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Min Li

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

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Information Theorymath.ITMachine Learningmath.APComputer Visionhep-exnlin.SIphysics.ins-detArtificial Intelligenceeess.SPmath.OCcond-mat.mtrl-scihep-latmath.NAmath.PRphysics.atom-phphysics.comp-phquant-phastro-ph.EPmath-phmath.MPMethodologynlin.PSNumerical AnalysisQuantitative Methodsastro-ph.COBiomoleculesComputationComputational Geometrycond-mat.str-elDatabasesDistributed, Parallel, and Cluster Computingeess.IVEmerging TechnologiesHardware Architecturemath.COmath.FAMolecular NetworksMultimediaNeural and Evolutionary Computingnucl-exOther Computer Sciencephysics.data-anphysics.opticsphysics.plasm-phRoboticsSocial and Information NetworksSoftware Engineering

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Published work

75 published item(s)

preprint2026arXiv

An efficient solver based on low-rank approximation and Neumann matrix series for unsteady diffusion-type partial differential equations with random coefficients

In this paper, we develop an efficient numerical solver for unsteady diffusion-type partial differential equations with random coefficients. A major computational challenge in such problems lies in repeatedly handling large-scale linear systems arising from spatial and temporal discretizations under uncertainty. To address this issue, we propose a novel generalized low-rank matrix approximation to represent the stochastic stiffness matrices, and approximate their inverses using the Neumann matrix series expansion. This approach transforms high-dimensional matrix inversion into a sequence of low-dimensional matrix multiplications. Therefore, the solver significantly reduces the computational cost and storage requirements while maintaining high numerical accuracy. The error analysis of the proposed solver is also provided. Finally, we apply the method to two classic uncertainty quantification problems: unsteady stochastic diffusion equations and the associated distributed optimal control problems. Numerical results demonstrate the feasibility and effectiveness of the proposed solver.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Correlated states in charge-transfer heterostructures based on rhombohedral multilayer graphene

Charge transfer is a common phenomenon in van der Waals heterostructures with proper work function mismatch, which enables electrostatic gating to control band alignment and interlayer charge distributions. This provides a tunable platform for studying coupled bilayer correlated electronic systems. Here, we theoretically investigate heterostructures of rhombohedral multilayer graphene (RMG) and an insulating substrate with gate-tunable band alignment. We first develop a self-consistent electrostatic theory for layer charge densities incorporating charge transfer, which reproduces the experimentally observed broadened and bent charge neutrality region. When the substrate's band edge has a much larger effective mass than RMG, its carriers can form a Wigner crystal at low densities. This creates a quantum superlattice that induces topological flat bands in the RMG layer, which may lead to Chern insulators driven by intralayer Coulomb interactions. Conversely, with comparable effective masses, we find an interlayer excitonic insulator state at charge neutrality stabilized by interlayer Coulomb coupling. Our work establishes these charge-transfer heterostructures as a rich platform for topological and excitonic correlated states, opening an avenue for ``charge-transferonics''.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis

LLM-integrated software, which embeds or interacts with large language models (LLMs) as functional components, exhibits probabilistic and context-dependent behaviors that fundamentally differ from those of traditional software. This shift introduces a new category of integration defects that arise not only from code errors but also from misaligned interactions among LLM-specific artifacts, including prompts, API calls, configurations, and model outputs. However, existing defect localization techniques are ineffective at identifying these LLM-specific integration defects because they fail to capture cross-layer dependencies across heterogeneous artifacts, cannot exploit incomplete or misleading error traces, and lack semantic reasoning capabilities for identifying root causes. To address these challenges, we propose LIDL, a multi-agent framework for defect localization in LLM-integrated software. LIDL (1) constructs a code knowledge graph enriched with LLM-aware annotations that represent interaction boundaries across source code, prompts, and configuration files, (2) fuses three complementary sources of error evidence inferred by LLMs to surface candidate defect locations, and (3) applies context-aware validation that uses counterfactual reasoning to distinguish true root causes from propagated symptoms. We evaluate LIDL on 146 real-world defect instances collected from 105 GitHub repositories and 16 agent-based systems. The results show that LIDL significantly outperforms five state-of-the-art baselines across all metrics, achieving a Top-3 accuracy of 0.64 and a MAP of 0.48, which represents a 64.1% improvement over the best-performing baseline. Notably, LIDL achieves these gains while reducing cost by 92.5%, demonstrating both high accuracy and cost efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Network Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is a cornerstone technology for 6G networks, offering unified support for high-rate communication and high-accuracy sensing. While existing literature extensively covers link-level designs, the transition toward large-scale deployment necessitates a fundamental understanding of network-level performance. This paper investigates a network ISAC model where a source node communicates with a destination via a relay network, while intermediate nodes concurrently perform cooperative sensing over specific spatial regions. We formulate a novel optimization framework that captures the interplay between multi-node routing and sensing coverage. For a one-dimensional path network, we provide an analytical characterization of the complete sensing-throughput region. Extending this to general network topologies, we establish that the sensing-throughput Pareto boundary is piecewise linear and provide physical interpretations for each segment. Our results reveal the fundamental trade-offs between sensing coverage and communication routing, offering key insights for the design of future 6G heterogeneous networks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PD-4DGS:Progressive Decomposition of 4D Gaussian Splatting for Bandwidth-Adaptive Dynamic Scene Streaming

4D Gaussian Splatting (4DGS) enables high-quality dynamic novel view synthesis, yet current models remain monolithic bitstreams that clients must download in full before any frame can be rendered, causing black-screen waits of tens to hundreds of seconds on mobile bandwidth and leaving 4DGS incompatible with modern adaptive-bitrate delivery. Progressive 3DGS compression alleviates this for static scenes, but it acts only on spatial anchors and cannot partition the temporal deformation networks that dominate dynamic-scene size. We present PD-4DGS, the first framework for progressive compression and on-demand transmission of 4DGS. Hierarchical Deformation Decomposition (HDD) externalises the coarse-to-fine motion hierarchy already latent in 4DGS into three independently transmittable layers -- a static scaffold, a global deformation, and a local refinement -- so that any prefix of the bitstream is already renderable, turning a single training run into a scalable, DASH/HLS-compatible bitstream. A Gaussian-entropy attribute rate-distortion loss together with a temporal mask consistency regulariser shrink the base layer while suppressing low-bitrate flicker; a capacity-weighted rollout schedule, gated online by a learnt activation rate rho, then prevents deformation-network under-training without any per-scene hyperparameter. On the Dycheck iPhone benchmark, PD-4DGS cuts the streamed bitstream by >60% at matched rendering fidelity and reduces first-frame latency from 73--930 s to ~1.7 s on a 2 Mbps link, uniquely enabling true on-demand progressive streaming for 4DGS.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

StellarF: A Physics-Informed LoRA Framework for Stellar Flare Forecasting with Historical & Statistical Data

Stellar flare forecasting represents a critical frontier in astrophysics, offering profound insights into stellar activity mechanisms and exoplanetary habitability assessments. Yet the inherent unpredictability of flare activity, rooted in stellar diversity and evolutionary stages, underpins the field's core challenges: (1) sparse, incomplete, noisy lightcurve data from traditional observations; (2) ineffective multi-scale flare evolution capture via single representations; (3) poor physical interpretability in data-driven models lacking physics-informed priors. To address these challenges, we propose StellarF, a physics-informed framework synergizing general Al with astrophysical domain knowledge via three core components: a unified preprocessing pipeline for lightcurve refinement (missing-value imputation, temporal patch partitioning, adaptive sample filtering); a Low-Rank Adaptation (LoRA)-finetuned large language model (LLM) backbone enhanced by first-order difference augmentation, flare statistical information, and flare historical record modules for multimodal fusion instead of only simple representations; and a novel physics-informed loss embedding a minimum rising rate prior, appended to the cross-entropy loss, to align with flare physics. Extensive experiments on Kepler and TESS datasets show StellarF achieves state-of-the-art performance across key metrics, setting new benchmarks for flare forecasting. This work bridges general AI with astrophysics, offering a practical, physically interpretable paradigm for transient event forecasting in time-domain astronomy.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models

