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Researcher profile

Xue Li

Xue Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningComputation and LanguageArtificial IntelligenceComputer Visioncond-mat.mtrl-sciComputational Engineering, Finance, and Sciencecond-mat.mes-hallcond-mat.str-elCryptography and Securitycs.CYDistributed, Parallel, and Cluster Computingeess.SYInformation Retrievalmath.NANeural and Evolutionary ComputingNumerical Analysisphysics.chem-phquant-phQuantitative MethodsRoboticsSocial and Information NetworksSystems and Control

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Published work

22 published item(s)

preprint2026arXiv

ReCoVR: Closing the Loop in Interactive Composed Video Retrieval

Composed video retrieval (CoVR) searches for target videos using a reference video and a modification text, but existing methods are restricted to a single interaction round and cannot support the progressive nature of real-world visual search. To bridge this gap, we first formalize interactive composed video retrieval, a multi-turn extension of CoVR, where users progressively refine their search intent through natural-language feedback across turns. Adapting existing interactive retrieval methods to this setting reveals two structural weaknesses: reliance on a single retrieval channel and an open-loop retrieval design that consumes user feedback but does not diagnose whether its own retrieval trajectory is drifting or stagnating. To address these limitations, we propose ReCoVR (Reflexive Composed Video Retrieval), a dual-pathway architecture built on reflexive perception, where the system treats its retrieval history as diagnostic evidence alongside user feedback. Specifically, an Intent Pathway routes heterogeneous feedback to complementary retrieval channels, while a Reflection Pathway performs trajectory-level reflection to monitor result evolution and correct retrieval errors across turns. Experiments on multiple benchmarks show that ReCoVR consistently outperforms interactive baselines, notably achieving 74.30% R@1 after just one interactive round on the WebVid-CoVR-Test dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Unicron: Economizing Self-Healing LLM Training at Scale

Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current failure recovery methods in cloud-based settings inadequately address the diverse and complex scenarios that arise, focusing narrowly on erasing downtime for individual tasks without considering the overall cost impact on a cluster. We introduce Unicron, a workload manager designed for efficient self-healing in large-scale language model training. Unicron optimizes the training process by minimizing failure-related costs across multiple concurrent tasks within a cluster. Its key features include in-band error detection for real-time error identification without extra overhead, a dynamic cost-aware plan generation mechanism for optimal reconfiguration, and an efficient transition strategy to reduce downtime during state changes. Deployed on a 128-GPU distributed cluster, Unicron demonstrates up to a 1.9x improvement in training efficiency over state-of-the-art methods, significantly reducing failure recovery costs and enhancing the reliability of large-scale language model training.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Integrating Dependency Tree Into Self-attention for Sentence Representation

Recent progress on parse tree encoder for sentence representation learning is notable. However, these works mainly encode tree structures recursively, which is not conducive to parallelization. On the other hand, these works rarely take into account the labels of arcs in dependency trees. To address both issues, we propose Dependency-Transformer, which applies a relation-attention mechanism that works in concert with the self-attention mechanism. This mechanism aims to encode the dependency and the spatial positional relations between nodes in the dependency tree of sentences. By a score-based method, we successfully inject the syntax information without affecting Transformer's parallelizability. Our model outperforms or is comparable to the state-of-the-art methods on four tasks for sentence representation and has obvious advantages in computational efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Observation of short-period helical spin order and magnetic transition in a non-chiral centrosymmetric helimagnet

The search for materials exhibiting nanoscale spiral order continues to be fuelled by the promise of emergent inductors. Although such spin textures have been reported in many materials, most of them exhibit long periods or are limited to operate far below room temperature. Here, we present the real-space observation of an ordered helical spin order with a period of 3.2 nm in a non-chiral centrosymmetric helimagnet MnCoSi at room temperature via multi-angle and multi-azimuth approach of Lorentz transmission electron microscopy (TEM). A magnetic transition from the ordered helical spin order to a cycloidal spin order below 228 K is clearly revealed by in situ neutron powder diffraction and Lorentz TEM, which is closely correlated with temperature-induced variation in magneto-crystalline anisotropy. These results reveal the origin of spiral ordered spin textures in non-chiral centrosymmetric helimagnet, which can serve as a new strategy for searching materials with nanoscale spin order with potential applications in emergent electromagnetism.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation

Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping. However, without a practical sim2real strategy, the policy trained in simulation could fail on the real task. There are also wide researches in the sim2real strategies, but most of those methods rely on heavy image rendering, domain randomization training, or tuning. In this work, we solve the insertion task using a pure visual reinforcement learning solution with minimum infrastructure requirement. We also propose a novel sim2real strategy, Real2Sim, which provides a novel and easier solution in policy adaptation. We discuss the advantage of Real2Sim compared with Sim2Real.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Recovery Time Metric Demonstrated on Real-world Electric Grid for Hurricane Impacted Outages

This work proposes a methodology for estimating recovery times for transmission lines and substations, and is demonstrated on a real-world 1269-bus power system model of Puerto Rico under 20 hurricane scenarios, or stochastic realizations of asset failure under the meteorological conditions of Hurricane Maria. The method defines base recovery times for system components and identifies factors that impact these base values by means of multipliers. While the method is tested on transmission lines and substation failures due to hurricanes, it is based on a generic process that could be applied to any system component or event as a general recovery time estimation framework. The results show that given the two failure modes under study (transmission towers and substations), transmission towers appear to have a greater impact on recovery time estimates despite substations being given longer base outage times. Additionally, average recovery times for the simulated hurricanes across 20 scenarios is ~28,000 work crew days.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Signature Entrenchment and Conceptual Changes in Automated Theory Repair

Human beliefs change, but so do the concepts that underpin them. The recent Abduction, Belief Revision and Conceptual Change (ABC) repair system combines several methods from automated theory repair to expand, contract, or reform logical structures representing conceptual knowledge in artificial agents. In this paper we focus on conceptual change: repair not only of the membership of logical concepts, such as what animals can fly, but also concepts themselves, such that birds may be divided into flightless and flying birds, by changing the signature of the logical theory used to represent them. We offer a method for automatically evaluating entrenchment in the signature of a Datalog theory, in order to constrain automated theory repair to succinct and intuitive outcomes. Formally, signature entrenchment measures the inferential contributions of every logical language element used to express conceptual knowledge, i.e., predicates and the arguments, ranking possible repairs to retain valuable logical concepts and reject redundant or implausible alternatives. This quantitative measurement of signature entrenchment offers a guide to the plausibility of conceptual changes, which we aim to contrast with human judgements of concept entrenchment in future work.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification

In this paper, we propose TEDL, a two-stage learning approach to quantify uncertainty for deep learning models in classification tasks, inspired by our findings in experimenting with Evidential Deep Learning (EDL) method, a recently proposed uncertainty quantification approach based on the Dempster-Shafer theory. More specifically, we observe that EDL tends to yield inferior AUC compared with models learnt by cross-entropy loss and is highly sensitive in training. Such sensitivity is likely to cause unreliable uncertainty estimation, making it risky for practical applications. To mitigate both limitations, we propose a simple yet effective two-stage learning approach based on our analysis on the likely reasons causing such sensitivity, with the first stage learning from cross-entropy loss, followed by a second stage learning from EDL loss. We also re-formulate the EDL loss by replacing ReLU with ELU to avoid the Dying ReLU issue. Extensive experiments are carried out on varied sized training corpus collected from a large-scale commercial search engine, demonstrating that the proposed two-stage learning framework can increase AUC significantly and greatly improve training robustness.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Abnormal Critical Fluctuations Revealed by Magnetic Resonance in the Two-Dimensional Ferromagnetic Insulators

Phase transitions and critical phenomena, which are dominated by fluctuations and correlations, are one of the fields replete with physical paradigms and unexpected discoveries. Especially for two-dimensional magnetism, the limitation of the Ginzburg criterion leads to enhanced fluctuations breaking down the mean-field theory near a critical point. Here, by means of magnetic resonance, we investigate the behavior of critical fluctuations in the two-dimensional ferromagnetic insulators $\rm CrXTe_3 (X=Si, Ge)$. After deriving the classical and quantum models of magnetic resonance, we deem the dramatic anisotropic shift of the measured $g$ factor to originate from fluctuations with anisotropic interactions. The deduction of the $g$ factor behind the fluctuations is consistent with the spin-only state (${g\approx}$ 2.050(10) for $\rm CrSiTe_3$ and 2.039(10) for $\rm CrGeTe_3$). Furthermore, the abnormal enhancement of $g$ shift, supplemented by specific heat and magnetometry measurements, suggests that $\rm CrSiTe_3$ exhibits a more typical two-dimensional nature than $\rm CrGeTe_3$ and may be closer to the quantum critical point.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

An ensemble solver for segregated cardiovascular FSI

Computational models are increasingly used for diagnosis and treatment of cardiovascular disease. To provide a quantitative hemodynamic understanding that can be effectively used in the clinic, it is crucial to quantify the variability in the outputs from these models due to multiple sources of uncertainty. To quantify this variability, the analyst invariably needs to generate a large collection of high-fidelity model solutions, typically requiring a substantial computational effort. In this paper, we show how an explicit-in-time ensemble cardiovascular solver offers superior performance with respect to the embarrassingly parallel solution with implicit-in-time algorithms, typical of an inner-outer loop paradigm for non-intrusive uncertainty propagation. We discuss in detail the numerics and efficient distributed implementation of a segregated FSI cardiovascular solver on both CPU and GPU systems, and demonstrate its applicability to idealized and patient-specific cardiovascular models, analyzed under steady and pulsatile flow conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification

Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning. Existing methods attempt to solve this problem by clustering extracted embeddings to generate pseudo labels. However, most methods ignore the intra-class gap caused by camera style variance, and some methods are relatively complex and indirect although they try to solve the negative impact of the camera style on feature distribution. To solve this problem, we propose a camera-aware style separation and contrastive learning method (CA-UReID), which directly separates camera styles in the feature space with the designed camera-aware attention module. It can explicitly divide the learnable feature into camera-specific and camera-agnostic parts, reducing the influence of different cameras. Moreover, to further narrow the gap across cameras, we design a camera-aware contrastive center loss to learn more discriminative embedding for each identity. Extensive experiments demonstrate the superiority of our method over the state-of-the-art methods on the unsupervised person ReID task.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

En route to high Tc superconductivity via Rb substitution of guest metal atoms in SrB3C3 clathrate

Recently, a host/guest clathrate SrB3C3 with sp3-bonded boron-carbon framework was synthesized at around 50 GPa. On the basis of electron count, the structure is understood as guest Sr2+ cations intercalated in the (B3C3)3- framework. Previous calculations suggest that SrB3C3 is a hole conductor with an estimated superconducting critical temperature (Tc) of 42 K at ambient pressure. If atoms with similar radius, such as Rb, can substitute Sr2+ in the lattice, the electronic as well as superconductivity properties of this material will be modified significantly. Here, we perform extensive simulations on the stability and physical properties of Rb-Sr-B3C3 system using first-principles density functional calculation in combination with cluster expansion and CALYPSO structure prediction method. We predict a phonon-mediated superconductor Rb0.5Sr0.5B3C3 with a remarkably high Tc of 78 K at ambient pressure, which is a significant improvement from the estimated value (42 K) in SrB3C3. The current results suggest that substitution of alkali atom in synthesized clathrate SrB3C3 is a viable route toward high-Tc compounds.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The effects of ionic liquids on the thermodynamics of H2 activation by frustrated Lewis pairs: a density functional theory study

Nowadays, hydrogen activation by frustrated Lewis pairs (FLPs) and their applications have been demonstrated to be one of emerge research topics in the field of catalysis. Previous studies have shown that the thermodynamics of these reaction is determined by electronic structures of FLPs and solvents. Herein, we investigated the systems consisting of typical FLPs and ionic liquids (ILs), which are well known by their large number of types and excellent solvent effects. The density functional theory (DFT) calculations were performed to study the thermodynamics for H2 activation by both inter- and intra-molecular FLPs, as well as the individual components. The results show that the computed overall Gibbs free energies in ILs are more negative than that computed in toluene. Through the thermodynamics partitioning, we find that ILs favor the H-H cleavage elemental step, while disfavored the elemental steps of proton attachment, hydride attachment and zwitterionic stabilization. Moreover, the results show that these effects are strongly dependent on the type of FLPs, where intra-molecular FLPs are more effected compared to the inter-molecular FLPs.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking

Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods. To solve this problem we develop C-FAR, a novel method for Fast, Automated and Reproducible assessment of multiple hierarchical clustering algorithms simultaneously. Our algorithm takes any number of hierarchical clustering trees as input, then strategically queries pairs for human feedback, and outputs an optimal clustering among those nominated by these trees. While it is applicable to large dataset in any domain that utilizes pairwise comparisons for assessment, our flagship application is the cluster aggregation step in spike-sorting, the task of assigning waveforms (spikes) in recordings to neurons. On simulated data of 96 neurons under adverse conditions, including drifting and 25\% blackout, our algorithm produces near-perfect tracking relative to the ground truth. Our runtime scales linearly in the number of input trees, making it a competitive computational tool. These results indicate that C-FAR is highly suitable as a model selection and assessment tool in clustering tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Disentanglement Then Reconstruction: Learning Compact Features for Unsupervised Domain Adaptation

Recent works in domain adaptation always learn domain invariant features to mitigate the gap between the source and target domains by adversarial methods. The category information are not sufficiently used which causes the learned domain invariant features are not enough discriminative. We propose a new domain adaptation method based on prototype construction which likes capturing data cluster centers. Specifically, it consists of two parts: disentanglement and reconstruction. First, the domain specific features and domain invariant features are disentangled from the original features. At the same time, the domain prototypes and class prototypes of both domains are estimated. Then, a reconstructor is trained by reconstructing the original features from the disentangled domain invariant features and domain specific features. By this reconstructor, we can construct prototypes for the original features using class prototypes and domain prototypes correspondingly. In the end, the feature extraction network is forced to extract features close to these prototypes. Our contribution lies in the technical use of the reconstructor to obtain the original feature prototypes which helps to learn compact and discriminant features. As far as we know, this idea is proposed for the first time. Experiment results on several public datasets confirm the state-of-the-art performance of our method.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Enhanced Ferroelectricity and Spin Current Waves in M-Type Barium Hexaferrite

The intrinsic ferroelectricity and related dielectric properties of M-type Barium Hexaferrite (BaFe12O19) with excellent magnetic performance are reported in this paper. A classic electric polarization (P-E) hysteresis loop with full saturation, together with two nonlinear reversal current peaks in the I-V curve and huge change of dielectric constant in the vicinity of Curie temperature, have all demonstrated themselves as sufficient experimental evidences to verify the ferroelectricity of BaFe12O19 ceramics. It holds a large remnant polarization at 108 uC/cm2 and a suitable coercive field at 117 kV/cm. Two peaks at 194C and 451C in the temperature-dependent dielectric spectrum of BaFe12O19 ceramics are considered to be the phase transition from ferro- to antiferro- and antiferro- to para-electric structures. A conventional strong magnetic hysteresis loop was also observed. The magnetically induced polarization upon the BaFe12O19 ceramics was achieved in the form of alternating spin current waves. A ME-coupling voltage with an amplitude of 23mV on an applied magnetic field at 500mT was achieved. These combined multiple functional responses of large ferroelectrics and strong ferromagnetism reveal the excellent multiferroic features of BaFe12O19, which would bring forth the great opportunity to create novel electric devices with active coupling effect between magnetic and electric orders.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Hyperfine Structure and Coherent Dynamics of Rare Earth Spins Explored with Electron-Nuclear Double Resonance at Sub-Kelvin Temperatures

An experimental platform of ultralow-temperature pulsed ENDOR (electron-nuclear double resonance) spectroscopy is constructed for the bulk materials. Coherent property of the coupled electron and nuclear spins of the rare-earth (RE) dopants in a crystal (143Nd3+:Y2SiO5) is investigated from 100 mK to 6 K. At the lowest working temperatures, two-pulse-echo coherence time exceeding 2 ms and 40 ms are achieved for the electron and nuclear spins, while the electronic Zeeman and hyperfine population lifetimes are more than 15 s and 10 min. With the aid of the near-unity electron spin polarization at 100 mK, the complete hyperfine level structure with 16 energy levels is measured using ENDOR technique without the assistance of the reconstructed spin Hamiltonian. These results demonstrate the suitability of the deeply cooled paramagnetic RE-doped solids for memory components aimed for quantum communication and quantum computation. The developed experimental platform is expected to be a powerful tool for paramagnetic materials from various research fields.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Learning Various Length Dependence by Dual Recurrent Neural Networks

Recurrent neural networks (RNNs) are widely used as a memory model for sequence-related problems. Many variants of RNN have been proposed to solve the gradient problems of training RNNs and process long sequences. Although some classical models have been proposed, capturing long-term dependence while responding to short-term changes remains a challenge. To this problem, we propose a new model named Dual Recurrent Neural Networks (DuRNN). The DuRNN consists of two parts to learn the short-term dependence and progressively learn the long-term dependence. The first part is a recurrent neural network with constrained full recurrent connections to deal with short-term dependence in sequence and generate short-term memory. Another part is a recurrent neural network with independent recurrent connections which helps to learn long-term dependence and generate long-term memory. A selection mechanism is added between two parts to help the needed long-term information transfer to the independent neurons. Multiple modules can be stacked to form a multi-layer model for better performance. Our contributions are: 1) a new recurrent model developed based on the divide-and-conquer strategy to learn long and short-term dependence separately, and 2) a selection mechanism to enhance the separating and learning of different temporal scales of dependence. Both theoretical analysis and extensive experiments are conducted to validate the performance of our model, and we also conduct simple visualization experiments and ablation analyses for the model interpretability. Experimental results indicate that the proposed DuRNN model can handle not only very long sequences (over 5000 time steps), but also short sequences very well. Compared with many state-of-the-art RNN models, our model has demonstrated efficient and better performance.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

PrivColl: Practical Privacy-Preserving Collaborative Machine Learning

Collaborative learning enables two or more participants, each with their own training dataset, to collaboratively learn a joint model. It is desirable that the collaboration should not cause the disclosure of either the raw datasets of each individual owner or the local model parameters trained on them. This privacy-preservation requirement has been approached through differential privacy mechanisms, homomorphic encryption (HE) and secure multiparty computation (MPC), but existing attempts may either introduce the loss of model accuracy or imply significant computational and/or communicational overhead. In this work, we address this problem with the lightweight additive secret sharing technique. We propose PrivColl, a framework for protecting local data and local models while ensuring the correctness of training processes. PrivColl employs secret sharing technique for securely evaluating addition operations in a multiparty computation environment, and achieves practicability by employing only the homomorphic addition operations. We formally prove that it guarantees privacy preservation even though the majority (n-2 out of n) of participants are corrupted. With experiments on real-world datasets, we further demonstrate that PrivColl retains high efficiency. It achieves a speedup of more than 45X over the state-of-the-art MPC/HE based schemes for training linear/logistic regression, and 216X faster for training neural network.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications

Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people's life. Current suicidal ideation detection methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This paper is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of suicidal ideation detection are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and datasets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Learning Private Neural Language Modeling with Attentive Aggregation

Mobile keyboard suggestion is typically regarded as a word-level language modeling problem. Centralized machine learning technique requires massive user data collected to train on, which may impose privacy concerns for sensitive personal typing data of users. Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server. To obtain a global model for prediction, existing FL algorithms simply average the client models and ignore the importance of each client during model aggregation. Furthermore, there is no optimization for learning a well-generalized global model on the central server. To solve these problems, we propose a novel model aggregation with the attention mechanism considering the contribution of clients models to the global model, together with an optimization technique during server aggregation. Our proposed attentive aggregation method minimizes the weighted distance between the server model and client models through iterative parameters updating while attends the distance between the server model and client models. Through experiments on two popular language modeling datasets and a social media dataset, our proposed method outperforms its counterparts in terms of perplexity and communication cost in most settings of comparison.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks

In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record. Accurately capturing the list of medications currently taken by a given patient is extremely challenging due to undefined errors and omissions. We present a general robust framework that explicitly models the possible contamination through overtime decay mechanism on the input billing codes and noise injection into the recurrent hidden states, respectively. By doing this, billing codes are reformulated into its temporal patterns with decay rates on each medical variable, and the hidden states of RNNs are regularised by random noises which serve as dropout to improved RNNs robustness towards data variability in terms of missing values and multiple errors. The proposed method is extensively evaluated on real health care data to demonstrate its effectiveness in suggesting medication orders from contaminated values.

0 citations0 reviewsNo code linkedOpen access