Hailong Ma, Xiangxiang Chu, Bo Zhang, Shaohua Wan, Bo Zhang
In recent years, deep learning methods have achieved impressive results with higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks by utilizing deeper layers. However, their application is quite limited since they require high computing power. In addition, most of the existing methods rarely take full advantage of the intermediate features which are helpful for restoration. To address these issues, we propose a moderate-size SISR net work named matrixed channel attention network (MCAN) by constructing a matrix ensemble of multi-connected channel attention blocks (MCAB). Several models of different sizes are released to meet various practical requirements. Conclusions can be drawn from our extensive benchmark experiments that the proposed models achieve better performance with much fewer multiply-adds and parameters. Our models will be made publicly available.
Bo Zhang, Ting Ye, Siyu Heng, Michael Z. Levy, Dylan S. Small
The novel coronavirus disease (COVID-19) is a highly contagious respiratory disease that was first detected in Wuhan, China in December 2019, and has since spread around the globe, claiming more than 69,000 lives by the time this protocol is written. It has been widely acknowledged that the most effective public policy to mitigate the pandemic is \emph{social and physical distancing}: keeping at least six feet away from people, working from home, closing non-essential businesses, etc. There have been a lot of anecdotal evidences suggesting that social distancing has a causal effect on disease mitigation; however, few studies have investigated the effect of social distancing on disease mitigation in a transparent and statistically-sound manner. We propose to perform an optimal non-bipartite matching to pair counties with similar observed covariates but vastly different average social distancing scores during the first week (March 16th through Match 22nd) of President's \emph{15 Days to Slow the Spread} campaign. We have produced a total of $302$ pairs of two U.S. counties with good covariate balance on a total of $16$ important variables. Our primary outcome will be the average observed illness collected by Kinsa Inc. two weeks after the intervention period. Although the observed illness does not directly measure COVID-19, it reflects a real-time aspect of the pandemic, and unlike confirmed cases, it is much less confounded by counties' testing capabilities. We also consider observed illness three weeks after the intervention period as a secondary outcome. We will test a proportional treatment effect using a randomization-based test with covariance adjustment and conduct a sensitivity analysis.
Xiangxiang Chu, Tianbao Zhou, Bo Zhang, Jixiang Li
Differentiable Architecture Search (DARTS) is now a widely disseminated weight-sharing neural architecture search method. However, it suffers from well-known performance collapse due to an inevitable aggregation of skip connections. In this paper, we first disclose that its root cause lies in an unfair advantage in exclusive competition. Through experiments, we show that if either of two conditions is broken, the collapse disappears. Thereby, we present a novel approach called Fair DARTS where the exclusive competition is relaxed to be collaborative. Specifically, we let each operation's architectural weight be independent of others. Yet there is still an important issue of discretization discrepancy. We then propose a zero-one loss to push architectural weights towards zero or one, which approximates an expected multi-hot solution. Our experiments are performed on two mainstream search spaces, and we derive new state-of-the-art results on CIFAR-10 and ImageNet. Our code is available on https://github.com/xiaomi-automl/fairdarts .
Bo Zhang, Benjamin Hilton, Christopher Short, Anton Souslov, Alexey Snezhko
An active colloidal fluid comprised of self-propelled spinning particles injecting energy and angular momentum at the microscale demonstrates spontaneous collective states that range from flocks to coherent vortices. Despite their seeming simplicity, the emergent far-from-equilibrium behavior of these fluids remains poorly understood, presenting a challenge to the design and control of next-generation active materials. When confined in a ring, such so-called polar active fluids acquire chirality once the spontaneous flow chooses a direction. In a perfect ring, this chirality is indefinitely long-lived. Here, we combine experiments on self-propelled colloidal Quincke rollers and mesoscopic simulations of continuum Toner-Tu equations to explore how such chiral states can be controlled and manipulated by obstacles. For different obstacle geometries three dynamic steady states have been realized: long-lived chiral flow, an apolar state in which the flow breaks up into counter-rotating vortices and an unconventional collective state with flow having an oscillating chirality. The chirality reversal proceeds through the formation of intermittent vortex chains in the vicinity of an obstacle. We demonstrate that the frequency of collective states with oscillating chirality can be tuned by obstacle parameters. We vary obstacle shapes to design chiral states that are independent of initial conditions. Building on our findings, we realize a system with two triangular obstacles that force the active fluid towards a state with a density imbalance of active particles across the ring. Our results demonstrate how spontaneous polar active flows in combination with size and geometry of scatterers can be used to control dynamic patterns of polar active liquids for materials design.
Bo Zhang, Siyu Heng, Ting Ye, Dylan S. Small
Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much social distancing is needed to achieve public health effects. In this article, we develop a design-based framework to make inference about the dose-response relationship between social distancing and COVID-19 related death toll and case numbers. We first discuss how to embed observational data with a time-independent, continuous treatment dose into an approximate randomized experiment, and develop a randomization-based procedure that tests if a structured dose-response relationship fits the data. We then generalize the design and testing procedure to accommodate a time-dependent, treatment dose trajectory, and generalize a dose-response relationship to a longitudinal setting. Finally, we apply the proposed design and testing procedures to investigate the effect of social distancing during the phased reopening in the United States on public health outcomes using data compiled from sources including Unacast, the United States Census Bureau, and the County Health Rankings and Roadmaps Program. We rejected a primary analysis null hypothesis that stated the social distancing from April 27, 2020, to June 28, 2020, had no effect on the COVID-19-related death toll from June 29, 2020, to August 2, 2020 (p-value < 0.001), and found that it took more reduction in mobility to prevent exponential growth in case numbers for non-rural counties compared to rural counties.
Xiangxiang Chu, Bo Zhang
Simplicity is the ultimate sophistication. Differentiable Architecture Search (DARTS) has now become one of the mainstream paradigms of neural architecture search. However, it largely suffers from the well-known performance collapse issue due to the aggregation of skip connections. It is thought to have overly benefited from the residual structure which accelerates the information flow. To weaken this impact, we propose to inject unbiased random noise to impede the flow. We name this novel approach NoisyDARTS. In effect, a network optimizer should perceive this difficulty at each training step and refrain from overshooting, especially on skip connections. In the long run, since we add no bias to the gradient in terms of expectation, it is still likely to converge to the right solution area. We also prove that the injected noise plays a role in smoothing the loss landscape, which makes the optimization easier. Our method features extreme simplicity and acts as a new strong baseline. We perform extensive experiments across various search spaces, datasets, and tasks, where we robustly achieve state-of-the-art results. Our code is available at https://github.com/xiaomi-automl/NoisyDARTS.
Bo Zhang, Jiao Li, Fan Yang, Jian-Ping Xiong, Jian-Ning Fu, Chao Liu, Hao Tian, Yin-Bi Li, Jia-Xin Wang, Cai-Xia Liang, Yu-Tao Zhou, Wei-kai Zong, Cheng-Qun Yang, Nian Liu, Yong-Hui Hou
May 25, 2021·astro-ph.SR·PDF Radial velocity (RV) is among the most fundamental physical quantities obtainable from stellar spectra and is rather important in the analysis of time-domain phenomena. The LAMOST Medium-Resolution Survey (MRS) DR7 contains 5 million single-exposure stellar spectra at spectral resolution $R\sim7\,500$. However, the temporal variation of the RV zero-points (RVZPs) of the MRS survey, which makes the RVs from multiple epochs inconsistent, has not been addressed. In this paper, we measure the RVs of the 3.8 million single-exposure spectra (for 0.6 million stars) with signal-to-noise ratio (SNR) higher than 5 based on cross-correlation function (CCF) method, and propose a robust method to self-consistently determine the RVZPs exposure-by-exposure for each spectrograph with the help of \textit{Gaia} DR2 RVs. Such RVZPs are estimated for 3.6 million RVs and can reach a mean precision of $\sim 0.38\,\mathrm{km\,s}^{-1}$. The result of the temporal variation of RVZPs indicates that our algorithm is efficient and necessary before we use the absolute RVs to perform time-domain analysis. Validating the results with APOGEE DR16 shows that our absolute RVs can reach an overall precision of 0.84/0.80 $\mathrm{km\,s}^{-1}$ in the blue/red arm at $50<\mathrm{SNR}<100$, while 1.26/1.99 $\mathrm{km\,s}^{-1}$ at $5<\mathrm{SNR}<10$. The cumulative distribution function (CDF) of the standard deviations of multiple RVs ($N_\mathrm{obs}\geq 8$) for 678 standard stars reach 0.45/0.54, 1.07/1.39, and 1.45/1.86 $\mathrm{km\,s}^{-1}$ in the blue/red arm at 50\%, 90\%, and 95\% levels, respectively. The catalogs of the RVs, RVZPs, and selected candidate RV standard stars are available at \url{https://github.com/hypergravity/paperdata}.
Bo Zhang, Pedro V. Sander, Chi-Ying Tsui, Amine Bermak
In this paper, we propose an image compression algorithm called Microshift. We employ an algorithm hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, then the sub-quantized values are further compressed. Two methods, the FAST and MRF model, are proposed to recover the bit-depth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. Our compression algorithm compresses images to 1.25 bits per pixel on average with PSNR of 33.16 dB, outperforming other on-chip compression algorithms. Then, we propose a hardware architecture and implement the algorithm on an FPGA and ASIC. The results on the VLSI design further validate the low hardware complexity and high power efficiency, showing our method is promising, particularly for low-power wireless vision sensor networks.
Bo Zhang, Alexey Snezhko
Large density fluctuations observed in active systems and hyperuniformity are two seemingly incompatible phenomena. However, the formation of hyperuniform states has been recently predicted in non-equilibrium fluids formed by chiral particles performing circular motion with the same handedness. Here we report evidence of hyperuniformity realized in a chiral active fluid comprised of pear-shaped Quincke rollers of arbitrary handedness. We show that hyperuniformity and large density fluctuations, triggered by dynamic clustering, coexist in this system at different length scales. The system loses its hyperuniformity as the curvature of particles' motion increases transforming them into localized spinners. Our results experimentally demonstrate a novel hyperuniform active fluid and provide new insights into an interplay between chirality, activity and hyperuniformity.
Chuyi Li, Lulu Li, Hongliang Jiang, Kaiheng Weng, Yifei Geng, Liang Li, Zaidan Ke, Qingyuan Li, Meng Cheng, Weiqiang Nie, Yiduo Li, Bo Zhang, Yufei Liang, Linyuan Zhou, Xiaoming Xu, Xiangxiang Chu, Xiaoming Wei, Xiaolin Wei
For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this technical report, we strive to push its limits to the next level, stepping forward with an unwavering mindset for industry application. Considering the diverse requirements for speed and accuracy in the real environment, we extensively examine the up-to-date object detection advancements either from industry or academia. Specifically, we heavily assimilate ideas from recent network design, training strategies, testing techniques, quantization, and optimization methods. On top of this, we integrate our thoughts and practice to build a suite of deployment-ready networks at various scales to accommodate diversified use cases. With the generous permission of YOLO authors, we name it YOLOv6. We also express our warm welcome to users and contributors for further enhancement. For a glimpse of performance, our YOLOv6-N hits 35.9% AP on the COCO dataset at a throughput of 1234 FPS on an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 43.5% AP at 495 FPS, outperforming other mainstream detectors at the same scale~(YOLOv5-S, YOLOX-S, and PPYOLOE-S). Our quantized version of YOLOv6-S even brings a new state-of-the-art 43.3% AP at 869 FPS. Furthermore, YOLOv6-M/L also achieves better accuracy performance (i.e., 49.5%/52.3%) than other detectors with a similar inference speed. We carefully conducted experiments to validate the effectiveness of each component. Our code is made available at https://github.com/meituan/YOLOv6.
Bo Zhang, Chen Zhang, Fang Ma, Dawei Song
Neural text matching models have been used in a range of applications such as question answering and natural language inference, and have yielded a good performance. However, these neural models are of a limited adaptability, resulting in a decline in performance when encountering test examples from a different dataset or even a different task. The adaptability is particularly important in the few-shot setting: in many cases, there is only a limited amount of labeled data available for a target dataset or task, while we may have access to a richly labeled source dataset or task. However, adapting a model trained on the abundant source data to a few-shot target dataset or task is challenging. To tackle this challenge, we propose a Meta-Weight Regulator (MWR), which is a meta-learning approach that learns to assign weights to the source examples based on their relevance to the target loss. Specifically, MWR first trains the model on the uniformly weighted source examples, and measures the efficacy of the model on the target examples via a loss function. By iteratively performing a (meta) gradient descent, high-order gradients are propagated to the source examples. These gradients are then used to update the weights of source examples, in a way that is relevant to the target performance. As MWR is model-agnostic, it can be applied to any backbone neural model. Extensive experiments are conducted with various backbone text matching models, on four widely used datasets and two tasks. The results demonstrate that our proposed approach significantly outperforms a number of existing adaptation methods and effectively improves the cross-dataset and cross-task adaptability of the neural text matching models in the few-shot setting.