Incoherent k-space undersampling and deep learning-based reconstruction methods have shown great success in accelerating MRI. However, the performance of most previous methods will degrade dramatically under high acceleration factors, e.g., 8$\times$ or higher. Recently, denoising diffusion models (DM) have demonstrated promising results in solving this issue; however, one major drawback of the DM methods is the long inference time due to a dramatic number of iterative reverse posterior sampling steps. In this work, a Single Step Diffusion Model-based reconstruction framework, namely SSDM-MRI, is proposed for restoring MRI images from highly undersampled k-space. The proposed method achieves one-step reconstruction by first training a conditional DM and then iteratively distilling this model four times using an iterative selective distillation algorithm, which works synergistically with a shortcut reverse sampling strategy for model inference. Comprehensive experiments were carried out on both publicly available fastMRI brain and knee images, as well as an in-house multi-echo GRE (QSM) subject. Overall, the results showed that SSDM-MRI outperformed other methods in terms of numerical metrics (e.g., PSNR and SSIM), error maps, image fine details, and latent susceptibility information hidden in MRI phase images. In addition, the reconstruction time for a 320$\times$320 brain slice of SSDM-MRI is only 0.45 second, which is only comparable to that of a simple U-net, making it a highly effective solution for MRI reconstruction tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

DeepSAT: An EDA-Driven Learning Framework for SAT

We present DeepSAT, a novel end-to-end learning framework for the Boolean satisfiability (SAT) problem. Unlike existing solutions trained on random SAT instances with relatively weak supervision, we propose applying the knowledge of the well-developed electronic design automation (EDA) field for SAT solving. Specifically, we first resort to logic synthesis algorithms to pre-process SAT instances into optimized and-inverter graphs (AIGs). By doing so, the distribution diversity among various SAT instances can be dramatically reduced, which facilitates improving the generalization capability of the learned model. Next, we regard the distribution of SAT solutions being a product of conditional Bernoulli distributions. Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling. To effectively train the generative model, with the help of logic simulation tools, we obtain the probabilities of nodes in the AIG being logic `1' as rich supervision. We conduct comprehensive experiments on various SAT problems. Our results show that, DeepSAT achieves significant accuracy improvements over state-of-the-art learning-based SAT solutions, especially when generalized to SAT instances that are relatively large or with diverse distributions.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Imaging of muon track in CsI(Tl) crystal with single photon sensitive camera

As a novel approach on visual photon imaging by a single photon sensitive camera and PMTs, this work is trying to measure and identify muon tracks from the 2-D images of CsI(Tl) crystal (scintillator detectors). It is possible that muon tracks can be seen directly with a good signal-to-noise ratio neither with further amplification nor external light, which provides an evolution method for particle measurement in the photon-starved regime of scintillation detectors. The setup of the crystal and camera testing system and the identification algorithm of muon track will be discussed in detail including the system calibration, identification model, signal-to-noise ratio, muon track confirmation, and an expectation on further improvements and applications.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation

To assist robots in teleoperation tasks, haptic rendering which allows human operators access a virtual touch feeling has been developed in recent years. Most previous haptic rendering methods strongly rely on data collected by tactile sensors. However, tactile data is not widely available for robots due to their limited reachable space and the restrictions of tactile sensors. To eliminate the need for tactile data, in this paper we propose a novel method named as Vis2Hap to generate haptic rendering from visual inputs that can be obtained from a distance without physical interaction. We take the surface texture of objects as key cues to be conveyed to the human operator. To this end, a generative model is designed to simulate the roughness and slipperiness of the object's surface. To embed haptic cues in Vis2Hap, we use height maps from tactile sensors and spectrograms from friction coefficients as the intermediate outputs of the generative model. Once Vis2Hap is trained, it can be used to generate height maps and spectrograms of new surface textures, from which a friction image can be obtained and displayed on a haptic display. The user study demonstrates that our proposed Vis2Hap method enables users to access a realistic haptic feeling similar to that of physical objects. The proposed vision-based haptic rendering has the potential to enhance human operators' perception of the remote environment and facilitate robotic manipulation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A self-contained and self-explanatory DNA storage system

Current research on DNA storage usually focuses on the improvement of storage density by developing effective encoding and decoding schemes while lacking the consideration on the uncertainty in ultra-long-term data storage and retention. Consequently, the current DNA storage systems are often not self-contained, implying that they have to resort to external tools for the restoration of the stored DNA data. This may result in high risks in data loss since the required tools might not be available due to the high uncertainty in far future. To address this issue, we propose in this paper a self-contained DNA storage system that can bring self-explanatory to its stored data without relying on any external tool. To this end, we design a specific DNA file format whereby a separate storage scheme is developed to reduce the data redundancy while an effective indexing is designed for random read operations to the stored data file. We verified through experimental data that the proposed self-contained and self-explanatory method can not only get rid of the reliance on external tools for data restoration but also minimise the data redundancy brought about when the amount of data to be stored reaches a certain scale.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Afterpulse measurement of JUNO 20-inch PMTs

In this article we present the large photo-multiplier tube (PMT) afterpulse measurement results of Jiangmen Underground Neutrino Observatory (JUNO) experiment. Totally 11 dynode-PMTs (R12860) from Hamamatsu company and 150 micro-channel plate PMTs (MCP-PMTs, GDB-6201) from NNVT company were tested, an afterpulse model is built according to the afterpulse time distribution and probability of occurrence for these two types of PMTs. The average ratio between the total afterpulse charge with the delay between 0.5 $μ$ s and 20 $μ$ s to the primary pulse charge is 5.6%(13.2%) for the tested MCP-PMTs (dynode-PMTs). JUNO experiment will deploy 20,012 20-inch PMTs, and this study will benefit the detector simulation, event reconstruction and data analysis of JUNO experiment.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Boosting Distributed Training Performance of the Unpadded BERT Model

Pre-training models are an important tool in Natural Language Processing (NLP), while the BERT model is a classic pre-training model whose structure has been widely adopted by followers. It was even chosen as the reference model for the MLPerf training benchmark. The distributed training performance optimization of BERT models plays an important role in accelerating the solutions of most NLP tasks. BERT model often uses padding tensors as its inputs, leading to excessive redundant computations. Thus, removing these redundant computations is essential to improve the distributed training performance. This paper designs a new approach to train BERT models with variable-length inputs efficiently. Firstly, we propose a general structure for the variable-length BERT models, and accelerate the encoder layer via our grouped multi-stream FMHA (Fused Multi-Head Attention) method. Secondly, through data exchange, we address the unbalanced workload problem caused by the variable-length inputs, which overlaps highly with the training process. Finally, we optimize the overall performance of the BERT model, such as kernel fusion, and operator optimization. Our experimental results show that our highly optimized BERT model achieves state-of-the-art throughput and ranks first in MLPerf Training v2.0 within the same GPU configuration. The optimizations in this paper can be applied to more BERT-like models in our future works.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DeepGate: Learning Neural Representations of Logic Gates

Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic. Most solutions apply well-developed DL models to solve specific EDA problems. While demonstrating promising results, they require careful model tuning for every problem. The fundamental question on "How to obtain a general and effective neural representation of circuits?" has not been answered yet. In this work, we take the first step towards solving this problem. We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate. Specifically, we propose transforming circuits into unified and-inverter graph format for learning and using signal probabilities as the supervision task in DeepGate. We then introduce a novel graph neural network that uses strong inductive biases in practical circuits as learning priors for signal probability prediction. Our experimental results show the efficacy and generalization capability of DeepGate.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DeepTPI: Test Point Insertion with Deep Reinforcement Learning

Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this paper, we propose a novel TPI approach based on deep reinforcement learning (DRL), named DeepTPI. Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement. Specifically, we model circuits as directed graphs and design a graph-based value network to estimate the action values for inserting different test points. The policy of the DRL agent is defined as selecting the action with the maximum value. Moreover, we apply the general node embeddings from a pre-trained model to enhance node features, and propose a dedicated testability-aware attention mechanism for the value network. Experimental results on circuits with various scales show that DeepTPI significantly improves test coverage compared to the commercial DFT tool. The code of this work is available at https://github.com/cure-lab/DeepTPI.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FKreg: A MATLAB toolbox for fast Multivariate Kernel Regression

Kernel smooth is the most fundamental technique for data density and regression estimation. However, time-consuming is the biggest obstacle for the application that the direct evaluation of kernel smooth for $N$ samples needs ${O}\left( {{N}^{2}} \right)$ operations. People have developed fast smooth algorithms using the idea of binning with FFT. Unfortunately, the accuracy is not controllable, and the implementation for multivariable and its bandwidth selection for the fast method is not available. Hence, we introduce a new MATLAB toolbox for fast multivariate kernel regression with the idea of non-uniform FFT (NUFFT), which implemented the algorithm for $M$ gridding points with ${O}\left( N+M\log M \right)$ complexity and accuracy controllability. The bandwidth selection problem utilizes the Fast Monte-Carlo algorithm to estimate the degree of freedom (DF), saving enormous cross-validation time even better when data share the same grid space for multiple regression. Up to now, this is the first toolbox for fast-binning high-dimensional kernel regression. Moreover, the estimation for local polynomial regression, the conditional variance for the heteroscedastic model, and the complex-valued datasets are also implemented in this toolbox. The performance is demonstrated with simulations and an application on the quantitive EEG.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition

Graph convolutional networks (GCNs) have emerged as dominant methods for skeleton-based action recognition. However, they still suffer from two problems, namely, neighborhood constraints and entangled spatiotemporal feature representations. Most studies have focused on improving the design of graph topology to solve the first problem but they have yet to fully explore the latter. In this work, we design a disentangled spatiotemporal transformer (DSTT) block to overcome the above limitations of GCNs in three steps: (i) feature disentanglement for spatiotemporal decomposition;(ii) global spatiotemporal attention for capturing correlations in the global context; and (iii) local information enhancement for utilizing more local information. Thereon, we propose a novel architecture, named Hierarchical Graph Convolutional skeleton Transformer (HGCT), to employ the complementary advantages of GCN (i.e., local topology, temporal dynamics and hierarchy) and Transformer (i.e., global context and dynamic attention). HGCT is lightweight and computationally efficient. Quantitative analysis demonstrates the superiority and good interpretability of HGCT.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ill_posedness for a two_component Novikov system in Besov space

In this paper, we consider the Cauchy problem for a two-component Novikov system on the line. By specially constructed initial data $(ρ_0, u_0)$ in $B_{p, \infty}^{s-1}(\mathbb{R})\times B_{p, \infty}^s(\mathbb{R})$ with $s>\max\{2+\frac{1}{p}, \frac{5}{2}\}$ and $1\leq p \leq \infty$, we show that any energy bounded solution starting from $(ρ_0, u_0)$ does not converge back to $(ρ_0, u_0)$ in the metric of $B_{p, \infty}^{s-1}(\mathbb{R})\times B_{p, \infty}^s(\mathbb{R})$ as time goes to zero, thus results in discontinuity of the data-to-solution map and ill-posedness.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ill-posedness for the two component Degasperis-Procesi equation in critical Besov space

In this paper, we study the Cauchy problem for the two component Degasperis-Procesi equation in critical Besov space $B^1_{\infty,1}(\mathbb R)$. By presenting a new construction of initial data, we proved the norm inflation of the corresponding solutions in $B^1_{\infty,1}(\mathbb R)$ and hence ill-posedness. This is quite different from the local well-posedness result for the Degasperis-Procesi equation in critical Besov space $B^1_{\infty,1}(\mathbb R)$ due to the coupled structure of density function.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Imaging of CsI(Tl) crystal event and double-slit Young's interference by a single photon sensitive camera

We will discuss an imaging measurement with a single photon sensitive and low noise camera aiming to a new paradigm in the optical readout of scintillation detectors. The features of the single photon sensitive camera will be characterized and demonstrated with a measurement on double-slit Young's interference in single photon mode. An imaging test on CsI(Tl) crystal and alpha source will be performed further for preliminary measurements on the noise level and sensitivity of the system with a 1/2", f/1.4 lens, which reaches an sensitivity on light intensity around 1/10 of the 3-inch PMT and shows a potential to realize an imaging of single alpha event. An application proposal to scintillation detectors will be further discussed, where it is usually assumed that the imaging is not possible in such a photon-starved and large-emittance regime.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Information-Theoretic Limits of Integrated Sensing and Communication with Correlated Sensing and Channel States for Vehicular Networks

In connected vehicular networks, it is vital to have vehicular nodes that are capable of sensing about surrounding environments and exchanging messages with each other for automating and coordinating purpose. Towards this end, integrated sensing and communication (ISAC), combining both sensing and communication systems to jointly utilize their resources and to pursue mutual benefits, emerges as a new cost-effective solution. In ISAC, the hardware and spectrum co-sharing leads to a fundamental tradeoff between sensing and communication performance, which is not well understood except for very simple cases with the same sensing and channel states, and perfect channel state information at the receiver (CSIR). In this paper, a general point-to-point ISAC model is proposed to account for the scenarios that the sensing state is different from but correlated with the channel state, and the CSIR is not necessarily perfect. For the model considered, the optimal tradeoff is characterized by a capacity-distortion function that quantifies the best communication rate for a given sensing distortion constraint requirement. An iterative algorithm is proposed to compute such tradeoff, and a few non-trivial examples are constructed to demonstrate the benefits of ISAC as compared to the separation-based approach.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Linear-Quadratic Large-Population Problem with Partial Information: Hamiltonian Approach and Riccati Approach

This paper studies a class of partial information linear-quadratic mean-field game problems. A general stochastic large-population system is considered, where the diffusion term of the dynamic of each agent can depend on the state and control. We study both the control constrained case and unconstrained case. In control constrained case, by using Hamiltonian approach and convex analysis, the explicit decentralized strategies can be obtained through projection operator. The corresponding Hamiltonian type consistency condition system is derived, which turns out to be a nonlinear mean-field forward-backward stochastic differential equation with projection operator. The well-posedness of such kind of equations is proved by using discounting method. Moreover, the corresponding $\varepsilon$-Nash equilibrium property is verified. In control unconstrained case, the decentralized strategies can be further represented explicitly as the feedback of filtered state through Riccati approach. The existence and uniqueness of a solution to a new Riccati type consistency condition system is also discussed. As an application, a general inter-bank borrowing and lending problem is studied to illustrate that the effect of partial information cannot be ignored.

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preprint2022arXiv

Linear-Quadratic Mean Field Games of Controls with Non-Monotone Data

In this paper, we study a class of linear-quadratic (LQ) mean field games of controls with common noises and their corresponding $N$-player games. The theory of mean field game of controls considers a class of mean field games where the interaction is via the joint law of both the state and control. By the stochastic maximum principle, we first analyze the limiting behavior of the representative player and obtain his/her optimal control in a feedback form with the given distributional flow of the population and its control. The mean field equilibrium is determined by the Nash certainty equivalence (NCE) system. Thanks to the common noise, we do not require any monotonicity conditions for the solvability of the NCE system. We also study the master equation arising from LQ mean field games of controls, which is a finite-dimensional second-order parabolic equation. It can be shown that the master equation admits a unique classical solution over an arbitrary time horizon without any monotonicity conditions. Beyond that, we can solve the $N$-player games directly by further assuming the non-degeneracy of the idiosyncratic noises. As byproducts, we prove the quantitative convergence results from the $N$-player game to the mean field game and the propagation of chaos property for the related optimal trajectories.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Mass Testing and Characterization of 20-inch PMTs for JUNO

Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3 % at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5,000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK).

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Maximum Temperatures in Evolving Protoplanetary Discs and Composition of Planetary Building Blocks

The maximum temperature and radial temperature profile in a protoplanetary disc are important for the condensation of different elements in the disc. We simulate the evolution of a set of protoplanetary discs from the collapse of their progenitor molecular cloud cores as well as the dust decoupling within the discs as they evolve. We show how the initial properties of the cloud cores affect the thermal history of the protoplanetary discs using a simple viscous disc model. Our results show that the maximum midplane temperature in the disc occurs within 0.5 AU. It increases with the initial cloud temperature and decreases with its angular velocity and the viscosity of the disc. From the observed properties of the molecular cloud cores we find the median value of the maximum temperature is around 1250 K, with roughly 90% of them being less than 1500 K - a value that is lower than the 50% condensation temperatures of most refractory elements. Therefore, only cloud cores with high initial temperatures or low angular velocities and/or low viscosities within the planet-forming discs will result in refractory-rich planetesimals. To reproduce the volatile depletion pattern of CM, CO, and CV chondrites and the terrestrial planets in Solar system, one must either have rare properties of the initial molecular cloud cores like high core temperature, or other sources of energy to heat the disc to sufficiently high temperatures. Alternatively, the volatile depletion observed in these chondrites may be inherited from the progenitor molecular cloud.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation

The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. Here, we propose PGMG, a pharmacophore-guided deep learning approach for bioactivate molecule generation. Through the guidance of pharmacophore, PGMG provides a flexible strategy to generate bioactive molecules with structural diversity in various scenarios using a trained variational autoencoder. We show that PGMG can generate molecules matching given pharmacophore models while maintaining a high level of validity, uniqueness, and novelty. In the case studies, we demonstrate the application of PGMG to generate bioactive molecules in ligand-based and structure-based drug de novo design, as well as in lead optimization scenarios. Overall, the flexibility and effectiveness of PGMG make it a useful tool for accelerating the drug discovery process.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Rethinking the Misalignment Problem in Dense Object Detection

Object detection aims to localize and classify the objects in a given image, and these two tasks are sensitive to different object regions. Therefore, some locations predict high-quality bounding boxes but low classification scores, and some locations are quite the opposite. A misalignment exists between the two tasks, and their features are spatially entangled. In order to solve the misalignment problem, we propose a plug-in Spatial-disentangled and Task-aligned operator (SALT). By predicting two task-aware point sets that are located in each task's sensitive regions, SALT can reassign features from those regions and align them to the corresponding anchor point. Therefore, features for the two tasks are spatially aligned and disentangled. To minimize the difference between the two regression stages, we propose a Self-distillation regression (SDR) loss that can transfer knowledge from the refined regression results to the coarse regression results. On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results. Extensive experiments on the MS-COCO dataset show that our proposed methods can consistently boost different state-of-the-art dense detectors by $\sim$2 AP. Notably, SALT-Net with Res2Net-101-DCN backbone achieves 53.8 AP on the MS-COCO test-dev.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning

Human leukocyte antigen (HLA) is an important molecule family in the field of human immunity, which recognizes foreign threats and triggers immune responses by presenting peptides to T cells. In recent years, the synthesis of tumor vaccines to induce specific immune responses has become the forefront of cancer treatment. Computationally modeling the binding patterns between peptide and HLA can greatly accelerate the development of tumor vaccines. However, most of the prediction methods performance is very limited and they cannot fully take advantage of the analysis of existing biological knowledge as the basis of modeling. In this paper, we propose TripHLApan, a novel pan-specific prediction model, for HLA molecular peptide binding prediction. TripHLApan exhibits powerful prediction ability by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. The comprehensive evaluations demonstrate the effectiveness of TripHLApan in predicting HLA-I and HLA-II peptide binding in different test environments. The predictive power of HLA-I is further demonstrated in the latest data set. In addition, we show that TripHLApan has strong binding reconstitution ability in the samples of a melanoma patient. In conclusion, TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines.

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preprint2021arXiv

BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions

Motivation: Exploring drug-protein interactions (DPIs) work as a pivotal step in drug discovery. The fast expansion of available biological data enables computational methods effectively assist in experimental methods. Among them, deep learning methods extract features only from basic characteristics, such as protein sequences, molecule structures. Others achieve significant improvement by learning from not only sequences/molecules but the protein-protein and drug-drug associations (PPAs and DDAs). The PPAs and DDAs are generally obtained by using computational methods. However, existing computational methods have some limitations, resulting in low-quality PPAs and DDAs that hamper the prediction performance. Therefore, we hope to develop a novel supervised learning method to learn the PPAs and DDAs effectively and thereby improve the prediction performance of the specific task of DPI. Results: In this research, we propose a novel deep learning framework, namely BridgeDPI. BridgeDPI introduces a class of nodes named hyper-nodes, which bridge different proteins/drugs to work as PPAs and DDAs. The hyper-nodes can be supervised learned for the specific task of DPI since the whole process is an end-to-end learning. Consequently, such a model would improve prediction performance of DPI. In three real-world datasets, we further demonstrate that BridgeDPI outperforms state-of-the-art methods. Moreover, ablation studies verify the effectiveness of the hyper-nodes. Last, in an independent verification, BridgeDPI explores the candidate bindings among COVID-19's proteins and various antiviral drugs. And the predictive results accord with the statement of the World Health Organization and Food and Drug Administration, showing the validity and reliability of BridgeDPI.

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preprint2021arXiv

JUNO Physics and Detector

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton LS detector at 700-m underground. An excellent energy resolution and a large fiducial volume offer exciting opportunities for addressing many important topics in neutrino and astro-particle physics. With 6 years of data, the neutrino mass ordering can be determined at 3-4 sigma and three oscillation parameters can be measured to a precision of 0.6% or better by detecting reactor antineutrinos. With 10 years of data, DSNB could be observed at 3-sigma; a lower limit of the proton lifetime of 8.34e33 years (90% C.L.) can be set by searching for p->nu_bar K^+; detection of solar neutrinos would shed new light on the solar metallicity problem and examine the vacuum-matter transition region. A core-collapse supernova at 10 kpc would lead to ~5000 IBD and ~2000 (300) all-flavor neutrino-proton (electron) scattering events. Geo-neutrinos can be detected with a rate of ~400 events/year. We also summarize the final design of the JUNO detector and the key R&D achievements. All 20-inch PMTs have been tested. The average photon detection efficiency is 28.9% for the 15,000 MCP PMTs and 28.1% for the 5,000 dynode PMTs, higher than the JUNO requirement of 27%. Together with the >20 m attenuation length of LS, we expect a yield of 1345 p.e. per MeV and an effective energy resolution of 3.02%/\sqrt{E (MeV)}$ in simulations. The underwater electronics is designed to have a loss rate <0.5% in 6 years. With degassing membranes and a micro-bubble system, the radon concentration in the 35-kton water pool could be lowered to <10 mBq/m^3. Acrylic panels of radiopurity <0.5 ppt U/Th are produced. The 20-kton LS will be purified onsite. Singles in the fiducial volume can be controlled to ~10 Hz. The JUNO experiment also features a double calorimeter system with 25,600 3-inch PMTs, a LS testing facility OSIRIS, and a near detector TAO.

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preprint2020arXiv

Dust Condensation in Evolving Discs and the Composition of Planetary Building Blocks

Partial condensation of dust from the Solar nebula is likely responsible for the diverse chemical compositions of chondrites and rocky planets/planetesimals in the inner Solar system. We present a forward physical-chemical model of a protoplanetary disc to predict the chemical compositions of planetary building blocks that may form from such a disc. Our model includes the physical evolution of the disc and the condensation, partial advection, and decoupling of the dust within it. The chemical composition of the condensate changes with time and radius. We compare the results of two dust condensation models: one where an element condenses when the midplane temperature in the disc is lower than the 50\% condensation temperature ($\rm T_{50}$) of that element and the other where the condensation of the dust is calculated by a Gibbs free energy minimization technique assuming chemical equilibrium at local disc temperature and pressure. The results of two models are generally consistent with some systematic differences of $\sim 10$\% depending upon the radial distance and an element's condensation temperature. Both models predict compositions similar to CM, CO, and CV chondrites provided that the decoupling timescale of the dust is on the order of the evolution timescale of the disc or longer. If the decoupling timescale is too short, the composition deviates significantly from the measured values. These models may contribute to our understanding of the chemical compositions of chondrites, and ultimately the terrestrial planets in the solar system, and may constrain the potential chemical compositions of rocky exoplanets.

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preprint2020arXiv

General stationary solutions of the nonlocal nonlinear Schrödinger equation and their relevance to the PT-symmetric system

With the stationary solution assumption, we establish the connection between the nonlocal nonlinear Schrödinger (NNLS) equation and an elliptic equation. Then, we obtain the general stationary solutions and discuss the relevance of their smoothness and boundedness to some integral constants. Those solutions, which cover the known results in the literature, include the unbounded Jacobi elliptic-function and hyperbolic-function solutions, the bounded sn-, cn- and dn-function solutions, as well as the hyperbolic soliton solutions. By the imaginary translation transformation of the NNLS equation, we also derive the complex-amplitude stationary solutions, in which all the bounded cases obey either the \PT- or anti-\PT-symmetric relation. In particular, the complex tanh-function solution can exhibit no spatial localization in addition to the dark and anti-dark soliton profiles, which is sharp contrast with the common dark soliton. Considering the physical relevance to \PT-symmetric system, we show that the complex-amplitude stationary solutions can yield a wide class of complex and time-independent \PT-symmetric potentials, and the symmetry breaking does not occur in the \PT-symmetric linear system with the associated potentials.

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preprint2020arXiv

Joint Beam Training and Data Transmission Design for Covert Millimeter-Wave Communication

Covert communication prevents legitimate transmission from being detected by a warden while maintaining certain covert rate at the intended user. Prior works have considered the design of covert communication over conventional low-frequency bands, but few works so far have explored the higher-frequency millimeter-wave (mmWave) spectrum. The directional nature of mmWave communication makes it attractive for covert transmission. However, how to establish such directional link in a covert manner in the first place remains as a significant challenge. In this paper, we consider a covert mmWave communication system, where legitimate parties Alice and Bob adopt beam training approach for directional link establishment. Accounting for the training overhead, we develop a new design framework that jointly optimizes beam training duration, training power and data transmission power to maximize the effective throughput of Alice-Bob link while ensuring the covertness constraint at warden Willie is met. We further propose a dual-decomposition successive convex approximation algorithm to solve the problem efficiently. Numerical studies demonstrate interesting tradeoff among the key design parameters considered and also the necessity of joint design of beam training and data transmission for covert mmWave communication.

