arXiv subjects

Ting Li

Publications and source records attributed to Ting Li.

At least 91 records · Page 5Linked to original sources

arXiv2023

Active Encoding of Flexural Wave with Non-Diffractive Talbot Effect

This study employs the theory of conformal transformation to devise a Mikaelian lens for flexural waves manipulation. We investigate the propagation patterns of flexural waves in the lens under scenarios of plane wave and point source incidence. Additionally, the study explores the Talbot effect generated by interference patterns of multiple sources. Within the Mikaelian lens, the Talbot effect displays non diffractive characteristics, facilitating propagation over considerable distances. Leveraging the non-diffractive attributes of the Talbot effect in the Mikaelian lens, the paper discusses the feasibility of encoding flexural waves based on active interference sources. Simulation and experimental validation attest to the lens's effective active encoding. This research introduces novel perspectives on flexural wave encoding, showcasing potential applications in flexural wave communication, detection, and related fields.

physics.app-ph↗

arXiv2023

Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networks

In this paper, we first study the fundamental limit of clustering networks when a multi-layer network is present. Under the mixture multi-layer stochastic block model (MMSBM), we show that the minimax optimal network clustering error rate, which takes an exponential form and is characterized by the Renyi divergence between the edge probability distributions of the component networks. We propose a novel two-stage network clustering method including a tensor-based initialization algorithm involving both node and sample splitting and a refinement procedure by likelihood-based Lloyd algorithm. Network clustering must be accompanied by node community detection. Our proposed algorithm achieves the minimax optimal network clustering error rate and allows extreme network sparsity under MMSBM. Numerical simulations and real data experiments both validate that our method outperforms existing methods. Oftentimes, the edges of networks carry count-type weights. We then extend our methodology and analysis framework to study the minimax optimal clustering error rate for mixture of discrete distributions including Binomial, Poisson, and multi-layer Poisson networks. The minimax optimal clustering error rates in these discrete mixtures all take the same exponential form characterized by the Renyi divergences. These optimal clustering error rates in discrete mixtures can also be achieved by our proposed two-stage clustering algorithm.

math.ST↗

arXiv2023

Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision Making

A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately. We propose three optimal allocation strategies in a dynamic setting where treatments are sequentially assigned over time. These strategies are designed to minimize the variance of the treatment effect estimator when data follow a non-Markov decision process or a (time-varying) Markov decision process. We further develop estimation procedures based on existing off-policy evaluation (OPE) methods and conduct extensive experiments in various environments to demonstrate the effectiveness of the proposed methodologies. In theory, we prove the optimality of the proposed treatment allocation design and establish upper bounds for the mean squared errors of the resulting treatment effect estimators.

stat.ME↗

arXiv2023

Partial Eruption of Solar Filaments. I. Configuration and Formation of Double-decker Filaments

Partial eruptions of solar filaments are the typical representative of solar eruptive behavior diversity. Here we investigate a typical filament partial eruption event and present integrated evidence for configuration of the pre-eruption filament and its formation. The CHASE H$α$ observations reveal structured Doppler velocity distribution within the pre-eruption filament, where distinct redshift only appeared in the east narrow part of the south filament region and then disappeared after the partial eruption while the north part dominated by blueshift remained. Combining the SDO, ASO-S observations, and NLFFF modeling results, we verify that there were two independent material flow systems within the pre-flare filament, whose magnetic topology is a special double-decker configuration consisting of two magnetic flux ropes (MFRs) with opposite magnetic twist. During the formation of this filament system, continuous magnetic flux cancellation and footpoint motion were observed around its north end. Therefore, we propose a new double-decker formation scenario that the two MFRs composing such double-decker configuration originated from two magnetic systems with different initial connections and opposite magnetic twist. Subsequent magnetic reconnection with surrounding newly-emerging fields resulted in the motion of footpoint of the upper MFR to the region around footpoint of the lower MFR, thus leading to eventual formation of the double-decker configuration consisting of two MFRs with similar footpoints but opposite signs of magnetic twist. These results provide a potential way to determine unambiguously the progenitor configuration of a partial-eruptive filament and reveal a special type of double-decker MFR configuration and a new double-decker formation scenario.

astro-ph.SR↗

arXiv2023

Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference

Accurate acquisition of crowd flow at Points of Interest (POIs) is pivotal for effective traffic management, public service, and urban planning. Despite this importance, due to the limitations of urban sensing techniques, the data quality from most sources is inadequate for monitoring crowd flow at each POI. This renders the inference of accurate crowd flow from low-quality data a critical and challenging task. The complexity is heightened by three key factors: 1) The scarcity and rarity of labeled data, 2) The intricate spatio-temporal dependencies among POIs, and 3) The myriad correlations between precise crowd flow and GPS reports. To address these challenges, we recast the crowd flow inference problem as a self-supervised attributed graph representation learning task and introduce a novel Contrastive Self-learning framework for Spatio-Temporal data (CSST). Our approach initiates with the construction of a spatial adjacency graph founded on the POIs and their respective distances. We then employ a contrastive learning technique to exploit large volumes of unlabeled spatio-temporal data. We adopt a swapped prediction approach to anticipate the representation of the target subgraph from similar instances. Following the pre-training phase, the model is fine-tuned with accurate crowd flow data. Our experiments, conducted on two real-world datasets, demonstrate that the CSST pre-trained on extensive noisy data consistently outperforms models trained from scratch.

cs.LG↗

arXiv2023

DUFormer: Solving Power Line Detection Task in Aerial Images using Semantic Segmentation

Unmanned aerial vehicles (UAVs) are frequently used for inspecting power lines and capturing high-resolution aerial images. However, detecting power lines in aerial images is difficult,as the foreground data(i.e, power lines) is small and the background information is abundant.To tackle this problem, we introduce DUFormer, a semantic segmentation algorithm explicitly designed to detect power lines in aerial images. We presuppose that it is advantageous to train an efficient Transformer model with sufficient feature extraction using a convolutional neural network(CNN) with a strong inductive bias.With this goal in mind, we introduce a heavy token encoder that performs overlapping feature remodeling and tokenization. The encoder comprises a pyramid CNN feature extraction module and a power line feature enhancement module.After successful local feature extraction for power lines, feature fusion is conducted.Then,the Transformer block is used for global modeling. The final segmentation result is achieved by amalgamating local and global features in the decode head.Moreover, we demonstrate the importance of the joint multi-weight loss function in power line segmentation. Our experimental results show that our proposed method outperforms all state-of-the-art methods in power line segmentation on the publicly accessible TTPLA dataset.

cs.CV↗

arXiv2023

GPU Accelerated Color Correction and Frame Warping for Real-time Video Stitching

Traditional image stitching focuses on a single panorama frame without considering the spatial-temporal consistency in videos. The straightforward image stitching approach will cause temporal flicking and color inconstancy when it is applied to the video stitching task. Besides, inaccurate camera parameters will cause artifacts in the image warping. In this paper, we propose a real-time system to stitch multiple video sequences into a panoramic video, which is based on GPU accelerated color correction and frame warping without accurate camera parameters. We extend the traditional 2D-Matrix (2D-M) color correction approach and a present spatio-temporal 3D-Matrix (3D-M) color correction method for the overlap local regions with online color balancing using a piecewise function on global frames. Furthermore, we use pairwise homography matrices given by coarse camera calibration for global warping followed by accurate local warping based on the optical flow. Experimental results show that our system can generate highquality panorama videos in real time.

cs.CV↗

arXiv2023

Observation of two splitting processes in a partial filament eruption on the sun: the role of breakout reconnection

Partial filament eruptions have often been observed, however, the physical mechanisms that lead to filament splitting are not yet fully understood. In this study, we present a unique event of a partial filament eruption that undergoes two distinct splitting processes. The first process involves vertical splitting and is accompanied by brightenings inside the filament, which may result from internal magentic reconnection within the filament. Following the first splitting process, the filament is separated into an upper part and a lower part. Subsequently, the upper part undergoes a second splitting, which is accompanied by a coronal blowout jet. An extrapolation of the coronal magnetic field reveals a hyperbolic flux tube structure above the filament, indicating the occurrence of breakout reconnection that reduces the constraning field above. Consequently, the filament is lifted up, but at a nonuniform speed. The high-speed part reaches the breakout current sheet to generate the blowout jet, while the low-speed part falls back to the solar surface, resulting in the second splitting. In addition, continuous brightenings are observed along the flare ribbons, suggesting the occurrence of slipping reconnection process. This study presents, for the first time, the unambiguous observation of a two-stage filament splitting process, advancing our understanding of the complex dynamics of solar eruptions.

astro-ph.SR↗

arXiv2023

Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing

Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on average treatment effects, situations with skewed and heavy-tailed outcome distributions may benefit from alternative criteria, such as quantiles. However, assessing dynamic quantile treatment effects (QTE) remains a challenge, particularly when dealing with data from ride-sourcing platforms that involve sequential decision-making across time and space. In this paper, we establish a formal framework to calculate QTE conditional on characteristics independent of the treatment. Under specific model assumptions, we demonstrate that the dynamic conditional QTE (CQTE) equals the sum of individual CQTEs across time, even though the conditional quantile of cumulative rewards may not necessarily equate to the sum of conditional quantiles of individual rewards. This crucial insight significantly streamlines the estimation and inference processes for our target causal estimand. We then introduce two varying coefficient decision process (VCDP) models and devise an innovative method to test the dynamic CQTE. Moreover, we expand our approach to accommodate data from spatiotemporal dependent experiments and examine both conditional quantile direct and indirect effects. To showcase the practical utility of our method, we apply it to three real-world datasets from a ride-sourcing platform. Theoretical findings and comprehensive simulation studies further substantiate our proposal.

stat.ME↗

arXiv2023

Complete replacement of magnetic flux in a flux rope during a coronal mass ejection

Solar coronal mass ejections are the most energetic events in the Solar System. In their standard formation model, a magnetic flux rope builds up into a coronal mass ejection through magnetic reconnection that continually converts overlying, untwisted magnetic flux into twisted flux enveloping the pre-existing rope. However, only a minority of coronal mass ejections carry a coherent magnetic flux rope as their core structure, which casts doubt on the universality of this orderly wrapping process. Here we provide observational evidence of a different formation and eruption mechanism of a magnetic flux rope from an S-shaped thread, where its magnetic flux is fully replaced via flare reconnections. One of the footpoints of the sigmoidal feature slipped and expanded during the formation, and then moved to a completely new place, associated with the highly dynamical evolution of flare ribbons and a twofold increase in magnetic flux through the footpoint, during the eruption. Such a configuration is not predicted by standard formation models or numerical simulations and highlights the three-dimensional nature of magnetic reconnections between the flux rope and the surrounding magnetic field.

astro-ph.SR↗

arXiv2023

A Partially Functional Linear Modeling Framework for Integrating Genetic, Imaging, and Clinical Data

This paper is motivated by the joint analysis of genetic, imaging, and clinical (GIC) data collected in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We propose a regression framework based on partially functional linear regression models to map high-dimensional GIC-related pathways for Alzheimer's Disease (AD). We develop a joint model selection and estimation procedure by embedding imaging data in the reproducing kernel Hilbert space and imposing the L0 penalty for the coefficients of genetic variables. We apply the proposed method to the ADNI dataset to identify important features from tens of thousands of genetic polymorphisms (reduced from millions using a preprocessing step) and study the effects of a certain set of informative genetic variants and the baseline hippocampus surface on thirteen future cognitive scores measuring different aspects of cognitive function. We explore the shared and different heritability patterns of these cognitive scores. Analysis results suggest that both the hippocampal and genetic data have heterogeneous effects on different scores, with the trend that the value of both hippocampi is negatively associated with the severity of cognition deficits. Polygenic effects are observed for all thirteen cognitive scores. The well-known APOE4 genotype only explains a small part of cognitive function. Shared genetic etiology exists, however, greater genetic heterogeneity exists within disease classifications after accounting for the baseline diagnosis status. These analyses are useful in further investigation of functional mechanisms for AD evolution.

stat.ME↗

arXiv2023

On the limit distribution for stochastic differential equations driven by cylindrical non-symmetric $α$-stable Lévy processes

This article deals with the limit distribution for a stochastic differential equation driven by a non-symmetric cylindrical $α$-stable process. Under suitable conditions, it is proved that the solution of this equation converges weakly to that of a stochastic differential equation driven by a Brownian motion in the Skorohod space as $α\rightarrow2$. Also, the rate of weak convergence, which depends on $ 2-α$, for the solution towards the solution of the limit equation is obtained. For illustration, the results are applied to a simple one-dimensional stochastic differential equation, which implies the rate of weak convergence is optimal.

math.PR↗

arXiv2023

rMultiNet: An R Package For Multilayer Networks Analysis

This paper develops an R package rMultiNet to analyze multilayer network data. We provide two general frameworks from recent literature, e.g. mixture multilayer stochastic block model(MMSBM) and mixture multilayer latent space model(MMLSM) to generate the multilayer network. We also provide several methods to reveal the embedding of both nodes and layers followed by further data analysis methods, such as clustering. Three real data examples are processed in the package. The source code of rMultiNet is available at https://github.com/ChenyuzZZ73/rMultiNet.

stat.ML↗

arXiv2022

Artificial gauge field enabled low-crosstalk, broadband, half-wavelength-pitched waveguide arrays

Dense waveguide arrays with half-wavelength-pitch, low-crosstalk, broadband, and flexible routing capability are essential for integrated photonics. However, achieving such performance is challenging due to the relatively weaker confinement of dielectric waveguides and the increased interactions among densely packed waveguides. Here, leveraging the artificial gauge field mechanism, we demonstrate half-wavelength-pitched dense waveguide arrays, consisting of 64 waveguides, in silicon with -30dB crosstalk suppression from 1480nm to 1550nm. The waveguide array features negligible insertion loss for 90-degree bending. Our approach enables flexibly routing a large-scale dense waveguide array that significantly reduces on-chip estate, leading to a high-density photonic integrated circuit, and may open up opportunities for important device performance improvement, such as half-wavelength-pitch OPA and ultra-dense space-division multiplexing

physics.optics↗

arXiv2022

Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting

Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously. However, most existing works focus more on learning with the explicit prior graph structure, while ignoring potential information from the implicit graph structure, yielding incomplete structure modeling. Some recent works attempt to learn the intrinsic or implicit graph structure directly while lacking a way to combine explicit prior structure with implicit structure together. In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure. RGSL consists of two innovative modules. First, we derive an implicit dense similarity matrix through node embedding, and learn the sparse graph structure using the Regularized Graph Generation (RGG) based on the Gumbel Softmax trick. Second, we propose a Laplacian Matrix Mixed-up Module (LM3) to fuse the explicit graph and implicit graph together. We conduct experiments on three real-word datasets. Results show that the proposed RGSL model outperforms existing graph forecasting algorithms with a notable margin, while learning meaningful graph structure simultaneously. Our code and models are made publicly available at https://github.com/alipay/RGSL.git.

cs.LG↗

arXiv2022

A Necessary and Sufficient Condition for Complete phase synchronization of high-dimensional nonidentical Kuramoto oscillators

For original Kuramoto models with nonidentical oscillators, it is impossible to realize complete phase synchronization. However, this paper reveals that complete phase synchronization can be achieved for a large class of high-dimensional Kuramoto models with nonidentical oscillators. Under the topology of strongly connected digraphs, a necessary and sufficient condition for complete phase synchronization is obtained. Finally, some simulations are provided to validate the obtained theoretic results.

math.DS↗

arXiv2022

Dynamic Property and Magnetic Nonpotentiality of Two Types of Confined Solar Flares

We analyze 152 large confined flares (GOES class $\geq$M1.0 and $\leq$$45^{\circ}$ from disk center) during 2010$-$2019, and classify them into two types according to the criterion taken from the work of Li et al. (2019). "Type I" flares are characterized by slipping motions of flare loops and ribbons and a stable filament underlying the flare loops. "Type II" flares are associated with the failed eruptions of the filaments, which can be explained by the classical 2D flare model. A total of 59 flares are "Type I" flares (about 40\%) and 93 events are "Type II" flares (about 60\%). There are significant differences in distributions of the total unsigned magnetic flux ($Φ$$_\mathrm{AR}$) of active regions (ARs) producing the two types of confined flares, with "Type I" confined flares from ARs with a larger $Φ$$_{AR}$ than "Type II". We calculate the mean shear angle $Ψ$$_\mathrm{HFED}$ within the core of an AR prior to the flare onset, and find that it is slightly smaller for "Type I" flares than that for "Type II" events. The relative non-potentiality parameter $Ψ$$_\mathrm{HFED}$/$Φ$$_\mathrm{AR}$ has the best performance in distinguishing the two types of flares. About 73\% of "Type I" confined flares have $Ψ$$_\mathrm{HFED}$/$Φ$$_\mathrm{AR}$$<$1.0$\times$$10^{-21}$ degree Mx$^{-1}$, and about 66\% of "Type II" confined events have $Ψ$$_\mathrm{HFED}$/$Φ$$_\mathrm{AR}$$\geq$1.0$\times$$10^{-21}$ degree Mx$^{-1}$. We suggest that "Type I" confined flares cannot be explained by the standard flare model in 2D/3D, and the occurrence of multiple slipping magnetic reconnections within the complex magnetic systems probably leads to the observed flare.

astro-ph.SR↗

arXiv2022

Continuous-stage symplectic adapted exponential methods for charged-particle dynamics with arbitrary electromagnetic fields

This paper is devoted to the numerical symplectic approximation of the charged-particle dynamics (CPD) with arbitrary electromagnetic fields. By utilizing continuous-stage methods and exponential integrators, a general class of symplectic methods is formulated for CPD under a homogeneous magnetic field. Based on the derived symplectic conditions, two practical symplectic methods up to order four are constructed where the error estimates show that the proposed second order scheme has a uniform accuracy in the position w.r.t. the strength of the magnetic field. Moreover, the symplectic methods are extended to CPD under non-homogeneous magnetic fields and three algorithms are formulated. Rigorous error estimates are investigated for the proposed methods and one method is proved to have a uniform accuracy in the position w.r.t. the strength of the magnetic field. Numerical experiments are provided for CPD under homogeneous and non-homogeneous magnetic fields, and the numerical results support the theoretical analysis and demonstrate the remarkable numerical behavior of our methods.

math.NA↗