Satellite Alignment: I. Distribution of Substructures and Their Dependence On Assembly History From N-Body Simulations

Observations have shown that the spatial distribution of satellite galaxies is not random, but aligned with the major axes of central galaxies. This alignment is dependent on galaxy properties, such that red satellites are more strongly aligned than blue satellites. Theoretical work done to interpret this phenomena has found that it is due to the non-spherical nature of dark matter halos. However, most studies over-predict the alignment signal under the assumption that the central galaxy shape follows the shape of the host halo. It is also not clear whether the color dependence of alignment is due to an assembly bias or an evolution effect. In this paper we study these problems using a cosmological N-body simulation. Subhalos are used to trace the positions of satellite galaxies. It is found that the shape of dark matter halos are mis-aligned at different radii. If the central galaxy shares the same shape as the inner host halo, then the alignment effect is weaker and agrees with observational data. However, it predicts almost no dependence of alignment on the color of satellite galaxies, though the late accreted subhalos show stronger alignment with the outer layer of the host halo than their early accreted counterparts. We find that this is due to the limitation of pure N-body simulations that satellites galaxies without associated subhalos ('orphan galaxies') are not resolved. These orphan (mostly red) satellites often reside in the inner region of host halos and should follow the shape of the host halo in the inner region.Comment: 12 pages, 11 figures, Published on Ap

The Distribution of Satellites Around Central Galaxies in a Cosmological Hydrodynamical Simulation

Observations have shown that the spatial distribution of satellite galaxies is not random, but rather is aligned with the major axes of central galaxies (CGs). The strength of the alignment is dependent on the properties of both the satellites and centrals. Theoretical studies using dissipationless N-body simulations are limited by their inability to directly predict the shape of CGs. Using hydrodynamical simulations including gas cooling, star formation, and feedback, we carry out a study of galaxy alignment and its dependence on the galaxy properties predicted directly from the simulations.We found that the observed alignment signal is well produced, as is the color dependence: red satellites and red centrals both show stronger alignments than their blue counterparts. The reason for the stronger alignment of red satellites is that most of them stay in the inner region of the dark matter halo where the shape of the CG better traces the dark matter distribution. The dependence of alignment on the color of CGs arises from the halo mass dependence, since the alignment between the shape of the central stellar component and the inner halo increases with halo mass. We also find that the alignment of satellites is most strongly dependent on their metallicity, suggesting that the metallicity of satellites, rather than color, is a better tracer of galaxy alignment on small scales. This could be tested in future observational studies.Comment: ApJ Letter, accepted. Four figures, no table. The resolution of Fig 1 was downgraded due to the limitation of file size. Updated to match the version in pres

Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems

Designing an e-commerce recommender system that serves hundreds of millions of active users is a daunting challenge. From a human vision perspective, there're two key factors that affect users' behaviors: items' attractiveness and their matching degree with users' interests. This paper proposes Telepath, a vision-based bionic recommender system model, which understands users from such perspective. Telepath is a combination of a convolutional neural network (CNN), a recurrent neural network (RNN) and deep neural networks (DNNs). Its CNN subnetwork simulates the human vision system to extract key visual signals of items' attractiveness and generate corresponding activations. Its RNN and DNN subnetworks simulate cerebral cortex to understand users' interest based on the activations generated from browsed items. In practice, the Telepath model has been launched to JD's recommender system and advertising system. For one of the major item recommendation blocks on the JD app, click-through rate (CTR), gross merchandise value (GMV) and orders have increased 1.59%, 8.16% and 8.71% respectively. For several major ads publishers of JD demand-side platform, CTR, GMV and return on investment have increased 6.58%, 61.72% and 65.57% respectively by the first launch, and further increased 2.95%, 41.75% and 41.37% respectively by the second launch.Comment: 8 pages, 11 figures, 1 tabl

Sub-Nyquist optical pulse sampling for photonic blind source separation

We propose and experimentally demonstrate an optical pulse sampling method for photonic blind source separation. The photonic system processes and separates wideband signals based on the statistical information of the mixed signals, and thus the sampling frequency can be orders of magnitude lower than the bandwidth of the signals. The ultra-fast optical pulses collect samples of the signals at very low sampling rates, and each sample is short enough to maintain the statistical properties of the signals. The low sampling frequency reduces the workloads of the analog to digital conversion and digital signal processing systems. In the meantime, the short pulse sampling maintains the accuracy of the sampled signals, so the statistical properties of the under-sampled signals are the same as the statistical properties of the original signals. The linear power range measurement shows that the sampling system with ultra-narrow optical pulse achieves a 30dB power dynamic range

Sub-Nyquist optical pulse sampling for photonic blind source separation

We propose and experimentally demonstrate an optical pulse sampling method for photonic blind source separation. The photonic system processes and separates wideband signals based on the statistical information of the mixed signals, and thus the sampling frequency can be orders of magnitude lower than the bandwidth of the signals. The ultra-fast optical pulses collect samples of the signals at very low sampling rates, and each sample is short enough to maintain the statistical properties of the signals. The low sampling frequency reduces the workloads of the analog to digital conversion and digital signal processing systems. In the meantime, the short pulse sampling maintains the accuracy of the sampled signals, so the statistical properties of the under-sampled signals are the same as the statistical properties of the original signals. The linear power range measurement shows that the sampling system with ultra-narrow optical pulse achieves a 30dB power dynamic range

Egocentric Audio-Visual Noise Suppression

This paper studies audio-visual suppression for egocentric videos -- where the speaker is not captured in the video. Instead, potential noise sources are visible on screen with the camera emulating the off-screen speaker's view of the outside world. This setting is different from prior work in audio-visual speech enhancement that relies on lip and facial visuals. In this paper, we first demonstrate that egocentric visual information is helpful for noise suppression. We compare object recognition and action classification based visual feature extractors, and investigate methods to align audio and visual representations. Then, we examine different fusion strategies for the aligned features, and locations within the noise suppression model to incorporate visual information. Experiments demonstrate that visual features are most helpful when used to generate additive correction masks. Finally, in order to ensure that the visual features are discriminative with respect to different noise types, we introduce a multi-task learning framework that jointly optimizes audio-visual noise suppression and video based acoustic event detection. This proposed multi-task framework outperforms the audio only baseline on all metrics, including a 0.16 PESQ improvement. Extensive ablations reveal the improved performance of the proposed model with multiple active distractors, over all noise types and across different SNRs.Comment: Under Review at ICASSP 202