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preprint2020arXiv

Millimeter-Wave Beam Search with Iterative Deactivation and Beam Shifting

Millimeter Wave (mmWave) communications rely on highly directional beams to combat severe propagation loss. In this paper, an adaptive beam search algorithm based on spatial scanning, called Iterative Deactivation and Beam Shifting (IDBS), is proposed for mmWave beam alignment. IDBS does not require advance information such as the Signal-to-Noise Ratio (SNR) and channel statistics, and matches the training overhead to the unknown SNR to achieve satisfactory performance. The algorithm works by gradually deactivating beams using a Bayesian probability criterion based on a uniform improper prior, where beam deactivation can be implemented with low-complexity operations that require computing a low-degree polynomial or a search through a look-up table. Numerical results confirm that IDBS adapts to different propagation scenarios such as line-of-sight and non-line-of-sight and to different SNRs. It can achieve better tradeoffs between training overhead and beam alignment accuracy than existing non-adaptive algorithms that have fixed training overheads.

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preprint2020arXiv

Multi-View Photometric Stereo: A Robust Solution and Benchmark Dataset for Spatially Varying Isotropic Materials

We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo (MVPS) technique that works for general isotropic materials. Our algorithm is suitable for perspective cameras and nearby point light sources. Our data capture setup is simple, which consists of only a digital camera, some LED lights, and an optional automatic turntable. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. The spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. In experiments, we demonstrate our algorithm with two different setups: a studio setup for highest precision and a desktop setup for best usability. According to our experiments, under the studio setting, the captured shapes are accurate to 0.5 millimeters and the captured reflectance has a relative root-mean-square error (RMSE) of 9%. We also quantitatively evaluate state-of-the-art MVPS on a newly collected benchmark dataset, which is publicly available for inspiring future research.

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preprint2020arXiv

Numerical methods for stochastic Volterra integral equations with weakly singular kernels

In this paper, we first establish the existence, uniqueness and Hölder continuity of the solution to stochastic Volterra integral equations with weakly singular kernels. Then, we propose a $θ$-Euler-Maruyama scheme and a Milstein scheme to solve the equations numerically and we obtain the strong rates of convergence for both schemes in $L^{p}$ norm for any $p\geq 1$. For the $θ$-Euler-Maruyama scheme the rate is $\min\{1-α,\frac{1}{2}-β\}~ % (0<α<1, 0< β<\frac{1}{2})$ and for the Milstein scheme the rate is $\min\{1-α,1-2β\}$ when $α\neq \frac 12$, where $(0<α<1, 0< β<\frac{1}{2})$. These results on the rates of convergence are significantly different from that of the similar schemes for the stochastic Volterra integral equations with regular kernels. The difficulty to obtain our results is the lack of Itô formula for the equations. To get around of this difficulty we use instead the Taylor formula and then carry a sophisticated analysis on the equation the solution satisfies.

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preprint2020arXiv

Robust Adaptive Beam Tracking for Mobile Millimetre Wave Communications

Millimetre wave (mmWave) beam tracking is a challenging task because tracking algorithms are required to provide consistent high accuracy with low probability of loss of track and minimal overhead. To meet these requirements, we propose in this paper a new analog beam tracking framework namely Adaptive Tracking with Stochastic Control (ATSC). Under this framework, beam direction updates are made using a novel mechanism based on measurements taken from only two beam directions perturbed from the current data beam. To achieve high tracking accuracy and reliability, we provide a systematic approach to jointly optimise the algorithm parameters. The complete framework includes a method for adapting the tracking rate together with a criterion for realignment (perceived loss of track). ATSC adapts the amount of tracking overhead that matches well to the mobility level, without incurring frequent loss of track, as verified by an extensive set of experiments under both representative statistical channel models as well as realistic urban scenarios simulated by ray-tracing software. In particular, numerical results show that ATSC can track dominant channel directions with high accuracy for vehicles moving at 72 km/hour in complicated urban scenarios, with an overhead of less than 1\%.

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preprint2020arXiv

The stabilizing index and cyclic index of coalescence and Cartesian product of uniform hypergraphs

Let $G$ be connected uniform hypergraph and let $\mathcal{A}(G)$ be the adjacency tensor of $G$. The stabilizing index of $G$ is the number of eigenvectors of $\mathcal{A}(G)$ associated with the spectral radius, and the cyclic index of $G$ is the number of eigenvalues of $\mathcal{A}(G)$ with modulus equal to the spectral radius. Let $G_1 \odot G_2$ and $G_1 \Box G_2$ be the coalescence and Cartesian product of connected $m$-uniform hypergraphs $G_1$ and $G_2$ respectively. In this paper, we give explicit formulas for the the stabilizing indices and cyclic indices of $G_1 \odot G_2$ and $G_1 \Box G_2$ in terms of those of $G_1$ and $G_2$ or the invariant divisors of their incidence matrices over $\mathbb{Z}_m$, respectively.

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preprint2020arXiv

UAV-Enabled Confidential Data Collection in Wireless Networks

This work, for the first time, considers confidential data collection in the context of unmanned aerial vehicle (UAV) wireless networks, where the scheduled ground sensor node (SN) intends to transmit confidential information to the UAV without being intercepted by other unscheduled ground SNs. Specifically, a full-duplex (FD) UAV collects data from each scheduled SN on the ground and generates artificial noise (AN) to prevent the scheduled SN's confidential information from being wiretapped by other unscheduled SNs. We first derive the reliability outage probability (ROP) and secrecy outage probability (SOP) of a considered fixed-rate transmission, based on which we formulate an optimization problem that maximizes the minimum average secrecy rate (ASR) subject to some specific constraints. We then transform the formulated optimization problem into a convex problem with the aid of first-order restrictive approximation technique and penalty method. The resultant problem is a generalized nonlinear convex programming (GNCP) and solving it directly still leads to a high complexity, which motivates us to further approximate this problem as a second-order cone program (SOCP) in order to reduce the computational complexity. Finally, we develop an iteration procedure based on penalty successive convex approximation (P-SCA) algorithm to pursue the solution to the formulated optimization problem. Our examination shows that the developed joint design achieves a significant performance gain compared to a benchmark scheme.

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preprint2016arXiv

Breather transition dynamics, Peregrine combs/walls and modulation instability in a variable-coefficient nonlinear Schrödinger equation with higher-order effects

We study a variable-coefficient nonlinear Schrödinger (vc-NLS) equation with higher-order effects. We show that the breather solution can be converted into four types of nonlinear waves on constant backgrounds including the multi-peak solitons, antidark soliton, periodic wave and W-shaped soliton. The transition condition requiring the group velocity dispersion (GVD) and third-order dispersion (TOD) to scale linearly is obtained analytically. We display several kinds of elastic interactions between the transformed nonlinear waves. We discuss the dispersion management of multi-peak soliton, which indicates that the GVD coefficient controls the number of peaks of the wave while the TOD coefficient has compression effect. The gain or loss has influence on the amplitudes of the multi-peak soliton. We further derive the breather multiple births by using multiple compression points of Akhmediev breathers in optical fiber systems with periodic dispersion. The number of ABs depends on the amplitude of the modulation but not on its wavelength, which affects their separation distance. In the limiting case, the breather multiple births reduce to the Peregrine combs. We discuss the effects of TOD coefficient on the spatiotemporal characteristics of Peregrine combs. When the amplitude of the modulation is equal to 1, the Peregrine comb is converted into a Peregrine wall that can be seen as intermediate state between rogue wave and W-shaped soliton. We finally find that the modulational stability regions with zero growth rate coincide with the transition condition using rogue wave eigenvalues. Our results could be useful for the experimental control and manipulation of the formation of generalized Peregrine rogue waves in diverse physical systems modeled by vc-NLS equation with higher-order effects.

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preprint2016arXiv

Darboux transformation and analytic solutions of the discrete \PT-symmetric nonlocal nonlinear Schrodinger equation

In this letter, for the discrete parity-time-symmetric nonlocal nonlinear Schrödinger equation, we construct the Darboux transformation, which provides an algebraic iterative algorithm to obtain a series of analytic solutions from a known one. To illustrate, the breathing-soliton solutions, periodic-wave solutions and localized rational soliton solutions are derived with the zero and plane-wave solutions as the seeds. The properties of those solutions are also discussed, and particularly the asymptotic analysis reveals all possible cases of the interaction between the discrete rational dark and antidark solitons.

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preprint2016arXiv

Design and Analysis of Transmit Beamforming for Millimetre Wave Base Station Discovery

In this paper, we develop an analytical framework for the initial access (a.k.a. Base Station (BS) discovery) in a millimeter-wave (mm-wave) communication system and propose an effective strategy for transmitting the Reference Signals (RSs) used for BS discovery. Specifically, by formulating the problem of BS discovery at User Equipments (UEs) as hypothesis tests, we derive a detector based on the Generalised Likelihood Ratio Test (GLRT) and characterise the statistical behaviour of the detector. The theoretical results obtained allow analysis of the impact of key system parameters on the performance of BS discovery, and show that RS transmission with narrow beams may not be helpful in improving the overall BS discovery performance due to the cost of spatial scanning. Using the method of large deviations, we identify the desirable beam pattern that minimises the average miss-discovery probability of UEs within a targeted detectable region. We then propose to transmit the RS with sequential scanning, using a pre-designed codebook with narrow and/or wide beams to approximate the desirable patterns. The proposed design allows flexible choices of the codebook sizes and the associated beam widths to better approximate the desirable patterns. Numerical results demonstrate the effectiveness of the proposed method.

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preprint2016arXiv

Quenching effect in below-threshold high harmonic generation

We theoretically demonstrate the quenching effect in below-threshold high harmonic generation (HHG) by using the time-dependent density-functional theory (TDDFT) and solving the time-dependent Schrödinger equation (TDSE). It is shown that the HHG is substantially suppressed in particular harmonic orders in the below-threshold region when multi-electron interaction comes into play. The position of the suppression is determined by the energy gap between the highest occupied orbital and the higher-lying orbital of the target. We show that the quenching effect is due to a new class of multi-electron dynamics involving electron-electron energy transfer, which is analog to the fluorescence quenching owing to the energy transfer between molecules in fluorescent material. This work reveals the important role of the multi-electron interaction on HHG especially in the below-threshold region.

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preprint2016arXiv

Rational Solitons in the Parity-Time-Symmetric Nonlocal Nonlinear Schrödinger Model

In this paper, via the generalized Darboux transformation, rational soliton solutions are derived for the parity-time-symmetric nonlocal nonlinear Schrödinger (NLS) model with the defocusing-type nonlinearity. We find that the first-order solution can exhibit the elastic interactions of rational antidark-antidark, dark-antidark, and antidark-dark soliton pairs on a continuous wave background, but there is no phase shift for the interacting solitons. Also, we discuss the degenerate case in which only one rational dark or antidark soliton survives. Moreover, we reveal that the second-order rational solution displays the interactions between two solitons with combined-peak-valley structures in the near-field regions, but each interacting soliton vanishes or evolves into a rational dark or antidark soliton as $|z|\ra \infty$. In addition, we numerically examine the stability of the first- and second-order rational soliton solutions.

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preprint2016arXiv

Stationary nonlinear waves, superposition modes and modulational instability characteristics in the AB system

We study the AB system describing marginally unstable baroclinic wave packets in geophysical fluids and also ultra-short pulses in nonlinear optics. We show that the breather can be converted into different types of stationary nonlinear waves on constant backgrounds, including the multi-peak soliton, M-shaped soliton, W-shaped soliton and periodic wave. We also investigate the nonlinear interactions between these waves, which display some novel patterns due to the non-propagating characteristics of the solitons: (1) Two antidark solitons can produce a W-shaped soliton instead of a higher-order antidark one, (2) The interaction between an antidark soliton and a W-shaped soliton can not only generate a higher-order antidark soliton, but also form a W-shaped solion pair, (3) The interactions between an oscillation W-shaped soliton and an oscillation M-shaped soliton show the multipeak structures. We find that the transition occurs at a modulational stability region in a low perturbation frequency region.

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preprint2015arXiv

A Majorized ADMM with Indefinite Proximal Terms for Linearly Constrained Convex Composite Optimization

This paper presents a majorized alternating direction method of multipliers (ADMM) with indefinite proximal terms for solving linearly constrained $2$-block convex composite optimization problems with each block in the objective being the sum of a non-smooth convex function and a smooth convex function, i.e., $\min_{x \in {\cal X}, \; y \in {\cal Y}}\{p(x)+f(x) + q(y)+g(y)\mid A^* x+B^* y = c\}$. By choosing the indefinite proximal terms properly, we establish the global convergence and $O(1/k)$ ergodic iteration-complexity of the proposed method for the step-length $τ\in (0, (1+\sqrt{5})/2)$. The computational benefit of using indefinite proximal terms within the ADMM framework instead of the current requirement of positive semidefinite ones is also demonstrated numerically. This opens up a new way to improve the practical performance of the ADMM and related methods.

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preprint2015arXiv

Adaptive Primal-Dual Hybrid Gradient Methods for Saddle-Point Problems

The Primal-Dual hybrid gradient (PDHG) method is a powerful optimization scheme that breaks complex problems into simple sub-steps. Unfortunately, PDHG methods require the user to choose stepsize parameters, and the speed of convergence is highly sensitive to this choice. We introduce new adaptive PDHG schemes that automatically tune the stepsize parameters for fast convergence without user inputs. We prove rigorous convergence results for our methods, and identify the conditions required for convergence. We also develop practical implementations of adaptive schemes that formally satisfy the convergence requirements. Numerical experiments show that adaptive PDHG methods have advantages over non-adaptive implementations in terms of both efficiency and simplicity for the user.

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preprint2015arXiv

Anti-dark and Mexican-hat solitons in the Sasa-Satsuma equation on the continuous wave background

In this letter, via the Darboux transformation method we construct new analytic soliton solutions for the Sasa-Satsuma equation which describes the femtosecond pulses propagation in a monomode fiber. We reveal that two different types of femtosecond solitons, i.e., the anti-dark (AD) and Mexican-hat (MH) solitons, can form on a continuous wave (CW) background, and numerically study their stability under small initial perturbations. Different from the common bright and dark solitons, the AD and MH solitons can exhibit both the resonant and elastic interactions, as well as various partially/completely inelastic interactions which are composed of such two fundamental interactions. In addition, we find that the energy exchange between some interacting soliton and the CW background may lead to one AD soliton changing into an MH one, or one MH soliton into an AD one.

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preprint2015arXiv

Assembly-Dependent Interfacial Property of Carbon Nanotube Fibers with Epoxy and Its Enhancement via Generalized Surface Sizing

In building up composite structures using carbon nanotube (CNT) fibers, the fiber-to-matrix interfacial shear strength (IFSS) is one of the most important issues. Originating from the assembly characteristics of CNT fiber, the IFSS strongly depends on the fiber's twisting level and densification level. Furthermore, there are rich ways to modify the fiber surface and thus enhance the IFSS, including the physical and chemical modification on fiber surface, the infiltration of matrix resin into CNT fiber, and the introduction of silane coupling agent. A new feature differing from carbon fibers is that all these treatments either change the fiber's surface structure or form a several-hundreds-nm-thick interphase inside rather than around the fiber. These "generalized" treatments obviously extend the common concept of surface sizing and can be used for various forms of CNT assembly structures.

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preprint2015arXiv

Constraining cosmology with pairwise velocity estimator

In this paper, we develop a full statistical method for the pairwise velocity estimator previously proposed, and apply Cosmicflows-2 catalogue to this method to constrain cosmology. We first calculate the covariance matrix for line-of-sight velocities for a given catalogue, and then simulate the mock full-sky surveys from it, and then calculate the variance for the pairwise velocity field. By applying the $8315$ independent galaxy samples and compressed $5224$ group samples from Cosmicflows-2 catalogue to this statistical method, we find that the joint constraint on $Ω^{0.6}_{\rm m}h$ and $σ_{8}$ is completely consistent with the WMAP 9-year and Planck 2015 best-fitting cosmology. Currently, there is no evidence for the modified gravity models or any dynamic dark energy models from this practice, and the error-bars need to be reduced in order to provide any concrete evidence against/to support $Λ$CDM cosmology.

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preprint2015arXiv

Identifying the Absorption Bump with Deep Learning

The pervasive interstellar dust grains provide significant insights to understand the formation and evolution of the stars, planetary systems, and the galaxies, and may harbor the building blocks of life. One of the most effective way to analyze the dust is via their interaction with the light from background sources. The observed extinction curves and spectral features carry the size and composition information of dust. The broad absorption bump at 2175 Angstrom is the most prominent feature in the extinction curves. Traditionally, statistical methods are applied to detect the existence of the absorption bump. These methods require heavy preprocessing and the co-existence of other reference features to alleviate the influence from the noises. In this paper, we apply Deep Learning techniques to detect the broad absorption bump. We demonstrate the key steps for training the selected models and their results. The success of Deep Learning based method inspires us to generalize a common methodology for broader science discovery problems. We present our on-going work to build the DeepDis system for such kind of applications.

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preprint2015arXiv

Multidimensional BSDEs with uniformly continuous coefficients: the general result

In this paper, by introducing a new notion of envelope of the stochastic process, we construct a family of random differential equations whose solutions can be viewed as solutions of a family of ordinary differential equations and prove that the multidimensional backward stochastic differential equations (BSDEs for short) with the general uniformly continuous coefficients are uniquely solvable. As a result, we solve the open problem of multidimensional BSDEs with uniformly continuous coefficients.

