Lick Slit Spectra of Thirty-Eight Objective Prism QSO Candidates and Low Metallicity Halo Stars

We present Lick Observatory slit spectra of 38 objects which were claimed to have pronounced ultraviolet excess and emission lines by Zhan \& Chen. Most of our spectra have FWHM spectral resolutions of about 4~\AA , and relatively high S/N of about 10 -- 50, although some have FWHM $\simeq 15$~\AA ~or lower S/N. We find eleven QSOs, four galaxies at $z \simeq 0.1$, twenty-two stars and one unidentified object with a low S/N spectrum. Six of the QSOs show absorption systems, including Q0000+027A with a relatively strong associated C~IV absorption system, and Q0008+008 (V$\simeq 18.9$) with a damped Ly$\alpha$ system with an H~I column density of $10^{21}$ cm$^{-2}$. The stars include a wide variety of spectral types. There is one new DA4 white dwarf at 170~pc, one sdB at 14~kpc, and three M stars. The rest are of types F, G and K. We have measured the equivalent widths of the Ca~II~K line, the G-band and the Balmer lines in ten stars with the best spectra, and we derive metallicities. Seven of them are in the range $-2.5 \leq$~[Fe/H]~$\leq -1.7$, while the others are less metal poor. If the stars are dwarfs, then they are at distances of 1 to 7~kpc, but if they are giants, typical distances will be about 10~kpc.Comment: (Plain Tex, 21 pages, including tables. Send email to 'travell_oir%[email protected]' for 12 pages of figures) To appear in the %%Astronomical Journal, August, 199

Theory of control of spin/photon interface for quantum networks

A cavity coupling a charged nanodot and a fiber can act as a quantum interface, through which a stationary spin qubit and a flying photon qubit can be inter-converted via cavity-assisted Raman process. This Raman process can be controlled to generate or annihilate an arbitrarily shaped single-photon wavepacket by pulse-shaping the controlling laser field. This quantum interface forms the basis for many essential functions of a quantum network, including sending, receiving, transferring, swapping, and entangling qubits at distributed quantum nodes as well as a deterministic source and an efficient detector of a single photon wavepacket with arbitrarily specified shape and average photon number. Numerical study of noise effects on the operations shows high fidelity.Comment: 4 pages, 2 figure

Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification. However, most of the current dFC studies are based on detecting discrete major brain status via spatial clustering, which ignores rich spatiotemporal dynamics contained in such chronnectome. We propose Deep Chronnectome Learning for exhaustively mining the comprehensive information, especially the hidden higher-level features, i.e., the dFC time series that may add critical diagnostic power for MCI classification. To this end, we devise a new Fully-connected Bidirectional Long Short-Term Memory Network (Full-BiLSTM) to effectively learn the periodic brain status changes using both past and future information for each brief time segment and then fuse them to form the final output. We have applied our method to a rigorously built large-scale multi-site database (i.e., with 164 data from NCs and 330 from MCIs, which can be further augmented by 25 folds). Our method outperforms other state-of-the-art approaches with an accuracy of 73.6% under solid cross-validations. We also made extensive comparisons among multiple variants of LSTM models. The results suggest high feasibility of our method with promising value also for other brain disorder diagnoses.Comment: The paper has been accepted by MICCAI201