Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018

Factors Controlling Vase Life of Waxflowers (Chamelaucium Desf. Varieties and Hybrids)

Factors affecting vase life of Chamelaucium Desf. hybrids and hybrids between Chamelaucium and Verticordia Desf. were studied. Both genotype and cultivar determined the length of vase life of waxflowers. The use of sucrose or other sugar types and concentrations supplemented with 8-hydroxyquniline sulphate and/or silver thiosulphate extended the vase life of waxflowers cultivars depending on genotype. Flowers strongly competed with leaves for carbohydrates and water, affecting the vase life of waxflowers cultivars

Design of nanostructured photocatalysts for hydrogen production and environmental application

Tableau d'honneur de la Faculté des études supérieures et postdorales, 2014-2015Au cours des dernières décennies, la photocatalyse par les semiconducteurs a été intensivement étudiée pour une grande variété d'applications, y compris la production d'hydrogène à partir de la dissociation de l'eau et la décomposition des polluants dans l'air et l'eau. Actuellement, TiO2 est le matériau photocatalytique le plus largement étudié en raison de son faible coût et ses propriétés physiques et chimiques exceptionnelles. Cependant, la rapide recombinaison électron-trou et son absorption dans la région de l’ultra-violet le rendent inefficace sous la lumière du soleil. Cette thèse vise à développer des photocatalyseurs efficaces à base de TiO2 en appliquant différentes stratégies telles que le contrôle de la morphologie des nanoparticules de TiO2, le couplage du TiO2 avec des métaux et d’autres semi-conducteurs, et l'optimisation de la porosité des photocatalyseurs. Nous avons mis au point une méthode de synthèse solvo-thermique pour produire des nanocristaux de TiO2 hautement cristallins de différentes formes, tel que rhombique, sphérique, et sous forme de tige. Les nanocristaux TiO2 obtenus ont ensuite été décorés par des clusters d'Ag de taille contrôlée pour former des hybrides Ag-TiO2 ayant une performance photocatalytique supérieure à celle du photocatalyseur conventionnel Ag-TiO2-P25. Nous avons également développé une technique non-hydrolytique pour la synthèse de nanodisques uniformes de TiO2 de diamètre contrôlé entre de 12 nm et 35 nm. Ces nanodisques ont ensuite été utilisés comme blocs de construction pour la synthèse des photocatalyseurs multi-composants solubles dans l'eau à base de CdS-Titanate-Ni; ces derniers sont très actifs pour la production d'hydrogène grâce à leur absorption efficace de lumière visible et leur séparation efficace d’électrons et trous. Finalement, nous avons construit un assemblage tridimensionnel ordonné de nanosphères creuses de coquille mince de Au/TiO2, en utilisant les blocs de construction de nanodiques de titanate. Ces photocatalyseurs présentent non seulement une surface spécifique très élevée, mais aussi un comportement photonique et une diffusion multiple de la lumière, ce qui améliore significativement l'absorption de la lumière visible. Ces nanosphères creuses de structure ordonnée tridimensionnelle présentent une activité photocatalytique induite par la lumière visible, étant plusieurs fois plus élevée que celle des nanopoudres conventionnelles d’Au/TiO2.Semiconductor photocatalysis has been intensively studied over the past decades for a wide variety of applications including hydrogen production from water splitting and decomposition of pollutants in air and water. Currently, TiO2 is the most widely investigated photocatalytic material because of its low cost and outstanding physical and chemical properties. However, its fast electron-hole recombination and light absorption only in ultra-violet region make it inefficient working under sunlight. The goal of the research presented in this thesis is to design efficient TiO2 based photocatalysts by applying various strategies encompassing controlling the morphology of TiO2 particles, coupling TiO2 with metals, and other semiconductors and optimizing porosity of the photocatalysts. We have developed a solvothermal synthetic method for producing highly crystalline TiO2 nanocrystals with various shapes, such as rhombic, spherical, and bar. The obtained TiO2 nanocrystals were then decorated with size-controlled Ag clusters to form Ag-TiO2 hybrids which exhibit superior photocatalytic performance in comparison to conventional Ag-TiO2-P25 photocatalyst. We have also developed a nonhydrolytic technique for the synthesis of uniform titanate nanodisks with controlled diameter in the range of 12 nm to 35 nm. These nanodisks were then used as building blocks for the design of water-soluble CdS–Titanate–Ni multicomponent photocatalysts which are highly active for hydrogen generation due to their effective visible light absorption and efficient charge separation. Finally, we have constructed a three-dimensional ordered assembly of thin-shell Au/TiO2 hollow nanospheres from titanate nanodisk building blocks. The designed photocatalysts exhibit not only a very high specific surface area but also photonic behavior and multiple light scattering, which significantly enhances visible light absorption. As a result, Au/TiO2 hollow nanospheres with three-dimensional ordered structure exhibit a visible-light-driven photocatalytic activity that is several times higher than conventional Au/TiO2 nanopowders

