Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA–disease associations. In this research, we proposed a lncRNA–disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA–disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA–disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA–disease associations and isolated diseases.

Fringe projection profilometry (FPP) has become increasingly important in dynamic 3-D shape measurement. In FPP, it is necessary to retrieve the phase of the measured object before shape profiling. However, traditional phase retrieval techniques often require a large number of fringes, which may generate motion-induced error for dynamic objects. In this paper, a novel phase retrieval technique based on deep learning is proposed, which uses an end-to-end deep convolution neural network to transform a single or two fringes into the phase retrieval required fringes. When the object’s surface is located in a restricted depth, the presented network only requires a single fringe as the input, which otherwise requires two fringes in an unrestricted depth. The proposed phase retrieval technique is first theoretically analyzed, and then numerically and experimentally verified on its applicability for dynamic 3-D measurement.

Abstract:The primary role of attention is in the control of action. A model of attention is presented. In this paper, the model takes the entire Norman & Shallice's theory into acconnt and mainly concentrates on the interactions between Contention Scheduling and Supervisory Attentional System. An AI agent is imported as an implemental approach for the model. Simulations are chosen based on different cases to demonstrate how the model reacts to random intermptions while executing a routine action.

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In recent years, finger vein recognition has become an important sub-field in biometrics and been applied to realworld applications. The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets. In order to motivate research on finger vein recognition, we released the largest finger vein data set up to now and hold finger vein recognition competitions based on our data set every year. In 2017, International Competition on Finger Vein Recognition (ICFVR) is held jointly with IJCB 2017. 11 teams registered and 10 of them joined the final evaluation. The winner of this year dramatically improved the EER from 2.64% to 0.483% compared to the winner of last year. In this paper, we introduce the process and results of ICFVR 2017 and give insights on development of state-of-art finger vein recognition algorithms.

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As a core resource of scientific knowledge, academic documents have been frequently used by scholars, especially newcomers to a given field. In the era of big data, scientific documents such as academic articles, patents, technical reports, and webpages are booming. The rapid daily growth of scientific documents indicates that a large amount of knowledge is proposed, improved, and used (Zhang et al., 2021). In scientific documents, knowledge entities (KEs) refer to the knowledge mentioned or cited by authors, such as algorithms, models, theories, datasets and software, diseases, drugs, and genes, reflecting rich resources in diverse problemsolving scenarios (

We present a simple method for generating translations with the Moses toolkit (Koehn et al., 2007) from existing hypotheses produced by other translation engines. As the structures underlying these translation engines are not known, an evaluationbased strategy is applied to select systems for combination. The experiments show promising improvements in terms of BLEU.