Finding lumbar vertebrae by evidence gathering

Low back pain is a very common problem and lumbar segmental instability is one of the causes. It is essential to investigate lumbar spine movement in order to understand instability better and as an aid to diagnosis. Digital videofluoroscopy (DVF) provides a method of quantifying the motion of individual vertebra. In this paper, we apply a new version of the Hough transform (HT) to locate the lumbar vertebra automatically in DVF image sequences. At present, this algorithm has been applied to a calibration model and to the vertebra L3 in DVF images, and has shown to provide satisfactory results. Further work will concentrate on reducing the computational time for realtime application, on developing a spatiotemporal sequences method and on determining the spinal kinematics based on the extracted parameters

Vanishing Gamow-Teller Transition Rate for A=14 and the Nucleon-Nucleon Interaction in the Medium

The problem of the near vanishing of the Gamow-Teller transition ($GT$) in the A=14 system between the lowest $J=0^+~ T=1$ and $J=1^+~ T=0$ states is revisited. The model space is extended from the valence space $(p^{-2})$ to the valence space plus all 2$\hbar \omega$ excitations. The question is addressed as to what features of the effective nucleon-nucleon interaction in the medium are required to obtain the vanishing $GT$ strength in this extended space. It turns out that a combination of a realistic strength of the tensor force combined with a spin-orbit interaction which is enhanced as compared to the free interaction yields a vanishing $GT$ strength. Such an interaction can be derived from a microscopic meson exchange potential if the enhancement of the small component of the Dirac spinors for the nucleons is taken into account.Comment: RevTex file, 7 pages, four postscript figures. submitted to Phys. Rev. C as a brief repor

Effects of the Spin-Orbit and Tensor Interactions on the M1M1 and E2E2 Excitations in Light Nuclei

The effects of varying the spin-orbit and tensor components of a realistic interaction on $M1$ excitation rates and $B(E2)'s$ are studied on nuclei in the $0p$ and $1s-0d$ shells. Not only the total $M1$ but also the spin and orbital parts separately are studied. The single-particle energies are first calculated with the same interaction that is used between the valence nucleons. Later this stringent condition is relaxed somewhat and the $1s$ level is raised relative to $0d$. For nuclei up to $^{28}Si$, much better results i.e stronger $B(M1)$ rates are obtained by increasing the strength of the spin-orbit interaction relative to the free value. This is probably also true for $^{32}S$, but $^{36}Ar$ presents some difficulties. The effects of weakening the tensor interaction are also studied. On a more subtle level, the optimum spin-orbit interaction in the lower half of the $s-d$ shell, as far as $M1$ excitations are concerned, is substantially larger than the difference $E(J=3/2^+)_1-E(J=5/2^+)_1=5.2~MeV$ in $^{17}O$. A larger spin-orbit splitting is also needed to destroy the triaxiality in $^{22}Ne$. Also studied are how much $M1$ orbital and spin strength lies in an observable region and how much is buried in the grass at higher energies. It is noted that for many nuclei the sum $B(M1)_{orbital}+B(M1)_{spin}$ is very close to $B(M1)_{total}$, indicating that the summed cross terms are very small.Comment: 39 pages, revtex 3.

Resource offload consolidation based on deep-reinforcement learning approach in cyber-physical systems.

In cyber-physical systems, it is advantageous to leverage cloud with edge resources to distribute the workload for processing and computing user data at the point of generation. Services offered by cloud are not flexible enough against variations in the size of underlying data, which leads to increased latency, violation of deadline and higher cost. On the other hand, resolving above-mentioned issues with edge devices with limited resources is also challenging. In this work, a novel reinforcement learning algorithm, Capacity-Cost Ratio-Reinforcement Learning (CCR-RL), is proposed which considers both resource utilization and cost for the target cyber-physical systems. In CCR-RL, the task offloading decision is made considering data arrival rate, edge device computation power, and underlying transmission capacity. Then, a deep learning model is created to allocate resources based on the underlying communication and computation rate. Moreover, new algorithms are proposed to regulate the allocation of communication and computation resources for the workload among edge devices and edge servers. The simulation results demonstrate that the proposed method can achieve a minimal latency and a reduced processing cost compared to the state-of-the-art schemes

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support

Measuring the elements of the optical density matrix

Most methods for experimentally reconstructing the quantum state of light involve determining a quasiprobability distribution such as the Wigner function. In this paper we present a scheme for measuring individual density matrix elements in the photon number state representation. Remarkably, the scheme is simple, involving two beam splitters and a reference field in a coherent state.Comment: 6 pages and 1 figur

Allowed Gamow-Teller Excitations from the Ground State of 14N

Motivated by the proposed experiment $^{14}N(d,{^2He})^{14}C$, we study the final states which can be reached via the allowed Gamow-Teller mechanism. Much emphasis has been given in the past to the fact that the transition matrix element from the $J^{\pi}=1^+ T=0$ ground state of $^{14}N$ to the $J^{\pi}=0^+ T=1$ ground state of $^{14}C$ is very close to zero, despite the fact that all the quantum numbers are right for an allowed transition. We discuss this problem, but, in particular, focus on the excitations to final states with angular momenta $1^+$ and $2^+$. We note that the summed strength to the $J^{\pi}=2^+ T=1$ states, calculated with a wide variety of interactions, is significantly larger than that to the $J^{\pi}=1^+ T=1$ final states.Comment: Submitted to Phys. Rev.

Large-space shell-model calculations for light nuclei

An effective two-body interaction is constructed from a new Reid-like $NN$ potential for a large no-core space consisting of six major shells and is used to generate the shell-model properties for light nuclei from $A$=2 to 6. (For practical reasons, the model space is partially truncated for $A$=6.) Binding energies and other physical observables are calculated and compare favorably with experiment.Comment: prepared using LaTex, 21 manuscript pages, no figure