arXiv subjects

Quan Zhou

Publications and source records attributed to Quan Zhou.

At least 109 records · Page 6Linked to original sources

arXiv2023

Yule's "nonsense correlation" solved: Part II

In 1926, G. Udny Yule considered the following: given a sequence of pairs of random variables $\{X_k,Y_k \}$ ($k=1,2, \ldots, n$), and letting $X_i = S_i$ and $Y_ i= S'_i$ where $S_i$ and $S'_i$ are the partial sums of two independent random walks, what is the distribution of the empirical correlation coefficient \begin{equation*} ρ_n = \frac{\sum_{i=1}^n S_i S^\prime_i - \frac{1}{n}(\sum_{i=1}^n S_i)(\sum_{i=1}^n S^\prime_i)}{\sqrt{\sum_{i=1}^n S^2_i - \frac{1}{n}(\sum_{i=1}^n S_i)^2}\sqrt{\sum_{i=1}^n (S^\prime_i)^2 - \frac{1}{n}(\sum_{i=1}^n S^\prime_i)^2}}? \end{equation*} Yule empirically observed the distribution of this statistic to be heavily dispersed and frequently large in absolute value, leading him to call it "nonsense correlation." This unexpected finding led to his formulation of two concrete questions, each of which would remain open for more than ninety years: (i) Find (analytically) the variance of $ρ_n$ as $n \rightarrow \infty$ and (ii): Find (analytically) the higher order moments and the density of $ρ_n$ as $n \rightarrow \infty$. In 2017, Ernst, Shepp, and Wyner considered the empirical correlation coefficient \begin{equation*} ρ:= \frac{\int_0^1W_1(t)W_2(t) dt - \int_0^1W_1(t) dt \int_0^1 W_2(t) dt}{\sqrt{\int_0^1 W^2_1(t) dt - (\int_0^1W_1(t) dt)^2} \sqrt{\int_0^1 W^2_2(t) dt - (\int_0^1W_2(t) dt)^2}}\end{equation*} of two independent Wiener processes $W_1,W_2$, the limit to which $ρ_n$ converges weakly, as was first shown by Phillips (1986). Using tools from integral equation theory, Ernst et al. (2017) closed question (i) by explicitly calculating the second moment of $ρ$ to be .240522. This paper begins where Ernst et al. (2017) leaves off. We succeed in closing question (ii) by explicitly calculating all moments of $ρ$ (up to order 16).

math.ST↗

arXiv2023

Subgroup Fairness in Two-Sided Markets

It is well known that two-sided markets are unfair in a number of ways. For instance, female workers at Uber earn less than their male colleagues per mile driven. Similar observations have been made for other minority subgroups in other two-sided markets. Here, we suggest a novel market-clearing mechanism for two-sided markets, which promotes equalisation of the pay per hour worked across multiple subgroups, as well as within each subgroup. In the process, we introduce a novel notion of subgroup fairness (which we call Inter-fairness), which can be combined with other notions of fairness within each subgroup (called Intra-fairness), and the utility for the customers (Customer-Care) in the objective of the market-clearing problem. While the novel non-linear terms in the objective complicate market clearing by making the problem non-convex, we show that a certain non-convex augmented Lagrangian relaxation can be approximated to any precision in time polynomial in the number of market participants using semi-definite programming. This makes it possible to implement the market-clearing mechanism efficiently. On the example of driver-ride assignment in an Uber-like system, we demonstrate the efficacy and scalability of the approach, and trade-offs between Inter- and Intra-fairness.

cs.AI↗

arXiv2023

Rate-Perception Optimized Preprocessing for Video Coding

In the past decades, lots of progress have been done in the video compression field including traditional video codec and learning-based video codec. However, few studies focus on using preprocessing techniques to improve the rate-distortion performance. In this paper, we propose a rate-perception optimized preprocessing (RPP) method. We first introduce an adaptive Discrete Cosine Transform loss function which can save the bitrate and keep essential high frequency components as well. Furthermore, we also combine several state-of-the-art techniques from low-level vision fields into our approach, such as the high-order degradation model, efficient lightweight network design, and Image Quality Assessment model. By jointly using these powerful techniques, our RPP approach can achieve on average, 16.27% bitrate saving with different video encoders like AVC, HEVC, and VVC under multiple quality metrics. In the deployment stage, our RPP method is very simple and efficient which is not required any changes in the setting of video encoding, streaming, and decoding. Each input frame only needs to make a single pass through RPP before sending into video encoders. In addition, in our subjective visual quality test, 87% of users think videos with RPP are better or equal to videos by only using the codec to compress, while these videos with RPP save about 12% bitrate on average. Our RPP framework has been integrated into the production environment of our video transcoding services which serve millions of users every day.

eess.IV↗

arXiv2023

Collective excitations of the Chern-insulator states in commensurate double moiré superlattices of twisted bilayer graphene on hexagonal boron nitride

We study the collective excitation modes of the Chern insulator states in magic-angle twisted bilayer graphene aligned with hexagonal boron nitride (TBG/BN) at odd integer fillings ($ν$) of the flat bands. For the $1 \times 1$ commensurate double moiré superlattices in TBG/BN at three twist angles ($θ'$) between BN and graphene, self-consistent Hartree-Fock calculations show that the electron-electron interaction and the broken $C_{2z}$ symmetry lead to the Chern-insulator ground states with valley-spin flavor polarized HF bands at odd $ν$. In the active-band approximation, the HF bands in the same flavor of TBG/BN are much more separated than those of the pristine TBG with TBG/BN having a larger intra-flavor band gap so that the energies of the lowest intra-flavor exciton modes of TBG/BN computed within the time-dependent HF method are much higher than those of TBG and reach about 20 meV, and the exciton wavefunctions of TBG/BN become less localized than those of TBG. The inter-flavor valley-wave modes in TBG/BN have excitation energies higher than 2.5 meV which is also much larger than that of TBG, while the spin-wave modes all have zero excitation gap. In contrast to TBG with particle-hole symmetric excitation modes for positive and negative $ν$, the excitation spectrums and gaps of TBG/BN at positive $ν$ are rather different from those at negative $ν$. The quantitative behavior of the excitation spectrum of TBG/BN also varies with $θ'$. Full HF calculations demonstrate that more HF bands besides the two central bands can have rather large contributions from the single-particle flat-band states, then the lowest exciton modes that determine the optical properties of the Chern insulator states in TBG/BN are generally the ones between the remote and flat-like bands, while the valley-wave modes have similar energies as those in the active-band approximation.

cond-mat.mes-hall↗

arXiv2023

Fast Replica Exchange Stochastic Gradient Langevin Dynamics

Application of the replica exchange (i.e., parallel tempering) technique to Langevin Monte Carlo algorithms, especially stochastic gradient Langevin dynamics (SGLD), has scored great success in non-convex learning problems, but one potential limitation is the computational cost caused by running multiple chains. Upon observing that a large variance of the gradient estimator in SGLD essentially increases the temperature of the stationary distribution, we propose expediting tempering schemes for SGLD by directly estimating the bias caused by the stochastic gradient estimator. This simple idea enables us to simulate high-temperature chains at a negligible computational cost (compared to that of the low-temperature chain) while preserving the convergence to the target distribution. Our method is fundamentally different from the recently proposed m-reSGLD (multi-variance replica exchange SGLD) method in that the latter suffers from the low accuracy of the gradient estimator (e.g., the chain can fail to converge to the target) while our method benefits from it. Further, we derive a swapping rate that can be easily evaluated, providing another significant improvement over m-reSGLD. To theoretically demonstrate the advantage of our method, we develop convergence bounds in Wasserstein distances. Numerical examples for Gaussian mixture and inverse PDE models are also provided, which show that our method can converge quicker than the vanilla multi-variance replica exchange method.

math.NA↗

arXiv2022

Empirical Analysis of AI-based Energy Management in Electric Vehicles: A Case Study on Reinforcement Learning

Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents an empirical analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The empirical analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity does not produce a more desirable energy-saving solution but better balances battery power and fuel consumption. The equivalent energy optimization objective based on the instantaneous state of charge (SOC) variation is parameter sensitive and can help RL-EMSs to achieve more efficient energy-cost strategies.

cs.AI↗

arXiv2022

X-ray Performance of a Small Pixel Size sCMOS Sensor and the Effect of Depletion Depth

In recent years, scientific Complementary Metal Oxide Semiconductor (sCMOS) devices have been increasingly applied in X-ray detection, thanks to their attributes such as high frame rate, low dark current, high radiation tolerance and low readout noise. We tested the basic performance of a backside-illuminated (BSI) sCMOS sensor, which has a small pixel size of 6.5 um * 6.5 um. At a temperature of -20C, The readout noise is 1.6 e, the dark current is 0.5 e/pixel/s, and the energy resolution reaches 204.6 eV for single-pixel events. The effect of depletion depth on the sensor's performance was also examined, using three versions of the sensors with different deletion depths. We found that the sensor with a deeper depletion region can achieve a better energy resolution for events of all types of pixel splitting patterns, and has a higher efficiency in collecting photoelectrons produced by X-ray photons. We further study the effect of depletion depth on charge diffusion with a center-of-gravity (CG) model. Based on this work, a highly depleted sCMOS is recommended for applications of soft X-ray spectroscop.

physics.ins-det↗

arXiv2022

Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning

The future transportation system will be a multi-agent network where connected AI agents can work together to address the grand challenges in our age, e.g., mitigation of real-world driving energy consumption. Distinguished from the existing research on vehicle energy management, which decoupled multiple inputs and multiple outputs (MIMO) control into single-output(MISO) control, this paper studied a multi-agent deep reinforcement learning (MADRL) framework to deal with multiple control outputs simultaneously. A new hand-shaking strategy is proposed for the DRL agents by introducing an independence ratio, and a parametric study is conducted to obtain the best setting for the MADRL framework. The study suggested that the MADRL with an independence ratio of 0.2 is the best, and more than 2.4% of energy can be saved over the conventional DRL framework.

cs.LG↗

arXiv2022

Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems

The multiple-try Metropolis (MTM) algorithm is an extension of the Metropolis-Hastings (MH) algorithm by selecting the proposed state among multiple trials according to some weight function. Although MTM has gained great popularity owing to its faster empirical convergence and mixing than the standard MH algorithm, its theoretical mixing property is rarely studied in the literature due to its complex proposal scheme. We prove that MTM can achieve a mixing time bound smaller than that of MH by a factor of the number of trials under a general setting applicable to high-dimensional model selection problems with discrete state spaces. Our theoretical results motivate a new class of weight functions called locally balanced weight functions and guide the choice of the number of trials, which leads to improved performance over standard MTM algorithms. We support our theoretical results by extensive simulation studies and real data applications with several Bayesian model selection problems.

stat.ME↗

arXiv2022

DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention

The recent advances of compressing high-accuracy convolution neural networks (CNNs) have witnessed remarkable progress for real-time object detection. To accelerate detection speed, lightweight detectors always have few convolution layers using single-path backbone. Single-path architecture, however, involves continuous pooling and downsampling operations, always resulting in coarse and inaccurate feature maps that are disadvantageous to locate objects. On the other hand, due to limited network capacity, recent lightweight networks are often weak in representing large scale visual data. To address these problems, this paper presents a dual-path network, named DPNet, with a lightweight attention scheme for real-time object detection. The dual-path architecture enables us to parallelly extract high-level semantic features and low-level object details. Although DPNet has nearly duplicated shape with respect to single-path detectors, the computational costs and model size are not significantly increased. To enhance representation capability, a lightweight self-correlation module (LSCM) is designed to capture global interactions, with only few computational overheads and network parameters. In neck, LSCM is extended into a lightweight crosscorrelation module (LCCM), capturing mutual dependencies among neighboring scale features. We have conducted exhaustive experiments on MS COCO and Pascal VOC 2007 datasets. The experimental results demonstrate that DPNet achieves state-of the-art trade-off between detection accuracy and implementation efficiency. Specifically, DPNet achieves 30.5% AP on MS COCO test-dev and 81.5% mAP on Pascal VOC 2007 test set, together mwith nearly 2.5M model size, 1.04 GFLOPs, and 164 FPS and 196 FPS for 320 x 320 input images of two datasets.

cs.CV↗

arXiv2022

Deep Neural Network for Blind Visual Quality Assessment of 4K Content

The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement of spatial resolution. However, existing blind image quality assessment (BIQA) methods are not suitable for the original and upscaled 4K contents due to the expanded resolution and specific distortions. In this paper, we propose a deep learning-based BIQA model for 4K content, which on one hand can recognize true and pseudo 4K content and on the other hand can evaluate their perceptual visual quality. Considering the characteristic that high spatial resolution can represent more abundant high-frequency information, we first propose a Grey-level Co-occurrence Matrix (GLCM) based texture complexity measure to select three representative image patches from a 4K image, which can reduce the computational complexity and is proven to be very effective for the overall quality prediction through experiments. Then we extract different kinds of visual features from the intermediate layers of the convolutional neural network (CNN) and integrate them into the quality-aware feature representation. Finally, two multilayer perception (MLP) networks are utilized to map the quality-aware features into the class probability and the quality score for each patch respectively. The overall quality index is obtained through the average pooling of patch results. The proposed model is trained through the multi-task learning manner and we introduce an uncertainty principle to balance the losses of the classification and regression tasks. The experimental results show that the proposed model outperforms all compared BIQA metrics on four 4K content quality assessment databases.

cs.MM↗

arXiv2022

The CDF W-mass, muon g-2, and dark matter in a $U(1)_{L_μ-L_τ}$ model with vector-like leptons

We study the CDF $W$-mass, muon $g-2$, and dark matter observables in a local $U(1)_{L_μ-L_τ}$ model in which the new particles include three vector-like leptons ($E_1,~ E_2,~ N$), a new gauge boson $Z'$, a scalar $S$ (breaking $U(1)_{L_μ-L_τ}$), a scalar dark matter $X_I$ and its partner $X_R$. We find that the CDF $W$-mass disfavors $m_{E_1}= m_{E_2}={m_N}$ or $s_L=s_R=0$ where $s_{L(R)}$ is mixing parameter of left (right)-handed fields of vector-like leptons. A large mass splitting between $E_1$ and $E_2$ is favored when the differences between $s_L$ and $s_R$ becomes small. The muon $g-2$ anomaly can be simultaneously explained for appropriate difference between $s_L$ $(m_{E_1})$ and $s_R$ $(m_{E_2})$, and some regions are excluded by the diphoton signal data of the 125 GeV Higgs. Combined with the CDF $W$-mass, muon $g-2$ anomaly and other relevant constraints, the correct dark matter relic density is mainly obtained in two different scenarios: (i) $X_IX_I\to Z'Z',~ SS$ for $m_{Z'}(m_S)<m_{X_I}$ and (ii) the co-annihilation processes for $min(m_{E_1},m_{E_2},m_N,m_{X_R})$ closed to $m_{X_I}$. Finally, we use the direct searches for $2\ell+E_T^{miss}$ event at the LHC to constrain the model, and show the allowed mass ranges of the vector-like leptons and dark matter.

hep-ph↗

arXiv2022

Dimension-free Mixing for High-dimensional Bayesian Variable Selection

Yang et al. (2016) proved that the symmetric random walk Metropolis--Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. We propose a novel MCMC sampler using an informed proposal scheme, which we prove achieves a much faster mixing time that is independent of the number of covariates, under the same assumptions. To the best of our knowledge, this is the first high-dimensional result which rigorously shows that the mixing rate of informed MCMC methods can be fast enough to offset the computational cost of local posterior evaluation. Motivated by the theoretical analysis of our sampler, we further propose a new approach called "two-stage drift condition" to studying convergence rates of Markov chains on general state spaces, which can be useful for obtaining tight complexity bounds in high-dimensional settings. The practical advantages of our algorithm are illustrated by both simulation studies and real data analysis.

stat.ME↗

arXiv2022

Rapid Convergence of Informed Importance Tempering

Informed Markov chain Monte Carlo (MCMC) methods have been proposed as scalable solutions to Bayesian posterior computation on high-dimensional discrete state spaces, but theoretical results about their convergence behavior in general settings are lacking. In this article, we propose a class of MCMC schemes called informed importance tempering (IIT), which combine importance sampling and informed local proposals, and derive generally applicable spectral gap bounds for IIT estimators. Our theory shows that IIT samplers have remarkable scalability when the target posterior distribution concentrates on a small set. Further, both our theory and numerical experiments demonstrate that the informed proposal should be chosen with caution: the performance of some proposals may be very sensitive to the shape of the target distribution. We find that the "square-root proposal weighting" scheme tends to perform well in most settings.

stat.CO↗

arXiv2021

DPNET: Dual-Path Network for Efficient Object Detectioj with Lightweight Self-Attention

Object detection often costs a considerable amount of computation to get satisfied performance, which is unfriendly to be deployed in edge devices. To address the trade-off between computational cost and detection accuracy, this paper presents a dual path network, named DPNet, for efficient object detection with lightweight self-attention. In backbone, a single input/output lightweight self-attention module (LSAM) is designed to encode global interactions between different positions. LSAM is also extended into a multiple-inputs version in feature pyramid network (FPN), which is employed to capture cross-resolution dependencies in two paths. Extensive experiments on the COCO dataset demonstrate that our method achieves state-of-the-art detection results. More specifically, DPNet obtains 29.0% AP on COCO test-dev, with only 1.14 GFLOPs and 2.27M model size for a 320x320 image.

cs.CV↗

arXiv2021

DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary

Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years. However, most of them ignore the benefit of boundary information. This paper introduces a lightweight dual-resolution network, called DRBANet, aiming to refine semantic segmentation results with the aid of boundary information. DRBANet adopts dual parallel architecture, including: high resolution branch (HRB) and low resolution branch (LRB). Specifically, HRB mainly consists of a set of Efficient Inverted Bottleneck Modules (EIBMs), which learn feature representations with larger receptive fields. LRB is composed of a series of EIBMs and an Extremely Lightweight Pyramid Pooling Module (ELPPM), where ELPPM is utilized to capture multi-scale context through hierarchical residual connections. Finally, a boundary supervision head is designed to capture object boundaries in HRB. Extensive experiments on Cityscapes and CamVid datasets demonstrate that our method achieves promising trade-off between segmentation accuracy and running efficiency.

cs.CV↗

arXiv2021

BubbleNet: Inferring micro-bubble dynamics with semi-physics-informed deep learning

Micro-bubbles and bubbly flows are widely observed and applied in chemical engineering, medicine, involves deformation, rupture, and collision of bubbles, phase mixture, etc. We study bubble dynamics by setting up two numerical simulation cases: bubbly flow with a single bubble and multiple bubbles, both confined in the microchannel, with parameters corresponding to their medical backgrounds. Both the cases have their medical background applications. Multiphase flow simulation requires high computation accuracy due to possible component losses that may be caused by sparse meshing during the computation. Hence, data-driven methods can be adopted as an useful tool. Based on physics-informed neural networks (PINNs), we propose a novel deep learning framework BubbleNet, which entails three main parts: deep neural networks (DNN) with sub nets for predicting different physics fields; the semi-physics-informed part, with only the fluid continuum condition and the pressure Poisson equation $\mathcal{P}$ encoded within; the time discretized normalizer (TDN), an algorithm to normalize field data per time step before training. We apply the traditional DNN and our BubbleNet to train the coarsened simulation data and predict the physics fields of both the two bubbly flow cases. The BubbleNets are trained for both with and without $\mathcal{P}$, from which we conclude that the 'physics-informed' part can serve as inner supervision. Results indicate our framework can predict the physics fields more accurately, estimating the prediction absolute errors. Our deep learning predictions outperform traditional numerical methods computed with similar data density meshing. The proposed network can potentially be applied to many other engineering fields.

physics.flu-dyn↗

arXiv2021

Apache Submarine: A Unified Machine Learning Platform Made Simple

As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists to improve their productivity. However, existing machine learning platforms are ill-equipped to address the "Machine Learning tech debts" such as glue code, reproducibility, and portability. Furthermore, existing platforms only take expert data scientists into consideration, and thus they are inflexible for infrastructure administrators and non-user-friendly for citizen data scientists. We propose Submarine, a unified machine learning platform, to address the challenges.

cs.DC↗