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1 |
Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm
پیاده سازی انتخاب ویژگی گراف-نظری توسط الگوریتم بهینه سازی تقریبی کوانتومی-2022 Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(pn2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.
Index Terms: Feature selection | graph theory | parameterized quantum circuit | quantum approximation optimization algorithm | quantum computing. |
مقاله انگلیسی |
2 |
An uncertainty law for microbial evolution
قانون عدم قطعیت برای تکامل میکروبها-2020 Medical practice would benefit from a thorough understanding of constraints and uncertainty in mi- crobial evolution. Higher order epistasis refers to unexpected effects of multiple mutations even if both single mutations and pairwise effects have been accounted for. Recent studies show that higher order epistasis is abundant in nature, for bacteria as well as higher organisms. However, the importance of higher order effects has been debated. It has been suggested that such effects cannot be interpreted, and should not be considered. Here, we show conclusively that higher order epistasis changes the adaptive prospects for a population. The conclusion is based on an exhaustive search of 193,270,310 hyper-cube graphs and applications of graph theory. Our results are more precise, yet more universal, than related research since they depend on mathematical theory, rather than sampling or simulations. Moreover, the uncertainty we establish for microbial evolution, due to higher order epistasis is not sensitive for detailed model assumptions, such as the baseline being additive or log-additive fitness. Keywords: Fitness landscapes| epistasis | higher order epistasis | predictability |
مقاله انگلیسی |
3 |
Network properties of healthy and Alzheimer brains
خواص شبکه مغز سالم و آلزایمر-2020 The application of graph theory in diffusion weighted resonance magnetic images have
allowed the description of the brain as a complex network, often called structural network.
For many years, the small-world properties of brain networks have been studied
and reported. However, few studies have gone beyond of clustering and characteristic
path length. In this work, we compare the structural connection network of a healthy
brain and a brain affected by Alzheimer’s disease with artificial small-world networks.
Based on statistical analysis, we demonstrate how artificial networks can be constructed
using Newman–Watts procedure. The network quantifiers of both structural matrices
are identified inside a probabilistic valley. Despite of similarities between structural
connection matrices and artificial small-world networks, increased assortativity can be
found in the Alzheimer brain. Due to limited experimental data, we cannot define
a direct link between Alzheimer’s disease and assortativity. Nevertheless, we intend
to call attention for an important network quantifier that has been neglected. Our
results indicate that network quantifiers can be helpful to identify abnormalities in real
structural connections, for instance Alzheimer’s disease that disrupts the communication
among neurons. One of our main results is to show that the network indicators of
the Alzheimer brain are almost identical with the small-world network, except the
assortativity. Keywords: Network | Human brain | Alzheimer’s disease | Small-world |
مقاله انگلیسی |
4 |
Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males
بررسی خستگی مؤثر بر شبکه های عملکردی مغزی مبتنی بر الکترونسفالوگرافی در هنگام رانندگی واقعی در مردان جوان-2019 In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived
as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects
participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for
detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range
and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction
method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional
connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in
multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was
significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the
frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and
higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based
on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were
applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning
algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy
of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain
network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue. Keywords: Electroencephalography (EEG) | Driver fatigue | Phase lag index | Graph theory | Functional connectivity | Brain network |
مقاله انگلیسی |
5 |
Cross-subject network investigation of the EEG microstructure: A sleep spindles study
بررسی شبکه ای موضعی از ساختار EEG: یک مطالعه sleep spindles-2019 Background: The microstructural EEG elements and their functional networks relate to many neurophysiological
functions of the brain and can reveal abnormalities. Despite the blooming variety of methods for estimating
connectivity in the EEG of a single subject, a common pitfall is seen in relevant studies; grand averaging is used
for estimating the characteristic connectivity patterns of a group of subjects. This averaging may distort results
and fail to account for the internal variability of connectivity results across the subjects of a group.
New Method: In this study, we propose a novel methodology for the cross-subject network investigation of EEG
graphoelements. We used dimensionality reduction techniques in order to reveal internal connectivity properties
and to examine how consistent these are across a number of subjects. In addition, graph theoretical measures
were utilized to prioritize regions according to their network attributes.
Results: As proof of concept, we applied this method on fast sleep spindles across 10 healthy subjects.
Neurophysiological findings revealed subnetworks of the spindle events across subjects, highlighting a predominance
for occipito-parietal areas and their connectivity with frontal regions.
Comparison with existing methods: This is a new approach for the examination of within-group connectivities in
EEG research. The results accounted for more than 85% of the overall data variance and the detected subnetworks
were found to be meaningful down-projections of the grand average of the group, suggesting sufficient
performance for the proposed methodology.
Conclusion: We conclude that the proposed methodology can serve as an observatory tool for the EEG connectivity
patterns across subjects, providing a supplementary analysis of the existing topography techniques. Keywords:EEG networks | PCA | EEG microstructure | Sleep spindle networks | Graph theory | Pattern recognition |
مقاله انگلیسی |
6 |
IOT and big data based cooperative logistical delivery scheduling method and cloud robot system
اینترنت اشیا و داده های بزرگ مبتنی بر همکاری لایسنسسی برنامه ریزی تحویل و سیستم ربات ابر-2018 Many studies have been done for logistics delivery scheduling technologies, but the cooperating and
relaying of resources in the process of logistics delivery remains elusive. We proposed IOT and big data based
cooperative logistical delivery scheduling method and cloud robot system, After obtaining the big data of
logistics delivery resources and requirements from logistics delivery companies through the IOT and/or Internet,
establishing the map of logistics delivery routes based on the big data of logistics delivery resources, the
logistics delivery route corresponding to the logistics delivery requirements is selected from the map of
logistics delivery routes by using the shortest route algorithm of the graph theory, and then the logistics delivery
resources corresponding to the logistics delivery route are scheduled to the corresponding logistics delivery
requirements, which can greatly improve the cooperative scheduling of logistics delivery resources among
different logistics delivery companies to enhance the level of logistics delivery resources utilization, reduce the
logistics delivery logistics delivery costs, and improve customer experience.
Keywords: logistical delivery, cooperative scheduling, IOT, big data, cloud robot |
مقاله انگلیسی |
7 |
The lean and resilient management of the supply chain and its impact on performance
مدیرت زنجیره تامین متکی و برجهنده و تاثیر آن روی عملکرد-2018 The relationship between lean management and resilience in the supply chain, whether negative or positive, is still not clear from the existing literature. This paper aims to investigate the relationship and links between lean and resilient supply chain (SC) practices and their impact on SC performance. To achieve this objective, the aerospace manufacturing sector (AMS) is chosen as the study sector because of the importance of both paradigms. Interpretive Structural Modeling (ISM) approach is used in order to identify linkages among various lean and resilience practices and SC performance metrics through a single systemic framework. ISM is an interactive learning process based on graph theory where experts knowledge is extracted and converted into a powerful well-structured model. For that purpose, a heterogeneous panel of experts in the AMS was formed, providing a complete view of all SC levels in the sector. The final ISM model revealed that lean SC practices act as drivers for resilient SC practices, since implementing the former in isolation could lead to a more vulnerable SC. The findings also show that lean SC practices lead to a higher performance improvement than resilient SC practices. This is due to the fact that resilient SC practices do not exert influence over all SC performance metrics as it occurs with lean SC practices. In addition, several managerial implications regarding the most convenient practices in terms of the companys objectives are drawn from this study.
