Xiamen University

About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.

3-D Object Retrieval With Hausdorff Distance Learning

Abstract: In view-based 3-D object retrieval, each object is described by a set of views. Group matching thus plays an important role. Previous research efforts have shown the effectiveness of Hausdorff distance in group matching. In this paper, we propose a 3-D object retrieval scheme with Hausdorff distance learning. In our approach, relevance feedback information is employed to select positive and negative view pairs with a probabilistic strategy, and a view-level Mahalanobis distance metric is learned. This Mahalanobis distance metric is adopted in estimating the Hausdorff distances between objects, based on which the objects in the 3-D database are ranked. We conduct experiments on three testing data sets, and the results demonstrate that the proposed Hausdorff learning approach can improve 3-D object retrieval performance.

Synthesis of single crystalline hexagonal nanobricks of LiNi1/3Co1/3Mn1/3O2 with high percentage of exposed {010} active facets as high rate performance cathode material for lithium-ion battery

Abstract: Single crystalline LiNi1/3Co1/3Mn1/3O2 (LNCM) hexagonal nanobricks with a high percentage of exposed {010} facets are synthesized by using Ni1/3Co1/3Mn1/3(OH)2 hexagonal nanosheets as both template and precursor, and exhibit excellent high rate performance as a cathode of lithium ion batteries.

Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network

Abstract: As a challenging pattern recognition task, automatic real-time emotion recognition based on multi-channel EEG signals is becoming an important computer-aided method for emotion disorder diagnose in neurology and psychiatry. Traditional machine learning approaches require to design and extract various features from single or multiple channels based on comprehensive domain knowledge. Consequently, these approaches may be an obstacle for non-domain experts. On the contrast, deep learning approaches have been used successfully in many recent literatures to learn features and classify different types of data. In this paper, baseline signals are considered and a simple but effective pre-processing method has been proposed to improve the recognition accuracy. Meanwhile, a hybrid neural network which combines `Convolutional Neural Network (CNN)’ and `Recurrent Neural Network (RNN)’ has been applied to classify human emotion states by effectively learning compositional spatial-temporal representation of raw EEG streams. The CNN module is used to mine the inter-channel correlation among physically adjacent EEG signals by converting the chain-like EEG sequence into 2D-like frame sequence. The LSTM module is adopted to mine contextual information. Experiments are carried out in a segment-level emotion identification task, on the DEAP benchmarking dataset. Our experimental results indicate that the proposed pre-processing method can increase emotion recognition accuracy by 32% approximately and the model achieves a high performance with a mean accuracy of 90.80% and 91.03% on valence and arousal classification task respectively.

Quadruple bonding in C 2 and analogous eight-valence electron species

Abstract: The bonding order of multiply bonded main-group elements is conventionally thought to be limited to triple bonds. Now, using high-level theoretical methods, it is shown that C2 and its isoelectronic molecules CN+, BN and CB− are quadruply bonded, featuring not only one σ - and two π-bonds, but also one weak ‘inverted’ bond.