164 research outputs found
Atlanta’s Desegregation Era Social Studies Curriculum: An Examination of Georgia History Textbooks
No full textAuthor accepted manuscript version of a chapter published in:
Bohan, C. H. & Randolph, P. (2012). Desegregation era social studies curriculum: An examination of Georgia History textbooks. Chapter seven in C. Woyshner and C. H. Bohan (Eds.) Histories of social studies and race, 1890–2000. (pp. 135−158). New York: Palgrave MacMillan.</p
Network properties data and code used in "Ecological plasticity governs ecosystem services in multilayer networks".
No full textCode and network properties data used in the analyses presented in "Ecological plasticity governs ecosystem services in multilayer networks". Further information can be requested of the author David A. Bohan ([email protected])
Selective Compression of Medical Images via Intelligent Segmentation and 3D-SPIHT Coding
No full textABSTRACT
SELECTIVE COMPRESSION OF MEDICAL IMAGES VIA INTELLIGENT SEGMENTATION AND 3D-SPIHT CODING
by
Bohan Fan
The University of Wisconsin-Milwaukee, 2018
Under the Supervision of Professor Zeyun Yu
With increasingly high resolutions of 3D volumetric medical images being widely used in clinical patient treatments, efficient image compression techniques have become in great demand due to the cost in storage and time for transmission. While various algorithms are available, the conflicts between high compression rate and the downgraded quality of the images can partially be harmonized by using the region of interest (ROI) coding technique. Instead of compressing the entire image, we can segment the image by critical diagnosis zone (the ROI zone) and background zone, and apply lossless compression or low compression rate to the former and high compression rate to the latter, without losing much clinically important information.
In this thesis, we explore a medical image transmitting process that utilizes a deep learning network, called 3D-Unet to segment the region of interest area of volumetric images and 3D-SPIHT algorithm to encode the images for compression, which can be potentially used in medical data sharing scenario. In our experiments, we train a 3D-Unet on a dataset of spine images with their label ground truth, and use the trained model to extract the vertebral bodies of testing data. The segmented vertebral regions are dilated to generate the region of interest, which are subject to the 3D-SPIHT algorithm with low compress ratio while the rest of the image (background) is coded with high compress ratio to achieve an excellent balance of image quality in region of interest and high compression ratio elsewhere
Selective Compression of Medical Images via Intelligent Segmentation and 3D-SPIHT Coding
No full textABSTRACT SELECTIVE COMPRESSION OF MEDICAL IMAGES VIA INTELLIGENT SEGMENTATION AND 3D-SPIHT CODING by Bohan Fan The University of Wisconsin-Milwaukee, 2018 Under the Supervision of Professor Zeyun Yu With increasingly high resolutions of 3D volumetric medical images being widely used in clinical patient treatments, efficient image compression techniques have become in great demand due to the cost in storage and time for transmission. While various algorithms are available, the conflicts between high compression rate and the downgraded quality of the images can partially be harmonized by using the region of interest (ROI) coding technique. Instead of compressing the entire image, we can segment the image by critical diagnosis zone (the ROI zone) and background zone, and apply lossless compression or low compression rate to the former and high compression rate to the latter, without losing much clinically important information. In this thesis, we explore a medical image transmitting process that utilizes a deep learning network, called 3D-Unet to segment the region of interest area of volumetric images and 3D-SPIHT algorithm to encode the images for compression, which can be potentially used in medical data sharing scenario. In our experiments, we train a 3D-Unet on a dataset of spine images with their label ground truth, and use the trained model to extract the vertebral bodies of testing data. The segmented vertebral regions are dilated to generate the region of interest, which are subject to the 3D-SPIHT algorithm with low compress ratio while the rest of the image (background) is coded with high compress ratio to achieve an excellent balance of image quality in region of interest and high compression ratio elsewhere
Learning Lines with Ordinal Constraints
No full textWe study the problem of finding a mapping f from a set of points into the real line, under ordinal triple constraints. An ordinal constraint for a triple of points (u,v,w) asserts that |f(u)-f(v)| < |f(u)-f(w)|. We present an approximation algorithm for the dense case of this problem. Given an instance that admits a solution that satisfies (1-ε)-fraction of all constraints, our algorithm computes a solution that satisfies (1-O(ε^{1/8}))-fraction of all constraints, in time O(n⁷) + (1/ε)^{O(1/ε^{1/8})} n
Research on the Marketing Strategy of Banking and Finance Business Given Big Data Technology
No full textIndividuals’ consumption patterns and financial demands are changing significantly as a result of the quick advancement of modern information technology and the integration of financial technology into everyday life, which has negative effects on and presents challenges for banks’ traditional retail operations. The competition among Chinese commercial banks in the retail sector is getting more and more intense due to the country’s rapid economic expansion and increase in household wealth. In order to improve its core competitiveness, maximise its retail business marketing strategy, and investigate a transformation path that is consistent with its own retail business marketing, Bank F must actively use financial technology innovation. In addition to the epidemic impacting the global economic recovery, the domestic economy is currently going through a transition and expedited structural adjustment, which raises credit risk and increases competitive pressure. A drop in the intermediate business income of the banking industry and a consistent decline in nett profit growth are the results of the financial market reform and the quick expansion of Internet finance, which have caused substantial changes in the market environment. Due to the fact that rural commercial banks have changed their course to aggressively promote inclusive finance and develop retail lending, how to optimise and innovate the marketing strategy of the retail lending business based on the Internet and the use of big data has emerged as a crucial concern for rural commercial banks
Practitioner Insights Into The Ethics Of Live Streaming
No full textThe live streaming industry, worth USD 38.87 billion in 2022 and expected to grow to USD 256.56 billion by 2032 (CMI, 2023), has become a major force in digital media. However, its rapid growth has also raised significant ethical concerns, which as a digital marketing executive, I have witnessed first-hand. Streamers often use deceptive tactics, such as creating false urgency or faking their own popularity, to induce viewers into making impulsive purchases. Recent scandals, including misleading product promotions and fabricated claims by top streamers, further highlight the industry's ethical issues. There is an urgent need to address these issues that threaten trust, consumer well-being, and the industry's future.
Existing research on live streaming has primarily focused on consumer behaviours that enhance profitability, such as purchase intentions and gifting, while overlooking broader ethical challenges. Studies on live streaming ethics often address only one or two aspects, such as malicious selling and privacy violation, with little examination of the mechanisms driving unethical practices. To address these gaps, this study explored the ethical challenges of live streaming and the mechanisms that drive unethical behaviour using a qualitative approach and semi-structured interviews with 16 industry practitioners, including streamers, marketing managers, marketing agency owners, and e-commerce experts.
The findings revealed 19 distinct ethical issues in live streaming, which were systematically categorised into six types: deception, coercion, unhealthy streamer-viewer relationships, privacy violations, dissemination of harmful values, and exploitation of legal loopholes. These categories encompass a wide range of unethical practices, such as false advertising, fake testimonials, and forced endorsements. Additionally, the study identified three critical mechanisms driving these issues: asymmetric power relations between streamers and viewers; psychological manipulation of viewers; and insufficient regulatory frameworks. These mechanisms operate individually and interact in a cyclical manner, reinforcing one another and intensifying ethical challenges within the live streaming ecosystem.
This research makes several theoretical contributions. Firstly, it fills critical gaps in the marketing literature by offering a holistic examination of ethical challenges in live streaming. Secondly, it enriches existing research by identifying the underlying mechanisms contributing to unethical practices. Thirdly, it provides a comprehensive analysis of the interplay between these mechanisms, highlighting feedback loops that intensify unethical behaviours. Lastly, it expands the understanding of parasocial relationships by revealing their darker implications, demonstrating how these one-sided bonds can be weaponised for financial and emotional exploitation.
Practically, this study offers insights into the perspectives of practitioners within the industry. It highlights the need for stronger regulations and industry accountability in live streaming, including criminal penalties for unethical practices, mandatory background checks for streamers, and stricter content moderation. Platforms must enhance transparency and promote ethical conduct, while consumer education initiatives are essential to empower viewers and protect them from exploitation. Overall, this research lays a foundation for future work on ethical live streaming, opening avenues for exploring ethical challenges from a global perspective, examining psychological mechanisms from a consumer perspective, and finally, investigating the overall well-being of streamers
Sex-Specific Associations of Red Meat and Processed Meat Consumption with Serum Metabolites in the UK Biobank
No full textRed meat consumption has been found to closely related to cardiometabolic health, with sex disparity. However, the specific metabolic factors corresponding to red meat consumption in men and women have not been examined previously. We analyzed the sex-specific associations of meat consumption, with 167 metabolites using multivariable regression, controlling for age, ethnicity, Townsend deprivation index, education, physical activity, smoking, and drinking status among ~79,644 UK Biobank participants. We also compared the sex differences using an established formula. After accounting for multiple testing with false discovery rate < 5% and controlling for confounders, the positive associations of unprocessed red meat consumption with branched-chain amino acids and several lipoproteins, and the inverse association with glycine were stronger in women, while the positive associations with apolipoprotein A1, creatinine, and monounsaturated fatty acids were more obvious in men. For processed meat, the positive associations with branched-chain amino acids, several lipoproteins, tyrosine, lactate, glycoprotein acetyls and inverse associations with glutamine, and glycine were stronger in women than in men. The study suggests that meat consumption has sex-specific associations with several metabolites. This has important implication to provide dietary suggestions for individuals with or at high risk of cardiometabolic disease, with consideration of sex difference
Geometric Algorithms for Learning and Data Poisoning
No full textGeometric methods have provided many powerful tools in algorithm design and learning. In this dissertation, I will present our geometric algorithms for learning and data poisoning. In the first part of my dissertation, I introduce our approach to solve the learning line problem with ordinal constraints. Our goal is to find a mapping from a set of points into the real line, such that the number of constraints for the triple of points that encode their proximity information could be preserved as many as possible. Our algorithm computes a solution that satisfies (1-O(\eps^{1/8}))-fraction of all constraints, in time O(n^7) + (1/\eps)^{O(1/\eps^{1/8})} n.
In the second part of my dissertation, I introduce our geometric algorithms for data poisoning. The key motivation of our study is to provide a systematic way of injecting nearly-optimal poisoning to data with provable worse case guarantee, so that the robustness of the clustering algorithms could be evaluated on some nearly optimal poisoned data set. I first propose our poisoning algorithms against -center and -means clustering. Our algorithms can achieve -approximation with respect to optimal poisoning for -center data poisoning problem in time . For -means data poisoning problem, we get an bi-criteria approximation algorithm for general case. The algorithm achieves -approximation with respect to optimal poisoning, that is, if the cost of optimal poisoning is cost_k(X\cup P_\OPT), our algorithm compute a poisoning, with cost at least . Then I propose our poisoning algorithm for -NN clustering. Our algorithm computes an \eps n-additive approximation of the optimal poisoning in n\cdot 2^{2^{O(d+k/\eps)}} time
Analysis on the Negative Impact of AI Development on Employment and Its Countermeasures
No full textWhile benefiting society, the technological progress of artificial intelligence (AI) has also brought a rising number of unemployed people and breeded polarization in income distribution by threatening the low-skill and labor-intensive industry. To solve the negative impact of AI, policies about the taxation and subsidy on AI and the income-supporting program can be proposed. However, neither of them will work well to achieve sustainable social development. In the long run, technological progress will not be influenced by government policies, and capital will find its own path to a rapid growth. Income-supporting programs are short-term solutions, being ineffective and not sustainable. Based on the literature collected, the author came up with two practical methods to deal with the negative impact brought by AI to employment: the industrial relocation as a short-term solution and the reframing of the education system as a long-term solution
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