Search

Neurobiological markers of stress symptom progression for healthy survivors from a disaster (e.g., an earthquake) would greatly help with early intervention to prevent the development of stress-related disorders. However, the relationship between the neurobiological alterations and the symptom progression over time is unclear. Here, we examined 44 healthy survivors of the Wenchuan earthquake in China in a longitudinal resting-state fMRI study to observe the alterations of brain functions related to depressive or anxiety symptom progression. Using multi-variate pattern analysis to the fMRI data, we successfully predicted the depressive or anxiety symptom severity for these survivors in short- (25 days) and long-term (2 years) and the symptom severity changes over time. Several brain areas (e.g., the frontolimbic and striatal areas) and the functional connectivities located within the fronto-striato-thalamic and default-mode networks were found to be correlated with the symptom progression and might play important roles in the adaptation to trauma. [ABSTRACT FROM AUTHOR]

Conductive polymer composites require a three-dimensional (3D) network to impart electrical conductivity. A general method that is applicable to most polymers for achieving a desirable graphene 3D network is still a challenge. We have developed a facile technique to fabricate highly electrical conductive composite using vacuum-assisted infusion of epoxy into graphene sponge (GS) scaffold. Macroscopic GSs were synthesized from graphene oxide solution by a hydrothermal method combined with freeze drying. The GS/epoxy composites prepared display consistent isotropic electrical conductivity around 1 S/m, and it is found to be close to that of the pristine GS. Compared with neat epoxy, GS/epoxy has a 12-orders-of-magnitude increase in electrical conductivity, attributed to the compactly interconnected graphene network constructed in the polymer matrix. This method can be extended to other materials to fabricate highly conductive composites for practical applications such as electronic devices, sensors, actuators, and electromagnetic shielding. [ABSTRACT FROM AUTHOR]

Many existing adaptive beamformers possess robustness against arbitrary array steering vector (ASV) mismatches within presumed uncertainty set. However, when the array facing a large steering direction error, their performance degrade significantly since the uncertainty in steering direction generally gives rise to an outstanding mismatch in ASV. In the applications of microphone array, large steering direction error is often unavoidable because of the motion of target speaker. Meanwhile, in addition to conventional adaptive beamformers, microphone array also requests a controlled frequency response to target signal. In this paper, we propose a new adaptive microphone array implemented in frequency domain with controlled mainlobe and frequency response. A compact ASV uncertainty set explicitly modelling steering direction error and the other arbitrary ASV errors is exploited to derive beamformer with robust constraints on array magnitude response. Numerical results show that the proposed microphone array not only produces large controlled robust response region and robust frequency response, but also achieves high performance in SINR enhancement. [ABSTRACT FROM AUTHOR]

Abstract   The focus of this paper is on joint feature re-extraction and classification in cases when the training data set is small. An iterative semi-supervised support vector machine (SVM) algorithm is proposed, where each iteration consists both feature re-extraction and classification, and the feature re-extraction is based on the classification results from the previous iteration. Feature extraction is first discussed in the framework of Rayleigh coefficient maximization. The effectiveness of common spatial pattern (CSP) feature, which is commonly used in Electroencephalogram (EEG) data analysis and EEG-based brain computer interfaces (BCIs), can be explained by Rayleigh coefficient maximization. Two other features are also defined using the Rayleigh coefficient. These features are effective for discriminating two classes with different means or different variances. If we extract features based on Rayleigh coefficient maximization, a large training data set with labels is required in general; otherwise, the extracted features are not reliable. Thus we present an iterative semi-supervised SVM algorithm embedded with feature re-extraction. This iterative algorithm can be used to extract these three features reliably and perform classification simultaneously in cases where the training data set is small. Each iteration is composed of two main steps: (i) the training data set is updated/augmented using unlabeled test data with their predicted labels; features are re-extracted based on the augmented training data set. (ii) The re-extracted features are classified by a standard SVM. Regarding parameter setting and model selection of our algorithm, we also propose a semi-supervised learning-based method using the Rayleigh coefficient, in which both training data and test data are used. This method is suitable when cross-validation model selection may not work for small training data set. Finally, the results of data analysis are presented to demonstrate the validity of our approach. [ABSTRACT FROM AUTHOR]