Miracle-dz/UniFAR-FTUs huggingface dataset:
Translation Selection for Japanese-English Noun-Noun Compounds

We presenta methodfor compositionallytranslatingJapaneseNN compoundsinto English,usinga wordlevel transferdictionary and target languagemonolingualcorpus. The methodinterpolatesover fullyspecifiedandpartial translationdata,basedon corpusevidence.In evaluation,we demonstratethat interpolationover the two datatypesis superiorto usingeitherone,andshow thatour methodperformsat an F-scoreof 0.68over translation-alignedinputsand0.66overa randomsampleof 500NN compounds.
Fuel Nozzle Spray Pattern Classifier

In this study an industrial problem of classification of faulty fuel nozzles is considered and a solution is proposed by analyzing their spray pattern through vision based algorithms. The proposed solution is more reliable, accurate, cheap, and descriptive as compared to the manual techniques which are time consuming and error prone. We capture the dependency of spray patterns on imaging parameters using direction dependent enhancement, adaptive filtering and statistical feature extraction. In this study directional features of spray patterns affected by various disorders are extracted and are then used for classification of different fuel nozzles using Euclidean distance classifier. Moreover nozzle spray patterns are processed for spray angle measurement.
Process Model Evolution in the SPADE Environment

Software processes are long-lived entities. Careful design and thorough validation of software process models are necessary to ensure the quality of the process. They do not prevent, however, process models from undergoing change. Change requests may occur in the context of reuse, i.e. statically, in order to support software process model customization. They can also occur dynamically, while software process models are being executed, in order to support timely reaction as data are gathered from the field during process enactment. We discuss the mechanisms a process language should possess in order to support changes. We illustrate the solution adopted in the context of the SPADE environment and discuss how the proposed mechanisms can be used to model different policies for changing a software process model. >
Transformational Journeys: Volunteer Tourism, Non-Elite Youth and the Politics of the Self

This thesis examines short volunteer tourism trips involving young people from non-elite backgrounds, run by youth groups based on London council estates and travelling to Kenya and Zimbabwe. Based on ethnographic work both in the UK and during trips, this thesis argues that participants narrate the trips as sites of self-transformation. They see themselves as becoming more ‘grateful’ for their relative wealth, ‘charitable’, and ‘aspirational’ in terms of having the desire and dreams to make their own future. Though these findings echo those pertaining to middle-class volunteers, there is a disciplinary aspect to the way imaginaries of personal transformation through volunteer tourism mesh with longer-standing efforts at reforming working-class youth, and amplify pressures on young subjects to take individual responsibility for their own betterment. Rather than examine these problematic politics as an imposition of discursive power, this thesis makes a close reading of emotion and affect in volunteer tourism. Affective circulations, embodied acts, and cultivated emotions constitute depoliticised approaches to global poverty and national inequality and give them a visceral felt authenticity. However, emotional intensities also disrupt the overarching narrative. Young people emphasise their enjoyment of the trips as spaces of collective fun rather than individual reform, express desires to connect across transnational difference, and contrast their enjoyment of status and meaningful labour abroad with the constraints they face in UK society. This thesis argues that volunteer tourism is a site for subject formation that is deeply entangled with both relations between ‘the west and the rest’ and young subjects’ social navigations in the national context. It offers a corrective to mapping a global-local dualism onto classed subjects and totalising analyses of volunteer tourism which assume an archetypal elite white volunteer acting at the whim of ‘neocolonialism’ and ‘neoliberalism’. It contributes to better understanding the relationship between emotion, affect and politics, and using this understanding, offers a more nuanced reading of the transnational encounters of volunteer tourism.
Adaptive filtering in the presence of outliers

Adaptive filters aimed at detecting a signal of interest in the presence of noise undergo a significant degradation in terms of the output signal to interference and noise ratio when the training samples contain signal-like components. It is thus crucial to take into account this contamination when estimating the noise covariance matrix which is used to compute the adaptive filter. In this paper, we consider a covariance matrix estimation scheme which takes into account the possible presence of outliers, without censoring any training sample. A Bayesian model is formulated where the amplitude of the signal component of each training sample is assumed to follow a Bernoulli-Gaussian distribution. Additionally, the noise covariance matrix is assigned some non informative prior distribution, namely a maximum entropy distribution. The posterior distributions of these variables are derived and an efficient Markov Chain Monte Carlo method is presented to obtain the minimum mean-square error estimates. The new scheme is shown to outperform robust schemes based on diagonal loading.
Pareto Lévy Measures and Multivariate Regular Variation

We consider regular variation of a Lévy process X := ( X t) t≥0 in with Lévy measure Π, emphasizing the dependence between jumps of its components. By transforming the one-dimensional marginal Lévy measures to those of a standard 1-stable Lévy process, we decouple the marginal Lévy measures from the dependence structure. The dependence between the jumps is modeled by a so-called Pareto Lévy measure, which is a natural standardization in the context of regular variation. We characterize multivariate regularly variation of X by its one-dimensional marginal Lévy measures and the Pareto Lévy measure. Moreover, we define upper and lower tail dependence coefficients for the Lévy measure, which also apply to the multivariate distributions of the process. Finally, we present graphical tools to visualize the dependence structure in terms of the spectral density and the tail integral for homogeneous and nonhomogeneous Pareto Lévy measures.
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

This paper describes the generic obstacle and lane detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an on-board monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.
COMPARING COMPENSATORY REACTIONS IN YOUNG AND OLDER ADULTS IN RESPONSE TO PLATFORM PERTURBATIONS DURING GAIT

COMPARING COMPENSATORY REACTIONS IN YOUNG AND OLDER ADULTS IN RESPONSE TO PLATFORM PERTURBATIONS DURING GAIT Emily Isabel McIntosh Advisor: University of Guelph, 2013 Dr. Lori Ann Vallis The overall objective of this thesis was to compare compensatory reactions in young and older adults following unexpected platform perturbations in the frontal and sagittal planes during gait. Eleven young (aged 18-30) and ten community-dwelling older adults (aged 65+) completed a general health questionnaire, Mini BESTest, hip strength tests, and 45 walking trials on a motion platform. The walking trials were completed in a randomized block design on a 5 m pathway that moved following right heel contact after at least two steps. Young adults had stronger hips and performed better on the Mini BESTest than older adults. Compensatory reactions (measured through gait parameters and centre of mass motion) indicated that lateral perturbations were more difficult for both groups. These measures showed that young adults were able to recover more quickly than older adults. This could be attributed to altered base of support changes that occurred immediately following platform motions.
Analysis of Relative Concentration of Ethanol and Other Odorous Compounds (OCs) Emitted from the Working Surface at a Landfill in China

Estimating odor emissions from landfill sites is a complicated task because of the various chemical and biological species that exist in landfill gases. In this study, the relative concentration of ethanol and other odorous compounds emitted from the working surface at a landfill in China was analyzed. Gas sampling was conducted at the landfill on a number of selected days from March 2012 to March 2014, which represented different periods throughout the two years. A total of 41, 59, 66, 54, 63, 54, 41, and 42 species of odorous compounds were identified and quantified in eight sampling activities, respectively; a number of 86 species of odorous compounds were identified and quantified all together in the study. The measured odorous compounds were classified into six different categories (Oxygenated compounds, Halogenated compounds, Terpenes, Sulfur compounds, Aromatics, and Hydrocarbons). The total average concentrations of the oxygenated compounds, sulfur compounds, aromatics, halogenated compounds, hydrocarbons, and terpenes were 2.450 mg/m3, 0.246 mg/m3, 0.203 mg/m3, 0.319 mg/m3, 0.530 mg/m3, and 0.217 mg/m3, respectively. The relative concentrations of 59 odorous compounds with respect to the concentration of ethyl alcohol (1000 ppm) were determined. The dominant contaminants that cause odor pollution around the landfill are ethyl sulfide, methyl mercaptan, acetaldehyde, and hydrogen sulfide; dimethyl disulfide and dimethyl sulfide also contribute to the pollution to a certain degree.
The filter wheel and filters development for the X-IFU instruments onboard Athena

