Scalable Data Augmentation for Deep Learning

Scalable Data Augmentation (SDA) provides a framework for training deep learning models using auxiliary hidden layers. Scalable MCMC is available for network training and inference. SDA provides a number of computational advantages over traditional algorithms, such as avoiding backtracking, local modes and can perform optimization with stochastic gradient descent (SGD) in TensorFlow. Standard deep neural networks with logit, ReLU and SVM activation functions are straightforward to implement. To illustrate our architectures and methodology, we use P\'{o}lya-Gamma logit data augmentation for a number of standard datasets. Finally, we conclude with directions for future research

Adversarial Bayesian Simulation

In the absence of explicit or tractable likelihoods, Bayesians often resort to approximate Bayesian computation (ABC) for inference. Our work bridges ABC with deep neural implicit samplers based on generative adversarial networks (GANs) and adversarial variational Bayes. Both ABC and GANs compare aspects of observed and fake data to simulate from posteriors and likelihoods, respectively. We develop a Bayesian GAN (B-GAN) sampler that directly targets the posterior by solving an adversarial optimization problem. B-GAN is driven by a deterministic mapping learned on the ABC reference by conditional GANs. Once the mapping has been trained, iid posterior samples are obtained by filtering noise at a negligible additional cost. We propose two post-processing local refinements using (1) data-driven proposals with importance reweighting, and (2) variational Bayes. We support our findings with frequentist-Bayesian results, showing that the typical total variation distance between the true and approximate posteriors converges to zero for certain neural network generators and discriminators. Our findings on simulated data show highly competitive performance relative to some of the most recent likelihood-free posterior simulators

A CT-Based Airway Segmentation Using U2^2-net Trained by the Dice Loss Function

Airway segmentation from chest computed tomography scans has played an essential role in the pulmonary disease diagnosis. The computer-assisted airway segmentation based on the U-net architecture is more efficient and accurate compared to the manual segmentation. In this paper we employ the U$^2$-net trained by the Dice loss function to model the airway tree from the multi-site CT scans based on 299 training CT scans provided by the ATM'22. The derived saliency probability map from the training is applied to the validation data to extract the corresponding airway trees. The observation shows that the majority of the segmented airway trees behave well from the perspective of accuracy and connectivity. Refinements such as non-airway regions labeling and removing are applied to certain obtained airway tree models to display the largest component of the binary results.Comment: 8 pages, 5 figure

Long-term outcomes after elective sterilization procedures — a comparative retrospective cohort study of Medicaid patients

Objectives: The objectives were to compare the long-termoutcomes, including hysterectomy, chronic pelvic pain (CPP) and abnormal uterine bleeding (AUB), in women post hysteroscopic sterilization (HS) and laparoscopic tubal ligation (TL) in the Medicaid population. Study design: This was a retrospective observational cohort analysis using data from the US Medicaid Analytic Extracts Encounters database.Women aged 18 to 49 years with at least one claimfor HS (n=3929) or TL (n=10,875) between July 1, 2009, through December 31, 2010, were included. Main outcome measures were hysterectomy, CPP or AUB in the 24 months poststerilization. Propensity score matching was used to control for patient demographics and baseline characteristics. Logistic regression analysis investigated the variables associated with a 24-month rate of each outcome in the HS versus laparoscopic TL cohorts. Results: Postmatching analyses were performed at 6, 12 and 24 months post index procedure. At 24 months, hysterectomy was more common in the laparoscopic TL than the HS group (3.5% vs. 2.1%; p=.0023), as was diagnosis of CPP (26.8% vs. 23.5%; p=.0050). No significant differences in AUB diagnoseswere observed. Logistic regression identified HS as being associated with lower risk of hysterectomy ( odds ratio [OR] 0.77 [95% confidence interval {CI} 0.60–0.97]; p=.0274) and lower risk of CPP diagnosis (OR 0.91 [95% CI 0.83–0.99]; p= .0336) at 24 months poststerilization. Conclusion: In Medicaid patients,HS is associated with a significantly lower risk of hysterectomyor CPP diagnosis 24 months poststerilization versus laparoscopic TL. Incidence of AUB poststerilization is not significantly different.While some differences in outcomes were statistically significant, the effect sizes were small, and the conclusion is one of equivalence and not clinical superiority. Implications statement: This propensity score matching analysis confirms that pelvic pain andAUBarecommon in women before and after sterilization regardless of whether the procedure is performed hysteroscopically or laparoscopically. Moreover, HS is associated with a significantly lower risk of hysterectomy or a CPP diagnosis in the 24 months poststerilization when compared to TL

A locally active discrete memristor model and its application in a hyperchaotic map

© 2022 Springer Nature Switzerland AG. Part of Springer Nature. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s11071-021-07132-5The continuous memristor is a popular topic of research in recent years, however, there is rare discussion about the discrete memristor model, especially the locally active discrete memristor model. This paper proposes a locally active discrete memristor model for the first time and proves the three fingerprints characteristics of this model according to the definition of generalized memristor. A novel hyperchaotic map is constructed by coupling the discrete memristor with a two-dimensional generalized square map. The dynamical behaviors are analyzed with attractor phase diagram, bifurcation diagram, Lyapunov exponent spectrum, and dynamic behavior distribution diagram. Numerical simulation analysis shows that there is significant improvement in the hyperchaotic area, the quasi-periodic area and the chaotic complexity of the two-dimensional map when applying the locally active discrete memristor. In addition, antimonotonicity and transient chaos behaviors of system are reported. In particular, the coexisting attractors can be observed in this discrete memristive system, resulting from the different initial values of the memristor. Results of theoretical analysis are well verified with hardware experimental measurements. This paper lays a great foundation for future analysis and engineering application of the discrete memristor and relevant the study of other hyperchaotic maps.Peer reviewedFinal Accepted Versio