Interpretations of Domain Adaptations via Layer Variational Analysis

Transfer learning is known to perform efficiently in many applications empirically, yet limited literature reports the mechanism behind the scene. This study establishes both formal derivations and heuristic analysis to formulate the theory of transfer learning in deep learning. Our framework utilizing layer variational analysis proves that the success of transfer learning can be guaranteed with corresponding data conditions. Moreover, our theoretical calculation yields intuitive interpretations towards the knowledge transfer process. Subsequently, an alternative method for network-based transfer learning is derived. The method shows an increase in efficiency and accuracy for domain adaptation. It is particularly advantageous when new domain data is sufficiently sparse during adaptation. Numerical experiments over diverse tasks validated our theory and verified that our analytic expression achieved better performance in domain adaptation than the gradient descent method.Comment: Published at ICLR 202

Combining handcrafted features with latent variables in machine learning for prediction of radiationâ induced lung damage

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/1/mp13497.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149351/2/mp13497_am.pd

Deep reinforcement learning for automated radiation adaptation in lung cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141551/1/mp12625.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141551/2/mp12625_am.pd

Rethinking CycleGAN: Improving Quality of GANs for Unpaired Image-to-Image Translation

An unpaired image-to-image (I2I) translation technique seeks to find a mapping between two domains of data in a fully unsupervised manner. While the initial solutions to the I2I problem were provided by the generative adversarial neural networks (GANs), currently, diffusion models (DM) hold the state-of-the-art status on the I2I translation benchmarks in terms of FID. Yet, they suffer from some limitations, such as not using data from the source domain during the training, or maintaining consistency of the source and translated images only via simple pixel-wise errors. This work revisits the classic CycleGAN model and equips it with recent advancements in model architectures and model training procedures. The revised model is shown to significantly outperform other advanced GAN- and DM-based competitors on a variety of benchmarks. In the case of Male2Female translation of CelebA, the model achieves over 40% improvement in FID score compared to the state-of-the-art results. This work also demonstrates the ineffectiveness of the pixel-wise I2I translation faithfulness metrics and suggests their revision. The code and trained models are available at https://github.com/LS4GAN/uvcgan

The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy

With the continuous increase in radiotherapy patient-specific data from multimodality imaging and biotechnology molecular sources, knowledge-based response-adapted radiotherapy (KBR-ART) is emerging as a vital area for radiation oncology personalized treatment. In KBR-ART, planned dose distributions can be modified based on observed cues in patients’ clinical, geometric, and physiological parameters. In this paper, we present current developments in the field of adaptive radiotherapy (ART), the progression toward KBR-ART, and examine several applications of static and dynamic machine learning approaches for realizing the KBR-ART framework potentials in maximizing tumor control and minimizing side effects with respect to individual radiotherapy patients. Specifically, three questions required for the realization of KBR-ART are addressed: (1) what knowledge is needed; (2) how to estimate RT outcomes accurately; and (3) how to adapt optimally. Different machine learning algorithms for KBR-ART application shall be discussed and contrasted. Representative examples of different KBR-ART stages are also visited

Association Between Thyroid Disorders and Colorectal Cancer Risk in Adult Patients in Taiwan