keywords: Lean supply chain management |Resilient supply chain management |Interpretive structural modeling |Aerospace manugacturing sector |
مقاله انگلیسی |
8 |
Socio-cyber network: The potential of cyber-physical system to define human behaviors using big data analytics
شبکه اجتماعی سایری: پتانسیل سیستم فیزیکی سایبری برای تعریف رفتارهای انسانی با استفاده از تجزیه و تحلیل داده های بزرگ-2018 The growing gap between users and the big data analytics requires innovative tools that address the chal
lenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient
to analyze such massive volume of data. Moreover, advancements in the field of big data application and
data science leads toward a new paradigm of human behavior, where various smart devices integrate
with each other and establish a relationship. However, majority of the systems are either memoryless or
computational inefficient, which are unable to define or predict human behavior. Therefore, keeping in
view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of
big data within their requirements. Hence, this paper presents a system architecture that integrates social
network with the technical network. We derive a novel notion of ‘Socio-Cyber Network’, where a friendship
is made based on the geo-location information of the user, where trust index is used based on graphs
theory. The proposed graph theory provides a better understanding of extraction knowledge from the
data and finding relationship between different users. To check the efficiency of the proposed algorithms
exploited in the proposed system architecture, we have implemented our proposed system using Hadoop
and MapReduce. MapReduce for cyber-physical system (CPS) is supported by a parallel algorithm that
efficiently process a huge volume of data sets. The system is implemented using Spark GraphX tool at
the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the
system is evaluated in terms of efficiency by considering the system throughput and processing time. The
results show that the proposed system is more scalable and efficient.
Keywords: Big data ، Socio-cyber network ، Human behavior ، Graphs ، Friendship ، Trust index |
مقاله انگلیسی |
9 |
DTRM: A new reputation mechanism to enhance data trustworthiness for high-performance cloud computing
DTRM: مکانیزم اعتبار جدید برای افزایش اطمینان داده ها برای محاسبات ابری با کارایی بالا-2018 Cloud computing and the mobile Internet have been the two most influential information technology
revolutions, which intersect in mobile cloud computing (MCC). The burgeoning MCC enables the large
scale collection and processing of big data, which demand trusted, authentic, and accurate data to ensure
an important but often overlooked aspect of big data — data veracity. Troublesome internal attacks
launched by internal malicious users is one key problem that reduces data veracity and remains difficult
to handle. To enhance data veracity and thus improve the performance of big data computing in MCC, this
paper proposes a Data Trustworthiness enhanced Reputation Mechanism (DTRM) which can be used to
defend against internal attacks. In the DTRM, the sensitivity-level based data category, Metagraph theory
based user group division, and reputation transferring methods are integrated into the reputation query
and evaluation process. The extensive simulation results based on real datasets show that the DTRM
outperforms existing classic reputation mechanisms under bad mouthing attacks and mobile attacks.
Keywords: Cloud computing ، Reputation mechanism ، Trustworthiness ، Data veracity |
مقاله انگلیسی |
10 |
The lean and resilient management of the supply chain and its impact on performance
مدیریت لاغر و انعطاف پذیر از زنجیره تامین و تاثیر آن بر عملکرد-2018 The relationship between lean management and resilience in the supply chain, whether negative or positive, is
still not clear from the existing literature. This paper aims to investigate the relationship and links between lean
and resilient supply chain (SC) practices and their impact on SC performance. To achieve this objective, the
aerospace manufacturing sector (AMS) is chosen as the study sector because of the importance of both para
digms. Interpretive Structural Modeling (ISM) approach is used in order to identify linkages among various lean
and resilience practices and SC performance metrics through a single systemic framework. ISM is an interactive
learning process based on graph theory where experts knowledge is extracted and converted into a powerful
well-structured model. For that purpose, a heterogeneous panel of experts in the AMS was formed, providing a
complete view of all SC levels in the sector. The final ISM model revealed that lean SC practices act as drivers for
resilient SC practices, since implementing the former in isolation could lead to a more vulnerable SC. The
findings also show that lean SC practices lead to a higher performance improvement than resilient SC practices.
This is due to the fact that resilient SC practices do not exert influence over all SC performance metrics as it
occurs with lean SC practices. In addition, several managerial implications regarding the most convenient
practices in terms of the companys objectives are drawn from this study.
Keywords: Lean supply chain management ، Resilient supply chain management ، Interpretive structural modeling ، Aerospace manugacturing sector |
مقاله انگلیسی |