Athena is the large mission selected by ESA in 2013 to investigate the science theme “Hot and Energetic Universe” and presently scheduled for launch in 2028. One of the two instruments located at the focus of the 12 m-long Athena telescope is the X-ray Integral Field Unit (X-IFU). This is an array of TES microcalorimeters that will be operated at temperatures of 50 mK in order to perform high resolution spectroscopy with an energy resolution down to 2.5 eV at energies < 7 keV. In order to cope with the large dynamical range of X-ray fluxes spanned by the celestial objects Athena will be observing, the X-IFU will be equipped with a filter wheel. This will allow the user to fine tune the instrument set-up based on the nature of the target, thus optimizing the scientific outcomes of the observation. A few positions of the filter wheel will also be used to host a calibration source and to allow the measurement of the instrument intrinsic background.
Dynamic spectrum access with non-stationary Multi-Armed Bandit

Dynamic spectrum access (DSA) is an emerging notion in cognitive radio, aiming to improve the spectrum usage with reliable secondary access to the spectral resources. The main challenge in DSA is the detection of spectral opportunities and their efficient utilization without causing interference to the primary users. For this goal, we propose to make use of a reinforcement learning approach: the Multi Armed Bandit (MAB). The MAB approach provides the secondary users with the rules and policies necessary to achieve a tradeoff between exploitation and exploration in DSA. Different MAB strategies are tested on an IEEE802.11 medium access model and evaluated in dynamic environment. Our study shows that the MAB constitute a viable solution for the DSA. Adding to that, the performances of the MAB algorithms can be improved with a finite tuning of the internal parameters.
When to use Quantile Normalization?

Normalization and preprocessing are essential steps for the analysis of high-throughput data including next-generation sequencing and microarrays. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation from noisy data. These methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Transforming the data to remove these differences has the potential to remove interesting biologically driven global variation and therefore may not be appropriate depending on the type and source of variation. Currently, it is up to the subject matter experts, for example biologists, to determine if the stated assumptions are appropriate or not. Here, we propose a data-driven method to test for the assumptions of global normalization methods. We demonstrate the utility of our method (quantro), by applying it to multiple gene expression and DNA methylation and show examples of when global normalization methods are not appropriate. We also perform a Monte Carlo simulation study to illustrate how our method generally outperforms the current approach. An R-package implementing our method is available on Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/quantro.html).
Bimodal personal recognition using hand images

Presence of unique palmprint patterns makes palmprints suitable for personal recognition. In low resolution images, these patterns mainly consist of multisized and multidirectional principal lines and wrinkles. This prompted us to use wavelet based multiresolution analysis (MRA) to extract these features. Most of the palmprint feature extraction works based on wavelets fail to consider spatial location of energies. We have proposed a new method to extract these features in the form of spatially localized wavelet energy signatures (SLWES) which comprise spatial and frequency information at different resolutions. SLWES features characterize palmprints effectively and are represented in a vector form. We have also identified a few new hand geometrical features suitable for personal recognition, and thirty hand geometry features (including new) are extracted to form a geometrical feature vector. These feature vectors are then examined for their individual and combined (fusion) identification performances. We experimented these methods on our database and obtained 97.5% accuracy for combined mode, which is comparable with similar bimodal (geometrical and palmprints) identification methods.
Static and dynamic modeling of thermal microgripper

The performance of any micro-component affects the overall performance of the system especially in the micro-level. In this research work, the thermal and structural performances of microgripper based on silicon are studied. The present study is part of a larger effort in developing, modeling and characterization procedures toward understanding the behavior of microgripper. This is essential to develop better control algorithms and to facilitate the study of robot arms dynamic on gripping performance. The structural stiffness and failure of microgripper in static loading are characterized for two actuation modes: in-plane and off-plane. Moreover, building block attempts are introduced for dynamical modeling of microgripper/sensor and environment. The electromechanical system model of sensor is experimentally identified and utilized in identifying the performance of hypothesized lumped models of microgripper
Brain activation detection from functional magnetic resonance imaging data using likelihood based hypothesis tests

