Investigation of new learning methods for visual recognition

Visual recognition is one of the most difficult and prevailing problems in computer vision and pattern recognition due to the challenges in understanding the semantics and contents of digital images. Two major components of a visual recognition system are discriminatory feature representation and efficient and accurate pattern classification. This dissertation therefore focuses on developing new learning methods for visual recognition. Based on the conventional sparse representation, which shows its robustness for visual recognition problems, a series of new methods is proposed. Specifically, first, a new locally linear K nearest neighbor method, or LLK method, is presented. The LLK method derives a new representation, which is an approximation to the ideal representation, by optimizing an objective function based on a host of criteria for sparsity, locality, and reconstruction. The novel representation is further processed by two new classifiers, namely, an LLK based classifier (LLKc) and a locally linear nearest mean based classifier (LLNc), for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Second, a new generative and discriminative sparse representation (GDSR) method is proposed by taking advantage of both a coarse modeling of the generative information and a modeling of the discriminative information. The proposed GDSR method integrates two new criteria, namely, a discriminative criterion and a generative criterion, into the conventional sparse representation criterion. A new generative and discriminative sparse representation based classification (GDSRc) method is then presented based on the derived new representation. Finally, a new Score space based multiple Metric Learning (SML) method is presented for a challenging visual recognition application, namely, recognizing kinship relations or kinship verification. The proposed SML method, which goes beyond the conventional Mahalanobis distance metric learning, not only learns the distance metric but also models the generative process of features by taking advantage of the score space. The SML method is optimized by solving a constrained, non-negative, and weighted variant of the sparse representation problem. To assess the feasibility of the proposed new learning methods, several visual recognition tasks, such as face recognition, scene recognition, object recognition, computational fine art analysis, action recognition, fine grained recognition, as well as kinship verification are applied. The experimental results show that the proposed new learning methods achieve better performance than the other popular methods

Multi-phase modelling of multi-species ionic migration in concrete

Chloride-induced corrosion of reinforcing steel in concrete is a worldwide problem. In order to predict how chlorides penetrate in concrete and how other ionic species in con-crete pore solution affect the penetration of chlorides, this thesis presents a numerical study on multi-phase modelling of ionic transport in concrete dominated by migration process. There are many advantages in rapid chloride migration test (RCM) method and numeri-cal approach. However, most of models in the literature predicting chloride diffusivity in concrete are diffusion models, which not consider the action of externally applied electric field. In view of this, the specific aim of this thesis is to develop a rational nu-merical migration model to simulate chloride migration tests. By using this model, the diffusion coefficient of chlorides in concrete will be efficiently predicted. Furthermore, other mechanisms of ionic transportation in composite materials can be scientifically in-vestigated in the meantime. In most existing work, researchers tend to use the assumption of electro-neutrality con-dition, which ensures that no external charge can be imported (Bockris and Reddy, 1998), to determine the electrostatic potential within concrete as well as considering a 1-D problem with only one phase structure and single species (i.e. the chlorides) for pre-dicting the ionic migration. In contrast, this thesis presents a number of sets of multi-phase migration models in more than one dimension and uses the Poisson’s equation for controlling the multi-species interactions. By solving both mass conservation and Pois-son’s equations, the distribution profiles of each ionic species and electrostatic potential at any required time are successfully obtained. Some significant factors, i.e. the influ-ence of dimensions, aggregates, interfacial transition zones (ITZs), cracks and binding effect have also been discussed in detail. The results reveal a series of important features which may not be seen from existing numerical models. For quantitative study, this thesis also provides the prediction method of chloride diffu-sivity not only by the traditional stationary diffusion models but also by the migration models presented in the thesis. The obtained results are compared with three proven analytical models, i.e., Maxwell’s model (Dormieux and Lemarchand, 2000), Brug-geman’s equation (Bruggeman’s, 1935) and the lower bound of the effective diffusion coefficient proposed by Li et al. (2012) as well as validated against experimental data sets of an accelerated chloride migration test (ACMT) brought by Yang and Su (2002).School of Civil Engineering, University of Birmingham;School of Marine Science and Engineering, University of Plymouth; China Scholarship Counci

Transport model study of nuclear stopping in heavy ion collisions over an energy range from 0.09A GeV to 160A GeV

Nuclear stopping in the heavy ion collisions over a beam energy range from SIS, AGS up to SPS is studied in the framework of the modified UrQMD transport model, in which mean field potentials of both formed and "pre-formed" hadrons (from string fragmentation) and medium modified nucleon-nucleon elastic cross sections are considered. It is found that the nuclear stopping is influenced by both the stiffness of the equation of state and the medium modifications of nucleon-nucleon cross sections at SIS energies. At the high SPS energies, the two-bump structure is shown in the experimental rapidity distribution of free protons, which can be understood with the consideration of the "pre-formed" hadron potentials.Comment: 15 pages, 7 figure

部分線形モデルの差分推定量の漸近理論

本論文は部分線形モデルの重み付き差分法による推定量の漸近理論に関して論じる。一定の正則条件のもとで推定量の漸近正視性を確認する。さらに、漸近的にセミパラメトリックモデルの効率性を達成するための差分の次数のオーダーを明らかにし、重み付き差分の重みが満たすべき制約を明らかにする。 　This paper studies the asymptotic theory on a weighted difference-based estimator of partially linear models. Regularity conditions for asymptotic normality are provided. Moreover, we derive the order of the difference, under which the semiparametric effciency bound could be achieved. The restrictions on weights are provided as well

Transport discovery of emerging robust helical surface states in Z2=0Z_2=0 systems

We study the possibility of realizing robust helical surface states in $Z_2=0$ systems. We find that the combination of anisotropy and finite-size confinement leads to the emergence of robust helical edge states in both 2D and 3D $Z_2=0$ systems. By investigating an anisotropic Bernevig-Hughes-Zhang model in a finite sample, we demonstrate that the transport manifestation of the surface states is robust against non-magnetic disorder, resembling that of a $Z_2 = 1$ phase. Notably, the effective energy gap for the robust helical states can be efficiently engineered, allowing for potential applications as valley filters and valley valves. The realization of emerging robust helical surface states in realistic material is also discussed.Comment: 5 pages, 4 figures; submitted to Phys. Rev. Lett. on Nov. 25. 201

Residue cross sections of 50^{50}Ti-induced fusion reactions based on the two-step model

$^{50}$Ti-induced fusion reactions to synthesize superheavy elements are studied systematically with the two-step model developed recently, where fusion process is divided into approaching phase and formation phase. Furthermore, the residue cross sections for different neutron evaporation channels are evaluated with the statistical evaporation model. In general, the calculated cross sections are much smaller than that of $^{48}$Ca-induced fusion reactions, but the results are within the detection capability of experimental facilities nowadays. The maximum calculated residue cross section for producing superheavy element $Z=119$ is in the reaction $^{50}$Ti+$^{247}$Bk in $3n$ channels with $\sigma_{\rm res}(3n)=0.043$ pb at $E^{*}$ = 37.0 MeV.Comment: 6 pages, 7 figure