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

K-quantum Nonlinear Jaynes-Cummings Model in Two Trapped Ions

A k-quantum nonlinear Jaynes-Cummings model for two trapped ions interacting with laser beams resonant to k-th red side-band of center-of-mass mode, far from Lamb-Dicke regime, has been obtained. The exact analytic solution showed the existence of quantum collapses and revivals of the occupation of two atoms.Comment: 8 pages, 3 figure

Quantum-State Engineering of Multiple Trapped Ions for Center-of-Mass Mode

We propose a scheme to generate a superposition with arbitrary coefficients on a line in phase space for the center-of-mass vibrational mode of N ions by means of isolating all other spectator vibrational modes from the center-of-mass mode. It can be viewed as the generation of previous methods for preparing motional states of one ion. For large number of ions, we need only one cyclic operatin to generate such a superposition of many coherent states.Comment: 14 pages, revte

Constraints on the Local Cosmic Void from the Pantheon Supernovae Data

In principle, the local cosmic void can be simply modeled by the spherically symmetric Lemaitre-Tolman-Bondi (LTB) metric. In practice, the real local cosmic void is probably not spherically symmetric. In this paper, to reconstruct a more realistic profile of the local cosmic void, we divide it into several segments. Each segment with certain solid angle is modeled by its own LTB metric. Meanwhile, we divide the 1048 type Ia supernovae (SNIa) of the Pantheon Survey into corresponding subsets according to their distribution in the galactic coordinate system. Obviously, each SNIa subset can only be used to reconstruct the profile of one segment. Finally, we can patch together an irregular profile for the local cosmic void with the whole Pantheon sample. Note that, the paucity of each data subset lead us to focus on the inner part of each void segment and assume that the half radii of the void segments are sufficient to constrain the whole segment. We find that, despite $2\sigma$ signals of anisotropy limited to the depth of the void segments, the constraints on every void segment are consistent with $\Lambda$CDM model at $95\%$ CL. Moreover, our constraints are too weak to challenge the cosmic homogeneity and isotropy.Comment: 12 pages, 9 figure

Probabilistic Risk Assessment of Reservoir Operation

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive