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Yan Li

Yan Li contributes to research discovery and scholarly infrastructure.

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preprint2026arXiv

AcademiClaw: When Students Set Challenges for AI Agents

Benchmarks within the OpenClaw ecosystem have thus far evaluated exclusively assistant-level tasks, leaving the academic-level capabilities of OpenClaw largely unexamined. We introduce AcademiClaw, a bilingual benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic workflows -- homework, research projects, competitions, and personal projects -- that they found current AI agents unable to solve effectively. Curated from 230 student-submitted candidates through rigorous expert review, the final task set spans 25+ professional domains, ranging from olympiad-level mathematics and linguistics problems to GPU-intensive reinforcement learning and full-stack system debugging, with 16 tasks requiring CUDA GPU execution. Each task executes in an isolated Docker sandbox and is scored on task completion by multi-dimensional rubrics combining six complementary techniques, with an independent five-category safety audit providing additional behavioral analysis. Experiments on six frontier models show that even the best achieves only a 55\% pass rate. Further analysis uncovers sharp capability boundaries across task domains, divergent behavioral strategies among models, and a disconnect between token consumption and output quality, providing fine-grained diagnostic signals beyond what aggregate metrics reveal. We hope that AcademiClaw and its open-sourced data and code can serve as a useful resource for the OpenClaw community, driving progress toward agents that are more capable and versatile across the full breadth of real-world academic demands. All data and code are available at https://github.com/GAIR-NLP/AcademiClaw.

preprint2025arXiv

Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking

Unmanned Aerial Vehicles (UAVs) play an important role in various applications, where precise trajectory tracking is crucial. However, conventional control algorithms for trajectory tracking often exhibit limited performance due to the underactuated, nonlinear, and highly coupled dynamics of quadrotor systems. To address these challenges, we propose HBO-PID, a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization (HBO) framework with the classical PID controller to achieve accurate and robust trajectory tracking. By explicitly modeling input-dependent noise variance, the proposed method can better adapt to dynamic and complex environments, and therefore improve the accuracy and robustness of trajectory tracking. To accelerate the convergence of optimization, we adopt a two-stage optimization strategy that allow us to more efficiently find the optimal controller parameters. Through experiments in both simulation and real-world scenarios, we demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) methods. Compared to SOTA methods, it improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.

preprint2024arXiv

Investigation of two-particle contributions to nucleon matrix elements

We investigate contributions of excited states to nucleon matrix elements by studying the two- and three-point functions using nucleon and pion-nucleon interpolating fields. This study is carried out using twisted mass fermion ensembles with pion masses 346 MeV and 131 MeV. We compare the results obtained using these two ensembles and show preliminary results for nucleon charges.

preprint2023arXiv

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are three-fold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws of the vehicle engine into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods.

preprint2023arXiv

Emergent Electronic Kagome Lattice in Correlated Charge-Density-Wave State of 1T-TaS$_2$

Quantum materials with tunable correlated and/or topological electronic states, such as the electronic Kagome lattice, provide an ideal platform to study the exotic quantum properties. However, the real-space investigations on the correlated electronic Kagome lattice have been rarely reported. Herein, we report on the electronic Kagome lattice emerging in the correlated charge-density-wave (CDW) state of 1T-TaS$_2$ at ~200 K via variable-temperature scanning tunneling microscopy (VT-STM). This emergent Kagome lattice can be considered a fractional electron-filling superstructure with reduced translational and rotational symmetries, confirmed by STM measurements and density functional theory simulations. The characteristic band structure and density of states of this electronic Kagome lattice are further explored based on theoretical calculations. Our results demonstrate a self-organized electronic Kagome lattice from the correlated CDW state via the effective tuning parameter of temperature and provide a platform to directly explore the interplay of correlated electrons and topological physics.

preprint2023arXiv

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. In this work, we propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder (BVAE) equipped with diffusion, denoise, and disentanglement, namely D3VAE. Specifically, a coupled diffusion probabilistic model is proposed to augment the time series data without increasing the aleatoric uncertainty and implement a more tractable inference process with BVAE. To ensure the generated series move toward the true target, we further propose to adapt and integrate the multiscale denoising score matching into the diffusion process for time series forecasting. In addition, to enhance the interpretability and stability of the prediction, we treat the latent variable in a multivariate manner and disentangle them on top of minimizing total correlation. Extensive experiments on synthetic and real-world data show that D3VAE outperforms competitive algorithms with remarkable margins. Our implementation is available at https://github.com/PaddlePaddle/PaddleSpatial/tree/main/research/D3VAE.

preprint2023arXiv

Statistics of weakly nonlinear waves on currents with strong vertical shear

We investigate how the presence of a vertically sheared current affects wave statistics, including the probability of rogue waves, and apply it to a real-world case using measured spectral and shear current data from the Mouth of the Columbia River. A theory for weakly nonlinear waves valid to second order in wave steepness is derived, and used to analyze statistical properties of surface waves; the theory extends the classic theory by Longuet-Higgins [J. Fluid Mech. 12, 3 (1962)] to allow for an arbitrary depth-dependent background flow, $U(z)$, with $U$ the horizontal velocity along the main direction of wave propagation and $z$ the vertical axis. Numerical statistics are collected from a large number of realisations of random, irregular sea-states following a JONSWAP spectrum, on linear and exponential model currents of varying strengths. A number of statistical quantities are presented and compared to a range of theoretical expressions from the literature; in particular the distribution of wave surface elevation, surface maxima, and crest height; the exceedance probability including the probability of rogue waves; the maximum crest height among $N_s$ waves, and the skewness of the surface elevation distribution. We find that compared to no-shear conditions, opposing vertical shear ($U'(z)>0$) leads to increased wave height and increased skewness of the nonlinear-wave elevation distribution, while a following shear ($U'(z)<0$) has opposite effects. With the wave spectrum and velocity profile measured in the Columbia River estuary by Zippel & Thomson [J. Geophys. Res: Oceans 122, 3311 (2017)] our second--order theory predicts that the probability of rogue waves is significantly reduced and enhanced during ebb and flood, respectively, adding support to the notion that shear currents need to be accounted for in wave modelling and prediction.

preprint2023arXiv

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. Though one could lower the complexity of Transformers by inducing the sparsity in point-wise self-attentions for LTTF, the limited information utilization prohibits the model from exploring the complex dependencies comprehensively. To this end, we propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects: (i) an encoder-decoder architecture incorporating a linear complexity without sacrificing information utilization is proposed on top of sliding-window attention and Stationary and Instant Recurrent Network (SIRN); (ii) a module derived from the normalizing flow is devised to further improve the information utilization by inferring the outputs with the latent variables in SIRN directly; (iii) the inter-series correlation and temporal dynamics in time-series data are modeled explicitly to fuel the downstream self-attention mechanism. Extensive experiments on seven real-world datasets demonstrate that Conformer outperforms the state-of-the-art methods on LTTF and generates reliable prediction results with uncertainty quantification.

preprint2022arXiv

A flying Schrödinger cat in multipartite entangled states

Schrödinger's cat originates from the famous thought experiment querying the counterintuitive quantum superposition of macroscopic objects. As a natural extension, several "cats" (quasi-classical objects) can be prepared into coherent quantum superposition states, which is known as multipartite cat states demonstrating quantum entanglement among macroscopically distinct objects. Here we present a highly scalable approach to deterministically create flying multipartite Schrödinger cat states, by reflecting coherent state photons from a microwave cavity containing a superconducting qubit. We perform full quantum state tomography on the cat states with up to four photonic modes and confirm the existence of quantum entanglement among them. We also witness the hybrid entanglement between discrete-variable states (the qubit) and continuous-variable states (the flying multipartite cat) through a joint quantum state tomography. Our work demonstrates an important experimental control method in the microwave region and provides an enabling step for implementing a series of quantum metrology and quantum information processing protocols based on cat states.

preprint2022arXiv

Benchmarking Domain Generalization on EEG-based Emotion Recognition

Electroencephalography (EEG) based emotion recognition has demonstrated tremendous improvement in recent years. Specifically, numerous domain adaptation (DA) algorithms have been exploited in the past five years to enhance the generalization of emotion recognition models across subjects. The DA methods assume that calibration data (although unlabeled) exists in the target domain (new user). However, this assumption conflicts with the application scenario that the model should be deployed without the time-consuming calibration experiments. We argue that domain generalization (DG) is more reasonable than DA in these applications. DG learns how to generalize to unseen target domains by leveraging knowledge from multiple source domains, which provides a new possibility to train general models. In this paper, we for the first time benchmark state-of-the-art DG algorithms on EEG-based emotion recognition. Since convolutional neural network (CNN), deep brief network (DBN) and multilayer perceptron (MLP) have been proved to be effective emotion recognition models, we use these three models as solid baselines. Experimental results show that DG achieves an accuracy of up to 79.41\% on the SEED dataset for recognizing three emotions, indicting the potential of DG in zero-training emotion recognition when multiple sources are available.

preprint2022arXiv

CMEs and SEPs During November-December 2020: A Challenge for Real-Time Space Weather Forecasting

Predictions of coronal mass ejections (CMEs) and solar energetic particles (SEPs) are a central issue in space weather forecasting. In recent years, interest in space weather predictions has expanded to include impacts at other planets beyond Earth as well as spacecraft scattered throughout the heliosphere. In this sense, the scope of space weather science now encompasses the whole heliospheric system, and multi-point measurements of solar transients can provide useful insights and validations for prediction models. In this work, we aim to analyse the whole inner heliospheric context between two eruptive flares that took place in late 2020, i.e. the M4.4 flare of November 29 and the C7.4 flare of December 7. This period is especially interesting because the STEREO-A spacecraft was located ~60° east of the Sun-Earth line, giving us the opportunity to test the capabilities of "predictions at 360°" using remote-sensing observations from the Lagrange L1 and L5 points as input. We simulate the CMEs that were ejected during our period of interest and the SEPs accelerated by their shocks using the WSA-Enlil-SEPMOD modelling chain and four sets of input parameters, forming a "mini-ensemble". We validate our results using in-situ observations at six locations, including Earth and Mars. We find that, despite some limitations arising from the models' architecture and assumptions, CMEs and shock-accelerated SEPs can be reasonably studied and forecast in real time at least out to several tens of degrees away from the eruption site using the prediction tools employed here.

preprint2022arXiv

Compactness and existence results of the prescribing fractional $Q$-curvatures problem on $\mathbb{S}^n$

This paper is devoted to establishing the compactness and existence results of the solutions to the prescribing fractional $Q$-curvatures problem of order $2σ$ on $n$-dimensional standard sphere when $ n-2σ=2$, $σ=1+m/2,$ $m\in \mathbb{N}_{+}.$ The compactness results are novel and optimal. In addition, we prove a degree-counting formula of all solutions to achieve the existence. From our results, we can know where blow up occur. Furthermore, the sequence of solutions that blow up precisely at any finite distinct location can be constructed. It is worth noting that our results include the case of multiple harmonic.

preprint2022arXiv

Core overshoot constrained by the absence of a solar convective core and some solar-like stars

Convective-core overshoot mixing is a significant uncertainty in stellar evolution. Because numerical simulations and turbulent convection models predict exponentially decreasing radial rms turbulent velocity, a popular treatment of the overshoot mixing is to apply a diffusion process with exponentially decreasing diffusion coefficient. It is important to investigate the parameters of the diffusion coefficient because they determine the efficiency of the mixing in the overshoot region. In this paper, we have investigated the effects of the core overshoot mixing on the properties of the core in solar models and have constrained the parameters of the overshoot model by using helioseismic inferences and the observation of the solar 8B neutrino flux. For solar-mass stars, the core overshoot mixing helps to prolong the lifetime of the convective core developed at the ZAMS. If the strength of the mixing is sufficiently high, the convective core in a solar model could survive till the present solar age, leading to large deviations of the sound-speed and density profiles comparing with the helioseismic inferences. The 8B neutrino flux also favours a radiative solar core. Those provide a constraint on the parameters of the exponential diffusion model of the convective overshoot mixing. A limited asteroseismic investigation of 13 Kepler low-mass stars with 1.0 < M < 1.5 shows a mass-dependent range of the overshoot parameter. The overshoot mixing processes for different elements are analyzed in detail. It is found that the exponential diffusion overshoot model leads to different effective overshoot mixing lengths for elements with different nuclear equilibrium timescale.

preprint2022arXiv

Data-Driven Quantum Approximate Optimization Algorithm for Cyber-Physical Power Systems

Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems, especially for Distributed Energy Resources (DERs) dominant cyber-physical systems that have been widely developed to promote energy sustainability. The systems' maximum power or data sections are essential for monitoring, operation, and control, while high computational effort is required. Quantum Approximate Optimization Algorithm (QAOA) provides a promising means to search for these sections by leveraging quantum resources. However, its performance highly relies on the critical parameters, especially for weighted graphs. We present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density, and verify the strategy with 39,774 instances. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots the practical application of quantum technique to power systems in noisy intermediate-scale quantum devices, heralding its next-generation computation in the quantum era.

preprint2022arXiv

Deep Forest with Hashing Screening and Window Screening

As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy to improve the performance of our approach, called window screening, which can achieve higher accuracy without hyperparameter tuning on different datasets. Our experimental results show that HW-Forest has higher accuracy than other models, and the time cost is also reduced.

preprint2022arXiv

Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network

This paper presents a deep learning assisted synthesis approach for direct end-to-end generation of RF/mm-wave passive matching network with 3D EM structures. Different from prior approaches that synthesize EM structures from target circuit component values and target topologies, our proposed approach achieves the direct synthesis of the passive network given the network topology from desired performance values as input. We showcase the proposed synthesis Neural Network (NN) model on an on-chip 1:1 transformer-based impedance matching network. By leveraging parameter sharing, the synthesis NN model successfully extracts relevant features from the input impedance and load capacitors, and predict the transformer 3D EM geometry in a 45nm SOI process that will match the standard 50$Ω$ load to the target input impedance while absorbing the two loading capacitors. As a proof-of-concept, several example transformer geometries were synthesized, and verified in Ansys HFSS to provide the desired input impedance.

preprint2022arXiv

Dissipation induced information scrambling in a collision model

In this paper, we present a collision model to stroboscopically simulate the dynamics of information in dissipative systems. In particular, an all-optical scheme is proposed to investigate the information scrambling of bosonic systems with Gaussian environmental states. By varying the states of environments, we find that in the presence of dissipation the transient tripartite mutual information of system modes may show negative value signaling the appearance of information scrambling. We also find that dynamical indivisibility based non-Markovianity play dual roles in affecting the dynamics of information.

preprint2022arXiv

Equivariant $\mathbb R$-test configurations and semistable limits of $\mathbb Q$-Fano group compactifications

Let $G$ be a connected, complex reductive group. In this paper, we classify $G\times G$-equivariant normal $\mathbb R$-test configurations of a polarized $G$-compactification. Then for $\mathbb Q$-Fano $G$-compactifications, we express the H-invariant of its equivariant normal $\mathbb R$-test configurations in terms of the combinatory data. Based on \cite{Han-Li}, we compute the semistable limit of a K-unstable Fano $G$-compactification. As an application, we show that for the two K-unstable Fano $SO_4(\mathbb C)$-compactifications, the corresponding semistable limits are indeed the limit spaces of the normalized Kähler-Ricci flow.

preprint2022arXiv

Experimental preparation of generalized cat states for itinerant microwave photons

Generalized cat states represent arbitrary superpositions of coherent states, which are of great importance in various quantum information processing protocols. Here we demonstrate a versatile approach to creating generalized itinerant cat states in the microwave domain, by reflecting coherent state photons from a microwave cavity containing a superconducting qubit. We show that, with a coherent control of the qubit state, a full control over the coherent state superposition can be realized. The prepared cat states are verified through quantum state tomography of the qubit state dependent reflection photon field. We further quantify quantum coherence in the prepared cat states based on the resource theory, revealing a good experimental control on the coherent state superpositions. The photon number statistic and the squeezing properties are also analyzed. Remarkably, fourth-order squeezing is observed in the experimental states. Those results open up new possibilities of applying generalized cat states for the purpose of quantum information processing.

preprint2022arXiv

Generalization of Weinberg's Compositeness Relations

We generalize the time-honored Weinberg's compositeness relations by including the range corrections through considering a general form factor. In Weinberg's derivation, he considered the effective range expansion up to $\mathcal{O}(p^2)$ and made two additional approximations: neglecting the non-pole term in the Low equation; approximating the form factor by a constant. We lift the second approximation, and work out an analytic expression for the form factor. For a positive effective range, the form factor is of a single-pole form. An integral representation of the compositeness is obtained and is expected to have a smaller uncertainty than that derived from Weinberg's relations. We also establish an exact relation between the wave function of a bound state and the phase of the scattering amplitude neglecting the non-pole term. The deuteron is analyzed as an example, and the formalism can be applied to other cases where range corrections are important.

preprint2022arXiv

Gradient estimates for elliptic systems from composite materials with closely spaced stiff $C^{1,γ}$ inclusions

This paper is devoted to establishing the pointwise upper and lower bounds estimates of the gradient of the solutions to a class of general elliptic systems with Hölder continuous coefficients in a narrow region where the upper and lower boundaries is $C^{1,γ},$ $0<γ<1$, weaker than the previous $C^{2,γ}$ assumption. These estimates play a key role in the damage analysis of composite materials. From our results, the damage may initiate from the narrowest place.

preprint2022arXiv

Ground-to-Air Communications Beyond 5G: Coordinated Multi-Point Transmission Based on Poisson-Delaunay Triangulation

This paper designs a novel ground-to-air communication scheme to serve unmanned aerial vehicles (UAVs) through legacy terrestrial base stations (BSs). In particular, a tractable coordinated multi-point (CoMP) transmission based on the geometry of Poisson-Delaunay triangulation is developed, which provides reliable and seamless connectivity for UAVs. An effective dynamic frequency allocation scheme is designed to eliminate inter-cell interference by using the theory of circle packing. For exact performance evaluation, the handoff probability of a typical UAV is characterized, and then the coverage probability with handoffs is attained. Simulation and numerical results corroborate that the proposed scheme outperforms the conventional CoMP scheme with three nearest cooperating BSs in terms of handoff and coverage probabilities. Moreover, as each UAV has a fixed and unique CoMP BS set, it avoids the real-time dynamic BS searching process, thus reducing the feedback overhead.

preprint2022arXiv

Inverse scattering problem with a bare state

In hadron physics, molecular-like multihadron states can interact with compact multiquark states. The latter are modeled as bare states in the Hilbert space of a potential model. In this work, we study several potential models relevant to the bare state, and solve their inverse scattering problems. The first model, called "cc", is a separable potential model. We show that it can approximate (single-channel short-range) $S$-wave near-threshold physics with an error of $\mathcal{O}(β^3/M_V^3)$, where $β$ sets the maximum momentum of the near-threshold region and $M_V$ is the typical scale of the potential. The second model, called "bc", serves as the bare-state-dominance approximation, where interaction between continuum states is ignored. Under this model, even though the bare state is always crucial for a bound state's generation, a shallow bound state naturally tends to have a small bare-state proportion. Therefore, we need other quantities to quantify the importance of the bare state. The last model, called "bcc", is a combination of the first two models. This model not only serves as a correction to the bare-state-dominance approximation, but can also be used to understand the interplay between quark and hadron degrees of freedom. This model naturally leads to the presence of a Castillejo-Dalitz-Dyson (CDD) zero. We consider the energy decomposition of a bound state. The potential ratio of the bare-continuum interaction to the continuum self-interaction is proposed to understand how the bound state is generated. Model independently, an inequality for the potential ratio is derived. Based on the model "bcc", the CDD zero can be used to estimate the potential ratio. Finally, we apply these studies to the deuteron, $ρ$ meson, and $D_{s0}^*(2317)$, and analyze their properties.

preprint2022arXiv

K-stability and polystable degenerations of polarized spherical varieties

In this paper, we study the K-stability of polarized spherical varieties. After reduction, it can be treated as a variational problem of the reduced functional of the Futaki invariant on the associated moment polytope. With the convexity constraint of the problem, the minimizers are shown to satisfy the homogeneous Monge-Ampère equation (HMA). When the spherical variety has rank two, a simpler characterization can be established through properties of the HMA. As an application, we determine the strict semistability and polystable degenerations for Fano spherical varieties of rank two.

preprint2022arXiv

Measuring the tuning curve of spontaneous parameter down-conversion using a comet-tail-like pattern

The comet-tail-like interference patterns are observed using photons from the spontaneous parametric down-conversion (SPDC) process. The patterns are caused by the angular-spectrum-dependent interference and the diffraction of a blazed grating. We present the theoretical explanation and simulation results for these patterns, which are in good agreement with the experimental results. The most significant feature of the patterns is the bright parabolic contour profile, from which, one can deduce the parameter of the parabolic tuning curve of the SPDC process. This method could be helpful in designing experiments based on SPDC.

preprint2022arXiv

Modularized Bilinear Koopman Operator for Modeling and Predicting Transients of Microgrids

Modularized Koopman Bilinear Form (M-KBF) is presented to model and predict the transient dynamics of microgrids in the presence of disturbances. As a scalable data-driven approach, M-KBF divides the identification and prediction of the high-dimensional nonlinear system into the individual study of subsystems; and thus, alleviating the difficulty of intensively handling high volume data and overcoming the curse of dimensionality. For each subsystem, Koopman bilinear form is applied to efficiently identify its model by developing eigenfunctions via the extended dynamic mode decomposition method with an eigenvalue-based order truncation. Extensive tests show that M-KBF can provide accurate transient dynamics prediction for the nonlinear microgrids and verify the plug-and-play modeling and prediction function, which offers a potent tool for identifying high-dimensional systems. The modularity feature of M-KBF enables the provision of fast and precise prediction for the microgrid operation and control, paving the way towards online applications.

preprint2022arXiv

Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably

We investigate the role of noise in optimization algorithms for learning over-parameterized models. Specifically, we consider the recovery of a rank one matrix $Y^*\in R^{d\times d}$ from a noisy observation $Y$ using an over-parameterization model. We parameterize the rank one matrix $Y^*$ by $XX^\top$, where $X\in R^{d\times d}$. We then show that under mild conditions, the estimator, obtained by the randomly perturbed gradient descent algorithm using the square loss function, attains a mean square error of $O(σ^2/d)$, where $σ^2$ is the variance of the observational noise. In contrast, the estimator obtained by gradient descent without random perturbation only attains a mean square error of $O(σ^2)$. Our result partially justifies the implicit regularization effect of noise when learning over-parameterized models, and provides new understanding of training over-parameterized neural networks.

preprint2022arXiv

Nonoverlapping (delta, gamma)-approximate pattern matching

Pattern matching can be used to calculate the support of patterns, and is a key issue in sequential pattern mining (or sequence pattern mining). Nonoverlapping pattern matching means that two occurrences cannot use the same character in the sequence at the same position. Approximate pattern matching allows for some data noise, and is more general than exact pattern matching. At present, nonoverlapping approximate pattern matching is based on Hamming distance, which cannot be used to measure the local approximation between the subsequence and pattern, resulting in large deviations in matching results. To tackle this issue, we present a Nonoverlapping Delta and gamma approximate Pattern matching (NDP) scheme that employs the (delta, gamma)-distance to give an approximate pattern matching, where the local and the global distances do not exceed delta and gamma, respectively. We first transform the NDP problem into a local approximate Nettree and then construct an efficient algorithm, called the local approximate Nettree for NDP (NetNDP). We propose a new approach called the Minimal Root Distance which allows us to determine whether or not a node has root paths that satisfy the global constraint and to prune invalid nodes and parent-child relationships. NetNDP finds the rightmost absolute leaf of the max root, searches for the rightmost occurrence from the rightmost absolute leaf, and deletes this occurrence. We iterate the above steps until there are no new occurrences. Numerous experiments are used to verify the performance of the proposed algorithm.

preprint2022arXiv

On a Fractional Nirenberg problem involving the square root of the Laplacian on $\mathbb{S}^{3}$

In this paper, we are devoted to establishing the compactness and existence results of the solutions to the fractional Nirenberg problem for $n=3,$ $σ=1/2,$ when the prescribing $σ$-curvature function satisfies the $(n-2σ)$-flatness condition near its critical points. The compactness results are new and optimal. In addition, we obtain a degree-counting formula of all solutions. From our results, we can know where blow up occur. Moreover, for any finite distinct points, the sequence of solutions that blow up precisely at these points can be constructed. We extend the results of Li in \cite[CPAM, 1996]{LYY} from the local problem to nonlocal cases.

preprint2022arXiv

On-Time Communications Over Fading Channels

We consider the on-time transmissions of a sequence of packets over a fading channel.Different from traditional in-time communications, we investigate how many packets can be received $δ$-on-time, meaning that the packet is received with a deviation no larger than $δ$ slots. In this framework, we first derive the on-time reception rate of the random transmissions over the fading channel when no controlling is used. To improve the on-time reception rate, we further propose to schedule the transmissions by delaying, dropping, or repeating the packets. Specifically, we model the scheduling over the fading channel as a Markov decision process (MDP) and then obtain the optimal scheduling policy using an efficient iterative algorithm. For a given sequence of packet transmissions, we analyze the on-time reception rate for the random transmissions and the optimal scheduling. Our analytical and simulation results show that the on-time reception rate of random transmissions decreases (to zero) with the sequence length.By using the optimal packet scheduling, the on-time reception rate converges to a much larger constant. Moreover, we show that the on-time reception rate increases if the target reception interval and/or the deviation tolerance $δ$ is increased, or the randomness of the fading channel is reduced.

preprint2022arXiv

One-off Negative Sequential Pattern Mining

Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this paper discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this paper proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency, but also has better mining performance than the state-of-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.

preprint2022arXiv

OPP-Miner: Order-preserving sequential pattern mining

A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values. Moreover, existing methods mainly neglect the order relationships between time series values. To tackle these issues, inspired by order-preserving matching, this paper proposes an Order-Preserving sequential Pattern (OPP) mining method, which represents patterns based on the order relationships of the time series data. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath the time series data. To obtain frequent trends in time series, we propose the OPP-Miner algorithm to mine patterns with the same trend (sub-sequences with the same relative order). OPP-Miner employs the filtration and verification strategies to calculate the support and uses pattern fusion strategy to generate candidate patterns. To compress the result set, we also study finding the maximal OPPs. Experiments validate that OPP-Miner is not only efficient and scalable but can also discover similar sub-sequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance.

preprint2022arXiv

PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement

A Physics-Informed Dynamic Graph Neural Network (PIDGeuN) is presented to accurately, efficiently and robustly predict the nonlinear transient dynamics of microgrids in the presence of disturbances. The graph-based architecture of PIDGeuN provides a natural representation of the microgrid topology. Using only the state information that is practically measurable, PIDGeuN employs a time delay embedding formulation to fully reproduce the system dynamics, avoiding the dependency of conventional methods on internal dynamic states such as controllers. Based on a judiciously designed message passing mechanism, the PIDGeuN incorporates two physics-informed techniques to improve its prediction performance, including a physics-data-infusion approach to determining the inter-dependencies between buses, and a loss term to respect the known physical law of the power system, i.e., the Kirchhoff's law, to ensure the feasibility of the model prediction. Extensive tests show that PIDGeuN can provide accurate and robust prediction of transient dynamics for nonlinear microgrids over a long-term time period. Therefore, the PIDGeuN offers a potent tool for the modeling of large scale networked microgrids (NMs), with potential applications to predictive or preventive control in real time applications for the stable and resilient operations of NMs.

preprint2022arXiv

Principal Amalgamation Analysis for Microbiome Data

In recent years microbiome studies have become increasingly prevalent and large-scale. Through high-throughput sequencing technologies and well-established analytical pipelines, relative abundance data of operational taxonomic units and their associated taxonomic structures are routinely produced. Since such data can be extremely sparse and high dimensional, there is often a genuine need for dimension reduction to facilitate data visualization and downstream statistical analysis. We propose Principal Amalgamation Analysis (PAA), a novel amalgamation-based and taxonomy-guided dimension reduction paradigm for microbiome data. Our approach aims to aggregate the compositions into a smaller number of principal compositions, guided by the available taxonomic structure, by minimizing a properly measured loss of information. The choice of the loss function is flexible and can be based on familiar diversity indices for preserving either within-sample or between-sample diversity in the data. To enable scalable computation, we develop a hierarchical PAA algorithm to trace the entire trajectory of successive simple amalgamations. Visualization tools including dendrogram, scree plot, and ordination plot are developed. The effectiveness of PAA is demonstrated using gut microbiome data from a preterm infant study and an HIV infection study.

