Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data

Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence-to-sequence Autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as query-by-example Spoken Term Detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch phonetic structure from the audio segments of the target language if the source and target languages are similar. In query-by-example STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.Comment: arXiv admin note: text overlap with arXiv:1603.0098

Purchasing power parity in G-7 countries: Further evidence based on ADL test for threshold cointegration

This study applies a newly-developed Autoregressive Distributed Lag (ADL) test for threshold cointegration, proposed by Li and Lee (2010) to test the validity of long-run purchasing power parity (PPP) for G-7 countries over the January 1994 to April 2010. The empirical results indicate that PPP only holds true for Canada and France two countries. Our results have important policy implications for the G-7 countries under study.Purchasing Power Parity; G-7 Countries; ADL Test; Threshold Cointegration

Development of Compact P-Band Vector Reflectometer

A compact and low cost portable vector reflectometer is designed for a reliable measurement of reflection coefficient, S11. This reflectometer focuses on return loss measurement of frequency ranges from 450 MHz to 550 MHz. The detection of magnitude and phase is based on the utilization of surface mount Analog Devices AD8302 gain/phase detector. The data acquisition is controlled by using Arduino-Nano 3.0 microcontroller, with the use of two analog to digital converter (ADC) and a digital to analog converter (DAC). One port (Open, short and matched load) calibration technique is used to eliminate systematic errors prior to data acquisition. The evaluation of the reflectometer is done by comparing the result of the measurement to that of vector network analyzer

The Impact of Peak Hydrogeneration for Reserving Environmental Flow in Dachia River, Taiwan

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images

The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.Comment: 25 pages, 6 figures, 2 table

Adaptive circular enclosure colour distribution geometrical model utilizing point-in-polygon for segregation between lips and skin pixels

This paper is inspired from various boundary determination techniques which are used for segregating colours between background, skin and lips. Basic concept for this technique is based on colour segmentation with CIELAB colourspace utilized for justifiable reasons. Using LAB colour-space, lips colours were compiled into a colour-map and processed accordingly to our proposed algorithm of adaptive circular enclosure. Algorithm output was determined as a series of coordinates symbolizing boundary values surrounding colourmap. Separation of colours is based on these boundaries by creating a freeform polygon that defines if colour-value either belongs within colour-boundary polygon or not. This technique is famously known as the point in-polygon technique. Proposed technique evaluation uses XM2VTS database based on false positive and false-negative to compute segmentation error. Simulation shows proposed algorithm yields segmented error of 5.55% with accuracy of 94.45%