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Li Chen

Li Chen appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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math.AP Computer Vision Machine Learning astro-ph.GA math-ph math.MP physics.flu-dyn astro-ph.HE math.NA astro-ph.SR cond-mat.mtrl-sci math.FA cond-mat.str-el Information Retrieval math.DG math.PR Artificial Intelligence cond-mat.mes-hall Applications Computation and Language eess.IV math.GN math.MG physics.comp-ph physics.geo-ph physics.optics physics.soc-ph Populations and Evolution astro-ph.IM cond-mat.dis-nn cond-mat.quant-gas cond-mat.stat-mech Cryptography and Security Data Structures and Algorithms Distributed, Parallel, and Cluster Computing Human-Computer Interaction Information Theory math.CA math.IT physics.chem-ph quant-ph astro-ph cs.CY eess.AS math.OC nlin.AO Performance Sound Biological Physics Biomolecules Computation Computational Geometry Discrete Mathematics econ.TH eess.SP Graphics math.CO math.GT Neural and Evolutionary Computing Neurons and Cognition Numerical Analysis Quantitative Methods Robotics Social and Information Networks

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preprint2026arXiv

Doping induced itinerant ferromagnetism and enhanced ferroelectricity in BL-InSe

The microscopic coexistence of ferroelectricity and ferromagnetism in solids remains a fundamental challenge in condensed matter physics, with far-reaching implications for multifunctional materials and next-generation electronic devices. Using first-principles calculations, we predict emergent sliding ferroelectricity and doping-mediated ferromagnetism in bilayer (BL) InSe. The energetically favored AB stacked BL-InSe spontaneously breaks the out-of-plane mirror symmetry, resulting in a switchable polarization with a saturated component of 0.089 pC/m and a low transition barrier of 28.8 meV per unit cell. Strikingly, low-concentration electrostatic doping enhances rather than suppresses the ferroelectric polarization due to the abnormal layer-dependent electronic occupation in BL-InSe, in contrast to the conventional screening paradigm. In addition, the characteristic Mexican-hat-shaped valence band enables doping-induced itinerant half-metallic ferromagnetism, where the interlayer spin density difference scales linearly with doping concentration and can be reversed by switching the polarization direction. These results demonstrate the coexistence of ferroelectric and ferromagnetic orders in BL-InSe and establish a viable platform for realizing voltage-tunable multiferroicity through stacking and carrier doping in otherwise nonpolar and nonmagnetic semiconductors.

preprint2026arXiv

Engineering Ideal 2D Type-II Nodal Line Semimetals via Stacking and Intercalation of van der Waals Layers

Two-dimensional type-II topological semimetals (TSMs), characterized by strongly tilted Dirac cones, have attracted intense interest for their unconventional electronic properties and exotic transport behaviors. However, rational design remains challenging due to the sensitivity of band tilting to lattice geometry, atomic coordination, and symmetry constraints. Here, we present a bottom-up approach to engineer ideal type-II nodal line semimetals (NLSMs) in van der Waals bilayers via atomic intercalation. Using monolayer $h$-AlN as a prototype, we show that fluorine-intercalated bilayer AlN (F@BL-AlN) hosts a symmetry-protected type-II nodal loop precisely at the Fermi level, enabled by preserved mirror symmetry ($\mathcal{M}_z$) and tailored interlayer hybridization. First-principles calculations reveal that fluorine not only tunes interlayer coupling but also aligns the Fermi energy with the nodal line, stabilizing the type-II NLSM phase. The system exhibits tunable electronic properties under external electric and strain fields and features a van Hove singularity that induces spontaneous ferromagnetism, realizing a ferromagnetic topological semimetal state. This work provides a versatile platform for designing type-II NLSMs and offers practical guidance for their experimental realization.

preprint2026arXiv

InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation

Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on discrete tokenization. A central bottleneck is the tokenizer: aggressive downsampling and quantization often discard the fine-grained structures needed to preserve readable glyphs and distinctive facial features. We attribute this gap to standard discrete-tokenizer objectives being weakly aligned with text legibility and facial fidelity, as these objectives typically optimize generic reconstruction while compressing diverse content uniformly. To address this, we propose InsightTok, a simple yet effective discrete visual tokenization framework that enhances text and face fidelity through localized, content-aware perceptual losses. With a compact 16k codebook and a 16x downsampling rate, InsightTok significantly outperforms prior tokenizers in text and face reconstruction without compromising general reconstruction quality. These gains consistently transfer to autoregressive image generation in InsightAR, producing images with clearer text and more faithful facial details. Overall, our results highlight the potential of specialized supervision in tokenizer training for advancing discrete image generation.

preprint2026arXiv

Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials

Digital sensing faces challenges in developing sustainable methods to extend the applicability of customized e-noses to complex body odor volatilome (BOV). To address this challenge, we developed MORE-ML, a computational framework that integrates quantum-mechanical (QM) property data of e-nose molecular building blocks with machine learning (ML) methods to predict sensing-relevant properties. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between BOV molecules and mucin-derived receptors. This dataset provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of MORE-QX property space revealed weak correlations between QM properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Explainable AI methods further highlighted which QM properties most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for molecular receptors in BOV sensing. This approach establishes a foundation for advancing artificial sensing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications.

preprint2026arXiv

Normalized Solutions for Schrödinger-Bopp-Podolsky Systems with Critical Choquard-Type Nonlinearity on Bounded Domains

In this paper, we study normalized solutions for the following critical Schrödinger-Bopp-Podolsky system: $$-Δu + q(x)ϕu = λu + |u|^{p-2}u + \bigl(I_α* |u|^{3+α}\bigr)|u|^{1+α}u,\quad \text{in } Ω_r,$$ $$-Δϕ+ Δ^2ϕ= q(x)u^2, \ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\ \text{ in } Ω_r,$$ where $Ω_r \subset \mathbb R^3$ is a smooth bounded domain, $p \in \left(2, \frac{8}{3}\right)$, $q(x) \in C(\barΩ_r) \backslash \{0\}$ and $λ\in \mathbb R$ is the Lagrange multiplier associated with the constraint $\int_{Ω_r} |u|^2\, \mathrm d x = b^2$ for some $b > 0$. Here $α> 0$, $I_α$ denotes the Riesz potential, and the domain parameter $r$ reflects the size of $Ω_r$ whose precise definition will be given in Section 3. By applying a special minimax principle together with a truncation technique, we prove that there exists $b^* > 0$ such that the system admits multiple normalized solutions whenever $b \in (0, b^*)$ under Navier boundary conditions.

preprint2026arXiv

RecGPT-Mobile: On-Device Large Language Models for User Intent Understanding in Taobao Feed Recommendation

Predicting a user's next search query from recent interaction behaviors is a critical problem in modern e-commerce systems, particularly in scenarios where user intent evolves rapidly. Large Language Models (LLMs) offer strong semantic reasoning capabilities and have recently been adopted to enhance training data construction for next-query prediction. However, due to resource constraints on mobile devices, existing applications are deployed on cloud servers, resulting in high inference costs. In this paper, we propose RecGPT-Mobile, a framework that designs a lightweight LLM-based intent understanding agent to improve recommendation quality in mobile e-commerce scenarios. By deploying LLMs directly on mobile devices, our approach can capture evolving interests of users more quickly and adjust the recommendation results in real time. Extensive offline analyses and online experiments demonstrate that our method significantly improves the accuracy of recommendation results, laying a practical path for LLM deployment in production-scale recommendation systems on mobile devices, as well as a scalable solution for integrating LLMs into real-world next-query prediction systems.

preprint2026arXiv

Thompson Sampling for Repeated Newsvendor

In this paper, we investigate the performance of Thompson Sampling (TS) for online learning with censored feedback, focusing primarily on the classic repeated newsvendor model--a foundational framework in inventory management--and demonstrating how our techniques can be naturally extended to a broader class of problems. We first model demand using a Weibull distribution and initialize TS with a Gamma prior to dynamically adjust order quantities. Our analysis establishes optimal (up to logarithmic factors) frequentist regret bounds for TS without imposing restrictive prior assumptions. More importantly, it yields novel and highly interpretable insights on how TS addresses the exploration-exploitation trade-off in the repeated newsvendor setting. Specifically, our results show that when past order quantities are sufficiently large to overcome censoring, TS accurately estimates the unknown demand parameters, leading to near-optimal ordering decisions. Conversely, when past orders are relatively small, TS automatically increases future order quantities to gather additional demand information. Then, we extend our analysis to general parametric distribution family and provide proof for Bayesian regret. Extensive numerical simulations further demonstrate that TS outperforms more conservative and widely-used approaches such as online convex optimization, upper confidence bounds, and myopic Bayesian dynamic programming.

preprint2025arXiv

Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models

This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model (LLM) safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By leveraging an attack LLM to generate a high volume of diverse adversarial prompts and then fine-tuning this attack model with a preference dataset, Jailbreak-Zero achieves Pareto optimality across the crucial objectives of policy coverage, attack strategy diversity, and prompt fidelity to real user inputs. The empirical evidence demonstrates the superiority of this method, showcasing significantly higher attack success rates against both open-source and proprietary models like GPT-40 and Claude 3.5 when compared to existing state-of-the-art techniques. Crucially, Jailbreak-Zero accomplishes this while producing human-readable and effective adversarial prompts with minimal need for human intervention, thereby presenting a more scalable and comprehensive solution for identifying and mitigating the safety vulnerabilities of LLMs.

preprint2024arXiv

Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification

Protein structures represent the key to deciphering biological functions. The more detailed form of similarity among these proteins is sometimes overlooked by the conventional structural comparison methods. In contrast, further advanced methods, such as Triangular Spatial Relationship (TSR), have been demonstrated to make finer differentiations. Still, the classical implementation of TSR does not provide for the integration of secondary structure information, which is important for a more detailed understanding of the folding pattern of a protein. To overcome these limitations, we developed the SSE-TSR approach. The proposed method integrates secondary structure elements (SSEs) into TSR-based protein representations. This allows an enriched representation of protein structures by considering 18 different combinations of helix, strand, and coil arrangements. Our results show that using SSEs improves the accuracy and reliability of protein classification to varying degrees. We worked with two large protein datasets of 9.2K and 7.8K samples, respectively. We applied the SSE-TSR approach and used a neural network model for classification. Interestingly, introducing SSEs improved performance statistics for Dataset 1, with accuracy moving from 96.0% to 98.3%. For Dataset 2, where the performance statistics were already good, further small improvements were found with the introduction of SSE, giving an accuracy of 99.5% compared to 99.4%. These results show that SSE integration can dramatically improve TSR key discrimination, with significant benefits in datasets with low initial accuracies and only incremental gains in those with high baseline performance. Thus, SSE-TSR is a powerful bioinformatics tool that improves protein classification and understanding of protein function and interaction.

preprint2024arXiv

Joint Channel Estimation and Data Recovery for Millimeter Massive MIMO: Using Pilot to Capture Principal Components

Channel state information (CSI) is important to reap the full benefits of millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The traditional channel estimation methods using pilot frames (PF) lead to excessive overhead. To reduce the demand for PF, data frames (DF) can be adopted for joint channel estimation and data recovery. However, the computational complexity of the DF-based methods is prohibitively high. To reduce the computational complexity, we propose a joint channel estimation and data recovery (JCD) method assisted by a small number of PF for mmWave massive MIMO systems. The proposed method has two stages. In Stage 1, differing from the traditional PF-based methods, the proposed PF-assisted method is utilized to capture the angle of arrival (AoA) of principal components (PC) of channels. In Stage 2, JCD is designed for parallel implementation based on the multi-user decoupling strategy. The theoretical analysis demonstrates that the PF-assisted JCD method can achieve equivalent performance to the Bayesian-optimal DF-based method, while greatly reducing the computational complexity. Simulation results are also presented to validate the analytical results.

preprint2024arXiv

LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving

A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map learning primarily focuses on either detecting geometry-based lanelines or perceiving topology relationships of centerlines. Both of these methods ignore the intrinsic relationship of lanelines and centerlines, that lanelines bind centerlines. While simply predicting both types of lane in one model is mutually excluded in learning objective, we advocate lane segment as a new representation that seamlessly incorporates both geometry and topology information. Thus, we introduce LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure. Our algorithm features two key modifications. One is a lane attention module to capture pivotal region details within the long-range feature space. Another is an identical initialization strategy for reference points, which enhances the learning of positional priors for lane attention. On the OpenLane-V2 dataset, LaneSegNet outperforms previous counterparts by a substantial gain across three tasks, \textit{i.e.}, map element detection (+4.8 mAP), centerline perception (+6.9 DET$_l$), and the newly defined one, lane segment perception (+5.6 mAP). Furthermore, it obtains a real-time inference speed of 14.7 FPS. Code is accessible at https://github.com/OpenDriveLab/LaneSegNet.

preprint2024arXiv

Parabolic Anderson model in bounded domains of recurrent metric measure spaces

A metric measure space equipped with a Dirichlet form is called recurrent if its Hausdorff dimension is less than its walk dimension. In bounded domains of such spaces we study the parabolic Anderson models \[ \partial_{t} u(t,x) = Δu(t,x) + βu(t,x) \, \dot{W}_α(t,x) \] where the noise $W_α$ is white in time and colored in space when $α>0$ while for $α=0$ it is also white in space. Both Dirichlet and Neumann boundary conditions are considered. Besides proving existence and uniqueness in the Itô sense we also get precise $L^p$ estimates for the moments and intermittency properties of the solution as a consequence. Our study reveals new exponents which are intrinsically associated to the geometry of the underlying space and the results for instance apply in metric graphs or fractals like the Sierpiński gasket for which we prove scaling invariance properties of the models.

preprint2023arXiv

Personalized Prompt Learning for Explainable Recommendation

Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system's ease of use, and gain users' trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus, how to effectively fuse IDs into such models becomes a critical issue. Inspired by recent advancement in prompt learning, we come up with two solutions: find alternative words to represent IDs (called discrete prompt learning), and directly input ID vectors to a pre-trained model (termed continuous prompt learning). In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages. To bridge the gap, we further propose two training strategies: sequential tuning and recommendation as regularization. Extensive experiments show that our continuous prompt learning approach equipped with the training strategies consistently outperforms strong baselines on three datasets of explainable recommendation.

preprint2023arXiv

Reversible Attack based on Local Visual Adversarial Perturbation

Adding perturbations to images can mislead classification models to produce incorrect results. Recently, researchers exploited adversarial perturbations to protect image privacy from retrieval by intelligent models. However, adding adversarial perturbations to images destroys the original data, making images useless in digital forensics and other fields. To prevent illegal or unauthorized access to sensitive image data such as human faces without impeding legitimate users, the use of reversible adversarial attack techniques is increasing. The original image can be recovered from its reversible adversarial examples. However, existing reversible adversarial attack methods are designed for traditional imperceptible adversarial perturbations and ignore the local visible adversarial perturbation. In this paper, we propose a new method for generating reversible adversarial examples based on local visible adversarial perturbation. The information needed for image recovery is embedded into the area beyond the adversarial patch by the reversible data hiding technique. To reduce image distortion, lossless compression and the B-R-G (bluered-green) embedding principle are adopted. Experiments on CIFAR-10 and ImageNet datasets show that the proposed method can restore the original images error-free while ensuring good attack performance.

preprint2023arXiv

Rigorous Derivation of the Degenerate Parabolic-Elliptic Keller-Segel System from a Moderately Interacting Stochastic Particle System. Part I Partial Differential Equation

The aim of this paper is to provide the analysis result for the partial differential equations arising from the rigorous derivation of the degenerate parabolic-elliptic Keller-Segel system from a moderately interacting stochastic particle system. The rigorous derivation is divided into two articles. In this paper, we establish the solution theory of the degenerate parabolic-elliptic Keller-Segel problem and its non-local version, which will be used in the second paper for the discussion of the mean-field limit. A parabolic regularized system is introduced to bridge the stochastic particle model and the degenerate Keller-Segel system. We derive the existence of the solution to this regularized system by constructing approximate solutions, giving uniform estimates and taking the limits, where a crucial step is to obtain the L infty Bernstein type estimate for the gradient of the approximate solution. Based on this, we obtain the well-posedness of the corresponding non-local equation through perturbation method. Finally, the weak solution of the degenerate Keller-Segel system is obtained by using a nonlinear version of Aubin-Lions lemma.

preprint2023arXiv

Visual Point Cloud Forecasting enables Scalable Autonomous Driving

In contrast to extensive studies on general vision, pre-training for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require features encompassing semantics, 3D geometry, and temporal information simultaneously for joint perception, prediction, and planning, posing dramatic challenges for pre-training. To resolve this, we bring up a new pre-training task termed as visual point cloud forecasting - predicting future point clouds from historical visual input. The key merit of this task captures the synergic learning of semantics, 3D structures, and temporal dynamics. Hence it shows superiority in various downstream tasks. To cope with this new problem, we present ViDAR, a general model to pre-train downstream visual encoders. It first extracts historical embeddings by the encoder. These representations are then transformed to 3D geometric space via a novel Latent Rendering operator for future point cloud prediction. Experiments show significant gain in downstream tasks, e.g., 3.1% NDS on 3D detection, ~10% error reduction on motion forecasting, and ~15% less collision rate on planning.

preprint2022arXiv

A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome

Adversarial examples pose many security threats to convolutional neural networks (CNNs). Most defense algorithms prevent these threats by finding differences between the original images and adversarial examples. However, the found differences do not contain features about the classes, so these defense algorithms can only detect adversarial examples without recovering the correct labels. In this regard, we propose the Adversarial Feature Genome (AFG), a novel type of data that contains both the differences and features about classes. This method is inspired by an observed phenomenon, namely the Adversarial Feature Separability (AFS), where the difference between the feature maps of the original images and adversarial examples becomes larger with deeper layers. On top of that, we further develop an adversarial example recognition framework that detects adversarial examples and can recover the correct labels. In the experiments, the detection and classification of adversarial examples by AFGs has an accuracy of more than 90.01\% in various attack scenarios. To the best of our knowledge, our method is the first method that focuses on both attack detecting and recovering. AFG gives a new data-driven perspective to improve the robustness of CNNs. The source code is available at https://github.com/GeoX-Lab/Adv_Fea_Genome.

preprint2022arXiv

A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data

The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation of a large number of defect data will waste a lot of manpower and material resources. Therefore, this paper reviews the methods of surface defect detection of industrial products based on a small number of labeled data, and this method is divided into traditional image processing-based industrial product surface defect detection methods and deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data. The traditional image processing-based industrial product surface defect detection methods are divided into statistical methods, spectral methods and model methods. Deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data are divided into based on data augmentation, based on transfer learning, model-based fine-tuning, semi-supervised, weak supervised and unsupervised.

preprint2022arXiv

An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Location Deep Features

Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challenging, while the unused location representations are available to supply the semantics classification. This study designs a novel and efficient framework with a new module InCNet constructed lightweight model YOLCO (You Only Look Cytology Once). It directly extracts feature inside the single cell (cluster) instead of the traditional way that from image tile with a fixed size. The InCNet (Inline Connection Network) enriches the multi-scale connectivity without efficiency loss. The proposal allows the input size enlarged to megapixel that can stitch the WSI by the average repeats decreased from $10^3\sim10^4$ to $10^1\sim10^2$ for collecting features and predictions at two scales. Based on Transformer for classifying the integrated multi-scale multi-task WSI features, the experimental results appear $0.872$ AUC score better than the best conventional model on our dataset ($n$=2,019) from four scanners. The code is available at https://github.com/Chrisa142857/You-Only-Look-Cytopathology-Once , where the deployment version has the speed $\sim$70 s/WSI.

preprint2022arXiv

Besov class via heat semigroup on Dirichlet spaces III: BV functions and sub-Gaussian heat kernel estimates

With a view toward fractal spaces, by using a Korevaar-Schoen space approach, we introduce the class of bounded variation (BV) functions in a general framework of strongly local Dirichlet spaces with a heat kernel satisfying sub-Gaussian estimates. Under a weak Bakry-Émery curvature type condition, which is new in this setting, this BV class is identified with a heat semigroup based Besov class. As a consequence of this identification, properties of BV functions and associated BV measures are studied in detail. In particular, we prove co-area formulas, global $L^1$ Sobolev embeddings and isoperimetric inequalities. It is shown that for nested fractals or their direct products the BV class we define is dense in $L^1$. The examples of the unbounded Vicsek set, unbounded Sierpinski gasket and unbounded Sierpinski carpet are discussed.

preprint2022arXiv

C^0 estimates for Hessian quotient equations on HKT manifolds

We show the $C^0$ estimate for solutions to Hessian quotient equations on hyperKähler with torsion manifolds without any additional assumption on its hypercomplex structure by a clever use of the cone condition directly.

preprint2022arXiv

Combined mean-field and semiclassical limits of large fermionic systems

We study the time dependent Schrödinger equation for large spinless fermions with the semiclassical scale $\hbar = N^{-1/3}$ in three dimensions. By using the Husimi measure defined by coherent states, we rewrite the Schrödinger equation into a BBGKY type of hierarchy for the k particle Husimi measure. Further estimates are derived to obtain the weak compactness of the Husimi measure, and in addition uniform estimates for the remainder terms in the hierarchy are derived in order to show that in the semiclassical regime the weak limit of the Husimi measure is exactly the solution of the Vlasov equation.

preprint2022arXiv

Conversational Recommendation: A Grand AI Challenge

Animated avatars, which look and talk like humans, are iconic visions of the future of AI-powered systems. Through many sci-fi movies we are acquainted with the idea of speaking to such virtual personalities as if they were humans. Today, we talk more and more to machines like Apple's Siri, e.g., to ask them for the weather forecast. However, when asked for recommendations, e.g., for a restaurant to go to, the limitations of such devices quickly become obvious. They do not engage in a conversation to find out what we might prefer, they often do not provide explanations for what they recommend, and they may have difficulties remembering what was said one minute earlier. Conversational recommender systems promise to address these limitations. In this paper, we review existing approaches to build such systems, which developments we observe today, which challenges are still open and why the development of conversational recommenders represents one of the next grand challenges of AI.

preprint2022arXiv

Decentralized Federated Learning: Balancing Communication and Computing Costs

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus. It can provide a general decentralized SGD analytical framework. We establish strong convergence guarantees for the proposed DFL algorithm without the assumption of convex objectives. The convergence rate of DFL can be optimized to achieve the balance of communication and computing costs under constrained resources. For improving communication efficiency of DFL, compressed communication is further introduced to the proposed DFL as a new scheme, named DFL with compressed communication (C-DFL). The proposed C-DFL exhibits linear convergence for strongly convex objectives. Experiment results based on MNIST and CIFAR-10 datasets illustrate the superiority of DFL over traditional decentralized SGD methods and show that C-DFL further enhances communication efficiency.

preprint2022arXiv

Deep Learning with Label Noise: A Hierarchical Approach

Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the network architecture or careful tuning of the optimization procedure. In this work, we propose a simple hierarchical approach that incorporates a label hierarchy when training the deep learning models. Our approach requires no change of the network architecture or the optimization procedure. We investigate our hierarchical network through a wide range of simulated and real datasets and various label noise types. Our hierarchical approach improves upon regular deep neural networks in learning with label noise. Combining our hierarchical approach with pre-trained models achieves state-of-the-art performance in real-world noisy datasets.

preprint2022arXiv

Design of Coded Caching Schemes with Linear Subpacketizations Based on Injective Arc Coloring of Regular Digraphs

Coded caching is an effective technique to decongest the amount of traffic in the backhaul link. In such a scheme, each file hosted in the server is divided into a number of packets to pursue a low transmission rate based on the delicate design of contents cached into users and broadcast messages. However, the implementation complexity of this scheme increases with the number of packets. It is desirable to design a scheme with a small subpacketization level and a relatively low transmission rate. Recently, placement delivery array (PDA) was proposed to address the subpacketization bottleneck of coded caching. This paper investigates the design PDA from a new perspective, i.e., the injective arc coloring of regular digraphs. It is shown that the injective arc coloring of a regular digraph can yield a PDA with the same number of rows and columns. Based on this, a new class of regular digraphs are defined and the upper bounds on the injective chromatic index of such digraphs are derived. Consequently, some new coded caching schemes with a linear subpacketization level and a small transmission rate are proposed, one of which generalizes the existing scheme for the scenario with a more flexible number of users.

preprint2022arXiv

Design of Placement Delivery Arrays for Coded Caching with Small Subpacketizations and Flexible Memory Sizes

Coded caching is an emerging technique to reduce the data transmission load during the peak-traffic times. In such a scheme, each file in the data center or library is usually divided into a number of packets to pursue a low broadcasting rate based on the designed placements at each user's cache. However, the implementation complexity of this scheme increases as the number of packets increases. It is crucial to design a scheme with a small subpacketization level, while maintaining a relatively low transmission rate. It is known that the design of caches in users (i.e., the placement phase) and broadcasting (i.e., the delivery phase) can be unified in one matrix, namely the placement delivery array (PDA). This paper proposes a novel PDA construction by selecting proper orthogonal arrays (POAs), which generalizes some known constructions but with a more flexible memory size. Based on the proposed PDA construction, an effective transformation is further proposed to enable a coded caching scheme to have a smaller subpacketization level. Moreover, two new coded caching schemes with the coded placement are considered. It is shown that the proposed schemes yield a lower subpacketization level and transmission rate over some existing schemes.

preprint2022arXiv

Discovery of extended structure around open cluster COIN-Gaia 13 based on Gaia EDR3

COIN-Gaia 13 is a newly discovered open cluster revealed by Gaia DR2 data. It is a nearby open cluster with a distance of about 513 pc. Combined with the five-dimensional astrometric data of Gaia EDR3 with higher accuracy, we use the membership assignment algorithm (pyUPMASK) to determine the membership of COIN-Gaia 13 in a large extended spatial region. The cluster has found 478 candidate members. After obtaining reliable cluster members, we further study its basic properties and spatial distribution. Our results show that there is an obvious extended structure of the cluster in the X-Y plane. This elongated structure is distributed along the spiral arm, and the whole length is about 270 pc. The cluster age is 250 Myr, the total mass is about 439 M$_\odot$, and the tidal radius of the cluster is about 11 pc. Since more than half of the member stars (352 stars) are located outside twice the tidal radius, it is suspected that this cluster is undergoing the dynamic dissolution process. Furthermore, the spatial distribution and kinematic analysis indicate that the extended structure in COIN-Gaia 13 is more likely to be caused by the differential rotation of the Galaxy.

preprint2022arXiv

Enhanced Deep Animation Video Interpolation

Existing learning-based frame interpolation algorithms extract consecutive frames from high-speed natural videos to train the model. Compared to natural videos, cartoon videos are usually in a low frame rate. Besides, the motion between consecutive cartoon frames is typically nonlinear, which breaks the linear motion assumption of interpolation algorithms. Thus, it is unsuitable for generating a training set directly from cartoon videos. For better adapting frame interpolation algorithms from nature video to animation video, we present AutoFI, a simple and effective method to automatically render training data for deep animation video interpolation. AutoFI takes a layered architecture to render synthetic data, which ensures the assumption of linear motion. Experimental results show that AutoFI performs favorably in training both DAIN and ANIN. However, most frame interpolation algorithms will still fail in error-prone areas, such as fast motion or large occlusion. Besides AutoFI, we also propose a plug-and-play sketch-based post-processing module, named SktFI, to refine the final results using user-provided sketches manually. With AutoFI and SktFI, the interpolated animation frames show high perceptual quality.

preprint2022arXiv

ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention

Sparse Transformer has recently attracted a lot of attention since the ability for reducing the quadratic dependency on the sequence length. We argue that two factors, information bottleneck sensitivity and inconsistency between different attention topologies, could affect the performance of the Sparse Transformer. This paper proposes a well-designed model named ERNIE-Sparse. It consists of two distinctive parts: (i) Hierarchical Sparse Transformer (HST) to sequentially unify local and global information. (ii) Self-Attention Regularization (SAR) method, a novel regularization designed to minimize the distance for transformers with different attention topologies. To evaluate the effectiveness of ERNIE-Sparse, we perform extensive evaluations. Firstly, we perform experiments on a multi-modal long sequence modeling task benchmark, Long Range Arena (LRA). Experimental results demonstrate that ERNIE-Sparse significantly outperforms a variety of strong baseline methods including the dense attention and other efficient sparse attention methods and achieves improvements by 2.77% (57.78% vs. 55.01%). Secondly, to further show the effectiveness of our method, we pretrain ERNIE-Sparse and verified it on 3 text classification and 2 QA downstream tasks, achieve improvements on classification benchmark by 0.83% (92.46% vs. 91.63%), on QA benchmark by 3.24% (74.67% vs. 71.43%). Experimental results continue to demonstrate its superior performance.

preprint2022arXiv

Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

One major problem in black-box adversarial attacks is the high query complexity in the hard-label attack setting, where only the top-1 predicted label is available. In this paper, we propose a novel geometric-based approach called Tangent Attack (TA), which identifies an optimal tangent point of a virtual hemisphere located on the decision boundary to reduce the distortion of the attack. Assuming the decision boundary is locally flat, we theoretically prove that the minimum $\ell_2$ distortion can be obtained by reaching the decision boundary along the tangent line passing through such tangent point in each iteration. To improve the robustness of our method, we further propose a generalized method which replaces the hemisphere with a semi-ellipsoid to adapt to curved decision boundaries. Our approach is free of pre-training. Extensive experiments conducted on the ImageNet and CIFAR-10 datasets demonstrate that our approach can consume only a small number of queries to achieve the low-magnitude distortion. The implementation source code is released online at https://github.com/machanic/TangentAttack.

preprint2022arXiv

Global weak solutions to the Vlasov-Poisson-Fokker-Planck-Navier-Stokes system

We consider the compressible Vlasov-Poisson-Fokker-Planck-Navier-Stokes system in a three dimensional bounded domain with nonhomogeneous Dirichlet boundary conditions. The system describes the evolution of charged particles ensemble dispersed in an isentropic fluid. For the adiabatic coefficient $γ>3/2$, we establish the global existence of weak solutions to this system with arbitrary large initial and boundary data.

preprint2022arXiv

Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

Conversational recommender systems (CRSs) imitate human advisors to assist users in finding items through conversations and have recently gained increasing attention in domains such as media and e-commerce. Like in human communication, building trust in human-agent communication is essential given its significant influence on user behavior. However, inspiring user trust in CRSs with a "one-size-fits-all" design is difficult, as individual users may have their own expectations for conversational interactions (e.g., who, user or system, takes the initiative), which are potentially related to their personal characteristics. In this study, we investigated the impacts of three personal characteristics, namely personality traits, trust propensity, and domain knowledge, on user trust in two types of text-based CRSs, i.e., user-initiative and mixed-initiative. Our between-subjects user study (N=148) revealed that users' trust propensity and domain knowledge positively influenced their trust in CRSs, and that users with high conscientiousness tended to trust the mixed-initiative system.

preprint2022arXiv

Learning quantum dissipation by the neural ordinary differential equation

Quantum dissipation arises from the unavoidable coupling between a quantum system and its surrounding environment, which is known as a major obstacle in the quantum processing of information. Apart from its existence, how to trace the dissipation from observational data is a crucial topic that may stimulate manners to suppress the dissipation. In this paper, we propose to learn the quantum dissipation from dynamical observations using the neural ordinary differential equation, and then demonstrate this method concretely on two open quantum-spin systems -- a large spin system and a spin-1/2 chain. We also investigate the learning efficiency of the dataset, which provides useful guidance for data acquisition in experiments. Our work promisingly facilitates effective modeling and decoherence suppression in open quantum systems.

preprint2022arXiv

Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot

Equipped with a wide span of sensors, predominant autonomous driving solutions are becoming more modular-oriented for safe system design. Though these sensors have laid a solid foundation, most massive-production solutions up to date still fall into L2 phase. Among these, Comma.ai comes to our sight, claiming one $999 aftermarket device mounted with a single camera and board inside owns the ability to handle L2 scenarios. Together with open-sourced software of the entire system released by Comma.ai, the project is named Openpilot. Is it possible? If so, how is it made possible? With curiosity in mind, we deep-dive into Openpilot and conclude that its key to success is the end-to-end system design instead of a conventional modular framework. The model is briefed as Supercombo, and it can predict the ego vehicle's future trajectory and other road semantics on the fly from monocular input. Unfortunately, the training process and massive amount of data to make all these work are not publicly available. To achieve an intensive investigation, we try to reimplement the training details and test the pipeline on public benchmarks. The refactored network proposed in this work is referred to as OP-Deepdive. For a fair comparison of our version to the original Supercombo, we introduce a dual-model deployment scheme to test the driving performance in the real world. Experimental results on nuScenes, Comma2k19, CARLA, and in-house realistic scenarios verify that a low-cost device can indeed achieve most L2 functionalities and be on par with the original Supercombo model. In this report, we would like to share our latest findings, shed some light on the new perspective of end-to-end autonomous driving from an industrial product-level side, and potentially inspire the community to continue improving the performance. Our code, benchmarks are at https://github.com/OpenPerceptionX/Openpilot-Deepdive.

preprint2022arXiv

Maximum Flow and Minimum-Cost Flow in Almost-Linear Time

We give an algorithm that computes exact maximum flows and minimum-cost flows on directed graphs with $m$ edges and polynomially bounded integral demands, costs, and capacities in $m^{1+o(1)}$ time. Our algorithm builds the flow through a sequence of $m^{1+o(1)}$ approximate undirected minimum-ratio cycles, each of which is computed and processed in amortized $m^{o(1)}$ time using a new dynamic graph data structure. Our framework extends to algorithms running in $m^{1+o(1)}$ time for computing flows that minimize general edge-separable convex functions to high accuracy. This gives almost-linear time algorithms for several problems including entropy-regularized optimal transport, matrix scaling, $p$-norm flows, and $p$-norm isotonic regression on arbitrary directed acyclic graphs.

preprint2022arXiv

Minimum Coverage Instrumentation

Modern compilers leverage block coverage profile data to carry out downstream profile-guided optimizations to improve the runtime performance and the size of a binary. Given a control-flow graph $G=(V, E)$ of a function in the binary, where nodes in $V$ correspond to basic blocks (sequences of instructions that are always executed sequentially) and edges in $E$ represent jumps in the control flow, the goal is to know for each block $u \in V$ whether $u$ was executed during a session. To this end, extra instrumentation code that records when a block is executed needs to be added to the binary. This extra code creates a time and space overhead, which one would like to minimize as much as possible. Motivated by this application, we study the Minimum Coverage Instrumentation problem, where the goal is to find a minimum size subset of blocks to instrument such that the coverage of the remaining blocks in the graph can be inferred from the coverage status of the instrumented subset. Our main result is an algorithm to find an optimal instrumentation strategy and to carry out the inference in $O(|E|)$ time. We also study variants of this basic problem in which we are interested in learning the coverage of edges instead of the nodes, or when we are only allowed to instrument edges instead of the nodes.

preprint2022arXiv

New insights into the structure of open clusters in the Gaia era

With the help of Gaia data, it is noted that in addition to the core components, there are low-density outer halo components in the extended region of open clusters. To study the extended structure beyond the core radius of the cluster ($\sim$ 10 pc), based on Gaia EDR3 data, taking up to 50 pc as the searching radius, we use the pyUPMASK algorithm to re-determine the member stars of the open cluster within 1-2 kpc. We obtain the member stars of 256 open clusters, especially those located in the outer halo region of open clusters. Furthermore, we find that most open clusters' radial density profile in the outer region deviates from the King's profile. To better describe the internal and external structural characteristics of open clusters, we propose a double components model for description: core components with King model distribution and outer halo components with logarithmic Gaussian distribution, and then suggest using four radii ( $r_c$, $r_t$, $r_o$, $r_e$) for describing the structure and distribution profile of star clusters, where $r_t$ and $r_e$ represent the boundaries of core components and outer halo components respectively. Finally, we provide a catalog of 256 clusters with structural parameters. In addition, our study shows the sizes of these radii are statistically linear related, which indicates that the inner and outer regions of the cluster are interrelated and follow similar evolutionary processes. Further, we show that the structure of two components can be used to better trace the cluster evolution properties in different stages.

preprint2022arXiv

New Open Cluster candidates Found in Galactic Disk Using Gaia DR2/EDR3 Data

We report 541 new open cluster candidates in Gaia EDR3 through revisiting the cluster results from an earlier analysis of the Gaia DR2, which revealed nearly a thousand open cluster candidates in the solar neighborhood (mostly d < 3 kpc) resideing at Galactic latitudes |b| < 20 degrees. A subsequent comparison with lists of known clusters shows a large increases of the cluster samples within 2 kpc from the Sun. We assign membership probabilities to the stars through the open source pyUPMASK algorithm, and also estimate the physical parameters through isochrone fitting for each candidate. Most of the new candidates show small total proper motion dispersions and clear features in the color-magnitude diagrams. Besides, the metallicity gradient of the new candidates is consistent with those found in the literature. The cluster parameters and member stars are available at CDS via anonymous ftp to cdsarc.u-strasbg.fr(130.79.128.5) or via https://cdsarc.unistra.fr/viz-bin/cat/J/ApJS. The discovery of these new objects shows that the open cluster samples in Gaia data is still not complete, and more discoveries are expected in the future researches.

preprint2022arXiv

Pascal algebra of matrices and Pascal map on jet bundles

We identify and study a matrix algebra consisting of Pascal-type matrices. The generator of the matrix algebra is shown to well define a canonical bundle map, called the Pascal map on jet bundles, and we use it to give an intrinsic definition of point-wise contact between Hermitian vector bundles in terms of unitary equivalence of the Pascal maps.

preprint2022arXiv

PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark

Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.). Previous work struggled in complex cases due to their simple designs of the spatial transformation between front view and bird's eye view (BEV) and the lack of a realistic dataset. Towards these issues, we present PersFormer: an end-to-end monocular 3D lane detector with a novel Transformer-based spatial feature transformation module. Our model generates BEV features by attending to related front-view local regions with camera parameters as a reference. PersFormer adopts a unified 2D/3D anchor design and an auxiliary task to detect 2D/3D lanes simultaneously, enhancing the feature consistency and sharing the benefits of multi-task learning. Moreover, we release one of the first large-scale real-world 3D lane datasets: OpenLane, with high-quality annotation and scenario diversity. OpenLane contains 200,000 frames, over 880,000 instance-level lanes, 14 lane categories, along with scene tags and the closed-in-path object annotations to encourage the development of lane detection and more industrial-related autonomous driving methods. We show that PersFormer significantly outperforms competitive baselines in the 3D lane detection task on our new OpenLane dataset as well as Apollo 3D Lane Synthetic dataset, and is also on par with state-of-the-art algorithms in the 2D task on OpenLane. The project page is available at https://github.com/OpenPerceptionX/PersFormer_3DLane and OpenLane dataset is provided at https://github.com/OpenPerceptionX/OpenLane.

preprint2022arXiv

Quasi-periodic oscillations of the X-ray burst from the magnetar SGR J1935+2154 and associated with the fast radio burst FRB 200428

The origin(s) and mechanism(s) of fast radio bursts (FRBs), which are short radio pulses from cosmological distances, have remained a major puzzle since their discovery. We report a strong Quasi-Periodic Oscillation(QPO) of 40 Hz in the X-ray burst from the magnetar SGR J1935+2154 and associated with FRB 200428, significantly detected with the Hard X-ray Modulation Telescope (Insight-HXMT) and also hinted by the Konus-Wind data. QPOs from magnetar bursts have only been rarely detected; our 3.4 sigma (p-value is 2.9e-4) detection of the QPO reported here reveals the strongest QPO signal observed from magnetars (except in some very rare giant flares), making this X-ray burst unique among magnetar bursts. The two X-ray spikes coinciding with the two FRB pulses are also among the peaks of the QPO. Our results suggest that at least some FRBs are related to strong oscillation processes of neutron stars. We also show that we may overestimate the significance of the QPO signal and underestimate the errors of QPO parameters if QPO exists only in a fraction of the time series of a X-ray burst which we use to calculate the Leahy-normalized periodogram.

preprint2022arXiv

Robust Landmark-based Stent Tracking in X-ray Fluoroscopy

In clinical procedures of angioplasty (i.e., open clogged coronary arteries), devices such as balloons and stents need to be placed and expanded in arteries under the guidance of X-ray fluoroscopy. Due to the limitation of X-ray dose, the resulting images are often noisy. To check the correct placement of these devices, typically multiple motion-compensated frames are averaged to enhance the view. Therefore, device tracking is a necessary procedure for this purpose. Even though angioplasty devices are designed to have radiopaque markers for the ease of tracking, current methods struggle to deliver satisfactory results due to the small marker size and complex scenes in angioplasty. In this paper, we propose an end-to-end deep learning framework for single stent tracking, which consists of three hierarchical modules: U-Net based landmark detection, ResNet based stent proposal and feature extraction, and graph convolutional neural network (GCN) based stent tracking that temporally aggregates both spatial information and appearance features. The experiments show that our method performs significantly better in detection compared with the state-of-the-art point-based tracking models. In addition, its fast inference speed satisfies clinical requirements.

preprint2022arXiv

ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning

Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of tasks. To better predict the control signals and enhance user safety, an end-to-end approach that benefits from joint spatial-temporal feature learning is desirable. While there are some pioneering works on LiDAR-based input or implicit design, in this paper we formulate the problem in an interpretable vision-based setting. In particular, we propose a spatial-temporal feature learning scheme towards a set of more representative features for perception, prediction and planning tasks simultaneously, which is called ST-P3. Specifically, an egocentric-aligned accumulation technique is proposed to preserve geometry information in 3D space before the bird's eye view transformation for perception; a dual pathway modeling is devised to take past motion variations into account for future prediction; a temporal-based refinement unit is introduced to compensate for recognizing vision-based elements for planning. To the best of our knowledge, we are the first to systematically investigate each part of an interpretable end-to-end vision-based autonomous driving system. We benchmark our approach against previous state-of-the-arts on both open-loop nuScenes dataset as well as closed-loop CARLA simulation. The results show the effectiveness of our method. Source code, model and protocol details are made publicly available at https://github.com/OpenPerceptionX/ST-P3.

preprint2022arXiv

Tail Quantile Estimation for Non-preemptive Priority Queues

Motivated by applications in computing and telecommunication systems, we investigate the problem of estimating p-quantile of steady-state sojourn times in a single-server multi-class queueing system with non-preemptive priorities for p close to 1. The main challenge in this problem lies in efficient sampling from the tail event. To address this issue, we develop a regenerative simulation algorithm with importance sampling. In addition, we establish a central limit theorem for the estimator to construct the confidence interval. Numerical experiments show that our algorithm outperforms benchmark simulation methods. Our result contributes to the literature on rare event simulation for queueing systems.

preprint2022arXiv

Time-constrained Dynamic Mechanisms for College Admissions

Recent literature shows that dynamic matching mechanisms may outperform the standard mechanisms to deliver desirable results. We highlight an under-explored design dimension, the time constraints that students face under such a dynamic mechanism. First, we theoretically explore the effect of time constraints and show that the outcome can be worse than the outcome produced by the student-proposing deferred acceptance mechanism. Second, we present evidence from the Inner Mongolian university admissions that time constraints can prevent dynamic mechanisms from achieving stable outcomes, creating losers and winners among students.

preprint2021arXiv

BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented health crisis for the global. Digital contact tracing, as a transmission intervention measure, has shown its effectiveness on pandemic control. Despite intensive research on digital contact tracing, existing solutions can hardly meet users' requirements on privacy and convenience. In this paper, we propose BU-Trace, a novel permissionless mobile system for privacy-preserving intelligent contact tracing based on QR code and NFC technologies. First, a user study is conducted to investigate and quantify the user acceptance of a mobile contact tracing system. Second, a decentralized system is proposed to enable contact tracing while protecting user privacy. Third, an intelligent behavior detection algorithm is designed to ease the use of our system. We implement BU-Trace and conduct extensive experiments in several real-world scenarios. The experimental results show that BU-Trace achieves a privacy-preserving and intelligent mobile system for contact tracing without requesting location or other privacy-related permissions.

preprint2021arXiv

Dynamical reciprocity in interacting games: numerical results and mechanism analysis

We study the evolution of two mutually interacting games with both pairwise games as well as the public goods game on different topologies. On 2d square lattices, we reveal that the game-game interaction can promote the cooperation prevalence in all cases, and the cooperation-defection phase transitions even become absent and fairly high cooperation is expected when the interaction goes to be very strong. A mean-field theory is developed that points out new dynamical routes arising therein. Detailed analysis shows indeed that there are rich categories of interactions in either individual or bulk scenario: invasion, neutral, and catalyzed types; their combination puts cooperators at a persistent advantage position, which boosts the cooperation. The robustness of the revealed reciprocity is strengthened by the studies of model variants, including asymmetrical or time-varying interactions, games of different types, games with time-scale separation, different updating rules etc. The structural complexities of the underlying population, such as Newman--Watts small world networks, Erdős--Rényi random networks, and Barabási--Albert networks, also do not alter the working of the dynamical reciprocity. In particular, as the number of games engaged increases, the cooperation level continuously improves in general. Our work thus uncovers a new class of cooperation mechanism and indicates the great potential for human cooperation where concurrent issues are so often seen in the real world.

preprint2021arXiv

Neumann problem for a class of fully nonlinear equations

In this paper, we establish a priori estimates for a class of fully nonlinear equations with Neumann boundary conditions. By the continuity method, we have obtained the existence theorem for the Neumann problem.

preprint2021arXiv

Social hierarchy promotes the cooperation prevalence

Social hierarchy is important that can not be ignored in human socioeconomic activities and in the animal world. Here we incorporate this factor into the evolutionary game to see what impact it could have on the cooperation outcome. The probabilistic strategy adoption between two players is then not only determined by their payoffs, but also by their hierarchy difference -- players in the high rank are more likely to reproduce their strategies than the peers in the low rank. Through simulating the evolution of Prisoners' dilemma game with three hierarchical distributions, we find that the levels of cooperation are enhanced in all cases, and the enhancement is optimal in the uniform case. The enhancement is due to the fact that the presence of hierarchy facilitates the formation of cooperation clusters with high-rank players acting as the nucleation cores. This mechanism remains valid on Barabási-Albert scale-free networks, in particular the cooperation enhancement is maximal when the hubs are of higher social ranks. We also study a two-hierarchy model, where similar cooperation promotion is revealed and some theoretical analyses are provided. Our finding may partially explain why the social hierarchy is so ubiquitous on this planet.

preprint2021arXiv

Specht's invariant and localization of operator tuples

We introduce a set of operator-valued invariants for localization of operator tuples in the Cowen-Douglas class, with which a Specht-type criterion for unitary equivalence is obtained.

preprint2021arXiv

Speech enhancement with weakly labelled data from AudioSet

Speech enhancement is a task to improve the intelligibility and perceptual quality of degraded speech signal. Recently, neural networks based methods have been applied to speech enhancement. However, many neural network based methods require noisy and clean speech pairs for training. We propose a speech enhancement framework that can be trained with large-scale weakly labelled AudioSet dataset. Weakly labelled data only contain audio tags of audio clips, but not the onset or offset times of speech. We first apply pretrained audio neural networks (PANNs) to detect anchor segments that contain speech or sound events in audio clips. Then, we randomly mix two detected anchor segments containing speech and sound events as a mixture, and build a conditional source separation network using PANNs predictions as soft conditions for speech enhancement. In inference, we input a noisy speech signal with the one-hot encoding of "Speech" as a condition to the trained system to predict enhanced speech. Our system achieves a PESQ of 2.28 and an SSNR of 8.75 dB on the VoiceBank-DEMAND dataset, outperforming the previous SEGAN system of 2.16 and 7.73 dB respectively.

preprint2020arXiv

A Concise Proof of Discrete Jordan Curve Theorem

This paper gives a concise proof of the Jordan curve theorem on discrete surfaces. We also embed the discrete surface in the 2D plane to prove the original version of the Jordan curve theorem. This paper is a simple version of L. Chen, Note on the discrete Jordan curve theorem (revised version), arXiv:1312.0316. We seek to clarify and simplify some statements and proofs. Again, the purpose of this paper is to make the proof of the theorems easier to understand. In revision 2, we added Appendix B to make a self-contained proof on verifying simple connectedness of the Euclidean plane in this paper. In this revision, we added a special case for the proof of Theorem 3 in Appendix B that was found when we were revising a new paper for high dimensional contraction. It was easy to resolve in 2D. We put it in Appendix C of this paper.

preprint2020arXiv

A curvature flow approach to Lp-Christoffel-Minkowski problem for p>1

We study the motion of smooth, closed, strictly convex hypersurfaces in Rn+1 expanding in the direction of their normal vector field with speed depending on the k-th elementary symmetric polynomial of the principal radii of curvature. As an application, we gives a unifed fow approach to Lp-Christoffel-Minkowski problem for p > 1.

preprint2020arXiv

A Discrete Proof of The General Jordan-Schoenflies Theorem

In the early 1960s, Brown and Mazur proved the general Jordan-Schoenflies theorem. This fundamental theorem states: If we embed an $(n-1)$ sphere $S^{(n-1)}$ locally flatly in an $n$ sphere $S^{n}$, then it decomposes $S^{n}$ into two components. In addition, the embedded $S^{(n-1)}$ is the common boundary of the two components and each component is homeomorphic to the $n$-ball.\newline This paper gives a constructive proof of the theorem using the discrete method. More specifically, we prove the equivalent statements: Let $M$ be an $n$-manifold, which is homeomorphic to $S^{n}$. Then, every $(n-1)$-manifold $S$, a submanifold with local flatness in $M$, decomposes the space $M$ into two components where each component is homeomorphic to an $n$-ball. The method was chosen in order to evaluate the computability and computational costs among operations between cells regarding homeomorphism. In addition, methods within the proof can be extended to applications in design algorithms under the assumption that homeomorphic mappings are constructible and computable. In this new revision, We add some new detailed discussions.

preprint2020arXiv

A note on second order Riesz transforms in 3-dimensional Lie groups

We prove explicit $L^p$ bounds for second order Riesz transforms of the sub-Laplacian in the Lie groups $\mathbb H$, $\mathbb{SU}(2)$ and $\mathbb{SL}(2)$

preprint2020arXiv

A Systematic Analysis of the Phase Lags Associated with the Type-C Quasi-periodic Oscillation in GRS 1915+105

We present a systematic analysis of the phase lags associated with the type-C QPOs in GRS 1915+105 using RXTE data. Our sample comprises of 620 RXTE observations with type-C QPOs ranging from ~0.4 Hz to ~6.3 Hz. Based on our analysis, we confirm that the QPO phase lags decrease with QPO frequency, and change sign from positive to negative at a QPO frequency of ~2 Hz. In addition, we find that the slope of this relation is significantly different between QPOs below and above 2 Hz. The relation between the QPO lags and QPO rms can be well fitted with a broken line: as the QPO lags go from negative to positive, the QPO rms first increases, reaching its maximum at around zero lag, and then decreases. The phase-lag behaviour of the subharmonic of the QPO is similar to that of the QPO fundamental, where the subharmonic lags decrease with subharmonic frequency and change sign from positive to negative at a subharmonic frequency of ~1 Hz; on the contrary, the second harmonic of the QPO shows a quite different phase-lag behaviour, where all the second harmonics show hard lags that remain more or less constant. For both the QPO and its (sub)harmonics, the slope of the lag-energy spectra shows a similar evolution with frequency as the average phase lags. This suggests that the lag-energy spectra drives the average phase lags. We discuss the possibility for the change in lag sign, and the physical origin of the QPO lags.

preprint2020arXiv

Adversarial Example in Remote Sensing Image Recognition

With the wide application of remote sensing technology in various fields, the accuracy and security requirements for remote sensing images (RSIs) recognition are also increasing. In recent years, due to the rapid development of deep learning in the field of image recognition, RSI recognition models based on deep convolution neural networks (CNNs) outperform traditional hand-craft feature techniques. However, CNNs also pose security issues when they show their capability of accurate classification. By adding a very small variation of the adversarial perturbation to the input image, the CNN model can be caused to produce erroneous results with extremely high confidence, and the modification of the image is not perceived by the human eye. This added adversarial perturbation image is called an adversarial example, which poses a serious security problem for systems based on CNN model recognition results. This paper, for the first time, analyzes adversarial example problem of RSI recognition under CNN models. In the experiments, we used different attack algorithms to fool multiple high-accuracy RSI recognition models trained on multiple RSI datasets. The results show that RSI recognition models are also vulnerable to adversarial examples, and the models with different structures trained on the same RSI dataset also have different vulnerabilities. For each RSI dataset, the number of features also affects the vulnerability of the model. Many features are good for defensive adversarial examples. Further, we find that the attacked class of RSI has an attack selectivity property. The misclassification of adversarial examples of the RSIs are related to the similarity of the original classes in the CNN feature space. In addition, adversarial examples in RSI recognition are of great significance for the security of remote sensing applications, showing a huge potential for future research.

preprint2020arXiv

Algorithms for Deforming and Contracting Simply Connected Discrete Closed Manifolds (III)

In a recent paper, {\it Algorithms for Deforming and Contracting Simply Connected Discrete Closed Manifolds (II)}, we discussed two algorithms for deforming and contracting a simply connected discrete closed manifold into a discrete sphere. The first algorithm was a continuation of work that began in {\it Algorithms for Deforming and Contracting Simply Connected Discrete Closed Manifolds (I)}, the second algorithm contained a more direct treatment of contraction for discrete manifolds. In this paper, we clarify that we can use this same method on standard piecewise linear (PL) complexes on the triangulation of general smooth manifolds. Our discussion is based on triangulation techniques invented by Cairns, Whitehead, and Whitney more than half of a century ago. In this paper, we use PL or simplicial complexes to replace certain concepts of discrete manifolds in previous papers. Note that some details in the original papers related to discrete manifolds may also need to be slightly modified for this purpose. In this paper, we use the algorithmic procedure (a contraction process) to prove the following theorem: For a finite triangulation of a simply-connected closed and orientable 3-manifold $M$ in Euclidean space, if a (simply-connected closed) 2-cycle which was made by 2-cells of this triangulation separates $M$ into two connected components, then each of the components will also be simply-connected. In addition, we can algorithmically make $M$ to be homeomorphic to a 3-sphere. The relationship of the theorem to a generalized Jordan separation problem, the general Jordan-Schoenflies theorem, and other important problems are also discussed. This revision adds more figures and explanations as well as two more special cases. We will post the detailed algorithm/procedure for practical triangulations in the following papers.

preprint2020arXiv

An Integrated Quadratic Reconstruction for Finite Volume Schemes to Scalar Conservation Laws in Multiple Dimensions

We proposed a piecewise quadratic reconstruction method in multiple dimensions, which is in an integrated style, for finite volume schemes to scalar conservation laws. This integrated quadratic reconstruction is parameter-free and applicable on flexible grids. We show that the finite volume schemes with the new reconstruction satisfy a local maximum principle with properly setup on time steplength. Numerical examples are presented to show that the proposed scheme attains a third-order accuracy for smooth solutions in both 2D and 3D cases. It is indicated by numerical results that the local maximum principle is helpful to prevent overshoots in numerical solutions.

preprint2020arXiv

Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement

Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ARTLABEL.

preprint2020arXiv

Besov class via heat semigroup on Dirichlet spaces I: Sobolev type inequalities

We introduce heat semigroup-based Besov classes in the general framework of Dirichlet spaces. General properties of those classes are studied and quantitative regularization estimates for the heat semigroup in this scale of spaces are obtained. As a highlight of the paper, we obtain a far reaching $L^p$-analogue, $p \ge 1$, of the Sobolev inequality that was proved for $p=2$ by N. Varopoulos under the assumption of ultracontractivity for the heat semigroup. The case $p=1$ is of special interest since it yields isoperimetric type inequalities.

preprint2020arXiv

Besov class via heat semigroup on Dirichlet spaces II: BV functions and Gaussian heat kernel estimates

We introduce the class of bounded variation (BV) functions in a general framework of strictly local Dirichlet spaces with doubling measure. Under the 2-Poincaré inequality and a weak Bakry-Émery curvature type condition, this BV class is identified with the heat semigroup based Besov class $\mathbf{B}^{1,1/2}(X)$ that was introduced in our previous paper. Assuming furthermore a quasi Bakry-Émery curvature type condition, we identify the Sobolev class $W^{1,p}(X)$ with $\mathbf{B}^{p,1/2}(X)$ for $p>1$. Consequences of those identifications in terms of isoperimetric and Sobolev inequalities with sharp exponents are given.

preprint2020arXiv

Blurry Video Frame Interpolation

Existing works reduce motion blur and up-convert frame rate through two separate ways, including frame deblurring and frame interpolation. However, few studies have approached the joint video enhancement problem, namely synthesizing high-frame-rate clear results from low-frame-rate blurry inputs. In this paper, we propose a blurry video frame interpolation method to reduce motion blur and up-convert frame rate simultaneously. Specifically, we develop a pyramid module to cyclically synthesize clear intermediate frames. The pyramid module features adjustable spatial receptive field and temporal scope, thus contributing to controllable computational complexity and restoration ability. Besides, we propose an inter-pyramid recurrent module to connect sequential models to exploit the temporal relationship. The pyramid module integrates a recurrent module, thus can iteratively synthesize temporally smooth results without significantly increasing the model size. Extensive experimental results demonstrate that our method performs favorably against state-of-the-art methods.

preprint2020arXiv

Composite Signalling for DFRC: Dedicated Probing Signal or Not?

Dual-functional radar-communication (DFRC) is a promising new solution to simultaneously probe the radar target and transmit information in wireless networks. In this paper, we study the joint optimization of transmit and receive beamforming for the DFRC system. Specifically, the signal to interference plus noise ratio (SINR) of the radar is maximized under the SINR constraints of the communication user (CU), which characterizes the optimal tradeoff between radar and communication. In addition to simply using the communication signal for target probing, we further consider to exploit dedicated probing signals to enhance the radar sensing performance. We commence by studying the single-CU scenario, where a closed-form solution to the beamforming design problem is provided. It is then proved that a dedicated radar probing signal is not needed. As a further step, we consider a more complicated multi-CU scenario, where the beamforming design is formulated as a non-convex quadratically constrained quadratic programming. The optimal solutions are obtained by applying semidefinite relaxation with guaranteed rank-1 property. It is shown that under the multi-CU scenario, the dedicated probing signal should be employed to improve the radar performance at the cost of implementing an additional interference cancellation at the CU. Finally, the numerical simulations are provided to verify the effectiveness of the proposed algorithm.

preprint2020arXiv

Construction of a series of new $ν=2/5$ fractional quantum Hall wave functions by conformal field theory

In this paper, a series of $ν=2/5$ fractional quantum Hall wave functions are constructed from conformal field theory(CFT). They share the same topological properties with states constructed by Jain's composite fermion approach. Upon exact lowest Landau level(LLL) projection, some of Jain composite fermion states would not survive if constraints on Landau level indices given in the appendices of this paper were not satisfied. By contrast, states constructed from CFT always stay in LLL. These states are characterized by different topological shifts and multibody relative angular momenta. As a by-product, in the appendices we prove the necessary conditions for general $ ν=p/(2p+1) $ composite fermion states to have nonvanishing LLL projection.

preprint2020arXiv

Content Adaptive and Error Propagation Aware Deep Video Compression

Recently, learning based video compression methods attract increasing attention. However, the previous works suffer from error propagation due to the accumulation of reconstructed error in inter predictive coding. Meanwhile, the previous learning based video codecs are also not adaptive to different video contents. To address these two problems, we propose a content adaptive and error propagation aware video compression system. Specifically, our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Based on the learned long-term temporal information, our approach effectively alleviates error propagation in reconstructed frames. More importantly, instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system. The proposed approach updates the parameters for encoder according to the rate-distortion criterion but keeps the decoder unchanged in the inference stage. Therefore, the encoder is adaptive to different video contents and achieves better compression performance by reducing the domain gap between the training and testing datasets. Our method is simple yet effective and outperforms the state-of-the-art learning based video codecs on benchmark datasets without increasing the model size or decreasing the decoding speed.

preprint2020arXiv

Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification

Remote sensing image scene classification is a fundamental but challenging task in understanding remote sensing images. Recently, deep learning-based methods, especially convolutional neural network-based (CNN-based) methods have shown enormous potential to understand remote sensing images. CNN-based methods meet with success by utilizing features learned from data rather than features designed manually. The feature-learning procedure of CNN largely depends on the architecture of CNN. However, most of the architectures of CNN used for remote sensing scene classification are still designed by hand which demands a considerable amount of architecture engineering skills and domain knowledge, and it may not play CNN's maximum potential on a special dataset. In this paper, we proposed an automatically architecture learning procedure for remote sensing scene classification. We designed a parameters space in which every set of parameters represents a certain architecture of CNN (i.e., some parameters represent the type of operators used in the architecture such as convolution, pooling, no connection or identity, and the others represent the way how these operators connect). To discover the optimal set of parameters for a given dataset, we introduced a learning strategy which can allow efficient search in the architecture space by means of gradient descent. An architecture generator finally maps the set of parameters into the CNN used in our experiments.

preprint2020arXiv

Discovery of oscillations above 200 keV in a black hole X-ray binary with Insight-HXMT

Low-frequency quasi-periodic oscillations (LFQPOs) are commonly found in black hole X-ray binaries, and their origin is still under debate. The properties of LFQPOs at high energies (above 30 keV) are closely related to the nature of the accretion flow in the innermost regions, and thus play a crucial role in critically testing various theoretical models. The Hard X-ray Modulation Telescope (Insight-HXMT) is capable of detecting emissions above 30 keV, and is therefore an ideal instrument to do so. Here we report the discovery of LFQPOs above 200 keV in the new black hole MAXI J1820+070 in the X-ray hard state, which allows us to understand the behaviours of LFQPOs at hundreds of kiloelectronvolts. The phase lag of the LFQPO is constant around zero below 30 keV, and becomes a soft lag (that is, the high-energy photons arrive first) above 30 keV. The soft lag gradually increases with energy and reaches ~0.9s in the 150-200 keV band. The detection at energies above 200 keV, the large soft lag and the energy-related behaviors of the LFQPO pose a great challenge for most currently existing models, but suggest that the LFQPO probably originates from the precession of a small-scale jet.

preprint2020arXiv

Existence of smooth even solutions to the dual Orlicz-Minkowski problem

In this paper we study the dual Orlicz-Minkowski problem, which is a generalization of the dual Minkowski problem in convex geometry. By considering a geometric flow involving Gauss curvature and functions of normal vectors and radial vectors, we obtain a new existence result of smooth even solutions to this problem for smooth even measures.

preprint2020arXiv

Exploring open cluster properties with Gaia and LAMOST

In Gaia DR2, the unprecedented high-precision level reached in sub-mas for astrometry and mmag for photometry. Using cluster members identified with these astrometry and photometry in Gaia DR2, we can obtain a reliable determination of cluster properties. However, because of the shortcoming of Gaia spectroscopic observation in dealing with densely crowded cluster region, the number of radial velocity and metallicity for cluster member stars from Gaia DR2 is still lacking. In this study, we aim to improve the cluster properties by combining the LAMOST spectra. In particular, we provide the list of cluster members with spectroscopic parameters as an add-value catalog in LAMOST DR5, which can be used to perform detailed study for a better understanding on the stellar properties, by using their spectra and fundamental properties from the host cluster. We cross-matched the spectroscopic catalog in LAMOST DR5 with the identified cluster members in Cantat-Gaudin et al.2018 and then used members with spectroscopic parameters to derive statistical properties of open clusters. We obtained a list of 8811 members with spectroscopic parameters and a catalog of 295 cluster properties. In addition, we study the radial and vertical metallicity gradient and age-metallicity relation with the compiled open clusters as tracers, finding slopes of -0.053$\pm$0.004 dex kpc$^{-1}$, -0.252$\pm$0.039 dex kpc$^{-1}$ and 0.022$\pm$0.008 dex Gyr$^{-1}$, respectively. Both slopes of metallicity distribution relation for young clusters (0.1 Gyr < Age < 2 Gyr) and the age-metallicity relation for clusters within 6 Gyr are consistent with literature results. In order to fully study the chemical evolution history in the disk, more spectroscopic observations for old and distant open clusters are needed for further investigation.

preprint2020arXiv

Fast Dynamic Cuts, Distances and Effective Resistances via Vertex Sparsifiers

We present a general framework of designing efficient dynamic approximate algorithms for optimization on undirected graphs. In particular, we develop a technique that, given any problem that admits a certain notion of vertex sparsifiers, gives data structures that maintain approximate solutions in sub-linear update and query time. We illustrate the applicability of our paradigm to the following problems. (1) A fully-dynamic algorithm that approximates all-pair maximum-flows/minimum-cuts up to a nearly logarithmic factor in $\tilde{O}(n^{2/3})$ amortized time against an oblivious adversary, and $\tilde{O}(m^{3/4})$ time against an adaptive adversary. (2) An incremental data structure that maintains $O(1)$-approximate shortest path in $n^{o(1)}$ time per operation, as well as fully dynamic approximate all-pair shortest path and transshipment in $\tilde{O}(n^{2/3+o(1)})$ amortized time per operation. (3) A fully-dynamic algorithm that approximates all-pair effective resistance up to an $(1+ε)$ factor in $\tilde{O}(n^{2/3+o(1)} ε^{-O(1)})$ amortized update time per operation. The key tool behind result (1) is the dynamic maintenance of an algorithmic construction due to Madry [FOCS' 10], which partitions a graph into a collection of simpler graph structures (known as j-trees) and approximately captures the cut-flow and metric structure of the graph. The $O(1)$-approximation guarantee of (2) is by adapting the distance oracles by [Thorup-Zwick JACM `05]. Result (3) is obtained by invoking the random-walk based spectral vertex sparsifier by [Durfee et al. STOC `19] in a hierarchical manner, while carefully keeping track of the recourse among levels in the hierarchy.

preprint2020arXiv

Fast Kernel k-means Clustering Using Incomplete Cholesky Factorization

Kernel-based clustering algorithm can identify and capture the non-linear structure in datasets, and thereby it can achieve better performance than linear clustering. However, computing and storing the entire kernel matrix occupy so large memory that it is difficult for kernel-based clustering to deal with large-scale datasets. In this paper, we employ incomplete Cholesky factorization to accelerate kernel clustering and save memory space. The key idea of the proposed kernel $k$-means clustering using incomplete Cholesky factorization is that we approximate the entire kernel matrix by the product of a low-rank matrix and its transposition. Then linear $k$-means clustering is applied to columns of the transpose of the low-rank matrix. We show both analytically and empirically that the performance of the proposed algorithm is similar to that of the kernel $k$-means clustering algorithm, but our method can deal with large-scale datasets.

preprint2020arXiv

FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides

Multi-focus image fusion technologies compress different focus depth images into an image in which most objects are in focus. However, although existing image fusion techniques, including traditional algorithms and deep learning-based algorithms, can generate high-quality fused images, they need multiple images with different focus depths in the same field of view. This criterion may not be met in some cases where time efficiency is required or the hardware is insufficient. The problem is especially prominent in large-size whole slide images. This paper focused on the multi-focus image fusion of cytopathological digital slide images, and proposed a novel method for generating fused images from single-focus or few-focus images based on conditional generative adversarial network (GAN). Through the adversarial learning of the generator and discriminator, the method is capable of generating fused images with clear textures and large depth of field. Combined with the characteristics of cytopathological images, this paper designs a new generator architecture combining U-Net and DenseBlock, which can effectively improve the network's receptive field and comprehensively encode image features. Meanwhile, this paper develops a semantic segmentation network that identifies the blurred regions in cytopathological images. By integrating the network into the generative model, the quality of the generated fused images is effectively improved. Our method can generate fused images from only single-focus or few-focus images, thereby avoiding the problem of collecting multiple images of different focus depths with increased time and hardware costs. Furthermore, our model is designed to learn the direct mapping of input source images to fused images without the need to manually design complex activity level measurements and fusion rules as in traditional methods.

preprint2020arXiv

Flow by Gauss curvature to Dual Orlicz-Minkowski problems

In this paper we study a normalised anisotropic Gauss curvature flow of strictly convex, closed hypersurfaces in the Euclidean space R^n+1. We prove that the flow exists for all time and converges smoothly to the unique, strictly convex solution of a Monge-Amp`ere type equation. Our argument provides a parabolic proof in the smooth category for the existence of solutions to the Dual Orlicz-Minkowski problem introduced by Zhu, Xing and Ye.

preprint2020arXiv

Frozen Patterns of Impacted Droplets: From Conical Tips to Toroidal Shapes

We report frozen patterns for the water droplets impacting on a cold substrate through fast-speed images. These patterns can be manipulated by several physical parameters (the droplet size, falling height, and substrate temperature), and the scaling analysis has a remarkable agreement with the phase diagram. The observed double-concentric toroidal shape is attributed to the correlation between the impacting dynamics and freezing process, as confirmed by the spatiotemporal evolution of the droplet temperature, the identified timescale associated with the morphology and solidification ($t_{inn}\simeq τ_{sol}$), and the ice front-advection model. These results for frozen patterns provide insight into the complex interplay of the rapid impacting hydrodynamics, the transient heat transfer, and the intricate solidification process.

preprint2020arXiv

Fully nonlinear equations of Krylov type on Riemannian manifolds with negative curvature

In this paper, we consider fully nonlinear equations of Krylov type on Riemannian manifolds with negative curvature which naturally arise in conformal geometry. Moreover, we prove the a priori estimates for solutions to these equations and establish the existence results. Our results can be viewed as an extension of previous results given by Gursky-Viaclovsky and Li-Sheng.

preprint2020arXiv

Horo-convex hypersurfaces with prescribed shifted Gauss curvatures in $\mathbb{H}^{n+1}$

In this paper, we consider prescribed shifted Gauss curvature equations for horo-convex hypersurfaces in $\mathbb{H}^{n+1}$. Under some sufficient condition, we obtain an existence result by the standard degree theory based on the a prior estimates for the solutions to the equations. Different from the prescribed Weingarten curvature problem in space forms, we do not impose a sign condition for radial derivative of the functions in the right-hand side of the equations to prove the existence due to the horo-covexity of hypersurfaces in $\mathbb{H}^{n+1}$.

preprint2020arXiv

Inverse Gauss curvature flows with free boundaries in a cone

We consider strictly convex hypersurfaces with the boundary which meets a strictly convex cone perpendicularly. We prove that if these hypersurfaces expand inside this cone, driven by the power of the Gauss curvature, then the evolution exists for all the time and the evolving hypersurfaces converge smoothly to a piece of round sphere after rescaling.

preprint2020arXiv

LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS): Scientific goals and survey plan

Since September 2018, LAMOST starts a new 5-year medium-resolution spectroscopic survey (MRS) using bright/gray nights. We present the scientific goals of LAMOST-MRS and propose a near optimistic strategy of the survey. A complete footprint is also provided. Not only the regular medium-resolution survey, but also a time-domain spectroscopic survey is being conducted since 2018 and will be end in 2023. According to the detailed survey plan, we expect that LAMOST-MRS can observe about 2 million stellar spectra with ~7500 and limiting magnitude of around G=15 mag. Moreover, it will also provide about 200 thousand stars with averagely 60-epoch observations and limiting magnitude of G~14 mag. These high quality spectra will give around 20 elemental abundances, rotational velocities, emission line profiles as well as precise radial velocity with uncertainty less than 1 km/s. With these data, we expect that LAMOST can effectively leverage sciences on stellar physics, e.g. exotic binary stars, detailed observation of many types of variable stars etc., planet host stars, emission nebulae, open clusters, young pre-main-sequence stars etc.

preprint2020arXiv

Large-scale Real-time Personalized Similar Product Recommendations

Similar product recommendation is one of the most common scenes in e-commerce. Many recommendation algorithms such as item-to-item Collaborative Filtering are working on measuring item similarities. In this paper, we introduce our real-time personalized algorithm to model product similarity and real-time user interests. We also introduce several other baseline algorithms including an image-similarity-based method, item-to-item collaborative filtering, and item2vec, and compare them on our large-scale real-world e-commerce dataset. The algorithms which achieve good offline results are also tested on the online e-commerce website. Our personalized method achieves a 10% improvement on the add-cart number in the real-world e-commerce scenario.

preprint2020arXiv

On a class of reaction-diffusion equations with aggregation

In this paper, global-in-time existence and blow up results are shown for a reaction-diffusion equation appearing in the theory of aggregation phenomena (including chemotaxis). Properties of the corresponding steady-state problem are also presented. Moreover, the stability around constant equilibria and the non-existence of non-constant solutions are studied in certain cases.

preprint2020arXiv

On the global classical solution to compressible Euler system with singular velocity alignment

We consider a compressible Euler system with singular velocity alignment, known as the Euler-alignment system, describing the flocking behaviors of large animal groups. We establish a local well-posedness theory for the system, as well as a global well-posedness theory for small initial data. We also show the asymptotic flocking behavior, where solutions converge to a constant steady state exponentially in time.

preprint2020arXiv

SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

preprint2020arXiv

Spin squeezing in a spin-orbit coupled Bose-Einstein condensate

We study the spin squeezing in a spin-1/2 Bose-Einstein condensates (BEC) with Raman induced spin-orbit coupling (SOC). Under the condition of two-photon resonance and weak Raman coupling strength, the system possesses two degenerate ground states, using which we construct an effective two-mode model. The Hamiltonian of the two-mode model takes the form of the one-axis-twisting Hamiltonian which is known to generate spin squeezing. More importantly, we show that the SOC provides a convenient control knob to adjust the spin nonlinearity responsible for spin squeezing. Specifically, the spin nonlinearity strength can be tuned to be comparable to the two-body density-density interaction, hence is much larger than the intrinsic spin-dependent interaction strength in conventional two-component BEC systems such as $^{87}$Rb and $^{23}$Na in the absence of the SOC. We confirm the spin squeezing by carrying out a fully beyond-mean-field numerical calculation using the truncated Wigner method. Additionally, the experimental implementation is also discussed.

preprint2020arXiv

Stable Sparse Subspace Embedding for Dimensionality Reduction

Sparse random projection (RP) is a popular tool for dimensionality reduction that shows promising performance with low computational complexity. However, in the existing sparse RP matrices, the positions of non-zero entries are usually randomly selected. Although they adopt uniform sampling with replacement, due to large sampling variance, the number of non-zeros is uneven among rows of the projection matrix which is generated in one trial, and more data information may be lost after dimension reduction. To break this bottleneck, based on random sampling without replacement in statistics, this paper builds a stable sparse subspace embedded matrix (S-SSE), in which non-zeros are uniformly distributed. It is proved that the S-SSE is stabler than the existing matrix, and it can maintain Euclidean distance between points well after dimension reduction. Our empirical studies corroborate our theoretical findings and demonstrate that our approach can indeed achieve satisfactory performance.

preprint2020arXiv

The classical Neumann problem for a class of mixed Hessian equations

In this paper, we establish global C^2 estimates for a class of mixed Hessian equations with Neumann boundary condition, and obtain the existence theorem of k-admissible solutions for the classical Neumann problem of these mixed Hessian equations.

preprint2020arXiv

The Neumann problem for a class of mixed complex Hessian equations

In this paper, we consider the Neumann problem of a class of mixed complex Hessian equations, and establish the global C^1 estimates a nd reduce the global second derivative estimate to the estimate of double normal second derivatives on the boundary. In particular, we can prove the global C^2 estimates and the existence theorems when k=n.

preprint2020arXiv

Uniform Interpolation Constrained Geodesic Learning on Data Manifold

In this paper, we propose a method to learn a minimizing geodesic within a data manifold. Along the learned geodesic, our method can generate high-quality interpolations between two given data samples. Specifically, we use an autoencoder network to map data samples into latent space and perform interpolation via an interpolation network. We add prior geometric information to regularize our autoencoder for the convexity of representations so that for any given interpolation approach, the generated interpolations remain within the distribution of the data manifold. Before the learning of a geodesic, a proper Riemannianmetric should be defined. Therefore, we induce a Riemannian metric by the canonical metric in the Euclidean space which the data manifold is isometrically immersed in. Based on this defined Riemannian metric, we introduce a constant speed loss and a minimizing geodesic loss to regularize the interpolation network to generate uniform interpolation along the learned geodesic on the manifold. We provide a theoretical analysis of our model and use image translation as an example to demonstrate the effectiveness of our method.

preprint2020arXiv

Uniqueness of solutions to Lp-Christoffel-Minkowski problem for p<1

$L_p$-Christoffel-Minkowski problem arises naturally in the $L_p$-Brunn-Minkowski theory. It connects both curvature measures and area measures of convex bodies and is a fundamental problem in convex geometric analysis. Since the lack of Firey's extension of Brunn-Minkowski inequality and constant rank theorem for $p<1$, the existence and uniqueness of $L_p$-Brunn-Minkowski problem are difficult problems. In this paper, we prove a uniqueness theorem for solutions to $L_p$-Christoffel-Minkowski problem with $p<1$ and constant prescribed data. Our proof is motivated by the idea of Brendle-Choi-Daskaspoulos's work on asymptotic behavior of flows by powers of the Gaussian curvature. One of the highlights of our arguments is that we introduce a new auxiliary function $Z$ which is the key to our proof.

preprint2020arXiv

User Validation of Recommendation Serendipity Metrics

Though it has been recognized that recommending serendipitous (i.e., surprising and relevant) items can be helpful for increasing users' satisfaction and behavioral intention, how to measure serendipity in the offline environment is still an open issue. In recent years, a number of metrics have been proposed, but most of them were based on researchers' assumptions due to the serendipity's subjective nature. In order to validate these metrics' actual performance, we collected over 10,000 users' real feedback data and compared with the metrics' results. It turns out the user profile based metrics, especially content-based ones, perform better than those based on item popularity, in terms of estimating the unexpectedness facet of recommendations. Moreover, the full metrics, which involve the unexpectedness component, relevance, timeliness, and user curiosity, can more accurately indicate the recommendation's serendipity degree, relative to those that just involve some of them. The application of these metrics to several recommender algorithms further consolidates their practical usage, because the comparison results are consistent with those from user evaluation. Thus, this work is constructive for filling the gap between offline measurement and user study on recommendation serendipity.

preprint2020arXiv

Vertex nomination: The canonical sampling and the extended spectral nomination schemes

Suppose that one particular block in a stochastic block model is of interest, but block labels are only observed for a few of the vertices in the network. Utilizing a graph realized from the model and the observed block labels, the vertex nomination task is to order the vertices with unobserved block labels into a ranked nomination list with the goal of having an abundance of interesting vertices near the top of the list. There are vertex nomination schemes in the literature, including the optimally precise canonical nomination scheme~$\mathcal{L}^C$ and the consistent spectral partitioning nomination scheme~$\mathcal{L}^P$. While the canonical nomination scheme $\mathcal{L}^C$ is provably optimally precise, it is computationally intractable, being impractical to implement even on modestly sized graphs. With this in mind, an approximation of the canonical scheme---denoted the {\it canonical sampling nomination scheme} $\mathcal{L}^{CS}$---is introduced; $\mathcal{L}^{CS}$ relies on a scalable, Markov chain Monte Carlo-based approximation of $\mathcal{L}^{C}$, and converges to $\mathcal{L}^{C}$ as the amount of sampling goes to infinity. The spectral partitioning nomination scheme is also extended to the {\it extended spectral partitioning nomination scheme}, $\mathcal{L}^{EP}$, which introduces a novel semisupervised clustering framework to improve upon the precision of $\mathcal{L}^P$. Real-data and simulation experiments are employed to illustrate the precision of these vertex nomination schemes, as well as their empirical computational complexity. Keywords: vertex nomination, Markov chain Monte Carlo, spectral partitioning, Mclust MSC[2010]: 60J22, 65C40, 62H30, 62H25

preprint2020arXiv

Xiaomingbot: A Multilingual Robot News Reporter

This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation. Its system summarizes Chinese news that it automatically generates from data tables. Next, it translates the summary or the full article into multiple languages, and reads the multilingual rendition through synthesized speech. Notably, Xiaomingbot utilizes a voice cloning technology to synthesize the speech trained from a real person's voice data in one input language. The proposed system enjoys several merits: it has an animated avatar, and is able to generate and read multilingual news. Since it was put into practice, Xiaomingbot has written over 600,000 articles, and gained over 150,000 followers on social media platforms.

preprint2019arXiv

BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading

Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microaneurysms, are essential to DR grading in medical imaging, but they could easily be influenced by other objects. To address these challenges, we propose a new deep learning architecture, called BiRA-Net, which combines the attention model for feature extraction and bilinear model for fine-grained classification. Furthermore, in considering the distance between different grades of different DR categories, we propose a new loss function, called grading loss, which leads to improved training convergence of the proposed approach. Experimental results are provided to demonstrate the superior performance of the proposed approach.

preprint2019arXiv

Combined Mean Field Limit and Non-relativistic Limit of Vlasov-Maxwell Particle System to Vlasov-Poisson System

In this paper we consider the mean field limit and non-relativistic limit of relativistic Vlasov-Maxwell particle system to Vlasov-Poisson equation. With the relativistic Vlasov-Maxwell particle system being a starting point, we carry out the estimates (with respect to $N$ and $c$) between the characteristic equation of both Vlasov-Maxwell particle model and Vlasov-Poisson equation, where the probabilistic method is exploited. In the last step, we take both large $N$ limit and non-relativistic limit (meaning $c$ tending to infinity) to close the argument.

preprint2019arXiv

Direct comparison of many-body methods for realistic electronic Hamiltonians

A large collaboration carefully benchmarks 20 first principles many-body electronic structure methods on a test set of 7 transition metal atoms, and their ions and monoxides. Good agreement is attained between the 3 systematically converged methods, resulting in experiment-free reference values. These reference values are used to assess the accuracy of modern emerging and scalable approaches to the many-electron problem. The most accurate methods obtain energies indistinguishable from experimental results, with the agreement mainly limited by the experimental uncertainties. Comparison between methods enables a unique perspective on calculations of many-body systems of electrons.

preprint2019arXiv

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.

preprint2017arXiv

Conical square functions for degenerate elliptic operators

The aim of this paper is to study the boundedness of different conical square functions that arise naturally from second order divergence form degenerate elliptic operators. More precisely, let $L_w=w^{-1}\,{\rm div}(w\,A\,\nabla)$ where $w\in A_2$ and $A$ is an $n\times n$ bounded, complex-valued, uniformly elliptic matrix. D. Cruz-Uribe and C. Rios solved the $L^2(w)$-Kato square root problem obtaining that $\sqrt{L_w}$ is equivalent to the gradient on $L^2(w)$. The same authors in collaboration with the second named author of this paper studied the $L^p(w)$-boundedness of operators that are naturally associated with $L_w$, such as the functional calculus, Riesz transforms, or vertical square functions. The theory developed admitted also weighted estimates (i.e., estimates in $L^p(v dw)$ for $v\in A_\infty(w)$), and in particular a class of "degeneracy" weights $w$ was found in such a way that the classical $L^2$-Kato problem can be solved. In this paper, continuing this line of research, and also that originated in some recent results by the second and third named authors of the current paper, we study the boundedness on $L^p(w)$ and on $L^p(v dw)$, with $v\in A_\infty(w)$, of the conical square functions that one can construct using the heat or Poisson semigroup associated with $L_w$. As a consequence of our methods, we find a class of degeneracy weights $w$ for which $L^2$-estimates for these conical square functions hold. This opens the door to the study of weighted and unweighted Hardy spaces and of boundary value problems associated with $L_w$.

preprint2016arXiv

A systematic study of effects of stellar rotation, age spread and binaries on color-magnitude diagrams with extended main-sequence turn-offs

Stellar rotation, age spread and binary stars are thought to be three most possible causes of the peculiar color-magnitude diagrams (CMDs) of some star clusters, which exhibit extended main-sequence turn-offs (eMSTOs). It is far from getting a clear answer. This paper studies the effects of three above causes on the CMDs of star clusters systematically. A rapid stellar evolutionary code and a recently published database of rotational effects of single stars have been used, via an advanced stellar population synthesis technique. As a result, we find a consistent result for rotation to recent works, which suggests that rotation is able to explain, at least partially, the eMSTOs of clusters, if clusters are not too old ($<$ 2.0\,Gyr). In addition, an age spread of 200 to 500\,Myr reproduces extended turn-offs for all clusters younger than 2.5\,Gyr, in particular, for those younger than 2.2\,Gyr. Age spread also results in extended red clumps (eRCs) for clusters younger than 0.5\,Gyr. The younger the clusters, the clearer the eRC structures. Moreover, it is shown that binaries (including interactive binaries) affect the spread of MSTO slightly for old clusters, but they can contribute to the eMSTOs of clusters younger than 0.5\,Gyr. Our result suggests a possible way to disentangle the roles of stellar rotation and age spread, i.e., checking the existence of CMDs with both eMSTO and eRC in clusters younger than 0.5\,Gyr.

preprint2016arXiv

Cultivating Software Performance in Cloud Computing

There exist multitudes of cloud performance metrics, including workload performance, application placement, software/hardware optimization, scalability, capacity, reliability, agility and so on. In this paper, we consider jointly optimizing the performance of the software applications in the cloud. The challenges lie in bringing a diversity of raw data into tidy data format, unifying performance data from multiple systems based on timestamps, and assessing the quality of the processed performance data. Even after verifying the quality of cloud performance data, additional challenges block optimizing cloud computing. In this paper, we identify the challenges of cloud computing from the perspectives of computing environment, data collection, performance analytics and production environment.

preprint2016arXiv

Hardy spaces on metric measure spaces with generalized sub-gaussian heat kernel estimates

Hardy space theory has been studied on manifolds or metric measure spaces equipped with either Gaussian or sub-Gaussian heat kernel behaviour. However, there are natural examples where one finds a mix of both behaviour (locally Gaussian and at infinity sub-Gaussian) in which case the previous theory doesn't apply. Still we define molecular and square function Hardy spaces using appropriate scaling, and we show that they agree with Lebesgue spaces in some range. Besides, counterexamples are given in this setting that the $H^p$ space corresponding to Gaussian estimates may not coincide with $L ^p$. As a motivation for this theory, we show that the Riesz transform maps our Hardy space $H^1$ into $L^1$ .

preprint2016arXiv

Mean Field Limit and Propagation of Chaos for a Pedestrian Flow Model

In this paper a rigorous proof of the mean field limit for a pedestrian flow model in two dimensions is given by using a probabilistic method. The model under investigation is an interacting particle system coupled to the eikonal equation on the microscopic scale. For stochastic initial data, it is proved that the solution of the $N$-particle pedestrian flow system with properly chosen cut-off converges in the probability sense to the solution of the characteristics of the non-cut-off Vlasov equation. Furthermore, the result on propagation of chaos is also deduced in terms of bounded Lipschitz distance.

preprint2016arXiv

On Study of the Binarized Deep Neural Network for Image Classification

Recently, the deep neural network (derived from the artificial neural network) has attracted many researchers' attention by its outstanding performance. However, since this network requires high-performance GPUs and large storage, it is very hard to use it on individual devices. In order to improve the deep neural network, many trials have been made by refining the network structure or training strategy. Unlike those trials, in this paper, we focused on the basic propagation function of the artificial neural network and proposed the binarized deep neural network. This network is a pure binary system, in which all the values and calculations are binarized. As a result, our network can save a lot of computational resource and storage. Therefore, it is possible to use it on various devices. Moreover, the experimental results proved the feasibility of the proposed network.

preprint2016arXiv

Optical Modes in PT-Symmetric Double-Channel Waveguides

We investigate the unique properties of various analytical optical modes, including the fundamental modes and the excited modes, in a double-channel waveguide with parity-time (PT) symmetry. Based on these optical modes, the dependence of the threshold values for the gain/loss parameter, i.e., PT symmetry breaking points, on the structure parameters is discussed. We find that the threshold value for the excited modes is larger than that of the fundamental mode. In addition, the beam dynamics in the double-channel waveguide with PT symmetry is also investigated.

preprint2016arXiv

Performance evaluation of the cavities on nucleate boiling at microscale level

Nucleate boiling heat transfer (NBHT) from enhanced structures is an effective way to dissipate high heat flux. In the present study, the cavities behaviours for nucleation on roughened surface are numerically studied on the entire ebullition cycle based on a phase-change lattice Boltzmann method without introducing any artificial disturbances. The adopted model is firstly validated with the Laplace law and the two phase coexistence curve, and then applied to investigate the effects of cavity structure on NBHT. The bubble departure diameter, departure frequency and total boiling heat flux of an ebullition cycle are also explored. It is demonstrated that the cavity widths and the cavity grooves show significant influence on the features of NBHT. Cavity with circular groove in the present research shows the best performance for NBHT in terms of the averaged heat flux and bubble release frequency. When a specific cavity is combined with other different cavities on roughened surfaces its nucleation process on different roughened surfaces may differ greatly.

preprint2016arXiv

Permeability prediction of organic shale with generalized lattice Boltzmann model considering surface diffusion effect

Gas flow in shale is associated with both organic matter (OM) and inorganic matter (IOM) which contain nanopores ranging in size from a few to hundreds of nanometers. In addition to the noncontinuum effect which leads to an apparent permeability of gas higher than the intrinsic permeability, the surface diffusion of adsorbed gas in organic pores also can influence the apparent permeability through its own transport mechanism. In this study, a generalized lattice Boltzmann model (GLBM) is employed for gas flow through the reconstructed shale matrix consisting of OM and IOM. The Expectation-Maximization (EM) algorithm is used to assign the pore size distribution to each component, and the dusty gas model (DGM) and generalized Maxwell-Stefan model (GMS) are adopted to calculate the apparent permeability accounting for multiple transport mechanisms including viscous flow, Knudsen diffusion and surface diffusion. Effects of pore radius and pressure on permeability of both IOM and OM as well as effects of Langmuir parameters on OM are investigated. Moreover, the effect of total organic content and distribution on the apparent permeability of the reconstructed shale matrix is also studied. It is found that the distribution of OM and IOM has a negligible influence on apparent permeability, whereas the total organic content and the surface diffusion play a significant role in gas transport in shale matrix.

preprint2016arXiv

Selecting M-giants with infra-red photometry: Distances, metallicities and the Sagittarius stream

Using a spectroscopically confirmed sample of M-giants, M-dwarfs and quasars from the LAMOST survey, we assess how well WISE $\&$ 2MASS color-cuts can be used to select M-giant stars. The WISE bands are very efficient at separating M-giants from M-dwarfs and we present a simple classification that can produce a clean and relatively complete sample of M-giants. We derive a new photometric relation to estimate the metallicity for M-giants, calibrated using data from the APOGEE survey. We find a strong correlation between the $(W1-W2)$ color and $\rm [M/H]$, where almost all of the scatter is due to photometric uncertainties. We show that previous photometric distance relations, which are mostly based on stellar models, may be biased and devise a new empirical distance relation, investigating trends with metallicity and star formation history. Given these relations, we investigate the properties of M-giants in the Sagittarius stream. The offset in the orbital plane between the leading and trailing tails is reproduced and, by identifying distant M-giants in the direction of the Galactic anti-center, we confirm that the previously detected debris in the outer halo is the apocenter of the trailing tail. We also find tentative evidence supporting an existing overdensity near the leading tail in the Northern Galactic hemisphere, possibly an extension to the trailing tail (so-called Branch C). We have measured the metallicity distribution along the stream, finding a clear metallicity offset between the leading and trailing tails, in agreement with models for the stream formation. We include an online table of M-giants to facilitate further studies.

preprint2015arXiv

A Joint Graph Inference Case Study: the C.elegans Chemical and Electrical Connectomes

We investigate joint graph inference for the chemical and electrical connectomes of the \textit{Caenorhabditis elegans} roundworm. The \textit{C.elegans} connectomes consist of $253$ non-isolated neurons with known functional attributes, and there are two types of synaptic connectomes, resulting in a pair of graphs. We formulate our joint graph inference from the perspectives of seeded graph matching and joint vertex classification. Our results suggest that connectomic inference should proceed in the joint space of the two connectomes, which has significant neuroscientific implications.

preprint2015arXiv

A nonlocal reaction diffusion equation and its relation with Fujita exponent

This paper is concerned with a type of nonlinear reaction-diffusion equation, which arises from the population dynamics. The equation includes a certain type reaction term $u^α(1- σ\int_{\R^n}u^βdx)$ of dimension $n \ge 1$ and $σ>0$. An energy-methods-based proof on the existence of global solutions is presented and the qualitative behavior of solution which is decided by the choice of $α,β$ is exhibited. More precisely, for $1 \le α<1+(1-2/p)β$, where $p$ is the exponent appears in Sobolev's embedding theorem defined in \er{p}, the equation admits a unique global solution for any nonnegative initial data. Especially, in the case of $n\geq 2$ and $β=1$, the exponent $α<1+2/n$ is exactly the well-known Fujita exponent. The global existence result obtained in this paper shows that by switching on the nonlocal effect, i.e., from $σ=0$ to $σ>0$, the solution's behavior differs distinctly, that's, from finite time blow-up to global existence.

preprint2015arXiv

A sample of galaxy pairs identified from the LAMOST spectral survey and the Sloan Digital Sky Survey

A small fraction($<10\%$) of SDSS main sample galaxies(MGs) have not been targeted with spectroscopy due to the the fiber collision effect. These galaxies have been compiled into the input catalog of the LAMOST extra-galactic survey and named as the complementary galaxy sample. In this paper, we introduce the project and the status of the spectroscopies of the complementary galaxies in the first two years of the LAMOST spectral survey(till Sep. of 2014). Moreover, we present a sample of 1,102 galaxy pairs identified from the LAMOST complementary galaxies and SDSS MGs, which are defined as that the two members have a projected distance smaller than 100 kpc and the recessional velocity difference smaller than 500 $\rm kms^{-1}$. Compared with the SDSS only selected galaxy pairs, the LAMOST-SDSS pairs take the advantages of not being biased toward large separations and therefor play as a useful supplement to the statistical studies of galaxy interaction and galaxy merging.

preprint2015arXiv

A simple and robust single-pixel computational ghost imaging

A simple and robust experiment demonstrating computational ghost imaging with structured illumination and a single-pixel detector has been performed. Our experimental setup utilizes a general computer for generating pseudo-randomly patterns on the liquid crystal display screen to illuminate a partially-transmissive object. With an incoherent light source, this object is imaged. The effects of light source, light path, and the number of measurements on the reconstruction quality of the object are discussed both theoretically and experimentally. The realization of computational ghost imaging with computer liquid crystal display is a further setup toward the practical application of ghost imaging with ordinary incoherent light.

preprint2015arXiv

Adaptive network models of collective decision making in swarming systems

We consider a class of adaptive network models where links can only be created or deleted between nodes in different states. These models provide an approximate description of a set of systems where nodes represent agents moving in physical or abstract space, the state of each node represents the agent's heading direction, and links indicate mutual awareness. We show analytically that the adaptive network description captures the phase transition to collective motion in swarming systems and that the properties of this transition are determined by the number of states (discrete heading directions) that can be accessed by each agent.

preprint2015arXiv

Algebraic approach to the study of zero modes of Haldane pseudopotentials

We consider lattice Hamiltonians that arise from putting Haldane pseudopotentials into a second quantized or "guiding-center-only" form. These are fascinating examples for frustration free lattice Hamiltonians. This is so since even though their highest density zero energy ground states, the Laughlin states, are known to have matrix-product structure (with unbounded bond dimension), the frustration free character of these lattice Hamiltonians seems obscure, {\em unless} one goes back to the original first quantized picture of analytic lowest Landau level wave functions. This step involves putting back additional degrees of freedom associated with dynamical momenta, and one wonders whether the addition of these degrees of freedom is truly necessary to recognize the frustration free character of the underlying lattice Hamiltonian. Fundamentally, these degrees of freedom have nothing to do with spectrum of a "guiding-center-only" Hamiltonian. Moreover, such constructions are unfamiliar and not available in the study of simpler (finite range) frustration free lattice Hamiltonians with matrix product ground states (of finite bond dimension). That the zero mode properties of "lattice versions" of pseudopotentials can be understood from a polynomial-free, intrinsically lattice point of view is also suggested by the fact that these pseudopotentials are constructed from an algebra of reasonably simply looking operators. Here we show that zero mode properties, and hence the frustration free character, of these lattice Hamitlonians can be understood as a consequence of algebraic structures that these operators are part of. We believe that our results will deepen insights into parent Hamiltonians of matrix product states with infinite bond dimensions, as could be of use, especially, in the study of fractional Chern insulators.

preprint2015arXiv

Algorithms for Deforming and Contracting Simply Connected Discrete Closed Manifolds (I)

In this exploration paper, we design algorithms for deforming and contracting a simply connected discrete closed manifold to a discrete sphere. Such a contraction is a kind of shrinking or reducing process. In our algorithms, we need to assume an ambient space for the discrete manifold, and this ambient space also a simply connected discrete space in higher dimensions. Our algorithm would work for most of cases. For some special cases, we will make detailed analysis in the next paper. In other words, this paper has not provided a complete proof for each case. The algorithm designed in this paper is in polynomial time.

preprint2015arXiv

Brewing Analytics Quality for Cloud Performance

Cloud computing has become increasingly popular. Many options of cloud deployments are available. Testing cloud performance would enable us to choose a cloud deployment based on the requirements. In this paper, we present an innovative process, implemented in software, to allow us to assess the quality of the cloud performance data. The process combines performance data from multiple machines, spanning across user experience data, workload performance metrics, and readily available system performance data. Furthermore, we discuss the major challenges of bringing raw data into tidy data formats in order to enable subsequent analysis, and describe how our process has several layers of assessment to validate the quality of the data processing procedure. We present a case study to demonstrate the effectiveness of our proposed process, and conclude our paper with several future research directions worth investigating.

preprint2015arXiv

Changes in porosity, permeability and surface area during rock dissolution: effects of mineralogical heterogeneity

Effects of heterogeneity of mineral distribution and reaction rate on the rock dissolution process are investigated using a pore-scale reactive transport model based on the lattice Boltzmann method. Coupled fluid flow, species transport, chemical reaction and solid structure alternation due to dissolution are simulated. Effects of mineral distributions and chemical heterogeneity on the dissolution behaviors and evolutions of hydrologic properties are studied under different reactive transport conditions. Simulation results show that the coupling between advection, diffusion and reaction as well as the mineralogical heterogeneity leads to complex reactive transport behaviors and complicated temporal evolutions of hydrologic properties including porosity, permeability and reactive surface. Diverse relationships between surface area and volume are predicted, which cannot be described by simple models such as the spherical-grain model. Porosity-permeability relationships also differ under different mineral distributions and reactive transport conditions. Simulation results indicate that it is extremely challenging to propose general relationships for hydrologic properties for dissolution of rocks with mineralogical heterogeneity, due to the complicated interactions between reactive transport and mineralogical heterogeneity.

preprint2015arXiv

Estimation of distances to stars with stellar parameters from LAMOST

We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each star's absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each ~5-degree diameter "plate" that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show that our method recovers distances for most stars within ~20%, but with some systematic overestimation of distances to halo giants. We apply our code to the LAMOST database, and show that the current precision of LAMOST stellar parameters permits measurements of distances with ~40% error bars. This precision should improve as the LAMOST data pipelines continue to be refined.

preprint2015arXiv

Excitation, detection, and electrostatic manipulation of terahertz-frequency range plasmons in a two-dimensional electron system

Terahertz time domain spectroscopy employing free-space radiation has frequently been used to probe the elementary excitations of low-dimensional systems. The diffraction limit blocks its use for the in-plane study of individual laterally defined nanostructures, however. Here, we demonstrate a planar terahertz-frequency plasmonic circuit in which photoconductive material is monolithically integrated with a two-dimensional electron system. Plasmons with a broad spectral range (up to ~400 GHz) are excited by injecting picosecond-duration pulses, generated and detected by a photoconductive semiconductor, into a high mobility two-dimensional electron system. Using voltage modulation of a Schottky gate overlying the two-dimensional electron system, we form a tuneable plasmonic cavity, and observe electrostatic manipulation of the plasmon resonances. Our technique offers a direct route to access the picosecond dynamics of confined transport in a broad range of lateral nanostructures.

preprint2015arXiv

Explanation of a special color-magnitude diagram of star cluster NGC 1651 from different models

The color-magnitude diagram (CMD) of globular cluster NGC1651 has special structures including a broad main sequence, an extended main sequence turn-off and an extended red giant clump. The reason for such special CMDs remains unclear. In order to test how different the results from various stellar population assumptions are, we study a high-quality CMD of NGC1651 from the Hubble Space Telescope archive via eight kinds of models. Distance modulus, extinction, age ranges, star formation mode, fraction of binaries, and fraction of rotational stars are determined and then compared. The results show that stellar populations both with and without age spread can reproduce the special structure of the observed CMD. A composite population with extended star formation from 1.8\,Gyrs ago to 1.4\,Gyrs ago, which contains 50 per cent binaries and 70 per cent rotational stars, fits the observed CMD best. Meanwhile, a 1.5\,Gyr-old simple population that consists of rotational stars can also fit the observed CMD well. The results of CMD fitting are shown to depend strongly on stellar population type (simple or composite), and fraction of rotators. If the member stars of NGC1651 formed in a single star burst, the effect of stellar rotation should be very important for the explanation of observed CMDs. Otherwise, the effect may be small. It is also possible that the special observed CMD is a result of the combined effects of stellar binarity, rotation and age spread. Therefore, further work on stellar population type and fraction of rotational stars of intermediate-age clusters are necessary to understand their observed CMDs.

preprint2015arXiv

Giant negative thermal expansion covering room temperature in nanocrystalline GaNxMn3

Materials with negative thermal expansion (NTE), which contract upon heating, are of great interest both technically and fundamentally. Here, we report giant NTE covering room temperature in mechanically milled antiperovksite GaNxMn3 compounds. The micrograin GaNxMn3 exhibits a large volume contraction at the antiferromagnetic (AFM) to paramagnetic (PM) (AFM-PM) transition within a temperature window (ΔT) of only a few kelvins. The grain size reduces to ~ 30 nm after slight milling, while ΔT is broadened to 50K. The corresponding coefficient of linear thermal expansion (α) reaches ~ -70 ppm/K, which is almost two times larger than those obtained in chemically doped antiperovskite compounds. Further reducing grain size to ~ 10 nm, ΔT exceeds 100 K and α remains as large as -30 ppm/K (-21 ppm/K) for x = 1.0 (x = 0.9). Excess atomic displacements together with the reduced structural coherence, revealed by high-energy X-ray pair distribution functions, are suggested to delay the AFM-PM transition. By controlling the grain size via mechanically alloying or grinding, giant NTE may also be achievable in other materials with large lattice contraction due to electronic or magnetic phase transitions.

preprint2015arXiv

Global existence and asymptotic behavior of solutions to a nonlocal Fisher-KPP type problem

In this work, we consider a nonlocal Fisher-KPP reaction-diffusion problem with Neumann boundary condition and nonnegative initial data in a bounded domain in $\mathbb{R}^n (n \ge 1)$, with reaction term $u^α(1-m(t))$, where $m(t)$ is the total mass at time $t$. When $α\ge 1$ and the initial mass is greater than or equal to one, the problem has a unique nonnegative classical solution. While if the initial mass is less than one, then the problem admits a unique global solution for $n=1,2$ with any $1 \le α<2$ or $n \ge 3$ with any $1 \le α< 1+2/n$. Moreover, the asymptotic convergence to the solution of the heat equation is proved. Finally, some numerical simulations in dimensions $n=1,2$ are exhibited. Especially, for $α>2$ and the initial mass is less than one, our numerical results show that the solution exists globally in time and the mass tends to one as time goes to infinity.

preprint2015arXiv

Integrated Surface-enhanced Raman Spectroscopy chip based on liquid core waveguide

We propose an integrated surface enhanced Raman scattering (SERS) chip based on liquid-core waveguide with total reflection, through which the depression of leaky mode enable a long propagating distance. An Raman enhancement factor for rhodamine 6G of 2.5*105 is obtained, and a excellent repeatability is shown. The peaks in the SERS spectrum of DNA of silkworm clearly illustrate the information of the molecule structure. The integration of the SERS substrate, micro-fluid, and liquid-core waveguide make such a SERS chip attractive for biochemical detection with high performance.

preprint2015arXiv

Investigation of methane adsorption and its effect on gas transport in shale matrix through microscale and mesoscale simulations

Methane adsorption and its effect on fluid flow in shale matrix are investigated through multi-scale simulation scheme by using molecular dynamics (MD) and lattice Boltzmann (LB) methods. Equilibrium MD simulations are conducted to study methane adsorption on the organic and inorganic walls of nanopores in shale matrix with different pore sizes and pressures. Density and pressure distributions within the adsorbed layer and the free gas region are discussed. The illumination of the MD results on larger scale LB simulations is presented. Pressure-dependent thickness of adsorbed layer should be adopted and the transport of adsorbed layer should be properly considered in LB simulations. LB simulations, which are based on a generalized Navier-Stokes equation for flow through low-permeability porous media with slippage, are conducted by taking into consideration the effects of adsorbed layer. It is found that competitive effects of slippage and adsorbed layer exist on the permeability of shale matrix, leading to different changing trends of the apparent permeability.

preprint2015arXiv

Member candidates of the star clusters from LAMOST DR2 data

In this work, we provide 2189 photometric- and kinematic-selected member candidates of 24 star clusters from the LAMOST DR2 catalog. We perform two-step membership identification: selection along the stellar track in the color-magnitude diagram, i.e., photometric identification, and the selection from the distribution of radial velocities, i.e. the kinematic identification. We find that the radial velocity from the LAMOST data are very helpful in the membership identification. The mean probability of membership is 40\% for the radial velocity selected sample. With these 24 star clusters, we investigate the performance of the radial velocity and metallicity estimated in the LAMOST pipeline. We find that the systematic offset in radial velocity and metallicity are $0.85\pm1.26$\,\kms\ and $-0.08\pm0.04$\,dex, with dispersions of $5.47_{-0.71}^{+1.16}$\,\kms\ and $0.13_{-0.02}^{+0.04}$\,dex, respectively. Finally, we propose that the photometric member candidates of the clusters covered by the LAMOST footprints should be assigned higher priority so that more member stars can be observed.

preprint2015arXiv

Numerical Predictions of Effective Thermal Conductivities for Three-dimensional Four-directional Braided Composites Using the Lattice Boltzmann Method

In this paper, a multiple-relaxation-time lattice Boltzmann model with an off-diagonal collision matrix was adopted to predict the effective thermal conductivities of the anisotropic heterogeneous materials whose components are also anisotropic. The half lattice division scheme was adopted to deal with the internal boundaries to guarantee the heat flux continuity at the interfaces. Accuracy of the model was confirmed by comparisons with benchmark results and existing simulation data. The present method was then adopted to numerically predict the transverse and longitudinal effective thermal conductivities of three-dimensional (3D) four-directional braided composites. Some corresponding experiments based on the Hot Disk method were conducted to measure their transverse and longitudinal effective thermal conductivities. The predicted data fit the experiment data well. Influences of fiber volume fractions and interior braiding angles on the effective thermal conductivities of 3D four-directional braided composites were then studied. The results show that a larger fiber volume fraction leads to a larger effective thermal conductivity along the transverse and longitudinal directions; a larger interior braiding angle brings a larger transverse thermal conductivity but a smaller one along the longitudinal direction. It is also shown that for anisotropic materials the periodic boundary condition is different from the adiabatic boundary condition and for periodic microstructure unit cell the periodic boundary condition should be used. Key words: effective thermal conductivities, anisotropic, multi-relaxation-time, lattice Boltzmann method, three-dimensional four-directional braided composites

preprint2015arXiv

Phase Transitions in Cooperative Coinfections: Simulation Results for Networks and Lattices

We study the spreading of two mutually cooperative diseases on different network topologies, and with two microscopic realizations, both of which are stochastic versions of an SIR type model studied by us recently in mean field approximation. There it had been found that cooperativity can lead to first-order spreading/extinction transitions. However, due to the rapid mixing implied by the mean field assumption, first order transitions required non-zero initial densities of sick individuals. For the stochastic model studied here the results depend strongly on the underlying network. First order transitions are found when there are few short but many long loops: (i) No first order transitions exist on trees and on 2-d lattices with local contacts (ii) They do exist on Erdos-Renyi (ER) networks, on d-dimensional lattices with d >= 4, and on 2-d lattices with sufficiently long-ranged contacts; (iii) On 3-d lattices with local contacts the results depend on the microscopic details of the implementation; (iv) While single infected seeds can always lead to infinite epidemics on regular lattices, on ER networks one sometimes needs finite initial densities of infected nodes; (v) In all cases the first order transitions are actually "hybrid", i.e. they display also power law scaling usually associated with second order transitions. On regular lattices, our model can also be interpreted as the growth of an interface due to cooperative attachment of two species of particles. Critically pinned interfaces in this model seem to be in different universality classes than standard critically pinned interfaces in models with forbidden overhangs. Finally, the detailed results mentioned above hold only when both diseases propagate along the same network of links. If they use different links, results can be rather different in detail, but are similar overall.

preprint2015arXiv

Riesz transform for $1 \leq p \le 2$ without Gaussian heat kernel bound

We study the $L^p$ boundedness of Riesz transform as well as the reverse inequality on Riemannian manifolds and graphs under the volume doubling property and a sub-Gaussian heat kernel upper bound. We prove that the Riesz transform is then bounded on $L^p$ for $1 \textless{} p \textless{} 2$, which shows that Gaussian estimates of the heat kernel are not a necessary condition for this.In the particular case of Vicsek manifolds and graphs, we show that the reverse inequality does not hold for $1 \textless{} p \textless{} 2$. This yields a full picture of the ranges of $p\in (1,+\infty)$ for which respectively the Riesz transform is $L^p$ -bounded and the reverse inequality holds on $L^p$ on such manifolds and graphs. This picture is strikingly different from the Euclidean one.

preprint2015arXiv

Spectral Clustering for Divide-and-Conquer Graph Matching

We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with existing state-of-the-art seeded graph matching procedures. We justify our approach by proving that modestly correlated, large stochastic block model random graphs are correctly matched utilizing very few seeds through our divide-and-conquer procedure. We also demonstrate the effectiveness of our approach in matching very large graphs in simulated and real data examples, showing up to a factor of 8 improvement in runtime with minimal sacrifice in accuracy.

preprint2015arXiv

Spin-orbit-angular-momentum coupling in a spin-1 Bose-Einstein condensate

We propose a simple model with spin and orbit angular momentum coupling in a spin-1 Bose- Einstein condensate, where three internal atomic states are Raman coupled by a pair of co- propagating Laguerre-Gaussian beams. The resulting Raman transition imposes a transfer of orbital angular momentum between photons and the condensate in a spin-dependent way. Focusing on a regime where the single-particle ground state is nearly three-fold degenerate, we show that the weak interatomic interaction in the condensate produces a rich phase diagram, and that a many-body Rabi oscillation between two quantum phases can be induced by a sudden quench of the quadratic Zeeman shift. We carried out our calculations using both a variational method and a full numerical method, and found excellent agreement.

preprint2015arXiv

The lattice Boltzmann method for isothermal micro-gaseous flow and its application in shale gas flow: a review

The lattice Boltzmann method (LBM) has experienced tremendous advances and been well accepted as a popular method of simulation of various fluid flow mechanisms on pore scale in tight formations. With the introduction of an effective relaxation time and slip boundary conditions, the LBM has been successfully extended to solve micro-gaseous related transport and phenomena. As gas flow in shale matrix is mostly in the slip flow and transition flow regimes, given the difficulties of experimental techniques to determine extremely low permeability, it appears that the computational methods especially the LBM can be an attractive choice for simulation of these micro-gaseous flows. In this paper an extensive overview on a number of relaxation time and boundary conditions used in LBM-like models for micro-gaseous flow are carried out and their advantages and disadvantages are discussed. Furthermore, potential application of the LBM in flow simulation in shale gas reservoirs on pore scale and representative elementary volume(REV) scale is evaluated and summarised. Our review indicates that the LBM is capable of capturing gas flow in continuum to slip flow regimes which cover significant proportion of the pores in shale gas reservoirs and identifies opportunities for future research.

preprint2015arXiv

The M-giant star candidates identified in the LAMOST data release 1

We perform a discrimination procedure with the spectral index diagram of TiO5 and CaH2+CaH3 to separate M giants from M dwarfs. Using the M giant spectra identified from the LAMOST DR1 with high signal-to-noise ratio (SNR), we have successfully assembled a set of M giant templates, which show more reliable spectral features. Combining with the M dwarf/subdwarf templates in Zhong et al. (2015), we present an extended M-type templates library which includes not only M dwarfs with well-defined temperature and metallicity grid but also M giants with subtype from M0 to M6. Then, the template-fit algorithm were used to automatically identify and classify M giant stars from the LAMOST DR1. The result of M giant stars catalog is cross-matched with 2MASS JHKs and WISE W1/W2 infrared photometry. In addition, we calculated the heliocentric radial velocity of all M giant stars by using the cross-correlation method with the template spectrum in a zero-velocity restframe. Using the relationship between the absolute infrared magnitude MJ and our classified spectroscopic subtype, we derived the spectroscopic distance of M giants with uncertainties of about 40%. A catalog of 8639 M giants is provided. As an additional search result, we also present 101690 M dwarfs/subdwarfs catalog which were classified by our classification pipeline.

preprint2015arXiv

The NuSTAR View of a QPO Evolution of GRS 1915+105

We report a timing analysis of the black hole binary GRS 1915+105 with the NuSTAR observatory. A strong type-C QPO below 2 Hz appears in the power density spectrum during the whole observation, whose frequency is correlated with the 3-25 keV count rate. The QPO shows a sudden increase in frequency along with an increase in flux and a softening of the spectrum. We discuss the possible origin of the QPO and the reasons that lead to the QPO frequency variation. It is suggested that the reflection component has little influence on QPO frequency and the increase in QPO frequency could be associated with the inward motion of the outer part of the disk.

preprint2014arXiv

A generalized lattice Boltzmann model for flow through tight porous media with Klinkenberg's effect

Gas slippage occurs when the mean free path of the gas molecules is in the order of the characteristic pore size of a porous medium. This phenomenon leads to the Klinkenberg's effect where the measured permeability of a gas (apparent permeability) is higher than that of the liquid (intrinsic permeability). A generalized lattice Boltzmann model is proposed for flow through porous media that includes Klinkenberg's effect, which is based on the model of Guo et al. (Z.L. Guo et al., Phys.Rev.E 65, 046308 (2002)). The second-order Beskok and Karniadakis-Civan's correlation (A. Beskok and G. Karniadakis, Microscale Thermophysical Engineering 3, 43-47 (1999), F. Civan, Transp Porous Med 82, 375-384 (2010)) is adopted to calculate the apparent permeability based on intrinsic permeability and Knudsen number. Fluid flow between two parallel plates filled with porous media is simulated to validate model. Simulations performed in a heterogeneous porous medium with components of different porosity and permeability indicate that the Klinkenberg's effect plays significant role on fluid flow in low-permeability porous media, and it is more pronounced as the Knudsen number increases. Fluid flow in a shale matrix with and without fractures is also studied, and it is found that the fractures greatly enhance the fluid flow and the Klinkenberg's effect leads to higher global permeability of the shale matrix.

preprint2014arXiv

An Ultra Low Mass and Small Radius Compact Object in 4U 1746-37?

Photospheric radius expansion (PRE) bursts have already been used to constrain the masses and radii of neutron stars. RXTE observed three PRE bursts in 4U 1746-37, all with low touchdown fluxes. We discuss here the possibility of low mass neutron star in 4U 1746-37 because the Eddington luminosity depends on stellar mass. With typical values of hydrogen mass fraction and color correction factor, a Monte-Carlo simulation was applied to constrain the mass and radius of neutron star in 4U 1746-37. 4U 1746-37 has a high inclination angle. Two geometric effects, the reflection of the far side accretion disc and the obscuration of the near side accretion disc have also been included in the mass and radius constraints of 4U 1746-37. If the reflection of the far side accretion disc is accounted, a low mass compact object (mass of $0.41\pm0.14~M_{\odot}$ and radius of $8.73\pm1.54~\rm km$ at 68% confidence) exists in 4U 1746-37. If another effect operated, 4U 1746-37 may contain an ultra low mass and small radius object ($M=0.21\pm0.06~M_{\odot},~R=6.26\pm0.99~\rm km$ at 68% confidence). Combined all possibilities, the mass of 4U 1746-37 is $0.41^{+0.70}_{-0.30}~M_\odot$ at 99.7% confidence. For such low mass NS, it could be reproduced by a self-bound compact star, i.e., quark star or quark-cluster star.

preprint2014arXiv

Correlations in Horizontal Branch Oscillations and Break Components in XTE J1701-462 and GX 17+2

We studied the horizontal branch oscillations (HBO) and the band-limited components observed in the power spectra of the transient neutron star low-mass X-ray binary XTE J1701-462 and the persistent "Sco-like" Z source GX 17+2. These two components were studied based on the state-resolved spectra. We found that the frequencies of XTE J1701-462 lie on the known correlations (WK and PBK), showing consistency with other types of X-ray binaries (black holes, atoll sources and millisecond X-ray pulsars). However, GX 17+2 is shifted from the WK correlation like other typical Z sources. We suggest that the WK/PBK main track forms a boundary which separates persistent sources from transient sources. The characteristic frequencies of break and HBO are independent of accretion rate in both sources, although it depends on spectral models. We also report the energy dependence of the HBO and break frequencies in XTE J1701-462 and how the temporal properties change with spectral state in XTE J1701-462 and GX 17+2. We studied the correlation between rms at the break and the HBO frequency. We suggest that HBO and break components for both sources probably arise from a similar physical mechanism: Comptonization emission from the corona. These two components could be caused by same kind of oscillation in a corona who with uneven density, and they could be generated from different areas of corona. We further suggest that different proportions of the Comptonization component in the total flux cause the different distribution between GX 17+2 and XTE J1701-462 in the $rms_{\rm{break}}$-$rms_{\rm{HBO}}$ diagram.

preprint2014arXiv

Counterfactual quantum-information transfer

We demonstrate quantum information can be transferred between two distant participants without any physical particles travelling between them. The key procedure of the counterfactual scheme is to entangle two nonlocal qubits with each other without interaction, so the scheme can also be used to generate nonlocal entanglement counterfactually. We here illustrate the scheme by using flying photon qubits and stationary electron-spin qubits assisted by quantum dots inside double-sided optical microcavities. Unlike the typical teleportation, the present scheme does not require prior entanglement sharing or classical communication between the two distant participants.

preprint2014arXiv

Cross-correlations between soft and hard light curves depending on luminosity in the transient neutron star XTE J1701-462

Using all the observations from Rossi X-ray Timing Explorer for accreting neutron star XTE J1701-462, we carry out a systematic study on the cross-correlation between its soft and hard light curves. The anti-correlations appear at the hard vertex and on the upper normal branch (NB) in the Cyg-like interval, occur on the horizontal branch (HB) and/or upper NB in the first and second Sco-like intervals, and display at the upper flaring branch (FB) in the third Sco-like interval. We suggest that the anti-correlation might evolve with luminosity, because with decreasing of luminosity, the source evolved from a Cyg-like Z source, via a Sco-like Z source, to an atoll source. In the Cyg-like interval, the positive correlations are mostly distributed on the HB, which is not consistent with that of the Cyg-like Z source GX 5-1 and Cyg X-2 whose HBs host ambiguous correlations and anti-correlations. From our spectral analyses, it is found that the ratio of the hard emission to the soft emission basically keeps unvaried from the HB to the hard vertex, which might result in the positive correlation. Additionally, we compare the spectra in the third Sco-like interval with those of atoll source 4U 1735-44 and find that their spectral evolution along the tracks in the color-color diagrams are similar, indicating that in this interval, XTE J1701-462 approaches an atoll source. Both hard time lags and soft time lags are detected and the possible origins of them are discussed.

preprint2014arXiv

Fixing the Reference Frame for PPMXL Proper Motions Using Extragalactic Sources

We quantify and correct systematic errors in PPMXL proper motions using extragalactic sources from the first two LAMOST data releases and the Veron-Cetty & Veron Catalog of Quasars. Although the majority of the sources are from the Veron catalog, LAMOST makes important contributions in regions that are not well-sampled by previous catalogs, particularly at low Galactic latitudes and in the south Galactic cap. We show that quasars in PPMXL have measureable and significant proper motions, which reflect the systematic zero-point offsets present in the catalog. We confirm the global proper motion shifts seen by Wu, Ma, & Zhou (2011), and additionally find smaller-scale fluctuations of the QSO-derived corrections to an absolute frame. We average the proper motions of 158,106 extragalactic objects in bins of 3x3 degrees and present a table of proper motion corrections.

preprint2014arXiv

Lattice Boltzmann prediction of transport properties in reconstructed nanostructures of organic matters in shales

Size, morphology and distributions of pores in organic matters of shale matrix are discussed based on high resolution images from experiments in the literature. 150 nanoscale structures of the organic matters are then reconstructed by randomly placing pore spheres with different diameters and overlap tolerances. Effects of porosity, the mean diameter and the overlap tolerance on void space connectivity and pore size distribution are studied. Further, a pore-scale model based on the Lattice Boltzmann method is developed to predict the Knudsen diffusivity and permeability of the reconstructed organic matters. The simulation results show that the mean pore diameter and overlap tolerance significantly affect the transport properties. The predicted Knudsen effective diffusivity is compared with Bruggeman equation and it is found that this equation underestimate the tortuosity. A modified Bruggeman equation is proposed based on the simulation results. The predicted intrinsic permeability is in acceptable agreement with Kozeny-Carman (KC) equation. In addition, a relationship is developed to determine the apparent permeability based on Knudsen diffusivity and intrinsic permeability. The predicted apparent permeability is compared with that predicted by various corrections in the literature. Knudsen's corrections match best with our numerical results and are recommended to calculate the apparent permeability.

preprint2014arXiv

Nanoscale simulation of shale transport properties using the lattice Boltzmann method: permeability and diffusivity

