Causal effect analysis is an important and popular method in clinical statistics, which typically conducted based on the observational data. However, the analysis based on observed data may be affected by unobserved variables, which may produce bias, leading to estimate the causal effects inaccurately. Existing methods ignore the unobserved variables or are unable to find appropriate proxy variables to weaken this bias, which fail to provide reliable estimation of the causal effects. To address this problem, this paper proposes the instrumental variable method, a more accurate computational method than traditional approaches in the realm of clinical statistic for drug efficacy analysis. This method incorporates the effect of unobserved into the error term, to estimate the accurate causal effect. Under some mild assumptions, the variables in the observational data is considered as instrumental variables. Then, the proposed method calculates the effects of butylphthalide (i.e., a drug) on patients with acute ischemic stroke (AIS) in the presence of unobserved variables. The causal effect of monthly prognosis and the confidence interval of this causal estimator is estimated. The study results show that the butylphthalide has a significant positive effect on the prognostic recovery of patients with acute ischemic stroke.

Traffic flow prediction is an important problem in the field of intelligent transportation systems. Most existing traffic-flow prediction methods have made good progress, which however still face the following two key challenges. (1) The underlying pattern of traffic flow depends on not only the historical information along the timeline, but also the information of spatially adjacent areas, making it a challenging problem on how to balance the two temporal-spatial patterns; (2) Due to fact that time information has the characteristic of multiple granularity, such as hour, day and week, how to capture the multi-grained temporal patterns is another challenge problem. In this paper, we design a multi-grained spatio-temporal deep regression model (MGSTDR) to address the above challenges. By extending the typical autoregressive integrated moving average model (ARIMA) on the basis of multi-grained spatio-temporal traffic flow information, the proposed model can effectively use historical information along the timeline as well as the information of adjacent regions, such that the prediction of multi-grained spatio-temporal traffic flow can be performed. Experimental results on several datasets demonstrate that the proposed model outperforms existing benchmark methods on the task of multi-granularity, and particularly obtains an approximately 5.66% improvement in the hourly traffic flow prediction.

The goal of speech synthesis style control is to convert natural language into corresponding expressive audio output. The speech synthesis style control algorithm based on Transformer can improve synthesis speed while maintain its quality. But there still exist some shortcomings. Firstly, there is a problem of missing style in synthesized audio, when there is a large disparity between the length of the style reference audio and text. Secondly, the decoding process based on vanilla attention is prone to problems of repeating, omission and skipping. To address the above problems, a temporal alignment style control speech synthesis algorithm TATTS is proposed, which can effectively utilize temporal information in the encoding and decoding processes, respectively. In the encoding process, TATTS proposes a temporal alignment cross-attention module to jointly train style audio and text representations, which can solve the alignment problem of unequal-length audio and texts. In the decoding process, TATTS considers the monotonicity of audio timing. And a stepwise monotonic multi-head attention mechanism in the Transformer decoder is proposed to solve the problem of misreading in synthesized audio. The experimental results show that, compared with the baseline model, TATTS has increased the naturalness index of audio results on the LJSpeech and VCTK datasets by 3.8% and 4.8%, respectively, and the style similarity index on the VCTK dataset has increased by 10%. Experimental results demonstrate the effectiveness of the synthetic algorithm, and the ability to style control and transfer.

Target speaker extraction aims to extract the speech of the specific speaker from mixed audio, which usually treats the enrolled audio of the target speaker as auxiliary information. Existing approaches mainly have the following limitations: the auxiliary network for speaker recognition cannot capture the critical information from enrolled audio, and the second one is the lack of an interactive learning mechanism between mixed and enrolled audio embedding. These limitations lead to speaker confusion when the difference between the enrolled and target audio is significant. To address this, a speaker-aware cross-attention speaker extraction network (SACAN) is proposed. First, SACAN introduces an attention-based speaker aggregation module in the speaker recognition auxiliary network, which effectively aggregates critical information about target speaker characteristics. Then, it uses mixed audio to enhance target speaker embedding. After that, to promote the integration of speaker embedding and mixed audio embedding, SACAN builds an interactive learning mechanism through cross-attention and enhances the speaker perception ability of the model. The experimental results show that SACAN improves by 0.0133 and 1.0695 in terms of STOI and SI-SDRi when compared with the benchmark model, validating the effectiveness of the proposed module in speaker confusion assessment and ablation experiments.

Cross-network node classification aims to transfer knowledge extracted from a source network with sufficient labeled nodes to predict labels for nodes in a target network. Existing methods mainly focus on associating two networks in a shared embedding space, where the distribution discrepancy of node embedding across two networks isminimized. However, the relationship across two networks remains underexplored. In this paper, we propose a method that learns cross-network relationship among nodes from different networks and performs information aggregation across networks based on our learned relationship. To achieve this, we introduce a labeled Fused Gromov-Wasserstein model based on optimal transport, which exploits feature, structure and label information to construct node association between two networks. Based on the cross-network relationship, we design a cross-network graph convolutional network to learn node embeddings. Experimental results on several benchmark datasets show the superiority of the proposed method over state-of-the-art methods.