Utterance Augmentation for Speaker Recognition

The speaker recognition problem is to automatically recognize a person from their voice. The training of a speaker recognition model typically requires a very large training corpus, e.g., multiple voice samples from a very large number of individuals. In the diverse domains of application of speaker recognition, it is often impractical to obtain a training corpus of the requisite size. This disclosure describes techniques that augment utterances, e.g., by cutting, splitting, shuffling, etc., such that the need for collections of raw voice samples from individuals is substantially reduced. In effect, the original model works better on the augmented utterances on the target domain

The Effect of Roughness on the Nonlinear Flow in a Single Fracture with Sudden Aperture Change

AbstractAbrupt changes in aperture (sudden expansion and contraction) are commonly seen in naturally occurred or artificial single fractures. The relevant research mainly focuses on the changes in fluid properties caused by the sudden expansion of the aperture in smooth parallel fractures. To investigate the effects of roughness on the nonlinear flow properties in a single rough fracture with abruptly aperture change (SF-AC), the flow characteristics of the fractures under different Reynolds numbers Re (50~2000) are simulated by the turbulence k-ε steady-state modulus with the Naiver-Stokes equation. The results show that, in a rough SF-AC, the growth of the eddy and the flow path deflection of the mainstream zone are more obvious than those in a smooth SF-AC, and the discrepancies between the rough and smooth SF-ACs become even more obvious when the relative roughness and/or Re values become greater. The increase of the fracture roughness leads to the generation of more local eddies on the rough SF-ACs and enhances the flow path deflection in the sudden expansion fracture. The number of eddies increases with Re, and the size of eddy area increases linearly with Re at first. When Re reaches a value of 300-500, the growth rate of the eddy size slows down and then stabilizes. Groundwater flow in a rough SF-AC follows a clearly visible nonlinear (or non-Darcy) flow law other than the linear Darcy’s law. The Forchheimer equation fits the hydraulic gradient-velocity (J-v) better than the linear Darcy’s law. The corresponding critical Re value at which the nonlinear flow starts to dominate in a rough SF-AC is around 300~500

Oncostatin M Protects Rod and Cone Photoreceptors and Promotes Regeneration of Cone Outer Segment in a Rat Model of Retinal Degeneration

Retinitis pigmentosa (RP) is a group of photoreceptor degenerative disorders that lead to loss of vision. Typically, rod photoreceptors degenerate first, resulting in loss of night and peripheral vision. Secondary cone degeneration eventually affects central vision, leading to total blindness. Previous studies have shown that photoreceptors could be protected from degeneration by exogenous neurotrophic factors, including ciliary neurotrophic factor (CNTF), a member of the IL-6 family of cytokines. Using a transgenic rat model of retinal degeneration (the S334-ter rat), we investigated the effects of Oncostatin M (OSM), another member of the IL-6 family of cytokines, on photoreceptor protection. We found that exogenous OSM protects both rod and cone photoreceptors. In addition, OSM promotes regeneration of cone outer segments in early stages of cone degeneration. Further investigation showed that OSM treatment induces STAT3 phosphorylation in Müller cells but not in photoreceptors, suggesting that OSM not directly acts on photoreceptors and that the protective effects of OSM on photoreceptors are mediated by Müller cells. These findings support the therapeutic strategy using members of IL-6 family of cytokines for retinal degenerative disorders. They also provide evidence that activation of the STAT3 pathway in Müller cells promotes photoreceptor survival. Our work highlights the importance of Müller cell-photoreceptor interaction in the retina, which may serve as a model of glia-neuron interaction in general

On the radiated EMI current extraction of dc transmission line based on corona current statistical measurements

Corona-originated discharge of DC transmission lines is the main reason for the radiated electromagnetic interference (EMI) field in the vicinity of transmission lines. A joint time-frequency analysis technique was proposed to extract the radiated EMI current (excitation current) of DC corona based on corona current statistical measurements. A reduced-scale experimental platform was setup to measure the statistical distributions of current waveform parameters of aluminum conductor steel reinforced. Based on the measured results, the peak value, root-mean-square value and average value with 9 kHz and 200 Hz band-with of 0.5 MHz radiated EMI current were calculated by the technique proposed and validated with conventional excitation function method. Radio interference (RI) was calculated based on the radiated EMI current and a wire-to-plate platform was built for the validity of the RI computation results. The reason for the certain deviation between the computations and measurements was detailed analyzed

