arXiv subjects

Jun Ma

Publications and source records attributed to Jun Ma.

At least 361 records · Page 20Linked to original sources

arXiv2021

Excedance-type polynomials and gamma-positivity

The object of this paper is to give a systematic treatment of excedance-type polynomials. We first give a sufficient condition for a sequence of polynomials to have alternatingly increasing property, and then we present a systematic study of the joint distribution of excedances, fixed points and cycles of permutations and derangements, signed or not, colored or not. Let $p\in [0,1]$ and $q\in [0,1]$ be two given real numbers. We prove that the cyc q-Eulerian polynomials of permutations are bi-gamma-positive, and the fix and cyc (p,q)-Eulerian polynomials of permutations are alternatingly increasing, and so they are unimodal with modes in the middle, where fix and cyc are the fixed point and cycle statistics. When p=1 and q=1/2, we find a combinatorial interpretation of the bi-gamma-coefficients of the (p,q)-Eulerian polynomials. We then study excedance and flag excedance statistics of signed permutations and colored permutations. In particular, we establish the relationships between the (p,q)-Eulerian polynomials and some multivariate Eulerian polynomials. Our results unify and generalize a variety of recent results.

math.CO↗

arXiv2021

Receding Horizon Motion Planning for Multi-Agent Systems: A Velocity Obstacle Based Probabilistic Method

In this paper, a novel and innovative methodology for feasible motion planning in the multi-agent system is developed. On the basis of velocity obstacles characteristics, the chance constraints are formulated in the receding horizon control (RHC) problem, and geometric information of collision cones is used to generate the feasible regions of velocities for the host agent. By this approach, the motion planning is conducted at the velocity level instead of the position level. Thus, it guarantees a safer collision-free trajectory for the multi-agent system, especially for the systems with high-speed moving agents. Moreover, a probability threshold of potential collisions can be satisfied during the motion planning process. In order to validate the effectiveness of the methodology, different scenarios for multiple agents are investigated, and the simulation results clearly show that the proposed approach can effectively avoid potential collisions with a collision probability less than a specific threshold.

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arXiv2021

Generalized Iterative Super-Twisting Sliding Mode Control: A Case Study on Flexure-Joint Dual-Drive H-Gantry Stage

Mechatronic systems are commonly used in the industry, where fast and accurate motion performance is always required to guarantee manufacturing precision and efficiency. Nevertheless, the system model and parameters are difficult to be obtained accurately. Moreover, the high-order modes, strong coupling in the multi-axis systems, or unmodeled frictions will bring uncertain dynamics to the system. To overcome the above-mentioned issues and enhance the motion performance, this paper introduces a novel intelligent and totally model-free control method for mechatronic systems with unknown dynamics. In detail, a 2-degree-of-freedom (DOF) architecture is designed, which organically merges a generalized super-twisting algorithm with a unique iterative learning law. The controller solely utilizes the input-output data collected in iterations such that it works without any knowledge of the system parameters. The rigorous proof of convergence ability is given and a case study on flexture-joint dual-drive H-gantry stage is shown to validate the effectiveness of the proposed method.

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arXiv2021

Eulerian polynomials for multipermutations and signed multipermutations

In this paper, we study Eulerian polynomials for permutations and signed permutations of the multiset $\{1,1,2,2,\ldots,n,n\}$. Properties of these polynomials, including recurrence relations and unimodality are discussed. In particular, we give a unified proof of the fact that these polynomials are unimodal with modes in the middle.

