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Yan Li

Publications and source records attributed to Yan Li.

At least 361 records · Page 20Linked to original sources
arXiv2023

Cross-Domain Product Representation Learning for Rich-Content E-Commerce

The proliferation of short video and live-streaming platforms has revolutionized how consumers engage in online shopping. Instead of browsing product pages, consumers are now turning to rich-content e-commerce, where they can purchase products through dynamic and interactive media like short videos and live streams. This emerging form of online shopping has introduced technical challenges, as products may be presented differently across various media domains. Therefore, a unified product representation is essential for achieving cross-domain product recognition to ensure an optimal user search experience and effective product recommendations. Despite the urgent industrial need for a unified cross-domain product representation, previous studies have predominantly focused only on product pages without taking into account short videos and live streams. To fill the gap in the rich-content e-commerce area, in this paper, we introduce a large-scale cRoss-dOmain Product Ecognition dataset, called ROPE. ROPE covers a wide range of product categories and contains over 180,000 products, corresponding to millions of short videos and live streams. It is the first dataset to cover product pages, short videos, and live streams simultaneously, providing the basis for establishing a unified product representation across different media domains. Furthermore, we propose a Cross-dOmain Product rEpresentation framework, namely COPE, which unifies product representations in different domains through multimodal learning including text and vision. Extensive experiments on downstream tasks demonstrate the effectiveness of COPE in learning a joint feature space for all product domains.

cs.CV↗
arXiv2023

RIP-based Performance Guarantee for Low Rank Matrix Recovery via $L_{*-F}$ Minimization

In the undetermined linear system $\bm{b}=\mathcal{A}(\bm{X})+\bm{s}$, vector $\bm{b}$ and operator $\mathcal{A}$ are the known measurements and $\bm{s}$ is the unknown noise. In this paper, we investigate sufficient conditions for exactly reconstructing desired matrix $\bm{X}$ being low-rank or approximately low-rank. We use the difference of nuclear norm and Frobenius norm ($L_{*-F}$) as a surrogate for rank function and establish a new nonconvex relaxation of such low rank matrix recovery, called the $L_{*-F}$ minimization, in order to approximate the rank function closer. For such nonconvex and nonsmooth constrained $L_{*-F}$ minimization problems, based on whether the noise level is $0$, we give the upper bound estimation of the recovery error respectively. Particularly, in the noise-free case, one sufficient condition for exact recovery is presented. If linear operator $\mathcal{A}$ satisfies the restricted isometry property with $δ_{4r}<\frac{\sqrt{2r}-1}{\sqrt{2r}-1+\sqrt{2}(\sqrt{2r}+1)}$, then $r$-\textbf{rank} matrix $\bm{X}$ can be exactly recovered without other assumptions. In addition, we also take insights into the regularized $L_{*-F}$ minimization model since such regularized model is more widely used in algorithm design. We provide the recovery error estimation of this regularized $L_{*-F}$ minimization model via RIP tool. To our knowledge, this is the first result on exact reconstruction of low rank matrix via regularized $L_{*-F}$ minimization.

math.OC↗
arXiv2023

First-order Policy Optimization for Robust Policy Evaluation

We adopt a policy optimization viewpoint towards policy evaluation for robust Markov decision process with $\mathrm{s}$-rectangular ambiguity sets. The developed method, named first-order policy evaluation (FRPE), provides the first unified framework for robust policy evaluation in both deterministic (offline) and stochastic (online) settings, with either tabular representation or generic function approximation. In particular, we establish linear convergence in the deterministic setting, and $\tilde{\mathcal{O}}(1/ε^2)$ sample complexity in the stochastic setting. FRPE also extends naturally to evaluating the robust state-action value function with $(\mathrm{s}, \mathrm{a})$-rectangular ambiguity sets. We discuss the application of the developed results for stochastic policy optimization of large-scale robust MDPs.

math.OC↗
arXiv2023

Nonprobability follow-up sample analysis: an application to SARS-CoV-2 infection prevalence estimation

Public health policy makers are faced with making crucial decisions rapidly during infectious disease outbreaks such as that caused by SARS-CoV-2. Ideally, rapidly deployed representative health surveys could provide needed data for such decisions. Under the constraints of a limited timeframe and resources, it may be infeasible to implement random based (probability) sampling that yields a population representative survey sample with high response rates. As an alternative, a volunteer (nonprobability) sample is often collected using outreach methods such as social media and web surveys. Compared to a probability sample, a nonprobability sample is subject to selection bias. In addition, when participants are followed longitudinally nonresponse often occurs at later follow up timepoints. As a result, estimates of cross-sectional parameters at later timepoints will be subject to selection bias and nonresponse bias. In this paper, we create kernel-weighted pseudoweights (KW) for the baseline survey participants and construct nonresponse-adjusted kw (kwNR) for respondents at each follow-visit to estimate the population mean at the follow-up visits. We develop Taylor Linearization variance estimation that accounts for variability due to estimating both pseudoweights and the nonresponse adjustments. Simulations are conducted to evaluate the proposed kwNR-weighted estimates. We investigate covariate effects on each of the following: baseline sample participation propensity, follow-up response propensity and the mean of the outcome. We apply the proposed kwNR-weighted methods to the SARS-Cov-2 antibody seropositivity longitudinal study, which begins with a baseline survey early in the pandemic, and collects data at six- and twelve-month post baseline follow-ups.

