LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION

This thesis studies machine learning problems involved in visual recognition on a variety of computer vision tasks. It attacks the challenge of scaling-up learning to efficiently handle more training data in object recognition, more noise in brain activation patterns, and learning more capable visual similarity models. For learning similarity models, one challenge is to capture from data the subtle correlations that preserve the notion of similarity relevant to the task. Most previous work focused on improving feature learning and metric learning separately. Instead, we propose a unified deep-learning modeling framework that jointly optimizes the two through back-propagation. We model the feature mapping using a convolutional neural network and the metric function using a multi-layer fully-connected network. Enabled by large datasets and a sampler to handle the intrinsic imbalance between positive and negative samples, we are able to learn such models efficiently. We apply this approach to patch-based image matching and cross-domain clothing-item matching. For analyzing activation patterns in images acquired using functional Magnetic Resonance Imaging (fMRI), a technology widely used in neuroscience to study human brain, challenges are small number of examples and high level of noise. The common ways of increasing the signal to noise ratio include adding more repetitions, averaging trials, and analyzing statistics maps solved based on a general linear model. In collaboration with neuroscientists, we developed a machine learning approach that allows us to analyze individual trials directly. This approach uses multi-voxel patterns over regions of interest as feature representation, and helps discover effects previous analyses missed. For multi-class object recognition, one challenge is learning a non-one-vs-all multi-class classifier with large numbers of categories each with large numbers of examples. A common approach is data parallelization in a synchronized fashion: evenly and randomly distribute the data into splits, learn a full model on each split and average the models. We reformulate the overall learning problem in a consensus optimization framework and propose a more principled synchronized approach to distributed training. Moreover, we develop an efficient algorithm for solving the sub-problem by reducing it to a standard problem with warm start.Doctor of Philosoph

Bilingual teachers' contextualization in teaching Chinese as a foreign language in Australian schools

This research focuses on the practice of contextualization in teaching Chinese as a foreign language among a cohort of bilingual language teacher-researchers. It aims to extend the prevalent emphasis in the current literature that acknowledges the role of context in language education; however, these research studies primarily give voice to linguistic contexts or relegates context into a static physical space such as ‘environment’. This research is grounded in a social constructionism perspective whereby context is regarded as a dynamic relation-building process, or more accurately, a contextualizing process, enabled through various sociocultural activities. The data reveal that the teacher-researchers employed various forms of contextualization in teaching and linked these to particular teaching content through identifiable, purposeful activities, resulting in a variety of students’ responses. This research provides an evidence-based understanding of contextualization in CFL teaching for a more sustainable second language education

Cryogenic hybrid magnonic circuits based on spalled YIG thin films

Yttrium iron garnet (YIG) magnonics has sparked extensive research interests toward harnessing magnons (quasiparticles of collective spin excitation) for signal processing. In particular, YIG magnonics-based hybrid systems exhibit great potentials for quantum information science because of their wide frequency tunability and excellent compatibility with other platforms. However, the broad application and scalability of thin-film YIG devices in the quantum regime has been severely limited due to the substantial microwave loss in the host substrate for YIG, gadolinium gallium garnet (GGG), at cryogenic temperatures. In this study, we demonstrate that substrate-free YIG thin films can be obtained by introducing the controlled spalling and layer transfer technology to YIG/GGG samples. Our approach is validated by measuring a hybrid device consisting of a superconducting resonator and a spalled YIG film, which gives a strong coupling feature indicating the good coherence of our system. This advancement paves the way for enhanced on-chip integration and the scalability of YIG-based quantum devices.Comment: 10 pages, 8 figure

MatchNet: Unifying feature and metric learning for patch-based matching

Motivated by recent successes on learning feature rep-resentations and on learning feature comparison functions, we propose a unified approach to combining both for train-ing a patch matching system. Our system, dubbed Match-Net, consists of a deep convolutional network that extracts features from patches and a network of three fully con-nected layers that computes a similarity between the ex-tracted features. To ensure experimental repeatability, we train MatchNet on standard datasets and employ an input sampler to augment the training set with synthetic exemplar pairs that reduce overfitting. Once trained, we achieve bet-ter computational efficiency during matching by disassem-bling MatchNet and separately applying the feature com-putation and similarity networks in two sequential stages. We perform a comprehensive set of experiments on stan-dard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods. Our results confirm that our unified approach im-proves accuracy over previous state-of-the-art results on patch matching datasets, while reducing the storage re-quirement for descriptors. We make pre-trained MatchNet publicly available.1 1

Electron on solid neon -- a new solid-state single-electron qubit platform

The promise of quantum computing has driven a persistent quest for new qubit platforms with long coherence, fast operation, and large scalability. Electrons, ubiquitous elementary particles of nonzero charge, spin, and mass, have commonly been perceived as paradigmatic local quantum information carriers. Despite superior controllability and configurability, their practical performance as qubits via either motional or spin states depends critically on their material environment. Here we report our experimental realization of a new qubit platform based upon isolated single electrons trapped on an ultraclean solid neon surface in vacuum. By integrating an electron trap in a circuit quantum electrodynamics architecture, we achieve strong coupling between the motional states of a single electron and microwave photons in an on-chip superconducting resonator. Qubit gate operations and dispersive readout are used to measure the energy relaxation time $T_1$ of $15~\mu$s and phase coherence time $T_2$ over 200 ns, indicating that the electron-on-solid-neon qubit already performs near the state of the art as a charge qubit.Comment: 7 pages, 3 figure

Network analysis of the relationships between problematic smartphone use and anxiety, and depression in a sample of Chinese college students

BackgroundProblematic smartphone use (PSU) is associated with both anxiety and depression. However, the relationships between components of PSU and symptoms of anxiety or depression have not been investigated. Hence, the aim of this study was to closely examine the relationships between PSU and anxiety and depression to identify the pathological mechanisms underpinning those relationships. A second aim was to identify important bridge nodes to identify potential targets for intervention.MethodsSymptom-level network structures of PSU and anxiety, and PSU and depression were constructed to investigate the connections between the variables and evaluate the bridge expected influence (BEI) of each node. Network analysis using data from 325 Chinese healthy college students was performed.ResultsFive strongest edges appeared within the communities in both the PSU-anxiety and PSU-depression networks. The “Withdrawal” component had more connections with symptoms of anxiety or depression than any other PSU node. In particular, the edges between “Withdrawal” and “Restlessness” and between “Withdrawal” and “Concentration difficulties” were the strongest cross-community edges in the PSU-anxiety network and PSU-depression network, respectively. Furthermore, “Withdrawal” had the highest BEI in the PSU community in both networks.ConclusionsThese findings provide preliminary evidence of the pathological pathways linking PSU with anxiety and depression, with “Withdrawal” linking PSU with both anxiety and depression. Hence, “Withdrawal” may be a potential target for preventing and intervening in cases of anxiety or depression