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Yi Li

Publications and source records attributed to Yi Li.

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

Randomness Certification from Multipartite Quantum Steering for Arbitrary Dimensional Systems

Entanglement in bipartite systems has been applied for the generation of secure random numbers, which are playing an important role in cryptography or scientific numerical simulations. Here, we propose to use multipartite entanglement distributed between trusted and untrusted parties for generating randomness of arbitrary dimensional systems. We show that the distributed structure of several parties leads to additional protection against possible attacks by an eavesdropper, resulting in more secure randomness generated than in the corresponding bipartite scenario. Especially, randomness can be certified in the group of untrusted parties, even there is no randomness exists in either of them individually. We prove that the necessary and sufficient resource for quantum randomness in this scenario is multipartite quantum steering when two measurement settings are performed on the untrusted parties. However, the sufficiency no longer holds with more measurement settings. Finally, we apply our analysis to some experimentally realized states and show that more randomness can be extracted in comparison to the existing analysis.

quant-ph↗
arXiv2023

A New Integrative Method for Multigroup Comparisons of Censored Survival Outcomes in Multiple Observational Studies

In observational studies, covariate imbalance generates confounding, resulting in biased comparisons. Although propensity score-based weighting approaches facilitate unconfounded group comparisons for implicit target populations, existing techniques may not directly or efficiently analyze multiple studies with multiple groups and provide results generalizable to larger populations. Moreover, few methods deliver precise inferences for various estimands with censored survival outcomes. We propose a new concordant target population approach, which constructs generalized balancing weights and realistic target populations. Our method can incorporate researcher-specified natural population attributes and synthesize information by appropriately compensating for over- or under-represented groups to achieve covariate balance. The constructed {concordant} weights are agnostic to specific estimators, estimands, and outcomes and maximize the effective sample size (ESS) for more precise inferences. Simulation studies and descriptive comparisons of glioblastoma outcomes of racial groups in multiple TCGA studies demonstrate the strategy's practical advantages. Unlike existing weighting techniques, the proposed concordant target population revealed a drastically different result: Blacks were more vulnerable and endured significantly worse prognoses; Asians had the best outcomes with a median overall survival of 1,024 (SE: 15.2) days, compared to 384 (SE: 1.2) and 329 (SE: 19.7) days for Whites and Blacks, respectively.

stat.ME↗
arXiv2023

Unconfounded Meta-analytical Frameworks for Multivariate Outcomes in Multigroup Observational Studies using Concordant Weights

While meta-analyzing retrospective cancer patient cohorts, an investigation of differences in the expressions of target oncogenes across cancer subtypes is of substantial interest because the results may uncover novel tumorigenesis mechanisms and improve screening and treatment strategies. Weighting methods facilitate unconfounded comparisons of multigroup potential outcomes in multiple observational studies. For example, Guha et al. (2022) introduced concordant weights, allowing integrative analyses of survival outcomes by maximizing the effective sample size. However, it remains unclear how to use this or other weighting approaches to analyze a variety of continuous, categorical, ordinal, or multivariate outcomes, especially when research interests prioritize uncommon or unplanned estimands suggested by post hoc analyses; examples include percentiles and moments of group potential outcomes and pairwise correlations of multivariate outcomes. This paper proposes a unified meta-analytical approach accommodating various types of endpoints and fosters new estimators compatible with most weighting frameworks. Asymptotic properties of the estimators are investigated under mild assumptions. For undersampled groups, we devise small-sample procedures for quantifying estimation uncertainty. We meta-analyze multi-site TCGA breast cancer data, shedding light on the differential mRNA expression patterns of eight targeted genes for the subtypes infiltrating ductal carcinoma and infiltrating lobular carcinoma.

stat.ME↗
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

Observation of Large-Number Corner Modes in $\mathbb{Z}$-class Higher-Order Topolectrical Circuits