preprint2022arXiv

Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction

Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the research filed of singe image dehazing. To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios. Specifically, the dehazing network is optimized under the consistency-regularized framework with the proposed Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the negative information to facilitate the traditional positive-orient dehazing objective function, by squeezing the dehazed image to its clean target from different directions. Meanwhile, the consistency regularization keeps consistent outputs given multi-level hazy images, thus improving the model robustness. Extensive experimental results on two synthetic and three real-world datasets demonstrate that our method significantly surpasses the state-of-the-art approaches.

preprint2022arXiv

Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection

Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singularity problem and bias in the measurement. The time complexity of DTW is quadratic to the length of time series, making it inapplicable in real-time applications. In this paper, we propose a novel time series dissimilarity measure named RobustDTW to reduce the effects of noises and outliers. Specifically, the RobustDTW estimates the trend and optimizes the time warp in an alternating manner by utilizing our designed temporal graph trend filtering. To improve efficiency, we propose a multi-level framework that estimates the trend and the warp function at a lower resolution, and then repeatedly refines them at a higher resolution. Based on the proposed RobustDTW, we further extend it to periodicity detection and outlier time series detection. Experiments on real-world datasets demonstrate the superior performance of RobustDTW compared to DTW variants in both outlier time series detection and periodicity detection.

preprint2022arXiv

SAMCNet for Spatial-configuration-based Classification: A Summary of Results

The goal of spatial-configuration-based classification is to build a classifier to distinguish two classes (e.g., responder, non-responder) based on the spatial arrangements (e.g., spatial interactions between different point categories) given multi-category point data from two classes. This problem is important for generating hypotheses in medical pathology towards discovering new immunotherapies for cancer treatment as well as for other applications in biomedical research and microbial ecology. This problem is challenging due to an exponential number of category subsets which may vary in the strength of spatial interactions. Most prior efforts on using human selected spatial association measures may not be sufficient for capturing the relevant (e.g., surrounded by) spatial interactions which may be of biological significance. In addition, the related deep neural networks are limited to category pairs and do not explore larger subsets of point categories. To overcome these limitations, we propose a Spatial-interaction Aware Multi-Category deep neural Network (SAMCNet) architecture and contribute novel local reference frame characterization and point pair prioritization layers for spatial-configuration-based classification. Extensive experimental results on multiple cancer datasets show that the proposed architecture provides higher prediction accuracy over baseline methods.

preprint2022arXiv

Steady-state susceptibility in continuous phase transitions of dissipative systems

In this work, we explore the critical behaviors of fidelity susceptibility and trace distance susceptibility associated to the steady states of dissipative systems at continuous phase transitions. We investigate on two typical models, one is the dissipative spin-1/2 XYZ model on two-dimensional square lattice and the other is a driven-dissipative Kerr oscillator. We find that the susceptibilities of fidelity and trace distance exhabit singular behaviors near the critical points of phase transitions in both models. The critical points, in thermodynamic limit, extracted from the scalings of the critical controlling parameters to the system size or nonlinearity agree well with the existed results.

preprint2022arXiv

Two-dimensional modeling of the tearing-mode-governed magnetic reconnection in the large-scale current sheet above the two-ribbon flare

We attempt to model magnetic reconnection during the two-ribbon flare in the gravitationally stratified solar atmosphere with the Lundquist number of $S=10^6$ using 2D simulations. We found that the tearing mode instability leads to the inhomogeneous turbulence inside the reconnecting current sheet (CS) and invokes the fast phase of reconnection. Fast reconnection brings an extra dissipation of magnetic field which enhances the reconnection rate in an apparent way. The energy spectrum in the CS shows the power-law pattern and the dynamics of plasmoids governs the associated spectral index. We noticed that the energy dissipation occurs at a scale $l_{ko}$ of 100-200~km, and the associated CS thickness ranges from 1500 to 2500~km, which follows the Taylor scale $l_T=l_{ko} S^{1/6}$. The termination shock(TS) appears in the turbulent region above flare loops, which is an important contributor to heating flare loops. Substantial magnetic energy is converted into both kinetic and thermal energies via TS, and the cumulative heating rate is greater than the rate of the kinetic energy transfer. In addition, the turbulence is somehow amplified by TS, of which the amplitude is related to the local geometry of the TS.

preprint2022arXiv

Understanding COVID-19 Effects on Mobility: A Community-Engaged Approach

Given aggregated mobile device data, the goal is to understand the impact of COVID-19 policy interventions on mobility. This problem is vital due to important societal use cases, such as safely reopening the economy. Challenges include understanding and interpreting questions of interest to policymakers, cross-jurisdictional variability in choice and time of interventions, the large data volume, and unknown sampling bias. The related work has explored the COVID-19 impact on travel distance, time spent at home, and the number of visitors at different points of interest. However, many policymakers are interested in long-duration visits to high-risk business categories and understanding the spatial selection bias to interpret summary reports. We provide an Entity Relationship diagram, system architecture, and implementation to support queries on long-duration visits in addition to fine resolution device count maps to understand spatial bias. We closely collaborated with policymakers to derive the system requirements and evaluate the system components, the summary reports, and visualizations.

preprint2021arXiv

Adjusted Logistic Propensity Weighting Methods for Population Inference using Nonprobability Volunteer-Based Epidemiologic Cohorts

Many epidemiologic studies forgo probability sampling and turn to nonprobability volunteer-based samples because of cost, response burden, and invasiveness of biological samples. However, finite population inference is difficult to make from the nonprobability samples due to the lack of population representativeness. Aiming for making inferences at the population level using nonprobability samples, various inverse propensity score weighting (IPSW) methods have been studied with the propensity defined by the participation rate of population units in the nonprobability sample. In this paper, we propose an adjusted logistic propensity weighting (ALP) method to estimate the participation rates for nonprobability sample units. Compared to existing IPSW methods, the proposed ALP method is easy to implement by ready-to-use software while producing approximately unbiased estimators for population quantities regardless of the nonprobability sample rate. The efficiency of the ALP estimator can be further improved by scaling the survey sample weights in propensity estimation. Taylor linearization variance estimators are proposed for ALP estimators of finite population means that account for all sources of variability. The proposed ALP methods are evaluated numerically via simulation studies and empirically using the naïve unweighted National Health and Nutrition Examination Survey III sample, while taking the 1997 National Health Interview Survey as the reference, to estimate the 15-year mortality rates.

preprint2021arXiv

Dynamic list coloring of 1-planar graphs

A graph is $k$-planar if it can be drawn in the plane so that each edge is crossed at most $k$ times. Typically, the class of 1-planar graphs is among the most investigated graph families within the so-called "beyond planar graphs". A dynamic $\ell$-list coloring of a graph is a proper coloring so that each vertex receives a color from a list of $\ell$ distinct candidate colors assigned to it, and meanwhile, there are at least two colors appearing in the neighborhood of every vertex of degree at least two. In this paper, we prove that each 1-planar graph has a dynamic $11$-list coloring. Moreover, we show a relationship between the dynamic coloring of 1-planar graphs and the proper coloring of 2-planar graphs, which states that the dynamic (list) chromatic number of the class of 1-planar graphs is at least the (list) chromatic number of the class of 2-planar graphs.

preprint2021arXiv

IFoodCloud: A Platform for Real-time Sentiment Analysis of Public Opinion about Food Safety in China

The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues. In order to systematically collect and analyse public opinion on food safety, we developed IFoodCloud, a platform for the real-time sentiment analysis of public opinion on food safety in China. It collects data from more than 3,100 public sources that can be used to explore public opinion trends, public sentiment, and regional attention differences of food safety incidents. At the same time, we constructed a sentiment classification model using multiple lexicon-based and deep learning-based algorithms integrated with IFoodCloud that provide an unprecedented rapid means of understanding the public sentiment toward specific food safety incidents. Our best model's F1-score achieved 0.9737. Further, three real-world cases are presented to demonstrate the application and robustness. IFoodCloud could be considered a valuable tool for promote scientisation of food safety supervision and risk communication.

preprint2021arXiv

Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization

Numerous empirical evidences have corroborated the importance of noise in nonconvex optimization problems. The theory behind such empirical observations, however, is still largely unknown. This paper studies this fundamental problem through investigating the nonconvex rectangular matrix factorization problem, which has infinitely many global minima due to rotation and scaling invariance. Hence, gradient descent (GD) can converge to any optimum, depending on the initialization. In contrast, we show that a perturbed form of GD with an arbitrary initialization converges to a global optimum that is uniquely determined by the injected noise. Our result implies that the noise imposes implicit bias towards certain optima. Numerical experiments are provided to support our theory.

preprint2021arXiv

Pursuing Sources of Heterogeneity in Modeling Clustered Population

Researchers often have to deal with heterogeneous population with mixed regression relationships, increasingly so in the era of data explosion. In such problems, when there are many candidate predictors, it is not only of interest to identify the predictors that are associated with the outcome, but also to distinguish the true sources of heterogeneity, i.e., to identify the predictors that have different effects among the clusters and thus are the true contributors to the formation of the clusters. We clarify the concepts of the source of heterogeneity that account for potential scale differences of the clusters and propose a regularized finite mixture effects regression to achieve heterogeneity pursuit and feature selection simultaneously. As the name suggests, the problem is formulated under an effects-model parameterization, in which the cluster labels are missing and the effect of each predictor on the outcome is decomposed to a common effect term and a set of cluster-specific terms. A constrained sparse estimation of these effects leads to the identification of both the variables with common effects and those with heterogeneous effects. We propose an efficient algorithm and show that our approach can achieve both estimation and selection consistency. Simulation studies further demonstrate the effectiveness of our method under various practical scenarios. Three applications are presented, namely, an imaging genetics study for linking genetic factors and brain neuroimaging traits in Alzheimer's disease, a public health study for exploring the association between suicide risk among adolescents and their school district characteristics, and a sport analytics study for understanding how the salary levels of baseball players are associated with their performance and contractual status.

