Showing papers by "University of Electronic Science and Technology of China published in 2010"
TL;DR: The mTLE alterations observed in functional connectivity and topological properties may be used to define tentative disease markers, including altered small-world properties in patients, along with smaller degree of connectivity, increased n-to-1 connectivity, smaller absolute clustering coefficients and shorter absolute path length.
Abstract: Background
The functional architecture of the human brain has been extensively described in terms of functional connectivity networks, detected from the low–frequency coherent neuronal fluctuations that can be observed in a resting state condition. Little is known, so far, about the changes in functional connectivity and in the topological properties of functional networks, associated with different brain diseases.
Methodology/Principal Findings
In this study, we investigated alterations related to mesial temporal lobe epilepsy (mTLE), using resting state functional magnetic resonance imaging on 18 mTLE patients and 27 healthy controls. Functional connectivity among 90 cortical and subcortical regions was measured by temporal correlation. The related values were analyzed to construct a set of undirected graphs. Compared to controls, mTLE patients showed significantly increased connectivity within the medial temporal lobes, but also significantly decreased connectivity within the frontal and parietal lobes, and between frontal and parietal lobes. Our findings demonstrated that a large number of areas in the default-mode network of mTLE patients showed a significantly decreased number of connections to other regions. Furthermore, we observed altered small-world properties in patients, along with smaller degree of connectivity, increased n-to-1 connectivity, smaller absolute clustering coefficients and shorter absolute path length.
Conclusions/Significance
We suggest that the mTLE alterations observed in functional connectivity and topological properties may be used to define tentative disease markers.
485 citations
TL;DR: It is shown that the closed loop tracking control system is stochastically stable in meansquare and the estimation errors converge to zero in mean square as well.
442 citations
TL;DR: Among the three cortical networks, the greatest clustering coefficient and the longest absolute path length in AD are found, which might indicate that the organization of the cortical network was the least optimal in AD.
Abstract: Recently, many researchers have used graph theory to study the aberrant brain structures in Alzheimer's disease (AD) and have made great progress. However, the characteristics of the cortical network in Mild Cognitive Impairment (MCI) are still largely unexplored. In this study, the gray matter volumes obtained from magnetic resonance imaging (MRI) for all brain regions except the cerebellum were parcellated into 90 areas using the automated anatomical labeling (AAL) template to construct cortical networks for 98 normal controls (NCs), 113 MCIs and 91 ADs. The measurements of the network properties were calculated for each of the three groups respectively. We found that all three cortical networks exhibited small-world properties and those strong interhemispheric correlations existed between bilaterally homologous regions. Among the three cortical networks, we found the greatest clustering coefficient and the longest absolute path length in AD, which might indicate that the organization of the cortical network was the least optimal in AD. The small-world measures of the MCI network exhibited intermediate values. This finding is logical given that MCI is considered to be the transitional stage between normal aging and AD. Out of all the between-group differences in the clustering coefficient and absolute path length, only the differences between the AD and normal control groups were statistically significant. Compared with the normal controls, the MCI and AD groups retained their hub regions in the frontal lobe but showed a loss of hub regions in the temporal lobe. In addition, altered interregional correlations were detected in the parahippocampus gyrus, medial temporal lobe, cingulum, fusiform, medial frontal lobe, and orbital frontal gyrus in groups with MCI and AD. Similar to previous studies of functional connectivity, we also revealed increased interregional correlations within the local brain lobes and disrupted long distance interregional correlations in groups with MCI and AD.
388 citations
TL;DR: In this paper, the effects of differently dimensional nanoparticles on the crystallization behavior of poly(l-lactide) matrices were investigated using time-resolved Fourier transform infrared spectroscopy (FTIR) and wide-angle X-ray diffraction (WAXD).
Abstract: Low-dimensional nanoparticles have a strong ability to induce the crystallization of polymer matrices. One-dimensional carbon nanotubes (CNTs) and two-dimensional graphene nanosheets (GNSs), both of which are both carbon-based nanoparticles, provide a good opportunity to investigate the effects of differently dimensional nanoparticles on the crystallization behavior of a polymer. For this purpose, respective nanocomposites of CNTs and GNSs with poly(l-lactide) (PLLA) as matrix were prepared by solution coagulation. Time-resolved Fourier-transform infrared spectroscopy (FTIR) and synchrotron wide-angle X-ray diffraction (WAXD) were performed to probe chain conformational changes and to determine the crystallization kinetics during the isothermal crystallization of the PLLA nanocomposites and neat PLLA, especially in the early stages. Both CNTs and GNSs could serve as nucleating agents in accelerating the crystallization kinetics of PLLA; however, the ability of CNTs to induce crystallization was stronger t...