IMPORTANCE Thyroid hormones have been shown to affect several important pathways in cancer development, including colorectal cancer (CRC). Clinical studies examining the association between thyroid disorders and colorectal cancer have conflicting results and have predominantly involved white populations. OBJECTIVE To determine if a diagnosis of hyperthyroidism or hypothyroidism is associated with the risk of developing colorectal cancer in an East Asian population. DESIGN, SETTING, AND PARTICIPANTS This nationwide population-based case-control study was conducted from April 27, 2018, to November 8, 2018, using the Taiwanese National Health Insurance Research Database. Participants were adults (n = 139 426) either with a new diagnosis (between 2008 and 2013) of primary colorectal cancer without a history of cancer, or without cancer. Cases and controls were matched 11 by age, sex, and index date. Diagnosis of hyperthyroidism or hypothyroidism prior to the diagnosis of colorectal cancer (or the same index date in controls) was then determined. MAIN OUTCOMES AND MEASURES Risk differences in developing colorectal cancer among patients with a medical history of hyperthyroidism or hypothyroidism. RESULTS A total of 139 426 patients were included in the study, and 69 713 individuals made up each case and control group, which were both predominantly male (39 872 [57.2%]). The mean (SD) age for those with CRC was 65.8 (13.7) years and for those without CRC was 66.0 (13.6) years. Both hyperthyroidism (adjusted odds ratio [aOR], 0.77; 95% CI, 0.69-0.86; P < .001) and hypothyroidism (aOR, 0.78; 95% CI, 0.65-0.94; P = .008) were associated with a decreased risk of being diagnosed with colorectal cancer. An inverse association of rectal cancer was found among patients aged 50 years or older with a history of hypothyroidism despite treatment (aOR, 0.54; 95% CI, 0.39-0.74; P < .001). A history of hyperthyroidism in all age groups was associated with a lower risk of colon cancer (aOR, 0.74; 95% CI, 0.64-0.85; P < .001), with a stronger association seen among those younger than 50 years (aOR, 0.55; 95% CI, 0.36-0.85; P = .007). CONCLUSIONS AND RELEVANCE In this study, hypothyroidism appeared to be associated with a lower risk of rectal cancer, whereas hyperthyroidism appeared to be associated with a lower risk of colon cancer. Because of this, biochemical in vivo research and epidemiologic studies appear to be needed to further clarify the nature of these associations.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Identification of the genetic determinants of Salmonella enterica serotype Typhimurium that may regulate the expression of the type 1 fimbriae in response to solid agar and static broth culture conditions

<p>Abstract</p> <p>Background</p> <p>Type 1 fimbriae are the most commonly found fimbrial appendages on the outer membrane of <it>Salmonella enterica </it>serotype Typhimurium. Previous investigations indicate that static broth culture favours <it>S</it>. Typhimurium to produce type 1 fimbriae, while non-fimbriate bacteria are obtained by growth on solid agar media. The phenotypic expression of type 1 fimbriae in <it>S</it>. Typhimurium is the result of the interaction and cooperation of several genes in the <it>fim </it>gene cluster. Other gene products that may also participate in the regulation of type 1 fimbrial expression remain uncharacterized.</p> <p>Results</p> <p>In the present study, transposon insertion mutagenesis was performed on <it>S</it>. Typhimurium to generate a library to screen for those mutants that would exhibit different type 1 fimbrial phenotypes than the parental strain. Eight-two mutants were obtained from 7,239 clones screened using the yeast agglutination test. Forty-four mutants produced type 1 fimbriae on both solid agar and static broth media, while none of the other 38 mutants formed type 1 fimbriae in either culture condition. The flanking sequences of the transposons from 54 mutants were cloned and sequenced. These mutants can be classified according to the functions or putative functions of the open reading frames disrupted by the transposon. Our current results indicate that the genetic determinants such as those involved in the fimbrial biogenesis and regulation, global regulators, transporter proteins, prophage-derived proteins, and enzymes of different functions, to name a few, may play a role in the regulation of type 1 fimbrial expression in response to solid agar and static broth culture conditions. A complementation test revealed that transforming a recombinant plasmid possessing the coding sequence of a NAD(P)H-flavin reductase gene <it>ubiB </it>restored an <it>ubiB </it>mutant to exhibit the type 1 fimbrial phenotype as its parental strain.</p> <p>Conclusion</p> <p>Genetic determinants other than the <it>fim </it>genes may involve in the regulation of type 1 fimbrial expression in <it>S</it>. Typhimurium. How each gene product may influence type 1 fimbrial expression is an interesting research topic which warrants further investigation.</p

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements