Topology-selective jamming of fully-connected, code-division random-access networks
NASA Technical Reports Server (NTRS)
Polydoros, Andreas; Cheng, Unjeng
1990-01-01
The purpose is to introduce certain models of topology selective stochastic jamming and examine its impact on a class of fully-connected, spread-spectrum, slotted ALOHA-type random access networks. The theory covers dedicated as well as half-duplex units. The dominant role of the spatial duty factor is established, and connections with the dual concept of time selective jamming are discussed. The optimal choices of coding rate and link access parameters (from the users' side) and the jamming spatial fraction are numerically established for DS and FH spreading.
Kartsakli, Elli; Antonopoulos, Angelos; Alonso, Luis; Verikoukis, Christos
2014-01-01
Relay sensor networks are often employed in end-to-end healthcare applications to facilitate the information flow between patient worn sensors and the medical data center. Medium access control (MAC) protocols, based on random linear network coding (RLNC), are a novel and suitable approach to efficiently handle data dissemination. However, several challenges arise, such as additional delays introduced by the intermediate relay nodes and decoding failures, due to channel errors. In this paper, we tackle these issues by adopting a cloud architecture where the set of relays is connected to a coordinating entity, called cloud manager. We propose a cloud-assisted RLNC-based MAC protocol (CLNC-MAC) and develop a mathematical model for the calculation of the key performance metrics, namely the system throughput, the mean completion time for data delivery and the energy efficiency. We show the importance of central coordination in fully exploiting the gain of RLNC under error-prone channels. PMID:24618727
MATIN: A Random Network Coding Based Framework for High Quality Peer-to-Peer Live Video Streaming
Barekatain, Behrang; Khezrimotlagh, Dariush; Aizaini Maarof, Mohd; Ghaeini, Hamid Reza; Salleh, Shaharuddin; Quintana, Alfonso Ariza; Akbari, Behzad; Cabrera, Alicia Triviño
2013-01-01
In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay. PMID:23940530
Minimal Increase Network Coding for Dynamic Networks.
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. PMID:26867211
Minimal Increase Network Coding for Dynamic Networks.
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
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
Composite Random Fiber Networks
NASA Astrophysics Data System (ADS)
Picu, Catalin; Shahsavari, Ali
2013-03-01
Systems made from fibers are common in the biological and engineering worlds. In many instances, as for example in skin, where elastin and collagen fibers are present, the fiber network is composite, in the sense that it contains fibers of very different properties. The relationship between microstructural parameters and the elastic moduli of random fiber networks containing a single type of fiber is understood. In this work we address a similar target for the composite networks. We show that linear superposition of the contributions to stiffness of individual sub-networks does not apply and interesting non-linear effects are observed. A physical basis of these effects is proposed.
Spatially embedded random networks.
Barnett, L; Di Paolo, E; Bullock, S
2007-11-01
Many real-world networks analyzed in modern network theory have a natural spatial element; e.g., the Internet, social networks, neural networks, etc. Yet, aside from a comparatively small number of somewhat specialized and domain-specific studies, the spatial element is mostly ignored and, in particular, its relation to network structure disregarded. In this paper we introduce a model framework to analyze the mediation of network structure by spatial embedding; specifically, we model connectivity as dependent on the distance between network nodes. Our spatially embedded random networks construction is not primarily intended as an accurate model of any specific class of real-world networks, but rather to gain intuition for the effects of spatial embedding on network structure; nevertheless we are able to demonstrate, in a quite general setting, some constraints of spatial embedding on connectivity such as the effects of spatial symmetry, conditions for scale free degree distributions and the existence of small-world spatial networks. We also derive some standard structural statistics for spatially embedded networks and illustrate the application of our model framework with concrete examples. PMID:18233726
Spatially embedded random networks
NASA Astrophysics Data System (ADS)
Barnett, L.; di Paolo, E.; Bullock, S.
2007-11-01
Many real-world networks analyzed in modern network theory have a natural spatial element; e.g., the Internet, social networks, neural networks, etc. Yet, aside from a comparatively small number of somewhat specialized and domain-specific studies, the spatial element is mostly ignored and, in particular, its relation to network structure disregarded. In this paper we introduce a model framework to analyze the mediation of network structure by spatial embedding; specifically, we model connectivity as dependent on the distance between network nodes. Our spatially embedded random networks construction is not primarily intended as an accurate model of any specific class of real-world networks, but rather to gain intuition for the effects of spatial embedding on network structure; nevertheless we are able to demonstrate, in a quite general setting, some constraints of spatial embedding on connectivity such as the effects of spatial symmetry, conditions for scale free degree distributions and the existence of small-world spatial networks. We also derive some standard structural statistics for spatially embedded networks and illustrate the application of our model framework with concrete examples.
NASA Astrophysics Data System (ADS)
Donnelly, Isaac
Random walks on lattices are a well used model for diffusion on continuum. They have been to model subdiffusive systems, systems with forcing and reactions as well as a combination of the three. We extend the traditional random walk framework to the network to obtain novel results. As an example due to the small graph diameter, the early time behaviour of subdiffusive dynamics dominates the observed system which has implications for models of the brain or airline networks. I would like to thank the Australian American Fulbright Association.
Quantifying randomness in real networks
NASA Astrophysics Data System (ADS)
Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri
2015-10-01
Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.
Quantifying randomness in real networks.
Orsini, Chiara; Dankulov, Marija M; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri
2015-10-20
Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks--the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain--and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs.
Quantifying randomness in real networks
Orsini, Chiara; Dankulov, Marija M.; Colomer-de-Simón, Pol; Jamakovic, Almerima; Mahadevan, Priya; Vahdat, Amin; Bassler, Kevin E.; Toroczkai, Zoltán; Boguñá, Marián; Caldarelli, Guido; Fortunato, Santo; Krioukov, Dmitri
2015-01-01
Represented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks—the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain—and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs. PMID:26482121
Entanglement-assisted random access codes
Pawlowski, Marcin; Zukowski, Marek
2010-04-15
An (n,m,p) random access code (RAC) makes it possible to encode n bits in an m-bit message in such a way that a receiver of the message can guess any of the original n bits with probability p greater than (1/2). In quantum RACs (QRACs), one transmits n qubits. The full set of primitive entanglement-assisted random access codes (EARACs) is introduced, in which parties are allowed to share a two-qubit singlet. It is shown that via a concatenation of these, one can build for any n an (n,1,p) EARAC. QRACs for n>3 exist only if parties also share classical randomness. We show that EARACs outperform the best of known QRACs not only in the success probabilities but also in the amount of communication needed in the preparatory stage of the protocol. Upper bounds on the performance of EARACs are given and shown to limit also QRACs.
Secure Computation from Random Error Correcting Codes
NASA Astrophysics Data System (ADS)
Chen, Hao; Cramer, Ronald; Goldwasser, Shafi; de Haan, Robbert; Vaikuntanathan, Vinod
Secure computation consists of protocols for secure arithmetic: secret values are added and multiplied securely by networked processors. The striking feature of secure computation is that security is maintained even in the presence of an adversary who corrupts a quorum of the processors and who exercises full, malicious control over them. One of the fundamental primitives at the heart of secure computation is secret-sharing. Typically, the required secret-sharing techniques build on Shamir's scheme, which can be viewed as a cryptographic twist on the Reed-Solomon error correcting code. In this work we further the connections between secure computation and error correcting codes. We demonstrate that threshold secure computation in the secure channels model can be based on arbitrary codes. For a network of size n, we then show a reduction in communication for secure computation amounting to a multiplicative logarithmic factor (in n) compared to classical methods for small, e.g., constant size fields, while tolerating t < ({1 over 2} - {ɛ}) {n} players to be corrupted, where ɛ> 0 can be arbitrarily small. For large networks this implies considerable savings in communication. Our results hold in the broadcast/negligible error model of Rabin and Ben-Or, and complement results from CRYPTO 2006 for the zero-error model of Ben-Or, Goldwasser and Wigderson (BGW). Our general theory can be extended so as to encompass those results from CRYPTO 2006 as well. We also present a new method for constructing high information rate ramp schemes based on arbitrary codes, and in particular we give a new construction based on algebraic geometry codes.
Universality in random quantum networks
NASA Astrophysics Data System (ADS)
Novotný, Jaroslav; Alber, Gernot; Jex, Igor
2015-12-01
Networks constitute efficient tools for assessing universal features of complex systems. In physical contexts, classical as well as quantum networks are used to describe a wide range of phenomena, such as phase transitions, intricate aspects of many-body quantum systems, or even characteristic features of a future quantum internet. Random quantum networks and their associated directed graphs are employed for capturing statistically dominant features of complex quantum systems. Here, we develop an efficient iterative method capable of evaluating the probability of a graph being strongly connected. It is proven that random directed graphs with constant edge-establishing probability are typically strongly connected, i.e., any ordered pair of vertices is connected by a directed path. This typical topological property of directed random graphs is exploited to demonstrate universal features of the asymptotic evolution of large random qubit networks. These results are independent of our knowledge of the details of the network topology. These findings suggest that other highly complex networks, such as a future quantum internet, may also exhibit similar universal properties.
Percolation on correlated random networks
NASA Astrophysics Data System (ADS)
Agliari, E.; Cioli, C.; Guadagnini, E.
2011-09-01
We consider a class of random, weighted networks, obtained through a redefinition of patterns in an Hopfield-like model, and, by performing percolation processes, we get information about topology and resilience properties of the networks themselves. Given the weighted nature of the graphs, different kinds of bond percolation can be studied: stochastic (deleting links randomly) and deterministic (deleting links based on rank weights), each mimicking a different physical process. The evolution of the network is accordingly different, as evidenced by the behavior of the largest component size and of the distribution of cluster sizes. In particular, we can derive that weak ties are crucial in order to maintain the graph connected and that, when they are the most prone to failure, the giant component typically shrinks without abruptly breaking apart; these results have been recently evidenced in several kinds of social networks.
BCH codes for large IC random-access memory systems
NASA Technical Reports Server (NTRS)
Lin, S.; Costello, D. J., Jr.
1983-01-01
In this report some shortened BCH codes for possible applications to large IC random-access memory systems are presented. These codes are given by their parity-check matrices. Encoding and decoding of these codes are discussed.
Coherent diffractive imaging using randomly coded masks
Seaberg, Matthew H.; D'Aspremont, Alexandre; Turner, Joshua J.
2015-12-07
We experimentally demonstrate an extension to coherent diffractive imaging that encodes additional information through the use of a series of randomly coded masks, removing the need for typical object-domain constraints while guaranteeing a unique solution to the phase retrieval problem. Phase retrieval is performed using a numerical convex relaxation routine known as “PhaseCut,” an iterative algorithm known for its stability and for its ability to find the global solution, which can be found efficiently and which is robust to noise. The experiment is performed using a laser diode at 532.2 nm, enabling rapid prototyping for future X-ray synchrotron and even free electron laser experiments.
Applications of Coding in Network Communications
ERIC Educational Resources Information Center
Chang, Christopher SungWook
2012-01-01
This thesis uses the tool of network coding to investigate fast peer-to-peer file distribution, anonymous communication, robust network construction under uncertainty, and prioritized transmission. In a peer-to-peer file distribution system, we use a linear optimization approach to show that the network coding framework significantly simplifies…
Local leaders in random networks
NASA Astrophysics Data System (ADS)
Blondel, Vincent D.; Guillaume, Jean-Loup; Hendrickx, Julien M.; de Kerchove, Cristobald; Lambiotte, Renaud
2008-03-01
We consider local leaders in random uncorrelated networks, i.e., nodes whose degree is higher than or equal to the degree of all their neighbors. An analytical expression is found for the probability for a node of degree k to be a local leader. This quantity is shown to exhibit a transition from a situation where high-degree nodes are local leaders to a situation where they are not, when the tail of the degree distribution behaves like the power law ˜k-γc with γc=3 . Theoretical results are verified by computer simulations, and the importance of finite-size effects is discussed.
Organization of growing random networks
Krapivsky, P. L.; Redner, S.
2001-06-01
The organizational development of growing random networks is investigated. These growing networks are built by adding nodes successively, and linking each to an earlier node of degree k with an attachment probability A{sub k}. When A{sub k} grows more slowly than linearly with k, the number of nodes with k links, N{sub k}(t), decays faster than a power law in k, while for A{sub k} growing faster than linearly in k, a single node emerges which connects to nearly all other nodes. When A{sub k} is asymptotically linear, N{sub k}(t){similar_to}tk{sup {minus}{nu}}, with {nu} dependent on details of the attachment probability, but in the range 2{lt}{nu}{lt}{infinity}. The combined age and degree distribution of nodes shows that old nodes typically have a large degree. There is also a significant correlation in the degrees of neighboring nodes, so that nodes of similar degree are more likely to be connected. The size distributions of the in and out components of the network with respect to a given node{emdash}namely, its {open_quotes}descendants{close_quotes} and {open_quotes}ancestors{close_quotes}{emdash}are also determined. The in component exhibits a robust s{sup {minus}2} power-law tail, where s is the component size. The out component has a typical size of order lnt, and it provides basic insights into the genealogy of the network.
Hierarchy in directed random networks
NASA Astrophysics Data System (ADS)
Mones, Enys
2013-02-01
In recent years, the theory and application of complex networks have been quickly developing in a markable way due to the increasing amount of data from real systems and the fruitful application of powerful methods used in statistical physics. Many important characteristics of social or biological systems can be described by the study of their underlying structure of interactions. Hierarchy is one of these features that can be formulated in the language of networks. In this paper we present some (qualitative) analytic results on the hierarchical properties of random network models with zero correlations and also investigate, mainly numerically, the effects of different types of correlations. The behavior of the hierarchy is different in the absence and the presence of giant components. We show that the hierarchical structure can be drastically different if there are one-point correlations in the network. We also show numerical results suggesting that the hierarchy does not change monotonically with the correlations and there is an optimal level of nonzero correlations maximizing the level of hierarchy.
On Delay and Security in Network Coding
ERIC Educational Resources Information Center
Dikaliotis, Theodoros K.
2013-01-01
In this thesis, delay and security issues in network coding are considered. First, we study the delay incurred in the transmission of a fixed number of packets through acyclic networks comprised of erasure links. The two transmission schemes studied are routing with hop-by-hop retransmissions, where every node in the network simply stores and…
Network Coding for Function Computation
ERIC Educational Resources Information Center
Appuswamy, Rathinakumar
2011-01-01
In this dissertation, the following "network computing problem" is considered. Source nodes in a directed acyclic network generate independent messages and a single receiver node computes a target function f of the messages. The objective is to maximize the average number of times f can be computed per network usage, i.e., the "computing…
The weight distribution and randomness of linear codes
NASA Technical Reports Server (NTRS)
Cheung, K.-M.
1989-01-01
Finding the weight distributions of block codes is a problem of theoretical and practical interest. Yet the weight distributions of most block codes are still unknown except for a few classes of block codes. Here, by using the inclusion and exclusion principle, an explicit formula is derived which enumerates the complete weight distribution of an (n,k,d) linear code using a partially known weight distribution. This expression is analogous to the Pless power-moment identities - a system of equations relating the weight distribution of a linear code to the weight distribution of its dual code. Also, an approximate formula for the weight distribution of most linear (n,k,d) codes is derived. It is shown that for a given linear (n,k,d) code over GF(q), the ratio of the number of codewords of weight u to the number of words of weight u approaches the constant Q = q(-)(n-k) as u becomes large. A relationship between the randomness of a linear block code and the minimum distance of its dual code is given, and it is shown that most linear block codes with rigid algebraic and combinatorial structure also display certain random properties which make them similar to random codes with no structure at all.
Systematic network coding for two-hop lossy transmissions
NASA Astrophysics Data System (ADS)
Li, Ye; Blostein, Steven; Chan, Wai-Yip
2015-12-01
In this paper, we consider network transmissions over a single or multiple parallel two-hop lossy paths. These scenarios occur in applications such as sensor networks or WiFi offloading. Random linear network coding (RLNC), where previously received packets are re-encoded at intermediate nodes and forwarded, is known to be a capacity-achieving approach for these networks. However, a major drawback of RLNC is its high encoding and decoding complexity. In this work, a systematic network coding method is proposed. We show through both analysis and simulation that the proposed method achieves higher end-to-end rate as well as lower computational cost than RLNC for finite field sizes and finite-sized packet transmissions.
Randomizing Genome-Scale Metabolic Networks
Samal, Areejit; Martin, Olivier C.
2011-01-01
Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have “unusual” properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints. PMID:21779409
Efficient codes and balanced networks.
Denève, Sophie; Machens, Christian K
2016-03-01
Recent years have seen a growing interest in inhibitory interneurons and their circuits. A striking property of cortical inhibition is how tightly it balances excitation. Inhibitory currents not only match excitatory currents on average, but track them on a millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. We review, together with experimental evidence, recent theoretical approaches that investigate the advantages of such tight balance for coding and computation. These studies suggest a possible revision of the dominant view that neurons represent information with firing rates corrupted by Poisson noise. Instead, tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of magnitude more precise than a Poisson rate code. Moreover, tight balance may provide a template that allows cortical neurons to construct high-dimensional population codes and learn complex functions of their inputs.
Self-Control in Sparsely Coded Networks
NASA Astrophysics Data System (ADS)
Dominguez, D. R. C.; Bollé, D.
1998-03-01
A complete self-control mechanism is proposed in the dynamics of neural networks through the introduction of a time-dependent threshold, determined in function of both the noise and the pattern activity in the network. Especially for sparsely coded models this mechanism is shown to considerably improve the storage capacity, the basins of attraction, and the mutual information content.
Spike Code Flow in Cultured Neuronal Networks.
Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei
2016-01-01
We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network. PMID:27217825
Spike Code Flow in Cultured Neuronal Networks.
Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei
2016-01-01
We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.
MINET (momentum integral network) code documentation
Van Tuyle, G J; Nepsee, T C; Guppy, J G
1989-12-01
The MINET computer code, developed for the transient analysis of fluid flow and heat transfer, is documented in this four-part reference. In Part 1, the MINET models, which are based on a momentum integral network method, are described. The various aspects of utilizing the MINET code are discussed in Part 2, The User's Manual. The third part is a code description, detailing the basic code structure and the various subroutines and functions that make up MINET. In Part 4, example input decks, as well as recent validation studies and applications of MINET are summarized. 32 refs., 36 figs., 47 tabs.
Multicast Reduction Network Source Code
Lee, G.
2006-12-19
MRNet is a software tree-based overlay network developed at the University of Wisconsin, Madison that provides a scalable communication mechanism for parallel tools. MRNet, uses a tree topology of networked processes between a user tool and distributed tool daemons. This tree topology allows scalable multicast communication from the tool to the daemons. The internal nodes of the tree can be used to distribute computation and alalysis on data sent from the tool daemons to the tool. This release covers minor implementation to port this software to the BlueGene/L architecuture and for use with a new implementation of the Dynamic Probe Class Library.
Multicast Reduction Network Source Code
2006-12-19
MRNet is a software tree-based overlay network developed at the University of Wisconsin, Madison that provides a scalable communication mechanism for parallel tools. MRNet, uses a tree topology of networked processes between a user tool and distributed tool daemons. This tree topology allows scalable multicast communication from the tool to the daemons. The internal nodes of the tree can be used to distribute computation and alalysis on data sent from the tool daemons to themore » tool. This release covers minor implementation to port this software to the BlueGene/L architecuture and for use with a new implementation of the Dynamic Probe Class Library.« less
Thermodynamics of random reaction networks.
Fischer, Jakob; Kleidon, Axel; Dittrich, Peter
2015-01-01
Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha) and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.
Thermodynamics of random reaction networks.
Fischer, Jakob; Kleidon, Axel; Dittrich, Peter
2015-01-01
Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha) and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks. PMID:25723751
Random walk centrality for temporal networks
NASA Astrophysics Data System (ADS)
Rocha, Luis E. C.; Masuda, Naoki
2014-06-01
Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.
Autoregressive cascades on random networks
NASA Astrophysics Data System (ADS)
Iyer, Srikanth K.; Vaze, Rahul; Narasimha, Dheeraj
2016-04-01
A network cascade model that captures many real-life correlated node failures in large networks via load redistribution is studied. The considered model is well suited for networks where physical quantities are transmitted, e.g., studying large scale outages in electrical power grids, gridlocks in road networks, and connectivity breakdown in communication networks, etc. For this model, a phase transition is established, i.e., existence of critical thresholds above or below which a small number of node failures lead to a global cascade of network failures or not. Theoretical bounds are obtained for the phase transition on the critical capacity parameter that determines the threshold above and below which cascade appears or disappears, respectively, that are shown to closely follow numerical simulation results.
Population coding in sparsely connected networks of noisy neurons
Tripp, Bryan P.; Orchard, Jeff
2012-01-01
This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons. PMID:22586391
Molecular codes in biological and chemical reaction networks.
Görlich, Dennis; Dittrich, Peter
2013-01-01
Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.
Routing in Networks with Random Topologies
NASA Technical Reports Server (NTRS)
Bambos, Nicholas
1997-01-01
We examine the problems of routing and server assignment in networks with random connectivities. In such a network the basic topology is fixed, but during each time slot and for each of tis input queues, each server (node) is either connected to or disconnected from each of its queues with some probability.
Random aggregation models for the formation and evolution of coding and non-coding DNA
NASA Astrophysics Data System (ADS)
Provata, A.
A random aggregation model with influx is proposed for the formation of the non-coding DNA regions via random co-aggregation and influx of biological macromolecules such as viruses, parasite DNA, and replication segments. The constant mixing (transpositions) and influx drives the system in an out-of-equilibrium steady state characterised by a power law size distribution. The model predicts the long range distributions found in the noncoding eucaryotic DNA and explains the observed correlations. For the formation of coding DNA a random closed aggregation model is proposed which predicts short range coding size distributions. The closed aggregation process drives the system in an almost “frozen” stable state which is robust to external perturbations and which is characterised by well defined space and time scales, as observed in coding sequences.
Molecular Codes in Biological and Chemical Reaction Networks
Görlich, Dennis; Dittrich, Peter
2013-01-01
Shannon’s theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process “meaningful” information from those that do not. Here, we present a formal method to assess a system’s semantic capacity by analyzing a reaction network’s capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems posses different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life. PMID:23372756
A Network Coding Based Routing Protocol for Underwater Sensor Networks
Wu, Huayang; Chen, Min; Guan, Xin
2012-01-01
Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime. PMID:22666045
Network coding for quantum cooperative multicast
NASA Astrophysics Data System (ADS)
Xu, Gang; Chen, Xiu-Bo; Li, Jing; Wang, Cong; Yang, Yi-Xian; Li, Zongpeng
2015-11-01
Cooperative communication is starting to attract substantial research attention in quantum information theory. However, given a specific network, it is still unknown whether quantum cooperative communication can be successfully performed. In this paper, we investigate network coding for quantum cooperative multicast (QCM) over the classic butterfly network. A very reasonable definition of QCM is first introduced. It not only perfectly focuses on the basic idea of quantum cooperative communication, but also wonderfully reflects the characteristic of classical multicast over a specific network structure. Next, we design QCM protocol for two-level systems and generalize the protocol into d-dimensional Hilbert space. It is shown that our protocols have significant advantages in terms of resource cost and compatibility with classical multicast. Besides, the success probability, which only depends on the coefficients of the initial quantum states, is carefully analyzed. In particular if the source nodes choose the quantum equatorial states, success probability can reach 1.
Sparse coding for layered neural networks
NASA Astrophysics Data System (ADS)
Katayama, Katsuki; Sakata, Yasuo; Horiguchi, Tsuyoshi
2002-07-01
We investigate storage capacity of two types of fully connected layered neural networks with sparse coding when binary patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network, in which a transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately. We derive recursion relations for order parameters by means of the signal-to-noise ratio method, and then apply the self-control threshold method proposed by Dominguez and Bollé to both layered networks with monotonic transfer functions. We find that a critical value αC of storage capacity is about 0.11|a ln a| -1 ( a≪1) for both layered networks, where a is a neuronal activity. It turns out that the basin of attraction is larger for both layered networks when the self-control threshold method is applied.
Analysis of quantum network coding for realistic repeater networks
NASA Astrophysics Data System (ADS)
Satoh, Takahiko; Ishizaki, Kaori; Nagayama, Shota; Van Meter, Rodney
2016-03-01
Quantum repeater networks have attracted attention for the implementation of long-distance and large-scale sharing of quantum states. Recently, researchers extended classical network coding, which is a technique for throughput enhancement, into quantum information. The utility of quantum network coding (QNC) has been shown under ideal conditions, but it has not been studied previously under conditions of noise and shortage of quantum resources. We analyzed QNC on a butterfly network, which can create end-to-end Bell pairs at twice the rate of the standard quantum network repeater approach. The joint fidelity of creating two Bell pairs has a small penalty for QNC relative to entanglement swapping. It will thus be useful when we care more about throughput than fidelity. We found that the output fidelity drops below 0.5 when the initial Bell pairs have fidelity F <0.90 , even with perfect local gates. Local gate errors have a larger impact on quantum network coding than on entanglement swapping.
Learning and coding in biological neural networks
NASA Astrophysics Data System (ADS)
Fiete, Ila Rani
How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and
Random interactions in higher order neural networks
NASA Technical Reports Server (NTRS)
Baldi, Pierre; Venkatesh, Santosh S.
1993-01-01
Recurrent networks of polynomial threshold elements with random symmetric interactions are studied. Precise asymptotic estimates are derived for the expected number of fixed points as a function of the margin of stability. In particular, it is shown that there is a critical range of margins of stability (depending on the degree of polynomial interaction) such that the expected number of fixed points with margins below the critical range grows exponentially with the number of nodes in the network, while the expected number of fixed points with margins above the critical range decreases exponentially with the number of nodes in the network. The random energy model is also briefly examined and links with higher order neural networks and higher order spin glass models made explicit.
Random walk centrality in interconnected multilayer networks
NASA Astrophysics Data System (ADS)
Solé-Ribalta, Albert; De Domenico, Manlio; Gómez, Sergio; Arenas, Alex
2016-06-01
Real-world complex systems exhibit multiple levels of relationships. In many cases they require to be modeled as interconnected multilayer networks, characterizing interactions of several types simultaneously. It is of crucial importance in many fields, from economics to biology and from urban planning to social sciences, to identify the most (or the less) influent nodes in a network using centrality measures. However, defining the centrality of actors in interconnected complex networks is not trivial. In this paper, we rely on the tensorial formalism recently proposed to characterize and investigate this kind of complex topologies, and extend two well known random walk centrality measures, the random walk betweenness and closeness centrality, to interconnected multilayer networks. For each of the measures we provide analytical expressions that completely agree with numerically results.
Sampled-Data Consensus Over Random Networks
NASA Astrophysics Data System (ADS)
Wu, Junfeng; Meng, Ziyang; Yang, Tao; Shi, Guodong; Johansson, Karl Henrik
2016-09-01
This paper considers the consensus problem for a network of nodes with random interactions and sampled-data control actions. We first show that consensus in expectation, in mean square, and almost surely are equivalent for a general random network model when the inter-sampling interval and network size satisfy a simple relation. The three types of consensus are shown to be simultaneously achieved over an independent or a Markovian random network defined on an underlying graph with a directed spanning tree. For both independent and Markovian random network models, necessary and sufficient conditions for mean-square consensus are derived in terms of the spectral radius of the corresponding state transition matrix. These conditions are then interpreted as the existence of critical value on the inter-sampling interval, below which global mean-square consensus is achieved and above which the system diverges in mean-square sense for some initial states. Finally, we establish an upper bound on the inter-sampling interval below which almost sure consensus is reached, and a lower bound on the inter-sampling interval above which almost sure divergence is reached. Some numerical simulations are given to validate the theoretical results and some discussions on the critical value of the inter-sampling intervals for the mean-square consensus are provided.
Spatial versus sequential correlations for random access coding
NASA Astrophysics Data System (ADS)
Tavakoli, Armin; Marques, Breno; Pawłowski, Marcin; Bourennane, Mohamed
2016-03-01
Random access codes are important for a wide range of applications in quantum information. However, their implementation with quantum theory can be made in two very different ways: (i) by distributing data with strong spatial correlations violating a Bell inequality or (ii) using quantum communication channels to create stronger-than-classical sequential correlations between state preparation and measurement outcome. Here we study this duality of the quantum realization. We present a family of Bell inequalities tailored to the task at hand and study their quantum violations. Remarkably, we show that the use of spatial and sequential quantum correlations imposes different limitations on the performance of quantum random access codes: Sequential correlations can outperform spatial correlations. We discuss the physics behind the observed discrepancy between spatial and sequential quantum correlations.
In-network Coding for Resilient Sensor Data Storage and Efficient Data Mule Collection
NASA Astrophysics Data System (ADS)
Albano, Michele; Gao, Jie
In a sensor network of n nodes in which k of them have sensed interesting data, we perform in-network erasure coding such that each node stores a linear combination of all the network data with random coefficients. This scheme greatly improves data resilience to node failures: as long as there are k nodes that survive an attack, all the data produced in the sensor network can be recovered with high probability. The in-network coding storage scheme also improves data collection rate by mobile mules and allows for easy scheduling of data mules.
Weight distributions for turbo codes using random and nonrandom permutations
NASA Technical Reports Server (NTRS)
Dolinar, S.; Divsalar, D.
1995-01-01
This article takes a preliminary look at the weight distributions achievable for turbo codes using random, nonrandom, and semirandom permutations. Due to the recursiveness of the encoders, it is important to distinguish between self-terminating and non-self-terminating input sequences. The non-self-terminating sequences have little effect on decoder performance, because they accumulate high encoded weight until they are artificially terminated at the end of the block. From probabilistic arguments based on selecting the permutations randomly, it is concluded that the self-terminating weight-2 data sequences are the most important consideration in the design of constituent codes; higher-weight self-terminating sequences have successively decreasing importance. Also, increasing the number of codes and, correspondingly, the number of permutations makes it more and more likely that the bad input sequences will be broken up by one or more of the permuters. It is possible to design nonrandom permutations that ensure that the minimum distance due to weight-2 input sequences grows roughly as the square root of (2N), where N is the block length. However, these nonrandom permutations amplify the bad effects of higher-weight inputs, and as a result they are inferior in performance to randomly selected permutations. But there are 'semirandom' permutations that perform nearly as well as the designed nonrandom permutations with respect to weight-2 input sequences and are not as susceptible to being foiled by higher-weight inputs.
Weighted networks as randomly reinforced urn processes
NASA Astrophysics Data System (ADS)
Caldarelli, Guido; Chessa, Alessandro; Crimaldi, Irene; Pammolli, Fabio
2013-02-01
We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights are determined by a reinforcement mechanism. We develop a statistical test and a procedure based on it to study the evolution of networks over time, detecting the “dominance” of some edges with respect to the others and then assessing if a given instance of the network is taken at its steady state or not. Distance from the steady state can be considered as a measure of the relevance of the observed properties of the network. Our results are quite general, in the sense that they are not based on a particular probability distribution or functional form of the random weights. Moreover, the proposed tool can be applied also to dense networks, which have received little attention by the network community so far, since they are often problematic. We apply our procedure in the context of the International Trade Network, determining a core of “dominant edges.”
On the Dynamics of Random Neuronal Networks
NASA Astrophysics Data System (ADS)
Robert, Philippe; Touboul, Jonathan
2016-10-01
We study the mean-field limit and stationary distributions of a pulse-coupled network modeling the dynamics of a large neuronal assemblies. Our model takes into account explicitly the intrinsic randomness of firing times, contrasting with the classical integrate-and-fire model. The ergodicity properties of the Markov process associated to finite networks are investigated. We derive the large network size limit of the distribution of the state of a neuron, and characterize their invariant distributions as well as their stability properties. We show that the system undergoes transitions as a function of the averaged connectivity parameter, and can support trivial states (where the network activity dies out, which is also the unique stationary state of finite networks in some cases) and self-sustained activity when connectivity level is sufficiently large, both being possibly stable.
Secure quantum network coding for controlled repeater networks
NASA Astrophysics Data System (ADS)
Shang, Tao; Li, Jiao; Liu, Jian-wei
2016-07-01
To realize efficient quantum communication based on quantum repeater, we propose a secure quantum network coding scheme for controlled repeater networks, which adds a controller as a trusted party and is able to control the process of EPR-pair distribution. As the key operations of quantum repeater, local operations and quantum communication are designed to adopt quantum one-time pad to enhance the function of identity authentication instead of local operations and classical communication. Scheme analysis shows that the proposed scheme can defend against active attacks for quantum communication and realize long-distance quantum communication with minimal resource consumption.
Handheld laser scanner automatic registration based on random coding
NASA Astrophysics Data System (ADS)
He, Lei; Yu, Chun-ping; Wang, Li
2011-06-01
Current research on Laser Scanner often focuses mainly on the static measurement. Little use has been made of dynamic measurement, that are appropriate for more problems and situations. In particular, traditional Laser Scanner must Keep stable to scan and measure coordinate transformation parameters between different station. In order to make the scanning measurement intelligently and rapidly, in this paper ,we developed a new registration algorithm for handleheld laser scanner based on the positon of target, which realize the dynamic measurement of handheld laser scanner without any more complex work. the double camera on laser scanner can take photograph of the artificial target points to get the three-dimensional coordinates, this points is designed by random coding. And then, a set of matched points is found from control points to realize the orientation of scanner by the least-square common points transformation. After that the double camera can directly measure the laser point cloud in the surface of object and get the point cloud data in an unified coordinate system. There are three major contributions in the paper. Firstly, a laser scanner based on binocular vision is designed with double camera and one laser head. By those, the real-time orientation of laser scanner is realized and the efficiency is improved. Secondly, the coding marker is introduced to solve the data matching, a random coding method is proposed. Compared with other coding methods,the marker with this method is simple to match and can avoid the shading for the object. Finally, a recognition method of coding maker is proposed, with the use of the distance recognition, it is more efficient. The method present here can be used widely in any measurement from small to huge obiect, such as vehicle, airplane which strengthen its intelligence and efficiency. The results of experiments and theory analzing demonstrate that proposed method could realize the dynamic measurement of handheld laser
Mass-balanced randomization of metabolic networks
Basler, Georg; Ebenhöh, Oliver; Selbig, Joachim; Nikoloski, Zoran
2011-01-01
Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem. Results: We first review the shortcomings of the existing generic sampling scheme—switch randomization—and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties. Contact: basler@mpimp-golm.mpg.de; nikoloski@mpimp-golm.mpg.de Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21436128
NASA Astrophysics Data System (ADS)
Zhou, Yu-Qian; Gao, Fei; Li, Dan-Dan; Li, Xin-Hui; Wen, Qiao-Yan
2016-09-01
We have proved that new randomness can be certified by partially free sources using 2 →1 quantum random access code (QRAC) in the framework of semi-device-independent (SDI) protocols [Y.-Q. Zhou, H.-W. Li, Y.-K. Wang, D.-D. Li, F. Gao, and Q.-Y. Wen, Phys. Rev. A 92, 022331 (2015), 10.1103/PhysRevA.92.022331]. To improve the effectiveness of the randomness generation, here we propose the SDI randomness expansion using 3 →1 QRAC and obtain the corresponding classical and quantum bounds of the two-dimensional quantum witness. Moreover, we get the condition which should be satisfied by the partially free sources to successfully certify new randomness, and the analytic relationship between the certified randomness and the two-dimensional quantum witness violation.
A random interacting network model for complex networks.
Goswami, Bedartha; Shekatkar, Snehal M; Rheinwalt, Aljoscha; Ambika, G; Kurths, Jürgen
2015-01-01
We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. PMID:26657032
A random interacting network model for complex networks
NASA Astrophysics Data System (ADS)
Goswami, Bedartha; Shekatkar, Snehal M.; Rheinwalt, Aljoscha; Ambika, G.; Kurths, Jürgen
2015-12-01
We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems.
Scalable networks for discrete quantum random walks
Fujiwara, S.; Osaki, H.; Buluta, I.M.; Hasegawa, S.
2005-09-15
Recently, quantum random walks (QRWs) have been thoroughly studied in order to develop new quantum algorithms. In this paper we propose scalable quantum networks for discrete QRWs on circles, lines, and also in higher dimensions. In our method the information about the position of the walker is stored in a quantum register and the network consists of only one-qubit rotation and (controlled){sup n}-NOT gates, therefore it is purely computational and independent of the physical implementation. As an example, we describe the experimental realization in an ion-trap system.
Randomness and preserved patterns in cancer network
NASA Astrophysics Data System (ADS)
Rai, Aparna; Menon, A. Vipin; Jalan, Sarika
2014-09-01
Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.
Quantum Random Access Codes Using Single d-Level Systems.
Tavakoli, Armin; Hameedi, Alley; Marques, Breno; Bourennane, Mohamed
2015-05-01
Random access codes (RACs) are used by a party to, with limited communication, access an arbitrary subset of information held by another party. Quantum resources are known to enable RACs that break classical limitations. Here, we study quantum and classical RACs with high-level communication. We derive average performances of classical RACs and present families of high-level quantum RACs. Our results show that high-level quantum systems can significantly increase the advantage of quantum RACs over their classical counterparts. We demonstrate our findings in an experimental realization of a quantum RAC with four-level communication.
Resilience of complex networks to random breakdown
Paul, Gerald; Sreenivasan, Sameet; Stanley, H. Eugene
2005-11-01
Using Monte Carlo simulations we calculate f{sub c}, the fraction of nodes that are randomly removed before global connectivity is lost, for networks with scale-free and bimodal degree distributions. Our results differ from the results predicted by an equation for f{sub c} proposed by Cohen et al. We discuss the reasons for this disagreement and clarify the domain for which the proposed equation is valid.
Quantum games on evolving random networks
NASA Astrophysics Data System (ADS)
Pawela, Łukasz
2016-09-01
We study the advantages of quantum strategies in evolutionary social dilemmas on evolving random networks. We focus our study on the two-player games: prisoner's dilemma, snowdrift and stag-hunt games. The obtained result show the benefits of quantum strategies for the prisoner's dilemma game. For the other two games, we obtain regions of parameters where the quantum strategies dominate, as well as regions where the classical strategies coexist.
Symmetry in critical random Boolean network dynamics.
Hossein, Shabnam; Reichl, Matthew D; Bassler, Kevin E
2014-04-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce an alternative approach to the analysis of heterogeneous complex systems.
Driven synchronization in random networks of oscillators.
Hindes, Jason; Myers, Christopher R
2015-07-01
Synchronization is a universal phenomenon found in many non-equilibrium systems. Much recent interest in this area has overlapped with the study of complex networks, where a major focus is determining how a system's connectivity patterns affect the types of behavior that it can produce. Thus far, modeling efforts have focused on the tendency of networks of oscillators to mutually synchronize themselves, with less emphasis on the effects of external driving. In this work, we discuss the interplay between mutual and driven synchronization in networks of phase oscillators of the Kuramoto type, and explore how the structure and emergence of such states depend on the underlying network topology for simple random networks with a given degree distribution. We find a variety of interesting dynamical behaviors, including bifurcations and bistability patterns that are qualitatively different for heterogeneous and homogeneous networks, and which are separated by a Takens-Bogdanov-Cusp singularity in the parameter region where the coupling strength between oscillators is weak. Our analysis is connected to the underlying dynamics of oscillator clusters for important states and transitions. PMID:26232970
Symmetry in critical random Boolean network dynamics
NASA Astrophysics Data System (ADS)
Hossein, Shabnam; Reichl, Matthew D.; Bassler, Kevin E.
2014-04-01
Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce an alternative approach to the analysis of heterogeneous complex systems.
Optimal bounds for parity-oblivious random access codes
NASA Astrophysics Data System (ADS)
Chailloux, André; Kerenidis, Iordanis; Kundu, Srijita; Sikora, Jamie
2016-04-01
Random access coding is an information task that has been extensively studied and found many applications in quantum information. In this scenario, Alice receives an n-bit string x, and wishes to encode x into a quantum state {ρ }x, such that Bob, when receiving the state {ρ }x, can choose any bit i\\in [n] and recover the input bit x i with high probability. Here we study two variants: parity-oblivious random access codes (RACs), where we impose the cryptographic property that Bob cannot infer any information about the parity of any subset of bits of the input apart from the single bits x i ; and even-parity-oblivious RACs, where Bob cannot infer any information about the parity of any even-size subset of bits of the input. In this paper, we provide the optimal bounds for parity-oblivious quantum RACs and show that they are asymptotically better than the optimal classical ones. Our results provide a large non-contextuality inequality violation and resolve the main open problem in a work of Spekkens et al (2009 Phys. Rev. Lett. 102 010401). Second, we provide the optimal bounds for even-parity-oblivious RACs by proving their equivalence to a non-local game and by providing tight bounds for the success probability of the non-local game via semidefinite programming. In the case of even-parity-oblivious RACs, the cryptographic property holds also in the device independent model.
Anomalous Anticipatory Responses in Networked Random Data
Nelson, Roger D.; Bancel, Peter A.
2006-10-16
We examine an 8-year archive of synchronized, parallel time series of random data from a world spanning network of physical random event generators (REGs). The archive is a publicly accessible matrix of normally distributed 200-bit sums recorded at 1 Hz which extends from August 1998 to the present. The primary question is whether these data show non-random structure associated with major events such as natural or man-made disasters, terrible accidents, or grand celebrations. Secondarily, we examine the time course of apparently correlated responses. Statistical analyses of the data reveal consistent evidence that events which strongly affect people engender small but significant effects. These include suggestions of anticipatory responses in some cases, leading to a series of specialized analyses to assess possible non-random structure preceding precisely timed events. A focused examination of data collected around the time of earthquakes with Richter magnitude 6 and greater reveals non-random structure with a number of intriguing, potentially important features. Anomalous effects in the REG data are seen only when the corresponding earthquakes occur in populated areas. No structure is found if they occur in the oceans. We infer that an important contributor to the effect is the relevance of the earthquake to humans. Epoch averaging reveals evidence for changes in the data some hours prior to the main temblor, suggestive of reverse causation.
Spiking network simulation code for petascale computers
Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M.; Plesser, Hans E.; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz
2014-01-01
Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today. PMID:25346682
Spiking network simulation code for petascale computers.
Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M; Plesser, Hans E; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz
2014-01-01
Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today. PMID:25346682
Spiking network simulation code for petascale computers.
Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M; Plesser, Hans E; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz
2014-01-01
Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today.
Random walks in directed modular networks
NASA Astrophysics Data System (ADS)
Comin, Cesar H.; Viana, Mateus P.; Antiqueira, Lucas; Costa, Luciano da F.
2014-12-01
Because diffusion typically involves symmetric interactions, scant attention has been focused on studying asymmetric cases. However, important networked systems underlain by diffusion (e.g. cortical networks and WWW) are inherently directed. In the case of undirected diffusion, it can be shown that the steady-state probability of the random walk dynamics is fully correlated with the degree, which no longer holds for directed networks. We investigate the relationship between such probability and the inward node degree, which we call efficiency, in modular networks. Our findings show that the efficiency of a given community depends mostly on the balance between its ingoing and outgoing connections. In addition, we derive analytical expressions to show that the internal degree of the nodes does not play a crucial role in their efficiency, when considering the Erdős-Rényi and Barabási-Albert models. The results are illustrated with respect to the macaque cortical network, providing subsidies for improving transportation and communication systems.
Random Birth-and-Death Networks
NASA Astrophysics Data System (ADS)
Zhang, Xiaojun; He, Zheng; Rayman-Bacchus, Lez
2016-02-01
In this paper, a baseline model termed as random birth-and-death network (RBDN) model is considered, in which at each time step, a new node is added into the network with probability p (0
network with probability q=1-p. This model allows for fluctuations in size, reflecting the behaviour of networks in many different disciplines including physics, ecology and economics. The purpose of this study is to develop the RBDN model and explore its basic statistical properties. For different p, we first discuss the network size of RBDN, then combining the stochastic process rules based Markov chain method and the probability generating function method, we provide the exact solutions of the degree distributions. Finally, the tail characteristics of the degree distributions are explored after simulation verification. Our results show that the tail of the degree distribution for RBDN exhibits a Poisson tail in the case of 0
Sparse coding for hyperspectral images using random dictionary and soft thresholding
NASA Astrophysics Data System (ADS)
Oguslu, Ender; Iftekharuddin, Khan; Li, Jiang
2012-05-01
Many techniques have been recently developed for classification of hyperspectral images (HSI) including support vector machines (SVMs), neural networks and graph-based methods. To achieve good performances for the classification, a good feature representation of the HSI is essential. A great deal of feature extraction algorithms have been developed such as principal component analysis (PCA) and independent component analysis (ICA). Sparse coding has recently shown state-of-the-art performances in many applications including image classification. In this paper, we present a feature extraction method for HSI data motivated by a recently developed sparse coding based image representation technique. Sparse coding consists of a dictionary learning step and an encoding step. In the learning step, we compared two different methods, L1-penalized sparse coding and random selection for the dictionary learning. In the encoding step, we utilized a soft threshold activation function to obtain feature representations for HSI. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center (KSC) and compared our results with those obtained by a recently proposed method, supervised locally linear embedding weighted k-nearest-neighbor (SLLE-WkNN) classifier. We have achieved better performances on this dataset in terms of the overall accuracy with a random dictionary. We conclude that this simple feature extraction framework might lead to more efficient HSI classification systems.
Random walk with priorities in communicationlike networks
NASA Astrophysics Data System (ADS)
Bastas, Nikolaos; Maragakis, Michalis; Argyrakis, Panos; ben-Avraham, Daniel; Havlin, Shlomo; Carmi, Shai
2013-08-01
We study a model for a random walk of two classes of particles (A and B). Where both species are present in the same site, the motion of A's takes precedence over that of B's. The model was originally proposed and analyzed in Maragakis [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.77.020103 77, 020103(R) (2008)]; here we provide additional results. We solve analytically the diffusion coefficients of the two species in lattices for a number of protocols. In networks, we find that the probability of a B particle to be free decreases exponentially with the node degree. In scale-free networks, this leads to localization of the B's at the hubs and arrest of their motion. To remedy this, we investigate several strategies to avoid trapping of the B's, including moving an A instead of the hindered B, allowing a trapped B to hop with a small probability, biased walk toward non-hub nodes, and limiting the capacity of nodes. We obtain analytic results for lattices and networks, and we discuss the advantages and shortcomings of the possible strategies.
Code 672 observational science branch computer networks
NASA Technical Reports Server (NTRS)
Hancock, D. W.; Shirk, H. G.
1988-01-01
In general, networking increases productivity due to the speed of transmission, easy access to remote computers, ability to share files, and increased availability of peripherals. Two different networks within the Observational Science Branch are described in detail.
Improving Residents' Code Status Discussion Skills: A Randomized Trial
Neely, Kathy J.; Sharma, Rashmi K.; Cohen, Elaine R.; McGaghie, William C.; Wayne, Diane B.
2012-01-01
Abstract Background Inpatient Code Status Discussions (CSDs) are commonly facilitated by resident physicians, despite inadequate training. We studied the efficacy of a CSD communication skills training intervention for internal medicine residents. Methods This was a prospective, randomized controlled trial of a multimodality communication skills educational intervention for postgraduate year (PGY) 1 residents. Intervention group residents completed a 2 hour teaching session with deliberate practice of communication skills, online modules, self-reflection, and a booster training session in addition to assigned clinical rotations. Control group residents completed clinical rotations alone. CSD skills of residents in both groups were assessed 2 months after the intervention using an 18 item behavioral checklist during a standardized patient encounter. Average scores for intervention and control group residents were calculated and between-group differences on the CSD skills assessment were evaluated using two-tailed independent sample t tests. Results Intervention group residents displayed higher overall scores on the simulated CSD (75.1% versus 53.2%, p<0.0001) than control group residents. The intervention group also displayed a greater number of key CSD communication behaviors and facilitated significantly longer conversations. The training, evaluation, and feedback sessions were rated highly. Conclusion A focused, multimodality curriculum can improve resident performance of simulated CSDs. Skill improvement lasted for at least 2 months after the intervention. Further studies are needed to assess skill retention and to set minimum performance standards. PMID:22690890
Random symmetry breaking and freezing in chaotic networks.
Peleg, Y; Kinzel, W; Kanter, I
2012-09-01
Parameter space of a driven damped oscillator in a double well potential presents either a chaotic trajectory with sign oscillating amplitude or a nonchaotic trajectory with a fixed sign amplitude. A network of such delay coupled damped oscillators is shown to present chaotic dynamics while the sign amplitude of each damped oscillator is randomly frozen. This phenomenon of random broken global symmetry of the network simultaneous with random freezing of each degree of freedom is accompanied by the existence of exponentially many randomly frozen chaotic attractors with the size of the network. Results are exemplified by a network of modified Duffing oscillators with infinite range pseudoinverse delayed interactions. PMID:23031002
Optimal Grouping and Matching for Network-Coded Cooperative Communications
Sharma, S; Shi, Y; Hou, Y T; Kompella, S; Midkiff, S F
2011-11-01
Network-coded cooperative communications (NC-CC) is a new advance in wireless networking that exploits network coding (NC) to improve the performance of cooperative communications (CC). However, there remains very limited understanding of this new hybrid technology, particularly at the link layer and above. This paper fills in this gap by studying a network optimization problem that requires joint optimization of session grouping, relay node grouping, and matching of session/relay groups. After showing that this problem is NP-hard, we present a polynomial time heuristic algorithm to this problem. Using simulation results, we show that our algorithm is highly competitive and can produce near-optimal results.
Mean first return time for random walks on weighted networks
NASA Astrophysics Data System (ADS)
Jing, Xing-Li; Ling, Xiang; Long, Jiancheng; Shi, Qing; Hu, Mao-Bin
2015-11-01
Random walks on complex networks are of great importance to understand various types of phenomena in real world. In this paper, two types of biased random walks on nonassortative weighted networks are studied: edge-weight-based random walks and node-strength-based random walks, both of which are extended from the normal random walk model. Exact expressions for stationary distribution and mean first return time (MFRT) are derived and examined by simulation. The results will be helpful for understanding the influences of weights on the behavior of random walks.
Optimal Near-Hitless Network Failure Recovery Using Diversity Coding
ERIC Educational Resources Information Center
Avci, Serhat Nazim
2013-01-01
Link failures in wide area networks are common and cause significant data losses. Mesh-based protection schemes offer high capacity efficiency but they are slow, require complex signaling, and instable. Diversity coding is a proactive coding-based recovery technique which offers near-hitless (sub-ms) restoration with a competitive spare capacity…
Transition to Chaos in Random Neuronal Networks
NASA Astrophysics Data System (ADS)
Kadmon, Jonathan; Sompolinsky, Haim
2015-10-01
Firing patterns in the central nervous system often exhibit strong temporal irregularity and considerable heterogeneity in time-averaged response properties. Previous studies suggested that these properties are the outcome of the intrinsic chaotic dynamics of the neural circuits. Indeed, simplified rate-based neuronal networks with synaptic connections drawn from Gaussian distribution and sigmoidal nonlinearity are known to exhibit chaotic dynamics when the synaptic gain (i.e., connection variance) is sufficiently large. In the limit of an infinitely large network, there is a sharp transition from a fixed point to chaos, as the synaptic gain reaches a critical value. Near the onset, chaotic fluctuations are slow, analogous to the ubiquitous, slow irregular fluctuations observed in the firing rates of many cortical circuits. However, the existence of a transition from a fixed point to chaos in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work, we investigate rate-based dynamics of neuronal circuits composed of several subpopulations with randomly diluted connections. Nonzero connections are either positive for excitatory neurons or negative for inhibitory ones, while single neuron output is strictly positive with output rates rising as a power law above threshold, in line with known constraints in many biological systems. Using dynamic mean field theory, we find the phase diagram depicting the regimes of stable fixed-point, unstable-dynamic, and chaotic-rate fluctuations. We focus on the latter and characterize the properties of systems near this transition. We show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as the single population with Gaussian connectivity. In these architectures, the large mean excitatory and inhibitory inputs dynamically balance each other, amplifying the effect of the residual fluctuations. Importantly, the existence of a transition to chaos
Executable Code Recognition in Network Flows Using Instruction Transition Probabilities
NASA Astrophysics Data System (ADS)
Kim, Ikkyun; Kang, Koohong; Choi, Yangseo; Kim, Daewon; Oh, Jintae; Jang, Jongsoo; Han, Kijun
The ability to recognize quickly inside network flows to be executable is prerequisite for malware detection. For this purpose, we introduce an instruction transition probability matrix (ITPX) which is comprised of the IA-32 instruction sets and reveals the characteristics of executable code's instruction transition patterns. And then, we propose a simple algorithm to detect executable code inside network flows using a reference ITPX which is learned from the known Windows Portable Executable files. We have tested the algorithm with more than thousands of executable and non-executable codes. The results show that it is very promising enough to use in real world.
Distributed Estimation, Coding, and Scheduling in Wireless Visual Sensor Networks
ERIC Educational Resources Information Center
Yu, Chao
2013-01-01
In this thesis, we consider estimation, coding, and sensor scheduling for energy efficient operation of wireless visual sensor networks (VSN), which consist of battery-powered wireless sensors with sensing (imaging), computation, and communication capabilities. The competing requirements for applications of these wireless sensor networks (WSN)…
Scaling solutions for connectivity and conductivity of continuous random networks.
Galindo-Torres, S A; Molebatsi, T; Kong, X-Z; Scheuermann, A; Bringemeier, D; Li, L
2015-10-01
Connectivity and conductivity of two-dimensional fracture networks (FNs), as an important type of continuous random networks, are examined systematically through Monte Carlo simulations under a variety of conditions, including different power law distributions of the fracture lengths and domain sizes. The simulation results are analyzed using analogies of the percolation theory for discrete random networks. With a characteristic length scale and conductivity scale introduced, we show that the connectivity and conductivity of FNs can be well described by universal scaling solutions. These solutions shed light on previous observations of scale-dependent FN behavior and provide a powerful method for quantifying effective bulk properties of continuous random networks. PMID:26565157
Duality of rate coding and temporal coding in multilayered feedforward networks.
Masuda, Naoki; Aihara, Kazuyuki
2003-01-01
A functional role for precise spike timing has been proposed as an alternative hypothesis to rate coding. We show in this article that both the synchronous firing code and the population rate code can be used dually in a common framework of a single neural network model. Furthermore, these two coding mechanisms are bridged continuously by several modulatable model parameters, including shared connectivity, feedback strength, membrane leak rate, and neuron heterogeneity. The rates of change of these parameters are closely related to the response time and the timescale of learning. PMID:12590821
Accurate multiple network alignment through context-sensitive random walk
2015-01-01
Background Comparative network analysis can provide an effective means of analyzing large-scale biological networks and gaining novel insights into their structure and organization. Global network alignment aims to predict the best overall mapping between a given set of biological networks, thereby identifying important similarities as well as differences among the networks. It has been shown that network alignment methods can be used to detect pathways or network modules that are conserved across different networks. Until now, a number of network alignment algorithms have been proposed based on different formulations and approaches, many of them focusing on pairwise alignment. Results In this work, we propose a novel multiple network alignment algorithm based on a context-sensitive random walk model. The random walker employed in the proposed algorithm switches between two different modes, namely, an individual walk on a single network and a simultaneous walk on two networks. The switching decision is made in a context-sensitive manner by examining the current neighborhood, which is effective for quantitatively estimating the degree of correspondence between nodes that belong to different networks, in a manner that sensibly integrates node similarity and topological similarity. The resulting node correspondence scores are then used to predict the maximum expected accuracy (MEA) alignment of the given networks. Conclusions Performance evaluation based on synthetic networks as well as real protein-protein interaction networks shows that the proposed algorithm can construct more accurate multiple network alignments compared to other leading methods. PMID:25707987
Coded Cooperation for Multiway Relaying in Wireless Sensor Networks.
Si, Zhongwei; Ma, Junyang; Thobaben, Ragnar
2015-01-01
Wireless sensor networks have been considered as an enabling technology for constructing smart cities. One important feature of wireless sensor networks is that the sensor nodes collaborate in some manner for communications. In this manuscript, we focus on the model of multiway relaying with full data exchange where each user wants to transmit and receive data to and from all other users in the network. We derive the capacity region for this specific model and propose a coding strategy through coset encoding. To obtain good performance with practical codes, we choose spatially-coupled LDPC (SC-LDPC) codes for the coded cooperation. In particular, for the message broadcasting from the relay, we construct multi-edge-type (MET) SC-LDPC codes by repeatedly applying coset encoding. Due to the capacity-achieving property of the SC-LDPC codes, we prove that the capacity region can theoretically be achieved by the proposed MET SC-LDPC codes. Numerical results with finite node degrees are provided, which show that the achievable rates approach the boundary of the capacity region in both binary erasure channels and additive white Gaussian channels. PMID:26131675
Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.
Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime
2016-01-01
It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence. PMID:27239189
Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.
Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime
2016-01-01
It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.
A Multicast Routing to Improve Multicast Capacity with Minimal Network Coding Cellsin WDM Networks
NASA Astrophysics Data System (ADS)
Liu, Huanlin; Zhou, Bangtao; Chen, Yong
2014-12-01
Multicast routing based on network coding can save large wavelength channels with high network coding operation costs for the all-optical WDM network. An optical multicast routing algorithm based on minimum network coding cells to reach the multicast max-flow is proposed in the paper. The algorithm selects path in the K shortest paths from source to each destination which meets the two criteria. One is to select paths making the least probability of dropping multicast max-flow. The other is to make the path lowest potential path coding cells in the K shortest paths. We investigate that the previously proposed multicast algorithms based on network coding choose the shortest paths or link-disjoint highest shared links to construct network coding sub-graph which may result in the large coding operation cost and decrease the possibility of getting the maximal multicast flow. The simulation results show that the proposed algorithm can effectively reduce the network coding cells and improve the possibility of achieving the multicast max-flow by using our designed two path selection criteria. In addition, the proposed multicast algorithm deteriorates a very limited multicast link costs which is very close to the lowest link costs. So, the proposed algorithm can get the cost-effective multicast routing for the optical networks.
NASA Astrophysics Data System (ADS)
Ejaz, Saqib; Yang, Feng-Fan
2015-03-01
The parallel encoding and decoding structure of turbo codes makes them natural candidate for coded-cooperative scenarios. In this paper, we focus on one of the key components of turbo codes i.e., interleaver, and analyze its effect on the performance of coded-cooperative communication. The impact of an interleaver on the overall performance of cooperative systems depends on the type of an interleaver and its location in the cooperative encoding scheme. We consider code matched interleaver (CMI) as an optimum choice and present its role in a coded-cooperation scenario. The search and convergence of CMI for long interleaver sizes is an issue; therefore, a modification in the search conditions is included without any compromise on the performance of CMI. We also present analytical method to determine maximum S-constraint length for a CMI design. Further, we analyze the performance of two different encoding schemes of turbo codes, i.e., distributed turbo code (DTC) and distributed multiple turbo code (DMTC) after inclusion of CMI. Monte Carlo simulations show that CMI increases the diversity gain relative to other conventional interleavers such as uniform random interleaver. The channel is assumed to be Rayleigh fading among all communication nodes.
Phase transitions for information diffusion in random clustered networks
NASA Astrophysics Data System (ADS)
Lim, Sungsu; Shin, Joongbo; Kwak, Namju; Jung, Kyomin
2016-09-01
We study the conditions for the phase transitions of information diffusion in complex networks. Using the random clustered network model, a generalisation of the Chung-Lu random network model incorporating clustering, we examine the effect of clustering under the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous contact rates. For this purpose, we exploit the branching process to analyse information diffusion in random unclustered networks with arbitrary contact rates, and provide novel iterative algorithms for estimating the conditions and sizes of global cascades, respectively. Showing that a random clustered network can be mapped into a factor graph, which is a locally tree-like structure, we successfully extend our analysis to random clustered networks with heterogeneous contact rates. We then identify the conditions for phase transitions of information diffusion using our method. Interestingly, for various contact rates, we prove that random clustered networks with higher clustering coefficients have strictly lower phase transition points for any given degree sequence. Finally, we confirm our analytical results with numerical simulations of both synthetically-generated and real-world networks.
Phase transitions for information diffusion in random clustered networks
NASA Astrophysics Data System (ADS)
Lim, Sungsu; Shin, Joongbo; Kwak, Namju; Jung, Kyomin
2016-08-01
We study the conditions for the phase transitions of information diffusion in complex networks. Using the random clustered network model, a generalisation of the Chung-Lu random network model incorporating clustering, we examine the effect of clustering under the Susceptible-Infected-Recovered (SIR) epidemic diffusion model with heterogeneous contact rates. For this purpose, we exploit the branching process to analyse information diffusion in random unclustered networks with arbitrary contact rates, and provide novel iterative algorithms for estimating the conditions and sizes of global cascades, respectively. Showing that a random clustered network can be mapped into a factor graph, which is a locally tree-like structure, we successfully extend our analysis to random clustered networks with heterogeneous contact rates. We then identify the conditions for phase transitions of information diffusion using our method. Interestingly, for various contact rates, we prove that random clustered networks with higher clustering coefficients have strictly lower phase transition points for any given degree sequence. Finally, we confirm our analytical results with numerical simulations of both synthetically-generated and real-world networks.
Incorporation of Condensation Heat Transfer in a Flow Network Code
NASA Technical Reports Server (NTRS)
Anthony, Miranda; Majumdar, Alok; McConnaughey, Paul K. (Technical Monitor)
2001-01-01
In this paper we have investigated the condensation of water vapor in a short tube. A numerical model of condensation heat transfer was incorporated in a flow network code. The flow network code that we have used in this paper is Generalized Fluid System Simulation Program (GFSSP). GFSSP is a finite volume based flow network code. Four different condensation models were presented in the paper. Soliman's correlation has been found to be the most stable in low flow rates which is of particular interest in this application. Another highlight of this investigation is conjugate or coupled heat transfer between solid or fluid. This work was done in support of NASA's International Space Station program.
A random spatial network model based on elementary postulates
Karlinger, M.R.; Troutman, B.M.
1989-01-01
In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. -from Authors
The influence of the property of random coded patterns on fluctuation-correlation ghost imaging
NASA Astrophysics Data System (ADS)
Wang, Chenglong; Gong, Wenlin; Shao, Xuehui; Han, Shensheng
2016-06-01
According to the reconstruction feature of fluctuation-correlation ghost imaging (GI), we define a normalized characteristic matrix and the influence of the property of random coded patterns on GI is investigated based on the theory of matrix analysis. Both simulative and experimental results demonstrate that for different random coded patterns, the quality of fluctuation-correlation GI can be predicted by some parameters extracted from the normalized characteristic matrix, which suggests its potential application in the optimization of random coded patterns for GI system.
Energy coding in neural network with inhibitory neurons.
Wang, Ziyin; Wang, Rubin; Fang, Ruiyan
2015-04-01
This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential.
Code generation: a strategy for neural network simulators.
Goodman, Dan F M
2010-10-01
We demonstrate a technique for the design of neural network simulation software, runtime code generation. This technique can be used to give the user complete flexibility in specifying the mathematical model for their simulation in a high level way, along with the speed of code written in a low level language such as C+ +. It can also be used to write code only once but target different hardware platforms, including inexpensive high performance graphics processing units (GPUs). Code generation can be naturally combined with computer algebra systems to provide further simplification and optimisation of the generated code. The technique is quite general and could be applied to any simulation package. We demonstrate it with the 'Brian' simulator ( http://www.briansimulator.org ).
Dynamical coding of sensory information with competitive networks.
Rabinovich, M I; Huerta, R; Volkovskii, A; Abarbanel, H D; Stopfer, M; Laurent, G
2000-01-01
Based on experiments with the locust olfactory system, we demonstrate that model sensory neural networks with lateral inhibition can generate stimulus specific identity-temporal patterns in the form of stimulus-dependent switching among small and dynamically changing neural ensembles (each ensemble being a group of synchronized projection neurons). Networks produce this switching mode of dynamical activity when lateral inhibitory connections are strongly non-symmetric. Such coding uses 'winner-less competitive' (WLC) dynamics. In contrast to the well known winner-take-all competitive (WTA) networks and Hopfield nets, winner-less competition represents sensory information dynamically. Such dynamics are reproducible, robust against intrinsic noise and sensitive to changes in the sensory input. We demonstrate the validity of sensory coding with WLC networks using two different formulations of the dynamics, namely the average and spiking dynamics of projection neurons (PN).
Code System to Generate Latin Hypercube and Random Samples.
1999-02-25
Version: 00 LHS was written for the generation of multi variate samples either completely at random or by a constrained randomization termed Latin hypercube sampling (LHS). The generation of these samples is based on user-specified parameters which dictate the characteristics of the generated samples, such as type of sample (LHS or random), sample size, number of samples desired, correlation structure on input variables, and type of distribution specified on each variable. The following distributions aremore » built into the program: normal, lognormal, uniform, loguniform, triangular, and beta. In addition, the samples from the uniform and loguniform distributions may be modified by changing the frequency of the sampling within subintervals, and a subroutine which can be modified by the user to generate samples from other distributions (including empirical data) is provided.« less
Code System to Generate Latin Hypercube and Random Samples.
IMAN, RONALD L.
1999-02-25
Version: 00 LHS was written for the generation of multi variate samples either completely at random or by a constrained randomization termed Latin hypercube sampling (LHS). The generation of these samples is based on user-specified parameters which dictate the characteristics of the generated samples, such as type of sample (LHS or random), sample size, number of samples desired, correlation structure on input variables, and type of distribution specified on each variable. The following distributions are built into the program: normal, lognormal, uniform, loguniform, triangular, and beta. In addition, the samples from the uniform and loguniform distributions may be modified by changing the frequency of the sampling within subintervals, and a subroutine which can be modified by the user to generate samples from other distributions (including empirical data) is provided.
Spectral coarse graining for random walks in bipartite networks
NASA Astrophysics Data System (ADS)
Wang, Yang; Zeng, An; Di, Zengru; Fan, Ying
2013-03-01
Many real-world networks display a natural bipartite structure, yet analyzing and visualizing large bipartite networks is one of the open challenges in complex network research. A practical approach to this problem would be to reduce the complexity of the bipartite system while at the same time preserve its functionality. However, we find that existing coarse graining methods for monopartite networks usually fail for bipartite networks. In this paper, we use spectral analysis to design a coarse graining scheme specific for bipartite networks, which keeps their random walk properties unchanged. Numerical analysis on both artificial and real-world networks indicates that our coarse graining can better preserve most of the relevant spectral properties of the network. We validate our coarse graining method by directly comparing the mean first passage time of the walker in the original network and the reduced one.
Performance of wireless sensor networks under random node failures
Bradonjic, Milan; Hagberg, Aric; Feng, Pan
2011-01-28
Networks are essential to the function of a modern society and the consequence of damages to a network can be large. Assessing network performance of a damaged network is an important step in network recovery and network design. Connectivity, distance between nodes, and alternative routes are some of the key indicators to network performance. In this paper, random geometric graph (RGG) is used with two types of node failure, uniform failure and localized failure. Since the network performance are multi-facet and assessment can be time constrained, we introduce four measures, which can be computed in polynomial time, to estimate performance of damaged RGG. Simulation experiments are conducted to investigate the deterioration of networks through a period of time. With the empirical results, the performance measures are analyzed and compared to provide understanding of different failure scenarios in a RGG.
Current-reinforced random walks for constructing transport networks.
Ma, Qi; Johansson, Anders; Tero, Atsushi; Nakagaki, Toshiyuki; Sumpter, David J T
2013-03-01
Biological systems that build transport networks, such as trail-laying ants and the slime mould Physarum, can be described in terms of reinforced random walks. In a reinforced random walk, the route taken by 'walking' particles depends on the previous routes of other particles. Here, we present a novel form of random walk in which the flow of particles provides this reinforcement. Starting from an analogy between electrical networks and random walks, we show how to include current reinforcement. We demonstrate that current-reinforcement results in particles converging on the optimal solution of shortest path transport problems, and avoids the self-reinforcing loops seen in standard density-based reinforcement models. We further develop a variant of the model that is biologically realistic, in the sense that the particles can be identified as ants and their measured density corresponds to those observed in maze-solving experiments on Argentine ants. For network formation, we identify the importance of nonlinear current reinforcement in producing networks that optimize both network maintenance and travel times. Other than ant trail formation, these random walks are also closely related to other biological systems, such as blood vessels and neuronal networks, which involve the transport of materials or information. We argue that current reinforcement is likely to be a common mechanism in a range of systems where network construction is observed.
Muller's ratchet in random graphs and scale-free networks
NASA Astrophysics Data System (ADS)
Campos, Paulo R. A.; Combadão, Jaime; Dionisio, Francisco; Gordo, Isabel
2006-10-01
Muller’s ratchet is an evolutionary process that has been implicated in the extinction of asexual species, the evolution of mitochondria, the degeneration of the Y chromosome, the evolution of sex and recombination and the evolution of microbes. Here we study the speed of Muller’s ratchet in a population subdivided into many small subpopulations connected by migration, and distributed on a network. We compare the speed of the ratchet in two distinct types of topologies: scale free networks and random graphs. The difference between the topologies is noticeable when the average connectivity of the network and the migration rate is large. In this situation we observe that the ratchet clicks faster in scale free networks than in random graphs. So contrary to intuition, scale free networks are more prone to loss of genetic information than random graphs. On the other hand, we show that scale free networks are more robust to the random extinction than random graphs. Since these complex networks have been shown to describe well real-life systems, our results open a framework for studying the evolution of microbes and disease epidemics.
Cascading Failures in Spatially-Embedded Random Networks
Asztalos, Andrea; Sreenivasan, Sameet; Szymanski, Boleslaw K.; Korniss, Gyorgy
2014-01-01
Cascading failures constitute an important vulnerability of interconnected systems. Here we focus on the study of such failures on networks in which the connectivity of nodes is constrained by geographical distance. Specifically, we use random geometric graphs as representative examples of such spatial networks, and study the properties of cascading failures on them in the presence of distributed flow. The key finding of this study is that the process of cascading failures is non-self-averaging on spatial networks, and thus, aggregate inferences made from analyzing an ensemble of such networks lead to incorrect conclusions when applied to a single network, no matter how large the network is. We demonstrate that this lack of self-averaging disappears with the introduction of a small fraction of long-range links into the network. We simulate the well studied preemptive node removal strategy for cascade mitigation and show that it is largely ineffective in the case of spatial networks. We introduce an altruistic strategy designed to limit the loss of network nodes in the event of a cascade triggering failure and show that it performs better than the preemptive strategy. Finally, we consider a real-world spatial network viz. a European power transmission network and validate that our findings from the study of random geometric graphs are also borne out by simulations of cascading failures on the empirical network. PMID:24400101
Sequential defense against random and intentional attacks in complex networks
NASA Astrophysics Data System (ADS)
Chen, Pin-Yu; Cheng, Shin-Ming
2015-02-01
Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is first proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.
Sequential defense against random and intentional attacks in complex networks.
Chen, Pin-Yu; Cheng, Shin-Ming
2015-02-01
Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is first proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.
Critical scaling in the rheology of damped random spring networks
NASA Astrophysics Data System (ADS)
Tighe, Brian
2011-11-01
Physical, biological, and engineered materials ranging from foams and emulsions to bioppolymer and bar-joint networks can be modelled as random networks of springs. We study the oscillatory rheology of random networks immersed in a viscous background fluid, and show how their response is intimately tied to the presence or absence of floppy modes in the zero frequency limit. The rheology displays dynamic critical scaling with three different regimes: viscous fluid, elastic solid, and shear thinning power law fluid. We give scaling arguments to explain all of the critical exponents and confirm our predictions with numerics. Supported by the Dutch Organization for Scientific Research (NWO).
A scaling law for random walks on networks
Perkins, Theodore J.; Foxall, Eric; Glass, Leon; Edwards, Roderick
2014-01-01
The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics. PMID:25311870
A scaling law for random walks on networks
NASA Astrophysics Data System (ADS)
Perkins, Theodore J.; Foxall, Eric; Glass, Leon; Edwards, Roderick
2014-10-01
The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics.
A scaling law for random walks on networks.
Perkins, Theodore J; Foxall, Eric; Glass, Leon; Edwards, Roderick
2014-01-01
The dynamics of many natural and artificial systems are well described as random walks on a network: the stochastic behaviour of molecules, traffic patterns on the internet, fluctuations in stock prices and so on. The vast literature on random walks provides many tools for computing properties such as steady-state probabilities or expected hitting times. Previously, however, there has been no general theory describing the distribution of possible paths followed by a random walk. Here, we show that for any random walk on a finite network, there are precisely three mutually exclusive possibilities for the form of the path distribution: finite, stretched exponential and power law. The form of the distribution depends only on the structure of the network, while the stepping probabilities control the parameters of the distribution. We use our theory to explain path distributions in domains such as sports, music, nonlinear dynamics and stochastic chemical kinetics. PMID:25311870
Intergroup networks as random threshold graphs
NASA Astrophysics Data System (ADS)
Saha, Sudipta; Ganguly, Niloy; Mukherjee, Animesh; Krueger, Tyll
2014-04-01
Similar-minded people tend to form social groups. Due to pluralistic homophily as well as a sort of heterophily, people also participate in a wide variety of groups. Thus, these groups generally overlap with each other; an overlap between two groups can be characterized by the number of common members. These common members can play a crucial role in the transmission of information between the groups. As a step towards understanding the information dissemination, we perceive the system as a pruned intergroup network and show that it maps to a very basic graph theoretic concept known as a threshold graph. We analyze several structural properties of this network such as degree distribution, largest component size, edge density, and local clustering coefficient. We compare the theoretical predictions with the results obtained from several online social networks (LiveJournal, Flickr, YouTube) and find a good match.
Chimeras in random non-complete networks of phase oscillators.
Laing, Carlo R; Rajendran, Karthikeyan; Kevrekidis, Ioannis G
2012-03-01
We consider the simplest network of coupled non-identical phase oscillators capable of displaying a "chimera" state (namely, two subnetworks with strong coupling within the subnetworks and weaker coupling between them) and systematically investigate the effects of gradually removing connections within the network, in a random but systematically specified way. We average over ensembles of networks with the same random connectivity but different intrinsic oscillator frequencies and derive ordinary differential equations (ODEs), whose fixed points describe a typical chimera state in a representative network of phase oscillators. Following these fixed points as parameters are varied we find that chimera states are quite sensitive to such random removals of connections, and that oscillations of chimera states can be either created or suppressed in apparent bifurcation points, depending on exactly how the connections are gradually removed.
Hao, Kun; Jin, Zhigang; Shen, Haifeng; Wang, Ying
2015-01-01
Efficient routing protocols for data packet delivery are crucial to underwater sensor networks (UWSNs). However, communication in UWSNs is a challenging task because of the characteristics of the acoustic channel. Network coding is a promising technique for efficient data packet delivery thanks to the broadcast nature of acoustic channels and the relatively high computation capabilities of the sensor nodes. In this work, we present GPNC, a novel geographic routing protocol for UWSNs that incorporates partial network coding to encode data packets and uses sensor nodes' location information to greedily forward data packets to sink nodes. GPNC can effectively reduce network delays and retransmissions of redundant packets causing additional network energy consumption. Simulation results show that GPNC can significantly improve network throughput and packet delivery ratio, while reducing energy consumption and network latency when compared with other routing protocols.
Hao, Kun; Jin, Zhigang; Shen, Haifeng; Wang, Ying
2015-01-01
Efficient routing protocols for data packet delivery are crucial to underwater sensor networks (UWSNs). However, communication in UWSNs is a challenging task because of the characteristics of the acoustic channel. Network coding is a promising technique for efficient data packet delivery thanks to the broadcast nature of acoustic channels and the relatively high computation capabilities of the sensor nodes. In this work, we present GPNC, a novel geographic routing protocol for UWSNs that incorporates partial network coding to encode data packets and uses sensor nodes’ location information to greedily forward data packets to sink nodes. GPNC can effectively reduce network delays and retransmissions of redundant packets causing additional network energy consumption. Simulation results show that GPNC can significantly improve network throughput and packet delivery ratio, while reducing energy consumption and network latency when compared with other routing protocols. PMID:26029955
Randomizing bipartite networks: the case of the World Trade Web
Saracco, Fabio; Di Clemente, Riccardo; Gabrielli, Andrea; Squartini, Tiziano
2015-01-01
Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the World Trade Web, comparing the observed values of a number of structural quantities of interest with the expected ones, calculated via our randomization procedure. Interestingly, the behavior of the World Trade Web in this new representation is strongly different from the monopartite analogue, showing highly non-trivial patterns of self-organization. PMID:26029820
Connectivity in Random Grain Boundary Networks
Kumar, M; Schuh, C A; King, W E
2002-10-22
Mechanical properties of FCC metals and alloys can be improved by exercising control over the population of grain boundary types in the microstructure. The existing studies also suggest that such properties tend to have percolative mechanisms that depend on the topology of the grain boundary network. With the emergence of SEM-based automated electron backscatter diffraction (EBSD), statistically significant datasets of interface crystallography can be analyzed in a routine manner, giving new insight into the topology and percolative properties of grain boundary networks. In this work, we review advanced analysis techniques for EBSD datasets to quantify microstructures in terms of grain boundary character and triple junction distributions, as well as detailed percolation-theory based cluster analysis.
Brainwashing random asymmetric “neural” networks
NASA Astrophysics Data System (ADS)
McGuire, P. C.; Littlewort, G. C.; Rafelski, J.
1991-11-01
An algorithm for synaptic modification (plasticity) is described by which a recurrently connected network of neuron-like units can organize itself to produce a sequence of activation states that does not repeat itself for a very long time. During the self-organization stage, the connections between the units undergo non-Hebbian modifications, which tend to decorrelate the activity of the units, thereby lengthening the period of the cyclic modes inherent in the network. It is shown that the peridiodicity of the activity rises exponentially with the amount of exposure to this plasticity algorithm. Threshold is also a critical parameter in determining cycle lengths, as is the rate of decay of the fields that accumulate at silent units.
Random field Ising model and community structure in complex networks
NASA Astrophysics Data System (ADS)
Son, S.-W.; Jeong, H.; Noh, J. D.
2006-04-01
We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)
QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding
Razzaque, Mohammad Abdur; Javadi, Saeideh S.; Coulibaly, Yahaya; Hira, Muta Tah
2015-01-01
Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network's QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts. PMID:25551485
Opportunistic quantum network coding based on quantum teleportation
NASA Astrophysics Data System (ADS)
Shang, Tao; Du, Gang; Liu, Jian-wei
2016-04-01
It seems impossible to endow opportunistic characteristic to quantum network on the basis that quantum channel cannot be overheard without disturbance. In this paper, we propose an opportunistic quantum network coding scheme by taking full advantage of channel characteristic of quantum teleportation. Concretely, it utilizes quantum channel for secure transmission of quantum states and can detect eavesdroppers by means of quantum channel verification. What is more, it utilizes classical channel for both opportunistic listening to neighbor states and opportunistic coding by broadcasting measurement outcome. Analysis results show that our scheme can reduce the times of transmissions over classical channels for relay nodes and can effectively defend against classical passive attack and quantum active attack.
Selective randomized load balancing and mesh networks with changing demands
NASA Astrophysics Data System (ADS)
Shepherd, F. B.; Winzer, P. J.
2006-05-01
We consider the problem of building cost-effective networks that are robust to dynamic changes in demand patterns. We compare several architectures using demand-oblivious routing strategies. Traditional approaches include single-hop architectures based on a (static or dynamic) circuit-switched core infrastructure and multihop (packet-switched) architectures based on point-to-point circuits in the core. To address demand uncertainty, we seek minimum cost networks that can carry the class of hose demand matrices. Apart from shortest-path routing, Valiant's randomized load balancing (RLB), and virtual private network (VPN) tree routing, we propose a third, highly attractive approach: selective randomized load balancing (SRLB). This is a blend of dual-hop hub routing and randomized load balancing that combines the advantages of both architectures in terms of network cost, delay, and delay jitter. In particular, we give empirical analyses for the cost (in terms of transport and switching equipment) for the discussed architectures, based on three representative carrier networks. Of these three networks, SRLB maintains the resilience properties of RLB while achieving significant cost reduction over all other architectures, including RLB and multihop Internet protocol/multiprotocol label switching (IP/MPLS) networks using VPN-tree routing.
A chemical reaction network solver for the astrophysics code NIRVANA
NASA Astrophysics Data System (ADS)
Ziegler, U.
2016-02-01
Context. Chemistry often plays an important role in astrophysical gases. It regulates thermal properties by changing species abundances and via ionization processes. This way, time-dependent cooling mechanisms and other chemistry-related energy sources can have a profound influence on the dynamical evolution of an astrophysical system. Modeling those effects with the underlying chemical kinetics in realistic magneto-gasdynamical simulations provide the basis for a better link to observations. Aims: The present work describes the implementation of a chemical reaction network solver into the magneto-gasdynamical code NIRVANA. For this purpose a multispecies structure is installed, and a new module for evolving the rate equations of chemical kinetics is developed and coupled to the dynamical part of the code. A small chemical network for a hydrogen-helium plasma was constructed including associated thermal processes which is used in test problems. Methods: Evolving a chemical network within time-dependent simulations requires the additional solution of a set of coupled advection-reaction equations for species and gas temperature. Second-order Strang-splitting is used to separate the advection part from the reaction part. The ordinary differential equation (ODE) system representing the reaction part is solved with a fourth-order generalized Runge-Kutta method applicable for stiff systems inherent to astrochemistry. Results: A series of tests was performed in order to check the correctness of numerical and technical implementation. Tests include well-known stiff ODE problems from the mathematical literature in order to confirm accuracy properties of the solver used as well as problems combining gasdynamics and chemistry. Overall, very satisfactory results are achieved. Conclusions: The NIRVANA code is now ready to handle astrochemical processes in time-dependent simulations. An easy-to-use interface allows implementation of complex networks including thermal processes
Biomolecular network motif counting and discovery by color coding.
Alon, Noga; Dao, Phuong; Hajirasouliha, Iman; Hormozdiari, Fereydoun; Sahinalp, S Cenk
2008-07-01
Protein-protein interaction (PPI) networks of many organisms share global topological features such as degree distribution, k-hop reachability, betweenness and closeness. Yet, some of these networks can differ significantly from the others in terms of local structures: e.g. the number of specific network motifs can vary significantly among PPI networks. Counting the number of network motifs provides a major challenge to compare biomolecular networks. Recently developed algorithms have been able to count the number of induced occurrences of subgraphs with k < or = 7 vertices. Yet no practical algorithm exists for counting non-induced occurrences, or counting subgraphs with k > or = 8 vertices. Counting non-induced occurrences of network motifs is not only challenging but also quite desirable as available PPI networks include several false interactions and miss many others. In this article, we show how to apply the 'color coding' technique for counting non-induced occurrences of subgraph topologies in the form of trees and bounded treewidth subgraphs. Our algorithm can count all occurrences of motif G' with k vertices in a network G with n vertices in time polynomial with n, provided k = O(log n). We use our algorithm to obtain 'treelet' distributions for k < or = 10 of available PPI networks of unicellular organisms (Saccharomyces cerevisiae Escherichia coli and Helicobacter Pyloris), which are all quite similar, and a multicellular organism (Caenorhabditis elegans) which is significantly different. Furthermore, the treelet distribution of the unicellular organisms are similar to that obtained by the 'duplication model' but are quite different from that of the 'preferential attachment model'. The treelet distribution is robust w.r.t. sparsification with bait/edge coverage of 70% but differences can be observed when bait/edge coverage drops to 50%. PMID:18586721
Construction and Analysis of Random Networks with Explosive Percolation
NASA Astrophysics Data System (ADS)
Friedman, Eric J.; Landsberg, Adam S.
2009-12-01
The existence of explosive phase transitions in random (Erdös Rényi-type) networks has been recently documented by Achlioptas, D’Souza, and Spencer [Science 323, 1453 (2009)SCIEAS0036-807510.1126/science.1167782] via simulations. In this Letter we describe the underlying mechanism behind these first-order phase transitions and develop tools that allow us to identify (and predict) when a random network will exhibit an explosive transition. Several interesting new models displaying explosive transitions are also presented.
Enhanced networked server management with random remote backups
NASA Astrophysics Data System (ADS)
Kim, Song-Kyoo
2003-08-01
In this paper, the model is focused on available server management in network environments. The (remote) backup servers are hooked up by VPN (Virtual Private Network) and replace broken main severs immediately. A virtual private network (VPN) is a way to use a public network infrastructure and hooks up long-distance servers within a single network infrastructure. The servers can be represent as "machines" and then the system deals with main unreliable and random auxiliary spare (remote backup) machines. When the system performs a mandatory routine maintenance, auxiliary machines are being used for backups during idle periods. Unlike other existing models, the availability of auxiliary machines is changed for each activation in this enhanced model. Analytically tractable results are obtained by using several mathematical techniques and the results are demonstrated in the framework of optimized networked server allocation problems.
Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network
Lin, Kai; Wang, Di; Hu, Long
2016-01-01
With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods. PMID:27376302
Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network.
Lin, Kai; Wang, Di; Hu, Long
2016-01-01
With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods. PMID:27376302
Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network.
Lin, Kai; Wang, Di; Hu, Long
2016-07-01
With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods.
Random Resistor Network Model of Minimal Conductivity in Graphene
NASA Astrophysics Data System (ADS)
Cheianov, Vadim V.; Fal'Ko, Vladimir I.; Altshuler, Boris L.; Aleiner, Igor L.
2007-10-01
Transport in undoped graphene is related to percolating current patterns in the networks of n- and p-type regions reflecting the strong bipolar charge density fluctuations. Finite transparency of the p-n junctions is vital in establishing the macroscopic conductivity. We propose a random resistor network model to analyze scaling dependencies of the conductance on the doping and disorder, the quantum magnetoresistance and the corresponding dephasing rate.
Optimal control of coupled PDE networks with automated code generation
NASA Astrophysics Data System (ADS)
Papadopoulos, D.
2012-09-01
The purpose of this work is to present a framework for the optimal control of coupled PDE networks. A coupled PDE network is a system of partial differential equations coupled together. Such systems can be represented as a directed graph. A domain specific language (DSL)—an extension of the DOT language—is used for the description of such a coupled PDE network. The adjoint equations and the gradient, required for its optimal control, are computed with the help of a computer algebra system (CAS). Automated code generation techniques have been used for the generation of the PDE systems of both the direct and the adjoint equations. Both the direct and adjoint equations are solved with the standard finite element method. Finally, for the numerical optimization of the system standard optimization techniques are used such as BFGS and Newton conjugate gradient.
Listening to the noise: random fluctuations reveal gene network parameters
Munsky, Brian; Khammash, Mustafa
2009-01-01
The cellular environment is abuzz with noise. The origin of this noise is attributed to the inherent random motion of reacting molecules that take part in gene expression and post expression interactions. In this noisy environment, clonal populations of cells exhibit cell-to-cell variability that frequently manifests as significant phenotypic differences within the cellular population. The stochastic fluctuations in cellular constituents induced by noise can be measured and their statistics quantified. We show that these random fluctuations carry within them valuable information about the underlying genetic network. Far from being a nuisance, the ever-present cellular noise acts as a rich source of excitation that, when processed through a gene network, carries its distinctive fingerprint that encodes a wealth of information about that network. We demonstrate that in some cases the analysis of these random fluctuations enables the full identification of network parameters, including those that may otherwise be difficult to measure. This establishes a potentially powerful approach for the identification of gene networks and offers a new window into the workings of these networks.
Analytic method for calculating properties of random walks on networks
NASA Technical Reports Server (NTRS)
Goldhirsch, I.; Gefen, Y.
1986-01-01
A method for calculating the properties of discrete random walks on networks is presented. The method divides complex networks into simpler units whose contribution to the mean first-passage time is calculated. The simplified network is then further iterated. The method is demonstrated by calculating mean first-passage times on a segment, a segment with a single dangling bond, a segment with many dangling bonds, and a looplike structure. The results are analyzed and related to the applicability of the Einstein relation between conductance and diffusion.
Dynamic quality of service differentiation using fixed code weight in optical CDMA networks
NASA Astrophysics Data System (ADS)
Kakaee, Majid H.; Essa, Shawnim I.; Abd, Thanaa H.; Seyedzadeh, Saleh
2015-11-01
The emergence of network-driven applications, such as internet, video conferencing, and online gaming, brings in the need for a network the environments with capability of providing diverse Quality of Services (QoS). In this paper, a new code family of novel spreading sequences, called a Multi-Service (MS) code, has been constructed to support multiple services in Optical- Code Division Multiple Access (CDMA) system. The proposed method uses fixed weight for all services, however reducing the interfering codewords for the users requiring higher QoS. The performance of the proposed code is demonstrated using mathematical analysis. It shown that the total number of served users with satisfactory BER of 10-9 using NB=2 is 82, while they are only 36 and 10 when NB=3 and 4 respectively. The developed MS code is compared with variable-weight codes such as Variable Weight-Khazani Syed (VW-KS) and Multi-Weight-Random Diagonal (MW-RD). Different numbers of basic users (NB) are used to support triple-play services (audio, data and video) with different QoS requirements. Furthermore, reference to the BER of 10-12, 10-9, and 10-3 for video, data and audio, respectively, the system can support up to 45 total users. Hence, results show that the technique can clearly provide a relative QoS differentiation with lower value of basic users can support larger number of subscribers as well as better performance in terms of acceptable BER of 10-9 at fixed code weight.
Random Time Identity Based Firewall In Mobile Ad hoc Networks
NASA Astrophysics Data System (ADS)
Suman, Patel, R. B.; Singh, Parvinder
2010-11-01
A mobile ad hoc network (MANET) is a self-organizing network of mobile routers and associated hosts connected by wireless links. MANETs are highly flexible and adaptable but at the same time are highly prone to security risks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized control. Firewall is an effective means of protecting a local network from network-based security threats and forms a key component in MANET security architecture. This paper presents a review of firewall implementation techniques in MANETs and their relative merits and demerits. A new approach is proposed to select MANET nodes at random for firewall implementation. This approach randomly select a new node as firewall after fixed time and based on critical value of certain parameters like power backup. This approach effectively balances power and resource utilization of entire MANET because responsibility of implementing firewall is equally shared among all the nodes. At the same time it ensures improved security for MANETs from outside attacks as intruder will not be able to find out the entry point in MANET due to the random selection of nodes for firewall implementation.
Gain Control Network Conditions in Early Sensory Coding
Serrano, Eduardo; Nowotny, Thomas; Levi, Rafael; Smith, Brian H.; Huerta, Ramón
2013-01-01
Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models. PMID:23874176
Non-random retention of protein-coding overlapping genes in Metazoa
Soldà, Giulia; Suyama, Mikita; Pelucchi, Paride; Boi, Silvia; Guffanti, Alessandro; Rizzi, Ermanno; Bork, Peer; Tenchini, Maria Luisa; Ciccarelli, Francesca D
2008-01-01
Background Although the overlap of transcriptional units occurs frequently in eukaryotic genomes, its evolutionary and biological significance remains largely unclear. Here we report a comparative analysis of overlaps between genes coding for well-annotated proteins in five metazoan genomes (human, mouse, zebrafish, fruit fly and worm). Results For all analyzed species the observed number of overlapping genes is always lower than expected assuming functional neutrality, suggesting that gene overlap is negatively selected. The comparison to the random distribution also shows that retained overlaps do not exhibit random features: antiparallel overlaps are significantly enriched, while overlaps lying on the same strand and those involving coding sequences are highly underrepresented. We confirm that overlap is mostly species-specific and provide evidence that it frequently originates through the acquisition of terminal, non-coding exons. Finally, we show that overlapping genes tend to be significantly co-expressed in a breast cancer cDNA library obtained by 454 deep sequencing, and that different overlap types display different patterns of reciprocal expression. Conclusion Our data suggest that overlap between protein-coding genes is selected against in Metazoa. However, when retained it may be used as a species-specific mechanism for the reciprocal regulation of neighboring genes. The tendency of overlaps to involve non-coding regions of the genes leads to the speculation that the advantages achieved by an overlapping arrangement may be optimized by evolving regulatory non-coding transcripts. PMID:18416813
Random Evolution of Idiotypic Networks: Dynamics and Architecture
NASA Astrophysics Data System (ADS)
Brede, Markus; Behn, Ulrich
The paper deals with modelling a subsystem of the immune system, the so-called idiotypic network (INW). INWs, conceived by N.K. Jerne in 1974, are functional networks of interacting antibodies and B cells. In principle, Jernes' framework provides solutions to many issues in immunology, such as immunological memory, mechanisms for antigen recognition and self/non-self discrimination. Explaining the interconnection between the elementary components, local dynamics, network formation and architecture, and possible modes of global system function appears to be an ideal playground of statistical mechanics. We present a simple cellular automaton model, based on a graph representation of the system. From a simplified description of idiotypic interactions, rules for the random evolution of networks of occupied and empty sites on these graphs are derived. In certain biologically relevant parameter ranges the resultant dynamics leads to stationary states. A stationary state is found to correspond to a specific pattern of network organization. It turns out that even these very simple rules give rise to a multitude of different kinds of patterns. We characterize these networks by classifying `static' and `dynamic' network-patterns. A type of `dynamic' network is found to display many features of real INWs.
MFPT calculation for random walks in inhomogeneous networks
NASA Astrophysics Data System (ADS)
Wijesundera, Isuri; Halgamuge, Malka N.; Nirmalathas, Ampalavanapillai; Nanayakkara, Thrishantha
2016-11-01
Knowing the expected arrival time at a particular state, also known as the mean first passage time (MFPT), often plays an important role for a large class of random walkers in their respective state-spaces. Contrasting to ideal conditions required by recent advancements on MFPT estimations, many naturally occurring random walkers encounter inhomogeneity of transport characteristics in the networks they walk on. This paper presents a heuristic method to divide an inhomogeneous network into homogeneous network primitives (NPs) optimized using particle swarm optimizer, and to use a 'hop-wise' MFPT calculation method. This methodology's potential is demonstrated through simulated random walks and with a case study using the dataset of past cyclone tracks over the North Atlantic Ocean. Parallel processing was used to increase calculation efficiency. The predictions using the proposed method are compared to real data averages and predictions assuming homogeneous transport properties. The results show that breaking the problem into NPs reduces the average error from 18.8% to 5.4% with respect to the homogeneous network assumption.
Multilayer neural network with randomized learning: Model and applications
Terekhov, S.A.
1995-03-01
A randomized annealing-simulation scheme for learning by a multilayer neural network is examined. The fractal properties of the learning trajectories in the phase space of the network are studied. It is proposed that the learning temperature be controlled by the phenomenological characteristics of the trajectory. The errors of generalization of a multilayer perceptron and of the method of splines in time-series prediction are compared. It is shown that for nonsmooth functions that generate a stochastic time series, a neural network is preferable to a spline for the same number of parameters. The learning scheme is used to construct a cybernetic neural-network model of the phenomenon of magnetic implosion as well as for fault classification in the coolant system of a nuclear-power-plant reactor.
Improved Iterative Decoding of Network-Channel Codes for Multiple-Access Relay Channel.
Majumder, Saikat; Verma, Shrish
2015-01-01
Cooperative communication using relay nodes is one of the most effective means of exploiting space diversity for low cost nodes in wireless network. In cooperative communication, users, besides communicating their own information, also relay the information of other users. In this paper we investigate a scheme where cooperation is achieved using a common relay node which performs network coding to provide space diversity for two information nodes transmitting to a base station. We propose a scheme which uses Reed-Solomon error correcting code for encoding the information bit at the user nodes and convolutional code as network code, instead of XOR based network coding. Based on this encoder, we propose iterative soft decoding of joint network-channel code by treating it as a concatenated Reed-Solomon convolutional code. Simulation results show significant improvement in performance compared to existing scheme based on compound codes.
Improved Iterative Decoding of Network-Channel Codes for Multiple-Access Relay Channel
Majumder, Saikat; Verma, Shrish
2015-01-01
Cooperative communication using relay nodes is one of the most effective means of exploiting space diversity for low cost nodes in wireless network. In cooperative communication, users, besides communicating their own information, also relay the information of other users. In this paper we investigate a scheme where cooperation is achieved using a common relay node which performs network coding to provide space diversity for two information nodes transmitting to a base station. We propose a scheme which uses Reed-Solomon error correcting code for encoding the information bit at the user nodes and convolutional code as network code, instead of XOR based network coding. Based on this encoder, we propose iterative soft decoding of joint network-channel code by treating it as a concatenated Reed-Solomon convolutional code. Simulation results show significant improvement in performance compared to existing scheme based on compound codes. PMID:27347526
Low-dimensional dynamics of structured random networks
NASA Astrophysics Data System (ADS)
Aljadeff, Johnatan; Renfrew, David; Vegué, Marina; Sharpee, Tatyana O.
2016-02-01
Using a generalized random recurrent neural network model, and by extending our recently developed mean-field approach [J. Aljadeff, M. Stern, and T. Sharpee, Phys. Rev. Lett. 114, 088101 (2015), 10.1103/PhysRevLett.114.088101], we study the relationship between the network connectivity structure and its low-dimensional dynamics. Each connection in the network is a random number with mean 0 and variance that depends on pre- and postsynaptic neurons through a sufficiently smooth function g of their identities. We find that these networks undergo a phase transition from a silent to a chaotic state at a critical point we derive as a function of g . Above the critical point, although unit activation levels are chaotic, their autocorrelation functions are restricted to a low-dimensional subspace. This provides a direct link between the network's structure and some of its functional characteristics. We discuss example applications of the general results to neuroscience where we derive the support of the spectrum of connectivity matrices with heterogeneous and possibly correlated degree distributions, and to ecology where we study the stability of the cascade model for food web structure.
Chirp- and random-based coded ultrasonic excitation for localized blood-brain barrier opening.
Kamimura, H A S; Wang, S; Wu, S-Y; Karakatsani, M E; Acosta, C; Carneiro, A A O; Konofagou, E E
2015-10-01
Chirp- and random-based coded excitation methods have been proposed to reduce standing wave formation and improve focusing of transcranial ultrasound. However, no clear evidence has been shown to support the benefits of these ultrasonic excitation sequences in vivo. This study evaluates the chirp and periodic selection of random frequency (PSRF) coded-excitation methods for opening the blood-brain barrier (BBB) in mice. Three groups of mice (n = 15) were injected with polydisperse microbubbles and sonicated in the caudate putamen using the chirp/PSRF coded (bandwidth: 1.5–1.9 MHz, peak negative pressure: 0.52 MPa, duration: 30 s) or standard ultrasound (frequency: 1.5 MHz, pressure: 0.52 MPa, burst duration: 20 ms, duration: 5 min) sequences. T1-weighted contrast-enhanced MRI scans were performed to quantitatively analyze focused ultrasound induced BBB opening. The mean opening volumes evaluated from the MRI were mm3, mm3and mm3 for the chirp, random and regular sonications, respectively. The mean cavitation levels were V.s, V.s and V.s for the chirp, random and regular sonications, respectively. The chirp and PSRF coded pulsing sequences improved the BBB opening localization by inducing lower cavitation levels and smaller opening volumes compared to results of the regular sonication technique. Larger bandwidths were associated with more focused targeting but were limited by the frequency response of the transducer, the skull attenuation and the microbubbles optimal frequency range. The coded methods could therefore facilitate highly localized drug delivery as well as benefit other transcranial ultrasound techniques that use higher pressure levels and higher precision to induce the necessary bioeffects in a brain region while avoiding damage to the surrounding healthy tissue. PMID:26394091
Emergent classical geometries on boundaries of randomly connected tensor networks
NASA Astrophysics Data System (ADS)
Chen, Hua; Sasakura, Naoki; Sato, Yuki
2016-03-01
It is shown that classical spaces with geometries emerge on boundaries of randomly connected tensor networks with appropriately chosen tensors in the thermodynamic limit. With variation of the tensors the dimensions of the spaces can be freely chosen, and the geometries—which are curved in general—can be varied. We give the explicit solvable examples of emergent flat tori in arbitrary dimensions, and the correspondence from the tensors to the geometries for general curved cases. The perturbative dynamics in the emergent space is shown to be described by an effective action which is invariant under the spatial diffeomorphism due to the underlying orthogonal group symmetry of the randomly connected tensor network. It is also shown that there are various phase transitions among spaces, including extended and point-like ones, under continuous change of the tensors.
Intergranular degradation assessment via random grain boundary network analysis
Kumar, Mukul; Schwartz, Adam J.; King, Wayne E.
2002-01-01
A method is disclosed for determining the resistance of polycrystalline materials to intergranular degradation or failure (IGDF), by analyzing the random grain boundary network connectivity (RGBNC) microstructure. Analysis of the disruption of the RGBNC microstructure may be assess the effectiveness of materials processing in increasing IGDF resistance. Comparison of the RGBNC microstructures of materials exposed to extreme operating conditions to unexposed materials may be used to diagnose and predict possible onset of material failure due to
Gate modulation of anodically etched gallium arsenide nanowire random network
NASA Astrophysics Data System (ADS)
Aikawa, Shinya; Yamada, Kohei; Asoh, Hidetaka; Ono, Sachiko
2016-06-01
Gallium arsenide nanowires (GaAs NWs) formed by anodic etching show an electrically semi-insulating behavior because of charge carrier depletion caused by high interface state density. Here, we demonstrate the gate modulation of an anodically etched GaAs NW random network. By applying a reverse bias voltage after anodic etching of bulk GaAs, hydrogen ion exposure of the depleted NW region occurs, and then the interface state density is possibly decreased owing to the reduction in the amount of excess As generated at the interface between the amorphous Ga2O3 and GaAs layers. Consequently, the drain current of the thin-film transistor (TFT) with the GaAs NW random network was increased and was changed by the gate voltage. In contrast, the random network film remained in the insulator in the absence of reverse electrolysis treatment. The TFT performance is still insufficient but may be improved by optimizing the hydrogen ion exposure conditions.
SIR dynamics in random networks with heterogeneous connectivity.
Volz, Erik
2008-03-01
Random networks with specified degree distributions have been proposed as realistic models of population structure, yet the problem of dynamically modeling SIR-type epidemics in random networks remains complex. I resolve this dilemma by showing how the SIR dynamics can be modeled with a system of three nonlinear ODE's. The method makes use of the probability generating function (PGF) formalism for representing the degree distribution of a random network and makes use of network-centric quantities such as the number of edges in a well-defined category rather than node-centric quantities such as the number of infecteds or susceptibles. The PGF provides a simple means of translating between network and node-centric variables and determining the epidemic incidence at any time. The theory also provides a simple means of tracking the evolution of the degree distribution among susceptibles or infecteds. The equations are used to demonstrate the dramatic effects that the degree distribution plays on the final size of an epidemic as well as the speed with which it spreads through the population. Power law degree distributions are observed to generate an almost immediate expansion phase yet have a smaller final size compared to homogeneous degree distributions such as the Poisson. The equations are compared to stochastic simulations, which show good agreement with the theory. Finally, the dynamic equations provide an alternative way of determining the epidemic threshold where large-scale epidemics are expected to occur, and below which epidemic behavior is limited to finite-sized outbreaks. PMID:17668212
Non-coding RNAs and complex distributed genetic networks
NASA Astrophysics Data System (ADS)
Zhdanov, Vladimir
2011-08-01
In eukaryotic cells, the mRNA-protein interplay can be dramatically influenced by non-coding RNAs (ncRNAs). Although this new paradigm is now widely accepted, an understanding of the effect of ncRNAs on complex genetic networks is lacking. To clarify what may happen in this case, we propose a mean-field kinetic model describing the influence of ncRNA on a complex genetic network with a distributed architecture including mutual protein-mediated regulation of many genes transcribed into mRNAs. ncRNA is considered to associate with mRNAs and inhibit their translation and/or facilitate degradation. Our results are indicative of the richness of the kinetics under consideration. The main complex features are found to be bistability and oscillations. One could expect to find kinetic chaos as well. The latter feature has however not been observed in our calculations. In addition, we illustrate the difference in the regulation of distributed networks by mRNA and ncRNA.
Nonlinear system modeling with random matrices: echo state networks revisited.
Zhang, Bai; Miller, David J; Wang, Yue
2012-01-01
Echo state networks (ESNs) are a novel form of recurrent neural networks (RNNs) that provide an efficient and powerful computational model approximating nonlinear dynamical systems. A unique feature of an ESN is that a large number of neurons (the "reservoir") are used, whose synaptic connections are generated randomly, with only the connections from the reservoir to the output modified by learning. Why a large randomly generated fixed RNN gives such excellent performance in approximating nonlinear systems is still not well understood. In this brief, we apply random matrix theory to examine the properties of random reservoirs in ESNs under different topologies (sparse or fully connected) and connection weights (Bernoulli or Gaussian). We quantify the asymptotic gap between the scaling factor bounds for the necessary and sufficient conditions previously proposed for the echo state property. We then show that the state transition mapping is contractive with high probability when only the necessary condition is satisfied, which corroborates and thus analytically explains the observation that in practice one obtains echo states when the spectral radius of the reservoir weight matrix is smaller than 1.
A simple model of global cascades on random networks
NASA Astrophysics Data System (ADS)
Watts, Duncan J.
2002-04-01
The origin of large but rare cascades that are triggered by small initial shocks is a phenomenon that manifests itself as diversely as cultural fads, collective action, the diffusion of norms and innovations, and cascading failures in infrastructure and organizational networks. This paper presents a possible explanation of this phenomenon in terms of a sparse, random network of interacting agents whose decisions are determined by the actions of their neighbors according to a simple threshold rule. Two regimes are identified in which the network is susceptible to very large cascadesherein called global cascadesthat occur very rarely. When cascade propagation is limited by the connectivity of the network, a power law distribution of cascade sizes is observed, analogous to the cluster size distribution in standard percolation theory and avalanches in self-organized criticality. But when the network is highly connected, cascade propagation is limited instead by the local stability of the nodes themselves, and the size distribution of cascades is bimodal, implying a more extreme kind of instability that is correspondingly harder to anticipate. In the first regime, where the distribution of network neighbors is highly skewed, it is found that the most connected nodes are far more likely than average nodes to trigger cascades, but not in the second regime. Finally, it is shown that heterogeneity plays an ambiguous role in determining a system's stability: increasingly heterogeneous thresholds make the system more vulnerable to global cascades; but an increasingly heterogeneous degree distribution makes it less vulnerable.
NASA Astrophysics Data System (ADS)
Yu, Mingchao; Sadeghi, Parastoo; Aboutorab, Neda
2015-12-01
We consider broadcasting a block of packets to multiple wireless receivers under random packet erasures using instantly decodable network coding (IDNC). The sender first broadcasts each packet uncoded once, then generates coded packets according to receivers' feedback about their missing packets. We focus on strict IDNC (S-IDNC), where each coded packet includes at most one missing packet of every receiver. But, we will also study its relation with generalized IDNC (G-IDNC), where this condition is relaxed. We characterize two fundamental performance limits of S-IDNC: (1) the number of transmissions to complete the broadcast, which measures throughput and (2) average packet decoding delay, which measures how fast each packet is decoded at each receiver on average. We derive a closed-form expression for the expected minimum number of transmissions in terms of the number of packets and receivers and the erasure probability. We prove that it is NP-hard to minimize the average packet decoding delay of S-IDNC. We also prove that the graph models of S- and G-IDNC share the same chromatic number. Next, we design efficient S-IDNC transmission schemes and coding algorithms with full/intermittent receiver feedback. We present simulation results to corroborate the developed theory and compare our schemes with existing ones.
An effective packetization algorithm of LT codes for stable video streaming over wireless network
NASA Astrophysics Data System (ADS)
Lee, Dongju; Kim, Wan; Song, Hwangjun
2011-09-01
In this work, we propose an effective And-Or tree based packetization algorithm of Luby Transform (LT) codes to provide stable video streaming services by minimizing the deterioration of video streaming service quality caused by lost packets over error-prone wireless network. To accomplish our goal, the proposed packetization algorithm considers the relationships among encoded symbols of LT codes based on an And-Or tree analysis tool, and then puts the these encoded symbols into packets to minimize the packet loss effect during packet transmission and improve the decoding success rate of LT codes by reducing the correlations among packets. We conduct a mathematical analysis to prove performance of our packetization algorithm of LT codes compared with conventional packetization algorithm. Finally, the proposed system is fully implemented in Java and C/C++, and widely tested to show that the proposed packetization algorithm works reasonably well. The experimental results are provided to demonstrate that the proposed packetization algorithm supports more stable video streaming services with higher peak signal-to-nose ratio (PSNR) than the conventional packetization algorithm with various packet loss patterns, including random and burst packet loss patterns.
Security authentication with a three-dimensional optical phase code using random forest classifier.
Markman, Adam; Carnicer, Artur; Javidi, Bahram
2016-06-01
An object with a unique three-dimensional (3D) optical phase mask attached is analyzed for security and authentication. These 3D optical phase masks are more difficult to duplicate or to have a mathematical formulation compared with 2D masks and thus have improved security capabilities. A quick response code was modulated using a random 3D optical phase mask generating a 3D optical phase code (OPC). Due to the scattering of light through the 3D OPC, a unique speckle pattern based on the materials and structure in the 3D optical phase mask is generated and recorded on a CCD device. Feature extraction is performed by calculating the mean, variance, skewness, kurtosis, and entropy for each recorded speckle pattern. The random forest classifier is used for authentication. Optical experiments demonstrate the feasibility of the authentication scheme. PMID:27409445
NASA Technical Reports Server (NTRS)
Rizzi, Stephen A.; Muravyov, Alexander A.
2002-01-01
Two new equivalent linearization implementations for geometrically nonlinear random vibrations are presented. Both implementations are based upon a novel approach for evaluating the nonlinear stiffness within commercial finite element codes and are suitable for use with any finite element code having geometrically nonlinear static analysis capabilities. The formulation includes a traditional force-error minimization approach and a relatively new version of a potential energy-error minimization approach, which has been generalized for multiple degree-of-freedom systems. Results for a simply supported plate under random acoustic excitation are presented and comparisons of the displacement root-mean-square values and power spectral densities are made with results from a nonlinear time domain numerical simulation.
Random access to mobile networks with advanced error correction
NASA Technical Reports Server (NTRS)
Dippold, Michael
1990-01-01
A random access scheme for unreliable data channels is investigated in conjunction with an adaptive Hybrid-II Automatic Repeat Request (ARQ) scheme using Rate Compatible Punctured Codes (RCPC) Forward Error Correction (FEC). A simple scheme with fixed frame length and equal slot sizes is chosen and reservation is implicit by the first packet transmitted randomly in a free slot, similar to Reservation Aloha. This allows the further transmission of redundancy if the last decoding attempt failed. Results show that a high channel utilization and superior throughput can be achieved with this scheme that shows a quite low implementation complexity. For the example of an interleaved Rayleigh channel and soft decision utilization and mean delay are calculated. A utilization of 40 percent may be achieved for a frame with the number of slots being equal to half the station number under high traffic load. The effects of feedback channel errors and some countermeasures are discussed.
A hippocampal network for spatial coding during immobility and sleep
Kay, K.; Sosa, M.; Chung, J.E.; Karlsson, M.P.; Larkin, M.C.; Frank, L.M.
2016-01-01
How does an animal know where it is when it stops moving? Hippocampal place cells fire at discrete locations as subjects traverse space, thereby providing an explicit neural code for current location during locomotion. In contrast, during awake immobility, the hippocampus is thought to be dominated by neural firing representing past and possible future experience. The question of whether and how the hippocampus constructs a representation of current location in the absence of locomotion has stood unresolved. Here we report that a distinct population of hippocampal neurons, located in the CA2 subregion, signals current location during immobility, and furthermore does so in association with a previously unidentified hippocampus-wide network pattern. In addition, signaling of location persists into brief periods of desynchronization prevalent in slow-wave sleep. The hippocampus thus generates a distinct representation of current location during immobility, pointing to mnemonic processing specific to experience occurring in the absence of locomotion. PMID:26934224
Non-Coding RNAs in Neural Networks, REST-Assured.
Rossbach, Michael
2011-01-01
In the nervous system, several key steps in cellular complexity and development are regulated by non-coding RNAs (ncRNAs) and the repressor element-1 silencing transcription factor/neuron-restrictive silencing factor (REST/NRSF). REST recruits gene regulatory complexes to regulatory sequences, among them the repressor element-1/neuron-restrictive silencer element, and mediates developmental stage-specific gene expression or repression, chromatin (re-)organization or silencing for protein-coding genes as well as for several ncRNAs like microRNAs, short interfering RNAs or long ncRNAs. NcRNAs are far from being just transcriptional noise and are involved in chromatin accessibility, transcription and post-transcriptional processing, trafficking, or RNA editing. REST and its cofactor CoREST are both highly regulated through various ncRNAs. The importance of the correct regulation within the ncRNA network, the ncRNAome, is demonstrated when it comes to a deregulation of REST and/or ncRNAs associated with molecular pathophysiology underlying diverse disorders including neurodegenerative diseases or brain tumors. PMID:22303307
Mechanical Behavior of Homogeneous and Composite Random Fiber Networks
NASA Astrophysics Data System (ADS)
Shahsavari, Ali
Random fiber networks are present in many biological and non-biological materials such as paper, cytoskeleton, and tissue scaffolds. Mechanical behavior of networks is controlled by the mechanical properties of the constituent fibers and the architecture of the network. To characterize these two main factors, different parameters such as fiber density, fiber length, average segment length, nature of the cross-links at the fiber intersections, ratio of bending to axial behavior of fibers have been considered. Random fiber networks are usually modeled by representing each fiber as a Timoshenko or an Euler-Bernoulli beam and each cross-link as either a welded or rotating joint. In this dissertation, the effect of these modeling options on the dependence of the overall linear network modulus on microstructural parameters is studied. It is concluded that Timoshenko beams can be used for the whole range of density and fiber stiffness parameters, while the Euler-Bernoulli model can be used only at relatively low densities. In the low density-low bending stiffness range, elastic strain energy is stored in the bending mode of the deformation, while in the other extreme range of parameters, the energy is stored predominantly in the axial and shear deformation modes. It is shown that both rotating and welded joint models give the same rules for scaling of the network modulus with different micromechanical parameters. The elastic modulus of sparsely cross-linked random fiber networks, i.e. networks in which the degree of cross-linking varies, is studied. The relationship between the micromechanical parameters - fiber density, fiber axial and bending stiffness, and degree of cross-linking - and the overall elastic modulus is presented in terms of a master curve. It is shown that the master plot with various degrees of cross-linking can be collapsed to a curve which is also valid for fully cross-linked networks. Random fiber networks in which fibers are bonded to each other are
Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization.
Zhao, Zhi-Qin; Han, Guo-Sheng; Yu, Zu-Guo; Li, Jinyan
2015-08-01
Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request.
First Passage Time for Random Walks in Heterogeneous Networks
NASA Astrophysics Data System (ADS)
Hwang, S.; Lee, D.-S.; Kahng, B.
2012-08-01
The first passage time (FPT) for random walks is a key indicator of how fast information diffuses in a given system. Despite the role of FPT as a fundamental feature in transport phenomena, its behavior, particularly in heterogeneous networks, is not yet fully understood. Here, we study, both analytically and numerically, the scaling behavior of the FPT distribution to a given target node, averaged over all starting nodes. We find that random walks arrive quickly at a local hub, and therefore, the FPT distribution shows a crossover with respect to time from fast decay behavior (induced from the attractive effect to the hub) to slow decay behavior (caused by the exploring of the entire system). Moreover, the mean FPT is independent of the degree of the target node in the case of compact exploration. These theoretical results justify the necessity of using a random jump protocol (empirically used in search engines) and provide guidelines for designing an effective network to make information quickly accessible.
NASA Astrophysics Data System (ADS)
Wei, Pei; Gu, Rentao; Ji, Yuefeng
2014-06-01
As an innovative and promising technology, network coding has been introduced to passive optical networks (PON) in recent years to support inter optical network unit (ONU) communication, yet the signaling process and dynamic bandwidth allocation (DBA) in PON with network coding (NC-PON) still need further study. Thus, we propose a joint signaling and DBA scheme for efficiently supporting differentiated services of inter ONU communication in NC-PON. In the proposed joint scheme, the signaling process lays the foundation to fulfill network coding in PON, and it can not only avoid the potential threat to downstream security in previous schemes but also be suitable for the proposed hybrid dynamic bandwidth allocation (HDBA) scheme. In HDBA, a DBA cycle is divided into two sub-cycles for applying different coding, scheduling and bandwidth allocation strategies to differentiated classes of services. Besides, as network traffic load varies, the entire upstream transmission window for all REPORT messages slides accordingly, leaving the transmission time of one or two sub-cycles to overlap with the bandwidth allocation calculation time at the optical line terminal (the OLT), so that the upstream idle time can be efficiently eliminated. Performance evaluation results validate that compared with the existing two DBA algorithms deployed in NC-PON, HDBA demonstrates the best quality of service (QoS) support in terms of delay for all classes of services, especially guarantees the end-to-end delay bound of high class services. Specifically, HDBA can eliminate queuing delay and scheduling delay of high class services, reduce those of lower class services by at least 20%, and reduce the average end-to-end delay of all services over 50%. Moreover, HDBA also achieves the maximum delay fairness between coded and uncoded lower class services, and medium delay fairness for high class services.
Computational capabilities of random automata networks for reservoir computing.
Snyder, David; Goudarzi, Alireza; Teuscher, Christof
2013-04-01
This paper underscores the conjecture that intrinsic computation is maximal in systems at the "edge of chaos". We study the relationship between dynamics and computational capability in random Boolean networks (RBN) for reservoir computing (RC). RC is a computational paradigm in which a trained readout layer interprets the dynamics of an excitable component (called the reservoir) that is perturbed by external input. The reservoir is often implemented as a homogeneous recurrent neural network, but there has been little investigation into the properties of reservoirs that are discrete and heterogeneous. Random Boolean networks are generic and heterogeneous dynamical systems and here we use them as the reservoir. A RBN is typically a closed system; to use it as a reservoir we extend it with an input layer. As a consequence of perturbation, the RBN does not necessarily fall into an attractor. Computational capability in RC arises from a tradeoff between separability and fading memory of inputs. We find the balance of these properties predictive of classification power and optimal at critical connectivity. These results are relevant to the construction of devices which exploit the intrinsic dynamics of complex heterogeneous systems, such as biomolecular substrates.
Analysis of complex contagions in random multiplex networks
NASA Astrophysics Data System (ADS)
Yaǧan, Osman; Gligor, Virgil
2012-09-01
We study the diffusion of influence in random multiplex networks where links can be of r different types, and, for a given content (e.g., rumor, product, or political view), each link type is associated with a content-dependent parameter ci in [0,∞] that measures the relative bias type i links have in spreading this content. In this setting, we propose a linear threshold model of contagion where nodes switch state if their “perceived” proportion of active neighbors exceeds a threshold τ. Namely a node connected to mi active neighbors and ki-mi inactive neighbors via type i links will turn active if ∑cimi/∑ciki exceeds its threshold τ. Under this model, we obtain the condition, probability and expected size of global spreading events. Our results extend the existing work on complex contagions in several directions by (i) providing solutions for coupled random networks whose vertices are neither identical nor disjoint, (ii) highlighting the effect of content on the dynamics of complex contagions, and (iii) showing that content-dependent propagation over a multiplex network leads to a subtle relation between the giant vulnerable component of the graph and the global cascade condition that is not seen in the existing models in the literature.
Predicting genetic interactions with random walks on biological networks
Chipman, Kyle C; Singh, Ambuj K
2009-01-01
Background Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse
A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin
2016-01-01
Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay. PMID:27618044
A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks.
Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin
2016-01-01
Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay. PMID:27618044
A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks.
Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin
2016-09-07
Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay.
Technology Infusion of CodeSonar into the Space Network Ground Segment
NASA Technical Reports Server (NTRS)
Benson, Markland J.
2009-01-01
This slide presentation reviews the applicability of CodeSonar to the Space Network software. CodeSonar is a commercial off the shelf system that analyzes programs written in C, C++ or Ada for defects in the code. Software engineers use CodeSonar results as an input to the existing source code inspection process. The study is focused on large scale software developed using formal processes. The systems studied are mission critical in nature but some use commodity computer systems.
Network Randomization and Dynamic Defense for Critical Infrastructure Systems
Chavez, Adrian R.; Martin, Mitchell Tyler; Hamlet, Jason; Stout, William M.S.; Lee, Erik
2015-04-01
Critical Infrastructure control systems continue to foster predictable communication paths, static configurations, and unpatched systems that allow easy access to our nation's most critical assets. This makes them attractive targets for cyber intrusion. We seek to address these attack vectors by automatically randomizing network settings, randomizing applications on the end devices themselves, and dynamically defending these systems against active attacks. Applying these protective measures will convert control systems into moving targets that proactively defend themselves against attack. Sandia National Laboratories has led this effort by gathering operational and technical requirements from Tennessee Valley Authority (TVA) and performing research and development to create a proof-of-concept solution. Our proof-of-concept has been tested in a laboratory environment with over 300 nodes. The vision of this project is to enhance control system security by converting existing control systems into moving targets and building these security measures into future systems while meeting the unique constraints that control systems face.
Topological and Dynamical Complexity of Random Neural Networks
NASA Astrophysics Data System (ADS)
Wainrib, Gilles; Touboul, Jonathan
2013-03-01
Random neural networks are dynamical descriptions of randomly interconnected neural units. These show a phase transition to chaos as a disorder parameter is increased. The microscopic mechanisms underlying this phase transition are unknown and, similar to spin glasses, shall be fundamentally related to the behavior of the system. In this Letter, we investigate the explosion of complexity arising near that phase transition. We show that the mean number of equilibria undergoes a sharp transition from one equilibrium to a very large number scaling exponentially with the dimension on the system. Near criticality, we compute the exponential rate of divergence, called topological complexity. Strikingly, we show that it behaves exactly as the maximal Lyapunov exponent, a classical measure of dynamical complexity. This relationship unravels a microscopic mechanism leading to chaos which we further demonstrate on a simpler disordered system, suggesting a deep and underexplored link between topological and dynamical complexity.
Impact of dynamic rate coding aspects of mobile phone networks on forensic voice comparison.
Alzqhoul, Esam A S; Nair, Balamurali B T; Guillemin, Bernard J
2015-09-01
Previous studies have shown that landline and mobile phone networks are different in their ways of handling the speech signal, and therefore in their impact on it. But the same is also true of the different networks within the mobile phone arena. There are two major mobile phone technologies currently in use today, namely the global system for mobile communications (GSM) and code division multiple access (CDMA) and these are fundamentally different in their design. For example, the quality of the coded speech in the GSM network is a function of channel quality, whereas in the CDMA network it is determined by channel capacity (i.e., the number of users sharing a cell site). This paper examines the impact on the speech signal of a key feature of these networks, namely dynamic rate coding, and its subsequent impact on the task of likelihood-ratio-based forensic voice comparison (FVC). Surprisingly, both FVC accuracy and precision are found to be better for both GSM- and CDMA-coded speech than for uncoded. Intuitively one expects FVC accuracy to increase with increasing coded speech quality. This trend is shown to occur for the CDMA network, but, surprisingly, not for the GSM network. Further, in respect to comparisons between these two networks, FVC accuracy for CDMA-coded speech is shown to be slightly better than for GSM-coded speech, particularly when the coded-speech quality is high, but in terms of FVC precision the two networks are shown to be very similar.
Universality classes of the generalized epidemic process on random networks.
Chung, Kihong; Baek, Yongjoo; Ha, Meesoon; Jeong, Hawoong
2016-05-01
We present a self-contained discussion of the universality classes of the generalized epidemic process (GEP) on Poisson random networks, which is a simple model of social contagions with cooperative effects. These effects lead to rich phase transitional behaviors that include continuous and discontinuous transitions with tricriticality in between. With the help of a comprehensive finite-size scaling theory, we numerically confirm static and dynamic scaling behaviors of the GEP near continuous phase transitions and at tricriticality, which verifies the field-theoretical results of previous studies. We also propose a proper criterion for the discontinuous transition line, which is shown to coincide with the bond percolation threshold.
Universality classes of the generalized epidemic process on random networks
NASA Astrophysics Data System (ADS)
Chung, Kihong; Baek, Yongjoo; Ha, Meesoon; Jeong, Hawoong
2016-05-01
We present a self-contained discussion of the universality classes of the generalized epidemic process (GEP) on Poisson random networks, which is a simple model of social contagions with cooperative effects. These effects lead to rich phase transitional behaviors that include continuous and discontinuous transitions with tricriticality in between. With the help of a comprehensive finite-size scaling theory, we numerically confirm static and dynamic scaling behaviors of the GEP near continuous phase transitions and at tricriticality, which verifies the field-theoretical results of previous studies. We also propose a proper criterion for the discontinuous transition line, which is shown to coincide with the bond percolation threshold.
Universality classes of the generalized epidemic process on random networks.
Chung, Kihong; Baek, Yongjoo; Ha, Meesoon; Jeong, Hawoong
2016-05-01
We present a self-contained discussion of the universality classes of the generalized epidemic process (GEP) on Poisson random networks, which is a simple model of social contagions with cooperative effects. These effects lead to rich phase transitional behaviors that include continuous and discontinuous transitions with tricriticality in between. With the help of a comprehensive finite-size scaling theory, we numerically confirm static and dynamic scaling behaviors of the GEP near continuous phase transitions and at tricriticality, which verifies the field-theoretical results of previous studies. We also propose a proper criterion for the discontinuous transition line, which is shown to coincide with the bond percolation threshold. PMID:27300907
Bouchaud-Mézard model on a random network
NASA Astrophysics Data System (ADS)
Ichinomiya, Takashi
2012-09-01
We studied the Bouchaud-Mézard (BM) model, which was introduced to explain Pareto's law in a real economy, on a random network. Using “adiabatic and independent” assumptions, we analytically obtained the stationary probability distribution function of wealth. The results show that wealth condensation, indicated by the divergence of the variance of wealth, occurs at a larger J than that obtained by the mean-field theory, where J represents the strength of interaction between agents. We compared our results with numerical simulation results and found that they were in good agreement.
Power-law relations in random networks with communities.
Stegehuis, Clara; van der Hofstad, Remco; van Leeuwaarden, Johan S H
2016-07-01
Most random graph models are locally tree-like-do not contain short cycles-rendering them unfit for modeling networks with a community structure. We introduce the hierarchical configuration model (HCM), a generalization of the configuration model that includes community structures, while properties such as the size of the giant component, and the size of the giant percolating cluster under bond percolation can still be derived analytically. Viewing real-world networks as realizations of HCM, we observe two previously undiscovered power-law relations: between the number of edges inside a community and the community sizes, and between the number of edges going out of a community and the community sizes. We also relate the power-law exponent τ of the degree distribution with the power-law exponent of the community-size distribution γ. In the case of extremely dense communities (e.g., complete graphs), this relation takes the simple form τ=γ-1. PMID:27575143
Exploring MEDLINE Space with Random Indexing and Pathfinder Networks
Cohen, Trevor
2008-01-01
The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search. PMID:18999236
Power-law relations in random networks with communities
NASA Astrophysics Data System (ADS)
Stegehuis, Clara; van der Hofstad, Remco; van Leeuwaarden, Johan S. H.
2016-07-01
Most random graph models are locally tree-like—do not contain short cycles—rendering them unfit for modeling networks with a community structure. We introduce the hierarchical configuration model (HCM), a generalization of the configuration model that includes community structures, while properties such as the size of the giant component, and the size of the giant percolating cluster under bond percolation can still be derived analytically. Viewing real-world networks as realizations of HCM, we observe two previously undiscovered power-law relations: between the number of edges inside a community and the community sizes, and between the number of edges going out of a community and the community sizes. We also relate the power-law exponent τ of the degree distribution with the power-law exponent of the community-size distribution γ . In the case of extremely dense communities (e.g., complete graphs), this relation takes the simple form τ =γ -1 .
Unbiased degree-preserving randomization of directed binary networks.
Roberts, E S; Coolen, A C C
2012-04-01
Randomizing networks using a naive "accept-all" edge-swap algorithm is generally biased. Building on recent results for nondirected graphs, we construct an ergodic detailed balance Markov chain with nontrivial acceptance probabilities for directed graphs, which converges to a strictly uniform measure and is based on edge swaps that conserve all in and out degrees. The acceptance probabilities can also be generalized to define Markov chains that target any alternative desired measure on the space of directed graphs in order to generate graphs with more sophisticated topological features. This is demonstrated by defining a process tailored to the production of directed graphs with specified degree-degree correlation functions. The theory is implemented numerically and tested on synthetic and biological network examples.
Chirp- and random-based coded ultrasonic excitation for localized blood-brain barrier opening
NASA Astrophysics Data System (ADS)
Kamimura, H. A. S.; Wang, S.; Wu, S.-Y.; Karakatsani, M. E.; Acosta, C.; Carneiro, A. A. O.; Konofagou, E. E.
2015-10-01
Chirp- and random-based coded excitation methods have been proposed to reduce standing wave formation and improve focusing of transcranial ultrasound. However, no clear evidence has been shown to support the benefits of these ultrasonic excitation sequences in vivo. This study evaluates the chirp and periodic selection of random frequency (PSRF) coded-excitation methods for opening the blood-brain barrier (BBB) in mice. Three groups of mice (n = 15) were injected with polydisperse microbubbles and sonicated in the caudate putamen using the chirp/PSRF coded (bandwidth: 1.5-1.9 MHz, peak negative pressure: 0.52 MPa, duration: 30 s) or standard ultrasound (frequency: 1.5 MHz, pressure: 0.52 MPa, burst duration: 20 ms, duration: 5 min) sequences. T1-weighted contrast-enhanced MRI scans were performed to quantitatively analyze focused ultrasound induced BBB opening. The mean opening volumes evaluated from the MRI were 9.38+/- 5.71 mm3, 8.91+/- 3.91 mm3and 35.47+/- 5.10 mm3 for the chirp, random and regular sonications, respectively. The mean cavitation levels were 55.40+/- 28.43 V.s, 63.87+/- 29.97 V.s and 356.52+/- 257.15 V.s for the chirp, random and regular sonications, respectively. The chirp and PSRF coded pulsing sequences improved the BBB opening localization by inducing lower cavitation levels and smaller opening volumes compared to results of the regular sonication technique. Larger bandwidths were associated with more focused targeting but were limited by the frequency response of the transducer, the skull attenuation and the microbubbles optimal frequency range. The coded methods could therefore facilitate highly localized drug delivery as well as benefit other transcranial ultrasound techniques that use higher pressure levels and higher precision to induce the necessary bioeffects in a brain region while avoiding damage to the surrounding healthy tissue.
Chirp- and random-based coded ultrasonic excitation for localized blood-brain barrier opening
Kamimura, HAS; Wang, S; Wu, S-Y; Karakatsani, ME; Acosta, C; Carneiro, AAO; Konofagou, EE
2015-01-01
Chirp- and random-based coded excitation methods have been proposed to reduce standing wave formation and improve focusing of transcranial ultrasound. However, no clear evidence has been shown to support the benefits of these ultrasonic excitation sequences in vivo. This study evaluates the chirp and periodic selection of random frequency (PSRF) coded-excitation methods for opening the blood-brain barrier (BBB) in mice. Three groups of mice (n=15) were injected with polydisperse microbubbles and sonicated in the caudate putamen using the chirp/PSRF coded (bandwidth: 1.5-1.9 MHz, peak negative pressure: 0.52 MPa, duration: 30 s) or standard ultrasound (frequency: 1.5 MHz, pressure: 0.52 MPa, burst duration: 20 ms, duration: 5 min) sequences. T1-weighted contrast-enhanced MRI scans were performed to quantitatively analyze focused ultrasound induced BBB opening. The mean opening volumes evaluated from the MRI were 9.38±5.71 mm3, 8.91±3.91 mm3 and 35.47 ± 5.10 mm3 for the chirp, random and regular sonications, respectively. The mean cavitation levels were 55.40±28.43 V.s, 63.87±29.97 V.s and 356.52±257.15 V.s for the chirp, random and regular sonications, respectively. The chirp and PSRF coded pulsing sequences improved the BBB opening localization by inducing lower cavitation levels and smaller opening volumes compared to results of the regular sonication technique. Larger bandwidths were associated with more focused targeting but were limited by the frequency response of the transducer, the skull attenuation and the microbubbles optimal frequency range. The coded methods could therefore facilitate highly localized drug delivery as well as benefit other transcranial ultrasound techniques that use higher pressure levels and higher precision to induce the necessary bioeffects in a brain region while avoiding damage to the surrounding healthy tissue. PMID:26394091
Randomizing world trade. I. A binary network analysis
NASA Astrophysics Data System (ADS)
Squartini, Tiziano; Fagiolo, Giorgio; Garlaschelli, Diego
2011-10-01
The international trade network (ITN) has received renewed multidisciplinary interest due to recent advances in network theory. However, it is still unclear whether a network approach conveys additional, nontrivial information with respect to traditional international-economics analyses that describe world trade only in terms of local (first-order) properties. In this and in a companion paper, we employ a recently proposed randomization method to assess in detail the role that local properties have in shaping higher-order patterns of the ITN in all its possible representations (binary or weighted, directed or undirected, aggregated or disaggregated by commodity) and across several years. Here we show that, remarkably, the properties of all binary projections of the network can be completely traced back to the degree sequence, which is therefore maximally informative. Our results imply that explaining the observed degree sequence of the ITN, which has not received particular attention in economic theory, should instead become one the main focuses of models of trade.
Intercluster Connection in Cognitive Wireless Mesh Networks Based on Intelligent Network Coding
NASA Astrophysics Data System (ADS)
Chen, Xianfu; Zhao, Zhifeng; Jiang, Tao; Grace, David; Zhang, Honggang
2009-12-01
Cognitive wireless mesh networks have great flexibility to improve spectrum resource utilization, within which secondary users (SUs) can opportunistically access the authorized frequency bands while being complying with the interference constraint as well as the QoS (Quality-of-Service) requirement of primary users (PUs). In this paper, we consider intercluster connection between the neighboring clusters under the framework of cognitive wireless mesh networks. Corresponding to the collocated clusters, data flow which includes the exchanging of control channel messages usually needs four time slots in traditional relaying schemes since all involved nodes operate in half-duplex mode, resulting in significant bandwidth efficiency loss. The situation is even worse at the gateway node connecting the two colocated clusters. A novel scheme based on network coding is proposed in this paper, which needs only two time slots to exchange the same amount of information mentioned above. Our simulation shows that the network coding-based intercluster connection has the advantage of higher bandwidth efficiency compared with the traditional strategy. Furthermore, how to choose an optimal relaying transmission power level at the gateway node in an environment of coexisting primary and secondary users is discussed. We present intelligent approaches based on reinforcement learning to solve the problem. Theoretical analysis and simulation results both show that the intelligent approaches can achieve optimal throughput for the intercluster relaying in the long run.
Random walk approach for dispersive transport in pipe networks
NASA Astrophysics Data System (ADS)
Sämann, Robert; Graf, Thomas; Neuweiler, Insa
2016-04-01
Keywords: particle transport, random walk, pipe, network, HYSTEM-EXTAN, OpenGeoSys After heavy pluvial events in urban areas the available drainage system may be undersized at peak flows (Fuchs, 2013). Consequently, rainwater in the pipe network is likely to spill out through manholes. The presence of hazardous contaminants in the pipe drainage system represents a potential risk to humans especially when the contaminated drainage water reaches the land surface. Real-time forecasting of contaminants in the drainage system needs a quick calculation. Numerical models to predict the fate of contaminants are usually based on finite volume methods. Those are not applicable here because of their volume averaging elements. Thus, a more efficient method is preferable, which is independent from spatial discretization. In the present study, a particle-based method is chosen to calculate transport paths and spatial distribution of contaminants within a pipe network. A random walk method for particles in turbulent flow in partially filled pipes has been developed. Different approaches for in-pipe-mixing and node-mixing with respect to the geometry in a drainage network are shown. A comparison of dispersive behavior and calculation time is given to find the fastest model. The HYSTEM-EXTRAN (itwh, 2002) model is used to provide hydrodynamic conditions in the pipe network according to surface runoff scenarios in order to real-time predict contaminant transport in an urban pipe network system. The newly developed particle-based model will later be coupled to the subsurface flow model OpenGeoSys (Kolditz et al., 2012). References: Fuchs, L. (2013). Gefährdungsanalyse zur Überflutungsvorsorge kommunaler Entwässerungssysteme. Sanierung und Anpassung von Entwässerungssystemen-Alternde Infrastruktur und Klimawandel, Österreichischer Wasser-und Abfallwirtschaftsverband, Wien, ISBN, 978-3. itwh (2002). Modellbeschreibung, Institut für technisch-wissenschaftliche Hydrologie Gmb
Incorporating Contact Network Structure in Cluster Randomized Trials
NASA Astrophysics Data System (ADS)
Staples, Patrick C.; Ogburn, Elizabeth L.; Onnela, Jukka-Pekka
2015-12-01
Whenever possible, the efficacy of a new treatment is investigated by randomly assigning some individuals to a treatment and others to control, and comparing the outcomes between the two groups. Often, when the treatment aims to slow an infectious disease, clusters of individuals are assigned to each treatment arm. The structure of interactions within and between clusters can reduce the power of the trial, i.e. the probability of correctly detecting a real treatment effect. We investigate the relationships among power, within-cluster structure, cross-contamination via between-cluster mixing, and infectivity by simulating an infectious process on a collection of clusters. We demonstrate that compared to simulation-based methods, current formula-based power calculations may be conservative for low levels of between-cluster mixing, but failing to account for moderate or high amounts can result in severely underpowered studies. Power also depends on within-cluster network structure for certain kinds of infectious spreading. Infections that spread opportunistically through highly connected individuals have unpredictable infectious breakouts, making it harder to distinguish between random variation and real treatment effects. Our approach can be used before conducting a trial to assess power using network information, and we demonstrate how empirical data can inform the extent of between-cluster mixing.
Distributed Clone Detection in Static Wireless Sensor Networks: Random Walk with Network Division
Khan, Wazir Zada; Aalsalem, Mohammed Y.; Saad, N. M.
2015-01-01
Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads. PMID:25992913
Distributed clone detection in static wireless sensor networks: random walk with network division.
Khan, Wazir Zada; Aalsalem, Mohammed Y; Saad, N M
2015-01-01
Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.
Wetmore, Kelly M.; Price, Morgan N.; Waters, Robert J.; Lamson, Jacob S.; He, Jennifer; Hoover, Cindi A.; Blow, Matthew J.; Bristow, James; Butland, Gareth; Arkin, Adam P.; Deutschbauer, Adam
2015-05-12
Transposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with any transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli, Phaeobacter inhibens, Pseudomonas stutzeri, Shewanella amazonensis, and Shewanella oneidensis. To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens, we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative D-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling. A large challenge in microbiology is the functional assessment of the millions of uncharacterized genes identified by genome sequencing. Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach to assign phenotypes and functions to genes
Wetmore, Kelly M.; Price, Morgan N.; Waters, Robert J.; Lamson, Jacob S.; He, Jennifer; Hoover, Cindi A.; Blow, Matthew J.; Bristow, James; Butland, Gareth; Arkin, Adam P.; et al
2015-05-12
Transposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with anymore » transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli, Phaeobacter inhibens, Pseudomonas stutzeri, Shewanella amazonensis, and Shewanella oneidensis. To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens, we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative D-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling. A large challenge in microbiology is the functional assessment of the millions of uncharacterized genes identified by genome sequencing. Transposon mutagenesis coupled to next-generation sequencing (TnSeq) is a powerful approach to assign phenotypes and functions to genes. However, the current strategies for TnSeq are
The EUROCET Network: Support for Coding, Vigilance and Surveillance
Mareri, Maura; Filippetti, Marzia; Ghirardini, Angelo; Vespasiano, Francesca; Ciaccio, Paola Di; Nanni Costa, Alessandro
2011-01-01
Background In the last years, there have been increasing concerns about the safety and traceability of human tissues and cells in Europe. In order to regulate this part of medical practice, the European Commission issued 3 directives between 2004 and 2006 and endorsed EUROCET to support member states in fulfilling some of their obligations. Mathods EUROCET created a connection with the European Union (EU) Competent Authorities (CAs) and set up a website where lists of the CAs, the authorized Tissue Establishments (TEs) and the activity data are published and updated. Moreover, EUROCET is involved within the Vigilance and Surveillance of Substances of Human Origin (SOHO V&S) project, aiming to support the EU member states in the establishment of vigilance and surveillance systems for tissues and cells. EUROCET is also working with EU stakeholders to develop a common coding system concerning donation and products. Results There are 33 countries in EUROCET and 57 CAs. 3,974 TEs are recorded: 1,108 for tissues, 1,480 for haematopoietic progenitor cells and 1,386 for assisted reproduction. On the website, it is possible to find the 2010 activity data report. Conclusion Based on its cooperation with the CAs, EUROCET represents them in the European network. Nowadays, the EU member states can rely on a web portal and database in order to put the tissue and cell directives into practice. PMID:22403518
NASA Technical Reports Server (NTRS)
Lee, P. J.
1984-01-01
Performance of two coding systems is analyzed for a noisy optical channel with M(=2(L)-ary orthogonal signaling and random gain photomultiplier detection. The considered coding systems are the Reed Solomon (RS) coding with error only correction decoding and the interleaved binary convolutional system with soft decision Viterbi decoding. The required average number of received signal photons per information bit, N sub b, for a desired bit error of 0.000001 is found for a set of commonly used parameters and with a high background noise level. We find that the interleaved binary convolutional coding system is preferable to the RS coding system in performance complexity tradeoffs.
Rombaut, Joost; Pizurica, Aleksandra; Philips, Wilfried
2009-04-01
In video communication over lossy packet networks (e.g., the Internet), packet loss errors can severely damage the transmitted video. The damaged video can largely be repaired with passive error concealment, where neighboring information is used to estimate missing information. We address the problem of passive error concealment for wavelet coded data with dispersive packetization. The reported techniques of this kind have many problems and usually fail in the reconstruction of high-frequency content. This paper presents a novel locally adaptive error concealment method for subband coded I-frames based on an inhomogeneous Gaussian Markov random field model. We estimate the parameters of this model from a local context of each lost coefficient, and we interpolate the lost coefficients accordingly. The results demonstrate a significant improvement over the reported related methods both in terms of objective performance measures and visually. The biggest improvement of the proposed method compared to the state-of-the-art in the field is the correct reconstruction of high-frequency information such as textures and edges.
A large scale code resolution service network in the Internet of Things.
Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan
2012-11-07
In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS.
A Large Scale Code Resolution Service Network in the Internet of Things
Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan
2012-01-01
In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT's advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS. PMID:23202207
A large scale code resolution service network in the Internet of Things.
Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan
2012-01-01
In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS. PMID:23202207
Distributed fault estimation with randomly occurring uncertainties over sensor networks
NASA Astrophysics Data System (ADS)
Dong, Hongli; Wang, Zidong; Bu, Xianye; Alsaadi, Fuad E.
2016-07-01
This paper is concerned with the distributed fault estimation problem for a class of uncertain stochastic systems over sensor networks. The norm-bounded uncertainty enters into the system in a random way governed by a set of Bernoulli distributed white sequence. The purpose of the addressed problem is to design distributed fault estimators, via available output measurements from not only the individual sensor, but also its neighbouring sensors, such that the fault estimation error converges to zero exponentially in the mean square while the disturbance rejection attenuation is constrained to a give level by means of the ? performance index. Intensive stochastic analysis is carried out to obtain sufficient conditions for ensuring the exponential stability as well as prescribed ? performance for the overall estimation error dynamics. Simulation results are provided to demonstrate the effectiveness of the proposed fault estimation technique in this paper.
Spatial Markov model of anomalous transport through random lattice networks.
Kang, Peter K; Dentz, Marco; Le Borgne, Tanguy; Juanes, Ruben
2011-10-28
Flow through lattice networks with quenched disorder exhibits a strong correlation in the velocity field, even if the link transmissivities are uncorrelated. This feature, which is a consequence of the divergence-free constraint, induces anomalous transport of passive particles carried by the flow. We propose a Lagrangian statistical model that takes the form of a continuous time random walk with correlated velocities derived from a genuinely multidimensional Markov process in space. The model captures the anomalous (non-Fickian) longitudinal and transverse spreading, and the tail of the mean first-passage time observed in the Monte Carlo simulations of particle transport. We show that reproducing these fundamental aspects of transport in disordered systems requires honoring the correlation in the Lagrangian velocity.
Information Filtering via Biased Random Walk on Coupled Social Network
Dong, Qiang; Fu, Yan
2014-01-01
The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods. PMID:25147867
Wetmore, Kelly M.; Price, Morgan N.; Waters, Robert J.; Lamson, Jacob S.; He, Jennifer; Hoover, Cindi A.; Blow, Matthew J.; Bristow, James; Butland, Gareth
2015-01-01
ABSTRACT Transposon mutagenesis with next-generation sequencing (TnSeq) is a powerful approach to annotate gene function in bacteria, but existing protocols for TnSeq require laborious preparation of every sample before sequencing. Thus, the existing protocols are not amenable to the throughput necessary to identify phenotypes and functions for the majority of genes in diverse bacteria. Here, we present a method, random bar code transposon-site sequencing (RB-TnSeq), which increases the throughput of mutant fitness profiling by incorporating random DNA bar codes into Tn5 and mariner transposons and by using bar code sequencing (BarSeq) to assay mutant fitness. RB-TnSeq can be used with any transposon, and TnSeq is performed once per organism instead of once per sample. Each BarSeq assay requires only a simple PCR, and 48 to 96 samples can be sequenced on one lane of an Illumina HiSeq system. We demonstrate the reproducibility and biological significance of RB-TnSeq with Escherichia coli, Phaeobacter inhibens, Pseudomonas stutzeri, Shewanella amazonensis, and Shewanella oneidensis. To demonstrate the increased throughput of RB-TnSeq, we performed 387 successful genome-wide mutant fitness assays representing 130 different bacterium-carbon source combinations and identified 5,196 genes with significant phenotypes across the five bacteria. In P. inhibens, we used our mutant fitness data to identify genes important for the utilization of diverse carbon substrates, including a putative d-mannose isomerase that is required for mannitol catabolism. RB-TnSeq will enable the cost-effective functional annotation of diverse bacteria using mutant fitness profiling. PMID:25968644
Influence of weight heterogeneity on random walks in scale-free networks
NASA Astrophysics Data System (ADS)
Li, Ling; Guan, Jihong; Qi, Zhaohui
2016-07-01
Many systems are best described by weighted networks, in which the weights of the edges are heterogeneous. In this paper, we focus on random walks in weighted network, investigating the impacts of weight heterogeneity on the behavior of random walks. We study random walks in a family of weighted scale-free tree-like networks with power-law weight distribution. We concentrate on three cases of random walk problems: with a trap located at a hub node, a leaf adjacent to a hub node, and a farthest leaf node from a hub. For all these cases, we calculate analytically the global mean first passage time (GMFPT) measuring the efficiency of random walk, as well as the leading scaling of GMFPT. We find a significant decrease in the dominating scaling of GMFPT compared with the corresponding binary networks in all three random walk problems, which implies that weight heterogeneity has a significant influence on random walks in scale-free networks.
Regoui, Chaouki; Durand, Guillaume; Belliveau, Luc; Léger, Serge
2013-01-01
This paper presents a novel hybrid DNA encryption (HyDEn) approach that uses randomized assignments of unique error-correcting DNA Hamming code words for single characters in the extended ASCII set. HyDEn relies on custom-built quaternary codes and a private key used in the randomized assignment of code words and the cyclic permutations applied on the encoded message. Along with its ability to detect and correct errors, HyDEn equals or outperforms existing cryptographic methods and represents a promising in silico DNA steganographic approach. PMID:23984392
Tulpan, Dan; Regoui, Chaouki; Durand, Guillaume; Belliveau, Luc; Léger, Serge
2013-01-01
This paper presents a novel hybrid DNA encryption (HyDEn) approach that uses randomized assignments of unique error-correcting DNA Hamming code words for single characters in the extended ASCII set. HyDEn relies on custom-built quaternary codes and a private key used in the randomized assignment of code words and the cyclic permutations applied on the encoded message. Along with its ability to detect and correct errors, HyDEn equals or outperforms existing cryptographic methods and represents a promising in silico DNA steganographic approach.
Multi-Level Construction of Polar Codes for Half-Duplex Wireless Coded-Cooperative Networks
NASA Astrophysics Data System (ADS)
Ejaz, Saqib; FengFan, Yang; Soliman, Tamer H. M.
