transitions included growth, which occurred in 10-20% of the colonies, followed in decending order by partial mortality (3-16%), colony fission (<5%) and ramet fusion (<3%). Recruitment and whole colony mortality rates were low (<0.7 colonies/m(2)) with minimal interannual variation. Transition matrices indicated that the Arabian Gulf assemblages have declining growth rates (λ<1) whereas the massive coral population is stable (λ = 1) in the Gulf of Oman. Projection models indicated that (i) the Arabian Gulf population and area cover declines would be exacerbated under 10-year and 16-year disturbance scenarios as the vital rates do not allow for recovery to pre-disturbance levels during these timeframes, and (ii) the Gulf of Oman assemblage could return to its pre-disturbance area cover but its overall population size would not fully recover under the same scenarios.

Demography and Population Dynamics of Massive Coral Communities in Adjacent High Latitude Regions (United Arab Emirates)

PubMed Central

Foster, Kristi A.; Foster, Greg

2013-01-01

Individual massive coral colonies, primarily faviids and poritids, from three distinct assemblages within the southeastern Arabian Gulf and northwestern Gulf of Oman (United Arab Emirates) were studied from 2006–2009. Annual photographic censuses of approximately 2000 colonies were used to describe the demographics (size class frequencies, abundance, area cover) and population dynamics under “normal” environmental conditions. Size class transitions included growth, which occurred in 10–20% of the colonies, followed in decending order by partial mortality (3–16%), colony fission (<5%) and ramet fusion (<3%). Recruitment and whole colony mortality rates were low (<0.7 colonies/m2) with minimal interannual variation. Transition matrices indicated that the Arabian Gulf assemblages have declining growth rates (λ<1) whereas the massive coral population is stable (λ = 1) in the Gulf of Oman. Projection models indicated that (i) the Arabian Gulf population and area cover declines would be exacerbated under 10-year and 16-year disturbance scenarios as the vital rates do not allow for recovery to pre-disturbance levels during these timeframes, and (ii) the Gulf of Oman assemblage could return to its pre-disturbance area cover but its overall population size would not fully recover under the same scenarios. PMID:23990923

Embedding dynamical networks into distributed models

NASA Astrophysics Data System (ADS)

Innocenti, Giacomo; Paoletti, Paolo

2015-07-01

Large networks of interacting dynamical systems are well-known for the complex behaviours they are able to display, even when each node features a quite simple dynamics. Despite examples of such networks being widespread both in nature and in technological applications, the interplay between the local and the macroscopic behaviour, through the interconnection topology, is still not completely understood. Moreover, traditional analytical methods for dynamical response analysis fail because of the intrinsically large dimension of the phase space of the network which makes the general problem intractable. Therefore, in this paper we develop an approach aiming to condense all the information in a compact description based on partial differential equations. By focusing on propagative phenomena, rigorous conditions under which the original network dynamical properties can be successfully analysed within the proposed framework are derived as well. A network of Fitzhugh-Nagumo systems is finally used to illustrate the effectiveness of the proposed method.

Clustering promotes switching dynamics in networks of noisy neurons

NASA Astrophysics Data System (ADS)

Franović, Igor; Klinshov, Vladimir

2018-02-01

Macroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.

Isophote Shapes Of Early-Type Galaxies In Massive Clusters At Z 1 And 0

NASA Astrophysics Data System (ADS)

Mitsuda, Kazuma; Doi, Mamoru; Morokuma, Tomoki; Suzuki, Nao; Yasuda, Naoki; Perlmutter, Saul; Aldering, Greg; Meyers, Joshua

2017-06-01

Dynamics of early-type galaxies (ETGs), whether they are supported by rotation or dispersion, is a clue to understand their assembly history. We compare the isophote shape parameter a4 between z ˜ 1 and 0 as a proxy for dynamics to investigate the epoch at which the dynamical properties are established. We create cluster ETG samples with stellar masses of log(M✽/M⦿) ≥ 10.5 with spectroscopic redshifts. We have 130 ETGs from the Hubble Space Telescope Cluster Supernova Survey for z ˜ 1 and 355 ETGs from the Sloan Digital Sky Survey for z ˜ 0. We find similar dependence of the a4 parameter on the mass at z ˜ 1 and 0; the main population changes from disky (a4 > 0) to boxy (a4 ≤ 0) at a critical mass of log(M✽/M⦿) 11.5 with the massive end dominated by boxy ETGs. The disky ETG fraction is consistent between these redshifts. Although uncertainties are large, the results suggest that the isophote shapes and probably dynamical properties of cluster ETGs are already in place at z > 1 and do not significantly evolve in z < 1, despite significant size evolution. The constant disky fraction imply that the processes responsible for the size evolution is not enough violent to convert the dynamical properties of ETGs.

Collective relaxation dynamics of small-world networks

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N , average degree k , and topological randomness q . We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q , including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Collective relaxation dynamics of small-world networks.

PubMed

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N, average degree k, and topological randomness q. We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q, including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

Chaotic Dynamics of Trans-Neptunian Objects Perturbed by Planet Nine

NASA Astrophysics Data System (ADS)

Hadden, Sam; Li, Gongjie; Payne, Matthew J.; Holman, Matthew J.

