Semi-supervised and unsupervised extensions to maximum-margin structured prediction

University of Technology Sydney. Faculty of Engineering and Information Technology.Structured prediction is the backbone of various computer vision and machine learning applications. Inspired by the success of maximum-margin classifiers in the recent years; in this thesis, we will present novel semi-supervised and unsupervised extensions to structured prediction via maximum-margin classifiers. For semi-supervised structured prediction, we have tackled the problem of recognizing actions from single images. Action recognition from a single image is an important task for applications such as image annotation, robotic navigation, video surveillance and several others. We propose approaching action recognition by first partitioning the entire image into “superpixels”, and then using their latent classes as attributes of the action. The action class is predicted based on a graphical model composed of measurements from each superpixel and a fully-connected graph of superpixel classes. The model is learned using a latent structural SVM approach, and an efficient, greedy algorithm is proposed to provide inference over the graph. Differently from most existing methods, the proposed approach does not require annotation of the actor (usually provided as a bounding box). For the unsupervised extension of structured prediction, we considered the case of labeling binary sequences. This case is important in a detection scenario, where one is interested in detecting an action or an event. In particular, we address the unsupervised SVM relaxation recently proposed in (Li et al. 2013) and extend it for structured prediction by merging it with structural SVM. The main contribution of the proposed extension (named Well-SSVM) is a re-organization of the feature map and loss function of structural SVM that permits finding the violating labelings required by the relaxation. Experiments on synthetic and real datasets in a fully unsupervised setting reveal a competitive performance as opposed to other unsupervised algorithms such as k-means and latent structural SVM. Finally, we approached the problem of unsupervised structured prediction by M³ Networks. M³ Networks are an alternative formulation of maximum-margin structured prediction that can satisfy the complete set of constraints for decomposable feature and loss functions; hence, the entire set of constraints is considered during the search for the optimal margin as opposed to Structural SVM. In the thesis, we present the interpretation of M³ Networks in Well-SSVM, thus allowing us to use in a semi-supervised and unsupervised scenario

Higher Order Couplings in the Clustering of Biased Tracers of Large-Scale Structure

The Large-Scale Structure (LSS) of the Universe, i.e. the distribution of matter and luminous tracers (such as galaxies), contains a wealth of information about the origin, composition, and evolution of the Universe. In order to extract this information, the non-linearities present in late-time observables provided by LSS surveys must be understood well. In general, there are three main sources of non-linearities: (1) non-linear matter clustering due to gravity; (2) non-linear biasing, i.e. the relation between the distribution of tracers and dark matter; and (3) primordial non-Gaussianity, which induces non-linearities in the initial conditions. The Effective Field Theory of Large-Scale Structure (EFTofLSS) provides a powerful framework to model the non-linear clustering due to gravity. In this thesis, we focus on understanding the non-linearities due to galaxy biasing using the EFTofLSS and numerical N-body simulations. This thesis is comprised of the following three projects: In the first part, we present a novel method to constrain quadratic and cubic galaxy bias parameters in dark matter simulations. The natural statistics to constrain quadratic and cubic bias parameters are tree-level bispectrum and trispectrum, respectively. Since these statistics are computationally quite expensive, we use efficient squared and cubic field estimators that contain integrated bispectrum and trispectrum information. We use the constraints to model the one-loop halo-matter power spectrum and show that the results agree with simulations up to kmax = 0.1h Mpc 1 once an additional derivative bias is implemented (Published in: Abidi & Baldauf, JCAP07(2018)029). In the second part, we develop a formalism to reconstruct the linear density field based on quadratic couplings in galaxy clustering. We employ a quadratic estimator inspired by Cosmic Microwave Background (CMB) lensing reconstruction. We incorporate non-linearities due to gravity, galaxy biasing and primordial non-Gaussianity, and verify our predictions with N-body simulations. We perform a Fisher matrix analysis on how the reconstructed field in combination with the biased tracer field can improve constraints on local type primordial non-Gaussianity. We find significant improvement on constraints due to cosmic variance cancellation resulting from the additional correlated modes of the reconstructed field, similar to multi-tracer analyses. In the third part, we develop a method to constrain non-linear galaxy bias parameters using the two- and three-point functions of projected galaxy clustering in correlation with CMB lensing convergence. The project thus aims to bring the methodology developed in project 1 above closer to data. We develop the quadratic field method for projected fields to avoid complications from non-linear redshift space distortions. We perform a Fisher forecast to show that this method can indeed be used to put constraints on bias parameters and the amplitude of matter fluctuations. Finally, using N-body simulations we ascertain that the projected statistics do indeed reduce the impact of finger-of-god corrections.My PhD was generously funded by the Cambridge Commonwealth, European and International Trust and the Higher Education Commission Pakistan. I have additionally received invaluable financial assistance from St. Edmunds College, Cambridge, the Cambridge Philosophical Society, the Centre for Theoretical Cosmology, Dr Blake Sherwin's EPRC grant, the Postgraduate Lundgren Award, and the Santander Award

How Pakistan\u27s media spreads the message about reproductive and sexual health

Abstract, issue, and pagination are not provided by the author/publishe

Participant Experience of the First Massive Open Online Course (MOOC) from Pakistan

