Mining topical relevant patterns for multi-document summarization

Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.

Microstrip square ring antenna for dual-band operation

This paper presents a generalized approach to design an electromagnetically coupled microstrip ring antenna for dual-band operation. By widening two opposite sides of a square ring antenna, its fractional bandwidth at the primary resonance mode can be increased significantly so that it may be used for practical applications. By attaching a stub to the inner edge of the side opposite to the feed arm, some of the losses in electrical length caused by widening can be regained. More importantly, this addition also alters the current distribution on the antenna and directs radiations at the second resonant frequency towards boresight. It has also been observed that for the dual frequency configurations studied, the ratio of the resonant frequencies (center dot r(2)center dot center dot r(1)) can range between 1.55 and 2.01. This shows flexibility in designing dual frequency antennas with a desired pair of resonant frequencies.

Joint evaluation of multiple speech patterns for speech recognition and training

We are addressing the novel problem of jointly evaluating multiple speech patterns for automatic speech recognition and training. We propose solutions based on both the non-parametric dynamic time warping (DTW) algorithm, and the parametric hidden Markov model (HMM). We show that a hybrid approach is quite effective for the application of noisy speech recognition. We extend the concept to HMM training wherein some patterns may be noisy or distorted. Utilizing the concept of ``virtual pattern'' developed for joint evaluation, we propose selective iterative training of HMMs. Evaluating these algorithms for burst/transient noisy speech and isolated word recognition, significant improvement in recognition accuracy is obtained using the new algorithms over those which do not utilize the joint evaluation strategy.

Effect of surface radiation on conjugate natural convection in a horizontal annulus driven by inner heat generating solid cylinder

This paper reports a numerical study of the laminar conjugate natural convection heat transfer with and without the interaction of the surface radiation in a horizontal cylindrical annulus formed between an inner heat generating solid circular cylinder and an outer isothermal circular boundary. Numerical solutions are obtained by solving the governing equations with a pressure correction method on a collocated (non-staggered) mesh. Steady-state results are presented for the flow and temperature distributions and Nusselt numbers for the heat generation based Grashof number ranging from 10(7) to 10(10), solid-to-fluid thermal conductivity ratios of 1, 5, 10, 50 and 100, radius ratios of 0.226 and 0.452 and surface emissivities of 0-0.8 with air as the working medium. It is observed that surface radiation reduces the convective heat transfer in the annulus compared to the pure natural convection case and enhances the overall Nusselt number.

Season of birth and location of residence: Defining the role of ultraviolet radiation exposure on childhood temperament and behaviour

This thesis added new insight to research knowledge about the role that season and ultraviolet radiation (UV) exposure during pregnancy has on children's temperament and behaviours, using a nation-wide longitudinal study. It was found that young children born in summer months are likely to have problematic behaviours. The thesis also found that summer-born children are likely to receive lowest levels of UV exposure during the gestational period. Finally, this work showed that low gestational UV exposure is associated with an increased risk of behavioural problems in children.

DMT of Multi-hop Cooperative Networks-Part II: Layered and Multi-Antenna Networks

We consider single-source, single-sink (ss-ss) multi-hop relay networks, with slow-fading Rayleigh links. This two part paper aims at giving explicit protocols and codes to achieve the optimal diversity-multiplexing tradeoff (DMT) of two classes of multi-hop networks: K-parallel-path (KPP) networks and Layered networks. While single-antenna KPP networks were the focus of the first part, we consider layered and multi-antenna networks in this second part. We prove that a linear DMT between the maximum diversity d(max). and the maximum multiplexing gain of 1 is achievable for single-antenna fully-connected layered networks under the half-duplex constraint. This is shown to be equal to the optimal DMT if the number of relaying layers is less than 4. For the multiple-antenna case, we provide an achievable DMT, which is significantly better than known lower bounds for half duplex networks. Along the way, we compute the DMT of parallel MIMO channels in terms of the DMT of the component channel. For arbitrary ss-ss single-antenna directed acyclic networks with full-duplex relays, we prove that a linear tradeoff between maximum diversity and maximum multiplexing gain is achievable using an amplify-and-forward (AF) protocol. Explicit short-block-length codes are provided for all the proposed protocols. Two key implications of the results in the two-part paper are that the half-duplex constraint does not necessarily entail rate loss by a factor of two as previously believed and that simple AN protocols are often sufficient to attain the best possible DMT.

Joint decoding of multiple speech patterns for robust speech recognition

We are addressing a new problem of improving automatic speech recognition performance, given multiple utterances of patterns from the same class. We have formulated the problem of jointly decoding K multiple patterns given a single Hidden Markov Model. It is shown that such a solution is possible by aligning the K patterns using the proposed Multi Pattern Dynamic Time Warping algorithm followed by the Constrained Multi Pattern Viterbi Algorithm The new formulation is tested in the context of speaker independent isolated word recognition for both clean and noisy patterns. When 10 percent of speech is affected by a burst noise at -5 dB Signal to Noise Ratio (local), it is shown that joint decoding using only two noisy patterns reduces the noisy speech recognition error rate to about 51 percent, when compared to the single pattern decoding using the Viterbi Algorithm. In contrast a simple maximization of individual pattern likelihoods, provides only about 7 percent reduction in error rate.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.

Si, CdTe and CdZnTe radiation detectors for imaging applications

The structure and operation of CdTe, CdZnTe and Si pixel detectors based on crystalline semiconductors, bump bonding and CMOS technology and developed mainly at Oy Simage Ltd. And Oy Ajat Ltd., Finland for X- and gamma ray imaging are presented. This detector technology evolved from the development of Si strip detectors at the Finnish Research Institute for High Energy Physics (SEFT) which later merged with other physics research units to form the Helsinki Institute of Physics (HIP). General issues of X-ray imaging such as the benefits of the method of direct conversion of X-rays to signal charge in comparison to the indirect method and the pros and cons of photon counting vs. charge integration are discussed. A novel design of Si and CdTe pixel detectors and the analysis of their imaging performance in terms of SNR, MTF, DQE and dynamic range are presented in detail. The analysis shows that directly converting crystalline semiconductor pixel detectors operated in the charge integration mode can be used in X-ray imaging very close to the theoretical performance limits in terms of efficiency and resolution. Examples of the application of the developed imaging technology to dental intra oral and panoramic and to real time X-ray imaging are given. A CdTe photon counting gamma imager is introduced. A physical model to calculate the photo peak efficiency of photon counting CdTe pixel detectors is developed and described in detail. Simulation results indicates that the charge sharing phenomenon due to diffusion of signal charge carriers limits the pixel size of photon counting detectors to about 250 μm. Radiation hardness issues related to gamma and X-ray imaging detectors are discussed.

X-ray scattering and diffraction enhanced imaging studies of in vitro breast tissues

This thesis is a study of the x-ray scattering properties of tissues and tumours of the breast. Clinical radiography is based on the absorption of the x-rays when passing right through the human body and gives information about the densities of the tissues. Besides being absorbed, x-rays may change their direction within the tissues due to elastic scattering or even to refraction. The phenomenon of scattering is a nuisance to radiography in general, and to mammography in particular, because it reduces the quality of the images. However, scattered x-rays bear very useful information about the structure of the tissues at the supra-molecular level. Some pathologies, like breast cancer, produce alterations to the structures of the tissues, being especially evident in collagen-rich tissues. On the other hand, the change of direction due to refraction of the x-rays on the tissue boundaries can be mapped. The diffraction enhanced imaging (DEI) technique uses a perfect crystal to convert the angular deviations of the x-rays into intensity variations, which can be recorded as images. This technique is of especial interest in the cases were the densities of the tissues are very similar (like in mammography) and the absorption images do not offer enough contrast. This thesis explores the structural differences existing in healthy and pathological collagen in breast tissue samples by the small-angle x-ray scattering (SAXS) technique and compares these differences with the morphological information found in the DEI images and the histo-pathology of the same samples. Several breast tissue samples were studied by SAXS technique in the European Synchrotron Radiation Facility (ESRF) in Grenoble, France. Scattering patterns of the different tissues of the breast were acquired and compared with the histology of the samples. The scattering signals from adipose tissue (fat), connective tissue (collagen) and necrotic tissue were identified. Moreover, a clear distinction could be done between the scattering signals from healthy collagen and from collagen from an invasive tumour. Scattering from collagen is very characteristic. It includes several scattering peaks and scattering features that carry information about the size and the spacing of the collagen fibrils in the tissues. It was found that the collagen fibrils in invaded tumours were thinner and had a d-spacing length 0,7% longer that fibrils from healthy tumours. The scattering signals from the breast tissues were compared with the histology by building colour-coded maps across the samples. They were also imaged with the DEI technique. There was a total agreement between the scattering maps, the morphological features seen in the images and the information of the histo- pathological examination. The thesis demonstrates that the x-ray scattering signal can be used to characterize tissues and that it carries important information about the pathological state of the breast tissues, thus showing the potential of the SAXS technique as a possible diagnostic tool for breast cancer.