Variational Speech Waveform Compression to Catalyze Semantic Communications

We propose a novel neural waveform compression method to catalyze emerging speech semantic communications. By introducing nonlinear transform and variational modeling, we effectively capture the dependencies within speech frames and estimate the probabilistic distribution of the speech feature more accurately, giving rise to better compression performance. In particular, the speech signals are analyzed and synthesized by a pair of nonlinear transforms, yielding latent features. An entropy model with hyperprior is built to capture the probabilistic distribution of latent features, followed with quantization and entropy coding. The proposed waveform codec can be optimized flexibly towards arbitrary rate, and the other appealing feature is that it can be easily optimized for any differentiable loss function, including perceptual loss used in semantic communications. To further improve the fidelity, we incorporate residual coding to mitigate the degradation arising from quantization distortion at the latent space. Results indicate that achieving the same performance, the proposed method saves up to 27% coding rate than widely used adaptive multi-rate wideband (AMR-WB) codec as well as emerging neural waveform coding methods

Wireless Deep Speech Semantic Transmission

In this paper, we propose a new class of high-efficiency semantic coded transmission methods for end-to-end speech transmission over wireless channels. We name the whole system as deep speech semantic transmission (DSST). Specifically, we introduce a nonlinear transform to map the speech source to semantic latent space and feed semantic features into source-channel encoder to generate the channel-input sequence. Guided by the variational modeling idea, we build an entropy model on the latent space to estimate the importance diversity among semantic feature embeddings. Accordingly, these semantic features of different importance can be allocated with different coding rates reasonably, which maximizes the system coding gain. Furthermore, we introduce a channel signal-to-noise ratio (SNR) adaptation mechanism such that a single model can be applied over various channel states. The end-to-end optimization of our model leads to a flexible rate-distortion (RD) trade-off, supporting versatile wireless speech semantic transmission. Experimental results verify that our DSST system clearly outperforms current engineered speech transmission systems on both objective and subjective metrics. Compared with existing neural speech semantic transmission methods, our model saves up to 75% of channel bandwidth costs when achieving the same quality. An intuitive comparison of audio demos can be found at https://ximoo123.github.io/DSST

Chromosomal imbalances in nasopharyngeal carcinoma: a meta-analysis of comparative genomic hybridization results

Nasopharyngeal carcinoma (NPC) is a highly prevalent disease in Southeast Asia and its prevalence is clearly affected by genetic background. Various theories have been suggested for its high incidence in this geographical region but to these days no conclusive explanation has been identified. Chromosomal imbalances identifiable through comparative genomic hybridization may shed some light on common genetic alterations that may be of relevance to the onset and progression of NPC. Review of the literature, however, reveals contradictory results among reported findings possibly related to factors associated with patient selection, stage of disease, differences in methodological details etc. To increase the power of the analysis and attempt to identify commonalities among the reported findings, we performed a meta-analysis of results described in NPC tissues based on chromosomal comparative genomic hybridization (CGH). This meta-analysis revealed consistent patters in chromosomal abnormalities that appeared to cluster in specific "hot spots" along the genome following a stage-dependent progression

Population phylogenomic analysis of mitochondrial DNA in wild boars and domestic pigs revealed multiple domestication events in East Asia

A fine-grained mitochondrial DNA phylogenomic analysis was conducted in domestic pigs and wild boars, revealing that pig domestication in East Asia occurred in the Mekong and the middle and downstream regions of the Yangtze river

Lentivirus-mediated RNA interference targeting the H19 gene inhibits cell proliferation and apoptosis in human choriocarcinoma cell line JAR

BACKGROUND: H19 is a paternally imprinted gene that has been shown to be highly expressed in the trophoblast tissue. Results from previous studies have initiated a debate as to whether noncoding RNA H19 acts as a tumor suppressor or as a tumor promotor in trophoblast tissue. In the present study, we developed lentiviral vectors expressing H19-specific small interfering RNA (siRNA) to specifically block the expression of H19 in the human choriocarcinoma cell line JAR. Using this approach, we investigated the impact of the H19 gene on the proliferation, invasion and apoptosis of JAR cells. Moreover, we examined the effect of H19 knockdown on the expression of insulin-like growth factor 2 (IGF2), hairy and enhancer of split homologue-1 (HES-1) and dual-specific phosphatase 5 (DUSP5) genes. RESULTS: H19 knockdown inhibited apoptosis and proliferation of JAR cells, but had no significant impact on cell invasion. In addition, H19 knockdown resulted in significant upregulation of HES-1 and DUSP5 expression, but not IGF2 expression in JAR cells. CONCLUSIONS: The finding that H19 downregulation could simultaneously inhibit proliferation and apoptosis of JAR cells highlights a putative dual function for H19 in choriocarcinoma and may explain the debate on whether H19 acts as a tumor suppressor or a tumor promotor in trophoblast tissue. Furthermore, upregulation of HES-1 and DUSP5 may mediate H19 downregulation-induced suppression of proliferation and apoptosis of JAR cells