Showing papers by "University of Electronic Science and Technology of China published in 2011"

Link prediction in complex networks: A survey

Abstract: Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

Efficient design and assembly of custom TALEN and other TAL effector-based constructs for DNA targeting

Abstract: TALENs are important new tools for genome engineering. Fusions of transcription activator-like (TAL) effectors of plant pathogenic Xanthomonas spp. to the FokI nuclease, TALENs bind and cleave DNA in pairs. Binding specificity is determined by customizable arrays of polymorphic amino acid repeats in the TAL effectors. We present a method and reagents for efficiently assembling TALEN constructs with custom repeat arrays. We also describe design guidelines based on naturally occurring TAL effectors and their binding sites. Using software that applies these guidelines, in nine genes from plants, animals and protists, we found candidate cleavage sites on average every 35bp. Each of 15 sites selected from this set was cleaved in a yeast-based assay with TALEN pairs constructed with our reagents. We used two of the TALEN pairs to mutate HPRT1 in human cells and ADH1 in Arabidopsis thaliana protoplasts. Our reagents include a plasmid construct for making custom TAL effectors and one for TAL effector fusions to additional proteins of interest. Using the former, we constructed de novo a functional analog of AvrHah1 of Xanthomonas gardneri. The complete plasmid set is available through the non-profit repository AddGene

Leaders in social networks, the Delicious case.

Abstract: Finding pertinent information is not limited to search engines. Online communities can amplify the influence of a small number of power users for the benefit of all other users. Users' information foraging in depth and breadth can be greatly enhanced by choosing suitable leaders. For instance in delicious.com, users subscribe to leaders' collection which lead to a deeper and wider reach not achievable with search engines. To consolidate such collective search, it is essential to utilize the leadership topology and identify influential users. Google's PageRank, as a successful search algorithm in the World Wide Web, turns out to be less effective in networks of people. We thus devise an adaptive and parameter-free algorithm, the LeaderRank, to quantify user influence. We show that LeaderRank outperforms PageRank in terms of ranking effectiveness, as well as robustness against manipulations and noisy data. These results suggest that leaders who are aware of their clout may reinforce the development of social networks, and thus the power of collective search.

Temperature dependence of graphene oxide reduced by hydrazine hydrate

Abstract: Graphene oxide (GO) was successfully prepared by a modified Hummer's method. The reduction effect and mechanism of the as-prepared GO reduced with hydrazine hydrate at different temperatures and time were characterized by x-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), elemental analysis (EA), x-ray diffractions (XRD), Raman spectroscopy and thermo-gravimetric analysis (TGA). The results showed that the reduction effect of GO mainly depended on treatment temperature instead of treatment time. Desirable reduction of GO can only be obtained at high treatment temperature. Reduced at 95 °C for 3 h, the C/O atomic ratio of GO increased from 3.1 to 15.1, which was impossible to obtain at low temperatures, such as 80, 60 or 15 °C, even for longer reduction time. XPS, 13C NMR and FTIR results show that most of the epoxide groups bonded to graphite during the oxidation were removed from GO and form the sp2 structure after being reduced by hydrazine hydrate at high temperature (>60 °C), leading to the electric conductivity of GO increasing from 1.5 × 10 − 6 to 5 S cm − 1, while the hydroxyls on the surface of GO were not removed by hydrazine hydrate even at high temperature. Additionally, the FTIR, XRD and Raman spectrum indicate that the GO reduced by hydrazine hydrate can not be entirely restored to the pristine graphite structures. XPS and FTIR data also suggest that carbonyl and carboxyl groups can be reduced by hydrazine hydrate and possibly form hydrazone, but not a C = C structure.