2015-11-01
In this paper, Plotkin's construction is employed to buildup longer length polar codes with the help of shorter length polar codes. Firstly, we present the multi-level code construction steps for non-cooperative communication schemes. Secondly, we extend the proposed multi-level polar code construction to coded-cooperative scenarios due to the parallel split in the proposed encoding scheme. Since, relay plays a pivotal role in the overall bit error rate (BER) performance of the coded-cooperative schemes, therefore, an efficient criteria of information bit selection at the relay is also presented. Furthermore, we propose a novel joint successive cancellation decoding scheme, which is employed at the destination and provides significant coding gains. Various numerical simulations show that the proposed polar coded-cooperative scheme (PCCS) scheme not only outperforms non-cooperative polar coded scheme but also the existing cooperative schemes for polar codes under identical conditions over an additive white Gaussian noise (AWGN) and quasi-static Rayleigh fading channels.
NASA Astrophysics Data System (ADS)
Reichenbach, Tobias; Hudspeth, A. J.
2012-11-01
Frequency discrimination is a fundamental task of the auditory system. The mammalian inner ear, or cochlea, provides a place code in which different frequencies are detected at different spatial locations. However, a temporal code based on spike timing is also available: action potentials evoked in an auditory-nerve fiber by a low-frequency tone occur at a preferred phase of the stimulus—they exhibit phase locking—and thus provide temporal information about the tone's frequency. Humans employ this temporal information for discrimination of low frequencies. How might such temporal information be read out in the brain? Here we employ statistical and numerical methods to demonstrate that recurrent random neural networks in which connections between neurons introduce characteristic time delays, and in which neurons require temporally coinciding inputs for spike initiation, can perform sharp frequency discrimination when stimulated with phase-locked inputs. Although the frequency resolution achieved by such networks is limited by the noise in phase locking, the resolution for realistic values reaches the tiny frequency difference of 0.2% that has been measured in humans.
Lossy cutset coding of bilevel images based on Markov random fields.
Reyes, Matthew G; Neuhoff, David L; Pappas, Thrasyvoulos N
2014-04-01
An effective, low complexity method for lossy compression of scenic bilevel images, called lossy cutset coding, is proposed based on a Markov random field model. It operates by losslessly encoding pixels in a square grid of lines, which is a cutset with respect to a Markov random field model, and preserves key structural information, such as borders between black and white regions. Relying on the Markov random field model, the decoder takes a MAP approach to reconstructing the interior of each grid block from the pixels on its boundary, thereby creating a piecewise smooth image that is consistent with the encoded grid pixels. The MAP rule, which reduces to finding the block interiors with fewest black-white transitions, is directly implementable for the most commonly occurring block boundaries, thereby avoiding the need for brute force or iterative solutions. Experimental results demonstrate that the new method is computationally simple, outperforms the current lossy compression technique most suited to scenic bilevel images, and provides substantially lower rates than lossless techniques, e.g., JBIG, with little loss in perceived image quality.
Channel coding in the space station data system network
NASA Technical Reports Server (NTRS)
Healy, T.
1982-01-01
A detailed discussion of the use of channel coding for error correction, privacy/secrecy, channel separation, and synchronization is presented. Channel coding, in one form or another, is an established and common element in data systems. No analysis and design of a major new system would fail to consider ways in which channel coding could make the system more effective. The presence of channel coding on TDRS, Shuttle, the Advanced Communication Technology Satellite Program system, the JSC-proposed Space Operations Center, and the proposed 30/20 GHz Satellite Communication System strongly support the requirement for the utilization of coding for the communications channel. The designers of the space station data system have to consider the use of channel coding.
Patient-Physician Communication About Code Status Preferences: A Randomized Controlled Trial
Rhondali, Wadih; Perez-Cruz, Pedro; Hui, David; Chisholm, Gary B.; Dalal, Shalini; Baile, Walter; Chittenden, Eva; Bruera, Eduardo
2013-01-01
Purpose Code status discussions are important in cancer care. The best modality for such discussions has not been established. Our objective was to determine the impact of a physician ending a code status discussion with a question (autonomy approach) versus a recommendation (beneficence approach) on patients' do-not-resuscitate (DNR) preference. Methods Patients in a supportive care clinic watched two videos showing a physician-patient discussion regarding code status. Both videos were identical except for the ending: one ended with the physician asking for the patient's code status preference and the other with the physician recommending DNR. Patients were randomly assigned to watch the videos in different sequences. The main outcome was the proportion of patients choosing DNR for the video patient. Results 78 patients completed the study. 74% chose DNR after the question video, 73% after the recommendation video. Median physician compassion score was very high and not different for both videos. 30/30 patients who had chosen DNR for themselves and 30/48 patients who had not chosen DNR for themselves chose DNR for the video patient (100% v/s 62%). Age (OR=1.1/year) and white ethnicity (OR=9.43) predicted DNR choice for the video patient. Conclusion Ending DNR discussions with a question or a recommendation did not impact DNR choice or perception of physician compassion. Therefore, both approaches are clinically appropriate. All patients who chose DNR for themselves and most patients who did not choose DNR for themselves chose DNR for the video patient. Age and race predicted DNR choice. PMID:23564395
Properties of networks with partially structured and partially random connectivity
Ahmadian, Yashar; Fumarola, Francesco; Miller, Kenneth D.
2016-01-01
Networks studied in many disciplines, including neuroscience and mathematical biology, have connectivity that may be stochastic about some underlying mean connectivity represented by a nonnormal matrix. Furthermore the stochasticity may not be i.i.d. across elements of the connectivity matrix. More generally, the problem of understanding the behavior of stochastic matrices with nontrivial mean structure and correlations arises in many settings. We address this by characterizing large random N × N matrices of the form A = M + LJR, where M, L and R are arbitrary deterministic matrices and J is a random matrix of zero-mean independent and identically distributed elements. M can be nonnormal, and L and R allow correlations that have separable dependence on row and column indices. We first provide a general formula for the eigenvalue density of A. For A nonnormal, the eigenvalues do not suffice to specify the dynamics induced by A, so we also provide general formulae for the transient evolution of the magnitude of activity and frequency power spectrum in an N -dimensional linear dynamical system with a coupling matrix given by A. These quantities can also be thought of as characterizing the stability and the magnitude of the linear response of a nonlinear network to small perturbations about a fixed point. We derive these formulae and work them out analytically for some examples of M, L and R motivated by neurobiological models. We also argue that the persistence as N → ∞ of a finite number of randomly distributed outlying eigenvalues outside the support of the eigenvalue density of A, as previously observed, arises in regions of the complex plane Ω where there are nonzero singular values of L−1(z1 − M)R−1 (for z ∈ Ω) that vanish as N → ∞. When such singular values do not exist and L and R are equal to the identity, there is a correspondence in the normalized Frobenius norm (but not in the operator norm) between the support of the spectrum of A for
Properties of networks with partially structured and partially random connectivity
NASA Astrophysics Data System (ADS)
Ahmadian, Yashar; Fumarola, Francesco; Miller, Kenneth D.
2015-01-01
Networks studied in many disciplines, including neuroscience and mathematical biology, have connectivity that may be stochastic about some underlying mean connectivity represented by a non-normal matrix. Furthermore, the stochasticity may not be independent and identically distributed (iid) across elements of the connectivity matrix. More generally, the problem of understanding the behavior of stochastic matrices with nontrivial mean structure and correlations arises in many settings. We address this by characterizing large random N ×N matrices of the form A =M +L J R , where M ,L , and R are arbitrary deterministic matrices and J is a random matrix of zero-mean iid elements. M can be non-normal, and L and R allow correlations that have separable dependence on row and column indices. We first provide a general formula for the eigenvalue density of A . For A non-normal, the eigenvalues do not suffice to specify the dynamics induced by A , so we also provide general formulas for the transient evolution of the magnitude of activity and frequency power spectrum in an N -dimensional linear dynamical system with a coupling matrix given by A . These quantities can also be thought of as characterizing the stability and the magnitude of the linear response of a nonlinear network to small perturbations about a fixed point. We derive these formulas and work them out analytically for some examples of M ,L , and R motivated by neurobiological models. We also argue that the persistence as N →∞ of a finite number of randomly distributed outlying eigenvalues outside the support of the eigenvalue density of A , as previously observed, arises in regions of the complex plane Ω where there are nonzero singular values of L-1(z 1 -M ) R-1 (for z ∈Ω ) that vanish as N →∞ . When such singular values do not exist and L and R are equal to the identity, there is a correspondence in the normalized Frobenius norm (but not in the operator norm) between the support of the spectrum
Damage spreading in spatial and small-world random boolean networks
Lu, Qiming; Teuscher, Christof
2008-01-01
Random Boolean Networks (RBNs) are often used as generic models for certain dynamics of complex systems, ranging from social networks, neural networks, to gene or protein interaction networks. Traditionally, RBNs are interconnected randomly and without considering any spatial arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ({bar K} << 1) and that the critical connectivity of stability K{sub s} changes compared to random networks. At higher {bar K}, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key trade-offs between damage spreading (robustness), the network wiring cost, and the network's communication characteristics.
Degree distribution of random birth-and-death network with network size decline
NASA Astrophysics Data System (ADS)
Xiao-Jun, Zhang; Hui-Lan, Yang
2016-06-01
In this paper, we provide a general method to obtain the exact solutions of the degree distributions for random birth-and-death network (RBDN) with network size decline. First, by stochastic process rules, the steady state transformation equations and steady state degree distribution equations are given in the case of m ≥ 3 and 0 < p < 1/2, then the average degree of network with n nodes is introduced to calculate the degree distributions. Specifically, taking m = 3 for example, we explain the detailed solving process, in which computer simulation is used to verify our degree distribution solutions. In addition, the tail characteristics of the degree distribution are discussed. Our findings suggest that the degree distributions will exhibit Poisson tail property for the declining RBDN. Project supported by the National Natural Science Foundation of China (Grant No. 61273015) and the Chinese Scholarship Council.
Degree distribution of random birth-and-death network with network size decline
NASA Astrophysics Data System (ADS)
Xiao-Jun, Zhang; Hui-Lan, Yang
2016-06-01
In this paper, we provide a general method to obtain the exact solutions of the degree distributions for random birth-and-death network (RBDN) with network size decline. First, by stochastic process rules, the steady state transformation equations and steady state degree distribution equations are given in the case of m ≥ 3 and 0 < p < 1/2, then the average degree of network with n nodes is introduced to calculate the degree distributions. Specifically, taking m = 3 for example, we explain the detailed solving process, in which computer simulation is used to verify our degree distribution solutions. In addition, the tail characteristics of the degree distribution are discussed. Our findings suggest that the degree distributions will exhibit Poisson tail property for the declining RBDN. Project supported by the National Natural Science Foundation of China (Grant No. 61273015) and the Chinese Scholarship Council.
Entanglement distribution over quantum code-division multiple-access networks
NASA Astrophysics Data System (ADS)
Zhu, Chang-long; Yang, Nan; Liu, Yu-xi; Nori, Franco; Zhang, Jing
2015-10-01
We present a method for quantum entanglement distribution over a so-called code-division multiple-access network, in which two pairs of users share the same quantum channel to transmit information. The main idea of this method is to use different broadband chaotic phase shifts, generated by electro-optic modulators and chaotic Colpitts circuits, to encode the information-bearing quantum signals coming from different users and then recover the masked quantum signals at the receiver side by imposing opposite chaotic phase shifts. The chaotic phase shifts given to different pairs of users are almost uncorrelated due to the randomness of chaos and thus the quantum signals from different pair of users can be distinguished even when they are sent via the same quantum channel. It is shown that two maximally entangled states can be generated between two pairs of users by our method mediated by bright coherent lights, which can be more easily implemented in experiments compared with single-photon lights. Our method is robust under the channel noises if only the decay rates of the information-bearing fields induced by the channel noises are not quite high. Our study opens up new perspectives for addressing and transmitting quantum information in future quantum networks.
Hybrid scheduling mechanisms for Next-generation Passive Optical Networks based on network coding
NASA Astrophysics Data System (ADS)
Zhao, Jijun; Bai, Wei; Liu, Xin; Feng, Nan; Maier, Martin
2014-10-01
Network coding (NC) integrated into Passive Optical Networks (PONs) is regarded as a promising solution to achieve higher throughput and energy efficiency. To efficiently support multimedia traffic under this new transmission mode, novel NC-based hybrid scheduling mechanisms for Next-generation PONs (NG-PONs) including energy management, time slot management, resource allocation, and Quality-of-Service (QoS) scheduling are proposed in this paper. First, we design an energy-saving scheme that is based on Bidirectional Centric Scheduling (BCS) to reduce the energy consumption of both the Optical Line Terminal (OLT) and Optical Network Units (ONUs). Next, we propose an intra-ONU scheduling and an inter-ONU scheduling scheme, which takes NC into account to support service differentiation and QoS assurance. The presented simulation results show that BCS achieves higher energy efficiency under low traffic loads, clearly outperforming the alternative NC-based Upstream Centric Scheduling (UCS) scheme. Furthermore, BCS is shown to provide better QoS assurance.
Complementary feeding: a Global Network cluster randomized controlled trial
2011-01-01
Background Inadequate and inappropriate complementary feeding are major factors contributing to excess morbidity and mortality in young children in low resource settings. Animal source foods in particular are cited as essential to achieve micronutrient requirements. The efficacy of the recommendation for regular meat consumption, however, has not been systematically evaluated. Methods/Design A cluster randomized efficacy trial was designed to test the hypothesis that 12 months of daily intake of beef added as a complementary food would result in greater linear growth velocity than a micronutrient fortified equi-caloric rice-soy cereal supplement. The study is being conducted in 4 sites of the Global Network for Women's and Children's Health Research located in Guatemala, Pakistan, Democratic Republic of the Congo (DRC) and Zambia in communities with toddler stunting rates of at least 20%. Five clusters per country were randomized to each of the food arms, with 30 infants in each cluster. The daily meat or cereal supplement was delivered to the home by community coordinators, starting when the infants were 6 months of age and continuing through 18 months. All participating mothers received nutrition education messages to enhance complementary feeding practices delivered by study coordinators and through posters at the local health center. Outcome measures, obtained at 6, 9, 12, and 18 months by a separate assessment team, included anthropometry; dietary variety and diversity scores; biomarkers of iron, zinc and Vitamin B12 status (18 months); neurocognitive development (12 and 18 months); and incidence of infectious morbidity throughout the trial. The trial was supervised by a trial steering committee, and an independent data monitoring committee provided oversight for the safety and conduct of the trial. Discussion Findings from this trial will test the efficacy of daily intake of meat commencing at age 6 months and, if beneficial, will provide a strong rationale
Resonant spatiotemporal learning in large random recurrent networks.
Daucé, Emmanuel; Quoy, Mathias; Doyon, Bernard
2002-09-01
Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives stimulating spatiotemporal signals, and the secondary layer is a fully connected random recurrent network. This secondary layer spontaneously displays complex chaotic dynamics. All connections have a constant time delay. We use for our experiments a Hebbian (covariance) learning rule. This rule slowly modifies the weights under the influence of a periodic stimulus. The effect of learning is twofold: (i) it simplifies the secondary-layer dynamics, which eventually stabilizes to a periodic orbit; and (ii) it connects the secondary layer to the primary layer, and realizes a feedback from the secondary to the primary layer. This feedback signal is added to the incoming signal, and matches it (i.e., the secondary layer performs a one-step prediction of the forthcoming stimulus). After learning, a resonant behavior can be observed: the system resonates with familiar stimuli, which activates a feedback signal. In particular, this resonance allows the recognition and retrieval of partial signals, and dynamic maintenance of the memory of past stimuli. This resonance is highly sensitive to the temporal relationships and to the periodicity of the presented stimuli. When we present stimuli which do not match in time or space, the feedback remains silent. The number of different stimuli for which resonant behavior can be learned is analyzed. As with Hopfield networks, the capacity is proportional to the size of the second, recurrent layer. Moreover, the high capacity displayed allows the implementation of our model on real-time systems interacting with their environment. Such an implementation is reported in the case of a simple behavior-based recognition task on a mobile robot. Finally, we present some functional analogies with biological systems in terms of autonomy and dynamic binding, and present
Damage Spreading in Spatial and Small-world Random Boolean Networks
Lu, Qiming; Teuscher, Christof
2014-02-18
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Knowledge extraction from evolving spiking neural networks with rank order population coding.
Soltic, Snjezana; Kasabov, Nikola
2010-12-01
This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.
NASA Astrophysics Data System (ADS)
Cui, Laizhong; Jiang, Yong; Wu, Jianping; Xia, Shutao
Most large-scale Peer-to-Peer (P2P) live streaming systems are constructed as a mesh structure, which can provide robustness in the dynamic P2P environment. The pull scheduling algorithm is widely used in this mesh structure, which degrades the performance of the entire system. Recently, network coding was introduced in mesh P2P streaming systems to improve the performance, which makes the push strategy feasible. One of the most famous scheduling algorithms based on network coding is R2, with a random push strategy. Although R2 has achieved some success, the push scheduling strategy still lacks a theoretical model and optimal solution. In this paper, we propose a novel optimal pull-push scheduling algorithm based on network coding, which consists of two stages: the initial pull stage and the push stage. The main contributions of this paper are: 1) we put forward a theoretical analysis model that considers the scarcity and timeliness of segments; 2) we formulate the push scheduling problem to be a global optimization problem and decompose it into local optimization problems on individual peers; 3) we introduce some rules to transform the local optimization problem into a classical min-cost optimization problem for solving it; 4) We combine the pull strategy with the push strategy and systematically realize our scheduling algorithm. Simulation results demonstrate that decode delay, decode ratio and redundant fraction of the P2P streaming system with our algorithm can be significantly improved, without losing throughput and increasing overhead.
Damage spreading in spatial and small-world random Boolean networks.
Lu, Qiming; Teuscher, Christof
2014-02-01
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities (K ≪ 1) and that the critical connectivity of stability K(s) changes compared to random networks. At higher K, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size N increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Funabiki, N; Kitamichi, J
1998-01-01
A gradual neural network (GNN) algorithm is presented for the jointly time-slot/code assignment problem (JTCAP) in a packet radio network in this paper. The goal of this newly defined problem is to find a simultaneous assignment of a time-slot and a code to each communication link, whereas time-slots and codes have been independently assigned in existing algorithms. A time/code division multiple access protocol is adopted for conflict-free communications, where packets are transmitted in repetition of fixed-length time-slots with specific codes. GNN seeks the time-slot/code assignment with the minimum number of time-slots subject to two constraints: 1) the number of codes must not exceed its upper limit and 2) any couple of links within conflict distance must not be assigned to the same time-slot/code pair. The restricted problem for only one code is known to be NP-complete. The performance of GNN is verified through solving 3000 instances with 100-500 nodes and 100-1000 links. The comparison with the lower bound and a greedy algorithm shows the superiority of GNN in terms of the solution quality with the comparable computation time.
Stability and dynamical properties of material flow systems on random networks
NASA Astrophysics Data System (ADS)
Anand, K.; Galla, T.
2009-04-01
The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.
A Multicast Sparse-Grooming Algorithm Based on Network Coding in WDM Networks
NASA Astrophysics Data System (ADS)
Zhang, Shengfeng; Peng, Han; Sui, Meng; Liu, Huanlin
2015-03-01
To improve the limited number of wavelength utilization and decrease the traffic blocking probability in sparse-grooming wavelength-division multiplexing (WDM) networks, a multicast sparse-grooming algorithm based on network coding (MCSA-NC) is put forward to solve the routing problem for dynamic multicast requests in this paper. In the proposed algorithm, a traffic partition strategy, that the coarse-granularity multicast request with grooming capability on the source node is split into several fine-granularity multicast requests, is designed so as to increase the probability for traffic grooming successfully in MCSA-NC. Besides considering that multiple destinations should receive the data from source of the multicast request at the same time, the traditional transmission mechanism is improved by constructing edge-disjoint paths for each split multicast request. Moreover, in order to reduce the number of wavelengths required and further decrease the traffic blocking probability, a light-tree reconfiguration mechanism is presented in the MCSA-NC, which can select a minimal cost light tree from the established edge-disjoint paths for a new multicast request.
NASA Astrophysics Data System (ADS)
Kondo, Yoshihisa; Yomo, Hiroyuki; Yamaguchi, Shinji; Davis, Peter; Miura, Ryu; Obana, Sadao; Sampei, Seiichi
This paper proposes multipoint-to-multipoint (MPtoMP) real-time broadcast transmission using network coding for ad-hoc networks like video game networks. We aim to achieve highly reliable MPtoMP broadcasting using IEEE 802.11 media access control (MAC) that does not include a retransmission mechanism. When each node detects packets from the other nodes in a sequence, the correctly detected packets are network-encoded, and the encoded packet is broadcasted in the next sequence as a piggy-back for its native packet. To prevent increase of overhead in each packet due to piggy-back packet transmission, network coding vector for each node is exchanged between all nodes in the negotiation phase. Each user keeps using the same coding vector generated in the negotiation phase, and only coding information that represents which user signal is included in the network coding process is transmitted along with the piggy-back packet. Our simulation results show that the proposed method can provide higher reliability than other schemes using multi point relay (MPR) or redundant transmissions such as forward error correction (FEC). We also implement the proposed method in a wireless testbed, and show that the proposed method achieves high reliability in a real-world environment with a practical degree of complexity when installed on current wireless devices.
Classification of melanoma lesions using sparse coded features and random forests
NASA Astrophysics Data System (ADS)
Rastgoo, Mojdeh; Lemaître, Guillaume; Morel, Olivier; Massich, Joan; Garcia, Rafael; Meriaudeau, Fabrice; Marzani, Franck; Sidibé, Désiré
2016-03-01
Malignant melanoma is the most dangerous type of skin cancer, yet it is the most treatable kind of cancer, conditioned by its early diagnosis which is a challenging task for clinicians and dermatologists. In this regard, CAD systems based on machine learning and image processing techniques are developed to differentiate melanoma lesions from benign and dysplastic nevi using dermoscopic images. Generally, these frameworks are composed of sequential processes: pre-processing, segmentation, and classification. This architecture faces mainly two challenges: (i) each process is complex with the need to tune a set of parameters, and is specific to a given dataset; (ii) the performance of each process depends on the previous one, and the errors are accumulated throughout the framework. In this paper, we propose a framework for melanoma classification based on sparse coding which does not rely on any pre-processing or lesion segmentation. Our framework uses Random Forests classifier and sparse representation of three features: SIFT, Hue and Opponent angle histograms, and RGB intensities. The experiments are carried out on the public PH2 dataset using a 10-fold cross-validation. The results show that SIFT sparse-coded feature achieves the highest performance with sensitivity and specificity of 100% and 90.3% respectively, with a dictionary size of 800 atoms and a sparsity level of 2. Furthermore, the descriptor based on RGB intensities achieves similar results with sensitivity and specificity of 100% and 71.3%, respectively for a smaller dictionary size of 100 atoms. In conclusion, dictionary learning techniques encode strong structures of dermoscopic images and provide discriminant descriptors.
Levy, W B; Sanyal, A; Rodriguez, P; Sullivan, D W; Wu, X B
2005-06-01
The trace version of classical conditioning is used as a prototypical hippocampal-dependent task to study the recoding sequence prediction theory of hippocampal function. This theory conjectures that the hippocampus is a random recoder of sequences and that, once formed, the neuronal codes are suitable for prediction. As such, a trace conditioning paradigm, which requires a timely prediction, seems by far the simplest of the behaviorally-relevant paradigms for studying hippocampal recoding. Parameters that affect the formation of these random codes include the temporal aspects of the behavioral/cognitive paradigm and certain basic characteristics of hippocampal region CA3 anatomy and physiology such as connectivity and activity. Here we describe some of the dynamics of code formation and describe how biological and paradigmatic parameters affect the neural codes that are formed. In addition to a backward cascade of coding neurons, we point out, for the first time, a higher-order dynamic growing out of the backward cascade-a particular forward and backward stabilization of codes as training progresses. We also observe that there is a performance compromise involved in the setting of activity levels due to the existence of three behavioral failure modes. Each of these behavioral failure modes exists in the computational model and, presumably, natural selection produced the compromise performance observed by psychologists. Thus, examining the parametric sensitivities of the codes and their dynamic formation gives insight into the constraints on natural computation and into the computational compromises ensuing from these constraints.
Statistical Inference for Valued-Edge Networks: The Generalized Exponential Random Graph Model
Desmarais, Bruce A.; Cranmer, Skyler J.
2012-01-01
Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks based on both endogenous and exogenous factors, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We address this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges have continuous values (bounded or unbounded), thus greatly expanding the scope of networks applied researchers can subject to statistical analysis. PMID:22276151
All-optical code routing in interconnected optical CDMA and WDM ring networks.
Deng, Yanhua; Fok, Mable P; Prucnal, Paul R; Wang, Ting
2010-11-01
We propose an all-optical hybrid network composed of optical code division multiple access (CDMA) rings interconnecting through a reconfigurable wavelength division multiplexing (WDM) metro area ring. This network retains the advantages of both the optical CDMA and WDM techniques, including asynchronous access and differentiated quality of service, while removing the hard limit on the number of subscribers and increasing network flexibility. The all-optical network is enabled by using nonlinear optical loop mirrors in an add/drop router (ADR) that performs code conversion, dropping, and switching asynchronously. We experimentally demonstrate the functionalities of the ADR in the proposed scheme asynchronously and obtain error-free performance. The bit-error rate measurements show acceptable power penalties for different code routes.
A neutron spectrum unfolding computer code based on artificial neural networks
NASA Astrophysics Data System (ADS)
Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.
2014-02-01
The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in
ERIC Educational Resources Information Center
Brandon, Paul R.; Harrison, George M.; Lawton, Brian E.
2013-01-01
When evaluators plan site-randomized experiments, they must conduct the appropriate statistical power analyses. These analyses are most likely to be valid when they are based on data from the jurisdictions in which the studies are to be conducted. In this method note, we provide software code, in the form of a SAS macro, for producing statistical…
Biometrics based key management of double random phase encoding scheme using error control codes
NASA Astrophysics Data System (ADS)
Saini, Nirmala; Sinha, Aloka
2013-08-01
In this paper, an optical security system has been proposed in which key of the double random phase encoding technique is linked to the biometrics of the user to make it user specific. The error in recognition due to the biometric variation is corrected by encoding the key using the BCH code. A user specific shuffling key is used to increase the separation between genuine and impostor Hamming distance distribution. This shuffling key is then further secured using the RSA public key encryption to enhance the security of the system. XOR operation is performed between the encoded key and the feature vector obtained from the biometrics. The RSA encoded shuffling key and the data obtained from the XOR operation are stored into a token. The main advantage of the present technique is that the key retrieval is possible only in the simultaneous presence of the token and the biometrics of the user which not only authenticates the presence of the original input but also secures the key of the system. Computational experiments showed the effectiveness of the proposed technique for key retrieval in the decryption process by using the live biometrics of the user.
When Are Popescu-Rohrlich Boxes and Random Access Codes Equivalent?
NASA Astrophysics Data System (ADS)
Grudka, Andrzej; Horodecki, Karol; Horodecki, Michał; Kłobus, Waldemar; Pawłowski, Marcin
2014-09-01
We study a problem of interconvertibility of two supraquantum resources: one is the so-called Popescu-Rohrlich (PR) box, which violates Clauser-Horne-Shimony-Holt inequality up to the maximal algebraic bound, and the second is the so-called random access code (RAC). The latter is a functionality that enables Bob (receiver) to choose one of two bits of Alice. It is known that a PR box supplemented with one bit of communication can be used to simulate a RAC. We ask the converse question: to what extent can a RAC can simulate a PR box? To this end, we introduce a "racbox": a box such that when it is supplemented with one bit of communication it offers a RAC. As said, a PR box can simulate a racbox. The question we raise is whether any racbox can simulate a PR box. We show that a nonsignaling racbox, indeed, can simulate a PR box; hence, these two resources are equivalent. We also provide an example of a signaling racbox that cannot simulate a PR box. We give a resource inequality between racboxes and PR boxes and show that it is saturated.
NASA Astrophysics Data System (ADS)
Zhang, Yongsheng; Xiong, Hongkai; He, Zhihai; Yu, Songyu
2010-07-01
An important issue in Wyner-Ziv video coding is the reconstruction of Wyner-Ziv frames with decoded bit-planes. So far, there are two major approaches: the Maximum a Posteriori (MAP) reconstruction and the Minimum Mean Square Error (MMSE) reconstruction algorithms. However, these approaches do not exploit smoothness constraints in natural images. In this paper, we model a Wyner-Ziv frame by Markov random fields (MRFs), and produce reconstruction results by finding an MAP estimation of the MRF model. In the MRF model, the energy function consists of two terms: a data term, MSE distortion metric in this paper, measuring the statistical correlation between side-information and the source, and a smoothness term enforcing spatial coherence. In order to better describe the spatial constraints of images, we propose a context-adaptive smoothness term by analyzing the correspondence between the output of Slepian-Wolf decoding and successive frames available at decoders. The significance of the smoothness term varies in accordance with the spatial variation within different regions. To some extent, the proposed approach is an extension to the MAP and MMSE approaches by exploiting the intrinsic smoothness characteristic of natural images. Experimental results demonstrate a considerable performance gain compared with the MAP and MMSE approaches.
Quantum discord in quantum random access codes and its connection to dimension witnesses
NASA Astrophysics Data System (ADS)
Yao, Yao; Li, Hong-Wei; Zou, Xu-Bo; Huang, Jing-Zheng; Zhang, Chun-Mei; Yin, Zhen-Qiang; Chen, Wei; Guo, Guang-Can; Han, Zheng-Fu
2012-12-01
We exploit quantum discord (and geometric discord) to detect quantum correlations present in a well-known communication model called quantum random access codes (QRACs), which has a variety of applications in quantum information theory. In spite of the fact that there is no entanglement between the two parts involved in this model, analytical derivation shows that the quantum discord is nonzero and highlights that quantum discord might be regarded as a figure of merit to characterize the quantum feature of QRACs, since this model has no classical counterparts. To gain further insight, we also investigate the dynamical behavior of quantum discord under some specific state rotations. In the two-state case, the connection between quantum discord and dimension witness is graphically discussed and intriguingly, our results illustrate that these two quantities are monotonically related to each other. For state encodings in the real |0>-|1> plane, we derive an explicit analytical expression of the geometric discord and find that geometric discord reaches the maximal value for the optimal encoding strategy. However, for arbitrary state encodings in the Bloch sphere, our numerical simulations reveal that maximal geometric discord could not coincide with optimal 2→1 QRAC.
Ding, Derui; Wang, Zidong; Shen, Bo; Shu, Huisheng
2012-05-01
In this paper, the state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring phenomena from sensor measurements. The randomly occurring phenomena include randomly occurring sensor saturations (ROSSs) and randomly varying sensor delays (RVSDs) that result typically from networked environments. A novel sensor model is proposed to describe the ROSSs and the RVSDs within a unified framework via two sets of Bernoulli-distributed white sequences with known conditional probabilities. Rather than employing the commonly used Lipschitz-type function, a more general sector-like nonlinear function is used to describe the nonlinearities existing in the network. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that, for all probabilistic sensor saturations and sensor delays, the dynamics of the estimation error is guaranteed to be exponentially mean-square stable and the effect from the exogenous disturbances to the estimation accuracy is attenuated at a given level by means of an H∞-norm. In terms of a novel Lyapunov-Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semidefinite programming method. A simulation example is provided to show the usefulness of the proposed state estimation conditions. PMID:24806122
Wu, Zhilu; Jiang, Lihui; Ren, Guanghui; Zhao, Nan; Zhao, Yaqin
2015-01-01
Interference alignment (IA) has been put forward as a promising technique which can mitigate interference and effectively increase the throughput of wireless sensor networks (WSNs). However, the number of users is strictly restricted by the IA feasibility condition, and the interference leakage will become so strong that the quality of service will degrade significantly when there are more users than that IA can support. In this paper, a novel joint spatial-code clustered (JSCC)-IA scheme is proposed to solve this problem. In the proposed scheme, the users are clustered into several groups so that feasible IA can be achieved within each group. In addition, each group is assigned a pseudo noise (PN) code in order to suppress the inter-group interference via the code dimension. The analytical bit error rate (BER) expressions of the proposed JSCC-IA scheme are formulated for the systems with identical and different propagation delays, respectively. To further improve the performance of the JSCC-IA scheme in asymmetric networks, a random grouping selection (RGS) algorithm is developed to search for better grouping combinations. Numerical results demonstrate that the proposed JSCC-IA scheme is capable of accommodating many more users to communicate simultaneously in the same frequency band with better performance. PMID:25602270
Neural Network Approach to Locating Cryptography in Object Code
Jason L. Wright; Milos Manic
2009-09-01
Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.
Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks
Podobnik, Boris; Lipic, Tomislav; Horvatic, Davor; Majdandzic, Antonio; Bishop, Steven R.; Eugene Stanley, H.
2015-01-01
Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science. PMID:26387609
Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks
NASA Astrophysics Data System (ADS)
Podobnik, Boris; Lipic, Tomislav; Horvatic, Davor; Majdandzic, Antonio; Bishop, Steven R.; Eugene Stanley, H.
2015-09-01
Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science.
Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.
Podobnik, Boris; Lipic, Tomislav; Horvatic, Davor; Majdandzic, Antonio; Bishop, Steven R; Eugene Stanley, H
2015-09-21
Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science.
Steady state and mean recurrence time for random walks on stochastic temporal networks.
Speidel, Leo; Lambiotte, Renaud; Aihara, Kazuyuki; Masuda, Naoki
2015-01-01
Random walks are basic diffusion processes on networks and have applications in, for example, searching, navigation, ranking, and community detection. Recent recognition of the importance of temporal aspects on networks spurred studies of random walks on temporal networks. Here we theoretically study two types of event-driven random walks on a stochastic temporal network model that produces arbitrary distributions of interevent times. In the so-called active random walk, the interevent time is reinitialized on all links upon each movement of the walker. In the so-called passive random walk, the interevent time is reinitialized only on the link that has been used the last time, and it is a type of correlated random walk. We find that the steady state is always the uniform density for the passive random walk. In contrast, for the active random walk, it increases or decreases with the node's degree depending on the distribution of interevent times. The mean recurrence time of a node is inversely proportional to the degree for both active and passive random walks. Furthermore, the mean recurrence time does or does not depend on the distribution of interevent times for the active and passive random walks, respectively. PMID:25679656
Physical-Layer Network Coding for VPN in TDM-PON
NASA Astrophysics Data System (ADS)
Wang, Qike; Tse, Kam-Hon; Chen, Lian-Kuan; Liew, Soung-Chang
2012-12-01
We experimentally demonstrate a novel optical physical-layer network coding (PNC) scheme over time-division multiplexing (TDM) passive optical network (PON). Full-duplex error-free communications between optical network units (ONUs) at 2.5 Gb/s are shown for all-optical virtual private network (VPN) applications. Compared to the conventional half-duplex communications set-up, our scheme can increase the capacity by 100% with power penalty smaller than 3 dB. Synchronization of two ONUs is not required for the proposed VPN scheme
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.
Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli
2016-01-01
In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks
Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli
2016-01-01
In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423
Financial Time Series Prediction Using Elman Recurrent Random Neural Networks.
Wang, Jie; Wang, Jun; Fang, Wen; Niu, Hongli
2016-01-01
In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. PMID:27293423
A code switching technique for distributed spread spectrum packet radio networks
NASA Astrophysics Data System (ADS)
Sousa, E. S.; Silvester, J. A.
A protocol for the use of spreading codes in a spread spectrum packet radio network is presented. Throughput results for a single-hop homogeneous network in heavy traffic are given. With the protocol, each terminal is assigned two unique spreading codes: one that the terminal uses to monitor the channel when it is idle, and a different code that the terminal switches to after transmitting an initial addressing header, which is transmitted on the destination's monitoring code. Limiting throughput results are obtained. Under the assumption of exponentially distributed packet lengths a limiting throughput per terminal pair corresponding to a utilization of .3431 for a system with an infinite number of users and infinite bandwidth is obtained.
Reaction-Diffusion Processes on Random and Scale-Free Networks
NASA Astrophysics Data System (ADS)
Banerjee, Subhasis; Mallick, Shrestha Basu; Bose, Indrani
We study the discrete Gierer-Meinhardt model of reaction-diffusion on three different types of networks: regular, random and scale-free. The model dynamics lead to the formation of stationary Turing patterns in the steady state in certain parameter regions. Some general features of the patterns are studied through numerical simulation. The results for the random and scale-free networks show a marked difference from those in the case of the regular network. The difference may be ascribed to the small world character of the first two types of networks.
Time-series analysis of networks: Exploring the structure with random walks
NASA Astrophysics Data System (ADS)
Weng, Tongfeng; Zhao, Yi; Small, Michael; Huang, Defeng David
2014-08-01
We generate time series from scale-free networks based on a finite-memory random walk traversing the network. These time series reveal topological and functional properties of networks via their temporal correlations. Remarkably, networks with different node-degree mixing patterns exhibit distinct self-similar characteristics. In particular, assortative networks are transformed into time series with long-range correlation, while disassortative networks are transformed into time series exhibiting anticorrelation. These relationships are consistent across a diverse variety of real networks. Moreover, we show that multiscale analysis of these time series can describe and classify various physical networks ranging from social and technological to biological networks according to their functional origin. These results suggest that there is a unified dynamical mechanism that governs the structural organization of many seemingly different networks.
Enhancing network robustness against targeted and random attacks using a memetic algorithm
NASA Astrophysics Data System (ADS)
Tang, Xianglong; Liu, Jing; Zhou, Mingxing
2015-08-01
In the past decades, there has been much interest in the elasticity of infrastructures to targeted and random attacks. In the recent work by Schneider C. M. et al., Proc. Natl. Acad. Sci. U.S.A., 108 (2011) 3838, the authors proposed an effective measure (namely R, here we label it as R t to represent the measure for targeted attacks) to evaluate network robustness against targeted node attacks. Using a greedy algorithm, they found that the optimal structure is an onion-like one. However, real systems are often under threats of both targeted attacks and random failures. So, enhancing networks robustness against both targeted and random attacks is of great importance. In this paper, we first design a random-robustness index (Rr) . We find that the onion-like networks destroyed the original strong ability of BA networks in resisting random attacks. Moreover, the structure of an R r -optimized network is found to be different from that of an onion-like network. To design robust scale-free networks (RSF) which are resistant to both targeted and random attacks (TRA) without changing the degree distribution, a memetic algorithm (MA) is proposed, labeled as \\textit{MA-RSF}\\textit{TRA} . In the experiments, both synthetic scale-free networks and real-world networks are used to validate the performance of \\textit{MA-RSF}\\textit{TRA} . The results show that \\textit{MA-RSF} \\textit{TRA} has a great ability in searching for the most robust network structure that is resistant to both targeted and random attacks.
Parameters affecting the resilience of scale-free networks to random failures.
Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran; Saia, Jared
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 degree 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.
Kim, Daehee; Kim, Dongwan; An, Sunshin
2016-01-01
Code dissemination in wireless sensor networks (WSNs) is a procedure for distributing a new code image over the air in order to update programs. Due to the fact that WSNs are mostly deployed in unattended and hostile environments, secure code dissemination ensuring authenticity and integrity is essential. Recent works on dynamic packet size control in WSNs allow enhancing the energy efficiency of code dissemination by dynamically changing the packet size on the basis of link quality. However, the authentication tokens attached by the base station become useless in the next hop where the packet size can vary according to the link quality of the next hop. In this paper, we propose three source authentication schemes for code dissemination supporting dynamic packet size. Compared to traditional source authentication schemes such as μTESLA and digital signatures, our schemes provide secure source authentication under the environment, where the packet size changes in each hop, with smaller energy consumption. PMID:27409616
Kim, Daehee; Kim, Dongwan; An, Sunshin
2016-01-01
Code dissemination in wireless sensor networks (WSNs) is a procedure for distributing a new code image over the air in order to update programs. Due to the fact that WSNs are mostly deployed in unattended and hostile environments, secure code dissemination ensuring authenticity and integrity is essential. Recent works on dynamic packet size control in WSNs allow enhancing the energy efficiency of code dissemination by dynamically changing the packet size on the basis of link quality. However, the authentication tokens attached by the base station become useless in the next hop where the packet size can vary according to the link quality of the next hop. In this paper, we propose three source authentication schemes for code dissemination supporting dynamic packet size. Compared to traditional source authentication schemes such as μTESLA and digital signatures, our schemes provide secure source authentication under the environment, where the packet size changes in each hop, with smaller energy consumption. PMID:27409616
NetCoDer: A Retransmission Mechanism for WSNs Based on Cooperative Relays and Network Coding.
Valle, Odilson T; Montez, Carlos; Medeiros de Araujo, Gustavo; Vasques, Francisco; Moraes, Ricardo
2016-01-01
Some of the most difficult problems to deal with when using Wireless Sensor Networks (WSNs) are related to the unreliable nature of communication channels. In this context, the use of cooperative diversity techniques and the application of network coding concepts may be promising solutions to improve the communication reliability. In this paper, we propose the NetCoDer scheme to address this problem. Its design is based on merging cooperative diversity techniques and network coding concepts. We evaluate the effectiveness of the NetCoDer scheme through both an experimental setup with real WSN nodes and a simulation assessment, comparing NetCoDer performance against state-of-the-art TDMA-based (Time Division Multiple Access) retransmission techniques: BlockACK, Master/Slave and Redundant TDMA. The obtained results highlight that the proposed NetCoDer scheme clearly improves the network performance when compared with other retransmission techniques. PMID:27258280
NetCoDer: A Retransmission Mechanism for WSNs Based on Cooperative Relays and Network Coding
Valle, Odilson T.; Montez, Carlos; Medeiros de Araujo, Gustavo; Vasques, Francisco; Moraes, Ricardo
2016-01-01
Some of the most difficult problems to deal with when using Wireless Sensor Networks (WSNs) are related to the unreliable nature of communication channels. In this context, the use of cooperative diversity techniques and the application of network coding concepts may be promising solutions to improve the communication reliability. In this paper, we propose the NetCoDer scheme to address this problem. Its design is based on merging cooperative diversity techniques and network coding concepts. We evaluate the effectiveness of the NetCoDer scheme through both an experimental setup with real WSN nodes and a simulation assessment, comparing NetCoDer performance against state-of-the-art TDMA-based (Time Division Multiple Access) retransmission techniques: BlockACK, Master/Slave and Redundant TDMA. The obtained results highlight that the proposed NetCoDer scheme clearly improves the network performance when compared with other retransmission techniques. PMID:27258280
Energy-efficient population coding constrains network size of a neuronal array system
NASA Astrophysics Data System (ADS)
Yu, Lianchun; Zhang, Chi; Liu, Liwei; Yu, Yuguo
2016-01-01
We consider the open issue of how the energy efficiency of the neural information transmission process, in a general neuronal array, constrains the network size, and how well this network size ensures the reliable transmission of neural information in a noisy environment. By direct mathematical analysis, we have obtained general solutions proving that there exists an optimal number of neurons in the network, where the average coding energy cost (defined as energy consumption divided by mutual information) per neuron passes through a global minimum for both subthreshold and superthreshold signals. With increases in background noise intensity, the optimal neuronal number decreases for subthreshold signals and increases for suprathreshold signals. The existence of an optimal number of neurons in an array network reveals a general rule for population coding that states that the neuronal number should be large enough to ensure reliable information transmission that is robust to the noisy environment but small enough to minimize energy cost.
A web of possibilities: network-based discovery of protein interaction codes.
Winter, Daniel L; Erce, Melissa A; Wilkins, Marc R
2014-12-01
Many proteins, including p53, the FoxO transcription factors, RNA polymerase II, pRb, and the chaperones, have extensive post-translational modifications (PTMs). Many of these modifications modulate protein-protein interactions, controlling interaction presence/absence and specificity. Here we propose the notion of the interaction code, a widespread means by which modifications are used to control interactions in the proteome. Minimal interaction codes are likely to exist on proteins that have two modifications and two or more interaction partners. By contrast, complex interaction codes are likely to be found on "date hub" proteins that have many interactions, many PTMs, or are targeted by many modifying and demodifying enzymes. Proteins with new interaction codes should be discoverable by examining protein interaction networks, annotated with PTMs and protein-modifying enzyme-substrate links. Multiple instances or combinations of phosphorylation, acetylation, methylation, O-GlcNAc, or ubiquitination will likely form interaction codes, especially when colocated on a protein's single interaction interface. A network-based example of code discovery is given, predicting the yeast protein Npl3p to have a methylation/phosphorylation-dependent interaction code.
Alternative knowledge acquisition: Developing a pulse coded neural network
Dress, W.B.
1987-01-01
After a Rip-van-Winkle nap of more than 20 years, the ideas of biologically motivated computing are re-emerging. Instrumental to this awakening have been the highly publicized contributions of John Hopfield and major advances in the neurosciences. In 1982, Hopfield showed how a system of maximally coupled neutron-like elements described by a Hamiltonian formalism (a linear, conservative system) could behave in a manner startlingly suggestive of the way humans might go about solving problems and retrieving memories. Continuing advances in the neurosciences are providing a coherent basis in suggesting how nature's neurons might function. A particular model is described for an artificial neural system designed to interact with (learn from and manipulate) a simulated (or real) environment. The model is based on early work by Iben Browning. The Browning model, designed to investigate computer-based intelligence, contains a particular simplification based on observations of frequency coding of information in the brain and information flow from receptors to the brain and back to effectors. The ability to act on and react to the environment was seen as an important principle, leading to self-organization of the system.
Evolutionary Computation with Spatial Receding Horizon Control to Minimize Network Coding Resources
Leeson, Mark S.
2014-01-01
The minimization of network coding resources, such as coding nodes and links, is a challenging task, not only because it is a NP-hard problem, but also because the problem scale is huge; for example, networks in real world may have thousands or even millions of nodes and links. Genetic algorithms (GAs) have a good potential of resolving NP-hard problems like the network coding problem (NCP), but as a population-based algorithm, serious scalability and applicability problems are often confronted when GAs are applied to large- or huge-scale systems. Inspired by the temporal receding horizon control in control engineering, this paper proposes a novel spatial receding horizon control (SRHC) strategy as a network partitioning technology, and then designs an efficient GA to tackle the NCP. Traditional network partitioning methods can be viewed as a special case of the proposed SRHC, that is, one-step-wide SRHC, whilst the method in this paper is a generalized N-step-wide SRHC, which can make a better use of global information of network topologies. Besides the SRHC strategy, some useful designs are also reported in this paper. The advantages of the proposed SRHC and GA for the NCP are illustrated by extensive experiments, and they have a good potential of being extended to other large-scale complex problems. PMID:24883371
Random walks on non-homogenous weighted Koch networks
NASA Astrophysics Data System (ADS)
Dai, Meifeng; Li, Xingyi; Xi, Lifeng
2013-09-01
In this paper, we introduce new models of non-homogenous weighted Koch networks on real traffic systems depending on the three scaling factors r1,r2,r3∈(0,1). Inspired by the definition of the average weighted shortest path (AWSP), we define the average weighted receiving time (AWRT). Assuming that the walker, at each step, starting from its current node, moves uniformly to any of its neighbors, we show that in large network, the AWRT grows as power-law function of the network order with the exponent, represented by θ(r1,r2,r3)=log4(1+r1+r2+r3). Moreover, the AWSP, in the infinite network order limit, only depends on the sum of scaling factors r1,r2,r3.
Phenotype accessibility and noise in random threshold gene regulatory networks.
Pinho, Ricardo; Garcia, Victor; Feldman, Marcus W
2014-01-01
Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes
Phenotype Accessibility and Noise in Random Threshold Gene Regulatory Networks
Feldman, Marcus W.
2015-01-01
Evolution requires phenotypic variation in a population of organisms for selection to function. Gene regulatory processes involved in organismal development affect the phenotypic diversity of organisms. Since only a fraction of all possible phenotypes are predicted to be accessed by the end of development, organisms may evolve strategies to use environmental cues and noise-like fluctuations to produce additional phenotypic diversity, and hence to enhance the speed of adaptation. We used a generic model of organismal development --gene regulatory networks-- to investigate how different levels of noise on gene expression states (i.e. phenotypes) may affect access to new, unique phenotypes, thereby affecting phenotypic diversity. We studied additional strategies that organisms might adopt to attain larger phenotypic diversity: either by augmenting their genome or the number of gene expression states. This was done for different types of gene regulatory networks that allow for distinct levels of regulatory influence on gene expression or are more likely to give rise to stable phenotypes. We found that if gene expression is binary, increasing noise levels generally decreases phenotype accessibility for all network types studied. If more gene expression states are considered, noise can moderately enhance the speed of discovery if three or four gene expression states are allowed, and if there are enough distinct regulatory networks in the population. These results were independent of the network types analyzed, and were robust to different implementations of noise. Hence, for noise to increase the number of accessible phenotypes in gene regulatory networks, very specific conditions need to be satisfied. If the number of distinct regulatory networks involved in organismal development is large enough, and the acquisition of more genes or fine tuning of their expression states proves costly to the organism, noise can be useful in allowing access to more unique phenotypes
Cross, Katy A.; Iacoboni, Marco
2011-01-01
Using functional magnetic resonance imaging, we show that a distributed fronto-parietal visuomotor integration network is recruited to overcome automatic responses to both biological and non-biological cues. Activity levels in these areas are similar for both cue types. The functional connectivity of this network, however, reveals differential coupling with thalamus and precuneus (biological cues) and extrastriate cortex (non biological cues). This suggests that a set of cortical areas equally activated in two tasks may accomplish task goals differently depending on their network interactions. This supports models of brain organization that emphasize efficient coding through changing patterns of integration between regions of specialized function. PMID:21976418
Hybrid decode-amplify-forward (HDAF) scheme in distributed Alamouti-coded cooperative network
NASA Astrophysics Data System (ADS)
Gurrala, Kiran Kumar; Das, Susmita
2015-05-01
In this article, a signal-to-noise ratio (SNR)-based hybrid decode-amplify-forward scheme in a distributed Alamouti-coded cooperative network is proposed. Considering a flat Rayleigh fading channel environment, the MATLAB simulation and analysis are carried out. In the cooperative scheme, two relays are employed, where each relay is transmitting each row Alamouti code. The selection of SNR threshold depends on the target rate information. The closed form expressions of symbol error rate (SER), the outage probability and average channel capacity with tight upper bounds are derived and compared with the simulation done in MATLAB environment. Furthermore, the impact of relay location on the SER performance is analysed. It is observed that the proposed hybrid relaying technique outperforms the individual amplify and forward and decode and forward ones in the distributed Alamouti-coded cooperative network.
Integrating non-coding RNAs in JAK-STAT regulatory networks.
Witte, Steven; Muljo, Stefan A
2014-01-01
Being a well-characterized pathway, JAK-STAT signaling serves as a valuable paradigm for studying the architecture of gene regulatory networks. The discovery of untranslated or non-coding RNAs, namely microRNAs and long non-coding RNAs, provides an opportunity to elucidate their roles in such networks. In principle, these regulatory RNAs can act as downstream effectors of the JAK-STAT pathway and/or affect signaling by regulating the expression of JAK-STAT components. Examples of interactions between signaling pathways and non-coding RNAs have already emerged in basic cell biology and human diseases such as cancer, and can potentially guide the identification of novel biomarkers or drug targets for medicine.
A reaction-diffusion-based coding rate control mechanism for camera sensor networks.
Yamamoto, Hiroshi; Hyodo, Katsuya; Wakamiya, Naoki; Murata, Masayuki
2010-01-01
A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal. PMID:22163620
A Reaction-Diffusion-Based Coding Rate Control Mechanism for Camera Sensor Networks
Yamamoto, Hiroshi; Hyodo, Katsuya; Wakamiya, Naoki; Murata, Masayuki
2010-01-01
A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal. PMID:22163620
Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T
2015-07-21
Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population. PMID:26150519
k-core percolation on complex networks: Comparing random, localized, and targeted attacks.
Yuan, Xin; Dai, Yang; Stanley, H Eugene; Havlin, Shlomo
2016-06-01
The type of malicious attack inflicting on networks greatly influences their stability under ordinary percolation in which a node fails when it becomes disconnected from the giant component. Here we study its generalization, k-core percolation, in which a node fails when it loses connection to a threshold k number of neighbors. We study and compare analytically and by numerical simulations of k-core percolation the stability of networks under random attacks (RA), localized attacks (LA) and targeted attacks (TA), respectively. By mapping a network under LA or TA into an equivalent network under RA, we find that in both single and interdependent networks, TA exerts the greatest damage to the core structure of a network. We also find that for Erdős-Rényi (ER) networks, LA and RA exert equal damage to the core structure, whereas for scale-free (SF) networks, LA exerts much more damage than RA does to the core structure. PMID:27415275
NASA Astrophysics Data System (ADS)
Ji, Xingpei; Wang, Bo; Liu, Dichen; Dong, Zhaoyang; Chen, Guo; Zhu, Zhenshan; Zhu, Xuedong; Wang, Xunting
2016-10-01
Whether the realistic electrical cyber-physical interdependent networks will undergo first-order transition under random failures still remains a question. To reflect the reality of Chinese electrical cyber-physical system, the "partial one-to-one correspondence" interdependent networks model is proposed and the connectivity vulnerabilities of three realistic electrical cyber-physical interdependent networks are analyzed. The simulation results show that due to the service demands of power system the topologies of power grid and its cyber network are highly inter-similar which can effectively avoid the first-order transition. By comparing the vulnerability curves between electrical cyber-physical interdependent networks and its single-layer network, we find that complex network theory is still useful in the vulnerability analysis of electrical cyber-physical interdependent networks.
Vulnerability of networks: Fractional percolation on random graphs
NASA Astrophysics Data System (ADS)
Shang, Yilun
2014-01-01
We present a theoretical framework for understanding nonbinary, nonindependent percolation on networks with general degree distributions. The model incorporates a partially functional (PF) state of nodes so that both intensity and extensity of error are characterized. Two connected nodes in a PF state cannot sustain the load and therefore break their link. We give exact solutions for the percolation threshold, the fraction of giant cluster, and the mean size of small clusters. The robustness-fragility transition point for scale-free networks with a degree distribution pk∝k-α is identified to be α =3. The analysis reveals that scale-free networks are vulnerable to targeted attack at hubs: a more complete picture of their Achilles' heel turns out to be not only the hubs themselves but also the edges linking them together.
Drug-target interaction prediction by random walk on the heterogeneous network.
Chen, Xing; Liu, Ming-Xi; Yan, Gui-Ying
2012-07-01
Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.
Representation of nonlinear random transformations by non-gaussian stochastic neural networks.
Turchetti, Claudio; Crippa, Paolo; Pirani, Massimiliano; Biagetti, Giorgio
2008-06-01
The learning capability of neural networks is equivalent to modeling physical events that occur in the real environment. Several early works have demonstrated that neural networks belonging to some classes are universal approximators of input-output deterministic functions. Recent works extend the ability of neural networks in approximating random functions using a class of networks named stochastic neural networks (SNN). In the language of system theory, the approximation of both deterministic and stochastic functions falls within the identification of nonlinear no-memory systems. However, all the results presented so far are restricted to the case of Gaussian stochastic processes (SPs) only, or to linear transformations that guarantee this property. This paper aims at investigating the ability of stochastic neural networks to approximate nonlinear input-output random transformations, thus widening the range of applicability of these networks to nonlinear systems with memory. In particular, this study shows that networks belonging to a class named non-Gaussian stochastic approximate identity neural networks (SAINNs) are capable of approximating the solutions of large classes of nonlinear random ordinary differential transformations. The effectiveness of this approach is demonstrated and discussed by some application examples.
All-optical OFDM network coding scheme for all-optical virtual private communication in PON
NASA Astrophysics Data System (ADS)
Li, Lijun; Gu, Rentao; Ji, Yuefeng; Bai, Lin; Huang, Zhitong
2014-03-01
A novel optical orthogonal frequency division multiplexing (OFDM) network coding scheme is proposed over passive optical network (PON) system. The proposed scheme for all-optical virtual private network (VPN) does not only improve transmission efficiency, but also realize full-duplex communication mode in a single fiber. Compared with the traditional all-optical VPN architectures, the all-optical OFDM network coding scheme can support higher speed, more flexible bandwidth allocation, and higher spectrum efficiency. In order to reduce the difficulty of alignment for encoding operation between inter-communication traffic, the width of OFDM subcarrier pulse is stretched in our proposed scheme. The feasibility of all-optical OFDM network coding scheme for VPN is verified, and the relevant simulation results show that the full-duplex inter-communication traffic stream can be transmitted successfully. Furthermore, the tolerance of misalignment existing in inter-ONUs traffic is investigated and analyzed for all-optical encoding operation, and the difficulty of pulse alignment is proved to be lower.
Quantum secure direct communication network with superdense coding and decoy photons
NASA Astrophysics Data System (ADS)
Deng, Fu-Guo; Li, Xi-Han; Li, Chun-Yan; Zhou, Ping; Zhou, Hong-Yu
2007-07-01
A quantum secure direct communication network scheme is proposed with quantum superdense coding and decoy photons. The servers on a passive optical network prepare and measure the quantum signal, i.e. a sequence of the d-dimensional Bell states. After confirming the security of the photons received from the receiver, the sender codes his secret message on them directly. For preventing a dishonest server from eavesdropping, some decoy photons prepared by measuring one photon in the Bell states are used to replace some original photons. One of the users on the network can communicate to any other one. This scheme has the advantage of high capacity, and it is more convenient than others as only a sequence of photons is transmitted in quantum line.
Code to generate random identifiers and select QA/QC samples
Mehnert, Edward
1992-01-01
SAMPLID is a PC-based, FORTRAN-77 code which generates unique numbers for identification of samples, selection of QA/QC samples, and generation of labels. These procedures are tedious, but using a computer code such as SAMPLID can increase efficiency and reduce or eliminate errors and bias. The algorithm, used in SAMPLID, for generation of pseudorandom numbers is free of statistical flaws present in commonly available algorithms.
Synchronized states and multistability in a random network of coupled discontinuous maps
Nag, Mayurakshi; Poria, Swarup
2015-08-15
The synchronization behavior of coupled chaotic discontinuous maps over a ring network with dynamic random connections is reported in this paper. It is observed that random rewiring stabilizes one of the two strongly unstable fixed points of the local map. Depending on initial conditions, the network synchronizes to different unstable fixed points, which signifies the existence of synchronized multistability in the complex network. Moreover, the length of discontinuity of the local map has an important role in generating windows of different synchronized fixed points. Synchronized fixed point and synchronized periodic orbits are found in the network depending on coupling strength and different parameter values of the local map. We have identified the existence of period subtracting bifurcation with respect to coupling strength in the network. The range of coupling strength for the occurrence of synchronized multistable spatiotemporal fixed points is determined. This range strongly depends upon the dynamic rewiring probability and also on the local map.
Development of flow network analysis code for block type VHTR core by linear theory method
Lee, J. H.; Yoon, S. J.; Park, J. W.; Park, G. C.
2012-07-01
VHTR (Very High Temperature Reactor) is high-efficiency nuclear reactor which is capable of generating hydrogen with high temperature of coolant. PMR (Prismatic Modular Reactor) type reactor consists of hexagonal prismatic fuel blocks and reflector blocks. The flow paths in the prismatic VHTR core consist of coolant holes, bypass gaps and cross gaps. Complicated flow paths are formed in the core since the coolant holes and bypass gap are connected by the cross gap. Distributed coolant was mixed in the core through the cross gap so that the flow characteristics could not be modeled as a simple parallel pipe system. It requires lot of effort and takes very long time to analyze the core flow with CFD analysis. Hence, it is important to develop the code for VHTR core flow which can predict the core flow distribution fast and accurate. In this study, steady state flow network analysis code is developed using flow network algorithm. Developed flow network analysis code was named as FLASH code and it was validated with the experimental data and CFD simulation results. (authors)
Learning random networks for compression of still and moving images
NASA Technical Reports Server (NTRS)
Gelenbe, Erol; Sungur, Mert; Cramer, Christopher
1994-01-01
Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.
Synchronization in the random-field Kuramoto model on complex networks.
Lopes, M A; Lopes, E M; Yoon, S; Mendes, J F F; Goltsev, A V
2016-07-01
We study the impact of random pinning fields on the emergence of synchrony in the Kuramoto model on complete graphs and uncorrelated random complex networks. We consider random fields with uniformly distributed directions and homogeneous and heterogeneous (Gaussian) field magnitude distribution. In our analysis, we apply the Ott-Antonsen method and the annealed-network approximation to find the critical behavior of the order parameter. In the case of homogeneous fields, we find a tricritical point above which a second-order phase transition gives place to a first-order phase transition when the network is either fully connected or scale-free with the degree exponent γ>5. Interestingly, for scale-free networks with 2<γ≤5, the phase transition is of second-order at any field magnitude, except for degree distributions with γ=3 when the transition is of infinite order at K_{c}=0 independent of the random fields. Contrary to the Ising model, even strong Gaussian random fields do not suppress the second-order phase transition in both complete graphs and scale-free networks, although the fields increase the critical coupling for γ>3. Our simulations support these analytical results. PMID:27575149
Synchronization in the random-field Kuramoto model on complex networks
NASA Astrophysics Data System (ADS)
Lopes, M. A.; Lopes, E. M.; Yoon, S.; Mendes, J. F. F.; Goltsev, A. V.
2016-07-01
We study the impact of random pinning fields on the emergence of synchrony in the Kuramoto model on complete graphs and uncorrelated random complex networks. We consider random fields with uniformly distributed directions and homogeneous and heterogeneous (Gaussian) field magnitude distribution. In our analysis, we apply the Ott-Antonsen method and the annealed-network approximation to find the critical behavior of the order parameter. In the case of homogeneous fields, we find a tricritical point above which a second-order phase transition gives place to a first-order phase transition when the network is either fully connected or scale-free with the degree exponent γ >5 . Interestingly, for scale-free networks with 2 <γ ≤5 , the phase transition is of second-order at any field magnitude, except for degree distributions with γ =3 when the transition is of infinite order at Kc=0 independent of the random fields. Contrary to the Ising model, even strong Gaussian random fields do not suppress the second-order phase transition in both complete graphs and scale-free networks, although the fields increase the critical coupling for γ >3 . Our simulations support these analytical results.
Building Damage-Resilient Dominating Sets in Complex Networks against Random and Targeted Attacks
NASA Astrophysics Data System (ADS)
Molnár, F.; Derzsy, N.; Szymanski, B. K.; Korniss, G.
2015-02-01
We study the vulnerability of dominating sets against random and targeted node removals in complex networks. While small, cost-efficient dominating sets play a significant role in controllability and observability of these networks, a fixed and intact network structure is always implicitly assumed. We find that cost-efficiency of dominating sets optimized for small size alone comes at a price of being vulnerable to damage; domination in the remaining network can be severely disrupted, even if a small fraction of dominator nodes are lost. We develop two new methods for finding flexible dominating sets, allowing either adjustable overall resilience, or dominating set size, while maximizing the dominated fraction of the remaining network after the attack. We analyze the efficiency of each method on synthetic scale-free networks, as well as real complex networks.
Building damage-resilient dominating sets in complex networks against random and targeted attacks.
Molnár, F; Derzsy, N; Szymanski, B K; Korniss, G
2015-02-09
We study the vulnerability of dominating sets against random and targeted node removals in complex networks. While small, cost-efficient dominating sets play a significant role in controllability and observability of these networks, a fixed and intact network structure is always implicitly assumed. We find that cost-efficiency of dominating sets optimized for small size alone comes at a price of being vulnerable to damage; domination in the remaining network can be severely disrupted, even if a small fraction of dominator nodes are lost. We develop two new methods for finding flexible dominating sets, allowing either adjustable overall resilience, or dominating set size, while maximizing the dominated fraction of the remaining network after the attack. We analyze the efficiency of each method on synthetic scale-free networks, as well as real complex networks.
Coded unicast downstream traffic in a wireless network: analysis and WiFi implementation
NASA Astrophysics Data System (ADS)
Cohen, Asaf; Biton, Erez; Kampeas, Joseph; Gurewitz, Omer
2013-12-01
In this article, we design, analyze and implement a network coding based scheme for the problem of transmitting multiple unicast streams from a single access point to multiple receivers. In particular, we consider the scenario in which an access point has access to infinite streams of data to be distributed to their intended receivers. After each time slot, the access point receives acknowledgments on previous transmissions. Based on the acknowledgements, it decides on the structure of a coded or uncoded packet to be broadcast to all receivers in the next slot. The goal of the access point is to maximize the cumulative throughput or discounted cumulative throughput in the system. We first rigorously model the relevant coding problem and the information available to the access point and the receivers. We then formulate the problem using a Markov decision process with an infinite horizon, analyze the value function under the uncoded and coded policies and, despite the exponential number of states, devise greedy and semi-greedy policies with a running time which is polynomial with high probability. We then analyze the two users case in more detail and show the optimality of the semi-greedy policy in that case. Finally, we describe a simple implementation of the suggested concepts within a WiFi open-source driver. The implementation performs the network coding such that the enhanced WiFi architecture is transparent above the MAC layer.
Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models.
Krivitsky, Pavel N; Handcock, Mark S; Morris, Martina
2011-07-01
Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS). PMID:21691424
Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models
Krivitsky, Pavel N.; Handcock, Mark S.; Morris, Martina
2011-01-01
Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS). PMID:21691424
Hsieh, Liang-Tien; Ranganath, Charan
2015-11-01
Episodic memory entails the ability to remember what happened when. Although the available evidence indicates that the hippocampus plays a role in structuring serial order information during retrieval of event sequences, information processed in the hippocampus must be conveyed to other cortical and subcortical areas in order to guide behavior. However, the extent to which other brain regions contribute to the temporal organization of episodic memory remains unclear. Here, we examined multivoxel activity pattern changes during retrieval of learned and random object sequences, focusing on a neocortical "core recollection network" that includes the medial prefrontal cortex, retrosplenial cortex, and angular gyrus, as well as on striatal areas including the caudate nucleus and putamen that have been implicated in processing of sequence information. The results demonstrate that regions of the core recollection network carry information about temporal positions within object sequences, irrespective of object information. This schematic coding of temporal information is in contrast to the putamen, which carried information specific to objects in learned sequences, and the caudate, which carried information about objects, irrespective of sequence context. Our results suggest a role for the cortical recollection network in the representation of temporal structure of events during episodic retrieval, and highlight the possible mechanisms by which the striatal areas may contribute to this process. More broadly, the results indicate that temporal sequence retrieval is a useful paradigm for dissecting the contributions of specific brain regions to episodic memory. PMID:26209802
Hsieh, Liang-Tien; Ranganath, Charan
2015-11-01
Episodic memory entails the ability to remember what happened when. Although the available evidence indicates that the hippocampus plays a role in structuring serial order information during retrieval of event sequences, information processed in the hippocampus must be conveyed to other cortical and subcortical areas in order to guide behavior. However, the extent to which other brain regions contribute to the temporal organization of episodic memory remains unclear. Here, we examined multivoxel activity pattern changes during retrieval of learned and random object sequences, focusing on a neocortical "core recollection network" that includes the medial prefrontal cortex, retrosplenial cortex, and angular gyrus, as well as on striatal areas including the caudate nucleus and putamen that have been implicated in processing of sequence information. The results demonstrate that regions of the core recollection network carry information about temporal positions within object sequences, irrespective of object information. This schematic coding of temporal information is in contrast to the putamen, which carried information specific to objects in learned sequences, and the caudate, which carried information about objects, irrespective of sequence context. Our results suggest a role for the cortical recollection network in the representation of temporal structure of events during episodic retrieval, and highlight the possible mechanisms by which the striatal areas may contribute to this process. More broadly, the results indicate that temporal sequence retrieval is a useful paradigm for dissecting the contributions of specific brain regions to episodic memory.
Adaptive coded spreading OFDM signal for dynamic-λ optical access network
NASA Astrophysics Data System (ADS)
Liu, Bo; Zhang, Lijia; Xin, Xiangjun
2015-12-01
This paper proposes and experimentally demonstrates a novel adaptive coded spreading (ACS) orthogonal frequency division multiplexing (OFDM) signal for dynamic distributed optical ring-based access network. The wavelength can be assigned to different remote nodes (RNs) according to the traffic demand of optical network unit (ONU). The ACS can provide dynamic spreading gain to different signals according to the split ratio or transmission length, which offers flexible power budget for the network. A 10×13.12 Gb/s OFDM access with ACS is successfully demonstrated over two RNs and 120 km transmission in the experiment. The demonstrated method may be viewed as one promising for future optical metro access network.
Protection of HEVC Video Delivery in Vehicular Networks with RaptorQ Codes
Martínez-Rach, Miguel; López, Otoniel; Malumbres, Manuel Pérez
2014-01-01
With future vehicles equipped with processing capability, storage, and communications, vehicular networks will become a reality. A vast number of applications will arise that will make use of this connectivity. Some of them will be based on video streaming. In this paper we focus on HEVC video coding standard streaming in vehicular networks and how it deals with packet losses with the aid of RaptorQ, a Forward Error Correction scheme. As vehicular networks are packet loss prone networks, protection mechanisms are necessary if we want to guarantee a minimum level of quality of experience to the final user. We have run simulations to evaluate which configurations fit better in this type of scenarios. PMID:25136675
Rosvall, Martin; Bergstrom, Carl T.
2011-01-01
To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network — the optimal number of levels and modular partition at each level — with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks. PMID:21494658
Emergence of multilevel selection in the prisoner's dilemma game on coevolving random networks
NASA Astrophysics Data System (ADS)
Szolnoki, Attila; Perc, Matjaž
2009-09-01
We study the evolution of cooperation in the prisoner's dilemma game, whereby a coevolutionary rule is introduced that molds the random topology of the interaction network in two ways. First, existing links are deleted whenever a player adopts a new strategy or its degree exceeds a threshold value; second, new links are added randomly after a given number of game iterations. These coevolutionary processes correspond to the generic formation of new links and deletion of existing links that, especially in human societies, appear frequently as a consequence of ongoing socialization, change of lifestyle or death. Due to the counteraction of deletions and additions of links the initial heterogeneity of the interaction network is qualitatively preserved, and thus cannot be held responsible for the observed promotion of cooperation. Indeed, the coevolutionary rule evokes the spontaneous emergence of a powerful multilevel selection mechanism, which despite the sustained random topology of the evolving network, maintains cooperation across the whole span of defection temptation values.
Trend-driven information cascades on random networks
NASA Astrophysics Data System (ADS)
Kobayashi, Teruyoshi
2015-12-01
Threshold models of global cascades have been extensively used to model real-world collective behavior, such as the contagious spread of fads and the adoption of new technologies. A common property of those cascade models is that a vanishingly small seed fraction can spread to a finite fraction of an infinitely large network through local infections. In social and economic networks, however, individuals' behavior is often influenced not only by what their direct neighbors are doing, but also by what the majority of people are doing as a trend. A trend affects individuals' behavior while individuals' behavior creates a trend. To analyze such a complex interplay between local- and global-scale phenomena, I generalize the standard threshold model by introducing a type of node called global nodes (or trend followers), whose activation probability depends on a global-scale trend, specifically the percentage of activated nodes in the population. The model shows that global nodes play a role as accelerating cascades once a trend emerges while reducing the probability of a trend emerging. Global nodes thus either facilitate or inhibit cascades, suggesting that a moderate share of trend followers may maximize the average size of cascades.
Optimally conductive networks in randomly dispersed CNT:graphene hybrids
Shim, Wonbo; Kwon, Youbin; Jeon, Seung-Yeol; Yu, Woong-Ryeol
2015-01-01
A predictive model is proposed that quantitatively describes the synergistic behavior of the electrical conductivities of CNTs and graphene in CNT:graphene hybrids. The number of CNT-to-CNT, graphene-to-graphene, and graphene-to-CNT contacts is calculated assuming a random distribution of CNTs and graphene particles in the hybrids and using an orientation density function. Calculations reveal that the total number of contacts reaches a maximum at a specific composition and depends on the particle sizes of the graphene and CNTs. The hybrids, prepared using inkjet printing, are distinguished by higher electrical conductivities than that of 100% CNT or graphene at certain composition ratios. These experimental results provide strong evidence that this approach involving constituent element contacts is suitable for investigating the properties of particulate hybrid materials. PMID:26564249
All-optical swapping of spectral amplitude code labels for packet-switched networks
NASA Astrophysics Data System (ADS)
Chen, Lawrence R.
2008-08-01
Packet-switched networks have attracted considerable attention as a basis for next-generation optical networks due to their advantages in terms of flexibility and network efficiency over traditional circuit-switched networks. Optical code multi-protocol label switching (OC-MPLS) promises fast, flexible, power-efficient switching by keeping signals in the optical domain and avoiding costly conversions to the electrical domain. In this paper, we review the use of spectral amplitude codes (SACs) for implementing OC-MPLS labels. We discuss the principles and features, as well as key enabling technologies required for their processing. In particular, we compare three different approaches for low cost all-optical swapping of SAC labels. All approaches are based on semiconductor fiber lasers and exploit nonlinearity in a semiconductor device: the first uses cross-absorption modulation in an electroabsorption modulator, the second uses cross-gain modulation (XGM) in a semiconductor optical amplifier (SOA), and the third makes use of XGM in an SOA as well as injection locking in a Fabry-Pérot laser. We present the static and dynamic responses of each for swapping a multi-wavelength input label to a multi-wavelength output label. The benefits and limitations of each approach as well as future improvements are discussed. We also present the results of systems experiments which demonstrate error-free all-optical label swapping, recognition, and switching of multi-rate packets in packet-switched networks using multi-wavelength labels.
Nonequilibrium, Drift-Flux Code System for Two-Phase Flow Network Analysis
2000-08-01
Version: 00 SOLA-LOOP is designed for the solution of transient two-phase flow in networks composed of one-dimensional components. The fluid dynamics is described by a nonequilibrium, drift-flux formulation of the fluid conservation laws. Although developed for nuclear reactor safety analysis, SOLA-LOOP may be used as the basis for other types of special-purpose network codes. The program can accommodate almost any set of constitutive relations, property tables, or other special features required for different applications.
NASA Astrophysics Data System (ADS)
Park, Joon-Sang; Lee, Uichin; Oh, Soon Young; Gerla, Mario; Lun, Desmond Siumen; Ro, Won Woo; Park, Joonseok
Vehicular ad hoc networks (VANET) aims to enhance vehicle navigation safety by providing an early warning system: any chance of accidents is informed through the wireless communication between vehicles. For the warning system to work, it is crucial that safety messages be reliably delivered to the target vehicles in a timely manner and thus reliable and timely data dissemination service is the key building block of VANET. Data mulling technique combined with three strategies, network codeing, erasure coding and repetition coding, is proposed for the reliable and timely data dissemination service. Particularly, vehicles in the opposite direction on a highway are exploited as data mules, mobile nodes physically delivering data to destinations, to overcome intermittent network connectivity cause by sparse vehicle traffic. Using analytic models, we show that in such a highway data mulling scenario the network coding based strategy outperforms erasure coding and repetition based strategies.
Role Analysis in Networks using Mixtures of Exponential Random Graph Models
Salter-Townshend, Michael; Murphy, Thomas Brendan
2014-01-01
A novel and flexible framework for investigating the roles of actors within a network is introduced. Particular interest is in roles as defined by local network connectivity patterns, identified using the ego-networks extracted from the network. A mixture of Exponential-family Random Graph Models is developed for these ego-networks in order to cluster the nodes into roles. We refer to this model as the ego-ERGM. An Expectation-Maximization algorithm is developed to infer the unobserved cluster assignments and to estimate the mixture model parameters using a maximum pseudo-likelihood approximation. The flexibility and utility of the method are demonstrated on examples of simulated and real networks. PMID:26101465
Size effects and internal length scales in the elasticity of random fiber networks
NASA Astrophysics Data System (ADS)
Picu, Catalin; Berkache, Kamel; Shahsavari, Ali; Ganghoffer, Jean-Francois
Random fiber networks are the structural element of many biological and man-made materials, including connective tissue, various consumer products and packaging materials. In all cases of practical interest the scale at which the material is used and the scale of the fiber diameter or the mean segment length of the network are separated by several orders of magnitude. This precludes solving boundary value problems defined on the scale of the application while resolving every fiber in the system, and mandates the development of continuum equivalent models. To this end, we study the intrinsic geometric and mechanical length scales of the network and the size effect associated with them. We consider both Cauchy and micropolar continuum models and calibrate them based on the discrete network behavior. We develop a method to predict the characteristic length scales of the problem and the minimum size of a representative element of the network based on network structural parameters and on fiber properties.
Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements
NASA Astrophysics Data System (ADS)
Kaneko, Kunihiko
1990-03-01
A Network of chaotic elements is investigated with the use of globally coupled maps. A simple coding of many attractors with clustering is shown. Through the coding, the attractors are organized so that their change exhibits bifurcation-like phenomena. A precision-dependent tree is constructed which leads to the similarity of our attractor with those of spin-glasses. Hierarchical dynamics is constructed on the tree, which leads to the dynamical change of trees and the temporal change of effective degrees of freedom. By a simple input on a site, we can switch among attractors and tune the strength of chaos. A threshold on a cluster size is found, beyond which a peculiar “posi-nega” switch occurs. Possible application to biological information processing is discussed with the emphasis on the fuzzy switch (chaotic search) and hierarchical code (categorization).
Data Security in Ad Hoc Networks Using Randomization of Cryptographic Algorithms
NASA Astrophysics Data System (ADS)
Krishna, B. Ananda; Radha, S.; Keshava Reddy, K. Chenna
Ad hoc networks are a new wireless networking paradigm for mobile hosts. Unlike traditional mobile wireless networks, ad hoc networks do not rely on any fixed infrastructure. Instead, hosts rely on each other to keep the network connected. The military tactical and other security-sensitive operations are still the main applications of ad hoc networks, although there is a trend to adopt ad hoc networks for commercial uses due to their unique properties. One main challenge in design of these networks is how to feasibly detect and defend the major attacks against data, impersonation and unauthorized data modification. Also, in the same network some nodes may be malicious whose objective is to degrade the network performance. In this study, we propose a security model in which the packets are encrypted and decrypted using multiple algorithms where the selection scheme is random. The performance of the proposed model is analyzed and it is observed that there is no increase in control overhead but a slight delay is introduced due to the encryption process. We conclude that the proposed security model works well for heavily loaded networks with high mobility and can be extended for more cryptographic algorithms.
Analysis of bHLH coding genes using gene co-expression network approach.
Srivastava, Swati; Sanchita; Singh, Garima; Singh, Noopur; Srivastava, Gaurava; Sharma, Ashok
2016-07-01
Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species.
Principles of odor coding and a neural network for odor discrimination.
Schild, D
1988-12-01
A concept of olfactory coding is proposed. It describes the stimulus responses of all receptor cells by the use of vector spaces. The morphological convergence pattern between receptor cells and glomeruli is given in the same vector space as the receptor cell activities. The overall input of a glomerulus follows as the scalar product of the receptor cell activity vector and the vector of the glomerulus' convergence pattern. The proposed coding concept shows how the network of the olfactory bulb succeeds in discriminating odors with high selectivity. It is concluded that sets of mitral cells coding similar odors work very much in the way of mutually inhibited matched filters. This solves one main problem both in olfaction as well as real-time odor detection by an artificial nose, i.e., how the fairly low degree of selectivity of receptor cells or sensors is overcome by the neural network following the receptor stage. The formal description of olfactory coding suggests that quality perception which is invariant under concentration shifts is accomplished by an associative memory in the olfactory bulb.
Coevolution of information processing and topology in hierarchical adaptive random Boolean networks
NASA Astrophysics Data System (ADS)
Górski, Piotr J.; Czaplicka, Agnieszka; Hołyst, Janusz A.
2016-02-01
Random Boolean Networks (RBNs) are frequently used for modeling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive random Boolean Network (HARBN) as a system consisting of distinct adaptive RBNs (ARBNs) - subnetworks - connected by a set of permanent interlinks. We investigate mean node information, mean edge information as well as mean node degree. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. The main natural feature of ARBNs, i.e. their adaptability, is preserved in HARBNs and they evolve towards critical configurations which is documented by power law distributions of network attractor lengths. The mean information processed by a single node or a single link increases with the number of interlinks added to the system. The mean length of network attractors and the mean steady-state connectivity possess minima for certain specific values of the quotient between the density of interlinks and the density of all links in networks. It means that the modular network displays extremal values of its observables when subnetworks are connected with a density a few times lower than a mean density of all links.
PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 2, Code manual
Reifman, J.; Wei, T.Y.C.
1995-09-01
We recommend the reader first review Volume 1 of this document, Code Theory, before reading Volume 2. In this volume we make extensive use of terms and concepts described and defined in Volume 1 which are not redefined here to the same extent. To try to reduce the amount of redundant information, we have restricted this volume to the presentation of the expert system code and refer back to the theory described in Volume 1 when necessary. Verification and validation of the results are presented in Volume 3, Application, of this document. Volume 3 also presents the implementation of the component characteristics diagnostic approach through artificial neural networks discussed in Volume 1. We decided to present the component characteristics approach in Volume 3, as opposed to write a separate code manual for it, because the approach, although general, requires a case-by-case analysis. The purpose of this volume is to present the details of the expert system (ES) portion o the PRODIAG process diagnostic program. In addition, we present here the graphical diagnostics interface (GDI) and illustrate the combined use of the ES and GDI with a sample problem. For completeness, we provide the file names of all files, programs and major subroutines of these two systems, ES and GDI, and their corresponding location in the Reactor Analysis Division (RA) computer network and Reactor Engineering Division (RE) computer network as of 30 September 1995.
Hierarchical surface code for network quantum computing with modules of arbitrary size
NASA Astrophysics Data System (ADS)
Li, Ying; Benjamin, Simon C.
2016-10-01
The network paradigm for quantum computing involves interconnecting many modules to form a scalable machine. Typically it is assumed that the links between modules are prone to noise while operations within modules have a significantly higher fidelity. To optimize fault tolerance in such architectures we introduce a hierarchical generalization of the surface code: a small "patch" of the code exists within each module and constitutes a single effective qubit of the logic-level surface code. Errors primarily occur in a two-dimensional subspace, i.e., patch perimeters extruded over time, and the resulting noise threshold for intermodule links can exceed ˜10 % even in the absence of purification. Increasing the number of qubits within each module decreases the number of qubits necessary for encoding a logical qubit. But this advantage is relatively modest, and broadly speaking, a "fine-grained" network of small modules containing only about eight qubits is competitive in total qubit count versus a "course" network with modules containing many hundreds of qubits.
Automatic network-adaptive ultra-low-bit-rate video coding
NASA Astrophysics Data System (ADS)
Chien, Wei-Jung; Lam, Tuyet-Trang; Abousleman, Glen P.; Karam, Lina J.
2006-05-01
This paper presents a software-only, real-time video coder/decoder (codec) for use with low-bandwidth channels where the bandwidth is unknown or varies with time. The codec incorporates a modified JPEG2000 core and interframe predictive coding, and can operate with network bandwidths of less than 1 kbits/second. The encoder and decoder establish two virtual connections over a single IP-based communications link. The first connection is UDP/IP guaranteed throughput, which is used to transmit the compressed video stream in real time, while the second is TCP/IP guaranteed delivery, which is used for two-way control and compression parameter updating. The TCP/IP link serves as a virtual feedback channel and enables the decoder to instruct the encoder to throttle back the transmission bit rate in response to the measured packet loss ratio. It also enables either side to initiate on-the-fly parameter updates such as bit rate, frame rate, frame size, and correlation parameter, among others. The codec also incorporates frame-rate throttling whereby the number of frames decoded is adjusted based upon the available processing resources. Thus, the proposed codec is capable of automatically adjusting the transmission bit rate and decoding frame rate to adapt to any network scenario. Video coding results for a variety of network bandwidths and configurations are presented to illustrate the vast capabilities of the proposed video coding system.
Stability of Dominating Sets in Complex Networks against Random and Targeted Attacks
NASA Astrophysics Data System (ADS)
Molnar, F.; Derzsy, N.; Szymanski, B. K.; Korniss, G.
2014-03-01
Minimum dominating sets (MDS) are involved in efficiently controlling and monitoring many social and technological networks. However, MDS influence over the entire network may be significantly reduced when some MDS nodes are disabled due to random breakdowns or targeted attacks against nodes in the network. We investigate the stability of domination in scale-free networks in such scenarios. We define stability as the fraction of nodes in the network that are still dominated after some nodes have been removed, either randomly, or by targeting the highest-degree nodes. We find that although the MDS is the most cost-efficient solution (requiring the least number of nodes) for reaching every node in an undamaged network, it is also very sensitive to damage. Further, we investigate alternative methods for finding dominating sets that are less efficient (more costly) than MDS but provide better stability. Finally we construct an algorithm based on greedy node selection that allows us to precisely control the balance between domination stability and cost, to achieve any desired stability at minimum cost, or the best possible stability at any given cost. Analysis of our method shows moderate improvement of domination cost efficiency against random breakdowns, but substantial improvements against targeted attacks. Supported by DARPA, DTRA, ARL NS-CTA, ARO, and ONR.
Altimetry and lidar using AlGaAs lasers modulated with pseudo-random codes
NASA Technical Reports Server (NTRS)
Abshire, James B.; Rall, Jonathan A. R.; Manizade, Serdar S.
1992-01-01
We have demonstrated a prototype laser altimeter and lidar which uses a low power AlGaAs laser modulated with a pseudorandom noise (PN) code, a 20 cm diameter telescope, and a photon counting receiver. Measurements to tree canopies over a 400 m horizontal path show strong signals with 5.3 mW average power and a 6.4 msec averaging time. Computer simulations predict lidar performance for various types of scattering performance.
Yin, Jun; Yang, Yuwang; Wang, Lei
2016-01-01
Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on NC and CS (Compressed Data Gathering—CDG) have been proposed. However, these schemes assume that the sparsity of the network sensed data is constant and the value of the sparsity is known before starting each data gathering epoch, thus they ignore the variation of the data observed by the WSNs which are deployed in practical circumstances. In this paper, we present a complete design of the feedback CDG scheme where the sink node adaptively queries those interested nodes to acquire an appropriate number of measurements. The adaptive measurement-formation procedure and its termination rules are proposed and analyzed in detail. Moreover, in order to minimize the number of overall transmissions in the formation procedure of each measurement, we have developed a NP-complete model (Maximum Leaf Nodes Minimum Steiner Nodes—MLMS) and realized a scalable greedy algorithm to solve the problem. Experimental results show that the proposed measurement-formation method outperforms previous schemes, and experiments on both datasets from ocean temperature and practical network deployment also prove the effectiveness of our proposed feedback CDG scheme. PMID:27043574
A Markov model for the temporal dynamics of balanced random networks of finite size.
Lagzi, Fereshteh; Rotter, Stefan
2014-01-01
The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between
A Markov model for the temporal dynamics of balanced random networks of finite size
Lagzi, Fereshteh; Rotter, Stefan
2014-01-01
The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between
A Random Walk on WASP-12b with the Bayesian Atmospheric Radiative Transfer (BART) Code
NASA Astrophysics Data System (ADS)
Harrington, Joseph; Cubillos, Patricio; Blecic, Jasmina; Challener, Ryan; Rojo, Patricio; Lust, Nathaniel B.; Bowman, Oliver; Blumenthal, Sarah D.; Foster, Andrew S. D.; Foster, Austin James; Stemm, Madison; Bruce, Dylan
2016-01-01
We present the Bayesian Atmospheric Radiative Transfer (BART) code for atmospheric property retrievals from transit and eclipse spectra, and apply it to WASP-12b, a hot (~3000 K) exoplanet with a high eclipse signal-to-noise ratio. WASP-12b has been controversial. We (Madhusudhan et al. 2011, Nature) claimed it was the first planet with a high C/O abundance ratio. Line et al. (2014, ApJ) suggested a high CO2 abundance to explain the data. Stevenson et al. (2014, ApJ, atmospheric model by Madhusudhan) add additional data and reaffirm the original result, stating that C2H2 and HCN, not included in the Line et al. models, explain the data. We explore several modeling configurations and include Hubble, Spitzer, and ground-based eclipse data.BART consists of a differential-evolution Markov-Chain Monte Carlo sampler that drives a line-by-line radiative transfer code through the phase space of thermal- and abundance-profile parameters. BART is written in Python and C. Python modules generate atmospheric profiles from sets of MCMC parameters and integrate the resulting spectra over observational bandpasses, allowing high flexibility in modeling the planet without interacting with the fast, C portions that calculate the spectra. BART's shared memory and optimized opacity calculation allow it to run on a laptop, enabling classroom use. Runs can scale constant abundance profiles, profiles of thermochemical equilibrium abundances (TEA) calculated by the included TEA code, or arbitrary curves. Several thermal profile parameterizations are available. BART is an open-source, reproducible-research code. Users must release any code or data modifications if they publish results from it, and we encourage the community to use it and to participate in its development via http://github.com/ExOSPORTS/BART.This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. J. Blecic holds a NASA Earth and Space Science
Non-coding RNAs and a layered architecture of genetic networks
NASA Astrophysics Data System (ADS)
Zhdanov, Vladimir P.
2010-12-01
In eukaryotic cells, protein-coding sequences constitute a relatively small part of the genome. The rest of the genome is transcribed to non-coding RNAs (ncRNAs). Such RNAs form the cornerstone of a regulatory network that operates in parallel with the protein network. Their biological functions are based primarily on the ability to pair with and deactivate target messenger RNAs (mRNAs). To clarify the likely role of ncRNAs in complex genetic networks, we present and comprehensively analyze a kinetic model of one of the key counterparts of the network architectures. Specifically, the genes transcribed to ncRNAs are considered to interplay with a hierarchical two-layer set of genes transcribed to mRNAs. The genes forming the bottom layer are regulated from the top and negatively self-regulated. If the former regulation is positive, the dependence of the RNA populations on the governing parameters is found to be often non-monotonous. Specifically, the model predicts bistability. If the regulation is negative, the dependence of the RNA populations on the governing parameters is monotonous. In particular, the population of the mRNAs, corresponding to the genes forming the bottom layer, is nearly constant.
Kagami, Haruna; Akutsu, Tatsuya; Maegawa, Shingo; Hosokawa, Hiroshi; Nacher, Jose C
2015-01-01
Deciphering the association between life molecules and human diseases is currently an important task in systems biology. Research over the past decade has unveiled that the human genome is almost entirely transcribed, producing a vast number of non-protein-coding RNAs (ncRNAs) with potential regulatory functions. More recent findings suggest that many diseases may not be exclusively linked to mutations in protein-coding genes. The combination of these arguments poses the question of whether ncRNAs that play a critical role in network control are also enriched with disease-associated ncRNAs. To address this question, we mapped the available annotated information of more than 350 human disorders to the largest collection of human ncRNA-protein interactions, which define a bipartite network of almost 93,000 interactions. Using a novel algorithmic-based controllability framework applied to the constructed bipartite network, we found that ncRNAs engaged in critical network control are also statistically linked to human disorders (P-value of P = 9.8 × 10(-109)). Taken together, these findings suggest that the addition of those genes that encode optimized subsets of ncRNAs engaged in critical control within the pool of candidate genes could aid disease gene prioritization studies. PMID:26459019
NASA Astrophysics Data System (ADS)
Kagami, Haruna; Akutsu, Tatsuya; Maegawa, Shingo; Hosokawa, Hiroshi; Nacher, Jose C.
2015-10-01
Deciphering the association between life molecules and human diseases is currently an important task in systems biology. Research over the past decade has unveiled that the human genome is almost entirely transcribed, producing a vast number of non-protein-coding RNAs (ncRNAs) with potential regulatory functions. More recent findings suggest that many diseases may not be exclusively linked to mutations in protein-coding genes. The combination of these arguments poses the question of whether ncRNAs that play a critical role in network control are also enriched with disease-associated ncRNAs. To address this question, we mapped the available annotated information of more than 350 human disorders to the largest collection of human ncRNA-protein interactions, which define a bipartite network of almost 93,000 interactions. Using a novel algorithmic-based controllability framework applied to the constructed bipartite network, we found that ncRNAs engaged in critical network control are also statistically linked to human disorders (P-value of P = 9.8 × 10-109). Taken together, these findings suggest that the addition of those genes that encode optimized subsets of ncRNAs engaged in critical control within the pool of candidate genes could aid disease gene prioritization studies.
Method and system for pattern analysis using a coarse-coded neural network
NASA Technical Reports Server (NTRS)
Spirkovska, Liljana (Inventor); Reid, Max B. (Inventor)
1994-01-01
A method and system for performing pattern analysis with a neural network coarse-coding a pattern to be analyzed so as to form a plurality of sub-patterns collectively defined by data. Each of the sub-patterns comprises sets of pattern data. The neural network includes a plurality fields, each field being associated with one of the sub-patterns so as to receive the sub-pattern data therefrom. Training and testing by the neural network then proceeds in the usual way, with one modification: the transfer function thresholds the value obtained from summing the weighted products of each field over all sub-patterns associated with each pattern being analyzed by the system.
Data compression in wireless sensors network using MDCT and embedded harmonic coding.
Alsalaet, Jaafar K; Ali, Abduladhem A
2015-05-01
One of the major applications of wireless sensors networks (WSNs) is vibration measurement for the purpose of structural health monitoring and machinery fault diagnosis. WSNs have many advantages over the wired networks such as low cost and reduced setup time. However, the useful bandwidth is limited, as compared to wired networks, resulting in relatively low sampling. One solution to this problem is data compression which, in addition to enhancing sampling rate, saves valuable power of the wireless nodes. In this work, a data compression scheme, based on Modified Discrete Cosine Transform (MDCT) followed by Embedded Harmonic Components Coding (EHCC) is proposed to compress vibration signals. The EHCC is applied to exploit harmonic redundancy present is most vibration signals resulting in improved compression ratio. This scheme is made suitable for the tiny hardware of wireless nodes and it is proved to be fast and effective. The efficiency of the proposed scheme is investigated by conducting several experimental tests.
NASA Astrophysics Data System (ADS)
Ma, Jun; Xu, Ying; Wang, Chunni; Jin, Wuyin
2016-11-01
Regular spatial patterns could be observed in spatiotemporal systems far from equilibrium states. Artificial networks with different topologies are often designed to reproduce the collective behaviors of nodes (or neurons) which the local kinetics of node is described by kinds of oscillator models. It is believed that the self-organization of network much depends on the bifurcation parameters and topology connection type. Indeed, the boundary effect is every important on the pattern formation of network. In this paper, a regular network of Hindmarsh-Rose neurons is designed in a two-dimensional square array with nearest-neighbor connection type. The neurons on the boundary are excited with random stimulus. It is found that spiral waves, even a pair of spiral waves could be developed in the network under appropriate coupling intensity. Otherwise, the spatial distribution of network shows irregular states. A statistical variable is defined to detect the collective behavior by using mean field theory. It is confirmed that regular pattern could be developed when the synchronization degree is low. The potential mechanism could be that random perturbation on the boundary could induce coherence resonance-like behavior thus spiral wave could be developed in the network.
ERIC Educational Resources Information Center
Gruenenfelder, Thomas M.; Pisoni, David B.
2009-01-01
Purpose: The mental lexicon of words used for spoken word recognition has been modeled as a complex network or graph. Do the characteristics of that graph reflect processes involved in its growth (M. S. Vitevitch, 2008) or simply the phonetic overlap between similar-sounding words? Method: Three pseudolexicons were generated by randomly selecting…
3D self-consistent percolative model for networks of randomly aligned carbon nanotubes
NASA Astrophysics Data System (ADS)
Colasanti, S.; Deep Bhatt, V.; Abdellah, A.; Lugli, P.
2015-10-01
A numerical percolative model for simulations of random networks of carbon nanotubes is presented. This algorithm takes into account the real 3D nature of these networks, allowing for a better understanding of their electrical properties. The nanotubes are modeled as non-rigid bendable cylinders with geometrical properties derived according to some statistical distributions inferred from the experiments. For the transport mechanisms we refer to the theory of one-dimensional ballistic channels which is based on the computation of the density of states. The behavior of the entire network is then simulated by coupling a SPICE program with an iterative algorithm that calculates self-consistently the electrostatic potential and the current flow in each node of the network. We performed several simulations on the resistivity of networks with different thicknesses and over different simulation domains. Our results confirm the percolative nature of the electrical transport, which are more pronounced in films close to their percolation threshold.
Model for disease dynamics of a waterborne pathogen on a random network.
Li, Meili; Ma, Junling; van den Driessche, P
2015-10-01
A network epidemic SIWR model for cholera and other diseases that can be transmitted via the environment is developed and analyzed. The person-to-person contacts are modeled by a random contact network, and the contagious environment is modeled by an external node that connects to every individual. The model is adapted from the Miller network SIR model, and in the homogeneous mixing limit becomes the Tien and Earn deterministic cholera model without births and deaths. The dynamics of our model shows excellent agreement with stochastic simulations. The basic reproduction number [Formula: see text] is computed, and on a Poisson network shown to be the sum of the basic reproduction numbers of the person-to-person and person-to-water-to-person transmission pathways. However, on other networks, [Formula: see text] depends nonlinearly on the transmission along the two pathways. Type reproduction numbers are computed and quantify measures to control the disease. Equations giving the final epidemic size are obtained.
Rich, Scott; Booth, Victoria; Zochowski, Michal
2016-01-01
The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics
Hsieh, Liang-Tien; Ranganath, Charan
2015-01-01
Episodic memory entails the ability to remember what happened when. Although the available evidence indicates that the hippocampus plays a role in structuring serial order information during retrieval of event sequences, information processed in the hippocampus must be conveyed to other cortical and subcortical areas in order to guide behavior. However, the extent to which other brain regions contribute to the temporal organization of episodic memory remains unclear. Here, we examined multivoxel activity pattern changes during retrieval of learned and random object sequences, focusing on a neocortical “core recollection network” that includes the medial prefrontal cortex, retrosplenial cortex, and angular gyrus, as well as on striatal areas including the caudate nucleus and putamen that have been implicated in processing of sequence information. The results demonstrate that regions of the core recollection network carry information about temporal positions within object sequences, irrespective of object information. This schematic coding of temporal information is in contrast to the putamen, which carried information specific to objects in learned sequences, and the caudate, which carried information about objects, irrespective of sequence context. Our results suggest a role for the cortical recollection network in the representation of temporal structure of events during episodic retrieval, and highlight the possible mechanisms by which the striatal areas may contribute to this process. More broadly, the results indicate that temporal sequence retrieval is a useful paradigm for dissecting the contributions of specific brain regions to episodic memory. PMID:26209802
Plasmon-enhanced random lasing in bio-compatible networks of cellulose nanofibers
NASA Astrophysics Data System (ADS)
Zhang, R.; Knitter, S.; Liew, S. F.; Omenetto, F. G.; Reinhard, B. M.; Cao, H.; Dal Negro, L.
2016-01-01
We report on plasmon-enhanced random lasing in bio-compatible light emitting Hydroxypropyl Cellulose (HPC) nanofiber networks doped with gold nanoparticles. HPC nanofibers with a diameter of 260 ± 30 nm were synthesized by a one step, cost-effective and facile electrospinning technique from a solution-containing Rhodamine 6G and Au nanoparticles. Nanoparticles of controlled diameters from 10 nm to 80 nm were dispersed inside the nanofibers and optically characterized using photoluminescence, dark-field spectroscopy, and coherent backscattering measurements. Plasmon-enhanced random lasing was demonstrated with a lower threshold than that in dye-doped identical HPC networks without Au nanoparticles. These findings provide an effective approach for plasmon-enhanced random lasers based on a bio-compatible host matrix that is particularly attractive for biophotonic applications such as fluorescence sensing, optical tagging, and detection.