preprint2021arXiv

Towards high partial waves in lattice QCD with a dumbbell-like operator

An extended two-hadron operator is developed to extract the spectra of irreducible representations (irreps) in the finite volume. The irreps of the group for the finite volume system are projected using a coordinate-space operator. The correlation function of this operator is computationally efficient to extract lattice spectra of the specific irrep. In particular, this new formulation only requires propagators to be computed from two distinct source locations, at fixed spatial separation. We perform a proof-of-principle study on a $24^3 \times 48$ lattice volume with $m_π\approx 900$ MeV by isolating various spectra of the $ππ$ system with isospin-2 including a range of total momenta and irreps. By applying the Lüscher formalism, the phase shifts of $S$-, $D$- and $G$-wave $ππ$ scattering with isospin-2 are extracted from the spectra.

preprint2020arXiv

A Physics Model-Guided Online Bayesian Framework for Energy Management of Extended Range Electric Delivery Vehicles

Increasing the fuel economy of hybrid electric vehicles (HEVs) and extended range electric vehicles (EREVs) through optimization-based energy management strategies (EMS) has been an active research area in transportation. However, it is difficult to apply optimization-based EMS to current in-use EREVs because insufficient knowledge is known about future trips, and because such methods are computationally expensive for large-scale deployment. As a result, most past research has been validated on standard driving cycles or on recorded high-resolution data from past real driving cycles. This paper improves an in-use rule-based EMS that is used in a delivery vehicle fleet equipped with two-way vehicle-to-cloud connectivity. A physics model-guided online Bayesian framework is described and validated on large number of in-use driving samples of EREVs used for last-mile package delivery. The framework includes: a database, a preprocessing module, a vehicle model and an online Bayesian algorithm module. It uses historical 0.2 Hz resolution trip data as input and outputs an updated parameter to the engine control logic on the vehicle to reduce fuel consumption on the next trip. The key contribution of this work is a framework that provides an immediate solution for fuel use reduction of in-use EREVs. The framework was also demonstrated on real-world EREVs delivery vehicles operating on actual routes. The results show an average of 12.8% fuel use reduction among tested vehicles for 155 real delivery trips. The presented framework is extendable to other EREV applications including passenger vehicles, transit buses, and other vocational vehicles whose trips are similar day-to-day.

preprint2020arXiv

Adversarial Attacks on Reinforcement Learning based Energy Management Systems of Extended Range Electric Delivery Vehicles

Adversarial examples are firstly investigated in the area of computer vision: by adding some carefully designed ''noise'' to the original input image, the perturbed image that cannot be distinguished from the original one by human, can fool a well-trained classifier easily. In recent years, researchers also demonstrated that adversarial examples can mislead deep reinforcement learning (DRL) agents on playing video games using image inputs with similar methods. However, although DRL has been more and more popular in the area of intelligent transportation systems, there is little research investigating the impacts of adversarial attacks on them, especially for algorithms that do not take images as inputs. In this work, we investigated several fast methods to generate adversarial examples to significantly degrade the performance of a well-trained DRL- based energy management system of an extended range electric delivery vehicle. The perturbed inputs are low-dimensional state representations and close to the original inputs quantified by different kinds of norms. Our work shows that, to apply DRL agents on real-world transportation systems, adversarial examples in the form of cyber-attack should be considered carefully, especially for applications that may lead to serious safety issues.

preprint2020arXiv

Air-to-Air Communications Beyond 5G: A Novel 3D CoMP Transmission Scheme

In this paper, a novel $3$D cellular model consisting of aerial base stations (aBSs) and aerial user equipments (aUEs) is proposed, by integrating the coordinated multi-point (CoMP) transmission technique with the theory of stochastic geometry. For this new $3$D architecture, a tractable model for aBSs' deployment based on the binomial-Delaunay tetrahedralization is developed, which ensures seamless coverage for a given space. In addition, a versatile and practical frequency allocation scheme is designed to eliminate the inter-cell interference effectively. Based on this model, performance metrics including the achievable data rate and coverage probability are derived for two types of aUEs: {\it i)} the general aUE (i.e., an aUE having distinct distances from its serving aBSs) and {\it ii)} the worst-case aUE (i.e., an aUE having equal distances from its serving aBSs). Simulation and numerical results demonstrate that the proposed approach emphatically outperforms the conventional binomial-Voronoi tessellation without CoMP. Insightfully, it provides a similar performance to the binomial-Voronoi tessellation which utilizes the conventional CoMP scheme; yet, introducing a considerably reduced computational complexity and backhaul/signaling overhead.

preprint2020arXiv

An extended Flaherty-Keller formula for an elastic composite with densely packed convex inclusions

In this paper, we are concerned with the effective elastic property of a two-phase high-contrast periodic composite with densely packed inclusions. The equations of linear elasticity are assumed. We first give a novel proof of the Flaherty-Keller formula for elliptic inclusions, which improves a recent result of Kang and Yu (Calc.Var.Partial Differential Equations, 2020). We construct an auxiliary function consisting of the Keller function and an additional corrected function depending on the coefficients of Lamé system and the geometry of inclusions, to capture the full singular term of the gradient. On the other hand, this method allows us to deal with the inclusions of arbitrary shape, even with zero curvature. An extended Flaherty-Keller formula is proved for m-convex inclusions, m > 2, curvilinear squares with round off angles, which minimize the elastic modulus under the same volume fraction of hard inclusions.

preprint2020arXiv

Bilinear Graph Neural Network with Neighbor Interactions

Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co-occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well-known GCN and GAT, respectively. Empirical results on three public benchmarks of semi-supervised node classification verify the effectiveness of BGNN -- BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5%) in classification accuracy.Codes are available at: https://github.com/zhuhm1996/bgnn.

preprint2020arXiv

Chip-to-chip quantum teleportation and multi-photon entanglement in silicon

Exploiting semiconductor fabrication techniques, natural carriers of quantum information such as atoms, electrons, and photons can be embedded in scalable integrated devices. Integrated optics provides a versatile platform for large-scale quantum information processing and transceiving with photons. Scaling up the integrated devices for quantum applications requires highperformance single-photon generation and photonic qubit-qubit entangling operations. However, previous demonstrations report major challenges in producing multiple bright, pure and identical single-photons, and entangling multiple photonic qubits with high fidelity. Another notable challenge is to noiselessly interface multiphoton sources and multiqubit operators in a single device. Here we demonstrate on-chip genuine multipartite entanglement and quantum teleportation in silicon, by coherently controlling an integrated network of microresonator nonlinear single-photon sources and linear-optic multiqubit entangling circuits. The microresonators are engineered to locally enhance the nonlinearity, producing multiple frequencyuncorrelated and indistinguishable single-photons, without requiring any spectral filtering. The multiqubit states are processed in a programmable linear circuit facilitating Bell-projection and fusion operation in a measurement-based manner. We benchmark key functionalities, such as intra-/inter-chip teleportation of quantum states, and generation of four-photon Greenberger-HorneZeilinger entangled states. The production, control, and transceiving of states are all achieved in micrometer-scale silicon chips, fabricated by complementary metal-oxide-semiconductor processes. Our work lays the groundwork for scalable on-chip multiphoton technologies for quantum computing and communication.

preprint2020arXiv

Coordinated Multi-Point Transmission: A Poisson-Delaunay Triangulation Based Approach

Coordinated multi-point (CoMP) transmission is a cooperating technique among base stations (BSs) in a cellular network, with outstanding capability at inter-cell interference (ICI) mitigation. ICI is a dominant source of error, and has detrimental effects on system performance if not managed properly. Based on the theory of Poisson-Delaunay triangulation, this paper proposes a novel analytical model for CoMP operation in cellular networks. Unlike the conventional CoMP operation that is dynamic and needs on-line updating occasionally, the proposed approach enables the cooperating BS set of a user equipment (UE) to be fixed and off-line determined according to the location information of BSs. By using the theory of stochastic geometry, the coverage probability and spectral efficiency of a typical UE are analyzed, and simulation results corroborate the effectiveness of the proposed CoMP scheme and the developed performance analysis.

preprint2020arXiv

Deep Reinforcement Learning with Robust and Smooth Policy

Deep reinforcement learning (RL) has achieved great empirical successes in various domains. However, the large search space of neural networks requires a large amount of data, which makes the current RL algorithms not sample efficient. Motivated by the fact that many environments with continuous state space have smooth transitions, we propose to learn a smooth policy that behaves smoothly with respect to states. We develop a new framework -- \textbf{S}mooth \textbf{R}egularized \textbf{R}einforcement \textbf{L}earning ($\textbf{SR}^2\textbf{L}$), where the policy is trained with smoothness-inducing regularization. Such regularization effectively constrains the search space, and enforces smoothness in the learned policy. Moreover, our proposed framework can also improve the robustness of policy against measurement error in the state space, and can be naturally extended to distribubutionally robust setting. We apply the proposed framework to both on-policy (TRPO) and off-policy algorithm (DDPG). Through extensive experiments, we demonstrate that our method achieves improved sample efficiency and robustness.

preprint2020arXiv

Dipole coupling of a tunable hole double quantum dot in germanium hut wire to a microwave resonator