Altered functional–structural coupling of large-scale brain networks in idiopathic generalized epilepsy

Abstract: The human brain is a large-scale integrated network in the functional and structural domain. Graph theoretical analysis provides a novel framework for analysing such complex networks. While previous neuroimaging studies have uncovered abnormalities in several specific brain networks in patients with idiopathic generalized epilepsy characterized by tonic–clonic seizures, little is known about changes in whole-brain functional and structural connectivity networks. Regarding functional and structural connectivity, networks are intimately related and share common small-world topological features. We predict that patients with idiopathic generalized epilepsy would exhibit a decoupling between functional and structural networks. In this study, 26 patients with idiopathic generalized epilepsy characterized by tonic–clonic seizures and 26 age- and sex-matched healthy controls were recruited. Resting-state functional magnetic resonance imaging signal correlations and diffusion tensor image tractography were used to generate functional and structural connectivity networks. Graph theoretical analysis revealed that the patients lost optimal topological organization in both functional and structural connectivity networks. Moreover, the patients showed significant increases in nodal topological characteristics in several cortical and subcortical regions, including mesial frontal cortex, putamen, thalamus and amygdala relative to controls, supporting the hypothesis that regions playing important roles in the pathogenesis of epilepsy may display abnormal hub properties in network analysis. Relative to controls, patients showed further decreases in nodal topological characteristics in areas of the default mode network, such as the posterior cingulate gyrus and inferior temporal gyrus. Most importantly, the degree of coupling between functional and structural connectivity networks was decreased, and exhibited a negative correlation with epilepsy duration in patients. Our findings suggest that the decoupling of functional and structural connectivity may reflect the progress of long-term impairment in idiopathic generalized epilepsy, and may be used as a potential biomarker to detect subtle brain abnormalities in epilepsy. Overall, our results demonstrate for the first time that idiopathic generalized epilepsy is reflected in a disrupted topological organization in large-scale brain functional and structural networks, thus providing valuable information for better understanding the pathophysiological mechanisms of generalized tonic–clonic seizures. * Abbreviations : AAL : automated anatomical labelling GTCS : generalized tonic–clonic seizures IGE : idiopathic generalized epilepsy

Control of nonlinear systems with partial state constraints using a barrier Lyapunov function

Abstract: This article addresses the problem of control design for strict-feedback systems with constraints on the states. To prevent the states from violating the constraints, we employ a barrier Lyapunov function (BLF), which grows to infinity whenever its arguments approaches some finite limits. Based on BLF-based backstepping, we show that asymptotic output tracking is achieved without violation of any constraint, provided that the initial states and control parameters are feasible. We also establish sufficient conditions to ensure feasibility, which can be checked offline without precise knowledge of the initial states. The feasibility conditions are relaxed when handling the partial state constraint problem as compared to the full state constraint problem. In the presence of parametric uncertainties, BLF-based adaptive backstepping is useful in preventing the states from transgressing the constrained region during the transient stages of online parameter adaptation. To relax the feasibility conditions, asymme...

Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI.

Abstract: Studies of in mesial temporal lobe epilepsy (mTLE) patients with hippocampal sclerosis (HS) have reported reductions in both functional and structural connectivity between hippocampal structures and adjacent brain regions. However, little is known about the connectivity among the default mode network (DMN) in mTLE. Here, we hypothesized that both functional and structural connectivity within the DMN were disturbed in mTLE. To test this hypothesis, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) were applied to examine the DMN connectivity of 20 mTLE patients, and 20 gender- and age-matched healthy controls. Combining these two techniques, we explored the changes in functional (temporal correlation coefficient derived from fMRI) and structural (path length and connection density derived from DTI tractography) connectivity of the DMN. Compared to the controls, we found that both functional and structural connectivity were significantly decreased between the posterior cingulate cortex (PCC)/precuneus (PCUN) and bilateral mesial temporal lobes (mTLs) in patients. No significant between-group difference was found between the PCC/PCUN and medial prefrontal cortex (mPFC). In addition, functional connectivity was found to be correlated with structural connectivity in two pairwise regions, namely between the PCC/PCUN and bilateral mTLs, respectively. Our results suggest that the decreased functional connectivity within the DMN in mTLE may be a consequence of the decreased connection density underpinning the degeneration of structural connectivity.

A novel high-performance photovoltaic–thermoelectric hybrid device

Abstract: A novel photovoltaic–thermoelectric (PV–TE) hybrid device composed of a series-connected dye-sensitized solar cell (DSSC), a solar selective absorber (SSA) and a TE generator is created. The conversion efficiency of the DSSC was enhanced significantly by using the SSA and TE generator to utilize residual sunlight transmitted through the DSSC. The hybrid device comprising a DSSC as a “top cell” for high-energy photons and an SSA coated TE generator as a “bottom cell” for low-energy photons gave rise to an overall conversion efficiency larger than 13%. Although our hybrid device was not yet optimized but served as proof-of-principle for harvesting electricity from solar light and heat simultaneously with high conversion efficiency by a single device, this study would give some enlightenment for the development of high-performance PV–TE hybrid devices.

Electromagnetic and microwave-absorbing properties of magnetic nickel ferrite nanocrystals

Abstract: The electromagnetic and microwave absorbing properties of nickel ferrite nanocrystals were investigated for the first time. There were two frequencies corresponding to the maximum reflection loss in a wide thickness range from 3.0 to 5.0 mm, which may be bought by the nanosize effect and the good crystallization of the nanocrystals.

Altered functional connectivity in default mode network in absence epilepsy: A resting-state fMRI study

Abstract: Dysfunctional default mode network (DMN) has been observed in various mental disorders, including epilepsy (see review Broyd et al. (2009): Neurosci Biobehav Rev 33:279-296). Because interic- tal epileptic discharges may affect DMN, resting-state fMRI was used in this study to determine DMN functional connectivity in 14 healthy controls and 12 absence epilepsy patients. To avoid interictal epi- leptic discharge effects, testing was performed within interictal durations when there were no interictal epileptic discharges. Cross-correlation functional connectivity analysis with seed at posterior cingulate cortex, as well as region-wise calculation in DMN, revealed decreased integration within DMN in the absence epilepsy patients. Region-wise functional connectivity among the frontal, parietal, and tempo- ral lobe was significantly decreased in the patient group. Moreover, functional connectivity between the frontal and parietal lobe revealed a significant negative correlation with epilepsy duration. These findings indicated DMN abnormalities in patients with absence epilepsy, even during resting interictal durations without interictal epileptic discharges. Abnormal functional connectivity in absence epilepsy may reflect abnormal anatomo-functional architectural integration in DMN, as a result of cognitive mental impairment and unconsciousness during absence seizure. Hum Brain Mapp 00:000-000, 2010. V C 2010 Wiley-Liss, Inc.

Reducing Mutual Coupling of Closely Spaced Microstrip MIMO Antennas for WLAN Application

Abstract: An efficient mutual coupling reduction method is introduced in this letter for extremely closely placed dual-element microstrip antennas positioned on a finite-sized ground plane for WLAN MIMO application at 5.8 GHz. High isolation can be achieved through a simple slot structure on the ground between the microstrip antennas. The position, length, and width of the slot have been optimized for maximizing the isolation. It is found that more than 40 dB isolation can be achieved between two parallel microstrip antennas sharing a common ground plane. The space distance of these antennas is 17.5 mm ≈ 0.33λ0 from element center to center (side by side of 1.6 mm ≈ 0.031λ0) when the ground plane size is 0.85λ0 × 0.55λ0. Along with this letter, several prototypes were fabricated, and their performances measured to validate the obtained IE3D moment method-based simulation results.

Millimeter-Wave Substrate Integrated Waveguide Long Slot Leaky-Wave Antennas and Two-Dimensional Multibeam Applications