NASA Astrophysics Data System (ADS)
Stoytchev, M.; Genack, A. Z.
1997-04-01
We have measured the transmission of microwave radiation through a nearly cubic metal-wire network with a lattice constant of 1 cm and a length of 8 cm on each side. We observe a sharp drop in the transmission below a cutoff frequency νc=9.33 GHz when the structure is empty. The gap persists when the network is filled with randomly positioned Teflon spheres, but νc is reduced by the ratio of the phase velocities in air and in a random medium of Teflon spheres and air. When aluminum spheres are substituted for some of the Teflon spheres, transmission peaks appear within the gap. The average transmission in an ensemble of random configurations shows a broadening of the band edge associated with precursors to localized states in this small sample. This indicates that the statistics of the localization transition can be studied in larger samples.
Observer-based H(infinity) control for networked nonlinear systems with random packet losses.
Li, Jian Guo; Yuan, Jing Qi; Lu, Jun Guo
2010-01-01
This paper investigates the observer-based H(infinity) control problem of networked nonlinear systems with global Lipschitz nonlinearities and random communication packet losses. The random packet loss is modelled as a Bernoulli distributed white sequence with a known conditional probability distribution. In the presence of random packet losses, sufficient conditions for the existence of an observer-based feedback controller are derived, such that the closed-loop networked nonlinear system is exponentially stable in the mean-square sense, and a prescribed H(infinity) disturbance-rejection-attenuation performance is also achieved. Then a linear matrix inequality (LMI) approach for designing such an observer-based H(infinity) controller is presented. Finally, a simulation example is used to demonstrate the effectiveness of the proposed method.
Takizawa, Kenichi; Hamaguchi, Kiyoshi
2010-01-01
This paper presents a digital fountain code as a design criterion in order to mitigate packet erasure in wireless wearable body area networks (WBANs). First, we measure its radio propagation around the human body between two antennas attached to participants, and then analyze the measurement results from the standpoint of occurrence ratio of packet erasure. Then, we evaluate the application of digital fountain code into such WBANs where a rateless code is introduced as such code in order to provide a design criterion for the code. PMID:21096974
Random vs. Combinatorial Methods for Discrete Event Simulation of a Grid Computer Network
NASA Technical Reports Server (NTRS)
Kuhn, D. Richard; Kacker, Raghu; Lei, Yu
2010-01-01
This study compared random and t-way combinatorial inputs of a network simulator, to determine if these two approaches produce significantly different deadlock detection for varying network configurations. Modeling deadlock detection is important for analyzing configuration changes that could inadvertently degrade network operations, or to determine modifications that could be made by attackers to deliberately induce deadlock. Discrete event simulation of a network may be conducted using random generation, of inputs. In this study, we compare random with combinatorial generation of inputs. Combinatorial (or t-way) testing requires every combination of any t parameter values to be covered by at least one test. Combinatorial methods can be highly effective because empirical data suggest that nearly all failures involve the interaction of a small number of parameters (1 to 6). Thus, for example, if all deadlocks involve at most 5-way interactions between n parameters, then exhaustive testing of all n-way interactions adds no additional information that would not be obtained by testing all 5-way interactions. While the maximum degree of interaction between parameters involved in the deadlocks clearly cannot be known in advance, covering all t-way interactions may be more efficient than using random generation of inputs. In this study we tested this hypothesis for t = 2, 3, and 4 for deadlock detection in a network simulation. Achieving the same degree of coverage provided by 4-way tests would have required approximately 3.2 times as many random tests; thus combinatorial methods were more efficient for detecting deadlocks involving a higher degree of interactions. The paper reviews explanations for these results and implications for modeling and simulation.
Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks
Huang, Rimao; Qiu, Xuesong; Rui, Lanlan
2011-01-01
Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate. PMID:22163789
Han, I; Bond, S; Welty, R; Du, Y; Yoo, S; Reinhardt, C; Behymer, E; Sperry, V; Kobayashi, N
2004-02-12
This project is focused on the development of advanced components and system technologies for secure data transmission on high-speed fiber optic data systems. This work capitalizes on (1) a strong relationship with outstanding faculty at the University of California-Davis who are experts in high speed fiber-optic networks, (2) the realization that code division multiple access (CDMA) is emerging as a bandwidth enhancing technique for fiber optic networks, (3) the realization that CDMA of sufficient complexity forms the basis for almost unbreakable one-time key transmissions, (4) our concepts for superior components for implementing CDMA, (5) our expertise in semiconductor device processing and (6) our Center for Nano and Microtechnology, which is where the majority of the experimental work was done. Here we present a novel device concept, which will push the limits of current technology, and will simultaneously solve system implementation issues by investigating new state-of-the-art fiber technologies. This will enable the development of secure communication systems for the transmission and reception of messages on deployed commercial fiber optic networks, through the CDMA phase encoding of broad bandwidth pulses. CDMA technology has been developed as a multiplexing technology, much like wavelength division multiplexing (WDM) or time division multiplexing (TDM), to increase the potential number of users on a given communication link. A novel application of the techniques created for CDMA is to generate secure communication through physical layer encoding. Physical layer encoding devices are developed which utilize semiconductor waveguides with fast carrier response times to phase encode spectral components of a secure signal. Current commercial technology, most commonly a spatial light modulator, allows phase codes to be changed at rates of only 10's of Hertz ({approx}25ms response). The use of fast (picosecond to nanosecond) carrier dynamics of semiconductors, as
A neural network model of general olfactory coding in the insect antennal lobe.
Getz, W M; Lutz, A
1999-08-01
A central problem in olfaction is understanding how the quality of olfactory stimuli is encoded in the insect antennal lobe (or in the analogously structured vertebrate olfactory bulb) for perceptual processing in the mushroom bodies of the insect protocerebrum (or in the vertebrate olfactory cortex). In the study reported here, a relatively simple neural network model, inspired by our current knowledge of the insect antennal lobes, is used to investigate how each of several features and elements of the network, such as synapse strengths, feedback circuits and the steepness of neural activation functions, influences the formation of an olfactory code in neurons that project from the antennal lobes to the mushroom bodies (or from mitral cells to olfactory cortex). An optimal code in these projection neurons (PNs) should minimize potential errors by the mushroom bodies in misidentifying the quality of an odor across a range of concentrations while maximizing the ability of the mushroom bodies to resolve odors of different quality. Simulation studies demonstrate that the network is able to produce codes independent or virtually independent of concentration over a given range. The extent of this range is moderately dependent on a parameter that characterizes how long it takes for the voltage in an activated neuron to decay back to its resting potential, strongly dependent on the strength of excitatory feedback by the PNs onto antennal lobe intrinsic neurons (INs), and overwhelmingly dependent on the slope of the activation function that transforms the voltage of depolarized neurons into the rate at which spikes are produced. Although the code in the PNs is degraded by large variations in the concentration of odor stimuli, good performance levels are maintained when the complexity of stimuli, as measured by the number of component odorants, is doubled. When excitatory feedback from the PNs to the INs is strong, the activity in the PNs undergoes transitions from initial
Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks.
Scarpetta, Silvia; Giacco, Ferdinando
2013-04-01
We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at different time scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stable precise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units. PMID:23053861
Differential Code Biases Temporal and Spatial Variation: A case study of permanent network in Egypt
NASA Astrophysics Data System (ADS)
Mousa, Ashraf; Abd, Mohamed; Rabah, Moustafa; Mowafi, Mahmoud; Awad, Ahmed
2016-07-01
Measurements of Global Positioning Satellite System (GPS) receivers are affected by systematic offsets related to group and phase delays of the signal generation and processing chain. One of the important factors affecting the ionosphere Total Electron Content (TEC) estimation accuracy is the hardware Differential Code Bias (DCBs) inherited in both GPS satellites and receivers. The resulting code and phase biases depend on the transmission frequency and the employed signal modulation. An efficient algorithm using the geometry conditions between satellite and tracking receivers is proposed to determine the receiver differential code bias (DCB) using Egyptian permanent reference stations. This method does not require a traditional single-layer ionosphere model and can be used for estimating DCBs of receivers in a regional network. This paper estimates receiver DCBs for nine receivers located within Egyptian network. The results showed that the estimated mean value of the receiver DCB varied from -28 ns (nanosecond) to 39 ns. It is clear from the results that DCB values for Egyptian sites do not vary much with latitude and longitude, except at ABSM and ASWN. DCB values increase gradually with increasing height.
Numerical simulation of fibrous biomaterials with randomly distributed fiber network structure.
Jin, Tao; Stanciulescu, Ilinca
2016-08-01
This paper presents a computational framework to simulate the mechanical behavior of fibrous biomaterials with randomly distributed fiber networks. A random walk algorithm is implemented to generate the synthetic fiber network in 2D used in simulations. The embedded fiber approach is then adopted to model the fibers as embedded truss elements in the ground matrix, which is essentially equivalent to the affine fiber kinematics. The fiber-matrix interaction is partially considered in the sense that the two material components deform together, but no relative movement is considered. A variational approach is carried out to derive the element residual and stiffness matrices for finite element method (FEM), in which material and geometric nonlinearities are both included. Using a data structure proposed to record the network geometric information, the fiber network is directly incorporated into the FEM simulation without significantly increasing the computational cost. A mesh sensitivity analysis is conducted to show the influence of mesh size on various simulation results. The proposed method can be easily combined with Monte Carlo (MC) simulations to include the influence of the stochastic nature of the network and capture the material behavior in an average sense. The computational framework proposed in this work goes midway between homogenizing the fiber network into the surrounding matrix and accounting for the fully coupled fiber-matrix interaction at the segment length scale, and can be used to study the connection between the microscopic structure and the macro-mechanical behavior of fibrous biomaterials with a reasonable computational cost. PMID:26342926
Random network peristalsis in Physarum polycephalum organizes fluid flows across an individual.
Alim, Karen; Amselem, Gabriel; Peaudecerf, François; Brenner, Michael P; Pringle, Anne
2013-08-13
Individuals can function as integrated organisms only when information and resources are shared across a body. Signals and substrates are commonly moved using fluids, often channeled through a network of tubes. Peristalsis is one mechanism for fluid transport and is caused by a wave of cross-sectional contractions along a tube. We extend the concept of peristalsis from the canonical case of one tube to a random network. Transport is maximized within the network when the wavelength of the peristaltic wave is of the order of the size of the network. The slime mold Physarum polycephalum grows as a random network of tubes, and our experiments confirm peristalsis is used by the slime mold to drive internal cytoplasmic flows. Comparisons of theoretically generated contraction patterns with the patterns exhibited by individuals of P. polycephalum demonstrate that individuals maximize internal flows by adapting patterns of contraction to size, thus optimizing transport throughout an organism. This control of fluid flow may be the key to coordinating growth and behavior, including the dynamic changes in network architecture seen over time in an individual. PMID:23898203
Timme, Marc; Geisel, Theo; Wolf, Fred
2006-03-01
We analyze the dynamics of networks of spiking neural oscillators. First, we present an exact linear stability theory of the synchronous state for networks of arbitrary connectivity. For general neuron rise functions, stability is determined by multiple operators, for which standard analysis is not suitable. We describe a general nonstandard solution to the multioperator problem. Subsequently, we derive a class of neuronal rise functions for which all stability operators become degenerate and standard eigenvalue analysis becomes a suitable tool. Interestingly, this class is found to consist of networks of leaky integrate-and-fire neurons. For random networks of inhibitory integrate-and-fire neurons, we then develop an analytical approach, based on the theory of random matrices, to precisely determine the eigenvalue distributions of the stability operators. This yields the asymptotic relaxation time for perturbations to the synchronous state which provides the characteristic time scale on which neurons can coordinate their activity in such networks. For networks with finite in-degree, i.e., finite number of presynaptic inputs per neuron, we find a speed limit to coordinating spiking activity. Even with arbitrarily strong interaction strengths neurons cannot synchronize faster than at a certain maximal speed determined by the typical in-degree.
Random and Directed Walk-Based Top-k Queries in Wireless Sensor Networks
Fu, Jun-Song; Liu, Yun
2015-01-01
In wireless sensor networks, filter-based top-k query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors’ readings and declines in the overall range of all the readings. In this work, a random walk-based top-k query approach called RWTQ and a directed walk-based top-k query approach called DWTQ are proposed. At the beginning of a top-k query, one or several tokens are sent to the specific node(s) in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the “right” way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime. PMID:26016914
Cooperative multi-user detection and ranging based on pseudo-random codes
NASA Astrophysics Data System (ADS)
Morhart, C.; Biebl, E. M.
2009-05-01
We present an improved approach for a Round Trip Time of Flight distance measurement system. The system is intended for the usage in a cooperative localisation system for automotive applications. Therefore, it is designed to address a large number of communication partners per measurement cycle. By using coded signals in a time divison multiple access order, we can detect a large number of pedestrian sensors with just one car sensor. We achieve this by using very short transmit bursts in combination with a real time correlation algorithm. Futhermore, the correlation approach offers real time data, concerning the time of arrival, that can serve as a trigger impulse for other comunication systems. The distance accuracy of the correlation result was further increased by adding a fourier interpolation filter. The system performance was checked with a prototype at 2.4 GHz. We reached a distance measurement accuracy of 12 cm at a range up to 450 m.
Hybrid information privacy system: integration of chaotic neural network and RSA coding
NASA Astrophysics Data System (ADS)
Hsu, Ming-Kai; Willey, Jeff; Lee, Ting N.; Szu, Harold H.
2005-03-01
Electronic mails are adopted worldwide; most are easily hacked by hackers. In this paper, we purposed a free, fast and convenient hybrid privacy system to protect email communication. The privacy system is implemented by combining private security RSA algorithm with specific chaos neural network encryption process. The receiver can decrypt received email as long as it can reproduce the specified chaos neural network series, so called spatial-temporal keys. The chaotic typing and initial seed value of chaos neural network series, encrypted by the RSA algorithm, can reproduce spatial-temporal keys. The encrypted chaotic typing and initial seed value are hidden in watermark mixed nonlinearly with message media, wrapped with convolution error correction codes for wireless 3rd generation cellular phones. The message media can be an arbitrary image. The pattern noise has to be considered during transmission and it could affect/change the spatial-temporal keys. Since any change/modification on chaotic typing or initial seed value of chaos neural network series is not acceptable, the RSA codec system must be robust and fault-tolerant via wireless channel. The robust and fault-tolerant properties of chaos neural networks (CNN) were proved by a field theory of Associative Memory by Szu in 1997. The 1-D chaos generating nodes from the logistic map having arbitrarily negative slope a = p/q generating the N-shaped sigmoid was given first by Szu in 1992. In this paper, we simulated the robust and fault-tolerance properties of CNN under additive noise and pattern noise. We also implement a private version of RSA coding and chaos encryption process on messages.
Advances in Exponential Random Graph (p*) Models Applied to a Large Social Network
Goodreau, Steven M.
2007-01-01
Recent advances in statistical network analysis based on the family of exponential random graph (ERG) models have greatly improved our ability to conduct inference on dependence in large social networks (Snijders 2002, Pattison and Robins 2002, Handcock 2002, Handcock 2003, Snijders et al. 2006, Hunter et al. 2005, Goodreau et al. 2005, previous papers this issue). This paper applies advances in both model parameterizations and computational algorithms to an examination of the structure observed in an adolescent friendship network of 1,681 actors from the National Longitudinal Study of Adolescent Health (AddHealth). ERG models of social network structure are fit using the R package statnet, and their adequacy assessed through comparison of model predictions with the observed data for higher-order network statistics. For this friendship network, the commonly used model of Markov dependence leads to the problems of degeneracy discussed by Handcock (2002, 2003). On the other hand, model parameterizations introduced by Snijders et al (2006) and Hunter and Handcock (2006) avoid degeneracy and provide reasonable fit to the data. Degree-only models did a poor job of capturing observed network structure; those that did best included terms both for heterogeneous mixing on exogenous attributes (grade and self-reported race) as well as endogenous clustering. Networks simulated from this model were largely consistent with the observed network on multiple higher-order network statistics, including the number of triangles, the size of the largest component, the overall reachability, the distribution of geodesic distances, the degree distribution, and the shared partner distribution. The ability to fit such models to large datasets and to make inference about the underling processes generating the network represents a major advance in the field of statistical network analysis. PMID:18449326
JPEG image transmission over mobile network with an efficient channel coding and interleaving
NASA Astrophysics Data System (ADS)
El-Bendary, Mohsen A. M. Mohamed; Abou El-Azm, Atef E.; El-Fishawy, Nawal A.; Al-Hosarey, Farid Shawki M.; Eltokhy, Mostafa A. R.; Abd El-Samie, Fathi E.; Kazemian, H. B.
2012-11-01
This article studies improving of coloured JPEG image transmission over mobile wireless personal area network through the Bluetooth networks. This article uses many types of enhanced data rate and asynchronous connectionless packets. It presents a proposed chaotic interleaving technique for improving a transmission of coloured images over burst error environment through merging it with error control scheme. The computational complexity of the used different error control schemes is considered. A comparison study between different scenarios of the image transmission is held in to choose an effective technique. The simulation experiments are carried over the correlated fading channel using the widely accepted Jakes' model. Our experiments reveal that the proposed chaotic interleaving technique enhances quality of the received coloured image. Our simulation results show that the convolutional codes with longer constraint length are effective if its complexity is ignored. It reveals also that the standard error control scheme of old Bluetooth versions is ineffective in the case of coloured image transmission over mobile Bluetooth network. Finally, the proposed scenarios of the standard error control scheme with the chaotic interleaver perform better than the convolutional codes with reducing the complexity.
Yuan, Xin; Shao, Shuai; Stanley, H Eugene; Havlin, Shlomo
2015-09-01
The stability of networks is greatly influenced by their degree distributions and in particular by their breadth. Networks with broader degree distributions are usually more robust to random failures but less robust to localized attacks. To better understand the effect of the breadth of the degree distribution we study two models in which the breadth is controlled and compare their robustness against localized attacks (LA) and random attacks (RA). We study analytically and by numerical simulations the cases where the degrees in the networks follow a bi-Poisson distribution, P(k)=αe^{-λ_{1}}λ_{1}^{k}/k!+(1-α)e^{-λ_{2}}λ_{2}^{k}/k!,α∈[0,1], and a Gaussian distribution, P(k)=Aexp(-(k-μ)^{2}/2σ^{2}), with a normalization constant A where k≥0. In the bi-Poisson distribution the breadth is controlled by the values of α, λ_{1}, and λ_{2}, while in the Gaussian distribution it is controlled by the standard deviation, σ. We find that only when α=0 or α=1, i.e., degrees obeying a pure Poisson distribution, are LA and RA the same. In all other cases networks are more vulnerable under LA than under RA. For a Gaussian distribution with an average degree μ fixed, we find that when σ^{2} is smaller than μ the network is more vulnerable against random attack. When σ^{2} is larger than μ, however, the network becomes more vulnerable against localized attack. Similar qualitative results are also shown for interdependent networks. PMID:26465441
NASA Astrophysics Data System (ADS)
Yuan, Xin; Shao, Shuai; Stanley, H. Eugene; Havlin, Shlomo
2015-09-01
The stability of networks is greatly influenced by their degree distributions and in particular by their breadth. Networks with broader degree distributions are usually more robust to random failures but less robust to localized attacks. To better understand the effect of the breadth of the degree distribution we study two models in which the breadth is controlled and compare their robustness against localized attacks (LA) and random attacks (RA). We study analytically and by numerical simulations the cases where the degrees in the networks follow a bi-Poisson distribution, P (k ) =α e-λ1λ/1kk ! +(1 -α ) e-λ2λ/2kk ! ,α ∈[0 ,1 ] , and a Gaussian distribution, P (k ) =A exp(-(k/-μ) 22 σ2 ), with a normalization constant A where k ≥0 . In the bi-Poisson distribution the breadth is controlled by the values of α , λ1, and λ2, while in the Gaussian distribution it is controlled by the standard deviation, σ . We find that only when α =0 or α =1 , i.e., degrees obeying a pure Poisson distribution, are LA and RA the same. In all other cases networks are more vulnerable under LA than under RA. For a Gaussian distribution with an average degree μ fixed, we find that when σ2 is smaller than μ the network is more vulnerable against random attack. When σ2 is larger than μ , however, the network becomes more vulnerable against localized attack. Similar qualitative results are also shown for interdependent networks.
Surveying Multidisciplinary Aspects in Real-Time Distributed Coding for Wireless Sensor Networks
Braccini, Carlo; Davoli, Franco; Marchese, Mario; Mongelli, Maurizio
2015-01-01
Wireless Sensor Networks (WSNs), where a multiplicity of sensors observe a physical phenomenon and transmit their measurements to one or more sinks, pertain to the class of multi-terminal source and channel coding problems of Information Theory. In this category, “real-time” coding is often encountered for WSNs, referring to the problem of finding the minimum distortion (according to a given measure), under transmission power constraints, attainable by encoding and decoding functions, with stringent limits on delay and complexity. On the other hand, the Decision Theory approach seeks to determine the optimal coding/decoding strategies or some of their structural properties. Since encoder(s) and decoder(s) possess different information, though sharing a common goal, the setting here is that of Team Decision Theory. A more pragmatic vision rooted in Signal Processing consists of fixing the form of the coding strategies (e.g., to linear functions) and, consequently, finding the corresponding optimal decoding strategies and the achievable distortion, generally by applying parametric optimization techniques. All approaches have a long history of past investigations and recent results. The goal of the present paper is to provide the taxonomy of the various formulations, a survey of the vast related literature, examples from the authors' own research, and some highlights on the inter-play of the different theories. PMID:25633597
Regulatory networks of non-coding RNAs in brown/beige adipogenesis
Xu, Shaohai; Chen, Peng; Sun, Lei
2015-01-01
BAT (brown adipose tissue) is specialized to burn fatty acids for heat generation and energy expenditure to defend against cold and obesity. Accumulating studies have demonstrated that manipulation of BAT activity through various strategies can regulate metabolic homoeostasis and lead to a healthy phenotype. Two classes of ncRNA (non-coding RNA), miRNA and lncRNA (long non-coding RNA), play crucial roles in gene regulation during tissue development and remodelling. In the present review, we summarize recent findings on regulatory role of distinct ncRNAs in brown/beige adipocytes, and discuss how these ncRNA regulatory networks contribute to brown/beige fat development, differentiation and function. We suggest that targeting ncRNAs could be an attractive approach to enhance BAT activity for protecting the body against obesity and its pathological consequences. PMID:26283634
Shi, Cindy
2015-07-17
The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.
NASA Astrophysics Data System (ADS)
Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.
2016-07-01
Recursive distributed filtering and fixed-point smoothing algorithms are proposed from measurements through sensor networks perturbed by random parameter matrices and additive noises. The proposed observation model provides a unified framework to consider some network-induced random phenomena. Using an innovation approach, intermediate distributed optimal least-squares (LS) linear estimators are firstly obtained at each sensor node, processing the available output measurements, not only from the own sensor but also from its neighbouring sensors according to the network topology. After that, the proposed distributed estimators are designed at each node as the LS matrix-weighted linear combination of the intermediate estimators within its neighbourhood. The proposed algorithms use only covariance information and do not require the state-space model of the signal. To compare the accuracy of the estimators, recursive expressions for the estimation error covariance matrices are also derived. A simulation example shows the effectiveness of the proposed estimation algorithms and some of the network-induced uncertainties covered by the observation model with random parameter matrices considered in this paper.
Spatiotemporal coding in the cortex: information flow-based learning in spiking neural networks.
Deco, G; Schürmann, B
1999-05-15
We introduce a learning paradigm for networks of integrate-and-fire spiking neurons that is based on an information-theoretic criterion. This criterion can be viewed as a first principle that demonstrates the experimentally observed fact that cortical neurons display synchronous firing for some stimuli and not for others. The principle can be regarded as the postulation of a nonparametric reconstruction method as optimization criteria for learning the required functional connectivity that justifies and explains synchronous firing for binding of features as a mechanism for spatiotemporal coding. This can be expressed in an information-theoretic way by maximizing the discrimination ability between different sensory inputs in minimal time. PMID:10226189
Jesse, Stephen; Kalinin, Sergei V; Kumar, Amit; Ovchinnikov, Oleg S; Guo, Senli; Griggio, Flavio; Trolier-Mckinstry, Susan E
2011-01-01
The spatial variability of the polarization dynamics in thin film ferroelectric capacitors was probed by recognition analysis of spatially-resolved spectroscopic data. Switching spectroscopy piezoresponse force microscopy was used to measure local hysteresis loops and map them on a 2D random-bond, random-field Ising model. A neural-network based recognition approach was utilized to analyze the hysteresis loops and their spatial variability. Strong variability is observed in the polarization dynamics around macroscopic cracks due to the modified local elastic and electric boundary conditions, with most pronounced effect on the length scale of ~100 nm away from the crack.
NASA Technical Reports Server (NTRS)
Schallhorn, Paul; Majumdar, Alok
2012-01-01
This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.
Modelling and Simulation of National Electronic Product Code Network Demonstrator Project
NASA Astrophysics Data System (ADS)
Mo, John P. T.
The National Electronic Product Code (EPC) Network Demonstrator Project (NDP) was the first large scale consumer goods track and trace investigation in the world using full EPC protocol system for applying RFID technology in supply chains. The NDP demonstrated the methods of sharing information securely using EPC Network, providing authentication to interacting parties, and enhancing the ability to track and trace movement of goods within the entire supply chain involving transactions among multiple enterprise. Due to project constraints, the actual run of the NDP was 3 months only and was unable to consolidate with quantitative results. This paper discusses the modelling and simulation of activities in the NDP in a discrete event simulation environment and provides an estimation of the potential benefits that can be derived from the NDP if it was continued for one whole year.
Associative fear learning enhances sparse network coding in primary sensory cortex
Gdalyahu, Amos; Tring, Elaine; Polack, Pierre-Olivier; Gruver, Robin; Golshani, Peyman; Fanselow, Michael S.; Silva, Alcino J.; Trachtenberg, Joshua T.
2012-01-01
Summary Several models of associative learning predict that stimulus processing changes during association formation. How associative learning reconfigures neural circuits in primary sensory cortex to "learn" associative attributes of a stimulus remains unknown. Using 2-photon in-vivo calcium imaging to measure responses of networks of neurons in primary somatosensory cortex, we discovered that associative fear learning, in which whisker stimulation is paired with foot shock, enhances sparse population coding and robustness of the conditional stimulus, yet decreases total network activity. Fewer cortical neurons responded to stimulation of the trained whisker than in controls, yet their response strength was enhanced. These responses were not observed in mice exposed to a non-associative learning procedure. Our results define how the cortical representation of a sensory stimulus is shaped by associative fear learning. These changes are proposed to enhance efficient sensory processing after associative learning. PMID:22794266
Kroeger, P.G.; Kennett, R.J.; Colman, J.; Ginsberg, T. )
1991-10-01
This report documents the THATCH code, which can be used to model general thermal and flow networks of solids and coolant channels in two-dimensional r-z geometries. The main application of THATCH is to model reactor thermo-hydraulic transients in High-Temperature Gas-Cooled Reactors (HTGRs). The available modules simulate pressurized or depressurized core heatup transients, heat transfer to general exterior sinks or to specific passive Reactor Cavity Cooling Systems, which can be air or water-cooled. Graphite oxidation during air or water ingress can be modelled, including the effects of added combustion products to the gas flow and the additional chemical energy release. A point kinetics model is available for analyzing reactivity excursions; for instance due to water ingress, and also for hypothetical no-scram scenarios. For most HTGR transients, which generally range over hours, a user-selected nodalization of the core in r-z geometry is used. However, a separate model of heat transfer in the symmetry element of each fuel element is also available for very rapid transients. This model can be applied coupled to the traditional coarser r-z nodalization. This report described the mathematical models used in the code and the method of solution. It describes the code and its various sub-elements. Details of the input data and file usage, with file formats, is given for the code, as well as for several preprocessing and postprocessing options. The THATCH model of the currently applicable 350 MW{sub th} reactor is described. Input data for four sample cases are given with output available in fiche form. Installation requirements and code limitations, as well as the most common error indications are listed. 31 refs., 23 figs., 32 tabs.
Identify the diversity of mesoscopic structures in networks: A mixed random walk approach
NASA Astrophysics Data System (ADS)
Ma, Yifang; Jiang, Xin; Li, Meng; Shen, Xin; Guo, Quantong; Lei, Yanjun; Zheng, Zhiming
2013-10-01
Community or cluster structure, which can provide insight into the natural partitions and inner connections of a network, is a key feature in studying the mesoscopic structure of complex systems. Although numerous methods for community detection have been proposed ever since, there is still a lack of understanding on how to quantify the diversity of pre-divided community structures, or rank the roles of communities in participating in specific dynamic processes. Inspired by the Law of Mass Action in chemical kinetics, we introduce here the community random walk energy (CRWE), which reflects a potential based on the diffusion phase of a mixed random walk process taking place on the network, to identify the configuration of community structures. The difference of CRWE allows us to distinguish the intrinsic topological diversity between individual communities, on condition that all the communities are pre-arranged in the network. We illustrate our method by performing numerical simulations on constructive community networks and a real social network with distinct community structures. As an application, we apply our method to characterize the diversity of human genome communities, which provides a possible use of our method in inferring the genetic similarity between human populations.
NASA Astrophysics Data System (ADS)
Nightingale, James; Wang, Qi; Grecos, Christos; Goma, Sergio
2014-02-01
High Efficiency Video Coding (HEVC), the latest video compression standard (also known as H.265), can deliver video streams of comparable quality to the current H.264 Advanced Video Coding (H.264/AVC) standard with a 50% reduction in bandwidth. Research into SHVC, the scalable extension to the HEVC standard, is still in its infancy. One important area for investigation is whether, given the greater compression ratio of HEVC (and SHVC), the loss of packets containing video content will have a greater impact on the quality of delivered video than is the case with H.264/AVC or its scalable extension H.264/SVC. In this work we empirically evaluate the layer-based, in-network adaptation of video streams encoded using SHVC in situations where dynamically changing bandwidths and datagram loss ratios require the real-time adaptation of video streams. Through the use of extensive experimentation, we establish a comprehensive set of benchmarks for SHVC-based highdefinition video streaming in loss prone network environments such as those commonly found in mobile networks. Among other results, we highlight that packet losses of only 1% can lead to a substantial reduction in PSNR of over 3dB and error propagation in over 130 pictures following the one in which the loss occurred. This work would be one of the earliest studies in this cutting-edge area that reports benchmark evaluation results for the effects of datagram loss on SHVC picture quality and offers empirical and analytical insights into SHVC adaptation to lossy, mobile networking conditions.
Baek, Yongjoo; Ha, Meesoon; Jeong, Hawoong
2014-12-01
We investigate the totally asymmetric simple exclusion process on closed and directed random regular networks, which is a simple model of active transport in the one-dimensional segments coupled by junctions. By a pair mean-field theory and detailed numerical analyses, it is found that the correlations at junctions induce two notable deviations from the simple mean-field theory, which neglects these correlations: (1) the narrower range of particle density for phase coexistence and (2) the algebraic decay of density profile with exponent 1/2 even outside the maximal-current phase. We show that these anomalies are attributable to the effective slow bonds formed by the network junctions.
Random networks of spiking neurons: instability in the Xenopus tadpole moto-neural pattern.
Fulvi Mari, C
2000-07-01
A large network of integrate-and-fire neurons is studied analytically when the synaptic weights are independently randomly distributed according to a Gaussian distribution with arbitrary mean and variance. The relevant order parameters are identified, and it is shown that such network is statistically equivalent to an ensemble of independent integrate-and-fire neurons with each input signal given by the sum of a self-interaction deterministic term and a Gaussian colored noise. The model is able to reproduce the quasisynchronous oscillations, and the dropout of their frequency, of the central nervous system neurons of the swimming Xenopus tadpole. Predictions from the model are proposed for future experiments.
NASA Astrophysics Data System (ADS)
Zhang, Chongfu; Qiu, Kun; Xu, Bo; Ling, Yun
2008-05-01
This paper proposes an all-optical label processing scheme that uses the multiple optical orthogonal codes sequences (MOOCS)-based optical label for optical packet switching (OPS) (MOOCS-OPS) networks. In this scheme, each MOOCS is a permutation or combination of the multiple optical orthogonal codes (MOOC) selected from the multiple-groups optical orthogonal codes (MGOOC). Following a comparison of different optical label processing (OLP) schemes, the principles of MOOCS-OPS network are given and analyzed. Firstly, theoretical analyses are used to prove that MOOCS is able to greatly enlarge the number of available optical labels when compared to the previous single optical orthogonal code (SOOC) for OPS (SOOC-OPS) network. Then, the key units of the MOOCS-based optical label packets, including optical packet generation, optical label erasing, optical label extraction and optical label rewriting etc., are given and studied. These results are used to verify that the proposed MOOCS-OPS scheme is feasible.
NASA Astrophysics Data System (ADS)
Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.
2013-07-01
In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called "Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres", (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the "Robust design of artificial neural networks methodology" and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of 252Cf, 241AmBe and 239PuBe neutron sources measured with a Bonner spheres system.
Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.
2013-07-03
In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.
All-time dynamics of continuous-time random walks on complex networks
NASA Astrophysics Data System (ADS)
Teimouri, Hamid; Kolomeisky, Anatoly B.
2013-02-01
The concept of continuous-time random walks (CTRW) is a generalization of ordinary random walk models, and it is a powerful tool for investigating a broad spectrum of phenomena in natural, engineering, social, and economic sciences. Recently, several theoretical approaches have been developed that allowed to analyze explicitly dynamics of CTRW at all times, which is critically important for understanding mechanisms of underlying phenomena. However, theoretical analysis has been done mostly for systems with a simple geometry. Here we extend the original method based on generalized master equations to analyze all-time dynamics of CTRW models on complex networks. Specific calculations are performed for models on lattices with branches and for models on coupled parallel-chain lattices. Exact expressions for velocities and dispersions are obtained. Generalized fluctuations theorems for CTRW models on complex networks are discussed.
Kalnaus, Sergiy; Sabau, Adrian S; Newman, Sarah M; Tenhaeff, Wyatt E; Daniel, Claus; Dudney, Nancy J
2011-01-01
The effective DC conductivity of particulate composite electrolytes was obtained by solving electrostatics equations using random resistors network method in three dimensions. The composite structure was considered to consist of three phases: matrix, particulate filler, and conductive shell that surrounded each particle; each phase possessing a different conductivity. Different particle size distributions were generated using Monte Carlo simulations. Unlike effective medium formulations, it was shown that the random resistors network method was able to predict percolation thresholds for the effective composite conductivity. It was found that the mean particle radius has a higher influence on the effective composite conductivity compared to the effect of type of the particle size distributions that were considered. The effect of the shell thickness on the composite conductivity has been investigated. It was found that the conductivity enhancement due to the presence of the conductive shell phase becomes less evident as the shell thickness increases.
NASA Astrophysics Data System (ADS)
Liu, Chengyin; Xu, Chunchuan; Teng, Jun
2016-09-01
The Random Decrement Technique (RDT), based on decentralized computing approaches implemented in wireless sensor networks (WSNs), has shown advantages for modal parameter and data aggregation identification. However, previous studies of RDT-based approaches from ambient vibration data are based on the assumption of a broad-band stochastic process input excitation. The process normally is modeled by filtered white or white noise. In addition, the choice of the triggering condition in RDT is closely related to data communication. In this project, research has been conducted to study the nonstationary white noise excitations as the input to verify the random decrement technique. A local extremum triggering condition is chosen and implemented for the purpose of minimum data communication in a RDT-based distributed computing strategy. Numerical simulation results show that the proposed technique is capable of minimizing the amount of data transmitted over the network with accuracy in modal parameters identification.
Comprehensive Reconstruction and Visualization of Non-Coding Regulatory Networks in Human
Bonnici, Vincenzo; Russo, Francesco; Bombieri, Nicola; Pulvirenti, Alfredo; Giugno, Rosalba
2014-01-01
Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape. PMID:25540777
Comprehensive reconstruction and visualization of non-coding regulatory networks in human.
Bonnici, Vincenzo; Russo, Francesco; Bombieri, Nicola; Pulvirenti, Alfredo; Giugno, Rosalba
2014-01-01
Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape.
Comprehensive reconstruction and visualization of non-coding regulatory networks in human.
Bonnici, Vincenzo; Russo, Francesco; Bombieri, Nicola; Pulvirenti, Alfredo; Giugno, Rosalba
2014-01-01
Research attention has been powered to understand the functional roles of non-coding RNAs (ncRNAs). Many studies have demonstrated their deregulation in cancer and other human disorders. ncRNAs are also present in extracellular human body fluids such as serum and plasma, giving them a great potential as non-invasive biomarkers. However, non-coding RNAs have been relatively recently discovered and a comprehensive database including all of them is still missing. Reconstructing and visualizing the network of ncRNAs interactions are important steps to understand their regulatory mechanism in complex systems. This work presents ncRNA-DB, a NoSQL database that integrates ncRNAs data interactions from a large number of well established on-line repositories. The interactions involve RNA, DNA, proteins, and diseases. ncRNA-DB is available at http://ncrnadb.scienze.univr.it/ncrnadb/. It is equipped with three interfaces: web based, command-line, and a Cytoscape app called ncINetView. By accessing only one resource, users can search for ncRNAs and their interactions, build a network annotated with all known ncRNAs and associated diseases, and use all visual and mining features available in Cytoscape. PMID:25540777
Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa
2016-01-01
This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices.
Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa
2016-01-01
This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices. PMID:27338387
Caballero-Águila, Raquel; Hermoso-Carazo, Aurora; Linares-Pérez, Josefa
2016-01-01
This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times, with different characteristics at each sensor. The measured outputs are subject to uncertainties modeled by random parameter matrices, thus providing a unified framework to describe a wide variety of network-induced phenomena; moreover, the additive noises are assumed to be one-step autocorrelated and cross-correlated. Under these conditions, without requiring the knowledge of the signal evolution model, but using only the first and second order moments of the processes involved in the observation model, recursive algorithms for the optimal linear distributed and centralized filters under the least-squares criterion are derived by an innovation approach. Firstly, local estimators based on the measurements received from each sensor are obtained and, after that, the distributed fusion filter is generated as the least-squares matrix-weighted linear combination of the local estimators. Also, a recursive algorithm for the optimal linear centralized filter is proposed. In order to compare the estimators performance, recursive formulas for the error covariance matrices are derived in all the algorithms. The effects of the delays in the filters accuracy are analyzed in a numerical example which also illustrates how some usual network-induced uncertainties can be dealt with using the current observation model described by random matrices. PMID:27338387
Optimal system size for complex dynamics in random neural networks near criticality
Wainrib, Gilles; García del Molino, Luis Carlos
2013-12-15
In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.
Randomized algorithms for stability and robustness analysis of high-speed communication networks.
Alpcan, Tansu; Başar, Tamer; Tempo, Roberto
2005-09-01
This paper initiates a study toward developing and applying randomized algorithms for stability of high-speed communication networks. The focus is on congestion and delay-based flow controllers for sources, which are "utility maximizers" for individual users. First, we introduce a nonlinear algorithm for such source flow controllers, which uses as feedback aggregate congestion and delay information from bottleneck nodes of the network, and depends on a number of parameters, among which are link capacities, user preference for utility, and pricing. We then linearize this nonlinear model around its unique equilibrium point and perform a robustness analysis for a special symmetric case with a single bottleneck node. The "symmetry" here captures the scenario when certain utility and pricing parameters are the same across all active users, for which we derive closed-form necessary and sufficient conditions for stability and robustness under parameter variations. In addition, the ranges of values for the utility and pricing parameters for which stability is guaranteed are computed exactly. These results also admit counterparts for the case when the pricing parameters vary across users, but the utility parameter values are still the same. In the general nonsymmetric case, when closed-form derivation is not possible, we construct specific randomized algorithms which provide a probabilistic estimate of the local stability of the network. In particular, we use Monte Carlo as well as quasi-Monte Carlo techniques for the linearized model. The results obtained provide a complete analysis of congestion control algorithms for internet style networks with a single bottleneck node as well as for networks with general random topologies. PMID:16252829
Kim, Youngbok; Jeon, Sie-Wook; Kwon, Won-Bae; Park, Chang-Soo
2012-01-01
We propose and demonstrate a fiber Bragg grating (FBG) sensor network employing the code division multiple access (CDMA) technique to identify information from individual sensors. To detect information without considering time delays between sensors, a sliding correlation method is applied, in which two different signals with the same pseudo-random binary sequence (PRBS) pattern, but slightly different frequencies, are applied to the source and detector sides. Moreover, for time domain detection, a wavelength-to-time conversion technique using a wavelength dispersive medium is introduced. The experimental results show that the proposed sensor network has a wide strain dynamic range of 2,400 με and a low crosstalk of 950:1. PMID:22778619
Kim, Youngbok; Jeon, Sie-Wook; Kwon, Won-Bae; Park, Chang-Soo
2012-01-01
We propose and demonstrate a fiber Bragg grating (FBG) sensor network employing the code division multiple access (CDMA) technique to identify information from individual sensors. To detect information without considering time delays between sensors, a sliding correlation method is applied, in which two different signals with the same pseudo-random binary sequence (PRBS) pattern, but slightly different frequencies, are applied to the source and detector sides. Moreover, for time domain detection, a wavelength-to-time conversion technique using a wavelength dispersive medium is introduced. The experimental results show that the proposed sensor network has a wide strain dynamic range of 2,400 με and a low crosstalk of 950:1.
Synaptic signal streams generated by ex vivo neuronal networks contain non-random, complex patterns.
Lee, Sangmook; Zemianek, Jill M; Shultz, Abraham; Vo, Anh; Maron, Ben Y; Therrien, Mikaela; Courtright, Christina; Guaraldi, Mary; Yanco, Holly A; Shea, Thomas B
2014-11-01
Cultured embryonic neurons develop functional networks that transmit synaptic signals over multiple sequentially connected neurons as revealed by multi-electrode arrays (MEAs) embedded within the culture dish. Signal streams of ex vivo networks contain spikes and bursts of varying amplitude and duration. Despite the random interactions inherent in dissociated cultures, neurons are capable of establishing functional ex vivo networks that transmit signals among synaptically connected neurons, undergo developmental maturation, and respond to exogenous stimulation by alterations in signal patterns. These characteristics indicate that a considerable degree of organization is an inherent property of neurons. We demonstrate herein that (1) certain signal types occur more frequently than others, (2) the predominant signal types change during and following maturation, (3) signal predominance is dependent upon inhibitory activity, and (4) certain signals preferentially follow others in a non-reciprocal manner. These findings indicate that the elaboration of complex signal streams comprised of a non-random distribution of signal patterns is an emergent property of ex vivo neuronal networks.
Zimmerman, D.A.; Wilson, J.L.
1992-01-01
TUBA is a computer code for the generation of synthetic two-dimensional random fields via the Turning Bands Method. It is primarily used to generate synthetic permeability fields for hydrologic and petroleum engineering applications, but it has applications wherever synthetic random fields are employed. This is version 2.0 of TUBA, a completely redesigned and rewritten code. It generates stationary or non-stationary, isotropic and anisotropic, and point or areal average random fields. Five functional covariance models are available in the code. These are Gaussian, Bessel, Telis, and Generalized Covariance models. The user can supply other forms. The random fields can be generated onto a gridded system (e.g., at the nodes of a point centered finite difference model, or the blocks of a block centered model), or at arbitrary locations in space (e.g., at the Gauss points of a finite element grid). TUBA can be used to generate the field values in local areas at much greater resolution than the original simulated field. The fields can be generated with a normal or a lognormal distribution. The size of the simulation is limited only by the virtual memory capabilities of the computer on which it is run. Random fields with over a million nodes have been generated with TUBA on a 386PC running Xenix. The code has been run on 286 and 386 PC's running DOS, on Sun 3's and 4's using Unix, and on Dec VAX's running VMS.
Zimmerman, D.A.; Wilson, J.L.
1992-01-01
TUBA is a computer code for the generation of synthetic two-dimensional random fields via the Turning Bands Method. It is primarily used to generate synthetic permeability fields for hydrologic and petroleum engineering applications, but it has applications wherever synthetic random fields are employed. This is version 2.0 of TUBA, a completely redesigned and rewritten code. It generates stationary or non-stationary, isotropic and anisotropic, and point or areal average random fields. Five functional covariance models are available in the code. These are Gaussian, Bessel, Telis, and Generalized Covariance models. The user can supply other forms. The random fields can be generated onto a gridded system (e.g., at the nodes of a point centered finite difference model, or the blocks of a block centered model), or at arbitrary locations in space (e.g., at the Gauss points of a finite element grid). TUBA can be used to generate the field values in local areas at much greater resolution than the original simulated field. The fields can be generated with a normal or a lognormal distribution. The size of the simulation is limited only by the virtual memory capabilities of the computer on which it is run. Random fields with over a million nodes have been generated with TUBA on a 386PC running Xenix. The code has been run on 286 and 386 PC`s running DOS, on Sun 3`s and 4`s using Unix, and on Dec VAX`s running VMS.
Aceves, S M; Flowers, D L; Chen, J; Babaimopoulos, A
2006-08-29
We have developed an artificial neural network (ANN) based combustion model and have integrated it into a fluid mechanics code (KIVA3V) to produce a new analysis tool (titled KIVA3V-ANN) that can yield accurate HCCI predictions at very low computational cost. The neural network predicts ignition delay as a function of operating parameters (temperature, pressure, equivalence ratio and residual gas fraction). KIVA3V-ANN keeps track of the time history of the ignition delay during the engine cycle to evaluate the ignition integral and predict ignition for each computational cell. After a cell ignites, chemistry becomes active, and a two-step chemical kinetic mechanism predicts composition and heat generation in the ignited cells. KIVA3V-ANN has been validated by comparison with isooctane HCCI experiments in two different engines. The neural network provides reasonable predictions for HCCI combustion and emissions that, although typically not as good as obtained with the more physically representative multi-zone model, are obtained at a much reduced computational cost. KIVA3V-ANN can perform reasonably accurate HCCI calculations while requiring only 10% more computational effort than a motored KIVA3V run. It is therefore considered a valuable tool for evaluation of engine maps or other performance analysis tasks requiring multiple individual runs.
Blatti, Charles; Sinha, Saurabh
2016-01-01
Motivation: Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or ‘properties’ such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene–gene or gene–property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. Results: We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. Availability and Implementation: DRaWR was implemented as
NASA Astrophysics Data System (ADS)
Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.
2013-07-01
In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural
Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.
2013-07-03
In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural
Cheng, Hongju; Su, Zhihuang; Lloret, Jaime; Chen, Guolong
2014-11-06
Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime.
Biased random walk in spatially embedded networks with total cost constraint
NASA Astrophysics Data System (ADS)
Niu, Rui-Wu; Pan, Gui-Jun
2016-11-01
We investigate random walk with a bias toward a target node in spatially embedded networks with total cost restriction introduced by Li et al. (2010). Precisely, The network is built from a two-dimension regular lattice to be improved by adding long-range shortcuts with probability P(rij) ∼rij-α, where rij is the Manhattan distance between sites i and j, and α is a variable exponent, the total length of the long-range connections is restricted. Bias is represented as a probability p of the packet or particle to travel at every hop toward the node which has the smallest Manhattan distance to the target node. By studying the mean first passage time (MFPT) for different exponent log < l >, we find that the best transportation condition is obtained with an exponent α = d + 1(d = 2) for all p. The special phenomena can be possibly explained by the theory of information entropy, we find that when α = d + 1(d = 2), the spatial network with total cost restriction becomes an optimal network which has a maximum information entropy. In addition, the scaling of the MFPT with the size of the network is also investigated, and finds that the scaling of the MFPT with L follows a linear distribution for all p > 0.
Effects of community structure on the dynamics of random threshold networks
NASA Astrophysics Data System (ADS)
Wang, Rui-Sheng; Albert, Réka
2013-01-01
Random threshold networks (RTNs) have been widely used as models of neural or genetic regulatory networks. Network topology plays a central role in the dynamics of these networks. Recently it has been shown that many social and biological networks are scale-free and also exhibit community structure, in which autonomous modules are wired together to perform relatively independent functions. In this study we use both synchronous and asynchronous models of RTNs to systematically investigate how community structure affects the dynamics of RTNs with scale-free topology. Extensive simulation experiments show that RTNs with high modularity have more attractors than those RTNs with low modularity, and RTNs with smaller communities tend to have more attractors. Damage resulting from perturbation of initial conditions spreads less effectively in RTNs with higher modularity and RTNs with smaller communities. In addition, RTNs with high modularity can coordinate their internal dynamics better than RTNs with low modularity under the synchronous update scheme, and it is the other way around under the asynchronous update. This study shows that community structure has a strong effect on the dynamics of RTNs.
Sadeh, Sadra; Rotter, Stefan
2014-01-01
Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity.
Sadeh, Sadra; Rotter, Stefan
2014-01-01
Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity. PMID:25469704
Improved Neural Networks with Random Weights for Short-Term Load Forecasting.
Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo
2015-01-01
An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.
Improved Neural Networks with Random Weights for Short-Term Load Forecasting
Lang, Kun; Zhang, Mingyuan; Yuan, Yongbo
2015-01-01
An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting. PMID:26629825
The Brain Matures with Stronger Functional Connectivity and Decreased Randomness of Its Network
Smit, Dirk J. A.; Boersma, Maria; Schnack, Hugo G.; Micheloyannis, Sifis; Boomsma, Dorret I.; Hulshoff Pol, Hilleke E.; Stam, Cornelis J.; de Geus, Eco J. C.
