Automatic spoken language identification utilizing acoustic and phonetic speech information

Automatic spoken Language Identi¯cation (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the speaker. The trend of globalization and the pervasive popularity of the Internet will amplify the need for the capabilities spoken language identi¯ca- tion systems provide. A prominent application arises in call centers dealing with speakers speaking di®erent languages. Another important application is to index or search huge speech data archives and corpora that contain multiple languages. The aim of this research is to develop techniques targeted at producing a fast and more accurate automatic spoken LID system compared to the previous National Institute of Standards and Technology (NIST) Language Recognition Evaluation. Acoustic and phonetic speech information are targeted as the most suitable fea- tures for representing the characteristics of a language. To model the acoustic speech features a Gaussian Mixture Model based approach is employed. Pho- netic speech information is extracted using existing speech recognition technol- ogy. Various techniques to improve LID accuracy are also studied. One approach examined is the employment of Vocal Tract Length Normalization to reduce the speech variation caused by di®erent speakers. A linear data fusion technique is adopted to combine the various aspects of information extracted from speech. As a result of this research, a LID system was implemented and presented for evaluation in the 2003 Language Recognition Evaluation conducted by the NIST.

Unsupervised Domain Adaptation by Domain Invariant Projection

Domain-invariant representations are key to addressing the domain shift problem where the training and test exam- ples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be di- rectly suitable for such a comparison, since some of the fea- tures may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Domain Invariant Projection approach: An unsupervised domain adaptation method that overcomes this issue by extracting the information that is invariant across the source and tar- get domains. More specifically, we learn a projection of the data to a low-dimensional latent space where the distance between the empirical distributions of the source and target examples is minimized. We demonstrate the effectiveness of our approach on the task of visual object recognition and show that it outperforms state-of-the-art methods on a stan- dard domain adaptation benchmark dataset

视觉伺服分类及其动态过程

对视觉伺服进行了综述性的介绍,系统地介绍了机器人视觉伺服控制的发展历史以及现状·从控制模型给出了视觉伺服控制系统的分类·针对两种最基本的分类方式基于位置的视觉伺服和基于图像的视觉伺服进行了重点介绍·对于视觉系统和图像特征的选取问题进行了讨论,此外还对视觉伺服系统的动态过程进行了分析,指出视觉系统的延时是目前伺服控制的研究所面临的最大问题·对未来视觉伺服研究的方向进行了总结·

A planar Penning trap

In this thesis I present theoretical and experimental results concern- ing the operation and properties of a new kind of Penning trap, the planar trap. It consists of circular electrodes printed on an isolating surface, with an homogeneous magnetic field pointing perpendicular to that surface. The motivation of such geometry is to be found in the construction of an array of planar traps for quantum informa- tional purposes. The open access to radiation of this geometry, and the long coherence times expected for Penning traps, make the planar trap a good candidate for quantum computation. Several proposals for quantum 2-qubit interactions are studied and estimates for their rates are given. An expression for the electrostatic potential is presented, and its fea- tures exposed. A detailed study of the anharmonicity of the potential is given theoretically and is later demonstrated by experiment and numerical simulations, showing good agreement. Size scalability of this trap has been studied by replacing the original planar trap by a trap twice smaller in the experimental setup. This substitution shows no scale effect apart from those expected for the scaling of the parameters of the trap. A smaller lifetime for trapped electrons is seen for this smaller trap, but is clearly matched to a bigger misalignment of the trap’s surface and the magnetic field, due to its more difficult hand manipulation. I also give a hint that this trap may be of help in studying non-linear dynamics for a sextupolarly perturbed Penning trap.

Predicting Transcript Production Rates in Yeast With Sparse Linear Models

To provide biological insights into transcriptional regulation, a couple of groups have recently presented models relating the promoter DNA-bound transcription factors (TFs) to downstream gene’s mean transcript level or transcript production rates over time. However, transcript production is dynamic in response to changes of TF concentrations over time. Also, TFs are not the only factors binding to promoters; other DNA binding factors (DBFs) bind as well, especially nucleosomes, resulting in competition between DBFs for binding at same genomic location. Additionally, not only TFs, but also some other elements regulate transcription. Within core promoter, various regulatory elements influence RNAPII recruitment, PIC formation, RNAPII searching for TSS, and RNAPII initiating transcription. Moreover, it is proposed that downstream from TSS, nucleosomes resist RNAPII elongation.

Here, we provide a machine learning framework to predict transcript production rates from DNA sequences. We applied this framework in the S. cerevisiae yeast for two scenarios: a) to predict the dynamic transcript production rate during the cell cycle for native promoters; b) to predict the mean transcript production rate over time for synthetic promoters. As far as we know, our framework is the first successful attempt to have a model that can predict dynamic transcript production rates from DNA sequences only: with cell cycle data set, we got Pearson correlation coefficient Cp = 0.751 and coefficient of determination r2 = 0.564 on test set for predicting dynamic transcript production rate over time. Also, for DREAM6 Gene Promoter Expression Prediction challenge, our fitted model outperformed all participant teams, best of all teams, and a model combining best team’s k-mer based sequence features and another paper’s biologically mechanistic features, in terms of all scoring metrics.

Moreover, our framework shows its capability of identifying generalizable fea- tures by interpreting the highly predictive models, and thereby provide support for associated hypothesized mechanisms about transcriptional regulation. With the learned sparse linear models, we got results supporting the following biological insights: a) TFs govern the probability of RNAPII recruitment and initiation possibly through interactions with PIC components and transcription cofactors; b) the core promoter amplifies the transcript production probably by influencing PIC formation, RNAPII recruitment, DNA melting, RNAPII searching for and selecting TSS, releasing RNAPII from general transcription factors, and thereby initiation; c) there is strong transcriptional synergy between TFs and core promoter elements; d) the regulatory elements within core promoter region are more than TATA box and nucleosome free region, suggesting the existence of still unidentified TAF-dependent and cofactor-dependent core promoter elements in yeast S. cerevisiae; e) nucleosome occupancy is helpful for representing +1 and -1 nucleosomes’ regulatory roles on transcription.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

Fluid-structure interaction analysis of full scale vehicle-barrier impact using coupled SPH-FEA

Portable water-filled barriers (PWFB) are roadside structures used to separate moving traffic from work-zones. Numerical PWFB modelling is preferred in the design stages prior to actual testing. This paper aims to study the fluid-structure interaction of PWFB under vehicular impact using several methods. The strategy to treat water as non-structural mass was proposed and the errors were investigated. It was found that water can be treated with the FEA-NSM model for velocities higher than 80kmh-1. However, full SPH/FEA model is still the best treatment for water and necessary for lower impact velocities. The findings in this paper can be used as guidelines for modelling and designing PWFB.

Porohyperelastic analysis of single osteocyte using AFM and inverse FEA

Osteocytes are the mature cells and perform as mechanosensors within the bone. The mechanical property of osteocytes plays an important role to fulfill these functions. However, little researches have been done to investigate the mechanical deformation properties of single osteocytes. Atomic Force Microscopy (AFM) is a state-of-art experimental facility for high resolution imaging of tissues, cells and any surfaces as well as for probing mechanical properties of the samples both qualitatively and quantitatively. In this paper, the experimental study based on AFM is firstly used to obtain forceindentation curves of single round osteocytes. The porohyperelastic (PHE) model of a single osteocyte is then developed by using the inverse finite element analysis (FEA) to identify and extract mechanical properties from the experiment results. It has been found that the PHE model is a good candidature for biomechanics studies of osteocytes.

Porohyperelastic analysis of single chondrocyte using AFM and inverse FEA

The aim of this paper is to determine the creep and relaxation responses of single chondrocytes in vitro. Firstly, Atomic Force Microscopy (AFM) was used to obtain the force-indentation curves of single chondrocytes at the strain-rate of 7.05 s-1. This result was then employed in inverse finite element analysis (FEA) using porohyperelastic (PHE) idealization of the cells to determine their mechanical properties. The PHE model results agreed well with AFM experimental data. This PHE model was then utilized to study chondrocyte’s creep and relaxation behaviors. The results revealed that the effect of fluid was predominant for cell’s mechanical behaviors and that the PHE is a good model for biomechanics studies of chondrocytes.

KARYOTYPE AND BANDING-PATTERNS OF FEA TREE RAT (CHIROMYSCUS-CHIROPUS)

Fea's tree rat (Chiromyscus chiropus) is a very rare species which there are only a few specimens in the world. The chromosomes of two male specimens, collected from Xishuanbanna, Yunnan, are analysed by several banding technique (G-, C-bands, as well as Ag-staining). The diploid chromosome number is 22, and autosomes comprise 5 pairs of metacentrics, 2 pairs of subacrocentrics, and 3 pairs of acrocentrics. The X chromosome is a acrocentric, and Y is a micro-chromosome, almost a point, which could be a marker chromosome of the species and the genus. The centromeric C-bands are very faint, and C-bands of Nos. 1, 2, 9 and Y chromosome are negative. Only one pair Ag-NORs was found on No. 10 in the silver-stained karyotype. The relationship between morphologic and chromosomal features was discussed, and C-banded karyotype evolutionary trend has also been discussed. Moreover, the conventional karyotype of Niviventer confucianus was described.