Abstract: Two types of substrate integrated waveguide (SIW) long slot leaky-wave antennas with controllable sidelobe level are proposed and demonstrated in this paper. The first prototype is able to achieve an excellent sidelobe level of -27.7ndB by properly meandering a long slot etched on the broadside of a straight SIW section from the centerline toward the sidewall then back. But it is known that an asymmetrically curved slot would worsen the cross-polar level. To overcome this drawback, a modified leaky-wave antenna is proposed, which has a straight long slot etched on the broadside of a meandering SIW section. It yields an outstanding sidelobe level of -29.3 dB and also improves the cross-polar level by more than 11 dB at 35 GHz. Experimental results agree well with simulations, thus validating our design. Then, a two-dimensional (2-D) multibeam antenna is developed by combining such 14 leaky-wave antennas with an SIW beamforming network (BFN). It has features of scanning both in elevation orientation by varying frequency and in cross-plane direction by using the BFN. Excited at ports 1-10 of such a 2-D multibeam antenna at 35 GHz, angular region of 86.6° in azimuth can effectively be covered by 3 dB beam-width of ten pencil beams. Varying frequency from 33 GHz to 37 GHz, the angular region of 37.5° and 38.9° in elevation can be covered by 3 dB beam-width of those continuous scanning beams excited at ports 6 and 8 respectively.

Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors

Abstract: Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.

Simplified Relay Selection and Power Allocation in Cooperative Cognitive Radio Systems

Abstract: In this paper, we investigate joint relay selection and power allocation to maximize system throughput with limited interference to licensed (primary) users in cognitive radio (CR) systems. As these two problems are coupled together, we first develop an optimal approach based on the dual method and then propose a suboptimal approach to reduce complexity while maintaining reasonable performance. From our simulation results, the proposed approaches can increase the system throughput by over 50%.

Word Learning in Infant- and Adult-Directed Speech

Abstract: Infant-directed speech (IDS), compared with adult-directed speech (ADS), is characterized by a slower rate, a higher fundamental frequency, greater pitch variations, longer pauses, repetitive intonational structures, and shorter sentences. Despite studies on the properties of IDS, there is no direct demonstration of its effects for word learning in infants. This study examined whether 21- and 27-month-old children learned novel words better in IDS than in ADS. Two major findings emerged. First, 21-month-olds reliably learned words only in the IDS condition, although children with relatively larger vocabulary than their peers learned in the ADS condition as well. Second, 27-month-olds reliably learned the words in the ADS condition. These results support the implicitly held assumption that IDS does in fact facilitate word mapping at the start of lexical acquisition and that its influence wanes as language development proceeds.

Measuring Economic Impacts of Drought: A Review and Discussion

Abstract: Purpose – The purpose of this paper is to provide useful information for members of the weather community and policy makers, to help them understand the full scope of drought economic impacts and assessment methodologies, and to help determine the feasibility of future drought mitigation programs.Design/methodology/approach – To accomplish the objective, the paper reviews the literature of drought economic impact studies in both agricultural and non‐agricultural sectors, summarizes the methods and data employed, compares the various results, and investigates the problems and limitations of previous studies.Findings – The paper concludes with a discussion of the challenges and directions of future improvement on drought economic impact assessment.Originality/value – This paper gives a comprehensive review of drought economic impacts and the associated quantitative assessment methodologies, which provides valuable information to rational decisions supporting drought mitigation policies and programs.

The small world yields the most effective information spreading

Abstract: The spreading dynamics of information and diseases are usually analyzed by using a unified framework and analogous models. In this paper, we propose a model to emphasize the essential difference between information spreading and epidemic spreading, where the memory effects, the social reinforcement and the non-redundancy of contacts are taken into account. Under certain conditions, the information spreads faster and broader in regular networks than in random networks, which to some extent supports the recent experimental observation of spreading in online society (Centola D 2010 Science 329 1194). At the same time, the simulation result indicates that the random networks tend to be favorable for effective spreading when the network size increases. This challenges the validity of the above-mentioned experiment for large-scale systems. More importantly, we show that the spreading effectiveness can be sharply enhanced by introducing a little randomness into the regular structure, namely the small-world networks yield the most effective information spreading. This work provides insights into the role of local clustering in information spreading.

Efficient Flexible Phosphorescent Polymer Light‐Emitting Diodes Based on Silver Nanowire‐Polymer Composite Electrode

Abstract: Blue, green, and red electrophosphorescent polymer light-emitting diodes have been fabricated on silver nanowire-polymer composite electrode. The devices are 20%-50% more efficient than control devices on ITO/glass and exhibit small efficiency roll-off at high luminances. The blue PLEDs were repeatedly bent to 1.5 mm radius concave or convex with calculated strain in the emissive layer approximately 5% (tensile or compressive).

FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms

Abstract: Various methods and algorithms have been developed for multiclass classification problems in recent years. How to select an effective algorithm for a multiclass classification task is an important yet difficult issue. Since the multiclass algorithm selection normally involves more than one criterion, such as accuracy and computation time, the selection process can be modeled as a multiple criteria decision making (MCDM) problem. While the evaluations of algorithms provided by different MCDM methods are in agreement sometimes, there are situations where MCDM methods generate very different results. To resolve this disagreement and help decision makers pick the most suitable classifier(s), this paper proposes a fusion approach to produce a weighted compatible MCDM ranking of multiclass classification algorithms. Several multiclass datasets from different domains are used in the experimental study to test the proposed fusion approach. The results prove that MCDM methods are useful tools for evaluating multiclass classification algorithms and the fusion approach is capable of identifying a compromised solution when different MCDM methods generate conflicting rankings.

Feature selection and parameter optimization for support vector machines

Abstract: Research highlights? A genetic algorithm with feature chromosomes (GAFC) is proposed. ? The asymptotic behaviors of support vector machines (SVM) are fused with GA. ? The GAFC has not only the search ability of GA, but also has the search ability of feature chromosomes. ? The GAFC obtained good performances by optimizing feature subset and parameters of SVM simultaneously. Support vector machines (SVM) are an emerging data classification technique with many diverse applications. The feature subset selection, along with the parameter setting in the SVM training procedure significantly influences the classification accuracy. In this paper, the asymptotic behaviors of support vector machines are fused with genetic algorithm (GA) and the feature chromosomes are generated, which thereby directs the search of genetic algorithm to the straight line of optimal generalization error in the superparameter space. On this basis, a new approach based on genetic algorithm with feature chromosomes, termed GA with feature chromosomes, is proposed to simultaneously optimize the feature subset and the parameters for SVM.To evaluate the proposed approach, the experiment adopts several real world datasets from the UCI database and from the Benchmark database. Compared with the GA without feature chromosomes, the grid search, and other approaches, the proposed approach not only has higher classification accuracy and smaller feature subsets, but also has fewer processing time.

Ensuring supply chain quality performance through applying the SCOR model

Abstract: The objective of adopting quality standards such as ISO 9000 series is to help companies develop and maintain supply chain processes that meet certain performance metrics, such as those provided by the Supply Chain Operations Reference model (SCOR). Based on the survey data from 232 companies that have obtained ISO 9000 certification, this study extends the five decision areas (Plan, Source, Make, Deliver, and Return) of the SCOR model by integrating quality assurance measures in the supply chain process. The results show that individually, each decision area has a positive impact on both customer-facing supply chain quality performance and internal-facing firm level business performance. Collectively, ‘Plan’ and ‘Source’ decisions are more important to customer-facing supply chain performance (reliability, response, and flexibility), and ‘Make’ decisions positively affect internal-facing performance metrics (cost and asset).

Adaptive Spatial Modulation for Wireless MIMO Transmission Systems

Abstract: In this letter, an adaptive spatial modulation (ASM) transmission scheme is proposed to achieve better system performance under a fixed data rate. The proposed scheme is based on a developed modulation order selection criterion (MOSC), so as to minimize the conditioned pairwise error probability (PEP) for each channel realization. Furthermore, the special information-conveying mode of spatial modulation (SM) is utilized to reduce the complexity of the proposed algorithm. Simulation results show that the proposed adaptive SM scheme provides considerable system performance improvement compared to original SM system.

Ensemble of software defect predictors: an ahp-based evaluation method

Abstract: Classification algorithms that help to identify software defects or faults play a crucial role in software risk management. Experimental results have shown that ensemble of classifiers are often mo...