Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved. A recent study [1] presented a time contrastive learning (TCL) concept to explore the non-stationarity of brain signals for classification of brain states. Speech signals have similar non-stationarity property, and TCL further has the advantage of having no need for labeled data. We therefore present a TCL based BN feature extraction method. The method uniformly partitions each speech utterance in a training dataset into a predefined number of multi-frame segments. Each segment in an utterance corresponds to one class, and class labels are shared across utterances. DNNs are then trained to discriminate all speech frames among the classes to exploit the temporal structure of speech. In addition, we propose a segment-based unsupervised clustering algorithm to re-assign class labels to the segments. TD-SV experiments were conducted on the RedDots challenge database. The TCL-DNNs were trained using speech data of fixed pass-phrases that were excluded from the TD-SV evaluation set, so the learned features can be considered phrase-independent. We compare the performance of the proposed TCL bottleneck (BN) feature with those of short-time cepstral features and BN features extracted from DNNs discriminating speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels and boundaries are generated by three different automatic speech recognition (ASR) systems. Experimental results show that the proposed TCL-BN outperforms cepstral features and speaker+pass-phrase discriminant BN features, and its performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Entangling two distant nanocavities via a waveguide

In this paper, we investigate the generation of continuous variable entanglement between two spatially-separate nanocavities mediated by a coupled resonator optical waveguide in photonic crystals. By solving the exact dynamics of the cavity system coupled to the waveguide, the entanglement and purity of the two-mode cavity state are discussed in detail for the initially separated squeezing inputs. It is found that the stable and pure entangled state of the two distant nanocavities can be achieved with the requirement of only a weak cavity-waveguide coupling when the cavities are resonant with the band center of the waveguide. The strong couplings between the cavities and the waveguide lead to the entanglement sudden death and sudden birth. When the frequencies of the cavities lie outside the band of the waveguide, the waveguide-induced cross frequency shift between the cavities can optimize the achievable entanglement. It is also shown that the entanglement can be easily manipulated through the changes of the cavity frequencies within the waveguide band.Comment: 8 pages, 8 figure

Penerapan Strategi Pembelajaran Inkuiri Dipadukan Media Audio Visual Untuk Meningkatkan Kualitas Pembelajaran Biologi Siswa Kelas VII D SMP N 1 Jaten

– The aim of this research is improve quality of biology learning for conditioning class, student attitude in class, the performance of teacher and student motivation of learning in student class VII-D 1st Junior High School Of Jaten through the application of strategies for inquiry learning combined audio visual media. This research was classroom action research with planning, action, observation, and reflection steps. Data was collected using questionnaire, observation, and interview. The validation of data using method and observer triangulation techniques. The data analyzed by descriptive. The result in cycles I describes that mean of observation data in conditioning class indicators are 70,20%, students\u27 attitudes in class are 62,77%, performance of teachers in the learning are 80% and students motivation in learning are 68,18%. For the questionnaire, observation data in conditioning class indicators are 74,53%, students\u27 attitudes in class are 74,13%, and students motivation in learning are 74,38%. The result in cycles II describes that mean of observation data in conditioning class indicators are 80,81%, students\u27 attitudes in class are 80.09%, performance of teachers in the learning are 96,67% and student motivation in learning are 83,71%. For the questionnaire, observation data in conditioning class indicators are 83,87%, students\u27 attitudes in class are 82,49%, and students motivation in learning are 79,43%. In addition, this research also uses interview to know effect of research in quality of biology learning. The result of interview shows that students\u27 attitudes more positive, students motivation more increase and classroom climate more conducive on learning activities. The conclusion of research describes that the combination of audio visual media in inquiry learning strategies can improve quality of biology learning for conditioning class, students\u27 attitudes, performance of teachers in the learning and motivation of learning in student class VII-D in 1st junior high school of jaten

A Fractal Model for the Maximum Droplet Diameter in Gas-Liquid Mist Flow

Distribution characteristics of liquid droplet size are described using the fractal theory for liquid droplet size distribution in gas-liquid mist flow. Thereby, the fractal expression of the maximum droplet diameter is derived. The fractal model for maximum droplet diameter is obtained based on the internal relationship between maximum droplet diameter and the droplet fractal dimension, which is obtained by analyzing the balance between total droplet surface energy and total gas turbulent kinetic energy. Fractal model predictions of maximum droplet diameter agree with the experimental data. Maximum droplet diameter and droplet fractal dimension are both found to be related to the superficial velocity of gas and liquid. Maximum droplet diameter decreases with an increase in gas superficial velocity but increases with an increase in liquid superficial velocity. Droplet fractal dimension increases with an increase in gas superficial velocity but decreases with an increase in liquid superficial velocity. These are all consistent with the physical facts

Pressure Transient Analysis of Dual Fractal Reservoir

A dual fractal reservoir transient flow model was created by embedding a fracture system simulated by a tree-shaped fractal network into a matrix system simulated by fractal porous media. The dimensionless bottom hole pressure model was created using the Laplace transform and Stehfest numerical inversion methods. According to the model's solution, the bilogarithmic type curves of the dual fractal reservoirs are illustrated, and the influence of different fractal factors on pressure transient responses is discussed. This semianalytical model provides a practical and reliable method for empirical applications

Neddylation inhibitor MLN4924 suppresses cilia formation by modulating AKT1

Abstract The primary cilium is a microtubule-based sensory organelle. The molecular mechanism that regulates ciliary dynamics remains elusive. Here, we report an unexpected finding that MLN4924, a small molecule inhibitor of NEDD8-activating enzyme (NAE), blocks primary ciliary formation by inhibiting synthesis/assembly and promoting disassembly. This is mainly mediated by MLN4924-induced phosphorylation of AKT1 at Ser473 under serum-starved, ciliary-promoting conditions. Indeed, pharmaceutical inhibition (by MK2206) or genetic depletion (via siRNA) of AKT1 rescues MLN4924 effect, indicating its causal role. Interestingly, pAKT1-Ser473 activity regulates both ciliary synthesis/assembly and disassembly in a MLN4924 dependent manner, whereas pAKT-Thr308 determines the ciliary length in MLN4924-independent but VHL-dependent manner. Finally, MLN4924 inhibits mouse hair regrowth, a process requires ciliogenesis. Collectively, our study demonstrates an unexpected role of a neddylation inhibitor in regulation of ciliogenesis via AKT1, and provides a proof-of-concept for potential utility of MLN4924 in the treatment of human diseases associated with abnormal ciliogenesis.https://deepblue.lib.umich.edu/bitstream/2027.42/148214/1/13238_2019_Article_614.pd

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin