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Yan Xu

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preprint2026arXiv

PIVOT: Bridging Planning and Execution in LLM Agents via Trajectory Refinement

Large language model (LLM)-based agents frequently generate seemingly coherent plans that fail upon execution due to infeasible actions, constraint violations, and compounding errors over extended horizons. PIVOT (Plan-Inspect-eVOlve Trajectories) addresses this plan-execution misalignment through a self-supervised framework that treats trajectories as optimizable objects iteratively refined via environment interaction. The framework comprises four stages: PLAN generates candidate trajectories; INSPECT executes them and computes structured losses with textual gradients encoding plan-execution discrepancies; EVOLVE applies these signals to produce improved trajectories; and VERIFY performs a final global check against task constraints. A monotonic acceptance process ensures a non-decreasing solution quality. Empirical evaluations on DeepPlanning and GAIA demonstrate state-of-the-art performance: with human-in-the-loop (HITL) feedback, PIVOT establishes a strong upper bound up to 94% relative improvement in constraint satisfaction, while its fully autonomous variant retains substantial gains, showing that the core trajectory-refinement mechanism remains effective without external supervision. At the same time, PIVOT remains computationally efficient, requiring up to 3x to 5x fewer tokens than competing refinement methods. These findings establish that (self- or human-supervised) feedback-based trajectory optimization is a principled methodology for mitigating plan-execution gaps in autonomous agent systems.

preprint2022arXiv

Analytic smoothing effect of the time variable for the spatially homogeneous Landau equation

In this work, we study the Cauchy problem of the spatially homogeneous Landau equation with hard potentials in a close-to-quilibrium framework. We prove that the solution to the Cauchy problem enjoys the analytic regularizing effect of the time variable with an L2 initial datum for positive time. So that the smoothing effect of Cauchy problem for the spatially homogeneous Landau equation with hard potentials is exactly same as heat equation.

preprint2022arXiv

Can Question Rewriting Help Conversational Question Answering?

Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA. We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.

preprint2022arXiv

Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning

Visual grounding is a task to locate the target indicated by a natural language expression. Existing methods extend the generic object detection framework to this problem. They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text. However, modeling the visual features from these predefined locations may fail to fully exploit the visual context and attribute information in the text query, which limits their performance. In this paper, we propose a transformer-based framework for accurate visual grounding by establishing text-conditioned discriminative features and performing multi-stage cross-modal reasoning. Specifically, we develop a visual-linguistic verification module to focus the visual features on regions relevant to the textual descriptions while suppressing the unrelated areas. A language-guided feature encoder is also devised to aggregate the visual contexts of the target object to improve the object's distinctiveness. To retrieve the target from the encoded visual features, we further propose a multi-stage cross-modal decoder to iteratively speculate on the correlations between the image and text for accurate target localization. Extensive experiments on five widely used datasets validate the efficacy of our proposed components and demonstrate state-of-the-art performance. Our code is public at https://github.com/yangli18/VLTVG.

preprint2022arXiv

Iterative Adaptively Regularized LASSO-ADMM Algorithm for CFAR Estimation of Sparse Signals: IAR-LASSO-ADMM-CFAR Algorithm

The least-absolute shrinkage and selection operator (LASSO) is a regularization technique for estimating sparse signals of interest emerging in various applications and can be efficiently solved via the alternating direction method of multipliers (ADMM), which will be termed as LASSO-ADMM algorithm. The choice of the regularization parameter has significant impact on the performance of LASSO-ADMM algorithm. However, the optimization for the regularization parameter in the existing LASSO-ADMM algorithms has not been solved yet. In order to optimize this regularization parameter, we propose an efficient iterative adaptively regularized LASSO-ADMM (IAR-LASSO-ADMM) algorithm by iteratively updating the regularization parameter in the LASSO-ADMM algorithm. Moreover, a method is designed to iteratively update the regularization parameter by adding an outer iteration to the LASSO-ADMM algorithm. Specifically, at each outer iteration the zero support of the estimate obtained by the inner LASSO-ADMM algorithm is utilized to estimate the noise variance, and the noise variance is utilized to update the threshold according to a pre-defined const false alarm rate (CFAR). Then, the resulting threshold is utilized to update both the non-zero support of the estimate and the regularization parameter, and proceed to the next inner iteration. In addition, a suitable stopping criterion is designed to terminate the outer iteration process to obtain the final non-zero support of the estimate of the sparse measurement signals. The resulting algorithm is termed as IAR-LASSO-ADMM-CFAR algorithm. Finally, simulation results have been presented to show that the proposed IAR-LASSO-ADMM-CFAR algorithm outperforms the conventional LASSO-ADMM algorithm and other existing algorithms in terms of reconstruction accuracy, and its sparsity order estimate is more accurate than the existing algorithms.

preprint2022arXiv

Nanoscale three-dimensional magnetic sensing with a probabilistic nanomagnet driven by spin-orbit torque

Detection of vector magnetic fields at nanoscale dimensions is critical in applications ranging from basic material science, to medical diagnostic. Meanwhile, an all-electric operation is of great significance for achieving a simple and compact sensing system. Here, we propose and experimentally demonstrate a simple approach to sensing a vector magnetic field at nanoscale dimensions, by monitoring a probabilistic nanomagnet's transition probability from a metastable state, excited by a driving current due to SOT, to a settled state. We achieve sensitivities for Hx, Hy, and Hz of 1.02%/Oe, 1.09%/Oe and 3.43%/Oe, respectively, with a 200 x 200 nm^2 nanomagnet. The minimum detectable field is dependent on the driving pulse events N, and is expected to be as low as 1 uT if N = 3 x 10^6.

preprint2022arXiv

Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

To diversify and enrich generated dialogue responses, knowledge-grounded dialogue has been investigated in recent years. The existing methods tackle the knowledge grounding challenge by retrieving the relevant sentences over a large corpus and augmenting the dialogues with explicit extra information. Despite their success, however, the existing works have drawbacks in inference efficiency. This paper proposes KnowExpert, a framework to bypass the explicit retrieval process and inject knowledge into the pre-trained language models with lightweight adapters and adapt to the knowledge-grounded dialogue task. To the best of our knowledge, this is the first attempt to tackle this challenge without retrieval in this task under an open-domain chit-chat scenario. The experimental results show that Knowexpert performs comparably with some retrieval-based baselines while being time-efficient in inference, demonstrating the effectiveness of our proposed method.

preprint2022arXiv

RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization

6-DoF object pose estimation from a monocular image is challenging, and a post-refinement procedure is generally needed for high-precision estimation. In this paper, we propose a framework based on a recurrent neural network (RNN) for object pose refinement, which is robust to erroneous initial poses and occlusions. During the recurrent iterations, object pose refinement is formulated as a non-linear least squares problem based on the estimated correspondence field (between a rendered image and the observed image). The problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm enabling end-to-end training. The correspondence field estimation and pose refinement are conducted alternatively in each iteration to recover the object poses. Furthermore, to improve the robustness to occlusion, we introduce a consistency-check mechanism based on the learned descriptors of the 3D model and observed 2D images, which downweights the unreliable correspondences during pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and YCB-Video datasets validate the effectiveness of our method and demonstrate state-of-the-art performance.

preprint2022arXiv

Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling

The correct ego-motion estimation basically relies on the understanding of correspondences between adjacent LiDAR scans. However, given the complex scenarios and the low-resolution LiDAR, finding reliable structures for identifying correspondences can be challenging. In this paper, we delve into structure reliability for accurate self-supervised ego-motion estimation and aim to alleviate the influence of unreliable structures in training, inference and mapping phases. We improve the self-supervised LiDAR odometry substantially from three aspects: 1) A two-stage odometry estimation network is developed, where we obtain the ego-motion by estimating a set of sub-region transformations and averaging them with a motion voting mechanism, to encourage the network focusing on representative structures. 2) The inherent alignment errors, which cannot be eliminated via ego-motion optimization, are down-weighted in losses based on the 3D point covariance estimations. 3) The discovered representative structures and learned point covariances are incorporated in the mapping module to improve the robustness of map construction. Our two-frame odometry outperforms the previous state of the arts by 16%/12% in terms of translational/rotational errors on the KITTI dataset and performs consistently well on the Apollo-Southbay datasets. We can even rival the fully supervised counterparts with our mapping module and more unlabeled training data.

preprint2022arXiv

SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks

Recent learning-based LiDAR odometry methods have demonstrated their competitiveness. However, most methods still face two substantial challenges: 1) the 2D projection representation of LiDAR data cannot effectively encode 3D structures from the point clouds; 2) the needs for a large amount of labeled data for training limit the application scope of these methods. In this paper, we propose a self-supervised LiDAR odometry method, dubbed SelfVoxeLO, to tackle these two difficulties. Specifically, we propose a 3D convolution network to process the raw LiDAR data directly, which extracts features that better encode the 3D geometric patterns. To suit our network to self-supervised learning, we design several novel loss functions that utilize the inherent properties of LiDAR point clouds. Moreover, an uncertainty-aware mechanism is incorporated in the loss functions to alleviate the interference of moving objects/noises. We evaluate our method's performances on two large-scale datasets, i.e., KITTI and Apollo-SouthBay. Our method outperforms state-of-the-art unsupervised methods by 27%/32% in terms of translational/rotational errors on the KITTI dataset and also performs well on the Apollo-SouthBay dataset. By including more unlabelled training data, our method can further improve performance comparable to the supervised methods.

preprint2022arXiv

Transformer based multiple instance learning for weakly supervised histopathology image segmentation

Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. The development of weakly supervised segmentation algorithm alleviates the problem of medical image annotation that it is time-consuming and labor-intensive. As a subset of weakly supervised learning, Multiple Instance Learning (MIL) has been proven to be effective in segmentation. However, there is a lack of related information between instances in MIL, which limits the further improvement of segmentation performance. In this paper, we propose a novel weakly supervised method for pixel-level segmentation in histopathology images, which introduces Transformer into the MIL framework to capture global or long-range dependencies. The multi-head self-attention in the Transformer establishes the relationship between instances, which solves the shortcoming that instances are independent of each other in MIL. In addition, deep supervision is introduced to overcome the limitation of annotations in weakly supervised methods and make the better utilization of hierarchical information. The state-of-the-art results on the colon cancer dataset demonstrate the superiority of the proposed method compared with other weakly supervised methods. It is worth believing that there is a potential of our approach for various applications in medical images.

preprint2022arXiv

WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques.

preprint2021arXiv

CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results

As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Recently, a large-scale face anti-spoofing dataset, CelebA-Spoof which comprised of 625,537 pictures of 10,177 subjects has been released. It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects. This paper reports methods and results in the CelebA-Spoof Challenge 2020 on Face AntiSpoofing which employs the CelebA-Spoof dataset. The model evaluation is conducted online on the hidden test set. A total of 134 participants registered for the competition, and 19 teams made valid submissions. We will analyze the top ranked solutions and present some discussion on future work directions.

preprint2021arXiv

Multi-hop Question Generation with Graph Convolutional Network

Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation. The code is publicly available at https://github.com/HLTCHKUST/MulQG}{https://github.com/HLTCHKUST/MulQG .

preprint2021arXiv

Multi-Passband Observations of A Solar Flare over the He I 10830 Å line

This study presents a C3.0 flare observed by the BBSO/GST and IRIS, on 2018-May-28 around 17:10 UT. The Near Infrared Imaging Spectropolarimeter (NIRIS) of GST was set to spectral imaging mode to scan five spectral positions at $\pm$ 0.8 Å, $\pm$ 0.4 Åand line center of He I 10830. At the flare ribbon's leading edge the line is observed to undergo enhanced absorption, while the rest of the ribbon is observed to be in emission. When in emission, the contrast compared to the pre-flare ranges from about $30~\%$ to nearly $100~\%$ at different spectral positions. Two types of spectra, "convex" shape with higher intensity at line core and "concave" shape with higher emission in the line wings, are found at the trailing and peak flaring areas, respectively. On the ribbon front, negative contrasts, or enhanced absorption, of about $\sim 10\% - 20\%$ appear in all five wavelengths. This observation strongly suggests that the negative flares observed in He I 10830 with mono-filtergram previously were not caused by pure Doppler shifts of this spectral line. Instead, the enhanced absorption appears to be a consequence of flare energy injection, namely non-thermal collisional ionization of helium caused by the precipitation of high energy electrons, as found in our recent numerical modeling results. In addition, though not strictly simultaneous, observations of Mg II from the IRIS spacecraft, show an obvious central reversal pattern at the locations where enhanced absorption of He I 10830 is seen, which is in consistent with previous observations.

preprint2020arXiv

A New Comprehensive Data Set of Solar Filaments of 100 yr Interval. I

Filaments are very common physical phenomena on the Sun and are often taken as important proxies of solar magnetic activities. The study of filaments has become a hot topic in the space weather research. For a more comprehensive understanding of filaments, especially for an understanding of solar activities of multiple solar cycles, it is necessary to perform a combined multifeature analysis by constructing a data set of multiple solar cycle data. To achieve this goal, we constructed a centennial data set that covers the H$α$ data from five observatories around the world. During the data set construction, we encountered varieties of problems, such as data fusion, accurate determination of the solar edge, classifying data by quality, dynamic threshold, and so on, which arose mainly due to multiple sources and a large time span of data. But fortunately, these problems were well solved. The data set includes seven types of data products and eight types of feature parameters with which we can implement the functions of data searching and statistical analyses. It has the characteristics of better continuity and highly complementary to space observation data, especially in the wavelengths not covered by space observations, and covers many solar cycles (including more than 60 yr of high-cadence data). We expect that this new comprehensive data set as well as the tools will help researchers to significantly speed up their search for features or events of interest, for either statistical or case study purposes, and possibly help them get a better and more comprehensive understanding of solar filament mechanisms.

preprint2020arXiv

A Public Website for the Automated Assessment and Validation of SARS-CoV-2 Diagnostic PCR Assays

Summary: Polymerase chain reaction-based assays are the current gold standard for detecting and diagnosing SARS-CoV-2. However, as SARS-CoV-2 mutates, we need to constantly assess whether existing PCR-based assays will continue to detect all known viral strains. To enable the continuous monitoring of SARS-CoV-2 assays, we have developed a web-based assay validation algorithm that checks existing PCR-based assays against the ever-expanding genome databases for SARS-CoV-2 using both thermodynamic and edit-distance metrics. The assay screening results are displayed as a heatmap, showing the number of mismatches between each detection and each SARS-CoV-2 genome sequence. Using a mismatch threshold to define detection failure, assay performance is summarized with the true positive rate (recall) to simplify assay comparisons. Availability: https://covid19.edgebioinformatics.org/#/assayValidation. Contact: Jason Gans (jgans@lanl.gov) and Patrick Chain (pchain@lanl.gov)

preprint2020arXiv

An ultraweak-local discontinuous Galerkin method for PDEs with high order spatial derivatives

In this paper, we develop a new discontinuous Galerkin method for solving several types of partial differential equations (PDEs) with high order spatial derivatives. We combine the advantages of local discontinuous Galerkin (LDG) method and ultra-weak discontinuous Galerkin (UWDG) method. Firstly, we rewrite the PDEs with high order spatial derivatives into a lower order system, then apply the UWDG method to the system. We first consider the fourth order and fifth order nonlinear PDEs in one space dimension, and then extend our method to general high order problems and two space dimensions. The main advantage of our method over the LDG method is that we have introduced fewer auxiliary variables, thereby reducing memory and computational costs. The main advantage of our method over the UWDG method is that no internal penalty terms are necessary in order to ensure stability for both even and odd order PDEs. We prove stability of our method in the general nonlinear case and provide optimal error estimates for linear PDEs for the solution itself as well as for the auxiliary variables approximating its derivatives. A key ingredient in the proof of the error estimates is the construction of the relationship between the derivative and the element interface jump of the numerical solution and the auxiliary variable solution of the solution derivative. With this relationship, we can then use the discrete Sobolev and Poincaré inequalities to obtain the optimal error estimates. The theoretical findings are confirmed by numerical experiments.

preprint2020arXiv

Comparison of Enhanced Absorption in He I 10830 Å in Observations and Modeling During the Early Phase of a Solar Flare

The He I 10830 Å triplet is a very informative indicator of chromospheric activities as the helium is the second most abundant element in the solar atmosphere. Taking advantage of the high resolution of the 1.6 m Goode Solar Telescope (GST) at Big Bear Solar Observatory (BBSO), previous observations have shown clear evidence of the enhanced absorption, instead of typically-observed emission, for two M-class flares. In this study, we analyze the evolution of the He I 10830 10830 Å emission in numerical models and compare it with observations. The models represent the RADYN simulation results obtained from the F-CHROMA database. We consider the models with the injected electron spectra parameters close to observational estimates for the 2013-August-17 flare event ($δ=8$, $E_c = \{15,20\}$ keV, $F=\{1\times 10^{11}, 3\times{}10^{11}\}$ erg cm$^{-2}$) in detail, as well as other available models. The modeling results agree well with observations, in the sense of both the maximum intensity decrease (-17.1%, compared to the observed value of -13.7%) and the trend of temporal variation (initial absorption phase followed by the emission). All models demonstrate the increased number densities and decreased ratio of the upper and lower level populations of He I 10830 10830 Å transition in the initial phase, which enhances the opacity and forms an absorption feature. Models suggest that the temperatures and free electron densities at heights of 1.3-1.5 Mm should be larger than $\sim 10^4$ K and $6\times 10^{11}$ cm$^{-3}$ thresholds for the line to start being in emission.

preprint2020arXiv

Differential rotation of the halo traced by the K-giant stars

We use K-giant stars selected from the LAMOST DR5 to study the variation of the rotational velocity of the galactic halo at different space positions. Modelling the rotational velocity distribution with both the halo and disk components, we find that the rotational velocity of the halo population decreases almost linearly with increasing vertical distance to the galactic disk plane, $Z$, at fixed galactocentric radius, $R$. The samples are separated into two parts with $6<R<12$ kpc and $12<R<20$ kpc. We derive that the decreasing rates along $Z$ for the two subsamples are $-3.07\pm0.63$ and $-1.89\pm0.37$ km s$^{-1}$ kpc$^{-1}$, respectively. Compared with the TNG simulations, we suggest that this trend is probably caused by the interaction between the disk and halo. The results from the simulations show that only the oblate halo can provide a decreasing rotational velocity with an increasing $Z$. This indicates that the Galactic halo is oblate with galactocentric radius $R<20$ kpc. On the other hand, the flaring of the disk component (mainly the thick disk) is clearly traced by this study, with $R$ between 12 and 20 kpc, the disk can vertically extend to $6\sim10$ kpc above the disk plane. What is more interesting is that, we find the Gaia-Enceladus-Sausage (GES) component has a significant contribution only in the halo with $R<12$ kpc, i.e. a fraction of 23$-$47\%. While in the outer subsample, the contribution is too low to be well constrained.

preprint2020arXiv

Differentially Private Combinatorial Cloud Auction

Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this paper, we design a differentially private combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time.

preprint2020arXiv

Estimating 3D Camera Pose from 2D Pedestrian Trajectories

We consider the task of re-calibrating the 3D pose of a static surveillance camera, whose pose may change due to external forces, such as birds, wind, falling objects or earthquakes. Conventionally, camera pose estimation can be solved with a PnP (Perspective-n-Point) method using 2D-to-3D feature correspondences, when 3D points are known. However, 3D point annotations are not always available or practical to obtain in real-world applications. We propose an alternative strategy for extracting 3D information to solve for camera pose by using pedestrian trajectories. We observe that 2D pedestrian trajectories indirectly contain useful 3D information that can be used for inferring camera pose. To leverage this information, we propose a data-driven approach by training a neural network (NN) regressor to model a direct mapping from 2D pedestrian trajectories projected on the image plane to 3D camera pose. We demonstrate that our regressor trained only on synthetic data can be directly applied to real data, thus eliminating the need to label any real data. We evaluate our method across six different scenes from the Town Centre Street and DUKEMTMC datasets. Our method achieves an improvement of $\sim50\%$ on both position and orientation prediction accuracy when compared to other SOTA methods.

preprint2020arXiv

Fair Auction and Trade Framework for Cloud VM Allocation based on Blockchain

Cloud auctions provide cost-effective strategies for cloud VM allocation. Most existing cloud auctions simply assume that the auctioneer is trustable, and thus the fairness of auctions can be easily achieved. However, in fact, such a trustable auctioneer may not exist, and the fairness is non-trivial to guarantee. In this work, for the first time, we propose a decentralized cloud VM auction and trade framework based on blockchain. We realize both auction fairness and trade fairness among participants (e.g., cloud provider and cloud users) in this system, which guarantees the interest of each party will not suffer any loss as long as it follows the protocol. Furthermore, we implement our system through the local blockchain and Ethereum official test blockchain, carry out experimental simulations, and demonstrate the feasibility of our system.

