TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

Efficient Deterministic Finite Automata Minimization Based on Backward Depth Information.

PubMed

Liu, Desheng; Huang, Zhiping; Zhang, Yimeng; Guo, Xiaojun; Su, Shaojing

2016-01-01

Obtaining a minimal automaton is a fundamental issue in the theory and practical implementation of deterministic finite automatons (DFAs). A minimization algorithm is presented in this paper that consists of two main phases. In the first phase, the backward depth information is built, and the state set of the DFA is partitioned into many blocks. In the second phase, the state set is refined using a hash table. The minimization algorithm has a lower time complexity O(n) than a naive comparison of transitions O(n2). Few states need to be refined by the hash table, because most states have been partitioned by the backward depth information in the coarse partition. This method achieves greater generality than previous methods because building the backward depth information is independent of the topological complexity of the DFA. The proposed algorithm can be applied not only to the minimization of acyclic automata or simple cyclic automata, but also to automata with high topological complexity. Overall, the proposal has three advantages: lower time complexity, greater generality, and scalability. A comparison to Hopcroft's algorithm demonstrates experimentally that the algorithm runs faster than traditional algorithms.

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

Ifcwall Reconstruction from Unstructured Point Clouds

NASA Astrophysics Data System (ADS)

Bassier, M.; Klein, R.; Van Genechten, B.; Vergauwen, M.

2018-05-01

The automated reconstruction of Building Information Modeling (BIM) objects from point cloud data is still ongoing research. A key aspect is the creation of accurate wall geometry as it forms the basis for further reconstruction of objects in a BIM. After segmenting and classifying the initial point cloud, the labelled segments are processed and the wall topology is reconstructed. However, the preocedure is challenging due to noise, occlusions and the complexity of the input data.In this work, a method is presented to automatically reconstruct consistent wall geometry from point clouds. More specifically, the use of room information is proposed to aid the wall topology creation. First, a set of partial walls is constructed based on classified planar primitives. Next, the rooms are identified using the retrieved wall information along with the floors and ceilings. The wall topology is computed by the intersection of the partial walls conditioned on the room information. The final wall geometry is defined by creating IfcWallStandardCase objects conform the IFC4 standard. The result is a set of walls according to the as-built conditions of a building. The experiments prove that the used method is a reliable framework for wall reconstruction from unstructured point cloud data. Also, the implementation of room information reduces the rate of false positives for the wall topology. Given the walls, ceilings and floors, 94% of the rooms is correctly identified. A key advantage of the proposed method is that it deals with complex rooms and is not bound to single storeys.

Maximum likelihood: Extracting unbiased information from complex networks

NASA Astrophysics Data System (ADS)

Garlaschelli, Diego; Loffredo, Maria I.

2008-07-01

The choice of free parameters in network models is subjective, since it depends on what topological properties are being monitored. However, we show that the maximum likelihood (ML) principle indicates a unique, statistically rigorous parameter choice, associated with a well-defined topological feature. We then find that, if the ML condition is incompatible with the built-in parameter choice, network models turn out to be intrinsically ill defined or biased. To overcome this problem, we construct a class of safely unbiased models. We also propose an extension of these results that leads to the fascinating possibility to extract, only from topological data, the “hidden variables” underlying network organization, making them “no longer hidden.” We test our method on World Trade Web data, where we recover the empirical gross domestic product using only topological information.

Riemann surfaces of complex classical trajectories and tunnelling splitting in one-dimensional systems

NASA Astrophysics Data System (ADS)

Harada, Hiromitsu; Mouchet, Amaury; Shudo, Akira

2017-10-01

The topology of complex classical paths is investigated to discuss quantum tunnelling splittings in one-dimensional systems. Here the Hamiltonian is assumed to be given as polynomial functions, so the fundamental group for the Riemann surface provides complete information on the topology of complex paths, which allows us to enumerate all the possible candidates contributing to the semiclassical sum formula for tunnelling splittings. This naturally leads to action relations among classically disjoined regions, revealing entirely non-local nature in the quantization condition. The importance of the proper treatment of Stokes phenomena is also discussed in Hamiltonians in the normal form.

Visualization of Topology through Simulation

NASA Astrophysics Data System (ADS)

Mulderig, Andrew; Beaucage, Gregory; Vogtt, Karsten; Jiang, Hanqiu

Complex structures can be decomposed into their minimal topological description coupled with complications of tortuosity. We have found that a stick figure representation can account for the topological content of any structure and coupling with scaling measures of tortuosity we can reconstruct an object. This deconstruction is native to static small-angle scattering measurements where we can obtain quantitative measures of the tortuous structure and the minimal topological structure. For example, a crumpled sheet of paper is composed of a minimal sheet structure and parameters reflecting the extent of crumpling. This quantification yields information that can be used to calculate the hydrodynamic radius, radius of gyration, structural conductive pathway, modulus, and other properties of complex structures. The approach is general and has been applied to a wide range of nanostructures from crumpled graphene to branched polymers and unfolded proteins and RNA. In this poster we will demonstrate how simple structural simulations can be used to reconstruct from these parameters a 3d representation of the complex structure through a heuristic approach. Several examples will be given from nano-fractal aggregates.

Coming to Grips with Ambiguity: Ion Mobility-Mass Spectrometry for Protein Quaternary Structure Assignment

NASA Astrophysics Data System (ADS)

Eschweiler, Joseph D.; Frank, Aaron T.; Ruotolo, Brandon T.

2017-10-01

Multiprotein complexes are central to our understanding of cellular biology, as they play critical roles in nearly every biological process. Despite many impressive advances associated with structural characterization techniques, large and highly-dynamic protein complexes are too often refractory to analysis by conventional, high-resolution approaches. To fill this gap, ion mobility-mass spectrometry (IM-MS) methods have emerged as a promising approach for characterizing the structures of challenging assemblies due in large part to the ability of these methods to characterize the composition, connectivity, and topology of large, labile complexes. In this Critical Insight, we present a series of bioinformatics studies aimed at assessing the information content of IM-MS datasets for building models of multiprotein structure. Our computational data highlights the limits of current coarse-graining approaches, and compelled us to develop an improved workflow for multiprotein topology modeling, which we benchmark against a subset of the multiprotein complexes within the PDB. This improved workflow has allowed us to ascertain both the minimal experimental restraint sets required for generation of high-confidence multiprotein topologies, and quantify the ambiguity in models where insufficient IM-MS information is available. We conclude by projecting the future of IM-MS in the context of protein quaternary structure assignment, where we predict that a more complete knowledge of the ultimate information content and ambiguity within such models will undoubtedly lead to applications for a broader array of challenging biomolecular assemblies. [Figure not available: see fulltext.

Efficient Deterministic Finite Automata Minimization Based on Backward Depth Information

PubMed Central

Liu, Desheng; Huang, Zhiping; Zhang, Yimeng; Guo, Xiaojun; Su, Shaojing

2016-01-01

Obtaining a minimal automaton is a fundamental issue in the theory and practical implementation of deterministic finite automatons (DFAs). A minimization algorithm is presented in this paper that consists of two main phases. In the first phase, the backward depth information is built, and the state set of the DFA is partitioned into many blocks. In the second phase, the state set is refined using a hash table. The minimization algorithm has a lower time complexity O(n) than a naive comparison of transitions O(n2). Few states need to be refined by the hash table, because most states have been partitioned by the backward depth information in the coarse partition. This method achieves greater generality than previous methods because building the backward depth information is independent of the topological complexity of the DFA. The proposed algorithm can be applied not only to the minimization of acyclic automata or simple cyclic automata, but also to automata with high topological complexity. Overall, the proposal has three advantages: lower time complexity, greater generality, and scalability. A comparison to Hopcroft’s algorithm demonstrates experimentally that the algorithm runs faster than traditional algorithms. PMID:27806102

Modeling and dynamical topology properties of VANET based on complex networks theory

NASA Astrophysics Data System (ADS)

Zhang, Hong; Li, Jie

2015-01-01

Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What's more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a macroscopic perspective.

Modeling and dynamical topology properties of VANET based on complex networks theory

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

Zhang, Hong; Li, Jie, E-mail: prof.li@foxmail.com

2015-01-15

Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate andmore » control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What’s more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a macroscopic perspective.« less

Topological Phenotypes Constitute a New Dimension in the Phenotypic Space of Leaf Venation Networks

PubMed Central

Ronellenfitsch, Henrik; Lasser, Jana; Daly, Douglas C.; Katifori, Eleni

2015-01-01

The leaves of angiosperms contain highly complex venation networks consisting of recursively nested, hierarchically organized loops. We describe a new phenotypic trait of reticulate vascular networks based on the topology of the nested loops. This phenotypic trait encodes information orthogonal to widely used geometric phenotypic traits, and thus constitutes a new dimension in the leaf venation phenotypic space. We apply our metric to a database of 186 leaves and leaflets representing 137 species, predominantly from the Burseraceae family, revealing diverse topological network traits even within this single family. We show that topological information significantly improves identification of leaves from fragments by calculating a “leaf venation fingerprint” from topology and geometry. Further, we present a phenomenological model suggesting that the topological traits can be explained by noise effects unique to specimen during development of each leaf which leave their imprint on the final network. This work opens the path to new quantitative identification techniques for leaves which go beyond simple geometric traits such as vein density and is directly applicable to other planar or sub-planar networks such as blood vessels in the brain. PMID:26700471

Identifying influential nodes in complex networks: A node information dimension approach

NASA Astrophysics Data System (ADS)

Bian, Tian; Deng, Yong

2018-04-01

In the field of complex networks, how to identify influential nodes is a significant issue in analyzing the structure of a network. In the existing method proposed to identify influential nodes based on the local dimension, the global structure information in complex networks is not taken into consideration. In this paper, a node information dimension is proposed by synthesizing the local dimensions at different topological distance scales. A case study of the Netscience network is used to illustrate the efficiency and practicability of the proposed method.

Multi-attribute integrated measurement of node importance in complex networks.

PubMed

Wang, Shibo; Zhao, Jinlou

2015-11-01

The measure of node importance in complex networks is very important to the research of networks stability and robustness; it also can ensure the security of the whole network. Most researchers have used a single indicator to measure the networks node importance, so that the obtained measurement results only reflect certain aspects of the networks with a loss of information. Meanwhile, because of the difference of networks topology, the nodes' importance should be described by combining the character of the networks topology. Most of the existing evaluation algorithms cannot completely reflect the circumstances of complex networks, so this paper takes into account the degree of centrality, the relative closeness centrality, clustering coefficient, and topology potential and raises an integrated measuring method to measure the nodes' importance. This method can reflect nodes' internal and outside attributes and eliminate the influence of network structure on the node importance. The experiments of karate network and dolphin network show that networks topology structure integrated measure has smaller range of metrical result than a single indicator and more universal. Experiments show that attacking the North American power grid and the Internet network with the method has a faster convergence speed than other methods.

Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction.

PubMed

Cang, Zixuan; Wei, Guo-Wei

2018-02-01

Protein-ligand binding is a fundamental biological process that is paramount to many other biological processes, such as signal transduction, metabolic pathways, enzyme construction, cell secretion, and gene expression. Accurate prediction of protein-ligand binding affinities is vital to rational drug design and the understanding of protein-ligand binding and binding induced function. Existing binding affinity prediction methods are inundated with geometric detail and involve excessively high dimensions, which undermines their predictive power for massive binding data. Topology provides the ultimate level of abstraction and thus incurs too much reduction in geometric information. Persistent homology embeds geometric information into topological invariants and bridges the gap between complex geometry and abstract topology. However, it oversimplifies biological information. This work introduces element specific persistent homology (ESPH) or multicomponent persistent homology to retain crucial biological information during topological simplification. The combination of ESPH and machine learning gives rise to a powerful paradigm for macromolecular analysis. Tests on 2 large data sets indicate that the proposed topology-based machine-learning paradigm outperforms other existing methods in protein-ligand binding affinity predictions. ESPH reveals protein-ligand binding mechanism that can not be attained from other conventional techniques. The present approach reveals that protein-ligand hydrophobic interactions are extended to 40Å  away from the binding site, which has a significant ramification to drug and protein design. Copyright © 2017 John Wiley & Sons, Ltd.

Two-stage Framework for a Topology-Based Projection and Visualization of Classified Document Collections

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

Oesterling, Patrick; Scheuermann, Gerik; Teresniak, Sven

During the last decades, electronic textual information has become the world's largest and most important information source available. People have added a variety of daily newspapers, books, scientific and governmental publications, blogs and private messages to this wellspring of endless information and knowledge. Since neither the existing nor the new information can be read in its entirety, computers are used to extract and visualize meaningful or interesting topics and documents from this huge information clutter. In this paper, we extend, improve and combine existing individual approaches into an overall framework that supports topological analysis of high dimensional document point cloudsmore » given by the well-known tf-idf document-term weighting method. We show that traditional distance-based approaches fail in very high dimensional spaces, and we describe an improved two-stage method for topology-based projections from the original high dimensional information space to both two dimensional (2-D) and three dimensional (3-D) visualizations. To show the accuracy and usability of this framework, we compare it to methods introduced recently and apply it to complex document and patent collections.« less

Occlusions in Camera Networks and Vision: The Bridge between Topological Recovery and Metric Reconstruction

DTIC Science & Technology

2009-05-18

serves as a didactic tool to understand the information required for the approach to coordinate free tracking and navigation problems. Observe that the...layout (left), and in the CN -Complex (right). These paths can be compared by using the algebraic topological tools covered in chapter 2. . . . 34 3.9...right). mathematical tools necessary to make our discussion formal; chapter 3 will present the construction of a simplicial representation called

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

PubMed Central

2012-01-01

Background Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges. Results To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. Conclusions We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes. PMID:23046740

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Comparative anatomy of the vestibular nuclear complex in submammalian vertebrates.

NASA Technical Reports Server (NTRS)

Mehler, W. R.

1972-01-01

A synopsis of the literature on the natural history of the vestibular nuclear complex (VNC) in lower vertebrates is presented in an attempt to assess the knowledge available. The review discloses that there is considerable descriptive information that is widely dispersed in the literature. However, information about the topology, number, and cellular composition of the cell groups that compose the VNC is sketchy. Major cytological and hodological information is still needed to establish which parts of the VNC actually are homologous.

A perspective on the advancement of natural language processing tasks via topological analysis of complex networks. Comment on "Approaching human language with complex networks" by Cong and Liu

NASA Astrophysics Data System (ADS)

Amancio, Diego Raphael

2014-12-01

Concepts and methods of complex networks have been applied to probe the properties of a myriad of real systems [1]. The finding that written texts modeled as graphs share several properties of other completely different real systems has inspired the study of language as a complex system [2]. Actually, language can be represented as a complex network in its several levels of complexity. As a consequence, morphological, syntactical and semantical properties have been employed in the construction of linguistic networks [3]. Even the character level has been useful to unfold particular patterns [4,5]. In the review by Cong and Liu [6], the authors emphasize the need to use the topological information of complex networks modeling the various spheres of the language to better understand its origins, evolution and organization. In addition, the authors cite the use of networks in applications aiming at holistic typology and stylistic variations. In this context, I will discuss some possible directions that could be followed in future research directed towards the understanding of language via topological characterization of complex linguistic networks. In addition, I will comment the use of network models for language processing applications. Additional prospects for future practical research lines will also be discussed in this comment.

Optimal network alignment with graphlet degree vectors.

PubMed

Milenković, Tijana; Ng, Weng Leong; Hayes, Wayne; Przulj, Natasa

2010-06-30

Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.

Representation and display of vector field topology in fluid flow data sets

NASA Technical Reports Server (NTRS)

Helman, James; Hesselink, Lambertus

1989-01-01

The visualization of physical processes in general and of vector fields in particular is discussed. An approach to visualizing flow topology that is based on the physics and mathematics underlying the physical phenomenon is presented. It involves determining critical points in the flow where the velocity vector vanishes. The critical points, connected by principal lines or planes, determine the topology of the flow. The complexity of the data is reduced without sacrificing the quantitative nature of the data set. By reducing the original vector field to a set of critical points and their connections, a representation of the topology of a two-dimensional vector field that is much smaller than the original data set but retains with full precision the information pertinent to the flow topology is obtained. This representation can be displayed as a set of points and tangent curves or as a graph. Analysis (including algorithms), display, interaction, and implementation aspects are discussed.

ClueNet: Clustering a temporal network based on topological similarity rather than denseness.

PubMed

Crawford, Joseph; Milenković, Tijana

2018-01-01

Network clustering is a very popular topic in the network science field. Its goal is to divide (partition) the network into groups (clusters or communities) of "topologically related" nodes, where the resulting topology-based clusters are expected to "correlate" well with node label information, i.e., metadata, such as cellular functions of genes/proteins in biological networks, or age or gender of people in social networks. Even for static data, the problem of network clustering is complex. For dynamic data, the problem is even more complex, due to an additional dimension of the data-their temporal (evolving) nature. Since the problem is computationally intractable, heuristic approaches need to be sought. Existing approaches for dynamic network clustering (DNC) have drawbacks. First, they assume that nodes should be in the same cluster if they are densely interconnected within the network. We hypothesize that in some applications, it might be of interest to cluster nodes that are topologically similar to each other instead of or in addition to requiring the nodes to be densely interconnected. Second, they ignore temporal information in their early steps, and when they do consider this information later on, they do so implicitly. We hypothesize that capturing temporal information earlier in the clustering process and doing so explicitly will improve results. We test these two hypotheses via our new approach called ClueNet. We evaluate ClueNet against six existing DNC methods on both social networks capturing evolving interactions between individuals (such as interactions between students in a high school) and biological networks capturing interactions between biomolecules in the cell at different ages. We find that ClueNet is superior in over 83% of all evaluation tests. As more real-world dynamic data are becoming available, DNC and thus ClueNet will only continue to gain importance.

Fast algorithm for automatically computing Strahler stream order

USGS Publications Warehouse

Lanfear, Kenneth J.

1990-01-01

An efficient algorithm was developed to determine Strahler stream order for segments of stream networks represented in a Geographic Information System (GIS). The algorithm correctly assigns Strahler stream order in topologically complex situations such as braided streams and multiple drainage outlets. Execution time varies nearly linearly with the number of stream segments in the network. This technique is expected to be particularly useful for studying the topology of dense stream networks derived from digital elevation model data.

Topological Aspects of Information Retrieval.

ERIC Educational Resources Information Center

Egghe, Leo; Rousseau, Ronald

1998-01-01

Discusses topological aspects of theoretical information retrieval, including retrieval topology; similarity topology; pseudo-metric topology; document spaces as topological spaces; Boolean information retrieval as a subsystem of any topological system; and proofs of theorems. (LRW)

Probing the Topological Properties of Complex Networks Modeling Short Written Texts

PubMed Central

Amancio, Diego R.

2015-01-01

In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well—many informative discoveries have been made this way—but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyses performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks. PMID:25719799

Self-determined mechanisms in complex networks

NASA Astrophysics Data System (ADS)

Liu, Yang; Yuan, Jian; Shan, Xiuming; Ren, Yong; Ma, Zhengxin

2008-03-01

Self-organized networks are pervasive in communication systems such as the Internet, overlay networks, peer-to-peer networks, and cluster-based services. These networks evolve into complex topologies, under specific driving forces, i.e. user demands, technological innovations, design objectives and so on. Our study focuses on the driving forces behind individual evolutions of network components, and their stimulation and domination to the self-organized networks which are defined as self-determined mechanisms in this paper. Understanding forces underlying the evolution of networks should enable informed design decisions and help to avoid unwanted surprises, such as congestion collapse. A case study on the macroscopic evolution of the Internet topology of autonomous systems under a specific driving force is then presented. Using computer simulations, it is found that the power-law degree distribution can originate from a connection preference to larger numbers of users, and that the small-world property can be caused by rapid growth in the number of users. Our results provide a new feasible perspective to understand intrinsic fundamentals in the topological evolution of complex networks.

Optimality problem of network topology in stocks market analysis

NASA Astrophysics Data System (ADS)

Djauhari, Maman Abdurachman; Gan, Siew Lee

2015-02-01

Since its introduction fifteen years ago, minimal spanning tree has become an indispensible tool in econophysics. It is to filter the important economic information contained in a complex system of financial markets' commodities. Here we show that, in general, that tool is not optimal in terms of topological properties. Consequently, the economic interpretation of the filtered information might be misleading. To overcome that non-optimality problem, a set of criteria and a selection procedure of an optimal minimal spanning tree will be developed. By using New York Stock Exchange data, the advantages of the proposed method will be illustrated in terms of the power-law of degree distribution.

Persistent topological features of dynamical systems

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

Maletić, Slobodan, E-mail: slobodan@hitsz.edu.cn; Institute of Nuclear Sciences Vinča, University of Belgrade, Belgrade; Zhao, Yi, E-mail: zhao.yi@hitsz.edu.cn

Inspired by an early work of Muldoon et al., Physica D 65, 1–16 (1993), we present a general method for constructing simplicial complex from observed time series of dynamical systems based on the delay coordinate reconstruction procedure. The obtained simplicial complex preserves all pertinent topological features of the reconstructed phase space, and it may be analyzed from topological, combinatorial, and algebraic aspects. In focus of this study is the computation of homology of the invariant set of some well known dynamical systems that display chaotic behavior. Persistent homology of simplicial complex and its relationship with the embedding dimensions are examinedmore » by studying the lifetime of topological features and topological noise. The consistency of topological properties for different dynamic regimes and embedding dimensions is examined. The obtained results shed new light on the topological properties of the reconstructed phase space and open up new possibilities for application of advanced topological methods. The method presented here may be used as a generic method for constructing simplicial complex from a scalar time series that has a number of advantages compared to the mapping of the same time series to a complex network.« less

On genera of curves from high-loop generalized unitarity cuts

NASA Astrophysics Data System (ADS)

Huang, Rijun; Zhang, Yang

2013-04-01

Generalized unitarity cut of a Feynman diagram generates an algebraic system of polynomial equations. At high-loop levels, these equations may define a complex curve or a (hyper-)surface with complicated topology. We study the curve cases, i.e., a 4-dimensional L-loop diagram with (4 L-1) cuts. The topology of a complex curve is classified by its genus. Hence in this paper, we use computational algebraic geometry to calculate the genera of curves from two and three-loop unitarity cuts. The global structure of degenerate on-shell equations under some specific kinematic configurations is also sketched. The genus information can also be used to judge if a unitary cut solution could be rationally parameterized.

Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function.

PubMed

Reimann, Michael W; Nolte, Max; Scolamiero, Martina; Turner, Katharine; Perin, Rodrigo; Chindemi, Giuseppe; Dłotko, Paweł; Levi, Ran; Hess, Kathryn; Markram, Henry

2017-01-01

The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence toward peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities.

Investigating the Cosmic Web with Topological Data Analysis

NASA Astrophysics Data System (ADS)

Cisewski-Kehe, Jessi; Wu, Mike; Fasy, Brittany; Hellwing, Wojciech; Lovell, Mark; Rinaldo, Alessandro; Wasserman, Larry

2018-01-01

Data exhibiting complicated spatial structures are common in many areas of science (e.g. cosmology, biology), but can be difficult to analyze. Persistent homology is a popular approach within the area of Topological Data Analysis that offers a new way to represent, visualize, and interpret complex data by extracting topological features, which can be used to infer properties of the underlying structures. In particular, TDA may be useful for analyzing the large-scale structure (LSS) of the Universe, which is an intricate and spatially complex web of matter. In order to understand the physics of the Universe, theoretical and computational cosmologists develop large-scale simulations that allow for visualizing and analyzing the LSS under varying physical assumptions. Each point in the 3D data set represents a galaxy or a cluster of galaxies, and topological summaries ("persistent diagrams") can be obtained summarizing the different ordered holes in the data (e.g. connected components, loops, voids).The topological summaries are interesting and informative descriptors of the Universe on their own, but hypothesis tests using the topological summaries would provide a way to make more rigorous comparisons of LSS under different theoretical models. For example, the received cosmological model has cold dark matter (CDM); however, while the case is strong for CDM, there are some observational inconsistencies with this theory. Another possibility is warm dark matter (WDM). It is of interest to see if a CDM Universe and WDM Universe produce LSS that is topologically distinct.We present several possible test statistics for two-sample hypothesis tests using the topological summaries, carryout a simulation study to investigate the suitableness of the proposed test statistics using simulated data from a variation of the Voronoi foam model, and finally we apply the proposed inference framework to WDM vs. CDM cosmological simulation data.

Improving protein complex classification accuracy using amino acid composition profile.

PubMed

Huang, Chien-Hung; Chou, Szu-Yu; Ng, Ka-Lok

2013-09-01

Protein complex prediction approaches are based on the assumptions that complexes have dense protein-protein interactions and high functional similarity between their subunits. We investigated those assumptions by studying the subunits' interaction topology, sequence similarity and molecular function for human and yeast protein complexes. Inclusion of amino acids' physicochemical properties can provide better understanding of protein complex properties. Principal component analysis is carried out to determine the major features. Adopting amino acid composition profile information with the SVM classifier serves as an effective post-processing step for complexes classification. Improvement is based on primary sequence information only, which is easy to obtain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder

PubMed Central

Chen, Jian-Huai; Yao, Zhi-Jian; Qin, Jiao-Long; Yan, Rui; Hua, Ling-Ling; Lu, Qing

2016-01-01

Background: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network. PMID:26960371

TopMaker: Technique Developed for Automatic Multiblock Topology Generation Using the Medial Axis

NASA Technical Reports Server (NTRS)

Rigby, David L.

2004-01-01

The TopMaker technique was developed in an effort to reduce the time required for grid generation in complex numerical studies. Topology generation accounts for much of the man-hours required for structured multiblock grids. With regard to structured multiblock grids, topology refers to how the blocks are arranged and connected. A two-dimensional multiblock topology generation technique has been developed at the NASA Glenn Research Center. Very general configurations can be addressed by the technique. A configuration is defined by a collection of non-intersecting closed curves, which will be referred to as loops. More than a single loop implies that holes exist in the domain, which poses no problem. This technique requires only the medial vertices and the touch points that define each vertex. From the information about the medial vertices, the connectivity between medial vertices is generated. The physical shape of the medial edge is not required. By applying a few simple rules to each medial edge, a multiblock topology can be generated without user intervention. The resulting topologies contain only the level of complexity dictated by the configurations. Grid lines remain attached to the boundary except at sharp concave turns, where a change in index family is introduced as would be desired. Keeping grid lines attached to the boundary is especially important in computational fluid dynamics, where highly clustered grids are used near no-slip boundaries. This technique is simple and robust and can easily be incorporated into the overall grid-generation process.

Edge states at the interface of non-Hermitian systems

NASA Astrophysics Data System (ADS)

Yuce, C.

2018-04-01

Topological edge states appear at the interface of two topologically distinct Hermitian insulators. We study the extension of this idea to non-Hermitian systems. We consider P T -symmetric and topologically distinct non-Hermitian insulators with real spectra and study topological edge states at the interface of them. We show that P T symmetry is spontaneously broken at the interface during the topological phase transition. Therefore, topological edge states with complex energy eigenvalues appear at the interface. We apply our idea to a complex extension of the Su-Schrieffer-Heeger model.

MIMO: an efficient tool for molecular interaction maps overlap

PubMed Central

2013-01-01

Background Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps. Results Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database. Conclusions MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways. PMID:23672344

Inferring topologies via driving-based generalized synchronization of two-layer networks

NASA Astrophysics Data System (ADS)

Wang, Yingfei; Wu, Xiaoqun; Feng, Hui; Lu, Jun-an; Xu, Yuhua

2016-05-01

The interaction topology among the constituents of a complex network plays a crucial role in the network’s evolutionary mechanisms and functional behaviors. However, some network topologies are usually unknown or uncertain. Meanwhile, coupling delays are ubiquitous in various man-made and natural networks. Hence, it is necessary to gain knowledge of the whole or partial topology of a complex dynamical network by taking into consideration communication delay. In this paper, topology identification of complex dynamical networks is investigated via generalized synchronization of a two-layer network. Particularly, based on the LaSalle-type invariance principle of stochastic differential delay equations, an adaptive control technique is proposed by constructing an auxiliary layer and designing proper control input and updating laws so that the unknown topology can be recovered upon successful generalized synchronization. Numerical simulations are provided to illustrate the effectiveness of the proposed method. The technique provides a certain theoretical basis for topology inference of complex networks. In particular, when the considered network is composed of systems with high-dimension or complicated dynamics, a simpler response layer can be constructed, which is conducive to circuit design. Moreover, it is practical to take into consideration perturbations caused by control input. Finally, the method is applicable to infer topology of a subnetwork embedded within a complex system and locate hidden sources. We hope the results can provide basic insight into further research endeavors on understanding practical and economical topology inference of networks.

Neural complexity: A graph theoretic interpretation

NASA Astrophysics Data System (ADS)

Barnett, L.; Buckley, C. L.; Bullock, S.

2011-04-01

One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end, Tononi [Proc. Natl. Acad. Sci. USA.PNASA60027-842410.1073/pnas.91.11.5033 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system’s dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns [Cereb. Cortex53OPAV1047-321110.1093/cercor/10.2.127 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.71.016114 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular, we explicitly establish a dependency of neural complexity on cyclic graph motifs.

Topological phase transitions and quantum Hall effect in the graphene family

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

Ledwith, Patrick John; Kort-Kamp, Wilton Junior de Melo; Dalvit, Diego Alejandro Roberto

Monolayer staggered materials of the graphene family present intrinsic spin-orbit coupling and can be driven through several topological phase transitions using external circularly polarized lasers and static electric or magnetic fields. We show how topological features arising from photoinduced phase transitions and the magnetic-field-induced quantum Hall effect coexist in these materials and simultaneously impact their Hall conductivity through their corresponding charge Chern numbers. We also show that the spectral response of the longitudinal conductivity contains signatures of the various phase-transition boundaries, that the transverse conductivity encodes information about the topology of the band structure, and that both present resonant peaksmore » which can be unequivocally associated with one of the four inequivalent Dirac cones present in these materials. As a result, this complex optoelectronic response can be probed with straightforward Faraday rotation experiments, allowing the study of the crossroads between quantum Hall physics, spintronics, and valleytronics.« less

How cells explore shape space: a quantitative statistical perspective of cellular morphogenesis.

PubMed

Yin, Zheng; Sailem, Heba; Sero, Julia; Ardy, Rico; Wong, Stephen T C; Bakal, Chris

2014-12-01

Through statistical analysis of datasets describing single cell shape following systematic gene depletion, we have found that the morphological landscapes explored by cells are composed of a small number of attractor states. We propose that the topology of these landscapes is in large part determined by cell-intrinsic factors, such as biophysical constraints on cytoskeletal organization, and reflects different stable signaling and/or transcriptional states. Cell-extrinsic factors act to determine how cells explore these landscapes, and the topology of the landscapes themselves. Informational stimuli primarily drive transitions between stable states by engaging signaling networks, while mechanical stimuli tune, or even radically alter, the topology of these landscapes. As environments fluctuate, the topology of morphological landscapes explored by cells dynamically adapts to these fluctuations. Finally we hypothesize how complex cellular and tissue morphologies can be generated from a limited number of simple cell shapes. © 2014 WILEY Periodicals, Inc.

Topological phase transitions and quantum Hall effect in the graphene family

NASA Astrophysics Data System (ADS)

Ledwith, P.; Kort-Kamp, W. J. M.; Dalvit, D. A. R.

2018-04-01

Monolayer staggered materials of the graphene family present intrinsic spin-orbit coupling and can be driven through several topological phase transitions using external circularly polarized lasers and static electric or magnetic fields. We show how topological features arising from photoinduced phase transitions and the magnetic-field-induced quantum Hall effect coexist in these materials and simultaneously impact their Hall conductivity through their corresponding charge Chern numbers. We also show that the spectral response of the longitudinal conductivity contains signatures of the various phase-transition boundaries, that the transverse conductivity encodes information about the topology of the band structure, and that both present resonant peaks which can be unequivocally associated with one of the four inequivalent Dirac cones present in these materials. This complex optoelectronic response can be probed with straightforward Faraday rotation experiments, allowing the study of the crossroads between quantum Hall physics, spintronics, and valleytronics.

Topological phase transitions and quantum Hall effect in the graphene family

DOE PAGES

Ledwith, Patrick John; Kort-Kamp, Wilton Junior de Melo; Dalvit, Diego Alejandro Roberto

2018-04-15

Monolayer staggered materials of the graphene family present intrinsic spin-orbit coupling and can be driven through several topological phase transitions using external circularly polarized lasers and static electric or magnetic fields. We show how topological features arising from photoinduced phase transitions and the magnetic-field-induced quantum Hall effect coexist in these materials and simultaneously impact their Hall conductivity through their corresponding charge Chern numbers. We also show that the spectral response of the longitudinal conductivity contains signatures of the various phase-transition boundaries, that the transverse conductivity encodes information about the topology of the band structure, and that both present resonant peaksmore » which can be unequivocally associated with one of the four inequivalent Dirac cones present in these materials. As a result, this complex optoelectronic response can be probed with straightforward Faraday rotation experiments, allowing the study of the crossroads between quantum Hall physics, spintronics, and valleytronics.« less

Complex network approach to fractional time series

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

Manshour, Pouya

In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacencymore » matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.« less

Evolution and development of brain networks: from Caenorhabditis elegans to Homo sapiens.

PubMed

Kaiser, Marcus; Varier, Sreedevi

2011-01-01

Neural networks show a progressive increase in complexity during the time course of evolution. From diffuse nerve nets in Cnidaria to modular, hierarchical systems in macaque and humans, there is a gradual shift from simple processes involving a limited amount of tasks and modalities to complex functional and behavioral processing integrating different kinds of information from highly specialized tissue. However, studies in a range of species suggest that fundamental similarities, in spatial and topological features as well as in developmental mechanisms for network formation, are retained across evolution. 'Small-world' topology and highly connected regions (hubs) are prevalent across the evolutionary scale, ensuring efficient processing and resilience to internal (e.g. lesions) and external (e.g. environment) changes. Furthermore, in most species, even the establishment of hubs, long-range connections linking distant components, and a modular organization, relies on similar mechanisms. In conclusion, evolutionary divergence leads to greater complexity while following essential developmental constraints.

ClueNet: Clustering a temporal network based on topological similarity rather than denseness

PubMed Central

Milenković, Tijana

2018-01-01

Network clustering is a very popular topic in the network science field. Its goal is to divide (partition) the network into groups (clusters or communities) of “topologically related” nodes, where the resulting topology-based clusters are expected to “correlate” well with node label information, i.e., metadata, such as cellular functions of genes/proteins in biological networks, or age or gender of people in social networks. Even for static data, the problem of network clustering is complex. For dynamic data, the problem is even more complex, due to an additional dimension of the data—their temporal (evolving) nature. Since the problem is computationally intractable, heuristic approaches need to be sought. Existing approaches for dynamic network clustering (DNC) have drawbacks. First, they assume that nodes should be in the same cluster if they are densely interconnected within the network. We hypothesize that in some applications, it might be of interest to cluster nodes that are topologically similar to each other instead of or in addition to requiring the nodes to be densely interconnected. Second, they ignore temporal information in their early steps, and when they do consider this information later on, they do so implicitly. We hypothesize that capturing temporal information earlier in the clustering process and doing so explicitly will improve results. We test these two hypotheses via our new approach called ClueNet. We evaluate ClueNet against six existing DNC methods on both social networks capturing evolving interactions between individuals (such as interactions between students in a high school) and biological networks capturing interactions between biomolecules in the cell at different ages. We find that ClueNet is superior in over 83% of all evaluation tests. As more real-world dynamic data are becoming available, DNC and thus ClueNet will only continue to gain importance. PMID:29738568

Efficiently computing and deriving topological relation matrices between complex regions with broad boundaries

NASA Astrophysics Data System (ADS)

Du, Shihong; Guo, Luo; Wang, Qiao; Qin, Qimin

The extended 9-intersection matrix is used to formalize topological relations between uncertain regions while it is designed to satisfy the requirements at a concept level, and to deal with the complex regions with broad boundaries (CBBRs) as a whole without considering their hierarchical structures. In contrast to simple regions with broad boundaries, CBBRs have complex hierarchical structures. Therefore, it is necessary to take into account the complex hierarchical structure and to represent the topological relations between all regions in CBBRs as a relation matrix, rather than using the extended 9-intersection matrix to determine topological relations. In this study, a tree model is first used to represent the intrinsic configuration of CBBRs hierarchically. Then, the reasoning tables are presented for deriving topological relations between child, parent and sibling regions from the relations between two given regions in CBBRs. Finally, based on the reasoning, efficient methods are proposed to compute and derive the topological relation matrix. The proposed methods can be incorporated into spatial databases to facilitate geometric-oriented applications.

Effects in the network topology due to node aggregation: Empirical evidence from the domestic maritime transportation in Greece

NASA Astrophysics Data System (ADS)

Tsiotas, Dimitrios; Polyzos, Serafeim

2018-02-01

This article studies the topological consistency of spatial networks due to node aggregation, examining the changes captured between different network representations that result from nodes' grouping and they refer to the same socioeconomic system. The main purpose of this study is to evaluate what kind of topological information remains unalterable due to node aggregation and, further, to develop a framework for linking the data of an empirical network with data of its socioeconomic environment, when the latter are available for hierarchically higher levels of aggregation, in an effort to promote the interdisciplinary research in the field of complex network analysis. The research question is empirically tested on topological and socioeconomic data extracted from the Greek Maritime Network (GMN) that is modeled as a non-directed multilayer (bilayer) graph consisting of a port-layer, where nodes represent ports, and a prefecture-layer, where nodes represent coastal and insular prefectural groups of ports. The analysis highlights that the connectivity (degree) of the GMN is the most consistent aspect of this multilayer network, which preserves both the topological and the socioeconomic information through node aggregation. In terms of spatial analysis and regional science, such effects illustrate the effectiveness of the prefectural administrative division for the functionality of the Greek maritime transportation system. Overall, this approach proposes a methodological framework that can enjoy further applications about the grouping effects induced on the network topology, providing physical, technical, socioeconomic, strategic or political insights.

Using Lattice Topology Information to Investigate Persistent Scatterers at Facades in Urban Areas

NASA Astrophysics Data System (ADS)

Schack, L.; Soergel, U.

2013-05-01

Modern spaceborne SAR sensors like TerraSAR-X offer ground resolution of up to one meter in range and azimuth direction. Buildings, roads, bridges, and other man-made structures appear in such data often as regular patterns of strong and temporally stable points (Persistent Scatterer, PS). As one step in the process of unveiling what object structure actually causes the PS (i.e., physical nature) we compare those regular structures in SAR data to their correspondences in optical imagery. We use lattices as a common data representation for visible facades. By exploiting the topology information given by the lattices we can complete gaps in the structures which is one step towards the understanding of the complex scattering characteristics of distinct facade objects.

Topology and Oligomerization of Mono- and Oligomeric Proteins Regulate Their Half-Lives in the Cell.

PubMed

Mallik, Saurav; Kundu, Sudip

2018-06-05

To find additional structural constraints (besides disordered segments) that regulate protein half-life in the cell, we herein assess the influence of native topology of monomeric and sequestration of oligomeric proteins into multimeric complexes in yeast, human, and mouse. Native topology acts as a molecular marker of globular protein's mechanical resistance and consequently captures their half-life variations on genome scale. Sequestration into multimeric complexes elongates oligomeric protein half-life in the cell, presumably by burying ubiquitinoylation sites and disordered segments required for proteasomal recognition. The latter effect is stronger for proteins associated with multiple complexes and for those binding early during complex self-assembly, including proteins that oligomerize with large proportions of surface buried. After gene duplication, diversification of topology and sequestration into non-identical sets of complexes alter half-lives of paralogous proteins during the course of evolution. Thus, native topology and sequestration into multimeric complexes reflect designing principles of proteins to regulate their half-lives. Copyright © 2018 Elsevier Ltd. All rights reserved.

Rule-based topology system for spatial databases to validate complex geographic datasets

NASA Astrophysics Data System (ADS)

Martinez-Llario, J.; Coll, E.; Núñez-Andrés, M.; Femenia-Ribera, C.

2017-06-01

A rule-based topology software system providing a highly flexible and fast procedure to enforce integrity in spatial relationships among datasets is presented. This improved topology rule system is built over the spatial extension Jaspa. Both projects are open source, freely available software developed by the corresponding author of this paper. Currently, there is no spatial DBMS that implements a rule-based topology engine (considering that the topology rules are designed and performed in the spatial backend). If the topology rules are applied in the frontend (as in many GIS desktop programs), ArcGIS is the most advanced solution. The system presented in this paper has several major advantages over the ArcGIS approach: it can be extended with new topology rules, it has a much wider set of rules, and it can mix feature attributes with topology rules as filters. In addition, the topology rule system can work with various DBMSs, including PostgreSQL, H2 or Oracle, and the logic is performed in the spatial backend. The proposed topology system allows users to check the complex spatial relationships among features (from one or several spatial layers) that require some complex cartographic datasets, such as the data specifications proposed by INSPIRE in Europe and the Land Administration Domain Model (LADM) for Cadastral data.

Creating complex molecular topologies by configuring DNA four-way junctions

NASA Astrophysics Data System (ADS)

Liu, Di; Chen, Gang; Akhter, Usman; Cronin, Timothy M.; Weizmann, Yossi

2016-10-01

The realization of complex topologies at the molecular level represents a grand challenge in chemistry. This necessitates the manipulation of molecular interactions with high precision. Here we show that single-stranded DNA (ssDNA) knots and links can be created by utilizing the inherent topological properties that pertain to the DNA four-way junction, at which the two helical strands form a node and can be configured conveniently and connected for complex topological construction. Using this strategy, we produced series of ssDNA topoisomers with the same sequences. By finely designing the curvature and torsion, double-stranded DNA knots were accessed by hybridizing and ligating the complementary strands with the knotted ssDNA templates. Furthermore, we demonstrate the use of a constructed ssDNA knot both to probe the topological conversion catalysed by DNA topoisomerase and to study the DNA replication under topological constraint.

Complex Road Intersection Modelling Based on Low-Frequency GPS Track Data

NASA Astrophysics Data System (ADS)

Huang, J.; Deng, M.; Zhang, Y.; Liu, H.

2017-09-01

It is widely accepted that digital map becomes an indispensable guide for human daily traveling. Traditional road network maps are produced in the time-consuming and labour-intensive ways, such as digitizing printed maps and extraction from remote sensing images. At present, a large number of GPS trajectory data collected by floating vehicles makes it a reality to extract high-detailed and up-to-date road network information. Road intersections are often accident-prone areas and very critical to route planning and the connectivity of road networks is mainly determined by the topological geometry of road intersections. A few studies paid attention on detecting complex road intersections and mining the attached traffic information (e.g., connectivity, topology and turning restriction) from massive GPS traces. To the authors' knowledge, recent studies mainly used high frequency (1 s sampling rate) trajectory data to detect the crossroads regions or extract rough intersection models. It is still difficult to make use of low frequency (20-100 s) and easily available trajectory data to modelling complex road intersections geometrically and semantically. The paper thus attempts to construct precise models for complex road intersection by using low frequency GPS traces. We propose to firstly extract the complex road intersections by a LCSS-based (Longest Common Subsequence) trajectory clustering method, then delineate the geometry shapes of complex road intersections by a K-segment principle curve algorithm, and finally infer the traffic constraint rules inside the complex intersections.

Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

PubMed

Martínez-Romero, Marcos; Vázquez-Naya, José M; Rabuñal, Juan R; Pita-Fernández, Salvador; Macenlle, Ramiro; Castro-Alvariño, Javier; López-Roses, Leopoldo; Ulla, José L; Martínez-Calvo, Antonio V; Vázquez, Santiago; Pereira, Javier; Porto-Pazos, Ana B; Dorado, Julián; Pazos, Alejandro; Munteanu, Cristian R

2010-05-01

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

Positivity of the universal pairing in 3 dimensions

NASA Astrophysics Data System (ADS)

Calegari, Danny; Freedman, Michael H.; Walker, Kevin

2010-01-01

Associated to a closed, oriented surface S is the complex vector space with basis the set of all compact, oriented 3 -manifolds which it bounds. Gluing along S defines a Hermitian pairing on this space with values in the complex vector space with basis all closed, oriented 3 -manifolds. The main result in this paper is that this pairing is positive, i.e. that the result of pairing a nonzero vector with itself is nonzero. This has bearing on the question of what kinds of topological information can be extracted in principle from unitary (2+1) -dimensional TQFTs. The proof involves the construction of a suitable complexity function c on all closed 3 -manifolds, satisfying a gluing axiom which we call the topological Cauchy-Schwarz inequality, namely that c(AB) le max(c(AA),c(BB)) for all A,B which bound S , with equality if and only if A=B . The complexity function c involves input from many aspects of 3 -manifold topology, and in the process of establishing its key properties we obtain a number of results of independent interest. For example, we show that when two finite-volume hyperbolic 3 -manifolds are glued along an incompressible acylindrical surface, the resulting hyperbolic 3 -manifold has minimal volume only when the gluing can be done along a totally geodesic surface; this generalizes a similar theorem for closed hyperbolic 3 -manifolds due to Agol-Storm-Thurston.

An Adaptive Complex Network Model for Brain Functional Networks

PubMed Central

Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

2009-01-01

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

The Topology of Symmetric Tensor Fields

NASA Technical Reports Server (NTRS)

Levin, Yingmei; Batra, Rajesh; Hesselink, Lambertus; Levy, Yuval

1997-01-01

Combinatorial topology, also known as "rubber sheet geometry", has extensive applications in geometry and analysis, many of which result from connections with the theory of differential equations. A link between topology and differential equations is vector fields. Recent developments in scientific visualization have shown that vector fields also play an important role in the analysis of second-order tensor fields. A second-order tensor field can be transformed into its eigensystem, namely, eigenvalues and their associated eigenvectors without loss of information content. Eigenvectors behave in a similar fashion to ordinary vectors with even simpler topological structures due to their sign indeterminacy. Incorporating information about eigenvectors and eigenvalues in a display technique known as hyperstreamlines reveals the structure of a tensor field. The simplify and often complex tensor field and to capture its important features, the tensor is decomposed into an isotopic tensor and a deviator. A tensor field and its deviator share the same set of eigenvectors, and therefore they have a similar topological structure. A a deviator determines the properties of a tensor field, while the isotopic part provides a uniform bias. Degenerate points are basic constituents of tensor fields. In 2-D tensor fields, there are only two types of degenerate points; while in 3-D, the degenerate points can be characterized in a Q'-R' plane. Compressible and incompressible flows share similar topological feature due to the similarity of their deviators. In the case of the deformation tensor, the singularities of its deviator represent the area of vortex core in the field. In turbulent flows, the similarities and differences of the topology of the deformation and the Reynolds stress tensors reveal that the basic addie-viscosity assuptions have their validity in turbulence modeling under certain conditions.

Complexity of chemical graphs in terms of size, branching, and cyclicity.

PubMed

Balaban, A T; Mills, D; Kodali, V; Basak, S C

2006-08-01

Chemical graph complexity depends on many factors, but the main ones are size, branching, and cyclicity. Some molecular descriptors embrace together all these three parameters, which cannot then be disentangled. The topological index J (and its refinements that include accounting for bond multiplicity and the presence of heteroatoms) was designed to compensate in a significant measure for graph size and cyclicity, and therefore it contains information mainly on branching. In order to separate these factors, two new indices (F and G) related with J are proposed, which allow to group together graphs with the same size into families of constitutional formulas differing in their branching and cyclicity. A comparison with other topological indices revealed that a few other topological indices vary similarly with index G, notably DN2S4 among the triplet indices, and TOTOP among the indices contained in the Molconn-Z program. This comparison involved all possible chemical graphs (i.e. connected planar graphs with vertex degrees not higher than four) with four through six vertices, and all possible alkanes with four through nine carbon atoms.

Oak Ridge Reservation. Physical Characteristics and National Resources

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

Parr, Patricia Dreyer; Joan, F. Hughes

The topology, geology, hydrology, vegetation, and wildlife of the Oak Ridge Reservation (ORR) provide a complex and intricate array of resources that directly impact land stewardship and use decisions. The purpose of this document is to consolidate general information regarding the natural resources and physical characteristics of the ORR.

Self-consistent adjoint analysis for topology optimization of electromagnetic waves

NASA Astrophysics Data System (ADS)

Deng, Yongbo; Korvink, Jan G.

2018-05-01

In topology optimization of electromagnetic waves, the Gâteaux differentiability of the conjugate operator to the complex field variable results in the complexity of the adjoint sensitivity, which evolves the original real-valued design variable to be complex during the iterative solution procedure. Therefore, the self-inconsistency of the adjoint sensitivity is presented. To enforce the self-consistency, the real part operator has been used to extract the real part of the sensitivity to keep the real-value property of the design variable. However, this enforced self-consistency can cause the problem that the derived structural topology has unreasonable dependence on the phase of the incident wave. To solve this problem, this article focuses on the self-consistent adjoint analysis of the topology optimization problems for electromagnetic waves. This self-consistent adjoint analysis is implemented by splitting the complex variables of the wave equations into the corresponding real parts and imaginary parts, sequentially substituting the split complex variables into the wave equations with deriving the coupled equations equivalent to the original wave equations, where the infinite free space is truncated by the perfectly matched layers. Then, the topology optimization problems of electromagnetic waves are transformed into the forms defined on real functional spaces instead of complex functional spaces; the adjoint analysis of the topology optimization problems is implemented on real functional spaces with removing the variational of the conjugate operator; the self-consistent adjoint sensitivity is derived, and the phase-dependence problem is avoided for the derived structural topology. Several numerical examples are implemented to demonstrate the robustness of the derived self-consistent adjoint analysis.

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

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

Potok, Thomas E; Schuman, Catherine D; Young, Steven R

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determinemore » network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.« less

Topological Quantum Information Processing Mediated Via Hybrid Topological Insulator Structures

DTIC Science & Technology

2013-11-13

manipulation, entanglement and detection ofMajorana fermions in diamond-topological insulator - superconductor heterojunctions. Furthennore, we propose to...the formation, manipulation, entanglement and detection of Majorana fermions in diamond-topological insulator - superconductor heterojunctions...Interactions between Superconductors and Topological Insulators Recent advances have revealed a new type of information processing, topological quantum

Topological analysis of long-chain branching patterns in polyolefins.

PubMed

Bonchev, D; Markel, E; Dekmezian, A

2001-01-01

Patterns in molecular topology and complexity for long-chain branching are quantitatively described. The Wiener number, the topological complexity index, and a new index of 3-starness are used to quantify polymer structure. General formulas for these indices were derived for the cases of 3-arm star, H-shaped, and B-arm comb polymers. The factors affecting complexity in monodisperse polymer systems are ranked as follows: number of arms > arm length > arm central position approximately equal to arm clustering > total molecular weight approximately equal to backbone molecular weight. Topological indices change rapidly and then plateau as the molecular weight of branches on a polyolefin backbone increases from 0 to 5 kD. Complexity calculations relate 2-arm or 3-arm comb structures to the corresponding 3-arm stars of equivalent complexity but much higher molecular weight. In a subsequent paper, we report the application of topological analysis for developing structure/property relationships for monodisperse polymers. While the focus of the present work is on the description of monodisperse, well-defined architectures, the methods may be extended to the description of polydisperse systems.

Estimating topological properties of weighted networks from limited information

NASA Astrophysics Data System (ADS)

Gabrielli, Andrea; Cimini, Giulio; Garlaschelli, Diego; Squartini, Angelo

A typical problem met when studying complex systems is the limited information available on their topology, which hinders our understanding of their structural and dynamical properties. A paramount example is provided by financial networks, whose data are privacy protected. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here we develop a reconstruction method, based on statistical mechanics concepts, that exploits the empirical link density in a highly non-trivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems. Acknoweledgement to ``Growthcom'' ICT - EC project (Grant No: 611272) and ``Crisislab'' Italian Project.

Switching chiral solitons for algebraic operation of topological quaternary digits

NASA Astrophysics Data System (ADS)

Kim, Tae-Hwan; Cheon, Sangmo; Yeom, Han Woong

2017-02-01

Chiral objects can be found throughout nature; in condensed matter chiral objects are often excited states protected by a system's topology. The use of chiral topological excitations to carry information has been demonstrated, where the information is robust against external perturbations. For instance, reading, writing, and transfer of binary information have been demonstrated with chiral topological excitations in magnetic systems, skyrmions, for spintronic devices. The next step is logic or algebraic operations of such topological bits. Here, we show experimentally the switching between chiral topological excitations or chiral solitons of different chirality in a one-dimensional electronic system with Z4 topological symmetry. We found that a fast-moving achiral soliton merges with chiral solitons to switch their handedness. This can lead to the realization of algebraic operation of Z4 topological charges. Chiral solitons could be a platform for storage and operation of robust topological multi-digit information.

