Mandarin speech perception in combined electric and acoustic stimulation.

For deaf individuals with residual low-frequency acoustic hearing, combined use of a cochlear implant (CI) and hearing aid (HA) typically provides better speech understanding than with either device alone. Because of coarse spectral resolution, CIs do not provide fundamental frequency (F0) information that contributes to understanding of tonal languages such as Mandarin Chinese. The HA can provide good representation of F0 and, depending on the range of aided acoustic hearing, first and second formant (F1 and F2) information. In this study, Mandarin tone, vowel, and consonant recognition in quiet and noise was measured in 12 adult Mandarin-speaking bimodal listeners with the CI-only and with the CI+HA. Tone recognition was significantly better with the CI+HA in noise, but not in quiet. Vowel recognition was significantly better with the CI+HA in quiet, but not in noise. There was no significant difference in consonant recognition between the CI-only and the CI+HA in quiet or in noise. There was a wide range in bimodal benefit, with improvements often greater than 20 percentage points in some tests and conditions. The bimodal benefit was compared to CI subjects' HA-aided pure-tone average (PTA) thresholds between 250 and 2000 Hz; subjects were divided into two groups: "better" PTA (<50 dB HL) or "poorer" PTA (>50 dB HL). The bimodal benefit differed significantly between groups only for consonant recognition. The bimodal benefit for tone recognition in quiet was significantly correlated with CI experience, suggesting that bimodal CI users learn to better combine low-frequency spectro-temporal information from acoustic hearing with temporal envelope information from electric hearing. Given the small number of subjects in this study (n = 12), further research with Chinese bimodal listeners may provide more information regarding the contribution of acoustic and electric hearing to tonal language perception

Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan Dan Konsekuensinya Pada Loyalitas (Studi Pada Obyek Wisata Di Kabupaten Malang)

Studi ini meneliti kepuasan wisatawan yang mengunjungi obyek wisata yang ada di Kabupaten Malang dengan menggunakan konsep dasar Swedish Customer Satisfaction Barometer (SCSB). Tujuan penelitian untuk menganalisis pengaruh langsung kualitas layanan (service quality) terhadap kepuasan wisatawan domestik (customer satisfaction), menganalisis pengaruh langsung harapan konsumen (customer expectation) terhadap kepuasan wisatawan domestik (customer satisfaction), dan menganalisis pengaruh langsung kepuasan konsumen (customer satisfaction) terhadap loyalitas konsumen (customer loyalty) wisatawan domestik. Sampel penelitian adalah wisatawan domestik yang berkunjung ke objek wisata (Pantai Sendang Biru, Pantai Ngliyep dan Pantai Bale Kambang), yaitu sebanyak 150 responden. Teknik analisis data yang digunakan adalah Structural Equation Modelling (SEM) dengan menggunakan bantuan program AMOS. Hasil penelitian menunjukkan bahwa ada pengaruh langsung antara kualitas layanan dan kepuasan pelanggan, tidak ada pengaruh yang signifikan anatara harapan dengan kepuasan pelanggan, ada pengaruh langsung antara kepuasan pelanggan dengan loyalitas konsumen. Variabel kualitas layanan yaitu reliability dan emphaty memiliki pengaruh yang paling besar terhadap kepuasan pelanggan sedangkan responsiveness, assurance, dan tangible memilki pengaruh yang cukup signifikan

Estimating Extreme Value Index by Subsampling for Massive Datasets with Heavy-Tailed Distributions

Modern statistical analyses often encounter datasets with massive sizes and heavy-tailed distributions. For datasets with massive sizes, traditional estimation methods can hardly be used to estimate the extreme value index directly. To address the issue, we propose here a subsampling-based method. Specifically, multiple subsamples are drawn from the whole dataset by using the technique of simple random subsampling with replacement. Based on each subsample, an approximate maximum likelihood estimator can be computed. The resulting estimators are then averaged to form a more accurate one. Under appropriate regularity conditions, we show theoretically that the proposed estimator is consistent and asymptotically normal. With the help of the estimated extreme value index, a normal range can be established for a heavy-tailed random variable. Observations that fall outside the range should be treated as suspected records and can be practically regarded as outliers. Extensive simulation experiments are provided to demonstrate the promising performance of our method. A real data analysis is also presented for illustration purpose

When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluate the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.Comment: 7 pages, 4 figures, 7 table