Profiles of Technological Capabilities of the Chinese Academy of Sciences(CAS)--A Comparison of Patenting Activities of the CAS with other National Level Institutions

The purpose is to compare patent activities within Chinese Academy of Sciences(CAS) with France National Center for Scientific Research(CNR)and Max Planck Society(MPS)which have similar national role outside China on patent statistics

LSTM Neural Networks With Attention Mechanisms for Accelerated Prediction of Charge Density at Onset Condition of DC Corona Discharge

The onset process of corona discharge is naturally nonlinear and dynamic. The conventionally physical-based onset model and numerical computation of onset charge distribution are hampered by the computational power and given time. Here, in order to efficiently model this highly nonlinear dynamic process, a long short-term memory (LSTM) neural networks with attention mechanisms is proposed for accelerated charge density prediction under different atmospheric conditions, which adaptively choose charge-related input variables at each time step and hidden states relating to charge density all time steps. Our results demonstrate that this well trained model could make instant predictions with high accuracy under given target atmospheric conditions. Results show that the proposed model substantially reduces the computing time compared to physical-based methods. This work provides insights into applying LSTM neural networks to the charge density prediction of other discharge modes as well

Autonomous Power Control MAC Protocol for Mobile Ad Hoc Networks

Battery energy limitation has become a performance bottleneck for mobile ad hoc networks. IEEE 802.11 has been adopted as the current standard MAC protocol for ad hoc networks. However, it was developed without considering energy efficiency. To solve this problem, many modifications on IEEE 802.11 to incorporate power control have been proposed in the literature. The main idea of these power control schemes is to use a maximum possible power level for transmitting RTS/CTS and the lowest acceptable power for sending DATA/ACK. However, these schemes may degrade network throughput and reduce the overall energy efficiency of the network. This paper proposes autonomous power control MAC protocol (APCMP), which allows mobile nodes dynamically adjusting power level for transmitting DATA/ACK according to the distances between the transmitter and its neighbors. In addition, the power level for transmitting RTS/CTS is also adjustable according to the power level for DATA/ACK packets. In this paper, the performance of APCMP protocol is evaluated by simulation and is compared with that of other protocols.</p

Quality Assessment for Comparing Image Enhancement Algorithms

As the image enhancement algorithms developed in recent years, how to compare the performances of different image enhancement algorithms becomes a novel task. In this paper, we propose a framework to do quality assessment for comparing image enhancement algorithms. Not like traditional image quality assessment approaches, we focus on the relative quality ranking between enhanced images rather than giving an absolute quality score for a single enhanced image. We construct a dataset which contains source images in bad visibility and their enhanced images processed by different enhancement algorithms, and then do subjective assessment in a pair-wise way to get the relative ranking of these enhanced images. A rank function is trained to fit the subjective assessment results

The Impact of Stable Customer Relationships on Enterprises’ Technological Innovation Based on the Mediating Effect of the Competitive Advantage of Enterprises

Technological innovation and stable customer relationships are both important factors for the sustainable development of enterprises. However, it remains unclear whether there is a relationship between stable customer relationships and technological innovation. In this work, we manually collected data regarding customer relationships and the innovation of manufacturing companies listed in the A-Share index in China from 2009 to 2016. Through empirical analysis, this work used a two-way fixed effect model and intermediary effect model tests to explore the impact of stable customer relationships on technological innovation. The empirical research found the following. (1) Stable customer relationships significantly promote the technological innovation of enterprises, and the empirical results are still valid after a variety of robust tests. The competitive advantage of enterprises forms a part of the intermediary role in the relationship above. (2) Comparing the samples of large-scale enterprises, state-owned enterprises, mature enterprises, and low-capital-intensive enterprises, the research found that stable customer relationships can significantly promote corporate technological innovation in small-scale enterprises, non-state-owned enterprises, young enterprises, and highly capital-intensive enterprises. This article enriches and deepens our understanding of the mechanism by which stable customer relationships affect enterprises’ technological innovation. At the same time, this research is helpful for better evaluating the impact of establishing a stable customer relationship on the sustainable competitive advantage of enterprises