Topological properties of flat electroencephalography's state space

NASA Astrophysics Data System (ADS)

Ken, Tan Lit; Ahmad, Tahir bin; Mohd, Mohd Sham bin; Ngien, Su Kong; Suwa, Tohru; Meng, Ong Sie

2016-02-01

Neuroinverse problem are often associated with complex neuronal activity. It involves locating problematic cell which is highly challenging. While epileptic foci localization is possible with the aid of EEG signals, it relies greatly on the ability to extract hidden information or pattern within EEG signals. Flat EEG being an enhancement of EEG is a way of viewing electroencephalograph on the real plane. In the perspective of dynamical systems, Flat EEG is equivalent to epileptic seizure hence, making it a great platform to study epileptic seizure. Throughout the years, various mathematical tools have been applied on Flat EEG to extract hidden information that is hardly noticeable by traditional visual inspection. While these tools have given worthy results, the journey towards understanding seizure process completely is yet to be succeeded. Since the underlying structure of Flat EEG is dynamic and is deemed to contain wealthy information regarding brainstorm, it would certainly be appealing to explore in depth its structures. To better understand the complex seizure process, this paper studies the event of epileptic seizure via Flat EEG in a more general framework by means of topology, particularly, on the state space where the event of Flat EEG lies.

Copper coordination polymers from cavitand ligands: hierarchical spaces from cage and capsule motifs, and other topologies† †Electronic supplementary information (ESI) available: Further details of crystallographic studies, thermogravimetric analyses, Raman spectroscopy. CCDC 1401250–1401256. For ESI and crystallographic data in CIF or other electronic format see DOI: 10.1039/c5sc01801c Click here for additional data file. Click here for additional data file.

PubMed Central

Thorp-Greenwood, Flora L.; Ronson, Tanya K.

2015-01-01

The cyclotriveratrylene-type ligands (±)-tris(iso-nicotinoyl)cyclotriguaiacylene L1 (±)-tris(4-pyridylmethyl)cyclotriguaiacylene L2 and (±)-tris{4-(4-pyridyl)benzyl}cyclotriguaiacylene L3 all feature 4-pyridyl donor groups and all form coordination polymers with CuI and/or CuII cations that show a remarkable range of framework topologies and structures. Complex [CuI 4CuII 1.5(L1)3(CN)6]·CN·n(DMF) 1 features a novel 3,4-connected framework of cyano-linked hexagonal metallo-cages. In complexes [Cu3(L2)4(H2O)3]·6(OTf)·n(DMSO) 2 and [Cu2(L3)2Br2(H2O)(DMSO)]·2Br·n(DMSO) 3 capsule-like metallo-cryptophane motifs are formed which linked through their metal vertices into a hexagonal 2D network of (43.123)(42.122) topology or a coordination chain. Complex [Cu2(L1)2(OTf)2(NMP)2(H2O)2]·2(OTf)·2NMP 4 has an interpenetrating 2D 3,4-connected framework of (4.62.8)(62.8)(4.62.82) topology with tubular channels. Complex [Cu(L1)(NCMe)]·BF4·2(CH3CN)·H2O 5 features a 2D network of 63 topology while the CuII analogue [Cu2(L1)2(NMP)(H2O)]·4BF4·12NMP·1.5H2O 6 has an interpenetrating (10,3)-b type structure and complex [Cu2(L2)2Br3(DMSO)]·Br·n(DMSO) 7 has a 2D network of 4.82 topology. Strategies for formation of coordination polymers with hierarchical spaces emerge in this work and complex 2 is shown to absorb fullerene-C60 through soaking the crystals in a toluene solution. PMID:28791086

Hippocampal Replay Captures the Unique Topological Structure of a Novel Environment

PubMed Central

Wu, Xiaojing

2014-01-01

Hippocampal place-cell replay has been proposed as a fundamental mechanism of learning and memory, which might support navigational learning and planning. An important hypothesis of relevance to these proposed functions is that the information encoded in replay should reflect the topological structure of experienced environments; that is, which places in the environment are connected with which others. Here we report several attributes of replay observed in rats exploring a novel forked environment that support the hypothesis. First, we observed that overlapping replays depicting divergent trajectories through the fork recruited the same population of cells with the same firing rates to represent the common portion of the trajectories. Second, replay tended to be directional and to flip the represented direction at the fork. Third, replay-associated sharp-wave–ripple events in the local field potential exhibited substructure that mapped onto the maze topology. Thus, the spatial complexity of our recording environment was accurately captured by replay: the underlying neuronal activities reflected the bifurcating shape, and both directionality and associated ripple structure reflected the segmentation of the maze. Finally, we observed that replays occurred rapidly after small numbers of experiences. Our results suggest that hippocampal replay captures learned information about environmental topology to support a role in navigation. PMID:24806672

D Topological Indoor Building Modeling Integrated with Open Street Map

NASA Astrophysics Data System (ADS)

Jamali, A.; Rahman, A. Abdul; Boguslawski, P.

2016-09-01

Considering various fields of applications for building surveying and various demands, geometry representation of a building is the most crucial aspect of a building survey. The interiors of the buildings need to be described along with the relative locations of the rooms, corridors, doors and exits in many kinds of emergency response, such as fire, bombs, smoke, and pollution. Topological representation is a challenging task within the Geography Information Science (GIS) environment, as the data structures required to express these relationships are particularly difficult to develop. Even within the Computer Aided Design (CAD) community, the structures for expressing the relationships between adjacent building parts are complex and often incomplete. In this paper, an integration of 3D topological indoor building modeling in Dual Half Edge (DHE) data structure and outdoor navigation network from Open Street Map (OSM) is presented.

Protein complex prediction in large ontology attributed protein-protein interaction networks.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

2013-01-01

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

A case study on topology optimized design for additive manufacturing

NASA Astrophysics Data System (ADS)

Gebisa, A. W.; Lemu, H. G.

2017-12-01

Topology optimization is an optimization method that employs mathematical tools to optimize material distribution in a part to be designed. Earlier developments of topology optimization considered conventional manufacturing techniques that have limitations in producing complex geometries. This has hindered the topology optimization efforts not to fully be realized. With the emergence of additive manufacturing (AM) technologies, the technology that builds a part layer upon a layer directly from three dimensional (3D) model data of the part, however, producing complex shape geometry is no longer an issue. Realization of topology optimization through AM provides full design freedom for the design engineers. The article focuses on topologically optimized design approach for additive manufacturing with a case study on lightweight design of jet engine bracket. The study result shows that topology optimization is a powerful design technique to reduce the weight of a product while maintaining the design requirements if additive manufacturing is considered.

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification.

PubMed

Turner, Alexander P; Caves, Leo S D; Stepney, Susan; Tyrrell, Andy M; Lones, Michael A

2017-01-01

This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

Probing lattice dynamics and electron-phonon coupling in the topological nodal-line semimetal ZrSiS

NASA Astrophysics Data System (ADS)

Singha, Ratnadwip; Samanta, Sudeshna; Chatterjee, Swastika; Pariari, Arnab; Majumdar, Dipanwita; Satpati, Biswarup; Wang, Lin; Singha, Achintya; Mandal, Prabhat

2018-03-01

Topological materials provide an exclusive platform to study the dynamics of relativistic particles in table-top experiments and offer the possibility of wide-scale technological applications. ZrSiS is a newly discovered topological nodal-line semimetal and has drawn enormous interests. In this paper, we have investigated the lattice dynamics and electron-phonon interaction in single-crystalline ZrSiS using Raman spectroscopy. Polarization and angle-resolved Raman data have been analyzed using crystal symmetries and theoretically calculated atomic vibrational patterns along with phonon dispersion spectra. Wavelength- and temperature-dependent measurements show the complex interplay of electron and phonon degrees of freedom, resulting in resonant phonon and quasielastic electron scattering through interband transition. Our high-pressure Raman studies reveal vibrational anomalies, which are the signature of structural phase transitions. Further investigations through high-pressure synchrotron x-ray diffraction clearly show pressure-induced structural transitions and coexistence of multiple phases, which also indicate possible electronic topological transitions in ZrSiS. This study not only provides the fundamental information on the phonon subsystem, but also sheds some light in understanding the topological nodal-line phase in ZrSiS and other isostructural systems.

Topological Quantum Information Processing Mediated Via Hybrid Topogical Insulator Structures

DTIC Science & Technology

2014-03-28

formation, manipulation, entanglement and detection of Majorana fermions in diamond-topological insulator - superconductor heterojunctions. Furthermore...between Superconductors and Topological Insulators Recent advances have revealed a new type of information processing, topological quantum...Topological Insulator - Superconductor Heterostructures," Physical Review B 84, 144507 (2011). 7 Hsiang-Hsuan Hung, Pouyan Ghaemi, Taylor L

A peptide extension dictates IgM assembly.

PubMed

Pasalic, Dzana; Weber, Benedikt; Giannone, Chiara; Anelli, Tiziana; Müller, Roger; Fagioli, Claudio; Felkl, Manuel; John, Christine; Mossuto, Maria Francesca; Becker, Christian F W; Sitia, Roberto; Buchner, Johannes

2017-10-10

Professional secretory cells can produce large amounts of high-quality complex molecules, including IgM antibodies. Owing to their multivalency, polymeric IgM antibodies provide an efficient first-line of defense against pathogens. To decipher the mechanisms of IgM assembly, we investigated its biosynthesis in living cells and faithfully reconstituted the underlying processes in vitro. We find that a conserved peptide extension at the C-terminal end of the IgM heavy (Ig-μ) chains, termed the tailpiece, is necessary and sufficient to establish the correct geometry. Alanine scanning revealed that hydrophobic amino acids in the first half of the tailpiece contain essential information for generating the correct topology. Assembly is triggered by the formation of a disulfide bond linking two tailpieces. This induces conformational changes in the tailpiece and the adjacent domain, which drive further polymerization. Thus, the biogenesis of large and topologically challenging IgM complexes is dictated by a local conformational switch in a peptide extension.

Measures for brain connectivity analysis: nodes centrality and their invariant patterns

NASA Astrophysics Data System (ADS)

da Silva, Laysa Mayra Uchôa; Baltazar, Carlos Arruda; Silva, Camila Aquemi; Ribeiro, Mauricio Watanabe; de Aratanha, Maria Adelia Albano; Deolindo, Camila Sardeto; Rodrigues, Abner Cardoso; Machado, Birajara Soares

2017-07-01

The high dynamical complexity of the brain is related to its small-world topology, which enable both segregated and integrated information processing capabilities. Several measures of connectivity estimation have already been employed to characterize functional brain networks from multivariate electrophysiological data. However, understanding the properties of each measure that lead to a better description of the real topology and capture the complex phenomena present in the brain remains challenging. In this work we compared four nonlinear connectivity measures and show that each method characterizes distinct features of brain interactions. The results suggest an invariance of global network parameters from different behavioral states and that more complete description may be reached considering local features, independently of the connectivity measure employed. Our findings also point to future perspectives in connectivity studies that combine distinct and complementary dependence measures in assembling higher dimensions manifolds.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

PubMed

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-09-25

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

PubMed Central

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-01-01

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks.

PubMed

Filho, Humberto A; Machicao, Jeaneth; Bruno, Odemir M

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology.

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks

PubMed Central

Filho, Humberto A.; Machicao, Jeaneth

2018-01-01

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology. PMID:29734359

The geometric nature of weights in real complex networks

NASA Astrophysics Data System (ADS)

Allard, Antoine; Serrano, M. Ángeles; García-Pérez, Guillermo; Boguñá, Marián

2017-01-01

The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Besides of providing a natural geometrical interpretation of their complex topologies, this hypothesis yields the recipe for sustainable Internet's routing protocols, sheds light on the hierarchical organization of biochemical pathways in cells, and allows for a rich characterization of the evolution of international trade. Here we present empirical evidence that this geometric interpretation also applies to the weighted organization of real complex networks. We introduce a very general and versatile model and use it to quantify the level of coupling between their topology, their weights and an underlying metric space. Our model accurately reproduces both their topology and their weights, and our results suggest that the formation of connections and the assignment of their magnitude are ruled by different processes.

Randic and Schultz molecular topological indices and their correlation with some X-ray absorption parameters

NASA Astrophysics Data System (ADS)

Khatri, Sunil; Kekre, Pravin A.; Mishra, Ashutosh

2016-10-01

The properties of a molecular system are affected by the topology of molecule. Therefore many studies have been made where the various physic-chemical properties are correlated with the topological indices. These studies have shown a very good correlation demonstrating the utility of the graph theoretical approach. It is, therefore, very natural to expect that the various physical properties obtained by the X-ray absorption spectra may also show correlation with the topological indices. Some complexes were used to establish correlation between topological indices and some X-ray absorption parameters like chemical shift. The chemical shift is on the higher energy side of the metal edge in these complexes. The result obtained in these studies shows that the topological indices of organic molecule acting as a legands can be used for estimating edge shift theoretically.

Anomalous resistivity and the evolution of magnetic field topology

NASA Technical Reports Server (NTRS)

Parker, E. N.

1993-01-01

This paper explores the topological restructuring of a force-free magnetic field caused by the hypothetical sudden onset of a localized region of strong anomalous resistivity. It is shown that the topological complexity increases, with the primitive planar force-free field with straight field lines developing field lines that wrap half a turn around each other, evidently providing a surface of tangential discontinuity in the wraparound region. It is suggested that the topological restructuring contributes to the complexity of the geomagnetic substorm, the aurora, and perhaps some of the flare activity on the sun, or other star, and the Galactic halo.

An ensemble framework for clustering protein-protein interaction networks.

PubMed

Asur, Sitaram; Ucar, Duygu; Parthasarathy, Srinivasan

2007-07-01

Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. Supplementary data are available at Bioinformatics online.

Estimating topological properties of weighted networks from limited information.

PubMed

Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

2015-10-01

A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.

Estimating topological properties of weighted networks from limited information

NASA Astrophysics Data System (ADS)

Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

2015-10-01

A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.

Dynamic information routing in complex networks

PubMed Central

Kirst, Christoph; Timme, Marc; Battaglia, Demian

2016-01-01

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

Topological Band Theory for Non-Hermitian Hamiltonians

NASA Astrophysics Data System (ADS)

Shen, Huitao; Zhen, Bo; Fu, Liang

2018-04-01

We develop the topological band theory for systems described by non-Hermitian Hamiltonians, whose energy spectra are generally complex. After generalizing the notion of gapped band structures to the non-Hermitian case, we classify "gapped" bands in one and two dimensions by explicitly finding their topological invariants. We find nontrivial generalizations of the Chern number in two dimensions, and a new classification in one dimension, whose topology is determined by the energy dispersion rather than the energy eigenstates. We then study the bulk-edge correspondence and the topological phase transition in two dimensions. Different from the Hermitian case, the transition generically involves an extended intermediate phase with complex-energy band degeneracies at isolated "exceptional points" in momentum space. We also systematically classify all types of band degeneracies.

A multi-element cosmological model with a complex space-time topology

NASA Astrophysics Data System (ADS)

Kardashev, N. S.; Lipatova, L. N.; Novikov, I. D.; Shatskiy, A. A.

2015-02-01

Wormhole models with a complex topology having one entrance and two exits into the same space-time of another universe are considered, as well as models with two entrances from the same space-time and one exit to another universe. These models are used to build a model of a multi-sheeted universe (a multi-element model of the "Multiverse") with a complex topology. Spherical symmetry is assumed in all the models. A Reissner-Norström black-hole model having no singularity beyond the horizon is constructed. The strength of the central singularity of the black hole is analyzed.

The Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Map

PubMed Central

Arai, Mamiko; Brandt, Vicky; Dabaghian, Yuri

2014-01-01

Learning arises through the activity of large ensembles of cells, yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap. We have taken spatial learning as our starting point, computationally modeling the activity of place cells using methods derived from algebraic topology, especially persistent homology. We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments (“learn” the space) within certain values of place cell firing rate, place field size, and cell population; we called this parameter space the learning region. Here we advance the model both technically and conceptually. To make the model more physiological, we explored the effects of theta precession on spatial learning in our virtual ensembles. Theta precession, which is believed to influence learning and memory, did in fact enhance learning in our model, increasing both speed and the size of the learning region. Interestingly, theta precession also increased the number of spurious loops during simplicial complex formation. We next explored how downstream readout neurons might define co-firing by grouping together cells within different windows of time and thereby capturing different degrees of temporal overlap between spike trains. Our model's optimum coactivity window correlates well with experimental data, ranging from ∼150–200 msec. We further studied the relationship between learning time, window width, and theta precession. Our results validate our topological model for spatial learning and open new avenues for connecting data at the level of individual neurons to behavioral outcomes at the neuronal ensemble level. Finally, we analyzed the dynamics of simplicial complex formation and loop transience to propose that the simplicial complex provides a useful working description of the spatial learning process. PMID:24945927

Computing Tutte polynomials of contact networks in classrooms

NASA Astrophysics Data System (ADS)

Hincapié, Doracelly; Ospina, Juan

2013-05-01

Objective: The topological complexity of contact networks in classrooms and the potential transmission of an infectious disease were analyzed by sex and age. Methods: The Tutte polynomials, some topological properties and the number of spanning trees were used to algebraically compute the topological complexity. Computations were made with the Maple package GraphTheory. Published data of mutually reported social contacts within a classroom taken from primary school, consisting of children in the age ranges of 4-5, 7-8 and 10-11, were used. Results: The algebraic complexity of the Tutte polynomial and the probability of disease transmission increases with age. The contact networks are not bipartite graphs, gender segregation was observed especially in younger children. Conclusion: Tutte polynomials are tools to understand the topology of the contact networks and to derive numerical indexes of such topologies. It is possible to establish relationships between the Tutte polynomial of a given contact network and the potential transmission of an infectious disease within such network

Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach

NASA Technical Reports Server (NTRS)

Aguilo, Miguel A.; Warner, James E.

2017-01-01

This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.

Adaptive Correction from Virtually Complex Dynamic Libraries: The Role of Noncovalent Interactions in Structural Selection and Folding.

PubMed

Lafuente, Maria; Atcher, Joan; Solà, Jordi; Alfonso, Ignacio

2015-11-16

The hierarchical self-assembling of complex molecular systems is dictated by the chemical and structural information stored in their components. This information can be expressed through an adaptive process that determines the structurally fittest assembly under given environmental conditions. We have set up complex disulfide-based dynamic covalent libraries of chemically and topologically diverse pseudopeptidic compounds. We show how the reaction evolves from very complex mixtures at short reaction times to the almost exclusive formation of a major compound, through the establishment of intramolecular noncovalent interactions. Our experiments demonstrate that the systems evolve through error-check and error-correction processes. The nature of these interactions, the importance of the folding and the effects of the environment are also discussed. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Directing folding pathways for multi-component DNA origami nanostructures with complex topology

NASA Astrophysics Data System (ADS)

Marras, A. E.; Zhou, L.; Kolliopoulos, V.; Su, H.-J.; Castro, C. E.

2016-05-01

Molecular self-assembly has become a well-established technique to design complex nanostructures and hierarchical mesoscale assemblies. The typical approach is to design binding complementarity into nucleotide or amino acid sequences to achieve the desired final geometry. However, with an increasing interest in dynamic nanodevices, the need to design structures with motion has necessitated the development of multi-component structures. While this has been achieved through hierarchical assembly of similar structural units, here we focus on the assembly of topologically complex structures, specifically with concentric components, where post-folding assembly is not feasible. We exploit the ability to direct folding pathways to program the sequence of assembly and present a novel approach of designing the strand topology of intermediate folding states to program the topology of the final structure, in this case a DNA origami slider structure that functions much like a piston-cylinder assembly in an engine. The ability to program the sequence and control orientation and topology of multi-component DNA origami nanostructures provides a foundation for a new class of structures with internal and external moving parts and complex scaffold topology. Furthermore, this work provides critical insight to guide the design of intermediate states along a DNA origami folding pathway and to further understand the details of DNA origami self-assembly to more broadly control folding states and landscapes.

Performance of children with developmental dyslexia on high and low topological entropy artificial grammar learning task.

PubMed

Katan, Pesia; Kahta, Shani; Sasson, Ayelet; Schiff, Rachel

2017-07-01

Graph complexity as measured by topological entropy has been previously shown to affect performance on artificial grammar learning tasks among typically developing children. The aim of this study was to examine the effect of graph complexity on implicit sequential learning among children with developmental dyslexia. Our goal was to determine whether children's performance depends on the complexity level of the grammar system learned. We conducted two artificial grammar learning experiments that compared performance of children with developmental dyslexia with that of age- and reading level-matched controls. Experiment 1 was a high topological entropy artificial grammar learning task that aimed to establish implicit learning phenomena in children with developmental dyslexia using previously published experimental conditions. Experiment 2 is a lower topological entropy variant of that task. Results indicated that given a high topological entropy grammar system, children with developmental dyslexia who were similar to the reading age-matched control group had substantial difficulty in performing the task as compared to typically developing children, who exhibited intact implicit learning of the grammar. On the other hand, when tested on a lower topological entropy grammar system, all groups performed above chance level, indicating that children with developmental dyslexia were able to identify rules from a given grammar system. The results reinforced the significance of graph complexity when experimenting with artificial grammar learning tasks, particularly with dyslexic participants.

The Topographical Mapping in Drosophila Central Complex Network and Its Signal Routing

PubMed Central

Chang, Po-Yen; Su, Ta-Shun; Shih, Chi-Tin; Lo, Chung-Chuan

2017-01-01

Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, we analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. We discovered that, except for a small number of “atypical” neuron types, the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules. Specifically, the topological mapping formed by these neurons can be reconstructed by applying a generation matrix on a small set of initial neurons. By analyzing how information flows propagate with or without the atypical neurons, we found that while the general pattern of signal propagation in the central complex follows the simple topological mapping formed by the “typical” neurons, some atypical neurons can substantially re-route the signal pathways, implying specific roles of these neurons in sensory signal integration. The present study provides insights into the organization principle and signal integration in the central complex. PMID:28443014

Information Flows? A Critique of Transfer Entropies

NASA Astrophysics Data System (ADS)

James, Ryan G.; Barnett, Nix; Crutchfield, James P.

2016-06-01

A central task in analyzing complex dynamics is to determine the loci of information storage and the communication topology of information flows within a system. Over the last decade and a half, diagnostics for the latter have come to be dominated by the transfer entropy. Via straightforward examples, we show that it and a derivative quantity, the causation entropy, do not, in fact, quantify the flow of information. At one and the same time they can overestimate flow or underestimate influence. We isolate why this is the case and propose several avenues to alternate measures for information flow. We also address an auxiliary consequence: The proliferation of networks as a now-common theoretical model for large-scale systems, in concert with the use of transferlike entropies, has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems. This interpretation thus fails to include the effects of polyadic dependencies. The net result is that much of the sophisticated organization of complex systems may go undetected.

Topology driven modeling: the IS metaphor.

PubMed

Merelli, Emanuela; Pettini, Marco; Rasetti, Mario

In order to define a new method for analyzing the immune system within the realm of Big Data, we bear on the metaphor provided by an extension of Parisi's model, based on a mean field approach. The novelty is the multilinearity of the couplings in the configurational variables. This peculiarity allows us to compare the partition function [Formula: see text] with a particular functor of topological field theory-the generating function of the Betti numbers of the state manifold of the system-which contains the same global information of the system configurations and of the data set representing them. The comparison between the Betti numbers of the model and the real Betti numbers obtained from the topological analysis of phenomenological data, is expected to discover hidden n-ary relations among idiotypes and anti-idiotypes. The data topological analysis will select global features, reducible neither to a mere subgraph nor to a metric or vector space. How the immune system reacts, how it evolves, how it responds to stimuli is the result of an interaction that took place among many entities constrained in specific configurations which are relational. Within this metaphor, the proposed method turns out to be a global topological application of the S[B] paradigm for modeling complex systems.

The structure of a ring-opened proliferating cell nuclear antigen-replication factor C complex revealed by fluorescence energy transfer.

PubMed

Zhuang, Zhihao; Yoder, Bonita L; Burgers, Peter M J; Benkovic, Stephen J

2006-02-21

Numerous proteins that function in DNA metabolic pathways are known to interact with the proliferating cell nuclear antigen (PCNA). The important function of PCNA in stimulating various cellular activities requires its topological linkage with DNA. Loading of the circular PCNA onto duplex DNA requires the activity of a clamp-loader [replication factor C (RFC)] complex and the energy derived from ATP hydrolysis. The mechanistic and structural details regarding PCNA loading by the RFC complex are still developing. In particular, the positive identification of a long-hypothesized structure of an open clamp-RFC complex as an intermediate in loading has remained elusive. In this study, we capture an open yeast PCNA clamp in a complex with RFC through fluorescence energy transfer experiments. We also follow the topological transitions of PCNA in the various steps of the clamp-loading pathway through both steady-state and stopped-flow fluorescence studies. We find that ATP effectively drives the clamp-loading process to completion with the formation of the closed PCNA bound to DNA, whereas ATPgammaS cannot. The information derived from this work complements that obtained from previous structural and mechanistic studies and provides a more complete picture of a eukaryotic clamp-loading pathway using yeast as a paradigm.

Principles of assembly reveal a periodic table of protein complexes.

PubMed

Ahnert, Sebastian E; Marsh, Joseph A; Hernández, Helena; Robinson, Carol V; Teichmann, Sarah A

2015-12-11

Structural insights into protein complexes have had a broad impact on our understanding of biological function and evolution. In this work, we sought a comprehensive understanding of the general principles underlying quaternary structure organization in protein complexes. We first examined the fundamental steps by which protein complexes can assemble, using experimental and structure-based characterization of assembly pathways. Most assembly transitions can be classified into three basic types, which can then be used to exhaustively enumerate a large set of possible quaternary structure topologies. These topologies, which include the vast majority of observed protein complex structures, enable a natural organization of protein complexes into a periodic table. On the basis of this table, we can accurately predict the expected frequencies of quaternary structure topologies, including those not yet observed. These results have important implications for quaternary structure prediction, modeling, and engineering. Copyright © 2015, American Association for the Advancement of Science.

Influence of network topology on cooperative problem-solving systems.

PubMed

Fontanari, José F; Rodrigues, Francisco A

2016-09-01

The idea of a collective intelligence behind the complex natural structures built by organisms suggests that the organization of social networks is selected so as to optimize problem-solving competence at the group level. Here we study the influence of the social network topology on the performance of a group of agents whose task is to locate the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence networks. In the case those messages convey accurate information on the proximity of the solution (i.e., for smooth fitness landscapes), we find that high connectivity as well as centralization boosts the group performance. For rugged landscapes, however, these characteristics are beneficial for small groups only. For large groups, it is advantageous to slow down the information transmission through the network to avoid local maximum traps. Long-range links and modularity have marginal effects on the performance of the group, except for a very narrow region of the model parameters.

Individual nodeʼs contribution to the mesoscale of complex networks

NASA Astrophysics Data System (ADS)

Klimm, Florian; Borge-Holthoefer, Javier; Wessel, Niels; Kurths, Jürgen; Zamora-López, Gorka

2014-12-01

The analysis of complex networks is devoted to the statistical characterization of the topology of graphs at different scales of organization in order to understand their functionality. While the modular structure of networks has become an essential element to better apprehend their complexity, the efforts to characterize the mesoscale of networks have focused on the identification of the modules rather than describing the mesoscale in an informative manner. Here we propose a framework to characterize the position every node takes within the modular configuration of complex networks and to evaluate their function accordingly. For illustration, we apply this framework to a set of synthetic networks, empirical neural networks, and to the transcriptional regulatory network of the Mycobacterium tuberculosis. We find that the architecture of both neuronal and transcriptional networks are optimized for the processing of multisensory information with the coexistence of well-defined modules of specialized components and the presence of hubs conveying information from and to the distinct functional domains.

Effects of topology on the adsorption of singly tethered ring polymers to attractive surfaces.

PubMed

Li, Bing; Sun, Zhao-Yan; An, Li-Jia

2015-07-14

We investigate the effect of topology on the equilibrium behavior of singly tethered ring polymers adsorbed on an attractive surface. We focus on the change of square radius of gyration Rg(2), the perpendicular component Rg⊥(2) and the parallel component Rg‖(2) to the adsorbing surface, the mean contacting number of monomers with the surface , and the monomer distribution along z-direction during transition from desorption to adsorption. We find that both of the critical point of adsorption εc and the crossover exponent ϕ depend on the knot type when the chain length of ring ranges from 48 to 400. The behaviors of Rg(2), Rg⊥(2), and Rg‖(2) are found to be dependent on the topology and the monomer-surface attractive strength. At weak adsorption, the polymer chains with more complex topology are more adsorbable than those with simple topology. However, at strong adsorption, the polymer chains with complex topology are less adsorbable. By analyzing the distribution of monomer along z-direction, we give a possible mechanism for the effect of topology on the adsorption behavior.

Dynamical mechanism of atrial fibrillation: A topological approach

NASA Astrophysics Data System (ADS)

Marcotte, Christopher D.; Grigoriev, Roman O.

2017-09-01

While spiral wave breakup has been implicated in the emergence of atrial fibrillation, its role in maintaining this complex type of cardiac arrhythmia is less clear. We used the Karma model of cardiac excitation to investigate the dynamical mechanisms that sustain atrial fibrillation once it has been established. The results of our numerical study show that spatiotemporally chaotic dynamics in this regime can be described as a dynamical equilibrium between topologically distinct types of transitions that increase or decrease the number of wavelets, in general agreement with the multiple wavelets' hypothesis. Surprisingly, we found that the process of continuous excitation waves breaking up into discontinuous pieces plays no role whatsoever in maintaining spatiotemporal complexity. Instead, this complexity is maintained as a dynamical balance between wave coalescence—a unique, previously unidentified, topological process that increases the number of wavelets—and wave collapse—a different topological process that decreases their number.

Complexity of generic biochemical circuits: topology versus strength of interactions.

PubMed

Tikhonov, Mikhail; Bialek, William

2016-12-06

The historical focus on network topology as a determinant of biological function is still largely maintained today, illustrated by the rise of structure-only approaches to network analysis. However, biochemical circuits and genetic regulatory networks are defined both by their topology and by a multitude of continuously adjustable parameters, such as the strength of interactions between nodes, also recognized as important. Here we present a class of simple perceptron-based Boolean models within which comparing the relative importance of topology versus interaction strengths becomes a quantitatively well-posed problem. We quantify the intuition that for generic networks, optimization of interaction strengths is a crucial ingredient of achieving high complexity, defined here as the number of fixed points the network can accommodate. We propose a new methodology for characterizing the relative role of parameter optimization for topologies of a given class.

Estimating Bayesian Phylogenetic Information Content

PubMed Central

Lewis, Paul O.; Chen, Ming-Hui; Kuo, Lynn; Lewis, Louise A.; Fučíková, Karolina; Neupane, Suman; Wang, Yu-Bo; Shi, Daoyuan

2016-01-01

Measuring the phylogenetic information content of data has a long history in systematics. Here we explore a Bayesian approach to information content estimation. The entropy of the posterior distribution compared with the entropy of the prior distribution provides a natural way to measure information content. If the data have no information relevant to ranking tree topologies beyond the information supplied by the prior, the posterior and prior will be identical. Information in data discourages consideration of some hypotheses allowed by the prior, resulting in a posterior distribution that is more concentrated (has lower entropy) than the prior. We focus on measuring information about tree topology using marginal posterior distributions of tree topologies. We show that both the accuracy and the computational efficiency of topological information content estimation improve with use of the conditional clade distribution, which also allows topological information content to be partitioned by clade. We explore two important applications of our method: providing a compelling definition of saturation and detecting conflict among data partitions that can negatively affect analyses of concatenated data. [Bayesian; concatenation; conditional clade distribution; entropy; information; phylogenetics; saturation.] PMID:27155008

Identification of Complex Carbon Nanotube Structures

NASA Technical Reports Server (NTRS)

Han, Jie; Saini, Subhash (Technical Monitor)

1998-01-01

A variety of complex carbon nanotube (CNT) structures have been observed experimentally. These include sharp bends, branches, tori, and helices. They are believed to be formed by using topological defects such as pentagons and heptagons to connect different CNT. The effects of type, number, and arrangement (separation and orientation) of defects on atomic structures and energetics of complex CNT are investigated using topology, quantum mechanics and molecular mechanics calculations. Energetically stable models are derived for identification of observed complex CNT structures.

Link prediction based on local weighted paths for complex networks

NASA Astrophysics Data System (ADS)

Yao, Yabing; Zhang, Ruisheng; Yang, Fan; Yuan, Yongna; Hu, Rongjing; Zhao, Zhili

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

A peptide extension dictates IgM assembly

PubMed Central

Pasalic, Dzana; Weber, Benedikt; Giannone, Chiara; Anelli, Tiziana; Müller, Roger; Fagioli, Claudio; Felkl, Manuel; John, Christine; Mossuto, Maria Francesca; Sitia, Roberto; Buchner, Johannes

2017-01-01

Professional secretory cells can produce large amounts of high-quality complex molecules, including IgM antibodies. Owing to their multivalency, polymeric IgM antibodies provide an efficient first-line of defense against pathogens. To decipher the mechanisms of IgM assembly, we investigated its biosynthesis in living cells and faithfully reconstituted the underlying processes in vitro. We find that a conserved peptide extension at the C-terminal end of the IgM heavy (Ig-μ) chains, termed the tailpiece, is necessary and sufficient to establish the correct geometry. Alanine scanning revealed that hydrophobic amino acids in the first half of the tailpiece contain essential information for generating the correct topology. Assembly is triggered by the formation of a disulfide bond linking two tailpieces. This induces conformational changes in the tailpiece and the adjacent domain, which drive further polymerization. Thus, the biogenesis of large and topologically challenging IgM complexes is dictated by a local conformational switch in a peptide extension. PMID:28973899

TopMaker: A Technique for Automatic Multi-Block Topology Generation Using the Medial Axis

NASA Technical Reports Server (NTRS)

Heidmann, James D. (Technical Monitor); Rigby, David L.

2004-01-01

A two-dimensional multi-block topology generation technique has been developed. Very general configurations are addressable by the technique. A configuration is defined by a collection of non-intersecting closed curves, which will be referred to as loops. More than a single loop implies that holes exist in the domain, which poses no problem. This technique requires only the medial vertices and the touch points that define each vertex. From the information about the medial vertices, the connectivity between medial vertices is generated. The physical shape of the medial edge is not required. By applying a few simple rules to each medial edge, the multiblock topology is generated with no user intervention required. The resulting topologies contain only the level of complexity dictated by the configurations. Grid lines remain attached to the boundary except at sharp concave turns where a change in index family is introduced as would be desired. Keeping grid lines attached to the boundary is especially important in the area of computational fluid dynamics where highly clustered grids are used near no-slip boundaries. This technique is simple and robust and can easily be incorporated into the overall grid generation process.

On the Prognostic Efficiency of Topological Descriptors for Magnetograms of Active Regions

NASA Astrophysics Data System (ADS)

Knyazeva, I. S.; Urtiev, F. A.; Makarenko, N. G.

2017-12-01

Solar flare prediction remains an important practical task of space weather. An increase in the amount and quality of observational data and the development of machine-learning methods has led to an improvement in prediction techniques. Additional information has been retrieved from the vector magnetograms; these have been recently supplemented by traditional line-of-sight (LOS) magnetograms. In this work, the problem of the comparative prognostic efficiency of features obtained on the basis of vector data and LOS magnetograms is discussed. Invariants obtained from a topological analysis of LOS magnetograms are used as complexity characteristics of magnetic patterns. Alternatively, the so-called SHARP parameters were used; they were calculated by the data analysis group of the Stanford University Laboratory on the basis of HMI/SDO vector magnetograms and are available online at the website (http://jsoc.stanford.edu/) with the solar dynamics observatory (SDO) database for the entire history of SDO observations. It has been found that the efficiency of large-flare prediction based on topological descriptors of LOS magnetograms in epignosis mode is at least s no worse than the results of prognostic schemes based on vector features. The advantages of the use of topological invariants based on LOS data are discussed.

Augmented Topological Descriptors of Pore Networks for Material Science.

PubMed

Ushizima, D; Morozov, D; Weber, G H; Bianchi, A G C; Sethian, J A; Bethel, E W

2012-12-01

One potential solution to reduce the concentration of carbon dioxide in the atmosphere is the geologic storage of captured CO2 in underground rock formations, also known as carbon sequestration. There is ongoing research to guarantee that this process is both efficient and safe. We describe tools that provide measurements of media porosity, and permeability estimates, including visualization of pore structures. Existing standard algorithms make limited use of geometric information in calculating permeability of complex microstructures. This quantity is important for the analysis of biomineralization, a subsurface process that can affect physical properties of porous media. This paper introduces geometric and topological descriptors that enhance the estimation of material permeability. Our analysis framework includes the processing of experimental data, segmentation, and feature extraction and making novel use of multiscale topological analysis to quantify maximum flow through porous networks. We illustrate our results using synchrotron-based X-ray computed microtomography of glass beads during biomineralization. We also benchmark the proposed algorithms using simulated data sets modeling jammed packed bead beds of a monodispersive material.

Structural complexity, movement bias, and metapopulation extinction risk in dendritic ecological networks

USGS Publications Warehouse

Campbell Grant, Evan H.

2011-01-01

Spatial complexity in metacommunities can be separated into 3 main components: size (i.e., number of habitat patches), spatial arrangement of habitat patches (network topology), and diversity of habitat patch types. Much attention has been paid to lattice-type networks, such as patch-based metapopulations, but interest in understanding ecological networks of alternative geometries is building. Dendritic ecological networks (DENs) include some increasingly threatened ecological systems, such as caves and streams. The restrictive architecture of dendritic ecological networks might have overriding implications for species persistence. I used a modeling approach to investigate how number and spatial arrangement of habitat patches influence metapopulation extinction risk in 2 DENs of different size and topology. Metapopulation persistence was higher in larger networks, but this relationship was mediated by network topology and the dispersal pathways used to navigate the network. Larger networks, especially those with greater topological complexity, generally had lower extinction risk than smaller and less-complex networks, but dispersal bias and magnitude affected the shape of this relationship. Applying these general results to real systems will require empirical data on the movement behavior of organisms and will improve our understanding of the implications of network complexity on population and community patterns and processes.

Optimal Learning Paths in Information Networks

PubMed Central

Rodi, G. C.; Loreto, V.; Servedio, V. D. P.; Tria, F.

2015-01-01

Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances. PMID:26030508

Visibility in the topology of complex networks

NASA Astrophysics Data System (ADS)

Tsiotas, Dimitrios; Charakopoulos, Avraam

2018-09-01

Taking its inspiration from the visibility algorithm, which was proposed by Lacasa et al. (2008) to convert a time-series into a complex network, this paper develops and proposes a novel expansion of this algorithm that allows generating a visibility graph from a complex network instead of a time-series that is currently applicable. The purpose of this approach is to apply the idea of visibility from the field of time-series to complex networks in order to interpret the network topology as a landscape. Visibility in complex networks is a multivariate property producing an associated visibility graph that maps the ability of a node "to see" other nodes in the network that lie beyond the range of its neighborhood, in terms of a control-attribute. Within this context, this paper examines the visibility topology produced by connectivity (degree) in comparison with the original (source) network, in order to detect what patterns or forces describe the mechanism under which a network is converted to a visibility graph. The overall analysis shows that visibility is a property that increases the connectivity in networks, it may contribute to pattern recognition (among which the detection of the scale-free topology) and it is worth to be applied to complex networks in order to reveal the potential of signal processing beyond the range of its neighborhood. Generally, this paper promotes interdisciplinary research in complex networks providing new insights to network science.

An object-oriented software approach for a distributed human tracking motion system

NASA Astrophysics Data System (ADS)

Micucci, Daniela L.

2003-06-01

Tracking is a composite job involving the co-operation of autonomous activities which exploit a complex information model and rely on a distributed architecture. Both information and activities must be classified and related in several dimensions: abstraction levels (what is modelled and how information is processed); topology (where the modelled entities are); time (when entities exist); strategy (why something happens); responsibilities (who is in charge of processing the information). A proper Object-Oriented analysis and design approach leads to a modular architecture where information about conceptual entities is modelled at each abstraction level via classes and intra-level associations, whereas inter-level associations between classes model the abstraction process. Both information and computation are partitioned according to level-specific topological models. They are also placed in a temporal framework modelled by suitable abstractions. Domain-specific strategies control the execution of the computations. Computational components perform both intra-level processing and intra-level information conversion. The paper overviews the phases of the analysis and design process, presents major concepts at each abstraction level, and shows how the resulting design turns into a modular, flexible and adaptive architecture. Finally, the paper sketches how the conceptual architecture can be deployed into a concrete distribute architecture by relying on an experimental framework.

Topological Quantum Buses: Coherent Quantum Information Transfer between Topological and Conventional Qubits

NASA Astrophysics Data System (ADS)

Bonderson, Parsa; Lutchyn, Roman M.

2011-04-01

We propose computing bus devices that enable quantum information to be coherently transferred between topological and conventional qubits. We describe a concrete realization of such a topological quantum bus acting between a topological qubit in a Majorana wire network and a conventional semiconductor double quantum dot qubit. Specifically, this device measures the joint (fermion) parity of these two different qubits by using the Aharonov-Casher effect in conjunction with an ancilliary superconducting flux qubit that facilitates the measurement. Such a parity measurement, together with the ability to apply Hadamard gates to the two qubits, allows one to produce states in which the topological and conventional qubits are maximally entangled and to teleport quantum states between the topological and conventional quantum systems.

Performance of Children with Developmental Dyslexia on High and Low Topological Entropy Artificial Grammar Learning Task

ERIC Educational Resources Information Center

Katan, Pesia; Kahta, Shani; Sasson, Ayelet; Schiff, Rachel

2017-01-01

Graph complexity as measured by topological entropy has been previously shown to affect performance on artificial grammar learning tasks among typically developing children. The aim of this study was to examine the effect of graph complexity on implicit sequential learning among children with developmental dyslexia. Our goal was to determine…

Cooperation-Induced Topological Complexity: A Promising Road to Fault Tolerance and Hebbian Learning

DTIC Science & Technology

2012-03-16

topological complexity a way to compare the efficiency of a scale-free network to the random network of Erdos and Renyi . All this is extensively dis- cussed in...an excellent review paper byArenas et al. (2008) showing very interesting comparisons of Erdos– Renyi networks and scale- free networks as a function

The sites and topology of mitochondrial superoxide production

PubMed Central

Brand, Martin D.

2010-01-01

Mitochondrial superoxide production is an important source of reactive oxygen species in cells, and may cause or contribute to ageing and the diseases of ageing. Seven major sites of superoxide production in mammalian mitochondria are known and widely accepted. In descending order of maximum capacity they are the ubiquinone binding sites in complex I (site IQ) and complex III (site IIIQo), glycerol 3-phosphate dehydrogenase, the flavin in complex I (site IF), the electron transferring flavoprotein:Q oxidoreductase (ETFQOR) of fatty acid beta oxidation, and pyruvate and 2-oxoglutarate dehydrogenases. None of these sites is fully characterized and for some we only have sketchy information. The topology of the sites is important because it determines whether or not a site will produce superoxide in the mitochondrial matrix and be able to damage mitochondrial DNA. All sites produce superoxide in the matrix; site IIIQo and glycerol 3-phosphate dehydrogenase also produce superoxide to the intermembrane space. The relative contribution of each site to mitochondrial reactive oxygen species generation in the absence of electron transport inhibitors is unknown in isolated mitochondria, in cells or in vivo, and may vary considerably with species, tissue, substrate, energy demand and oxygen tension. PMID:20064600

Sequential defense against random and intentional attacks in complex networks.

PubMed

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.

Biogenic manganese oxide nanoparticle formation by a multimeric multicopper oxidase Mnx.

PubMed

Romano, Christine A; Zhou, Mowei; Song, Yang; Wysocki, Vicki H; Dohnalkova, Alice C; Kovarik, Libor; Paša-Tolić, Ljiljana; Tebo, Bradley M

2017-09-29

Bacteria that produce Mn oxides are extraordinarily skilled engineers of nanomaterials that contribute significantly to global biogeochemical cycles. Their enzyme-based reaction mechanisms may be genetically tailored for environmental remediation applications or bioenergy production. However, significant challenges exist for structural characterization of the enzymes responsible for biomineralization. The active Mn oxidase in Bacillus sp. PL-12, Mnx, is a complex composed of a multicopper oxidase (MCO), MnxG, and two accessory proteins, MnxE and MnxF. MnxG shares sequence similarity with other, structurally characterized MCOs. MnxE and MnxF have no similarity to any characterized proteins. The ~200 kDa complex has been recalcitrant to crystallization, so its structure is unknown. Here, we show that native mass spectrometry defines the subunit topology and copper binding of Mnx, while high-resolution electron microscopy visualizes the protein and nascent Mn oxide minerals. These data provide critical structural information for understanding Mn biomineralization by such unexplored enzymes.Significant challenges exist for structural characterization of enzymes responsible for biomineralization. Here the authors show that native mass spectrometry and high resolution electron microscopy can define the subunit topology and copper binding of a manganese oxidizing complex, and describe early stage formation of its mineral products.

Identifying partial topology of complex dynamical networks via a pinning mechanism

NASA Astrophysics Data System (ADS)

Zhu, Shuaibing; Zhou, Jin; Lu, Jun-an

2018-04-01

In this paper, we study the problem of identifying the partial topology of complex dynamical networks via a pinning mechanism. By using the network synchronization theory and the adaptive feedback controlling method, we propose a method which can greatly reduce the number of nodes and observers in the response network. Particularly, this method can also identify the whole topology of complex networks. A theorem is established rigorously, from which some corollaries are also derived in order to make our method more cost-effective. Several numerical examples are provided to verify the effectiveness of the proposed method. In the simulation, an approach is also given to avoid possible identification failure caused by inner synchronization of the drive network.

Retinal Connectomics: Towards Complete, Accurate Networks

PubMed Central

Marc, Robert E.; Jones, Bryan W.; Watt, Carl B.; Anderson, James R.; Sigulinsky, Crystal; Lauritzen, Scott

2013-01-01

Connectomics is a strategy for mapping complex neural networks based on high-speed automated electron optical imaging, computational assembly of neural data volumes, web-based navigational tools to explore 1012–1015 byte (terabyte to petabyte) image volumes, and annotation and markup tools to convert images into rich networks with cellular metadata. These collections of network data and associated metadata, analyzed using tools from graph theory and classification theory, can be merged with classical systems theory, giving a more completely parameterized view of how biologic information processing systems are implemented in retina and brain. Networks have two separable features: topology and connection attributes. The first findings from connectomics strongly validate the idea that the topologies complete retinal networks are far more complex than the simple schematics that emerged from classical anatomy. In particular, connectomics has permitted an aggressive refactoring of the retinal inner plexiform layer, demonstrating that network function cannot be simply inferred from stratification; exposing the complex geometric rules for inserting different cells into a shared network; revealing unexpected bidirectional signaling pathways between mammalian rod and cone systems; documenting selective feedforward systems, novel candidate signaling architectures, new coupling motifs, and the highly complex architecture of the mammalian AII amacrine cell. This is but the beginning, as the underlying principles of connectomics are readily transferrable to non-neural cell complexes and provide new contexts for assessing intercellular communication. PMID:24016532

Inference of gene regulatory networks from time series by Tsallis entropy

PubMed Central

2011-01-01

Background The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 ≤ q ≤ 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/. PMID:21545720

Topological Privacy: Lattice Structures and Information Bubbles for Inference and Obfuscation

DTIC Science & Technology

2016-12-19

AFRL-AFOSR-VA-TR-2017-0036 Topological Privacy Michael Erdmann CARNEGIE MELLON UNIVERSITY 5000 FORBES AVENUE PITTSBURGH, PA 15213-3815 02/22/2017...PERSON 19b. TELEPHONE NUMBER (Include area code) 19-12-2016 Final 15-10-2013 - 14-10-2016 Topological Privacy Erdmann, Michael, A. Carnegie Mellon...Michael Erdmann Carnegie Mellon University me@cs.cmu.edu December 19, 2016 Abstract Information has intrinsic geometric and topological structure, arising

CAN WE PREDICT THE GLOBAL MAGNETIC TOPOLOGY OF A PRE-MAIN-SEQUENCE STAR FROM ITS POSITION IN THE HERTZSPRUNG-RUSSELL DIAGRAM?

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

Gregory, S. G.; Hillenbrand, L. A.; Donati, J.-F.

2012-08-20

Zeeman-Doppler imaging studies have shown that the magnetic fields of T Tauri stars can be significantly more complex than a simple dipole and can vary markedly between sources. We collect and summarize the magnetic field topology information obtained to date and present Hertzsprung-Russell (H-R) diagrams for the stars in the sample. Intriguingly, the large-scale field topology of a given pre-main-sequence (PMS) star is strongly dependent upon the stellar internal structure, with the strength of the dipole component of its multipolar magnetic field decaying rapidly with the development of a radiative core. Using the observational data as a basis, we arguemore » that the general characteristics of the global magnetic field of a PMS star can be determined from its position in the H-R diagram. Moving from hotter and more luminous to cooler and less luminous stars across the PMS of the H-R diagram, we present evidence for four distinct magnetic topology regimes. Stars with large radiative cores, empirically estimated to be those with a core mass in excess of {approx}40% of the stellar mass, host highly complex and dominantly non-axisymmetric magnetic fields, while those with smaller radiative cores host axisymmetric fields with field modes of higher order than the dipole dominant (typically, but not always, the octupole). Fully convective stars above {approx}> 0.5 M{sub Sun} appear to host dominantly axisymmetric fields with strong (kilo-Gauss) dipole components. Based on similarities between the magnetic properties of PMS stars and main-sequence M-dwarfs with similar internal structures, we speculate that a bistable dynamo process operates for lower mass stars ({approx}< 0.5 M{sub Sun} at an age of a few Myr) and that they will be found to host a variety of magnetic field topologies. If the magnetic topology trends across the H-R diagram are confirmed, they may provide a new method of constraining PMS stellar evolution models.« less

Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

2011-01-01

Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a formation estimation algorithm that is modular and robust to variations in the topology and link properties of the underlying formation network.

The Topology of Three-Dimensional Symmetric Tensor Fields

NASA Technical Reports Server (NTRS)

Lavin, Yingmei; Levy, Yuval; Hesselink, Lambertus

1994-01-01

We study the topology of 3-D symmetric tensor fields. The goal is to represent their complex structure by a simple set of carefully chosen points and lines analogous to vector field topology. The basic constituents of tensor topology are the degenerate points, or points where eigenvalues are equal to each other. First, we introduce a new method for locating 3-D degenerate points. We then extract the topological skeletons of the eigenvector fields and use them for a compact, comprehensive description of the tensor field. Finally, we demonstrate the use of tensor field topology for the interpretation of the two-force Boussinesq problem.

Topological quantum buses: coherent quantum information transfer between topological and conventional qubits.

PubMed

Bonderson, Parsa; Lutchyn, Roman M

2011-04-01

We propose computing bus devices that enable quantum information to be coherently transferred between topological and conventional qubits. We describe a concrete realization of such a topological quantum bus acting between a topological qubit in a Majorana wire network and a conventional semiconductor double quantum dot qubit. Specifically, this device measures the joint (fermion) parity of these two different qubits by using the Aharonov-Casher effect in conjunction with an ancilliary superconducting flux qubit that facilitates the measurement. Such a parity measurement, together with the ability to apply Hadamard gates to the two qubits, allows one to produce states in which the topological and conventional qubits are maximally entangled and to teleport quantum states between the topological and conventional quantum systems. © 2011 American Physical Society

Comparison of Point Matching Techniques for Road Network Matching

NASA Astrophysics Data System (ADS)

Hackeloeer, A.; Klasing, K.; Krisp, J. M.; Meng, L.