We propose a new approach, which is also based on a general linear model with correlated noise modelled as an AR process, but unlike the common GLM approach, it does not require a prewhitening step. Instead, statistical inference is based on the exact likelihood function that describes the statistics of the data including the temporal correlation structure of the noise. No approximations are made. The order of the AR process is assumed x ed. It is determined from practical null data sets (acquired in the absence of activity) using nite sample criteria for order selection. The present work focuses on the case of only one activation-related parameter. Three likelihood based binary hypothesis tests are proposed: the generalized likelihood ratio test (GLRT), the Wald test, and the Rao test. The null hypothesis H0 (no activation is present) is tested against the alternative hypothesis H1 (activation is present). For the computation of the test statistics, the maximum likelihood estimates of the unknown parameters under H0 (Rao), H1 (Wald) or both H0 and H1 (GLRT) are needed. They are obtained by maximizing the likelihood function with respect to all unknown parameters (including those of the AR model) simultaneously. Both one-sided and two-sided variants of the likelihood based tests are proposed.
Dimensions of Journalistic Workplace Autonomy: A Five-nation Comparison

This article examines how journalists perceive workplace autonomy in five European countries, based on an email survey (N = 2238) conducted in the United Kingdom, Germany, Italy, Sweden and Estonia. The article argues that the workplace level functions as a link between the macro level of external pressures and the micro level of perceived influences on news work. Using principal component analysis we explore the dimensionality of workplace autonomy based on a set of 20 survey questions. Regression analysis is then used on the dimensions found in order to determine what affects perception of autonomy in the different dimensions. The most salient explanatory variables are found on the country and organisational levels, whereas the variables age, experience, gender, managerial role and medium have no or limited effects. The results show the organisational and country levels being integrated and that national journalistic culture is the most salient factor explaining perception of autonomy.
Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.
THE AUSTRALIAN NATIONAL UNIVERSITY WORKING PAPERS IN ECONOMICS AND ECONOMETRICS CHASING PATENTS *

We examine the problem faced by a company that wishes to purchase patents in the hands of two di¤erent patent owners. Complementarity of these patents in the production process of the company is a prime e¢ciency reason for them being owned (or licenced) by the company. We show that this very same complementarity can lead to patent owners behaving strategically in bargaining, and delaying their sale to the company. When the company is highly leveraged, such ine¢cient delay is limited. Comparative statics results are also obtained. Relevant applications include assembly of patents for drug treatments from the human genome, and land assembly. Key-words: patents, complementarity, bargaining. JEL Classi.cation: C78, O31, O34. ¤Menezes gratefully acknowledges the . nancial support from CNPq and ARC.
A pumped storage power plant with double-fed induction machine and cascaded frequency converter

The paper presents a variable speed drive for pump-storage plant applications. A reversible turbine is energized by a double-fed induction machine (DFIM) exited on the rotor side with a cascaded frequency converter made up of M single-phase cells. The DFIM is started up as a synchronous machine by means of a CSI on the stator side and a VSI on the rotor side. An emphasis is placed on the control system aimed at the plant active and reactive power regulation. In order to balance out currents of the transformer secondary windings feeding single-phase active rectifiers, a phase disposition PWM strategy has been modified by introducing a consecutive cycling step-wise displacement of triangular wave offsets within a saw tooth period consisting of M triangle wave periods. The paper concludes by analyzing start-up responses and performance characteristics of the system in sub- and over-synchronous modes of operation.
A model-predictive controller for optimal hydro-power utilization of river reservoirs

This paper focuses on a two-layer control algorithm for operational management of river barrages, which is based on a model-predictive controller in the upper layer and local controllers at each barrage in the lower layer. The incorporation of hydro-power utilization into the objective function of the predictive controller will be discussed in detail. The process description is based on an efficient numerical schema (Godunov-method) for the full Saint-Venant-equations using a coarse spatial discretization. A simulation study for three barrages of the river Moselle shows the benefits of the suggested approach for a real inflow scenario.