2018-06-01

Observations of clustering among the orbits of the most distant trans-Neptunian objects (TNOs) has inspired interest in the possibility of an undiscovered ninth planet lurking in the outskirts of the solar system. Numerical simulations by a number of authors have demonstrated that, with appropriate choices of planet mass and orbit, such a planet can maintain clustering in the orbital elements of the population of distant TNOs, similar to the observed sample. However, many aspects of the rich underlying dynamical processes induced by such a distant eccentric perturber have not been fully explored. We report the results of our investigation of the dynamics of coplanar test-particles that interact with a massive body on an circular orbit (Neptune) and a massive body on a more distant, highly eccentric orbit (the putative Planet Nine). We find that a detailed examination of our idealized simulations affords tremendous insight into the rich test-particle dynamics that are possible. In particular, we find that chaos and resonance overlap plays an important role in particles’ dynamical evolution. We develop a simple mapping model that allows us to understand, in detail, the web of overlapped mean-motion resonances explored by chaotically evolving particles. We also demonstrate that gravitational interactions with Neptune can have profound effects on the orbital evolution of particles. Our results serve as a starting point for a better understanding of the dynamical behavior observed in more complicated simulations that can be used to constrain the mass and orbit of Planet Nine.

Social networks as a tool for science communication and public engagement: focus on Twitter.

PubMed

López-Goñi, Ignacio; Sánchez-Angulo, Manuel

2018-02-01

Social networks have been used to teach and engage people about the importance of science. The integration of social networks in the daily routines of faculties and scientists is strongly recommended to increase their personal brand, improve their skills, enhance their visibility, share and communicate science to society, promote scientific culture, and even as a tool for teaching and learning. Here we review the use of Twitter in science and comment on our previous experience of using this social network as a platform for a Massive Online Open Course (MOOC) in Spain and Latin America. We propose to extend this strategy to a pan-European Microbiology MOOC in the near future. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Characterization of attacks on public telephone networks

NASA Astrophysics Data System (ADS)

Lorenz, Gary V.; Manes, Gavin W.; Hale, John C.; Marks, Donald; Davis, Kenneth; Shenoi, Sujeet

2001-02-01

The U.S. Public Telephone Network (PTN) is a massively connected distributed information systems, much like the Internet. PTN signaling, transmission and operations functions must be protected from physical and cyber attacks to ensure the reliable delivery of telecommunications services. The increasing convergence of PTNs with wireless communications systems, computer networks and the Internet itself poses serious threats to our nation's telecommunications infrastructure. Legacy technologies and advanced services encumber well-known and as of yet undiscovered vulnerabilities that render them susceptible to cyber attacks. This paper presents a taxonomy of cyber attacks on PTNs in converged environments that synthesizes exploits in computer and communications network domains. The taxonomy provides an opportunity for the systematic exploration of mitigative and preventive strategies, as well as for the identification and classification of emerging threats.

Pardon the Disruption ... Innovation Changes How We Think about Higher Education

ERIC Educational Resources Information Center

DiSalvio, Philip

2012-01-01

This article illustrates the landscape-changing potential of the "disruptive innovation" taking place on the shores of the Charles River. Learning technologies now being used in the massive open online course (MOOC) movement, some suggest, will change the way people think about higher education. MOOCs are based on an open-networked learning…

When the Chips Are down: Taking Time to Pay Attention to Real Issues

ERIC Educational Resources Information Center

Model, David

2011-01-01

Global warming, deforestation, destruction of the oceans, hunger, poverty, human rights abuses and war crimes will, at best, be redressed by empty words and token gestures unless the public imbibes massive doses of caffeine. Unfortunately the public's attention seems to be focused elsewhere. Blackberries, cell phones, social networks on the…

Surveillance in the Information Age: Text Quantification, Anomaly Detection, and Empirical Evaluation

ERIC Educational Resources Information Center

Lu, Hsin-Min

2010-01-01

Deep penetration of personal computers, data communication networks, and the Internet has created a massive platform for data collection, dissemination, storage, and retrieval. Large amounts of textual data are now available at a very low cost. Valuable information, such as consumer preferences, new product developments, trends, and opportunities,…

Participant Association and Emergent Curriculum in a MOOC: Can the Community Be the Curriculum?

ERIC Educational Resources Information Center

Bell, Frances; Mackness, Jenny; Funes, Mariana

2016-01-01

We investigated how participants associated with each other and developed community in a Massive Open Online Course (MOOC) about Rhizomatic Learning (Rhizo14).We compared learner experiences in two social networking sites (SNSs), Facebook and Twitter. Our combination of thematic analysis of qualitative survey data with analysis of participant…

RAMBOT: A Connectionist Expert System That Learns by Example.

ERIC Educational Resources Information Center

Mozer, Michael C.

One solution to the problem of getting expert knowledge into expert systems would be to endow the systems with powerful learning procedures that could discover appropriate behaviors by observing an expert in action. A promising source of such learning procedures can be found in recent work on connectionist networks, which are massively parallel…