2013-05-01

Map conflation investigates the unique identification of geographical entities across different maps depicting the same geographic region. It involves a matching process which aims to find commonalities between geographic features. A specific subdomain of conflation called Road Network Matching establishes correspondences between road networks of different maps on multiple layers of abstraction, ranging from elementary point locations to high-level structures such as road segments or even subgraphs derived from the induced graph of a road network. The process of identifying points located on different maps by means of geometrical, topological and semantical information is called point matching. This paper provides an overview of various techniques for point matching, which is a fundamental requirement for subsequent matching steps focusing on complex high-level entities in geospatial networks. Common point matching approaches as well as certain combinations of these are described, classified and evaluated. Furthermore, a novel similarity metric called the Exact Angular Index is introduced, which considers both topological and geometrical aspects. The results offer a basis for further research on a bottom-up matching process for complex map features, which must rely upon findings derived from suitable point matching algorithms. In the context of Road Network Matching, reliable point matches provide an immediate starting point for finding matches between line segments describing the geometry and topology of road networks, which may in turn be used for performing a structural high-level matching on the network level.

The topological requirements for robust perfect adaptation in networks of any size.

PubMed

Araujo, Robyn P; Liotta, Lance A

2018-05-01

Robustness, and the ability to function and thrive amid changing and unfavorable environments, is a fundamental requirement for living systems. Until now it has been an open question how large and complex biological networks can exhibit robust behaviors, such as perfect adaptation to a variable stimulus, since complexity is generally associated with fragility. Here we report that all networks that exhibit robust perfect adaptation (RPA) to a persistent change in stimulus are decomposable into well-defined modules, of which there exist two distinct classes. These two modular classes represent a topological basis for all RPA-capable networks, and generate the full set of topological realizations of the internal model principle for RPA in complex, self-organizing, evolvable bionetworks. This unexpected result supports the notion that evolutionary processes are empowered by simple and scalable modular design principles that promote robust performance no matter how large or complex the underlying networks become.

Optimizing topological cascade resilience based on the structure of terrorist networks.

PubMed

Gutfraind, Alexander

2010-11-10

Complex socioeconomic networks such as information, finance and even terrorist networks need resilience to cascades--to prevent the failure of a single node from causing a far-reaching domino effect. We show that terrorist and guerrilla networks are uniquely cascade-resilient while maintaining high efficiency, but they become more vulnerable beyond a certain threshold. We also introduce an optimization method for constructing networks with high passive cascade resilience. The optimal networks are found to be based on cells, where each cell has a star topology. Counterintuitively, we find that there are conditions where networks should not be modified to stop cascades because doing so would come at a disproportionate loss of efficiency. Implementation of these findings can lead to more cascade-resilient networks in many diverse areas.

Plant hormone signaling during development: insights from computational models.

PubMed

Oliva, Marina; Farcot, Etienne; Vernoux, Teva

2013-02-01

Recent years have seen an impressive increase in our knowledge of the topology of plant hormone signaling networks. The complexity of these topologies has motivated the development of models for several hormones to aid understanding of how signaling networks process hormonal inputs. Such work has generated essential insights into the mechanisms of hormone perception and of regulation of cellular responses such as transcription in response to hormones. In addition, modeling approaches have contributed significantly to exploring how spatio-temporal regulation of hormone signaling contributes to plant growth and patterning. New tools have also been developed to obtain quantitative information on hormone distribution during development and to test model predictions, opening the way for quantitative understanding of the developmental roles of hormones. Copyright © 2012 Elsevier Ltd. All rights reserved.

Does the choice of nucleotide substitution models matter topologically?

PubMed

Hoff, Michael; Orf, Stefan; Riehm, Benedikt; Darriba, Diego; Stamatakis, Alexandros

2016-03-24

In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria. We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. We also assess, to which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Finally, we assess if the definition of the sample size (#sites versus #sites × #taxa) yields different models and, as a consequence, different tree topologies. We find that, all three factors (by order of impact: nucleotide model selection, information criterion used, sample size definition) can yield topologically substantially different final tree topologies (topological difference exceeding 10 %) for approximately 5 % of the tree inferences conducted on the 39 empirical datasets used in our study. We find that, using the best-fit nucleotide substitution model may change the final ML tree topology compared to an inference under a default GTR model. The effect is less pronounced when comparing distinct information criteria. Nonetheless, in some cases we did obtain substantial topological differences.

Four coordination polymers based on 5-tert-butyl isophthalic acid and rigid bis(imidazol-1yl)benzene linkers: Synthesis, luminescence detection of acetone and optical properties

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

Arıcı, Mürsel, E-mail: marici@ogu.edu.tr; Zafer Yeşilel, Okan; Büyükgüngör, Orhan

Four coordination polymers including, [Co(µ-Htbip){sub 2}(µ-dib)]{sub n} (1), [Co(µ-tbip)(µ-dmib){sub 0.5}]{sub n} (2), [Zn{sub 2}(µ-tbip)(µ{sub 3}-tbip)(µ-dmib){sub 1.5}]{sub n} (3) and [Cd(µ{sub 3}-tbip)(µ-dib){sub 0.5} (H{sub 2}O)]{sub n} (4) (tbip: 5-tert-butylisophthalate, dib: 1,4-bis(imidazol-1yl)benzene, dmib: 1,4-bis(imidazol-1yl)-2,5-dimethylbenzene), were hydrothermally synthesized and characterized by elemental analysis, IR spectra, single crystal and powder X-ray diffraction and thermal analysis (TG/DTA). The structural diversity is observed depending on ligands and coordination number of metal centers in the synthesized complexes. The tbip ligand displayed five different coordination modes in its complexes. In 1 and 2, complex 1 is 3D framework with the dia topology while complex 2 has 2D structuremore » with the sql topology depending on coordination geometries of Co ions. Complex 3 is 3D framework with the fsh 4,6-conn topology and complex 4 has 2D 4-connected sql topology. Photoluminescent properties of complex 3 dispersed in various organic solvents were investigated and the results showed that 3 dispersed in methanol could be used as a fluorescent sensor for the detection of acetone. Moreover, thermal and optical properties of the complexes were also studied. - Graphical abstract: Four coordination polymers were hydrothermally synthesized and characterized by various techniques. The complexes showed the structural diversity depending on ligands and coordination number of metal centers. The tbip ligand displayed four different coordination modes in its complexes. In 1 and 2, complexes 1 and 2 are 3D and 2D structures with the dia and sql topologies depending on coordination geometries of Co ions, respectively. Complexes 3 and 4 are 3D and 2D structures with the fsh 4,6-conn and sql topology, respectively. Photoluminescent properties of complex 3 dispersed in various organic solvents were investigated and the results showed that 3 dispersed in methanol could be used as a fluorescent sensor for the detection of acetone. Moreover, thermal and optical properties of the complexes were also studied. - Highlights: • Four new 2D and 3D coordination polymers with 5-tert-butyl isophthalic acid and rigid bis(imidazol-1yl)benzene linkers. • The structural diversity depending on ligands and coordination number of metal centers. • Fluorescent sensor for the detection of acetone.« less

Graph theoretical analysis of complex networks in the brain

PubMed Central

Stam, Cornelis J; Reijneveld, Jaap C

2007-01-01

Since the discovery of small-world and scale-free networks the study of complex systems from a network perspective has taken an enormous flight. In recent years many important properties of complex networks have been delineated. In particular, significant progress has been made in understanding the relationship between the structural properties of networks and the nature of dynamics taking place on these networks. For instance, the 'synchronizability' of complex networks of coupled oscillators can be determined by graph spectral analysis. These developments in the theory of complex networks have inspired new applications in the field of neuroscience. Graph analysis has been used in the study of models of neural networks, anatomical connectivity, and functional connectivity based upon fMRI, EEG and MEG. These studies suggest that the human brain can be modelled as a complex network, and may have a small-world structure both at the level of anatomical as well as functional connectivity. This small-world structure is hypothesized to reflect an optimal situation associated with rapid synchronization and information transfer, minimal wiring costs, as well as a balance between local processing and global integration. The topological structure of functional networks is probably restrained by genetic and anatomical factors, but can be modified during tasks. There is also increasing evidence that various types of brain disease such as Alzheimer's disease, schizophrenia, brain tumours and epilepsy may be associated with deviations of the functional network topology from the optimal small-world pattern. PMID:17908336

Deterministic ripple-spreading model for complex networks.

PubMed

Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Hines, Evor L; Di Paolo, Ezequiel

2011-04-01

This paper proposes a deterministic complex network model, which is inspired by the natural ripple-spreading phenomenon. The motivations and main advantages of the model are the following: (i) The establishment of many real-world networks is a dynamic process, where it is often observed that the influence of a few local events spreads out through nodes, and then largely determines the final network topology. Obviously, this dynamic process involves many spatial and temporal factors. By simulating the natural ripple-spreading process, this paper reports a very natural way to set up a spatial and temporal model for such complex networks. (ii) Existing relevant network models are all stochastic models, i.e., with a given input, they cannot output a unique topology. Differently, the proposed ripple-spreading model can uniquely determine the final network topology, and at the same time, the stochastic feature of complex networks is captured by randomly initializing ripple-spreading related parameters. (iii) The proposed model can use an easily manageable number of ripple-spreading related parameters to precisely describe a network topology, which is more memory efficient when compared with traditional adjacency matrix or similar memory-expensive data structures. (iv) The ripple-spreading model has a very good potential for both extensions and applications.

Analysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension.

PubMed

Siyah Mansoory, Meysam; Oghabian, Mohammad Ali; Jafari, Amir Homayoun; Shahbabaie, Alireza

2017-01-01

Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obligatory for graph construction and analysis is consistently underestimated by LC, because not all the bivariate distributions, but only the marginals are Gaussian. In a number of studies, Mutual Information (MI) has been employed, as a similarity measure between each two time series of the brain regions, a pure nonlinear measure. Owing to the complex fractal organization of the brain indicating self-similarity, more information on the brain can be revealed by fMRI Fractal Dimension (FD) analysis. In the present paper, Box-Counting Fractal Dimension (BCFD) is introduced for graph theoretical analysis of fMRI data in 17 methamphetamine drug users and 18 normal controls. Then, BCFD performance was evaluated compared to those of LC and MI methods. Moreover, the global topological graph properties of the brain networks inclusive of global efficiency, clustering coefficient and characteristic path length in addict subjects were investigated too. Compared to normal subjects by using statistical tests (P<0.05), topological graph properties were postulated to be disrupted significantly during the resting-state fMRI. Based on the results, analyzing the graph topological properties (representing the brain networks) based on BCFD is a more reliable method than LC and MI.

Inferring Network Controls from Topology Using the Chomp Database

DTIC Science & Technology

2015-12-03

AFRL-AFOSR-VA-TR-2016-0033 INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE John Harer DUKE UNIVERSITY Final Report 12/03/2015...INFERRING NETWORK CONTROLS FROM TOPOLOGY USING THE CHOMP DATABASE 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-10-1-0436 5c. PROGRAM ELEMENT NUMBER 6...area of Topological Data Analysis (TDA) and it’s application to dynamical systems. The role of this work in the Complex Networks program is based on

Real topological entropy versus metric entropy for birational measure-preserving transformations

NASA Astrophysics Data System (ADS)

Abarenkova, N.; Anglès d'Auriac, J.-Ch.; Boukraa, S.; Maillard, J.-M.

2000-10-01

We consider a family of birational measure-preserving transformations of two complex variables, depending on one parameter for which simple rational expressions for the dynamical zeta function have been conjectured, together with an equality between the topological entropy and the logarithm of the Arnold complexity (divided by the number of iterations). Similar results have been obtained for the adaptation of these two concepts to dynamical systems of real variables, yielding to introduce a “real topological entropy” and a “real Arnold complexity”. We try to compare, here, the Kolmogorov-Sinai metric entropy and this real Arnold complexity, or real topological entropy, on this particular example of a one-parameter dependent birational transformation of two variables. More precisely, we analyze, using an infinite precision calculation, the Lyapunov characteristic exponents for various values of the parameter of the birational transformation, in order to compare these results with the ones for the real Arnold complexity. We find a quite surprising result: for this very birational example, and, in fact, for a large set of birational measure-preserving mappings generated by involutions, the Lyapunov characteristic exponents seem to be equal to zero or, at least, extremely small, for all the orbits we have considered, and for all values of the parameter. Birational measure-preserving transformations, generated by involutions, could thus allow to better understand the difference between the topological description and the probabilistic description of discrete dynamical systems. Many birational measure-preserving transformations, generated by involutions, seem to provide examples of discrete dynamical systems which can be topologically chaotic while they are metrically almost quasi-periodic. Heuristically, this can be understood as a consequence of the fact that their orbits seem to form some kind of “transcendental foliation” of the two-dimensional space of variables.

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

NASA Astrophysics Data System (ADS)

Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

2015-11-01

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.

Visualization of Morse connection graphs for topologically rich 2D vector fields.

PubMed

Szymczak, Andrzej; Sipeki, Levente

2013-12-01

Recent advances in vector field topologymake it possible to compute its multi-scale graph representations for autonomous 2D vector fields in a robust and efficient manner. One of these representations is a Morse Connection Graph (MCG), a directed graph whose nodes correspond to Morse sets, generalizing stationary points and periodic trajectories, and arcs - to trajectories connecting them. While being useful for simple vector fields, the MCG can be hard to comprehend for topologically rich vector fields, containing a large number of features. This paper describes a visual representation of the MCG, inspired by previous work on graph visualization. Our approach aims to preserve the spatial relationships between the MCG arcs and nodes and highlight the coherent behavior of connecting trajectories. Using simulations of ocean flow, we show that it can provide useful information on the flow structure. This paper focuses specifically on MCGs computed for piecewise constant (PC) vector fields. In particular, we describe extensions of the PC framework that make it more flexible and better suited for analysis of data on complex shaped domains with a boundary. We also describe a topology simplification scheme that makes our MCG visualizations less ambiguous. Despite the focus on the PC framework, our approach could also be applied to graph representations or topological skeletons computed using different methods.

Recoverable information and emergent conservation laws in fracton stabilizer codes

NASA Astrophysics Data System (ADS)

Schmitz, A. T.; Ma, Han; Nandkishore, Rahul M.; Parameswaran, S. A.

2018-04-01

We introduce a new quantity that we term recoverable information, defined for stabilizer Hamiltonians. For such models, the recoverable information provides a measure of the topological information as well as a physical interpretation, which is complementary to topological entanglement entropy. We discuss three different ways to calculate the recoverable information and prove their equivalence. To demonstrate its utility, we compute recoverable information for fracton models using all three methods where appropriate. From the recoverable information, we deduce the existence of emergent Z2 Gauss-law-type constraints, which in turn imply emergent Z2 conservation laws for pointlike quasiparticle excitations of an underlying topologically ordered phase.

Methods of information geometry in computational system biology (consistency between chemical and biological evolution).

PubMed

Astakhov, Vadim

2009-01-01

Interest in simulation of large-scale metabolic networks, species development, and genesis of various diseases requires new simulation techniques to accommodate the high complexity of realistic biological networks. Information geometry and topological formalisms are proposed to analyze information processes. We analyze the complexity of large-scale biological networks as well as transition of the system functionality due to modification in the system architecture, system environment, and system components. The dynamic core model is developed. The term dynamic core is used to define a set of causally related network functions. Delocalization of dynamic core model provides a mathematical formalism to analyze migration of specific functions in biosystems which undergo structure transition induced by the environment. The term delocalization is used to describe these processes of migration. We constructed a holographic model with self-poetic dynamic cores which preserves functional properties under those transitions. Topological constraints such as Ricci flow and Pfaff dimension were found for statistical manifolds which represent biological networks. These constraints can provide insight on processes of degeneration and recovery which take place in large-scale networks. We would like to suggest that therapies which are able to effectively implement estimated constraints, will successfully adjust biological systems and recover altered functionality. Also, we mathematically formulate the hypothesis that there is a direct consistency between biological and chemical evolution. Any set of causal relations within a biological network has its dual reimplementation in the chemistry of the system environment.

Hierarchical sequencing of online social graphs

NASA Astrophysics Data System (ADS)

Andjelković, Miroslav; Tadić, Bosiljka; Maletić, Slobodan; Rajković, Milan

2015-10-01

In online communications, patterns of conduct of individual actors and use of emotions in the process can lead to a complex social graph exhibiting multilayered structure and mesoscopic communities. Using simplicial complexes representation of graphs, we investigate in-depth topology of the online social network constructed from MySpace dialogs which exhibits original community structure. A simulation of emotion spreading in this network leads to the identification of two emotion-propagating layers. Three topological measures are introduced, referred to as the structure vectors, which quantify graph's architecture at different dimension levels. Notably, structures emerging through shared links, triangles and tetrahedral faces, frequently occur and range from tree-like to maximal 5-cliques and their respective complexes. On the other hand, the structures which spread only negative or only positive emotion messages appear to have much simpler topology consisting of links and triangles. The node's structure vector represents the number of simplices at each topology level in which the node resides and the total number of such simplices determines what we define as the node's topological dimension. The presented results suggest that the node's topological dimension provides a suitable measure of the social capital which measures the actor's ability to act as a broker in compact communities, the so called Simmelian brokerage. We also generalize the results to a wider class of computer-generated networks. Investigating components of the node's vector over network layers reveals that same nodes develop different socio-emotional relations and that the influential nodes build social capital by combining their connections in different layers.

A topological hierarchy for functions on triangulated surfaces.

PubMed

Bremer, Peer-Timo; Edelsbrunner, Herbert; Hamann, Bernd; Pascucci, Valerio

2004-01-01

We combine topological and geometric methods to construct a multiresolution representation for a function over a two-dimensional domain. In a preprocessing stage, we create the Morse-Smale complex of the function and progressively simplify its topology by cancelling pairs of critical points. Based on a simple notion of dependency among these cancellations, we construct a hierarchical data structure supporting traversal and reconstruction operations similarly to traditional geometry-based representations. We use this data structure to extract topologically valid approximations that satisfy error bounds provided at runtime.

Statistical Inferences from the Topology of Complex Networks

DTIC Science & Technology

2016-10-04

stable, does not lose any information, has continuous and discrete versions, and obeys a strong law of large numbers and a central limit theorem. The...paper (with J.A. Scott) “Categorification of persistent homology” [7] in the journal Discrete and Computational Geome- try and the paper “Metrics for...Generalized Persistence Modules” (with J.A. Scott and V. de Silva) in the journal Foundations of Computational Math - ematics [5]. These papers develop

Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy.

PubMed

Niu, Haijing; Wang, Jinhui; Zhao, Tengda; Shu, Ni; He, Yong

2012-01-01

The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.

Topology optimization of a gas-turbine engine part

NASA Astrophysics Data System (ADS)

Faskhutdinov, R. N.; Dubrovskaya, A. S.; Dongauzer, K. A.; Maksimov, P. V.; Trufanov, N. A.

2017-02-01

One of the key goals of aerospace industry is a reduction of the gas turbine engine weight. The solution of this task consists in the design of gas turbine engine components with reduced weight retaining their functional capabilities. Topology optimization of the part geometry leads to an efficient weight reduction. A complex geometry can be achieved in a single operation with the Selective Laser Melting technology. It should be noted that the complexity of structural features design does not affect the product cost in this case. Let us consider a step-by-step procedure of topology optimization by an example of a gas turbine engine part.

Visualization of 3-D tensor fields

NASA Technical Reports Server (NTRS)

Hesselink, L.

1996-01-01

Second-order tensor fields have applications in many different areas of physics, such as general relativity and fluid mechanics. The wealth of multivariate information in tensor fields makes them more complex and abstract than scalar and vector fields. Visualization is a good technique for scientists to gain new insights from them. Visualizing a 3-D continuous tensor field is equivalent to simultaneously visualizing its three eigenvector fields. In the past, research has been conducted in the area of two-dimensional tensor fields. It was shown that degenerate points, defined as points where eigenvalues are equal to each other, are the basic singularities underlying the topology of tensor fields. Moreover, it was shown that eigenvectors never cross each other except at degenerate points. Since we live in a three-dimensional world, it is important for us to understand the underlying physics of this world. In this report, we describe a new method for locating degenerate points along with the conditions for classifying them in three-dimensional space. Finally, we discuss some topological features of three-dimensional tensor fields, and interpret topological patterns in terms of physical properties.

Handedness- and brain size-related efficiency differences in small-world brain networks: a resting-state functional magnetic resonance imaging study.

PubMed

Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu

2015-05-01

The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.

Visual sensory networks and effective information transfer in animal groups.

PubMed

Strandburg-Peshkin, Ariana; Twomey, Colin R; Bode, Nikolai W F; Kao, Albert B; Katz, Yael; Ioannou, Christos C; Rosenthal, Sara B; Torney, Colin J; Wu, Hai Shan; Levin, Simon A; Couzin, Iain D

2013-09-09

Social transmission of information is vital for many group-living animals, allowing coordination of motion and effective response to complex environments. Revealing the interaction networks underlying information flow within these groups is a central challenge. Previous work has modeled interactions between individuals based directly on their relative spatial positions: each individual is considered to interact with all neighbors within a fixed distance (metric range), a fixed number of nearest neighbors (topological range), a 'shell' of near neighbors (Voronoi range), or some combination (Figure 1A). However, conclusive evidence to support these assumptions is lacking. Here, we employ a novel approach that considers individual movement decisions to be based explicitly on the sensory information available to the organism. In other words, we consider that while spatial relations do inform interactions between individuals, they do so indirectly, through individuals' detection of sensory cues. We reconstruct computationally the visual field of each individual throughout experiments designed to investigate information propagation within fish schools (golden shiners, Notemigonus crysoleucas). Explicitly considering visual sensing allows us to more accurately predict the propagation of behavioral change in these groups during leadership events. Furthermore, we find that structural properties of visual interaction networks differ markedly from those of metric and topological counterparts, suggesting that previous assumptions may not appropriately reflect information flow in animal groups. Copyright © 2013 Elsevier Ltd. All rights reserved.

Surface smoothing, decimation, and their effects on 3D biological specimens.

PubMed

Veneziano, Alessio; Landi, Federica; Profico, Antonio

2018-06-01

Smoothing and decimation filters are commonly used to restore the realistic appearance of virtual biological specimens, but they can cause a loss of topological information of unknown extent. In this study, we analyzed the effect of smoothing and decimation on a 3D mesh to highlight the consequences of an inappropriate use of these filters. Topological noise was simulated on four anatomical regions of the virtual reconstruction of an orangutan cranium. Sequential levels of smoothing and decimation were applied, and their effects were analyzed on the overall topology of the 3D mesh and on linear and volumetric measurements. Different smoothing algorithms affected mesh topology and measurements differently, although the influence on the latter was generally low. Decimation always produced detrimental effects on both topology and measurements. The application of smoothing and decimation, both separate and combined, is capable of recovering topological information. Based on the results, objective guidelines are provided to minimize information loss when using smoothing and decimation on 3D meshes. © 2018 Wiley Periodicals, Inc.

Optimization of topological quantum algorithms using Lattice Surgery is hard

NASA Astrophysics Data System (ADS)

Herr, Daniel; Nori, Franco; Devitt, Simon

The traditional method for computation in the surface code or the Raussendorf model is the creation of holes or ''defects'' within the encoded lattice of qubits which are manipulated via topological braiding to enact logic gates. However, this is not the only way to achieve universal, fault-tolerant computation. In this work we turn attention to the Lattice Surgery representation, which realizes encoded logic operations without destroying the intrinsic 2D nearest-neighbor interactions sufficient for braided based logic and achieves universality without using defects for encoding information. In both braided and lattice surgery logic there are open questions regarding the compilation and resource optimization of quantum circuits. Optimization in braid-based logic is proving to be difficult to define and the classical complexity associated with this problem has yet to be determined. In the context of lattice surgery based logic, we can introduce an optimality condition, which corresponds to a circuit with lowest amount of physical qubit requirements, and prove that the complexity of optimizing the geometric (lattice surgery) representation of a quantum circuit is NP-hard.

Topological entanglement Rényi entropy and reduced density matrix structure.

PubMed

Flammia, Steven T; Hamma, Alioscia; Hughes, Taylor L; Wen, Xiao-Gang

2009-12-31

We generalize the topological entanglement entropy to a family of topological Rényi entropies parametrized by a parameter alpha, in an attempt to find new invariants for distinguishing topologically ordered phases. We show that, surprisingly, all topological Rényi entropies are the same, independent of alpha for all nonchiral topological phases. This independence shows that topologically ordered ground-state wave functions have reduced density matrices with a certain simple structure, and no additional universal information can be extracted from the entanglement spectrum.

Topological Entanglement Rényi Entropy and Reduced Density Matrix Structure

NASA Astrophysics Data System (ADS)

Flammia, Steven T.; Hamma, Alioscia; Hughes, Taylor L.; Wen, Xiao-Gang

2009-12-01

We generalize the topological entanglement entropy to a family of topological Rényi entropies parametrized by a parameter α, in an attempt to find new invariants for distinguishing topologically ordered phases. We show that, surprisingly, all topological Rényi entropies are the same, independent of α for all nonchiral topological phases. This independence shows that topologically ordered ground-state wave functions have reduced density matrices with a certain simple structure, and no additional universal information can be extracted from the entanglement spectrum.

Topology-Optimized Multilayered Metaoptics

NASA Astrophysics Data System (ADS)

Lin, Zin; Groever, Benedikt; Capasso, Federico; Rodriguez, Alejandro W.; Lončar, Marko

2018-04-01

We propose a general topology-optimization framework for metasurface inverse design that can automatically discover highly complex multilayered metastructures with increased functionalities. In particular, we present topology-optimized multilayered geometries exhibiting angular phase control, including a single-piece nanophotonic metalens with angular aberration correction, as well as an angle-convergent metalens that focuses light onto the same focal spot regardless of the angle of incidence.

Occam’s Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel

NASA Astrophysics Data System (ADS)

Mahoney, John R.; Aghamohammadi, Cina; Crutchfield, James P.

2016-02-01

A stochastic process’ statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process’ cryptic order-a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.

Occam's Quantum Strop: Synchronizing and Compressing Classical Cryptic Processes via a Quantum Channel.

PubMed

Mahoney, John R; Aghamohammadi, Cina; Crutchfield, James P

2016-02-15

A stochastic process' statistical complexity stands out as a fundamental property: the minimum information required to synchronize one process generator to another. How much information is required, though, when synchronizing over a quantum channel? Recent work demonstrated that representing causal similarity as quantum state-indistinguishability provides a quantum advantage. We generalize this to synchronization and offer a sequence of constructions that exploit extended causal structures, finding substantial increase of the quantum advantage. We demonstrate that maximum compression is determined by the process' cryptic order--a classical, topological property closely allied to Markov order, itself a measure of historical dependence. We introduce an efficient algorithm that computes the quantum advantage and close noting that the advantage comes at a cost-one trades off prediction for generation complexity.

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

Flammia, Steven T.; Hamma, Alioscia; Hughes, Taylor L.

We generalize the topological entanglement entropy to a family of topological Renyi entropies parametrized by a parameter alpha, in an attempt to find new invariants for distinguishing topologically ordered phases. We show that, surprisingly, all topological Renyi entropies are the same, independent of alpha for all nonchiral topological phases. This independence shows that topologically ordered ground-state wave functions have reduced density matrices with a certain simple structure, and no additional universal information can be extracted from the entanglement spectrum.

Pathway enrichment analysis approach based on topological structure and updated annotation of pathway.

PubMed

Yang, Qian; Wang, Shuyuan; Dai, Enyu; Zhou, Shunheng; Liu, Dianming; Liu, Haizhou; Meng, Qianqian; Jiang, Bin; Jiang, Wei

2017-08-16

Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Variable speed limit strategies analysis with mesoscopic traffic flow model based on complex networks

NASA Astrophysics Data System (ADS)

Li, Shu-Bin; Cao, Dan-Ni; Dang, Wen-Xiu; Zhang, Lin

As a new cross-discipline, the complexity science has penetrated into every field of economy and society. With the arrival of big data, the research of the complexity science has reached its summit again. In recent years, it offers a new perspective for traffic control by using complex networks theory. The interaction course of various kinds of information in traffic system forms a huge complex system. A new mesoscopic traffic flow model is improved with variable speed limit (VSL), and the simulation process is designed, which is based on the complex networks theory combined with the proposed model. This paper studies effect of VSL on the dynamic traffic flow, and then analyzes the optimal control strategy of VSL in different network topologies. The conclusion of this research is meaningful to put forward some reasonable transportation plan and develop effective traffic management and control measures to help the department of traffic management.

Does topological information matter for power grid vulnerability?

PubMed

Ouyang, Min; Yang, Kun

2014-12-01

Power grids, which are playing an important role in supporting the economy of a region as well as the life of its citizens, could be attacked by terrorists or enemies to damage the region. Depending on different levels of power grid information collected by the terrorists, their attack strategies might be different. This paper groups power grid information into four levels: no information, purely topological information (PTI), topological information with generator and load nodes (GLNI), and full information (including component physical properties and flow parameters information), and then identifies possible attack strategies for each information level. Analyzing and comparing power grid vulnerability under these attack strategies from both terrorists' and utility companies' point of view give rise to an approach to quantify the relative values of these three types of information, including PTI, GLNI, and component parameter information (CPI). This approach can provide information regarding the extent to which topological information matters for power system vulnerability decisions. Taking several test systems as examples, results show that for small attacks with p ≤ 0.1, CPI matters the most; when taking attack cost into consideration and assuming that the terrorists take the optimum cost-efficient attack intensity, then CPI has the largest cost-based information value.

Does topological information matter for power grid vulnerability?

NASA Astrophysics Data System (ADS)

Ouyang, Min; Yang, Kun

2014-12-01

Power grids, which are playing an important role in supporting the economy of a region as well as the life of its citizens, could be attacked by terrorists or enemies to damage the region. Depending on different levels of power grid information collected by the terrorists, their attack strategies might be different. This paper groups power grid information into four levels: no information, purely topological information (PTI), topological information with generator and load nodes (GLNI), and full information (including component physical properties and flow parameters information), and then identifies possible attack strategies for each information level. Analyzing and comparing power grid vulnerability under these attack strategies from both terrorists' and utility companies' point of view give rise to an approach to quantify the relative values of these three types of information, including PTI, GLNI, and component parameter information (CPI). This approach can provide information regarding the extent to which topological information matters for power system vulnerability decisions. Taking several test systems as examples, results show that for small attacks with p ≤ 0.1, CPI matters the most; when taking attack cost into consideration and assuming that the terrorists take the optimum cost-efficient attack intensity, then CPI has the largest cost-based information value.

Nonreciprocal lasing in topological cavities of arbitrary geometries

NASA Astrophysics Data System (ADS)

Bahari, Babak; Ndao, Abdoulaye; Vallini, Felipe; El Amili, Abdelkrim; Fainman, Yeshaiahu; Kanté, Boubacar

2017-11-01

Resonant cavities are essential building blocks governing many wave-based phenomena, but their geometry and reciprocity fundamentally limit the integration of optical devices. We report, at telecommunication wavelengths, geometry-independent and integrated nonreciprocal topological cavities that couple stimulated emission from one-way photonic edge states to a selected waveguide output with an isolation ratio in excess of 10 decibels. Nonreciprocity originates from unidirectional edge states at the boundary between photonic structures with distinct topological invariants. Our experimental demonstration of lasing from topological cavities provides the opportunity to develop complex topological circuitry of arbitrary geometries for the integrated and robust generation and transport of photons in classical and quantum regimes.

Automatic discovery of the communication network topology for building a supercomputer model

NASA Astrophysics Data System (ADS)

Sobolev, Sergey; Stefanov, Konstantin; Voevodin, Vadim

2016-10-01

The Research Computing Center of Lomonosov Moscow State University is developing the Octotron software suite for automatic monitoring and mitigation of emergency situations in supercomputers so as to maximize hardware reliability. The suite is based on a software model of the supercomputer. The model uses a graph to describe the computing system components and their interconnections. One of the most complex components of a supercomputer that needs to be included in the model is its communication network. This work describes the proposed approach for automatically discovering the Ethernet communication network topology in a supercomputer and its description in terms of the Octotron model. This suite automatically detects computing nodes and switches, collects information about them and identifies their interconnections. The application of this approach is demonstrated on the "Lomonosov" and "Lomonosov-2" supercomputers.

Topological Properties of Some Integrated Circuits for Very Large Scale Integration Chip Designs

NASA Astrophysics Data System (ADS)

Swanson, S.; Lanzerotti, M.; Vernizzi, G.; Kujawski, J.; Weatherwax, A.

2015-03-01

This talk presents topological properties of integrated circuits for Very Large Scale Integration chip designs. These circuits can be implemented in very large scale integrated circuits, such as those in high performance microprocessors. Prior work considered basic combinational logic functions and produced a mathematical framework based on algebraic topology for integrated circuits composed of logic gates. Prior work also produced an historically-equivalent interpretation of Mr. E. F. Rent's work for today's complex circuitry in modern high performance microprocessors, where a heuristic linear relationship was observed between the number of connections and number of logic gates. This talk will examine topological properties and connectivity of more complex functionally-equivalent integrated circuits. The views expressed in this article are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense or the U.S. Government.

Fuzzy spaces topology change as a possible solution to the black hole information loss paradox

NASA Astrophysics Data System (ADS)

Silva, C. A. S.

2009-06-01

The black hole information loss paradox is one of the most intricate problems in modern theoretical physics. A proposal to solve this is one related with topology change. However it has found some obstacles related to unitarity and cluster decomposition (locality). In this Letter we argue that modelling the black hole's event horizon as a noncommutative manifold - the fuzzy sphere - we can solve the problems with topology change, getting a possible solution to the black hole information loss paradox.

Topology of Document Retrieval Systems.

ERIC Educational Resources Information Center

Everett, Daniel M.; Cater, Steven C.

1992-01-01

Explains the use of a topological structure to examine the closeness between documents in retrieval systems and analyzes the topological structure of a vector-space model, a fuzzy-set model, an extended Boolean model, a probabilistic model, and a TIRS (Topological Information Retrieval System) model. Proofs for the results are appended. (17…

Spectrum-Based and Collaborative Network Topology Analysis and Visualization

ERIC Educational Resources Information Center

Hu, Xianlin

2013-01-01

Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying network topology is very important to network analysis. In this dissertation, we study networks by…

Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.

PubMed

Fan, Yue; Wang, Xiao; Peng, Qinke

2017-01-01

Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.

Circuit topology of proteins and nucleic acids.

PubMed

Mashaghi, Alireza; van Wijk, Roeland J; Tans, Sander J

2014-09-02

Folded biomolecules display a bewildering structural complexity and diversity. They have therefore been analyzed in terms of generic topological features. For instance, folded proteins may be knotted, have beta-strands arranged into a Greek-key motif, or display high contact order. In this perspective, we present a method to formally describe the topology of all folded linear chains and hence provide a general classification and analysis framework for a range of biomolecules. Moreover, by identifying the fundamental rules that intrachain contacts must obey, the method establishes the topological constraints of folded linear chains. We also briefly illustrate how this circuit topology notion can be applied to study the equivalence of folded chains, the engineering of artificial RNA structures and DNA origami, the topological structure of genomes, and the role of topology in protein folding. Copyright © 2014 Elsevier Ltd. All rights reserved.

Curvature and temperature of complex networks.

PubMed

Krioukov, Dmitri; Papadopoulos, Fragkiskos; Vahdat, Amin; Boguñá, Marián

2009-09-01

We show that heterogeneous degree distributions in observed scale-free topologies of complex networks can emerge as a consequence of the exponential expansion of hidden hyperbolic space. Fermi-Dirac statistics provides a physical interpretation of hyperbolic distances as energies of links. The hidden space curvature affects the heterogeneity of the degree distribution, while clustering is a function of temperature. We embed the internet into the hyperbolic plane and find a remarkable congruency between the embedding and our hyperbolic model. Besides proving our model realistic, this embedding may be used for routing with only local information, which holds significant promise for improving the performance of internet routing.

Effective field theories for topological insulators by functional bosonization

NASA Astrophysics Data System (ADS)

Chan, AtMa; Hughes, Taylor L.; Ryu, Shinsei; Fradkin, Eduardo

2013-02-01

Effective field theories that describe the dynamics of a conserved U(1) current in terms of “hydrodynamic” degrees of freedom of topological phases in condensed matter are discussed in general dimension D=d+1 using the functional bosonization technique. For noninteracting topological insulators (superconductors) with a conserved U(1) charge and characterized by an integer topological invariant [more specifically, they are topological insulators in the complex symmetry classes (class A and AIII), and in the “primary series” of topological insulators, in the eight real symmetry classes], we derive the BF-type topological field theories supplemented with the Chern-Simons (when D is odd) or the θ (when D is even) terms. For topological insulators characterized by a Z2 topological invariant (the first and second descendants of the primary series), their topological field theories are obtained by dimensional reduction. Building on this effective field theory description for noninteracting topological phases, we also discuss, following the spirit of the parton construction of the fractional quantum Hall effect by Block and Wen, the putative “fractional” topological insulators and their possible effective field theories, and use them to determine the physical properties of these nontrivial quantum phases.

Combined shape and topology optimization for minimization of maximal von Mises stress

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

Lian, Haojie; Christiansen, Asger N.; Tortorelli, Daniel A.

Here, this work shows that a combined shape and topology optimization method can produce optimal 2D designs with minimal stress subject to a volume constraint. The method represents the surface explicitly and discretizes the domain into a simplicial complex which adapts both structural shape and topology. By performing repeated topology and shape optimizations and adaptive mesh updates, we can minimize the maximum von Mises stress using the p-norm stress measure with p-values as high as 30, provided that the stress is calculated with sufficient accuracy.

Combined shape and topology optimization for minimization of maximal von Mises stress

DOE PAGES

Lian, Haojie; Christiansen, Asger N.; Tortorelli, Daniel A.; ...

2017-01-27

Here, this work shows that a combined shape and topology optimization method can produce optimal 2D designs with minimal stress subject to a volume constraint. The method represents the surface explicitly and discretizes the domain into a simplicial complex which adapts both structural shape and topology. By performing repeated topology and shape optimizations and adaptive mesh updates, we can minimize the maximum von Mises stress using the p-norm stress measure with p-values as high as 30, provided that the stress is calculated with sufficient accuracy.

Evolution of heliospheric magnetized configurations via topological invariants

NASA Astrophysics Data System (ADS)

Roth, Ilan

2013-07-01

The analogy between magnetohydrodynamics (MHD) and knot theory is utilized in presenting a new method for an analysis of stability and evolution of complex magnetic heliospheric flux tubes. Planar projection of a three-dimensional magnetic configuration depicts the structure as a two-dimensional diagram with crossings, to which one may assign mathematical operations leading to robust topological invariants. These invariants enrich the topological information of magnetic configurations beyond helicity. It is conjectured that the field which emerges from the solar photosphere is structured as one of the simplest knots-unknot or prime knot-and these flux ropes are then stretched while carried by the solar wind into the interplanetary medium. Preservation of invariants for small diffusivity and large cross section of the emerging magnetic flux makes them impervious to large scale reconnection, allowing us to predict the observed structures at 1 AU as elongated prime knots. Similar structures may be observed in magnetic clouds which got disconnected from their footpoints and in ion drop-out configurations from a compact flare source in solar impulsive solar events. Observation of small scale magnetic features consistent with prime knots may indicate spatial intermittency and non-Gaussian statistics in the turbulent cascade process. For flux tubes with higher resistivity, magnetic energy decay rate should decrease with increased knot complexity as the invariants are then harder to be violated. These observations could be confirmed if adjacent satellites happen to measure distinctly oriented magnetic fields with directionally varying suprathermal particle fluxes.

Epidemics in Complex Networks: The Diversity of Hubs

NASA Astrophysics Data System (ADS)

Kitsak, Maksim; Gallos, Lazaros K.; Havlin, Shlomo; Stanley, H. Eugene; Makse, Hernan A.

2009-03-01

Many complex systems are believed to be vulnerable to spread of viruses and information owing to their high level of interconnectivity. Even viruses of low contagiousness easily proliferate the Internet. Rumors, fads, and innovation ideas are prone to efficient spreading in various social systems. Another commonly accepted standpoint is the importance of the most connected elements (hubs) in the spreading processes. We address following questions. Do all hubs conduct epidemics in the same manner? How does the epidemics spread depend on the structure of the network? What is the most efficient way to spread information over the system? We analyze several large-scale systems in the framework of of the susceptible/infective/removed (SIR) disease spread model which can also be mapped to the problem of rumor or fad spreading. We show that hubs are often ineffective in the transmission of virus or information owing to the highly heterogeneous topology of most networks. We also propose a new tool to evaluate the efficiency of nodes in spreading virus or information.

Topologically-protected one-way leaky waves in nonreciprocal plasmonic structures

NASA Astrophysics Data System (ADS)

Hassani Gangaraj, S. Ali; Monticone, Francesco

2018-03-01

We investigate topologically-protected unidirectional leaky waves on magnetized plasmonic structures acting as homogeneous photonic topological insulators. Our theoretical analyses and numerical experiments aim at unveiling the general properties of these exotic surface waves, and their nonreciprocal and topological nature. In particular, we study the behavior of topological leaky modes in stratified structures composed of a magnetized plasma at the interface with isotropic conventional media, and we show how to engineer their propagation and radiation properties, leading to topologically-protected backscattering-immune wave propagation, and highly directive and tunable radiation. Taking advantage of the non-trivial topological properties of these leaky modes, we also theoretically demonstrate advanced functionalities, including arbitrary re-routing of leaky waves on the surface of bodies with complex shapes, as well as the realization of topological leaky-wave (nano)antennas with isolated channels of radiation that are completely independent and separately tunable. Our findings help shedding light on the behavior of topologically-protected modes in open wave-guiding structures, and may open intriguing directions for future antenna generations based on topological structures, at microwaves and optical frequencies.

Proteome-wide Subcellular Topologies of E. coli Polypeptides Database (STEPdb)*

PubMed Central

Orfanoudaki, Georgia; Economou, Anastassios

2014-01-01

Cell compartmentalization serves both the isolation and the specialization of cell functions. After synthesis in the cytoplasm, over a third of all proteins are targeted to other subcellular compartments. Knowing how proteins are distributed within the cell and how they interact is a prerequisite for understanding it as a whole. Surface and secreted proteins are important pathogenicity determinants. Here we present the STEP database (STEPdb) that contains a comprehensive characterization of subcellular localization and topology of the complete proteome of Escherichia coli. Two widely used E. coli proteomes (K-12 and BL21) are presented organized into thirteen subcellular classes. STEPdb exploits the wealth of genetic, proteomic, biochemical, and functional information on protein localization, secretion, and targeting in E. coli, one of the best understood model organisms. Subcellular annotations were derived from a combination of bioinformatics prediction, proteomic, biochemical, functional, topological data and extensive literature re-examination that were refined through manual curation. Strong experimental support for the location of 1553 out of 4303 proteins was based on 426 articles and some experimental indications for another 526. Annotations were provided for another 320 proteins based on firm bioinformatic predictions. STEPdb is the first database that contains an extensive set of peripheral IM proteins (PIM proteins) and includes their graphical visualization into complexes, cellular functions, and interactions. It also summarizes all currently known protein export machineries of E. coli K-12 and pairs them, where available, with the secretory proteins that use them. It catalogs the Sec- and TAT-utilizing secretomes and summarizes their topological features such as signal peptides and transmembrane regions, transmembrane topologies and orientations. It also catalogs physicochemical and structural features that influence topology such as abundance, solubility, disorder, heat resistance, and structural domain families. Finally, STEPdb incorporates prediction tools for topology (TMHMM, SignalP, and Phobius) and disorder (IUPred) and implements the BLAST2STEP that performs protein homology searches against the STEPdb. PMID:25210196

Topology for efficient information dissemination in ad-hoc networking

NASA Technical Reports Server (NTRS)

Jennings, E.; Okino, C. M.

2002-01-01

In this paper, we explore the information dissemination problem in ad-hoc wirless networks. First, we analyze the probability of successful broadcast, assuming: the nodes are uniformly distributed, the available area has a lower bould relative to the total number of nodes, and there is zero knowledge of the overall topology of the network. By showing that the probability of such events is small, we are motivated to extract good graph topologies to minimize the overall transmissions. Three algorithms are used to generate topologies of the network with guaranteed connectivity. These are the minimum radius graph, the relative neighborhood graph and the minimum spanning tree. Our simulation shows that the relative neighborhood graph has certain good graph properties, which makes it suitable for efficient information dissemination.

Topological surface states in nodal superconductors.

PubMed

Schnyder, Andreas P; Brydon, Philip M R

2015-06-24

Topological superconductors have become a subject of intense research due to their potential use for technical applications in device fabrication and quantum information. Besides fully gapped superconductors, unconventional superconductors with point or line nodes in their order parameter can also exhibit nontrivial topological characteristics. This article reviews recent progress in the theoretical understanding of nodal topological superconductors, with a focus on Weyl and noncentrosymmetric superconductors and their protected surface states. Using selected examples, we review the bulk topological properties of these systems, study different types of topological surface states, and examine their unusual properties. Furthermore, we survey some candidate materials for topological superconductivity and discuss different experimental signatures of topological surface states.

Small-World Brain Networks Revisited

PubMed Central

Bassett, Danielle S.; Bullmore, Edward T.

2016-01-01

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex. PMID:27655008

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

PubMed Central

Lee, Esther J.

2018-01-01

Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals. PMID:29451874

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

AlignNemo: a local network alignment method to integrate homology and topology.

PubMed

Ciriello, Giovanni; Mina, Marco; Guzzi, Pietro H; Cannataro, Mario; Guerra, Concettina

2012-01-01

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo.

Effect of chunk strength on the performance of children with developmental dyslexia on artificial grammar learning task may be related to complexity.

PubMed

Schiff, Rachel; Katan, Pesia; Sasson, Ayelet; Kahta, Shani

2017-07-01

There's a long held view that chunks play a crucial role in artificial grammar learning performance. We compared chunk strength influences on performance, in high and low topological entropy (a measure of complexity) grammar systems, with dyslexic children, age-matched and reading-level-matched control participants. Findings show that age-matched control participants' performance reflected equivalent influence of chunk strength in the two topological entropy conditions, as typically found in artificial grammar learning experiments. By contrast, dyslexic children and reading-level-matched controls' performance reflected knowledge of chunk strength only under the low topological entropy condition. In the low topological entropy grammar system, they appeared completely unable to utilize chunk strength to make appropriate test item selections. In line with previous research, this study suggests that for typically developing children, it is the chunks that are attended during artificial grammar learning and create a foundation on which implicit associative learning mechanisms operate, and these chunks are unitized to different strengths. However, for children with dyslexia, it is complexity that may influence the subsequent memorability of chunks, independently of their strength.

Understanding the topological characteristics and flow complexity of urban traffic congestion

NASA Astrophysics Data System (ADS)

Wen, Tzai-Hung; Chin, Wei-Chien-Benny; Lai, Pei-Chun

2017-05-01

For a growing number of developing cities, the capacities of streets cannot meet the rapidly growing demand of cars, causing traffic congestion. Understanding the spatial-temporal process of traffic flow and detecting traffic congestion are important issues associated with developing sustainable urban policies to resolve congestion. Therefore, the objective of this study is to propose a flow-based ranking algorithm for investigating traffic demands in terms of the attractiveness of street segments and flow complexity of the street network based on turning probability. Our results show that, by analyzing the topological characteristics of streets and volume data for a small fraction of street segments in Taipei City, the most congested segments of the city were identified successfully. The identified congested segments are significantly close to the potential congestion zones, including the officially announced most congested streets, the segments with slow moving speeds at rush hours, and the areas near significant landmarks. The identified congested segments also captured congestion-prone areas concentrated in the business districts and industrial areas of the city. Identifying the topological characteristics and flow complexity of traffic congestion provides network topological insights for sustainable urban planning, and these characteristics can be used to further understand congestion propagation.

Observation of symmetry-protected topological band with ultracold fermions

PubMed Central

Song, Bo; Zhang, Long; He, Chengdong; Poon, Ting Fung Jeffrey; Hajiyev, Elnur; Zhang, Shanchao; Liu, Xiong-Jun; Jo, Gyu-Boong

2018-01-01

Symmetry plays a fundamental role in understanding complex quantum matter, particularly in classifying topological quantum phases, which have attracted great interests in the recent decade. An outstanding example is the time-reversal invariant topological insulator, a symmetry-protected topological (SPT) phase in the symplectic class of the Altland-Zirnbauer classification. We report the observation for ultracold atoms of a noninteracting SPT band in a one-dimensional optical lattice and study quench dynamics between topologically distinct regimes. The observed SPT band can be protected by a magnetic group and a nonlocal chiral symmetry, with the band topology being measured via Bloch states at symmetric momenta. The topology also resides in far-from-equilibrium spin dynamics, which are predicted and observed in experiment to exhibit qualitatively distinct behaviors in quenching to trivial and nontrivial regimes, revealing two fundamental types of spin-relaxation dynamics related to bulk topology. This work opens the way to expanding the scope of SPT physics with ultracold atoms and studying nonequilibrium quantum dynamics in these exotic systems. PMID:29492457

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow.

PubMed

Zubek, Julian; Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow

PubMed Central

Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems. PMID:28809957

Design of complex bone internal structure using topology optimization with perimeter control.

PubMed

Park, Jaejong; Sutradhar, Alok; Shah, Jami J; Paulino, Glaucio H

2018-03-01

Large facial bone loss usually requires patient-specific bone implants to restore the structural integrity and functionality that also affects the appearance of each patient. Titanium alloys (e.g., Ti-6Al-4V) are typically used in the interfacial porous coatings between the implant and the surrounding bone to promote stability. There exists a property mismatch between the two that in general leads to complications such as stress-shielding. This biomechanical discrepancy is a hurdle in the design of bone replacements. To alleviate the mismatch, the internal structure of the bone replacements should match that of the bone. Topology optimization has proven to be a good technique for designing bone replacements. However, the complex internal structure of the bone is difficult to mimic using conventional topology optimization methods without additional restrictions. In this work, the complex bone internal structure is recovered using a perimeter control based topology optimization approach. By restricting the solution space by means of the perimeter, the intricate design complexity of bones can be achieved. Three different bone regions with well-known physiological loadings are selected to illustrate the method. Additionally, we found that the target perimeter value and the pattern of the initial distribution play a vital role in obtaining the natural curvatures in the bone internal structures as well as avoiding excessive island patterns. Copyright © 2018 Elsevier Ltd. All rights reserved.

Structure, Function, and Propagation of Information across Living Two, Four, and Eight Node Degree Topologies.

PubMed

Alagapan, Sankaraleengam; Franca, Eric; Pan, Liangbin; Leondopulos, Stathis; Wheeler, Bruce C; DeMarse, Thomas B

2016-01-01

In this study, we created four network topologies composed of living cortical neurons and compared resultant structural-functional dynamics including the nature and quality of information transmission. Each living network was composed of living cortical neurons and were created using microstamping of adhesion promoting molecules and each was "designed" with different levels of convergence embedded within each structure. Networks were cultured over a grid of electrodes that permitted detailed measurements of neural activity at each node in the network. Of the topologies we tested, the "Random" networks in which neurons connect based on their own intrinsic properties transmitted information embedded within their spike trains with higher fidelity relative to any other topology we tested. Within our patterned topologies in which we explicitly manipulated structure, the effect of convergence on fidelity was dependent on both topology and time-scale (rate vs. temporal coding). A more detailed examination using tools from network analysis revealed that these changes in fidelity were also associated with a number of other structural properties including a node's degree, degree-degree correlations, path length, and clustering coefficients. Whereas information transmission was apparent among nodes with few connections, the greatest transmission fidelity was achieved among the few nodes possessing the highest number of connections (high degree nodes or putative hubs). These results provide a unique view into the relationship between structure and its affect on transmission fidelity, at least within these small neural populations with defined network topology. They also highlight the potential role of tools such as microstamp printing and microelectrode array recordings to construct and record from arbitrary network topologies to provide a new direction in which to advance the study of structure-function relationships.

Interdisciplinary challenges in the study of power grid resilience and stability and their relation to extreme weather events

NASA Astrophysics Data System (ADS)

Heitzig, J.; Fujiwara, N.; Aihara, K.; Kurths, J.

2014-10-01

This topical issue collects contributions to the interdisciplinary study of power grid stability in face of increasing volatility of energy production and consumption due to increasing renewable energy infeed and changing climatic conditions. The individual papers focus on different aspects of this field and bring together modern achievements from various disciplines, in particular complex systems science, nonlinear data analysis, control theory, electrical engineering, and climatology. Main topics considered here are prediction and volatility of renewable infeed, modelling and theoretical analysis of power grid topology, dynamics and stability, relationships between stability and complex network topology, and improvements via topological changes or control. Impacts for the design of smart power grids are discussed in detail.

An efficient approach to the travelling salesman problem using self-organizing maps.

PubMed

Vieira, Frederico Carvalho; Dória Neto, Adrião Duarte; Costa, José Alfredo Ferreira

2003-04-01

This paper presents an approach to the well-known Travelling Salesman Problem (TSP) using Self-Organizing Maps (SOM). The SOM algorithm has interesting topological information about its neurons configuration on cartesian space, which can be used to solve optimization problems. Aspects of initialization, parameters adaptation, and complexity analysis of the proposed SOM based algorithm are discussed. The results show an average deviation of 3.7% from the optimal tour length for a set of 12 TSP instances.

On a biologically inspired topology optimization method

NASA Astrophysics Data System (ADS)

Kobayashi, Marcelo H.

2010-03-01

This work concerns the development of a biologically inspired methodology for the study of topology optimization in engineering and natural systems. The methodology is based on L systems and its turtle interpretation for the genotype-phenotype modeling of the topology development. The topology is analyzed using the finite element method, and optimized using an evolutionary algorithm with the genetic encoding of the L system and its turtle interpretation, as well as, body shape and physical characteristics. The test cases considered in this work clearly show the suitability of the proposed method for the study of engineering and natural complex systems.

Entropic manifestations of topological order in three dimensions

NASA Astrophysics Data System (ADS)

Bullivant, Alex; Pachos, Jiannis K.

2016-03-01

We evaluate the entanglement entropy of exactly solvable Hamiltonians corresponding to general families of three-dimensional topological models. We show that the modification to the entropic area law due to three-dimensional topological properties is richer than the two-dimensional case. In addition to the reduction of the entropy caused by a nonzero vacuum expectation value of contractible loop operators, a topological invariant emerges that increases the entropy if the model consists of nontrivially braiding anyons. As a result the three-dimensional topological entanglement entropy provides only partial information about the two entropic topological invariants.

Stochastic cycle selection in active flow networks.

PubMed

Woodhouse, Francis G; Forrow, Aden; Fawcett, Joanna B; Dunkel, Jörn

2016-07-19

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Stochastic cycle selection in active flow networks

NASA Astrophysics Data System (ADS)

Woodhouse, Francis; Forrow, Aden; Fawcett, Joanna; Dunkel, Jorn

2016-11-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. By connecting concepts from lattice field theory, graph theory and transition rate theory, we show how topology controls dynamics in a generic model for actively driven flow on a network. Through theoretical and numerical analysis we identify symmetry-based rules to classify and predict the selection statistics of complex flow cycles from the network topology. Our conceptual framework is applicable to a broad class of biological and non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ice-type models.

Fault detection of Tennessee Eastman process based on topological features and SVM

NASA Astrophysics Data System (ADS)

Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

2018-03-01

Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

Evaluating the effect of aging on interference resolution with time-varying complex networks analysis

PubMed Central

Ariza, Pedro; Solesio-Jofre, Elena; Martínez, Johann H.; Pineda-Pardo, José A.; Niso, Guiomar; Maestú, Fernando; Buldú, Javier M.

2015-01-01

In this study we used graph theory analysis to investigate age-related reorganization of functional networks during the active maintenance of information that is interrupted by external interference. Additionally, we sought to investigate network differences before and after averaging network parameters between both maintenance and interference windows. We compared young and older adults by measuring their magnetoencephalographic recordings during an interference-based working memory task restricted to successful recognitions. Data analysis focused on the topology/temporal evolution of functional networks during both the maintenance and interference windows. We observed that: (a) Older adults require higher synchronization between cortical brain sites in order to achieve a successful recognition, (b) The main differences between age groups arise during the interference window, (c) Older adults show reduced ability to reorganize network topology when interference is introduced, and (d) Averaging network parameters leads to a loss of sensitivity to detect age differences. PMID:26029079

The effect of the neural activity on topological properties of growing neural networks.

PubMed

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

Stochastic cycle selection in active flow networks

PubMed Central

Woodhouse, Francis G.; Forrow, Aden; Fawcett, Joanna B.; Dunkel, Jörn

2016-01-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models. PMID:27382186

Three-Level De-Multiplexed Dual-Branch Complex Delta-Sigma Transmitter.

PubMed

Arfi, Anis Ben; Elsayed, Fahmi; Aflaki, Pouya M; Morris, Brad; Ghannouchi, Fadhel M

2018-02-20

In this paper, a dual-branch topology driven by a Delta-Sigma Modulator (DSM) with a complex quantizer, also known as the Complex Delta Sigma Modulator (CxDSM), with a 3-level quantized output signal is proposed. By de-multiplexing the 3-level Delta-Sigma-quantized signal into two bi-level streams, an efficiency enhancement over the operational frequency range is achieved. The de-multiplexed signals drive a dual-branch amplification block composed of two switch-mode back-to-back power amplifiers working at peak power. A signal processing technique known as quantization noise reduction with In-band Filtering (QNRIF) is applied to each of the de-multiplexed streams to boost the overall performances; particularly the Adjacent Channel Leakage Ratio (ACLR). After amplification, the two branches are combined using a non-isolated combiner, preserving the efficiency of the transmitter. A comprehensive study on the operation of this topology and signal characteristics used to drive the dual-branch Switch-Mode Power Amplifiers (SMPAs) was established. Moreover, this work proposes a highly efficient design of the amplification block based on a back-to-back power topology performing a dynamic load modulation exploiting the non-overlapping properties of the de-multiplexed Complex DSM signal. For experimental validation, the proposed de-multiplexed 3-level Delta-Sigma topology was implemented on the BEEcube™ platform followed by the back-to-back Class-E switch-mode power amplification block. The full transceiver is assessed using a 4th-Generation mobile communications standard LTE (Long Term Evolution) standard 1.4 MHz signal with a peak to average power ratio (PAPR) of 8 dB. The dual-branch topology exhibited a good linearity and a coding efficiency of the transmitter chain higher than 72% across the band of frequency from 1.8 GHz to 2.7 GHz.

Efficient embedding of complex networks to hyperbolic space via their Laplacian

PubMed Central

Alanis-Lobato, Gregorio; Mier, Pablo; Andrade-Navarro, Miguel A.

2016-01-01

The different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was produced by the afore-mentioned model. Here, a different strategy is followed in the form of the Laplacian-based Network Embedding, a simple yet accurate, efficient and data driven manifold learning approach, which allows for the quick geometric analysis of big networks. Comparisons against existing embedding and prediction techniques highlight its applicability to network evolution and link prediction. PMID:27445157

Efficient embedding of complex networks to hyperbolic space via their Laplacian

NASA Astrophysics Data System (ADS)

Alanis-Lobato, Gregorio; Mier, Pablo; Andrade-Navarro, Miguel A.

2016-07-01

The different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was produced by the afore-mentioned model. Here, a different strategy is followed in the form of the Laplacian-based Network Embedding, a simple yet accurate, efficient and data driven manifold learning approach, which allows for the quick geometric analysis of big networks. Comparisons against existing embedding and prediction techniques highlight its applicability to network evolution and link prediction.

Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties.

PubMed

Ouma, Wilberforce Zachary; Pogacar, Katja; Grotewold, Erich

2018-04-01

Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These exponents are independent of the fraction of the GRN experimentally sampled, enabling prediction of properties of the complete GRN for an organism. We further demonstrate that the exponents describe inequalities in transcription factor (TF)-target gene recognition across GRNs. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. Consequently, architectural GRN organization drives not only phenotypic plasticity within a species, but is also likely implicated in species-specific phenotype.

Shape complexes: the intersection of label orderings and star convexity constraints in continuous max-flow medical image segmentation

PubMed Central

Baxter, John S. H.; Inoue, Jiro; Drangova, Maria; Peters, Terry M.

2016-01-01

Abstract. Optimization-based segmentation approaches deriving from discrete graph-cuts and continuous max-flow have become increasingly nuanced, allowing for topological and geometric constraints on the resulting segmentation while retaining global optimality. However, these two considerations, topological and geometric, have yet to be combined in a unified manner. The concept of “shape complexes,” which combine geodesic star convexity with extendable continuous max-flow solvers, is presented. These shape complexes allow more complicated shapes to be created through the use of multiple labels and super-labels, with geodesic star convexity governed by a topological ordering. These problems can be optimized using extendable continuous max-flow solvers. Previous approaches required computationally expensive coordinate system warping, which are ill-defined and ambiguous in the general case. These shape complexes are demonstrated in a set of synthetic images as well as vessel segmentation in ultrasound, valve segmentation in ultrasound, and atrial wall segmentation from contrast-enhanced CT. Shape complexes represent an extendable tool alongside other continuous max-flow methods that may be suitable for a wide range of medical image segmentation problems. PMID:28018937

Interaction surface and topology of Get3-Get4-Get5 protein complex, involved in targeting tail-anchored proteins to endoplasmic reticulum.

PubMed

Chang, Yi-Wei; Lin, Tai-Wen; Li, Yi-Chuan; Huang, Yu-Shan; Sun, Yuh-Ju; Hsiao, Chwan-Deng

2012-02-10

Recent work has uncovered the "GET system," which is responsible for endoplasmic reticulum targeting of tail-anchored proteins. Although structural information and the individual roles of most components of this system have been defined, the interactions and interplay between them remain to be elucidated. Here, we investigated the interactions between Get3 and the Get4-Get5 complex from Saccharomyces cerevisiae. We show that Get3 interacts with Get4-Get5 via an interface dominated by electrostatic forces. Using isothermal titration calorimetry and small-angle x-ray scattering, we further demonstrate that the Get3 homodimer interacts with two copies of the Get4-Get5 complex to form an extended conformation in solution.

Modes of Interaction between Individuals Dominate the Topologies of Real World Networks

PubMed Central

Lee, Insuk; Kim, Eiru; Marcotte, Edward M.

2015-01-01

We find that the topologies of real world networks, such as those formed within human societies, by the Internet, or among cellular proteins, are dominated by the mode of the interactions considered among the individuals. Specifically, a major dichotomy in previously studied networks arises from modeling networks in terms of pairwise versus group tasks. The former often intrinsically give rise to scale-free, disassortative, hierarchical networks, whereas the latter often give rise to single- or broad-scale, assortative, nonhierarchical networks. These dependencies explain contrasting observations among previous topological analyses of real world complex systems. We also observe this trend in systems with natural hierarchies, in which alternate representations of the same networks, but which capture different levels of the hierarchy, manifest these signature topological differences. For example, in both the Internet and cellular proteomes, networks of lower-level system components (routers within domains or proteins within biological processes) are assortative and nonhierarchical, whereas networks of upper-level system components (internet domains or biological processes) are disassortative and hierarchical. Our results demonstrate that network topologies of complex systems must be interpreted in light of their hierarchical natures and interaction types. PMID:25793969

Bayesian structural inference for hidden processes.

PubMed

Strelioff, Christopher C; Crutchfield, James P

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ε-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ε-machines, irrespective of estimated transition probabilities. Properties of ε-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Bayesian structural inference for hidden processes

NASA Astrophysics Data System (ADS)

Strelioff, Christopher C.; Crutchfield, James P.

2014-04-01

We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.

Topological defects in liquid crystals and molecular self-assembly (Conference Presentation)

NASA Astrophysics Data System (ADS)

Abbott, Nicholas L.

2017-02-01

Topological defects in liquid crystals (LCs) have been widely used to organize colloidal dispersions and template polymerizations, leading to a range of elastomers and gels with complex mechanical and optical properties. However, little is understood about molecular-level assembly processes within defects. This presentation will describe an experimental study that reveals that nanoscopic environments defined by LC topological defects can selectively trigger processes of molecular self-assembly. By using fluorescence microscopy, cryogenic transmission electron microscopy and super-resolution optical microscopy, key signatures of molecular self-assembly of amphiphilic molecules in topological defects are observed - including cooperativity, reversibility, and controlled growth of the molecular assemblies. By using polymerizable amphiphiles, we also demonstrate preservation of molecular assemblies templated by defects, including nanoscopic "o-rings" synthesized from "Saturn-ring" disclinations. Our results reveal that topological defects in LCs are a versatile class of three-dimensional, dynamic and reconfigurable templates that can direct processes of molecular self-assembly in a manner that is strongly analogous to other classes of macromolecular templates (e.g., polymer—surfactant complexes). Opportunities for the design of exquisitely responsive soft materials will be discussed using bacterial endotoxin as an example.

Quantum algorithms for topological and geometric analysis of data

PubMed Central

Lloyd, Seth; Garnerone, Silvano; Zanardi, Paolo

2016-01-01

Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers—the numbers of connected components, holes and voids—in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis. PMID:26806491

Entanglement entropy for (3+1)-dimensional topological order with excitations

NASA Astrophysics Data System (ADS)

Wen, Xueda; He, Huan; Tiwari, Apoorv; Zheng, Yunqin; Ye, Peng

2018-02-01

Excitations in (3+1)-dimensional [(3+1)D] topologically ordered phases have very rich structures. (3+1)D topological phases support both pointlike and stringlike excitations, and in particular the loop (closed string) excitations may admit knotted and linked structures. In this work, we ask the following question: How do different types of topological excitations contribute to the entanglement entropy or, alternatively, can we use the entanglement entropy to detect the structure of excitations, and further obtain the information of the underlying topological order? We are mainly interested in (3+1)D topological order that can be realized in Dijkgraaf-Witten (DW) gauge theories, which are labeled by a finite group G and its group 4-cocycle ω ∈H4[G ;U(1 ) ] up to group automorphisms. We find that each topological excitation contributes a universal constant lndi to the entanglement entropy, where di is the quantum dimension that depends on both the structure of the excitation and the data (G ,ω ) . The entanglement entropy of the excitations of the linked/unlinked topology can capture different information of the DW theory (G ,ω ) . In particular, the entanglement entropy introduced by Hopf-link loop excitations can distinguish certain group 4-cocycles ω from the others.

A new logistic dynamic particle swarm optimization algorithm based on random topology.

PubMed

Ni, Qingjian; Deng, Jianming

2013-01-01

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.

Exploring hyperspectral imaging data sets with topological data analysis.

PubMed

Duponchel, Ludovic

2018-02-13

Analytical chemistry is rapidly changing. Indeed we acquire always more data in order to go ever further in the exploration of complex samples. Hyperspectral imaging has not escaped this trend. It quickly became a tool of choice for molecular characterisation of complex samples in many scientific domains. The main reason is that it simultaneously provides spectral and spatial information. As a result, chemometrics has provided many exploration tools (PCA, clustering, MCR-ALS …) well-suited for such data structure at early stage. However we are today facing a new challenge considering the always increasing number of pixels in the data cubes we have to manage. The idea is therefore to introduce a new paradigm of Topological Data Analysis in order explore hyperspectral imaging data sets highlighting its nice properties and specific features. With this paper, we shall also point out the fact that conventional chemometric methods are often based on variance analysis or simply impose a data model which implicitly defines the geometry of the data set. Thus we will show that it is not always appropriate in the framework of hyperspectral imaging data sets exploration. Copyright © 2017 Elsevier B.V. All rights reserved.

Default cascades in complex networks: topology and systemic risk.

PubMed

Roukny, Tarik; Bersini, Hugues; Pirotte, Hugues; Caldarelli, Guido; Battiston, Stefano

2013-09-26

The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only--but substantially--when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.

Electrostatically Accelerated Encounter and Folding for Facile Recognition of Intrinsically Disordered Proteins

PubMed Central

Ganguly, Debabani; Zhang, Weihong; Chen, Jianhan

2013-01-01

Achieving facile specific recognition is essential for intrinsically disordered proteins (IDPs) that are involved in cellular signaling and regulation. Consideration of the physical time scales of protein folding and diffusion-limited protein-protein encounter has suggested that the frequent requirement of protein folding for specific IDP recognition could lead to kinetic bottlenecks. How IDPs overcome such potential kinetic bottlenecks to viably function in signaling and regulation in general is poorly understood. Our recent computational and experimental study of cell-cycle regulator p27 (Ganguly et al., J. Mol. Biol. (2012)) demonstrated that long-range electrostatic forces exerted on enriched charges of IDPs could accelerate protein-protein encounter via “electrostatic steering” and at the same time promote “folding-competent” encounter topologies to enhance the efficiency of IDP folding upon encounter. Here, we further investigated the coupled binding and folding mechanisms and the roles of electrostatic forces in the formation of three IDP complexes with more complex folded topologies. The surface electrostatic potentials of these complexes lack prominent features like those observed for the p27/Cdk2/cyclin A complex to directly suggest the ability of electrostatic forces to facilitate folding upon encounter. Nonetheless, similar electrostatically accelerated encounter and folding mechanisms were consistently predicted for all three complexes using topology-based coarse-grained simulations. Together with our previous analysis of charge distributions in known IDP complexes, our results support a prevalent role of electrostatic interactions in promoting efficient coupled binding and folding for facile specific recognition. These results also suggest that there is likely a co-evolution of IDP folded topology, charge characteristics, and coupled binding and folding mechanisms, driven at least partially by the need to achieve fast association kinetics for cellular signaling and regulation. PMID:24278008

Robustness and percolation of holes in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Andu; Maletić, Slobodan; Zhao, Yi

2018-07-01

Efficient robustness and fault tolerance of complex network is significantly influenced by its connectivity, commonly modeled by the structure of pairwise relations between network elements, i.e., nodes. Nevertheless, aggregations of nodes build higher-order structures embedded in complex network, which may be more vulnerable when the fraction of nodes is removed. The structure of higher-order aggregations of nodes can be naturally modeled by simplicial complexes, whereas the removal of nodes affects the values of topological invariants, like the number of higher-dimensional holes quantified with Betti numbers. Following the methodology of percolation theory, as the fraction of nodes is removed, new holes appear, which have the role of merger between already present holes. In the present article, relationship between the robustness and homological properties of complex network is studied, through relating the graph-theoretical signatures of robustness and the quantities derived from topological invariants. The simulation results of random failures and intentional attacks on networks suggest that the changes of graph-theoretical signatures of robustness are followed by differences in the distribution of number of holes per cluster under different attack strategies. In the broader sense, the results indicate the importance of topological invariants research for obtaining further insights in understanding dynamics taking place over complex networks.

Robust pinning control of heterogeneous complex networks with jointly connected topologies and time-varying parametric uncertainty

NASA Astrophysics Data System (ADS)

Manfredi, Sabato

2018-05-01

The pinning/leader control problems provide the design of the leader or pinning controller in order to guide a complex network to a desired trajectory or target (synchronisation or consensus). Let a time-invariant complex network, pinning/leader control problems include the design of the leader or pinning controller gain and number of nodes to pin in order to guide a network to a desired trajectory (synchronization or consensus). Usually, lower is the number of pinned nodes larger is the pinning gain required to assess network synchronisation. On the other side, realistic application scenario of complex networks is characterised by switching topologies, time-varying node coupling strength and link weight that make hard to solve the pinning/leader control problem. Additionally, the system dynamics at nodes can be heterogeneous. In this paper, we derive robust stabilisation conditions of time-varying heterogeneous complex networks with jointly connected topologies when coupling strength and link weight interactions are affected by time-varying uncertainties. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, we formulate low computationally demanding stabilisability conditions to design a pinning/leader control gain for robust network synchronisation. The effectiveness of the proposed approach is shown by several design examples applied to a paradigmatic well-known complex network composed of heterogeneous Chua's circuits.

Reconfigurable optical implementation of quantum complex networks

NASA Astrophysics Data System (ADS)

Nokkala, J.; Arzani, F.; Galve, F.; Zambrini, R.; Maniscalco, S.; Piilo, J.; Treps, N.; Parigi, V.

2018-05-01

Network theory has played a dominant role in understanding the structure of complex systems and their dynamics. Recently, quantum complex networks, i.e. collections of quantum systems arranged in a non-regular topology, have been theoretically explored leading to significant progress in a multitude of diverse contexts including, e.g., quantum transport, open quantum systems, quantum communication, extreme violation of local realism, and quantum gravity theories. Despite important progress in several quantum platforms, the implementation of complex networks with arbitrary topology in quantum experiments is still a demanding task, especially if we require both a significant size of the network and the capability of generating arbitrary topology—from regular to any kind of non-trivial structure—in a single setup. Here we propose an all optical and reconfigurable implementation of quantum complex networks. The experimental proposal is based on optical frequency combs, parametric processes, pulse shaping and multimode measurements allowing the arbitrary control of the number of the nodes (optical modes) and topology of the links (interactions between the modes) within the network. Moreover, we also show how to simulate quantum dynamics within the network combined with the ability to address its individual nodes. To demonstrate the versatility of these features, we discuss the implementation of two recently proposed probing techniques for quantum complex networks and structured environments.

Enhanced capital-asset pricing model for the reconstruction of bipartite financial networks.

PubMed

Squartini, Tiziano; Almog, Assaf; Caldarelli, Guido; van Lelyveld, Iman; Garlaschelli, Diego; Cimini, Giulio

2017-09-01

Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets are strongly affected by the interconnections among financial institutions. Yet, while the aggregate balance sheets of institutions are publicly disclosed, information on single positions is mostly confidential and, as such, unavailable. Standard approaches to reconstruct the network of financial interconnection produce unrealistically dense topologies, leading to a biased estimation of systemic risk. Moreover, reconstruction techniques are generally designed for monopartite networks of bilateral exposures between financial institutions, thus failing in reproducing bipartite networks of security holdings (e.g., investment portfolios). Here we propose a reconstruction method based on constrained entropy maximization, tailored for bipartite financial networks. Such a procedure enhances the traditional capital-asset pricing model (CAPM) and allows us to reproduce the correct topology of the network. We test this enhanced CAPM (ECAPM) method on a dataset, collected by the European Central Bank, of detailed security holdings of European institutional sectors over a period of six years (2009-2015). Our approach outperforms the traditional CAPM and the recently proposed maximum-entropy CAPM both in reproducing the network topology and in estimating systemic risk due to fire sales spillovers. In general, ECAPM can be applied to the whole class of weighted bipartite networks described by the fitness model.

Enhanced capital-asset pricing model for the reconstruction of bipartite financial networks

NASA Astrophysics Data System (ADS)

Squartini, Tiziano; Almog, Assaf; Caldarelli, Guido; van Lelyveld, Iman; Garlaschelli, Diego; Cimini, Giulio

2017-09-01

Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets are strongly affected by the interconnections among financial institutions. Yet, while the aggregate balance sheets of institutions are publicly disclosed, information on single positions is mostly confidential and, as such, unavailable. Standard approaches to reconstruct the network of financial interconnection produce unrealistically dense topologies, leading to a biased estimation of systemic risk. Moreover, reconstruction techniques are generally designed for monopartite networks of bilateral exposures between financial institutions, thus failing in reproducing bipartite networks of security holdings (e.g., investment portfolios). Here we propose a reconstruction method based on constrained entropy maximization, tailored for bipartite financial networks. Such a procedure enhances the traditional capital-asset pricing model (CAPM) and allows us to reproduce the correct topology of the network. We test this enhanced CAPM (ECAPM) method on a dataset, collected by the European Central Bank, of detailed security holdings of European institutional sectors over a period of six years (2009-2015). Our approach outperforms the traditional CAPM and the recently proposed maximum-entropy CAPM both in reproducing the network topology and in estimating systemic risk due to fire sales spillovers. In general, ECAPM can be applied to the whole class of weighted bipartite networks described by the fitness model.

CCTOP: a Consensus Constrained TOPology prediction web server.

PubMed

Dobson, László; Reményi, István; Tusnády, Gábor E

2015-07-01

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Complex networks with large numbers of labelable attractors

NASA Astrophysics Data System (ADS)

Mi, Yuanyuan; Zhang, Lisheng; Huang, Xiaodong; Qian, Yu; Hu, Gang; Liao, Xuhong

2011-09-01

Information storage in many functional subsystems of the brain is regarded by theoretical neuroscientists to be related to attractors of neural networks. The number of attractors is large and each attractor can be temporarily represented or suppressed easily by corresponding external stimulus. In this letter, we discover that complex networks consisting of excitable nodes have similar fascinating properties of coexistence of large numbers of oscillatory attractors, most of which can be labeled with a few nodes. According to a simple labeling rule, different attractors can be identified and the number of labelable attractors can be predicted from the analysis of network topology. With the cues of the labeling association, these attractors can be conveniently retrieved or suppressed on purpose.

Cd(II)-coordination polymers based on tetracarboxylic acid and diverse bis(imidazole) ligands: Synthesis, structural diversity and photoluminescence properties

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

Arıcı, Mürsel, E-mail: marici@ogu.edu.tr; Yeşilel, Okan Zafer; Taş, Murat

Three new Cd(II)-coordination polymers, namely, ([Cd{sub 2}(μ{sub 6}-ao{sub 2}btc)(μ-1,5-bipe){sub 2}]·2H{sub 2}O){sub n} (1), ([Cd{sub 2}(μ{sub 6}-ao{sub 2}btc)(μ-1,4-bix){sub 2}]{sub n}·2DMF) (2) and ([Cd{sub 2}(μ{sub 8}-abtc)(μ-1,4-betix)]·DMF·H{sub 2}O){sub n} (3) (ao{sub 2}btc=di-oxygenated form of 3,3′,5,5′-azobenzenetetracarboxylate, 1,5-bipe: 1,5-bis(imidazol-1yl)pentane, 1,4-bix=1,4-bis(imidazol-1ylmethyl)benzene, 1,4-betix=1,4-bis(2-ethylimidazol-1ylmethyl)benzene) were synthesized with 3,3′,5,5′-azobenzenetetracarboxylic acid and flexible, semi-flexible and semi-flexible substituted bis(imidazole) linkers. They were characterized by IR spectroscopy, elemental analysis, single-crystal X-ray diffraction, powder X-ray diffractions (PXRD) and thermal analyses (TG/DTA). Complexes 1–3 exhibited structural diversities depending on flexible, semi-flexible and semi-flexible substituted bis(imidazole) ligands. Complex 1 was 2D structure with 3,6L18 topology. Complex 2 had a 3D pillar-layered framework with the raremore » sqc27 topology. When semi-flexible substituted bis(imidazole) linker was used, 3D framework of complex 3 was obtained with the paddlewheel Cd{sub 2}(CO{sub 2}){sub 4}-type binuclear SBU. Moreover, thermal and photoluminescence properties of the complexes were determined in detailed. - Graphical abstract: In this study, three novel Cd(II)-coordination polymers were synthesized with 3,3′,5,5′-azobenzenetetracarboxylic acid and flexible, semi-flexible and semi-flexible substituted bis(imidazole) linkers. They were characterized by IR spectroscopy, elemental analysis, single-crystal X-ray diffraction, powder X-ray diffractions (PXRD) and thermal analyses (TG/DTA). Complexes 1–3 exhibited structural diversities depending on flexible, semi-flexible and semi-flexible substituted bis(imidazole) ligands. Complex 1 was 2D structure with 3,6L18 topology. Complex 2 had a 3D pillar-layered framework with the rare sqc27 topology. When semi-flexible substituted bis(imidazole) linker was used, 3D framework of complex 3 was obtained with the paddlewheel Cd{sub 2}(CO{sub 2}){sub 4}-type binuclear SBU. - Highlights: • Three new Cd(II)-coordination polymers with azobenzenetetracarboxylic acid and diverse bis(imidazole) linkers. • Complex 1 is 2D structure with 3,6L18 topology. • 3D pillar-layered framework of 2 with the rare sqc27 topology. • 3D framework of 3 with the paddlewheel Cd{sub 2}(CO{sub 2}){sub 4}-type SBU.« less

Topological Constraints in Directed Polymer Melts

NASA Astrophysics Data System (ADS)

Serna, Pablo; Bunin, Guy; Nahum, Adam

2015-11-01

Polymers in a melt may be subject to topological constraints, as in the example of unlinked polymer rings. How to do statistical mechanics in the presence of such constraints remains a fundamental open problem. We study the effect of topological constraints on a melt of directed polymers, using simulations of a simple quasi-2D model. We find that fixing the global topology of the melt to be trivial changes the polymer conformations drastically. Polymers of length L wander in the transverse direction only by a distance of order (ln L )ζ with ζ ≃1.5 . This is strongly suppressed in comparison with the Brownian L1 /2 scaling which holds in the absence of the topological constraint. It is also much smaller than the predictions of standard heuristic approaches—in particular the L1 /4 of a mean-field-like "array of obstacles" model—so our results present a sharp challenge to theory. Dynamics are also strongly affected by the constraints, and a tagged monomer in an infinite system performs logarithmically slow subdiffusion in the transverse direction. To cast light on the suppression of the strands' wandering, we analyze the topological complexity of subregions of the melt: the complexity is also logarithmically small, and is related to the wandering by a power law. We comment on insights the results give for 3D melts, directed and nondirected.

Topological Constraints in Directed Polymer Melts.

PubMed

Serna, Pablo; Bunin, Guy; Nahum, Adam

2015-11-27

Polymers in a melt may be subject to topological constraints, as in the example of unlinked polymer rings. How to do statistical mechanics in the presence of such constraints remains a fundamental open problem. We study the effect of topological constraints on a melt of directed polymers, using simulations of a simple quasi-2D model. We find that fixing the global topology of the melt to be trivial changes the polymer conformations drastically. Polymers of length L wander in the transverse direction only by a distance of order (lnL)^{ζ} with ζ≃1.5. This is strongly suppressed in comparison with the Brownian L^{1/2} scaling which holds in the absence of the topological constraint. It is also much smaller than the predictions of standard heuristic approaches-in particular the L^{1/4} of a mean-field-like "array of obstacles" model-so our results present a sharp challenge to theory. Dynamics are also strongly affected by the constraints, and a tagged monomer in an infinite system performs logarithmically slow subdiffusion in the transverse direction. To cast light on the suppression of the strands' wandering, we analyze the topological complexity of subregions of the melt: the complexity is also logarithmically small, and is related to the wandering by a power law. We comment on insights the results give for 3D melts, directed and nondirected.

The Anatomical Distance of Functional Connections Predicts Brain Network Topology in Health and Schizophrenia

PubMed Central

Vértes, Petra E.; Stidd, Reva; Lalonde, François; Clasen, Liv; Rapoport, Judith; Giedd, Jay; Bullmore, Edward T.; Gogtay, Nitin

2013-01-01

The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive “pruning” of short-distance functional connections in schizophrenia. PMID:22275481

Emergent chirality in the electric polarization texture of titanate superlattices.

PubMed

Shafer, Padraic; García-Fernández, Pablo; Aguado-Puente, Pablo; Damodaran, Anoop R; Yadav, Ajay K; Nelson, Christopher T; Hsu, Shang-Lin; Wojdeł, Jacek C; Íñiguez, Jorge; Martin, Lane W; Arenholz, Elke; Junquera, Javier; Ramesh, Ramamoorthy

2018-01-30

Chirality is a geometrical property by which an object is not superimposable onto its mirror image, thereby imparting a handedness. Chirality determines many important properties in nature-from the strength of the weak interactions according to the electroweak theory in particle physics to the binding of enzymes with naturally occurring amino acids or sugars, reactions that are fundamental for life. In condensed matter physics, the prediction of topologically protected magnetic skyrmions and related spin textures in chiral magnets has stimulated significant research. If the magnetic dipoles were replaced by their electrical counterparts, then electrically controllable chiral devices could be designed. Complex oxide BaTiO 3 /SrTiO 3 nanocomposites and PbTiO 3 /SrTiO 3 superlattices are perfect candidates, since "polar vortices," in which a continuous rotation of ferroelectric polarization spontaneously forms, have been recently discovered. Using resonant soft X-ray diffraction, we report the observation of a strong circular dichroism from the interaction between circularly polarized light and the chiral electric polarization texture that emerges in PbTiO 3 /SrTiO 3 superlattices. This hallmark of chirality is explained by a helical rotation of electric polarization that second-principles simulations predict to reside within complex 3D polarization textures comprising ordered topological line defects. The handedness of the texture can be topologically characterized by the sign of the helicity number of the chiral line defects. This coupling between the optical and novel polar properties could be exploited to encode chiral signatures into photon or electron beams for information processing.

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

PubMed

Li, Xiumin; Small, Michael

2012-06-01

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

Topological Creation of Acoustic Pseudospin Multipoles in a Flow-Free Symmetry-Broken Metamaterial Lattice

NASA Astrophysics Data System (ADS)

Zhang, Zhiwang; Wei, Qi; Cheng, Ying; Zhang, Ting; Wu, Dajian; Liu, Xiaojun

2017-02-01

The discovery of topological acoustics has revolutionized fundamental concepts of sound propagation, giving rise to strikingly unconventional acoustic edge modes immune to scattering. Because of the spinless nature of sound, the "spinlike" degree of freedom crucial to topological states in acoustic systems is commonly realized with circulating background flow or preset coupled resonator ring waveguides, which drastically increases the engineering complexity. Here we realize the acoustic pseudospin multipolar states in a simple flow-free symmetry-broken metamaterial lattice, where the clockwise (anticlockwise) sound propagation within each metamolecule emulates pseudospin down (pseudospin up). We demonstrate that tuning the strength of intermolecular coupling by simply contracting or expanding the metamolecule can induce the band inversion effect between the pseudospin dipole and quadrupole, which leads to a topological phase transition. Topologically protected edge states and reconfigurable topological one-way transmission for sound are further demonstrated. These results provide diverse routes to construct novel acoustic topological insulators with versatile applications.

The Partition Function in the Four-Dimensional Schwarz-Type Topological Half-Flat Two-Form Gravity

NASA Astrophysics Data System (ADS)

Abe, Mitsuko

We derive the partition functions of the Schwarz-type four-dimensional topological half-flat two-form gravity model on K3-surface or T4 up to on-shell one-loop corrections. In this model the bosonic moduli spaces describe an equivalent class of a trio of the Einstein-Kähler forms (the hyper-Kähler forms). The integrand of the partition function is represented by the product of some bar ∂ -torsions. bar ∂ -torsion is the extension of R-torsion for the de Rham complex to that for the bar ∂ -complex of a complex analytic manifold.

Going End to End to Deliver High-Speed Data

NASA Technical Reports Server (NTRS)

2005-01-01

By the end of the 1990s, the optical fiber "backbone" of the telecommunication and data-communication networks had evolved from megabits-per-second transmission rates to gigabits-per-second transmission rates. Despite this boom in bandwidth, however, users at the end nodes were still not being reached on a consistent basis. (An end node is any device that does not behave like a router or a managed hub or switch. Examples of end node objects are computers, printers, serial interface processor phones, and unmanaged hubs and switches.) The primary reason that prevents bandwidth from reaching the end nodes is the complex local network topology that exists between the optical backbone and the end nodes. This complex network topology consists of several layers of routing and switch equipment which introduce potential congestion points and network latency. By breaking down the complex network topology, a true optical connection can be achieved. Access Optical Networks, Inc., is making this connection a reality with guidance from NASA s nondestructive evaluation experts.

Tunnelling with a negative cosmological constant

NASA Astrophysics Data System (ADS)

Gibbons, G. W.

1996-02-01

The point of this paper is to see what light new results in hyperbolic geometry may throw on gravitational entropy and whether gravitational entropy is relevant for the quantum origin of the universe. We introduce some new gravitational instantons which mediate the birth from nothing of closed universes containing wormholes and suggest that they may contribute to the density matrix of the universe. We also discuss the connection between their gravitational action and the topological and volumetric entropies introduced in hyperbolic geometry. These coincide for hyperbolic 4-manifolds, and increase with increasing topological complexity of the 4-manifold. We raise the question of whether the action also increases with the topological complexity of the initial 3-geometry, measured either by its 3-volume or its Matveev complexity. We point out, in distinction to the non-supergravity case, that universes with domains of negative cosmological constant separated by supergravity domain walls cannot be born from nothing. Finally we point out that our wormholes provide examples of the type of Perpetual Motion machines envisaged by Frolov and Novikov.

A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

PubMed Central

Freytag, Saskia; Manitz, Juliane; Schlather, Martin; Kneib, Thomas; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Heinrich, Joachim; Bickeböller, Heike

2014-01-01

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). Here, the kernel converts genomic information of two individuals to a quantitative value reflecting their genetic similarity. With the selection of the kernel one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms. PMID:24434848

Crystalline metamaterials for topological properties at subwavelength scales

PubMed Central

Yves, Simon; Fleury, Romain; Berthelot, Thomas; Fink, Mathias; Lemoult, Fabrice; Lerosey, Geoffroy

2017-01-01

The exciting discovery of topological condensed matter systems has lately triggered a search for their photonic analogues, motivated by the possibility of robust backscattering-immune light transport. However, topological photonic phases have so far only been observed in photonic crystals and waveguide arrays, which are inherently physically wavelength scaled, hindering their application in compact subwavelength systems. In this letter, we tackle this problem by patterning the deep subwavelength resonant elements of metamaterials onto specific lattices, and create crystalline metamaterials that can develop complex nonlocal properties due to multiple scattering, despite their very subwavelength spatial scale that usually implies to disregard their structure. These spatially dispersive systems can support subwavelength topological phases, as we demonstrate at microwaves by direct field mapping. Our approach gives a straightforward tabletop platform for the study of photonic topological phases, and allows to envision applications benefiting the compactness of metamaterials and the amazing potential of topological insulators. PMID:28719573

Scaling behavior of knotted random polygons and self-avoiding polygons: Topological swelling with enhanced exponent.

PubMed

Uehara, Erica; Deguchi, Tetsuo

2017-12-07

We show that the average size of self-avoiding polygons (SAPs) with a fixed knot is much larger than that of no topological constraint if the excluded volume is small and the number of segments is large. We call it topological swelling. We argue an "enhancement" of the scaling exponent for random polygons with a fixed knot. We study them systematically through SAP consisting of hard cylindrical segments with various different values of the radius of segments. Here we mean by the average size the mean-square radius of gyration. Furthermore, we show numerically that the topological balance length of a composite knot is given by the sum of those of all constituent prime knots. Here we define the topological balance length of a knot by such a number of segments that topological entropic repulsions are balanced with the knot complexity in the average size. The additivity suggests the local knot picture.

Creation and manipulation of topological states in chiral nematic microspheres

PubMed Central

Orlova, Tetiana; Aßhoff, Sarah Jane; Yamaguchi, Tadatsugu; Katsonis, Nathalie; Brasselet, Etienne

2015-01-01

Topology is a universal concept that is encountered in daily life and is known to determine many static and dynamical properties of matter. Taming and controlling the topology of materials therefore constitutes a contemporary interdisciplinary challenge. Building on the controllable spatial properties of soft matter appears as a relevant strategy to address the challenge, in particular, because it may lead to paradigmatic model systems that allow checking theories experimentally. Here we report experimentally on a wealth of complex free-standing metastable topological architectures at the micron scale, in frustrated chiral nematic droplets. These results support recent works predicting the formation of free-standing knotted and linked disclination structures in confined chiral nematic fluids. We also demonstrate that various kinds of external fields (thermal, electrical and optical) can be used to achieve topological remote control. All this may foster the development of new devices based on topologically structured soft media. PMID:26145716

Statistical moments of quantum-walk dynamics reveal topological quantum transitions.

PubMed

Cardano, Filippo; Maffei, Maria; Massa, Francesco; Piccirillo, Bruno; de Lisio, Corrado; De Filippis, Giulio; Cataudella, Vittorio; Santamato, Enrico; Marrucci, Lorenzo

2016-04-22

Many phenomena in solid-state physics can be understood in terms of their topological properties. Recently, controlled protocols of quantum walk (QW) are proving to be effective simulators of such phenomena. Here we report the realization of a photonic QW showing both the trivial and the non-trivial topologies associated with chiral symmetry in one-dimensional (1D) periodic systems. We find that the probability distribution moments of the walker position after many steps can be used as direct indicators of the topological quantum transition: while varying a control parameter that defines the system phase, these moments exhibit a slope discontinuity at the transition point. Numerical simulations strongly support the conjecture that these features are general of 1D topological systems. Extending this approach to higher dimensions, different topological classes, and other typologies of quantum phases may offer general instruments for investigating and experimentally detecting quantum transitions in such complex systems.

Statistical moments of quantum-walk dynamics reveal topological quantum transitions

PubMed Central

Cardano, Filippo; Maffei, Maria; Massa, Francesco; Piccirillo, Bruno; de Lisio, Corrado; De Filippis, Giulio; Cataudella, Vittorio; Santamato, Enrico; Marrucci, Lorenzo

2016-01-01

Many phenomena in solid-state physics can be understood in terms of their topological properties. Recently, controlled protocols of quantum walk (QW) are proving to be effective simulators of such phenomena. Here we report the realization of a photonic QW showing both the trivial and the non-trivial topologies associated with chiral symmetry in one-dimensional (1D) periodic systems. We find that the probability distribution moments of the walker position after many steps can be used as direct indicators of the topological quantum transition: while varying a control parameter that defines the system phase, these moments exhibit a slope discontinuity at the transition point. Numerical simulations strongly support the conjecture that these features are general of 1D topological systems. Extending this approach to higher dimensions, different topological classes, and other typologies of quantum phases may offer general instruments for investigating and experimentally detecting quantum transitions in such complex systems. PMID:27102945

A role for chromatin topology in imprinted domain regulation.

PubMed

MacDonald, William A; Sachani, Saqib S; White, Carlee R; Mann, Mellissa R W

2016-02-01

Recently, many advancements in genome-wide chromatin topology and nuclear architecture have unveiled the complex and hidden world of the nucleus, where chromatin is organized into discrete neighbourhoods with coordinated gene expression. This includes the active and inactive X chromosomes. Using X chromosome inactivation as a working model, we utilized publicly available datasets together with a literature review to gain insight into topologically associated domains, lamin-associated domains, nucleolar-associating domains, scaffold/matrix attachment regions, and nucleoporin-associated chromatin and their role in regulating monoallelic expression. Furthermore, we comprehensively review for the first time the role of chromatin topology and nuclear architecture in the regulation of genomic imprinting. We propose that chromatin topology and nuclear architecture are important regulatory mechanisms for directing gene expression within imprinted domains. Furthermore, we predict that dynamic changes in chromatin topology and nuclear architecture play roles in tissue-specific imprint domain regulation during early development and differentiation.

Creation and manipulation of topological states in chiral nematic microspheres

NASA Astrophysics Data System (ADS)

Orlova, Tetiana; Aßhoff, Sarah Jane; Yamaguchi, Tadatsugu; Katsonis, Nathalie; Brasselet, Etienne

2015-07-01

Topology is a universal concept that is encountered in daily life and is known to determine many static and dynamical properties of matter. Taming and controlling the topology of materials therefore constitutes a contemporary interdisciplinary challenge. Building on the controllable spatial properties of soft matter appears as a relevant strategy to address the challenge, in particular, because it may lead to paradigmatic model systems that allow checking theories experimentally. Here we report experimentally on a wealth of complex free-standing metastable topological architectures at the micron scale, in frustrated chiral nematic droplets. These results support recent works predicting the formation of free-standing knotted and linked disclination structures in confined chiral nematic fluids. We also demonstrate that various kinds of external fields (thermal, electrical and optical) can be used to achieve topological remote control. All this may foster the development of new devices based on topologically structured soft media.

Scaling behavior of knotted random polygons and self-avoiding polygons: Topological swelling with enhanced exponent

NASA Astrophysics Data System (ADS)

Uehara, Erica; Deguchi, Tetsuo

2017-12-01

We show that the average size of self-avoiding polygons (SAPs) with a fixed knot is much larger than that of no topological constraint if the excluded volume is small and the number of segments is large. We call it topological swelling. We argue an "enhancement" of the scaling exponent for random polygons with a fixed knot. We study them systematically through SAP consisting of hard cylindrical segments with various different values of the radius of segments. Here we mean by the average size the mean-square radius of gyration. Furthermore, we show numerically that the topological balance length of a composite knot is given by the sum of those of all constituent prime knots. Here we define the topological balance length of a knot by such a number of segments that topological entropic repulsions are balanced with the knot complexity in the average size. The additivity suggests the local knot picture.

Crystalline metamaterials for topological properties at subwavelength scales

NASA Astrophysics Data System (ADS)

Yves, Simon; Fleury, Romain; Berthelot, Thomas; Fink, Mathias; Lemoult, Fabrice; Lerosey, Geoffroy

2017-07-01

The exciting discovery of topological condensed matter systems has lately triggered a search for their photonic analogues, motivated by the possibility of robust backscattering-immune light transport. However, topological photonic phases have so far only been observed in photonic crystals and waveguide arrays, which are inherently physically wavelength scaled, hindering their application in compact subwavelength systems. In this letter, we tackle this problem by patterning the deep subwavelength resonant elements of metamaterials onto specific lattices, and create crystalline metamaterials that can develop complex nonlocal properties due to multiple scattering, despite their very subwavelength spatial scale that usually implies to disregard their structure. These spatially dispersive systems can support subwavelength topological phases, as we demonstrate at microwaves by direct field mapping. Our approach gives a straightforward tabletop platform for the study of photonic topological phases, and allows to envision applications benefiting the compactness of metamaterials and the amazing potential of topological insulators.

The binary protein-protein interaction landscape of Escherichia coli

PubMed Central

Rajagopala, Seesandra V.; Vlasblom, James; Arnold, Roland; Franca-Koh, Jonathan; Pakala, Suman B.; Phanse, Sadhna; Ceol, Arnaud; Häuser, Roman; Siszler, Gabriella; Wuchty, Stefan; Emili, Andrew; Babu, Mohan; Aloy, Patrick; Pieper, Rembert; Uetz, Peter

2014-01-01

Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (~70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, approximately doubling the number of known binary PPIs in E. coli. Integration of binary PPIs and genetic interactions revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that could be mapped within multi-protein complexes were informative regarding internal topology and indicated that interactions within complexes are significantly more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily significant model microbe. PMID:24561554

Topology of foreign exchange markets using hierarchical structure methods

NASA Astrophysics Data System (ADS)

Naylor, Michael J.; Rose, Lawrence C.; Moyle, Brendan J.

2007-08-01

This paper uses two physics derived hierarchical techniques, a minimal spanning tree and an ultrametric hierarchical tree, to extract a topological influence map for major currencies from the ultrametric distance matrix for 1995-2001. We find that these two techniques generate a defined and robust scale free network with meaningful taxonomy. The topology is shown to be robust with respect to method, to time horizon and is stable during market crises. This topology, appropriately used, gives a useful guide to determining the underlying economic or regional causal relationships for individual currencies and to understanding the dynamics of exchange rate price determination as part of a complex network.

A topological coordinate system for the diamond cubic grid.

PubMed

Čomić, Lidija; Nagy, Benedek

2016-09-01

Topological coordinate systems are used to address all cells of abstract cell complexes. In this paper, a topological coordinate system for cells in the diamond cubic grid is presented and some of its properties are detailed. Four dependent coordinates are used to address the voxels (triakis truncated tetrahedra), their faces (hexagons and triangles), their edges and the points at their corners. Boundary and co-boundary relations, as well as adjacency relations between the cells, can easily be captured by the coordinate values. Thus, this coordinate system is apt for implementation in various applications, such as visualizations, morphological and topological operations and shape analysis.

Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications

PubMed Central

Tadić, Bosiljka; Andjelković, Miroslav; Boshkoska, Biljana Mileva; Levnajić, Zoran

2016-01-01

Human behaviour in various circumstances mirrors the corresponding brain connectivity patterns, which are suitably represented by functional brain networks. While the objective analysis of these networks by graph theory tools deepened our understanding of brain functions, the multi-brain structures and connections underlying human social behaviour remain largely unexplored. In this study, we analyse the aggregate graph that maps coordination of EEG signals previously recorded during spoken communications in two groups of six listeners and two speakers. Applying an innovative approach based on the algebraic topology of graphs, we analyse higher-order topological complexes consisting of mutually interwoven cliques of a high order to which the identified functional connections organise. Our results reveal that the topological quantifiers provide new suitable measures for differences in the brain activity patterns and inter-brain synchronisation between speakers and listeners. Moreover, the higher topological complexity correlates with the listener’s concentration to the story, confirmed by self-rating, and closeness to the speaker’s brain activity pattern, which is measured by network-to-network distance. The connectivity structures of the frontal and parietal lobe consistently constitute distinct clusters, which extend across the listener’s group. Formally, the topology quantifiers of the multi-brain communities exceed the sum of those of the participating individuals and also reflect the listener’s rated attributes of the speaker and the narrated subject. In the broader context, the presented study exposes the relevance of higher topological structures (besides standard graph measures) for characterising functional brain networks under different stimuli. PMID:27880802

Multifractal cross-correlation effects in two-variable time series of complex network vertex observables

NASA Astrophysics Data System (ADS)

OświÈ©cimka, Paweł; Livi, Lorenzo; DroŻdŻ, Stanisław

2016-10-01

We investigate the scaling of the cross-correlations calculated for two-variable time series containing vertex properties in the context of complex networks. Time series of such observables are obtained by means of stationary, unbiased random walks. We consider three vertex properties that provide, respectively, short-, medium-, and long-range information regarding the topological role of vertices in a given network. In order to reveal the relation between these quantities, we applied the multifractal cross-correlation analysis technique, which provides information about the nonlinear effects in coupling of time series. We show that the considered network models are characterized by unique multifractal properties of the cross-correlation. In particular, it is possible to distinguish between Erdös-Rényi, Barabási-Albert, and Watts-Strogatz networks on the basis of fractal cross-correlation. Moreover, the analysis of protein contact networks reveals characteristics shared with both scale-free and small-world models.

Harnessing glycomics technologies: integrating structure with function for glycan characterization

PubMed Central

Robinson, Luke N.; Artpradit, Charlermchai; Raman, Rahul; Shriver, Zachary H.; Ruchirawat, Mathuros; Sasisekharan, Ram

2013-01-01

Glycans, or complex carbohydrates, are a ubiquitous class of biological molecules which impinge on a variety of physiological processes ranging from signal transduction to tissue development and microbial pathogenesis. In comparison to DNA and proteins, glycans present unique challenges to the study of their structure and function owing to their complex and heterogeneous structures and the dominant role played by multivalency in their sequence-specific biological interactions. Arising from these challenges, there is a need to integrate information from multiple complementary methods to decode structure-function relationships. Focusing on acidic glycans, we describe here key glycomics technologies for characterizing their structural attributes, including linkage, modifications, and topology, as well as for elucidating their role in biological processes. Two cases studies, one involving sialylated branched glycans and the other sulfated glycosaminoglycans, are used to highlight how integration of orthogonal information from diverse datasets enables rapid convergence of glycan characterization for development of robust structure-function relationships. PMID:22522536

Realization of a topological phase transition in a gyroscopic lattice

NASA Astrophysics Data System (ADS)

Mitchell, Noah P.; Nash, Lisa M.; Irvine, William T. M.

2018-03-01

Topological metamaterials exhibit unusual behaviors at their boundaries, such as unidirectional chiral waves, that are protected by a topological feature of their band structures. The ability to tune such a material through a topological phase transition in real time could enable the use of protected waves for information storage and readout. Here we dynamically tune through a topological phase transition by breaking inversion symmetry in a metamaterial composed of interacting gyroscopes. Through the transition, we track the divergence of the edge modes' localization length and the change in Chern number characterizing the topology of the material's band structure. This Rapid Communication provides a new axis with which to tune the response of mechanical topological metamaterials.

Nuclear Pasta: Topology and Defects

NASA Astrophysics Data System (ADS)

da Silva Schneider, Andre; Horowitz, Charles; Berry, Don; Caplan, Matt; Briggs, Christian

2015-04-01

A layer of complex non-uniform phases of matter known as nuclear pasta is expected to exist at the base of the crust of neutron stars. Using large scale molecular dynamics we study the topology of some pasta shapes, the formation of defects and how these may affect properties of neutron star crusts.

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

PubMed Central

Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-01-01

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes. PMID:29642483

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

PubMed

Murakami, Masaya; Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

2018-04-08

Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

Default Cascades in Complex Networks: Topology and Systemic Risk

PubMed Central

Roukny, Tarik; Bersini, Hugues; Pirotte, Hugues; Caldarelli, Guido; Battiston, Stefano

2013-01-01

The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only – but substantially – when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011. PMID:24067913

Topological data analyses and machine learning for detection, classification and characterization of atmospheric rivers

NASA Astrophysics Data System (ADS)

Muszynski, G.; Kashinath, K.; Wehner, M. F.; Prabhat, M.; Kurlin, V.

2017-12-01

We investigate novel approaches to detecting, classifying and characterizing extreme weather events, such as atmospheric rivers (ARs), in large high-dimensional climate datasets. ARs are narrow filaments of concentrated water vapour in the atmosphere that bring much of the precipitation in many mid-latitude regions. The precipitation associated with ARs is also responsible for major flooding events in many coastal regions of the world, including the west coast of the United States and western Europe. In this study we combine ideas from Topological Data Analysis (TDA) with Machine Learning (ML) for detecting, classifying and characterizing extreme weather events, like ARs. TDA is a new field that sits at the interface between topology and computer science, that studies "shape" - hidden topological structure - in raw data. It has been applied successfully in many areas of applied sciences, including complex networks, signal processing and image recognition. Using TDA we provide ARs with a shape characteristic as a new feature descriptor for the task of AR classification. In particular, we track the change in topology in precipitable water (integrated water vapour) fields using the Union-Find algorithm. We use the generated feature descriptors with ML classifiers to establish reliability and classification performance of our approach. We utilize the parallel toolkit for extreme climate events analysis (TECA: Petascale Pattern Recognition for Climate Science, Prabhat et al., Computer Analysis of Images and Patterns, 2015) for comparison (it is assumed that events identified by TECA is ground truth). Preliminary results indicate that our approach brings new insight into the study of ARs and provides quantitative information about the relevance of topological feature descriptors in analyses of a large climate datasets. We illustrate this method on climate model output and NCEP reanalysis datasets. Further, our method outperforms existing methods on detection and classification of ARs. This work illustrates that TDA combined with ML may provide a uniquely powerful approach for detection, classification and characterization of extreme weather phenomena.

Topology of large-scale structure. IV - Topology in two dimensions

NASA Technical Reports Server (NTRS)

Melott, Adrian L.; Cohen, Alexander P.; Hamilton, Andrew J. S.; Gott, J. Richard, III; Weinberg, David H.

1989-01-01

In a recent series of papers, an algorithm was developed for quantitatively measuring the topology of the large-scale structure of the universe and this algorithm was applied to numerical models and to three-dimensional observational data sets. In this paper, it is shown that topological information can be derived from a two-dimensional cross section of a density field, and analytic expressions are given for a Gaussian random field. The application of a two-dimensional numerical algorithm for measuring topology to cross sections of three-dimensional models is demonstrated.

Topological Principles of Control in Dynamical Networks

NASA Astrophysics Data System (ADS)

Kim, Jason; Pasqualetti, Fabio; Bassett, Danielle

Networked biological systems, such as the brain, feature complex patterns of interactions. To predict and correct the dynamic behavior of such systems, it is imperative to understand how the underlying topological structure affects and limits the function of the system. Here, we use network control theory to extract topological features that favor or prevent network controllability, and to understand the network-wide effect of external stimuli on large-scale brain systems. Specifically, we treat each brain region as a dynamic entity with real-valued state, and model the time evolution of all interconnected regions using linear, time-invariant dynamics. We propose a simplified feed-forward scheme where the effect of upstream regions (drivers) on the connected downstream regions (non-drivers) is characterized in closed-form. Leveraging this characterization of the simplified model, we derive topological features that predict the controllability properties of non-simplified networks. We show analytically and numerically that these predictors are accurate across a large range of parameters. Among other contributions, our analysis shows that heterogeneity in the network weights facilitate controllability, and allows us to implement targeted interventions that profoundly improve controllability. By assuming an underlying dynamical mechanism, we are able to understand the complex topology of networked biological systems in a functionally meaningful way.

Fault detection techniques for complex cable shield topologies

NASA Astrophysics Data System (ADS)

Coonrod, Kurt H.; Davis, Stuart L.; McLemore, Donald P.

1994-09-01

This document presents the results of a basic principles study which investigated technical approaches for developing fault detection techniques for use on cables with complex shielding topologies. The study was limited to those approaches which could realistically be implemented on a fielded cable, i.e., approaches which would require partial disassembly of a cable were not pursued. The general approach used was to start with present transfer impedance measurement techniques and modify their use to achieve the best possible measurement range. An alternative test approach, similar to a sniffer type test, was also investigated.

Solvent-regulated assemblies of four Zn(II) coordination polymers constructed by flexible tetracarboxylates and pyridyl ligands

NASA Astrophysics Data System (ADS)

Fang, Kang; He, Xiang; Shao, Min; Li, Ming-Xing

2016-08-01

Four unique complexes with diverse coordination architectures were synthesized upon complexation of 5,5-(1,4-phenylenebis (methylene))bis (oxy)- diisophthalic acid (H4L) with zinc ions by using different solvent. namely, {[Zn(H2L) (bpp)]·DEF}n (1), {[Zn2(L) (bpp)2]·4H2O}n (2), {[Zn2(L) (pdp)2]·3H2O·DEF}n (3), {[Zn2(L) (pdp)2].4H2O}n (4). Complexes 1,2 and 3,4 are obtained by varying solvents to control their structures. The size of solvent molecular plays an important role to control different structure of these compounds. Compound 1 is 2D waved framework with (4, 4) grid layer as sql topology. Compound 3 displays a (4,6)-connected 2-nodal net with a fsc topology. Compounds 2 and 4 are all three-dimensional network simplified as (4,4)-connected 2-nodal net with a bbf topology. The photochemical properties of compounds 1-4 were tested in the solid state at room temperature, owing to their strong luminescent emissions, complexes 1-4 are good candidates for photoactive materials.

Deciphering protein signatures using color, morphological, and topological analysis of immunohistochemically stained human tissues

NASA Astrophysics Data System (ADS)

Zerhouni, Erwan; Prisacari, Bogdan; Zhong, Qing; Wild, Peter; Gabrani, Maria

2016-03-01

Images of tissue specimens enable evidence-based study of disease susceptibility and stratification. Moreover, staining technologies empower the evidencing of molecular expression patterns by multicolor visualization, thus enabling personalized disease treatment and prevention. However, translating molecular expression imaging into direct health benefits has been slow. Two major factors contribute to that. On the one hand, disease susceptibility and progression is a complex, multifactorial molecular process. Diseases, such as cancer, exhibit cellular heterogeneity, impeding the differentiation between diverse grades or types of cell formations. On the other hand, the relative quantification of the stained tissue selected features is ambiguous, tedious and time consuming, prone to clerical error, leading to intra- and inter-observer variability and low throughput. Image analysis of digital histopathology images is a fast-developing and exciting area of disease research that aims to address the above limitations. We have developed a computational framework that extracts unique signatures using color, morphological and topological information and allows the combination thereof. The integration of the above information enables diagnosis of disease with AUC as high as 0.97. Multiple staining show significant improvement with respect to most proteins, and an AUC as high as 0.99.

A topological framework for interactive queries on 3D models in the Web.

PubMed

Figueiredo, Mauro; Rodrigues, José I; Silvestre, Ivo; Veiga-Pires, Cristina

2014-01-01

Several technologies exist to create 3D content for the web. With X3D, WebGL, and X3DOM, it is possible to visualize and interact with 3D models in a web browser. Frequently, three-dimensional objects are stored using the X3D file format for the web. However, there is no explicit topological information, which makes it difficult to design fast algorithms for applications that require adjacency and incidence data. This paper presents a new open source toolkit TopTri (Topological model for Triangle meshes) for Web3D servers that builds the topological model for triangular meshes of manifold or nonmanifold models. Web3D client applications using this toolkit make queries to the web server to get adjacent and incidence information of vertices, edges, and faces. This paper shows the application of the topological information to get minimal local points and iso-lines in a 3D mesh in a web browser. As an application, we present also the interactive identification of stalactites in a cave chamber in a 3D web browser. Several tests show that even for large triangular meshes with millions of triangles, the adjacency and incidence information is returned in real time making the presented toolkit appropriate for interactive Web3D applications.

A Topological Framework for Interactive Queries on 3D Models in the Web

PubMed Central

Figueiredo, Mauro; Rodrigues, José I.; Silvestre, Ivo; Veiga-Pires, Cristina

2014-01-01

Several technologies exist to create 3D content for the web. With X3D, WebGL, and X3DOM, it is possible to visualize and interact with 3D models in a web browser. Frequently, three-dimensional objects are stored using the X3D file format for the web. However, there is no explicit topological information, which makes it difficult to design fast algorithms for applications that require adjacency and incidence data. This paper presents a new open source toolkit TopTri (Topological model for Triangle meshes) for Web3D servers that builds the topological model for triangular meshes of manifold or nonmanifold models. Web3D client applications using this toolkit make queries to the web server to get adjacent and incidence information of vertices, edges, and faces. This paper shows the application of the topological information to get minimal local points and iso-lines in a 3D mesh in a web browser. As an application, we present also the interactive identification of stalactites in a cave chamber in a 3D web browser. Several tests show that even for large triangular meshes with millions of triangles, the adjacency and incidence information is returned in real time making the presented toolkit appropriate for interactive Web3D applications. PMID:24977236

Characterization of topological structure on complex networks.

PubMed

Nakamura, Ikuo

2003-10-01

Characterizing the topological structure of complex networks is a significant problem especially from the viewpoint of data mining on the World Wide Web. "Page rank" used in the commercial search engine Google is such a measure of authority to rank all the nodes matching a given query. We have investigated the page-rank distribution of the real Web and a growing network model, both of which have directed links and exhibit a power law distributions of in-degree (the number of incoming links to the node) and out-degree (the number of outgoing links from the node), respectively. We find a concentration of page rank on a small number of nodes and low page rank on high degree regimes in the real Web, which can be explained by topological properties of the network, e.g., network motifs, and connectivities of nearest neighbors.

Modelling of nanoscale quantum tunnelling structures using algebraic topology method

NASA Astrophysics Data System (ADS)

Sankaran, Krishnaswamy; Sairam, B.

2018-05-01

We have modelled nanoscale quantum tunnelling structures using Algebraic Topology Method (ATM). The accuracy of ATM is compared to the analytical solution derived based on the wave nature of tunnelling electrons. ATM provides a versatile, fast, and simple model to simulate complex structures. We are currently expanding the method for modelling electrodynamic systems.

On the Axiomatization of Mathematical Understanding: Continuous Functions in the Transition to Topology

ERIC Educational Resources Information Center

Cheshire, Daniel C.

2017-01-01

The introduction to general topology represents a challenging transition for students of advanced mathematics. It requires the generalization of their previous understanding of ideas from fields like geometry, linear algebra, and real or complex analysis to fit within a more abstract conceptual system. Students must adopt a new lexicon of…

Exploring network operations for data and information networks

NASA Astrophysics Data System (ADS)

Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming

2017-01-01

Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.

Structure, mechanics, and binding mode heterogeneity of LEDGF/p75-DNA nucleoprotein complexes revealed by scanning force microscopy

NASA Astrophysics Data System (ADS)

Vanderlinden, Willem; Lipfert, Jan; Demeulemeester, Jonas; Debyser, Zeger; de Feyter, Steven

2014-04-01

LEDGF/p75 is a transcriptional coactivator implicated in the pathogenesis of AIDS and leukemia. In these contexts, LEDGF/p75 acts as a cofactor by tethering protein cargo to transcriptionally active regions in the human genome. Our study - based on scanning force microscopy (SFM) imaging - is the first to provide structural information on the interaction of LEDGF/p75 with DNA. Two novel approaches that allow obtaining insights into the DNA conformation inside nucleoprotein complexes revealed (1) that LEDGF/p75 can bind at least in three different binding modes, (2) how DNA topology and protein dimerization affect these binding modes, and (3) geometrical and mechanical aspects of the nucleoprotein complexes. These structural and mechanical details will help us to better understand the cellular mechanisms of LEDGF/p75 as a transcriptional coactivator and as a cofactor in disease.LEDGF/p75 is a transcriptional coactivator implicated in the pathogenesis of AIDS and leukemia. In these contexts, LEDGF/p75 acts as a cofactor by tethering protein cargo to transcriptionally active regions in the human genome. Our study - based on scanning force microscopy (SFM) imaging - is the first to provide structural information on the interaction of LEDGF/p75 with DNA. Two novel approaches that allow obtaining insights into the DNA conformation inside nucleoprotein complexes revealed (1) that LEDGF/p75 can bind at least in three different binding modes, (2) how DNA topology and protein dimerization affect these binding modes, and (3) geometrical and mechanical aspects of the nucleoprotein complexes. These structural and mechanical details will help us to better understand the cellular mechanisms of LEDGF/p75 as a transcriptional coactivator and as a cofactor in disease. Electronic supplementary information (ESI) available: SFM topographs of phage lambda DNA in situ, in the absence and presence of LEDGF/p75; model-independent tests for DNA chain equilibration in 2D; SFM topographs of plasmid DNA substrates I-IV in the absence of LEDGF/p75; proof-of-principle of bend angle determination on supercoiled plasmid DNA-EcoRV binding to cognate and non-cognate sites in pBR322 plasmid DNA. See DOI: 10.1039/c4nr00022f

Integrating digital topology in image-processing libraries.

PubMed

Lamy, Julien

2007-01-01

This paper describes a method to integrate digital topology informations in image-processing libraries. This additional information allows a library user to write algorithms respecting topological constraints, for example, a seed fill or a skeletonization algorithm. As digital topology is absent from most image-processing libraries, such constraints cannot be fulfilled. We describe and give code samples for all the structures necessary for this integration, and show a use case in the form of a homotopic thinning filter inside ITK. The obtained filter can be up to a hundred times as fast as ITK's thinning filter and works for any image dimension. This paper mainly deals of integration within ITK, but can be adapted with only minor modifications to other image-processing libraries.

Topology optimization and laser additive manufacturing in design process of efficiency lightweight aerospace parts

NASA Astrophysics Data System (ADS)

Fetisov, K. V.; Maksimov, P. V.

2018-05-01

The paper presents the application of topology optimization and laser additive manufacturing in the design of lightweight aerospace parts. At the beginning a brief overview of the topology optimization algorithm SIMP is given, one of the most commonly used algorithm in FEA software. After that, methodology of parts design with using topology optimization is discussed as well as issues related to designing for additive manufacturing. In conclusion, the practical application of the proposed methodologies is presented using the example of one complex assembly unit. As a result of the new design approach, the mass of product was reduced five times, and twenty parts were replaced by one.

Dynamic properties of epidemic spreading on finite size complex networks

NASA Astrophysics Data System (ADS)

Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

2005-11-01

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Latent Computational Complexity of Symmetry-Protected Topological Order with Fractional Symmetry.

PubMed

Miller, Jacob; Miyake, Akimasa

2018-04-27

An emerging insight is that ground states of symmetry-protected topological orders (SPTOs) possess latent computational complexity in terms of their many-body entanglement. By introducing a fractional symmetry of SPTO, which requires the invariance under 3-colorable symmetries of a lattice, we prove that every renormalization fixed-point state of 2D (Z_{2})^{m} SPTO with fractional symmetry can be utilized for universal quantum computation using only Pauli measurements, as long as it belongs to a nontrivial 2D SPTO phase. Our infinite family of fixed-point states may serve as a base model to demonstrate the idea of a "quantum computational phase" of matter, whose states share universal computational complexity ubiquitously.

Signalling Network Construction for Modelling Plant Defence Response

PubMed Central

Miljkovic, Dragana; Stare, Tjaša; Mozetič, Igor; Podpečan, Vid; Petek, Marko; Witek, Kamil; Dermastia, Marina; Lavrač, Nada; Gruden, Kristina

2012-01-01

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for modelling other biological systems, given that an adequate vocabulary is provided. PMID:23272172

Dodging the crisis of folding proteins with knots

NASA Astrophysics Data System (ADS)

Sulkowska, Joanna

2009-03-01

Proteins with nontrivial topology, containing knots and slipknots, have the ability to fold to their native states without any additional external forces invoked. A mechanism is suggested for folding of these proteins, such as YibK and YbeA, which involves an intermediate configuration with a slipknot. It elucidates the role of topological barriers and backtracking during the folding event. It also illustrates that native contacts are sufficient to guarantee folding in around 1-2% of the simulations, and how slipknot intermediates are needed to reduce the topological bottlenecks. As expected, simulations of proteins with similar structure but with knot removed fold much more efficiently, clearly demonstrating the origin of these topological barriers. Although these studies are based on a simple coarse-grained model, they are already able to extract some of the underlying principles governing folding in such complex topologies.

Jaccard Similarity Leads to the Marczewski-Steinhaus Topology for Information Retrieval.

ERIC Educational Resources Information Center

Rousseau, Ronald

1998-01-01

Demonstrates that if the similarity function of a retrieval system leads to a (pseudo-) metric, the retrieval, similarity and Everett-Cater metric topology coincide and are different from the discrete topology; this is the case if documents are represented by lists, using the Jaccard similarity measure. The corresponding metric is the…

Emergence of hysteresis loop in social contagions on complex networks.

PubMed

Su, Zhen; Wang, Wei; Li, Lixiang; Xiao, Jinghua; Stanley, H Eugene

2017-07-21

Understanding the spreading mechanisms of social contagions in complex network systems has attracted much attention in the physics community. Here we propose a generalized threshold model to describe social contagions. Using extensive numerical simulations and theoretical analyses, we find that a hysteresis loop emerges in the system. Specifically, the steady state of the system is sensitive to the initial conditions of the dynamics of the system. In the steady state, the adoption size increases discontinuously with the transmission probability of information about social contagions, and trial size exhibits a non-monotonic pattern, i.e., it first increases discontinuously then decreases continuously. Finally we study social contagions on heterogeneous networks and find that network topology does not qualitatively affect our results.

A new region-edge based level set model with applications to image segmentation

NASA Astrophysics Data System (ADS)

Zhi, Xuhao; Shen, Hong-Bin

2018-04-01

Level set model has advantages in handling complex shapes and topological changes, and is widely used in image processing tasks. The image segmentation oriented level set models can be grouped into region-based models and edge-based models, both of which have merits and drawbacks. Region-based level set model relies on fitting to color intensity of separated regions, but is not sensitive to edge information. Edge-based level set model evolves by fitting to local gradient information, but can get easily affected by noise. We propose a region-edge based level set model, which considers saliency information into energy function and fuses color intensity with local gradient information. The evolution of the proposed model is implemented by a hierarchical two-stage protocol, and the experimental results show flexible initialization, robust evolution and precise segmentation.

Bribery games on interdependent complex networks.

PubMed

Verma, Prateek; Nandi, Anjan K; Sengupta, Supratim

2018-08-07

Bribe demands present a social conflict scenario where decisions have wide-ranging economic and ethical consequences. Nevertheless, such incidents occur daily in many countries across the globe. Harassment bribery constitute a significant sub-set of such bribery incidents where a government official demands a bribe for providing a service to a citizen legally entitled to it. We employ an evolutionary game-theoretic framework to analyse the evolution of corrupt and honest strategies in structured populations characterized by an interdependent complex network. The effects of changing network topology, average number of links and asymmetry in size of the citizen and officer population on the proliferation of incidents of bribery are explored. A complex network topology is found to be beneficial for the dominance of corrupt strategies over a larger region of phase space when compared with the outcome for a regular network, for equal citizen and officer population sizes. However, the extent of the advantage depends critically on the network degree and topology. A different trend is observed when there is a difference between the citizen and officer population sizes. Under those circumstances, increasing randomness of the underlying citizen network can be beneficial to the fixation of honest officers up to a certain value of the network degree. Our analysis reveals how the interplay between network topology, connectivity and strategy update rules can affect population level outcomes in such asymmetric games. Copyright © 2018 Elsevier Ltd. All rights reserved.

Topological structures in the equities market network

PubMed Central

Leibon, Gregory; Pauls, Scott; Rockmore, Daniel; Savell, Robert

2008-01-01

We present a new method for articulating scale-dependent topological descriptions of the network structure inherent in many complex systems. The technique is based on “partition decoupled null models,” a new class of null models that incorporate the interaction of clustered partitions into a random model and generalize the Gaussian ensemble. As an application, we analyze a correlation matrix derived from 4 years of close prices of equities in the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotation (NASDAQ). In this example, we expose (i) a natural structure composed of 2 interacting partitions of the market that both agrees with and generalizes standard notions of scale (e.g., sector and industry) and (ii) structure in the first partition that is a topological manifestation of a well-known pattern of capital flow called “sector rotation.” Our approach gives rise to a natural form of multiresolution analysis of the underlying time series that naturally decomposes the basic data in terms of the effects of the different scales at which it clusters. We support our conclusions and show the robustness of the technique with a successful analysis on a simulated network with an embedded topological structure. The equities market is a prototypical complex system, and we expect that our approach will be of use in understanding a broad class of complex systems in which correlation structures are resident.

Quantum mechanical probability current as electromagnetic 4-current from topological EM fields

NASA Astrophysics Data System (ADS)

van der Mark, Martin B.

2015-09-01

Starting from a complex 4-potential A = αdβ we show that the 4-current density in electromagnetism and the probability current density in relativistic quantum mechanics are of identical form. With the Dirac-Clifford algebra Cl1,3 as mathematical basis, the given 4-potential allows topological solutions of the fields, quite similar to Bateman's construction, but with a double field solution that was overlooked previously. A more general nullvector condition is found and wave-functions of charged and neutral particles appear as topological configurations of the electromagnetic fields.

Synthesis and structure of 1,3-dimethyl-5-(p-sulfonamide-phenylazo)-6-aminouracil and its Ni(II) complex: Topological insights and investigation for noncovalent interactions

NASA Astrophysics Data System (ADS)

Debnath, Diptanu; Roy, Subhadip; Purkayastha, Atanu; Bauzá, Antonio; Choudhury, Rupasree; Ganguly, Rakesh; Frontera, Antonio; Misra, Tarun Kumar

2017-08-01

The azo-derivative, 1,3-dimethyl-5-(p-sulfonamide-phenylazo)-6-aminouracil (HL) containing 6-aminouracil (a biomolecule) and sulfonamide functionality (commonly found in sulfa-drugs), and its Ni(II) complex, NiIIL2 were synthesized. Single-crystal X-ray diffraction studies show that the ligand (HL) consists of an E conformation about the azo-linkage with a nearly planar geometry and the complex possesses distorted square planar geometry. The H-bonded underlying networks of HL and NiIIL2 were topologically classified revealing distinct topological types, namely tts and hxl, respectively. Moreover, topology of molecular packings in HL and NiIIL2 has also been discussed. Density functional theory (DFT) calculations, at the M06-2X/def2TZVP level of theory, are employed to characterize a great variety of non-covalent interactions that explicitly show the importance of antiparallel stacking interactions established by π--π+ interactions and H-bonds in the self-assembled dimmers in HL and lp-π/C-H⋯π interactions in NiIIL2. The results of NMR and UV-vis spectroscopies evidence that the ligand exists in hydrazone-imine-keto (B) tautomeric form in solution. The ligand absorption bands consist of the overlapping bands of π→π* and n→π* transitions. The complex experiences electronic transitions that consist of basically ILCT in character with some sort of participation of the atomic d-orbitals of the nickel. The pKa value of the ligand is found to be 4.09.

Vpliv topoloskih lastnosti kompleksnih mrez in dinamicnih lastnosti sklopljenih celicnih oscilatorjev na kolektivno dinamiko

NASA Astrophysics Data System (ADS)

Markovic, Rene

This doctor thesis is both theoretical and applicative. In the theoretical part of the thesis, we examine how the interplay of dynamical features of oscillators and structural properties of complex networks affect the collective behavior of the system. We show, that weakly dissipative and flexible oscillators synchronize best in a broad scale network topology, whereas on the other hand strongly dissipative and rigid oscillators exhibit maximal synchronization in a scale-free network topology. We provide an analytical explanation for this phenomenon and validate it by implementing various continuous as well as discrete mathematical models that exhibit different levels of dynamical complexity. In the continuation, we additionally investigate how speed of signal transmission in the network affects the collective dynamic of the system. Our results show that besides an optimal network topology, also an optimal information transmission speed exists, at which the system reaches the highest degree of global synchronization. In the second part we apply the findings and the methodology from our theoretical studies to the examination of the collective pancreatic beta cell activity in the islets of Langerhans, which represents the main mechanism for the regulation of blood glucose homeostasis by the secretion of the hormone insulin. We show that the beta cells dynamics is not synchronized on the global scale of the whole islets. Instead, the cells form local clusters of synchronized activity which tend to get less segregated under higher stimulatory glucose concentrations. Furthermore, higher glucose concentrations also lead to the presence of broad scale small world connectivity patterns in the functional beta cell network. The main findings thereby shed light on the physiology and collective behavior of the islets of Langerhans and point out the possibilities of pathological changes associated with changes in the intercellular communication pathways.

Our Expedition in Linear Neutral Platinum-Acetylide Complexes: The Preparation of Micro/nanostructure Materials, Complicated Topologies, and Dye-Sensitized Solar Cells.

PubMed

Xu, Lin; Yang, Hai-Bo

2016-06-01

During the past few decades, the construction of various kinds of platinum-acetylide complexes has attracted considerable attention, because of their wide applications in photovoltaic cells, non-linear optics, and bio-imaging materials. Among these platinum-acetylide complexes, the linear neutral platinum-acetylide complexes, due to their attractive properties, such as well-defined linear geometry, synthetic accessibility, and intriguing photoproperties, have emerged as a rising star in this field. In this personal account, we will discuss how we entered the field of linear neutral platinum-acetylide chemistry and what we found in this field. The preparation of various types of linear neutral platinum-acetylide complexes and their applications in the areas of micro/nanostructure materials, complicated topologies, and dye-sensitized solar cells will be summarized in this account. © 2016 The Chemical Society of Japan & Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using Hybrid Angle/Distance Information for Distributed Topology Control in Vehicular Sensor Networks

PubMed Central

Huang, Chao-Chi; Chiu, Yang-Hung; Wen, Chih-Yu

2014-01-01

In a vehicular sensor network (VSN), the key design issue is how to organize vehicles effectively, such that the local network topology can be stabilized quickly. In this work, each vehicle with on-board sensors can be considered as a local controller associated with a group of communication members. In order to balance the load among the nodes and govern the local topology change, a group formation scheme using localized criteria is implemented. The proposed distributed topology control method focuses on reducing the rate of group member change and avoiding the unnecessary information exchange. Two major phases are sequentially applied to choose the group members of each vehicle using hybrid angle/distance information. The operation of Phase I is based on the concept of the cone-based method, which can select the desired vehicles quickly. Afterwards, the proposed time-slot method is further applied to stabilize the network topology. Given the network structure in Phase I, a routing scheme is presented in Phase II. The network behaviors are explored through simulation and analysis in a variety of scenarios. The results show that the proposed mechanism is a scalable and effective control framework for VSNs. PMID:25350506

Dynamics of Complex Systems Built as Coupled Physical, Communication and Decision Layers

PubMed Central

Kühnlenz, Florian; Nardelli, Pedro H. J.

2016-01-01

This paper proposes a simple model to capture the complexity of multilayer systems where their constituent layers affect, and are affected by, each other. The physical layer is a circuit composed by a power source and resistors in parallel. Every individual agent aims at maximizing its own delivered power by adding, removing or keeping the resistors it has; the delivered power is in turn a non-linear function that depends on the other agents’ behavior, its own internal state, its global state perception, the information received from its neighbors via the communication network and a randomized selfishness. We develop an agent-based simulation to analyze the effects of number of agents (system size), communication network topology, communication errors and the minimum power gain that triggers a behavioral change on the system dynamic. Our results show that a wave-like behavior at macro-level (caused by individual changes in the decision layer) can only emerge for a specific system size. The ratio between cooperators and defectors depends on the minimum gain assumed—lower minimal gains lead to less cooperation, and vice-versa. Different communication network topologies imply different levels of power utilization and fairness at the physical layer, and a certain level of error in the communication layer induces more cooperation. PMID:26730590

VoroTop: Voronoi cell topology visualization and analysis toolkit

NASA Astrophysics Data System (ADS)

Lazar, Emanuel A.

2018-01-01

This paper introduces a new open-source software program called VoroTop, which uses Voronoi topology to analyze local structure in atomic systems. Strengths of this approach include its abilities to analyze high-temperature systems and to characterize complex structure such as grain boundaries. This approach enables the automated analysis of systems and mechanisms previously not possible.

Silver(I) coordination polymers assembled from flexible cyclotriphosphazene ligand: structures, topologies and investigation of the counteranion effects.

PubMed

Davarcı, Derya; Gür, Rüştü; Beşli, Serap; Şenkuytu, Elif; Zorlu, Yunus

2016-06-01

The reactions of a flexible ligand hexakis(3-pyridyloxy)cyclotriphosphazene (HPCP) with a variety of silver(I) salts (AgX; X = NO3(-), PF6(-), ClO4(-), CH3PhSO3(-), BF4(-) and CF3SO3(-)) afforded six silver(I) coordination polymers, namely {[Ag2(HPCP)]·(NO3)2·H2O}n (1), {[Ag2(HPCP)(CH3CN)]·(PF6)2}n (2), {[Ag2(HPCP)(CH3CN)]·(ClO4)2}n (3), [Ag3(HPCP)(CH3PhSO3)3]n (4), [Ag2(HPCP)(CH3CN)(BF4)2]n (5) and {[Ag(HPCP)]·(CF3SO3)}n (6). All of the isolated crystalline compounds were structurally determined by X-ray crystallography. Changing the counteranions in the reactions, which were conducted under similar conditions of M/L ratio (1:1), temperature and solvent, resulted in structures with different types of topologies. In complexes (1)-(6), the ligand HPCP shows different coordination modes with Ag(I) ions giving two-dimensional layered structures and three-dimensional frameworks with different topologies. Complex (1) displays a new three-dimensional framework adopting a (3,3,6)-connected 3-nodal net with point symbol {4.6(2)}2{4(2).6(10).8(3)}. Complexes (2) and (3) are isomorphous and have a two-dimensional layered structure showing the same 3,6L60 topology with point symbol {4.2(6)}2{4(8).6(6).8}. Complex (4) is a two-dimensional structure incorporating short Ag...Ag argentophilic interactions and has a uninodal 4-connected sql/Shubnikov tetragonal plane net with {4(4).6(2)} topology. Complex (5) exhibits a novel three-dimensional framework and more suprisingly contains twofold interpenetrated honeycomb-like networks, in which the single net has a trinodal (2,3,5)-connected 3-nodal net with point symbol {6(3).8(6).12}{6(3)}{8}. Complex (6) crystallizes in a trigonal crystal system with the space group R\\bar 3 and possesses a three-dimensional polymeric structure showing a binodal (4,6)-connected fsh net with the point symbol (4(3).6(3))2.(4(6).6(6).8(3)). The effect of the counteranions on the formation of coordination polymers is discussed in this study.

Interarrival times of message propagation on directed networks.

PubMed

Mihaljev, Tamara; de Arcangelis, Lucilla; Herrmann, Hans J

2011-08-01

One of the challenges in fighting cybercrime is to understand the dynamics of message propagation on botnets, networks of infected computers used to send viruses, unsolicited commercial emails (SPAM) or denial of service attacks. We map this problem to the propagation of multiple random walkers on directed networks and we evaluate the interarrival time distribution between successive walkers arriving at a target. We show that the temporal organization of this process, which models information propagation on unstructured peer to peer networks, has the same features as SPAM reaching a single user. We study the behavior of the message interarrival time distribution on three different network topologies using two different rules for sending messages. In all networks the propagation is not a pure Poisson process. It shows universal features on Poissonian networks and a more complex behavior on scale free networks. Results open the possibility to indirectly learn about the process of sending messages on networks with unknown topologies, by studying interarrival times at any node of the network.

Interarrival times of message propagation on directed networks

NASA Astrophysics Data System (ADS)

Mihaljev, Tamara; de Arcangelis, Lucilla; Herrmann, Hans J.

2011-08-01

One of the challenges in fighting cybercrime is to understand the dynamics of message propagation on botnets, networks of infected computers used to send viruses, unsolicited commercial emails (SPAM) or denial of service attacks. We map this problem to the propagation of multiple random walkers on directed networks and we evaluate the interarrival time distribution between successive walkers arriving at a target. We show that the temporal organization of this process, which models information propagation on unstructured peer to peer networks, has the same features as SPAM reaching a single user. We study the behavior of the message interarrival time distribution on three different network topologies using two different rules for sending messages. In all networks the propagation is not a pure Poisson process. It shows universal features on Poissonian networks and a more complex behavior on scale free networks. Results open the possibility to indirectly learn about the process of sending messages on networks with unknown topologies, by studying interarrival times at any node of the network.

Spatial networks

NASA Astrophysics Data System (ADS)

Barthélemy, Marc

2011-02-01

Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, and neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology. An important consequence of space on networks is that there is a cost associated with the length of edges which in turn has dramatic effects on the topological structure of these networks. We will thoroughly explain the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks. We will also discuss various processes which take place on these spatial networks, such as phase transitions, random walks, synchronization, navigation, resilience, and disease spread.

Decoherence of Topological Qubit in Linear Motions: Decoherence Impedance, Anti-Unruh and Information Backflow

NASA Astrophysics Data System (ADS)

Liu, Pei-Hua; Lin, Feng-Li

2017-08-01

In this work we study the decoherence of topological qubits in linear motions. The topological qubit is made of two spatially-separated Majorana zero modes which are the edge excitations of Kitaev chain [1]. In a previous work [2], it was shown by one of us and his collaborators that the decoherence of topological qubit is exactly solvable, moreover, topological qubit is robust against decoherence in the super-Ohmic environments. We extend the setup of [2] to consider the effect of motions on the decoherence of the topological qubits. Our results show the thermalization as expected by Unruh effect. Besides, we also find the so-called “anti-Unruh” phenomena which shows the rate of decoherence is anti-correlated with the acceleration in short-time scale. Moreover, we modulate the motion patterns of each Majorana modes and find information backflow and the preservation of coherence even with nonzero accelerations. This is the characteristics of the underlying non-Markovian reduced dynamics. We conclude that he topological qubit is in general more robust against decoherence than the usual qubits, and can be take into serious consideration for realistic implementation to have robust quantum computation and communication. This talk is based on our work in [3].

Weak signal transmission in complex networks and its application in detecting connectivity.

PubMed

Liang, Xiaoming; Liu, Zonghua; Li, Baowen

2009-10-01

We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

Multi-Dimensional Scaling based grouping of known complexes and intelligent protein complex detection.

PubMed

Rehman, Zia Ur; Idris, Adnan; Khan, Asifullah

2018-06-01

Protein-Protein Interactions (PPI) play a vital role in cellular processes and are formed because of thousands of interactions among proteins. Advancements in proteomics technologies have resulted in huge PPI datasets that need to be systematically analyzed. Protein complexes are the locally dense regions in PPI networks, which extend important role in metabolic pathways and gene regulation. In this work, a novel two-phase protein complex detection and grouping mechanism is proposed. In the first phase, topological and biological features are extracted for each complex, and prediction performance is investigated using Bagging based Ensemble classifier (PCD-BEns). Performance evaluation through cross validation shows improvement in comparison to CDIP, MCode, CFinder and PLSMC methods Second phase employs Multi-Dimensional Scaling (MDS) for the grouping of known complexes by exploring inter complex relations. It is experimentally observed that the combination of topological and biological features in the proposed approach has greatly enhanced prediction performance for protein complex detection, which may help to understand various biological processes, whereas application of MDS based exploration may assist in grouping potentially similar complexes. Copyright © 2018 Elsevier Ltd. All rights reserved.

BrainNet Viewer: a network visualization tool for human brain connectomics.

PubMed

Xia, Mingrui; Wang, Jinhui; He, Yong

2013-01-01

The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

Strain-induced topological transition in SrRu 2O 6 and CaOs 2O 6

DOE PAGES

Ochi, Masayuki; Arita, Ryotaro; Trivedi, Nandini; ...

2016-05-24

The topological property of SrRumore » $$_2$$O$$_6$$ and isostructural CaOs$$_2$$O$$_6$$ under various strain conditions is investigated using density functional theory. Based on an analysis of parity eigenvalues, we anticipate that a three-dimensional strong topological insulating state should be realized when band inversion is induced at the A point in the hexagonal Brillouin zone. For SrRu$$_2$$O$$_6$$, such a transition requires rather unrealistic tuning, where only the $c$ axis is reduced while other structural parameters are unchanged. However, given the larger spin-orbit coupling and smaller lattice constants in CaOs$$_2$$O$$_6$$, the desired topological transition does occur under uniform compressive strain. Our study paves a way to realize a topological insulating state in a complex oxide, which has not been experimentally demonstrated so far.« less

A Comparative Study of Power Supply Architectures In Wireless Electric Vehicle Charging Systems

NASA Astrophysics Data System (ADS)

Esteban, Bryan

Wireless inductive power transfer is a transformational and disruptive technology that enables the reliable and efficient transfer of electrical power over large air gaps for a host of unique applications. One such application that is now gaining much momentum worldwide is the wireless charging of electric vehicles (EVs). This thesis examines two of the primary power supply topologies being predominantly used for EV charging, namely the SLC and the LCL resonant full bridge inverter topologies. The study of both of these topologies is presented in the context of designing a 3 kW, primary side controlled, wireless EV charger with nominal operating parameters of 30 kHz centre frequency and range of coupling in the neighborhood of .18-.26. A comparison of both topologies is made in terms of their complexity, cost, efficiency, and power quality. The aim of the study is to determine which topology is better for wireless EV charging.

Experimental demonstration of topologically protected efficient sound propagation in an acoustic waveguide network

NASA Astrophysics Data System (ADS)

Wei, Qi; Tian, Ye; Zuo, Shu-Yu; Cheng, Ying; Liu, Xiao-Jun

2017-03-01

Acoustic topological states support sound propagation along the boundary in a one-way direction with inherent robustness against defects and disorders, leading to the revolution of the manipulation on acoustic waves. A variety of acoustic topological states relying on circulating fluid, chiral coupling, or temporal modulation have been proposed theoretically. However, experimental demonstration has so far remained a significant challenge, due to the critical limitations such as structural complexity and high losses. Here, we experimentally demonstrate an acoustic anomalous Floquet topological insulator in a waveguide network. The acoustic gapless edge states can be found in the band gap when the waveguides are strongly coupled. The scheme features simple structure and high-energy throughput, leading to the experimental demonstration of efficient and robust topologically protected sound propagation along the boundary. The proposal may offer a unique, promising application for design of acoustic devices in acoustic guiding, switching, isolating, filtering, etc.

Evolving neural networks through augmenting topologies.

PubMed

Stanley, Kenneth O; Miikkulainen, Risto

2002-01-01

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

Network topology analysis approach on China's QFII stock investment behavior

NASA Astrophysics Data System (ADS)

Zhang, Yongjie; Cao, Xing; He, Feng; Zhang, Wei

2017-05-01

In this paper, the investment behavior of QFII in China stock market from 2004 to 2015 is studied with the network topology method. Based on the nodes topological characteristics, stock holding fluctuations correlation is studied from the micro network level. We conclude that the QFII mutual stock holding network have both scale free and small world properties, which presented mainly small world characteristics from 2005 to 2011, and scale free characteristics from 2012 to 2015. Moreover, fluctuations correlation is different with different nodes topological characteristics. In different economic periods, QFII represented different connection patterns and they reacted to the market crash spontaneously. Thus, this paper provides the first evidence of complex network research on QFII' investment behavior in China as an emerging market.

Edge theory approach to topological entanglement entropy, mutual information, and entanglement negativity in Chern-Simons theories

NASA Astrophysics Data System (ADS)

Wen, Xueda; Matsuura, Shunji; Ryu, Shinsei

2016-06-01

We develop an approach based on edge theories to calculate the entanglement entropy and related quantities in (2+1)-dimensional topologically ordered phases. Our approach is complementary to, e.g., the existing methods using replica trick and Witten's method of surgery, and applies to a generic spatial manifold of genus g , which can be bipartitioned in an arbitrary way. The effects of fusion and braiding of Wilson lines can be also straightforwardly studied within our framework. By considering a generic superposition of states with different Wilson line configurations, through an interference effect, we can detect, by the entanglement entropy, the topological data of Chern-Simons theories, e.g., the R symbols, monodromy, and topological spins of quasiparticles. Furthermore, by using our method, we calculate other entanglement/correlation measures such as the mutual information and the entanglement negativity. In particular, it is found that the entanglement negativity of two adjacent noncontractible regions on a torus provides a simple way to distinguish Abelian and non-Abelian topological orders.

Geometry of complex networks and topological centrality

NASA Astrophysics Data System (ADS)

Ranjan, Gyan; Zhang, Zhi-Li

2013-09-01

We explore the geometry of complex networks in terms of an n-dimensional Euclidean embedding represented by the Moore-Penrose pseudo-inverse of the graph Laplacian (L). The squared distance of a node i to the origin in this n-dimensional space (lii+), yields a topological centrality index, defined as C∗(i)=1/lii+. In turn, the sum of reciprocals of individual node centralities, ∑i1/C∗(i)=∑ilii+, or the trace of L, yields the well-known Kirchhoff index (K), an overall structural descriptor for the network. To put into context this geometric definition of centrality, we provide alternative interpretations of the proposed indices that connect them to meaningful topological characteristics - first, as forced detour overheads and frequency of recurrences in random walks that has an interesting analogy to voltage distributions in the equivalent electrical network; and then as the average connectedness of i in all the bi-partitions of the graph. These interpretations respectively help establish the topological centrality (C∗(i)) of node i as a measure of its overall position as well as its overall connectedness in the network; thus reflecting the robustness of i to random multiple edge failures. Through empirical evaluations using synthetic and real world networks, we demonstrate how the topological centrality is better able to distinguish nodes in terms of their structural roles in the network and, along with Kirchhoff index, is appropriately sensitive to perturbations/re-wirings in the network.

Emergence of Rich-Club Topology and Coordinated Dynamics in Development of Hippocampal Functional Networks In Vitro

PubMed Central

Charlesworth, Paul; Kitzbichler, Manfred G.; Paulsen, Ole

2015-01-01

Recent studies demonstrated that the anatomical network of the human brain shows a “rich-club” organization. This complex topological feature implies that highly connected regions, hubs of the large-scale brain network, are more densely interconnected with each other than expected by chance. Rich-club nodes were traversed by a majority of short paths between peripheral regions, underlining their potential importance for efficient global exchange of information between functionally specialized areas of the brain. Network hubs have also been described at the microscale of brain connectivity (so-called “hub neurons”). Their role in shaping synchronous dynamics and forming microcircuit wiring during development, however, is not yet fully understood. The present study aimed to investigate the role of hubs during network development, using multi-electrode arrays and functional connectivity analysis during spontaneous multi-unit activity (MUA) of dissociated primary mouse hippocampal neurons. Over the first 4 weeks in vitro, functional connectivity significantly increased in strength, density, and size, with mature networks demonstrating a robust modular and small-world topology. As expected by a “rich-get-richer” growth rule of network evolution, MUA graphs were found to form rich-clubs at an early stage in development (14 DIV). Later on, rich-club nodes were a consistent topological feature of MUA graphs, demonstrating high nodal strength, efficiency, and centrality. Rich-club nodes were also found to be crucial for MUA dynamics. They often served as broker of spontaneous activity flow, confirming that hub nodes and rich-clubs may play an important role in coordinating functional dynamics at the microcircuit level. PMID:25855164

Spacetime topology change and black hole information

NASA Astrophysics Data System (ADS)

Hsu, Stephen D. H.

2007-01-01

Topology change-the creation of a disconnected baby universe-due to black hole collapse may resolve the information loss paradox. Evolution from an early time Cauchy surface to a final surface which includes a slice of the disconnected region can be unitary and consistent with conventional quantum mechanics. We discuss the issue of cluster decomposition, showing that any violations thereof are likely to be unobservably small. Topology change is similar to the black hole remnant scenario and only requires assumptions about the behavior of quantum gravity in Planckian regimes. It does not require non-locality or any modification of low-energy physics.

Mapping and discrimination of networks in the complexity-entropy plane

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

2017-10-01

Complex networks are usually characterized in terms of their topological, spatial, or information-theoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks, like infrastructure or social networks, on such a basis. Here we explore the possibility to classify complex networks by means of a statistical complexity measure that has formerly been successfully applied to distinguish different types of chaotic and stochastic time series. It is composed of a network's averaged per-node entropic measure characterizing the network's information content and the associated Jenson-Shannon divergence as a measure of disequilibrium. We study 29 real-world networks and show that networks of the same category tend to cluster in distinct areas of the resulting complexity-entropy plane. We demonstrate that within our framework, connectome networks exhibit among the highest complexity while, e.g., transportation and infrastructure networks display significantly lower values. Furthermore, we demonstrate the utility of our framework by applying it to families of random scale-free and Watts-Strogatz model networks. We then show in a second application that the proposed framework is useful to objectively construct threshold-based networks, such as functional climate networks or recurrence networks, by choosing the threshold such that the statistical network complexity is maximized.

Complex networks analysis of obstructive nephropathy data

NASA Astrophysics Data System (ADS)

Zanin, M.; Boccaletti, S.

2011-09-01

Congenital obstructive nephropathy (ON) is one of the most frequent and complex diseases affecting children, characterized by an abnormal flux of the urine, due to a partial or complete obstruction of the urinary tract; as a consequence, urine may accumulate in the kidney and disturb the normal operation of the organ. Despite important advances, pathological mechanisms are not yet fully understood. In this contribution, the topology of complex networks, based on vectors of features of control and ON subjects, is related with the severity of the pathology. Nodes in these networks represent genetic and metabolic profiles, while connections between them indicate an abnormal relation between their expressions. Resulting topologies allow discriminating ON subjects and detecting which genetic or metabolic elements are responsible for the malfunction.

Continually emerging mechanistic complexity of the multi-enzyme cellulosome complex.

PubMed

Smith, Steven P; Bayer, Edward A; Czjzek, Mirjam

2017-06-01

The robust plant cell wall polysaccharide-degrading properties of anaerobic bacteria are harnessed within elegant, marcomolecular assemblages called cellulosomes, in which proteins of complementary activities amass on scaffold protein networks. Research efforts have focused and continue to focus on providing detailed mechanistic insights into cellulosomal complex assembly, topology, and function. The accumulated information is expanding our fundamental understanding of the lignocellulosic biomass decomposition process and enhancing the potential of engineered cellulosomal systems for biotechnological purposes. Ongoing biochemical studies continue to reveal unexpected functional diversity within traditional cellulase families. Genomic, proteomic, and functional analyses have uncovered unanticipated cellulosomal proteins that augment the function of the native and designer cellulosomes. In addition, complementary structural and computational methods are continuing to provide much needed insights on the influence of cellulosomal interdomain linker regions on cellulosomal assembly and activity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Disentangling the many layers of eukaryotic transcriptional regulation.

PubMed

Lelli, Katherine M; Slattery, Matthew; Mann, Richard S

2012-01-01

Regulation of gene expression in eukaryotes is an extremely complex process. In this review, we break down several critical steps, emphasizing new data and techniques that have expanded current gene regulatory models. We begin at the level of DNA sequence where cis-regulatory modules (CRMs) provide important regulatory information in the form of transcription factor (TF) binding sites. In this respect, CRMs function as instructional platforms for the assembly of gene regulatory complexes. We discuss multiple mechanisms controlling complex assembly, including cooperative DNA binding, combinatorial codes, and CRM architecture. The second section of this review places CRM assembly in the context of nucleosomes and condensed chromatin. We discuss how DNA accessibility and histone modifications contribute to TF function. Lastly, new advances in chromosomal mapping techniques have provided increased understanding of intra- and interchromosomal interactions. We discuss how these topological maps influence gene regulatory models.

Selective Se-for-S substitution in Cs-bearing uranyl compounds

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

Gurzhiy, Vladislav V., E-mail: vladgeo17@mail.ru; Tyumentseva, Olga S.; Krivovichev, Sergey V.

Phase formation in the mixed sulfate-selenate aqueous system of uranyl nitrate and cesium nitrate has been investigated. Two types of crystalline compounds have been obtained and characterized using a number of experimental (single crystal XRD, FTIR, SEM) and theoretical (information-based complexity calculations, topological analysis) techniques. No miscibility gaps have been observed for Cs{sub 2}[(UO{sub 2}){sub 2}(TO{sub 4}){sub 3}] (T= S, Se), which crystallizes in tetragonal system, P-42{sub 1}m, a =9.616(1)–9.856(2), c =8.105(1)–8.159(1) Å, V =749.6(2)–792.5(3) Å{sup 3}. Nine phases with variable amount of S and Se have been structurally characterized. The structures of the Cs{sub 2}[(UO{sub 2}){sub 2}(TO{sub 4}){sub 3}]more » (T= S, Se) compounds are based upon the [(UO{sub 2}){sub 2}(TO{sub 4}){sub 3}]{sup 2-} layers of corner-sharing uranyl pentagonal bipyramids and TO{sub 4} tetrahedra. The layers contain two types of tetrahedral sites: T1 (3-connected, i.e. having three O atoms shared by adjacent uranyl polyhedra) and T2 (4-connected). The Se-for-S substitution in tetrahedral sites is highly selective with smaller S{sup 6+} cation showing a strong preference for the more tightly bonded T2 site. Crystallization in the pure Se system starts with the formation of Cs{sub 2}[(UO{sub 2})(SeO{sub 4}){sub 2}(H{sub 2}O)](H{sub 2}O) crystals, its subsequent dissolution and formation of Cs{sub 2}[(UO{sub 2}){sub 2}(SeO{sub 4}){sub 3}]. The information-based structural complexity calculations for these two phases support the rule that more topologically complex structures form at the latest stages of crystallization. - Graphical abstract: Nine phases representing the Cs{sub 2}[(UO{sub 2}){sub 2}(TO{sub 4}){sub 3}] (T= S, Se) solid solution series with variable amount of S and Se have been prepared by isothermal evaporation from aqueous solutions and characterized using a number of experimental and theoretical techniques. No immiscibility is observed between the pure sulfate and selenate compounds. The Se-for-S substitution in tetrahedral sites is highly selective with smaller S{sup 6+} cation showing a strong preference for the more tightly bonded 4-connected site. - Highlights: • Single crystals of novel mixed sulfate-selenate uranyl oxysalts were prepared by evaporation method. • Topological analysis and information-based complexity calculations were used for structure description. • The selective Se-for-S substitution was observed. • Evolution of phase formation in the aqueous Cs{sup +}–UO{sub 2}{sup 2+}–SO{sub 4}{sup 2–}–SeO{sub 4}{sup 2–} system was analyzed.« less

Global regularizing flows with topology preservation for active contours and polygons.

PubMed

Sundaramoorthi, Ganesh; Yezzi, Anthony

2007-03-01

Active contour and active polygon models have been used widely for image segmentation. In some applications, the topology of the object(s) to be detected from an image is known a priori, despite a complex unknown geometry, and it is important that the active contour or polygon maintain the desired topology. In this work, we construct a novel geometric flow that can be added to image-based evolutions of active contours and polygons in order to preserve the topology of the initial contour or polygon. We emphasize that, unlike other methods for topology preservation, the proposed geometric flow continually adjusts the geometry of the original evolution in a gradual and graceful manner so as to prevent a topology change long before the curve or polygon becomes close to topology change. The flow also serves as a global regularity term for the evolving contour, and has smoothness properties similar to curvature flow. These properties of gradually adjusting the original flow and global regularization prevent geometrical inaccuracies common with simple discrete topology preservation schemes. The proposed topology preserving geometric flow is the gradient flow arising from an energy that is based on electrostatic principles. The evolution of a single point on the contour depends on all other points of the contour, which is different from traditional curve evolutions in the computer vision literature.

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

PubMed

Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

The architecture of dynamic reservoir in the echo state network

NASA Astrophysics Data System (ADS)

Cui, Hongyan; Liu, Xiang; Li, Lixiang

2012-09-01

Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

PubMed Central

2009-01-01

Background The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes. Results We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality. PMID:19758426

Modeling Magnetic Flux-Ropes Structures

NASA Astrophysics Data System (ADS)

Nieves-Chinchilla, T.; Linton, M.; Hidalgo, M. A. U.; Vourlidas, A.; Savani, N.; Szabo, A.; Farrugia, C. J.; Yu, W.

2015-12-01

Flux-ropes are usually associated with magnetic structures embedded in the interplanetary Coronal Mass Ejections (ICMEs) with a depressed proton temperature (called Magnetic Clouds, MCs). However, small-scale flux-ropes in the solar wind are also identified with different formation, evolution, and dynamic involved. We present an analytical model to describe magnetic flux-rope topologies. The model is generalized to different grades of complexity. It extends the circular-cylindrical concept of Hidalgo et al. (2002) by introducing a general form for the radial dependence of the current density. This generalization provides information on the force distribution inside the flux rope in addition to the usual parameters of flux-rope geometrical information and orientation. The generalized model provides flexibility for implementation in 3-D MHD simulations.

Locating privileged spreaders on an online social network.

PubMed

Borge-Holthoefer, Javier; Rivero, Alejandro; Moreno, Yamir

2012-06-01

Social media have provided plentiful evidence of their capacity for information diffusion. Fads and rumors but also social unrest and riots travel fast and affect large fractions of the population participating in online social networks (OSNs). This has spurred much research regarding the mechanisms that underlie social contagion, and also who (if any) can unleash system-wide information dissemination. Access to real data, both regarding topology--the network of friendships--and dynamics--the actual way in which OSNs users interact, is crucial to decipher how the former facilitates the latter's success, understood as efficiency in information spreading. With the quantitative analysis that stems from complex network theory, we discuss who (and why) has privileged spreading capabilities when it comes to information diffusion. This is done considering the evolution of an episode of political protest which took place in Spain, spanning one month in 2011.

On the generality of the topological theory of visual shape perception.

PubMed

Kanbe, Fumio

2013-01-01

This study used a series of six closely related experiments to examine whether individuals use topological structures to discriminate figures. Strict control was exerted over the selection of stimuli, which were a specific type of randomly generated lined figures that can be classified using isomorphic sets defined by graph theory. Any two figures within an isomorphic set possessed the same topological structure. The experiments described here used a same/different discrimination task with simultaneously presented pairs of figures: (a) identical pairs (Id pairs), in which each pair of figures had the same topological and superficial properties; (b) nonidentical and isomorphic pairs (Iso pairs), in which each pair had the same topological but different superficial properties; and (c) nonidentical and nonisomorphic pairs (Noniso pairs), in which each pair had different topological properties. Within these experiments I varied the conditions related to the intersecting line segments, presentation of points defining each figure, figure complexity, stimulus aspect ratios, and the parity of the total line-segment lengths between the figures in each pair. These variations showed that the latencies for making accurate discriminations were shorter for Noniso pairs than for Iso pairs, suggesting that individuals are sensitive to topology when distinguishing figures.

The application of molecular topology for ulcerative colitis drug discovery.

PubMed

Bellera, Carolina L; Di Ianni, Mauricio E; Talevi, Alan

2018-01-01

Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

Towards understanding the behavior of physical systems using information theory

NASA Astrophysics Data System (ADS)

Quax, Rick; Apolloni, Andrea; Sloot, Peter M. A.

2013-09-01

One of the goals of complex network analysis is to identify the most influential nodes, i.e., the nodes that dictate the dynamics of other nodes. In the case of autonomous systems or transportation networks, highly connected hubs play a preeminent role in diffusing the flow of information and viruses; in contrast, in language evolution most linguistic norms come from the peripheral nodes who have only few contacts. Clearly a topological analysis of the interactions alone is not sufficient to identify the nodes that drive the state of the network. Here we show how information theory can be used to quantify how the dynamics of individual nodes propagate through a system. We interpret the state of a node as a storage of information about the state of other nodes, which is quantified in terms of Shannon information. This information is transferred through interactions and lost due to noise, and we calculate how far it can travel through a network. We apply this concept to a model of opinion formation in a complex social network to calculate the impact of each node by measuring how long its opinion is remembered by the network. Counter-intuitively we find that the dynamics of opinions are not determined by the hubs or peripheral nodes, but rather by nodes with an intermediate connectivity.

Data based identification and prediction of nonlinear and complex dynamical systems

NASA Astrophysics Data System (ADS)

Wang, Wen-Xu; Lai, Ying-Cheng; Grebogi, Celso

2016-07-01

The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. The classic approach to phase-space reconstruction through the methodology of delay-coordinate embedding has been practiced for more than three decades, but the paradigm is effective mostly for low-dimensional dynamical systems. Often, the methodology yields only a topological correspondence of the original system. There are situations in various fields of science and engineering where the systems of interest are complex and high dimensional with many interacting components. A complex system typically exhibits a rich variety of collective dynamics, and it is of great interest to be able to detect, classify, understand, predict, and control the dynamics using data that are becoming increasingly accessible due to the advances of modern information technology. To accomplish these goals, especially prediction and control, an accurate reconstruction of the original system is required. Nonlinear and complex systems identification aims at inferring, from data, the mathematical equations that govern the dynamical evolution and the complex interaction patterns, or topology, among the various components of the system. With successful reconstruction of the system equations and the connecting topology, it may be possible to address challenging and significant problems such as identification of causal relations among the interacting components and detection of hidden nodes. The "inverse" problem thus presents a grand challenge, requiring new paradigms beyond the traditional delay-coordinate embedding methodology. The past fifteen years have witnessed rapid development of contemporary complex graph theory with broad applications in interdisciplinary science and engineering. The combination of graph, information, and nonlinear dynamical systems theories with tools from statistical physics, optimization, engineering control, applied mathematics, and scientific computing enables the development of a number of paradigms to address the problem of nonlinear and complex systems reconstruction. In this Review, we describe the recent advances in this forefront and rapidly evolving field, with a focus on compressive sensing based methods. In particular, compressive sensing is a paradigm developed in recent years in applied mathematics, electrical engineering, and nonlinear physics to reconstruct sparse signals using only limited data. It has broad applications ranging from image compression/reconstruction to the analysis of large-scale sensor networks, and it has become a powerful technique to obtain high-fidelity signals for applications where sufficient observations are not available. We will describe in detail how compressive sensing can be exploited to address a diverse array of problems in data based reconstruction of nonlinear and complex networked systems. The problems include identification of chaotic systems and prediction of catastrophic bifurcations, forecasting future attractors of time-varying nonlinear systems, reconstruction of complex networks with oscillatory and evolutionary game dynamics, detection of hidden nodes, identification of chaotic elements in neuronal networks, reconstruction of complex geospatial networks and nodal positioning, and reconstruction of complex spreading networks with binary data.. A number of alternative methods, such as those based on system response to external driving, synchronization, and noise-induced dynamical correlation, will also be discussed. Due to the high relevance of network reconstruction to biological sciences, a special section is devoted to a brief survey of the current methods to infer biological networks. Finally, a number of open problems including control and controllability of complex nonlinear dynamical networks are discussed. The methods outlined in this Review are principled on various concepts in complexity science and engineering such as phase transitions, bifurcations, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of complex dynamical systems ranging from gene regulatory systems to social networks toward the ultimate goal of controlling such systems.

Characterization of known protein complexes using k-connectivity and other topological measures

PubMed Central

Gallagher, Suzanne R; Goldberg, Debra S

2015-01-01

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms. PMID:26913183

Robust interface between flying and topological qubits

PubMed Central

Xue, Zheng-Yuan; Gong, Ming; Liu, Jia; Hu, Yong; Zhu, Shi-Liang; Wang, Z. D.

2015-01-01

Hybrid architectures, consisting of conventional and topological qubits, have recently attracted much attention due to their capability in consolidating robustness of topological qubits and universality of conventional qubits. However, these two kinds of qubits are normally constructed in significantly different energy scales, and thus the energy mismatch is a major obstacle for their coupling, which can support the exchange of quantum information between them. Here we propose a microwave photonic quantum bus for a strong direct coupling between the topological and conventional qubits, where the energy mismatch is compensated by an external driving field. In the framework of tight-binding simulation and perturbation approach, we show that the energy splitting of Majorana fermions in a finite length nanowire, which we use to define topological qubits, is still robust against local perturbations due to the topology of the system. Therefore, the present scheme realizes a rather robust interface between the flying and topological qubits. Finally, we demonstrate that this quantum bus can also be used to generate multipartitie entangled states with the topological qubits. PMID:26216201

3D visualization software to analyze topological outcomes of topoisomerase reactions

PubMed Central

Darcy, I. K.; Scharein, R. G.; Stasiak, A.

2008-01-01

The action of various DNA topoisomerases frequently results in characteristic changes in DNA topology. Important information for understanding mechanistic details of action of these topoisomerases can be provided by investigating the knot types resulting from topoisomerase action on circular DNA forming a particular knot type. Depending on the topological bias of a given topoisomerase reaction, one observes different subsets of knotted products. To establish the character of topological bias, one needs to be aware of all possible topological outcomes of intersegmental passages occurring within a given knot type. However, it is not trivial to systematically enumerate topological outcomes of strand passage from a given knot type. We present here a 3D visualization software (TopoICE-X in KnotPlot) that incorporates topological analysis methods in order to visualize, for example, knots that can be obtained from a given knot by one intersegmental passage. The software has several other options for the topological analysis of mechanisms of action of various topoisomerases. PMID:18440983

Convergent evolution of gene networks by single-gene duplications in higher eukaryotes.

PubMed

Amoutzias, Gregory D; Robertson, David L; Oliver, Stephen G; Bornberg-Bauer, Erich

2004-03-01

By combining phylogenetic, proteomic and structural information, we have elucidated the evolutionary driving forces for the gene-regulatory interaction networks of basic helix-loop-helix transcription factors. We infer that recurrent events of single-gene duplication and domain rearrangement repeatedly gave rise to distinct networks with almost identical hub-based topologies, and multiple activators and repressors. We thus provide the first empirical evidence for scale-free protein networks emerging through single-gene duplications, the dominant importance of molecular modularity in the bottom-up construction of complex biological entities, and the convergent evolution of networks.

Gate-Variable Mid-Infrared Optical Transitions in a (Bi1-xSbx)2Te3 Topological Insulator.

PubMed

Whitney, William S; Brar, Victor W; Ou, Yunbo; Shao, Yinming; Davoyan, Artur R; Basov, D N; He, Ke; Xue, Qi-Kun; Atwater, Harry A

2017-01-11

We report mid-infrared spectroscopy measurements of ultrathin, electrostatically gated (Bi 1-x Sb x ) 2 Te 3 topological insulator films in which we observe several percent modulation of transmittance and reflectance as gating shifts the Fermi level. Infrared transmittance measurements of gated films were enabled by use of an epitaxial lift-off method for large-area transfer of topological insulator films from infrared-absorbing SrTiO 3 growth substrates to thermal oxidized silicon substrates. We combine these optical experiments with transport measurements and angle-resolved photoemission spectroscopy to identify the observed spectral modulation as a gate-driven transfer of spectral weight between both bulk and 2D topological surface channels and interband and intraband channels. We develop a model for the complex permittivity of gated (Bi 1-x Sb x ) 2 Te 3 and find a good match to our experimental data. These results open the path for layered topological insulator materials as a new candidate for tunable, ultrathin infrared optics and highlight the possibility of switching topological optoelectronic phenomena between bulk and spin-polarized surface regimes.

Topology of polymer chains under nanoscale confinement.

PubMed

Satarifard, Vahid; Heidari, Maziar; Mashaghi, Samaneh; Tans, Sander J; Ejtehadi, Mohammad Reza; Mashaghi, Alireza

2017-08-24

Spatial confinement limits the conformational space accessible to biomolecules but the implications for bimolecular topology are not yet known. Folded linear biopolymers can be seen as molecular circuits formed by intramolecular contacts. The pairwise arrangement of intra-chain contacts can be categorized as parallel, series or cross, and has been identified as a topological property. Using molecular dynamics simulations, we determine the contact order distributions and topological circuits of short semi-flexible linear and ring polymer chains with a persistence length of l p under a spherical confinement of radius R c . At low values of l p /R c , the entropy of the linear chain leads to the formation of independent contacts along the chain and accordingly, increases the fraction of series topology with respect to other topologies. However, at high l p /R c , the fraction of cross and parallel topologies are enhanced in the chain topological circuits with cross becoming predominant. At an intermediate confining regime, we identify a critical value of l p /R c , at which all topological states have equal probability. Confinement thus equalizes the probability of more complex cross and parallel topologies to the level of the more simple, non-cooperative series topology. Moreover, our topology analysis reveals distinct behaviours for ring- and linear polymers under weak confinement; however, we find no difference between ring- and linear polymers under strong confinement. Under weak confinement, ring polymers adopt parallel and series topologies with equal likelihood, while linear polymers show a higher tendency for series arrangement. The radial distribution analysis of the topology reveals a non-uniform effect of confinement on the topology of polymer chains, thereby imposing more pronounced effects on the core region than on the confinement surface. Additionally, our results reveal that over a wide range of confining radii, loops arranged in parallel and cross topologies have nearly the same contact orders. Such degeneracy implies that the kinetics and transition rates between the topological states cannot be solely explained by contact order. We expect these findings to be of general importance in understanding chaperone assisted protein folding, chromosome architecture, and the evolution of molecular folds.

Discriminating tests of information and topological indices. Animals and trees.

PubMed

Konstantinova, Elena V; Vidyuk, Maxim V

2003-01-01

In this paper we consider 13 information and topological indices based on the distance in a molecular graph with respect to their discrimination power. The numerical results of discriminating tests on 3490528 trees up to 21 vertices are given. The indices of the highest sensitivity are listed on the set of 1528775 alkane trees. The discrimination powers of indices are also examined on the classes of 849285 hexagonal, 298382 square, and 295365 triangular simply connected animals. The first class of animals corresponds to the structural formulas of planar benzenoid hydrocarbons. The values of all indices were calculated for all classes of animals as well as for the united set of 1443032 animals. The inspection of the data indicates the great sensitivity of four information indices and one topological index.

Networks of genetic loci and the scientific literature

NASA Astrophysics Data System (ADS)

Semeiks, J. R.; Grate, L. R.; Mian, I. S.

This work considers biological information graphs, networks in which nodes corre-spond to genetic loci (or "genes") and an (undirected) edge signifies that two genes are discussed in the same article(s) in the scientific literature ("documents"). Operations that utilize the topology of these graphs can assist researchers in the scientific discovery process. For example, a shortest path between two nodes defines an ordered series of genes and documents that can be used to explore the relationship(s) between genes of interest. This work (i) describes how topologies in which edges are likely to reflect genuine relationship(s) can be constructed from human-curated corpora of genes an-notated with documents (or vice versa), and (ii) illustrates the potential of biological information graphs in synthesizing knowledge in order to formulate new hypotheses and generate novel predictions for subsequent experimental study. In particular, the well-known LocusLink corpus is used to construct a biological information graph consisting of 10,297 nodes and 21,910 edges. The large-scale statistical properties of this gene-document network suggest that it is a new example of a power-law network. The segregation of genes on the basis of species and encoded protein molecular function indicate the presence of assortativity, the preference for nodes with similar attributes to be neighbors in a network. The practical utility of a gene-document network is illustrated by using measures such as shortest paths and centrality to analyze a subset of nodes corresponding to genes implicated in aging. Each release of a curated biomedical corpus defines a particular static graph. The topology of a gene-document network changes over time as curators add and/or remove nodes and/or edges. Such a dynamic, evolving corpus provides both the foundation for analyzing the growth and behavior of large complex networks and a substrate for examining trends in biological research.

Designed topology and site-selective metal composition in tetranuclear [MM'...M'M] linear complexes.

PubMed

Barrios, Leoní A; Aguilà, David; Roubeau, Olivier; Gamez, Patrick; Ribas-Ariño, Jordi; Teat, Simon J; Aromí, Guillem

2009-10-26

The ligand 1,3-bis[3-oxo-3-(2-hydroxyphenyl)propionyl]benzene (H(4)L), designed to align transition metals into tetranuclear linear molecules, reacts with M(II) salts (M=Ni, Co, Cu) to yield complexes with the expected [MMMM] topology. The novel complexes [Co(4)L(2)(py)(6)] (2; py=pyridine) and [Na(py)(2)][Cu(4)L(2)(py)(4)](ClO(4)) (3) have been crystallographically characterised. The metal sites in complexes 2 and 3, together with previously characterised [Ni(4)L(2)(py)(6)] (1), favour different coordination geometries. These have been exploited for the deliberate synthesis of the heterometallic complex [Cu(2)Ni(2)L(2)(py)(6)] (4). Complexes 1, 2, 3 and 4 exhibit antiferromagnetic interactions between pairs of metals within each cluster, leading to S=0 spin ground states, except for the latter cluster, which features two quasi-independent S=1/2 moieties within the molecule. Complex 4 gathers the structural and physical conditions, thus allowing it to be considered as prototype of a two-qbit quantum gate.

The topology of metabolic isotope labeling networks.

PubMed

Weitzel, Michael; Wiechert, Wolfgang; Nöh, Katharina

2007-08-29

Metabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures. With a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools. Exploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.

A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies

PubMed Central

2017-01-01

The authors use four criteria to examine a novel community detection algorithm: (a) effectiveness in terms of producing high values of normalized mutual information (NMI) and modularity, using well-known social networks for testing; (b) examination, meaning the ability to examine mitigating resolution limit problems using NMI values and synthetic networks; (c) correctness, meaning the ability to identify useful community structure results in terms of NMI values and Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks; and (d) scalability, or the ability to produce comparable modularity values with fast execution times when working with large-scale real-world networks. In addition to describing a simple hierarchical arc-merging (HAM) algorithm that uses network topology information, we introduce rule-based arc-merging strategies for identifying community structures. Five well-studied social network datasets and eight sets of LFR benchmark networks were employed to validate the correctness of a ground-truth community, eight large-scale real-world complex networks were used to measure its efficiency, and two synthetic networks were used to determine its susceptibility to two resolution limit problems. Our experimental results indicate that the proposed HAM algorithm exhibited satisfactory performance efficiency, and that HAM-identified and ground-truth communities were comparable in terms of social and LFR benchmark networks, while mitigating resolution limit problems. PMID:29121100

Functional Inference of Complex Anatomical Tendinous Networks at a Macroscopic Scale via Sparse Experimentation

PubMed Central

Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J.

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16th century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines. PMID:23144601

Functional inference of complex anatomical tendinous networks at a macroscopic scale via sparse experimentation.

PubMed

Saxena, Anupam; Lipson, Hod; Valero-Cuevas, Francisco J

2012-01-01

In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16(th) century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines.

A Dynamic Game on Network Topology for Counterinsurgency Applications

DTIC Science & Technology

2015-03-26

scenario. This study creates a dynamic game on network topology to provide insight into the effec- tiveness of offensive targeting strategies determined by...focused upon the diffusion of thoughts and innovations throughout complex social networks. Coleman et al. (1966) and Ryan & Gross (1950) investigated...free networks make them extremely resilient against errors but very vulnerable to attack. Most interest- ingly, a determined attacker can remove well

Temporal Comparisons of Internet Topology

DTIC Science & Technology

2014-06-01

Number CAIDA Cooperative Association of Internet Data Analysis CDN Content Delivery Network CI Confidence Interval DoS denial of service GMT Greenwich...the CAIDA data. Our methods include analysis of graph theoretical measures as well as complex network and statistical measures that will quantify the...tool that probes the Internet for topology analysis and performance [26]. Scamper uses network diagnostic tools, such as traceroute and ping, to probe

Molecular engineering of polymersome surface topology

PubMed Central

Ruiz-Pérez, Lorena; Messager, Lea; Gaitzsch, Jens; Joseph, Adrian; Sutto, Ludovico; Gervasio, Francesco Luigi; Battaglia, Giuseppe

2016-01-01

Biological systems exploit self-assembly to create complex structures whose arrangements are finely controlled from the molecular to mesoscopic level. We report an example of using fully synthetic systems that mimic two levels of self-assembly. We show the formation of vesicles using amphiphilic copolymers whose chemical nature is chosen to control both membrane formation and membrane-confined interactions. We report polymersomes with patterns that emerge by engineering interfacial tension within the polymersome surface. This allows the formation of domains whose topology is tailored by chemical synthesis, paving the avenue to complex supramolecular designs functionally similar to those found in viruses and trafficking vesicles. PMID:27152331

Effect of three bis-pyridyl-bis-amide ligands with various spacers on the structural diversity of new multifunctional cobalt(II) coordination polymers

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

Lin, Hong-Yan; Lu, Huizhe; Le, Mao

2015-03-15

Three new cobalt(II) coordination polymers [Co{sub 2}(1,4-NDC){sub 2}(3-bpye)(H{sub 2}O)] (1), [Co(1,4-NDC)(3-bpfp)(H{sub 2}O)] (2) and [Co(1,4-NDC)(3-bpcb)] (3) [3-bpye=N,N′-bis(3-pyridinecarboxamide)-1,2-ethane, 3-bpfp=bis(3-pyridylformyl)piperazine, 3-bpcb=N,N′-bis(3-pyridinecarboxamide)-1,4-benzene, and 1,4-H{sub 2}NDC=1,4-naphthalenedicarboxylic acid] have been hydrothermally synthesized. The structures of complexes 1–3 have been determined by X-ray single crystal diffraction analyses and further characterized by infrared spectroscopy (IR), powder X-ray diffraction (PXRD) and thermogravimetric analyses (TGA). Complex 1 is a 3D coordination structure with 8-connected (4{sup 20}.6{sup 8}) topology constructed from 3D [Co{sub 2}(1,4-NDC){sub 2}(H{sub 2}O)]{sub n} framework and bidentate 3-bpye ligands. Complex 2 shows 1D “cage+cage”-like chain formed by 1D [Co{sub 2}(1,4-NDC){sub 2}]{sub n} ribbon chains and [Co{sub 2}(3-bpfp){submore » 2}] loops, which are further linked by hydrogen bonding interactions to form a 3D supramolecular network. Complex 3 displays a 3D coordination network with a 6-connected (4{sup 12}.6{sup 3}) topology based on 2D [Co{sub 2}(1,4-NDC){sub 2}]{sub n} layers and bidentate 3-bpcb bridging ligands. The influences of different bis-pyridyl-bis-amide ligands with various spacers on the structures of title complexes are studied. Moreover, the fluorescent properties, electrochemical behaviors and magnetic properties of complexes 1–3 have been investigated. - Graphical abstract: Three multifunctional cobalt(II) complexes constructed from three bis-pyridyl-bis-amide and 1,4-naphthalenedicarboxylic acid have been hydrothermally synthesized and characterized. The fluorescent, electrochemical and magnetic properties of 1–3 have been investigated. - Highlights: • Three multifunctional cobalt(II) complexes based on various bis-pyridyl-bis-amide ligands. • Complex 1 is a 3D coordination structure with 8-connected (4{sup 20}.6{sup 8}) topology. • Complex 2 is a 1D “cage+cage”-like chain. • Complex 3 is a 3D coordination network with a 6-connected (4{sup 12}.6{sup 3}) topology. • The fluorescent, electrochemical and magnetic properties of 1–3 were reported.« less

Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion

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

Skraba, Primoz; Rosen, Paul; Wang, Bei

Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with amore » guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. Here, we apply our method to synthetic and simulation datasets to demonstrate its effectiveness.« less

Programmable Topology in New Families of Heterobimetallic Metal-Organic Frameworks.

PubMed

Muldoon, Patrick F; Liu, Chong; Miller, Carson C; Koby, S Benjamin; Gamble Jarvi, Austin; Luo, Tian-Yi; Saxena, Sunil; O'Keeffe, Michael; Rosi, Nathaniel L

2018-05-09

Using diverse building blocks, such as different heterometallic clusters, in metal-organic framework (MOF) syntheses greatly increases MOF complexity and leads to emergent synergistic properties. However, applying reticular chemistry to syntheses involving more than two molecular building blocks is challenging and there is limited progress in this area. We are therefore motivated to develop a strategy for achieving systematic and differential control over the coordination of multiple metals in MOFs. Herein, we report the design and synthesis of a diverse series of heterobimetallic MOFs with different metal ions and clusters severally distributed throughout two or three inorganic secondary building units (SBUs). By taking advantage of the bifunctional isonicotinate linker and its derivatives, which can coordinatively distinguish between early and late transition metals, we control the assembly and topology of up to three different inorganic SBUs in one-pot solvothermal reactions. Specifically, M 6 (μ 3 -O) n (μ 3 -OH) 8- n (CO 2 ) 12 (M = Zr 4+ , Hf 4+ , Dy 3+ ) SBUs are formed along with metal-pyridyl complexes. By controlling the geometry of the metal-pyridyl complexes, we direct the overall topology to produce eight new MOFs with fcu, ftw, and previously unreported trinodal pfm crystallographic nets.

Interplay of ICP and IXP over the Internet with power-law features

NASA Astrophysics Data System (ADS)

Fan, Zhongyan; Tang, Wallace Kit-Sang

The Internet is the largest artificial network consisting of billions of IP devices, managed by tens of thousands of autonomous systems (ASes). Due to its importance, the Internet has received much attention and its topological features, mainly in AS-level, have been widely explored from the complex network perspective. However, most of the previous studies assume a homogeneous model in which nodes are indistinguishable in nature. It may be good for a general study of topological structure, but unfortunately it fails to reflect the functionality. The Internet ecology is in fact heterogeneous and highly complex. It consists of various elements such as Internet Exchange Points (IXPs), Internet Content Providers (ICPs), and normal Autonomous System (ASes), realizing different roles in the Internet. In this paper, we propose level-structured network models for investigating how ICP performs under the AS-topology with power-law features and how IXP enhances its performance from a complex network perspective. Based on real data, our results reveal that the power-law nature of the Internet facilitates content delivery not only in efficiency but also in path redundancy. Moreover, the proposed multi-level framework is able to clearly illustrate the significant benefits gained by ICP from IXP peerings.

Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion.

PubMed

Skraba, Primoz; Rosen, Paul; Wang, Bei; Chen, Guoning; Bhatia, Harsh; Pascucci, Valerio

2016-02-29

Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with a guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. We apply our method to synthetic and simulation datasets to demonstrate its effectiveness.

Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion

DOE PAGES

Skraba, Primoz; Rosen, Paul; Wang, Bei; ...

2016-02-29

Vector field topology has been successfully applied to represent the structure of steady vector fields. Critical points, one of the essential components of vector field topology, play an important role in describing the complexity of the extracted structure. Simplifying vector fields via critical point cancellation has practical merit for interpreting the behaviors of complex vector fields such as turbulence. However, there is no effective technique that allows direct cancellation of critical points in 3D. This work fills this gap and introduces the first framework to directly cancel pairs or groups of 3D critical points in a hierarchical manner with amore » guaranteed minimum amount of perturbation based on their robustness, a quantitative measure of their stability. In addition, our framework does not require the extraction of the entire 3D topology, which contains non-trivial separation structures, and thus is computationally effective. Furthermore, our algorithm can remove critical points in any subregion of the domain whose degree is zero and handle complex boundary configurations, making it capable of addressing challenging scenarios that may not be resolved otherwise. Here, we apply our method to synthetic and simulation datasets to demonstrate its effectiveness.« less

Topological Evolution of a Fast Magnetic Breakout CME in 3-Dimensions

NASA Technical Reports Server (NTRS)

Lynch, B. J.; Antiochos, S. K.; DeVore, C. R.; Luhmann, J. G.; Zurbuchen, T. H.

2008-01-01

W present the extension of the magnetic breakout model for CME initiation to a fully 3-dimensional, spherical geometry. Given the increased complexity of the dynamic magnetic field interactions in 3-dimensions, we first present a summary of the well known axisymmetric breakout scenario in terms of the topological evolution associated with the various phases of the eruptive process. In this context, we discuss the completely analogous topological evolution during the magnetic breakout CME initiation process in the simplest 3-dimensional multipolar system. We show that an extended bipolar active region embedded in an oppositely directed background dipole field has all the necessary topological features required for magnetic breakout, i.e. a fan separatrix surface between the two distinct flux systems, a pair of spine fieldlines, and a true 3-dimensional coronal null point at their intersection. We then present the results of a numerical MHD simulation of this 3-dimensional system where boundary shearing flows introduce free magnetic energy, eventually leading to a fast magnetic breakout CME. The eruptive flare reconnection facilitates the rapid conversion of this stored free magnetic energy into kinetic energy and the associated acceleration causes the erupting field and plasma structure to reach an asymptotic eruption velocity of greater than or approx. equal to 1100 km/s over an approx.15 minute time period. The simulation results are discussed using the topological insight developed to interpret the various phases of the eruption and the complex, dynamic, and interacting magnetic field structures.

Foreign currency exchange network topology across the 2008 credit crisis

NASA Astrophysics Data System (ADS)

Sharif, Shamshuritawati; Ap, Nuraisah Che; Ruslan, Nuraimi

2017-05-01

A stable world currency exchange rate is a very important aspect to be considered for a developed country, i.e Malaysia. A better understanding about the currencies itself is needed nowadays. This project is about to understanding the fluctuation and to identify the most influential world currencies in the three different cases; before credit crisis, during credit crisis and after credit crisis. A network topology approach is use to examine the interrelationship between currencies based on correlation analysis. With this point of view, those relationships can be measured by a correlation structure among the currencies. The network can be analyse by filtering the important information using minimum spanning tree (MST) and interpret it using degree centrality as the centrality measure. This topology will give a useful guide to understand the behaviour and determine the most influential currency in the network as a part of a complex system. All currencies are compared among the three different cases; before credit crisis, during credit crisis and after credit crisis period. The result of this project shows that Unites State Dollar (USD), Brazilian Real (BRL), United Kingdom Pound (EUR) and Danish Krone (DKK) are the most influential currencies before the credit crisis period. With respect to during the credit crisis, New Zealand Dollar (NZD) dominates the network and it is followed by Singapore Dollar (SGD) for after the credit crisis period.

From the grid to the smart grid, topologically

NASA Astrophysics Data System (ADS)

Pagani, Giuliano Andrea; Aiello, Marco

2016-05-01

In its more visionary acceptation, the smart grid is a model of energy management in which the users are engaged in producing energy as well as consuming it, while having information systems fully aware of the energy demand-response of the network and of dynamically varying prices. A natural question is then: to make the smart grid a reality will the distribution grid have to be upgraded? We assume a positive answer to the question and we consider the lower layers of medium and low voltage to be the most affected by the change. In our previous work, we analyzed samples of the Dutch distribution grid (Pagani and Aiello, 2011) and we considered possible evolutions of these using synthetic topologies modeled after studies of complex systems in other technological domains (Pagani and Aiello, 2014). In this paper, we take an extra important step by defining a methodology for evolving any existing physical power grid to a good smart grid model, thus laying the foundations for a decision support system for utilities and governmental organizations. In doing so, we consider several possible evolution strategies and apply them to the Dutch distribution grid. We show how increasing connectivity is beneficial in realizing more efficient and reliable networks. Our proposal is topological in nature, enhanced with economic considerations of the costs of such evolutions in terms of cabling expenses and economic benefits of evolving the grid.

Brain anatomical networks in early human brain development.

PubMed

Fan, Yong; Shi, Feng; Smith, Jeffrey Keith; Lin, Weili; Gilmore, John H; Shen, Dinggang

2011-02-01

Recent neuroimaging studies have demonstrated that human brain networks have economic small-world topology and modular organization, enabling efficient information transfer among brain regions. However, it remains largely unknown how the small-world topology and modular organization of human brain networks emerge and develop. Using longitudinal MRI data of 28 healthy pediatric subjects, collected at their ages of 1 month, 1 year, and 2 years, we analyzed development patterns of brain anatomical networks derived from morphological correlations of brain regional volumes. The results show that the brain network of 1-month-olds has the characteristically economic small-world topology and nonrandom modular organization. The network's cost efficiency increases with the brain development to 1 year and 2 years, so does the modularity, providing supportive evidence for the hypothesis that the small-world topology and the modular organization of brain networks are established during early brain development to support rapid synchronization and information transfer with minimal rewiring cost, as well as to balance between local processing and global integration of information. Copyright © 2010. Published by Elsevier Inc.

Quon 3D language for quantum information

PubMed Central

Liu, Zhengwei; Wozniakowski, Alex; Jaffe, Arthur M.

2017-01-01

We present a 3D topological picture-language for quantum information. Our approach combines charged excitations carried by strings, with topological properties that arise from embedding the strings in the interior of a 3D manifold with boundary. A quon is a composite that acts as a particle. Specifically, a quon is a hemisphere containing a neutral pair of open strings with opposite charge. We interpret multiquons and their transformations in a natural way. We obtain a type of relation, a string–genus “joint relation,” involving both a string and the 3D manifold. We use the joint relation to obtain a topological interpretation of the C∗-Hopf algebra relations, which are widely used in tensor networks. We obtain a 3D representation of the controlled NOT (CNOT) gate that is considerably simpler than earlier work, and a 3D topological protocol for teleportation. PMID:28167790

Pruning artificial neural networks using neural complexity measures.

PubMed

Jorgensen, Thomas D; Haynes, Barry P; Norlund, Charlotte C F

2008-10-01

This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

Identifying protein complexes based on brainstorming strategy.

PubMed

Shen, Xianjun; Zhou, Jin; Yi, Li; Hu, Xiaohua; He, Tingting; Yang, Jincai

2016-11-01

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance. Copyright © 2016 Elsevier Inc. All rights reserved.

Automated Slicing for a Multi-Axis Metal Deposition System (Preprint)

DTIC Science & Technology

2006-09-01

experimented with different materials like H13 tool steel to build the part. Following the same slicing and scanning toolpath result, there is a geometric...and analysis tool -centroidal axis. Similar to medial axis, it contains geometry and topological information but is significantly computationally...geometry reasoning and analysis tool -centroidal axis. Similar to medial axis, it contains geometry and topological information but is significantly

Localizome: a server for identifying transmembrane topologies and TM helices of eukaryotic proteins utilizing domain information

PubMed Central

Lee, Sunghoon; Lee, Byungwook; Jang, Insoo; Kim, Sangsoo; Bhak, Jong

2006-01-01

The Localizome server predicts the transmembrane (TM) helix number and TM topology of a user-supplied eukaryotic protein and presents the result as an intuitive graphic representation. It utilizes hmmpfam to detect the presence of Pfam domains and a prediction algorithm, Phobius, to predict the TM helices. The results are combined and checked against the TM topology rules stored in a protein domain database called LocaloDom. LocaloDom is a curated database that contains TM topologies and TM helix numbers of known protein domains. It was constructed from Pfam domains combined with Swiss-Prot annotations and Phobius predictions. The Localizome server corrects the combined results of the user sequence to conform to the rules stored in LocaloDom. Compared with other programs, this server showed the highest accuracy for TM topology prediction: for soluble proteins, the accuracy and coverage were 99 and 75%, respectively, while for TM protein domain regions, they were 96 and 68%, respectively. With a graphical representation of TM topology and TM helix positions with the domain units, the Localizome server is a highly accurate and comprehensive information source for subcellular localization for soluble proteins as well as membrane proteins. The Localizome server can be found at . PMID:16845118

Topology-guided deformable registration with local importance preservation for biomedical images

NASA Astrophysics Data System (ADS)

Zheng, Chaojie; Wang, Xiuying; Zeng, Shan; Zhou, Jianlong; Yin, Yong; Feng, Dagan; Fulham, Michael

2018-01-01

The demons registration (DR) model is well recognized for its deformation capability. However, it might lead to misregistration due to erroneous diffusion direction when there are no overlaps between corresponding regions. We propose a novel registration energy function, introducing topology energy, and incorporating a local energy function into the DR in a progressive registration scheme, to address these shortcomings. The topology energy that is derived from the topological information of the images serves as a direction inference to guide diffusion transformation to retain the merits of DR. The local energy constrains the deformation disparity of neighbouring pixels to maintain important local texture and density features. The energy function is minimized in a progressive scheme steered by a topology tree graph and we refer to it as topology-guided deformable registration (TDR). We validated our TDR on 20 pairs of synthetic images with Gaussian noise, 20 phantom PET images with artificial deformations and 12 pairs of clinical PET-CT studies. We compared it to three methods: (1) free-form deformation registration method, (2) energy-based DR and (3) multi-resolution DR. The experimental results show that our TDR outperformed the other three methods in regard to structural correspondence and preservation of the local important information including texture and density, while retaining global correspondence.

GlycoDeNovo - an Efficient Algorithm for Accurate de novo Glycan Topology Reconstruction from Tandem Mass Spectra

NASA Astrophysics Data System (ADS)

Hong, Pengyu; Sun, Hui; Sha, Long; Pu, Yi; Khatri, Kshitij; Yu, Xiang; Tang, Yang; Lin, Cheng

2017-08-01

A major challenge in glycomics is the characterization of complex glycan structures that are essential for understanding their diverse roles in many biological processes. We present a novel efficient computational approach, named GlycoDeNovo, for accurate elucidation of the glycan topologies from their tandem mass spectra. Given a spectrum, GlycoDeNovo first builds an interpretation-graph specifying how to interpret each peak using preceding interpreted peaks. It then reconstructs the topologies of peaks that contribute to interpreting the precursor ion. We theoretically prove that GlycoDeNovo is highly efficient. A major innovative feature added to GlycoDeNovo is a data-driven IonClassifier which can be used to effectively rank candidate topologies. IonClassifier is automatically learned from experimental spectra of known glycans to distinguish B- and C-type ions from all other ion types. Our results showed that GlycoDeNovo is robust and accurate for topology reconstruction of glycans from their tandem mass spectra. [Figure not available: see fulltext.

Valley Topological Phases in Bilayer Sonic Crystals

NASA Astrophysics Data System (ADS)

Lu, Jiuyang; Qiu, Chunyin; Deng, Weiyin; Huang, Xueqin; Li, Feng; Zhang, Fan; Chen, Shuqi; Liu, Zhengyou

2018-03-01

Recently, the topological physics in artificial crystals for classical waves has become an emerging research area. In this Letter, we propose a unique bilayer design of sonic crystals that are constructed by two layers of coupled hexagonal array of triangular scatterers. Assisted by the additional layer degree of freedom, a rich topological phase diagram is achieved by simply rotating scatterers in both layers. Under a unified theoretical framework, two kinds of valley-projected topological acoustic insulators are distinguished analytically, i.e., the layer-mixed and layer-polarized topological valley Hall phases, respectively. The theory is evidently confirmed by our numerical and experimental observations of the nontrivial edge states that propagate along the interfaces separating different topological phases. Various applications such as sound communications in integrated devices can be anticipated by the intriguing acoustic edge states enriched by the layer information.

Driving factors of interactions between the exchange rate market and the commodity market: A wavelet-based complex network perspective

NASA Astrophysics Data System (ADS)

Wen, Shaobo; An, Haizhong; Chen, Zhihua; Liu, Xueyong

2017-08-01

In traditional econometrics, a time series must be in a stationary sequence. However, it usually shows time-varying fluctuations, and it remains a challenge to execute a multiscale analysis of the data and discover the topological characteristics of conduction in different scales. Wavelet analysis and complex networks in physical statistics have special advantages in solving these problems. We select the exchange rate variable from the Chinese market and the commodity price index variable from the world market as the time series of our study. We explore the driving factors behind the behavior of the two markets and their topological characteristics in three steps. First, we use the Kalman filter to find the optimal estimation of the relationship between the two markets. Second, wavelet analysis is used to extract the scales of the relationship that are driven by different frequency wavelets. Meanwhile, we search for the actual economic variables corresponding to different frequency wavelets. Finally, a complex network is used to search for the transfer characteristics of the combination of states driven by different frequency wavelets. The results show that statistical physics have a unique advantage over traditional econometrics. The Chinese market has time-varying impacts on the world market: it has greater influence when the world economy is stable and less influence in times of turmoil. The process of forming the state combination is random. Transitions between state combinations have a clustering feature. Based on these characteristics, we can effectively reduce the information burden on investors and correctly respond to the government's policy mix.

Emergence of rich-club topology and coordinated dynamics in development of hippocampal functional networks in vitro.

PubMed

Schroeter, Manuel S; Charlesworth, Paul; Kitzbichler, Manfred G; Paulsen, Ole; Bullmore, Edward T

2015-04-08

Recent studies demonstrated that the anatomical network of the human brain shows a "rich-club" organization. This complex topological feature implies that highly connected regions, hubs of the large-scale brain network, are more densely interconnected with each other than expected by chance. Rich-club nodes were traversed by a majority of short paths between peripheral regions, underlining their potential importance for efficient global exchange of information between functionally specialized areas of the brain. Network hubs have also been described at the microscale of brain connectivity (so-called "hub neurons"). Their role in shaping synchronous dynamics and forming microcircuit wiring during development, however, is not yet fully understood. The present study aimed to investigate the role of hubs during network development, using multi-electrode arrays and functional connectivity analysis during spontaneous multi-unit activity (MUA) of dissociated primary mouse hippocampal neurons. Over the first 4 weeks in vitro, functional connectivity significantly increased in strength, density, and size, with mature networks demonstrating a robust modular and small-world topology. As expected by a "rich-get-richer" growth rule of network evolution, MUA graphs were found to form rich-clubs at an early stage in development (14 DIV). Later on, rich-club nodes were a consistent topological feature of MUA graphs, demonstrating high nodal strength, efficiency, and centrality. Rich-club nodes were also found to be crucial for MUA dynamics. They often served as broker of spontaneous activity flow, confirming that hub nodes and rich-clubs may play an important role in coordinating functional dynamics at the microcircuit level. Copyright © 2015 the authors 0270-6474/15/355459-12$15.00/0.

Dynamical and topological aspects of consensus formation in complex networks

NASA Astrophysics Data System (ADS)

Chacoma, A.; Mato, G.; Kuperman, M. N.

2018-04-01

The present work analyzes a particular scenario of consensus formation, where the individuals navigate across an underlying network defining the topology of the walks. The consensus, associated to a given opinion coded as a simple message, is generated by interactions during the agent's walk and manifest itself in the collapse of the various opinions into a single one. We analyze how the topology of the underlying networks and the rules of interaction between the agents promote or inhibit the emergence of this consensus. We find that non-linear interaction rules are required to form consensus and that consensus is more easily achieved in networks whose degree distribution is narrower.

Computational Search for Strong Topological Insulators: An Exercise in Data Mining and Electronic Structure

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

Klintenberg, M.; Haraldsen, Jason T.; Balatsky, Alexander V.

In this paper, we report a data-mining investigation for the search of topological insulators by examining individual electronic structures for over 60,000 materials. Using a data-mining algorithm, we survey changes in band inversion with and without spin-orbit coupling by screening the calculated electronic band structure for a small gap and a change concavity at high-symmetry points. Overall, we were able to identify a number of topological candidates with varying structures and composition. Lastly, our overall goal is expand the realm of predictive theory into the determination of new and exotic complex materials through the data mining of electronic structure.

Computational Search for Strong Topological Insulators: An Exercise in Data Mining and Electronic Structure

DOE PAGES

Klintenberg, M.; Haraldsen, Jason T.; Balatsky, Alexander V.

2014-06-19

In this paper, we report a data-mining investigation for the search of topological insulators by examining individual electronic structures for over 60,000 materials. Using a data-mining algorithm, we survey changes in band inversion with and without spin-orbit coupling by screening the calculated electronic band structure for a small gap and a change concavity at high-symmetry points. Overall, we were able to identify a number of topological candidates with varying structures and composition. Lastly, our overall goal is expand the realm of predictive theory into the determination of new and exotic complex materials through the data mining of electronic structure.

Dual simulation of the massless lattice Schwinger model with topological term and non-zero chemical potential

NASA Astrophysics Data System (ADS)

Göschl, Daniel

2018-03-01

We discuss simulation strategies for the massless lattice Schwinger model with a topological term and finite chemical potential. The simulation is done in a dual representation where the complex action problem is solved and the partition function is a sum over fermion loops, fermion dimers and plaquette-occupation numbers. We explore strategies to update the fermion loops coupled to the gauge degrees of freedom and check our results with conventional simulations (without topological term and at zero chemical potential), as well as with exact summation on small volumes. Some physical implications of the results are discussed.

archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems.

PubMed

Delory, Benjamin M; Li, Mao; Topp, Christopher N; Lobet, Guillaume

2018-01-01

Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity.

Expected Number of Fixed Points in Boolean Networks with Arbitrary Topology.

PubMed

Mori, Fumito; Mochizuki, Atsushi

2017-07-14

Boolean network models describe genetic, neural, and social dynamics in complex networks, where the dynamics depend generally on network topology. Fixed points in a genetic regulatory network are typically considered to correspond to cell types in an organism. We prove that the expected number of fixed points in a Boolean network, with Boolean functions drawn from probability distributions that are not required to be uniform or identical, is one, and is independent of network topology if only a feedback arc set satisfies a stochastic neutrality condition. We also demonstrate that the expected number is increased by the predominance of positive feedback in a cycle.

archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems

PubMed Central

Delory, Benjamin M.; Li, Mao; Topp, Christopher N.; Lobet, Guillaume

2018-01-01

Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity. PMID:29636899

Bubble Divergences: Sorting out Topology from Cell Structure

NASA Astrophysics Data System (ADS)

Bonzom, Valentin; Smerlak, Matteo

2012-02-01

We conclude our analysis of bubble divergences in the flat spinfoam model. In [arXiv:1008.1476] we showed that the divergence degree of an arbitrary two-complex Gamma can be evaluated exactly by means of twisted cohomology. Here, we specialize this result to the case where Gamma is the two-skeleton of the cell decomposition of a pseudomanifold, and sharpen it with a careful analysis of the cellular and topological structures involved. Moreover, we explain in detail how this approach reproduces all the previous powercounting results for the Boulatov-Ooguri (colored) tensor models, and sheds light on algebraic-topological aspects of Gurau's 1/N expansion.

Zinc(II) and Cadmium(II) coordination polymers constructed from phenylenediacetate ligands

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

Sezer, Güneş Günay; Department of Chemistry, Eskişehir Osmangazi University, Eskişehir; Yeşilel, Okan Zafer

ABSTRACT: A series of new coordination polymers {[Zn(μ-opda)(μ-bpa)]·2H_2O}{sub n} (1), [Zn(μ{sub 3}-ppda)(μ-bpa)]{sub n} (2), [Cd(μ{sub 3}-ppda)(μ-bpa)]{sub n} (3), [Cd(μ{sub 3}-mpda)(μ-bpa)]{sub n} (4) and [Cd(μ{sub 3}-mpda)(μ-bipy)]{sub n} (5), (o/m/ppda=1,2/1,3/1,4-phenylenediacetate, bpa=1,2-bi(4-pyridyl)ethane, bipy=4,4′-bipyridine) were synthesized. Their structures were characterized by elemental analysis, IR spectroscopy, single-crystal and powder X-ray diffraction. Furthermore, the effect of metal sources (zinc acetate and zinc oxide) and acidity of the solution on the structure of the coordination polymers was discussed for complexes 1 and 5, respectively. The single-crystal X-ray crystallographic studies revealed that complexes 1, 3, 4 and 5 are uninodal (4)-connected 2D frameworks and display sql topology withmore » the point symbol of (4{sup 4}.6{sup 2}). Complex 2 is 3D coordination polymer and exhibits pcu topology with the point symbol of (4{sup 12}.6{sup 3}). In addition, the luminescent properties and thermal behavior of all complexes were also investigated. - Graphical abstract: Scheme 1. Topologies of Coordination Polymers Reported in This Paper.« less

Big words, halved brains and small worlds: complex brain networks of figurative language comprehension.

PubMed

Arzouan, Yossi; Solomon, Sorin; Faust, Miriam; Goldstein, Abraham

2011-04-27

Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity.

Distinguishing between incomplete lineage sorting and genomic introgressions: complete fixation of allospecific mitochondrial DNA in a sexually reproducing fish (Cobitis; Teleostei), despite clonal reproduction of hybrids.

PubMed

Choleva, Lukas; Musilova, Zuzana; Kohoutova-Sediva, Alena; Paces, Jan; Rab, Petr; Janko, Karel

2014-01-01

Distinguishing between hybrid introgression and incomplete lineage sorting causing incongruence among gene trees in that they exhibit topological differences requires application of statistical approaches that are based on biologically relevant models. Such study is especially challenging in hybrid systems, where usual vectors mediating interspecific gene transfers--hybrids with Mendelian heredity--are absent or unknown. Here we study a complex of hybridizing species, which are known to produce clonal hybrids, to discover how one of the species, Cobitis tanaitica, has achieved a pattern of mito-nuclear mosaic genome over the whole geographic range. We appplied three distinct methods, including the method using solely the information on gene tree topologies, and found that the contrasting mito-nuclear signal might not have resulted from the retention of ancestral polymorphism. Instead, we found two signs of hybridization events related to C. tanaitica; one concerning nuclear gene flow and the other suggested mitochondrial capture. Interestingly, clonal inheritance (gynogenesis) of contemporary hybrids prevents genomic introgressions and non-clonal hybrids are either absent or too rare to be detected among European Cobitis. Our analyses therefore suggest that introgressive hybridizations are rather old episodes, mediated by previously existing hybrids whose inheritance was not entirely clonal. Cobitis complex thus supports the view that the type of resulting hybrids depends on a level of genomic divergence between sexual species.

Knotty structures of the evolving heliospheric magnetic fields.

NASA Astrophysics Data System (ADS)

Roth, Ilan

2013-04-01

The analogy between MHD and knot theory is utilized in an analysis of structure, stability and evolution of complex magnetic heliospheric flux tubes. Planar projection of a three-dimensional magnetic configuration depicts the structure as a two-dimensional diagram with crossings, to which one may assign mathematical operations leading to robust topological invariants. These invariants enrich the topological information of magnetic configurations beyond helicity. It is conjectured that the field which emerges from the solar photosphere is structured as one of simplest knot invariants - unknot or prime knot, and these flux ropes are then stretched while carried by the solar wind into the interplanetary medium. Preservation of invariants for small diffusivity and large cross section of the emerging magnetic flux makes them impervious to large scale reconnection, allowing us to predict the observed structures at 1AU as elongated prime knots. Similar structures may be observed in magnetic clouds which got disconnected from their foot-points and in ion drop-out configurations from a compact flare source in solar impulsive solar events. Observation of small scale magnetic features consistent with prime knot may indicate spatial intermittency and non-Gaussian statistics in the turbulent cascade process. For flux tubes with higher resistivity, magnetic energy decay rate should decrease with increased knot complexity as the invariants are then harder to be violated. Future measurements are suggested for distinctly oriented magnetic fields with directionally varying suprathermal particle fluxes.

Homological scaffolds of brain functional networks

PubMed Central

Petri, G.; Expert, P.; Turkheimer, F.; Carhart-Harris, R.; Nutt, D.; Hellyer, P. J.; Vaccarino, F.

2014-01-01

Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci. 10, 186–198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects—homological cycles—associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle, we apply these tools to compare resting-state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin—the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain's functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo. PMID:25401177

Intelligent feature selection techniques for pattern classification of Lamb wave signals

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

Hinders, Mark K.; Miller, Corey A.

2014-02-18

Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crossholemore » tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.« less

Multiresolution persistent homology for excessively large biomolecular datasets

NASA Astrophysics Data System (ADS)

Xia, Kelin; Zhao, Zhixiong; Wei, Guo-Wei

2015-10-01

Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.

DNA Knots: Theory and Experiments

NASA Astrophysics Data System (ADS)

Sumners, D. W.

Cellular DNA is a long, thread-like molecule with remarkably complex topology. Enzymes that manipulate the geometry and topology of cellular DNA perform many vital cellular processes (including segregation of daughter chromosomes, gene regulation, DNA repair, and generation of antibody diversity). Some enzymes pass DNA through itself via enzyme-bridged transient breaks in the DNA; other enzymes break the DNA apart and reconnect it to different ends. In the topological approach to enzymology, circular DNA is incubated with an enzyme, producing an enzyme signature in the form of DNA knots and links. By observing the changes in DNA geometry (supercoiling) and topology (knotting and linking) due to enzyme action, the enzyme binding and mechanism can often be characterized. This paper will discuss some personal research history, and the tangle model for the analysis of site-specific recombination experiments on circular DNA.

How to obtain a cosmological constant from small exotic R4

NASA Astrophysics Data System (ADS)

Asselmeyer-Maluga, Torsten; Król, Jerzy

2018-03-01

In this paper we determine the cosmological constant as a topological invariant by applying certain techniques from low dimensional differential topology. We work with a small exotic R4 which is embedded into the standard R4. Any exotic R4 is a Riemannian smooth manifold with necessary non-vanishing curvature tensor. To determine the invariant part of such curvature we deal with a canonical construction of R4 where it appears as a part of the complex surface K 3 # CP(2) bar. Such R4's admit hyperbolic geometry. This fact simplifies significantly the calculations and enforces the rigidity of the expressions. In particular, we explain the smallness of the cosmological constant with a value consisting of a combination of (natural) topological invariant. Finally, the cosmological constant appears to be a topologically supported quantity.

Topological structure prediction in binary nanoparticle superlattices

DOE PAGES

Travesset, A.

2017-04-27

Systems of spherical nanoparticles with capping ligands have been shown to self-assemble into beautiful superlattices of fascinating structure and complexity. Here, I show that the spherical geometry of the nanoparticle imposes constraints on the nature of the topological defects associated with the capping ligand and that such topological defects control the structure and stability of the superlattices that can be assembled. Furthermore, all of these considerations form the basis for the orbifold topological model (OTM) described in this paper. Finally, the model quantitatively predicts the structure of super-lattices where capping ligands are hydrocarbon chains in excellent agreement with experimental results,more » explains the appearance of low packing fraction lattices as equilibrium, why certain similar structures are more stable (bccAB 6vs. CaB 6, AuCu vs. CsCl, etc.) and many other experimental observations.« less

Widespread spin polarization effects in photoemission from topological insulators

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

Jozwiak, C.; Chen, Y. L.; Fedorov, A. V.

2011-06-22

High-resolution spin- and angle-resolved photoemission spectroscopy (spin-ARPES) was performed on the three-dimensional topological insulator Bi{sub 2}Se{sub 3} using a recently developed high-efficiency spectrometer. The topological surface state's helical spin structure is observed, in agreement with theoretical prediction. Spin textures of both chiralities, at energies above and below the Dirac point, are observed, and the spin structure is found to persist at room temperature. The measurements reveal additional unexpected spin polarization effects, which also originate from the spin-orbit interaction, but are well differentiated from topological physics by contrasting momentum and photon energy and polarization dependencies. These observations demonstrate significant deviations ofmore » photoelectron and quasiparticle spin polarizations. Our findings illustrate the inherent complexity of spin-resolved ARPES and demonstrate key considerations for interpreting experimental results.« less

Automated and fast building of three-dimensional RNA structures.

PubMed

Zhao, Yunjie; Huang, Yangyu; Gong, Zhou; Wang, Yanjie; Man, Jianfen; Xiao, Yi

2012-01-01

Building tertiary structures of non-coding RNA is required to understand their functions and design new molecules. Current algorithms of RNA tertiary structure prediction give satisfactory accuracy only for small size and simple topology and many of them need manual manipulation. Here, we present an automated and fast program, 3dRNA, for RNA tertiary structure prediction with reasonable accuracy for RNAs of larger size and complex topology.

Multifractal analysis and topological properties of a new family of weighted Koch networks

NASA Astrophysics Data System (ADS)

Huang, Da-Wen; Yu, Zu-Guo; Anh, Vo

2017-03-01

Weighted complex networks, especially scale-free networks, which characterize real-life systems better than non-weighted networks, have attracted considerable interest in recent years. Studies on the multifractality of weighted complex networks are still to be undertaken. In this paper, inspired by the concepts of Koch networks and Koch island, we propose a new family of weighted Koch networks, and investigate their multifractal behavior and topological properties. We find some key topological properties of the new networks: their vertex cumulative strength has a power-law distribution; there is a power-law relationship between their topological degree and weight strength; the networks have a high weighted clustering coefficient of 0.41004 (which is independent of the scaling factor c) in the limit of large generation t; the second smallest eigenvalue μ2 and the maximum eigenvalue μn are approximated by quartic polynomials of the scaling factor c for the general Laplacian operator, while μ2 is approximately a quartic polynomial of c and μn= 1.5 for the normalized Laplacian operator. Then, we find that weighted koch networks are both fractal and multifractal, their fractal dimension is influenced by the scaling factor c. We also apply these analyses to six real-world networks, and find that the multifractality in three of them are strong.

Volumetric data analysis using Morse-Smale complexes

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

Natarajan, V; Pascucci, V

2005-10-13

The 3D Morse-Smale complex is a fundamental topological construct that partitions the domain of a real-valued function into regions having uniform gradient flow behavior. In this paper, we consider the construction and selective presentation of cells of the Morse-Smale complex and their use in the analysis and visualization of scientific datasets. We take advantage of the fact that cells of different dimension often characterize different types of features present in the data. For example, critical points pinpoint changes in topology by showing where components of the level sets are created, destroyed or modified in genus. Edges of the Morse-Smale complexmore » extract filament-like features that are not explicitly modeled in the original data. Interactive selection and rendering of portions of the Morse-Smale complex introduces fundamental data management challenges due to the unstructured nature of the complex even for structured inputs. We describe a data structure that stores the Morse-Smale complex and allows efficient selective traversal of regions of interest. Finally, we illustrate the practical use of this approach by applying it to cryo-electron microscopy data of protein molecules.« less

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

Li, Xiao-Ling; Liu, Guang-Zhen, E-mail: gzliuly@126.com; Xin, Ling-Yun

Two topologically new Mn(II) coordination polymers, namely ([Mn{sub 2}(H{sub 4}ipca)(4,4′-bpy){sub 1.5}(CH{sub 3}CH{sub 2}OH){sub 0.5}(H{sub 2}O){sub 1.5}]·0.5CH{sub 3}CH{sub 2}OH·2.5H{sub 2}O){sub n} (1) and (Mn{sub 4}(H{sub 4}ipca){sub 2}(bze)(H{sub 2}O){sub 4}){sub n} (2) were prepared by the solvothermal reactions of Mn(II) acetate with 5-(2’,3’-dicarboxylphenoxy)isophthalic acid (H{sub 4}ipca) in the presence of different N-donor coligands (4,4′-bpy=4,4′-bipyridyl and bze=1, 4-bis(1-imidazoly)benzene). The single crystal X-ray diffractions reveal that two complexes display 3D metal-organic frameworks with binuclear and tetranuclear Mn(II) units, respectively. Complex 1 features a (3,4,6)-connected porous framework based on dinuclear Mn(II) unit with the (4.5{sup 2}){sub 2}(4{sup 2}.6{sup 8}.8{sup 3}.9{sup 2})(5{sup 2}.8.9{sup 2}.10) new topology,more » and complex 2 possesses a (3,8)-connected network based on tetranuclear Mn(II) unit with the (4{sup 2}.6){sub 2}(4{sup 4}.6{sup 14}.7{sup 7}.8{sup 2}.9) new topology. Magnetic analyses indicate that both two compounds show weak antiferromagnetic interactions within binuclear and tetranuclear Mn(II) units. - Graphical abstract: Two topologically new Mn(II) metal-organic frameworks with dinuclear and tetranuclear Mn(II) units respectively were assembled by using 5-(2′,3′-Dicarboxylphenoxy)isophthalic acid and N-donor ancillary coligands. Magnetic analysis revealed the existence of dominant antiferromagnetic interactions within the polynuclear Mn(II) units. - Highlights: • Mixed ligand strategy produces two topologically new MOFs with dinuclear and tetranuclear Mn(II) respectively. • Magnetic fitting gives weak antiferromagnetic interactions within the polynuclear Mn(II) units.« less

Entanglement in 3D Kitaev spin liquids

NASA Astrophysics Data System (ADS)

Matern, S.; Hermanns, M.

2018-06-01

Quantum spin liquids are highly fascinating quantum liquids in which the spin degrees of freedom fractionalize. An interesting class of spin liquids are the exactly solvable, three-dimensional Kitaev spin liquids. Their fractionalized excitations are Majonara fermions, which may exhibit a variety of topological band structures—ranging from topologically protected Weyl semi-metals over nodal semi-metals to systems with Majorana Fermi surfaces. We study the entanglement spectrum of such Kitaev spin liquids and verify that it is closely related to the topologically protected edge spectrum. Moreover, we find that in some cases the entanglement spectrum contains even more information about the topological features than the surface spectrum, and thus provides a simple and reliable tool to probe the topology of a system.

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis

PubMed Central

Toppi, Jlenia; Astolfi, Laura; Risetti, Monica; Anzolin, Alessandra; Kober, Silvia E.; Wood, Guilherme; Mattia, Donatella

2018-01-01

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke. PMID:29379425

WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids.

PubMed

Hayat, Maqsood; Khan, Asifullah

2013-05-01

Membrane protein is the prime constituent of a cell, which performs a role of mediator between intra and extracellular processes. The prediction of transmembrane (TM) helix and its topology provides essential information regarding the function and structure of membrane proteins. However, prediction of TM helix and its topology is a challenging issue in bioinformatics and computational biology due to experimental complexities and lack of its established structures. Therefore, the location and orientation of TM helix segments are predicted from topogenic sequences. In this regard, we propose WRF-TMH model for effectively predicting TM helix segments. In this model, information is extracted from membrane protein sequences using compositional index and physicochemical properties. The redundant and irrelevant features are eliminated through singular value decomposition. The selected features provided by these feature extraction strategies are then fused to develop a hybrid model. Weighted random forest is adopted as a classification approach. We have used two benchmark datasets including low and high-resolution datasets. tenfold cross validation is employed to assess the performance of WRF-TMH model at different levels including per protein, per segment, and per residue. The success rates of WRF-TMH model are quite promising and are the best reported so far on the same datasets. It is observed that WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins. The accompanied web predictor is accessible at http://111.68.99.218/WRF-TMH/ .

Visible light communication and indoor positioning using a-SiCH device as receiver

NASA Astrophysics Data System (ADS)

Vieira, M. A.; Vieira, M.; Louro, P.; Vieira, P.; Fantoni, A.

2017-08-01

An indoor positioning system were trichromatic white LEDs are used both for illumination proposes and as transmitters and an optical processor, based on a-SiC:H technology, as mobile receiver is presented. OOK modulation scheme is used, and it provides a good trade-off between system performance and implementation complexity. The relationship between the transmitted data and the received digital output levels is decoded. The system topology for positioning is a self-positioning system in which the measuring unit is mobile. This unit receives the signals of several transmitters in known locations, and has the capability to compute its location based on the measured signals. LED bulbs work as transmitters, sending information together with different IDs related to their physical locations. A triangular topology for the unit cell is analysed. A 2D localization design, demonstrated by a prototype implementation is presented. Fine-grained indoor localization is tested. The received signal is used in coded multiplexing techniques for supporting communications and navigation concomitantly on the same channel. The position is estimated through the visible multilateration metodh using several non-collinear transmitters. The location and motion information is found by mapping position and estimates the location areas. Data analysis showed that by using a pinpin double photodiode based on a a-SiC:H heterostucture as receiver, and RBGLEDs as transmitters it is possible not only to determine the mobile target's position but also to infer the motion direction over time, along with the received information in each position.

Recipe for Topological Polaritons

NASA Astrophysics Data System (ADS)

Karzig, Torsten; Bardyn, Charles-Edouard; Lindner, Netanel; Refael, Gil

2015-03-01

The interaction between light and matter can give rise to novel topological states. This principle was recently exemplified in Floquet topological insulators, where classical light was used to induce a topological electronic band structure. Here, in contrast, we show that mixing single photons with excitons can result in new topological polaritonic states -- or ``topolaritons''. Taken separately, the underlying photons and excitons are topologically trivial. Combined appropriately, however, they give rise to non-trivial polaritonic bands with chiral edge modes allowing for unidirectional polariton propagation. The main ingredient in our construction is an exciton-photon coupling with a phase that winds in momentum space. We demonstrate how this winding emerges from spin-orbit coupling in the electronic system and an applied Zeeman field. We discuss the requirements for obtaining a sizable topological gap in the polariton spectrum. Funded by the Institute for Quantum Information and Matter, the Bi-National Science Foundation and I-Core: the Israeli Excellence Center ``Circle of Light'', and Darpa under funding for FENA, and the Swiss National Science Foundation.

Sensor-Topology Based Simplicial Complex Reconstruction from Mobile Laser Scanning

NASA Astrophysics Data System (ADS)

Guinard, S.; Vallet, B.

2018-05-01

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects. Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create triangles for each triplet of self-connected edges. Last, we improve this method with a regularization based on the co-planarity of triangles and collinearity of remaining edges. We compare our results to a naive simplicial complexes reconstruction based on edge length.

Structural and functional networks in complex systems with delay.

PubMed

Eguíluz, Víctor M; Pérez, Toni; Borge-Holthoefer, Javier; Arenas, Alex

2011-05-01

Functional networks of complex systems are obtained from the analysis of the temporal activity of their components, and are often used to infer their unknown underlying connectivity. We obtain the equations relating topology and function in a system of diffusively delay-coupled elements in complex networks. We solve exactly the resulting equations in motifs (directed structures of three nodes) and in directed networks. The mean-field solution for directed uncorrelated networks shows that the clusterization of the activity is dominated by the in-degree of the nodes, and that the locking frequency decreases with increasing average degree. We find that the exponent of a power law degree distribution of the structural topology γ is related to the exponent of the associated functional network as α=(2-γ)(-1) for γ<2. © 2011 American Physical Society

The dawn of the RNA World: Toward functional complexity through ligation of random RNA oligomers

PubMed Central

Briones, Carlos; Stich, Michael; Manrubia, Susanna C.

2009-01-01

A main unsolved problem in the RNA World scenario for the origin of life is how a template-dependent RNA polymerase ribozyme emerged from short RNA oligomers obtained by random polymerization on mineral surfaces. A number of computational studies have shown that the structural repertoire yielded by that process is dominated by topologically simple structures, notably hairpin-like ones. A fraction of these could display RNA ligase activity and catalyze the assembly of larger, eventually functional RNA molecules retaining their previous modular structure: molecular complexity increases but template replication is absent. This allows us to build up a stepwise model of ligation-based, modular evolution that could pave the way to the emergence of a ribozyme with RNA replicase activity, step at which information-driven Darwinian evolution would be triggered. PMID:19318464

Asynchronous State Estimation for Discrete-Time Switched Complex Networks With Communication Constraints.

PubMed

Zhang, Dan; Wang, Qing-Guo; Srinivasan, Dipti; Li, Hongyi; Yu, Li

2018-05-01

This paper is concerned with the asynchronous state estimation for a class of discrete-time switched complex networks with communication constraints. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the topology/coupling information. Also, the event-based communication, signal quantization, and the random packet dropout problems are studied due to the limited communication resource. With the help of switched system theory and by resorting to some stochastic system analysis method, a sufficient condition is proposed to guarantee the exponential stability of estimation error system in the mean-square sense and a prescribed performance level is also ensured. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem. Finally, the effectiveness of the proposed design approach is demonstrated by a simulation example.

Skeletonization and Partitioning of Digital Images Using Discrete Morse Theory.

PubMed

Delgado-Friedrichs, Olaf; Robins, Vanessa; Sheppard, Adrian

2015-03-01

We show how discrete Morse theory provides a rigorous and unifying foundation for defining skeletons and partitions of grayscale digital images. We model a grayscale image as a cubical complex with a real-valued function defined on its vertices (the voxel values). This function is extended to a discrete gradient vector field using the algorithm presented in Robins, Wood, Sheppard TPAMI 33:1646 (2011). In the current paper we define basins (the building blocks of a partition) and segments of the skeleton using the stable and unstable sets associated with critical cells. The natural connection between Morse theory and homology allows us to prove the topological validity of these constructions; for example, that the skeleton is homotopic to the initial object. We simplify the basins and skeletons via Morse-theoretic cancellation of critical cells in the discrete gradient vector field using a strategy informed by persistent homology. Simple working Python code for our algorithms for efficient vector field traversal is included. Example data are taken from micro-CT images of porous materials, an application area where accurate topological models of pore connectivity are vital for fluid-flow modelling.

Topology and Edge Modes in Quantum Critical Chains

NASA Astrophysics Data System (ADS)

Verresen, Ruben; Jones, Nick G.; Pollmann, Frank

2018-02-01

We show that topology can protect exponentially localized, zero energy edge modes at critical points between one-dimensional symmetry-protected topological phases. This is possible even without gapped degrees of freedom in the bulk—in contrast to recent work on edge modes in gapless chains. We present an intuitive picture for the existence of these edge modes in the case of noninteracting spinless fermions with time-reversal symmetry (BDI class of the tenfold way). The stability of this phenomenon relies on a topological invariant defined in terms of a complex function, counting its zeros and poles inside the unit circle. This invariant can prevent two models described by the same conformal field theory (CFT) from being smoothly connected. A full classification of critical phases in the noninteracting BDI class is obtained: Each phase is labeled by the central charge of the CFT, c ∈1/2 N , and the topological invariant, ω ∈Z . Moreover, c is determined by the difference in the number of edge modes between the phases neighboring the transition. Numerical simulations show that the topological edge modes of critical chains can be stable in the presence of interactions and disorder.

Nonlinear conduction via solitons in a topological mechanical insulator.

PubMed

Chen, Bryan Gin-ge; Upadhyaya, Nitin; Vitelli, Vincenzo

2014-09-09

Networks of rigid bars connected by joints, termed linkages, provide a minimal framework to design robotic arms and mechanical metamaterials built of folding components. Here, we investigate a chain-like linkage that, according to linear elasticity, behaves like a topological mechanical insulator whose zero-energy modes are localized at the edge. Simple experiments we performed using prototypes of the chain vividly illustrate how the soft motion, initially localized at the edge, can in fact propagate unobstructed all of the way to the opposite end. Using real prototypes, simulations, and analytical models, we demonstrate that the chain is a mechanical conductor, whose carriers are nonlinear solitary waves, not captured within linear elasticity. Indeed, the linkage prototype can be regarded as the simplest example of a topological metamaterial whose protected mechanical excitations are solitons, moving domain walls between distinct topological mechanical phases. More practically, we have built a topologically protected mechanism that can perform basic tasks such as transporting a mechanical state from one location to another. Our work paves the way toward adopting the principle of topological robustness in the design of robots assembled from activated linkages as well as in the fabrication of complex molecular nanostructures.

Molecular Knots

PubMed Central

Fielden, Stephen D. P.; Woltering, Steffen L.

2017-01-01

Abstract The first synthetic molecular trefoil knot was prepared in the late 1980s. However, it is only in the last few years that more complex small‐molecule knot topologies have been realized through chemical synthesis. The steric restrictions imposed on molecular strands by knotting can impart significant physical and chemical properties, including chirality, strong and selective ion binding, and catalytic activity. As the number and complexity of accessible molecular knot topologies increases, it will become increasingly useful for chemists to adopt the knot terminology employed by other disciplines. Here we give an overview of synthetic strategies towards molecular knots and outline the principles of knot, braid, and tangle theory appropriate to chemistry and molecular structure. PMID:28477423

A Topological Paradigm for Hippocampal Spatial Map Formation Using Persistent Homology

PubMed Central

Dabaghian, Y.; Mémoli, F.; Frank, L.; Carlsson, G.

2012-01-01

An animal's ability to navigate through space rests on its ability to create a mental map of its environment. The hippocampus is the brain region centrally responsible for such maps, and it has been assumed to encode geometric information (distances, angles). Given, however, that hippocampal output consists of patterns of spiking across many neurons, and downstream regions must be able to translate those patterns into accurate information about an animal's spatial environment, we hypothesized that 1) the temporal pattern of neuronal firing, particularly co-firing, is key to decoding spatial information, and 2) since co-firing implies spatial overlap of place fields, a map encoded by co-firing will be based on connectivity and adjacency, i.e., it will be a topological map. Here we test this topological hypothesis with a simple model of hippocampal activity, varying three parameters (firing rate, place field size, and number of neurons) in computer simulations of rat trajectories in three topologically and geometrically distinct test environments. Using a computational algorithm based on recently developed tools from Persistent Homology theory in the field of algebraic topology, we find that the patterns of neuronal co-firing can, in fact, convey topological information about the environment in a biologically realistic length of time. Furthermore, our simulations reveal a “learning region” that highlights the interplay between the parameters in combining to produce hippocampal states that are more or less adept at map formation. For example, within the learning region a lower number of neurons firing can be compensated by adjustments in firing rate or place field size, but beyond a certain point map formation begins to fail. We propose that this learning region provides a coherent theoretical lens through which to view conditions that impair spatial learning by altering place cell firing rates or spatial specificity. PMID:22912564

Insulator function and topological domain border strength scale with architectural protein occupancy

PubMed Central

2014-01-01

Background Chromosome conformation capture studies suggest that eukaryotic genomes are organized into structures called topologically associating domains. The borders of these domains are highly enriched for architectural proteins with characterized roles in insulator function. However, a majority of architectural protein binding sites localize within topological domains, suggesting sites associated with domain borders represent a functionally different subclass of these regulatory elements. How topologically associating domains are established and what differentiates border-associated from non-border architectural protein binding sites remain unanswered questions. Results By mapping the genome-wide target sites for several Drosophila architectural proteins, including previously uncharacterized profiles for TFIIIC and SMC-containing condensin complexes, we uncover an extensive pattern of colocalization in which architectural proteins establish dense clusters at the borders of topological domains. Reporter-based enhancer-blocking insulator activity as well as endogenous domain border strength scale with the occupancy level of architectural protein binding sites, suggesting co-binding by architectural proteins underlies the functional potential of these loci. Analyses in mouse and human stem cells suggest that clustering of architectural proteins is a general feature of genome organization, and conserved architectural protein binding sites may underlie the tissue-invariant nature of topologically associating domains observed in mammals. Conclusions We identify a spectrum of architectural protein occupancy that scales with the topological structure of chromosomes and the regulatory potential of these elements. Whereas high occupancy architectural protein binding sites associate with robust partitioning of topologically associating domains and robust insulator function, low occupancy sites appear reserved for gene-specific regulation within topological domains. PMID:24981874

A series of transition metal-organic frameworks based on a bipyridinium carboxylate ligand: Syntheses, structures and photoluminescent properties

NASA Astrophysics Data System (ADS)

Pei, Ru-Bo; Cao, Ming-Yang; Li, Lin-Ke; Dong, Xi-Yan; Zang, Shuang-Quan

2017-09-01

Based on a bipyridinium carboxylate ligand 1-(3,5-dicarboxy)-benzyl-1,2-di(pyridine-4-yl)ethylene chloride (H2L+Cl-), eight transition metal coordination polymers, namely, {[Zn(L)Cl]ṡ4H2O}n (1), {[Zn(L)H2O]ṡNO3ṡ2H2O}n (2), {[Zn(L) (H2O)]ṡ(NO3)0.5ṡ(Cl)0.5ṡ2H2O}n (3), {[Cd(L)(H2O)(NO3)]ṡ2H2O}n (4), {[Cd1.5(L) (Cl)2]ṡ2H2O}n (5), {[Cu(L)(H2O)]ṡNO3ṡH2O}n (6), {[Cu(HL)2(H2O)2]·Cl2·6H2O}n (7) and {[Ni(L)(H2O)Cl]ṡ4H2O}n (8) have been synthesized and characterized by single-crystal X-ray diffraction analyses. Complexes 1 and 8 display 2D wave-like layer structures with a 3-connected 63 topology. Complexes 2 and 6 demonstrate 3D 2-fold interpenetrating frameworks with uninodal, 3-connected (10,3)-d utp-topology. Another pair of 3D 2-fold interpenetrating frameworks 3 and 4 possess 3-connected, uninodal 103ThSi2 (ths)-topology. Complex 5 shows a 2D layer structure based on the extending of trinuclear Cd(II) subunits. Complex 7 presents 1D double-chain structure, in which the central Cu(II) ions are connected by the partially deprotonated ligand HL. Additionally, powder X-ray diffractions (PXRD) and thermogravimetric analyses of complexes 1-8, as well as the solid-state luminescent properties of d10 metal complexes 1-4 at room temperature have also been discussed.

Preparations, structures and properties of heterobimetallic complexes based on tetrahydrofuran-2,3,4,5-tetracarboxylate

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

Jia, Tian-Jing; Li, Shu-Mu; Cao, Wei

2013-05-01

Transition heterobimetallic metal-organic frameworks based on tetrahydrofuran-2,3,4,5-tetracarboxylicate (FTA), namely [M(H₂O)₆][Cu₂M(FTA)₂(H₂O)₂]·4H₂O [M=Mn (1), Co (2)], and [CuZn(FTA)(H₂O)₅]·H₂O (3) have been synthesized and characterized. Single-crystal X-ray diffraction indicates that complexes 1 and 2 are isomorphic. In 1 and 2, FTA ligand links the metal ions to a 2-D wave-like negative-charged layer with a topology of (4;6²)₂(4;6³;8²)₂(6). They possess 1-D channels with [M(H₂O)₆]²⁺ and lattice water molecules enclathrated. While in the complex 3, Cu²⁺ and Zn²⁺ ions are bridged by FTA to a 2-D neutral layer structure with a (8)₂(8⁴;12²) topology. Magnetic properties of 1–3 were analyzed in connection with their structures, whichmore » show that there exist weak antiferromagnetic interactions between metal ions. - Graphical abstract: Three heterobimetallic MOFs were constructed through the size-selectivity of TFA coordination sites for different transition metal ions based on the concept of “Hard and Soft Acids and Bases”. Highlights: • Complexes 1 and 3 contain 2-D wave-like negative-charged layers. • Complex 2 is a 2-D neutral layer structure with a (8)₂(8⁴;12²) topology. • Complexes 1–3 are the first example of heterobimetallic MOFs based on FTA. • The coordination sites of FTA show size-selectivity to metal ions.« less

Architecture and data processing alternatives for the TSE computer. Volume 2: Extraction of topological information from an image by the Tse computer

NASA Technical Reports Server (NTRS)

Jones, J. R.; Bodenheimer, R. E.

1976-01-01

A simple programmable Tse processor organization and arithmetic operations necessary for extraction of the desired topological information are described. Hardware additions to this organization are discussed along with trade-offs peculiar to the tse computing concept. An improved organization is presented along with the complementary software for the various arithmetic operations. The performance of the two organizations is compared in terms of speed, power, and cost. Software routines developed to extract the desired information from an image are included.

Bearing diagnostics: A method based on differential geometry

NASA Astrophysics Data System (ADS)

Tian, Ye; Wang, Zili; Lu, Chen; Wang, Zhipeng

2016-12-01

The structures around bearings are complex, and the working environment is variable. These conditions cause the collected vibration signals to become nonlinear, non-stationary, and chaotic characteristics that make noise reduction, feature extraction, fault diagnosis, and health assessment significantly challenging. Thus, a set of differential geometry-based methods with superiorities in nonlinear analysis is presented in this study. For noise reduction, the Local Projection method is modified by both selecting the neighborhood radius based on empirical mode decomposition and determining noise subspace constrained by neighborhood distribution information. For feature extraction, Hessian locally linear embedding is introduced to acquire manifold features from the manifold topological structures, and singular values of eigenmatrices as well as several specific frequency amplitudes in spectrograms are extracted subsequently to reduce the complexity of the manifold features. For fault diagnosis, information geometry-based support vector machine is applied to classify the fault states. For health assessment, the manifold distance is employed to represent the health information; the Gaussian mixture model is utilized to calculate the confidence values, which directly reflect the health status. Case studies on Lorenz signals and vibration datasets of bearings demonstrate the effectiveness of the proposed methods.

Deffuant model of opinion formation in one-dimensional multiplex networks

NASA Astrophysics Data System (ADS)

Shang, Yilun

2015-10-01

Complex systems in the real world often operate through multiple kinds of links connecting their constituents. In this paper we propose an opinion formation model under bounded confidence over multiplex networks, consisting of edges at different topological and temporal scales. We determine rigorously the critical confidence threshold by exploiting probability theory and network science when the nodes are arranged on the integers, {{Z}}, evolving in continuous time. It is found that the existence of ‘multiplexity’ impedes the convergence, and that working with the aggregated or summarized simplex network is inaccurate since it misses vital information. Analytical calculations are confirmed by extensive numerical simulations.

[Architecture of receptor-operated ionic channels of biological membranes].

PubMed

Bregestovski, P D

2011-01-01

Ion channels of biological membranes are the key proteins, which provide bioelectric functioning of living systems. These proteins are homo- or heterooligomers assembled from several identical or different subunits. Understanding the architectural organization and functioning of ion channels has been significantly extended due to resolving the crystal structure of several types of voltage-gated and receptor-operated channels. This review summarizes the information obtained from crystal structures of potassium, nicotinic acetylcholine receptor, P2X, and other ligand-gated ion channels. Despite the differences in the function, topology, ionic selectivity, and the subunit stoichiometry, a high similarity in the principles of organization of these macromolecular complexes has been revealed.

Reconfigureable network node

DOEpatents

Vanderveen, Keith B [Tracy, CA; Talbot, Edward B [Livermore, CA; Mayer, Laurence E [Davis, CA

2008-04-08

Nodes in a network having a plurality of nodes establish communication links with other nodes using available transmission media, as the ability to establish such links becomes available and desirable. The nodes predict when existing communications links will fail, become overloaded or otherwise degrade network effectiveness and act to establish substitute or additional links before the node's ability to communicate with the other nodes on the network is adversely affected. A node stores network topology information and programmed link establishment rules and criteria. The node evaluates characteristics that predict existing links with other nodes becoming unavailable or degraded. The node then determines whether it can form a communication link with a substitute node, in order to maintain connectivity with the network. When changing its communication links, a node broadcasts that information to the network. Other nodes update their stored topology information and consider the updated topology when establishing new communications links for themselves.

Spatial-temporal modeling of malware propagation in networks.

PubMed

Chen, Zesheng; Ji, Chuanyi

2005-09-01

Network security is an important task of network management. One threat to network security is malware (malicious software) propagation. One type of malware is called topological scanning that spreads based on topology information. The focus of this work is on modeling the spread of topological malwares, which is important for understanding their potential damages, and for developing countermeasures to protect the network infrastructure. Our model is motivated by probabilistic graphs, which have been widely investigated in machine learning. We first use a graphical representation to abstract the propagation of malwares that employ different scanning methods. We then use a spatial-temporal random process to describe the statistical dependence of malware propagation in arbitrary topologies. As the spatial dependence is particularly difficult to characterize, the problem becomes how to use simple (i.e., biased) models to approximate the spatially dependent process. In particular, we propose the independent model and the Markov model as simple approximations. We conduct both theoretical analysis and extensive simulations on large networks using both real measurements and synthesized topologies to test the performance of the proposed models. Our results show that the independent model can capture temporal dependence and detailed topology information and, thus, outperforms the previous models, whereas the Markov model incorporates a certain spatial dependence and, thus, achieves a greater accuracy in characterizing both transient and equilibrium behaviors of malware propagation.

Direct Bio-printing with Heterogeneous Topology Design.

PubMed

Ahsan, Amm Nazmul; Xie, Ruinan; Khoda, Bashir

2017-01-01

Bio-additive manufacturing is a promising tool to fabricate porous scaffold structures for expediting the tissue regeneration processes. Unlike the most traditional bulk material objects, the microstructures of tissue and organs are mostly highly anisotropic, heterogeneous, and porous in nature. However, modelling the internal heterogeneity of tissues/organs structures in the traditional CAD environment is difficult and oftentimes inaccurate. Besides, the de facto STL conversion of bio-models introduces loss of information and piles up more errors in each subsequent step (build orientation, slicing, tool-path planning) of the bio-printing process plan. We are proposing a topology based scaffold design methodology to accurately represent the heterogeneous internal architecture of tissues/organs. An image analysis technique is used that digitizes the topology information contained in medical images of tissues/organs. A weighted topology reconstruction algorithm is implemented to represent the heterogeneity with parametric functions. The parametric functions are then used to map the spatial material distribution. The generated information is directly transferred to the 3D bio-printer and heterogeneous porous tissue scaffold structure is manufactured without STL file. The proposed methodology is implemented to verify the effectiveness of the approach and the designed example structure is bio-fabricated with a deposition based bio-additive manufacturing system.

Efficient protein targeting to the inner nuclear membrane requires Atlastin-dependent maintenance of ER topology

PubMed Central

Pawar, Sumit; Ungricht, Rosemarie; Tiefenboeck, Peter; Leroux, Jean-Christophe

2017-01-01

Newly synthesized membrane proteins are targeted to the inner nuclear membrane (INM) by diffusion within the membrane system of the endoplasmic reticulum (ER), translocation through nuclear pore complexes (NPCs) and retention on nuclear partners. Using a visual in vitro assay we previously showed that efficient protein targeting to the INM depends on nucleotide hydrolysis. We now reveal that INM targeting is GTP-dependent. Exploiting in vitro reconstitution and in vivo analysis of INM targeting, we establish that Atlastins, membrane-bound GTPases of the ER, sustain the efficient targeting of proteins to the INM by their continued activity in preserving ER topology. When ER topology is altered, the long-range diffusional exchange of proteins in the ER network and targeting efficiency to the INM are diminished. Highlighting the general importance of proper ER topology, we show that Atlastins also influence NPC biogenesis and timely exit of secretory cargo from the ER. PMID:28826471

Experiments with arbitrary networks in time-multiplexed delay systems

NASA Astrophysics Data System (ADS)

Hart, Joseph D.; Schmadel, Don C.; Murphy, Thomas E.; Roy, Rajarshi

2017-12-01

We report a new experimental approach using an optoelectronic feedback loop to investigate the dynamics of oscillators coupled on large complex networks with arbitrary topology. Our implementation is based on a single optoelectronic feedback loop with time delays. We use the space-time interpretation of systems with time delay to create large networks of coupled maps. Others have performed similar experiments using high-pass filters to implement the coupling; this restricts the network topology to the coupling of only a few nearest neighbors. In our experiment, the time delays and coupling are implemented on a field-programmable gate array, allowing the creation of networks with arbitrary coupling topology. This system has many advantages: the network nodes are truly identical, the network is easily reconfigurable, and the network dynamics occur at high speeds. We use this system to study cluster synchronization and chimera states in both small and large networks of different topologies.

Coexistence of tunable Weyl points and topological nodal lines in ternary transition-metal telluride TaIrT e4

NASA Astrophysics Data System (ADS)

Zhou, Xiaoqing; Liu, Qihang; Wu, QuanSheng; Nummy, Tom; Li, Haoxiang; Griffith, Justin; Parham, Stephen; Waugh, Justin; Emmanouilidou, Eve; Shen, Bing; Yazyev, Oleg V.; Ni, Ni; Dessau, Daniel

2018-06-01

We report a combined theoretical and experimental study on TaIrT e4 , a potential candidate for a minimal model of type-II Weyl semimetals. Unexpectedly, an intriguing node structure with 12 Weyl points and a pair of nodal lines protected by mirror symmetry was found by first-principles calculations. Some signatures of the complex electronic structure, such as topologically nontrivial band crossings and topologically trivial Fermi arcs, are cross-validated by angle-resolved photoemission spectroscopy. Through external strain, the number of Weyl points can be reduced to a theoretical minimum of four, and the appearance of the nodal lines can be switched between different mirror planes in momentum space. The coexistence of switchable Weyl points and nodal lines establishes transition-metal chalcogenides as a unique test ground for topological state characterization and engineering.

Free-fermion descriptions of parafermion chains and string-net models

NASA Astrophysics Data System (ADS)

Meichanetzidis, Konstantinos; Turner, Christopher J.; Farjami, Ashk; Papić, Zlatko; Pachos, Jiannis K.

2018-03-01

Topological phases of matter remain a focus of interest due to their unique properties: fractionalization, ground-state degeneracy, and exotic excitations. While some of these properties can occur in systems of free fermions, their emergence is generally associated with interactions between particles. Here, we quantify the role of interactions in general classes of topological states of matter in one and two spatial dimensions, including parafermion chains and string-net models. Surprisingly, we find that certain topological states can be exactly described by free fermions, while others saturate the maximum possible distance from their optimal free-fermion description [C. J. Turner et al., Nat. Commun. 8, 14926 (2017), 10.1038/ncomms14926]. Our work opens the door to understanding the complexity of topological models by establishing new types of fermionization procedures to describe their low-energy physics, thus making them amenable to experimental realizations.

Differential Models for B-Type Open-Closed Topological Landau-Ginzburg Theories

NASA Astrophysics Data System (ADS)

Babalic, Elena Mirela; Doryn, Dmitry; Lazaroiu, Calin Iuliu; Tavakol, Mehdi

2018-05-01

We propose a family of differential models for B-type open-closed topological Landau-Ginzburg theories defined by a pair (X,W), where X is any non-compact Calabi-Yau manifold and W is any holomorphic complex-valued function defined on X whose critical set is compact. The models are constructed at cochain level using smooth data, including the twisted Dolbeault algebra of polyvector-valued forms and a twisted Dolbeault category of holomorphic factorizations of W. We give explicit proposals for cochain level versions of the bulk and boundary traces and for the bulk-boundary and boundary-bulk maps of the Landau-Ginzburg theory. We prove that most of the axioms of an open-closed TFT (topological field theory) are satisfied on cohomology and conjecture that the remaining two axioms (namely non-degeneracy of bulk and boundary traces and the topological Cardy constraint) are also satisfied.

New Methodology for Known Metabolite Identification in Metabonomics/Metabolomics: Topological Metabolite Identification Carbon Efficiency (tMICE).

PubMed

Sanchon-Lopez, Beatriz; Everett, Jeremy R

2016-09-02

A new, simple-to-implement and quantitative approach to assessing the confidence in NMR-based identification of known metabolites is introduced. The approach is based on a topological analysis of metabolite identification information available from NMR spectroscopy studies and is a development of the metabolite identification carbon efficiency (MICE) method. New topological metabolite identification indices are introduced, analyzed, and proposed for general use, including topological metabolite identification carbon efficiency (tMICE). Because known metabolite identification is one of the key bottlenecks in either NMR-spectroscopy- or mass spectrometry-based metabonomics/metabolomics studies, and given the fact that there is no current consensus on how to assess metabolite identification confidence, it is hoped that these new approaches and the topological indices will find utility.

Topological protection of multiparticle dissipative transport

NASA Astrophysics Data System (ADS)

Loehr, Johannes; Loenne, Michael; Ernst, Adrian; de Las Heras, Daniel; Fischer, Thomas M.

2016-06-01

Topological protection allows robust transport of localized phenomena such as quantum information, solitons and dislocations. The transport can be either dissipative or non-dissipative. Here, we experimentally demonstrate and theoretically explain the topologically protected dissipative motion of colloidal particles above a periodic hexagonal magnetic pattern. By driving the system with periodic modulation loops of an external and spatially homogeneous magnetic field, we achieve total control over the motion of diamagnetic and paramagnetic colloids. We can transport simultaneously and independently each type of colloid along any of the six crystallographic directions of the pattern via adiabatic or deterministic ratchet motion. Both types of motion are topologically protected. As an application, we implement an automatic topologically protected quality control of a chemical reaction between functionalized colloids. Our results are relevant to other systems with the same symmetry.

Exploring the free-energy landscape of carbohydrate-protein complexes: development and validation of scoring functions considering the binding-site topology

NASA Astrophysics Data System (ADS)

Eid, Sameh; Saleh, Noureldin; Zalewski, Adam; Vedani, Angelo

2014-12-01

Carbohydrates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the structure-based design of carbohydrate-based ligands. We assembled a diverse data set comprising 273 carbohydrate-protein crystal structures with known binding affinity and evaluated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as combinations thereof. Unfortunately, the tested functions were not capable of reproducing binding affinities in the studied complexes. To simplify the complex free-energy surface of carbohydrate-protein systems, we classified the studied proteins according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classification scheme reproduced binding affinities in the carbohydrate data set with an r 2 of 0.71 and root-mean-squared-error of 1.25 kcal/mol ( N = 236). The improvement in model performance underlines the significance of the differences in the local micro-environments of carbohydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and solvent exposure.

Software and Dataware for Energy Generation and Consumption Analysis System of Gas Processing Enterprises

NASA Astrophysics Data System (ADS)

Dolotovskii, I. V.; Dolotovskaya, N. V.; Larin, E. A.

2018-05-01

The article presents the architecture and content of a specialized analytical system for monitoring operational conditions, planning of consumption and generation of energy resources, long-term planning of production activities and development of a strategy for the development of the energy complex of gas processing enterprises. A compositional model of structured data on the equipment of the main systems of the power complex is proposed. The correctness of the use of software modules and the database of the analytical system is confirmed by comparing the results of measurements on the equipment of the electric power system and simulation at the operating gas processing plant. A high accuracy in the planning of consumption of fuel and energy resources has been achieved (the error does not exceed 1%). Information and program modules of the analytical system allow us to develop a strategy for improving the energy complex in the face of changing technological topology and partial uncertainty of economic factors.

Reversibility in Quantum Models of Stochastic Processes

NASA Astrophysics Data System (ADS)

Gier, David; Crutchfield, James; Mahoney, John; James, Ryan

Natural phenomena such as time series of neural firing, orientation of layers in crystal stacking and successive measurements in spin-systems are inherently probabilistic. The provably minimal classical models of such stochastic processes are ɛ-machines, which consist of internal states, transition probabilities between states and output values. The topological properties of the ɛ-machine for a given process characterize the structure, memory and patterns of that process. However ɛ-machines are often not ideal because their statistical complexity (Cμ) is demonstrably greater than the excess entropy (E) of the processes they represent. Quantum models (q-machines) of the same processes can do better in that their statistical complexity (Cq) obeys the relation Cμ >= Cq >= E. q-machines can be constructed to consider longer lengths of strings, resulting in greater compression. With code-words of sufficiently long length, the statistical complexity becomes time-symmetric - a feature apparently novel to this quantum representation. This result has ramifications for compression of classical information in quantum computing and quantum communication technology.

A pathway-based network analysis of hypertension-related genes

NASA Astrophysics Data System (ADS)

Wang, Huan; Hu, Jing-Bo; Xu, Chuan-Yun; Zhang, De-Hai; Yan, Qian; Xu, Ming; Cao, Ke-Fei; Zhang, Xu-Sheng

2016-02-01

Complex network approach has become an effective way to describe interrelationships among large amounts of biological data, which is especially useful in finding core functions and global behavior of biological systems. Hypertension is a complex disease caused by many reasons including genetic, physiological, psychological and even social factors. In this paper, based on the information of biological pathways, we construct a network model of hypertension-related genes of the salt-sensitive rat to explore the interrelationship between genes. Statistical and topological characteristics show that the network has the small-world but not scale-free property, and exhibits a modular structure, revealing compact and complex connections among these genes. By the threshold of integrated centrality larger than 0.71, seven key hub genes are found: Jun, Rps6kb1, Cycs, Creb312, Cdk4, Actg1 and RT1-Da. These genes should play an important role in hypertension, suggesting that the treatment of hypertension should focus on the combination of drugs on multiple genes.

Biogenic manganese oxide nanoparticle formation by a multimeric multicopper oxidase Mnx

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

Romano, Christine A.; Zhou, Mowei; Song, Yang

Bacteria that produce Mn oxides are extraordinarily skilled engineers of nanomaterials that contribute significantly to global biogeochemical cycles. Their enzyme-based reaction mechanisms may be genetically tailored for environmental remediation applications or bioenergy production. However, significant challenges exist for structural characterization of the enzymes responsible for biomineralization. The active Mn oxidase, Mnx, in Bacillus sp. PL-12 is a complex composed of a multicopper oxidase (MCO), MnxG, and two accessory proteins MnxE and MnxF. MnxG shares sequence similarity with other, structurally characterized MCOs. However, MnxE and MnxF have no similarity to any characterized proteins. The ~200 kDa complex has been recalcitrant tomore » crystallization, so its structure is unknown. In this study, native mass spectrometry defines the subunit topology and copper binding of the Mnx complex, while high resolution electron microscopy visualizes the protein and nascent Mn oxide minerals. These data provide critical structural information for conceptualizing how Mnx produces nanoparticulate Mn oxides.« less

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

Sensitivity and network topology in chemical reaction systems

NASA Astrophysics Data System (ADS)

Okada, Takashi; Mochizuki, Atsushi

2017-08-01

In living cells, biochemical reactions are catalyzed by specific enzymes and connect to one another by sharing substrates and products, forming complex networks. In our previous studies, we established a framework determining the responses to enzyme perturbations only from network topology, and then proved a theorem, called the law of localization, explaining response patterns in terms of network topology. In this paper, we generalize these results to reaction networks with conserved concentrations, which allows us to study any reaction system. We also propose network characteristics quantifying robustness. We compare E. coli metabolic network with randomly rewired networks, and find that the robustness of the E. coli network is significantly higher than that of the random networks.

Optimal Topology of Aircraft Rib and Spar Structures under Aeroelastic Loads

NASA Technical Reports Server (NTRS)

Stanford, Bret K.; Dunning, Peter D.

2014-01-01

Several topology optimization problems are conducted within the ribs and spars of a wing box. It is desired to locate the best position of lightening holes, truss/cross-bracing, etc. A variety of aeroelastic metrics are isolated for each of these problems: elastic wing compliance under trim loads and taxi loads, stress distribution, and crushing loads. Aileron effectiveness under a constant roll rate is considered, as are dynamic metrics: natural vibration frequency and flutter. This approach helps uncover the relationship between topology and aeroelasticity in subsonic transport wings, and can therefore aid in understanding the complex aircraft design process which must eventually consider all these metrics and load cases simultaneously.

Sampled-data consensus in switching networks of integrators based on edge events

NASA Astrophysics Data System (ADS)

Xiao, Feng; Meng, Xiangyu; Chen, Tongwen

2015-02-01

This paper investigates the event-driven sampled-data consensus in switching networks of multiple integrators and studies both the bidirectional interaction and leader-following passive reaction topologies in a unified framework. In these topologies, each information link is modelled by an edge of the information graph and assigned a sequence of edge events, which activate the mutual data sampling and controller updates of the two linked agents. Two kinds of edge-event-detecting rules are proposed for the general asynchronous data-sampling case and the synchronous periodic event-detecting case. They are implemented in a distributed fashion, and their effectiveness in reducing communication costs and solving consensus problems under a jointly connected topology condition is shown by both theoretical analysis and simulation examples.

Extended Maptree: a Representation of Fine-Grained Topology and Spatial Hierarchy of Bim

NASA Astrophysics Data System (ADS)

Wu, Y.; Shang, J.; Hu, X.; Zhou, Z.

2017-09-01

Spatial queries play significant roles in exchanging Building Information Modeling (BIM) data and integrating BIM with indoor spatial information. However, topological operators implemented for BIM spatial queries are limited to qualitative relations (e.g. touching, intersecting). To overcome this limitation, we propose an extended maptree model to represent the fine-grained topology and spatial hierarchy of indoor spaces. The model is based on a maptree which consists of combinatorial maps and an adjacency tree. Topological relations (e.g., adjacency, incidence, and covering) derived from BIM are represented explicitly and formally by extended maptrees, which can facilitate the spatial queries of BIM. To construct an extended maptree, we first use a solid model represented by vertical extrusion and boundary representation to generate the isolated 3-cells of combinatorial maps. Then, the spatial relationships defined in IFC are used to sew them together. Furthermore, the incremental edges of extended maptrees are labeled as removed 2-cells. Based on this, we can merge adjacent 3-cells according to the spatial hierarchy of IFC.

Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model

PubMed Central

McDonnell, Mark D.; Ward, Lawrence M.

2014-01-01

Abstract Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity. PMID:24743633

Scale-space measures for graph topology link protein network architecture to function.

PubMed

Hulsman, Marc; Dimitrakopoulos, Christos; de Ridder, Jeroen

2014-06-15

The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA © The Author 2014. Published by Oxford University Press.

Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks.

PubMed

Honegger, Thibault; Thielen, Moritz I; Feizi, Soheil; Sanjana, Neville E; Voldman, Joel

2016-06-22

The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry.

Microfluidic neurite guidance to study structure-function relationships in topologically-complex population-based neural networks

NASA Astrophysics Data System (ADS)

Honegger, Thibault; Thielen, Moritz I.; Feizi, Soheil; Sanjana, Neville E.; Voldman, Joel

2016-06-01

The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry.

Self-organization in multilayer network with adaptation mechanisms based on competition

NASA Astrophysics Data System (ADS)

Pitsik, Elena N.; Makarov, Vladimir V.; Nedaivozov, Vladimir O.; Kirsanov, Daniil V.; Goremyko, Mikhail V.

2018-04-01

The paper considers the phenomena of competition in multiplex network whose structure evolves corresponding to dynamics of it's elements, forming closed loop of self-learning with the aim to reach the optimal topology. Numerical analysis of proposed model shows that it is possible to obtain scale-invariant structures for corresponding parameters as well as the structures with homogeneous distribution of connections in the layers. Revealed phenomena emerges as the consequence of the self-organization processes related to structure-dynamical selflearning based on homeostasis and homophily, as well as the result of the competition between the network's layers for optimal topology. It was shown that in the mode of partial and cluster synchronization the network reaches scale-free topology of complex nature that is different from layer to layer. However, in the mode of global synchronization the homogeneous topologies on all layer of the network are observed. This phenomenon is tightly connected with the competitive processes that represent themselves as the natural mechanism of reaching the optimal topology of the links in variety of real-world systems.

Topological Alterations of the Intrinsic Brain Network in Patients with Functional Dyspepsia.

PubMed

Nan, Jiaofen; Zhang, Li; Zhu, Fubao; Tian, Xiaorui; Zheng, Qian; Deneen, Karen M von; Liu, Jixin; Zhang, Ming

2016-01-31

Previous studies reported that integrated information in the brain ultimately determines the subjective experience of patients with chronic pain, but how the information is integrated in the brain connectome of functional dyspepsia (FD) patients remains largely unclear. The study aimed to quantify the topological changes of the brain network in FD patients. Small-world properties, network efficiency and nodal centrality were utilized to measure the changes in topological architecture in 25 FD patients and 25 healthy controls based on functional magnetic resonance imaging. Pearson's correlation assessed the relationship of each topological property with clinical symptoms. FD patients showed an increase of clustering coefficients and local efficiency relative to controls from the perspective of a whole network as well as elevated nodal centrality in the right orbital part of the inferior frontal gyrus, left anterior cingulate gyrus and left hippocampus, and decreased nodal centrality in the right posterior cingulate gyrus, left cuneus, right putamen, left middle occipital gyrus and right inferior occipital gyrus. Moreover, the centrality in the anterior cingulate gyrus was significantly associated with symptom severity and duration in FD patients. Nevertheless, the inclusion of anxiety and depression scores as covariates erased the group differences in nodal centralities in the orbital part of the inferior frontal gyrus and hippocampus. The results suggest topological disruption of the functional brain networks in FD patients, presumably in response to disturbances of sensory information integrated with emotion, memory, pain modulation, and selective attention in patients.

Gapless topological order, gravity, and black holes

NASA Astrophysics Data System (ADS)

Rasmussen, Alex; Jermyn, Adam S.

2018-04-01

In this work we demonstrate that linearized gravity exhibits gapless topological order with an extensive ground state degeneracy. This phenomenon is closely related both to the topological order of the pyrochlore U (1 ) spin liquid and to recent work by Hawking and co-workers, who used the soft-photon and graviton theorems to demonstrate that the vacuum in linearized gravity is not unique. We first consider lattice models whose low-energy behavior is described by electromagnetism and linearized gravity, and then argue that the topological nature of these models carries over into the continuum. We demonstrate that these models can have many ground states without making assumptions about the topology of spacetime or about the high-energy nature of the theory, and show that the infinite family of symmetries described by Hawking and co-workers is simply the different topological sectors. We argue that in this context black holes appear as topological defects in the infrared theory, and that this suggests a potential approach to understanding both the firewall paradox and information encoding in gravitational theories. Finally, we use insights from the soft-boson theorems to make connections between deconfined gauge theories with continuous gauge groups and gapless topological order.

Real-space mapping of topological invariants using artificial neural networks

NASA Astrophysics Data System (ADS)

Carvalho, D.; García-Martínez, N. A.; Lado, J. L.; Fernández-Rossier, J.

2018-03-01

Topological invariants allow one to characterize Hamiltonians, predicting the existence of topologically protected in-gap modes. Those invariants can be computed by tracing the evolution of the occupied wave functions under twisted boundary conditions. However, those procedures do not allow one to calculate a topological invariant by evaluating the system locally, and thus require information about the wave functions in the whole system. Here we show that artificial neural networks can be trained to identify the topological order by evaluating a local projection of the density matrix. We demonstrate this for two different models, a one-dimensional topological superconductor and a two-dimensional quantum anomalous Hall state, both with spatially modulated parameters. Our neural network correctly identifies the different topological domains in real space, predicting the location of in-gap states. By combining a neural network with a calculation of the electronic states that uses the kernel polynomial method, we show that the local evaluation of the invariant can be carried out by evaluating a local quantity, in particular for systems without translational symmetry consisting of tens of thousands of atoms. Our results show that supervised learning is an efficient methodology to characterize the local topology of a system.

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

Zhang, Zuocheng; Feng, Xiao; Wang, Jing

The interplay between magnetism and topology, as exemplified in the magnetic skyrmion systems, has emerged as a rich playground for finding novel quantum phenomena and applications in future information technology. Magnetic topological insulators (TI) have attracted much recent attention, especially after the experimental realization of quantum anomalous Hall effect. Future applications of magnetic TI hinge on the accurate manipulation of magnetism and topology by external perturbations, preferably with a gate electric field. In this work, we investigate the magneto transport properties of Cr doped Bi 2(Se xTe 1-x) 3 TI across the topological quantum critical point (QCP). We find thatmore » the external gate voltage has negligible effect on the magnetic order for samples far away from the topological QCP. However, for the sample near the QCP, we observe a ferromagnetic (FM) to paramagnetic (PM) phase transition driven by the gate electric field. Theoretical calculations show that a perpendicular electric field causes a shift of electronic energy levels due to the Stark effect, which induces a topological quantum phase transition and consequently a magnetic phase transition. Finally, the in situ electrical control of the topological and magnetic properties of TI shed important new lights on future topological electronic or spintronic device applications.« less

Two-dimensional topological photonic systems

NASA Astrophysics Data System (ADS)

Sun, Xiao-Chen; He, Cheng; Liu, Xiao-Ping; Lu, Ming-Hui; Zhu, Shi-Ning; Chen, Yan-Feng

2017-09-01

The topological phase of matter, originally proposed and first demonstrated in fermionic electronic systems, has drawn considerable research attention in the past decades due to its robust transport of edge states and its potential with respect to future quantum information, communication, and computation. Recently, searching for such a unique material phase in bosonic systems has become a hot research topic worldwide. So far, many bosonic topological models and methods for realizing them have been discovered in photonic systems, acoustic systems, mechanical systems, etc. These discoveries have certainly yielded vast opportunities in designing material phases and related properties in the topological domain. In this review, we first focus on some of the representative photonic topological models and employ the underlying Dirac model to analyze the edge states and geometric phase. On the basis of these models, three common types of two-dimensional topological photonic systems are discussed: 1) photonic quantum Hall effect with broken time-reversal symmetry; 2) photonic topological insulator and the associated pseudo-time-reversal symmetry-protected mechanism; 3) time/space periodically modulated photonic Floquet topological insulator. Finally, we provide a summary and extension of this emerging field, including a brief introduction to the Weyl point in three-dimensional systems.

Object-oriented Persistent Homology

PubMed Central

Wang, Bao; Wei, Guo-Wei

2015-01-01

Persistent homology provides a new approach for the topological simplification of big data via measuring the life time of intrinsic topological features in a filtration process and has found its success in scientific and engineering applications. However, such a success is essentially limited to qualitative data classification and analysis. Indeed, persistent homology has rarely been employed for quantitative modeling and prediction. Additionally, the present persistent homology is a passive tool, rather than a proactive technique, for classification and analysis. In this work, we outline a general protocol to construct object-oriented persistent homology methods. By means of differential geometry theory of surfaces, we construct an objective functional, namely, a surface free energy defined on the data of interest. The minimization of the objective functional leads to a Laplace-Beltrami operator which generates a multiscale representation of the initial data and offers an objective oriented filtration process. The resulting differential geometry based object-oriented persistent homology is able to preserve desirable geometric features in the evolutionary filtration and enhances the corresponding topological persistence. The cubical complex based homology algorithm is employed in the present work to be compatible with the Cartesian representation of the Laplace-Beltrami flow. The proposed Laplace-Beltrami flow based persistent homology method is extensively validated. The consistence between Laplace-Beltrami flow based filtration and Euclidean distance based filtration is confirmed on the Vietoris-Rips complex for a large amount of numerical tests. The convergence and reliability of the present Laplace-Beltrami flow based cubical complex filtration approach are analyzed over various spatial and temporal mesh sizes. The Laplace-Beltrami flow based persistent homology approach is utilized to study the intrinsic topology of proteins and fullerene molecules. Based on a quantitative model which correlates the topological persistence of fullerene central cavity with the total curvature energy of the fullerene structure, the proposed method is used for the prediction of fullerene isomer stability. The efficiency and robustness of the present method are verified by more than 500 fullerene molecules. It is shown that the proposed persistent homology based quantitative model offers good predictions of total curvature energies for ten types of fullerene isomers. The present work offers the first example to design object-oriented persistent homology to enhance or preserve desirable features in the original data during the filtration process and then automatically detect or extract the corresponding topological traits from the data. PMID:26705370

Extracting insights from the shape of complex data using topology

PubMed Central

Lum, P. Y.; Singh, G.; Lehman, A.; Ishkanov, T.; Vejdemo-Johansson, M.; Alagappan, M.; Carlsson, J.; Carlsson, G.

2013-01-01

This paper applies topological methods to study complex high dimensional data sets by extracting shapes (patterns) and obtaining insights about them. Our method combines the best features of existing standard methodologies such as principal component and cluster analyses to provide a geometric representation of complex data sets. Through this hybrid method, we often find subgroups in data sets that traditional methodologies fail to find. Our method also permits the analysis of individual data sets as well as the analysis of relationships between related data sets. We illustrate the use of our method by applying it to three very different kinds of data, namely gene expression from breast tumors, voting data from the United States House of Representatives and player performance data from the NBA, in each case finding stratifications of the data which are more refined than those produced by standard methods. PMID:23393618

Extracting insights from the shape of complex data using topology.

PubMed

Lum, P Y; Singh, G; Lehman, A; Ishkanov, T; Vejdemo-Johansson, M; Alagappan, M; Carlsson, J; Carlsson, G

2013-01-01

This paper applies topological methods to study complex high dimensional data sets by extracting shapes (patterns) and obtaining insights about them. Our method combines the best features of existing standard methodologies such as principal component and cluster analyses to provide a geometric representation of complex data sets. Through this hybrid method, we often find subgroups in data sets that traditional methodologies fail to find. Our method also permits the analysis of individual data sets as well as the analysis of relationships between related data sets. We illustrate the use of our method by applying it to three very different kinds of data, namely gene expression from breast tumors, voting data from the United States House of Representatives and player performance data from the NBA, in each case finding stratifications of the data which are more refined than those produced by standard methods.

Transportation Network Topologies

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia (Editor)

2004-01-01

The existing U.S. hub-and-spoke air transportation system is reaching saturation. Major aspects of the current system, such as capacity, safety, mobility, customer satisfaction, security, communications, and ecological effects, require improvements. The changing dynamics - increased presence of general aviation, unmanned autonomous vehicles, military aircraft in civil airspace as part of homeland defense - contributes to growing complexity of airspace. The system has proven remarkably resistant to change. NASA Langley Research Center and the National Institute of Aerospace conducted a workshop on Transportation Network Topologies on 9-10 December 2003 in Williamsburg, Virginia. The workshop aimed to examine the feasibility of traditional methods for complex system analysis and design as well as potential novel alternatives in application to transportation systems, identify state-of-the-art models and methods, conduct gap analysis, and thus to lay a foundation for establishing a focused research program in complex systems applied to air transportation.

Electronic structure and acid-base properties of Kojic acid and its dimers. A DFT and quantum topology study

NASA Astrophysics Data System (ADS)

Aziz, Saadullah G.; Alyoubi, Abdulrahman O.; Elroby, Shaaban A.; Hilal, Rifaat H.

2017-10-01

Kojic acid is a polyfunctional heterocyclic compound, with several important reaction centres; it has a wide range of applications in the cosmetic, medicine, food, agriculture and chemical industries. The present study aims at better insight into its electronic structure and bonding characteristics. Thus, density functional theory at the M06-2x /6-311++G** level of theory is used to investigate its ground state electronic and acid-base properties. Protonation and deprotonation enthalpies are computed and analysed. The ability of Kojic acid to form both water complexes and dimers is explored. Several different complexes and dimer structures were examined. Natural bond order and quantum topology features of the charge density were analysed. The origin of the stability of the studied complexes and dimer structures can be traced to hydrogen bonding, π-conjugative and non-covalent dispersive interactions.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

PubMed

Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

PubMed Central

Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%. PMID:29387141

Genomic analysis of regulatory network dynamics reveals large topological changes

NASA Astrophysics Data System (ADS)

Luscombe, Nicholas M.; Madan Babu, M.; Yu, Haiyuan; Snyder, Michael; Teichmann, Sarah A.; Gerstein, Mark

2004-09-01

Network analysis has been applied widely, providing a unifying language to describe disparate systems ranging from social interactions to power grids. It has recently been used in molecular biology, but so far the resulting networks have only been analysed statically. Here we present the dynamics of a biological network on a genomic scale, by integrating transcriptional regulatory information and gene-expression data for multiple conditions in Saccharomyces cerevisiae. We develop an approach for the statistical analysis of network dynamics, called SANDY, combining well-known global topological measures, local motifs and newly derived statistics. We uncover large changes in underlying network architecture that are unexpected given current viewpoints and random simulations. In response to diverse stimuli, transcription factors alter their interactions to varying degrees, thereby rewiring the network. A few transcription factors serve as permanent hubs, but most act transiently only during certain conditions. By studying sub-network structures, we show that environmental responses facilitate fast signal propagation (for example, with short regulatory cascades), whereas the cell cycle and sporulation direct temporal progression through multiple stages (for example, with highly inter-connected transcription factors). Indeed, to drive the latter processes forward, phase-specific transcription factors inter-regulate serially, and ubiquitously active transcription factors layer above them in a two-tiered hierarchy. We anticipate that many of the concepts presented here-particularly the large-scale topological changes and hub transience-will apply to other biological networks, including complex sub-systems in higher eukaryotes.

Band warping, band non-parabolicity, and Dirac points in electronic and lattice structures

NASA Astrophysics Data System (ADS)

Resca, Lorenzo; Mecholsky, Nicholas A.; Pegg, Ian L.

2017-10-01

We illustrate at a fundamental level the physical and mathematical origins of band warping and band non-parabolicity in electronic and vibrational structures. We point out a robust presence of pairs of topologically induced Dirac points in a primitive-rectangular lattice using a p-type tight-binding approximation. We analyze two-dimensional primitive-rectangular and square Bravais lattices with implications that are expected to generalize to more complex structures. Band warping is shown to arise at the onset of a singular transition to a crystal lattice with a larger symmetry group, which allows the possibility of irreducible representations of higher dimensions, hence band degeneracy, at special symmetry points in reciprocal space. Band warping is incompatible with a multi-dimensional Taylor series expansion, whereas band non-parabolicities are associated with multi-dimensional Taylor series expansions to all orders. Still band non-parabolicities may merge into band warping at the onset of a larger symmetry group. Remarkably, while still maintaining a clear connection with that merging, band non-parabolicities may produce pairs of conical intersections at relatively low-symmetry points. Apparently, such conical intersections are robustly maintained by global topology requirements, rather than any local symmetry protection. For two p-type tight-binding bands, we find such pairs of conical intersections drifting along the edges of restricted Brillouin zones of primitive-rectangular Bravais lattices as lattice constants vary relatively to each other, until these conical intersections merge into degenerate warped bands at high-symmetry points at the onset of a square lattice. The conical intersections that we found appear to have similar topological characteristics as Dirac points extensively studied in graphene and other topological insulators, even though our conical intersections have none of the symmetry complexity and protection afforded by the latter more complex structures.

Thermalization of topological entropy after a quantum quench

NASA Astrophysics Data System (ADS)

Zeng, Yu; Hamma, Alioscia; Fan, Heng

2016-09-01

Topologically ordered quantum phases are robust in the sense that perturbations in the Hamiltonian of the system will not change the topological nature of the ground-state wave function. However, in order to exploit topological order for applications such as self-correcting quantum memories and information processing, these states need to be also robust both dynamically and at finite temperature in the presence of an environment. It is well known that systems like the toric code in two spatial dimensions are fragile in temperature. In this paper, we show a completely analytic treatment of the toric code away from equilibrium, after a quantum quench of the system Hamiltonian. We show that, despite being subject to unitary evolution (and at zero temperature), the long-time behavior of the topological entropy is thermal, therefore vanishing. If the quench preserves a local gauge structure, there is a residual long-lived topological entropy. This also is the thermal behavior in presence of such gauge constraints. The result is obtained by studying the time evolution of the topological 2-Rényi entropy in a fully analytical, exact way.

Infinite family of three-dimensional Floquet topological paramagnets

NASA Astrophysics Data System (ADS)

Potter, Andrew C.; Vishwanath, Ashvin; Fidkowski, Lukasz

2018-06-01

We uncover an infinite family of time-reversal symmetric 3 d interacting topological insulators of bosons or spins, in time-periodically driven systems, which we term Floquet topological paramagnets (FTPMs). These FTPM phases exhibit intrinsically dynamical properties that could not occur in thermal equilibrium and are governed by an infinite set of Z2-valued topological invariants, one for each prime number. The topological invariants are physically characterized by surface magnetic domain walls that act as unidirectional quantum channels, transferring quantized packets of information during each driving period. We construct exactly solvable models realizing each of these phases, and discuss the anomalous dynamics of their topologically protected surface states. Unlike previous encountered examples of Floquet SPT phases, these 3 d FTPMs are not captured by group cohomology methods and cannot be obtained from equilibrium classifications simply by treating the discrete time translation as an ordinary symmetry. The simplest such FTPM phase can feature anomalous Z2 (toric code) surface topological order, in which the gauge electric and magnetic excitations are exchanged in each Floquet period, which cannot occur in a pure 2 d system without breaking time reversal symmetry.

Phase coherent transport in hybrid superconductor-topological insulator devices

NASA Astrophysics Data System (ADS)

Finck, Aaron

2015-03-01

Heterostructures of superconductors and topological insulators are predicted to host unusual zero energy bound states known as Majorana fermions, which can robustly store and process quantum information. Here, I will discuss our studies of such heterostructures through phase-coherent transport, which can act as a unique probe of Majorana fermions. We have extensively explored topological insulator Josephson junctions through SQUID and single-junction diffraction patterns, whose unusual behavior give evidence for low-energy Andreev bound states. In topological insulator devices with closely spaced normal and superconducting leads, we observe prominent Fabry-Perot oscillations, signifying gate-tunable, quasi-ballistic transport that can elegantly interact with Andreev reflection. Superconducting disks deposited on the surface of a topological insulator generate Aharonov-Bohm-like oscillations, giving evidence for unusual states lying near the interface between the superconductor and topological insulator surface. Our results point the way towards sophisticated interferometers that can detect and read out the state of Majorana fermions in topological systems. This work was done in collaboration with Cihan Kurter, Yew San Hor, and Dale Van Harlingen. We acknowledge funding from Microsoft Project Q.

Spatiotemporal Dynamics and Fitness Analysis of Global Oil Market: Based on Complex Network

PubMed Central

Wang, Minggang; Fang, Guochang; Shao, Shuai

2016-01-01

We study the overall topological structure properties of global oil trade network, such as degree, strength, cumulative distribution, information entropy and weight clustering. The structural evolution of the network is investigated as well. We find the global oil import and export networks do not show typical scale-free distribution, but display disassortative property. Furthermore, based on the monthly data of oil import values during 2005.01–2014.12, by applying random matrix theory, we investigate the complex spatiotemporal dynamic from the country level and fitness evolution of the global oil market from a demand-side analysis. Abundant information about global oil market can be obtained from deviating eigenvalues. The result shows that the oil market has experienced five different periods, which is consistent with the evolution of country clusters. Moreover, we find the changing trend of fitness function agrees with that of gross domestic product (GDP), and suggest that the fitness evolution of oil market can be predicted by forecasting GDP values. To conclude, some suggestions are provided according to the results. PMID:27706147

Towards A Topological Framework for Integrating Semantic Information Sources

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

Joslyn, Cliff A.; Hogan, Emilie A.; Robinson, Michael

2014-09-07

In this position paper we argue for the role that topological modeling principles can play in providing a framework for sensor integration. While used successfully in standard (quantitative) sensors, we are developing this methodology in new directions to make it appropriate specifically for semantic information sources, including keyterms, ontology terms, and other general Boolean, categorical, ordinal, and partially-ordered data types. We illustrate the basics of the methodology in an extended use case/example, and discuss path forward.

Anonymous broadcasting of classical information with a continuous-variable topological quantum code

NASA Astrophysics Data System (ADS)

Menicucci, Nicolas C.; Baragiola, Ben Q.; Demarie, Tommaso F.; Brennen, Gavin K.

2018-03-01

Broadcasting information anonymously becomes more difficult as surveillance technology improves, but remarkably, quantum protocols exist that enable provably traceless broadcasting. The difficulty is making scalable entangled resource states that are robust to errors. We propose an anonymous broadcasting protocol that uses a continuous-variable surface-code state that can be produced using current technology. High squeezing enables large transmission bandwidth and strong anonymity, and the topological nature of the state enables local error mitigation.

Topology polymorphism graph for lung tumor segmentation in PET-CT images.

PubMed

Cui, Hui; Wang, Xiuying; Zhou, Jianlong; Eberl, Stefan; Yin, Yong; Feng, Dagan; Fulham, Michael

2015-06-21

Accurate lung tumor segmentation is problematic when the tumor boundary or edge, which reflects the advancing edge of the tumor, is difficult to discern on chest CT or PET. We propose a 'topo-poly' graph model to improve identification of the tumor extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the tumor from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate tumor delineation. We validated our method on 40 patients with non-small cell lung cancer where the tumors were manually delineated by a clinical expert. The studies were separated into an 'isolated' group (n = 20) where the lung tumor was located in the lung parenchyma and away from associated structures / tissues in the thorax and a 'complex' group (n = 20) where the tumor abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dice's similarity coefficient (DSC) to measure the spatial volume overlap and Hausdorff distance (HD) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881 ± 0.046 and HD of 5.311 ± 3.022 mm for the isolated cases and DSC of 0.870 ± 0.038 and HD of 9.370 ± 3.169 mm for the complex cases. Student's t-test showed that our model outperformed the other methods (p-values <0.05).

Enhanced collective influence: A paradigm to optimize network disruption

NASA Astrophysics Data System (ADS)

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

2017-04-01

The function of complex networks typically relies on the integrity of underlying structure. Sometimes, practical applications need to attack networks' function, namely inactivate and fragment networks' underlying structure. To effectively dismantle complex networks and regulate the function of them, a centrality measure, named CI (Morone and Makse, 2015), was proposed for node ranking. We observe that the performance of CI centrality in network disruption problem may deteriorate when it is used in networks with different topology properties. Specifically, the structural features of local network topology are overlooked in CI centrality, even though the local network topology of the nodes with a fixed CI value may have very different organization. To improve the ranking accuracy of CI, this paper proposes a variant ECI to CI by considering loop density and degree diversity of local network topology. And the proposed ECI centrality would degenerate into CI centrality with the reduction of the loop density and the degree diversity level. By comparing ECI with CI and classical centrality measures in both synthetic and real networks, the experimental results suggest that ECI can largely improve the performance of CI for network disruption. Based on the results, we analyze the correlation between the improvement and the properties of the networks. We find that the performance of ECI is positively correlated with assortative coefficient and community modularity and negatively correlated with degree inequality of networks, which can be used as guidance for practical applications.

Separator Reconnection at Earth's Dayside Magnetopause and the Tail: MMS Observations Compared to Global 3D Simulations

NASA Astrophysics Data System (ADS)

Buzulukova, N.; Dorelli, J.; Glocer, A.

2017-12-01

We present the results of global high resolution resistive magnetohydrodynamics (MHD BATS-R-US) simulations of Earth's magnetosphere. We extract location of magnetic separators with RECONX tool and compare the results with observations from the Magnetospheric Multiscale (MMS). A few cases are analysed including a southward IMF magnetopause crossing during October 16, 2015 that was previously identified as an electron diffusion region (EDR) event. The simulation predicts a complex time-dependent magnetic topology consisting of multiple separators and flux ropes. Despite the topological complexity, the predicted distance between MMS and the primary separator is less than 0.5 Earth radii. The simulation shows that the existence of IMF Bx results in a duskward shift of the location of the topological separator. The results are explained by a combined effect of solar wind draping and pile-up effect that modify the current density across the magnetopause and affect the location of the separator. The RECONX tool also is used to extract the separator location in the geomagnetic tail, and relate transient tail structures (bursty bulk flows) to the location of separator. These results suggest that global magnetic topology, rather than local magnetic geometry alone, determines the location of the separator reconnection both at the dayside magnetopause and in the tail. We show that the resistive MHD model helps to understand the global context of local MMS observations.

Chiralities of spiral waves and their transitions.

PubMed

Pan, Jun-ting; Cai, Mei-chun; Li, Bing-wei; Zhang, Hong

2013-06-01

The chiralities of spiral waves usually refer to their rotation directions (the turning orientations of the spiral temporal movements as time elapses) and their curl directions (the winding orientations of the spiral spatial geometrical structures themselves). Traditionally, they are the same as each other. Namely, they are both clockwise or both counterclockwise. Moreover, the chiralities are determined by the topological charges of spiral waves, and thus they are conserved quantities. After the inwardly propagating spirals were experimentally observed, the relationship between the chiralities and the one between the chiralities and the topological charges are no longer preserved. The chiralities thus become more complex than ever before. As a result, there is now a desire to further study them. In this paper, the chiralities and their transition properties for all kinds of spiral waves are systemically studied in the framework of the complex Ginzburg-Landau equation, and the general relationships both between the chiralities and between the chiralities and the topological charges are obtained. The investigation of some other models, such as the FitzHugh-Nagumo model, the nonuniform Oregonator model, the modified standard model, etc., is also discussed for comparison.

Optomechanical performance of 3D-printed mirrors with embedded cooling channels and substructures

NASA Astrophysics Data System (ADS)

Mici, Joni; Rothenberg, Bradley; Brisson, Erik; Wicks, Sunny; Stubbs, David M.

2015-09-01

Advances in 3D printing technology allow for the manufacture of topologically complex parts not otherwise feasible through conventional manufacturing methods. Maturing metal and ceramic 3D printing technologies are becoming more adept at printing complex shapes, enabling topologically intricate mirror substrates. One application area that can benefit from additive manufacturing is reflective optics used in high energy laser (HEL) systems that require materials with a low coefficient of thermal expansion (CTE), high specific stiffness, and (most importantly) high thermal conductivity to effectively dissipate heat from the optical surface. Currently, the limits of conventional manufacturing dictate the topology of HEL optics to be monolithic structures that rely on passive cooling mechanisms and high reflectivity coatings to withstand laser damage. 3D printing enables the manufacture of embedded cooling channels in metallic mirror substrates to allow for (1) active cooling and (2) tunable structures. This paper describes the engineering and analysis of an actively cooled composite optical structure to demonstrate the potential of 3D printing on the improvement of optomechanical systems.

X-ray fluorescence microscopy artefacts in elemental maps of topologically complex samples: Analytical observations, simulation and a map correction method

NASA Astrophysics Data System (ADS)

Billè, Fulvio; Kourousias, George; Luchinat, Enrico; Kiskinova, Maya; Gianoncelli, Alessandra

2016-08-01

XRF spectroscopy is among the most widely used non-destructive techniques for elemental analysis. Despite the known angular dependence of X-ray fluorescence (XRF), topological artefacts remain an unresolved issue when using X-ray micro- or nano-probes. In this work we investigate the origin of the artefacts in XRF imaging of topologically complex samples, which are unresolved problems in studies of organic matter due to the limited travel distances of low energy XRF emission from the light elements. In particular we mapped Human Embryonic Kidney (HEK293T) cells. The exemplary results with biological samples, obtained with a soft X-ray scanning microscope installed at a synchrotron facility were used for testing a mathematical model based on detector response simulations, and for proposing an artefact correction method based on directional derivatives. Despite the peculiar and specific application, the methodology can be easily extended to hard X-rays and to set-ups with multi-array detector systems when the dimensions of surface reliefs are in the order of the probing beam size.

Self-organized topology of recurrence-based complex networks

NASA Astrophysics Data System (ADS)

Yang, Hui; Liu, Gang

2013-12-01

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

Self-organized topology of recurrence-based complex networks.

PubMed

Yang, Hui; Liu, Gang

2013-12-01

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

Self-organized topology of recurrence-based complex networks

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

Yang, Hui, E-mail: huiyang@usf.edu; Liu, Gang

With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article ismore » to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.« less

Oscillations and Multiple Equilibria in Microvascular Blood Flow.

PubMed

Karst, Nathaniel J; Storey, Brian D; Geddes, John B

2015-07-01

We investigate the existence of oscillatory dynamics and multiple steady-state flow rates in a network with a simple topology and in vivo microvascular blood flow constitutive laws. Unlike many previous analytic studies, we employ the most biologically relevant models of the physical properties of whole blood. Through a combination of analytic and numeric techniques, we predict in a series of two-parameter bifurcation diagrams a range of dynamical behaviors, including multiple equilibria flow configurations, simple oscillations in volumetric flow rate, and multiple coexistent limit cycles at physically realizable parameters. We show that complexity in network topology is not necessary for complex behaviors to arise and that nonlinear rheology, in particular the plasma skimming effect, is sufficient to support oscillatory dynamics similar to those observed in vivo.

Hydrogen bonding between nitriles and hydrogen halides and the topological properties of molecular charge distributions

NASA Astrophysics Data System (ADS)

Boyd, Russell J.; Choi, Sai Cheng

1986-08-01

The topological properties of the charge density of the hydrogen-bonded complexes between nitrites and hydrogen chloride correlate linearly with theoretical estimates of the hydrogen-bond energy. At the 6-31G ** level, the hydrogenbond energies range from a low of 10 kJ/mol m NCCN—HC1 to a high of 38 kJ/mol in LiCN—HCl. A linear relationship between the charge density at the hydrogen-bond critical point and the NH internuclear distance of the RCN—HC1 complexes indicates that the generalization of the bond-length-bond-order relationship of CC bonds due to Bader, Tang, Tal and Biegler-König can be extended to intermolecular hydrogen bonding.

Topological Vulnerability Evaluation Model Based on Fractal Dimension of Complex Networks.

PubMed

Gou, Li; Wei, Bo; Sadiq, Rehan; Sadiq, Yong; Deng, Yong

2016-01-01

With an increasing emphasis on network security, much more attentions have been attracted to the vulnerability of complex networks. In this paper, the fractal dimension, which can reflect space-filling capacity of networks, is redefined as the origin moment of the edge betweenness to obtain a more reasonable evaluation of vulnerability. The proposed model combining multiple evaluation indexes not only overcomes the shortage of average edge betweenness's failing to evaluate vulnerability of some special networks, but also characterizes the topological structure and highlights the space-filling capacity of networks. The applications to six US airline networks illustrate the practicality and effectiveness of our proposed method, and the comparisons with three other commonly used methods further validate the superiority of our proposed method.

Trade-offs between robustness and small-world effect in complex networks

PubMed Central

Peng, Guan-Sheng; Tan, Suo-Yi; Wu, Jun; Holme, Petter

2016-01-01

Robustness and small-world effect are two crucial structural features of complex networks and have attracted increasing attention. However, little is known about the relation between them. Here we demonstrate that, there is a conflicting relation between robustness and small-world effect for a given degree sequence. We suggest that the robustness-oriented optimization will weaken the small-world effect and vice versa. Then, we propose a multi-objective trade-off optimization model and develop a heuristic algorithm to obtain the optimal trade-off topology for robustness and small-world effect. We show that the optimal network topology exhibits a pronounced core-periphery structure and investigate the structural properties of the optimized networks in detail. PMID:27853301

Multiresolution Distance Volumes for Progressive Surface Compression

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

Laney, D E; Bertram, M; Duchaineau, M A

2002-04-18

We present a surface compression method that stores surfaces as wavelet-compressed signed-distance volumes. Our approach enables the representation of surfaces with complex topology and arbitrary numbers of components within a single multiresolution data structure. This data structure elegantly handles topological modification at high compression rates. Our method does not require the costly and sometimes infeasible base mesh construction step required by subdivision surface approaches. We present several improvements over previous attempts at compressing signed-distance functions, including an 0(n) distance transform, a zero set initialization method for triangle meshes, and a specialized thresholding algorithm. We demonstrate the potential of sampled distancemore » volumes for surface compression and progressive reconstruction for complex high genus surfaces.« less

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

NASA Astrophysics Data System (ADS)

Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Verzini, M. J. Alconada; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Alimonti, G.; Alio, L.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Altheimer, A.; Gonzalez, B. Alvarez; Piqueras, D. Álvarez; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Coutinho, Y. Amaral; Amelung, C.; Amidei, D.; Santos, S. P. Amor Dos; Amorim, A.; Amoroso, S.; Amram, N.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Bella, L. Aperio; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Aurousseau, M.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bacci, C.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Bain, T.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barreiro, F.; da Costa, J. Barreiro Guimarães; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Basye, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Noccioli, E. Benhar; Garcia, J. A. Benitez; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Kuutmann, E. Bergeaas; Berger, N.; Berghaus, F.; Beringer, J.; Bernard, C.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertsche, C.; Bertsche, D.; Besana, M. I.; Besjes, G. J.; Bylund, O. Bessidskaia; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Biesuz, N. V.; Biglietti, M.; De Mendizabal, J. Bilbao; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Borroni, S.; Bortfeldt, J.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Bousson, N.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bozic, I.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Madden, W. D. Breaden; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Bronner, J.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; de Renstrom, P. A. Bruckman; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Bruschi, M.; Bruscino, N.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, L.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burghgrave, B.; Burke, S.; Burmeister, I.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Butt, A. I.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Urbán, S. Cabrera; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Caloba, L. P.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Camarda, S.; Camarri, P.; Cameron, D.; Armadans, R. Caminal; Campana, S.; Campanelli, M.; Campoverde, A.; Canale, V.; Canepa, A.; Bret, M. Cano; Cantero, J.; Cantrill, R.; Cao, T.; Garrido, M. D. M. Capeans; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, T.; Carlino, G.; Carminati, L.; Caron, S.; Carquin, E.; Carrillo-Montoya, G. D.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castaneda-Miranda, E.; Castelli, A.; Gimenez, V. Castillo; Castro, N. F.; Catastini, P.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Alberich, L. Cerda; Cerio, B. C.; Cerny, K.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cerv, M.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chalupkova, I.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chau, C. C.; Barajas, C. A. Chavez; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, K.; Chen, L.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, Y.; Cheplakov, A.; Cheremushkina, E.; Moursli, R. Cherkaoui El; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chislett, R. T.; Chitan, A.; Chizhov, M. V.; Choi, K.; Chouridou, S.; Chow, B. K. B.; Christodoulou, V.; Chromek-Burckhart, D.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciapetti, G.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coffey, L.; Cogan, J. G.; Colasurdo, L.; Cole, B.; Cole, S.; Colijn, A. P.; Collot, J.; Colombo, T.; Compostella, G.; Muiño, P. Conde; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Consorti, V.; Constantinescu, S.; Conta, C.; Conti, G.; Conventi, F.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Côté, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Ortuzar, M. Crispin; Cristinziani, M.; Croft, V.; Crosetti, G.; Donszelmann, T. Cuhadar; Cummings, J.; Curatolo, M.; Cúth, J.; Cuthbert, C.; Czirr, H.; Czodrowski, P.; D'Auria, S.; D'Onofrio, M.; De Sousa, M. J. Da Cunha Sargedas; Via, C. Da; Dabrowski, W.; Dafinca, A.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Dang, N. P.; Daniells, A. C.; Danninger, M.; Hoffmann, M. Dano; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Davey, W.; David, C.; Davidek, T.; Davies, E.; Davies, M.; Davison, P.; Davygora, Y.; Dawe, E.; Dawson, I.; Daya-Ishmukhametova, R. K.; De, K.; de Asmundis, R.; De Benedetti, A.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; De Regie, J. B. De Vivie; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Deigaard, I.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Deliyergiyev, M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; della Volpe, D.; Delmastro, M.; Delsart, P. A.; Deluca, C.; DeMarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Ciaccio, A.; Di Ciaccio, L.; Di Domenico, A.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Mattia, A.; Di Micco, B.; Di Nardo, R.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Diglio, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dohmae, T.; Dolejsi, J.; Dolezal, Z.; Dolgoshein, B. A.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Dubreuil, E.; Duchovni, E.; Duckeck, G.; Ducu, O. A.; Duda, D.; Dudarev, A.; Duflot, L.; Duguid, L.; Dührssen, M.; Dunford, M.; Yildiz, H. Duran; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Edson, W.; Edwards, N. C.; Ehrenfeld, W.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; Kacimi, M. El; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Endo, M.; Erdmann, J.; Ereditato, A.; Ernis, G.; Ernst, J.; Ernst, M.; Errede, S.; Ertel, E.; Escalier, M.; Esch, H.; Escobar, C.; Esposito, B.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Fabbri, L.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Giannelli, M. Faucci; Favareto, A.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenyuk, A. B.; Feremenga, L.; Martinez, P. Fernandez; Perez, S. Fernandez; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; de Lima, D. E. Ferreira; Ferrer, A.; Ferrere, D.; Ferretti, C.; Parodi, A. Ferretto; Fiascaris, M.; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, G. T.; Fletcher, G.; Fletcher, R. R. M.; Flick, T.; Floderus, A.; Castillo, L. R. Flores; Flowerdew, M. J.; Formica, A.; Forti, A.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; French, S. T.; Fressard-Batraneanu, S. M.; Friedrich, F.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Torregrosa, E. Fullana; Fulsom, B. G.; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, P.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Gao, J.; Gao, Y.; Gao, Y. S.; Walls, F. M. Garay; Garberson, F.; García, C.; Navarro, J. E. García; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gaur, B.; Gauthier, L.; Gauzzi, P.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Ge, P.; Gecse, Z.; Gee, C. N. P.; Geich-Gimbel, Ch.; Geisler, M. P.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; George, M.; George, S.; Gerbaudo, D.; Gershon, A.; Ghasemi, S.; Ghazlane, H.; Giacobbe, B.; Giagu, S.; Giangiobbe, V.; Giannetti, P.; Gibbard, B.; Gibson, S. M.; Gignac, M.; Gilchriese, M.; Gillam, T. P. S.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giorgi, F. M.; Giraud, P. 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2017-07-01

The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Analysis of Membrane Protein Topology in the Plant Secretory Pathway.

PubMed

Guo, Jinya; Miao, Yansong; Cai, Yi

2017-01-01

Topology of membrane proteins provides important information for the understanding of protein function and intermolecular associations. Integrate membrane proteins are generally transported from endoplasmic reticulum (ER) to Golgi and downstream compartments in the plant secretory pathway. Here, we describe a simple method to study membrane protein topology along the plant secretory pathway by transiently coexpressing a fluorescent protein (XFP)-tagged membrane protein and an ER export inhibitor protein, ARF1 (T31N), in tobacco BY-2 protoplast. By fractionation, microsome isolation, and trypsin digestion, membrane protein topology could be easily detected by either direct confocal microscopy imaging or western-blot analysis using specific XFP antibodies. A similar strategy in determining membrane protein topology could be widely adopted and applied to protein analysis in a broad range of eukaryotic systems, including yeast cells and mammalian cells.

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1.

PubMed

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Urrejola, P; Usai, G; Usanova, A; Vacavant, L; Vacek, V; Vachon, B; Valderanis, C; Valencic, N; Valentinetti, S; Valero, A; Valery, L; Valkar, S; Vallecorsa, S; Ferrer, J A Valls; Van Den Wollenberg, W; Van Der Deijl, P C; van der Geer, R; van der Graaf, H; van Eldik, N; van Gemmeren, P; Van Nieuwkoop, J; van Vulpen, I; van Woerden, M C; Vanadia, M; Vandelli, W; Vanguri, R; Vaniachine, A; Vannucci, F; Vardanyan, G; Vari, R; Varnes, E W; Varol, T; Varouchas, D; Vartapetian, A; Varvell, K E; Vazeille, F; Schroeder, T Vazquez; Veatch, J; Veloce, L M; Veloso, F; Velz, T; Veneziano, S; Ventura, A; Ventura, D; Venturi, M; Venturi, N; Venturini, A; Vercesi, V; Verducci, M; Verkerke, W; Vermeulen, J C; Vest, A; Vetterli, M C; Viazlo, O; Vichou, I; Vickey, T; Boeriu, O E Vickey; Viehhauser, G H A; Viel, S; Vigne, R; Villa, M; Perez, M Villaplana; Vilucchi, E; Vincter, M G; Vinogradov, V B; Vivarelli, I; Vlachos, S; Vladoiu, D; Vlasak, M; Vogel, M; Vokac, P; Volpi, G; Volpi, M; von der Schmitt, H; von Radziewski, H; von Toerne, E; Vorobel, V; Vorobev, K; Vos, M; Voss, R; Vossebeld, J H; Vranjes, N; Milosavljevic, M Vranjes; Vrba, V; Vreeswijk, M; Vuillermet, R; Vukotic, I; Vykydal, Z; Wagner, P; Wagner, W; Wahlberg, H; Wahrmund, S; Wakabayashi, J; Walder, J; Walker, R; Walkowiak, W; Wang, C; Wang, F; Wang, H; Wang, H; Wang, J; Wang, J; Wang, K; Wang, R; Wang, S M; Wang, T; Wang, T; Wang, X; Wanotayaroj, C; Warburton, A; Ward, C P; Wardrope, D R; Washbrook, A; Wasicki, C; Watkins, P M; Watson, A T; Watson, I J; Watson, M F; Watts, G; Watts, S; Waugh, B M; Webb, S; Weber, M S; Weber, S W; Webster, J S; Weidberg, A R; Weinert, B; Weingarten, J; Weiser, C; Weits, H; Wells, P S; Wenaus, T; Wengler, T; Wenig, S; Wermes, N; Werner, M; Werner, P; Wessels, M; Wetter, J; Whalen, K; Wharton, A M; White, A; White, M J; White, R; White, S; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Wienemann, P; Wiglesworth, C; Wiik-Fuchs, L A M; Wildauer, A; Wilkens, H G; Williams, H H; Williams, S; Willis, C; Willocq, S; Wilson, A; Wilson, J A; Wingerter-Seez, I; Winklmeier, F; Winter, B T; Wittgen, M; Wittkowski, J; Wollstadt, S J; Wolter, M W; Wolters, H; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wu, M; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xu, D; Xu, L; Yabsley, B; Yacoob, S; Yakabe, R; Yamada, M; Yamaguchi, D; Yamaguchi, Y; Yamamoto, A; Yamamoto, S; Yamanaka, T; Yamauchi, K; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, Y; Yao, W-M; Yap, Y C; Yasu, Y; Yatsenko, E; Wong, K H Yau; Ye, J; Ye, S; Yeletskikh, I; Yen, A L; Yildirim, E; Yorita, K; Yoshida, R; Yoshihara, K; Young, C; Young, C J S; Youssef, S; Yu, D R; Yu, J; Yu, J M; Yu, J; Yuan, L; Yuen, S P Y; Yurkewicz, A; Yusuff, I; Zabinski, B; Zaidan, R; Zaitsev, A M; Zalieckas, J; Zaman, A; Zambito, S; Zanello, L; Zanzi, D; Zeitnitz, C; Zeman, M; Zemla, A; Zeng, J C; Zeng, Q; Zengel, K; Zenin, O; Ženiš, T; Zerwas, D; Zhang, D; Zhang, F; Zhang, G; Zhang, H; Zhang, J; Zhang, L; Zhang, R; Zhang, X; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhong, J; Zhou, B; Zhou, C; Zhou, L; Zhou, L; Zhou, M; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Nedden, M Zur; Zurzolo, G; Zwalinski, L

2017-01-01

The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

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

Aad, G.; Abbott, B.; Abdallah, J.

The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections dependingmore » on the nature of the cluster. Lastly, topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.« less

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

DOE PAGES

Aad, G.; Abbott, B.; Abdallah, J.; ...

2017-07-24

The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections dependingmore » on the nature of the cluster. Lastly, topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.« less

Relay-based information broadcast in complex networks

NASA Astrophysics Data System (ADS)

Fan, Zhongyan; Han, Zeyu; Tang, Wallace K. S.; Lin, Dong

2018-04-01

Information broadcast (IB) is a critical process in complex network, usually accomplished by flooding mechanism. Although flooding is simple and no prior topological information is required, it consumes a lot of transmission overhead. Another extreme is the tree-based broadcast (TB), for which information is disseminated via a spanning tree. It achieves the minimal transmission overhead but the maintenance of spanning tree for every node is an obvious obstacle for implementation. Motivated by the success of scale-free network models for real-world networks, in this paper, we investigate the issues in IB by considering an alternative solution in-between these two extremes. A novel relay-based broadcast (RB) mechanism is proposed by employing a subset of nodes as relays. Information is firstly forwarded to one of these relays and then re-disseminated to others through the spanning tree whose root is the relay. This mechanism provides a trade-off solution between flooding and TB. On one hand, it saves up a lot of transmission overhead as compared to flooding; on the other hand, it costs much less resource for maintenance than TB as only a few spanning trees are needed. Based on two major criteria, namely the transmission overhead and the convergence time, the effectiveness of RB is confirmed. The impacts of relay assignment and network structures on performance are also studied in this work.

Evolution of disorder in Mediator complex and its functional relevance

PubMed Central

Nagulapalli, Malini; Maji, Sourobh; Dwivedi, Nidhi; Dahiya, Pradeep; Thakur, Jitendra K.

2016-01-01

Mediator, an important component of eukaryotic transcriptional machinery, is a huge multisubunit complex. Though the complex is known to be conserved across all the eukaryotic kingdoms, the evolutionary topology of its subunits has never been studied. In this study, we profiled disorder in the Mediator subunits of 146 eukaryotes belonging to three kingdoms viz., metazoans, plants and fungi, and attempted to find correlation between the evolution of Mediator complex and its disorder. Our analysis suggests that disorder in Mediator complex have played a crucial role in the evolutionary diversification of complexity of eukaryotic organisms. Conserved intrinsic disordered regions (IDRs) were identified in only six subunits in the three kingdoms whereas unique patterns of IDRs were identified in other Mediator subunits. Acquisition of novel molecular recognition features (MoRFs) through evolution of new subunits or through elongation of the existing subunits was evident in metazoans and plants. A new concept of ‘junction-MoRF’ has been introduced. Evolutionary link between CBP and Med15 has been provided which explain the evolution of extended-IDR in CBP from Med15 KIX-IDR junction-MoRF suggesting role of junction-MoRF in evolution and modulation of protein–protein interaction repertoire. This study can be informative and helpful in understanding the conserved and flexible nature of Mediator complex across eukaryotic kingdoms. PMID:26590257

DNA G-segment bending is not the sole determinant of topology simplification by type II DNA topoisomerases.

PubMed

Thomson, Neil H; Santos, Sergio; Mitchenall, Lesley A; Stuchinskaya, Tanya; Taylor, James A; Maxwell, Anthony

2014-08-21

DNA topoisomerases control the topology of DNA. Type II topoisomerases exhibit topology simplification, whereby products of their reactions are simplified beyond that expected based on thermodynamic equilibrium. The molecular basis for this process is unknown, although DNA bending has been implicated. To investigate the role of bending in topology simplification, the DNA bend angles of four enzymes of different types (IIA and IIB) were measured using atomic force microscopy (AFM). The enzymes tested were Escherichia coli topo IV and yeast topo II (type IIA enzymes that exhibit topology simplification), and Methanosarcina mazei topo VI and Sulfolobus shibatae topo VI (type IIB enzymes, which do not). Bend angles were measured using the manual tangent method from topographical AFM images taken with a novel amplitude-modulated imaging mode: small amplitude small set-point (SASS), which optimises resolution for a given AFM tip size and minimises tip convolution with the sample. This gave improved accuracy and reliability and revealed that all 4 topoisomerases bend DNA by a similar amount: ~120° between the DNA entering and exiting the enzyme complex. These data indicate that DNA bending alone is insufficient to explain topology simplification and that the 'exit gate' may be an important determinant of this process.

Role-separating ordering in social dilemmas controlled by topological frustration

NASA Astrophysics Data System (ADS)

Amaral, Marco A.; Perc, Matjaž; Wardil, Lucas; Szolnoki, Attila; da Silva Júnior, Elton J.; da Silva, Jafferson K. L.

2017-03-01

``Three is a crowd" is an old proverb that applies as much to social interactions as it does to frustrated configurations in statistical physics models. Accordingly, social relations within a triangle deserve special attention. With this motivation, we explore the impact of topological frustration on the evolutionary dynamics of the snowdrift game on a triangular lattice. This topology provides an irreconcilable frustration, which prevents anticoordination of competing strategies that would be needed for an optimal outcome of the game. By using different strategy updating protocols, we observe complex spatial patterns in dependence on payoff values that are reminiscent to a honeycomb-like organization, which helps to minimize the negative consequence of the topological frustration. We relate the emergence of these patterns to the microscopic dynamics of the evolutionary process, both by means of mean-field approximations and Monte Carlo simulations. For comparison, we also consider the same evolutionary dynamics on the square lattice, where of course the topological frustration is absent. However, with the deletion of diagonal links of the triangular lattice, we can gradually bridge the gap to the square lattice. Interestingly, in this case the level of cooperation in the system is a direct indicator of the level of topological frustration, thus providing a method to determine frustration levels in an arbitrary interaction network.

Topological characterization and early detection of bifurcations and chaos in complex systems using persistent homology.

PubMed

Mittal, Khushboo; Gupta, Shalabh

2017-05-01

Early detection of bifurcations and chaos and understanding their topological characteristics are essential for safe and reliable operation of various electrical, chemical, physical, and industrial processes. However, the presence of non-linearity and high-dimensionality in system behavior makes this analysis a challenging task. The existing methods for dynamical system analysis provide useful tools for anomaly detection (e.g., Bendixson-Dulac and Poincare-Bendixson criteria can detect the presence of limit cycles); however, they do not provide a detailed topological understanding about system evolution during bifurcations and chaos, such as the changes in the number of subcycles and their positions, lifetimes, and sizes. This paper addresses this research gap by using topological data analysis as a tool to study system evolution and develop a mathematical framework for detecting the topological changes in the underlying system using persistent homology. Using the proposed technique, topological features (e.g., number of relevant k-dimensional holes, etc.) are extracted from nonlinear time series data which are useful for deeper analysis of the system behavior and early detection of bifurcations and chaos. When applied to a Logistic map, a Duffing oscillator, and a real life Op-amp based Jerk circuit, these features are shown to accurately characterize the system dynamics and detect the onset of chaos.

Topological chaos, braiding and bifurcation of almost-cyclic sets.

PubMed

Grover, Piyush; Ross, Shane D; Stremler, Mark A; Kumar, Pankaj

2012-12-01

In certain two-dimensional time-dependent flows, the braiding of periodic orbits provides a way to analyze chaos in the system through application of the Thurston-Nielsen classification theorem (TNCT). We expand upon earlier work that introduced the application of the TNCT to braiding of almost-cyclic sets, which are individual components of almost-invariant sets [Stremler et al., "Topological chaos and periodic braiding of almost-cyclic sets," Phys. Rev. Lett. 106, 114101 (2011)]. In this context, almost-cyclic sets are periodic regions in the flow with high local residence time that act as stirrers or "ghost rods" around which the surrounding fluid appears to be stretched and folded. In the present work, we discuss the bifurcation of the almost-cyclic sets as a system parameter is varied, which results in a sequence of topologically distinct braids. We show that, for Stokes' flow in a lid-driven cavity, these various braids give good lower bounds on the topological entropy over the respective parameter regimes in which they exist. We make the case that a topological analysis based on spatiotemporal braiding of almost-cyclic sets can be used for analyzing chaos in fluid flows. Hence, we further develop a connection between set-oriented statistical methods and topological methods, which promises to be an important analysis tool in the study of complex systems.

DNA G-segment bending is not the sole determinant of topology simplification by type II DNA topoisomerases

NASA Astrophysics Data System (ADS)

Thomson, Neil H.; Santos, Sergio; Mitchenall, Lesley A.; Stuchinskaya, Tanya; Taylor, James A.; Maxwell, Anthony

2014-08-01

DNA topoisomerases control the topology of DNA. Type II topoisomerases exhibit topology simplification, whereby products of their reactions are simplified beyond that expected based on thermodynamic equilibrium. The molecular basis for this process is unknown, although DNA bending has been implicated. To investigate the role of bending in topology simplification, the DNA bend angles of four enzymes of different types (IIA and IIB) were measured using atomic force microscopy (AFM). The enzymes tested were Escherichia coli topo IV and yeast topo II (type IIA enzymes that exhibit topology simplification), and Methanosarcina mazei topo VI and Sulfolobus shibatae topo VI (type IIB enzymes, which do not). Bend angles were measured using the manual tangent method from topographical AFM images taken with a novel amplitude-modulated imaging mode: small amplitude small set-point (SASS), which optimises resolution for a given AFM tip size and minimises tip convolution with the sample. This gave improved accuracy and reliability and revealed that all 4 topoisomerases bend DNA by a similar amount: ~120° between the DNA entering and exiting the enzyme complex. These data indicate that DNA bending alone is insufficient to explain topology simplification and that the `exit gate' may be an important determinant of this process.

Differentiation of surface and bulk conductivities in topological insulator via four-probe spectroscopy

DOE PAGES

Zhang, Xiaoguang; McGuire, Michael A.; Chen, Yong P.; ...

2016-03-08

Topological insulators, with characteristic topological surface states, have emerged as a new state of matter with rich potentials for both fundamental physics and device applications. However, the experimental detection of the surface transport has been hampered by the unavoidable extrinsic conductivity associated with the bulk crystals. Here we show that a four-probe transport spectroscopy in a multi-probe scanning tunneling microscopy system can be used to differentiate conductivities from the surface states and the coexisting bulk states in topological insulators. We derive a scaling relation of measured resistance with respect to varying inter-probe spacing for two interconnected conduction channels, which allowsmore » quantitative determination of conductivities from both channels. Using this method, we demonstrate the separation of 2D and 3D conduction in topological insulators by comparing the conductance scaling of Bi 2Se 3, Bi 2Te 2Se, and Sb-doped Bi 2Se 3 with that of a pure 2D conductance of graphene on SiC substrate. We also report the 2D conductance enhancement due to the surface doping effect in topological insulators. This technique can be applied to reveal 2D to 3D crossover of conductance in other complex systems.« less

Role-separating ordering in social dilemmas controlled by topological frustration.

PubMed

Amaral, Marco A; Perc, Matjaž; Wardil, Lucas; Szolnoki, Attila; da Silva Júnior, Elton J; da Silva, Jafferson K L

2017-03-01

''Three is a crowd" is an old proverb that applies as much to social interactions as it does to frustrated configurations in statistical physics models. Accordingly, social relations within a triangle deserve special attention. With this motivation, we explore the impact of topological frustration on the evolutionary dynamics of the snowdrift game on a triangular lattice. This topology provides an irreconcilable frustration, which prevents anticoordination of competing strategies that would be needed for an optimal outcome of the game. By using different strategy updating protocols, we observe complex spatial patterns in dependence on payoff values that are reminiscent to a honeycomb-like organization, which helps to minimize the negative consequence of the topological frustration. We relate the emergence of these patterns to the microscopic dynamics of the evolutionary process, both by means of mean-field approximations and Monte Carlo simulations. For comparison, we also consider the same evolutionary dynamics on the square lattice, where of course the topological frustration is absent. However, with the deletion of diagonal links of the triangular lattice, we can gradually bridge the gap to the square lattice. Interestingly, in this case the level of cooperation in the system is a direct indicator of the level of topological frustration, thus providing a method to determine frustration levels in an arbitrary interaction network.

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

Xia, Kelin; Zhao, Zhixiong; Wei, Guo-Wei, E-mail: wei@math.msu.edu

Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topologicalmore » analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.« less

twzPEA: A Topology and Working Zone Based Pathway Enrichment Analysis Framework

USDA-ARS?s Scientific Manuscript database

Sensitive detection of involvement and adaptation of key signaling, regulatory, and metabolic pathways holds the key to deciphering molecular mechanisms such as those in the biomass-to-biofuel conversion process in yeast. Typical gene set enrichment analyses often do not use topology information in...

Modeling Spatial Relationships within a Fuzzy Framework.

ERIC Educational Resources Information Center

Petry, Frederick E.; Cobb, Maria A.

1998-01-01

Presents a model for representing and storing binary topological and directional relationships between 2-dimensional objects that is used to provide a basis for fuzzy querying capabilities. A data structure called an abstract spatial graph (ASG) is defined for the binary relationships that maintains all necessary information regarding topology and…

Quantum Entanglement and the Topological Order of Fractional Hall States

NASA Astrophysics Data System (ADS)

Rezayi, Edward

2015-03-01

Fractional quantum Hall states or, more generally, topological phases of matter defy Landau classification based on order parameter and broken symmetry. Instead they have been characterized by their topological order. Quantum information concepts, such as quantum entanglement, appear to provide the most efficient method of detecting topological order solely from the knowledge of the ground state wave function. This talk will focus on real-space bi-partitioning of quantum Hall states and will present both exact diagonalization and quantum Monte Carlo studies of topological entanglement entropy in various geometries. Results on the torus for non-contractible cuts are quite rich and, through the use of minimum entropy states, yield the modular S-matrix and hence uniquely determine the topological order, as shown in recent literature. Concrete examples of minimum entropy states from known quantum Hall wave functions and their corresponding quantum numbers, used in exact diagonalizations, will be given. In collaboration with Clare Abreu and Raul Herrera. Supported by DOE Grant DE-SC0002140.