Haoyang Liu - Academia.edu (original) (raw)

Papers by Haoyang Liu

Ecological Informatics, Sep 1, 2021

Abstract Video monitoring systems are successfully widely used in many on-land artificial intelli... more Abstract Video monitoring systems are successfully widely used in many on-land artificial intelligence applications. They have been introduced into fishery production in recent years, such as video-based live fish detection and biomass estimation. Such ways help protect the sea environment by avoiding overstocking and pollution by ocean disasters or human mistakes in the production. However, underwater detection and segmentation are now still challenging because of the complex and volatile environment. The paper proposes an efficient underwater fish school segmentation framework for live fish detection and counting in the high-density cage. Adaptive multi-scale Gaussian background models are first constructed frame by frame to separate the foreground fish groups from the background seawater. The fish groups are then divided into individual fish by density estimation using directional weighted convolution kernels. No other underwater video pre-processing algorithms are introduced in the framework. The framework only needs real-time video frames as input. It uses online segmentation algorithms to detect and count live fish. No other pre-collected labeled videos are used to train and fine-tune the framework. It shows robust detection and statistics results in a natural aquaculture deep-sea cage.

Nature Communications

Chirality has been a property of central importance in physics, chemistry and biology for more th... more Chirality has been a property of central importance in physics, chemistry and biology for more than a century. Recently, electrons were found to become spin polarized after transmitting through chiral molecules, crystals, and their hybrids. This phenomenon, called chirality-induced spin selectivity (CISS), presents broad application potentials and far-reaching fundamental implications involving intricate interplays among structural chirality, topological states, and electronic spin and orbitals. However, the microscopic picture of how chiral geometry influences electronic spin remains elusive, given the negligible spin-orbit coupling (SOC) in organic molecules. In this work, we address this issue via a direct comparison of magnetoconductance (MC) measurements on magnetic semiconductor-based chiral molecular spin valves with normal metal electrodes of contrasting SOC strengths. The experiment reveals that a heavy-metal electrode provides SOC to convert the orbital polarization induce...

Sensors

For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vi... more For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. Firstly, the IACMD is employed to simultaneously separate the distinct fault types and extract multiple resonance frequencies induced by them. Next, an adaptive bilateral wavelet hyper-dictionary that digs deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response is constructed to identify and reconstruct each type of fault-induced feature with the help of the orthogonal matching pursuit (OMP) algorithm. Finally, the fault characteristic frequency can be detected via an envelope demodulation analysis of the reconstructed signal. A simulation and two sets of experimental results confirm that the developed IACMDSR model is a powerful and versatile tool...

Remote Sensing

State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tra... more State-of-the-art multiple-object tracking methods are frequently applied to people or vehicle tracking, but rarely involve underwater-object tracking. Compared with the processing in non-underwater photos or videos, underwater fish tracking is challenging due to variations in light conditions, water turbidity levels, shape deformations, and the similar appearances of fish. This article proposes a robust underwater fish-school tracking algorithm (FSTA). The FSTA is based on the tracking-by-detection paradigm. To solve the problem of low recognition accuracy in an underwater environment, we add an amendment detection module that uses prior knowledge to modify the detection result. Second, we introduce an underwater data association algorithm for aquatic non-rigid organisms that recombines representation and location information to refine the data matching process and improve the tracking results. The Resnet50-IBN network is used as a re-identification network to track fish. We introdu...

arXiv (Cornell University), May 19, 2022

In this paper, we consider online convex optimization (OCO) with time-varying loss and constraint... more In this paper, we consider online convex optimization (OCO) with time-varying loss and constraint functions. Specifically, the decision maker chooses sequential decisions based only on past information, meantime the loss and constraint functions are revealed over time. We first develop a class of model-based augmented Lagrangian methods (MALM) for timevarying functional constrained OCO (without feedback delay). Under standard assumptions, we establish sublinear regret and sublinear constraint violation of MALM. Furthermore, we extend MALM to deal with time-varying functional constrained OCO with delayed feedback, in which the feedback information of loss and constraint functions is revealed to decision maker with delays. Without additional assumptions, we also establish sublinear regret and sublinear constraint violation for the delayed version of MALM. Finally, numerical results for several examples of constrained OCO including online network resource allocation, online logistic regression and online quadratically constrained quadratical program are presented to demonstrate the efficiency of the proposed algorithms.

Journal of Global Optimization

Entropy

Although commercial motion-capture systems have been widely used in various applications, the com... more Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method. This method uses bidirectional recurrent neural network (Bi-RNN) to build an a priori model from a large motion dataset to build human motion, thereby the low-dimensional motion measurements are mapped to whole-body posture. To improve the motion reconstruction performance for specific application scenarios, two fundamental problems in the model construction are investigated: training data selection and sparse sensor placement. The problem of deep-learning training data selection is to select independent and identically distributed (IID) data for a certain scenario fro...

The folded hypercube FQn is a variance of the hypercube network and is sup... more The folded hypercube FQn is a variance of the hypercube network and is superior to Qn in some properties[IEEE Trans.

ACS Biomaterials Science & Engineering, 2021

During the past decade, various novel tissue engineering (TE) strategies have been developed to m... more During the past decade, various novel tissue engineering (TE) strategies have been developed to maintain, repair, and restore the biomechanical functions of the musculoskeletal system. Silk fibroins are natural polymers with numerous advantageous properties such as good biocompatibility, high mechanical strength, and low degradation rate and are increasingly being recognized as a scaffolding material of choice in musculoskeletal TE applications. This current systematic review examines and summarizes the latest research on silk scaffolds in musculoskeletal TE applications within the past decade. Scientific databases searched include PubMed, Web of Science, Medline, Cochrane library, and Embase. The following keywords and search terms were used: musculoskeletal, tendon, ligament, intervertebral disc, muscle, cartilage, bone, silk, and tissue engineering. Our Review was limited to articles on musculoskeletal TE, which were published in English from 2010 to September 2019. The eligibility of the articles was assessed by two reviewers according to prespecified inclusion and exclusion criteria, after which an independent reviewer performed data extraction and a second independent reviewer validated the data obtained. A total of 1120 articles were reviewed from the databases. According to inclusion and exclusion criteria, 480 articles were considered as relevant for the purpose of this systematic review. Tissue engineering is an effective modality for repairing or replacing injured or damaged tissues and organs with artificial materials. This Review is intended to reveal the research status of silk-based scaffolds in the musculoskeletal system within the recent decade. In addition, a comprehensive translational research route for silk biomaterial from bench to bedside is described in this Review.

Veterinary Research, 2021

Riemerella anatipestifer causes epizootic infectious disease in poultry resulting in serious econ... more Riemerella anatipestifer causes epizootic infectious disease in poultry resulting in serious economic losses especially to the duck industry. In our previous study, R. anatipestifer was found to lyse duck erythrocytes in vitro. In the present study, a random Tn4351 mutagenesis library of hemolytic R. anatipestifer strain SX containing 4000 mutants was constructed to investigate the genetic basis of hemolytic activity. Thirty mutants with reduced hemolytic activity and one with increased hemolytic activity were screened and insertions in 24 genes were identified. Of these genes, four were predicted to encode outer membrane proteins, one encoded a cytoplasmic membrane protein, 11 encoded cytoplasmic proteins, and eight encoded proteins with unknown locations. Based on current annotations of the R. anatipestifer genomes, of the 24 genes, 7 (29.17%) were involved in iron utilization. The hemolytic activities of the complemented strains M2 (pRES-Riean_0790) and M18 (pRES-Riean_0653) were...

SSRN Electronic Journal, 2020

Agency MBSs with diverse characteristics are traded in parallel through individualized specified ... more Agency MBSs with diverse characteristics are traded in parallel through individualized specified pool (SP) contracts and standardized to-be-announced (TBA) contracts with delivery flexibility. This parallel trading environment generates distinctive effects on MBS pricing and trading: (1) Although cheapest-todeliver (CTD) issues are present in TBA trading and absent from SP trading by design, MBS heterogeneity associated with CTD discounts affects SP yields positively, with the effect stronger for lower-value SPs; (2) high selling pressure amplifies the effects of MBS heterogeneity on SP yields; (3) greater MBS heterogeneity dampens SP and TBA trading activities but increases their ratio.

Physical Review Fluids, 2021

Variable-density mixing in shock bubble interaction, a canonical flow of Richtermyer-Meshkov inst... more Variable-density mixing in shock bubble interaction, a canonical flow of Richtermyer-Meshkov instability, is studied by the high-resolution simulation. While the dissipation mainly controls the passive scalar mixing rate, an objective definition of variable-density mixing rate characterizing the macroscopic mixing formation is still lacking, and the fundamental behavior of mixing rate evolution is not yet well understood. Here, we first show that the variable-density mixing of shock bubble interaction is distinctly different from the previous observations in the passive scalar mixing. The widely-accepted hyperbolic conservation of the first moment of concentration in the scalar mixing, i.e., the conservation of the mean concentration, is violated in variable-density flows. We further combine the compositional transport equation and the divergence relation for the miscible flows to provide the evidence that the existence of density gradient accelerated mixing rate, decomposed by the accelerated dissipation term and redistributed diffusion term, contributes to the anomalous decrease or increase of the mean concentration depending on Atwood number. Further analyzing a number of simulations for the cylindrical or spherical bubbles under a broad range of shock Mach numbers, Reynolds numbers, and Péclet numbers, the density gradient accelerated mixing rate exhibits weak dependent on Péclet numbers, and identifies an Atwood number range with high mixing rate, which can be theoretically predicted based on the mode of hyperbolic conservation violation behavior.

Fannie Mae and Freddie Mac’s implicit government guarantee is widely argued to cause irresponsibl... more Fannie Mae and Freddie Mac’s implicit government guarantee is widely argued to cause irresponsible risk taking. Despite moral-hazard concerns, this paper presents evidence that Fannie Mae and Freddie Mac (the GSEs) more effectively managed home price risks during the 2000-2006 housing boom than private insurers. Mortgage origination data reveal that the GSEs were selecting loans with increasingly higher percentage of down payments, or lower loan to value ratios (LTV), in boom areas than in other areas. Furthermore, the decline of LTVs in boom areas stems entirely from the segment insured by the GSEs only, and none of the decline stems from the segment co-insured by private mortgage insurers. Private mortgage insurers also did not lower their exposure to home price risks along other dimensions, including the percentage of high LTV GSE loans they insured. To quantify how the GSEs’ portfolios would have performed under alternative home price scenarios, I build an insurance valuation mo...

arXiv: Optimization and Control, 2019

In this paper, we study first-order methods on a large variety of low-rank matrix optimization pr... more In this paper, we study first-order methods on a large variety of low-rank matrix optimization problems, whose solutions only live in a low dimensional eigenspace. Traditional first-order methods depend on the eigenvalue decomposition at each iteration which takes most of the computation time. In order to reduce the cost, we propose an inexact algorithm framework based on a polynomial subspace extraction. The idea is to use an additional polynomial-filtered iteration to extract an approximated eigenspace, and project the iteration matrix on this subspace, followed by an optimization update. The accuracy of the extracted subspace can be controlled by the degree of the polynomial filters. This kind of subspace extraction also enjoys the warm start property: the subspace of the current iteration is refined from the previous one. Then this framework is instantiated into two algorithms: the polynomial-filtered proximal gradient method and the polynomial-filtered alternating direction met...

Banking & Insurance eJournal, 2020

We study price dislocations and liquidity provision by dealers and the Federal Reserve (Fed) as t... more We study price dislocations and liquidity provision by dealers and the Federal Reserve (Fed) as the “dealer of last resort” in agency MBS markets during the COVID-19 crisis. As customers sold MBS to “scramble for cash,” dealers provided liquidity by taking inventory in the cash market and hedging inventory risk in the forward market. The cash and forward prices diverged significantly beyond the difference in the quality of MBS traded on the two markets. The Fed first facilitated dealers’ inventory hedging and then took holdings off dealers’ inventory directly. The price dislocations began to revert only after the Fed’s latter action, when customer selling was still strong.

IN THIS APPENDIX, we present implementation details for our maximum likelihood estimator. Additio... more IN THIS APPENDIX, we present implementation details for our maximum likelihood estimator. Additional details and code to run the estimator can be found at https://github. com/palmercj/EIV-QR. The main step is Step 5, the piecewise-linear sieve-ML estimator described in Section 3.1. Because this piecewise-linear estimator is computationally intensive, we use a series of preliminary steps to find start values in the neighborhood of the optimum.16 These steps significantly reduce the time required for convergence of the piecewise-linear estimator. (1) We estimate quantile regression on a grid of knots [t1 t2 tJ], where J is the number of knots, and denote the estimate as β̂QR(·). (2) We run 40 weighted least squares (WLS) iterations using β̂QR(·) from Step 1 as the start value. Using WLS in some fashion is a common technique in quantile regression computational programs and in our case is motivated by the fact that under a normality assumption of the EIV term ε, the maximum likelihood ...

The Annals of Statistics, 2021

We study the detection of a sparse change in a high-dimensional mean vector as a minimax testing ... more We study the detection of a sparse change in a high-dimensional mean vector as a minimax testing problem. Our first main contribution is to derive the exact minimax testing rate across all parameter regimes for n independent, p-variate Gaussian observations. This rate exhibits a phase transition when the sparsity level is of order p log log(8n) and has a very delicate dependence on the sample size: in a certain sparsity regime it involves a triple iterated logarithmic factor in n. Further, in a dense asymptotic regime, we identify the sharp leading constant, while in the corresponding sparse asymptotic regime, this constant is determined to within a factor of √ 2. Extensions that cover spatial and temporal dependence, primarily in the dense case, are also provided.

IEEE Transactions on Smart Grid, 2020

This paper investigates how to minimize the operational cost of cloud service provider (CSP) that... more This paper investigates how to minimize the operational cost of cloud service provider (CSP) that operates urban neighboring data centers (DCs) in the same electricity market and can conduct workload transfer among DCs. Due to the substantial electricity demand of DCs, their market power which can have impact on the locational marginal prices (LMPs) of the electricity market should be taken into consideration. We formulate a bilinear bilevel problem which regards the CSP as a price maker and explores cost-minimizing workload transfer strategies. The upper level is the operational cost minimization problem of CSP and the lower level corresponds to the economic dispatch problem of independent system operator (ISO) of electricity market which determines the electricity prices. It is challenging to directly solve the bilevel problem with bilinear term in the objective function. Hence, we first reformulate the original problem into a single level problem and then based on the property of the problem we develop a polytope cutting algorithm that attains the global optimal solution. The proposed algorithm solves linear optimizations iteratively by cutting the non-convex polytope feasible set into convex sets. In addition, considering the varying communication environment in practice, we analyze the impact of transfer price uncertainty on total cost of CSP, and show that the expected cost surprisingly decreases with the increasing uncertainty. Simulations based on the standard IEEE test cases show that the cost of CSP is significantly reduced and a win-win result for both the CSP and independent system operator (ISO) is possible.

SIAM Journal on Scientific Computing, 2018

In this paper, we consider the community detection problem under either the stochastic block mode... more In this paper, we consider the community detection problem under either the stochastic block model (SBM) assumption or the degree-correlated stochastic block model (DCSBM) assumption. The modularity maximization formulation for the community detection problem is NP-hard in general. In this paper, we propose a sparse and low-rank completely positive relaxation for the modularity maximization problem, we then develop an efficient row-by-row (RBR) type block coordinate descent (BCD) algorithm to solve the relaxation and prove an O(1/ √ N) convergence rate to a stationary point where N is the number of iterations. A fast rounding scheme is constructed to retrieve the community structure from the solution. Non-asymptotic high probability bounds on the misclassification rate are established to justify our approach. We further develop an asynchronous parallel RBR algorithm to speed up the convergence. Extensive numerical experiments on both synthetic and real world networks show that the proposed approach enjoys advantages in both clustering accuracy and numerical efficiency. Our numerical results indicate that the newly proposed method is a quite competitive alternative for community detection on sparse networks with over 50 million nodes.