Breast Ultrasound Image Segmentation Based on Uncertainty Reduction and Context Information

Breast cancer frequently occurs in women over the world. It was one of the most serious diseases and the second common cancer among women in 2019. The survival rate of stages 0 and 1 of breast cancer is closed to 100%. It is urgent to develop an approach that can detect breast cancer in the early stages. Breast ultrasound (BUS) imaging is low-cost, portable, and effective; therefore, it becomes the most crucial approach for breast cancer diagnosis. However, BUS images are of poor quality, low contrast, and uncertain. The computer-aided diagnosis (CAD) system is developed for breast cancer to prevent misdiagnosis. There have been many types of research for BUS image segmentation based on classic machine learning and computer vision methods, e.g., clustering methods, thresholding methods, level set, active contour, and graph cut. Since deep neural networks have been widely utilized in nature image semantic segmentation and achieved good results, deep learning approaches are also applied to BUS image segmentation. However, the previous methods still suffer some shortcomings. Firstly, the previous non-deep learning approaches highly depend on the manually selected features, such as texture, frequency, and intensity. Secondly, the previous deep learning approaches do not solve the uncertainty and noise in BUS images and deep learning architectures. Meanwhile, the previous methods also do not involve context information such as medical knowledge about breast cancer. In this work, three approaches are proposed to measure and reduce uncertainty and noise in deep neural networks. Also, three approaches are designed to involve medical knowledge and long-range distance context information in machine learning algorithms. The proposed methods are applied to breast ultrasound image segmentation. In the first part, three fuzzy uncertainty reduction architectures are designed to measure the uncertainty degree for pixels and channels in the convolutional feature maps. Then, medical knowledge constrained conditional random fields are proposed to reflect the breast layer structure and refine the segmentation results. A novel shape-adaptive convolutional operator is proposed to provide long-distance context information in the convolutional layer. Finally, a fuzzy generative adversarial network is proposed to reduce uncertainty. The new approaches are applied to 4 breast ultrasound image datasets: one multi-category dataset and three public datasets with pixel-wise ground truths for tumor and background. The proposed methods achieve the best performance among 15 BUS image segmentation methods on the four datasets

Laser Cooling of 85Rb Atoms to the Recoil Temperature Limit

We demonstrate the laser cooling of 85Rb atoms in a two-dimensional optical lattice. We follow the two-step degenerate Raman sideband cooling scheme [Kerman et al., Phys. Rev. Lett. 84, 439 (2000)], where a fast cooling of atoms to an auxiliary state is followed by a slow cooling to a dark state. This method has the advantage of independent control of the heating rate and cooling rate from the optical pumping beam. We operate the lattice at a Lamb-Dicke parameter eta=0.45 and show the cooling of spin-polarized 85Rb atoms to the recoil temperature in both dimension within 2.4 ms with the aid of adiabatic cooling

The ethical image in a topological perspective: the poetics of Gaston Bachelard

In the poetics of Gaston Bachelard, the natural images, especially the four elements (fire, water, air, earth), occupy the eminent place for literary imagination. Under this main frame, this paper tries to present the relation of ethics and aesthetics in focusing on the ethical image as a synthetic concept. It also argues that the poetic imagination in Bachelard presupposes a metaphysical base managing the being, the force, the will, and the action. There is a dynamic structure in this metaphysics of imagination. Notwithstanding the usual separation of different spheres in philosophy, Bachelard urges a primary fusion in the cosmic scale, i.e. the world and the human are communicative and correspondent. Taking these principles into consideration, this paper explores the topological dimension in those poetic images of elements and space. The ontological sense of being-there is evaluated by the dynamic function of “there” in restoring its ethical meaning. Likewise, the terms “in front of the fire”, “before the water”, “in the water” are given the topological accents. The verticality indicating the dynamic function of the flight, of the falling, and of standing upright is topologically effective. In accordance to the verticality, the concept of survival contains an effect of surpassing the existential conditions, the prefix “sur-“ means that tentative of the higher degree. In sum, the cosmicity as the very place of ethical and ontological unification reveals Bachelard’s concern on the imaginative transformation of the personality, the appropriation in the cosmos

Three Essays in Applied Econometrics: Agricultural and Energy Economics

This dissertation examines three empirical issues in energy and agricultural economics using econometrics models whose titles are: 1) Do Natural Hazards in the Gulf Coast Still Matter for State-Level Natural Gas Prices in the US? Evidence After the Shale Gas Boom; 2) Do Exploitations of Marcellus and Utica Shale Formations Improve Regional Economy in Ohio, Pennsylvania, and West Virginia? A Synthetic Control Analysis; and 3) How Did Covid-19 Impact US Household Food Spending? An Analysis Six Months In. The first essay assesses the impact of natural hazards on state-level natural gas prices and evaluates the effects of the shale gas boom on the hazard-price relationship. Property losses due to natural hazards in Texas and Louisiana are used to represent supply shocks in US natural gas market from the Gulf area. Panel distributed lag models are applied to a state-level panel data set from 1995 to 2016. Estimation results show that natural gas prices in both importing and exporting states have become less responsive to natural hazards in Texas, but more sensitive to hazard events in Louisiana since the shale boom. These results are robust to the break dates used, the geographical location of states considered, and the empirical specifications employed. The increasing importance of Louisiana in natural gas pricing is perhaps due to its role as the benchmark pricing location for US natural gas and its expansive pipeline networks. The second essay examines the impact of shale gas development on various economic outcomes in three Appalachian states: Ohio, Pennsylvania, and West Virginia. Four key economic indicators (poverty rate, population growth, employment growth, and income per capita growth) are considered. Estimation results obtained from the synthetic control method using 2002-2017 data are mixed. The shale development decreased the poverty rate and increased the employment growth rate in Pennsylvania and West Virginia in the short-run (2010 to 2013). In West Virginia, shale development also increased personal income per capita growth in the short run. However, most of the positive impacts disappeared or turned negative in the later post-boom period (2014 to 2017). The shale development did not bring significant economic benefits to Ohio. Nonetheless, shale development exerts a potential long-term negative effect on population growth in all three states. The third essay exploits a nationwide survey of primary grocery shoppers to estimate the impact of Covid-19 on household spending behavior. The survey was conducted in August 2020 when the economy had partially reopened in many areas of the country and consumers had different spending opportunities compared to when the Covid-19 lockdown began. Various sociodemographic information such as household income, age, Covid-19 severity level, access to grocery stores, and farmers markets were collected. Findings based on ordered Probit models show that food insecurity problems impacted middle-class households (those with income below $50,000 and those with income between$50,000 and $99,999). Households with children and/or the elderly (i.e., those that usually require higher food quality and nutrition intakes) had a higher probability of increasing their spending during Covid-19 than before. Furthermore, consumers’ food safety practice levels and the Covid-19 severity level within the country of their residences significantly affected their overall food grocery and local produce shopping behaviors

Integrated sensing, dynamics and control of a moble gantry crane

This thesis investigates the dynamics and control of a Rubber Tyred Gantry (RTG) crane which is commonly used in container handling operations. Both theoretical and experimental work has been undertaken to ensure the balance of this research. The concept of a Global Sensing System (GSS) is outlined, this being a closed loop automatic sensing system capable of guiding the lifting gear (spreader) to the location of the target container by using feedback signals from the crane's degrees of freedom. To acquire the crucial data for the coordinates and orientation of the swinging spreader a novel visual sensing system (VSS) is proposed. In addition algorithms used in the VSS for seeking the central coordinates of the clustered pixels from the digitised images are also developed. In order to investigate the feasibility of different control strategies in practice, a scaleddown, 1/8 full size, experimental crane rig has been constructed with a new level of functionality in that the spreader in this rig is equipped with multiple cables to emulate the characteristics of a full-size RTG crane. A Crane Application Programming Interface (CAPI) is proposed to reduce the complexity and difficulty in integrating the control software and hardware. It provides a relatively user-friendly environment in which the end-user can focus on implementing the more fundamental issues of control strategies, rather than spending significant amounts of time in low-level devicedependent programming. A control strategy using Feedback Linearization Control (FLC) is investigated. This can handle significant non-linearity in the dynamics of the RTG crane. Simulation results are provided, and so by means of the CAPI this controller is available for direct control of the experimental crane rig. The final part of the thesis is an integration of the analyses of the different subjects, and shows the feasibility of real-time implementation

Multi-omics Portraits of Cancer

Precision oncology demands accurate portrayal of a disease at all molecular levels. However, current large-scale studies of omics are often isolated by data types. I have been developing computational tools to conduct integrative analyses of omics data, identifying unique molecular etiology in each tumor. Particularly, this dissertation presents the following contributions to the computational omics of cancer: (1) uncovering the predisposition landscape in 33 cancers and how germline genome collaborates with somatic alterations in oncogenesis; (2) pioneering methods to combine genomic and proteomic data to identify treatment opportunities; and (3) revealing selective phosphorylation of kinase-substrate pairs. These findings advance our understanding of tumor biology on a systematic scale and inform clinical practice of cancer diagnosis and treatment design