preprint2020arXiv

Few-Shot Learning with Intra-Class Knowledge Transfer

We consider the few-shot classification task with an unbalanced dataset, in which some classes have sufficient training samples while other classes only have limited training samples. Recent works have proposed to solve this task by augmenting the training data of the few-shot classes using generative models with the few-shot training samples as the seeds. However, due to the limited number of the few-shot seeds, the generated samples usually have small diversity, making it difficult to train a discriminative classifier for the few-shot classes. To enrich the diversity of the generated samples, we propose to leverage the intra-class knowledge from the neighbor many-shot classes with the intuition that neighbor classes share similar statistical information. Such intra-class information is obtained with a two-step mechanism. First, a regressor trained only on the many-shot classes is used to evaluate the few-shot class means from only a few samples. Second, superclasses are clustered, and the statistical mean and feature variance of each superclass are used as transferable knowledge inherited by the children few-shot classes. Such knowledge is then used by a generator to augment the sparse training data to help the downstream classification tasks. Extensive experiments show that our method achieves state-of-the-art across different datasets and $n$-shot settings.

preprint2020arXiv

High Dimensional Three-Periods Locally Ideal MIP Formulations for the UC Problem

The thermal unit commitment (UC) problem often can be formulated as a mixed integer quadratic programming (MIQP), which is difficult to solve efficiently, especially for large-scale instances. The tighter characteristic reduces the search space, therefore, as a natural conse-quence, significantly reduces the computational burden. In the literature, many tightened formulations for single units with parts of constraints were reported without presenting how they were derived. In this paper, a sys-tematic approach is developed to formulate the tight formulations. The idea is using more new variables in high dimension space to capture all the states for single units within three periods, and then, using these state variables systematic derive three-periods locally ideal expressions for a subset of the constraints in UC. Meanwhile, the linear dependence relations of those new state variables are leveraged to keep the compactness of the obtained formulations. Based on this approach, we propose two tighter models, namely 3P-HD and 3P-HD-Pr. The proposed models and other four state-of-the-art models were tested on 51 instances, including 42 realistic instances and 9 8-unit-based instances, over a scheduling period of 24 h for systems ranging from 10 to 1080 generating units. The simulation results show that our proposed MIQP UC formulations are the tightest and can be solved most efficiently. After using piecewise technique to approxi-mate the quadratic operational cost function, the six UC MIQP formulations can be approximated by six corre-sponding mixed-integer linear programming (MILP) formulations. Our experiments show that the proposed 3P-HD and 3P-HD-Pr MILP formulations also perform the best in terms of tightness and solution times.

preprint2020arXiv

Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network

We propose a new machine learning approach to Stokes inversion based on a convolutional neural network (CNN) and the Milne-Eddington (ME) method. The Stokes measurements used in this study were taken by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory. By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS. Experimental results show that our CNN method produces smoother and cleaner magnetic maps than the widely used ME method. Furthermore, the CNN method is 4~6 times faster than the ME method, and is able to produce vector magnetic fields in near real-time, which is essential to space weather forecasting. Specifically, it takes ~50 seconds for the CNN method to process an image of 720 x 720 pixels comprising Stokes profiles of GST/NIRIS. Finally, the CNN-inferred results are highly correlated to the ME-calculated results and are closer to the ME's results with the Pearson product-moment correlation coefficient (PPMCC) being closer to 1 on average than those from other machine learning algorithms such as multiple support vector regression and multilayer perceptrons (MLP). In particular, the CNN method outperforms the current best machine learning method (MLP) by 2.6% on average in PPMCC according to our experimental study. Thus, the proposed physics-assisted deep learning-based CNN tool can be considered as an alternative, efficient method for Stokes inversion for high resolution polarimetric observations obtained by GST/NIRIS.

preprint2020arXiv

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.

preprint2020arXiv

MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask

Feature warping is a core technique in optical flow estimation; however, the ambiguity caused by occluded areas during warping is a major problem that remains unsolved. In this paper, we propose an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision. The proposed module can be easily integrated into end-to-end network architectures and enjoys performance gains while introducing negligible computational cost. The learned occlusion mask can be further fed into a subsequent network cascade with dual feature pyramids with which we achieve state-of-the-art performance. At the time of submission, our method, called MaskFlownet, surpasses all published optical flow methods on the MPI Sintel, KITTI 2012 and 2015 benchmarks. Code is available at https://github.com/microsoft/MaskFlownet.

preprint2020arXiv

SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noisy, some features benefit-ting multi-class discrimination are underexploited, such as shape information. In this paper, we propose a novel 3D shape signature to explore the shape information from point clouds. By incorporating operations of symmetry, convex hull and chebyshev fitting, the proposed shape sig-nature is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination. Based on the proposed shape signature, we develop the shape signature networks (SSN) for 3D object detection, which consist of pyramid feature encoding part, shape-aware grouping heads and explicit shape encoding objective. Experiments show that the proposed method performs remarkably better than existing methods on two large-scale datasets. Furthermore, our shape signature can act as a plug-and-play component and ablation study shows its effectiveness and good scalability

preprint2020arXiv

Structure of minimal 2-spheres of constant curvature in the complex hyperquadric

In this paper, the singular-value decomposition theory of complex matrices is explored to study constantly curved 2-spheres minimal in both $\mathbb{C}P^n$ and the hyperquadric of $\mathbb{C}P^n$. The moduli space of all those noncongruent ones is introduced, which can be described by certain complex symmetric matrices modulo an appropriate group action. Using this description, many examples, such as constantly curved holomorphic 2-spheres of higher degree, nonhomogenous minimal 2-spheres of constant curvature, etc., are constructed. Uniqueness is proven for the totally real constantly curved 2-sphere minimal in both the hyperquadric and $\mathbb{C}P^n$.

preprint2019arXiv

An optimal transport problem with backward martingale constraints motivated by insider trading

We study a single-period optimal transport problem on $\mathbb{R}^2$ with a covariance-type cost function $c(x,y) = (x_1-y_1)(x_2-y_2)$ and a backward martingale constraint. We show that a transport plan $γ$ is optimal if and only if there is a maximal monotone set $G$ that supports the $x$-marginal of $γ$ and such that $c(x,y) = \min_{z\in G}c(z,y)$ for every $(x,y)$ in the support of $γ$. We obtain sharp regularity conditions for the uniqueness of an optimal plan and for its representation in terms of a map. Our study is motivated by a variant of the classical Kyle model of insider trading from Rochet and Vila (1994).

preprint2019arXiv

Numerical simulations of strong-field processes in momentum space

The time-dependent Schrodinger equation (TDSE) is usually treated in real space in the textbook. However, it makes the numerical simulations of strong-field processes difficult due to the wide dispersion and fast oscillation of the electron wave packets under the interaction of intense laser fields. Here we demonstrate that the TDSE can be efficiently solved in the momentum space. The high-order harmonic generation and above-threshold ionization spectra obtained by numerical solutions of TDSE in momentum space agree well with previous studies in real space, but significantly reducing the computation cost.

preprint2019arXiv

Recursive Cascaded Networks for Unsupervised Medical Image Registration

We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration. The proposed architecture is simple in design and can be built on any base network. The moving image is warped successively by each cascade and finally aligned to the fixed image; this procedure is recursive in a way that every cascade learns to perform a progressive deformation for the current warped image. The entire system is end-to-end and jointly trained in an unsupervised manner. In addition, enabled by the recursive architecture, one cascade can be iteratively applied for multiple times during testing, which approaches a better fit between each of the image pairs. We evaluate our method on 3D medical images, where deformable registration is most commonly applied. We demonstrate that recursive cascaded networks achieve consistent, significant gains and outperform state-of-the-art methods. The performance reveals an increasing trend as long as more cascades are trained, while the limit is not observed. Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.

preprint2019arXiv

Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network

3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to obtain. Unsupervised learning methods ease the burden of manual annotation by exploiting unlabeled data without supervision. In this paper, we propose a new unsupervised learning method using convolutional neural networks under an end-to-end framework, Volume Tweening Network (VTN), for 3D medical image registration. We propose three innovative technical components: (1) An end-to-end cascading scheme that resolves large displacement; (2) An efficient integration of affine registration network; and (3) An additional invertibility loss that encourages backward consistency. Experiments demonstrate that our algorithm is 880x faster (or 3.3x faster without GPU acceleration) than traditional optimization-based methods and achieves state-of-theart performance in medical image registration.

preprint2016arXiv

Compressing Neural Language Models by Sparse Word Representations

Neural networks are among the state-of-the-art techniques for language modeling. Existing neural language models typically map discrete words to distributed, dense vector representations. After information processing of the preceding context words by hidden layers, an output layer estimates the probability of the next word. Such approaches are time- and memory-intensive because of the large numbers of parameters for word embeddings and the output layer. In this paper, we propose to compress neural language models by sparse word representations. In the experiments, the number of parameters in our model increases very slowly with the growth of the vocabulary size, which is almost imperceptible. Moreover, our approach not only reduces the parameter space to a large extent, but also improves the performance in terms of the perplexity measure.

preprint2016arXiv

Direct Urca processes involving singlet proton superfluidity in neutron star cooling

A detailed description of the baryon direct Urca processes A: $n\rightarrow p+e+\barν_{e}$, B: $Λ\rightarrow p+e+\barν_{e}$, C: $Ξ^{-}\rightarrowΛ+e+\barν_{e}$ related to the neutron star cooling is given in the relativistic mean field approximation.The contributions of the reactions B and C on the neutrino luminosity are calculated by means of the relativistic expressions of the neutrino energy losses.Our results show that the total neutrino luminosities of the reactions A, B, C within the mass range 1.603-2.067$M_{\odot}$ (1.515-1.840$M_{\odot}$ for TM1 model) for GM1 model are larger than the corresponding values for neutron stars in npe$μ$ matter. Although the hyperon direct Urca processes B and C reduce the neutrino emissivity of the reaction A, it illustrates the reactions B and C still make the total neutrino luminosity enhancement in the above mentioned areas.Furthermore, when we only consider the $^{1}S_{0}$ proton superfluidity in neutron star cooling, we find that although the neutrino emissivity of the reactions A and B is suppressed with the appearance of $^{1}S_{0}$ proton superfluidity, the total contribution of the reactions A, B, C can still quicken a massive neutron star cooling.These results could be used to help prove appearing hyperons in PSR J1614-2230 and J0348+0432 from neutron star cooling perspective.

preprint2016arXiv

Distilling Word Embeddings: An Encoding Approach

Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, which can reduce model complexity by a large margin as well as retain high accuracy, showing a good compromise between efficiency and performance. Experiments in two tasks reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.

preprint2016arXiv

Gland Instance Segmentation by Deep Multichannel Neural Networks

In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images. Our proposed system, deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirement by altering channels. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent methods of instance segmentation, we observe state-of-the-art results based on a number of evaluation metrics.

preprint2016arXiv

Gland Instance Segmentation by Deep Multichannel Side Supervision

In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional and boundary patterns, with side supervision (deep supervision on side responses) in gland histology images. Our proposed system, deep multichannel side supervision (DMCS), alleviates heavy feature design due to the use of convolutional neural networks guided by side supervision. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge, we observe state-of-the-art results based on a number of evaluation metrics.

preprint2016arXiv

How Transferable are Neural Networks in NLP Applications?

Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.

preprint2016arXiv

Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation

Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolutional neural networks or recurrent networks). They may fail to explore the potential representation space in different abstraction levels. In this paper, we propose deep recurrent neural networks (DRNNs) for relation classification to tackle this challenge. Further, we propose a data augmentation method by leveraging the directionality of relations. We evaluated our DRNNs on the SemEval-2010 Task~8, and achieve an F1-score of 86.1%, outperforming previous state-of-the-art recorded results.

preprint2016arXiv

Natural Language Inference by Tree-Based Convolution and Heuristic Matching

In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences. Experimental results show that our model outperforms existing sentence encoding-based approaches by a large margin.

preprint2016arXiv

Numerically Fitting The Electron Fermi Energy and The Electron Fraction in A Neutron Star

Based on the basic definition of Fermi energy of degenerate and relativistic electrons, we obtain a special solution to electron Fermi energy, $E_{\rm F}(e)$, and express $E_{\rm F}(e)$ as a function of electron fraction, $Y_{e}$, and matter density, $ρ$. Several useful analytical formulae for $Y_{e}$ and $ρ$ within classical models and the work of Dutra et al. 2014 (Type-2) in relativistic mean field theory are obtained using numerically fitting. When describing the mean-field Lagrangian, density, we adopt the TMA parameter set, which is remarkably consistent with with the updated astrophysical observations of neutron stars. Due to the importance of the density dependence of the symmetry energy, $S$, in nuclear astrophysics, a brief discussion on the symmetry parameters $S_v$ and $L$ (the slope of $S$) is presented. Combining these fit formulae with boundary conditions for different density regions, we can evaluate the value of $E_{\rm F}(e)$ in any given matter density, and obtain a schematic diagram of $E_{\rm F}(e)$ as a continuous function of $ρ$. Compared with previous study on the electron Fermi energy in other models, our methods of calculating $E_{\rm F}(e)$ are more simple and convenient, and can be universally suitable for the relativistic electron regions in the circumstances of common neutron stars. We have deduced a general expression of $E_{\rm F}(e)$ and $n_{e}$, which could be used to indirectly test whether one EoS of a NS is correct in our future studies on neutron star matter properties. Since URCA reactions are expected in the center of a massive star due to high-value electron Fermi energy and electron fraction, this study could be useful in the future studies on the NS thermal evolution.

preprint2016arXiv

Optimizing Quantiles in Preference-based Markov Decision Processes

In the Markov decision process model, policies are usually evaluated by expected cumulative rewards. As this decision criterion is not always suitable, we propose in this paper an algorithm for computing a policy optimal for the quantile criterion. Both finite and infinite horizons are considered. Finally we experimentally evaluate our approach on random MDPs and on a data center control problem.

preprint2016arXiv

The Energetics of White-light Flares Observed by SDO/HMI and RHESSI

White-light (WL) flares have been observed and studied more than a century since the first discovery. However, some fundamental physics behind the brilliant emission remains highly controversial. One of the important facts in addressing the flare energetics is the spatialtemporal correlation between the white-light emission and the hard X-ray radiation, presumably suggesting that the energetic electrons are the energy sources. In this study, we present a statistical analysis of 25 strong flares (?greater than or equal to M5) observed simultaneously by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) and the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). Among these events, WL emission was detected by SDO/HMI in 13 flares, associated with HXR emission. To quantitatively describe the strength of WL emission, equivalent area (EA) is defined as the integrated contrast enhancement over the entire flaring area. Our results show that the equivalent area is inversely proportional to the HXR power index, indicating that stronger WL emission tends to be associated with larger population of high energy electrons. However, no obvious correlation is found between WL emission and flux of non-thermal electrons at 50 keV. For the other group of 13 flares without detectable WL emission, the HXR spectra are softer (larger power index) than those flares with WL emission, especially for the X-class flares in this group.

preprint2016arXiv

Ultra-narrow Negative Flare Front Observed in Helium-10830~Å using the 1.6 m New Solar Telescope

Solar flares are sudden flashes of brightness on the Sun and are often associated with coronal mass ejections and solar energetic particles which have adverse effects in the near Earth environment. By definition, flares are usually referred to bright features resulting from excess emission. Using the newly commissioned 1.6~m New Solar Telescope at Big Bear Solar Observatory, here we show a striking "negative" flare with a narrow, but unambiguous "dark" moving front observed in He I 10830 Å, which is as narrow as 340 km and is associated with distinct spectral characteristics in H-alpha and Mg II lines. Theoretically, such negative contrast in He I 10830 Å can be produced under special circumstances, by nonthermal-electron collisions, or photoionization followed by recombination. Our discovery, made possible due to unprecedented spatial resolution, confirms the presence of the required plasma conditions and provides unique information in understanding the energy release and radiative transfer in astronomical objects.

preprint2016arXiv

Unprecedented Fine Structure of a Solar Flare Revealed by the 1.6~m New Solar Telescope

Solar flares signify the sudden release of magnetic energy and are sources of so called space weather. The fine structures (below 500 km) of flares are rarely observed and are accessible to only a few instruments world-wide. Here we present observation of a solar flare using exceptionally high resolution images from the 1.6~m New Solar Telescope (NST) equipped with high order adaptive optics at Big Bear Solar Observatory (BBSO). The observation reveals the process of the flare in unprecedented detail, including the flare ribbon propagating across the sunspots, coronal rain (made of condensing plasma) streaming down along the post-flare loops, and the chromosphere's response to the impact of coronal rain, showing fine-scale brightenings at the footpoints of the falling plasma. Taking advantage of the resolving power of the NST, we measure the cross-sectional widths of flare ribbons, post-flare loops and footpoint brighenings, which generally lie in the range of 80-200 km, well below the resolution of most current instruments used for flare studies. Confining the scale of such fine structure provides an essential piece of information in modeling the energy transport mechanism of flares, which is an important issue in solar and plasma physics.

preprint2015arXiv

Asymptotic properties of biorthogonal polynomials systems related to Hermite and Laguerre polynomials

In this paper, the structures to a family of biorthogonal polynomials that approximate to the Hermite and Generalized Laguerre polynomials are discussed respectively. Therefore, the asymptotic relation between several orthogonal polynomials and combinatorial polynomials are derived from the systems, which in turn verify the Askey scheme of hypergeometric orthogonal polynomials. As the applications of these properties, the asymptotic representations of the generalized Buchholz, Laguerre, Ultraspherical(Gegenbauer), Bernoulli, Euler, Meixner and Meixner-Pllaczekare polynomials are derived from the theorems directly. The relationship between Bernoulli and Euler polynomials are shown as a special case of the characterization theorem of the Appell sequence generated by $α$ scaling functions.

preprint2015arXiv

Discriminative Neural Sentence Modeling by Tree-Based Convolution

This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling. Our models leverage either constituency trees or dependency trees of sentences. The tree-based convolution process extracts sentences' structural features, and these features are aggregated by max pooling. Such architecture allows short propagation paths between the output layer and underlying feature detectors, which enables effective structural feature learning and extraction. We evaluate our models on two tasks: sentiment analysis and question classification. In both experiments, TBCNN outperforms previous state-of-the-art results, including existing neural networks and dedicated feature/rule engineering. We also make efforts to visualize the tree-based convolution process, shedding light on how our models work.

preprint2015arXiv

Rings and Radial Waves in the Disk of the Milky Way

We show that in the anticenter region, between Galactic longitudes of $110^\circ<l<229^\circ$, there is an oscillating asymmetry in the main sequence star counts on either side of the Galactic plane using data from the Sloan Digital Sky Survey. This asymmetry oscillates from more stars in the north at distances of about 2 kpc from the Sun to more stars in the south at 4-6 kpc from the Sun to more stars in the north at distances of 8-10 kpc from the Sun. We also see evidence that there are more stars in the south at distances of 12-16 kpc from the Sun. The three more distant asymmetries form roughly concentric rings around the Galactic center, opening in the direction of the Milky Way's spiral arms. The northern ring, 9 kpc from the Sun, is easily identified with the previously discovered Monoceros Ring. Parts of the southern ring at 14 kpc from the Sun (which we call the TriAnd Ring) have previously been identified as related to the Monoceros Ring and others have been called the Triangulum Andromeda Overdensity. The two nearer oscillations are approximated by a toy model in which the disk plane is offset by of the order 100 pc up and then down at different radii. We also show that the disk is not azimuthally symmetric around the Galactic anticenter and that there could be a correspondence between our observed oscillations and the spiral structure of the Galaxy. Our observations suggest that the TriAnd and Monoceros Rings (which extend to at least 25 kpc from the Galactic center) are primarily the result of disk oscillations.

preprint2015arXiv

Structure, Stability, and Evolution of Magnetic Flux Ropes from the Perspective of Magnetic Twist

We investigate the evolution of NOAA Active Region 11817 during 2013 August 10--12, when it developed a complex field configuration and produced four confined, followed by two eruptive, flares. These C-and-above flares are all associated with a magnetic flux rope (MFR) located along the major polarity inversion line, where shearing and converging photospheric flows are present. Aided by the nonlinear force-free field modeling, we identify the MFR through mapping magnetic connectivities and computing the twist number $\mathcal{T}_w$ for each individual field line. The MFR is moderately twisted ($|\mathcal{T}_w| < 2$) and has a well-defined boundary of high squashing factor $Q$. We found that the field line with the extremum $|\mathcal{T}_w|$ is a reliable proxy of the rope axis, and that the MFR's peak $|\mathcal{T}_w|$ temporarily increases within half an hour before each flare while it decreases after the flare peak for both confined and eruptive flares. This pre-flare increase in $|\mathcal{T}_w|$ has little effect on the active region's free magnetic energy or any other parameters derived for the whole region, due to its moderate amount and the MFR's relatively small volume, while its decrease after flares is clearly associated with the stepwise decrease in free magnetic energy due to the flare. We suggest that $\mathcal{T}_w$ may serve as a useful parameter in forewarning the onset of eruption, and therefore, the consequent space weather effects. The helical kink instability is identified as the prime candidate onset mechanism for the considered flares.

preprint2015arXiv

The effects of delta mesons on the baryonic direct Urca processes in neutron star matter

In the framework of relativistic mean field theory, the relativistic neutrino emissivity of the nucleonic and hyperonic direct Urca processes in the degenerate baryon matter of neutron stars are studied. We investigate particularly the influence of the isovector scalar interaction which is considered by exchanging $δ$ meson on the nucleonic and hyperonic direct Urca processes. The results indicate that $δ$ mesons lead to obvious enhancement of the total neutrino emissivity, which must result in more rapid cooling rate of neutron star matter.

preprint2015arXiv

The K giant stars from the LAMOST survey data II: the Hercules stream in radial migration

We estimate the age for the individual stars located at the lower part of the red giant branch from the LAMOST DR2 K giant sample. Taking into account the selection effects and the volume completeness, the age--metallicity map for the stars located between 0.3 and 1.5 kpc from the Sun is obtained. A significant substructure (denoted as the \it{narrow stripe}) located from (age, [Fe/H])$\sim$(5, 0.4) to (10 Gyr, -0.4 dex) in the age--metallicity map is clearly identified. Moreover, the \it{narrow stripe} stars are found the dominate contributors to several velocity substructures, including the well-known Hercules stream. The substantially large difference between the observed guiding-center radii and the birth radii inferred from the age--metallicity relation is evident that the \it{narrow stripe} stars have been radially migrated from about R$\sim4$ kpc to the solar neighborhood. This implies that the Hercules stream may not be owe to the resonance associated with the bar, but may be the kinematic imprint of the inner disk and later moved out due to radial migration. We estimate that the traveling speed of the radial migration are roughly 1.1$\pm0.1$ kpc Gyr$^{-1}$, equivalent with about $1.1\pm0.1$ km s$^{-1}$. This is in agreement with the median $v_R$ of $2.6^{+1.8}_{-1.9}$ km s$^{-1}$ of the \it{narrow stripe}. We also obtain that about one third stars in the solar neighborhood are radially migrated from around 4 kpc. Finally, we find that the radial migration does not lead to additional disk thickening according to the distribution of $z_{max}$.

preprint2015arXiv

Validation Of The Coronal Thick Target Source Model

We present detailed 3D modeling of a dense, coronal thick target X-ray flare using the GX Simulator tool, photospheric magnetic measurements, and microwave imaging and spectroscopy data. The developed model offers a remarkable agreement between the synthesized and observed spectra and images in both X-ray and microwave domains, which validates the entire model. The flaring loop parameters are chosen to reproduce the emission measure, temperature, and the nonthermal electron distribution at low energies derived from the X-ray spectral fit, while the remaining parameters, unconstrained by the X-ray data, are selected such as to match the microwave images and total power spectra. The modeling suggests that the accelerated electrons are trapped in the coronal part of the flaring loop, but away from where the magnetic field is minimal, and, thus, demonstrates that the data are clearly inconsistent with electron magnetic trapping in the weak diffusion regime mediated by the Coulomb collisions. Thus, the modeling supports the interpretation of the coronal thick-target sources as sites of electron acceleration in flares and supplies us with a realistic 3D model with physical parameters of the acceleration region and flaring loop.

preprint2014arXiv

Architecture of the Florida Power Grid as a Complex Network

We study the Florida high-voltage power grid as a technological network embedded in space. Measurements of geographical lengths of transmission lines, the mixing of generators and loads, the weighted clustering coefficient, as well as the organization of edge conductance weights show a complex architecture quite different from random-graph models usually considered. In particular, we introduce a parametrized mixing matrix to characterize the mixing pattern of generators and loads in the Florida Grid, which is intermediate between the random mixing case and the semi-bipartite case where generator-generator transmission lines are forbidden. Our observations motivate an investigation of optimization (design) principles leading to the structural organization of power grids. We thus propose two network optimization models for the Florida Grid as a case study. Our results show that the Florida Grid is optimized not only by reducing the construction cost (measured by the total length of power lines), but also through reducing the total pairwise edge resistance in the grid, which increases the robustness of power transmission between generators and loads against random line failures. We then embed our models in spatial areas of different aspect ratios and study how this geometric factor affects the network structure, as well as the box-counting fractal dimension of the grids generated by our models.

preprint2014arXiv

Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.

preprint2014arXiv

Comparison of Emission Properties of two Homologous Flares in AR 11283

Large, complex, active regions may produce multiple flares within a certain period of one or two days. These flares could occur in the same location with similar morphologies, commonly referred to as homologous flares. In 2011 September, active region NOAA 11283 produced a pair of homologous flares on the 6th and 7th, respectively. Both of them were white-light (WL) flares, as captured by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory in visible continuum at 617.3 nm which is believed to originate from the deep solar atmosphere.We investigate the WL emission of these X-class flares with HMIs seeing-free imaging spectroscopy. The durations of impulsive peaks in the continuum are about 4 minutes. We compare the WL with hard X-ray (HXR) observations for the September 6 flare and find a good correlation between the continuum and HXR both spatially and temporally. In absence of RHESSI data during the second flare on September 7, the derivative of the GOES soft X-ray is used and also found to be well correlated temporally with the continuum. We measure the contrast enhancements, characteristic sizes, and HXR fluxes of the twin flares, which are similar for both flares, indicating analogous triggering and heating processes. However, the September 7 flare was associated with conspicuous sunquake signals whereas no seismic wave was detected during the flare on September 6. Therefore, this comparison suggests that the particle bombardment may not play a dominant role in producing the sunquake events studied in this paper.

preprint2014arXiv

Global Energetics of Solar Flares: I. Magnetic Energies

We present the first part of a project on the global energetics of solar flares and coronal mass ejections (CMEs) that includes about 400 M- and X-class flares observed with AIA and HMI onboard SDO. We calculate the potential energy, free energy, and the flare-dissipated magnetic energy. We calculate these magnetic parameters using two different NLFFF codes: The COR-NLFFF code uses the line-of-sight magnetic field component $B_z$ from HMI to define the potential field, and the 2D coordinates of automatically detected coronal loops in 6 coronal wavelengths from AIA to measure the helical twist of coronal loops caused by vertical currents, while the PHOT-NLFFF code extrapolates the photospheric 3D vector fields. We find agreement between the two codes in the measurement of free energies and dissipated energies within a factor of $ \approx 3$. The size distributions of magnetic parameters exhibit powerlaw slopes that are approximately consistent with the fractal-diffusive self-organized criticality model. The magnetic parameters exhibit scaling laws for the nonpotential energy, $E_{np} \propto E_p^{1.02}$, for the free energy, $E_{free} \propto E_p^{1.7}$ and $E_{free} \propto B_φ^{1.0} L^{1.5}$, for the dissipated energy, $E_{diss} \propto E_p^{1.6}$ and $E_{diss} \propto E_{free}^{0.9}$, and the energy dissipation volume, $V \propto E_{diss}^{1.2}$. The potential energies vary in the range of $E_p = 1 \times 10^{31} - 4 \times 10^{33}$ erg, while the free energy has a ratio of $E_{free}/E_p \approx 1%-25%$. The Poynting flux amounts to $F_{flare} \approx 5 \times 10^{8} - 10^{10}$ erg cm$^{-2}$ s$^{-1}$ during flares, which averages to $F_{AR} \approx 6 \times 10^6$ erg cm$^{-2}$ s$^{-1}$ during the entire observation period and is comparable with the coronal heating rate requirement in active regions.

preprint2014arXiv

Singlet pairing gaps of neutrons and protons in hyperonic neutron stars

The $^{1}S_{0}$ nucleonic superfluids are investigated within the relativistic mean-field model and Bardeen-Cooper-Schrieffer theory in hyperonic neutron stars. The $^{1}S_{0}$ pairing gaps of neutrons and protons are calculated based on the Reid soft-core interaction as the nucleon-nucleon interaction. We have studied particularly the influence of hyperons degrees of freedom on the $^{1}S_{0}$ nucleonic pairing gap in neutron star matter. It is found that the appearance of hyperons has little impact on baryonic density range and size for the $^{1}S_{0}$ neutronic pairing gap, the $^{1}S_{0}$ protonic pairing gap also decreases slightly in this region $ρ_B=0.0-0.393$ fm$^{-3}$. However, if baryonic density becomes greater than 0.393 fm${^{-3}}$, the $^{1}S_{0}$ protonic pairing gap obviously increases. In addition, the protonic superfluid range is obviously enlarged due to the presence of hyperons. In our results, the hyperons change the $^{1}S_{0}$ protonic pairing gap which must change the cooling properties of neutron stars.

preprint2014arXiv

The K giant stars from the LAMOST survey data I: identification, metallicity, and distance

We present a support vector machine classifier to identify the K giant stars from the LAMOST survey directly using their spectral line features. The completeness of the identification is about 75% for tests based on LAMOST stellar parameters. The contamination in the identified K giant sample is lower than 2.5%. Applying the classification method to about 2 million LAMOST spectra observed during the pilot survey and the first year survey, we select 298,036 K giant candidates. The metallicities of the sample are also estimated with uncertainty of $0.13\sim0.29$\,dex based on the equivalent widths of Mg$_{\rm b}$ and iron lines. A Bayesian method is then developed to estimate the posterior probability of the distance for the K giant stars, based on the estimated metallicity and 2MASS photometry. The synthetic isochrone-based distance estimates have been calibrated using 7 globular clusters with a wide range of metallicities. The uncertainty of the estimated distance modulus at $K=11$\,mag, which is the median brightness of the K giant sample, is about 0.6\,mag, corresponding to $\sim30$% in distance. As a scientific verification case, the trailing arm of the Sagittarius stream is clearly identified with the selected K giant sample. Moreover, at about 80\,kpc from the Sun, we use our K giant stars to confirm a detection of stream members near the apo-center of the trailing tail. These rediscoveries of the features of the Sagittarius stream illustrate the potential of the LAMOST survey for detecting substructures in the halo of the Milky Way.

preprint2014arXiv

The velocity distribution in the solar neighbourhood from the LAMOST pilot survey

We use about 15,000 F/G nearby dwarf stars selected from the LAMOST pilot survey to map the U-V velocity distribution in the solar neighbourhood. An extreme deconvolution algorithm is applied to reconstruct an empirical multi-Gaussian model. In addition to the well known substructures, e.g., Sirius, Coma Berenices, Hyades-Pleiades over-densities, several new substructures are unveiled. A ripple-like structure from (U, V) = (-120, -5) to (103, -32)km/s is clearly seen in the U-V distribution. This structure seems associated with resonance induced by the Galactic bar, since it is extended in U while having a small dispersion in V at the same time. A ridge structure between (U, V) = (-60, 40) and (-15, 15) km/s is also found. Although similar substructures have been seen in the Hipparcos data, their origin is still unclear. Another compact over-density is seen at (U, V) = (-102, -24). With this large data sample, we find that the substructure located at V~70 km/s and the Arcturus group are essentially parallel in V, which may indicate that they originate from an unrelaxed disk component perturbed by the rotating bar.

preprint2014arXiv

Two-dimensional balanced sampling plans avoiding adjacent units

Hedayat et al. first introduced balanced sampling plans for the exclusion of contiguous units. Wright detailed the results of a preliminary investigation of two-dimensional balanced sampling plans avoiding adjacent units (2-BSAs), and pointed out explicitly three types of 2-BSAs, which have different adjacency scheme, namely "Row and Column", "Sharing a Border" and "Island". This paper will provide more details for the three types of 2-BSAs from the point of view of design theory.

preprint2013arXiv

DA white dwarfs observed in LAMOST pilot survey

A total of $\sim640,000$ objects from LAMOST pilot survey have been publicly released. In this work, we present a catalog of DA white dwarfs from the entire pilot survey. We outline a new algorithm for the selection of white dwarfs by fitting Sérsic profiles to the Balmer H$β$, H$γ$ and H$δ$ lines of the spectra, and calculating the equivalent width of the CaII K line. 2964 candidates are selected by constraining the fitting parameters and the equivalent width of CaII K line. All the spectra of candidates are visually inspected. We identify 230 (59 of them are already in Villanova and SDSS WD catalog) DA white dwarfs, 20 of which are DA white dwarfs with non-degenerate companions. In addition, 128 candidates are classified as DA white dwarf/subdwarfs, which means the classifications are ambiguous. The result is consistent with the expected DA white dwarf number estimated based on the LEGUE target selection algorithm.

preprint2013arXiv

He I D3 Observation of the 1984 May 22 M6.3 Solar Flare

He I D3 line has a unique response to the flare impact on the low solar atmosphere and can be a powerful diagnostic tool for energy transport processes. Using images obtained from the recently digitized films of Big Bear Solar Observatory, we report D3 observation of the M6.3 flare on 1984 May 22, which occurred in an active region with a circular magnetic polarity inversion line (PIL). The impulsive phase of the flare starts with a main elongated source that darkens in D3, inside of which bright emission kernels appear at the time of the initial small peak in hard X-rays (HXRs). These flare cores subsequently evolve into a sharp emission strand lying within the dark halo simultaneously with the main peak in HXRs, reversing the overall source contrast from -5% to 5%. The radiated energy in D3 during the main peak is estimated to be about 10^30 ergs, which is comparable to that carried by nonthermal electrons above 20 keV. Afterwards the flare proceeds along the circular PIL in the counterclockwise direction to form a dark circular ribbon in D3, which apparently mirrors the bright ribbons in Halpha and He I 10830 A. All these ribbons last for over one hour in the late gradual phase. We suggest that the present event resembles the so-called black-light flare that is proposed based on continuum images, and that D3 darkening and brightening features herein may be due to, respectively, the thermal conduction heating and the direct precipitation of high-energy electrons.

preprint2013arXiv

High-Cadence and High-Resolution Halpha Imaging Spectroscopy of a Circular Flare's Remote Ribbon with IBIS

We present an unprecedented high-resolution \ha\ imaging spectroscopic observation of a C4.1 flare taken with IBIS on 2011 October 22. The flare consists of a main circular ribbon that occurred in a parasitic magnetic configuration and a remote ribbon that was observed by the IBIS. Such a circular-ribbon flare with a remote brightening is predicted in 3D fan-spine reconnection but so far has been rarely observed. During the flare impulsive phase, we define "core" and "halo" structures in the observed ribbon. Examining the \ha\ emission spectra averaged in the flare core and halo areas, we find that only those from the flare cores show typical nonthermal electron beam heating characteristics. These characteristics include: broad and centrally reversed emission spectra, excess emission in the red wing with regard to the blue wing (i.e., red asymmetry), and redshifted bisectors of the emission spectra. We also observe rather quick timescales for the heating (30 s) and cooling (14--33 s) in the flare core locations. Therefore, we suggest that the flare cores revealed by IBIS track the sites of electron beam precipitation with exceptional spatial and temporal resolution. The flare cores show two-stage motion (a parallel motion along the ribbon followed by an expansion motion perpendicular to the ribbon) during the two impulsive phases of the flare. Some cores jump quickly (30 \kms) between discrete magnetic elements implying reconnection involving different flux tubes. We observe a very high temporal correlation ($\gtrsim0.9$) between the integrated \ha\ and HXR emission during the flare impulsive phase. A short time delay (4.6 s) is also found in the \ha\ emission spikes relative to HXR bursts. The ionization timescale of the cool chromosphere and the extra time taken for the electrons to travel to the remote ribbon site may contribute to this delay.

preprint2013arXiv

Study of Rapid Formation of a Delta Sunspot Associated with the 2012 July 2 C7.4 Flare Using High-resolution Observations of New Solar Telescope

Rapid, irreversible changes of magnetic topology and sunspot structure associated with flares have been systematically observed in recent years. The most striking features include the increase of horizontal field at the polarity inversion line (PIL) and the co-spatial penumbral darkening. A likely explanation of the above phenomenon is the back reaction to the coronal restructuring after eruptions: a coronal mass ejection carries the upward momentum while the downward momentum compresses the field lines near the PIL. Previous studies could only use low resolution (above 1") magnetograms and white-light images. Therefore, the changes are mostly observed for X-class flares. Taking advantage of the 0.1" spatial resolution and 15s temporal cadence of the New Solar Telescope at Big Bear Solar Observatory, we report in detail the rapid formation of sunspot penumbra at the PIL associated with the C7.4 flare on 2012 July 2. It is unambiguously shown that the solar granulation pattern evolves to alternating dark and bright fibril structure, the typical pattern of penumbra. Interestingly, the appearance of such a penumbra creates a new delta sunspot. The penumbral formation is also accompanied by the enhancement of horizontal field observed using vector magnetograms from the Helioseismic and Magnetic Imager. We explain our observations as due to the eruption of a flux rope following magnetic cancellation at the PIL. Subsequently the re-closed arcade fields are pushed down towards the surface to form the new penumbra. NLFFF extrapolation clearly shows both the flux rope close to the surface and the overlying fields.

preprint2013arXiv

Study of Two Successive Three-Ribbon Solar Flares on 2012 July 6

This Letter reports two rarely observed three-ribbon flares (M1.9 and C9.2) on 2012 July 6 in NOAA AR 11515, which we found with Halpha observations of 0.1" resolution from the New Solar Telescope and CaII H images from Hinode. The flaring site is characterized with an intriguing "fish-bone-like" morphology evidenced by both Halpha images and a nonlinear force-free field (NLFFF) extrapolation, where two semi-parallel rows of low-lying, sheared loops connect an elongated, parasitic negative field with the sandwiching positive fields. The NLFFF model also shows that the two rows of loops are asymmetric in height and have opposite twists, and are enveloped by large-scale field lines including open fields. The two flares occurred in succession in half an hour and are located at the two ends of the flaring region. The three ribbons of each flare run parallel to the PIL, with the outer two lying in the positive field and the central one in the negative field. Both flares show surge-like flows in Halpha apparently toward the remote region, while the C9.2 flare is also accompanied by EUV jets possibly along the open field lines. Interestingly, the 12-25 keV hard X-ray sources of the C9.2 flare first line up with the central ribbon then shift to concentrate on the top of the higher branch of loops. These results are discussed in favor of reconnection along the coronal null-line producing the three flare ribbons and the associated ejections.

preprint2012arXiv

Long-range adiabatic quantum state transfer through a linear array of quantum dots

We introduce an adiabatic long-range quantum communication proposal based on a quantum dot array. By adiabatically varying the external gate voltage applied on the system, the quantum information encoded in the electron can be transported from one end dot to another. We numerically solve the Schrödinger equation for a system with a given number of quantum dots. It is shown that this scheme is a simple and efficient protocol to coherently manipulate the population transfer under suitable gate pulses. The dependence of the energy gap and the transfer time on system parameters is analyzed and shown numerically. We also investigate the adiabatic passage in a more realistic system in the presence of inevitable fabrication imperfections. This method provides guidance for future realizations of adiabatic quantum state transfer in experiments.

preprint2012arXiv

Long-range adiabatic quantum state transfer through a tight-binding chain as a quantum data bus

We introduce a scheme based on adiabatic passage that allows for long-range quantum communication through tight-binding chain with always-on interaction. By adiabatically varying the external gate voltage applied on the system, the electron can be transported from the sender's dot to the aim one.We numerically solve the Schrödinger equation for a system with a given number of quantum dots. It is shown that this scheme is a simple and efficient protocol to coherently manipulate the population transfer under suitable gate pulses. The dependence of the energy gap and the transfer time on system parameters is analyzed and shown numerically. Our method provides a guidance for future realization of adiabatic quantum state transfer in experiments.

preprint2012arXiv

On Rings and Streams in the Galactic Anti-Center

We confirm that there are at least three separate low-latitude over-densities of blue F turnoff stars near the Milky Way anti-center: the Monoceros Ring, the Anti-Center Stream (ACS), and the Eastern Banded Structure (EBS). There might also be a small number of normal thick disk stars at the same location. The ACS is a tilted component that extends to higher Galactic latitude at lower Galactic longitude, 10 kpc from the Sun towards the anti-center. It has a sharp cutoff on the high latitude side. Distance, velocity, and proper motion measurements are consistent with previous orbit fits. The mean metallicity is [Fe/H]$=-0.96 \pm 0.03$, which is lower than the thick disk and Monoceros Ring. The Monoceros Ring is a higher density substructure that is present at $15\arcdeg<b<22\arcdeg$ at all longitudes probed in this survey. The structure likely continues towards lower latitudes. The distances are consistent with a constant distance from the Galactic Center of 17.6 kpc. The mean line-of-sight velocity of the structure is consistent with a thick disk rotation. However, the velocity dispersion of these stars is $\sim 15$ km s$^{-1}$, and the metallicity is [Fe/H]$=-0.80 \pm 0.01$. Both of these quantities are lower than the canonical thick disk. We suggest that this ring structure is likely different from the thick disk, though its association with the disk cannot be definitively ruled out. The Eastern Banded Structure (EBS) is detected primarily photometrically, near $(l,b)=(225\arcdeg,30\arcdeg)$, at a distance of 10.9 kpc from the Sun.

preprint2012arXiv

On the Relationship Between Coronal Magnetic Decay Index and CME Speed

Numerical simulations suggest that kink and torus instabilities are two potential contributors to the initiation and prorogation of eruptive events. A magnetic parameter named decay index (i.e., the coronal magnetic gradient of the overlying fields above the eruptive flux ropes) could play an important role in controlling kinematics of eruptions. Previous studies have identified a threshold range of the decay index that distinguishes between eruptive and confined configurations. Here we advance the study by investigating if there is a clear correlation between the decay index and CME speed. 38 CMEs associated with filament eruptions and/or two-ribbon flares are selected using the Halpha data from the Global Halpha Network. The filaments and flare ribbons observed in Halpha associated with the CMEs help to locate the magnetic polarity inversion line, along which the decay index is calculated based on the potential field extrapolation using MDI magnetograms as boundary conditions. The speeds of CMEs are obtained from the LASCO C2 CME catalog available online. We find that the mean decay index increases with CME speed for those CMEs with a speed below 1000 km/s, and stays flat around 2.2 for the CMEs with higher speeds. In addition, we present a case study of a partial filament eruption, in which the decay indexes show different values above the erupted/non-erupted part.

