arXiv subjects

Jun Ma

Publications and source records attributed to Jun Ma.

At least 379 records · Page 21Linked to original sources

arXiv2020

Estimating ages and metallicities of M31 star clusters from LAMOST DR6

Context. Determining the metallicities and ages of M31 clusters is fundamental to the study of the formation and evolution of M31 itself. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has carried out a systematic spectroscopic campaign of clusters and candidates in M31. Aims. We constructed a catalogue of 346 M31 clusters observed by LAMOST. By combining the information of the LAMOST spectra and the multi-band photometry, we developed a new algorithm to estimate the metallicities and ages of these clusters. Methods. We distinguish young clusters from old using random forest classifiers based on a empirical training data set selected from the literature. Ages of young clusters are derived from the spectral energy distribution (SED) fits of their multi-band photometric measurements. Their metallicities are estimated by fitting their observed spectral principal components extracted from the LAMOST spectra with those from the young metal-rich single stellar population (SSP) models. For old clusters, we built non-parameter random forest models between the spectral principal components and/or multi-band colours and the parameters of the clusters based on a training data set constructed from the SSP models. The ages and metallicities of the old clusters are then estimated by fitting their observed spectral principal components extracted from the LAMOST spectra and multi-band colours from the photometric measurements with the resultant random forest models. Results. We derived parameters of 53 young and 293 old clusters in our catalogue. Our resultant parameters are in good agreement with those from the literature. The ages of about 30 catalogued clusters and metallicities of about 40 sources are derived for the first time.

astro-ph.GA↗

arXiv2020

Two substructures in the nearby stellar halo found in Gaia and RAVE

We use the second Gaia data release (Gaia DR2), combined with RAVE spectroscopic surveys, to identify the substructures in the nearby stellar halo. We select 3,845 halo stars kinematically and chemically, and determine their density distribution in energy and angular momentum space. To select the substructures from overdensities, we reshuffle the velocities and estimate their significance. Two statistically significant substructures, GR-1 and GR-2, are identified. GR-1 has a high binding energy and small $z$-angular momentum. GR-2 is metal-rich but retrograde. They are both new substructure, and may be accretion debris of dwarf galaxies.

astro-ph.GA↗

arXiv2020

Existence of the Metal-Rich Stellar Halo and High-velocity Thick Disk in the Galaxy

Based on the second Gaia data release (DR2), combined with the LAMOST and APOGEE spectroscopic surveys, we study the kinematics and metallicity distribution of the high-velocity stars that have a relative speed of at least 220 ${\rm km\ s^{-1}}$ with respect to the local standard of rest in the Galaxy. The rotational velocity distribution of the high-velocity stars with [Fe/H] $>-1.0$ dex can be well described by a two-Gaussian model, with peaks at $V_ϕ\sim +164.2\pm0.7$ and $V_ϕ\sim +3.0\pm0.3$ ${\rm km\ s^{-1}}$, associated with the thick disk and halo, respectively. This implies that there should exist a high-velocity thick disk (HVTD) and a metal-rich stellar halo (MRSH) in the Galaxy. The HVTD stars have the same position as the halo in the Toomre diagram and but show the same rotational velocity and metallicity as the canonical thick disk. The MRSH stars have basically the same rotational velocity, orbital eccentricity, and position in the Lindblad and Toomre diagram as the canonical halo stars, but they are more metal-rich. Furthermore, the metallicity distribution function (MDF) of our sample stars are well fitted by a four-Gaussian model, associated with the outer-halo, inner-halo, MRSH, and HVTD, respectively. Chemical and kinematic properties and age imply that the MRSH and HVTD stars may form in situ.

astro-ph.GA↗

arXiv2020

Eulerian pairs and Eulerian recurrence systems

In this paper, we characterize a duality relation between Eulerian recurrences and Eulerian recurrence systems, which generalizes and unifies Hermite-Biehler decompositions of several enumerative polynomials, including flag descent polynomials for hyperoctahedral group, flag ascent-plateau polynomials for Stirling permutations, up-down run polynomials for symmetric group and alternating run polynomials for hyperoctahedral group. As applications, we derive some properties of associated enumerative polynomials. In particular, we find that both the ascent-plateau polynomials and left ascent-plateau polynomials for Stirling permutations are alternatingly increasing, and so they are unimodal with modes in the middle.

