An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.
a b s t r a c t Multi-domain network survivability is a key problem area and crankback signaling offers a very viable alternative for post-fault restoration. However, although some initial multi-domain crankback studies have been done, most have not considered post- fault recovery. Along these lines, this paper proposes a novel solution framework for
Distant supervision has been widely used for relation extraction but suffers from noise labeling problem. Neural network models are proposed to denoise with attention mechanism but cannot eliminate noisy data due to its non-zero weights. Hard decision is proposed to remove wrongly-labeled instances from the positive set though causes loss of useful information contained in removed instances. In this paper, we propose a novel generative neural framework named RDSGAN (Rank-based Distant Supervision GAN) which automatically generates valid instances for distant supervision relation extraction. Our framework combines soft attention and hard decision to learn the distribution of true positive instances via adversarial training and selects valid instances conforming to the distribution via rank-based distant supervision, which addresses the false positive problem. Experimental results show the superiority of our framework over strong baselines.
Learning low-dimensional representations of networked documents is a crucial task for documents linked in network structures.Relational Topic Models (RTMs) have shown their strengths in modeling both document contents and relations to discover the latent topic semantic representations.However, higher-order correlation structure information among documents is largely ignored in these methods.Therefore, we propose a novel graph relational topic model (GRTM) for document network, to fully explore and mix neighborhood information of documents on each order, based on the Higher-order Graph Attention Network (HGAT) with the log-normal prior in the graph attention.The proposed method can address the aforementioned issue via the information propagation among document-document based on the HGAT probabilistic encoder, to learn efficient networked document representations in the latent topic space, which can fully reflect document contents, along with document connections.Experiments on several real-world document network datasets show that, through fully exploring information in documents and document networks, our model achieves better performance on unsupervised representation learning and outperforms existing competitive methods in various downstream tasks.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The relationship between music and human emotions is a very interesting area.But how exactly music affects human emotions and what the valid parameters needed to classify the pattern is also an open question.In this paper, we consider the effect of four different types of music on human emotions through the nonlinear analysis of Heart rate variability (HRV).The t-test shows that the Largest lyapunov exponent (LLE) of HRV acquired from subjects with or without music stimulation is significantly different (p<0.01,Cohen's d>0.8).More importantly, the LLE of HRV is also influenced by different types of music respectively, and then we applied LLE as the affective features to recognize the subjects' emotional state which induced by specific types of music.An overall correct rate of 84 percent for quinary classification of amusement, excitement, sadness, fear and the baseline state by SVM classifier are obtained.
With the improvement of living standard, people pay more and more attention to their health. The detection of sleep information has always been a hotspot in both the academic and industrial area. However, traditional sleep monitoring systems need to bundle devices on users, which in return negatively affects the user experience. Observing this, in order to eliminate the outside restriction of the wearing devices, we design a novel user sleep information monitoring system based on the Non-contact mattress. In particular, the novel system realizes the entire process from the design of hardware device to the software development which includes ceramic piezoelectric sensor data acquisition, WiFi module upload data, the host computer processing data functions and user information display. The experimental results show that the novel system can accurately obtain the physiological information of human body under sleep condition.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)