The germanium (Ge) hut wire system has strong spin-orbit coupling, a long coherence time due to a very large heavy-light hole splitting, and the advantage of site-controlled large-scale hut wire positioning. These properties make the Ge hut wire a promising candidate for the realization of strong coupling of spin to superconducting resonators and scalability for multiple qubit coupling. We have coupled a reflection line resonator to a hole double quantum dot (DQD) formed in Ge hut wire. The amplitude and phase responses of the microwave resonator revealed that the charge stability diagrams of the DQD are in good agreement with those obtained from transport measurements. The DQD interdot tunneling rate is shown to be tunable from 6.2 GHz to 8.5 GHz, which demonstrates the ability to adjust the frequency detuning between the qubit and the resonator. Furthermore, we achieved a hole-resonator coupling strength of up to 15 MHz, with a charge qubit decoherence rate of 0.28 GHz. Meanwhile the hole spin-resonator coupling rate was estimated to be 3 MHz. These results suggest that holes of a DQD in a Ge hut wire are dipole coupled to microwave photons, potentially enabling tunable hole spin-photon interactions in Ge with an inherent spin-orbit coupling.

preprint2020arXiv

Edge Federation: Towards an Integrated Service Provisioning Model

Edge computing is a promising computing paradigm for pushing the cloud service to the network edge. To this end, edge infrastructure providers (EIPs) need to bring computation and storage resources to the network edge and allow edge service providers (ESPs) to provision latency-critical services to users. Currently, EIPs prefer to establish a series of private edge-computing environments to serve specific requirements of users. This kind of resource provisioning mechanism severely limits the development and spread of edge computing for serving diverse user requirements. To this end, we propose an integrated resource provisioning model, named edge federation, to seamlessly realize the resource cooperation and service provisioning across standalone edge computing providers and clouds. To efficiently schedule and utilize the resources across multiple EIPs, we systematically characterize the provisioning process as a large-scale linear programming (LP) problem and transform it into an easily solved form. Accordingly, we design a dynamic algorithm to tackle the varying service demands from users. We conduct extensive experiments over the base station networks in Toronto city. Compared with the existing fixed contract model and multihoming model, edge federation can reduce the overall cost of EIPs by 23.3% to 24.5%, and 15.5% to 16.3%, respectively.

preprint2020arXiv

Electronic Raman Scattering in Suspended Semiconducting Carbon Nanotubes

The electronic Raman scattering (ERS) features of single-walled carbon nanotubes (SWNTs) can reveal a wealth of information about their electronic structures, but have previously been thought to appear exclusively in metallic (M-) but not in semiconducting (S-) SWNTs. We report the experimental observation of the ERS features with an accuracy of 1 meV in suspended S-SWNTs, the processes of which are accomplished via the available high-energy electron-hole pairs. The ERS features can facilitate further systematic studies on the properties of SWNT, both metallic and semiconducting, with defined chirality.

preprint2020arXiv

Extra-cavity-enhanced difference-frequency generation at 1.63 μm

A 1632-nm laser has highly important applications in interfacing the wavelength of rubidium-based quantum memories (795 nm) and the telecom band (typically 1550 nm) by frequency conversion in three-wave mixing processes. A 1632-nm laser source based on pump-enhanced difference frequency generation is demonstrated. It has 300 mW of output power, in agreement with simulations, and a 55% quantum efficiency. An average power fluctuation of 0.51% over one hour was observed, and 200-kHz linewidth was measured using a delayed self-heterodyne method.

preprint2020arXiv

Finiteness of $\mathbb Q$-Fano compactifications of semisimple group with Kähler-Einstein metrics

In this note, we give a way to classify $\mathbb Q$-Fano compactifications of a semisimple group $G$. We will prove that there are only finitely many such $\mathbb Q$-Fano $G$-compactifications, which admits (singular) Kähler-Einstein metrics. As an application, this improves a former result in arXiv:2001.11320.

preprint2020arXiv

GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries

The automatic identification system (AIS), an automatic vessel-tracking system, has been widely adopted to perform intelligent traffic management and collision avoidance services in maritime Internet of Things (IoT) industries. With the rapid development of maritime transportation, tremendous numbers of AIS-based vessel trajectory data have been collected, which make trajectory data compression imperative and challenging. This paper mainly focuses on the compression and visualization of large-scale vessel trajectories and their Graphics Processing Unit (GPU)-accelerated implementations. The visualization was implemented to investigate the influence of compression on vessel trajectory data quality. In particular, the Douglas-Peucker (DP) and Kernel Density Estimation (KDE) algorithms, respectively utilized for trajectory compression and visualization, were significantly accelerated through the massively parallel computation capabilities of GPU architecture. Comprehensive experiments on trajectory compression and visualization have been conducted on large-scale AIS data of recording ship movements collected from 3 different water areas, i.e., the South Channel of Yangtze River Estuary, the Chengshan Jiao Promontory, and the Zhoushan Islands. Experimental results illustrated that (1) the proposed GPU-based parallel implementation frameworks could significantly reduce the computational time for both trajectory compression and visualization; (2) the influence of compressed vessel trajectories on trajectory visualization could be negligible if the compression threshold was selected suitably; (3) the Gaussian kernel was capable of generating more appropriate KDE-based visualization performance by comparing with other seven kernel functions.

preprint2020arXiv

How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.

preprint2020arXiv

Increasing two-photon entangled dimensions by shaping input beam profiles

Photon pair entangled in high dimensional orbital angular momentum (OAM) degree of freedom (DOF) has been widely regarded as a possible source in improving the capacity of quantum information processing. The need for the generation of a high dimensional maximally entangled state in the OAM DOF is therefore much desired. In this work, we demonstrate a simple method to generate a broader and flatter OAM spectrum, i.e. a larger spiral bandwidth (SB), of entangled photon pairs generated through spontaneous parametric down-conversion by modifying the pump beam profile. By investigating both experimentally and theoretically, we have found that an exponential pump profile that is roughly the inverse of the mode profiles of the single-mode fibers used for OAM detection will provide a much larger SB when compared to a Gaussian shaped pump.

preprint2020arXiv

Information scrambling in a collision model

The information scrambling in many-body systems is closely related to quantum chaotic dynamics, complexity, and gravity. Here we propose a collision model to simulate the information dynamics in an all-optical system. In our model the information is initially localized in the memory and evolves under the combined actions of many-body interactions and dissipation. We find that the information is scrambled if the memory and environmental particles are alternatively squeezed along two directions which are perpendicular to each other. Moreover, the disorder and imperfection of the interaction strength tend to prevent the information flow away to the environment and lead to the information scrambling in the memory. We analyze the spatial distributions of the correlations in the memory. Our proposal is possible to realize with current experimental techniques.

preprint2020arXiv

KIC 10736223: An Algol-type eclipsing binary just undergone the rapid mass-transfer stage

This paper reports the discovery of an Algol system KIC 10736223 that just past the rapid mass transfer stage. From the light curve and radial-velocity modelling we find KIC 10736223 to be a detached Algol system with the less-massive secondary nearly filling its Roche lobe. Based on the short-cadence Kepler data, we analyzed intrinsic oscillations of the pulsator and identified six secured independent $δ$ Scuti-type pulsation modes ($f_{1}$, $f_3$, $f_{9}$, $f_{19}$, $f_{42}$, and $f_{48}$). We compute two grids of theoretical models to reproduce the $δ$ Scuti freqiencies, and find fitting results of mass-accreting models meet well with those of single-star evolutionary models. The fundamental parameters of the primary star yielded with asteroseismology are $M$ = $1.57^{+0.05}_{-0.09}$ $M_{\odot}$, $Z$ = 0.009 $\pm$ 0.001, $R$ = $1.484^{+0.016}_{-0.028}$ $R_{\odot}$, $\log g$ = $4.291^{+0.004}_{-0.009}$, $T_{\rm eff}$ = $7748^{+230}_{-378}$ K, $L$ = $7.136^{+1.014}_{-1.519}$ $L_{\odot}$. The asteroseismic parameters match well with the dynamical parameters derived from the binary model. Moreover, our asteroseismic results show that the pulsator is an almost unevolved star with an age between 9.46-11.65 Myr for single-star evolutionary models and 2.67-3.14 Myr for mass-accreting models. Thereofore, KIC 10736223 may be an Algol system that has just undergone the rapid mass-transfer process.

preprint2020arXiv

Large spin to charge conversion in topological superconductor \b{eta}-PdBi2 at room temperature

\b{eta}-PdBi2 has attracted much attention for its prospective ability to possess simultaneously topological surface and superconducting states due to its unprecedented spin-orbit interaction (SOC). Whereas most works have focused solely on investigating its topological surface states, the coupling between spin and charge degrees of freedom in this class of quantum material remains unexplored. Here we first report a study of spin-to-charge conversion in a \b{eta}-PdBi2 ultrathin film grown by molecular beam epitaxy, utilizing a spin pumping technique to perform inverse spin Hall effect measurements. We find that the room temperature spin Hall angle of Fe/\b{eta}-PdBi2, θ_SH=0.037. This value is one order of magnitude larger than that of reported conventional superconductors, and is comparable to that of the best SOC metals and topological insulators. Our results provide an avenue for developing superconductor-based spintronic applications.

preprint2020arXiv

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well understood. Existing work that adapts GCN to recommendation lacks thorough ablation analyses on GCN, which is originally designed for graph classification tasks and equipped with many neural network operations. However, we empirically find that the two most common designs in GCNs -- feature transformation and nonlinear activation -- contribute little to the performance of collaborative filtering. Even worse, including them adds to the difficulty of training and degrades recommendation performance. In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering. Specifically, LightGCN learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted sum of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train, exhibiting substantial improvements (about 16.0\% relative improvement on average) over Neural Graph Collaborative Filtering (NGCF) -- a state-of-the-art GCN-based recommender model -- under exactly the same experimental setting. Further analyses are provided towards the rationality of the simple LightGCN from both analytical and empirical perspectives.

