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ردیف | عنوان | نوع |
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1 |
Reordering and Partitioning of Distributed Quantum Circuits
مرتب سازی مجدد و پارتیشن بندی مدارهای کوانتومی توزیع شده-2022 A new approach to reduce the teleportation cost and execution time in Distributed Quantum
Circuits (DQCs) was proposed in the present paper. DQCs, a well-known solution, have been applied to
solve the problem of maintaining a large number of qubits next to each other. In the distributed quantum
system, the qubits are transferred to another subsystem by a quantum protocol like teleportation. Hence,
a novel method was proposed to optimize the number of teleportation and to reduce the execution time for
generating DQC. To this end, first, the quantum circuit was reordered according to the qubits placement to
improve the computational execution time, and then the quantum circuit was modeled as a graph. Finally,
we combined the genetic algorithm (GA) and the modified tabu search algorithm (MTS) to partition the
graph model in order to obtain a distributed quantum circuit aimed at reducing the number of teleportation
costs. A significant reduction in teleportation cost (TC) and execution time (ET) was obtained in benchmark
circuits. In particular, we performed a more accurate optimization than the previous approaches, and the
proposed approach yielded the best results for several benchmark circuits.
INDEX TERMS: Quantum computing | distributed quantum circuit | optimization | genetic algorithm | teleportation. |
مقاله انگلیسی |
2 |
The role of multiple ties in knowledge networks: Complementarity in the Montefalco wine cluster
نقش پیوندهای متعدد در شبکه های دانش: مکمل در خوشه شراب Montefalco-2020 After decades of studies about pervasive, wide, and inclusive knowledge externalities and the advantages of being
there, recent literature on management, industrial marketing, economic geography, regional studies, and related
fields has stressed that knowledge spreads imperfectly, unevenly, and selectively within regional and cluster
contexts. In this respect, little is known about the role played by heterogeneous knowledge ties among the same
set of actors and to what extent they follow overlapping or different routes of exchanging knowledge. Thus, an
investigation of multiple knowledge networks in clusters is a fundamental approach to interpret the reasons for
innovation and economic performance.
With an original dataset comprised of data collected by surveys directly administered in local wineries in the
Montefalco wine region of Italy, this paper aims to analyse the roles played by different local knowledge ties
within a sector that is critically driven by the exchange of knowledge among economic actors. Social network
analysis and exponential random graph modelling were applied to investigate the driving forces of the knowledge
flows. The empirical results showed that economic and social ties positively affect the spread of knowledge,
but the former has a higher magnitude impact than the latter. Moreover, they follow complementary routes of
exchange rather than overlapping ones. We suggest that such a structure has implications for understanding the
diffusion of knowledge and structures of innovation in cluster contexts. Keywords: Multiple networks | Knowledge diffusion | ERGM | Industrial cluster | Wine industry |
مقاله انگلیسی |
3 |
Multilevel determinants of collaboration between organised criminal groups
عوامل تعیین کننده های چند سطحی از همکاری بین گروه های سازمان یافته جنایی-2020 Collaboration between members of different criminal groups is an important feature of crime that is considered
organised, as it allows criminals to access resources and skills in order to exploit illicit economic opportunities.