Background: In recent years, massive open online courses (MOOCs) have steadily gained popularity. It appears, however, that MOOC learners are concentrated mostly in the affluent English-speaking countries. MOOCs’ free-of-cost, easy accessibility should make them obviously attractive to participants from low-and-middle-income countries (LMIC). The reason why LMIC enrollments in MOOCs are so low is therefore unclear. In the year 2014, the first MOOC was launched from Pakistan. We administered a survey to the enrollees of this MOOC to explore concerns, fears, and limitations that might be deterring the LMIC audience from participating in MOOCs. Methods: The MOOC was a three-week course on bioinformatics that covered current concepts and techniques employed in the area of computer-based drug design. More than 230 participants enrolled for this course. At the end of the course, to examine the MOOC experience from their perspective, we invited the participants to take an online survey. Results: Fifty-four participants, mostly from Pakistan, completed the survey. The participants reported satisfaction with the course, and felt that the course participation was an enriching experience. Although they appeared eager to explore MOOC learning, we found that the learners from LMICs may not be completely comfortable with various aspects of online learning. Conclusion: Our results indicate that there is a definite market for MOOCs in LMICs. Computer accessibility and literacy must be enhanced in the LMICs to allow the citizens of these regions to feel comfortable with e-learning. Moreover, LMIC nations acknowledge their own unique learning cultures and experiences when they produce and share their MOOC offerings with the world

D-WISE: Diabetes Web-Centric Information and Support Environment: Conceptual Specification and Proposed Evaluation

AbstractObjectiveTo develop and evaluate Diabetes Web-Centric Information and Support Environment (D-WISE) that offers 1) a computerized decision-support system to assist physicians to A) use the Canadian Diabetes Association clinical practice guidelines (CDA CPGs) to recommend evidence-informed interventions; B) offer a computerized readiness assessment strategy to help physicians administer behaviour-change strategies to help patients adhere to disease self-management programs; and 2) a patient-specific diabetes self-management application, accessible through smart mobile devices, that offers behaviour-change interventions to engage patients in self-management.MethodsThe above-mentioned objectives were pursued through a knowledge management approach that involved 1) Translation of paper-based CDA CPGs and behaviour-change models as computerized decision-support tools that will assist physicians to offer evidence-informed and personalized diabetes management and behaviour-change strategies; 2) Engagement of patients in their diabetes care by generating a diabetes self-management program that takes into account their preferences, challenges and needs; 3) Empowering patients to self-manage their condition by providing them with personalized educational and motivational messages through a mobile self-management application. The theoretical foundation of our research is grounded in behaviour-change models and healthcare knowledge management.We used 1) knowledge modelling to computerize the paper-based CDA CPGs and behaviour-change models, in particular, the behaviour-change strategy elements of A) readiness-to-change assessments; B) motivation-enhancement interventions categorized along the lines of patients' being ready, ambivalent or not ready; and C) self-efficacy enhancement. The CDA CPGs and the behaviour-change models are modelled and computerized in terms of A) a diabetes management ontology that serves as the knowledge resource for all the services offered by D-WISE; B) decision support services that use logic-based reasoning algorithms to utilize the knowledge encoded within the diabetes management ontology to assist physicians by recommending patient-specific diabetes-management interventions and behaviour-change strategies; C) a mobile diabetes self-management application to engage and educate diabetes patients to self-manage their condition in a home-based setting while working in concert with their family physicians.ResultsWe have been successful in creating and conducting a usability assessment of the physician decision support tool. These results will be published once the patient self- management application has been evaluated.ConclusionsD-WISE will be evaluated through pilot studies measuring 1) the usability of the e-Health interventions; and 2) the impact of the interventions on patients' behaviour changes and diabetes control

A roadmap for offering MOOC from an LMIC institution

MOOCs are massive open online courses that are globally accessible, free of charge. Given their cost-free and open accessibility, it is surprising that only a few institutions have offered MOOCs from low- and middle-income countries (LMICs). Pakistan recently made this short list of LMICs as the first two MOOCs were launched from the country, in 2014 and 2016. Drawing from that experience, the organizers of that course present a roadmap for LMIC institutions for developing a MOOC, focusing especially on the technological and pedagogical limitations that an LMIC institution might find deterring

A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping

Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate

Happiness and Spirituality: An Empirical Analysis using Divine Perspectives in Pakistan

Happiness is the center of discussion among philosophers, theologians, psychologists and more recently among economists from past few decades. Easterlin (1974) claimed that money alone cannot buy happiness, factors such as social interactions, socio-demographic factors, religion and personal values influence happiness. Abundant literature has been produced onspirituality by philosophers and scholars of different religions, however, spirituality-happiness literature from Islamic point of view and particularly in the case of Muslim society is largely ignored. This study analytically explores and empirically tests the relationship between spirituality and happiness using Divine Economics Framework in case of 5 districts of Azad Kashmir(Pakistan), collected through Divine Economics Survey 2013. Findings of the study show that spirituality intrinsically matters in producing wellbeing and happiness