2012-01-01
We investigated the development of the brain's functional connectivity throughout the life span (ages 5 through 71 years) by measuring EEG activity in a large population-based sample. Connectivity was established with Synchronization Likelihood. Relative randomness of the connectivity patterns was established with Watts and Strogatz' (1998) graph parameters C (local clustering) and L (global path length) for alpha (∼10 Hz), beta (∼20 Hz), and theta (∼4 Hz) oscillation networks. From childhood to adolescence large increases in connectivity in alpha, theta and beta frequency bands were found that continued at a slower pace into adulthood (peaking at ∼50 yrs). Connectivity changes were accompanied by increases in L and C reflecting decreases in network randomness or increased order (peak levels reached at ∼18 yrs). Older age (55+) was associated with weakened connectivity. Semi-automatically segmented T1 weighted MRI images of 104 young adults revealed that connectivity was significantly correlated to cerebral white matter volume (alpha oscillations: r = 33, p<01; theta: r = 22, p<05), while path length was related to both white matter (alpha: max. r = 38, p<001) and gray matter (alpha: max. r = 36, p<001; theta: max. r = 36, p<001) volumes. In conclusion, EEG connectivity and graph theoretical network analysis may be used to trace structural and functional development of the brain. PMID:22615837
Ilday, Serim; Ilday, F Ömer; Hübner, René; Prosa, Ty J; Martin, Isabelle; Nogay, Gizem; Kabacelik, Ismail; Mics, Zoltan; Bonn, Mischa; Turchinovich, Dmitry; Toffoli, Hande; Toffoli, Daniele; Friedrich, David; Schmidt, Bernd; Heinig, Karl-Heinz; Turan, Rasit
2016-03-01
Multiscale self-assembly is ubiquitous in nature but its deliberate use to synthesize multifunctional three-dimensional materials remains rare, partly due to the notoriously difficult problem of controlling topology from atomic to macroscopic scales to obtain intended material properties. Here, we propose a simple, modular, noncolloidal methodology that is based on exploiting universality in stochastic growth dynamics and driving the growth process under far-from-equilibrium conditions toward a preplanned structure. As proof of principle, we demonstrate a confined-but-connected solid structure, comprising an anisotropic random network of silicon quantum-dots that hierarchically self-assembles from the atomic to the microscopic scales. First, quantum-dots form to subsequently interconnect without inflating their diameters to form a random network, and this network then grows in a preferential direction to form undulated and branching nanowire-like structures. This specific topology simultaneously achieves two scale-dependent features, which were previously thought to be mutually exclusive: good electrical conduction on the microscale and a bandgap tunable over a range of energies on the nanoscale.
Combating QR-Code-Based Compromised Accounts in Mobile Social Networks
Guo, Dong; Cao, Jian; Wang, Xiaoqi; Fu, Qiang; Li, Qiang
2016-01-01
Cyber Physical Social Sensing makes mobile social networks (MSNs) popular with users. However, such attacks are rampant as malicious URLs are spread covertly through quick response (QR) codes to control compromised accounts in MSNs to propagate malicious messages. Currently, there are generally two types of methods to identify compromised accounts in MSNs: one type is to analyze the potential threats on wireless access points and the potential threats on handheld devices’ operation systems so as to stop compromised accounts from spreading malicious messages; the other type is to apply the method of detecting compromised accounts in online social networks to MSNs. The above types of methods above focus neither on the problems of MSNs themselves nor on the interaction of sensors’ messages, which leads to the restrictiveness of platforms and the simplification of methods. In order to stop the spreading of compromised accounts in MSNs effectively, the attacks have to be traced to their sources first. Through sensors, users exchange information in MSNs and acquire information by scanning QR codes. Therefore, analyzing the traces of sensor-related information helps to identify the compromised accounts in MSNs. This paper analyzes the diversity of information sending modes of compromised accounts and normal accounts, analyzes the regularity of GPS (Global Positioning System)-based location information, and introduces the concepts of entropy and conditional entropy so as to construct an entropy-based model based on machine learning strategies. To achieve the goal, about 500,000 accounts of Sina Weibo and about 100 million corresponding messages are collected. Through the validation, the accuracy rate of the model is proved to be as high as 87.6%, and the false positive rate is only 3.7%. Meanwhile, the comparative experiments of the feature sets prove that sensor-based location information can be applied to detect the compromised accounts in MSNs. PMID:27657071
Assessment of BeiDou differential code bias variations from multi-GNSS network observations
NASA Astrophysics Data System (ADS)
Jin, S. G.; Jin, R.; Li, D.
2016-02-01
The differential code bias (DCB) of global navigation satellite systems (GNSSs) affects precise ionospheric modeling and applications. In this paper, daily DCBs of the BeiDou Navigation Satellite System (BDS) are estimated and investigated from 2-year multi-GNSS network observations (2013-2014) based on global ionospheric maps (GIMs) from the Center for Orbit Determination in Europe (CODE), which are compared with Global Positioning System (GPS) results. The DCB of BDS satellites is a little less stable than GPS solutions, especially for geostationary Earth orbit (GEO) satellites. The BDS GEO observations decrease the precision of inclined geosynchronous satellite orbit (IGSO) and medium Earth orbit (MEO) DCB estimations. The RMS of BDS satellites DCB decreases to about 0.2 ns when we remove BDS GEO observations. Zero-mean condition effects are not the dominant factor for the higher RMS of BDS satellites DCB. Although there are no obvious secular variations in the DCB time series, sub-nanosecond variations are visible for both BDS and GPS satellites DCBs during 2013-2014. For satellites in the same orbital plane, their DCB variations have similar characteristics. In addition, variations in receivers DCB in the same region are found with a similar pattern between BDS and GPS. These variations in both GPS and BDS DCBs are mainly related to the estimated error from ionospheric variability, while the BDS DCB intrinsic variation is in sub-nanoseconds.
Lepage, E.; Tavernier, H.; Bouhaddou, O.; Jais, JP.; Gisselbrecht, C.; Aurengo, A.; Boiron, M.
1989-01-01
The usual Randomized Clinical Trials (RCT) management using an anachronic procedure involving a flowsheet exchange between the remote centers and the coordinating center presents a number of inadequacies. Eligibility criteria are not always verified by the coordinating center before inclusion in the trial and randomization. Laboratory tests and therapeutic adjustments are frequently decided from memory by the clinician which often leads to data oversight and variability of therapeutic decisions. This results in protocol deviations and alteration of the efficiency of the RCT. HICREN is a medical consultation system designed to take into account the different difficulties encountered during RCT driving. The system integrates a clinical database with artificial intelligence technics to manage clinical trial data on non-expensive and widely available Minitel® terminals. Randomization is then possible, after eligibility criteria are satisfied, anytime and anywhere in France through the national telematic network. HICREN also includes an intuitive graphic interface to increase physician's compliance: a user friendly dialogue manager supports on line data entry with multi-windowing facilities and pull down menus. Interactive data validation is achieved through an interface to dedicated C programs. Patient follow up is achieved by an expert system that proposes appropriate dose of treatment according to the rules defined in the trial. At present, HICREN is implemented on the CISARC system for conducting three randomized clinical trials and one epidemiologic study.
Current flow in random resistor networks: the role of percolation in weak and strong disorder.
Wu, Zhenhua; López, Eduardo; Buldyrev, Sergey V; Braunstein, Lidia A; Havlin, Shlomo; Stanley, H Eugene
2005-04-01
We study the current flow paths between two edges in a random resistor network on a L X L square lattice. Each resistor has resistance e(ax) , where x is a uniformly distributed random variable and a controls the broadness of the distribution. We find that: (a) The scaled variable u identical with u congruent to L/a(nu) , where nu is the percolation connectedness exponent, fully determines the distribution of the current path length l for all values of u . For u > 1, the behavior corresponds to the weak disorder limit and l scales as l approximately L, while for u < 1 , the behavior corresponds to the strong disorder limit with l approximately L(d(opt) ), where d(opt) =1.22+/-0.01 is the optimal path exponent. (b) In the weak disorder regime, there is a length scale xi approximately a(nu), below which strong disorder and critical percolation characterize the current path. PMID:15903712
A design-by-treatment interaction model for network meta-analysis with random inconsistency effects.
Jackson, Dan; Barrett, Jessica K; Rice, Stephen; White, Ian R; Higgins, Julian P T
2014-09-20
Network meta-analysis is becoming more popular as a way to analyse multiple treatments simultaneously and, in the right circumstances, rank treatments. A difficulty in practice is the possibility of 'inconsistency' or 'incoherence', where direct evidence and indirect evidence are not in agreement. Here, we develop a random-effects implementation of the recently proposed design-by-treatment interaction model, using these random effects to model inconsistency and estimate the parameters of primary interest. Our proposal is a generalisation of the model proposed by Lumley and allows trials with three or more arms to be included in the analysis. Our methods also facilitate the ranking of treatments under inconsistency. We derive R and I(2) statistics to quantify the impact of the between-study heterogeneity and the inconsistency. We apply our model to two examples.
NASA Astrophysics Data System (ADS)
Hamaneh, Mehdi Bagheri; Haber, Jonah; Yu, Yi-Kuo
2015-11-01
A general model for random walks (RWs) on networks is proposed. It incorporates damping and time-dependent links, and it includes standard (undamped, noninteracting) RWs (SRWs), coalescing RWs, and coalescing-branching RWs as special cases. The exact, time-dependent solutions for the average numbers of visits (w ) to nodes and their fluctuations (σ2) are given, and the long-term σ -w relation is studied. Although σ ∝w1 /2 for SRWs, this power law can be fragile when coalescing-branching interaction is present. Damping, however, often strengthens it but with an exponent generally different from 1 /2 .
Power-law exponent of the Bouchaud-Mézard model on regular random networks
NASA Astrophysics Data System (ADS)
Ichinomiya, Takashi
2013-07-01
We study the Bouchaud-Mézard model on a regular random network. By assuming adiabaticity and independency, and utilizing the generalized central limit theorem and the Tauberian theorem, we derive an equation that determines the exponent of the probability distribution function of the wealth as x→∞. The analysis shows that the exponent can be smaller than 2, while a mean-field analysis always gives the exponent as being larger than 2. The results of our analysis are shown to be in good agreement with those of the numerical simulations.
Burioni, Raffaella; di Santo, Serena; di Volo, Matteo; Vezzani, Alessandro
2014-10-01
Self-organized quasiperiodicity is one of the most puzzling dynamical phases observed in systems of nonlinear coupled oscillators. The single dynamical units are not locked to the periodic mean field they produce, but they still feature a coherent behavior, through an unexplained complex form of correlation. We consider a class of leaky integrate-and-fire oscillators on random sparse and massive networks with dynamical synapses, featuring self-organized quasiperiodicity, and we show how complex collective oscillations arise from constructive interference of microscopic dynamics. In particular, we find a simple quantitative relationship between two relevant microscopic dynamical time scales and the macroscopic time scale of the global signal. We show that the proposed relation is a general property of collective oscillations, common to all the partially synchronous dynamical phases analyzed. We argue that an analogous mechanism could be at the origin of similar network dynamics.
Control Capacity and A Random Sampling Method in Exploring Controllability of Complex Networks
Jia, Tao; Barabási, Albert-László
2013-01-01
Controlling complex systems is a fundamental challenge of network science. Recent advances indicate that control over the system can be achieved through a minimum driver node set (MDS). The existence of multiple MDS's suggests that nodes do not participate in control equally, prompting us to quantify their participations. Here we introduce control capacity quantifying the likelihood that a node is a driver node. To efficiently measure this quantity, we develop a random sampling algorithm. This algorithm not only provides a statistical estimate of the control capacity, but also bridges the gap between multiple microscopic control configurations and macroscopic properties of the network under control. We demonstrate that the possibility of being a driver node decreases with a node's in-degree and is independent of its out-degree. Given the inherent multiplicity of MDS's, our findings offer tools to explore control in various complex systems. PMID:23912679
NASA Technical Reports Server (NTRS)
Benson, Markland J.
2008-01-01
Presents a source code analysis tool (CodeSonar) for use in the Space Network Ground Segment. The Space Network requires 99.9% proficiency and 97.0% availability of systems. Software has historically accounted for an annual average of 28% of the Space Network loss of availability and proficiency. CSCI A and CSCI B account for 42% of the previous eight months of software data loss. The technology infusion of CodeSonar into the Space Network Ground segment is meant to aid in determining the impact of the technology on the project both in the expenditure of effort and the technical results of the technology. Running a CodeSonar analysis and performing a preliminary review of the results averaged 3.5 minutes per finding (approximately 20 hours total). An additional 40 hours is estimated to analyze the 37 findings deemed too complex for the initial review. Using CodeSonar's tools to suppress known non-problems, delta tool runs will not repeat findings that have been marked as non-problems, further reducing the time needed for review. The 'non-interesting' finding rate of 70% is a large number, but filtering, search, and detailed contextual features of CodeSonar reduce the time per finding. Integration of the tool into the build process may also provide further savings by preventing developers from having to configure and operate the tool separately. These preliminary results show the tool to be easy to use and incorporate into the engineering process. These findings also provide significant potential improvements in proficiency and availability on the part of the software. As time-to-fix data become available a better cost trade can be made on person hours saved versus tool cost. Selective factors may be necessary to determine where best to apply CodeSonar to balance cost and benefits.
ERIC Educational Resources Information Center
German, D.; Sutcliffe, C. G.; Sirirojn, B.; Sherman, S. G.; Latkin, C. A.; Aramrattana, A.; Celentano, D. D.
2012-01-01
We examined the effect on depressive symptoms of a peer network-oriented intervention effective in reducing sexual risk behavior and methamphetamine (MA) use. Current Thai MA users aged 18-25 years and their drug and/or sex network members enrolled in a randomized controlled trial with 4 follow-ups over 12 months. A total of 415 index participants…
Bayesian Markov Random Field analysis for protein function prediction based on network data.
Kourmpetis, Yiannis A I; van Dijk, Aalt D J; Bink, Marco C A M; van Ham, Roeland C H J; ter Braak, Cajo J F
2010-02-24
Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S. cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature.
Active transport on disordered microtubule networks: the generalized random velocity model.
Kahana, Aviv; Kenan, Gilad; Feingold, Mario; Elbaum, Michael; Granek, Rony
2008-11-01
The motion of small cargo particles on microtubules by means of motor proteins in disordered microtubule networks is investigated theoretically using both analytical tools and computer simulations. Different network topologies in two and three dimensions are considered, one of which has been recently studied experimentally by Salman [Biophys. J. 89, 2134 (2005)]. A generalization of the random velocity model is used to derive the mean-square displacement of the cargo particle. We find that all cases belong to the class of anomalous superdiffusion, which is sensitive mainly to the dimensionality of the network and only marginally to its topology. Yet in three dimensions the motion is very close to simple diffusion, with sublogarithmic corrections that depend on the network topology. When details of the thermal diffusion in the bulk solution are included, no significant change to the asymptotic time behavior is found. However, a small asymmetry in the mean microtubule polarity affects the corresponding long-time behavior. We also study a three-dimensional model of the microtubule network in living animal cells. Three first-passage-time problems of intracellular transport are simulated and analyzed for different motor processivities: (i) cargo that originates near the nucleus and has to reach the membrane, (ii) cargo that originates from the membrane and has to reach the nucleus, and (iii) cargo that leaves the nucleus and has to reach a specific target in the cytoplasm. We conclude that while a higher motor processivity increases the transport efficiency in cases (i) and (ii), in case (iii) it has the opposite effect. We conjecture that the balance between the different network tasks, as manifested in cases (i) and (ii) versus case (iii), may be the reason for the evolutionary choice of a finite motor processivity.
Scaling of flow distance in random self-similar channel networks
Troutman, B.M.
2005-01-01
Natural river channel networks have been shown in empirical studies to exhibit power-law scaling behavior characteristic of self-similar and self-affine structures. Of particular interest is to describe how the distribution of distance to the outlet changes as a function of network size. In this paper, networks are modeled as random self-similar rooted tree graphs and scaling of distance to the root is studied using methods in stochastic branching theory. In particular, the asymptotic expectation of the width function (number of nodes as a function of distance to the outlet) is derived under conditions on the replacement generators. It is demonstrated further that the branching number describing rate of growth of node distance to the outlet is identical to the length ratio under a Horton-Strahler ordering scheme as order gets large, again under certain restrictions on the generators. These results are discussed in relation to drainage basin allometry and an application to an actual drainage network is presented. ?? World Scientific Publishing Company.
Path statistics, memory, and coarse-graining of continuous-time random walks on networks
NASA Astrophysics Data System (ADS)
Manhart, Michael; Kion-Crosby, Willow; Morozov, Alexandre V.
2015-12-01
Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs.
Ambient awareness: From random noise to digital closeness in online social networks
Levordashka, Ana; Utz, Sonja
2016-01-01
Ambient awareness refers to the awareness social media users develop of their online network in result of being constantly exposed to social information, such as microblogging updates. Although each individual bit of information can seem like random noise, their incessant reception can amass to a coherent representation of social others. Despite its growing popularity and important implications for social media research, ambient awareness on public social media has not been studied empirically. We provide evidence for the occurrence of ambient awareness and examine key questions related to its content and functions. A diverse sample of participants reported experiencing awareness, both as a general feeling towards their network as a whole, and as knowledge of individual members of the network, whom they had not met in real life. Our results indicate that ambient awareness can develop peripherally, from fragmented information and in the relative absence of extensive one-to-one communication. We report the effects of demographics, media use, and network variables and discuss the implications of ambient awareness for relational and informational processes online. PMID:27375343
Path statistics, memory, and coarse-graining of continuous-time random walks on networks.
Manhart, Michael; Kion-Crosby, Willow; Morozov, Alexandre V
2015-12-01
Continuous-time random walks (CTRWs) on discrete state spaces, ranging from regular lattices to complex networks, are ubiquitous across physics, chemistry, and biology. Models with coarse-grained states (for example, those employed in studies of molecular kinetics) or spatial disorder can give rise to memory and non-exponential distributions of waiting times and first-passage statistics. However, existing methods for analyzing CTRWs on complex energy landscapes do not address these effects. Here we use statistical mechanics of the nonequilibrium path ensemble to characterize first-passage CTRWs on networks with arbitrary connectivity, energy landscape, and waiting time distributions. Our approach can be applied to calculating higher moments (beyond the mean) of path length, time, and action, as well as statistics of any conservative or non-conservative force along a path. For homogeneous networks, we derive exact relations between length and time moments, quantifying the validity of approximating a continuous-time process with its discrete-time projection. For more general models, we obtain recursion relations, reminiscent of transfer matrix and exact enumeration techniques, to efficiently calculate path statistics numerically. We have implemented our algorithm in PathMAN (Path Matrix Algorithm for Networks), a Python script that users can apply to their model of choice. We demonstrate the algorithm on a few representative examples which underscore the importance of non-exponential distributions, memory, and coarse-graining in CTRWs.
RRW: repeated random walks on genome-scale protein networks for local cluster discovery
Macropol, Kathy; Can, Tolga; Singh, Ambuj K
2009-01-01
Background We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. Results We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. Conclusion RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters. PMID:19740439
NASA Astrophysics Data System (ADS)
Guimarães, Paulo R., Jr.; de Aguiar, Marcus A. M.; Bascompte, Jordi; Jordano, Pedro; Dos Reis, Sérgio Furtado
2005-03-01
Barabasi-Albert networks are constructed by adding nodes via preferential attachment to an initial core of nodes. We study the topology of small scale-free networks as a function of the size and average connectivity of their initial random core. We show that these two parameters may strongly affect the tail of the degree distribution, by consistently leading to broad-scale or single-scale networks. In particular, we argue that the size of the initial network core and its density of connections may be the main responsible for the exponential truncation of the power-law behavior observed in some small scale-free networks.
Analysis of the traffic running cost under random route choice behavior in a network with two routes
NASA Astrophysics Data System (ADS)
Tang, Tie-Qiao; Yu, Qiang; Liu, Kai
2016-05-01
In this paper, a car-following model is used to study each driver's three running costs in a network with two routes under the random route choice behavior. The numerical results indicate that each driver's three running costs and the corresponding total cost are relevant to the gap of the time the driver enters the network. The results can help us to further explore each driver's trip cost in a more complex network under other route choice behavior.
Rolls, David A.; Wang, Peng; McBryde, Emma; Pattison, Philippa; Robins, Garry
2015-01-01
We compare two broad types of empirically grounded random network models in terms of their abilities to capture both network features and simulated Susceptible-Infected-Recovered (SIR) epidemic dynamics. The types of network models are exponential random graph models (ERGMs) and extensions of the configuration model. We use three kinds of empirical contact networks, chosen to provide both variety and realistic patterns of human contact: a highly clustered network, a bipartite network and a snowball sampled network of a “hidden population”. In the case of the snowball sampled network we present a novel method for fitting an edge-triangle model. In our results, ERGMs consistently capture clustering as well or better than configuration-type models, but the latter models better capture the node degree distribution. Despite the additional computational requirements to fit ERGMs to empirical networks, the use of ERGMs provides only a slight improvement in the ability of the models to recreate epidemic features of the empirical network in simulated SIR epidemics. Generally, SIR epidemic results from using configuration-type models fall between those from a random network model (i.e., an Erdős-Rényi model) and an ERGM. The addition of subgraphs of size four to edge-triangle type models does improve agreement with the empirical network for smaller densities in clustered networks. Additional subgraphs do not make a noticeable difference in our example, although we would expect the ability to model cliques to be helpful for contact networks exhibiting household structure. PMID:26555701
Integrated coding-aware intra-ONU scheduling for passive optical networks with inter-ONU traffic
NASA Astrophysics Data System (ADS)
Li, Yan; Dai, Shifang; Wu, Weiwei
2016-12-01
Recently, with the soaring of traffic among optical network units (ONUs), network coding (NC) is becoming an appealing technique for improving the performance of passive optical networks (PONs) with such inter-ONU traffic. However, in the existed NC-based PONs, NC can only be implemented by buffering inter-ONU traffic at the optical line terminal (OLT) to wait for the establishment of coding condition, such passive uncertain waiting severely limits the effect of NC technique. In this paper, we will study integrated coding-aware intra-ONU scheduling in which the scheduling of inter-ONU traffic within each ONU will be undertaken by the OLT to actively facilitate the forming of coding inter-ONU traffic based on the global inter-ONU traffic distribution, and then the performance of PONs with inter-ONU traffic can be significantly improved. We firstly design two report message patterns and an inter-ONU traffic transmission framework as the basis for the integrated coding-aware intra-ONU scheduling. Three specific scheduling strategies are then proposed for adapting diverse global inter-ONU traffic distributions. The effectiveness of the work is finally evaluated by both theoretical analysis and simulations.
Chen, Huifang; Fan, Guangyu; Xie, Lei; Cui, Jun-Hong
2013-11-04
Due to the characteristics of underwater acoustic channel, media access control (MAC) protocols designed for underwater acoustic sensor networks (UWASNs) are quite different from those for terrestrial wireless sensor networks. Moreover, in a sink-oriented network with event information generation in a sensor field and message forwarding to the sink hop-by-hop, the sensors near the sink have to transmit more packets than those far from the sink, and then a funneling effect occurs, which leads to packet congestion, collisions and losses, especially in UWASNs with long propagation delays. An improved CDMA-based MAC protocol, named path-oriented code assignment (POCA) CDMA MAC (POCA-CDMA-MAC), is proposed for UWASNs in this paper. In the proposed MAC protocol, both the round-robin method and CDMA technology are adopted to make the sink receive packets from multiple paths simultaneously. Since the number of paths for information gathering is much less than that of nodes, the length of the spreading code used in the POCA-CDMA-MAC protocol is shorter greatly than that used in the CDMA-based protocols with transmitter-oriented code assignment (TOCA) or receiver-oriented code assignment (ROCA). Simulation results show that the proposed POCA-CDMA-MAC protocol achieves a higher network throughput and a lower end-to-end delay compared to other CDMA-based MAC protocols.
ERIC Educational Resources Information Center
Salisbury, Amy L.; Fallone, Melissa Duncan; Lester, Barry
2005-01-01
This review provides an overview and definition of the concept of neurobehavior in human development. Two neurobehavioral assessments used by the authors in current fetal and infant research are discussed: the NICU Network Neurobehavioral Assessment Scale and the Fetal Neurobehavior Coding System. This review will present how the two assessments…
Chen, Huifang; Fan, Guangyu; Xie, Lei; Cui, Jun-Hong
2013-01-01
Due to the characteristics of underwater acoustic channel, media access control (MAC) protocols designed for underwater acoustic sensor networks (UWASNs) are quite different from those for terrestrial wireless sensor networks. Moreover, in a sink-oriented network with event information generation in a sensor field and message forwarding to the sink hop-by-hop, the sensors near the sink have to transmit more packets than those far from the sink, and then a funneling effect occurs, which leads to packet congestion, collisions and losses, especially in UWASNs with long propagation delays. An improved CDMA-based MAC protocol, named path-oriented code assignment (POCA) CDMA MAC (POCA-CDMA-MAC), is proposed for UWASNs in this paper. In the proposed MAC protocol, both the round-robin method and CDMA technology are adopted to make the sink receive packets from multiple paths simultaneously. Since the number of paths for information gathering is much less than that of nodes, the length of the spreading code used in the POCA-CDMA-MAC protocol is shorter greatly than that used in the CDMA-based protocols with transmitter-oriented code assignment (TOCA) or receiver-oriented code assignment (ROCA). Simulation results show that the proposed POCA-CDMA-MAC protocol achieves a higher network throughput and a lower end-to-end delay compared to other CDMA-based MAC protocols. PMID:24193100
Physical states in the canonical tensor model from the perspective of random tensor networks
NASA Astrophysics Data System (ADS)
Narain, Gaurav; Sasakura, Naoki; Sato, Yuki
2015-01-01
Tensor models, generalization of matrix models, are studied aiming for quantum gravity in dimensions larger than two. Among them, the canonical tensor model is formulated as a totally constrained system with first-class constraints, the algebra of which resembles the Dirac algebra of general relativity. When quantized, the physical states are defined to be vanished by the quantized constraints. In explicit representations, the constraint equations are a set of partial differential equations for the physical wave-functions, which do not seem straightforward to be solved due to their non-linear character. In this paper, after providing some explicit solutions for N = 2 , 3, we show that certain scale-free integration of partition functions of statistical systems on random networks (or random tensor networks more generally) provides a series of solutions for general N. Then, by generalizing this form, we also obtain various solutions for general N. Moreover, we show that the solutions for the cases with a cosmological constant can be obtained from those with no cosmological constant for increased N. This would imply the interesting possibility that a cosmological constant can always be absorbed into the dynamics and is not an input parameter in the canonical tensor model. We also observe the possibility of symmetry enhancement in N = 3, and comment on an extension of Airy function related to the solutions.
Analysis of an epidemic model with awareness decay on regular random networks.
Juher, David; Kiss, Istvan Z; Saldaña, Joan
2015-01-21
The existence of a die-out threshold (different from the classic disease-invasion one) defining a region of slow extinction of an epidemic has been proved elsewhere for susceptible-aware-infectious-susceptible models without awareness decay, through bifurcation analysis. By means of an equivalent mean-field model defined on regular random networks, we interpret the dynamics of the system in this region and prove that the existence of bifurcation for this second epidemic threshold crucially depends on the absence of awareness decay. We show that the continuum of equilibria that characterizes the slow die-out dynamics collapses into a unique equilibrium when a constant rate of awareness decay is assumed, no matter how small, and that the resulting bifurcation from the disease-free equilibrium is equivalent to that of standard epidemic models. We illustrate these findings with continuous-time stochastic simulations on regular random networks with different degrees. Finally, the behaviour of solutions with and without decay in awareness is compared around the second epidemic threshold for a small rate of awareness decay.
NASA Astrophysics Data System (ADS)
De Bacco, Caterina; Guggiola, Alberto; Kühn, Reimer; Paga, Pierre
2016-05-01
Rare event statistics for random walks on complex networks are investigated using the large deviation formalism. Within this formalism, rare events are realised as typical events in a suitably deformed path-ensemble, and their statistics can be studied in terms of spectral properties of a deformed Markov transition matrix. We observe two different types of phase transition in such systems: (i) rare events which are singled out for sufficiently large values of the deformation parameter may correspond to localised modes of the deformed transition matrix; (ii) ‘mode-switching transitions’ may occur as the deformation parameter is varied. Details depend on the nature of the observable for which the rare event statistics is studied, as well as on the underlying graph ensemble. In the present paper we report results on rare events statistics for path averages of random walks in Erdős-Rényi and scale free networks. Large deviation rate functions and localisation properties are studied numerically. For observables of the type considered here, we also derive an analytical approximation for the Legendre transform of the large deviation rate function, which is valid in the large connectivity limit. It is found to agree well with simulations.
Li, Dongfang; Lu, Zhaojun; Zou, Xuecheng; Liu, Zhenglin
2015-01-01
Random number generators (RNG) play an important role in many sensor network systems and applications, such as those requiring secure and robust communications. In this paper, we develop a high-security and high-throughput hardware true random number generator, called PUFKEY, which consists of two kinds of physical unclonable function (PUF) elements. Combined with a conditioning algorithm, true random seeds are extracted from the noise on the start-up pattern of SRAM memories. These true random seeds contain full entropy. Then, the true random seeds are used as the input for a non-deterministic hardware RNG to generate a stream of true random bits with a throughput as high as 803 Mbps. The experimental results show that the bitstream generated by the proposed PUFKEY can pass all standard national institute of standards and technology (NIST) randomness tests and is resilient to a wide range of security attacks. PMID:26501283
Cui, Laizhong; Lu, Nan; Chen, Fu
2014-01-01
Most large-scale peer-to-peer (P2P) live streaming systems use mesh to organize peers and leverage pull scheduling to transmit packets for providing robustness in dynamic environment. The pull scheduling brings large packet delay. Network coding makes the push scheduling feasible in mesh P2P live streaming and improves the efficiency. However, it may also introduce some extra delays and coding computational overhead. To improve the packet delay, streaming quality, and coding overhead, in this paper are as follows. we propose a QoS driven push scheduling approach. The main contributions of this paper are: (i) We introduce a new network coding method to increase the content diversity and reduce the complexity of scheduling; (ii) we formulate the push scheduling as an optimization problem and transform it to a min-cost flow problem for solving it in polynomial time; (iii) we propose a push scheduling algorithm to reduce the coding overhead and do extensive experiments to validate the effectiveness of our approach. Compared with previous approaches, the simulation results demonstrate that packet delay, continuity index, and coding ratio of our system can be significantly improved, especially in dynamic environments. PMID:25114968
Foster, Jacob G; Foster, David V; Grassberger, Peter; Paczuski, Maya
2007-10-01
The simplest null models for networks, used to distinguish significant features of a particular network from a priori expected features, are random ensembles with the degree sequence fixed by the specific network of interest. These "fixed degree sequence" (FDS) ensembles are, however, famously resistant to analytic attack. In this paper we introduce ensembles with partially-fixed degree sequences (PFDS) and compare analytic results obtained for them with Monte Carlo results for the FDS ensemble. These results include link likelihoods, subgraph likelihoods, and degree correlations. We find that local structural features in the FDS ensemble can be reasonably well estimated by simultaneously fixing only the degrees of a few nodes, in addition to the total number of nodes and links. As test cases we use two protein interaction networks (Escherichia coli, Saccharomyces cerevisiae), the internet on the autonomous system (AS) level, and the World Wide Web. Fixing just the degrees of two nodes gives the mean neighbor degree as a function of node degree, k;{'}_{k} , in agreement with results explicitly obtained from rewiring. For power law degree distributions, we derive the disassortativity analytically. In the PFDS ensemble the partition function can be expanded diagrammatically. We obtain an explicit expression for the link likelihood to lowest order, which reduces in the limit of large, sparse undirected networks with L links and with k_{max}L to the simple formula P(k,k;{'})=kk;{'}(2L+kk;{'}) . In a similar limit, the probability for three nodes to be linked into a triangle reduces to the factorized expression P_{Delta}(k_{1},k_{2},k_{3})=P(k_{1},k_{2})P(k_{1},k_{3})P(k_{2},k_{3}) .
Zong, Shuang-Le; Zhao, Gang; Su, Li-Xin; Liang, Wei-Dong; Li, Li-Geng; Cheng, Guang; Wang, Ai-Jun; Cao, Xiao-Qiang; Zheng, Qiu-Tao; Li, Li-Dong; Kan, Shi-Lian
2016-03-01
The fifth metacarpal neck fractures (commonly termed boxer's fractures) are the most common type of metacarpal fractures. Many types of treatments are available in clinical practice, some of which have already been compared with other treatments by various researchers. However, a comprehensive treatment comparison is lacking. We estimated the comparative efficacy of different interventions for total complications, through a network meta-analysis of randomized controlled trials. We conducted a systematic search of the literature through October 2015. The outcome measurements were the total complications. We used a Bayesian network meta-analysis to combine direct and indirect evidence and to estimate the relative effects of treatment. We identified 6 RCTs registering a total of 288 patients who were eligible for our network meta-analysis. The literature's quality is relatively high. The median Structured Effectiveness for Quality Evaluation of Study score for the included trials was 33.8. The overall methodological quality was high. Of the 6 studies, all were 2-arm controlled trials comparing active intervention. Among the 4 treatments--conservative treatment (CT), antegrade intramedullary nailing (AIMN), transverse pinning (TP) with K-wires, and plate fixation (PF)--CT had the best rankings (ie, lowest risk of total complications), followed by PF, AIMN, and TP (ie, highest risk of total complications). Furthermore, we also presented the results using surface under the cumulative ranking curve. The surface under the cumulative ranking curve probabilities were 94.1%, 52.9%, 37.3%, and 15.7% for CT, PF, AIMN, and TP, respectively. In conclusion, current evidence suggested that conservative treatment is the optimum treatment for the fifth metacarpal neck fractures because of reduced total complication rates. Moreover, the TP with K-wires is the worst option with highly total complication rates. PF and AIMN therapy should be considered as the first-line choices. Larger
Yeh, Cheng-Yu; Yeh, Hsiang-Yuan; Arias, Carlos Roberto; Soo, Von-Wun
2012-01-01
With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73 GHz and 1 GB main memory running under windows operating system. PMID:22577352
Network-based genomic discovery: application and comparison of Markov random field models.
Wei, Peng; Pan, Wei
2010-01-01
As biological knowledge accumulates rapidly, gene networks encoding genome-wide gene-gene interactions have been constructed. As an improvement over the standard mixture model that tests all the genes iid a priori, Wei and Li (2007) and Wei and Pan (2008) proposed modeling a gene network as a Discrete- or Gaussian-Markov random field (DMRF or GMRF) respectively in a mixture model to analyze genomic data. However, how these methods compare in practical applications in not well understood and this is the aim here. We also propose two novel constraints in prior specifications for the GMRF model and a fully Bayesian approach to the DMRF model. We assess the accuracy of estimating the False Discovery Rate (FDR) by posterior probabilities in the context of MRF models. Applications to a ChIP-chip data set and simulated data show that the modified GMRF models has superior performance as compared with other models, while both MRF-based mixture models, with reasonable robustness to misspecified gene networks, outperform the standard mixture model.
Lee, Jae-Hyeok; Shim, Wooyoung; Choolakadavil Khalid, Najeeb; Kang, Won-Seok; Lee, Minsu; Kim, Hyo-Sop; Choi, Je; Lee, Gwang; Kim, Jae-Ho
2015-01-28
Studies on the interaction of cells with single-walled carbon nanotubes (SWCNTs) have been receiving increasing attention owing to their potential for various cellular applications. In this report, we investigated the interactions between biological cells and nanostructured SWCNTs films and focused on how morphological structures of SWCNT films affected cellular behavior such as cell proliferation and differentiation. One directionally aligned SWCNT Langmuir-Blodgett (LB) film and random network SWCNT film were fabricated by LB and vacuum filteration methods, respectively. We demonstrate that our SWCNT LB and network film based scaffolds do not show any cytotoxicity, while on the other hand, these scaffolds promote differentiation property of rat mesenchymal stem cells (rMSCs) when compared with that on conventional tissue culture polystyrene substrates. Especially, the SWCNT network film with average thickness and roughness values of 95 ± 5 and 9.81 nm, respectively, demonstrated faster growth rate and higher cell thickness for rMSCs. These results suggest that systematic manipulation of the thickness, roughness, and directional alignment of SWCNT films would provide the convenient strategy for controlling the growth and maintenance of the differentiation property of stem cells. The SWCNT film could be an alternative culture substrate for various stem cells, which often require close control of the growth and differentiation properties.
Asymptotic Analysis of Large Cooperative Relay Networks Using Random Matrix Theory
NASA Astrophysics Data System (ADS)
Li, Husheng; Han, Z.; Poor, H.
2008-12-01
Cooperative transmission is an emerging communication technology that takes advantage of the broadcast nature of wireless channels. In cooperative transmission, the use of relays can create a virtual antenna array so that multiple-input/multiple-output (MIMO) techniques can be employed. Most existing work in this area has focused on the situation in which there are a small number of sources and relays and a destination. In this paper, cooperative relay networks with large numbers of nodes are analyzed, and in particular the asymptotic performance improvement of cooperative transmission over direction transmission and relay transmission is analyzed using random matrix theory. The key idea is to investigate the eigenvalue distributions related to channel capacity and to analyze the moments of this distribution in large wireless networks. A performance upper bound is derived, the performance in the low signal-to-noise-ratio regime is analyzed, and two approximations are obtained for high and low relay-to-destination link qualities, respectively. Finally, simulations are provided to validate the accuracy of the analytical results. The analysis in this paper provides important tools for the understanding and the design of large cooperative wireless networks.
Pattern formation on networks with reactions: A continuous-time random-walk approach
NASA Astrophysics Data System (ADS)
Angstmann, C. N.; Donnelly, I. C.; Henry, B. I.
2013-03-01
We derive the generalized master equation for reaction-diffusion on networks from an underlying stochastic process, the continuous time random walk (CTRW). The nontrivial incorporation of the reaction process into the CTRW is achieved by splitting the derivation into two stages. The reactions are treated as birth-death processes and the first stage of the derivation is at the single particle level, taking into account the death process, while the second stage considers an ensemble of these particles including the birth process. Using this model we have investigated different types of pattern formation across the vertices on a range of networks. Importantly, the CTRW defines the Laplacian operator on the network in a non-ad hoc manner and the pattern formation depends on the structure of this Laplacian. Here we focus attention on CTRWs with exponential waiting times for two cases: one in which the rate parameter is constant for all vertices and the other where the rate parameter is proportional to the vertex degree. This results in nonsymmetric and symmetric CTRW Laplacians, respectively. In the case of symmetric Laplacians, pattern formation follows from the Turing instability. However in nonsymmetric Laplacians, pattern formation may be possible with or without a Turing instability.
Kelley, David R.; Snoek, Jasper; Rinn, John L.
2016-01-01
The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance—deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome. PMID:27197224
Kelley, David R; Snoek, Jasper; Rinn, John L
2016-07-01
The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance-deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome. PMID:27197224
Astrand, Elaine; Ibos, Guilhem; Duhamel, Jean-René; Ben Hamed, Suliann
2015-02-18
Despite an ever growing knowledge on how parietal and prefrontal neurons encode low-level spatial and color information or higher-level information, such as spatial attention, an understanding of how these cortical regions process neuronal information at the population level is still missing. A simple assumption would be that the function and temporal response profiles of these neuronal populations match that of its constituting individual cells. However, several recent studies suggest that this is not necessarily the case and that the single-cell approach overlooks dynamic changes in how information is distributed over the neuronal population. Here, we use a time-resolved population pattern analysis to explore how spatial position, spatial attention and color information are differentially encoded and maintained in the macaque monkey prefrontal (frontal eye fields) and parietal cortex (lateral intraparietal area). Overall, our work brings about three novel observations. First, we show that parietal and prefrontal populations operate in two distinct population regimens for the encoding of sensory and cognitive information: a stationary mode and a dynamic mode. Second, we show that the temporal dynamics of a heterogeneous neuronal population brings about complementary information to that of its functional subpopulations. Thus, both need to be investigated in parallel. Last, we show that identifying the neuronal configuration in which a neuronal population encodes given information can serve to reveal this same information in a different context. All together, this work challenges common views on neural coding in the parietofrontal network. PMID:25698752
Kelley, David R; Snoek, Jasper; Rinn, John L
2016-07-01
The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance-deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.
NASA Astrophysics Data System (ADS)
Nightingale, James; Wang, Qi; Grecos, Christos
2011-03-01
Users of the next generation wireless paradigm known as multihomed mobile networks expect satisfactory quality of service (QoS) when accessing streamed multimedia content. The recent H.264 Scalable Video Coding (SVC) extension to the Advanced Video Coding standard (AVC), offers the facility to adapt real-time video streams in response to the dynamic conditions of multiple network paths encountered in multihomed wireless mobile networks. Nevertheless, preexisting streaming algorithms were mainly proposed for AVC delivery over multipath wired networks and were evaluated by software simulation. This paper introduces a practical, hardware-based testbed upon which we implement and evaluate real-time H.264 SVC streaming algorithms in a realistic multihomed wireless mobile networks environment. We propose an optimised streaming algorithm with multi-fold technical contributions. Firstly, we extended the AVC packet prioritisation schemes to reflect the three-dimensional granularity of SVC. Secondly, we designed a mechanism for evaluating the effects of different streamer 'read ahead window' sizes on real-time performance. Thirdly, we took account of the previously unconsidered path switching and mobile networks tunnelling overheads encountered in real-world deployments. Finally, we implemented a path condition monitoring and reporting scheme to facilitate the intelligent path switching. The proposed system has been experimentally shown to offer a significant improvement in PSNR of the received stream compared with representative existing algorithms.
Dynamic fair node spectrum allocation for ad hoc networks using random matrices
NASA Astrophysics Data System (ADS)
Rahmes, Mark; Lemieux, George; Chester, Dave; Sonnenberg, Jerry
2015-05-01
Dynamic Spectrum Access (DSA) is widely seen as a solution to the problem of limited spectrum, because of its ability to adapt the operating frequency of a radio. Mobile Ad Hoc Networks (MANETs) can extend high-capacity mobile communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high potential impact, cognitive radio employs spectrum sensing to facilitate the identification of allocated frequencies not currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic location, while secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not using it. We populate a spatial radio environment map (REM) database with known information that can be leveraged in an ad hoc network to facilitate fair path use of the DSA-discovered links. Utilization of high-resolution geospatial data layers in RF propagation analysis is directly applicable. Random matrix theory (RMT) is useful in simulating network layer usage in nodes by a Wishart adjacency matrix. We use the Dijkstra algorithm for discovering ad hoc network node connection patterns. We present a method for analysts to dynamically allocate node-node path and link resources using fair division. User allocation of limited resources as a function of time must be dynamic and based on system fairness policies. The context of fair means that first available request for an asset is not envied as long as it is not yet allocated or tasked in order to prevent cycling of the system. This solution may also save money by offering a Pareto efficient repeatable process. We use a water fill queue algorithm to include Shapley value marginal contributions for allocation.
Lim, Mark J; Liu, Ziying; Braunschweiger, Karen I; Awad, Amany; Rothschild, Kenneth J
2013-01-01
RATIONALE Rapidly performing global proteomic screens is an important goal in the post-genomic era. Correlated matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and fluorescent imaging of photocleavable peptide-coded random bead-arrays was evaluated as a critical step in a new method for proteomic screening that combines many of the advantages of MS with fluorescence-based microarrays. METHODS Small peptide-coded model bead libraries containing up to 20 different bead species were constructed by attaching peptides to 30–34 µm diameter glass, agarose or TentaGel® beads using photocleavable biotin or a custom-designed photocleavable linker. The peptide-coded bead libraries were randomly arrayed into custom gold-coated micro-well plates with 45 µm diameter wells and subjected to fluorescence and MALDI mass spectrometric imaging (MALDI-MSI). RESULTS Photocleavable mass-tags from individual beads in these libraries were spatially localized as ∼65 µm spots using MALDI-MSI with high sensitivity and mass resolution. Fluorescently tagged beads were identified and correlated with their matching photocleavable mass-tags by comparing the fluorescence and MALDI-MS images of the same bead-array. Post-translational modification of the peptide Kemptide was also detected on individual beads in a photocleavable peptide-coded bead-array by MALDI-MSI alone, after exposure of the beads to protein kinase A (PKA). CONCLUSIONS Correlated MALDI-MS and fluorescent imaging of photocleavable peptide-coded random bead-arrays can provide a basis for performing global proteomic screening. © 2013 The Authors. Rapid Communications in Mass Spectrometry published by John Wiley & Sons, Ltd. PMID:24285390
Note: Network random walk model of two-state protein folding: Test of the theory
NASA Astrophysics Data System (ADS)
Berezhkovskii, Alexander M.; Murphy, Ronan D.; Buchete, Nicolae-Viorel
2013-01-01
We study two-state protein folding in the framework of a toy model of protein dynamics. This model has an important advantage: it allows for an analytical solution for the sum of folding and unfolding rate constants [A. M. Berezhkovskii, F. Tofoleanu, and N.-V. Buchete, J. Chem. Theory Comput. 7, 2370 (2011), 10.1021/ct200281d] and hence for the reactive flux at equilibrium. We use the model to test the Kramers-type formula for the reactive flux, which was derived assuming that the protein dynamics is described by a Markov random walk on a network of complex connectivity [A. Berezhkovskii, G. Hummer, and A. Szabo, J. Chem. Phys. 130, 205102 (2009), 10.1063/1.3139063]. It is shown that the Kramers-type formula leads to the same result for the reactive flux as the sum of the rate constants.
NASA Astrophysics Data System (ADS)
Lara, Silvia; Lai, Ying Tong; Love, Cameron; Ramakrishnan, Navneeth; Adam, Shaffique
In recent years, the Effective Medium Theory (EMT) and the Random Resistor Network (RRN) have been separately used to explain disorder induced magnetoresistance that is quadratic at low fields and linear at high fields. We demonstrate that the quadratic and linear coefficients of the magnetoresistance and the transition point from the quadratic to the linear regime depend only on the inhomogeneous carrier density profile. We use this to find a mapping between the two models using dimensionless parameters that determine the magnetoresistance and show numerically that they belong to the same universality class. This work is supported by the Singapore National Research Foundation (NRF-NRFF2012-01) and the Singapore Ministry of Education and Yale-NUS College through Grant Number R-607-265-01312.
NASA Astrophysics Data System (ADS)
Zhang, Ya; Tian, Yu-Ping
2010-11-01
This article studies the consensus problem for a group of sampled-data general linear dynamical agents over random communication networks. Dynamic output feedback protocols are applied to solve the consensus problem. When the sampling period is sufficiently small, it is shown that as long as the mean topology has globally reachable nodes, the mean square consensus can be achieved by selecting protocol parameters so that n - 1 specified subsystems are simultaneously stabilised. However, when the sampling period is comparatively large, it is revealed that differing from low-order integrator multi-agent systems the consensus problem may be unsolvable. By using the hybrid dynamical system theory, an allowable upper bound of sampling period is further proposed. Two approaches to designing protocols are also provided. Simulations are given to illustrate the validity of the proposed approaches.
Effective-medium theory of elastic waves in random networks of rods.
Katz, J I; Hoffman, J J; Conradi, M S; Miller, J G
2012-06-01
We formulate an effective medium (mean field) theory of a material consisting of randomly distributed nodes connected by straight slender rods, hinged at the nodes. Defining wavelength-dependent effective elastic moduli, we calculate both the static moduli and the dispersion relations of ultrasonic longitudinal and transverse elastic waves. At finite wave vector k the waves are dispersive, with phase and group velocities decreasing with increasing wave vector. These results are directly applicable to networks with empty pore space. They also describe the solid matrix in two-component (Biot) theories of fluid-filled porous media. We suggest the possibility of low density materials with higher ratios of stiffness and strength to density than those of foams, aerogels, or trabecular bone.
Zhang, Jingwen; Brackbill, Devon; Yang, Sijia; Becker, Joshua; Herbert, Natalie; Centola, Damon
2016-12-01
To identify what features of online social networks can increase physical activity, we conducted a 4-arm randomized controlled trial in 2014 in Philadelphia, PA. Students (n = 790, mean age = 25.2) at an university were randomly assigned to one of four conditions composed of either supportive or competitive relationships and either with individual or team incentives for attending exercise classes. The social comparison condition placed participants into 6-person competitive networks with individual incentives. The social support condition placed participants into 6-person teams with team incentives. The combined condition with both supportive and competitive relationships placed participants into 6-person teams, where participants could compare their team's performance to 5 other teams' performances. The control condition only allowed participants to attend classes with individual incentives. Rewards were based on the total number of classes attended by an individual, or the average number of classes attended by the members of a team. The outcome was the number of classes that participants attended. Data were analyzed using multilevel models in 2014. The mean attendance numbers per week were 35.7, 38.5, 20.3, and 16.8 in the social comparison, the combined, the control, and the social support conditions. Attendance numbers were 90% higher in the social comparison and the combined conditions (mean = 1.9, SE = 0.2) in contrast to the two conditions without comparison (mean = 1.0, SE = 0.2) (p = 0.003). Social comparison was more effective for increasing physical activity than social support and its effects did not depend on individual or team incentives.
Zhang, Jingwen; Brackbill, Devon; Yang, Sijia; Becker, Joshua; Herbert, Natalie; Centola, Damon
2016-12-01
To identify what features of online social networks can increase physical activity, we conducted a 4-arm randomized controlled trial in 2014 in Philadelphia, PA. Students (n = 790, mean age = 25.2) at an university were randomly assigned to one of four conditions composed of either supportive or competitive relationships and either with individual or team incentives for attending exercise classes. The social comparison condition placed participants into 6-person competitive networks with individual incentives. The social support condition placed participants into 6-person teams with team incentives. The combined condition with both supportive and competitive relationships placed participants into 6-person teams, where participants could compare their team's performance to 5 other teams' performances. The control condition only allowed participants to attend classes with individual incentives. Rewards were based on the total number of classes attended by an individual, or the average number of classes attended by the members of a team. The outcome was the number of classes that participants attended. Data were analyzed using multilevel models in 2014. The mean attendance numbers per week were 35.7, 38.5, 20.3, and 16.8 in the social comparison, the combined, the control, and the social support conditions. Attendance numbers were 90% higher in the social comparison and the combined conditions (mean = 1.9, SE = 0.2) in contrast to the two conditions without comparison (mean = 1.0, SE = 0.2) (p = 0.003). Social comparison was more effective for increasing physical activity than social support and its effects did not depend on individual or team incentives. PMID:27617191
Random set tracking and entropy based control applied to distributed sensor networks
NASA Astrophysics Data System (ADS)
Stein, David; Witkoskie, James; Theophanis, Stephen; Kuklinski, Walter
2007-04-01
This paper describes an integrated approach to sensor fusion and resource management applicable to sensor networks. The sensor fusion and tracking algorithm is based on the theory of random sets. Tracking is herein considered to be the estimation of parameters in a state space such that for a given target certain components, e.g., position and velocity, are time varying and other components, e.g., identifying features, are stationary. The fusion algorithm provides at each time step the posterior probability density function, known as the global density, on the state space, and the control algorithm identifies the set of sensors that should be used at the next time step in order to minimize, subject to constraints, an approximation of the expected entropy of the global density. The random set approach to target tracking models association ambiguity by statistically weighing all possible hypotheses and associations. Computational complexity is managed by approximating the posterior Global Density using a Gaussian mixture density and using an approach based on the Kulbach-Leibler metric to limit the number of components in the Gaussian mixture representation. A closed form approximation of the expected entropy of the global density, expressed as a Gaussian mixture density, at the next time step for a given set of proposed measurements is developed. Optimal sensor selection involves a search over subsets of sensors, and the computational complexity of this search is managed by employing the Mobius transformation. Field and simulated data from a sensor network comprised of multiple range radars, and acoustic arrays, that measure angle of arrival, are used to demonstrate the approach to sensor fusion and resource management.
NASA Astrophysics Data System (ADS)
Jin, Xin; Nie, Rencan; Zhou, Dongming; Yao, Shaowen; Chen, Yanyan; Yu, Jiefu; Wang, Quan
2016-11-01
A novel method for the calculation of DNA sequence similarity is proposed based on simplified pulse-coupled neural network (S-PCNN) and Huffman coding. In this study, we propose a coding method based on Huffman coding, where the triplet code was used as a code bit to transform DNA sequence into numerical sequence. The proposed method uses the firing characters of S-PCNN neurons in DNA sequence to extract features. Besides, the proposed method can deal with different lengths of DNA sequences. First, according to the characteristics of S-PCNN and the DNA primary sequence, the latter is encoded using Huffman coding method, and then using the former, the oscillation time sequence (OTS) of the encoded DNA sequence is extracted. Simultaneously, relevant features are obtained, and finally the similarities or dissimilarities of the DNA sequences are determined by Euclidean distance. In order to verify the accuracy of this method, different data sets were used for testing. The experimental results show that the proposed method is effective.
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation.
Roth, Holger R; Lu, Le; Liu, Jiamin; Yao, Jianhua; Seff, Ari; Cherry, Kevin; Kim, Lauren; Summers, Ronald M
2016-05-01
Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities ∼ 100% of but at high FP levels. By leveraging existing CADe systems, coordinates of regions or volumes of interest (ROI or VOI) are generated and function as input for a second tier, which is our focus in this study. In this second stage, we generate 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the ConvNets assign class (e.g., lesion, pathology) probabilities for a new set of random views that are then averaged to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three data sets: 59 patients for sclerotic metastasis detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.
Liu, Wei; Awate, Suyash P; Anderson, Jeffrey S; Fletcher, P Thomas
2014-10-15
We propose a hierarchical Markov random field model for estimating both group and subject functional networks simultaneously. The model takes into account the within-subject spatial coherence as well as the between-subject consistency of the network label maps. The statistical dependency between group and subject networks acts as a regularization, which helps the network estimation on both layers. We use Gibbs sampling to approximate the posterior density of the network labels and Monte Carlo expectation maximization to estimate the model parameters. We compare our method with two alternative segmentation methods based on K-Means and normalized cuts, using synthetic and real fMRI data. The experimental results show that our proposed model is able to identify both group and subject functional networks with higher accuracy on synthetic data, more robustness, and inter-session consistency on the real data.
Liu, Wei; Awate, Suyash P.; Anderson, Jeffrey S.; Fletcher, P. Thomas
2014-01-01
We propose a hierarchical Markov random field model that estimates both group and subject functional networks simultaneously. The model takes into account the within-subject spatial coherence as well as the between-subject consistency of the network label maps. The statistical dependency between group and subject networks acts as a regularization, which helps the network estimation on both layers. We use Gibbs sampling to approximate the posterior density of the network labels and Monte Carlo expectation maximization to estimate the model parameters. We compare our method with two alternative segmentation methods based on K-Means and normalized cuts, using synthetic and real fMRI data. The experimental results show our proposed model is able to identify both group and subject functional networks with higher accuracy, more robustness, and inter-session consistency. PMID:24954282
Chandrasekar, A; Rakkiyappan, R; Cao, Jinde
2015-10-01
This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results.
NASA Astrophysics Data System (ADS)
Olekhno, N. A.; Beltukov, Y. M.; Parshin, D. A.
2016-05-01
One of the methods for the description of plasmon resonances in disordered metal-dielectric nanocomposites represents an initial composite as an electric network in the form of a lattice whose bonds are randomly arranged complex impedances. In this work, a general method is used to describe resonances in binary networks consisting of two types of impedances, which are arbitrary functions of the frequency [Th. Jonckheere and J.M. Luck, J. Phys. A 31, 3687 (1998)]. The generalization of the low-frequency L- C model where metal and dielectric regions in the lattice are replaced by inductive bonds L and capacitive bonds C d, respectively, has been considered. To analyze the spectrum of resonances in the entire optical region, a more accurate model involves the replacement of the metal regions by bonds in the form of parallel LC circuits with the resonant frequency equal to the plasma frequency of the metal ωp. The spectral properties of this model, as well as the model of a nanocomposite consisting of two metals with different plasma frequencies, have been considered. Analytical relations between the spectra of all such systems and the spectra of the initial L- C model have been established in the matrix representation. General expressions describing the dependence of the resonance spectrum of composites with arbitrary geometry on the permittivity of the matrix have been obtained.
NASA Astrophysics Data System (ADS)
Sim, Sung-Han; Spencer, B. F.; Jo, Hongki; Carbonell-Márquez, Juan Francisco
Smart sensors have been recognized as a promising technology with the potential to overcome many of the inherent difficulties and limitations associated with traditional wired structural health monitoring (SHM) systems. The unique features offered by smart sensors, including wireless communication, on-board computation, and cost effectiveness, enable deployment of the dense array of sensors that are needed for monitoring of large-scale civil infrastructure. Despite the many advances in smart sensor technologies, power consumption is still considered as one of the most important challenges that should be addressed for the smart sensors to be more widely adopted in SHM applications. Data communication, the most significant source of the power consumption, can be reduced by appropriately selecting data processing schemes and the related network topology. This paper presents a new decentralized data aggregation approach for system identification based on the Random Decrement Technique (RDT). Following a brief overview of RDT, which is an output-only system identification approach, a hierarchical approach is described and shown to be suitable for implementation in the intrinsically decentralized computing environment found in wireless smart sensor networks (WSSNs). RDT-based decentralized data aggregation is then implemented on the Imote2 smart sensor platform based on the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. Finally, the efficacy of the decentralized RDT method is demonstrated experimentally in terms of the required data communication and the accuracy of identified dynamic properties.
Adaptive Code Division Multiple Access Protocol for Wireless Network-on-Chip Architectures
NASA Astrophysics Data System (ADS)
Vijayakumaran, Vineeth
Massive levels of integration following Moore's Law ushered in a paradigm shift in the way on-chip interconnections were designed. With higher and higher number of cores on the same die traditional bus based interconnections are no longer a scalable communication infrastructure. On-chip networks were proposed enabled a scalable plug-and-play mechanism for interconnecting hundreds of cores on the same chip. Wired interconnects between the cores in a traditional Network-on-Chip (NoC) system, becomes a bottleneck with increase in the number of cores thereby increasing the latency and energy to transmit signals over them. Hence, there has been many alternative emerging interconnect technologies proposed, namely, 3D, photonic and multi-band RF interconnects. Although they provide better connectivity, higher speed and higher bandwidth compared to wired interconnects; they also face challenges with heat dissipation and manufacturing difficulties. On-chip wireless interconnects is one other alternative proposed which doesn't need physical interconnection layout as data travels over the wireless medium. They are integrated into a hybrid NOC architecture consisting of both wired and wireless links, which provides higher bandwidth, lower latency, lesser area overhead and reduced energy dissipation in communication. However, as the bandwidth of the wireless channels is limited, an efficient media access control (MAC) scheme is required to enhance the utilization of the available bandwidth. This thesis proposes using a multiple access mechanism such as Code Division Multiple Access (CDMA) to enable multiple transmitter-receiver pairs to send data over the wireless channel simultaneously. It will be shown that such a hybrid wireless NoC with an efficient CDMA based MAC protocol can significantly increase the performance of the system while lowering the energy dissipation in data transfer. In this work it is shown that the wireless NoC with the proposed CDMA based MAC protocol
A study of physician collaborations through social network and exponential random graph
2013-01-01
Background Physician collaboration, which evolves among physicians during the course of providing healthcare services to hospitalised patients, has been seen crucial to effective patient outcomes in healthcare organisations and hospitals. This study aims to explore physician collaborations using measures of social network analysis (SNA) and exponential random graph (ERG) model. Methods Based on the underlying assumption that collaborations evolve among physicians when they visit a common hospitalised patient, this study first proposes an approach to map collaboration network among physicians from the details of their visits to patients. This paper terms this network as physician collaboration network (PCN). Second, SNA measures of degree centralisation, betweenness centralisation and density are used to examine the impact of SNA measures on hospitalisation cost and readmission rate. As a control variable, the impact of patient age on the relation between network measures (i.e. degree centralisation, betweenness centralisation and density) and hospital outcome variables (i.e. hospitalisation cost and readmission rate) are also explored. Finally, ERG models are developed to identify micro-level structural properties of (i) high-cost versus low-cost PCN; and (ii) high-readmission rate versus low-readmission rate PCN. An electronic health insurance claim dataset of a very large Australian health insurance organisation is utilised to construct and explore PCN in this study. Results It is revealed that the density of PCN is positively correlated with hospitalisation cost and readmission rate. In contrast, betweenness centralisation is found negatively correlated with hospitalisation cost and readmission rate. Degree centralisation shows a negative correlation with readmission rate, but does not show any correlation with hospitalisation cost. Patient age does not have any impact for the relation of SNA measures with hospitalisation cost and hospital readmission rate. The
Mason, Michael; Mennis, Jeremy; Way, Thomas; Zaharakis, Nikola; Campbell, Leah Floyd; Benotsch, Eric G; Keyser-Marcus, Lori; King, Laura
2016-10-01
Although adolescent tobacco use has declined in the last 10 years, African American high school seniors' past 30-day use has increased by 12 %, and as they age they are more likely to report lifetime use of tobacco. Very few urban youth are enrolled in evidenced-based smoking prevention and cessation programming. Therefore, we tested a text messaging smoking cessation intervention designed to engage urban youth through an automated texting program utilizing motivational interviewing-based peer network counseling. We recruited 200 adolescents (90.5 % African American) into a randomized controlled trial that delivered either the experimental intervention of 30 personalized motivational interviewing-based peer network counseling messages, or the attention control intervention, consisting of text messages covering general (non-smoking related) health habits. All adolescents were provided smart phones for the study and were assessed at baseline, and at 1, 3, and 6 months post intervention. Utilizing repeated measures general linear models we examined the effects of the intervention while controlling for race, gender, age, presence of a smoker in the home, and mental health counseling. At 6 months, participants in the experimental condition significantly decreased the number of days they smoked cigarettes and the number of cigarettes they smoked per day; they significantly increased their intentions not to smoke in the future; and significantly increased peer social support among girls. For boys, participants in the experimental condition significantly reduced the number of close friends in their networks who smoke daily compared to those in the control condition. Effect sizes ranged from small to large. These results provide encouraging evidence of the efficacy of text messaging interventions to reduce smoking among adolescents and our intervention holds promise as a large-scale public health preventive intervention platform.
Enhancing Image Processing Performance for PCID in a Heterogeneous Network of Multi-code Processors
NASA Astrophysics Data System (ADS)
Linderman, R.; Spetka, S.; Fitzgerald, D.; Emeny, S.
The Physically-Constrained Iterative Deconvolution (PCID) image deblurring code is being ported to heterogeneous networks of multi-core systems, including Intel Xeons and IBM Cell Broadband Engines. This paper reports results from experiments using the JAWS supercomputer at MHPCC (60 TFLOPS of dual-dual Xeon nodes linked with Infiniband) and the Cell Cluster at AFRL in Rome, NY. The Cell Cluster has 52 TFLOPS of Playstation 3 (PS3) nodes with IBM Cell Broadband Engine multi-cores and 15 dual-quad Xeon head nodes. The interconnect fabric includes Infiniband, 10 Gigabit Ethernet and 1 Gigabit Ethernet to each of the 336 PS3s. The results compare approaches to parallelizing FFT executions across the Xeons and the Cell's Synergistic Processing Elements (SPEs) for frame-level image processing. The experiments included Intel's Performance Primitives and Math Kernel Library, FFTW3.2, and Carnegie Mellon's SPIRAL. Optimization of FFTs in the PCID code led to a decrease in relative processing time for FFTs. Profiling PCID version 6.2, about one year ago, showed the 13 functions that accounted for the highest percentage of processing were all FFT processing functions. They accounted for over 88% of processing time in one run on Xeons. FFT optimizations led to improvement in the current PCID version 8.0. A recent profile showed that only two of the 19 functions with the highest processing time were FFT processing functions. Timing measurements showed that FFT processing for PCID version 8.0 has been reduced to less than 19% of overall processing time. We are working toward a goal of scaling to 200-400 cores per job (1-2 imagery frames/core). Running a pair of cores on each set of frames reduces latency by implementing parallel FFT processing. Our current results show scaling well out to 100 pairs of cores. These results support the next higher level of parallelism in PCID, where groups of several hundred frames each producing one resolved image are sent to cliques of several
Predictive coding accounts of shared representations in parieto-insular networks.
Ishida, Hiroaki; Suzuki, Keisuke; Grandi, Laura Clara
2015-04-01
The discovery of mirror neurons in the ventral premotor cortex (area F5) and inferior parietal cortex (area PFG) in the macaque monkey brain has provided the physiological evidence for direct matching of the intrinsic motor representations of the self and the visual image of the actions of others. The existence of mirror neurons implies that the brain has mechanisms reflecting shared self and other action representations. This may further imply that the neural basis self-body representations may also incorporate components that are shared with other-body representations. It is likely that such a mechanism is also involved in predicting other's touch sensations and emotions. However, the neural basis of shared body representations has remained unclear. Here, we propose a neural basis of body representation of the self and of others in both human and non-human primates. We review a series of behavioral and physiological findings which together paint a picture that the systems underlying such shared representations require integration of conscious exteroception and interoception subserved by a cortical sensory-motor network involving parieto-inner perisylvian circuits (the ventral intraparietal area [VIP]/inferior parietal area [PFG]-secondary somatosensory cortex [SII]/posterior insular cortex [pIC]/anterior insular cortex [aIC]). Based on these findings, we propose a computational mechanism of the shared body representation in the predictive coding (PC) framework. Our mechanism proposes that processes emerging from generative models embedded in these specific neuronal circuits play a pivotal role in distinguishing a self-specific body representation from a shared one. The model successfully accounts for normal and abnormal shared body phenomena such as mirror-touch synesthesia and somatoparaphrenia. In addition, it generates a set of testable experimental predictions. PMID:25447372
Neural network river forecasting through baseflow separation and binary-coded swarm optimization
NASA Astrophysics Data System (ADS)
Taormina, Riccardo; Chau, Kwok-Wing; Sivakumar, Bellie
2015-10-01
The inclusion of expert knowledge in data-driven streamflow modeling is expected to yield more accurate estimates of river quantities. Modular models (MMs) designed to work on different parts of the hydrograph are preferred ways to implement such approach. Previous studies have suggested that better predictions of total streamflow could be obtained via modular Artificial Neural Networks (ANNs) trained to perform an implicit baseflow separation. These MMs fit separately the baseflow and excess flow components as produced by a digital filter, and reconstruct the total flow by adding these two signals at the output. The optimization of the filter parameters and ANN architectures is carried out through global search techniques. Despite the favorable premises, the real effectiveness of such MMs has been tested only on a few case studies, and the quality of the baseflow separation they perform has never been thoroughly assessed. In this work, we compare the performance of MM against global models (GMs) for nine different gaging stations in the northern United States. Binary-coded swarm optimization is employed for the identification of filter parameters and model structure, while Extreme Learning Machines, instead of ANN, are used to drastically reduce the large computational times required to perform the experiments. The results show that there is no evidence that MM outperform global GM for predicting the total flow. In addition, the baseflow produced by the MM largely underestimates the actual baseflow component expected for most of the considered gages. This occurs because the values of the filter parameters maximizing overall accuracy do not reflect the geological characteristics of the river basins. The results indeed show that setting the filter parameters according to expert knowledge results in accurate baseflow separation but lower accuracy of total flow predictions, suggesting that these two objectives are intrinsically conflicting rather than compatible.
Non-binary coded modulation for FMF-based coherent optical transport networks
NASA Astrophysics Data System (ADS)
Lin, Changyu
The Internet has fundamentally changed the way of modern communication. Current trends indicate that high-capacity demands are not going to be saturated anytime soon. From Shannon's theory, we know that information capacity is a logarithmic function of signal-to-noise ratio (SNR), but a linear function of the number of dimensions. Ideally, we can increase the capacity by increasing the launch power, however, due to the nonlinear characteristics of silica optical fibers that imposes a constraint on the maximum achievable optical-signal-to-noise ratio (OSNR). So there exists a nonlinear capacity limit on the standard single mode fiber (SSMF). In order to satisfy never ending capacity demands, there are several attempts to employ additional degrees of freedom in transmission system, such as few-mode fibers (FMFs), which can dramatically improve the spectral efficiency. On the other hand, for the given physical links and network equipment, an effective solution to relax the OSNR requirement is based on forward error correction (FEC), as the response to the demands of high speed reliable transmission. In this dissertation, we first discuss the model of FMF with nonlinear effects considered. Secondly, we simulate the FMF based OFDM system with various compensation and modulation schemes. Thirdly, we propose tandem-turbo-product nonbinary byte-interleaved coded modulation (BICM) for next-generation high-speed optical transmission systems. Fourthly, we study the Q factor and mutual information as threshold in BICM scheme. Lastly, an experimental study of the limits of nonlinearity compensation with digital signal processing has been conducted.
Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen
2014-01-01
This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829
Horcholle-Bossavit, Ginette; Quenet, Brigitte; Foucart, Olivier
2007-01-01
For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated McCulloch and Pitts neurons (MC-P type) was developed [Quenet, B., Horn, D., 2003. The dynamic neural filter: a binary model of spatio-temporal coding. Neural Comput. 15 (2), 309-329]. Considering the update time as an intrinsic clock, this "Dynamic Neural Filter" (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce exact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal lobe (AL) in response to different odors [Wehr, M., Laurent, G., 1996. Odor encoding by temporal sequences of firing in oscillating neural assemblies. Nature 384, 162-166]. Here, in a first step, we separate the populations of PN and local inhibitory neurons (LN) and use the DNF as a guide for simulations based on biological plausible neurons (Hodgkin-Huxley: H-H type). We show that a parsimonious network of 10 H-H neurons generates action potentials whose timing represents the required codes. In a second step, we construct a new type of DNF in order to study the population dynamics when different delays are taken into account. We find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a formal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a coding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked to different cycles of oscillations which remind local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes.
ERIC Educational Resources Information Center
Kullgren, Jeffrey T.; Harkins, Kristin A.; Bellamy, Scarlett L.; Gonzales, Amy; Tao, Yuanyuan; Zhu, Jingsan; Volpp, Kevin G.; Asch, David A.; Heisler, Michele; Karlawish, Jason
2014-01-01
Background: Financial incentives and peer networks could be delivered through eHealth technologies to encourage older adults to walk more. Methods: We conducted a 24-week randomized trial in which 92 older adults with a computer and Internet access received a pedometer, daily walking goals, and weekly feedback on goal achievement. Participants…
A Multi-core Shared Tree Algorithm Based on Network Coding for Multi-point Optical Multicast
NASA Astrophysics Data System (ADS)
Liu, Huanlin; Yang, Yuming; Li, Yuan; Chen, Yong; Huang, Sheng
2015-03-01
With the growth of multi-point to multi-point multicast applications, the optical network bandwidth resource consumption is increasing rapidly. It attracted more and more researchers to improve the limited wavelength bandwidth utilization for multicast applications in wavelength division multiplexing (WDM) networks. In the paper, a multi-core shared multicast tree algorithm based on network coding is proposed to minimize the fiber link stress. The proposed algorithm includes three processes: searching the core node candidate set excluding core node loop path, selecting the core nodes from the convergence matrix based on heuristic algorithm, and constructing the multi-core nodes shared trees. The convergence matrix based on the heuristic method is constructed for selecting the core nodes from candidate core node set. To improve the limited wavelength utilization, we introduce network coding into the shared tree to compress the transmitting information. The simulation results show that the proposed algorithm's performance is better than the existing algorithms' performance in terms of link stress and balance degree.
Packet speech transmission on ATM networks using a variable rate embedded ADPCM coding scheme
NASA Astrophysics Data System (ADS)
Kondo, Kazuhiro; Ohno, Masashi
1994-02-01
We propose a packet speech coding scheme to vary the code bits of an embedded ADPCM coder between 6 bits per sample, for high prediction gain speech blocks, to 2 bits for blocks representing silence. Subjective quality of 3.2 MOS on a scale of 1 to 5 at an enhancement-bit loss rate of 30% was obtained in our tests.
Codes for a priority queue on a parallel data bus. [Deep Space Network
NASA Technical Reports Server (NTRS)
Wallis, D. E.; Taylor, H.
1979-01-01
Some codes for arbitration of priorities among subsystem computers or peripheral device controllers connected to a parallel data bus are described. At arbitration time, several subsystems present wire-OR, parallel code words to the bus, and the central computer can identify the subsystem of highest priority and determine which of two or more transmission services the subsystem requires. A mathematical discussion of the optimality of the codes with regard to the number of subsystems that may participate in the scheme for a given number of wires is presented along with the number of services that each subsystem may request.
NASA Astrophysics Data System (ADS)
Žeželj, M.; Stanković, I.
2016-10-01
Random networks of as-grown single-walled carbon nanotubes (CNTs) contain both metallic (m-CNTs) and semiconducting (s-CNTs) nanotubes in an approximate ratio of 1:2, which leads to a trade-off between on-conductance and the on/off ratio. We demonstrate how this design problem can be solved with a realistic numerical approach. We determine the CNT density, length, and channel dimensions under which CNT thin-film transistors simultaneously attain on-conductance higher than 1 μS and an on/off ratio higher than 104. The fact that asymmetric systems have more pronounced finite-size scaling behavior than symmetric systems allows us additional design freedom. A realization probability of the desired characteristics higher than 99% is obtained for the channels with aspect ratio {L}{{CH}}/{W}{{CH}}\\lt 1.2 and normalized size {L}{{CH}}{W}{{CH}}/{l}{{CNT}}2\\gt 250 when the CNT length is {l}{{CNT}}=4-20 μ {{m}} and the normalized density of CNTs is close to the value where the probability of percolation through only s-CNT pathways reaches its maximum.
Santana, Orlando
2016-01-01
Approximately 30% to 50% of patients will develop ischemic mitral regurgitation (MR) after a myocardial infarction, which is a result of progressive left ventricular remodeling and dysfunction of the subvalvular apparatus, and portends a poor long-term prognosis. Surgical treatment is centered on mitral valve repair utilizing a restrictive annuloplasty, or valve replacement with preservation of the subvalvular apparatus. In the recent Cardiothoracic Surgical Trials Network (CSTN) study, patients with severe ischemic MR were randomized to mitral valve repair with a restrictive annuloplasty versus chordal-sparing valve replacement, and concomitant coronary artery bypass grafting, if indicated. At 2-year follow-up, mitral valve repair was associated with a significantly higher incidence of moderate or greater recurrent MR and heart failure, with no difference in the indices of left ventricular reverse remodeling, as compared with valve replacement. The current appraisal aims to provide insight into the CSTN trial results, and discusses the evidence supporting a pathophysiologic-guided repair strategy incorporating combined annuloplasty and subvalvular repair techniques to optimize the outcomes of mitral valve repair in ischemic MR. PMID:26904260
Bao, Haibo; Cao, Jinde
2011-01-01
This paper is concerned with the state estimation problem for a class of discrete-time stochastic neural networks (DSNNs) with random delays. The effect of both variation range and distribution probability of the time delay are taken into account in the proposed approach. The stochastic disturbances are described in terms of a Brownian motion and the time-varying delay is characterized by introducing a Bernoulli stochastic variable. By employing a Lyapunov-Krasovskii functional, sufficient delay-distribution-dependent conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimator which can be checked readily by the Matlab toolbox. The main feature of the results obtained in this paper is that they are dependent on not only the bound but also the distribution probability of the time delay, and we obtain a larger allowance variation range of the delay, hence our results are less conservative than the traditional delay-independent ones. One example is given to illustrate the effectiveness of the proposed result. PMID:20950998
Random mapping network for tactical target reacquisition after loss of track
NASA Astrophysics Data System (ADS)
Church, Susan D.; Burman, Jerry A.
1991-08-01
Prior methods for tactical target reacquisition after a loss of track have used tree classifiers and template matchers. Examples of prior techniques include classifiers that are trained with a priori data which makes them somewhat intolerant to temporal and dynamic changes in the target pattern. Prior methods for reacquisition generally rely on proximity and area-based schemes. The disadvantage of these methods include their dependence on accurate and consistent segmentation in cluttered scenarios and their need for precise target position prediction. The random mapping network algorithm (RMNRA) offers a solution using a pattern memory and sparse feature matching technique. RMNRA assists the imaging tracker and improves tracking tenacity by reacquiring a tracked target after a loss of track has occurred. The reacquisition algorithm uses an associative memory to perform target pattern matching. The pattern matching technique is unique in that it is tolerant to some of the ambiguities that occur with classical template pattern matchers. Weighted pattern feature vectors are stored in a memory matrix to facilitate the matching of sensed and reference patterns dynamically over time. In addition, a sophisticated algorithm was designed to update the memory matrix over time to forget prior patterns as the target signature becomes stale over time and space. The algorithm has been implemented in real-time hardware and flight tested with an infrared sensor. The algorithm is discussed and results using real IR imagery are shown.
Wei, Qikang; Chen, Tao; Xu, Ruifeng; He, Yulan; Gui, Lin
2016-01-01
The recognition of disease and chemical named entities in scientific articles is a very important subtask in information extraction in the biomedical domain. Due to the diversity and complexity of disease names, the recognition of named entities of diseases is rather tougher than those of chemical names. Although there are some remarkable chemical named entity recognition systems available online such as ChemSpot and tmChem, the publicly available recognition systems of disease named entities are rare. This article presents a system for disease named entity recognition (DNER) and normalization. First, two separate DNER models are developed. One is based on conditional random fields model with a rule-based post-processing module. The other one is based on the bidirectional recurrent neural networks. Then the named entities recognized by each of the DNER model are fed into a support vector machine classifier for combining results. Finally, each recognized disease named entity is normalized to a medical subject heading disease name by using a vector space model based method. Experimental results show that using 1000 PubMed abstracts for training, our proposed system achieves an F1-measure of 0.8428 at the mention level and 0.7804 at the concept level, respectively, on the testing data of the chemical-disease relation task in BioCreative V. Database URL: http://219.223.252.210:8080/SS/cdr.html PMID:27777244
NASA Astrophysics Data System (ADS)
Ohdaira, Tetsushi
2014-07-01
Previous studies discussing cooperation employ the best decision that every player knows all information regarding the payoff matrix and selects the strategy of the highest payoff. Therefore, they do not discuss cooperation based on the altruistic decision with limited information (bounded rational altruistic decision). In addition, they do not cover the case where every player can submit his/her strategy several times in a match of the game. This paper is based on Ohdaira's reconsideration of the bounded rational altruistic decision, and also employs the framework of the prisoner's dilemma game (PDG) with sequential strategy. The distinction between this study and the Ohdaira's reconsideration is that the former covers the model of multiple groups, but the latter deals with the model of only two groups. Ohdaira's reconsideration shows that the bounded rational altruistic decision facilitates much more cooperation in the PDG with sequential strategy than Ohdaira and Terano's bounded rational second-best decision does. However, the detail of cooperation of multiple groups based on the bounded rational altruistic decision has not been resolved yet. This study, therefore, shows how randomness in the network composed of multiple groups affects the increase of the average frequency of mutual cooperation (cooperation between groups) based on the bounded rational altruistic decision of multiple groups. We also discuss the results of the model in comparison with related studies which employ the best decision.
Ince, Robin A. A.; Jaworska, Katarzyna; Gross, Joachim; Panzeri, Stefano; van Rijsbergen, Nicola J.; Rousselet, Guillaume A.; Schyns, Philippe G.
2016-01-01
A key to understanding visual cognition is to determine “where”, “when”, and “how” brain responses reflect the processing of the specific visual features that modulate categorization behavior—the “what”. The N170 is the earliest Event-Related Potential (ERP) that preferentially responds to faces. Here, we demonstrate that a paradigmatic shift is necessary to interpret the N170 as the product of an information processing network that dynamically codes and transfers face features across hemispheres, rather than as a local stimulus-driven event. Reverse-correlation methods coupled with information-theoretic analyses revealed that visibility of the eyes influences face detection behavior. The N170 initially reflects coding of the behaviorally relevant eye contralateral to the sensor, followed by a causal communication of the other eye from the other hemisphere. These findings demonstrate that the deceptively simple N170 ERP hides a complex network information processing mechanism involving initial coding and subsequent cross-hemispheric transfer of visual features. PMID:27550865
NASA Technical Reports Server (NTRS)
Spirkovska, Lilly; Reid, Max B.
1993-01-01
A higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.
Prior entanglement between senders enables perfect quantum network coding with modification
Hayashi, Masahito
2007-10-15
We find a protocol transmitting two quantum states crossly in the butterfly network only with prior entanglement between two senders. This protocol requires only one qubit transmission or two classical bits (cbits) transmission in each channel in the butterfly network. It is also proved that it is impossible without prior entanglement. More precisely, an upper bound of average fidelity is given in the butterfly network when prior entanglement is not allowed. The presented result concerns only the butterfly network, but our techniques can be applied to a more general graph.
NASA Astrophysics Data System (ADS)
Dasgupta, Sakyasingha; Nishikawa, Isao; Aihara, Kazuyuki; Toyoizumi, Taro
Source of cortical variability and its influence on signal processing remain an open question. We address the latter, by studying two types of balanced randomly connected networks of quadratic I-F neurons, with irregular spontaneous activity: (a) a deterministic network with strong connections generating noise by chaotic dynamics (b) a stochastic network with weak connections receiving noisy input. They are analytically tractable in the limit of large network-size and channel time-constant. Despite different sources of noise, spontaneous activity of these networks are identical unless majority of neurons are simultaneously recorded. However, the two networks show remarkably different sensitivity to external stimuli. In the former, input reverberates internally and can be read out over long time, but in the latter, inputs rapidly decay. This is further enhanced with activity-dependent plasticity at input synapses producing marked difference in decoding inputs from neural activity. We show, this leads to distinct performance of the two networks to integrate temporally separate signals from multiple sources, with the deterministic chaotic network activity serving as reservoir for Monte Carlo sampling to perform near optimal Bayesian integration, unlike its stochastic counterpart.
Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan
2014-01-01
The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442
ERIC Educational Resources Information Center
Gallagher, H. Colin; Robins, Garry
2015-01-01
As part of the shift within second language acquisition (SLA) research toward complex systems thinking, researchers have called for investigations of social network structure. One strand of social network analysis yet to receive attention in SLA is network statistical models, whereby networks are explained in terms of smaller substructures of…
Antoneli, Fernando; Ferreira, Renata C; Briones, Marcelo R S
2016-06-01
Here we propose a new approach to modeling gene expression based on the theory of random dynamical systems (RDS) that provides a general coupling prescription between the nodes of any given regulatory network given the dynamics of each node is modeled by a RDS. The main virtues of this approach are the following: (i) it provides a natural way to obtain arbitrarily large networks by coupling together simple basic pieces, thus revealing the modularity of regulatory networks; (ii) the assumptions about the stochastic processes used in the modeling are fairly general, in the sense that the only requirement is stationarity; (iii) there is a well developed mathematical theory, which is a blend of smooth dynamical systems theory, ergodic theory and stochastic analysis that allows one to extract relevant dynamical and statistical information without solving the system; (iv) one may obtain the classical rate equations form the corresponding stochastic version by averaging the dynamic random variables (small noise limit). It is important to emphasize that unlike the deterministic case, where coupling two equations is a trivial matter, coupling two RDS is non-trivial, specially in our case, where the coupling is performed between a state variable of one gene and the switching stochastic process of another gene and, hence, it is not a priori true that the resulting coupled system will satisfy the definition of a random dynamical system. We shall provide the necessary arguments that ensure that our coupling prescription does indeed furnish a coupled regulatory network of random dynamical systems. Finally, the fact that classical rate equations are the small noise limit of our stochastic model ensures that any validation or prediction made on the basis of the classical theory is also a validation or prediction of our model. We illustrate our framework with some simple examples of single-gene system and network motifs.
NASA Astrophysics Data System (ADS)
Borges, R. R.; Borges, F. S.; Lameu, E. L.; Batista, A. M.; Iarosz, K. C.; Caldas, I. L.; Viana, R. L.; Sanjuán, M. A. F.
2016-05-01
In this paper, we study the effects of spike timing-dependent plasticity on synchronisation in a network of Hodgkin-Huxley neurons. Neuron plasticity is a flexible property of a neuron and its network to change temporarily or permanently their biochemical, physiological, and morphological characteristics, in order to adapt to the environment. Regarding the plasticity, we consider Hebbian rules, specifically for spike timing-dependent plasticity (STDP), and with regard to network, we consider that the connections are randomly distributed. We analyse the synchronisation and desynchronisation according to an input level and probability of connections. Moreover, we verify that the transition for synchronisation depends on the neuronal network architecture, and the external perturbation level.
NASA Astrophysics Data System (ADS)
Jafari, Mehdi; Kasaei, Shohreh
2011-12-01
Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.
NASA Astrophysics Data System (ADS)
Jafari, Mehdi; Kasaei, Shohreh
2012-01-01
Automatic brain tissue segmentation is a crucial task in diagnosis and treatment of medical images. This paper presents a new algorithm to segment different brain tissues, such as white matter (WM), gray matter (GM), cerebral spinal fluid (CSF), background (BKG), and tumor tissues. The proposed technique uses the modified intraframe coding yielded from H.264/(AVC), for feature extraction. Extracted features are then imposed to an artificial back propagation neural network (BPN) classifier to assign each block to its appropriate class. Since the newest coding standard, H.264/AVC, has the highest compression ratio, it decreases the dimension of extracted features and thus yields to a more accurate classifier with low computational complexity. The performance of the BPN classifier is evaluated using the classification accuracy and computational complexity terms. The results show that the proposed technique is more robust and effective with low computational complexity compared to other recent works.
NASA Astrophysics Data System (ADS)
Lang, Jun; Hao, Zhengchao
2014-01-01
In this paper, we first propose the discrete multi-parameter fractional random transform (DMPFRNT), which can make the spectrum distributed randomly and uniformly. Then we introduce this new spectrum transform into the image fusion field and present a new approach for the remote sensing image fusion, which utilizes both adaptive pulse coupled neural network (PCNN) and the discrete multi-parameter fractional random transform in order to meet the requirements of both high spatial resolution and low spectral distortion. In the proposed scheme, the multi-spectral (MS) and panchromatic (Pan) images are converted into the discrete multi-parameter fractional random transform domains, respectively. In DMPFRNT spectrum domain, high amplitude spectrum (HAS) and low amplitude spectrum (LAS) components carry different informations of original images. We take full advantage of the synchronization pulse issuance characteristics of PCNN to extract the HAS and LAS components properly, and give us the PCNN ignition mapping images which can be used to determine the fusion parameters. In the fusion process, local standard deviation of the amplitude spectrum is chosen as the link strength of pulse coupled neural network. Numerical simulations are performed to demonstrate that the proposed method is more reliable and superior than several existing methods based on Hue Saturation Intensity representation, Principal Component Analysis, the discrete fractional random transform etc.
Hartman, Jessica H; Cothren, Steven D; Park, Sun-Ha; Yun, Chul-Ho; Darsey, Jerry A; Miller, Grover P
2013-07-01
Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.
NASA Astrophysics Data System (ADS)
Huang, Jen-Fa; Nieh, Ta-Chun; Chen, Kai-Sheng
2013-01-01
The cyclic period and free spectral range of arrayed-waveguide gratings (AWG) in a wavelength-division multiplexing/optical code division multiple access optical code division multiple access network are exploited. The total optical network unit (ONU) of network capacity is partitioned into groups of different wavelength in accordance with the geographical location of subscribers based on the radial distance of the ONU to the optical line terminal. Combining concentric circles round by round for ONU groups enables a fixed round-trip time in the data transmission and a significant increase in system performance. Using AWG router, the proposed topology of concentric circles retains signature orthogonality and minimizes wavelength collisions on the photo-detector. Furthermore, the adoption of extended M-sequence codes corresponding to the AWG codec provides a simpler, more efficient coding procedure and accommodates more users in a single group.
Sun, Jie; Shi, Hongbo; Wang, Zhenzhen; Zhang, Changjian; Liu, Lin; Wang, Letian; He, Weiwei; Hao, Dapeng; Liu, Shulin; Zhou, Meng
2014-08-01
Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play important roles in the development and progression of complex human diseases, and predicting novel human lncRNA-disease associations is a challenging and urgently needed task, especially at a time when increasing amounts of lncRNA-related biological data are available. In this study, we proposed a global network-based computational framework, RWRlncD, to infer potential human lncRNA-disease associations by implementing the random walk with restart method on a lncRNA functional similarity network. The performance of RWRlncD was evaluated by experimentally verified lncRNA-disease associations, based on leave-one-out cross-validation. We achieved an area under the ROC curve of 0.822, demonstrating the excellent performance of RWRlncD. Significantly, the performance of RWRlncD is robust to different parameter selections. Predictively highly-ranked lncRNA-disease associations in case studies of prostate cancer and Alzheimer's disease were manually confirmed by literature mining, providing evidence of the good performance and potential value of the RWRlncD method in predicting lncRNA-disease associations.
NASA Astrophysics Data System (ADS)
Habib, Christian
There is currently much work focused on developing packet-switched optical networks to overcome the limitations of existing optical networks. Switch design for packet-switched optical networks is particularly challenging, in part due to the lack of a practical optical memory system. As a result, optical labels and all-optical label processing have attracted much attention. This thesis examines a crucial label processing component of an optical packet switch, namely the label swapper. In this thesis, three different tabletop topologies for low-cost all-optical swapping of spectral amplitude code labels for packet-switched networks are examined in a proof-of-concept phase. The first uses cross-absorption modulation in an electro-absorption modulator within a semiconductor fiber ring laser (SFRL), the second uses cross-gain modulation (XGM) in a semiconductor optical amplifier (SOA) within an SFRL, and the third makes use of XGM in a SOA as well as injection locking in a Fabry-Perot laser diode for wavelength conversion. The benefits and limitations of each approach as well as future improvements are discussed. Building on these results, a high-performance integrated version of XGM swapper is designed, simulated, and masks are produced for fabrication using indium phosphide technology.
A fast technique for computing syndromes of BCH and RS codes. [deep space network
NASA Technical Reports Server (NTRS)
Reed, I. S.; Truong, T. K.; Miller, R. L.
1979-01-01
A combination of the Chinese Remainder Theorem and Winograd's algorithm is used to compute transforms of odd length over GF(2 to the m power). Such transforms are used to compute the syndromes needed for decoding CBH and RS codes. The present scheme requires substantially fewer multiplications and additions than the conventional method of computing the syndromes directly.
Technology Infusion of CodeSonar into the Space Network Ground Segment (RII07)
NASA Technical Reports Server (NTRS)
Benson, Markland
2008-01-01
The NASA Software Assurance Research Program (in part) performs studies as to the feasibility of technologies for improving the safety, quality, reliability, cost, and performance of NASA software. This study considers the application of commercial automated source code analysis tools to mission critical ground software that is in the operations and sustainment portion of the product lifecycle.
Liu, Andong; Yu, Li; Zhang, Wen-An
2011-01-01
The receding horizon H(∞) control (RHHC) problem is investigated in this paper for a class of networked control systems (NCSs) with random delay and packet disordering. A new model is proposed to describe the NCS with random delay which may be larger than one sampling period. The random delay is modeled as a Markov chain while the closed-loop system is described as a Markovian jump system. Sufficient conditions for the closed-loop NCS to be stochastically stable and the performance index to be upper bounded are derived by using the receding optimization principle. Furthermore, by solving a semi-definite programming (SDP) with linear matrix inequalities (LMIs) constraint, a piecewise-constant receding horizon H(∞) controller is obtained, and the designed piecewise-constant controller ensures that the closed-loop NCS achieves a prescribed H(∞) disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.
Stevens, Allen D.; Hernandez, Caleb; Jones, Seth; Moreira, Maria E.; Blumen, Jason R.; Hopkins, Emily; Sande, Margaret; Bakes, Katherine; Haukoos, Jason S.
2016-01-01
Background Medication dosing errors remain commonplace and may result in potentially life-threatening outcomes, particularly for pediatric patients where dosing often requires weight-based calculations. Novel medication delivery systems that may reduce dosing errors resonate with national healthcare priorities. Our goal was to evaluate novel, prefilled medication syringes labeled with color-coded volumes corresponding to the weight-based dosing of the Broselow Tape, compared to conventional medication administration, in simulated prehospital pediatric resuscitation scenarios. Methods We performed a prospective, block-randomized, cross-over study, where 10 full-time paramedics each managed two simulated pediatric arrests in situ using either prefilled, color-coded-syringes (intervention) or their own medication kits stocked with conventional ampoules (control). Each paramedic was paired with two emergency medical technicians to provide ventilations and compressions as directed. The ambulance patient compartment and the intravenous medication port were video recorded. Data were extracted from video review by blinded, independent reviewers. Results Median time to delivery of all doses for the intervention and control groups was 34 (95% CI: 28–39) seconds and 42 (95% CI: 36–51) seconds, respectively (difference = 9 [95% CI: 4–14] seconds). Using the conventional method, 62 doses were administered with 24 (39%) critical dosing errors; using the prefilled, color-coded syringe method, 59 doses were administered with 0 (0%) critical dosing errors (difference = 39%, 95% CI: 13–61%). Conclusions A novel color-coded, prefilled syringe decreased time to medication administration and significantly reduced critical dosing errors by paramedics during simulated prehospital pediatric resuscitations. PMID:26247145
NASA Astrophysics Data System (ADS)
Choo, Yen; Klug, Aaron
1994-11-01
We have used two selection techniques to study sequence-specific DNA recognition by the zinc finger, a small, modular DNA-binding minidomain. We have chosen zinc fingers because they bind as independent modules and so can be linked together in a peptide designed to bind a predetermined DNA site. In this paper, we describe how a library of zinc fingers displayed on the surface of bacteriophage enables selection of fingers capable of binding to given DNA triplets. The amino acid sequences of selected fingers which bind the same triplet are compared to examine how sequence-specific DNA recognition occurs. Our results can be rationalized in terms of coded interactions between zinc fingers and DNA, involving base contacts from a few α-helical positions. In the paper following this one, we describe a complementary technique which confirms the identity of amino acids capable of DNA sequence discrimination from these positions.
Lee, Dongyul; Lee, Chaewoo
2014-01-01
The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm.
Lee, Chaewoo
2014-01-01
The advancement in wideband wireless network supports real time services such as IPTV and live video streaming. However, because of the sharing nature of the wireless medium, efficient resource allocation has been studied to achieve a high level of acceptability and proliferation of wireless multimedia. Scalable video coding (SVC) with adaptive modulation and coding (AMC) provides an excellent solution for wireless video streaming. By assigning different modulation and coding schemes (MCSs) to video layers, SVC can provide good video quality to users in good channel conditions and also basic video quality to users in bad channel conditions. For optimal resource allocation, a key issue in applying SVC in the wireless multicast service is how to assign MCSs and the time resources to each SVC layer in the heterogeneous channel condition. We formulate this problem with integer linear programming (ILP) and provide numerical results to show the performance under 802.16 m environment. The result shows that our methodology enhances the overall system throughput compared to an existing algorithm. PMID:25276862
Structural Effects of Network Sampling Coverage I: Nodes Missing at Random1
Smith, Jeffrey A.; Moody, James
2013-01-01
Network measures assume a census of a well-bounded population. This level of coverage is rarely achieved in practice, however, and we have only limited information on the robustness of network measures to incomplete coverage. This paper examines the effect of node-level missingness on 4 classes of network measures: centrality, centralization, topology and homophily across a diverse sample of 12 empirical networks. We use a Monte Carlo simulation process to generate data with known levels of missingness and compare the resulting network scores to their known starting values. As with past studies (Borgatti et al 2006; Kossinets 2006), we find that measurement bias generally increases with more missing data. The exact rate and nature of this increase, however, varies systematically across network measures. For example, betweenness and Bonacich centralization are quite sensitive to missing data while closeness and in-degree are robust. Similarly, while the tau statistic and distance are difficult to capture with missing data, transitivity shows little bias even with very high levels of missingness. The results are also clearly dependent on the features of the network. Larger, more centralized networks are generally more robust to missing data, but this is especially true for centrality and centralization measures. More cohesive networks are robust to missing data when measuring topological features but not when measuring centralization. Overall, the results suggest that missing data may have quite large or quite small effects on network measurement, depending on the type of network and the question being posed. PMID:24311893
NASA Astrophysics Data System (ADS)
Bashtani, Farzad; Maini, Brij; Kantzas, Apostolos
2016-08-01
3D random networks are constructed in order to represent the tight Mesaverde formation which is located in north Wyoming, USA. The porous-space is represented by pore bodies of different shapes and sizes which are connected to each other by pore throats of varying length and diameter. Pore bodies are randomly distributed in space and their connectivity varies based on the connectivity number distribution which is used in order to generate the network. Network representations are then validated using publicly available mercury porosimetry experiments. The network modeling software solves the fundamental equations of two-phase immiscible flow incorporating wettability and contact angle variability. Quasi-static displacement is assumed. Single phase macroscopic properties (porosity, permeability) are calculated and whenever possible are compared to experimental data. Using this information drainage and imbibition capillary pressure, and relative permeability curves are predicted and (whenever possible) compared to experimental data. The calculated information is grouped and compared to available literature information on typical behavior of tight formations. Capillary pressure curve for primary drainage process is predicted and compared to experimental mercury porosimetry in order to validate the virtual porous media by history matching. Relative permeability curves are also calculated and presented.
NASA Astrophysics Data System (ADS)
Métens, S.; Monceau, P.; Renault, R.; Bottani, S.
2016-03-01
We start from a continuous extension of a mean field approach of the quorum percolation model, accounting for the response of in vitro neuronal cultures, to carry out a normal form analysis of the critical behavior. We highlight the effects of nonlinearities associated with this mean field approach even in the close vicinity of the critical point. Statistical properties of random networks with Gaussian in-degree are related to the outcoming links distribution. Finite size analysis of explicit Monte Carlo simulations enables us to confirm the relevance of the mean field approach on such networks and to show that the order parameter is weakly self-averaging; dynamical relaxation is investigated. Furthermore we derive a mean field equation taking into account the effect of inhibitory neurons and discuss the equivalence with a purely excitatory network.
Temporal coding in a silicon network of integrate-and-fire neurons.
Liu, Shih-Chii; Douglas, Rodney
2004-09-01
Spatio-temporal processing of spike trains by neuronal networks depends on a variety of mechanisms distributed across synapses, dendrites, and somata. In natural systems, the spike trains and the processing mechanisms cohere though their common physical instantiation. This coherence is lost when the natural system is encoded for simulation on a general purpose computer. By contrast, analog VLSI circuits are, like neurons, inherently related by their real-time physics, and so, could provide a useful substrate for exploring neuronlike event-based processing. Here, we describe a hybrid analog-digital VLSI chip comprising a set of integrate-and-fire neurons and short-term dynamical synapses that can be configured into simple network architectures with some properties of neocortical neuronal circuits. We show that, despite considerable fabrication variance in the properties of individual neurons, the chip offers a viable substrate for exploring real-time spike-based processing in networks of neurons.