A stochastic model of the influence of buffer gas collisions on Mollow spectra

In this paper we consider the influence of collisional fluctuations on the Mollow spectra of resonance fluorescence (RF). The fluctuations are taken into account by a simple shift of the constant detuning, involved in a set of optical Bloch equations by collision frequency noise which is modelled by a two-step random telegraph signal (RTS). We consider in detail the Mollow spectra for RF in the case of an arbitrary detuning of the laser frequency, where the emitter is a member of a statistical ensemble in thermodynamic equilibrium with the buffer gas at temperature $T$ which is treated as a colored environment, and velocity $v$ is distributed with the Maxwell-Boltzmann density

Probabilistic uncertainty quantification and experiment design for nonlinear models: Applications in systems biology

Despite the ever-increasing interest in understanding biology at the system level, there are several factors that hinder studies and analyses of biological systems. First, unlike systems from other applied fields whose parameters can be effectively identified, biological systems are usually unidentifiable, even in the ideal case when all possible system outputs are known with high accuracy. Second, the presence of multivariate bifurcations often leads the system to behaviors that are completely different in nature. In such cases, system outputs (as function of parameters/inputs) are usually discontinuous or have sharp transitions across domains with different behaviors. Finally, models from systems biology are usually strongly nonlinear with large numbers of parameters and complex interactions. This results in high computational costs of model simulations that are required to study the systems, an issue that becomes more and more problematic when the dimensionality of the system increases. Similarly, wet-lab experiments to gather information about the biological model of interest are usually strictly constrained by research budget and experimental settings. The choice of experiments/simulations for inference, therefore, needs to be carefully addressed. ^ The work presented in this dissertation develops strategies to address theoretical and practical limitations in uncertainty quantification and experimental design of non-linear mathematical models, applied in the context of systems biology. This work resolves those issues by focusing on three separate but related approaches: (i) the use of probabilistic frameworks for uncertainty quantification in the face of unidentifiability (ii) the use of behavior discrimination algorithms to study systems with discontinuous model responses and (iii) the use of effective sampling schemes and optimal experimental design to reduce the computational/experimental costs. ^ This cumulative work also places strong emphasis on providing theoretical foundations for the use of the proposed framework: theoretical properties of algorithms at each step in the process are investigated carefully to give more insights about how the algorithms perform, and in many cases, to provide feedback to improve the performance of existing approaches. Through the newly developed procedures, we successfully created a general probabilistic framework for uncertainty quantification and experiment design for non-linear models in the face of unidentifiability, sharp model responses with limited number of model simulations, constraints on experimental setting, and even in the absence of data. The proposed methods have strong theoretical foundations and have also proven to be effective in studies of expensive high-dimensional biological systems in various contexts

X-PDNet: Accurate Joint Plane Instance Segmentation and Monocular Depth Estimation with Cross-Task Distillation and Boundary Correction

Segmentation of planar regions from a single RGB image is a particularly important task in the perception of complex scenes. To utilize both visual and geometric properties in images, recent approaches often formulate the problem as a joint estimation of planar instances and dense depth through feature fusion mechanisms and geometric constraint losses. Despite promising results, these methods do not consider cross-task feature distillation and perform poorly in boundary regions. To overcome these limitations, we propose X-PDNet, a framework for the multitask learning of plane instance segmentation and depth estimation with improvements in the following two aspects. Firstly, we construct the cross-task distillation design which promotes early information sharing between dual-tasks for specific task improvements. Secondly, we highlight the current limitations of using the ground truth boundary to develop boundary regression loss, and propose a novel method that exploits depth information to support precise boundary region segmentation. Finally, we manually annotate more than 3000 images from Stanford 2D-3D-Semantics dataset and make available for evaluation of plane instance segmentation. Through the experiments, our proposed methods prove the advantages, outperforming the baseline with large improvement margins in the quantitative results on the ScanNet and the Stanford 2D-3D-S dataset, demonstrating the effectiveness of our proposals.Comment: Accepted to BMVC 202