RawNet: Fast End-to-End Neural Vocoder

Neural networks based vocoders have recently demonstrated the powerful ability to synthesize high quality speech. These models usually generate samples by conditioning on some spectrum features, such as Mel-spectrum. However, these features are extracted by using speech analysis module including some processing based on the human knowledge. In this work, we proposed RawNet, a truly end-to-end neural vocoder, which use a coder network to learn the higher representation of signal, and an autoregressive voder network to generate speech sample by sample. The coder and voder together act like an auto-encoder network, and could be jointly trained directly on raw waveform without any human-designed features. The experiments on the Copy-Synthesis tasks show that RawNet can achieve the comparative synthesized speech quality with LPCNet, with a smaller model architecture and faster speech generation at the inference step.Comment: Submitted to Interspeech 2019, Graz, Austri

A rapid low-cost real-time PCR for the detection of klebsiella pneumonia carbapenemase genes

<p>Abstract</p> <p>Background</p> <p><it>Klebsiella pneumonia</it> carbapenemases (KPCs) are able to hydrolyze the carbapenems, which cause many bacteria resistance to multiple classes of antibiotics, so the rapid dissemination of KPCs is worrisome. Laboratory identification of KPCs-harboring clinical isolates would be a key to limit the spread of the bacteria. This study would evaluate a rapid low-cost real-time PCR assay to detect KPCs.</p> <p>Methods</p> <p>Real-time PCR assay based on SYBR GreenIwas designed to amplify a 106bp product of the <it>bla</it>_KPC gene from the159 clinical Gram-negative isolates resistant to several classes of -lactam antibiotics through antimicrobial susceptibility testing. We confirmed the results of real-time PCR assay by the conventional PCR-sequencing. At the same time, KPCs of these clinical isolates were detected by the modified Hodge test (MHT). Then we compared the results of real-time PCR assay with those of MHT from the sensitivity and specificity. Moreover, we evaluated the sensitivity of the real-time PCR assay.</p> <p>Results</p> <p>The sensitivity and specificity of the results of the real-time PCR assay compared with those of MHT was 29/29(100%) and 130/130(100%), respectively. The results of the real-time PCR and the MHT were strongly consistent (Exact Sig. (2-tailed) =1. 000; McNemar test). The real-time PCR detection limit was about 0.8cfu using clinical isolates.</p> <p>Conclusion</p> <p>The real-time PCR assay could rapidly and accurately detect KPCs -harboring strains with high analytical sensitivity and specificity.</p

Distributed Time-Predictable Memory Interconnect for Multi-Core Architectures

Multi-core architectures are increasingly adopted in emerging real-time applications where execution time is required to be bounded in the worst case (i.e., time predictability) and low. Memory access latency is the main part forming the overall execution time. A promising approach towards time predictability is to employ distributed memory interconnects, either locally arbitrated interconnects or globally arbitrated interconnects, with arbitration schemes, and the pipelined tree-based structure can break the critical path of multiplexing into short steps with small logic size. It scales to a large number of processors that high clock frequency can be synthesised. This research explores timing behaviour of multi-core architectures with shared distributed memory interconnects and improves distributed time-predictable memory interconnects for multi-core architectures. The contributions are mainly threefold. First, the generic analytical flow is proposed for time-predictable behaviour of memory accesses across multi-core architectures with locally arbitrated interconnects. It guarantees time predictability and safely bound the worst case without exact memory access profiles. Second, the root queue modification with the root queue management is proposed for multi-core architectures with locally arbitrated interconnects that variation of memory access latency is reduced and timing behaviour analysis is facilitated. Third, Meshed Bluetree is proposed as the distributed time-predictable multi-memory interconnect, enabling multiple processors to simultaneously access multiple memory modules

Spectral-Based Graph Neural Networks for Complementary Item Recommendation

Modeling complementary relationships greatly helps recommender systems to accurately and promptly recommend the subsequent items when one item is purchased. Unlike traditional similar relationships, items with complementary relationships may be purchased successively (such as iPhone and Airpods Pro), and they not only share relevance but also exhibit dissimilarity. Since the two attributes are opposites, modeling complementary relationships is challenging. Previous attempts to exploit these relationships have either ignored or oversimplified the dissimilarity attribute, resulting in ineffective modeling and an inability to balance the two attributes. Since Graph Neural Networks (GNNs) can capture the relevance and dissimilarity between nodes in the spectral domain, we can leverage spectral-based GNNs to effectively understand and model complementary relationships. In this study, we present a novel approach called Spectral-based Complementary Graph Neural Networks (SComGNN) that utilizes the spectral properties of complementary item graphs. We make the first observation that complementary relationships consist of low-frequency and mid-frequency components, corresponding to the relevance and dissimilarity attributes, respectively. Based on this spectral observation, we design spectral graph convolutional networks with low-pass and mid-pass filters to capture the low-frequency and mid-frequency components. Additionally, we propose a two-stage attention mechanism to adaptively integrate and balance the two attributes. Experimental results on four e-commerce datasets demonstrate the effectiveness of our model, with SComGNN significantly outperforming existing baseline models.Comment: Accepted by AAAI-2

Thermal Error Modeling of the CNC Machine Tool Based on Data Fusion Method of Kalman Filter

This paper presents a modeling methodology for the thermal error of machine tool. The temperatures predicted by modified lumped-mass method and the temperatures measured by sensors are fused by the data fusion method of Kalman filter. The fused temperatures, instead of the measured temperatures used in traditional methods, are applied to predict the thermal error. The genetic algorithm is implemented to optimize the parameters in modified lumped-mass method and the covariances in Kalman filter. The simulations indicate that the proposed method performs much better compared with the traditional method of MRA, in terms of prediction accuracy and robustness under a variety of operating conditions. A compensation system is developed based on the controlling system of Siemens 840D. Validated by the compensation experiment, the thermal error after compensation has been reduced dramatically

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

It is a challenging task to accurately perform semantic segmentation due to the complexity of real picture scenes. Many semantic segmentation methods based on traditional deep learning insufficiently captured the semantic and appearance information of images, which put limit on their generality and robustness for various application scenes. In this paper, we proposed a novel strategy that reformulated the popularly-used convolution operation to multi-layer convolutional sparse coding block to ease the aforementioned deficiency. This strategy can be possibly used to significantly improve the segmentation performance of any semantic segmentation model that involves convolutional operations. To prove the effectiveness of our idea, we chose the widely-used U-Net model for the demonstration purpose, and we designed CSC-Unet model series based on U-Net. Through extensive analysis and experiments, we provided credible evidence showing that the multi-layer convolutional sparse coding block enables semantic segmentation model to converge faster, can extract finer semantic and appearance information of images, and improve the ability to recover spatial detail information. The best CSC-Unet model significantly outperforms the results of the original U-Net on three public datasets with different scenarios, i.e., 87.14% vs. 84.71% on DeepCrack dataset, 68.91% vs. 67.09% on Nuclei dataset, and 53.68% vs. 48.82% on CamVid dataset, respectively