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

Laplacian Denoising Autoencoder

While deep neural networks have been shown to perform remarkably well in many machine learning tasks, labeling a large amount of ground truth data for supervised training is usually very costly to scale. Therefore, learning robust representations with unlabeled data is critical in relieving human effort and vital for many downstream tasks. Recent advances in unsupervised and self-supervised learning approaches for visual data have benefited greatly from domain knowledge. Here we are interested in a more generic unsupervised learning framework that can be easily generalized to other domains. In this paper, we propose to learn data representations with a novel type of denoising autoencoder, where the noisy input data is generated by corrupting latent clean data in the gradient domain. This can be naturally generalized to span multiple scales with a Laplacian pyramid representation of the input data. In this way, the agent learns more robust representations that exploit the underlying data structures across multiple scales. Experiments on several visual benchmarks demonstrate that better representations can be learned with the proposed approach, compared to its counterpart with single-scale corruption and other approaches. Furthermore, we also demonstrate that the learned representations perform well when transferring to other downstream vision tasks

Analysis of service performance characteristics of debris flow check dams: A case study in Wudu District, Longnan City, Gansu Province

Check dams play a pivotal role in debris flow prevention and control engineering. However, their disaster prevention and mitigation capacity gradually decrease over service time due to repeated debris flow impacts. The study was carried out in 15 ditches and 55 check dams within Wudu District, Longnan City, Gansu Province. Seven key evaluation factors were selected for effectiveness and safety: reservoir siltation ratio, slope stability, drainage hole blockage, dam body damage, dam foundation damage, dam shoulder damage, and safety. The evaluation model of the serviceability of the indivusual dam and the comprehensive serviceability of the single trench of the barrage was established by using hierarchical analysis and fuzzy comprehensive evaluation method, and the serviceability was divided into four grades: excellent, good, medium and poor. The evaluation results show that the serviceability rating of individual dams is predominately "poor", accounting for 34.5%. Similarly, the collective serviceability rating of single trench dams for debris flow is predominately "poor", at 33.3%. The results of the evaluation are consistent with the fieldwork observations, providing a valuable reference for predicting the service performance and service life of barrage dams

Hybridization alters the gut microbial and metabolic profile concurrent with modifying intestinal functions in Tunchang pigs

IntroductionHybridization has been widely used among Chinese wild boars to improve their growth performance and maintain meat quality. Most studies have focused on the genetic basis for such variation. However, the differences in the gut environment between hybrid and purebred boars, which can have significant impacts on their health and productivity, have been poorly understood.MethodsIn the current study, metagenomics was used to detect the gut microbial diversity and composition in hybrid Batun (BT, Berkshire × Tunchang) pigs and purebred Tunchang (TC) pigs. Additionally, untargeted metabolomic analysis was used to detect differences in gut metabolic pathways. Furthermore, multiple molecular experiments were conducted to demonstrate differences in intestinal functions.ResultsAs a result of hybridization in TC pigs, a microbial change was observed, especially in Prevotella and Lactobacillus. Significant differences were found in gut metabolites, including fatty acyls, steroids, and steroid derivatives. Furthermore, the function of the intestinal barrier was decreased by hybridization, while the function of nutrient metabolism was increased.DiscussionEvidences were shown that hybridization changed the gut microbiome, gut metabolome, and intestinal functions of TC pigs. These findings supported our hypothesis that hybridization altered the gut microbial composition, thereby modifying the intestinal functions, even the host phenotypes. Overall, our study highlights the importance of considering the gut microbiome as a key factor in the evaluation of animal health and productivity, particularly in the context of genetic selection and breeding programs

An ultralight, supercompressible, superhydrophobic and multifunctional carbon aerogel with a specially designed structure

Abstract(#br)Compressible and ultralight carbon aerogels are attractive due to its compressibility, elasticity and conductivity. However, it is still a great challenge to enrich the properties of carbon aerogel to meet various requirements. Herein, we report an untralight, supercompressible, fatigue resistant, superhydrophobic and fire-resistant and multifunctional CNF-GO/glucose-kaolin carbon aerogel (C-NGGK) carbon aerogel. To achieve such excellent performances, calcined GO, CNFs, glucose and kaolin are used for forming low-density and continuous wave-shape rGO layers, reinforcing the mechanical strength of carbon layers, realizing superelasticity and fatigue resistance and resulting in a superhydrophobic surface for C-NGGK, respectively. The as-prepared C-NGGK demonstrates excellent superhydrophobicity with the water contact angle (WCA) of 124.9°, and the absorption efficiency of the C-NGGK samples for different oils and organic solvents are 75–255 times their own weight. These advantages show that the C-NGGK can be an ideal candidate for oil/water separation. In addition, there is also the prospect to be used for pressure sensors, while other potential applications include three-dimensional electrode materials for supercapacitors and batteries, catalyst carriers and various wearable devices

