arXiv subjects

Zhigang Li

Publications and source records attributed to Zhigang Li.

At least 55 records · Page 3Linked to original sources

arXiv2023

HT-MMIOW: A Hypothesis Test approach for Microbiome Mediation using Inverse Odds Weighting

The human microbiome has an important role in determining health. Mediation analyses quantify the contribution of the microbiome in the causal path between exposure and disease; however, current mediation models cannot fully capture the high dimensional, correlated, and compositional nature of microbiome data and do not typically accommodate dichotomous outcomes. We propose a novel approach that uses inverse odds weighting to test for the mediating effect of the microbiome. We use simulation to demonstrate that our approach gains power for high dimensional mediators, and it is agnostic to the effect of interactions between the exposure and mediators. Our application to infant gut microbiome data from the New Hampshire Birth Cohort Study revealed a mediating effect of 6-week infant gut microbiome on the relationship between maternal prenatal antibiotic use during pregnancy and incidence of childhood allergy by 5 years of age.

stat.AP↗

arXiv2022

Wafer-level substrate-free low-stress silicon nitride platform for THz metadevices and monolithically integrated narrowband metamaterial absorbers

The implementation of terahertz (THz) wafer-level metadevices is critical to advance the science for applications including (I) integrated focal plane array which can image for biology and (II) integrated narrowband absorbers for high spectral resolution THz spectroscopy. Substantial progress has been made in the development of THz metamaterials; however, a wafer-level low-stress THz metadevices platform remains a challenge. This paper experimentally demonstrates a substrate-free THz metadevices platform adopting engineered Si-rich and low-stress silicon nitride (SiNx) thin films, achieving an extensive THz transparency up to f = 2.5 THz. A new analytical model is first reported from the Lorentz model that can accurately predict spectral responses of metal insulator metal (MIM) metamaterial absorbers. The model is experimentally validated in the THz range and exploited for the first demonstration of a THz absorber, which exhibits performance approaching the predicted results. Our results show that the wafer-level SiNx platform will accelerate the development of large-scale, sophisticated substrate-free THz metadevices. The Lorentz model and its quadratic model will be a very practical method for designing THz metadevices.

physics.optics↗

arXiv2022

MarZIC: A Marginal mediation model for Zero-Inflated Compositional mediators with applications to microbiome data

The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data that is compounded by its compositional structure. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are actually not zero (i.e., false zeros). We develop a novel marginal mediation analysis method under the potential-outcomes framework to fill this gap and show the marginal model can also account for the compositional structure. The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches.

stat.ME↗

arXiv2022

Data-Driven Dispatchable Regions with Potentially Active Boundaries for Renewable Power Generation: Concept and Construction

The dispatchable region of volatile renewable power generation (RPG) quantifies how much uncertainty the power system can handle at a given operating point. State-of-the-art dispatchable region (DR) research has studied how system operational constraints influence the DR but has seldom considered the effect of the uncertainty features of RPG outputs. The traditional DR is generally described by a large number of boundaries, and it is computationally intensive to construct. To bridge these gaps, a novel type of DR is defined, which is enclosed by potentially active boundaries (PABs) that consider the operational constraints and uncertainty features of RPG outputs. The proposed DR is easier to construct because the PABs are only a small part of the traditional DR boundaries. The procedure for constructing the proposed DR is described in terms of the progressive search for PABs, which is formulated as a mixed-integer linear program by incorporating the discrete observed data points of RPG outputs as an approximate distribution. A parallel solution paradigm is also developed to expedite the construction procedure when using a large observed dataset. Simulation tests on the IEEE 30-bus and 118-bus systems verify the effectiveness and scalability of the proposed DR and the efficiency of the proposed algorithm.

eess.SY↗

arXiv2021

Contention-based Grant-free Transmission with Extremely Sparse Orthogonal Pilot Scheme

Due to the limited number of traditional orthogonal pilots, pilot collision will severely degrade the performance of contention-based grant-free transmission. To alleviate the pilot collision and exploit the spatial degree of freedom as much as possible, an extremely sparse orthogonal pilot scheme is proposed for uplink grant-free transmission. The proposed sparse pilot is used to perform active user detection and estimate the spatial channel. Then, inter-user interference suppression is performed by spatially combining the received data symbols using the estimated spatial channel. After that, the estimation and compensation of wireless channel and time/frequency offset are performed utilizing the geometric characteristics of combined data symbols. The task of pilot is much lightened, so that the extremely sparse orthogonal pilot can occupy minimized resources, and the number of orthogonal pilots can be increased significantly, which greatly reduces the probability of pilot collision. The numerical results show that the proposed extremely sparse orthogonal pilot scheme significantly improves the performance in high-overloading grant-free scenario.

cs.IT↗

arXiv2021

PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation

6D pose estimation from a single RGB image is a challenging and vital task in computer vision. The current mainstream deep model methods resort to 2D images annotated with real-world ground-truth 6D object poses, whose collection is fairly cumbersome and expensive, even unavailable in many cases. In this work, to get rid of the burden of 6D annotations, we formulate the 6D pose refinement as a Markov Decision Process and impose on the reinforcement learning approach with only 2D image annotations as weakly-supervised 6D pose information, via a delicate reward definition and a composite reinforced optimization method for efficient and effective policy training. Experiments on LINEMOD and T-LESS datasets demonstrate that our Pose-Free approach is able to achieve state-of-the-art performance compared with the methods without using real-world ground-truth 6D pose labels.

cs.CV↗

arXiv2021

Flow Regime Transition in Countercurrent Packed Column Monitored by ECT

Vertical packed columns are widely used in absorption, stripping and distillation processes. Flooding will occur in the vertical packed columns as a result of excessive liquid accumulation, which reduces mass transfer efficiency and causes a large pressure drop. Pressure drop measurements are typically used as the hydrodynamic parameter for predicting flooding. They are, however, only indicative of the occurrence of transition of the flow regime across the packed column. They offer limited spatial information to mass transfer packed column operators and designers. In this work, a new method using Electrical Capacitance Tomography (ECT) is implemented for the first time so that real-time flow regime monitoring at different vertical positions is achieved in a countercurrent packed bed column using ECT. Two normalisation methods are implemented to monitor the transition from pre-loading to flooding in a column of 200 mm diameter, 1200 mm height filled with plastic structured packing. Liquid distribution in the column can be qualitatively visualised via reconstructed ECT images. A flooding index is implemented to quantitatively indicate the progression of local flooding. In experiments, the degree of local flooding is quantified at various gas flow rates and locations of ECT sensor. ECT images were compared with pressure drop and visual observation. The experimental results demonstrate that ECT is capable of monitoring liquid distribution, identifying flow regime transitions and predicting local flooding.

physics.flu-dyn↗

arXiv2020

PDRS: A Fast Non-iterative Scheme for Massive Grant-free Access in Massive MIMO

Grant-free multiple-input multiple-output (MIMO) usually employs non-orthogonal pilots for joint user detection and channel estimation. However, existing methods are too complex for massive grant-free access in massive MIMO. This letter proposes pilot detection reference signal (PDRS) to greatly reduce the complexity. In PDRS scheme, no iteration is required. Direct weight estimation is also proposed to calculate combining weights without channel estimation. After combining, PDRS recovery errors are used to decide the pilot activity. The simulation results show that the proposed grant-free scheme performs good with a complexity reduced by orders of magnitude.

cs.IT↗

arXiv2020

Substrate-free THz focal plane metamaterial array with high absorption ratio

Microelectromechanical system (MEMS) focal plane array (FPA) with optical readout offers exciting opportunities for real-time terahertz (THz) imaging. However, conventional FPA suffers from a low THz absorption ratio, which further decreases the performance of THz imaging. Here, we present a simple and scalable approach for the realization of THz focal plane metamaterial array with a relatively high absorption ratio. The key idea is to combine the advantages of substrate-free structures with metamaterial. A 100 x100 THz FPA with a 150 x 150 μm pixel is designed, fabricated, and characterized. The dependence of the THz absorption ratio on the thickness of SiNx dielectric substrate film is investigated. The fabricated FPA exhibits a 90.6% resonant absorption at 1.36 THz, agreeing considerably with the theoretical simulation results. Our results imply that such a substrate-free THz focal plane metamaterial array enables the realization of THz imaging.

physics.app-ph↗

arXiv2020

Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations

While much progress has been made in 6-DoF object pose estimation from a single RGB image, the current leading approaches heavily rely on real-annotation data. As such, they remain sensitive to severe occlusions, because covering all possible occlusions with annotated data is intractable. In this paper, we introduce an approach to robustly and accurately estimate the 6-DoF pose in challenging conditions and without using any real pose annotations. To this end, we leverage the intuition that the poses predicted by a network from an image and from its counterpart synthetically altered to mimic occlusion should be consistent, and translate this to a self-supervised loss function. Our experiments on LINEMOD, Occluded-LINEMOD, YCB and new Randomization LINEMOD dataset evidence the robustness of our approach. We achieve state of the art performance on LINEMOD, and OccludedLINEMOD in without real-pose setting, even outperforming methods that rely on real annotations during training on Occluded-LINEMOD.

cs.CV↗

arXiv2020

Optimizing AD Pruning of Sponsored Search with Reinforcement Learning

Industrial sponsored search system (SSS) can be logically divided into three modules: keywords matching, ad retrieving, and ranking. During ad retrieving, the ad candidates grow exponentially. A query with high commercial value might retrieve a great deal of ad candidates such that the ranking module could not afford. Due to limited latency and computing resources, the candidates have to be pruned earlier. Suppose we set a pruning line to cut SSS into two parts: upstream and downstream. The problem we are going to address is: how to pick out the best $K$ items from $N$ candidates provided by the upstream to maximize the total system's revenue. Since the industrial downstream is very complicated and updated quickly, a crucial restriction in this problem is that the selection scheme should get adapted to the downstream. In this paper, we propose a novel model-free reinforcement learning approach to fixing this problem. Our approach considers downstream as a black-box environment, and the agent sequentially selects items and finally feeds into the downstream, where revenue would be estimated and used as a reward to improve the selection policy. To the best of our knowledge, this is first time to consider the system optimization from a downstream adaption view. It is also the first time to use reinforcement learning techniques to tackle this problem. The idea has been successfully realized in Baidu's sponsored search system, and online long time A/B test shows remarkable improvements on revenue.

cs.LG↗

arXiv2020

Global weak solutions for a two-component Novikov system

In this paper, we mainly consider about the existence and uniqueness of global weak solutions for the two-component Novikov system. We first recall some results and definitions of strong solutions and weak solutions for the system, then by using the method of approximation of smooth solutions, we prove the existence and uniqueness of global weak solutions of the system.

math.AP↗

arXiv2020

Cosmological parameter estimation from large-scale structure deep learning

We propose a light-weight deep convolutional neural network (CNN) to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy. The training set is based on 465 realizations of a cubic box with a side length of $256\ h^{-1}\ \rm Mpc$, sampled with $128^3$ particles interpolated over a cubic grid of $128^3$ voxels. These volumes have cosmological parameters varying within the flat $Λ$CDM parameter space of $0.16 \leq Ω_m \leq 0.46$ and $2.0 \leq 10^9 A_s \leq 2.3$. The neural network takes as an input cubes with $32^3$ voxels and has three convolution layers, three dense layers, together with some batch normalization and pooling layers. In the final predictions from the network we find a $2.5\%$ bias on the primordial amplitude $σ_8$ that can not easily be resolved by continued training. We correct this bias to obtain unprecedented accuracy in the cosmological parameter estimation with statistical uncertainties of $δΩ_m$=0.0015 and $δσ_8$=0.0029, which are several times better than the results of previous CNN works. Compared with a 2-point analysis method using clustering region of 0-130 and 10-130 $h^{-1}$ Mpc, the CNN constraints are several times and an order of magnitude more precise, respectively. Finally, we conduct preliminary checks of the error-tolerance abilities of the neural network, and find that it exhibits robustness against smoothing, masking, random noise, global variation, rotation, reflection, and simulation resolution. Those effects are well understood in typical clustering analysis, but had not been tested before for the CNN approach. Our work shows that CNN can be more promising than people expected in deriving tight cosmological constraints from the cosmic large scale structure.

astro-ph.CO↗

arXiv2020

Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Possibility of estimateing the non-linear systematics using fast simulations

The tomographic AP method is so far the best method in separating the Alcock-Paczynski (AP) signal from the redshift space distortion (RSD) effects and deriving powerful constraints on cosmological parameters using the $\lesssim40h^{-1}\ \rm Mpc$ clustering region. To guarantee that the method can be easily applied to the future large scale structure (LSS) surveys, we study the possibility of estimating the systematics of the method using fast simulation method. The major contribution of the systematics comes from the non-zero redshift evolution of the RSD effects, which is quantified by $\hatξ_{Δs}(μ,z)$ in our analysis, and estimated using the BigMultidark exact N-body simulation and approximate COLA simulation samples. We find about 5\%/10\% evolution when comparing the $\hatξ_{Δs}(μ,z)$ measured as $z=0.5$/$z=1$ to the measurements at $z=0$. We checked the inaccuracy in the 2pCFs computed using COLA, and find it 5-10 times smaller than the intrinsic systematics of the tomographic AP method, indicating that using COLA to estimate the systematics is good enough. Finally, we test the effect of halo bias, and find $\lesssim$1.5\% change in $\hatξ_{Δs}$ when varying the halo mass within the range of $2\times 10^{12}$ to $10^{14}$ $M_{\odot}$. We will perform more studies to achieve an accurate and efficient estimation of the systematics in redshift range of $z=0-1.5$.

astro-ph.CO↗

arXiv2020

Contention-based Grant-free Transmission with Independent Multi-pilot Scheme

Contention-based grant-free transmission is very promising for future massive machine-type communication (mMTC). In contention-based transmission, the random pilot collision is a challenging problem. To solve this problem, multiple pilots scheme is used to reduce the pilot collision probabliltiy. However, the existing work on multiple pilots relies on the low correlation of spatial channels, limiting its applicability. In this paper, an independent multi-pilot scheme is proposed, which utilizes the diversity of multiple pilots and is not limited by the spatial correlation. The receiver employs interference cancellation for both data symbols and multiple pilots to ensure the performance. The simulation results also show that the proposed independent multi-pilot scheme can significantly improve the BLER performance and increase the number of simultaneous access users.

cs.IT↗

arXiv2019

Toward accurate measurement of property-dependent galaxy clustering I. Comparison of the Vmax method and the "shuffled" method

Galaxy clustering provides insightful clues to our understanding of galaxy formation and evolution, as well as the universe. The redshift assignment for the random sample is one of the key steps to measure the galaxy clustering accurately. In this paper, by virtue of the mock galaxy catalogs, we investigate the effect of two redshift assignment methods on the measurement of galaxy two-point correlation functions (hereafter 2PCFs), the Vmax method and the "shuffled" method. We found that the shuffled method significantly underestimates both of the projected 2PCFs and the two-dimensional 2PCFs in redshift space. While the Vmax method does not show any notable bias on the 2PCFs for volume-limited samples. For flux-limited samples, the bias produced by the Vmax method is less than half of the shuffled method on large scales. Therefore, we strongly recommend the Vmax method to assign redshifts to random samples in the future galaxy clustering analysis.

astro-ph.CO↗

arXiv2019

Local well-posedness and global existence for a multi-component Novikov equation

Considered herein is a multi-component Novikov equation, which admits bi-Hamiltonian structure, infinitely many conserved quantities and peaked solutions. In this paper, we deduce two blow-up criteria for this system and global existence for some two-component case in $H^s$. Finally we verify that the system possesses peakons and periodic peakons.

math.AP↗

arXiv2019

IFAA: Robust association identification and Inference For Absolute Abundance in microbiome analyses

The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e.g., stool) can be considered as an approximation of RA in an entire ecosystem (e.g., gut). However, inference on RA suffers from the fact that RA are calculated by dividing absolute abundances (AA) over the common denominator (CD), the summation of all AA (i.e., library size). Because of that, perturbation in one taxon will result in a change in the CD and thus cause false changes in RA of all other taxa, and those false changes could lead to false positive/negative findings. We propose a novel analysis approach (IFAA) to make robust inference on AA of an ecosystem that can circumvent the issues induced by the CD problem and compositional structure of RA. IFAA can also address the confounding effect of library size and handle zero-inflated data structures. IFAA identifies microbial taxa associated with the covariates in Phase one and estimates the association parameters by employing an independent reference taxon in Phase two. Two real data applications are presented and extensive simulations show that IFAA outperforms other established existing approaches by a big margin in the presence of confounding effect of library size.

stat.AP↗