301 citations
TL;DR: In this article, a graphene nanosheet/ultra-high molecular weight polyethylene composite with a segregated structure has been fabricated using water/ethanol solvent-assisted dispersion and hot compression at 200 °C.
290 citations
TL;DR: A relationship between functional connectivity and disease severity was found in specific regions of RSNs, including medial and lateral prefrontal cortex, as well as parietal and occipital regions.
284 citations
TL;DR: The findings indicate that the DMN is widely affected even if a single network node is impaired, and suggests that the widespread functional impairments in mTLE may attribute to an aberrant DMN.
214 citations
TL;DR: This work identified that self-referential and default-mode networks (DMNs) play distinct and crucial roles in the human brain functional architecture and revealed the causal influences among these RSNs at different processing levels, and supplied information for a deeper understanding of the brain network dynamics.
Abstract: The human brain has been documented to be spatially organized in a finite set of specific coherent patterns, namely resting state networks (RSNs). The interactions among RSNs, being potentially dynamic and directional, may not be adequately captured by simple correlation or anticorrelation. In order to evaluate the possible effective connectivity within those RSNs, we applied a conditional Granger causality analysis (CGCA) to the RSNs retrieved by independent component analysis (ICA) from resting state functional magnetic resonance imaging (fMRI) data. Our analysis provided evidence for specific causal influences among the detected RSNs: default-mode, dorsal attention, core, central-executive, self-referential, somatosensory, visual, and auditory networks. In particular, we identified that self-referential and default-mode networks (DMNs) play distinct and crucial roles in the human brain functional architecture. Specifically, the former RSN exerted the strongest causal influence over the other RSNs, revealing a top-down modulation of self-referential mental activity (SRN) over sensory and cognitive processing. In quite contrast, the latter RSN was profoundly affected by the other RSNs, which may underlie an integration of information from primary function and higher level cognition networks, consistent with previous task-related studies. Overall, our results revealed the causal influences among these RSNs at different processing levels, and supplied information for a deeper understanding of the brain network dynamics.
198 citations
TL;DR: Visual and quantitative analysis show that the proposed algorithm significantly improves the fusion quality; compared to fusion methods including PCA, Brovey, discrete wavelet transform (DWT).
184 citations
TL;DR: This letter presents a secure CSS scheme by introducing a reputation-based mechanism to identify misbehaviors and mitigate their harmful effect on sensing performance, and presents a trusted node assistance scheme that starts with reliable CRs.
Abstract: Existing cooperative spectrum sensing (CSS) schemes are typically vulnerable to attacks where misbehaved cognitive radios (CRs) falsify sensing data. To ensure the robustness of spectrum sensing, this letter presents a secure CSS scheme by introducing a reputation-based mechanism to identify misbehaviors and mitigate their harmful effect on sensing performance. Encouraged by the fact that such secure CSS is sensitive to the correctness of reputations, we further present a trusted node assistance scheme. This scheme starts with reliable CRs. Sensing information from other CRs are incorporated into cooperative sensing only when their reputation is verified, which increases robustness of cooperative sensing. Simulations verify the effectiveness of the proposed schemes.
182 citations
TL;DR: The present study addressed the use of the infinity reference obtained by the reference electrode standardisation technique (REST) in the study of EEG default mode network (DMN) and showed that REST can exactly recover the true EEG network configuration.
TL;DR: The ALFF analysis may provide a useful tool in fMRI study of epilepsy, and individual analyses based on statistic parametric mapping revealed a moderate sensitivity and a fairly high specificity for the lateralization of unilateral mTLE.
Abstract: Various functional imaging tools have been used to detect epileptic activity in the neural network underlying mesial temporal lobe epilepsy (mTLE). In the present fMRI study, a data-driven approach was employed to map interictal epileptic activity in mTLE patients by measuring the amplitude of low-frequency fluctuation (ALFF) of the blood oxygen level-dependent (BOLD) signal. Twenty-four left mTLE patients and 26 right mTLE patients were investigated by comparing with 25 healthy subjects. In the patients, the regions showing increased ALFF were consistently distributed in the mesial temporal lobe, thalamus, and a few of other cortical and subcortical structures composing a mesial temporal epilepsy network proposed previously, while the regions showing decreased ALFF were mostly located in the areas of so-called default-mode network. Data of simultaneous EEG-fMRI from a portion of the patients suggested that the increases in ALFF might be associated with the interictal epileptic activity. Individual analyses based on statistic parametric mapping revealed a moderate sensitivity and a fairly high specificity for the lateralization of unilateral mTLE. We conclude that the ALFF analysis may provide a useful tool in fMRI study of epilepsy.
TL;DR: In this paper, a double-electrode approach has been used to minimize the electrostatic effect in fiber-based actuators, and an average piezoelectric coefficient d33 of −57.6 pm/V has been characterized from fabricated fibers and this value is about twice larger than the value reported in PVDF thin-films.
Abstract: Piezoelectric actuation of doubly clamped, electrospun poly (vinylidene fluoride) (PVDF) fibers fabricated by a direct-write process has been demonstrated. Near-field electrospinning (NFES) has been utilized to fabricate PVDF fibers with good piezoelectric properties by means of the in situ electrical poling and mechanical stretching process. Experimentally, PVDF fibers have responded to both piezoelectric and electrostatic effects and a double-electrode approach has been used to minimize the electrostatic effect. An average piezoelectric coefficient d33 of −57.6 pm/V has been characterized from fabricated fibers and this value is about twice larger than the value reported in PVDF thin-films. Various complex patterns of PVDF fibers have been deposited using NFES, enabling possible array formats for fiber-based actuators with possible applications including artificial muscles and switches.
TL;DR: This study is the first to reveal a network of abnormal effective connectivity of core structures in SAD and lends neurobiological support towards cognitive models considering disinhibition and an attentional bias towards negative stimuli as a core feature of the disorder.
Abstract: The amygdala is often found to be abnormally recruited in social anxiety disorder (SAD) patients. The question whether amygdala activation is primarily abnormal and affects other brain systems or whether it responds “normally” to an abnormal pattern of information conveyed by other brain structures remained unanswered. To address this question, we investigated a network of effective connectivity associated with the amygdala using Granger causality analysis on resting-state functional MRI data of 22 SAD patients and 21 healthy controls (HC). Implications of abnormal effective connectivity and clinical severity were investigated using the Liebowitz Social Anxiety Scale (LSAS). Decreased influence from inferior temporal gyrus (ITG) to amygdala was found in SAD, while bidirectional influences between amygdala and visual cortices were increased compared to HCs. Clinical relevance of decreased effective connectivity from ITG to amygdala was suggested by a negative correlation of LSAS avoidance scores and the value of Granger causality. Our study is the first to reveal a network of abnormal effective connectivity of core structures in SAD. This is in support of a disregulation in predescribed modules involved in affect control. The amygdala is placed in a central position of dysfunction characterized both by decreased regulatory influence of orbitofrontal cortex and increased crosstalk with visual cortex. The model which is proposed based on our results lends neurobiological support towards cognitive models considering disinhibition and an attentional bias towards negative stimuli as a core feature of the disorder.
TL;DR: The key findings are: under the current model settings, the optimal allocation scheme is to assign the supplier as the responsibility holder with appropriate restrictions on the corresponding rights to determine the wholesale price; inherent conflict exists between the economic and CSR performance criteria and, hence, the two maxima cannot be achieved simultaneously.
TL;DR: A recommendation algorithm that makes use of social tags based on the user-tag-object tripartite graphs that can significantly solve the cold-start problem in social tagging systems with heterogeneous object degree.
Abstract: Based on the user-tag-object tripartite graphs, we propose a recommendation algorithm that makes use of social tags. Besides its low cost of computational time, the experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can enhance the algorithmic accuracy and diversity. Especially, it provides more personalized recommendation when the assigned tags belong to more diverse topics. The proposed algorithm is particularly effective for small-degree objects, which reminds us of the well-known cold-start problem in recommender systems. Further empirical study shows that the proposed algorithm can significantly solve this problem in social tagging systems with heterogeneous object degree.
TL;DR: A novel wavelet-based approach for medical image fusion is presented, which is developed by taking into not only account the characteristics of human visual system (HVS) but also the physical meaning of the wavelet coefficients.
Abstract: A novel wavelet-based approach for medical image fusion is presented, which is developed by taking into not only account the characteristics of human visual system (HVS) but also the physical meaning of the wavelet coefficients. After the medical images to be fused are decomposed by the wavelet transform, different-fusion schemes for combining the coefficients are proposed: coefficients in low-frequency band are selected with a visibility-based scheme, and coefficients in high-frequency bands are selected with a variance based method. To overcome the presence of noise and guarantee the homogeneity of the fused image, all the coefficients are subsequently performed by a window-based consistency verification process. The fused image is finally constructed by the inverse wavelet transform with all composite coefficients. To quantitatively evaluate and prove the performance of the proposed method, series of experiments and comparisons with some existing fusion methods are carried out in the paper. Experimental results on simulated and real medical images indicate that the proposed method is effective and can get satisfactory fusion results.
TL;DR: In this article, a novel approach for estimating the direction of arrivals (DOAs) in time modulated linear arrays (TMLAs) with unidirectional phase center motion (UPCM) scheme is proposed.
Abstract: A novel approach for estimating the direction of arrivals (DOAs) in time modulated linear arrays (TMLAs) with unidirectional phase center motion (UPCM) scheme is proposed in this paper. Based on the fact that the main beams of the patterns at different sidebands can be directed at different directions, the corresponding received signals can be used to compose a received data space. Thus, the spatial locations of the far-field sources can be estimated by using multiple signal classification (MUSIC) algorithm. Simulation results of the DOA estimation in an 8-element TMLA with the UPCM scheme validate the proposed approach, where the performance such as the accuracy and resolution of the DOA estimation is obtained through Monte-Carlo simulations. As compared to the DOA estimation based on conventional uniform linear arrays (ULAs), a much better resolution performance is obtained.
TL;DR: This paper investigates collective rotating motions of second-order multi-agent systems with the help of Lyapunov theory for complex systems and proposes rotating formation protocols to make all agents move with a specific structure in a circular channel.
TL;DR: The present analysis provides a clear picture about the relation between the Zipf's law and Heaps' law without the help of any specific stochastic model, namely the Heps' law is indeed a derivative phenomenon from the ZipF's law.
Abstract: Background: Zipf’s law and Heaps’ law are observed in disparate complex systems. Of particular interests, these two laws often appear together. Many theoretical models and analyses are performed to understand their co-occurrence in real systems, but it still lacks a clear picture about their relation. Methodology/Principal Findings: We show that the Heaps’ law can be considered as a derivative phenomenon if the system obeys the Zipf’s law. Furthermore, we refine the known approximate solution of the Heaps’ exponent provided the Zipf’s exponent. We show that the approximate solution is indeed an asymptotic solution for infinite systems, while in the finite-size system the Heaps’ exponent is sensitive to the system size. Extensive empirical analysis on tens of disparate systems demonstrates that our refined results can better capture the relation between the Zipf’s and Heaps’ exponents. Conclusions/Significance: The present analysis provides a clear picture about the relation between the Zipf’s law and Heaps’ law without the help of any specific stochastic model, namely the Heaps’ law is indeed a derivative phenomenon from the Zipf’s law. The presented numerical method gives considerably better estimation of the Heaps’ exponent given the Zipf’s exponent and the system size. Our analysis provides some insights and implications of real complex systems. For example, one can naturally obtained a better explanation of the accelerated growth of scale-free networks.
TL;DR: In this article, the magnetic loose spheres were found to assemble into randomly dispersed loose nanoscale spheres with diameters ∼300nm in ethylene glycol in the presence of polyethylene and a small quantity of polymethyleneimine.
TL;DR: An overlapping communities detecting algorithm is proposed whose main strategies are finding an initial partial community from a node with maximal node strength and adding tight nodes to expand the partial community.
Abstract: Identification of communities is significant in understanding the structures and functions of networks. Since some nodes naturally belong to several communities, the study of overlapping communities has attracted increasing attention recently, and many algorithms have been designed to detect overlapping communities. In this paper, an overlapping communities detecting algorithm is proposed whose main strategies are finding an initial partial community from a node with maximal node strength and adding tight nodes to expand the partial community. Seven real-world complex networks and one synthetic network are used to evaluate the algorithm. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in weighted networks.