Topological corner states are exotic topological boundary states that are bounded to zero-dimensional geometry even the dimension of systems is large than one. As an elegant physical correspondence, their numbers are dictated by the bulk topological invariants. So far, all previous realizations of HOTIs are hallmarked by $\mathbb{Z}_2$ topological invariants and therefore have only one corner state at each corner. Here we report an experimental demonstration of $\mathbb{Z}$-class HOTI phases in electric circuits, hosting $N$ corner modes at each single corner structure. By measuring the impedance spectra and distributions, we clearly demonstrate the $\mathbb{Z}$-class HOTI phases, including the zero-energy corner modes and their density distributions. Moreover, we reveal that the local density of states (LDOS) at each corner for $N=4$ are equally distributed at four corner unit cells, prominently differing from $\mathbb{Z}_2$-class case where the LDOS only dominates over one corner unit cell. Our results extend the observation of HOTIs from $\mathbb{Z}_2$ class to $\mathbb{Z}$ class and the coexistence of spatially overlapped large number of corner modes which may enable exotic topological devices that require high degeneracy boundary states.

cond-mat.mes-hall↗
arXiv2023

CompSuite: A Dataset of Java Library Upgrade Incompatibility Issues

Modern software systems heavily rely on external libraries developed by third-parties to ensure efficient development. However, frequent library upgrades can lead to compatibility issues between the libraries and their client systems. In this paper, we introduce CompSuite, a dataset that includes 123 real-world Java client-library pairs where upgrading the library causes an incompatibility issue in the corresponding client. Each incompatibility issue in CompSuite is associated with a test case authored by the developers, which can be used to reproduce the issue. The dataset also provides a command-line interface that simplifies the execution and validation of each issue. With this infrastructure, users can perform an inspection of any incompatibility issue with the push of a button, or reproduce an issue step-by-step for a more detailed investigation. We make CompSuite publicly available to promote open science. We believe that various software analysis techniques, such as compatibility checking, debugging, and regression test selection, can benefit from CompSuite.

cs.SE↗
arXiv2023

A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG$^{\textbf{2}}$+ Track 3

In this technical report, we briefly introduce the solution of our team HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG$^{2}$+ Track 3. In this task, we propose an efficient two-stage framework to reconstruct a clear image from rainy frames. Firstly, a low-rank based video deraining method is utilized to generate pseudo GT, which fully takes the advantage of multi and aligned rainy frames. Secondly, a transformer-based single image deraining network Uformer is implemented to pre-train on large real rain dataset and then fine-tuned on pseudo GT to further improve image restoration. Moreover, in terms of visual pleasing effect, a comprehensive image processor module is utilized at the end of pipeline. Our overall framework is elaborately designed and able to handle both heavy rainy and foggy sequences provided in the final testing phase. Finally, we rank 1st on the average structural similarity (SSIM) and rank 2nd on the average peak signal-to-noise ratio (PSNR). Our code is available at https://github.com/yunguo224/UG2_Deraining.

cs.CV↗
arXiv2023

Dynamic Scene Adjustment for Player Engagement in VR Game

Virtual reality (VR) produces a highly realistic simulated environment with controllable environment variables. This paper proposes a Dynamic Scene Adjustment (DSA) mechanism based on the user interaction status and performance, which aims to adjust the VR experiment variables to improve the user's game engagement. We combined the DSA mechanism with a musical rhythm VR game. The experimental results show that the DSA mechanism can improve the user's game engagement (task performance).

cs.HC↗
arXiv2023

Integrating Magnons for Quantum Information

Magnons, the quanta of collective spin excitations in magnetically ordered materials, have distinct properties that make them uniquely appealing for quantum information applications. They can have ultra-small wavelengths down to the nanometer scale even at microwave frequencies. They can provide coupling to a diverse set of other quantum excitations, and their inherently gyrotropic dynamics forms the basis for pronounced non-reciprocities. In this article we discuss what the current research challenges are for integrating magnetic materials into quantum information systems and provide a perspective on how to address them.

cond-mat.mtrl-sci↗
arXiv2023

SViTT: Temporal Learning of Sparse Video-Text Transformers

Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size; the curse of dimensionality for multi-frame modeling; and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://svcl.ucsd.edu/projects/svitt.

cs.CV↗
arXiv2023

Skeleton-based action analysis for ADHD diagnosis

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder worldwide. While extensive research has focused on machine learning methods for ADHD diagnosis, most research relies on high-cost equipment, e.g., MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the action characteristics of ADHD are desired. Skeleton-based action recognition has gained attention due to the action-focused nature and robustness. In this work, we propose a novel ADHD diagnosis system with a skeleton-based action recognition framework, utilizing a real multi-modal ADHD dataset and state-of-the-art detection algorithms. Compared to conventional methods, the proposed method shows cost-efficiency and significant performance improvement, making it more accessible for a broad range of initial ADHD diagnoses. Through the experiment results, the proposed method outperforms the conventional methods in accuracy and AUC. Meanwhile, our method is widely applicable for mass screening.

cs.CV↗
arXiv2023

Ion-beam Assisted Sputtering of Titanium Nitride Thin Films

Titanium nitride is a material of interest for many superconducting devices such as nanowire microwave resonators and photon detectors. Thus, controlling the growth of TiN thin films with desirable properties is of high importance. In previous work on niobium nitride, ion beam-assisted sputtering (IBAS) reduced nitrogen sensitivity during deposition in tandem with an increase in nominal critical temperature. We have deposited thin films of titanium nitride by both, the conventional method of DC reactive magnetron sputtering and the IBAS method and compare their superconducting critical temperatures Tc as functions of thickness, sheet resistance, and nitrogen flow rate. We perform electrical and structural characterizations by electric transport and X-ray diffraction measurements. Compared to the conventional method of reactive sputtering, the IBAS technique has demonstrated a 10% increase in nominal critical temperature and 33% reduced sensitivity to nitrogen flow, without noticeable variation in the lattice structure. Additionally, we explore the behavior of superconducting Tc in ultra-thin films. Trends in films grown at high nitrogen concentrations follow predictions of mean-field theory in disordered films and show suppression of superconducting Tc due to geometric effects, while nitride films grown at low nitrogen concentrations strongly deviate from the theoretical models.

cond-mat.supr-con↗
arXiv2023

Evaluating AIGC Detectors on Code Content

Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with ChatGPT emerging as a leading AIGC model that produces high-quality responses across various applications, including software development and maintenance. Despite its potential, the misuse of ChatGPT poses significant concerns, especially in education and safetycritical domains. Numerous AIGC detectors have been developed and evaluated on natural language data. However, their performance on code-related content generated by ChatGPT remains unexplored. To fill this gap, in this paper, we present the first empirical study on evaluating existing AIGC detectors in the software domain. We created a comprehensive dataset including 492.5K samples comprising code-related content produced by ChatGPT, encompassing popular software activities like Q&A (115K), code summarization (126K), and code generation (226.5K). We evaluated six AIGC detectors, including three commercial and three open-source solutions, assessing their performance on this dataset. Additionally, we conducted a human study to understand human detection capabilities and compare them with the existing AIGC detectors. Our results indicate that AIGC detectors demonstrate lower performance on code-related data compared to natural language data. Fine-tuning can enhance detector performance, especially for content within the same domain; but generalization remains a challenge. The human evaluation reveals that detection by humans is quite challenging.

cs.SE↗
arXiv2023

Holographic torus correlators of stress tensor in $AdS_3/CFT_2$

In the context of $\rm AdS_3/CFT_2$, we investigate holographic correlators of the stress tensor of a conformal field theory (CFT) on a torus in this work. To calculate the correlators of the stress tensor, we employ the Einstein-Hilbert theory of gravity and perturbatively solve Einstein's equation in the bulk. We offer an explicit prescription to develop a recurrence relation that makes it simple to compute higher point correlators. The correlators and the recurrence relation are found to be consistent with what is known in CFTs. Following the spirit of the proposed cutoff $\rm AdS$/$T\bar{T}$ CFT holography, we then expand our computation program to investigate holographic torus correlators at a finite cutoff in the $\rm AdS_3$. A parallel recurrence relation associated with higher point correlators can be obtained.

hep-th↗
arXiv2023

Asynchronous and Error-prone Longitudinal Data Analysis via Functional Calibration

In many longitudinal settings, time-varying covariates may not be measured at the same time as responses and are often prone to measurement error. Naive last-observation-carried-forward methods incur estimation biases, and existing kernel-based methods suffer from slow convergence rates and large variations. To address these challenges, we propose a new functional calibration approach to efficiently learn longitudinal covariate processes based on sparse functional data with measurement error. Our approach, stemming from functional principal component analysis, calibrates the unobserved synchronized covariate values from the observed asynchronous and error-prone covariate values, and is broadly applicable to asynchronous longitudinal regression with time-invariant or time-varying coefficients. For regression with time-invariant coefficients, our estimator is asymptotically unbiased, root-n consistent, and asymptotically normal; for time-varying coefficient models, our estimator has the optimal varying coefficient model convergence rate with inflated asymptotic variance from the calibration. In both cases, our estimators present asymptotic properties superior to the existing methods. The feasibility and usability of the proposed methods are verified by simulations and an application to the Study of Women's Health Across the Nation, a large-scale multi-site longitudinal study on women's health during mid-life.

stat.ME↗
arXiv2023

Unidirectional Microwave Transduction with Chirality Selected Short-Wavelength Magnon Excitations

Nonreciprocal magnon propagation has recently become a highly potential approach of developing chip-embedded microwave isolators for advanced information processing. However, it is challenging to achieve large nonreciprocity in miniaturized magnetic thin-film devices because of the difficulty of distinguishing propagating surface spin waves along the opposite directions when the film thickness is small. In this work, we experimentally realize unidirectional microwave transduction with sub-micron-wavelength propagating magnons in a yttrium iron garnet (YIG) thin film delay line. We achieve a non-decaying isolation of 30 dB with a broad field-tunable band-pass frequency range up to 14 GHz. The large isolation is due to the selection of chiral magnetostatic surface spin waves with the Oersted field generated from the coplanar waveguide antenna. Increasing the geometry ratio between the antenna width and YIG thickness drastically reduces the nonreciprocity and introduces additional magnon transmission bands. Our results pave the way for on-chip microwave isolation and tunable delay line with short-wavelength magnonic excitations.

cond-mat.mes-hall↗
arXiv2023

CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications

Federated learning (FL) provides an emerging approach for collaboratively training semantic encoder/decoder models of semantic communication systems, without private user data leaving the devices. Most existing studies on trustworthy FL aim to eliminate data poisoning threats that are produced by malicious clients, but in many cases, eliminating model poisoning attacks brought by fake servers is also an important objective. In this paper, a certificateless authentication-based trustworthy federated learning (CATFL) framework is proposed, which mutually authenticates the identity of clients and server. In CATFL, each client verifies the server's signature information before accepting the delivered global model to ensure that the global model is not delivered by false servers. On the contrary, the server also verifies the server's signature information before accepting the delivered model updates to ensure that they are submitted by authorized clients. Compared to PKI-based methods, the CATFL can avoid too high certificate management overheads. Meanwhile, the anonymity of clients shields data poisoning attacks, while real-name registration may suffer from user-specific privacy leakage risks. Therefore, a pseudonym generation strategy is also presented in CATFL to achieve a trade-off between identity traceability and user anonymity, which is essential to conditionally prevent from user-specific privacy leakage. Theoretical security analysis and evaluation results validate the superiority of CATFL.

cs.CR↗
arXiv2022

Cross-Lingual Transfer Learning for Statistical Type Inference

Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a cross-lingual transfer learning framework, PLATO, for statistical type inference, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others, e.g., Python to JavaScript, Java to JavaScript, etc. PLATO is powered by a novel kernelized attention mechanism to constrain the attention scope of the backbone Transformer model such that the model is forced to base its prediction on commonly shared features among languages. In addition, we propose the syntax enhancement that augments the learning on the feature overlap among language domains. Furthermore, PLATO can also be used to improve the performance of the conventional supervised learning-based type inference by introducing cross-lingual augmentation, which enables the model to learn more general features across multiple languages. We evaluated PLATO under two settings: 1) under the cross-domain scenario that the target language data is not labeled or labeled partially, the results show that PLATO outperforms the state-of-the-art domain transfer techniques by a large margin, e.g., it improves the Python to TypeScript baseline by +5.40%@EM, +5.40%@weighted-F1, and 2) under the conventional monolingual supervised learning based scenario, PLATO improves the Python baseline by +4.40%@EM, +3.20%@EM (parametric).

cs.AI↗