Artificial Intelligence and Digital Twins play an integral role in driving innovation the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver field lane change prediction for manoeuvre anticipation. However, decision-making process such models complex non-transparent, hence reducing trustworthiness smart solution. This work presents innovative approach technical imp...

Pre-training is crucial for learning deep neural networks. Most of existing pre-training methods train simple models (e.g., restricted Boltzmann machines) and then stack them layer by layer to form the deep structure. This layerwise pre-training has found strong theoretical foundation and broad empirical support. However, it is not easy to employ such method to pre-train models without a clear ...

Complex network methodology is very useful for complex system explorer. However, the relationships among variables in complex system are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a synthetic method, named small-shuffle partial symbolic transfer entropy spectrum (SSPSTES), for inferring asso...

Testing a sequence of pre-ordered hypotheses to decide which of these can be rejected or accepted while controlling the familywise error rate (FWER) is of importance in many scientific studies such as clinical trials. In this paper, we first introduce a generalized fixed sequence procedure whose critical values are defined by using a function of the numbers of rejections and acceptances, and wh...

Abstract Convolutional neural networks (CNNs) have achieved tremendous success in visual recognition tasks but mainly rely on massive learnable parameters. To solve this problem, many effective and efficient convolution operators been proposed, such as group-wise convolution, point-wise depth-wise convolution. However, the above operations model optimize weight relationship within same convolut...

2 Methods 5 2.1 Multiple hypothesis testing framework . . . . . . . . . . . . . 5 2.2 Test statistics null distribution . . . . . . . . . . . . . . . . . 11 2.3 Single-step procedures for control of general Type I error rates θ(FVn) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4 Step-down procedures for control of the family-wise error rate 16 2.5 Augmentation multiple tes...

We consider a clinical trial with a primary and a secondary endpoint where the secondary endpoint is tested only if the primary endpoint is significant. The trial uses a group sequential procedure with two stages. The familywise error rate (FWER) of falsely concluding significance on either endpoint is to be controlled at a nominal level α. The type I error rate for the primary endpoint is cont...