A combination of single-carrier frequency-division mult-iple-access (SC-FDMA) and relay transmission is effective for performance improvement in uplink transmission. In SC-FDMA, a mapping strategy of user's spectrum has an enormous impact on system performance. In the relay communication, the optimum mapping strategy may differentiate from that in direct communication because of the independently distributed channels among nodes. In this letter, how each link should be considered in subcarrier mapping is studied and the impact of mapping strategies on the average bit error rate (BER) performance of single-user SC-FDMA relay communications will be given.

This paper first proposes a fast fingerprint identification method based on a weighted-mean of binary image, and further investigates optimization of the weights. The proposed method uses less computer memory than the conventional pattern matching method, and takes less computation time than both the feature extraction method and the pattern matching method. It is particularly effective on the fingerprints with a small angle of inclination. In order to improve the identification precision of the proposed basic method, three schemes of modifying the proposed basic method are also proposed. The performance of the proposed basic method and its modified schemes is evaluated by theoretical analysis and computer experiment using the fingerprint images recorded from a fingerprint read-in device. The numerical results showed that the proposed method using the modified schemes can improve both the true acceptance rate and the false rejection rate with less memory and complexity in comparison with the conventional pattern matching method and the feature extraction method.

To reduce mutual coupling of a two-level nested array (TLNA) with an even number of sensors, we propose an improved array configuration that exhibits all the good properties of the prototype optimal configuration under the constraint of a fixed number of sensors N and achieves reduction of mutual coupling. Compared with the prototype optimal TLNA (POTLNA), which inner level and outer level both have N/2 sensors, those of the improved optimal TLNA (IOTLNA) are N/2-1 and N/2+1. It is proved that the physical aperture and uniform degrees of freedom (uDOFs) of IOTLNA are the same as those of POTLNA, and the number of sensor pairs with small separations of IOTLNA is reduced. We also construct an improved optimal second-order super nested array (SNA) by using the IOTLNA as the parent nested array, termed IOTLNA-SNA, which has the same physical aperture and the same uDOFs, as well as the IOTLNA. Numerical simulations demonstrate the better performance of the improved array configurations.

Protein-Protein Interaction Extraction (PPIE) from biomedical literatures is an important task in biomedical text mining and has achieved great success on public datasets. However, in real-world applications, the existing PPI extraction methods are limited to label effort. Therefore, transfer learning method is applied to reduce the cost of manual labeling. Current transfer learning methods suffer from negative transfer and lower performance. To tackle this problem, an improved TrAdaBoost algorithm is proposed, that is, relative distribution is introduced to initialize the weights of TrAdaBoost to overcome the negative transfer caused by domain differences. To make further improvement on the performance of transfer learning, an approach combining active learning with the improved TrAdaBoost is presented. The experimental results on publicly available PPI corpora show that our method outperforms TrAdaBoost and SVM when the labeled data is insufficient,and on document classification corpora, it also illustrates that the proposed approaches can achieve better performance than TrAdaBoost and TPTSVM in final, which verifies the effectiveness of our methods.

This paper presents a fundamental locally one-dimensional (FLOD) method for 3-D thermal simulation. We first propose a locally one-dimensional (LOD) method for heat transfer equation within general inhomogeneous media. The proposed LOD method is then cast into compact form and formulated into the FLOD method with operator-free right-hand-side (RHS), which leads to computationally efficient update equations. Memory storage requirements and boundary conditions for both FLOD and LOD methods are detailed and compared. Stability analysis by means of analyzing the eigenvalues of amplification matrix substantiates the stability of the FLOD method. Additionally, the potential instability of the Douglas Gunn (DG) alternating-direction-implicit (ADI) method for inhomogeneous media is demonstrated. Numerical experiments justify the gain achieved in the overall efficiency for FLOD over LOD, DG-ADI and explicit methods. Furthermore, the relative maximum error of the FLOD method illustrates good trade-off between accuracy and efficiency.

Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.