Poaceae, one of the largest flowering plant families in angiosperms, evolved distinct inflorescence and flower morphology diverging from eudicots and other monocots. However, the mechanism underlying the specification of flower morphology in grasses remains unclear. Here we show that floral zygomorphy along the lemma-palea axis in rice (Oryza sativa) is partially or indirectly determined by the CYCLOIDEA (CYC)-like homolog RETARDED PALEA1 (REP1), which regulates palea identity and development. The REP1 gene is only expressed in palea primordium during early flower development, but during later floral stages is radially dispersed in stamens and the vascular bundles of the lemma and palea. The development of palea is significantly retarded in the rep1 mutant and its palea has five vascular bundles, which is similar to the vascular pattern of the wild-type lemma. Furthermore, ectopic expression of REP1 caused the asymmetrical overdifferentiation of the palea cells, altering their floral asymmetry. This work therefore extends the function of the TCP gene family members in defining the diversification of floral morphology in grasses and suggests that a common conserved mechanism controlling floral zygomorphy by CYC-like genes exists in both eudicots and the grasses.

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.

The public governance of epidemic outbreaks faces great uncertainty. Successful governance is only possible with a competent early warning system, which hinges upon efficient production, sharing, and use of relevant knowledge and information. In this process, functional scientific/professional communities are critical gatekeepers. Analyzing China's failed early warning for the COVID-19 outbreak, we show that an epidemic governance system dominated by bureaucratic forces is doomed to failure. In particular, we identify the lack of autonomy of scientific/professional communities-in this case, virologists, physicians, and epidemiologists-as one of the major contributing factors to the malfunction of the early warning system. Drawing upon the idea of community governance, we argue that only by empowering scientific/professional groups to exert efficient community governance can a state modernize its early warning system and perform better in combatting epidemics. ARTICLE HISTORY

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.

For practical automatic speaker verification (ASV) systems, replay attack poses a true risk. By replaying a pre-recorded speech signal of the genuine speaker, ASV systems tend to be easily fooled. An effective replay detection method is therefore highly desirable. In this study, we investigate a major difficulty in replay detection: the over-fitting problem caused by variability factors in speech signal. An F-ratio probing tool is proposed and three variability factors are investigated using this tool: speaker identity, speech content and playback & recording device. The analysis shows that device is the most influential factor that contributes the highest over-fitting risk. A frequency warping approach is studied to alleviate the over-fitting problem, as verified on the ASV-spoof 2017 database.