TL;DR: Some dynamic neighbour-based rules are adopted for the agents with the consideration of parameter uncertainties and external disturbances to make all agents asymptotically reach consensus while satisfying desired H∞ performance.
Abstract: This study is concerned with consensus problems for a class of multi-agent systems with second-order dynamics. Some dynamic neighbour-based rules are adopted for the agents with the consideration of parameter uncertainties and external disturbances. Sufficient conditions are derived to make all agents asymptotically reach consensus while satisfying desired H∞ performance. Finally, numerical simulations are provided to show the effectiveness of our theoretical results.
TL;DR: A method is proposed to optimize the projection matrix based on equiangular tight frame (ETF) design, which demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit.
Abstract: Compressive sensing (CS) is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame (ETF) design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore, an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit.
23 Jun 2010
TL;DR: The improved Apriori algorithm based on MapReduce mode is described, which can handle massive datasets with a large number of nodes on Hadoop platform.
Abstract: As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance inefficient. Recently the development of network and distributed technology makes cloud computing a reality in the implementation of association rules algorithm. In this paper we describe the improved Apriori algorithm based on MapReduce mode, which can handle massive datasets with a large number of nodes on Hadoop platform.
TL;DR: Wang et al. as mentioned in this paper investigated the correlation between degree and selection diversity and reported some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.
Abstract: Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, where users are connected with music groups and bookmarks, respectively. The degree distributions and degree-degree correlations for both users and objects are reported. We propose a new index, named collaborative similarity, to quantify the diversity of tastes based on the collaborative selection. Accordingly, the correlation between degree and selection diversity is investigated. We report some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.
TL;DR: The paper presents an algorithm for evaluating performance distribution of complex series-parallel multi-state systems with common cause failures caused by propagation of failures in system elements based on the universal generating function approach and a generalized reliability block diagram method.
TL;DR: In this article, a simple bandwidth enhancement method for low profile E-shaped patch antennas is presented, by introducing a distributed LC circuit to the E-shape patch antenna, a new resonant frequency close to that of the Eshaped patch is obtained, thus the bandwidth is widened.
Abstract: A simple bandwidth enhancement method for low profile E-shaped patch antennas is presented. By introducing a distributed LC circuit to the E-shaped patch antenna, a new resonant frequency close to that of the E-shaped patch is obtained, thus the bandwidth is widened. Moreover, the air thickness of the E-shaped patch antennas is reduced to only 0.0344λ0. A prototype antenna operated at AMPS band (824-894 MHz) was fabricated and measured. Measured results show that the designed low profile antenna has an impedance bandwidth over 9% for VSWR<; 2, with a satisfactory radiation performance within the bandwidth. The proposed method is also applicable to the design of other types of low profile slot-loaded patch antennas.
TL;DR: In this article, four types of plasmonic lenses for the purpose of superfocusing designed on the bases of approximate negative refractive index concept, subwavelength metallic structures, waveguide mode and curved chains of nanoparticles, respectively, were introduced.
Abstract: Four types of plasmonic lenses for the purpose of superfocusing designed on the bases of approximate negative refractive index concept, subwavelength metallic structures, waveguide mode were introduced, and curved chains of nanoparticles, respectively, were introduced. Imaging mechanism, fabrication, and characterization issues were presented. Theoretical analyses of the illumination with different polarization states on focusing performance of the plasmonic lenses were given also. In addition, a hybrid Au-Ag plasmonic lens with chirped slits for the purpose of avoiding oxidation of Ag film was presented.
TL;DR: In this study, mathematical modelling and analysis for consensus problems will be conducted for a group of autonomous mobile agents with double-integrator dynamics and time-varying interconnection delays, and the consensus stability of the multi-agent system is obtained.
Abstract: In this study, mathematical modelling and analysis for consensus problems will be conducted for a group of autonomous mobile agents with double-integrator dynamics and time-varying interconnection delays. To solve the problems, distributed control scheme for each agent will be proposed first. Then the consensus stability of the multi-agent system is obtained for the problem, where the dynamics of each agent is second-order with time-varying interconnection delays. In the convergence analysis, both fixed and switched interconnection topologies of the considered multi-agent system are investigated. Finally, some numerical examples are presented to validate the consensus algorithms.