京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第19505号 / 理博第4165号 / 新制||理||1598(附属図書館) / 32541 / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 津田 敏隆, 教授 石川 裕彦, 教授 余田 成男 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

The Integrated Nested Laplace Approximations (INLA) method has become a commonly used tool for researchers and practitioners to perform approximate Bayesian inference for various fields of applications. It has become essential to incorporate more complex models and expand the method’s capabilities with more features. In this dissertation, we contribute to the INLA method in different aspects. First, we present a new framework, INLA$^+$, based on dense matrices to perform approximate Bayesian inference. An application of the new approach is fitting disease-mapping models for count data with complex interactions. When the precision matrix is dense, the new approach scales better than the existing INLA method and utilizes the power of multiprocessors on shared and distributed memory architectures in today’s computational resources. Second, we propose an adaptive technique to improve gradient estimation for the convex gradient-based optimization framework in INLA. We propose a simple limited-memory technique for improving the accuracy of the numerical gradient of the marginal posterior of the hyperparameter by exploiting a coordinate transformation of the gradient and the history of previously taken descent directions. Third, we extend the commonly utilized Bayesian spatial model in disease mapping, known as the Besag model, into a non-stationary spatial model. This new model considers variations in spatial dependency among a predetermined number of sub-regions. The model incorporates multiple precision parameters, which enable different intensities of spatial dependence in each sub-region. To avoid overfitting and enhance generalization, we derive a joint penalized complexity prior for these parameters. These contributions expand the capabilities of the INLA method, improving its scalability, accuracy, and flexibility for a wider range of applications.

The present qualitative study investigated Spanish language maintenance among a familial/friend group of ten Ecuadorians that live in Northern California. The participants completed a survey and participated in an interview from which I retrieved information about the importance of Spanish and English, their self-reported confidence in Spanish and English, language attitudes, language use in private/familial contexts, and language use in public/social contexts. Previous studies regarding language maintenance and language shift in California were primarily focused on the Mexican-American population. California has the fourth largest population of Ecuadorians in the United States, yet there are no maintenance and shift studies for Ecuadorians in California. The collectivism and communal style of living that permeate Ecuadorian culture make the current study particularly unique and adds to past research on factors that affect maintenance and shift. Findings indicate that many of the members of this community are part of a dense network. This appears to encourage positive language attitudes. As a result, Spanish is used in many public and private contexts, which may help Spanish to be maintained by future generations. However, the current study also sheds light on the level of impact that spousal language may have regarding maintenance or shift for the future generation. The findings show that households with an English monolingual parent show a shift of importance and emotional attachment from Spanish to English. Thus, future generations are likely to see a shift to English if they are in a household with an English monolingual mother but may have an identity associated with their heritage because of the dense network to which they belong. However, future generations in households with two parents who speak Spanish are likely to maintain Spanish due to the network density and overall language attitudes.

Video from aerial surveillance can provide a rich source of data for analysts. From the time-critical perspective of wilderness search and rescue operations, information extracted from aerial videos can mean the difference between a successful search and an unsuccessful search. When using low-cost, payload-limited mini-UAVs, as opposed to more expensive platforms, several challenges arise, including jittery video, narrow fields of view, low resolution, and limited time on screen for key features. These challenges make it difficult for analysts to extract key information in a timely manner. Traditional approaches may address some of these issues, but no existing system effectively addresses all of them in a unified and efficient manner. Building upon a hierarchical dense image correspondence technique, we create a unifying framework for reducing jitter, enhancing resolution, and expanding the field of view while lengthening the time that features remain on screen. It also provides for easy extraction of moving objects in the scene. Our method incorporates locally adaptive warps which allows for robust image alignment even in the presence of parallax and without the aid of internal or external camera parameters. We accelerate the image registration process using commodity Graphics Processing Units (GPUs) to accomplish all of these tasks in near real-time with no external telemetry data.

Visual analysis of dense crowds is particularly challenging due to large number of individuals, occlusions, clutter, and fewer pixels per person which rarely occur in ordinary surveillance scenarios. This dissertation aims to address these challenges in images and videos of extremely dense crowds containing hundreds to thousands of humans. The goal is to tackle the fundamental problems of counting, detecting and tracking people in such images and videos using visual and contextual cues that are automatically derived from the crowded scenes. For counting in an image of extremely dense crowd, we propose to leverage multiple sources of information to compute an estimate of the number of individuals present in the image. Our approach relies on sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Furthermore, we employ a global consistency constraint on counts using Markov Random Field which caters for disparity in counts in local neighborhoods and across scales. We tested this approach on crowd images with the head counts ranging from 94 to 4543 and obtained encouraging results. Through this approach, we are able to count people in images of high-density crowds unlike previous methods which are only applicable to videos of low to medium density crowded scenes. However, the counting procedure just outputs a single number for a large patch or an entire image. With just the counts, it becomes difficult to measure the counting error for a query image with unknown number of people. For this, we propose to localize humans by finding repetitive patterns in the crowd image. Starting with detections from an underlying head detector, we correlate them within the image after their selection through several criteria: in a pre-defined grid, locally, or at multiple scales by automatically finding the patches that are most representative of recurring patterns in the crowd image. Finally, the set of generated hypotheses is selected using binary integer quadratic programming with Special Ordered Set (SOS) Type 1 constraints. Human Detection is another important problem in the analysis of crowded scenes where the goal is to place a bounding box on visible parts of individuals. Primarily applicable to images depicting medium to high density crowds containing several hundred humans, it is a crucial pre-requisite for many other visual tasks, such as tracking, action recognition or detection of anomalous behaviors, exhibited by individuals in a dense crowd. For detecting humans, we explore context in dense crowds in the form of locally-consistent scale prior which captures the similarity in scale in local neighborhoods with smooth variation over the image. Using the scale and confidence of detections obtained from an underlying human detector, we infer scale and confidence priors using Markov Random Field. In an iterative mechanism, the confidences of detections are modified to reflect consistency with the inferred priors, and the priors are updated based on the new detections. The final set of detections obtained are then reasoned for occlusion using Binary Integer Programming where overlaps and relations between parts of individuals are encoded as linear constraints. Both human detection and occlusion reasoning in this approach are solved with local neighbor-dependent constraints, thereby respecting the inter-dependence between individuals characteristic to dense crowd analysis. In addition, we propose a mechanism to detect different combinations of body parts without requiring annotations for individual combinations. Once human detection and localization is performed, we then use it for tracking people in dense crowds. Similar to the use of context as scale prior for human detection, we exploit it in the form of motion concurrence for tracking individuals in dense crowds. The proposed method for tracking provides an alternative and complementary approach to methods that require modeling of crowd flow. Simultaneously, it is less likely to fail in the case of dynamic crowd flows and anomalies by minimally relying on previous frames. The approach begins with the automatic identification of prominent individuals from the crowd that are easy to track. Then, we use Neighborhood Motion Concurrence to model the behavior of individuals in a dense crowd, this predicts the position of an individual based on the motion of its neighbors. When the individual moves with the crowd flow, we use Neighborhood Motion Concurrence to predict motion while leveraging five-frame instantaneous flow in case of dynamically changing flow and anomalies. All these aspects are then embedded in a framework which imposes hierarchy on the order in which positions of individuals are updated. The results are reported on eight sequences of medium to high density crowds and our approach performs on par with existing approaches without learning or modeling patterns of crowd flow. We experimentally demonstrate the efficacy and reliability of our algorithms by quantifying the performance of counting, localization, as well as human detection and tracking on new and challenging datasets containing hundreds to thousands of humans in a given scene.

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Rheological properties of idealised models which exhibit all the non-Newtonian flow phenomenology commonly seen in dense suspensions are investigated by particulate-dynamics computer-simulations. The objectives of these investigations are: (i) to establish the origins of various aspects of dense suspension rheology such as shear-thinning, shear thickening and dilatancy; (ii) to elucidate the different regions of a typical dense suspension rheogram by examining underlying structures and shear induced anisotropies in kinetic energy, diffusivity and pressure; (iii) to investigate the scaling of the simplest idealised model suspension; i.e. the hard-sphere model in Newtonian media and its relationship to the isokinetic flow curves obtained through non-equilibrium molecular dynamics (NEMD) simulations; (iv) to preliminarily determine the effect of perturbations present in all real colloidal suspensions, namely particle size polydispersity and a slight 'softness' of the interparticle potential. Non-equilibrium isokinetic simulations have been performed upon ;systems of particles interacting through the classical hard-sphere potential and a perturbation thereof, in which the hard-core is surrounded by a 'slightly soft' repulsive skin. The decision to base the present work upon isokinetic studies was made in order to obtain a better under- standing of suspension rheology by making a direct connection with previous NEMD studies of thermal systemst(93). These studies have shown that the non-linear behaviour exhibited by these systems under shear is atttributable to a shear-induced perturbation of the equilibrium phase behaviour. The present study shows this behaviour to correspond to the high shear region of the generalised suspension flow curve. / Science and Engineering Research Council and Unilever Research

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23614号 / 工博第4935号 / 新制||工||1771(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 黒瀬 良一, 教授 花崎 秀史, 教授 岩井 裕 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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