Frequency-Domain Response Based Timing Synchronization: A Near Optimal Sampling Phase Criterion for TDS-OFDM

In time-domain synchronous OFDM (TDS-OFDM) system for digital television terrestrial multimedia broadcasting (DTMB) standard, the baseband OFDM signal is upsampled and shaping filtered by square root raised cosine (SRRC) filter before digital-to-analog converter (DAC). Much of the work in the area of timing synchronization for TDS-OFDM focuses on frame synchronization and sampling clock frequency offset recovery, which does not consider the sampling clock phase offset due to the upsampling and SRRC filter. This paper evaluates the bit-error-rate (BER) effect of sampling clock phase offset in TDS-OFDM system. First, we provide the BER for M-order quadrature amplitude modulation (M-QAM) in uncoded TDS-OFDM system. Second, under the condition of the optimal BER criterion and additive white Gaussian noise (AWGN) channel, we propose a near optimal sampling phase estimation criterion based on frequency-domain response. Simulations demonstrate that the proposed criterion also has good performance in actual TDS-OFDM system with channel coding over multipath channels, and it is superior to the conventional symbol timing recovery methods for TDS-OFDM system.Comment: 6 pages, 7 figure

Tunable magnetism of a single-carbon vacancy in graphene

Removing a single-carbon vacancy introduces (quasi-)localized states for both and electrons in graphene. Interactions between the localized dangling bond and quasilocalized electrons of a single-carbon vacancy in graphene are predicted to control its magnetism. However, experimentally confirming this prediction through manipulating the interactions between the and electrons remains an outstanding challenge. Here we report the manipulation of magnetism of individual single-carbon vacancy in graphene by using a scanning tunnelling microscopy (STM) tip. Our spin-polarized STM measurements, complemented by density functional theory calculations, indicate that interactions between the localized and quasilocalized electrons could split the electrons into two states with opposite spins even when they are well above the Fermi level. Via the STM tip, we successfully manipulate both the magnitude and direction of magnetic moment of the electrons with respect to that of the electrons. Three different magnetic states of the single-carbon vacancy, exhibiting magnetic moments of about 1.6, 0.5, and 0 respectively, are realized in our experiment.Comment: 4 figure

Analysis on Correlations between Subsurface Kinetic Helicity and Photospheric Current Helicity in Active Regions

An investigation on correlations between photospheric current helicity and subsur- face kinetic helicity is carried out by analyzing vector magnetograms and subsurface velocities for two rapidly developing active regions. The vector magnetograms are from the SDO/HMI (Solar Dynamics Observatory / Helioseismic and Magnetic Im- ager) observed Stokes parameters, and the subsurface velocity is from time-distance data-analysis pipeline using HMI Dopplergrams. Over a span of several days, the evo- lution of the weighted current helicity shows a tendency similar to that of the weighted subsurface kinetic helicity, attaining a correlation coefficient above 0.60 for both ac- tive regions. Additionally, there seems to be a phase lag between the evolutions of the unweighted current and subsurface kinetic helicities for one of the active regions. The good correlation between these two helicities indicate that there is some intrinsic con- nection between the interior dynamics and photospheric magnetic twistedness inside active regions, which may help to interpret the well-known hemispheric preponder- ance of current-helicity distribution.Comment: 5 figure

Bright "merger-nova" from the remnant of a neutron star binary merger: A signature of a newly born, massive, millisecond magnetar

A massive millisecond magnetar may survive a merger of a neutron star (NS) binary, which would continuously power the merger ejecta. We develop a generic dynamic model for the merger ejecta with energy injection from the central magnetar. The ejecta emission (the "merger-nova") powered by the magnetar peaks in the UV band and the peak of lightcurve progressively shifts to an earlier epoch with increasing frequency. A magnetar-powered mergernova could have an optical peak brightness comparable to a supernova, which is a few tens or hundreds times brighter than the radioactive-powered merger-novae (the so-called macro-nova or kilo-nova). On the other hand, such a merger-nova would peak earlier and have a significantly shorter duration than that of a supernova. An early collapse of the magnetar could suppress the brightness of the optical emission and shorten its duration. Such millisecond-magnetar-powered merger-novae may be detected from NS-NS merger events without an observed short gamma-ray burst, and could be a bright electromagnetic counterpart for gravitational wave bursts due to NS-NS mergers. If detected, it suggests that the merger leaves behind a massive NS, which has important implications for the equation-of-state of nuclear matter.Comment: 6 pages, 4 figures, revised following the referee's report and accepted for publication by ApJ

Weak CS Emission in an Extremely Metal-poor Galaxy DDO 70

In most galaxies like the Milky Way, stars form in clouds of molecular gas. Unlike the CO emission that traces the bulk of molecular gas, the rotational transitions of HCN and CS molecules mainly probe the dense phase of molecular gas, which has a tight and almost linear relation with the far-infrared luminosity and star formation rate. However, it is unclear if dense molecular gas exists at very low metallicity, and if exists, how it is related to star formation. In this work, we report ALMA observations of the CS $J$=5$\rightarrow$4 emission line of DDO~70, a nearby gas-rich dwarf galaxy with $\sim7\%$ solar metallicity. We did not detect CS emission from all regions with strong CO emission. After stacking all CS spectra from CO-bright clumps, we find no more than a marginal detection of CS $J$=5$\rightarrow$4 transition, at a signal-to-noise ratio of $\sim 3.3$. This 3-$\sigma$ upper limit deviates from the $L^\prime_{\rm CS}$-$L_{\rm IR}$ and $L^\prime_{\rm CS}$-SFR relationships found in local star forming galaxies and dense clumps in the Milky Way, implying weaker CS emission at given IR luminosity and SFR. We discuss the possible mechanisms that suppress CS emission at low metallicity.Comment: 5 pages, 4 figures. Accepted for publication in MNRAS Letter

ReCoNet: Real-time Coherent Video Style Transfer Network

Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos. Some video style transfer models have been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time. In this paper, we propose a novel real-time video style transfer model, ReCoNet, which can generate temporally coherent style transfer videos while maintaining favorable perceptual styles. A novel luminance warping constraint is added to the temporal loss at the output level to capture luminance changes between consecutive frames and increase stylization stability under illumination effects. We also propose a novel feature-map-level temporal loss to further enhance temporal consistency on traceable objects. Experimental results indicate that our model exhibits outstanding performance both qualitatively and quantitatively.Comment: 16 pages, 7 figures. For supplementary material, see https://www.dropbox.com/s/go6f7uopjjsala7/ReCoNet%20Supplementary%20Material.pdf?dl=

Accelerating Flash Calculation through Deep Learning Methods

In the past two decades, researchers have made remarkable progress in accelerating flash calculation, which is very useful in a variety of engineering processes. In this paper, general phase splitting problem statements and flash calculation procedures using the Successive Substitution Method are reviewed, while the main shortages are pointed out. Two acceleration methods, Newton's method and the Sparse Grids Method are presented afterwards as a comparison with the deep learning model proposed in this paper. A detailed introduction from artificial neural networks to deep learning methods is provided here with the authors' own remarks. Factors in the deep learning model are investigated to show their effect on the final result. A selected model based on that has been used in a flash calculation predictor with comparison with other methods mentioned above. It is shown that results from the optimized deep learning model meet the experimental data well with the shortest CPU time. More comparison with experimental data has been conducted to show the robustness of our model

White-Light Flares on Close Binaries Observed with Kepler

Based on Kepler data, we present the results of a search for white-light flares on 1049 close binaries. We identify 234 flare binaries, on which 6818 flares are detected. We compare the flare-binary fraction in different binary morphologies ("detachedness"). The result shows that the fractions in over-contact and ellipsoidal binaries are approximately 10-20 percent lower than those in detached and semi-detached systems. We calculate the binary flares activity level (AL) of all the flare binaries, and discuss its variations along the orbital period (P_orb) and rotation period (P_rot, calculated for only detached binaries). We find that AL increases with decreasing P_orb or P_rot up to the critical values at P_orb near 3 days or P_rot near 1.5 days, thereafter, the AL starts decreasing no matter how fast the stars rotate. We examine the flaring rate as a function of orbital phase in 2 eclipsing binaries on which a large number of flares are detected. It appears that there is no correlation between flaring rate and orbital phase in these 2 binaries. In contrast, when we examine the function with 203 flares on 20 non-eclipse ellipsoidal binaries, bimodal distribution of amplitude weighted flare numbers shows up at orbital phase 0.25 and 0.75. Such variation could be larger than what is expected from the cross-section modification

Radiative decays of bottomonia into charmonia and light mesons

In the framework of nonrelativistic QCD, we study the radiative decays of bottomonia into charmonia, including $\Upsilon\to \chi_{cJ}\gamma$, $\Upsilon\to \eta_c\gamma$, $\eta_b\to J/\psi\gamma$, and $\chi_{bJ}\to J/\psi\gamma$. We give predictions for their branching ratios with numerical calculations. E.g., we predict the branching ratio for $\eta_b\to J/\psi\gamma$ is about $1\times 10^{-7}$. As a phenomenological model study, we further extend our calculation to the radiative decays of bottomonia into light mesons by assuming the $f_2(1270)$, $f_2'(1525)$ and other light mesons to be described by nonrelativistic $q\bar q ~(q=u,d,s)$ bound states with constituent quark masses. The calculated branching ratios for $\Upsilon\to f_2(1270)\gamma$ and $\Upsilon\to f_2'(1525)\gamma$ are roughly consistent with the CLEO data. Comparisons with radiative decays of charmonium into light mesons such as $J/\psi\to f_2(1270)\gamma$ are also given. In all calculations the QED contributions are taken into account and found to be significant in some processes

Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation

In this work, we demonstrate yet another approach to tackle the amodal segmentation problem. Specifically, we first introduce a new representation, namely a semantics-aware distance map (sem-dist map), to serve as our target for amodal segmentation instead of the commonly used masks and heatmaps. The sem-dist map is a kind of level-set representation, of which the different regions of an object are placed into different levels on the map according to their visibility. It is a natural extension of masks and heatmaps, where modal, amodal segmentation, as well as depth order information, are all well-described. Then we also introduce a novel convolutional neural network (CNN) architecture, which we refer to as semantic layering network, to estimate sem-dist maps layer by layer, from the global-level to the instance-level, for all objects in an image. Extensive experiments on the COCOA and D2SA datasets have demonstrated that our framework can predict amodal segmentation, occlusion and depth order with state-of-the-art performance.Comment: This paper is submitted to ACMMM1