Bo Zhang, Chao Liu, Chun-Qian Li, Li-Cai Deng, Tai-Sheng Yan, Jian-Rong Shi
Oct 29, 2019·astro-ph.SR·PDF Low-resolution spectra are proved competitive to high-resolution spectra in determining many stellar labels at comparable precision. It is useful to consider the spectral information content when assessing the capability of a stellar spectrum in deriving precise stellar labels. In this work, we quantify the information content brought by the LAMOST-II medium-resolution spectroscopic survey (MRS) using the gradient spectra and the coefficients-of-dependence (CODs). In general, the wavelength coverage of the MRS well constrains the stellar labels but the sensitivities of different stellar labels vary with spectral types and metallicity of the stars of interest and, therefore, affect the performance of the stellar label determination from the MRS spectra. Applying the SLAM to the synthetic spectra which mimic the MRS data, we find the precision of the fundamental stellar parameters Teff, logg and [M/H] are better when combining both the blue and red bands of the MRS. This is especially important for warm stars since the H$α$ line located in the red part plays a more important role in determining the effective temperature for warm stars. With blue and red parts together, we are able to reach similar performance to the low-resolution spectra except for warm stars. However, at [M/H]$\sim-2.0$ dex, the uncertainties of fundamental stellar labels estimated from MRS are substantially larger than those from low-resolution spectra. We also tested the uncertainties of Teff, logg and [M/H] of from MRS data induced from the radial velocity mismatch and find that a mismatch of about 1 km s$^{-1}$, which is typical for LAMOST MRS data, would not significantly affect the stellar label estimates. At last, reference precision limits are calculated using synthetic gradient spectra, according to which we expect abundances of at least 17 elements to be measured precisely from MRS spectra.
Jiti Gao, Guangming Pan, Yanrong Yang, Bo Zhang
Accurate estimation for extent of cross{sectional dependence in large panel data analysis is paramount to further statistical analysis on the data under study. Grouping more data with weak relations (cross{sectional dependence) together often results in less efficient dimension reduction and worse forecasting. This paper describes cross-sectional dependence among a large number of objects (time series) via a factor model and parameterizes its extent in terms of strength of factor loadings. A new joint estimation method, benefiting from unique feature of dimension reduction for high dimensional time series, is proposed for the parameter representing the extent and some other parameters involved in the estimation procedure. Moreover, a joint asymptotic distribution for a pair of estimators is established. Simulations illustrate the effectiveness of the proposed estimation method in the finite sample performance. Applications in cross-country macro-variables and stock returns from S&P 500 are studied.
Bo Zhang, Wei Liu
Traditional directional modulation (DM) designs are based on the assumption that there is no multi-path effect between transmitters and receivers. One problem with these designs is that the resultant systems will be vulnerable to eavesdroppers which are aligned with or very close to the desired directions, as the received modulation pattern at these positions is similar to the given one. To solve the problem, a two-ray multi-path model is studied for positional modulation and the coefficients design problem for a given array geometry and a location-optimised antenna array is solved, where the multi-path effect is exploited to generate a given modulation pattern at desired positions, with scrambled values at positions around them.
Bo Zhang, Bing Zhang
Dec 30, 2013·astro-ph.HE·PDF In this paper, we simulate the prompt emission light curves of gamma-ray bursts (GRBs) within the framework of the Internal-Collision-induced MAgnetic Reconnection and Turbulence (ICMART) model. This model applies to GRBs with a moderately-high magnetization parameter $σ$ in the emission region. We show that this model can produce highly variable light curves with both fast and slow components. The rapid variability is caused by many locally Doppler-boosted mini-emitters due to turbulent magnetic reconnection in a moderately-high-$σ$ flow. The run-away growth and subsequent depletion of these mini-emitters as a function time define a broad slow component for each ICMART event. A GRB light curve is usually composed of multiple ICMART events that are fundamentally driven by the erratic GRB central engine activity. Allowing variations of the model parameters, one is able to reproduce a variety of light curves and the power density spectra as observed.
Haiwen Zhang, Bo Zhang
This paper is concerned with the inverse problem of scattering of time-harmonic acoustic waves from a penetrable and buried obstacles. By introducing a related transmission scattering problem, a Newton iteration method is proposed to simultaneously reconstruct both the penetrable interface and the buried obstacle inside from far-field data. A main feature of our method is that we do not need to know the type of boundary conditions on the buried obstacle. In particular, the boundary condition on the buried obstacle can also be determined simultaneously by the method. Finally, numerical examples using multi-frequency data are carried out to illustrate the effectiveness of our method.