preprint2020arXiv

Partial Wave Mixing in Hamiltonian Effective Field Theory

The spectrum of excited states observed in the finite volume of lattice QCD is governed by the discrete symmetries of the cubic group. This finite group permits the mixing of orbital angular momentum quanta in the finite volume. As experimental results refer to specific angular momentum in a partial-wave decomposition, a formalism mapping the partial-wave scattering potentials to the finite volume is required. This formalism is developed herein for Hamiltonian effective field theory, an extension of chiral effective field theory incorporating the Lüscher relation linking the energy levels observed in finite volume to the scattering phase shift. The formalism provides an optimal set of rest-frame basis states maximally reducing the dimension of the Hamiltonian, and it should work in any Hamiltonian formalism. As a first example of the formalism's implementation, lattice QCD results for the spectrum of an isospin-2 $ππ$ scattering system are analyzed to determine the $s$, $d$, and $g$ partial-wave scattering information.

preprint2020arXiv

Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers

We propose using machine learning models for the direct synthesis of on-chip electromagnetic (EM) passive structures to enable rapid or even automated designs and optimizations of RF/mm-Wave circuits. As a proof of concept, we demonstrate the direct synthesis of a 1:1 transformer on a 45nm SOI process using our proposed neural network model. Using pre-existing transformer s-parameter files and their geometric design training samples, the model predicts target geometric designs.

preprint2020arXiv

Review on Set-Theoretic Methods for Safety Verification and Control of Power System

Increasing penetration of renewable energy introduces significant uncertainty into power systems. Traditional simulation-based verification methods may not be applicable due to the unknown-but-bounded feature of the uncertainty sets. Emerging set-theoretic methods have been intensively investigated to tackle this challenge. The paper comprehensively reviews these methods categorized by underlying mathematical principles, that is, set operation-based methods and passivity-based methods. Set operation-based methods are more computationally efficient, while passivity-based methods provide semi-analytical expression of reachable sets, which can be readily employed for control. Other features between different methods are also discussed and illustrated by numerical examples. A benchmark example is presented and solved by different methods to verify consistency.

preprint2020arXiv

Singular Kähler-Einstein metrics on $\mathbb Q$-Fano compactifications of Lie groups

In this paper, we prove an existence result for Kähler-Einstein metrics on $\mathbb Q$-Fano compactifications of Lie groups. As an application, we classify $\mathbb Q$-Fano compactifications of $SO_4(\mathbb C)$ which admit a Kähler-Einstein metric with the same volume as that of a smooth Fano compactification of $SO_4(\mathbb C)$.

preprint2020arXiv

TEA: Temporal Excitation and Aggregation for Action Recognition

Temporal modeling is key for action recognition in videos. It normally considers both short-range motions and long-range aggregations. In this paper, we propose a Temporal Excitation and Aggregation (TEA) block, including a motion excitation (ME) module and a multiple temporal aggregation (MTA) module, specifically designed to capture both short- and long-range temporal evolution. In particular, for short-range motion modeling, the ME module calculates the feature-level temporal differences from spatiotemporal features. It then utilizes the differences to excite the motion-sensitive channels of the features. The long-range temporal aggregations in previous works are typically achieved by stacking a large number of local temporal convolutions. Each convolution processes a local temporal window at a time. In contrast, the MTA module proposes to deform the local convolution to a group of sub-convolutions, forming a hierarchical residual architecture. Without introducing additional parameters, the features will be processed with a series of sub-convolutions, and each frame could complete multiple temporal aggregations with neighborhoods. The final equivalent receptive field of temporal dimension is accordingly enlarged, which is capable of modeling the long-range temporal relationship over distant frames. The two components of the TEA block are complementary in temporal modeling. Finally, our approach achieves impressive results at low FLOPs on several action recognition benchmarks, such as Kinetics, Something-Something, HMDB51, and UCF101, which confirms its effectiveness and efficiency.

preprint2020arXiv

The A Priori Estimate and Existence of the Positive Solution for A Nonlinear System Involving the Fractional Laplacian

In the paper, we consider the fractional elliptic system \begin{equation*}\left\{\begin{array}{ll} (- Δ)^{\frac{α_1}{2}}u(x)+\sum\limits^n_{i=1}b_i(x)\frac{\partial u}{\partial x_i}+B(x)u(x)=f(x,u,v),& \mbox { in } Ω,\\ (- Δ)^{\frac{α_2}{2}}v(x)+\sum\limits^n_{i=1}c_i(x)\frac{\partial v}{\partial x_i}+C(x)v(x)=g(x,u,v),& \mbox { in } Ω,\\ u=v=0, & \mbox { in } \mathbb{R}^n\setminusΩ, \end{array} \right.\label{a-1.2} \end{equation*} where $Ω$ is a bounded domain with $C^2$ boundary in $\mathbb{R}^n$ and $n>\max\{α_1,α_2\}$. We first utilize the blowing-up and re-scaling method to derive the a priori estimate for positive solutions when $1<α_1,α_2 <2$. Then for $0<α_1,α_2 <1$, we obtain the regularity estimate of positive solutions. On top of this, using the topological degree theory we prove the existence of positive solutions.

preprint2020arXiv

The finite dimensional subalgebra classification of infinite dimensional symmetry algebra of two dimensional coupled nonlinear Schrödinger equations

The symmetry group structures of two dimensional coupled nonlinear Shrödinger equations are considered. We first show that the equations admit infinite dimensional symmetry algebra as well as the corresponding symmetry group depending on four arbitrary functions of one variable. Then we show some physical symmetries and an affine loop algebra contained in the symmetry algebra of the equations. Third, we give the complete classifications of finite dimension (less than four) subalgebras of the symmetry algebra under the adjoint group of the symmetry group. These results provide the theoretical and computational basis for the further study of the equations with symmetry methods.

preprint2020arXiv

Tian's $α_{m,k}^{\hat K}$-invariants on group compactifications

In this paper, we compute Tian's $α_{m,k}^{K\times K}$-invariant on a polarized $G$-group compactification, where $K$ denotes a maximal compact subgroup of a connected complex reductive group $G$. We prove that Tian's conjecture (see Conjecture 1.1 below) is true for $α_{m,k}^{K\times K}$-invariant on such manifolds when $k=1$, but it fails in general by producing counter-examples when $k\ge 2$.

preprint2020arXiv

Transductive Zero-Shot Learning with Visual Structure Constraint

To recognize objects of the unseen classes, most existing Zero-Shot Learning(ZSL) methods first learn a compatible projection function between the common semantic space and the visual space based on the data of source seen classes, then directly apply it to the target unseen classes. However, in real scenarios, the data distribution between the source and target domain might not match well, thus causing the well-known \textbf{domain shift} problem. Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i.e. alleviate the above domain shift problem). Specifically, three different strategies (symmetric Chamfer-distance, Bipartite matching distance, and Wasserstein distance) are adopted to align the projected unseen semantic centers and visual cluster centers of test instances. We also propose a new training strategy to handle the real cases where many unrelated images exist in the test dataset, which is not considered in previous methods. Experiments on many widely used datasets demonstrate that the proposed visual structure constraint can bring substantial performance gain consistently and achieve state-of-the-art results. The source code is available at \url{https://github.com/raywzy/VSC}.

preprint2020arXiv

Ultra-slow sound in non-resonant meta-aerogel

The manipulation of sound with acoustic metamaterials is a field of intense research, where interaction via resonance is a common application despite the significant disadvantages. We propose a novel procedure for introducing well-designed coupling interfaces with a cell size of less than 10 nm into an ultra-soft porous medium, to prepare a meta-aerogel, where the sound propagation is significantly delayed in a non-resonant mode. The resultant sound velocity is shown as a scaling law with the mass density and the mass fraction ratio of the components, in accordance with our analytical model. We have prepared a meta-aerogel with the slowest sound velocity of 62 m/s. To the best of our knowledge, this is the lowest value in compact solid materials, with a prospect of further slowing down by our procedure. The development of such meta-aerogels can facilitate key applications in acoustic metamaterials intended to employ non-resonant type slow sound (or phase delay). Examples of the latter include deep subwavelength meta-surface and other focused imaging or transformation acoustics that require a high contrast of sound velocity.

preprint2019arXiv

A high-dimensional quantum frequency converter

In high dimensional quantum communication networks, quantum frequency convertor (QFC) is indispensable as an interface in the frequency domain. For example, many QFCs have been built to link atomic memories and fiber channels. However, almost all of QFCs work in a two-dimensional space. It is still a pivotal challenge to construct a high-quality QFC for some complex quantum states, e.g., a high dimensional single-photon state that refers to a qudit. Here, we firstly propose a high-dimensional QFC for an orbital angular momentum qudit via sum frequency conversion with a flat top beam pump. As a proof-of-principle demonstration, we realize quantum frequency conversions for a qudit from infrared to visible range. Based on the qudit quantum state tomography, the fidelities of converted state are 98.29(95.02)\%, 97.42(91.74)\%, and 86.75(67.04)\% for a qudit without (with) dark counts in 2,3, and 5 dimensions, respectively. The demonstration is very promising for constructing a high capacity quantum communication network.

preprint2019arXiv

Controllable selective coupling of Dyakonov surface wave at liquid crystal based interface

Highly directional and lossless surface wave has significant potential applications in the two-dimensional photonic circuits and devices. Here we experimentally demonstrate a selective Dyakonov surface wave coupling at the interface between a transparent polycarbonate material and nematic liquid crystal 5CB. By controlling the anisotropy of the nematic liquid crystal with an applied magnetic field, a single ray at a certain incident angle from a diverged incident beam can be selectively coupled into surface wave. The implementation of this property may lead to a new generation of on-chip integrated optics and two-dimensional photonic devices.

preprint2019arXiv

Exploring the convective core of the hybrid $δ$ Scuti-$γ$ Doradus star CoRoT 100866999 with asteroseismology

We computed a grid of theoretical models to fit the $δ$ Scuti frequencies of CoRoT 100866999 detected earlier from the CoRoT timeserials. The pulsating primary star is determined to be a main sequence star with a rotation period of $4.1^{+0.6}_{-0.5}$ days, rotating slower than the orbital motion. The fundamental parameters of the primary star are determined to be $M$ = $1.71^{+0.13}_{-0.04}$ $M_{\odot}$, $Z=0.012^{+0.004}_{-0.000}$, $f_{\rm ov}$ = $0.02^{+0.00}_{-0.02}$, $T_{\rm eff}$ = $8024^{+249}_{-297}$ K, $L$ = $11.898^{+2.156}_{-1.847}$ $L_{\odot}$, $\log g$ = $4.166^{+0.013}_{-0.002}$, $R$ = $1.787^{+0.040}_{-0.016}$ $R_{\odot}$, and $X_{\rm c}$ = 0.488$^{+0.011}_{-0.020}$, matching well those obtained from the eclipsing light curve analysis. Based on the model fittings, $p_1$ and $p_5$ are suggested to be two dipole modes, and $p_3$, $p_4$, $p_6$, and $p_7$ to be four quadrupole modes. In particular, $p_4$ and $p_7$ are identified as two components of one quintuplet. Based on the best-fitting model, we find that $p_1$ is a g mode and the other nonradial modes have pronounced mixed characters, which give strong constraints on the convective core. Finally, the relative size of the convective core of CoRoT 100866999 is determined to $R_{\rm conv}/R$ = $0.0931^{+0.0003}_{-0.0013}$.

preprint2019arXiv

Frequency up-conversion of an infrared image via a flat-top pump beam

The infrared imaging detection is an important and promising technology having wide applications. In this work, we report on the frequency up-conversion detection of an image based on sum frequency generation in a nonlinear crystal with a flat-top beam acting as the pump instead of a Gaussian beam, the up-converted image at 525 nm falls in the sensitive band of visible detectors and human eyes. Both theoretical simulations and the experimental results clearly demonstrate that using a flat-top beam as a pump can improve the fidelity of an image after the up-conversion compared with a Gaussian pump beam. Our scheme will be very promising for infrared image detection based on frequency up-conversion.

preprint2019arXiv

Partial Wave Mixing in Hamiltonian Effective Field Theory

We explore partial-wave mixing in the finite volume based on HEFT, and provide the P-Matrix to show the degree of partial-wave mixing. An example of isospin-2 $ππ$ scattering is used to check the consistency between HEFT and Lüscher's method.

preprint2018arXiv

One-dimensional van der Waals heterostructures

Property by design is one appealing idea in material synthesis but hard to achieve in practice. A recent successful example is the demonstration of van der Waals (vdW) heterostructures,1-3 in which atomic layers are stacked on each other and different ingredients can be combined beyond symmetry and lattice matching. This concept, usually described as a nanoscale Lego blocks, allows to build sophisticated structures layer by layer. However, this concept has been so far limited in two dimensional (2D) materials. Here we show a class of new material where different layers are coaxially (instead of planarly) stacked. As the structure is in one dimensional (1D) form, we name it "1D vdW heterostructures". We demonstrate a 5 nm diameter nanotube consisting of three different materials: an inner conductive carbon nanotube (CNT), a middle insulating hexagonal boron nitride nanotube (BNNT) and an outside semiconducting MoS2 nanotube. As the technique is highly applicable to other materials in the current 2D libraries,4-6 we anticipate our strategy to be a starting point for discovering a class of new semiconducting nanotube materials. A plethora of function-designable 1D heterostructures will appear after the combination of CNTs, BNNTs and semiconducting nanotubes.

preprint2017arXiv

Generation of Bose-Einstein Condensates' Ground State Through Machine Learning

We show that both single-component and two-component Bose-Einstein condensates' (BECs) ground states can be simulated by deep convolutional neural networks of the same structure. We trained the neural network via inputting the coupling strength in the dimensionless Gross-Pitaevskii equation (GPE) and outputting the ground state wave-function. After training, the neural network generates ground states faster than the method of imaginary time evolution, while the relative mean-square-error between predicted states and original states is in the magnitude between $10^{-5}$ and $10^{-4}$. We compared the eigen-energies based on predicted states and original states, it is shown that the neural network can predict eigen-energies in high precisions. Therefore, the BEC ground states, which are continuous wave-functions, can be represented by deep convolution neural networks.

Yan Li

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83 published item(s)

AcademiClaw: When Students Set Challenges for AI Agents

Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking

Investigation of two-particle contributions to nucleon matrix elements

Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

Emergent Electronic Kagome Lattice in Correlated Charge-Density-Wave State of 1T-TaS$_2$

Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

Statistics of weakly nonlinear waves on currents with strong vertical shear

Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

A flying Schrödinger cat in multipartite entangled states

Benchmarking Domain Generalization on EEG-based Emotion Recognition

CMEs and SEPs During November-December 2020: A Challenge for Real-Time Space Weather Forecasting

Compactness and existence results of the prescribing fractional $Q$-curvatures problem on $\mathbb{S}^n$

Core overshoot constrained by the absence of a solar convective core and some solar-like stars

Data-Driven Quantum Approximate Optimization Algorithm for Cyber-Physical Power Systems

Deep Forest with Hashing Screening and Window Screening

Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network

Dissipation induced information scrambling in a collision model

Equivariant $\mathbb R$-test configurations and semistable limits of $\mathbb Q$-Fano group compactifications

Experimental preparation of generalized cat states for itinerant microwave photons

Generalization of Weinberg&#39;s Compositeness Relations

Gradient estimates for elliptic systems from composite materials with closely spaced stiff $C^{1,γ}$ inclusions

Ground-to-Air Communications Beyond 5G: Coordinated Multi-Point Transmission Based on Poisson-Delaunay Triangulation

Inverse scattering problem with a bare state

K-stability and polystable degenerations of polarized spherical varieties

Measuring the tuning curve of spontaneous parameter down-conversion using a comet-tail-like pattern

Modularized Bilinear Koopman Operator for Modeling and Predicting Transients of Microgrids

Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably

Nonoverlapping (delta, gamma)-approximate pattern matching

On a Fractional Nirenberg problem involving the square root of the Laplacian on $\mathbb{S}^{3}$

On-Time Communications Over Fading Channels

One-off Negative Sequential Pattern Mining

OPP-Miner: Order-preserving sequential pattern mining

PIDGeuN: Graph Neural Network-Enabled Transient Dynamics Prediction of Networked Microgrids Through Full-Field Measurement

Principal Amalgamation Analysis for Microbiome Data

Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction

Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection

SAMCNet for Spatial-configuration-based Classification: A Summary of Results

Steady-state susceptibility in continuous phase transitions of dissipative systems

Two-dimensional modeling of the tearing-mode-governed magnetic reconnection in the large-scale current sheet above the two-ribbon flare

Understanding COVID-19 Effects on Mobility: A Community-Engaged Approach

Adjusted Logistic Propensity Weighting Methods for Population Inference using Nonprobability Volunteer-Based Epidemiologic Cohorts

Dynamic list coloring of 1-planar graphs

IFoodCloud: A Platform for Real-time Sentiment Analysis of Public Opinion about Food Safety in China

Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization

Pursuing Sources of Heterogeneity in Modeling Clustered Population

Towards high partial waves in lattice QCD with a dumbbell-like operator

A Physics Model-Guided Online Bayesian Framework for Energy Management of Extended Range Electric Delivery Vehicles

Adversarial Attacks on Reinforcement Learning based Energy Management Systems of Extended Range Electric Delivery Vehicles

Air-to-Air Communications Beyond 5G: A Novel 3D CoMP Transmission Scheme

An extended Flaherty-Keller formula for an elastic composite with densely packed convex inclusions

Bilinear Graph Neural Network with Neighbor Interactions

Chip-to-chip quantum teleportation and multi-photon entanglement in silicon

Coordinated Multi-Point Transmission: A Poisson-Delaunay Triangulation Based Approach

Deep Reinforcement Learning with Robust and Smooth Policy

Dipole coupling of a tunable hole double quantum dot in germanium hut wire to a microwave resonator

Edge Federation: Towards an Integrated Service Provisioning Model

Electronic Raman Scattering in Suspended Semiconducting Carbon Nanotubes

Extra-cavity-enhanced difference-frequency generation at 1.63 μm

Finiteness of $\mathbb Q$-Fano compactifications of semisimple group with Kähler-Einstein metrics

GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries

How to Retrain Recommender System? A Sequential Meta-Learning Method

Increasing two-photon entangled dimensions by shaping input beam profiles

Information scrambling in a collision model

KIC 10736223: An Algol-type eclipsing binary just undergone the rapid mass-transfer stage

Large spin to charge conversion in topological superconductor \b{eta}-PdBi2 at room temperature

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

Partial Wave Mixing in Hamiltonian Effective Field Theory

Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers

Review on Set-Theoretic Methods for Safety Verification and Control of Power System

Singular Kähler-Einstein metrics on $\mathbb Q$-Fano compactifications of Lie groups

TEA: Temporal Excitation and Aggregation for Action Recognition

The A Priori Estimate and Existence of the Positive Solution for A Nonlinear System Involving the Fractional Laplacian

Generalization of Weinberg's Compositeness Relations

Tian's $α_{m,k}^{\hat K}$-invariants on group compactifications

Generation of Bose-Einstein Condensates' Ground State Through Machine Learning