stat.AP↗
arXiv2023

First-order Policy Optimization for Robust Markov Decision Process

We consider the problem of solving robust Markov decision process (MDP), which involves a set of discounted, finite state, finite action space MDPs with uncertain transition kernels. The goal of planning is to find a robust policy that optimizes the worst-case values against the transition uncertainties, and thus encompasses the standard MDP planning as a special case. For $(\mathbf{s},\mathbf{a})$-rectangular uncertainty sets, we establish several structural observations on the robust objective, which facilitates the development of a policy-based first-order method, namely the robust policy mirror descent (RPMD). An $\mathcal{O}(\log(1/ε))$ iteration complexity for finding an $ε$-optimal policy is established with linearly increasing stepsizes. We further develop a stochastic variant of the robust policy mirror descent method, named SRPMD, when the first-order information is only available through online interactions with the nominal environment. We show that the optimality gap converges linearly up to the noise level, and consequently establish an $\tilde{\mathcal{O}}(1/ε^2)$ sample complexity by developing a temporal difference learning method for policy evaluation. Both iteration and sample complexities are also discussed for RPMD with a constant stepsize. To the best of our knowledge, all the aforementioned results appear to be new for policy-based first-order methods applied to the robust MDP problem.

cs.LG↗
arXiv2023

TALENT: Targeted Mining of Non-overlapping Sequential Patterns

With the widespread application of efficient pattern mining algorithms, sequential patterns that allow gap constraints have become a valuable tool to discover knowledge from biological data such as DNA and protein sequences. Among all kinds of gap-constrained mining, non-overlapping sequence mining can mine interesting patterns and satisfy the anti-monotonic property (the Apriori property). However, existing algorithms do not search for targeted sequential patterns, resulting in unnecessary and redundant pattern generation. Targeted pattern mining can not only mine patterns that are more interesting to users but also reduce the unnecessary redundant sequence generated, which can greatly avoid irrelevant computation. In this paper, we define and formalize the problem of targeted non-overlapping sequential pattern mining and propose an algorithm named TALENT (TArgeted mining of sequentiaL pattErN with consTraints). Two search methods including breadth-first and depth-first searching are designed to troubleshoot the generation of patterns. Furthermore, several pruning strategies to reduce the reading of sequences and items in the data and terminate redundant pattern extensions are presented. Finally, we select a series of datasets with different characteristics and conduct extensive experiments to compare the TALENT algorithm with the existing algorithms for mining non-overlapping sequential patterns. The experimental results demonstrate that the proposed targeted mining algorithm, TALENT, has excellent mining efficiency and can deal efficiently with many different query settings.

cs.DB↗
arXiv2023

Spin photonics on chip based on a twinning crystal metamaterial

Two-dimensional photonic circuits with high capacity are essential for a wide range of applications in next-generation photonic information technology and optoelectronics. Here we demonstrate a multi-channel spin-dependent photonic device based on a twinning crystal metamaterial. The structural symmetry and material symmetry of the twinning crystal metamaterial enable a total of 4 channels carrying different transverse spins because of the spin-momentum locking. The orientation of the anisotropy controls the propagation direction of each signal, and the rotation of the E-field with respect to energy flow determines the spin characteristics during input/output coupling. Leveraging this mechanism, the spin of an incident beam can be maintained during propagation on-chip and then delivered back into the free space, offering a new scheme for metamaterial-based spin-controlled nano-photonic applications.

physics.optics↗
arXiv2023

Mid-Infrared Upconversion Imaging Under Different Illumination Conditions

Converting the medium infrared field to the visible band is an effective image detection method. We propose a comprehensive theory of image up-conversion under continuous optical pumping, and discuss the relationship between the experimental parameters and imaging field of view, resolution, quantum efficiency, and conversion bandwidth. Theoretical predictions of upconversion imaging results are given based on numerical simulations, which show good agreement with experimental results. In particular, coherent and incoherent light illumination are studied separately and the advantages and disadvantages of their imaging performance are compared and analysed. This work provides a study of the upconversion image detection performance of the system, which is of great value in guiding the design of the detection system and bringing it to practical applications.

physics.optics↗
arXiv2023

Discovery and construction of surface kagome electronic states induced by p-d electronic hybridization

Kagome-lattice materials possess attractive properties for quantum computing applications, but their synthesis remains challenging. Herein, we show surface kagome electronic states (SKESs) on a Sn-terminated triangular Co3Sn2S2 surface, which are imprinted by vertical p-d electronic hybridization between the surface Sn (subsurface S) atoms and the buried Co kagome lattice network in the Co3Sn layer under the surface. Owing to the subsequent lateral hybridization of the Sn and S atoms in a corner-sharing manner, the kagome symmetry and topological electronic properties of the Co3Sn layer is proximate to the Sn surface. The SKESs and both hybridizations were verified via qPlus non-contact atomic force microscopy (nc-AFM) and density functional theory calculations. The construction of SKESs with tunable properties can be achieved by the atomic substitution of surface Sn (subsurface S) with other group III-V elements (Se or Te), which was demonstrated theoretically. This work exhibits the powerful capacity of nc-AFM in characterizing localized topological states and reveals the strategy for synthesis of large-area transition-metal-based kagome lattice materials using conventional surface deposition techniques.

cond-mat.mtrl-sci↗
arXiv2023

Towards Top-$K$ Non-Overlapping Sequential Patterns

Sequential pattern mining (SPM) has excellent prospects and application spaces and has been widely used in different fields. The non-overlapping SPM, as one of the data mining techniques, has been used to discover patterns that have requirements for gap constraints in some specific mining tasks, such as bio-data mining. And for the non-overlapping sequential patterns with gap constraints, the Nettree structure has been proposed to efficiently compute the support of the patterns. For pattern mining, users usually need to consider the threshold of minimum support (\textit{minsup}). This is especially difficult in the case of large databases. Although some existing algorithms can mine the top-$k$ patterns, they are approximate algorithms with fixed lengths. In this paper, a precise algorithm for mining \underline{T}op-$k$ \underline{N}on-\underline{O}verlapping \underline{S}equential \underline{P}atterns (TNOSP) is proposed. The top-$k$ solution of SPM is an effective way to discover the most frequent non-overlapping sequential patterns without having to set the \textit{minsup}. As a novel pattern mining algorithm, TNOSP can precisely search the top-$k$ patterns of non-overlapping sequences with different gap constraints. We further propose a pruning strategy named \underline{Q}ueue \underline{M}eta \underline{S}et \underline{P}runing (QMSP) to improve TNOSP's performance. TNOSP can reduce redundancy in non-overlapping sequential mining and has better performance in mining precise non-overlapping sequential patterns. The experimental results and comparisons on several datasets have shown that TNOSP outperformed the existing algorithms in terms of precision, efficiency, and scalability.

cs.DB↗
arXiv2023

Approximate Order-Preserving Pattern Mining for Time Series

The order-preserving pattern mining can be regarded as discovering frequent trends in time series, since the same order-preserving pattern has the same relative order which can represent a trend. However, in the case where data noise is present, the relative orders of many meaningful patterns are usually similar rather than the same. To mine similar relative orders in time series, this paper addresses an approximate order-preserving pattern (AOP) mining method based on (delta-gamma) distance to effectively measure the similarity, and proposes an algorithm called AOP-Miner to mine AOPs according to global and local approximation parameters. AOP-Miner adopts a pattern fusion strategy to generate candidate patterns generation and employs the screening strategy to calculate the supports of candidate patterns. Experimental results validate that AOP-Miner outperforms other competitive methods and can find more similar trends in time series.

cs.DB↗
arXiv2023

Reverse strain-induced snake states in graphene nanoribbons

Strain can tailor the band structures and properties of graphene nanoribbons (GNRs) with the well-known emergent pseudo-magnetic fields and the corresponding pseudo-Landau levels (pLLs). We design one type of the zigzag GNR (ZGNR) with reverse strains, producing pseudo-magnetic fields with opposite signs in the lower and upper half planes. Therefore, electrons propagate along the interface as "snake states", experiencing opposite Lorentz forces as they cross the zero field border line. By using the Landauer-Buttiker formalism combined with the nonequilibrium Green's function method, the existence and robustness of the reverse strain-induced snake states are further studied. Furthermore, the realization of long-thought pure valley currents in monolayer graphene systems is also proposed in our device.

cond-mat.mes-hall↗
arXiv2023

Frequency-tunable microwave quantum light source based on superconducting quantum circuits

A nonclassical light source is essential for implementing a wide range of quantum information processing protocols, including quantum computing, networking, communication, and metrology. In the microwave regime, propagating photonic qubits that transfer quantum information between multiple superconducting quantum chips serve as building blocks of large-scale quantum computers. In this context, spectral control of propagating single photons is crucial for interfacing different quantum nodes with varied frequencies and bandwidth. Here we demonstrate a microwave quantum light source based on superconducting quantum circuits that can generate propagating single photons, time-bin encoded photonic qubits and qudits. In particular, the frequency of the emitted photons can be tuned in situ as large as 200 MHz. Even though the internal quantum efficiency of the light source is sensitive to the working frequency, we show that the fidelity of the propagating photonic qubit can be well preserved with the time-bin encoding scheme. Our work thus demonstrates a versatile approach to realizing a practical quantum light source for future distributed quantum computing.

quant-ph↗
arXiv2023

Representative Pure Risk Estimation by Using Data from Epidemiologic Studies, Surveys, and Registries: Estimating Risks for Minority Subgroups

Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually underrepresent minorities. "Model-based" methods use population registries to improve externally validity of risk estimation but assume hazard ratios (HR) are generalizable from samples to the target finite population. "Pseudoweighting" methods improve representativeness of studies by using an external probability-based survey as the reference, but the resulting estimators can be biased due to propensity model misspecification or inefficient due to variable pseudoweights or small sample sizes of minorities in the cohort and/or survey. We propose a two-step pseudoweighting procedure that poststratifies the event rates among age/race/sex strata in the pseudoweighted cohort to the population rates to produce efficient and robust pure risk estimation (i.e., a cause-specific absolute risk in the absence of competing events). For developing an all-cause mortality risk model representative for the US, our findings suggest that HRs for minorities are not generalizable, and that surveys can have inadequate numbers of events for minorities. Poststratification on event rates is crucial for obtaining reliable risk estimation for minority subgroups.

stat.ME↗
arXiv2023

Equivariant $\mathbb R$-test configurations of polarized spherical varieties

Let $G$ be a connected, complex reductive Lie group and $G/H$ a spherical homogenous space. Let $(X,L)$ be a polarized $G$-variety which is a spherical embedding of $G/H$. In this paper we classify $G$-equivariant normal $\mathbb R$-test configurations of $(X,L)$ via combinatory data. In particular we classify the special ones, and prove a finiteness theorem of central fibres of $G$-equivariant special $\mathbb R$-test configurations. Also, as an application we study the semistable degeneration problem of a $\mathbb Q$-Fano spherical variety.

math.DG↗
arXiv2023

Quantum non-stationary phenomena of spin systems in collision models

We investigate the non-stationary phenomenon in a tripartite spin-1/2 system in the collision model (CM) framework. After introducing the dissipation through the system-environment collision for both Markovian and non-Markovian cases, we find the emergence of long-time oscillation in the dynamics of the system and the synchronization among subsystems. We connect the CM description and the quantum master equation in the continuous time limit and explain the existence of the stable oscillation by means of Liouvillian spectrum analysis. The time-dependence of the thermal property and the correlations are investigated, in particular we discuss the possibility of violation of the Landauer's principle in non-Markovian dynamics. In addition, we find that the imperfection of collective dissipation can be compensated by the randomness of the interaction sequence in our CM.

quant-ph↗
arXiv2023

Synchronization of persistent oscillations in spin systems with non-local dissipations

We explore the synchronization phenomenon in the quantum few-body system of spins with the non-local dissipation. Without the external driving, we find that the system can exhibit stable oscillatory behaviors in the long-time dynamics accompanied by the appearance of the purely imaginary eigenvalues of the Liouvillian. Moreover, the oscillations of the next-nearest-neighboring spins are completely synchronized revealed by the quantum trajectory analysis within the stochastic Schrödinger equation. The possibility of the appearance of the long-time oscillations in infinite-size lattice by means of cluster mean-field approximation is also discussed.

quant-ph↗
arXiv2023

Anchor Free remote sensing detector based on solving discrete polar coordinate equation

As the rapid development of depth learning, object detection in aviatic remote sensing images has become increasingly popular in recent years. Most of the current Anchor Free detectors based on key point detection sampling directly regression and classification features, with the design of object loss function based on the horizontal bounding box. It is more challenging for complex and diverse aviatic remote sensing object. In this paper, we propose an Anchor Free aviatic remote sensing object detector (BWP-Det) to detect rotating and multi-scale object. Specifically, we design a interactive double-branch(IDB) up-sampling network, in which one branch gradually up-sampling is used for the prediction of Heatmap, and the other branch is used for the regression of boundary box parameters. We improve a weighted multi-scale convolution (WmConv) in order to highlight the difference between foreground and background. We extracted Pixel level attention features from the middle layer to guide the two branches to pay attention to effective object information in the sampling process. Finally, referring to the calculation idea of horizontal IoU, we design a rotating IoU based on the split polar coordinate plane, namely JIoU, which is expressed as the intersection ratio following discretization of the inner ellipse of the rotating bounding box, to solve the correlation between angle and side length in the regression process of the rotating bounding box. Ultimately, BWP-Det, our experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show, achieves advanced performance with simpler models and fewer regression parameters.

cs.CV↗