402 research outputs found

    Minor stars in plane graphs with minimum degree five

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    The weight of a subgraph HH in GG is the sum of the degrees in GG of vertices of HH. The {\em height} of a subgraph HH in GG is the maximum degree of vertices of HH in GG. A star in a given graph is minor if its center has degree at most five in the given graph. Lebesgue (1940) gave an approximate description of minor 55-stars in the class of normal plane maps with minimum degree five. In this paper, we give two descriptions of minor 55-stars in plane graphs with minimum degree five. By these descriptions, we can extend several results and give some new results on the weight and height for some special plane graphs with minimum degree five.Comment: 11 pages, 3 figure

    Growing process

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    My MFA thesis exhibition, “growing process” is inspired by many occurrences in my life, and is especially influenced by my childhood memories. The artworks represent my loneliness and how I face and solve problems, which helped me mature as an adult. This monograph explores the shift and thought process in different life stages. I join a long tradition of ceramic production in China by using clay as a meaningful material. Clay may contain soil, carcasses and leaves, which decompose in the earth. There is a saying in China that “fallen leaves return to the roots,” which suggests a return to one\u27s origin. When a person dies, we bury the body in soil. Soil provides nutrition and breeds new life. It represents a process, a repetition, or a rebirth. The process of making a ceramic work is to make the structure by hand, which will always leave my finger prints on the clay, like a memory. Letting the clay become bone dry is a process of loss. Firing clay is a process of rebuilding. All of the stages make me think of the process of growing: making memories with people, losing people, and transforming. Although I take care of my own works, I cannot keep everything under control. I treat my works like treasures because I am always afraid to break any of them, as they are very fragile. It is a metaphor of avoiding loss, instead of letting them go. I use objects symbolically to connect to my body and soul as a means to express my feelings. Paper airplanes, feet, teeth and pinwheels are some of the ordinary objects we see around us. They may seem as if they are normal or unexceptional, but each object carries different memories with them; they always remind me of the occurrences that happened to me and through process, materiality and symbolism; I make them extraordinary

    Multilinear methods for disentangling variations with applications to facial analysis

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    Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of variability in the data. It is assumed that the multiplicative interactions of these factors emulate the entangled variability, giving rise to the rich structure of visual object appearance. Disentangling such unobserved factors from visual data is a challenging task, especially when the data have been captured in uncontrolled recording conditions (also referred to as “in-the-wild”) and label information is not available. The work presented in this thesis focuses on disentangling the variations contained in visual data, in particular applied to 2D and 3D faces. The motivation behind this work lies in recent developments in the field, such as (i) the creation of large, visual databases for face analysis, with (ii) the need of extracting information without the use of labels and (iii) the need to deploy systems under demanding, real-world conditions. In the first part of this thesis, we present a method to synthesise plausible 3D expressions that preserve the identity of a target subject. This method is supervised as the model uses labels, in this case 3D facial meshes of people performing a defined set of facial expressions, to learn. The ability to synthesise an entire facial rig from a single neutral expression has a large range of applications both in computer graphics and computer vision, ranging from the ecient and cost-e↵ective creation of CG characters to scalable data generation for machine learning purposes. Unlike previous methods based on multilinear models, the proposed approach is capable to extrapolate well outside the sample pool, which allows it to accurately reproduce the identity of the target subject and create artefact-free expression shapes while requiring only a small input dataset. We introduce global-local multilinear models that leverage the strengths of expression-specific and identity-specific local models combined with coarse motion estimations from a global model. The expression-specific and identity-specific local models are built from di↵erent slices of the patch-wise local multilinear model. Experimental results show that we achieve high-quality, identity-preserving facial expression synthesis results that outperform existing methods both quantitatively and qualitatively. In the second part of this thesis, we investigate how the modes of variations from visual data can be extracted. Our assumption is that visual data has an underlying structure consisting of factors of variation and their interactions. Finding this structure and the factors is important as it would not only help us to better understand visual data but once obtained we can edit the factors for use in various applications. Shape from Shading and expression transfer are just two of the potential applications. To extract the factors of variation, several supervised methods have been proposed but they require both labels regarding the modes of variations and the same number of samples under all modes of variations. Therefore, their applicability is limited to well-organised data, usually captured in well-controlled conditions. We propose a novel general multilinear matrix decomposition method that discovers the multilinear structure of possibly incomplete sets of visual data in unsupervised setting. We demonstrate the applicability of the proposed method in several computer vision tasks, including Shape from Shading (SfS) (in the wild and with occlusion removal), expression transfer, and estimation of surface normals from images captured in the wild. Finally, leveraging the unsupervised multilinear method proposed as well as recent advances in deep learning, we propose a weakly supervised deep learning method for disentangling multiple latent factors of variation in face images captured in-the-wild. To this end, we propose a deep latent variable model, where we model the multiplicative interactions of multiple latent factors of variation explicitly as a multilinear structure. We demonstrate that the proposed approach indeed learns disentangled representations of facial expressions and pose, which can be used in various applications, including face editing, as well as 3D face reconstruction and classification of facial expression, identity and pose.Open Acces

    The Effect of Interest Rates on Bank Risk-Taking: Evidence from Banks in China

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    There is a belief among some economists that 2008 financial crisis was caused by continuous low interest rates environment. They argue that low interest rate environment from the early to mid-2000s lead to the increase of banks’ risk-taking appetite. Many empirical studies conducted in western countries have proven the negative relationship between interest rates and bank risk-taking. In this paper, we examine whether or not this connection exists within the Chinese economy. We measure bank risk-taking behaviour based on the ratio of non-performing loans to total loans, and we find its relationship with two different kinds of interest rates: legal and market interest rates. In addition, we divided control variables into internal and external variables. We analyzed more than 800 observations made on Chinese banks between 2003 and 2012. Consistent with similar studies conducted in western countries, we found that low level interest rates substantially increased bank risk-taking behaviour

    DP-3-coloring of planar graphs without certain cycles

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    DP-coloring is a generalization of list coloring, which was introduced by Dvo\v{r}\'{a}k and Postle [J. Combin. Theory Ser. B 129 (2018) 38--54]. Zhang [Inform. Process. Lett. 113 (9) (2013) 354--356] showed that every planar graph with neither adjacent triangles nor 5-, 6-, 9-cycles is 3-choosable. Liu et al. [Discrete Math. 342 (2019) 178--189] showed that every planar graph without 4-, 5-, 6- and 9-cycles is DP-3-colorable. In this paper, we show that every planar graph with neither adjacent triangles nor 5-, 6-, 9-cycles is DP-3-colorable, which generalizes these results. Yu et al. gave three Bordeaux-type results by showing that (i) every planar graph with the distance of triangles at least three and no 4-, 5-cycles is DP-3-colorable; (ii) every planar graph with the distance of triangles at least two and no 4-, 5-, 6-cycles is DP-3-colorable; (iii) every planar graph with the distance of triangles at least two and no 5-, 6-, 7-cycles is DP-3-colorable. We also give two Bordeaux-type results in the last section: (i) every plane graph with neither 5-, 6-, 8-cycles nor triangles at distance less than two is DP-3-colorable; (ii) every plane graph with neither 4-, 5-, 7-cycles nor triangles at distance less than two is DP-3-colorable.Comment: 16 pages, 4 figure

    A Double Joint Bayesian Approach for J-Vector Based Text-dependent Speaker Verification

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    J-vector has been proved to be very effective in text-dependent speaker verification with short-duration speech. However, the current state-of-the-art back-end classifiers, e.g. joint Bayesian model, cannot make full use of such deep features. In this paper, we generalize the standard joint Bayesian approach to model the multi-faceted information in the j-vector explicitly and jointly. In our generalization, the j-vector was modeled as a result derived by a generative Double Joint Bayesian (DoJoBa) model, which contains several kinds of latent variables. With DoJoBa, we are able to explicitly build a model that can combine multiple heterogeneous information from the j-vectors. In verification step, we calculated the likelihood to describe whether the two j-vectors having consistent labels or not. On the public RSR2015 data corpus, the experimental results showed that our approach can achieve 0.02\% EER and 0.02\% EER for impostor wrong and impostor correct cases respectively
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