Indiana University-Purdue University Indianapolis (IUPUI) / The counting process is the fundamental of many real-world problems with event data. Poisson process, used as the background intensity of Hawkes process, is the most commonly used point process. The Hawkes process, a self-exciting point process fits to temporal event data, spatial-temporal event data, and event data with covariates. We study the Hawkes process that fits to heterogeneous drug overdose data via a novel semi-parametric approach. The counting process is also related to survival data based on the fact that they both study the occurrences of events over time. We fit a Cox model to temporal event data with a large corpus that is processed into high dimensional covariates. We study the significant features that influence the intensity of events.

X-ray Computed Tomography (CT) imaging has been playing a central role in clinical practice since it was invented in 1972. However, the traditional x-ray CT technique fails to distinguish different materials with similar density, especially for biological tissues. The lack of a quantitative imaging representation has constrained the application of CT technique from a broadening application such as personal or precision medicine. Therefore, my major thesis statement is to develop novel material-specific CT imaging techniques for molecular imaging in biological bodies. To achieve the goal, comprehensive studies were conducted to investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging (XFMI) has shown great promise as a low-cost molecular imaging modality for clinical and pre-clinical applications with high sensitivity. In this study, the effects of excitation beam spectrum on the molecular sensitivity of XFMI were experimentally investigated, by quantitatively deriving minimum detectable concentration (MDC) under a fixed surface entrance dose of 200 mR at three different excitation beam spectra. The result shows that the MDC can be readily increased by a factor of 5.26 via excitation spectrum optimization. Furthermore, a numerical model was developed and validated by the experimental data (≥0.976). The numerical model can be used to optimize XFMI system configurations to further improve the molecular sensitivity. Findings from this investigation could find applications for in vivo pre-clinical small-animal XFMI in the future. PCCT is an emerging technique that has the ability to distinguish photon energy and generate much richer image data that contains x-ray spectral information compared to conventional CT. In this study, a physics model was developed based on x-ray matter interaction physics to calculate the effective atomic number () and effective electron density () from PCCT image data for material identification. As the validation of the physics model, the and were calculated under various energy conditions for many materials. The relative standard deviations are mostly less than 1% (161 out of 168) shows that the developed model obtains good accuracy and robustness to energy conditions. To study the feasibility of applying the model with PCCT image data for material identification, both PCCT system numerical simulation and physical experiment were conducted. The result shows different materials can be clearly identified in the − map (with relative error ≤8.8%). The model has the value to serve as a material identification scheme for PCCT system for practical use in the future. As PCCT appears to be a significant breakthrough in CT imaging field, there exists severe data distortion problem in PCCT, which greatly limits the application of PCCT in practice. Lately, deep learning (DL) neural network has demonstrated tremendous success in medical imaging field. In this study, a deep learning neural network based PCCT data distortion correction method was proposed. When applying the algorithm to process the test dataset data, the accuracy of the PCCT data can be greatly improved (RMSE improved 73.7%). Compared with traditional data correction approaches such as maximum likelihood, the deep learning approach demonstrate superiority in terms of RMSE, SSIM, PSNR, and most importantly, runtime (4053.21 sec vs. 1.98 sec). The proposed method has the potential to facilitate the PCCT studies and applications in practice. / Doctor of Philosophy / X-ray Computed Tomography (CT) has played a central role in clinical imaging since it was invented in 1972. It has distinguishing characteristics of being able to generate three dimensional images with comprehensive inner structural information in fast speed (less than one second). However, traditional CT imaging lacks of material-specific capability due to the mechanism of image formation, which makes it cannot be used for molecular imaging. Molecular imaging plays a central role in present and future biomedical research and clinical diagnosis and treatment. For example, imaging of biological processes and molecular markers can provide unprecedented rich information, which has huge potentials for individualized therapies, novel drug design, earlier diagnosis, and personalized medicine. Therefore there exists a pressing need to enable the traditional CT imaging technique with material-specific capability for molecular imaging purpose. This dissertation conducted comprehensive study to separately investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging utilizes fluorescence signal to achieve molecular imaging in CT; Material identification can be achieved based on the rich image data from PCCT; The deep learning based correction method is an efficient approach for PCCT data distortion correction, and furthermore can boost its performance on material identification. With those techniques, the material-specific capability of CT can be greatly enhanced and the molecular imaging can be approached in biological bodies.

Black vulture (Coragyps atratus) and turkey vulture (Cathartes aura) roosting dynamics were studied at eight roosts near Radford, Virginia. Black vulture numbers at a permanent roost ranged from low monthly means in June 1982 and 1983 to peak monthly means in December 1981 and 1982. Turkey vulture numbers ranged from low monthly means in July 1982 and 1983 to peaks in December 1981 and 1983. Vultures used two temporary roosts at nearby landfills from March through October in 1983. Vultures marked with cattle eartags were observed moving among roosts. Road counts were poorly correlated (r = 0.39, P = 0.05, r = 0.39, P = 0.12, black vultures and turkey vultures respectively) with roosting vulture numbers, and may not be good indicators of vulture numbers. Long term monitoring of vulture populations is best accomplished by six counts in December, on the same date each year, as vultures leave permanent roosts. Movement of problem roosts may be most effective when accompanied by removal of attractants. Accretion of fecal material on metal leg bands constricted tarsi of black and turkey vultures. Teflon bands did not constrict the tarsus, but tag loss was high. Adult black vultures had longer tarsi and shorter wing chords than juveniles. Two nests were used in 1983 and 1984 by two pairs of black vultures, consisting of one marked and one unmarked bird. / Master of Science

The problem with which we shall concern ourselves in this paper is the self-scattering and self-absorption of beta particles by the source.

Physical activity plays a crucial role in maintaining overall health and reducing the risk of various chronic diseases. Step counting has emerged as a popular method for assessing physical activity levels, given its simplicity and ease of use. However, accurately measuring step counts in free-living environments presents significant challenges, with most activity trackers exhibiting a percent error above 20%. This study aims to address these challenges by creating a machine learning algorithm that leverages activity labels to improve step count accuracy in real-world conditions. Two approaches to balancing data were used: one employed a simpler oversampling technique, while the other adopted a more nuanced approach involving the removal of outliers. Models 1 and 2 were trained on each of these uniquely balanced datasets. Model 1 performed much better than Model 2 on testing datasets, but both achieved better than 20% error on new datasets, indicating their potential for more accurate step counting in real-world conditions. Despite challenges such as data imbalance, the study demonstrated the viability of using activity labels to enhance step counting accuracy. Future research should focus on addressing data imbalances and exploring more advanced machine learning techniques for more reliable activity monitoring.

"Crowd counting" is a term used to describe the process of calculating the number of people in a given context; however, crowd counting has multiple challenges especially when images representing a given crowd span multiple cameras or images. In this thesis, we propose a crowd counting camera array and correction (CCCAC) method using a camera array of scaled, adjusted, geometrically corrected, combined, processed, and then corrected images to determine the number of people within the newly created combined crowd field. The purpose of CCCAC is to transform and combine valid regions from multiple images from different sources and order as a uniform proportioned set of images for a collage or discrete summation through a new precision counting architecture. Determining counts in this manner within normalized view (collage), results in superior counting accuracy than processing individual images and summing totals with prior models. Finally, the output from the counting model is adjusted with learned results over time to perfect the counting ability of the entire counting system itself. Results show that CCCAC crowd counting corrected and uncorrected methods perform superior to raw image processing methods.

Object counting is a process of determining the quantity of specific objects in images. Accurate object counting is key for various applications in image understanding. The common applications are traffic monitoring, crowd management, wildlife migration monitoring, cell counting in medical images, plant and insect counting in agriculture, etc. Occlusions, complex backgrounds, changes in scale, and variations in object appearance in real-world settings make object counting challenging. This dissertation explores a progression of techniques to achieve robust localization and counting under diverse image modalities. The exploration initiates with addressing the challenges of vehicular target localization in cluttered environments using infrared (IR) imagery. We propose a network, called TCRNet-2, that processes target and clutter information in two parallel channels and then combines them to optimize the target-to-clutter ratio (TCR) metric. Next, we explore class-agnostic object counting in RGB images using vision transformers. The primary motivation for this work is that most current methods excel at counting known object types but struggle with unseen categories. To solve these drawbacks, we propose a class-agnostic object counting method. We introduce a dual-branch architecture with interconnected cross-attention that generates feature pyramids for robust object representations, and a dedicated feature aggregator module that further improves performance. Finally, we propose a novel framework that leverages vision-language models (VLM) for zero-shot object counting. While our earlier class-agnostic counting method demonstrates high efficacy in generalized counting tasks, it relies on user-defined exemplars of target objects, presenting a limitation. Additionally, the previous zero-shot counting method was a reference-less approach, which limits the ability to control the selection of the target object of interest in multi-class scenarios. To address these shortcomings, we propose to utilize vision-language models for zero-shot counting where object categories of interest can be specified by text prompts.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第25443号 / 情博第881号 / 新制||情||148(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 湊 真一, 教授 大木 英司, 教授 山本 章博 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

O Brasil possui a sétima maior reserva geológica de urânio do mundo, com aproximadamente 310 mil toneladas. A Província Uranífera de Lagoa Real, na região de Caetité e Lagoa Real, situado no centro sul da Bahia, é considerada a mais importante província monominerálica do Brasil. A população urbana que vive no distrito uranífero nas cidades de Caetité, Lagoa Real e Livramento de Nossa Senhora usa água potável oriunda do abastecimento público. Na área rural, caracterizada por freqüentes secas, os moradores recebem água de poços escavados e perfurados como também, de pequenas barragens e reservatórios abastecidos pelas chuvas. Este trabalho determinou os níveis de radioatividade alfa e beta total e as concentrações de urânio em diversos tipos de água consumidas pela população urbana e rural da Província Uranífera de Lagoa Real. As atividades α e β total foram determinadas com detector proporcional de fluxo gasoso e baixa radiação de fundo. As concentrações de urânio foram determinadas com o Espectrômetro de Massa com Fonte de Plasma Indutivo (ICP-MS). Os resultados obtidos foram comparados com as recomendações recentes de 2011 da Organização Mundial da Saúde, a portaria nº 2914 de 12/12/2011 do Ministério da Saúde e as resoluções do CONAMA. Os níveis de radiação natural variaram de 0,0041 ± 0,0004 Bq.L-1 a 0,80 ± 0,04 Bq.L-1 para a atividade alfa total e de 0,045 ± 0,003 a 3,0 ± 0,2 Bq.L-1 para a atividade beta total. Tendo como parâmetro a OMS e o MS, apenas duas amostras de água subterrânea, uma localizada na cidade de Lagoa Real e outra na cidade de Caetité apresentaram concentrações alfa total acima do valor de 0,5 Bq.L-1 descrito em suas recomendações, 0,80 ± 0,04 Bq.L-1 e 0,57 ± 0,03 Bq.L-1respectivamente. Para beta total, três amostras apresentaram níveis de radioatividade acima do limite de 1 Bq.L-1 recomendado pela Organização Mundial de Saúde e estabelecido pelo Ministério da Saúde; 3,0 ± 0,2 Bq.L-1; 1,63 ± 0,13 Bq.L-1 e 1,19 ± 0,07 Bq.L-1 todos situados no município de Lagoa Real. Duas amostras de água subterrânea no município de Caetité apresentaram concentrações de urânio acima do valor de 15 μg.L-1 determinado pelo CONAMA, 20,3 ± 0,3 μg.L-1 e 17,1± 0,3 μg.L-1. Em Lagoa Real, uma amostra apresentou níveis de urânio seis vezes superior ao limite estabelecido pelo Conselho Nacional de Meio Ambiente, 89,5 ± 1,5 μg.L-1. A Organização Mundial da Saúde em 2004 estabeleceu em suas recomendações o limite de 15 μg.L-1 como a concentração máxima de urânio na água potável. Em 2011 a OMS aumentou este limite para 30 μg.L-1. Levando-se em consideração as atuais recomendações da OMS, apenas a concentração de uma amostra de água apresentou níveis de urânio acima do recomendado, 89,5 ± 1,5 μg.L-1 no município de Lagoa Real. / Brazil has the seventh greatest geological uranium reserve in the world with approximately 310 thousand tons. The Lagoa Real Uranium Province, in the region of Caetité and Lagoa Real, situated in South Center Bahia, is considered the most important monomineralic province in Brazil. Urban population who lives in the uranium district in the cities of Caetité, Lagoa Real and Livramento de Nossa Senhora uses drinking water originated from public supply. In the rural area, characterized by frequent draughts, residents receive water from digged and drilled wells and from small dams and reservoirs, as well, which are supplied by the rains. This work determined the levels of total alpha and beta radioactivity and the uranium concentrations in several kinds of water consumed by urban and rural population from the Lagoa Real Uranium Province. Total α e β activities were determined with a low-level gas flow proportional detector. The uranium concentrations were determined with an inductive coupled plasma-mass spectrometer (ICP-MS). The results obtained were confronted with the latest World Health Organizations recommendations from 2011, the ordinance number 2914 of December 12 2011 from the Health Ministry and CONAMAs resolutions. Natural radiation levels varied from 0,0041 ± 0,0004 Bq.L-1 to 0,80 ± 0,04 Bq.L-1 for total alpha activity and from 0,045 ± 0,003 to 3,00 ± 0,2 Bq.L-1 for total beta activity. Having the WHO and the HM as parameter, just two underground water samples, one located in the city of Lagoa Real and the other in the city of Caetité presented total alpha concentration above the value of 0,5 Bq.L-1 described in its recommendations, 0,80 ± 0,040 Bq.L-1 and 0,57 ± 0,03 Bq.L-1 respectively. For total beta three samples presented radioactivity levels above the 1 Bq.L-1 limit recommended by the WHO and established by the Health Ministry; 3,00 ± 0,2 Bq.L-1; 1,63 ± 0,13 Bq.L-1 and 1,19 ± 0,07 Bq.L-1., all of them situated in the Lagoa Real town. Two samples of underground water from Caetité presented uranium concentrations above the value of 15 μg.L-1 determined by CONAMA, 20,3 ± 0,3 μg.L-1 and 17,1± 0,3 μg.L-1. In Lagoa Real one sample presented uranium levels six times over the limit established by the Environment National Council 89,5 ± 1,5 μg.L-1. In 2004 the World Health Organization established in its recommendations the 15 μg.L-1 limit as the maximum uranium concentration in drinking water. In 2011 the WHO increased that limit to 30 μg.L-1. Taking into account the current WHO recommendations only the concentration of one water sample presented uranium levels above the recommended, 89,5 ± 1,5 μg.L-1 in Lagoa Real.

O Brasil possui a sétima maior reserva geológica de urânio do mundo, com aproximadamente 310 mil toneladas. A Província Uranífera de Lagoa Real, na região de Caetité e Lagoa Real, situado no centro sul da Bahia, é considerada a mais importante província monominerálica do Brasil. A população urbana que vive no distrito uranífero nas cidades de Caetité, Lagoa Real e Livramento de Nossa Senhora usa água potável oriunda do abastecimento público. Na área rural, caracterizada por freqüentes secas, os moradores recebem água de poços escavados e perfurados como também, de pequenas barragens e reservatórios abastecidos pelas chuvas. Este trabalho determinou os níveis de radioatividade alfa e beta total e as concentrações de urânio em diversos tipos de água consumidas pela população urbana e rural da Província Uranífera de Lagoa Real. As atividades α e β total foram determinadas com detector proporcional de fluxo gasoso e baixa radiação de fundo. As concentrações de urânio foram determinadas com o Espectrômetro de Massa com Fonte de Plasma Indutivo (ICP-MS). Os resultados obtidos foram comparados com as recomendações recentes de 2011 da Organização Mundial da Saúde, a portaria nº 2914 de 12/12/2011 do Ministério da Saúde e as resoluções do CONAMA. Os níveis de radiação natural variaram de 0,0041 ± 0,0004 Bq.L-1 a 0,80 ± 0,04 Bq.L-1 para a atividade alfa total e de 0,045 ± 0,003 a 3,0 ± 0,2 Bq.L-1 para a atividade beta total. Tendo como parâmetro a OMS e o MS, apenas duas amostras de água subterrânea, uma localizada na cidade de Lagoa Real e outra na cidade de Caetité apresentaram concentrações alfa total acima do valor de 0,5 Bq.L-1 descrito em suas recomendações, 0,80 ± 0,04 Bq.L-1 e 0,57 ± 0,03 Bq.L-1respectivamente. Para beta total, três amostras apresentaram níveis de radioatividade acima do limite de 1 Bq.L-1 recomendado pela Organização Mundial de Saúde e estabelecido pelo Ministério da Saúde; 3,0 ± 0,2 Bq.L-1; 1,63 ± 0,13 Bq.L-1 e 1,19 ± 0,07 Bq.L-1 todos situados no município de Lagoa Real. Duas amostras de água subterrânea no município de Caetité apresentaram concentrações de urânio acima do valor de 15 μg.L-1 determinado pelo CONAMA, 20,3 ± 0,3 μg.L-1 e 17,1± 0,3 μg.L-1. Em Lagoa Real, uma amostra apresentou níveis de urânio seis vezes superior ao limite estabelecido pelo Conselho Nacional de Meio Ambiente, 89,5 ± 1,5 μg.L-1. A Organização Mundial da Saúde em 2004 estabeleceu em suas recomendações o limite de 15 μg.L-1 como a concentração máxima de urânio na água potável. Em 2011 a OMS aumentou este limite para 30 μg.L-1. Levando-se em consideração as atuais recomendações da OMS, apenas a concentração de uma amostra de água apresentou níveis de urânio acima do recomendado, 89,5 ± 1,5 μg.L-1 no município de Lagoa Real. / Brazil has the seventh greatest geological uranium reserve in the world with approximately 310 thousand tons. The Lagoa Real Uranium Province, in the region of Caetité and Lagoa Real, situated in South Center Bahia, is considered the most important monomineralic province in Brazil. Urban population who lives in the uranium district in the cities of Caetité, Lagoa Real and Livramento de Nossa Senhora uses drinking water originated from public supply. In the rural area, characterized by frequent draughts, residents receive water from digged and drilled wells and from small dams and reservoirs, as well, which are supplied by the rains. This work determined the levels of total alpha and beta radioactivity and the uranium concentrations in several kinds of water consumed by urban and rural population from the Lagoa Real Uranium Province. Total α e β activities were determined with a low-level gas flow proportional detector. The uranium concentrations were determined with an inductive coupled plasma-mass spectrometer (ICP-MS). The results obtained were confronted with the latest World Health Organizations recommendations from 2011, the ordinance number 2914 of December 12 2011 from the Health Ministry and CONAMAs resolutions. Natural radiation levels varied from 0,0041 ± 0,0004 Bq.L-1 to 0,80 ± 0,04 Bq.L-1 for total alpha activity and from 0,045 ± 0,003 to 3,00 ± 0,2 Bq.L-1 for total beta activity. Having the WHO and the HM as parameter, just two underground water samples, one located in the city of Lagoa Real and the other in the city of Caetité presented total alpha concentration above the value of 0,5 Bq.L-1 described in its recommendations, 0,80 ± 0,040 Bq.L-1 and 0,57 ± 0,03 Bq.L-1 respectively. For total beta three samples presented radioactivity levels above the 1 Bq.L-1 limit recommended by the WHO and established by the Health Ministry; 3,00 ± 0,2 Bq.L-1; 1,63 ± 0,13 Bq.L-1 and 1,19 ± 0,07 Bq.L-1., all of them situated in the Lagoa Real town. Two samples of underground water from Caetité presented uranium concentrations above the value of 15 μg.L-1 determined by CONAMA, 20,3 ± 0,3 μg.L-1 and 17,1± 0,3 μg.L-1. In Lagoa Real one sample presented uranium levels six times over the limit established by the Environment National Council 89,5 ± 1,5 μg.L-1. In 2004 the World Health Organization established in its recommendations the 15 μg.L-1 limit as the maximum uranium concentration in drinking water. In 2011 the WHO increased that limit to 30 μg.L-1. Taking into account the current WHO recommendations only the concentration of one water sample presented uranium levels above the recommended, 89,5 ± 1,5 μg.L-1 in Lagoa Real.