Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes within different phonetic structures. Both are key components of prosody in speech, which is different for different speakers. Models like cycle-consistent adversarial network (Cycle-GAN) and variational auto-encoder (VAE) have been successfully applied to voice conversion tasks without parallel data. However, due to the neural network architectures and feature vectors chosen for these approaches, the length of the predicted utterance has to be fixed to that of the input utterance, which limits the flexibility in mimicking the speaking rates and rhythmic patterns for the target speaker. On the other hand, sequence-to-sequence learning model was used to remove the above length constraint, but parallel training data are needed. In this paper, we propose an approach utilizing sequence-to-sequence model trained with unsupervised Cycle-GAN to perform the transformation between the phoneme posteriorgram sequences for different speakers. In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data. Preliminary evaluation on two datasets showed very encouraging results.Comment: 8 pages, 6 figures, Submitted to SLT 201

The Arabidopsis Malectin-Like/LRR-RLK IOS1 is Critical for BAK1-Dependent and BAK1-Independent Pattern-Triggered Immunity

Plasma membrane-localized pattern recognition receptors (PRRs) such as FLAGELLIN SENSING2 (FLS2), EF-TU RECEPTOR (EFR) and CHITIN ELICITOR RECEPTOR KINASE 1 (CERK1) recognize microbe-associated molecular patterns (MAMPs) to activate pattern-triggered immunity (PTI). A reverse genetics approach on genes responsive to the priming agent beta-aminobutyric acid (BABA) revealed IMPAIRED OOMYCETE SUSCEPTIBILITY1 (IOS1) as a critical PTI player. Arabidopsis thaliana ios1 mutants were hyper-susceptible to Pseudomonas syringae bacteria. Accordingly, ios1 mutants showed defective PTI responses, notably delayed up-regulation of the PTI-marker gene FLG22-INDUCED RECEPTOR-LIKE KINASE1 (FRK1), reduced callose deposition and mitogen-activated protein kinase activation upon MAMP treatment. Moreover, Arabidopsis lines over-expressing IOS1 were more resistant to bacteria and showed a primed PTI response. In vitro pull-down, bimolecular fluorescence complementation, co-immunoprecipitation, and mass spectrometry analyses supported the existence of complexes between the membrane-localized IOS1 and BRASSINOSTEROID INSENSITIVE1-ASSOCIATED KINASE1 (BAK1)-dependent PRRs FLS2 and EFR, as well as with the BAK1-independent PRR CERK1. IOS1 also associated with BAK1 in a ligand-independent manner, and positively regulated FLS2-BAK1 complex formation upon MAMP treatment. In addition, IOS1 was critical for chitin-mediated PTI. Finally, ios1 mutants were defective in BABA-induced resistance and priming. This work reveals IOS1 as a novel regulatory protein of FLS2-, EFR- and CERK1-mediated signaling pathways that primes PTI activation

Federated Deep Reinforcement Learning for THz-Beam Search with Limited CSI

Terahertz (THz) communication with ultra-wide available spectrum is a promising technique that can achieve the stringent requirement of high data rate in the next-generation wireless networks, yet its severe propagation attenuation significantly hinders its implementation in practice. Finding beam directions for a large-scale antenna array to effectively overcome severe propagation attenuation of THz signals is a pressing need. This paper proposes a novel approach of federated deep reinforcement learning (FDRL) to swiftly perform THz-beam search for multiple base stations (BSs) coordinated by an edge server in a cellular network. All the BSs conduct deep deterministic policy gradient (DDPG)-based DRL to obtain THz beamforming policy with limited channel state information (CSI). They update their DDPG models with hidden information in order to mitigate inter-cell interference. We demonstrate that the cell network can achieve higher throughput as more THz CSI and hidden neurons of DDPG are adopted. We also show that FDRL with partial model update is able to nearly achieve the same performance of FDRL with full model update, which indicates an effective means to reduce communication load between the edge server and the BSs by partial model uploading. Moreover, the proposed FDRL outperforms conventional non-learning-based and existing non-FDRL benchmark optimization methods

Konsep Demokrasi Politik Dalam Islam

Coexistence of chronic rhinosinusitis (CRS) with asthma appears to impair asthma control. Type-2 innate lymphoid cells (ILC2s) respond to the cytokines of thymic stromal lymphopoietin (TSLP), interleukin (IL)-25 and IL-33, thus contributing to airway diseases such as CRS and asthma. We investigate whether the augmented Th2-cytokines in CRS might be related to sinonasal tract ILC2s corresponding to enhanced IL-25, IL-33 and TSLP release in severe asthmatics, and be involved in asthma control. Twenty-eight asthmatics (12 non-severe and 16 severe) with CRS receiving nasal surgery were enrolled. The predicted FEV1 inversely associated with CRS severity of CT or endoscopy scores. Higher expression of Th2-driven cytokines (IL-4, IL-5, IL-9, and IL-13), TSLP, IL-25 and IL-33 in nasal tissues was observed in severe asthma. Severe asthmatics had higher ILC2 cell counts in their nasal tissues. ILC2 counts were positively correlated with Th2-cytokines. Nasal surgery significantly improved asthma control and lung function decline in severe asthma and CRS. The higher expression of IL-33/ILC2 axis-directed type 2 immune responses in nasal tissue of CRS brought the greater decline of lung function in severe asthma. ILC2-induced the upregulated activity of Th2-related cytokines in asthmatics with CRS may contribute to a recalcitrant status of asthma control

Interaction of Proliferation Cell Nuclear Antigen (PCNA) with c-Abl in Cell Proliferation and Response to DNA Damages in Breast Cancer

Cell proliferation in primary and metastatic tumors is a fundamental characteristic of advanced breast cancer. Further understanding of the mechanism underlying enhanced cell growth will be important in identifying novel prognostic markers and therapeutic targets. Here we demonstrated that tyrosine phosphorylation of the proliferating cell nuclear antigen (PCNA) is a critical event in growth regulation of breast cancer cells. We found that phosphorylation of PCNA at tyrosine 211 (Y211) enhanced its association with the non-receptor tyrosine kinase c-Abl. We further demonstrated that c-Abl facilitates chromatin association of PCNA and is required for nuclear foci formation of PCNA in cells stressed by DNA damage as well as in unperturbed cells. Targeting Y211 phosphorylation of PCNA with a cell-permeable peptide inhibited the phosphorylation and reduced the PCNA-Abl interaction. These results show that PCNA signal transduction has an important impact on the growth regulation of breast cancer cells

Parallel Synthesis for Autoregressive Speech Generation

Autoregressive neural vocoders have achieved outstanding performance in speech synthesis tasks such as text-to-speech and voice conversion. An autoregressive vocoder predicts a sample at some time step conditioned on those at previous time steps. Though it synthesizes natural human speech, the iterative generation inevitably makes the synthesis time proportional to the utterance length, leading to low efficiency. Many works were dedicated to generating the whole speech sequence in parallel and proposed GAN-based, flow-based, and score-based vocoders. This paper proposed a new thought for the autoregressive generation. Instead of iteratively predicting samples in a time sequence, the proposed model performs frequency-wise autoregressive generation (FAR) and bit-wise autoregressive generation (BAR) to synthesize speech. In FAR, a speech utterance is split into frequency subbands, and a subband is generated conditioned on the previously generated one. Similarly, in BAR, an 8-bit quantized signal is generated iteratively from the first bit. By redesigning the autoregressive method to compute in domains other than the time domain, the number of iterations in the proposed model is no longer proportional to the utterance length but to the number of subbands/bits, significantly increasing inference efficiency. Besides, a post-filter is employed to sample signals from output posteriors; its training objective is designed based on the characteristics of the proposed methods. Experimental results show that the proposed model can synthesize speech faster than real-time without GPU acceleration. Compared with baseline vocoders, the proposed model achieves better MUSHRA results and shows good generalization ability for unseen speakers and 44 kHz speech.Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processin