math.CO↗

arXiv2020

Peak thermoelectric power factor of holey silicon films

The thermoelectric properties of nanostructured silicon are not fully understood despite their initial promise. While the anomalously low thermal conductivity has attracted much work, the impact of nanostructuring on the power factor has mostly escaped attention. While initial reports did not find any significant changes to the power factor compared to the bulk, subsequent detailed measurements on p-type silicon nanowires showed a stark reduction in the Seebeck coefficient when compared to similarly doped bulk. The reduction is consistent with the disappearance of the phonon drag contribution, due to phonon boundary scattering. Here, we report measurements on a different nanostructure, holey silicon films, to test if similar loss of phonon drag can be observed. By devising experiments where all properties are measured on the same sample, we show that though these films possess electrical conductivity close to that in the bulk at comparable doping, they exhibit considerably smaller thermopower. The data are consistent with loss of phonon drag. At neck distances between 120 - 230 nm, the power factor at optimal doping is $\sim$ 50 percent that of the bulk. These insights are useful in the practical design of future thermoelectric devices based on nanostructured silicon.

cond-mat.mtrl-sci↗

arXiv2020

On Robust Stability and Performance with a Fixed-Order Controller Design for Uncertain Systems

Typically, it is desirable to design a control system that is not only robustly stable in the presence of parametric uncertainties but also guarantees an adequate level of system performance. However, most of the existing methods need to take all extreme models over an uncertain domain into consideration, which then results in costly computation. Also, since these approaches attempt (rather unrealistically) to guarantee the system performance over a full frequency range, a conservative design is always admitted. Here, taking a specific viewpoint of robust stability and performance under a stated restricted frequency range (which is applicable in rather many real-world situations), this paper provides an essential basis for the design of a fixed-order controller for a system with bounded parametric uncertainties. A Hurwitz polynomial is used in the design and the robust stability is characterized by the notion of positive realness, such that the required robust stability condition is then suitably successfully constructed. Also, the robust performance criteria in terms of sensitivity shaping under different frequency ranges are constructed based on an approach of bounded realness analysis. Necessary and sufficient conditions are provided for both the robust stability and robust performance criteria. Furthermore, these conditions are expressed in the framework of linear matrix inequality (LMI) constraints, and thus can be efficiently solved. Comparative simulations are provided to illustrate the effectiveness and efficiency of the proposed approach.

math.OC↗

arXiv2020

Grasping Detection Network with Uncertainty Estimation for Confidence-Driven Semi-Supervised Domain Adaptation

Data-efficient domain adaptation with only a few labelled data is desired for many robotic applications, e.g., in grasping detection, the inference skill learned from a grasping dataset is not universal enough to directly apply on various other daily/industrial applications. This paper presents an approach enabling the easy domain adaptation through a novel grasping detection network with confidence-driven semi-supervised learning, where these two components deeply interact with each other. The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence, which we referred it as the confidence-driven mean teacher. This approach largely prevents the student model to learn the incorrect/harmful information from the consistency loss, which speeds up the learning progress and improves the model accuracy. Our results show that the proposed network can achieve high success rate on the Cornell grasping dataset, and for domain adaptation with very limited data, the confidence-driven mean teacher outperforms the original mean teacher and direct training by more than 10% in evaluation loss especially for avoiding the overfitting and model diverging.

cs.CV↗

arXiv2020

The gamma-positivity of Eulerian polynomials and succession statistics

This paper is concerned with multivariate refinements of the gamma-positivity of Eulerian polynomials by using the succession and fixed point statistics. Properties of the enumerative polynomials for permutations, signed permutations and derangements, including generating functions and gamma-positivity are studied, which generalize and unify earlier results of Athanasiadis, Brenti, Chow, Petersen, Roselle, Stembridge, Shin and Zeng. In particular, we derive a formula expressing the joint distribution of excedance number and negative number statistics over the type B derangements in terms of the derangement polynomials.

math.CO↗

arXiv2020

A Mysterious Ring in Dark Space?

We report the discovery of a low-surface-brightness (27.42 mag arcsec^(-2) in g band) nebula, which has a ring-like shape in the Beijing-Arizona Sky Survey (BASS). Positive detections have been found in multiband data from far ultraviolet to far infrared, except the z band from BASS and W1, W2 from the Wide-field Infrared Survey Explorer. The reddening of the nebula E(B - V) ~ 0.02 mag is estimated from Infrared Astronomical Satellite (IRAS) 100 micron intensity and HI column density. With the help of the 3D reddening map from Pan-STARRS 1, the Two Micron All Sky Survey, and Gaia, the distance to the nebula of about 500 pc from Earth is derived. Such a low-surface-brightness nebula whose energy can be interpreted by the diffuse Galactic light could account for the optical counterpart of the infrared cirrus, which was detected by IRAS more than 30 yr ago. The ring-like structure might be the ultimate phase of an evolved planetary nebula, while the central white dwarf star has been ejected from the nebula for an unclear reason. On the other hand, the ring structure being a superposition of two close filaments might be another reasonable explanation. Considering the lack of spectroscopic data and uncertainty in the distance measurement, these interpretations need to be checked by future observations.

astro-ph.GA↗

arXiv2020

The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge

There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-quality 3D segmentations of these structures. Recently, methods based on deep learning have shown excellent results in automatic 3D segmentation, but they require large datasets for training, and there remains little consensus on which methods perform best. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem. A training set of 210 cross sectional CT images with kidney tumors was publicly released with corresponding semantic segmentation masks. 106 teams from five continents used this data to develop automated systems to predict the true segmentation masks on a test set of 90 CT images for which the corresponding ground truth segmentations were kept private. These predictions were scored and ranked according to their average So rensen-Dice coefficient between the kidney and tumor across all 90 cases. The winning team achieved a Dice of 0.974 for kidney and 0.851 for tumor, approaching the inter-annotator performance on kidney (0.983) but falling short on tumor (0.923). This challenge has now entered an "open leaderboard" phase where it serves as a challenging benchmark in 3D semantic segmentation.

eess.IV↗

arXiv2020

Trajectory Generation by Chance Constrained Nonlinear MPC with Probabilistic Prediction

Continued great efforts have been dedicated towards high-quality trajectory generation based on optimization methods, however, most of them do not suitably and effectively consider the situation with moving obstacles; and more particularly, the future position of these moving obstacles in the presence of uncertainty within some possible prescribed prediction horizon. To cater to this rather major shortcoming, this work shows how a variational Bayesian Gaussian mixture model (vBGMM) framework can be employed to predict the future trajectory of moving obstacles; and then with this methodology, a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of moving obstacles, and also incorporating presence of uncertainty within a prediction horizon. In this work, the full predictive conditional probability density function (PDF) with mean and covariance is obtained, and thus a future trajectory with uncertainty is formulated as a collision region represented by a confidence ellipsoid. To avoid the collision region, chance constraints are imposed to restrict the collision probability, and subsequently a nonlinear MPC problem is constructed with these chance constraints. It is shown that the proposed approach is able to predict the future position of the moving obstacles effectively; and thus based on the environmental information of the probabilistic prediction, it is also shown that the timing of collision avoidance can be earlier than the method without prediction. The tracking error and distance to obstacles of the trajectory with prediction are smaller compared with the method without prediction.

eess.SY↗

arXiv2020

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

To better understand early brain growth patterns in health and disorder, it is critical to accurately segment infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners. To promote methodological development in the community, iSeg-2019 challenge (http://iseg2019.web.unc.edu) provides a set of 6-month infant subjects from multiple sites with different protocols/scanners for the participating methods. Training/validation subjects are from UNC (MAP) and testing subjects are from UNC/UMN (BCP), Stanford University, and Emory University. By the time of writing, there are 30 automatic segmentation methods participating in iSeg-2019. We review the 8 top-ranked teams by detailing their pipelines/implementations, presenting experimental results and evaluating performance in terms of the whole brain, regions of interest, and gyral landmark curves. We also discuss their limitations and possible future directions for the multi-site issue. We hope that the multi-site dataset in iSeg-2019 and this review article will attract more researchers on the multi-site issue.

eess.IV↗

arXiv2020

Data-Driven Multi-Objective Controller Optimization for a Magnetically-Levitated Nanopositioning System

The performance achieved with traditional model-based control system design approaches typically relies heavily upon accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task; and the usually necessary practical approximations often render the automation system to operate in a non-optimal condition. This problem can be greatly aggravated in the case of a multi-axis magnetically-levitated nanopositioning system where the fully floating behavior and multi-axis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under non-optimal conditions. These motion data essentially contain rich information; therefore, the possibility exists to develop an intelligent automation system to learn from these motion data and to drive the system to operate towards optimality in a data-driven manner. Along this line then, this paper proposes a data-driven controller optimization approach that learns from the past non-optimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multi-objective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multi-objective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. Experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available.

eess.SY↗

arXiv2020

AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types.

cs.AI↗

arXiv2020

A genetic algorithm approach to fitting interferometric data of post-AGB objects: I. the case of the Ant nebula

We present GADRAD, a Python module that adopts heuristic search techniques in the form of genetic algorithms, to efficiently model post-asymptotic giant branch (post- AGB) disc environments. GADRAD systematically constructs the multi-dimensional pa- parameter probability density functions that arise from the fitting of radiative transfer and geometric models to optical interferometric data products. The result provides unbiased descriptions of the object's potential morphology, component luminosities and temperatures, dust composition, disc density profiles and mass. Correlation in the estimated parameters as well as potential degeneracies are revealed. Estimated probability distributions of the post-AGB environment parameters provide insight into the shaping processes that may occur in the transition from the post-AGB to the planetary nebula phase. We test parameter recovery on simulated artificial data products of a typical post-AGB environment. We then use GADRAD to model the mid-infrared spectrum and visibilities of the Ant nebula (Mz3), taken with the Very Large Telescope Interferometer's instrument MIDI. Our result is consistent with a large dusty disc with similar parameter values to those previously found by Chesneau et al., except for a larger dust mass of $3.5^{+7.5}_{-2.2}\times10^{-5}$ M$_{\odot}$. The parameter confidence intervals determined by GADRAD, can however be relied upon to impose additional constraints on all disc and system parameters. Based on our analysis and other considerations, we tentatively suggest that Mz3 is a pre-PN ejected during a magnetic (polar) common envelope interaction, where the binary may or may not have survived at the core of the nebula.

astro-ph.SR↗

arXiv2020

Segmentation Loss Odyssey

Loss functions are one of the crucial ingredients in deep learning-based medical image segmentation methods. Many loss functions have been proposed in existing literature, but are studied separately or only investigated with few other losses. In this paper, we present a systematic taxonomy to sort existing loss functions into four meaningful categories. This helps to reveal links and fundamental similarities between them. Moreover, we explore the relationship between the traditional region-based and the more recent boundary-based loss functions. The PyTorch implementations of these loss functions are publicly available at \url{https://github.com/JunMa11/SegLoss}.

eess.IV↗

arXiv2020

Plateaux on generalized Stirling permutations and partial $γ$-positivity

We prove that the enumerative polynomials of generalized Stirling permutations by the statistics of plateaux, descents and ascents are partial $γ$-positive. Specialization of our result to the Jacobi-Stirling permutations confirms a recent partial $γ$-positivity conjecture due to Ma, Yeh and the second named author. Our partial $γ$-positivity expansion, as well as a combinatorial interpretation for the corresponding $γ$-coefficients, are obtained via the machine of context-free grammars and a group action on generalized Stirling permutations. Besides, we also provide an alternative approach to the partial $γ$-positivity from the stability of certain multivariate polynomials.

math.CO↗

arXiv2020

TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories

Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for a single category of product, and thus do not apply to real-life e-Commerce scenarios, which often contain thousands of diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge extraction model that applies to thousands of product categories organized in a hierarchical taxonomy. Through category conditional self-attention and multi-task learning, our approach is both scalable, as it trains a single model for thousands of categories, and effective, as it extracts category-specific attribute values. Experiments on products from a taxonomy with 4,000 categories show that TXtract outperforms state-of-the-art approaches by up to 10% in F1 and 15% in coverage across all categories.

cs.CL↗