OBJECTIVE VOICE QUALITY PREDICTION FOR IP NETWORKS IN REAL TIME VOICE OVER INTERNET PROTOCOL APPLICATIONS

Presented herein is a system for monitoring/predicting objective voice quality in Voice Over Internet Protocol (VoIP) applications. The system is configured to , collect training datasets and generate online-training prediction models of voice quality

Dual Long Short-Term Memory Networks for Sub-Character Representation Learning

Characters have commonly been regarded as the minimal processing unit in Natural Language Processing (NLP). But many non-latin languages have hieroglyphic writing systems, involving a big alphabet with thousands or millions of characters. Each character is composed of even smaller parts, which are often ignored by the previous work. In this paper, we propose a novel architecture employing two stacked Long Short-Term Memory Networks (LSTMs) to learn sub-character level representation and capture deeper level of semantic meanings. To build a concrete study and substantiate the efficiency of our neural architecture, we take Chinese Word Segmentation as a research case example. Among those languages, Chinese is a typical case, for which every character contains several components called radicals. Our networks employ a shared radical level embedding to solve both Simplified and Traditional Chinese Word Segmentation, without extra Traditional to Simplified Chinese conversion, in such a highly end-to-end way the word segmentation can be significantly simplified compared to the previous work. Radical level embeddings can also capture deeper semantic meaning below character level and improve the system performance of learning. By tying radical and character embeddings together, the parameter count is reduced whereas semantic knowledge is shared and transferred between two levels, boosting the performance largely. On 3 out of 4 Bakeoff 2005 datasets, our method surpassed state-of-the-art results by up to 0.4%. Our results are reproducible, source codes and corpora are available on GitHub.Comment: Accepted & forthcoming at ITNG-201

Effect of insulin and metformin on methylation and glycolipid metabolism of peroxisome proliferator-activated receptor γ coactivator-1A of rat offspring with gestational diabetes mellitus

AbstractObjectiveTo discuss the effect of insulin and metformin on a methylation and glycolipid metabolism of peroxisome proliferator-activated receptor γ coactivator-1A (PPARGC1A) of rat offspring with gestational diabetes mellitus (GDM).MethodsA total of 45 pregnant rats received the intraperitoneal injection of streptozotocin to establish the pregnant rat model of GDM. A total of 21 pregnant rats with GDM were randomly divided into three groups, with 7 rats in each group, namely the insulin group, metformin group and control group. Rats in the insulin group received the abdominal subcutaneous injection of 1 mL/kg recombinant insulin glargine at 18:00 every day. Rats in the metformin group received the intragastric infusion of metformin hydrochloride at 18:00 every day, with the first dose of 300 mg/kg. The doses of two groups were adjusted every 3 d to maintain the blood glucose level at 2.65–7.62 mmol/L. Rats in the control group received the intragastric infusion of 1 mL normal saline at 18:00 every day. After the natural delivery of pregnant rats, 10 offspring rats were randomly selected from each group. At birth, 4 wk and 8 wk after the birth of offspring rats, the weight of offspring rats was measured. The blood glucose level of offspring rats was measured at 4 wk and 8 wk, while the level of serum insulin, triglyceride and leptin was measured at 8 wk.ResultsThe weight of offspring rats at birth in the insulin group and metformin group was significantly lower than the one in the control group (P < 0.05), and there was no significant difference at 4 wk and 8 wk among three groups (P > 0.05). The fasting blood glucose and random blood glucose in the insulin group and metformin group at 4 wk and 8 wk were all significantly lower than ones in the control group (P < 0.05); there was no significant difference between the insulin group and metformin group (P > 0.05). The expression of PPARGC1A mRNA in the insulin group and metformin group was significantly higher and the methylation level of PPARGC1A was significantly lower than the one in the control group (P < 0.05); but there was no significant difference between the insulin group and metformin group (P > 0.05). Insulin and leptin at 8 wk in the insulin group and metformin group were significantly higher, while triglyceride was significantly lower than the one in the control group (P < 0.05); triglyceride level in the insulin group was significantly higher than the one in the metformin group (P < 0.05). There was no significant difference in insulin and leptin level between the insulin group and metformin group (P > 0.05).ConclusionsGDM can induce the methylation of PPARGC1A of offspring rats to reduce the expression of PPARGC1A mRNA and then cause the disorder of glycolipid metabolism when the offspring rats grow up; the insulin or metformin in the treatment of pregnant rats with GDM can reduce the methylation level of PPARGC1A and thus improve the abnormal glycolipid metabolism of offspring rats