Collaboration across criminal groups is also difficult and risky due to the lack of institutions supporting peaceful
cooperation in illicit markets. Thus cross-group collaboration has been thought to take place mostly among small
and transient groups. This paper determines whether and under what conditions members of different, larger
organised crime groups collaborate with one another. To do so we use intelligence data from the Canadian
province of Alberta, centering on criminals and criminal groups engaged in multiple crime types in multiple
geographic locations. We apply a multilevel network analytical framework and exponential random graph
models using Bayesian techniques to uncover the determinants of cross-group criminal collaboration. We find
cross-group collaboration depends not only on co-location, but also on the types of groups to which the criminals
are affiliated, and on illicit market overlap between groups. When groups are operating in the same geographically-
situated illicit markets their members tend not to collaborate with one another, providing evidence
for the difficulty or undesirability of cross-group collaboration in illicit markets. Conversely, members of Outlaw
Motorcycle Gangs are more likely to collaborate across groups when markets overlap, suggesting the superior
capacity and motivation of biker gangs to coordinate criminal activity. Our paper contributes to the understanding
of criminal networks as complex, emergent, and spatially embedded market phenomena. Keywords: Illegal drugs | Multilevel networks | Exponential Random Graph Models | Illegal markets | Criminal groups |
مقاله انگلیسی |
4 |
Reconstructing the topology of financial networks from degree distributions and reciprocity
بازسازی توپولوژی شبکه های مالی از توزیع درجه و روابط متقابل-2019 A flexible probabilistic approach for the constructing of realistic topologies of interbank
networks is presented. This constitutes a challenging task, since information on bilateral
inter-banking activities is classified confidential and the number of banks in most European
countries is substantial. First, we analyze what information on European inter-banking
liabilities is publicly available. Second, we present an approach for the reconstruction of
network topologies satisfying known characteristics through an exponential random graph
model (ERGM), which incorporates the available information as side conditions. Third, we
conduct a case study calibrating the model to the Italian and the German interbank market.
Samples of both models are then analyzed with respect to different network statistics. The
relevance of the presented results stems from the urgent need of having realistic instances
of possible adjacency matrices as input in technical studies on the stability of inter-banking
networks. Various such studies exist, however, most of them rely on toy models for the
analyzed adjacency matrices. Keywords : Exponential random graph model | Financial network reconstruction | Maximum entropy |
مقاله انگلیسی |
5 |
An examination of DMO network identity using Exponential Random Graph Models
یک بررسی روی هویت شبکه DMO با استفاده از مدلهای گرافی تصادفی نمایی-2018 |
مقاله انگلیسی |
6 |
Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks
شبیه سازی مدل داده های بزرگ بر روی یک پایگاه داده گراف برای نظارت بر شبکه های حسگر چندرسانه ای بیسیم-2018 Sensors are present in various forms all around the world such as mobile phones, surveillance cameras,
smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors
are a part of some other system with similar sensors that compose a network. One of such networks
is composed of millions of sensors connected to the Internet which is called Internet of Things (IoT).
With the advances in wireless communication technologies, multimedia sensors and their networks are
expected to be major components in IoT. Many studies have already been done on wireless multimedia
sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All
those applications position sensor nodes and collect their data for a long time period with real-time
data flow, which is considered as big data. Big data may be structured or unstructured and needs to be
stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring
a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we
propose a big database model based on graph database model for handling data generated by wireless
multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query
big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based
NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL. We have run a number of query
experiments on our implemented simulator to show that which database system(s) for surveillance in
wireless multimedia sensor networks is efficient and scalable.
Keywords: Internet of things (IoT) ، Big graph databases ، NoSQL databases ، Wireless multimedia sensor networks ، Simulator |
مقاله انگلیسی |
7 |
A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model
یک روش کاوش قانون انجمنی معادلات داده های بزرگ برای پارامترهای ترانسفورماتور قدرت براساس مدل نمودار احتمالاتی-2018 The correlative change analysis of state parameters
can provide powerful technical supports for safe, reliable, and
high-efficient operation of the power transformers. However, the
analysis methods are primarily based on a single or a few state
parameters, and hence the potential failures can hardly be found
and predicted. In this paper, a data-driven method of association
rule mining for transformer state parameters has been proposed
by combining the Apriori algorithm and probabilistic graphical
model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned
cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations.
The result indicates that association rules among the numerical
solutions can be accurately mined. Finally, practical measured
data of five 500 kV transformers is analyzed by the proposed
method. The association rules of various state parameters have
been excavated, and then the mined association rules are used in
modifying the prediction results of single state parameters. The
results indicate that the application of the mined association rules
improves the accuracy of prediction. Therefore, the effectiveness
and feasibility of the proposed method in association rule mining
has been proved
Index Terms: Power transformers, state parameters, association rules, big data, data-driven method, Apriori algorithm, probabilistic graph, state prediction |
مقاله انگلیسی |
8 |
Structuring engineers implicit knowledge of forming process design by using a graph model
مهندسان ساختار دانش ضمنی تشکیل طراحی فرایند با استفاده از مدل نمودار-2018 Forming Process Design needs simultaneous consideration of multiple factors, accuracy of products, costs and cycle time. Consequently, its
design knowledge is based on personal experience and tends to be implicit and untransferable in general. To represent and transfer such implicit
and personal design knowledge of forming process, we propose a novel knowledge mining method based on graph theory. Experienced engineers’
knowledge is transcribed as a set of statements through a series of interviews. Then, the interrelationship among these statements is clarified by
translating them to a graph-based knowledge representation model. Implicit knowledge is extracted as structural characteristics of the graph.
Keywords: Implicit knowledge, Graph model, Knowledge transfer, Process design, Expert |
مقاله انگلیسی |
9 |
Information seeking in secondary schools: A multilevel network approach
اطلاعات در مدارس متوسطه: یک رویکرد شبکه چند سطحی-2017 In this study, we investigate information seeking interactions in secondary schools from a multilevel
network approach. Based on network-related theories, we examine the facilitating role of formal subunits.
We apply exponential random graph models for multilevel networks and summarize our findings by using
a meta-analysis technique. Our results indicate that formal subunits (e.g. subject departments) can, to
some extent, facilitate interactions, in loosely coupled organizations (e.g. secondary schools). Finally,
this study shows that a multilevel network approach can provide a more informative representation of
information seeking ties in knowledge-intensive organizations.
Keywords: Multilevel social network analysis | Exponential random graph models | Information seeking | Secondary schools | Formal subunits |
مقاله انگلیسی |
10 |
Hypergraph partitioning for social networks based on information entropy modularity
تقسیم بندی هایپرگراف برای شبکه های اجتماعی بر اساس مدولار بودن انتروپی اطلاعات-2017 A social network is a typical scale-free network with power-law degree distribution characteristics. It
demonstrates several natural imbalanced clusters when it is abstracted as a graph, and expands quickly under
its generative mechanism. Hypergraph is superior for modeling multi-user operations in social networks, and
partitioning the hypergraph modeled social networks could ease the scaling problems. However, todays popular
hypergraph partitioning tools are not sufficiently scalable; thus, unable to achieve high partitioning quality for
naturally imbalanced datasets. Recently proposed hypergraph partitioner, hyperpart, replaces the balance
constraint with an entropy constraint to achieve high-fidelity partitioning solutions, but it is not tailored for
scale-free networks, like social networks. In order to achieve scalable and high quality partitioning results for
hypergraph modeled social networks, we propose a partitioning method, EQHyperpart, which utilizes
information-Entropy-based modularity Q value (EQ) to direct the hypergraph partitioning process. This EQ
considers power-law degree distribution while describing the “natural” structure of scale-free networks. We
then apply simulated annealing and introduce a new definition of hyperedge cut, micro cut, to avoid the local
minima in convergence of partitioning, developing EQHyperpart into two specific partitioners, namely:
EQHyperpart-SA and EQHyperpart-MC. Finally, we evaluate the utility of our proposed method using
classical social network datasets, including Facebook dataset. Findings show that EQHyperpart partitioners
are more scalable than competing approaches, achieving a tradeoff between modularity retaining and cut size
minimizing under balance constraints, and an auto-tradeoff without balance constraints for hypergraph
modeled social networks.
Keywords: Scale-free network | Social network partitioning | Hypergraph partitioning | Information entropy | Modularity |
مقاله انگلیسی |