Horndeski theory is the most general scalar-tensor theory which leads to the second-order equations of motion. In this talk, a subclass of the Horndeski theory with shift symmetry is applied to study the galaxy rotation curve and the cosmo- logical evolution with identifying the scalar field with the dark matter, while the cosmological constant explains the dark energy. I show an analytic solution which reproduces the inverse-square-law density distribution of dark matter halo, so that the observed rotation curve can be explained at the galactic scale. I also discuss the dark-matter-like behavior of the scalar field at the cosmological scale and consider the consistent parameter region between the galactic and cosmological scales.

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One of the many possible approaches to this problem is the study of the bound stats of heavy quarks. Heavy mesons, in particular the bottomonium states, offer a theoretically simple environment in which to test new description of the low energy color interaction on a wide scale of energies: from few MeV, up to several GeV when annihilation processes are considered.

In this seminar we will first outline the basic ideas and the status of the bottomonium physics. We will then describe more in detail the potential of the measurement that will be performed at the Belle II experiment, ranging from the spectroscopy of the tetraquark-like states to the study of the hyperon-hyperon interactions.]]>

Within the LAPPD project, a photodetector production facility dedicated to fabricating 6 × 6 cm^{2} fast-timing MCP-PMTs was designed and built at Argonne National Laboratory. In this talk, I will report detailed design, fabrication and characterization of both 6 × 6 cm^{2} MCP-PMTs and 20 × 20 cm^{2} LAPPD^{TM} based on next-generation microchannel plates. The flexible photodetector design provides the potential of modifying individual components as well as the entire configuration to fit for different applications. Optimizations of current design driven by different applications such as particle identification, calorimeter and X-ray imaging will also be reported.

discretization, statistical averaging and renormalization. Here we implement the deep neural network scheme into the AdS/CFT correspondence, a renowned quantum gravity formulation. The neural network is identified with the bulk gravity spacetime, and the input data such as lattice QCD data as for the boundary QFT will automatically let the bulk metric "emerge", and with the emergent metric we can calculate other QCD observables such as Wilson loops. We discuss possible relation between quantum gravity and deep learning, also from

the viewpoint of solving inverse problems, which deep learning is generically good at. ]]>