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table

Experimental exploration of five-qubit quantum error correcting code with superconducting qubits

Quantum error correction is an essential ingredient for universal quantum computing. Despite tremendous experimental efforts in the study of quantum error correction, to date, there has been no demonstration in the realisation of universal quantum error correcting code, with the subsequent verification of all key features including the identification of an arbitrary physical error, the capability for transversal manipulation of the logical state, and state decoding. To address this challenge, we experimentally realise the $[\![5,1,3]\!]$ code, the so-called smallest perfect code that permits corrections of generic single-qubit errors. In the experiment, having optimised the encoding circuit, we employ an array of superconducting qubits to realise the $[\![5,1,3]\!]$ code for several typical logical states including the magic state, an indispensable resource for realising non-Clifford gates. The encoded states are prepared with an average fidelity of $57.1(3)\%$ while with a high fidelity of $98.6(1)\%$ in the code space. Then, the arbitrary single-qubit errors introduced manually are identified by measuring the stabilizers. We further implement logical Pauli operations with a fidelity of $97.2(2)\%$ within the code space. Finally, we realise the decoding circuit and recover the input state with an overall fidelity of $74.5(6)\%$, in total with $92$ gates. Our work demonstrates each key aspect of the $[\![5,1,3]\!]$ code and verifies the viability of experimental realization of quantum error correcting codes with superconducting qubits.Comment: 6 pages, 4 figures + Supplementary Material

Nanoquartz in Late Permian C1 coal and the high incidence of female lung cancer in the Pearl River Origin area: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>The Pearl River Origin area, Qujing District of Yunnan Province, has one of the highest female lung cancer mortality rates in China. Smoking was excluded as a cause of the lung cancer excess because almost all women were non-smokers. Crystalline silica embedded in the soot emissions from coal combustion was found to be associated with the lung cancer risk in a geographical correlation study. Lung cancer rates tend to be higher in places where the Late Permian C1 coal is produced. Therefore, we have hypothesized the two processes: C1 coal combustion --> nanoquartz in ambient air --> lung cancer excess in non-smoking women.</p> <p>Methods/Design</p> <p>We propose to conduct a retrospective cohort study to test the hypothesis above. We will search historical records and compile an inventory of the coal mines in operation during 1930–2009. To estimate the study subjects' retrospective exposure, we will reconstruct the historical exposure scenario by burning the coal samples, collected from operating or deserted coal mines by coal geologists, in a traditional firepit of an old house. Indoor air particulate samples will be collected for nanoquartz and polycyclic aromatic hydrocarbons (PAHs) analyses. Bulk quartz content will be quantified by X-ray diffraction analysis. Size distribution of quartz will be examined by electron microscopes and by centrifugation techniques. Lifetime cumulative exposure to nanoquartz will be estimated for each subject. Using the epidemiology data, we will examine whether the use of C1 coal and the cumulative exposure to nanoquartz are associated with an elevated risk of lung cancer.</p> <p>Discussion</p> <p>The high incidence rate of lung cancer in Xuan Wei, one of the counties in the current study area, was once attributed to high indoor air concentrations of PAHs. The research results have been cited for qualitative and quantitative cancer risk assessment of PAHs by the World Health Organization and other agencies. If nanoquartz is found to be the main underlying cause of the lung cancer epidemic in the study area, cancer potency estimates for PAHs by the international agencies based on the lung cancer data in this study setting should then be updated.</p

Insect-Specific microRNA Involved in the Development of the Silkworm Bombyx mori

MicroRNAs (miRNAs) are endogenous non-coding genes that participate in post-transcription regulation by either degrading mRNA or blocking its translation. It is considered to be very important in regulating insect development and metamorphosis. We conducted a large-scale screening for miRNA genes in the silkworm Bombyx mori using sequence-by-synthesis (SBS) deep sequencing of mixed RNAs from egg, larval, pupal, and adult stages. Of 2,227,930 SBS tags, 1,144,485 ranged from 17 to 25 nt, corresponding to 256,604 unique tags. Among these non-redundant tags, 95,184 were matched to the silkworm genome. We identified 3,750 miRNA candidate genes using a computational pipeline combining RNAfold and TripletSVM algorithms. We confirmed 354 miRNA genes using miRNA microarrays and then performed expression profile analysis on these miRNAs for all developmental stages. While 106 miRNAs were expressed in all stages, 248 miRNAs were egg- and pupa-specific, suggesting that insect miRNAs play a significant role in embryogenesis and metamorphosis. We selected eight miRNAs for quantitative RT-PCR analysis; six of these were consistent with our microarray results. In addition, we searched for orthologous miRNA genes in mammals, a nematode, and other insects and found that most silkworm miRNAs are conserved in insects, whereas only a small number of silkworm miRNAs has orthologs in mammals and the nematode. These results suggest that there are many miRNAs unique to insects