preprint2012arXiv

Quantum corrections to the dynamics of the Bose-Einstein condensate in a double-well potential

The dynamics of the Bose-Einstein condensate (BEC) in a double-well potential is of- ten investigated under the mean-field theory (MFT). This works successfully for large particle numbers with dynamical stability. But for dynamical instabilities, quantum cor- rections to the MFT becomes important [Phys.Rev.A 64, 013605(2001)]. Recently the adiabatic dynamics of the double-well BEC is investigated under the MFT in terms of a dark variable [Phys.Rev.A 81, 043621(2010)], which generalizes the adiabatic passage techniques in quantum optics to the nonlinear matter-wave case. We give a fully quan- tized version of it using second-quantization and introduce new correction terms from higher order interactions beyond the on-site interaction, which are interactions between the tunneling particle and the particle in the well and interactions between the tunneling particles. If only the on-site interaction is considered, this reduces to the usual two-mode BEC.

preprint2012arXiv

The LEGUE High Latitude Bright Survey Design for the LAMOST Pilot Survey

We describe the footprint and input catalog for bright nights in the LAMOST Pilot Survey, which began in October 2011. Targets are selected from two stripes in the north and south Galactic Cap regions, centered at $α$= 29$^\circ$, with 10$^\circ$ width in declination, covering right ascension of 135$^\circ-290^\circ$ and -30$^\circ$ to 30$^\circ$ respectively. We selected spectroscopic targets from a combination of the SDSS and 2MASS point source catalogs. The catalog of stars defining the field centers (as required by the Shack-Hartmann wavefront sensor at the center of the LAMOST field) consists of all V < 8m stars from the Hipparcos catalog. We employ a statistical selection algorithm that assigns priorities to targets based on their positions in multidimensional color/magnitude space. This scheme overemphasizes rare objects and de-emphasizes more populated regions of magnitude and color phase space, while ensuring a smooth, well-understood selection function. A demonstration of plate design is presented based on the Shack-Hartmann star catalog and an input catalog that was generated by our target selection routines.

preprint2012arXiv

The LEGUE Input Catalogue for Dark Night Observing in the LAMOST Pilot Survey

We outline the design of the dark nights portion of the LAMOST Pilot Survey, which began observations in October 2011. In particular, we focus on Milky Way stellar candidates that are targeted for the LEGUE (LAMOST Experiment for Galactic Understanding and Exploration) survey. We discuss the regions of sky in which spectroscopic candidates were selected, and the motivations for selecting each of these sky areas. Some limitations due to the unique design of the telescope are discussed, including the requirement that a bright (V < 8) star be placed at the center of each plate for wavefront sensing and active optics corrections. The target selection categories and scientific goals motivating them are briefly discussed, followed by a detailed overview of how these selection functions were realized. We illustrate the difference between the overall input catalog - Sloan Digital Sky Survey (SDSS) photometry - and the final targets selected for LAMOST observation.

preprint2012arXiv

The site conditions of the Guo Shou Jing Telescope

The weather at Xinglong Observing Station, where the Guo Shou Jing Telescope (GSJT) is located, is strongly affected by the monsoon climate in north-east China. The LAMOST survey strategy is constrained by these weather patterns. In this paper, we present a statistics on observing hours from 2004 to 2007, and the sky brightness, seeing, and sky transparency from 1995 to 2011 at the site. We investigate effects of the site conditions on the survey plan. Operable hours each month shows strong correlation with season: on average there are 8 operable hours per night available in December, but only 1-2 hours in July and August. The seeing and the sky transparency also vary with seasons. Although the seeing is worse in windy winters, and the atmospheric extinction is worse in the spring and summer, the site is adequate for the proposed scientific program of LAMOST survey. With a Monte Carlo simulation using historical data on the site condition, we find that the available observation hours constrain the survey footprint from 22h to 16h in right ascension; the sky brightness allows LAMOST to obtain the limit magnitude of V = 19.5mag with S/N = 10.

preprint2012arXiv

The Structure of Chromatic Polynomials of Planar Triangulation Graphs and Implications for Chromatic Zeros and Asymptotic Limiting Quantities

We present an analysis of the structure and properties of chromatic polynomials $P(G_{pt,\vec m},q)$ of one-parameter and multi-parameter families of planar triangulation graphs $G_{pt,\vec m}$, where ${\vec m} = (m_1,...,m_p)$ is a vector of integer parameters. We use these to study the ratio of $|P(G_{pt,\vec m},τ+1)|$ to the Tutte upper bound $(τ-1)^{n-5}$, where $τ=(1+\sqrt{5} \ )/2$ and $n$ is the number of vertices in $G_{pt,\vec m}$. In particular, we calculate limiting values of this ratio as $n \to \infty$ for various families of planar triangulations. We also use our calculations to study zeros of these chromatic polynomials. We study a large class of families $G_{pt,\vec m}$ with $p=1$ and $p=2$ and show that these have a structure of the form $P(G_{pt,m},q) = c_{_{G_{pt}},1}λ_1^m + c_{_{G_{pt}},2}λ_2^m + c_{_{G_{pt}},3}λ_3^m$ for $p=1$, where $λ_1=q-2$, $λ_2=q-3$, and $λ_3=-1$, and $P(G_{pt,\vec m},q) = \sum_{i_1=1}^3 \sum_{i_2=1}^3 c_{_{G_{pt}},i_1 i_2} λ_{i_1}^{m_1}λ_{i_2}^{m_2}$ for $p=2$. We derive properties of the coefficients $c_{_{G_{pt}},\vec i}$ and show that $P(G_{pt,\vec m},q)$ has a real chromatic zero that approaches $(1/2)(3+\sqrt{5} \ )$ as one or more of the $m_i \to \infty$. The generalization to $p \ge 3$ is given. Further, we present a one-parameter family of planar triangulations with real zeros that approach 3 from below as $m \to \infty$. Implications for the ground-state entropy of the Potts antiferromagnet are discussed.

preprint2011arXiv

Chromatic Polynomials of Planar Triangulations, the Tutte Upper Bound, and Chromatic Zeros

Tutte proved that if $G_{pt}$ is a planar triangulation and $P(G_{pt},q)$ is its chromatic polynomial, then $|P(G_{pt},τ+1)| \le (τ-1)^{n-5}$, where $τ=(1+\sqrt{5} \,)/2$ and $n$ is the number of vertices in $G_{pt}$. Here we study the ratio $r(G_{pt})=|P(G_{pt},τ+1)|/(τ-1)^{n-5}$ for a variety of planar triangulations. We construct infinite recursive families of planar triangulations $G_{pt,m}$ depending on a parameter $m$ linearly related to $n$ and show that if $P(G_{pt,m},q)$ only involves a single power of a polynomial, then $r(G_{pt,m})$ approaches zero exponentially fast as $n \to \infty$. We also construct infinite recursive families for which $P(G_{pt,m},q)$ is a sum of powers of certain functions and show that for these, $r(G_{pt,m})$ may approach a finite nonzero constant as $n \to \infty$. The connection between the Tutte upper bound and the observed chromatic zero(s) near to $τ+1$ is investigated. We report the first known graph for which the zero(s) closest to $τ+1$ is not real, but instead is a complex-conjugate pair. Finally, we discuss connections with nonzero ground-state entropy of the Potts antiferromagnet on these families of graphs.

preprint2011arXiv

Ground State Entropy of the Potts Antiferromagnet on Homeomorphic Expansions of Kagome Lattice Strips

We present exact calculations of the chromatic polynomial and resultant ground state entropy of the $q$-state Potts antiferromagnet on lattice strips that are homeomorphic expansions of a strip of the kagome lattice. The dependence of the ground state entropy on the form of homeomorphic expansion is elucidated.

preprint2011arXiv

Rapid Changes of Photospheric Magnetic Field after Tether-Cutting Reconnection and Magnetic Implosion

The rapid, irreversible change of the photospheric magnetic field has been recognized as an important element of the solar flare process. This Letter reports such a rapid change of magnetic fields during the 2011 February 13 M6.6 flare in NOAA AR 11158 that we found from the vector magnetograms of the Helioseismic and Magnetic Imager with 12-min cadence. High-resolution magnetograms of Hinode that are available at ~-5.5, -1.5, 1.5, and 4 hrs relative to the flare maximum are used to reconstruct three-dimensional coronal magnetic field under the nonlinear force-free field (NLFFF) assumption. UV and hard X-ray images are also used to illuminate the magnetic field evolution and energy release. The rapid change is mainly detected by HMI in a compact region lying in the center of the magnetic sigmoid, where the mean horizontal field strength exhibited a significant increase by 28%. The region lies between the initial strong UV and hard X-ray sources in the chromosphere, which are cospatial with the central feet of the sigmoid according to the NLFFF model. The NLFFF model further shows that strong coronal currents are concentrated immediately above the region, and that more intriguingly, the coronal current system underwent an apparent downward collapse after the sigmoid eruption. These results are discussed in favor of both the tether-cutting reconnection producing the flare and the ensuing implosion of the coronal field resulting from the energy release.

preprint2011arXiv

Solve the Master Equation by Python-An Introduction to the Python Computing Environment

A brief introduction to the Python computing environment is given. By solving the master equation encountered in quantum transport, we give an example of how to solve the ODE problems in Python. The ODE solvers used are the ZVODE routine in Scipy and the bsimp solver in GSL. For the former, the equation can be in its complex-valued form, while for the latter, it has to be rewritten to a real-valued form. The focus is on the detailed workflow of the implementation process, rather than on the syntax of the python language, with the hope to help readers simulate their own models in Python.

preprint2010arXiv

A Revisit of the Masuda Flare

We revisit the flare on 1992 January 13, which is now universally termed the "Masuda flare". The revisit is motivated not only by its uniqueness despite accumulating observations of \hxr coronal emission, but also by the improvement of Yohkoh hard X-ray imaging, which was achieved after the intensive investigations on this celebrated event. Through an uncertainty analysis, we show that the hard X-ray coronal source is located much closer to the soft X-ray loop in the re-calibrated HXT images than in the original ones. Specifically, the centroid of the M1-band (23--33 keV) coronal source is above the brightest pixel of the SXT loop by ~5000+/-1000 km (~9600 km in the original data); and above the apex of the 30% brightness contour of the SXT loop by ~2000+/-1000 km (~7000 km in the original data). We suggest that this change may naturally account for the fact that the spectrum of the coronal emission was reported to be extremely hard below ~20 keV in the pre-calibration investigations, whereas it has been considerably softer in the literature since Sato's re-calibration circa 1999. Still, the coronal spectrum is flatter at lower energies than at higher energies, owing to the lack of a similar source in the L-band (14--23 keV), which remains a puzzle.

preprint2010arXiv

Adiabatic quantum state transfer in non-uniform triple-quantum-dot system

We introduce an adiabatic quantum state transfer scheme in a non-uniform coupled triple-quantum-dot system. By adiabatically varying the external gate voltage applied on the sender and receiver, the electron can be transferred between them with high fidelity. By numerically solving the master equation for a system with always-on interaction, it is indicated that the transfer fidelity depends on the ration between the peak voltage and the maximum coupling constants. The effect of coupling mismatch on the transfer fidelity is also investigated and it is shown that there is a relatively large tolerance range to permit high fidelity quantum state transfer.

preprint2010arXiv

Evolution of Filament Barbs

We present a selected few cases in which the sense of chirality of filament barbs changed within as short as hours. We investigate in detail a quiescent filament on 2003 September 10 and 11. Of its four barbs displaying such changes only one overlay a small polarity inversion line inside the EUV filament channel (EFC). No magnetic elements with magnitude above the noise level were detected at the endpoints of all barbs. In particular, a pair of barbs first approached toward and then departed from each other in H-alpha, with the barb endpoints migrating as far as ~10". We conclude that the evolution of the barbs was driven by flux emergence and cancellation of small bipolar units at the EFC border.

preprint2010arXiv

Exact Results on Potts Model Partition Functions in a Generalized External Field and Weighted-Set Graph Colorings

We present exact results on the partition function of the $q$-state Potts model on various families of graphs $G$ in a generalized external magnetic field that favors or disfavors spin values in a subset $I_s = \{1,...,s\}$ of the total set of possible spin values, $Z(G,q,s,v,w)$, where $v$ and $w$ are temperature- and field-dependent Boltzmann variables. We remark on differences in thermodynamic behavior between our model with a generalized external magnetic field and the Potts model with a conventional magnetic field that favors or disfavors a single spin value. Exact results are also given for the interesting special case of the zero-temperature Potts antiferromagnet, corresponding to a set-weighted chromatic polynomial $Ph(G,q,s,w)$ that counts the number of colorings of the vertices of $G$ subject to the condition that colors of adjacent vertices are different, with a weighting $w$ that favors or disfavors colors in the interval $I_s$. We derive powerful new upper and lower bounds on $Z(G,q,s,v,w)$ for the ferromagnetic case in terms of zero-field Potts partition functions with certain transformed arguments. We also prove general inequalities for $Z(G,q,s,v,w)$ on different families of tree graphs. As part of our analysis, we elucidate how the field-dependent Potts partition function and weighted-set chromatic polynomial distinguish, respectively, between Tutte-equivalent and chromatically equivalent pairs of graphs.

preprint2010arXiv

The asymptotic properties of Eulerian numbers and refined Eulerian numbers: A Spline perspective

In this paper, the asymptotic formulas for Eulerian numbers, refined Eulerian numbers and the coefficients of descent polynomials are obtained directly from the spline interpretations of these numbers. Having related these numbers directly to B-splines [15], we can take advantage of many powerful spline techniques to derive various results of these numbers. The asymptotic formulas for the Eulerian numbers Ad;k agree with the previously known results which were given by L. Carlitz et al.(1972)[2] and S.Tanny (1973) [18], but the convergence order is much better. We also give the asymptotic representations of refined Eulerian numbers which is in terms of the Hermite polynomials.

preprint2010arXiv

The Orbit of the Orphan Stream

We use recent SEGUE spectroscopy and SDSS and SEGUE imaging data to measure the sky position, distance, and radial velocities of stars in the tidal debris stream that is commonly referred to as the "Orphan Stream." We fit orbital parameters to the data, and find a prograde orbit with an apogalacticon, perigalacticon, and eccentricity of 90 kpc, 16.4 kpc and 0.7, respectively. Neither the dwarf galaxy UMa II nor the Complex A gas cloud have velocities consistent with a kinematic association with the Orphan Stream. It is possible that Segue-1 is associated with the Orphan Stream, but no other known Galactic clusters or dwarf galaxies in the Milky Way lie along its orbit. The detected portion of the stream ranges from 19 to 47 kpc from the Sun and is an indicator of the mass interior to these distances. There is a marked increase in the density of Orphan Stream stars near (l,b)=(253,49) deg., which could indicate the presence of the progenitor at the edge of the SDSS data. If this is the progenitor, then the detected portion of the Orphan Stream is a leading tidal tail. We find blue horizontal branch (BHB) stars and F turnoff stars associated with the Orphan Stream. The turnoff color is (g-r)_0=0.22. The BHB stars have a low metallicity of [Fe/H]=-2.1. The orbit is best fit to a halo potential with a halo plus disk mass of about 2.6x10^11 Solar masses, integrated to 60 kpc from the Galactic center. Our best fit is found with a logarithmic halo speed of v_halo=73+/-24 km/s, a disk+bulge mass of M(R< 60 kpc) = 1.3x10^11 Solar masses, and a halo mass of M(R< 60 kpc) = 1.4x10^11 Solar masses. The Orphan Stream is projected to extend to 90 kpc from the Galactic center, and measurements of these distant parts of the stream would be a powerful probe of the mass of the Milky Way (truncated).

preprint2009arXiv

Weighted-Set Graph Colorings

We study a weighted-set graph coloring problem in which one assigns $q$ colors to the vertices of a graph such that adjacent vertices have different colors, with a vertex weighting $w$ that either disfavors or favors a given subset of $s$ colors contained in the set of $q$ colors. We construct and analyze a weighted-set chromatic polynomial $Ph(G,q,s,w)$ associated with this coloring. General properties of this weighted-set chromatic polynomial are proved, and illustrative calculations are presented for various families of graphs. This study extends a previous one for the case $s=1$ and reveals a number of interesting new features.

Yan Xu

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87 published item(s)

PIVOT: Bridging Planning and Execution in LLM Agents via Trajectory Refinement

Analytic smoothing effect of the time variable for the spatially homogeneous Landau equation

Can Question Rewriting Help Conversational Question Answering?

Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning

Iterative Adaptively Regularized LASSO-ADMM Algorithm for CFAR Estimation of Sparse Signals: IAR-LASSO-ADMM-CFAR Algorithm

Nanoscale three-dimensional magnetic sensing with a probabilistic nanomagnet driven by spin-orbit torque

Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust Correspondence Field Estimation and Pose Optimization

Robust Self-Supervised LiDAR Odometry via Representative Structure Discovery and 3D Inherent Error Modeling

SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks

Transformer based multiple instance learning for weakly supervised histopathology image segmentation

WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results

Multi-hop Question Generation with Graph Convolutional Network

Multi-Passband Observations of A Solar Flare over the He I 10830 Å line

A New Comprehensive Data Set of Solar Filaments of 100 yr Interval. I

A Public Website for the Automated Assessment and Validation of SARS-CoV-2 Diagnostic PCR Assays

An ultraweak-local discontinuous Galerkin method for PDEs with high order spatial derivatives

Comparison of Enhanced Absorption in He I 10830 Å in Observations and Modeling During the Early Phase of a Solar Flare

Differential rotation of the halo traced by the K-giant stars

Differentially Private Combinatorial Cloud Auction

Estimating 3D Camera Pose from 2D Pedestrian Trajectories

Fair Auction and Trade Framework for Cloud VM Allocation based on Blockchain

Few-Shot Learning with Intra-Class Knowledge Transfer

High Dimensional Three-Periods Locally Ideal MIP Formulations for the UC Problem

Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask

SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds

Structure of minimal 2-spheres of constant curvature in the complex hyperquadric

An optimal transport problem with backward martingale constraints motivated by insider trading

Numerical simulations of strong-field processes in momentum space

Recursive Cascaded Networks for Unsupervised Medical Image Registration

Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network

Compressing Neural Language Models by Sparse Word Representations

Direct Urca processes involving singlet proton superfluidity in neutron star cooling

Distilling Word Embeddings: An Encoding Approach

Gland Instance Segmentation by Deep Multichannel Neural Networks

Gland Instance Segmentation by Deep Multichannel Side Supervision

How Transferable are Neural Networks in NLP Applications?

Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation

Natural Language Inference by Tree-Based Convolution and Heuristic Matching

Numerically Fitting The Electron Fermi Energy and The Electron Fraction in A Neutron Star

Optimizing Quantiles in Preference-based Markov Decision Processes

The Energetics of White-light Flares Observed by SDO/HMI and RHESSI

Ultra-narrow Negative Flare Front Observed in Helium-10830~Å using the 1.6 m New Solar Telescope

Unprecedented Fine Structure of a Solar Flare Revealed by the 1.6~m New Solar Telescope

Asymptotic properties of biorthogonal polynomials systems related to Hermite and Laguerre polynomials

Discriminative Neural Sentence Modeling by Tree-Based Convolution

Rings and Radial Waves in the Disk of the Milky Way

Structure, Stability, and Evolution of Magnetic Flux Ropes from the Perspective of Magnetic Twist

The effects of delta mesons on the baryonic direct Urca processes in neutron star matter

The K giant stars from the LAMOST survey data II: the Hercules stream in radial migration

Validation Of The Coronal Thick Target Source Model

Architecture of the Florida Power Grid as a Complex Network

Building Program Vector Representations for Deep Learning

Comparison of Emission Properties of two Homologous Flares in AR 11283

Global Energetics of Solar Flares: I. Magnetic Energies

Singlet pairing gaps of neutrons and protons in hyperonic neutron stars

The K giant stars from the LAMOST survey data I: identification, metallicity, and distance

The velocity distribution in the solar neighbourhood from the LAMOST pilot survey

Two-dimensional balanced sampling plans avoiding adjacent units

DA white dwarfs observed in LAMOST pilot survey

He I D3 Observation of the 1984 May 22 M6.3 Solar Flare

High-Cadence and High-Resolution Halpha Imaging Spectroscopy of a Circular Flare's Remote Ribbon with IBIS

Study of Rapid Formation of a Delta Sunspot Associated with the 2012 July 2 C7.4 Flare Using High-resolution Observations of New Solar Telescope

Study of Two Successive Three-Ribbon Solar Flares on 2012 July 6

Long-range adiabatic quantum state transfer through a linear array of quantum dots

Long-range adiabatic quantum state transfer through a tight-binding chain as a quantum data bus

On Rings and Streams in the Galactic Anti-Center

On the Relationship Between Coronal Magnetic Decay Index and CME Speed

Quantum corrections to the dynamics of the Bose-Einstein condensate in a double-well potential

The LEGUE High Latitude Bright Survey Design for the LAMOST Pilot Survey

The LEGUE Input Catalogue for Dark Night Observing in the LAMOST Pilot Survey