The Estimation Of Age, Period And Cohort Effects For Lung Cancer

Lung cancer is one of the most fatal diseases in the world, so it is important to understand the pattern of lung cancer trends at population level. The age-period-cohort model (APC model) is used in this article to analyze the effects of age, year at diagnosis and year at birth on lung cancer in the U.S. The results suggest the age, period and cohort curvature effects are all significant on the lung cancer incidence and mortality. By looking at the plot of the age, period and cohort effects, we found historical events of interest like world war, sales strategies by tobacco companies and lung cancer screening technology may drive the period and cohort effects on the lung cancer in the U.S. The methodology used in this paper will be helpful for public health interventions evaluation and predicting the outcomes the future disease fluctuation driven by those interventions

Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time-consuming, labor-intensive, expensive and destructive to the specimen. Recently, the ability to virtually-stain unlabeled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain specific deep neural networks. Here, we present a new deep learning-based framework which generates virtually-stained images using label-free tissue, where different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabeled kidney tissue sections to generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones silver stain, and Masson's Trichrome stain. Using a single network, this approach multiplexes virtual staining of label-free tissue with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created on the same tissue cross-section, which is currently not feasible with standard histochemical staining methods.Comment: 19 pages, 5 figures, 2 table

Dynamic Portfolio Management with Reinforcement Learning

Dynamic Portfolio Management is a domain that concerns the continuous redistribution of assets within a portfolio to maximize the total return in a given period of time. With the recent advancement in machine learning and artificial intelligence, many efforts have been put in designing and discovering efficient algorithmic ways to manage the portfolio. This paper presents two different reinforcement learning agents, policy gradient actor-critic and evolution strategy. The performance of the two agents is compared during backtesting. We also discuss the problem set up from state space design, to state value function approximator and policy control design. We include the short position to give the agent more flexibility during assets redistribution and a constant trading cost of 0.25%. The agent is able to achieve 5% return in 10 days daily trading despite 0.25% trading cost

Outlier Detection Ensemble with Embedded Feature Selection

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Specifically, for each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS.Comment: 10pages, AAAI202

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要約のみTohoku University小野崇人課