math.CO↗

arXiv2021

Galaxy Clusters from the DESI Legacy Imaging Surveys. I. Cluster Detection

Based on the photometric redshift catalog of Zou H. et al. (2019), we apply a fast clustering algorithm to identify 540,432 galaxy clusters at $z\lesssim1$ in the DESI legacy imaging surveys, which cover a sky area of about 20,000 deg$^2$. Monte-Carlo simulations indicate that the false detection rate of our detecting method is about 3.1\%. The total masses of galaxy clusters are derived using a calibrated richness--mass relation that are based on the observations of X-ray emission and Sunyaev \& Zel'dovich effect. The median redshift and mass of our detected clusters are about 0.53 and $1.23\times10^{14} M_\odot$, respectively. Comparing with previous clusters identified using the data of the Sloan Digital Sky Survey (SDSS), we can recognize most of them, especially those with high richness. Our catalog will be used for further statistical studies on galaxy clusters and environmental effects on the galaxy evolution, etc.

astro-ph.GA↗

arXiv2021

Optimal Decentralized Control for Uncertain Systems by Symmetric Gauss-Seidel Semi-Proximal ALM

The H2 guaranteed cost decentralized control problem is investigated in this work. More specifically, on the basis of an appropriate H2 re-formulation that we put in place, the optimal control problem in the presence of parameter uncertainties is then suitably characterized by convex restriction and solved in parameter space. It is shown that a set of stabilizing decentralized controller gains for the uncertain system is parameterized in a convex set through appropriate convex restriction, and then an approximated conic optimization problem is constructed. This facilitates the use of the symmetric Gauss-Seidel (sGS) semi-proximal augmented Lagrangian method (ALM), which attains high computational effectiveness. A comprehensive analysis is given on the application of the approach in solving the optimal decentralized control problem; and subsequently, the preserved decentralized structure, robust stability, and robust performance are all suitably guaranteed with the proposed methodology. Furthermore, an illustrative example is presented to demonstrate the effectiveness of the proposed optimization approach.

math.OC↗

arXiv2021

Semi-Definite Relaxation Based ADMM for Cooperative Planning and Control of Connected Autonomous Vehicles

This paper investigates the cooperative planning and control problem for multiple connected autonomous vehicles (CAVs) in different scenarios. In the existing literature, most of the methods suffer from significant problems in computational efficiency. Besides, as the optimization problem is nonlinear and nonconvex, it typically poses great difficultly in determining the optimal solution. To address this issue, this work proposes a novel and completely parallel computation framework by leveraging the alternating direction method of multipliers (ADMM). The nonlinear and nonconvex optimization problem in the autonomous driving problem can be divided into two manageable subproblems; and the resulting subproblems can be solved by using effective optimization methods in a parallel framework. Here, the differential dynamic programming (DDP) algorithm is capable of addressing the nonlinearity of the system dynamics rather effectively; and the nonconvex coupling constraints with small dimensions can be approximated by invoking the notion of semi-definite relaxation (SDR), which can also be solved in a very short time. Due to the parallel computation and efficient relaxation of nonconvex constraints, our proposed approach effectively realizes real-time implementation and thus also extra assurance of driving safety is provided. In addition, two transportation scenarios for multiple CAVs are used to illustrate the effectiveness and efficiency of the proposed method.

cs.MA↗

arXiv2021

Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?

Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation. During the past five years, on the one hand, thousands of medical image segmentation methods have been proposed for various organs and lesions in different medical images, which become more and more challenging to fairly compare different methods. On the other hand, international segmentation challenges can provide a transparent platform to fairly evaluate and compare different methods. In this paper, we present a comprehensive review of the top methods in ten 3D medical image segmentation challenges during 2020, covering a variety of tasks and datasets. We also identify the "happy-families" practices in the cutting-edge segmentation methods, which are useful for developing powerful segmentation approaches. Finally, we discuss open research problems that should be addressed in the future. We also maintain a list of cutting-edge segmentation methods at \url{https://github.com/JunMa11/SOTA-MedSeg}.

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arXiv2020

Loss Ensembles for Extremely Imbalanced Segmentation

This short paper briefly presents our methodology details of automatic intracranial aneurysms segmentation from brain MR scans. We use ensembles of multiple models trained from different loss functions. Our method ranked first place in the ADAM challenge segmentation task. The code and trained models are publicly available at https://github.com/JunMa11/ADAM2020.

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arXiv2020

Exploring Large Context for Cerebral Aneurysm Segmentation

Automated segmentation of aneurysms from 3D CT is important for the diagnosis, monitoring, and treatment planning of the cerebral aneurysm disease. This short paper briefly presents the main technique details of the aneurysm segmentation method in the MICCAI 2020 CADA challenge. The main contribution is that we configure the 3D U-Net with a large patch size, which can obtain the large context. Our method ranked second on the MICCAI 2020 CADA testing dataset with an average Jaccard of 0.7593. Our code and trained models are publicly available at \url{https://github.com/JunMa11/CADA2020}.

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arXiv2020

Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI

Automatic evaluation of myocardium and pathology plays an important role in the quantitative analysis of patients suffering from myocardial infarction. In this paper, we present a cascaded convolutional neural network framework for myocardial infarction segmentation and classification in delayed-enhancement cardiac MRI. Specifically, we first use a 2D U-Net to segment the whole heart, including the left ventricle and the myocardium. Then, we crop the whole heart as a region of interest (ROI). Finally, a new 2D U-Net is used to segment the infraction and no-reflow areas in the whole heart ROI. The segmentation method can be applied to the classification task where the segmentation results with the infraction or no-reflow areas are classified as pathological cases. Our method took second place in the MICCAI 2020 EMIDEC segmentation task with Dice scores of 86.28%, 62.24%, and 77.76% for myocardium, infraction, and no-reflow areas, respectively, and first place in the classification task with an accuracy of 92%.

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arXiv2020

Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images

Automatic segmentation of head and neck tumors plays an important role in radiomics analysis. In this short paper, we propose an automatic segmentation method for head and neck tumors from PET and CT images based on the combination of convolutional neural networks (CNNs) and hybrid active contours. Specifically, we first introduce a multi-channel 3D U-Net to segment the tumor with the concatenated PET and CT images. Then, we estimate the segmentation uncertainty by model ensembles and define a segmentation quality score to select the cases with high uncertainties. Finally, we develop a hybrid active contour model to refine the high uncertainty cases. Our method ranked second place in the MICCAI 2020 HECKTOR challenge with average Dice Similarity Coefficient, precision, and recall of 0.752, 0.838, and 0.717, respectively.

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arXiv2020

Unifying Homophily and Heterophily Network Transformation via Motifs

Higher-order proximity (HOP) is fundamental for most network embedding methods due to its significant effects on the quality of node embedding and performance on downstream network analysis tasks. Most existing HOP definitions are based on either homophily to place close and highly interconnected nodes tightly in embedding space or heterophily to place distant but structurally similar nodes together after embedding. In real-world networks, both can co-exist, and thus considering only one could limit the prediction performance and interpretability. However, there is no general and universal solution that takes both into consideration. In this paper, we propose such a simple yet powerful framework called homophily and heterophliy preserving network transformation (H2NT) to capture HOP that flexibly unifies homophily and heterophily. Specifically, H2NT utilises motif representations to transform a network into a new network with a hybrid assumption via micro-level and macro-level walk paths. H2NT can be used as an enhancer to be integrated with any existing network embedding methods without requiring any changes to latter methods. Because H2NT can sparsify networks with motif structures, it can also improve the computational efficiency of existing network embedding methods when integrated. We conduct experiments on node classification, structural role classification and motif prediction to show the superior prediction performance and computational efficiency over state-of-the-art methods. In particular, DeepWalk-based H2 NT achieves 24% improvement in terms of precision on motif prediction, while reducing 46% computational time compared to the original DeepWalk.

cs.SI↗

arXiv2020

Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation

Convolutional Neural Networks (CNNs) have achieved high accuracy for cardiac structure segmentation if training cases and testing cases are from the same distribution. However, the performance would be degraded if the testing cases are from a distinct domain (e.g., new MRI scanners, clinical centers). In this paper, we propose a histogram matching (HM) data augmentation method to eliminate the domain gap. Specifically, our method generates new training cases by using HM to transfer the intensity distribution of testing cases to existing training cases. The proposed method is quite simple and can be used in a plug-and-play way in many segmentation tasks. The method is evaluated on MICCAI 2020 M\&Ms challenge, and achieves average Dice scores of 0.9051, 0.8405, and 0.8749, and Hausdorff Distances of 9.996, 12.49, and 12.68 for the left ventricular, myocardium, and right ventricular, respectively. Our results rank the third place in MICCAI 2020 M\&Ms challenge. The code and trained models are publicly available at \url{https://github.com/JunMa11/HM_DataAug}.

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arXiv2020

EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture

In this work, we address the Text-to-Speech (TTS) task by proposing a non-autoregressive architecture called EfficientTTS. Unlike the dominant non-autoregressive TTS models, which are trained with the need of external aligners, EfficientTTS optimizes all its parameters with a stable, end-to-end training procedure, while allowing for synthesizing high quality speech in a fast and efficient manner. EfficientTTS is motivated by a new monotonic alignment modeling approach (also introduced in this work), which specifies monotonic constraints to the sequence alignment with almost no increase of computation. By combining EfficientTTS with different feed-forward network structures, we develop a family of TTS models, including both text-to-melspectrogram and text-to-waveform networks. We experimentally show that the proposed models significantly outperform counterpart models such as Tacotron 2 and Glow-TTS in terms of speech quality, training efficiency and synthesis speed, while still producing the speeches of strong robustness and great diversity. In addition, we demonstrate that proposed approach can be easily extended to autoregressive models such as Tacotron 2.

eess.AS↗

arXiv2020

Mixed Information Flow for Cross-domain Sequential Recommendations

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this paper, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit. The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users' current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that mixed information flow network is able to further improve recommendation performance in different domains by modeling mixed information flow.

cs.IR↗

arXiv2020

New Determination of Fundamental Properties of Palomar 5 Using Deep DESI Imaging Data

The legacy imaging surveys for the Dark Energy Spectroscopic Instrument project provides multiplecolor photometric data, which are about 2 mag deeper than the SDSS. In this study, we redetermine the fundamental properties for an old halo globular cluster of Palomar 5 based on these new imaging data, including structure parameters, stellar population parameters, and luminosity and mass functions. These characteristics, together with its tidal tails, are key for dynamical studies of the cluster and constraining the mass model of the Milky Way. By fitting the King model to the radial surface density profile of Palomar 5, we derive the core radius of $r_c$ = 2.96' $\pm$ 0.11', tidal radius of $r_t$ = 17.99' $\pm$ 1.49', and concentration parameter of $c$ = 0.78 $\pm$ 0.04. We apply a Bayesian analysis method to derive the stellar population properties and get an age of 11.508 $\pm$ 0.027 Gyr, metallicity of [Fe/H] = -1.798 $\pm$ 0.014, reddening of $E(B-V)$ = 0.0552 $\pm$ 0.0005, and distance modulus of $(m-M)_0$ = 16.835 $\pm$ 0.006. The main-sequence luminosity and mass functions for both the cluster center, and tidal tails are investigated. The luminosity and mass functions at different distances from the cluster center suggest that there is obvious spatial mass segregation. Many faint low-mass stars have been evaporated at the cluster center and the tidal tails are enhanced by low-mass stars. Both the concentration and relaxation times suggest that Palomar 5 is a totally relaxed system.

astro-ph.GA↗

arXiv2020

Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, e.g., few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results: Based on the state-of-the-art network, we provide more than 40 pre-trained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average Dice Similarity Coefficient (DSC) scores of 97.3\%, 97.7\%, and 67.3\% and average Normalized Surface Dice (NSD) scores of 90.6\%, 91.4\%, and 70.0\% for left lung, right lung, and infection, respectively. Conclusions: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation and the largest number of pre-trained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.

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