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preprint2015arXiv

Selective enhancement of resonant multiphoton ionization with strong laser fields

High-resolution photoelectron momentum distributions of Xe atoms ionized by 800-nm linearly polarized laser fields have been traced at intensities from 1.1*1013 to 3.5*1013W/cm2 using velocity-map imaging techniques. At certain laser intensities, the momentum spectrum exhibits a distinct double-ring structure for low-order above-threshold ionization, which appears to be absent at lower or higher laser intensities. By investigating the intensity-resolved photoelectron energy spectrum, we find that this double-ring structure originates from resonant multiphoton ionization involving multiple Rydberg states of atoms. Varying the laser intensity, we can selectively enhance the resonant multiphoton ionization through certain atomic Rydberg states. The photoelectron angular distributions of multiphoton resonance are also investigated for the low-order above-threshold ionization.

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preprint2015arXiv

Streaking temporal double slit interference by an orthogonal two-color laser field

We investigate electron momentum distributions from single ionization of Ar by two orthogonally polarized laser pulses of different color. The two-color scheme is used to experimentally control the interference between electron wave packets released at different times within one laser cycle. This intracycle interference pattern is typically hard to resolve in an experiment. With the two-color control scheme these features become the dominant contribution to the electron momentum distribution. Furthermore the second color can be used for streaking of the otherwise interfering wave packets establishing a which-way marker. Our investigation shows that the visibility of the interference fringes depends on the degree of the which-way information determined by the controllable phase between the two pulses.

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preprint2015arXiv

The Trigger System for the External Target Experiment in CSR of HIRFL

A trigger system was designed for the external target experiment in the Cooling Storage Ring (CSR) of the Heavy Ion Research Facility in Lanzhou (HIRFL). Considering that different detectors are scattered in a large area, the trigger system is designed based on a master-slave structure and fiber-based serial data transmission technique. The trigger logic is organized in hierarchies, and flexible reconfiguration of the trigger function is achieved based on command register access or overall field-programmable gate array (FPGA) logic on-line reconfiguration controlled by remote computers. We also conducted tests to confirm the function of the trigger electronics, and the results indicate that this trigger system works well.

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preprint2014arXiv

A Convergent $3$-Block Semi-Proximal ADMM for Convex Minimization Problems with One Strongly Convex Block

In this paper, we present a semi-proximal alternating direction method of multipliers (ADMM) for solving $3$-block separable convex minimization problems with the second block in the objective being a strongly convex function and one coupled linear equation constraint. By choosing the semi-proximal terms properly, we establish the global convergence of the proposed semi-proximal ADMM for the step-length $τ\in (0, (1+\sqrt{5})/2)$ and the penalty parameter $σ\in (0, +\infty)$. In particular, if $σ>0$ is smaller than a certain threshold and the first and third linear operators in the linear equation constraint are injective, then all the three added semi-proximal terms can be dropped and consequently, the convergent $3$-block semi-proximal ADMM reduces to the directly extended $3$-block ADMM with $τ\in (0, (1+\sqrt{5})/2)$.

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preprint2014arXiv

Message and State Cooperation in a Relay Channel When Only the Relay Knows the State

A state-dependent relay channel is studied in which strictly causal channel state information is available at the relay and no state information is available at the source and destination. The source and the relay are connected via two unidirectional out-of-band orthogonal links of finite capacity, and a state-dependent memoryless channel connects the source and the relay, on one side, and the destination, on the other. Via the orthogonal links, the source can convey information about the message to be delivered to the destination to the relay while the relay can forward state information to the source. This exchange enables cooperation between the source and the relay on transmission of message and state information to the destination. First, two achievable schemes are proposed that exploit both message and state cooperation. It is shown that a transmission scheme inspired by noisy network coding performs better than a strategy based on block Markov coding and backward decoding. Next, based on the given achievable schemes and appropriate upper bounds, capacity results are identified for some special cases. Finally, a Gaussian model is studied, along with corresponding numerical results that illuminate the relative merits of state and message cooperation.

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preprint2014arXiv

Statistical computation of Boltzmann entropy and estimation of the optimal probability density function from statistical sample

In this work, we investigate the statistical computation of the Boltzmann entropy of statistical samples. For this purpose, we use both histogram and kernel function to estimate the probability density function of statistical samples. We find that, due to coarse-graining, the entropy is a monotonic increasing function of the bin width for histogram or bandwidth for kernel estimation, which seems to be difficult to select an optimal bin width/bandwidth for computing the entropy. Fortunately, we notice that there exists a minimum of the first derivative of entropy for both histogram and kernel estimation, and this minimum point of the first derivative asymptotically points to the optimal bin width or bandwidth. We have verified these findings by large amounts of numerical experiments. Hence, we suggest that the minimum of the first derivative of entropy be used as a selector for the optimal bin width or bandwidth of density estimation. Moreover, the optimal bandwidth selected by the minimum of the first derivative of entropy is purely data-based, independent of the unknown underlying probability density distribution, which is obviously superior to the existing estimators. Our results are not restricted to one-dimensional, but can also be extended to multivariate cases. It should be emphasized, however, that we do not provide a robust mathematical proof of these findings, and we leave these issues with those who are interested in them.

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preprint2013arXiv

A 3D Curve Offset Approach for Ruled Surface Generation in Engineering Design

Ruled surface is widely used in engineering design such as parting surface design of injection mold and checking surface design of checking fixture, which are usually generated by offsetting 3D curves. However, in 3D curve offset, there often exist break,interaction and overlapping problems which can't be solved by current CAD software automatically. This paper is targeted at developing a 3D curve offsetting algorithm for ruled surface generation, and three key technologies are introduced in details: An improved curve division method is proposed to reduce the offset accuracy error resulted from different offset distances and curvatures; An offsetting curve overlapping detection and elimination method is proposed; And then, a curve transition method is presented to improve curve offsetting quality for the break and intersection/overlapping regions, where a new algorithm for generating positive weights spherical rational quartic Bezier curve is proposed to bridge the breaks of offset curves to create a smooth ruled surface. Finally, two practical design cases, parting surface and checking surface generation, show that the proposed approach can enhance the efficiency and quality for ruled surface generation in engineering design.

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preprint2013arXiv

Molecular dynamics simulations of cumulative helium bombardments on tungsten surfaces

Molecular dynamics (MD) simulations were performed to study the cumulative bombardments of low-energy (60-200 eV) helium (He) atoms on tungsten (W) surfaces. The behaviour of He and the response of the W surface were investigated. The He incident energy and tungsten temperature play important roles on the formation and growth of He clusters. The temperature can promote the coalescence of He clusters and increase the size of the He clusters. A stratification phenomenon of the He depth distribution has been observed. The rupture of the He clusters and, at the same time, the escape and re-deposition of the W atoms have been observed. During the formation of He clusters, the interstitial W atoms are produced and evolve into bundles of <111> crowdions, which would be constrained around the He clusters for a long time. However, they will finally move onto the top surface along the <111> direction, which results in stacking the W atoms on the surface. The complex combination effects of the He clusters and the interstitial atoms result in the growth of the surfaces.

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preprint2013arXiv

Qunatum Parrondo's games constructed by quantum random walk

We construct a Parrondo's game using discrete time quantum walks. Two lossing games are represented by two different coin operators. By mixing the two coin operators $U_{A}(α_{A},β_{A},γ_{A})$ and $U_{B}(α_{B},β_{B},γ_{B})$, we may win the game. Here we mix the two games in position instead of time. With a number of selections of the parameters, we can win the game with sequences ABB, ABBB, \emph{et al}. If we set $β_{A}=45^{\circ},γ_{A}=0,α_{B}=0,β_{B}=88^{\circ}$, we find the game 1\emph{}with {\normalsize $U_{A}^{S}=U^{S}(-51^{\circ},45^{\circ},0)$, $U_{B}^{S}=U^{S}(0,88^{\circ},-16^{\circ})$ will win and get the most profit.}If we set $α_{A}=0,β_{A}=45^{\circ},α_{B}=0,β_{B}=88^{\circ}$ and{\normalsize{} the game 2 with $U_{A}^{S}=U^{S}(0,45^{\circ},-51^{\circ})$, $U_{B}^{S}=U^{S}(0,88^{\circ},-67^{\circ})$, will win most. And}game 1\emph{}{\normalsize is equivalent to the}game\emph{}2\emph{}with the changes of sequences and steps. But at a large enough steps, the game will loss at last.

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preprint2012arXiv

Degraded Broadcast Diamond Channels with Non-Causal State Information at the Source

A state-dependent degraded broadcast diamond channel is studied where the source-to-relays cut is modeled with two noiseless, finite-capacity digital links with a degraded broadcasting structure, while the relays-to-destination cut is a general multiple access channel controlled by a random state. It is assumed that the source has non-causal channel state information and the relays have no state information. Under this model, first, the capacity is characterized for the case where the destination has state information, i.e., has access to the state sequence. It is demonstrated that in this case, a joint message and state transmission scheme via binning is optimal. Next, the case where the destination does not have state information, i.e., the case with state information at the source only, is considered. For this scenario, lower and upper bounds on the capacity are derived for the general discrete memoryless model. Achievable rates are then computed for the case in which the relays-to-destination cut is affected by an additive Gaussian state. Numerical results are provided that illuminate the performance advantages that can be accrued by leveraging non-causal state information at the source.

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preprint2012arXiv

Molecular dynamics simulations with many-body potentials on multiple GPUs - the implementation, package and performance

Molecular dynamics (MD) is an important research tool extensively applied in materials science. Running MD on a graphics processing unit (GPU) is an attractive new approach for accelerating MD simulations. Currently, GPU implementations of MD usually run in a one-host-process-one-GPU (OHPOG) scheme. This scheme may pose a limitation on the system size that an implementation can handle due to the small device memory relative to the host memory. In this paper, we present a one-host-process-multiple-GPU (OHPMG) implementation of MD with embedded-atom-model or semi-empirical tight-binding many-body potentials. Because more device memory is available in an OHPMG process, the system size that can be handled is increased to a few million or more atoms. In comparison with the CPU implementation, in which Newton's third law is applied to improve the computational efficiency, our OHPMG implementation has achieved a 28.9x~86.0x speedup in double precision, depending on the system size, the cut-off ranges and the number of GPUs. The implementation can also handle a group of small boxes in one run by combining the small boxes into a large box. This approach greatly improves the GPU computing efficiency when a large number of MD simulations for small boxes are needed for statistical purposes.

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preprint2012arXiv

Multiple Access Channels with States Causally Known at Transmitters

It has been recently shown by Lapidoth and Steinberg that strictly causal state information can be beneficial in multiple access channels (MACs). Specifically, it was proved that the capacity region of a two-user MAC with independent states, each known strictly causally to one encoder, can be enlarged by letting the encoders send compressed past state information to the decoder. In this work, a generalization of the said strategy is proposed whereby the encoders compress also the past transmitted codewords along with the past state sequences. The proposed scheme uses a combination of long-message encoding, compression of the past state sequences and codewords without binning, and joint decoding over all transmission blocks. The proposed strategy has been recently shown by Lapidoth and Steinberg to strictly improve upon the original one. Capacity results are then derived for a class of channels that include two-user modulo-additive state-dependent MACs. Moreover, the proposed scheme is extended to state-dependent MACs with an arbitrary number of users. Finally, output feedback is introduced and an example is provided to illustrate the interplay between feedback and availability of strictly causal state information in enlarging the capacity region.

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preprint2012arXiv

Nonperturbative tuning of an improved relativistic heavy-quark action with application to bottom spectroscopy

We calculate the masses of bottom mesons using an improved relativistic action for the b-quarks and the RBC/UKQCD Iwasaki gauge configurations with 2+1 flavors of dynamical domain-wall light quarks. We analyze configurations with two lattice spacings: a^{-1} = 1.729 GeV (a ~ 0.11 fm) and a^{-1} = 2.281 GeV (a ~ 0.086 fm). We use an anisotropic, clover-improved Wilson action for the b-quark, and tune the three parameters of the action nonperturbatively such that they reproduce the experimental values of the B_s and B_s* heavy-light meson states. The masses and mass-splittings of the low-lying bottomonium states (such as the eta_b and Upsilon) can then be computed with no additional inputs, and comparison between these predictions and experiment provides a test of the validity of our method. We obtain bottomonium masses with total uncertainties of ~0.5-0.6% and fine-structure splittings with uncertainties of ~35-45%; for all cases we find good agreement with experiment. The parameters of the relativistic heavy-quark action tuned for b-quarks presented in this work can be used for precise calculations of weak matrix elements such as B-meson decay constants and mixing parameters with lattice discretization errors that are of the same size as in light pseudoscalar meson quantities. This general method can also be used for charmed meson masses and matrix elements if the parameters of the heavy-quark action are appropriately tuned.

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preprint2012arXiv

Quantum random walk in periodic potential on a line

We investigated the discrete-time quantum random walks on a line in periodic potential. The probability distribution with periodic potential is more complex compared to the normal quantum walks, and the standard deviation $σ$ has interesting behaviors for different period $q$ and parameter $θ$. We studied the behavior of standard deviation with variation in walk steps, period, and $θ$. The standard deviation increases approximately linearly with $θ$ and decreases with $1/q$ for $θ\in(0,π/4)$, and increases approximately linearly with $1/q$ for $θ\in[π/4,π/2)$. When $q=2$, the standard deviation is lazy for $θ\in[π/4+nπ,3π/4+nπ],n\in Z$.

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preprint2011arXiv

Message and State Cooperation in a Relay Channel When the Relay Has Strictly Causal State Information

A state-dependent relay channel is studied in which strictly causal channel state information is available at the relay and no state information is available at the source and destination. Source and relay are connected via two unidirectional out-of-band orthogonal links of finite capacity, and a state-dependent memoryless channel connects source and relay, on one side, and the destination, on the other. Via the orthogonal links, the source can convey information about the message to be delivered to the destination to the relay while the relay can forward state information to the source. This exchange enables cooperation between source and relay on both transmission of message and state information to the destination. First, an achievable scheme, inspired by noisy network coding, is proposed that exploits both message and state cooperation. Next, based on the given achievable rate and appropriate upper bounds, capacity results are identified for some special cases. Finally, a Gaussian model is studied, along with corresponding numerical results that illuminate the relative merits of state and message cooperation.

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preprint2010arXiv

THz generation by dual-color pulse in prealigned molecules

We demonstrate that the THz generation in air from a dual-color pulse, composed of the fundamental and second-harmonic waves, can be coherently controlled by field-free molecular alignment. By tuning its time delay to properly match various molecular alignment revivals, the THz generation from the dual-color pulse can be promoted or decreased due to the spatial cross-(de)focusing effect and the alignment-dependent ionization probabilities of the prealigned diatomic molecules of air. For the dual-color pulse of orthogonally polarized fundamental and second-harmonic waves, the polarization of the generated THz radiation can be controlled by the field-free molecular alignment, which functions as a transient dynamic wave-plate for the dual-color pulse with different phase velocities for the orthogonally polarized field components. The plasma effect on the THz generation of the dual-color pulse is also observed, leading to additional intensity and polarization control of the THz radiation.

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preprint2009arXiv

The finite temperature QCD using 2+1 flavors of domain wall fermions at N_t = 8

We study the region of the QCD phase transition using 2+1 flavors of domain wall fermions (DWF) and a $16^3 \times 8$ lattice volume with a fifth dimension of $L_s = 32$. The disconnected light quark chiral susceptibility, quark number susceptibility and the Polyakov loop suggest a chiral and deconfining crossover transition lying between 155 and 185 MeV for our choice of quark mass and lattice spacing. In this region the lattice scale deduced from the Sommer parameter $r_0$ is $a^{-1} \approx 1.3$ GeV, the pion mass is $\approx 300$ MeV and the kaon mass is approximately physical. The peak in the chiral susceptibility implies a pseudo critical temperature $T_c = 171(10)(17)$ MeV where the first error is associated with determining the peak location and the second with our unphysical light quark mass and non-zero lattice spacing. The effects of residual chiral symmetry breaking on the chiral condensate and disconnected chiral susceptibility are studied using several values of the valence $L_s$.

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preprint2007arXiv

Charm spectroscopy on dynamical 2+1 flavor domain wall fermion lattices with a relativistic heavy quark action

We present a preliminary calculation of the charmonium spectrum using the dynamical 2+1 flavor $24^3\times 64$ domain wall fermion lattice configurations generated by the RBC and UKQCD collaborations. We use the relativistic heavy quark action with 3 parameters non-perturbatively determined by matching to experimental quantities. Chiral extrapolation is done on four light sea quark masses from 0.005 to 0.03, with $m_s=0.04$ and $m_{res}=0.003$. We can either predict meson masses assuming the lattice spacing is known from other methods, or calculate the lattice spacing using those quantities.

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preprint2007arXiv

Q-DPM: An Efficient Model-Free Dynamic Power Management Technique

When applying Dynamic Power Management (DPM) technique to pervasively deployed embedded systems, the technique needs to be very efficient so that it is feasible to implement the technique on low end processor and tight-budget memory. Furthermore, it should have the capability to track time varying behavior rapidly because the time varying is an inherent characteristic of real world system. Existing methods, which are usually model-based, may not satisfy the aforementioned requirements. In this paper, we propose a model-free DPM technique based on Q-Learning. Q-DPM is much more efficient because it removes the overhead of parameter estimator and mode-switch controller. Furthermore, its policy optimization is performed via consecutive online trialing, which also leads to very rapid response to time varying behavior.

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