Inside-out growth or inside-out quenching? clues from colour gradients of local galaxies

We constrain the spatial gradient of star formation history within galaxies using the colour gradients in NUV-u and u-i for a local spatially-resolved galaxy sample. By splitting each galaxy into an inner and an outer part, we find that most galaxies show negative gradients in these two colours. We first rule out dust extinction gradient and metallicity gradient as the dominant source for the colour gradient. Then using stellar population models, we explore variations in star formation history to explain the colour gradients. As shown by our earlier work, a two-phase SFH consisting of an early secular evolution (growth) phase and a subsequent rapid evolution (quenching) phase is necessary to explain the observed colour distributions among galaxies. We explore two different inside-out growth models and two different inside-out quenching models by varying parameters of the SFH between inner and outer regions of galaxies. Two of the models can explain the observed range of colour gradients in NUV-u and u-i colours. We further distinguish them using an additional constraint provided by the u-i colour gradient distribution, under the assumption of constant galaxy formation rate and a common SFH followed by most galaxies. We find the best model is an inside-out growth model in which the inner region has a shorter e-folding time scale in the growth phase than the outer region. More spatially resolved ultraviolet (UV) observations are needed to improve the significance of the result.Comment: 11 pages, 7 figures, accepted for publication in MNRA

D2^2: Decentralized Training over Decentralized Data

While training a machine learning model using multiple workers, each of which collects data from their own data sources, it would be most useful when the data collected from different workers can be {\em unique} and {\em different}. Ironically, recent analysis of decentralized parallel stochastic gradient descent (D-PSGD) relies on the assumption that the data hosted on different workers are {\em not too different}. In this paper, we ask the question: {\em Can we design a decentralized parallel stochastic gradient descent algorithm that is less sensitive to the data variance across workers?} In this paper, we present D$^2$, a novel decentralized parallel stochastic gradient descent algorithm designed for large data variance \xr{among workers} (imprecisely, "decentralized" data). The core of D$^2$ is a variance blackuction extension of the standard D-PSGD algorithm, which improves the convergence rate from $O\left({\sigma \over \sqrt{nT}} + {(n\zeta^2)^{\frac{1}{3}} \over T^{2/3}}\right)$ to $O\left({\sigma \over \sqrt{nT}}\right)$ where $\zeta^{2}$ denotes the variance among data on different workers. As a result, D$^2$ is robust to data variance among workers. We empirically evaluated D$^2$ on image classification tasks where each worker has access to only the data of a limited set of labels, and find that D$^2$ significantly outperforms D-PSGD

Design and Evaluation of Digital Baseband Converter Sub-channel Delay Compensation Method on Bandwidth Synthesis

The effect of sub-channel delay on bandwidth synthesis is investigated to eliminate the “phase step” phenomenon in bandwidth synthesis during the test of CDBE (Chinese Digital Backend). Through formula derivation, we realize that sub-channel delay may cause phase discontinuity between different sub-channels. Theoretical analysis shows that sub-channel delay can induce bandwidth synthesis error in group delay measurement of the linear system. Furthermore, in the differential delay measurement between two stations, bandwidth synthesis error may occur when the LO (Local Oscillator) frequency differences of corresponding sub-channels are not identical. Error-free conditions are discussed under different applications. The phase errors among different sub-channels can be removed manually. However, the most effective way is the compensation of sub-channel delay. A sub-channel delay calculation method based on Modelsim is proposed. The compensation method is detailed. Simulation and field experiments are presented to verify our approach