Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks

Automatic body part recognition for CT slices can benefit various medical image applications. Recent deep learning methods demonstrate promising performance, with the requirement of large amounts of labeled images for training. The intrinsic structural or superior-inferior slice ordering information in CT volumes is not fully exploited. In this paper, we propose a convolutional neural network (CNN) based Unsupervised Body part Regression (UBR) algorithm to address this problem. A novel unsupervised learning method and two inter-sample CNN loss functions are presented. Distinct from previous work, UBR builds a coordinate system for the human body and outputs a continuous score for each axial slice, representing the normalized position of the body part in the slice. The training process of UBR resembles a self-organization process: slice scores are learned from inter-slice relationships. The training samples are unlabeled CT volumes that are abundant, thus no extra annotation effort is needed. UBR is simple, fast, and accurate. Quantitative and qualitative experiments validate its effectiveness. In addition, we show two applications of UBR in network initialization and anomaly detection.Comment: Oral presentation in ISBI1

Investment-Based Underperformance Following Seasoned Equity Offerings

Adding a return factor based on capital investment into standard, calendar-time factor regressions makes underperformance following seasoned equity offerings largely insignificant and reduces its magnitude by 37-46%. The reason is that issuers invest more than nonissuers matched on size and book-to-market. Moreover, the low-minus-high investment-to-asset factor earns a significant average return of 0.37% per month. Our evidence suggests that the underperformance results from the negative investment-expected return relation, as predicted by Carlson, Fisher, and Giammarino (2005).

Particle Acceleration and the Formation of Relativistic Outflows in Viscous Accretion Disks with Shocks

In this Letter, we present a new self-consistent theory for the production of the relativistic outflows observed from radio-loud black hole candidates and active galaxies as a result of particle acceleration in hot, viscous accretion disks containing standing, centrifugally-supported isothermal shocks. This is the first work to obtain the structure of such disks for a relatively large value of the Shakura-Sunyaev viscosity parameter ($\alpha=0.1$), and to consider the implications of the shock for the acceleration of relativistic particles in viscous disks. In our approach, the hydrodynamics and the particle acceleration are coupled and the solutions are obtained self-consistently based on a rigorous mathematical method. We find that particle acceleration in the vicinity of the shock can provide enough energy to power the observed relativistic jet in M87.Comment: published in ApJ