Porous structures of shales are reconstructed based on scanning electron microscopy (SEM) images of shale samples from Sichuan Basin, China. Characterization analyzes of the nanoscale reconstructed shales are performed, including porosity, pore size distribution, specific surface area and pore connectivity. The multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) fluid flow model and single-relaxation-time (SRT) LBM diffusion model are adopted to simulate the fluid flow and Knudsen diffusion process within the reconstructed shales, respectively. Tortuosity, intrinsic permeability and effective Knudsen diffusivity are numerically predicted. The tortuosity is much higher than that commonly employed in Bruggeman equation. Correction of the intrinsic permeability by taking into consideration the contribution of Knudsen diffusion, which leads to the apparent permeability, is performed. The correction factor under different Knudsen number and pressure are estimated and compared with existing corrections reported in the literature. For the wide pressure range under investigation, the correction factor is always greater than 1, indicating the Knudsen diffusion always plays a role on the transport mechanisms of shale gas in shales studied in the present study. Most of the values of correction factor are located in the transition regime, with no Darcy flow regime observed.

preprint2014arXiv

Nanoscale study of reactive transport in catalyst layer of proton exchange membrane fuel cells with precious and non-precious catalysts using lattice Boltzmann method

High-resolution porous structures of catalyst layer (CL) with multicomponent in proton exchange membrane fuel cells are reconstructed using a reconstruction method called quartet structure generation set. Characterization analyses of nanoscale structures are implemented including pore size distribution, specific area and phase connectivity. Pore-scale simulation methods based on the lattice Boltzmann method are developed and used to predict the macroscopic transport properties including effective diffusivity and proton conductivity. Nonuniform distributions of ionomer in CL generates more tortuous pathway for reactant transport and greatly reduces the effective diffusivity. Tortuosity of CL is much higher than conventional Bruggeman equation adopted. Knudsen diffusion plays a significant role in oxygen diffusion and significantly reduces the effective diffusivity. Reactive transport inside the CL is also investigated. Although the reactive surface area of non-precious metal catalyst (NPMC) CL is much higher than that of Pt CL, the oxygen reaction rate is quite lower in NPMC CL compared with that in Pt CL, due to much lower reaction rate. Micropores (a few nanometers) in NPMC CL although can increase reactive sites, contribute little to enhance the mass transport. Mesopores (few tens of nanometers) or macropores are required to increase the mass transport rate.

preprint2014arXiv

Pore-scale simulation of multicomponent multiphase reactive transport with dissolution and precipitation

Multicomponent multiphase reactive transport processes with dissolution-precipitation are widely encountered in energy and environment systems. A pore-scale two-phase multi-mixture model based on the lattice Boltzmann method (LBM) is developed for such complex transport processes, where each phase is considered as a mixture of miscible components in it. The liquid-gas fluid flow with large density ratio is simulated using the multicomponent multiphase pseudo-potential LB model; the transport of certain solute in the corresponding solvent is solved using the mass transport LB model; and the dynamic evolutions of the liquid-solid interface due to dissolution-precipitation are captured by an interface tracking scheme. The model developed can predict coupled multiple physicochemical processes including multiphase flow, multicomponent mass transport, homogeneous reactions in the bulk fluid and heterogeneous dissolution-precipitation reactions at the fluid-solid interface, and dynamic evolution of the solid matrix geometries at the pore-scale. The model is then applied to a physicochemical system encountered in shale gas/oil industry involving multiphase flow, multicomponent reactive transport and dissolution-precipitation, with several reactions whose rates can be several orders of magnitude different at a given temperature. The pore-scale phenomena and complex interaction between different sub-processes are investigated and discussed in detail.

preprint2014arXiv

Pore-scale study of dissolution-induced changes in hydrologic properties of rocks with binary minerals

A pore-scale numerical model for reactive transport processes based on the Lattice Boltzmann method is used to study the dissolution-induced changes in hydrologic properties of a fractured medium and a porous medium. The solid phase of both media consists of two minerals, and a structure reconstruction method called quartet structure generation set is employed to generate the distributions of both minerals. Emphasis is put on the effects of undissolved minerals on the changes of permeability and porosity under different Peclet and Damkohler numbers. The simulation results show porous layers formed by the undissolved mineral remain behind the dissolution reaction front. Due to the large flow resistance in these porous layers, the permeability increases very slowly or even remains at a small value although the porosity increases by a large amount. Besides, due to the heterogeneous characteristic of the dissolution, the chemical, mechanical and hydraulic apertures are very different from each other. Further, simulations in complex porous structures demonstrate that the existence of the porous layers of the nonreactive mineral suppresses the wormholing phenomena observed in the dissolution of mono-mineralic rocks.

preprint2014arXiv

Stochastic Blockmodeling for Online Advertising

Online advertising is an important and huge industry. Having knowledge of the website attributes can contribute greatly to business strategies for ad-targeting, content display, inventory purchase or revenue prediction. Classical inferences on users and sites impose challenge, because the data is voluminous, sparse, high-dimensional and noisy. In this paper, we introduce a stochastic blockmodeling for the website relations induced by the event of online user visitation. We propose two clustering algorithms to discover the instrinsic structures of websites, and compare the performance with a goodness-of-fit method and a deterministic graph partitioning method. We demonstrate the effectiveness of our algorithms on both simulation and AOL website dataset.

preprint2014arXiv

The accretion rate independence of horizontal branch oscillation in XTE J1701-462

We study the temporal and energy spectral properties of the unique neutron star low-mass X-ray binary XTE J1701-462. After assuming the HB/NB vertex as a reference position of accretion rate, the horizontal branch oscillation (HBO) of the HB/NB vertex is roughly 50 Hz. It indicates that the HBO is independent with the accretion rate or the source intensity. The spectral analysis shows $R_{\rm{in}}\propto\dot{M}_{\rm{Disk}}^{2.9\pm0.09}$ in the HB/NB vertex and $R_{\rm{in}}\propto\dot{M}_{\rm{Disk}}^{1.7\pm0.06}$ in the NB/FB vertex, which implies that different accretion rate may be produced in the HB/NB vertex and the NB/FB vertex. The Comptonization component could be fitted by constrained broken power law (CBPL) or nthComp. Different with GX 17+2, the frequencies of HBO positively correlate with the inner disk radius, which contradict with the prediction of Lense-Thirring precession model. XTE J1701-462, both in the Cyg-like phase and in the Sco-like phase, follows a positive correlation between the break frequency of broad band noise and the characteristic frequency of HBO, which is called the W-K relation. An anticorrelation between the frequency of HBO and photon energy is observed. Moreover, the rms of HBO increases with photon energy till ~10 keV. We discuss the possible origin of HBO from corona in XTE J1701-462.

preprint2014arXiv

The impossibility of exactly flat non-trivial Chern bands in strictly local periodic tight binding models

We investigate the possibility of exactly flat non-trivial Chern bands in tight binding models with local (strictly short-ranged) hopping parameters. We demonstrate that while any two of three criteria can be simultaneously realized (exactly flat band, non-zero Chern number, local hopping), it is not possible to simultaneously satisfy all three. Our theorem covers both the case of a single flat band, for which we give a rather elementary proof, as well as the case of multiple degenerate flat bands. In the latter case, our result is obtained as an application of $K$-theory. We also introduce a class of models on the Lieb lattice with nearest and next-nearest neighbor hopping parameters, which have an isolated exactly flat band of zero Chern number but, in general, non-zero Berry curvature.

preprint2014arXiv

The Nearest High-Velocity Stars Revealed by LAMOST Data Release 1

We report the discovery of 28 candidate high-velocity stars (HVSs) at heliocentric distances of less than 3 kpc, based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 1. Our sample of HVS candidates covers a much broader color range than the equivalent ranges discussed in previous studies and comprises the first and largest sample of HVSs in the solar neighborhood. The sufficiently accurate observed and derived parameters for all candidates allow us to ascertain their nature as genuine HVSs, while a subset of 12 objects represents the most promising candidates. Our results also highlight the great potential of discovering statistically large numbers of HVSs of different spectral types in LAMOST survey data. This will ultimately enable us to achieve a better understanding of the nature of Galactic HVSs and their ejection mechanisms, and to constrain the structure of the Galaxy.

preprint2014arXiv

Tuning Transport Properties of Topological Edge States of Bi(111) Bilayer Film by Edge Adsorption

Based on first-principles and tight-binding calculations, we report that the transport properties of topological edge states of zigzag Bi(111) nanoribbon can be significantly tuned by H edge adsorption. The Fermi velocity is increased by one order of magnitude, as the Dirac point is moved from Brillouin zone boundary to Brillouin zone center and the real-space distribution of Dirac states are made twice more delocalized. These intriguing changes are explained by an orbital filtering effect of edge H atoms, which removes certain components of $p$ orbits of edge Bi atoms that reshapes the topological edge states. In addition, the spin texture of the Dirac states is also modified, which is described by introducing an effective Hamiltonian. Our findings not only are of fundamental interest but also have practical implications in potential applications of topological insulators.

preprint2013arXiv

A Note on the Discrete Jordan Curve Theorem (Revised)

According to a general definition of discrete curves, surfaces, and manifolds. This paper focuses on the Jordan curve theorem in 2D discrete spaces. The Jordan curve theorem says that a (simply) closed curve separates a simply connected surface into two components. Based on the definition of discrete surfaces, we give three reasonable definitions of simply connected spaces. Theoretically, these three definition shall be equivalent. We have proved the Jordan curve theorem under the third definition of simply connected spaces. The Jordan theorem shows the relationship among an object, its boundary, and its outside area. After the publication of the first version of the paper ({\it L. Chen, Note on the discrete Jordan Curve Theorem. In: SPIE Conf. on Vision Geometry VIII, vol. 3811, pp. 82-94. (1999).}), we found some statements in the original proof of the Jordan Curve Theorem were not explained well. One case was not proven in details. In this revision, we added two more minor definitions and make the proof more solid and sound when it is needed for embedding a discrete surface into a Euclidean space. In this revision, we also proved that the third definition of simply connected spaces equivalent to the second definition of simply connected spaces.

preprint2013arXiv

Algorithms for Computing Topological Invariants in 2D and 3D Digital Spaces

Based on previous results of digital topology, this paper focuses on algorithms of topological invariants of objects in 2D and 3D Digital Spaces. We specifically interest in solving hole counting of 2D objects and genus of closed surface in 3D. We first prove a new formula for hole counting in 2D. The number of of holes is $h=1 + (|C_4|-|C_2|)/4$ where $C_4$ and $C_2$ are sets of inward and outward corner points, respectively. This paper mainly deals with algorithm design and implementation of practical computation of topological invariants in digital space. The algorithms relating to data structures, and pathological case detection and original data modification are main issues. This paper designed fast algorithms for topological invariants such as connected components, hole counting in 2D and boundary surface genus for 3D. For 2D images, we designed a linear time algorithm to solve hole counting problem. In 3D, we designed also O(n) time algorithm to get genus of the closed surface. These two algorithms are both in $O(\log n)$ space complexity.

preprint2013arXiv

Combined Effects of Binaries and Stellar Rotation on the Color-Magnitude Diagrams of Intermediate-Age Star Clusters

About seventy percent of intermediate-age star clusters in the Large Magellanic Clouds have been confirmed to have broad main sequence, multiple or extended turn-offs and dual red giant clumps. The observed result seems against the classical idea that such clusters are simple stellar populations. Although many models have been used for explaining the results via factors such as prolonged star formation history, metallicity spread, differential redenning, selection effect, observational uncertainty, stellar rotation, and binary interaction, the reason for the special color-magnitude diagrams is still uncertain. We revisit this question via the combination of stellar rotation and binary effects. As a result, it shows "golf club" color-magnitude diagrams with broad or multiple turn-offs, dual red clump, blue stragglers, red stragglers, and extended main sequences. Because both binaries and massive rotators are common, our result suggests that most color-magnitude diagrams including extended turn-off or multiple turn-offs can be explained using simple stellar populations including both binary and stellar rotation effects, or composite populations with two components.

preprint2013arXiv

Enhanced Archaeological Predictive Modelling in Space Archaeology

Identifying and preserving archaeological sites before they are destroyed is a very important issue. In this paper, we develop a greatly improved archaeological predictive model $APM_{enhanced}$ that predicts where archaeological sites will be found. This approach is applied to remotely-sensed multispectral bands and a single topographical band obtained from advanced remote sensing technologies such as satellites and Airborne Laser Scanning (ALS). Our $APM_{enhanced}$ is composed of band transformation, image analysis, feature extraction and classification. We evaluate our methodology on the sensor bands over Ft.Irwin, CA, USA. A nested bi-loop cross-validation and receiver operating characteristics curves are used to assess the performance of our algorithm. We first validate our method on the east swath of Ft.Irwin and then test on a separate dataset from the west swath of Ft.Irwin. A convex combination of two methodologies: $APM_{conventional}$, which has been used among archaeologists for many years, and our $APM_{enhanced}$, is demonstrated to yield superior classification performance compared to either alone at low false negative rates. We compare the performance of our methodology on different band combinations, chosen based on the archaeological importance for these sensor bands. We also compare the two types of $APM$s in the aspects of input data, output values, practicality and transferability.

preprint2013arXiv

Exact criterion for global existence and blow up to a degenerate Keller-Segel system

A degenerate Keller-Segel system with diffusion exponent $2n/(n+2)<m<2-\frac{2}{n}$ in multi dimension is studied. An exact criterion for global existence and blow up of solution is obtained. The estimates on $L^{2n/(n+2)}$ norm of the solution play important roles in our analysis. These estimates are closely related to the optimal constant in Haddy- Littlewood- Sobolev inequality. In the case of initial free energy less than a universal constant which depends on the inverse of total mass, there exists a constant such that if the $L^{2n/(n+2)}$ norm of initial data is less than this constant, then the weak solution exists globally; if the $L^{2n/(n+2)}$ norm of initial data is larger than the same constant, then the solution must blow-up in finite time. Our result shows that the total mass, which plays the deterministic role in two dimension case, might not be an appropriate criterion for existence and blow up discussion in multi-dimension, while the $L^{\frac{2n}{n+2}}$ norm of the initial data and the relation between initial free energy and initial mass are more important.

preprint2013arXiv

Mean-Field Limit for a Collision-Avoiding Flocking System and the Time-Asymptotic Flocking Dynamics for the Kinetic Equation

A Collision-Avoiding flocking particle system proposed in [8] is studied in this paper. The global wellposedness of its corresponding Vlasov-type kinetic equation is proved. As a corollary of the global stability result, the mean field limit of the particle system is obtained. Furthermore, the time-asymptotic flocking behavior of the solution to the kinetic equation is also derived. The technics used for local wellposedness and stability follow from similar ideas to those have been used in [3,22,14]. While in order to extend the local result globally, the main contribution here is to generate a series of new estimates for this Vlasov type equation, which imply that the growing of the characteristics can be controlled globally. Further estimates also show the long time flocking phenomena.

preprint2013arXiv

Outbreaks of coinfections: the critical role of cooperativity

Modeling epidemic dynamics plays an important role in studying how diseases spread, predicting their future course, and designing strategies to control them. In this letter, we introduce a model of SIR (susceptible-infected-removed) type which explicitly incorporates the effect of {\it cooperative coinfection}. More precisely, each individual can get infected by two different diseases, and an individual already infected with one disease has an increased probability to get infected by the other. Depending on the amount of this increase, we observe different threshold scenarios. Apart from the standard continuous phase transition for single disease outbreaks, we observe continuous transitions where both diseases must coexist, but also discontinuous transitions are observed, where a finite fraction of the population is already affected by both diseases at the threshold. All our results are obtained in a mean field model using rate equations, but we argue that they should hold also in more general frameworks.

preprint2013arXiv

Positivity of $|p|^a|q|^b+|q|^b|p|^a$

We show that $$J_{a,b,n}:=\frac12(|p|^a|q|^b+|q|^b|p|^a)$$ is positive, if $n\geq b+a$. (Here $q$ is the multiplication by $x$ and $p:= \mathrm{i}^{-1}\nabla$.) Furthermore we show that it generalizes the generalized Hardy inequalities for the fractional Laplacians.

preprint2013arXiv

Potential Importance of Binary Evolution in UV-Optical Spectral Fitting of Early-Type Galaxies

Binaries are very common in galaxies, and more than half of Galactic hot subdwarf stars, which are thought as a possible origin of UV-upturn of old stellar populations, are found in binaries. Previous works showed that binary evolution can make the spectra of binary star populations significantly different from those of single star populations. However, the effect of binary evolution has not been taken into account in most works of spectral fitting of galaxies. This paper studies the role of binary evolution in spectral fitting of early-type galaxies, via a stellar population synthesis model including both single and binary star populations. Spectra from ultraviolet to optical band are fitted to determine a few parameters of galaxies. The results show that the inclusion of binaries in stellar population models may lead to obvious change in the determination of some galaxy parameters and therefore it is potentially important for spectral studies. In particular, the ages of young components of composite stellar populations become much older when using binary star population models instead of single star population models. This implies that binary star population models will measure significantly different star formation histories (SFHs) for galaxies compared to single star population models. In addition, stellar population models with binary interactions measure larger dust extinctions than single star population models on average. It suggests that when using binary star population models instead of single star population models, negative extinctions are possibly unnecessary in spectral fitting of early-type galaxies.

preprint2013arXiv

Rotationally symmetric harmonic diffeomorphisms between surfaces

In this paper, we show that the nonexistence of rotationally symmetric harmonic diffeomorphism between the unit disk without the origin and a punctured disc with hyperbolic metric on the target.

preprint2013arXiv

The Cross Spectral Time lags evolution along branches in XTE J1701-462

We investigate the cross spectrum of XTE J1701-462 in various types of Neutron Star-Low Mass X-ray Binary subclasses, during its 2006-2007 outburst. We analyze the relation between the time lags and temporal variabilities. We find that the hard time lags accompany with the horizontal branch oscillations (HBOs) and the soft time lags dominate the noise in the low frequency range 0.1-10 Hz on HB. In the Cyg-like phase, the time lags decrease on the middle normal branch (NB) from HB/NB vertex to NB/FB vertex, whereas the time lags are roughly invariant in the Sco-like source. We discuss that the Compton upscattering by the corona introduces the soft lag in low frequency noise. We interpret that the variation of the Comptonization component from the disk emission lead to the HBOs' time lags evolution along the Z tracks. We also report the rms amplitude spectrum and phase lag spectrum for the NBO. The $\sim$ $160^{\circ}$ phase lag is found. We present that the rms amplitude of both the Cyg-like and the Sco-like NBO linearly increase with the photon energy in low energy bands, and it will drop in the highest energy band.

preprint2012arXiv

A Test for Radial Mixing Using Local Star Samples

We use samples of local main-sequence stars to show that the radial gradient of [Fe/H] in the thin disk of the Milky Way decreases with mean effective stellar temperature. Many of these stars are visiting the solar neighborhood from the inner and outer Galaxy. We use the angular momentum of each star about the Galactic center to determine the guiding center radius and to eliminate the effects of epicyclic motion, which would otherwise blur the estimated gradients. We interpret the effective temperature as a proxy for mean age, and conclude that the decreasing gradient is consistent with the predictions of radial mixing due to transient spiral patterns. We find some evidence that the trend of decreasing gradient with increasing mean age breaks to a constant gradient for samples of stars whose main-sequence life-times exceed the likely age of the thin disk.

preprint2012arXiv

Diamondoid Structure of Polymeric Nitrogen at High Pressures

High-pressure polymeric structures of nitrogen have attracted great attention owing to their potential application as high-energy-density materials. We report the density functional structural prediction of the unexpected stabilization of a diamondoid (or N10-cage) structure of polymeric nitrogen at high pressures. The structure adopts a highly symmetric body-centered cubic form with lattice sites occupied by N10 tetracyclic cages, each of which consists of 10 atoms and is covalently bonded with its six next-nearest N10 cages. The prediction of this diamondoid structure rules out the earlier proposed helical tunnel phase and demonstrates the high-order nature of polymeric nitrogen at extreme high pressures. Diamondoid nitrogen is a wide-gap insulator and energetically more favorable than the experimental cubic gauche and previously predicted layered Pba2 phases above 263 GPa, a pressure which is accessible to high pressure experiment.

preprint2012arXiv

Incompressible type limit analysis of a hydrodynamic model for charge-carrier transport

This paper is concerned with the rigorous analysis of the zero electron mass limit of the full Navier-Stokes-Poisson. This system has been introduced in the literature by Anile and Pennisi (see [5]) in order to describe a hydrodynamic model for charge-carrier transport in semiconductor devices. The purpose of this paper is to prove rigorously zero electron mass limit in the framework of general ill prepared initial data. In this situation the velocity field and the electronic fields develop fast oscillations in time. The main idea we will use in this paper is a combination of formal asymptotic expansion and rigorous uniform estimates on the error terms. Finally we prove the strong convergence of the full Navier Stokes Poisson system towards the incompressible Navier Stokes equations.

preprint2012arXiv

LAMOST Experiment for Galactic Understanding and Exploration (LEGUE) The survey science plan

We describe the current plans for a spectroscopic survey of millions of stars in the Milky Way galaxy using the Guo Shou Jing Telescope (GSJT, formerly the Large Area Multi-Object Spectroscopic Telescope - LAMOST). The survey will obtain spectra for 2.5 million stars brighter than $r<19$ during dark/grey time, and 5 million stars brighter than $r<17$ or $J<16$ on nights that are moonlit or have low transparency. The survey will begin in fall of 2012, and will run for at least four years. The telescope design constrains the optimal declination range for observations to $10^\circ<δ<50^\circ$, and site conditions lead to an emphasis on stars in the direction of the Galactic anticenter. The survey is divided into three parts with different target selection strategies: disk, anticenter, and spheroid. The resulting dataset will be used to study the merger history of the Milky Way, the substructure and evolution of the disks, the nature of the first generation of stars through identification of the lowest metallicity stars, and star formation through study of open clusters and the OB associations. Detailed design of the LEGUE survey will be completed after a review of the results of the pilot survey in summer 2012.

preprint2012arXiv

Multi-Output Recommender: Items, Groups and Friends, and Their Mutual Contributing Effects

Due to the development of social media technology, it becomes easier for users to gather together to form groups. Take the Last.fm for example, users can join groups they may be interested where they can share their loved songs and discuss topics about songs and singers. However, the number of groups grows over time, users need effective groups recommendations in order to meet more like-minded users.

preprint2012arXiv

Solving Groundwater Flow Equations Using Gradually Varied Functions

Finite difference method and finite element method are popular methods for solving groundwater flow equations. This paper presents a new method that uses gradually varied functions to solve such equation. In this paper, we have established a mathematical model based on gradually varied functions for groundwater data volume reconstruction. These functions do not rely on the rectangular Cartesian coordinate system. A gradually varied function can be defined in a general graph or network. Gradually varied functions are suitable for arbitrarily shaped aquifers. Two types of models are designed and implemented for real data processing: (1) the gradually varied model for individual (time) groundwater flow data, (2) the gradually varied model for sequential (time) groundwater flow data. In application, we used two sets of real data and one set of experimental data to test our methods.

preprint2012arXiv

Spectral Lags Obtained by CCF of Smoothed Lightcurves

We present a new technique to calculate the spectral lags of gamma-ray bursts (GRBs). Unlike previous processing methods, we first smooth the light curves of gamma-ray bursts in high and low energy bands using the "Loess" filter, then, we directly define the spectral lags as such to maximize the cross-correlation function (CCF) between two smoothed light curves. This method is suitable for various shapes of CCF; it effectively avoids the errors caused by manual selections for the fitting function and fitting interval. Using the method, we have carefully measured the spectral lags of individual pulses contained in BAT/Swift gamma-ray bursts with known redshifts, and confirmed the anti-correlation between the spectral lag and the isotropy luminosity. The distribution of spectral lags can be well fitted by four Gaussian components, with the centroids at 0.03 s, 0.09 s, 0.15 s, and 0.21 s, respectively. We find that some spectral lags of the multi-peak GRBs seem to evolve with time.

preprint2012arXiv

The selection of LEGUE disk targets for LAMOST's pilot survey

We describe the target selection algorithm for the low latitude disk portion of the LAMOST Pilot Survey, which aims to test systems in preparation for the LAMOST spectroscopic survey. We use the PPMXL (Roeser et al. 2010) astrometric catalog, which provides positions, proper motions, B/R/I magnitudes (mostly) from USNO-B (Monet et al. 2003) and J/H/Ks from The Two Micron All Sky Survey (2MASS, see Skrutskie et al. 2006) as well. We chose 8 plates along the Galactic plane, in the region $0^\circ<α<67^\circ$ and $42^\circ<δ<59^\circ$, that cover 22 known open clusters with a range of ages. Adjacent plates may have small overlapping. Each plate covers an area $2.5^\circ$ in radius,with central star (for Shack-Hartmann guider) brighter than $\sim8^{\rm th}$ magnitude. For each plate, we create an input catalog in the magnitude range $11.3<Imag<16.3$ and $Bmag$ available from PPMXL. The stars are selected to satisfy the requirements of the fiber positioning system and have a uniform distribution in the $I$ vs. $B-I$ color-magnitude diagram. Our final input catalog consists of 12,000 objects on each of 8 plates that are observable during the winter observing season in Xinglong Station of the National Astronomical Observatory of China.

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.

preprint2011arXiv

A stochastic maximum principle for backward delayed system via advanced stochastic differential equation (ASDE)

The main contributions of this paper are three fold. First, our primary concern is to investigate a class of stochastic recursive delayed control problems which arise naturally with sound backgrounds but have not been well-studied yet. For illustration purpose, some concrete examples are also provided here. We derive the stochastic maximum principle of sufficient condition to the optimal control in both cases with and without control delay. Second, it is interesting that a new class of time-advanced stochastic differential equations (ASDEs) is introduced as the adjoint process via duality relation. To our best knowledge, such equations have never been discussed in literature although they possess more academic values besides the control study here. Some existence and uniqueness result to ASDEs is presented. Third, to illustrate our theoretical results, some dynamic optimization problems are discussed based on our stochastic maximum principles. In particular, the optimal controls are derived explicitly by solving the associated time-advanced ordinary differential equation (AODE), the counterpart of the ASDE in its deterministic setup.

preprint2011arXiv

Cook's Theory and the Twentieth Century Mathematics

A comprehensive review on Cook's contribution in the theory of NP-Completeness with relations to modern mathematics.

preprint2011arXiv

Harmonic Functions for Data Reconstruction on 3D Manifolds

In computer graphics, smooth data reconstruction on 2D or 3D manifolds usually refers to subdivision problems. Such a method is only valid based on dense sample points. The manifold usually needs to be triangulated into meshes (or patches) and each node on the mesh will have an initial value. While the mesh is refined the algorithm will provide a smooth function on the redefined manifolds. However, when data points are not dense and the original mesh is not allowed to be changed, how is the "continuous and/or smooth" reconstruction possible? This paper will present a new method using harmonic functions to solve the problem. Our method contains the following steps: (1) Partition the boundary surfaces of the 3D manifold based on sample points so that each sample point is on the edge of the partition. (2) Use gradually varied interpolation on the edges so that each point on edge will be assigned a value. In addition, all values on the edge are gradually varied. (3) Use discrete harmonic function to fit the unknown points, i.e. the points inside each partition patch. The fitted function will be a harmonic or a local harmonic function in each partitioned area. The function on edge will be "near" continuous (or "near" gradually varied). If we need a smoothed surface on the manifold, we can apply subdivision algorithms. This paper has also a philosophical advantage over triangulation meshes. People usually use triangulation for data reconstruction. This paper employs harmonic functions, a generalization of triangulation because linearity is a form of harmonic. Therefore, local harmonic initialization is more sophisticated then triangulation. This paper is a conceptual and methodological paper. This paper does not focus on detailed mathematical analysis nor fine algorithm design.

preprint2011arXiv

Rapid dynamical mass segregation and properties of fractal star clusters

We investigate the evolution of young star clusters using N-body simulations. We confirm that subvirial and fractal-structured clusters will dynamically mass segregate on a short timescale (within 0.5 Myr). We adopt a modified minimum-spanning-tree (MST) method to measure the degree of mass segregation, demonstrating that the stars escaping from a cluster's potential are important for the temporal dependence of mass segregation in the cluster. The form of the initial velocity distribution will also affect the degree of mass segregation. If it depends on radius, the outer parts of the cluster would expand without undergoing collapse. In velocity space, we find 'inverse mass segregation,' which indicates that massive stars have higher velocity dispersions than their lower-mass counterparts.

preprint2011arXiv

Rate of Convergence in Nonlinear Hartree Dynamics with Factorized Initial Data

The mean field dynamics of an $N$-particle weekly interacting Boson system can be described by the nonlinear Hartree equation. In this paper, we present estimates on the 1/N rate of convergence of many-body Schrödinger dynamics to the one-body nonlinear Hartree dynamics with factorized initial data with two-body interaction potential $V$ in $L^3 (\mathbb{R}^3)+ L^{\infty} (\mathbb{R}^3)$.

preprint2011arXiv

Rate of Convergence Towards Hartree Dynamics

We consider a system of N bosons interacting through a two-body potential with, possibly, Coulomb-type singularities. We show that the difference between the many-body Schrödinger evolution in the mean-field regime and the effective nonlinear Hartree dynamics is at most of the order 1/N, for any fixed time. The N-dependence of the bound is optimal.

preprint2010arXiv

A Digital-Discrete Method For Smooth-Continuous Data Reconstruction

A systematic digital-discrete method for obtaining continuous functions with smoothness to a certain order (C^(n)) from sample data is designed. This method is based on gradually varied functions and the classical finite difference method. This new method has been applied to real groundwater data and the results have validated the method. This method is independent from existing popular methods such as the cubic spline method and the finite element method. The new digital-discrete method has considerable advantages for a large number of real data applications. This digital method also differs from other classical discrete methods that usually use triangulations. This method can potentially be used to obtain smooth functions such as polynomials through its derivatives f^(k) and the solution for partial differential equations such as harmonic and other important equations.

preprint2010arXiv

Applications of the Digital-Discrete Method in Smooth-Continuous Data Reconstruction

This paper presents some applications of using recently developed algorithms for smooth-continuous data reconstruction based on the digital-discrete method. The classical discrete method for data reconstruction is based on domain decomposition according to guiding (or sample) points. Then uses Splines (for polynomial) or finite elements method (for PDE) to fit the data. Our method is based on the gradually varied function that does not assume the property of the linearly separable among guiding points, i.e. no domain decomposition methods are needed. We also demonstrate the flexibility of the new method and the potential to solve variety of problems. The examples include some real data from water well logs and harmonic functions on closed 2D manifolds. This paper presented the results from six different algorithms. This method can be easily extended to higher multi-dimensions.

preprint2010arXiv

Applications of the Digital-Discrete Method in Smooth-Continuous Data Reconstruction

This paper presents some applications using recently developed algorithms for smooth-continuous data reconstruction based on the digital-discrete method. The classical discrete method for data reconstruction is based on domain decomposition according to guiding (or sample) points. Then the Spline method (for polynomial) or finite elements method (for PDE) is used to fit the data. Our method is based on the gradually varied function that does not assume the property of being linearly separable among guiding points, i.e. no domain decomposition methods are needed. We also demonstrate the flexibility of the new method and its potential to solve a variety of problems. The examples include some real data from water well logs and harmonic functions on closed 2D manifolds. This paper presents the results from six different algorithms. This method can be easily extended to higher multi-dimensions. We also include an advanced consideration related to the use of gradually varied mapping.

preprint2010arXiv

Digital-Discrete Surface Reconstruction: A true universal and nonlinear method

The most common problem in data reconstruction is to fit a function based on the observations of some sample (guiding) points. This paper provides a methodological point of view of digital-discrete surface reconstruction. We explain our method along with why it is a truly universal and nonlinear method unlike most popular methods, which are linear and restricted. This paper focuses on what the surface reconstruction problem is and why the digital-discrete method is important, necessary, and how it can be accomplished.

preprint2010arXiv

Gradual Variation Analysis for Groundwater Flow

Groundwater flow in Washington DC greatly influences the surface water quality in urban areas. The current methods of flow estimation, based on Darcy's Law and the groundwater flow equation, can be described by the diffusion equation (the transient flow) and the Laplace equation (the steady-state flow). The Laplace equation is a simplification of the diffusion equation under the condition that the aquifer has a recharging boundary. The practical way of calculation is to use numerical methods to solve these equations. The most popular system is called MODFLOW, which was developed by USGS. MODFLOW is based on the finite-difference method in rectangular Cartesian coordinates. MODFLOW can be viewed as a "quasi 3D" simulation since it only deals with the vertical average (no z-direction derivative). Flow calculations between the 2D horizontal layers use the concept of leakage. In this project, we have established a mathematical model based on gradually varied functions for groundwater data volume reconstruction. These functions do not rely on the rectangular Cartesian coordinate system. A gradually varied function can be defined in a general graph or network. Gradually varied functions are suitable for arbitrarily shaped aquifers. Two types of models are designed and implemented for real data processing: (1) the gradually varied model for individual (time) groundwater flow data, (2) the gradually varied model for sequential (time) groundwater flow data. In application, we also established a MySQL database to support the related research. The advantage of the gradually varied fitting and its related method does not need the strictly defined boundary condition as it is required in MODFLOW.

preprint2010arXiv

Smoothness and Smooth Extensions (I): Generalization of MWK Functions and Gradually Varied Functions

A mathematical smooth function means that the function has continuous derivatives to a certain degree C(k). We call it a k-smooth function or a smooth function if k can grow infinitively. Based on quantum physics, there is no such smooth surface in the real world on a very small scale. However, we do have a concept of smooth surfaces in practice since we always compare whether one surface is smoother than another one. This paper deals with the possible definitions of "natural" smoothness and their relationship to the original mathematical definition of smooth functions. The motivation of giving the definition of a smooth function is to study smooth extensions for practical applications. We observe this problem from two directions: From discrete to continuous, we suggest considering both micro smooth, the refinement of a smoothed function, and macro smooth, the best approximation using existing discrete space. (For two-dimensional or higher dimensional cases, we can use Hessian matrices.) From continuous to discrete, we suggest a new definition of natural smooth, it uses a scan from down scaling to up scaling to obtain the a ratio for sign changes by ignoring zero to represent the smoothness. For differentiable functions, mathematical smoothness does not mean a "good looking" smooth for a sampled set in discrete space. Finally, we discuss the Lipschitz continuity for defining the smoothness, which will be called discrete smoothness. This paper gives philosophical consideration of smoothness for practical problems, rather than a mathematical deduction or reduction, even though our inferences are based on solid mathematics.

preprint2010arXiv

XMM-Newton observation of the eclipsing binary Algol

We present an {\sl XMM-Newton} observation of the eclipsing binary Algol which contains an X-ray dark B8V primary and an X-ray bright K2IV secondary. The observation covered the optical secondary eclipse and captured an X-ray flare that was eclipsed by the B star. The EPIC and RGS spectra of Algol in its quiescent state are described by a two-temperature plasma model. The cool component has a temperature around 6.4$\times 10^{6}$ K while that of the hot component ranges from 2 to 4.0$\times 10^{7}$ K. Coronal abundances of C, N, O, Ne, Mg, Si and Fe were obtained for each component for both the quiescent and the flare phases, with generally upper limits for S and Ar, and C, N, and O for the hot component. F-tests show that the abundances need not to be different between the cool and the hot component and between the quiescent and the flare phase with the exception of Fe. Whereas the Fe abundance of the cool component remains constant at $\sim$0.14, the hot component shows an Fe abundance of $\sim$0.28, which increases to $\sim$0.44 during the flare. This increase is expected from the chromospheric evaporation model. The absorbing column density $N_H$ of the quiescent emission is 2.5$\times10^{20}$ cm$^{-2}$, while that of the flare-only emission is significantly lower and consistent with the column density of the interstellar medium. This observation substantiates earlier suggestions of the presence of X-ray absorbing material in the Algol system.

preprint2005arXiv

Polarization in Gamma-Ray Bursts Produced by Pinch Discharge

Large-voltage and high-temperature plasma columns produced by pinch discharge can generate gamma-ray flashes with energy spectra and spectral evolution consistent with that observed in gamma-ray bursts (GRBs). The inverse Compton scattering (ICS) during the discharge process can produce high linear polarization. The calculation indicates that the observed polarization depends on the angle between the observer's line-of-sight to the GRB and the direction of the pinch discharge, but only weakly depends on observed gamma-ray energy.

Li Chen

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182 published item(s)

Doping induced itinerant ferromagnetism and enhanced ferroelectricity in BL-InSe

Engineering Ideal 2D Type-II Nodal Line Semimetals via Stacking and Intercalation of van der Waals Layers

InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation

Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials

Normalized Solutions for Schrödinger-Bopp-Podolsky Systems with Critical Choquard-Type Nonlinearity on Bounded Domains

RecGPT-Mobile: On-Device Large Language Models for User Intent Understanding in Taobao Feed Recommendation

Thompson Sampling for Repeated Newsvendor

Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models

Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification

Joint Channel Estimation and Data Recovery for Millimeter Massive MIMO: Using Pilot to Capture Principal Components

LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving

Parabolic Anderson model in bounded domains of recurrent metric measure spaces

Personalized Prompt Learning for Explainable Recommendation

Reversible Attack based on Local Visual Adversarial Perturbation

Rigorous Derivation of the Degenerate Parabolic-Elliptic Keller-Segel System from a Moderately Interacting Stochastic Particle System. Part I Partial Differential Equation

Visual Point Cloud Forecasting enables Scalable Autonomous Driving

A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome

A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data

An Efficient Cervical Whole Slide Image Analysis Framework Based on Multi-scale Semantic and Location Deep Features

Besov class via heat semigroup on Dirichlet spaces III: BV functions and sub-Gaussian heat kernel estimates

C^0 estimates for Hessian quotient equations on HKT manifolds

Combined mean-field and semiclassical limits of large fermionic systems

Conversational Recommendation: A Grand AI Challenge

Decentralized Federated Learning: Balancing Communication and Computing Costs

Deep Learning with Label Noise: A Hierarchical Approach

Design of Coded Caching Schemes with Linear Subpacketizations Based on Injective Arc Coloring of Regular Digraphs

Design of Placement Delivery Arrays for Coded Caching with Small Subpacketizations and Flexible Memory Sizes

Discovery of extended structure around open cluster COIN-Gaia 13 based on Gaia EDR3

Enhanced Deep Animation Video Interpolation

ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention

Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Global weak solutions to the Vlasov-Poisson-Fokker-Planck-Navier-Stokes system

Impacts of Personal Characteristics on User Trust in Conversational Recommender Systems

Learning quantum dissipation by the neural ordinary differential equation

Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot

Maximum Flow and Minimum-Cost Flow in Almost-Linear Time

Minimum Coverage Instrumentation

New insights into the structure of open clusters in the Gaia era

New Open Cluster candidates Found in Galactic Disk Using Gaia DR2/EDR3 Data

Pascal algebra of matrices and Pascal map on jet bundles

PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark

Quasi-periodic oscillations of the X-ray burst from the magnetar SGR J1935+2154 and associated with the fast radio burst FRB 200428

Robust Landmark-based Stent Tracking in X-ray Fluoroscopy

ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning

Tail Quantile Estimation for Non-preemptive Priority Queues

Time-constrained Dynamic Mechanisms for College Admissions

BU-Trace: A Permissionless Mobile System for Privacy-Preserving Intelligent Contact Tracing

Dynamical reciprocity in interacting games: numerical results and mechanism analysis

Neumann problem for a class of fully nonlinear equations

Social hierarchy promotes the cooperation prevalence

Specht's invariant and localization of operator tuples

Speech enhancement with weakly labelled data from AudioSet

A Concise Proof of Discrete Jordan Curve Theorem

A curvature flow approach to Lp-Christoffel-Minkowski problem for p>1

A Discrete Proof of The General Jordan-Schoenflies Theorem

A note on second order Riesz transforms in 3-dimensional Lie groups

A Systematic Analysis of the Phase Lags Associated with the Type-C Quasi-periodic Oscillation in GRS 1915+105

Adversarial Example in Remote Sensing Image Recognition

Algorithms for Deforming and Contracting Simply Connected Discrete Closed Manifolds (III)

An Integrated Quadratic Reconstruction for Finite Volume Schemes to Scalar Conservation Laws in Multiple Dimensions

Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement

Besov class via heat semigroup on Dirichlet spaces I: Sobolev type inequalities

Besov class via heat semigroup on Dirichlet spaces II: BV functions and Gaussian heat kernel estimates

Blurry Video Frame Interpolation

Composite Signalling for DFRC: Dedicated Probing Signal or Not?

Construction of a series of new $ν=2/5$ fractional quantum Hall wave functions by conformal field theory

Content Adaptive and Error Propagation Aware Deep Video Compression

Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification

Discovery of oscillations above 200 keV in a black hole X-ray binary with Insight-HXMT

Existence of smooth even solutions to the dual Orlicz-Minkowski problem

Exploring open cluster properties with Gaia and LAMOST

Fast Dynamic Cuts, Distances and Effective Resistances via Vertex Sparsifiers

Fast Kernel k-means Clustering Using Incomplete Cholesky Factorization

FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides