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Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional timedelay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce highquality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a long-time distributional pattern, and therefore can be extracted from just dozens of frames.

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In this paper, the concepts of car maneuvers, fuzzy logic control (FLC), and sensor-based behaviors are merged to implement the human-like driving skills by an autonomous car-like mobile robot (CLMR). Four kinds of FLCs, fuzzy wall-following control, fuzzy corner control, fuzzy garage-parking control, and fuzzy parallel-parking control, are synthesized to accomplish the autonomous fuzzy behavior control (AFBC). Computer simulation results illustrate the effectiveness of the proposed control schemes. The setup of the CLMR is provided, where the implementation of the AFBC on a field-programmable gate array chip is also addressed. Finally, the real-time implementation experiments of the CLMR in the test ground demonstrate the feasibility in practical car maneuvers. Index Terms-Autonomous fuzzy behavior control (AFBC), car-like mobile robot (CLMR), field-programmable gate array (FPGA), fuzzy logic control (FLC), garage parking, parallel parking, real-time implementation.

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To trigger type I interferon (IFN) responses, pattern recognition receptors activate signaling cascades that lead to transcription of IFN and IFN-stimulated genes (ISGs). The promyelocytic leukemia (PML) protein has been implicated in these responses, although its role has not been defined. Here, we show that PML isoform II (PML-II) is specifically required for efficient induction of IFN-β transcription and of numerous ISGs, acting at the point of transcriptional complex assembly on target gene promoters. PML-II associated with specific transcription factors NF-κB and STAT1, as well as the coactivator CREB-binding protein (CBP), to facilitate transcriptional complex formation. The absence of PML-II substantially reduced binding of these factors and IFN regulatory factor 3 (IRF3) to IFN-β or ISGs promoters and sharply reduced gene activation. The unique C-terminal domain of PML-II was essential for its activity, while the N-terminal RBCC motif common to all PML isoforms was dispensable. We propose a model in which PML-II contributes to the transcription of multiple genes via the association of its C-terminal domain with relevant transcription complexes, which promotes the stable assembly of these complexes at promoters/enhancers of target genes, and that in this way PML-II plays a significant role in the development of type I IFN responses.

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