Haiwen Zhang, Bo Zhang
This paper is concerned with the direct and inverse acoustic or electromagnetic scattering problems by a locally perturbed, perfectly reflecting, infinite plane (which is called a locally rough surface in this paper). We propose a novel integral equation formulation for the direct scattering problem which is defined on a bounded curve (consisting of a bounded part of the infinite plane containing the local perturbation and the lower part of a circle) with two corners. This novel integral equation can be solved efficiently by using the Nystrom method with a graded mesh introduced previously by Kress and is capable of dealing with large wavenumber cases. For the inverse problem, we propose a Newton iteration method to reconstruct the local perturbation of the plane from multiple frequency far-field data, based on the novel integral equation formulation. Numerical examples are carried out to demonstrate that our reconstruction method is stable and accurate even for the case of multiple-scale profiles.
Bo Zhang, Xiao-Yan Chen, Chao Liu, Li Chen, Li-Cai Deng, Jin-Liang Hou, Zheng-Yi Shao, Fan Yang, Yue Wu, Ming Yang, Yong Zhang, Yong-Hui Hou, Yue-Fei Wang
Jun 13, 2015·astro-ph.SR·PDF In this work, we provide 2189 photometric- and kinematic-selected member candidates of 24 star clusters from the LAMOST DR2 catalog. We perform two-step membership identification: selection along the stellar track in the color-magnitude diagram, i.e., photometric identification, and the selection from the distribution of radial velocities, i.e. the kinematic identification. We find that the radial velocity from the LAMOST data are very helpful in the membership identification. The mean probability of membership is 40\% for the radial velocity selected sample. With these 24 star clusters, we investigate the performance of the radial velocity and metallicity estimated in the LAMOST pipeline. We find that the systematic offset in radial velocity and metallicity are $0.85\pm1.26$\,\kms\ and $-0.08\pm0.04$\,dex, with dispersions of $5.47_{-0.71}^{+1.16}$\,\kms\ and $0.13_{-0.02}^{+0.04}$\,dex, respectively. Finally, we propose that the photometric member candidates of the clusters covered by the LAMOST footprints should be assigned higher priority so that more member stars can be observed.
Bo Zhang, Jie Kong, Mudi Chen, Ke Qiao, Lorin S. Matthews, Truell W. Hyde
The spontaneous rotation of small dust clusters confined inside a cubical glass box in the sheath of a complex plasma was observed in experiment. Due to strong coupling between the dust particles, these clusters behave like a rigid-body where cluster rotation is contingent upon their configuration and symmetry. By evaluating the effects of distinct contributing forces, it is postulated that the rotation observed is driven by the net torque exerted on the cluster by the ion wake force. The configuration and symmetry of a cluster determines whether the net torque induced by the ion wake force is nonzero, in turn leading to cluster rotation. A COPTIC (Cartesian mesh, oblique boundary, particles and thermals in cell) simulation is employed to obtain the ion wake potential providing a theoretical model of cluster rotation which includes both the ion wake force and neutral drag and predicts rotation rates and direction in agreement with experimental results. These results are then used to diagnose the ion flow within the box.
Bo Zhang, Zhi-meng Zhang, Zhi-gang Deng, Wei Hong, Jian Teng, Shu-kai He, Wei-min Zhou, Yu-qiu Gu
Nonlinear Compton scattering (NCS) and nonlinear Breit-Wheeler (NBW) process are strongly multi-photon and highly nonlinear processes. In ultra intense lasers (normalized field amplitude $a_0 \gg 1$), radiation formation length is much shorter than a period and single NCS/NBW cannot be described as scatterings of electrons dressing plane waves with $γ$ photons for what they feel is a local constant crossed field. However, present theories in constant crossed fields are hard to give some important quantum features due to divergence problems, such as number of laser photons involved, instantaneous angular distribution and detailed spectrum. As an alternative, present understanding of single NCS/NBW in ultra intense lasers includes several classical and semi-quantum ideas such as forward emission, recoil reaction and spectrum cutoff. We investigated multi-photon effects on NCS/NBW in ultra intense lasers by extracting the number of laser photons involved in a single process in ultra intense lasers from formulae of existing theories. New features of single NCS in ultra intense lasers including fixed emission angle to instantaneous electron momentum, instantaneous deflection of electron, and disappearance of spectrum cutoff are deduced. Similar features of single NBW in ultra intense lasers including non-vanishing emission angles to instantaneous $γ$ photon momentum, disappearance of spectrum cutoff and appearance of spectrum lower limit are also obtained. Simulations show that corresponding signals of multi-photon effects are significant on $10$PW scale and stronger lasers.