arXiv subjects

Lili Jiang

Publications and source records attributed to Lili Jiang.

At least 19 recordsLinked to original sources

arXiv2025

Modeling Fairness in Recruitment AI via Information Flow

Avoiding bias and understanding the real-world consequences of AI-supported decision-making are critical to address fairness and assign accountability. Existing approaches often focus either on technical aspects, such as datasets and models, or on high-level socio-ethical considerations - rarely capturing how these elements interact in practice. In this paper, we apply an information flow-based modeling framework to a real-world recruitment process that integrates automated candidate matching with human decision-making. Through semi-structured stakeholder interviews and iterative modeling, we construct a multi-level representation of the recruitment pipeline, capturing how information is transformed, filtered, and interpreted across both algorithmic and human components. We identify where biases may emerge, how they can propagate through the system, and what downstream impacts they may have on candidates. This case study illustrates how information flow modeling can support structured analysis of fairness risks, providing transparency across complex socio-technical systems.

cs.CY↗

arXiv2025

Electrically tunable quantum interference of atomic spins on surfaces

Controlling quantum interference near avoided energy-level crossings is crucial for fast and reliable coherent manipulation in quantum information processing. However, achieving tunable quantum interference in atomically-precise engineered structures remains challenging. Here, we demonstrate electrical control of quantum interference using atomic spins on an insulating film in a scanning tunneling microscope. Using bias voltages applied across the tunnel junction, we modulate the atomically-confined magnetic interaction between the probe tip and surface atoms with a strong electric field, and drive the spin state rapidly through the energy-level anticrossing. This all-electrical manipulation allows us to achieve Landau-Zener-Stückelberg-Majorana (LZSM) interferometry on both single spins and pairs of interacting spins. The LZSM pattern exhibits multiphoton resonances, and its asymmetry suggests that the spin dynamics is influenced by spin-transfer torque of tunneling electrons. Multi-level LZSM spectra measured on coupled spins with tunable interactions show distinct interference patterns depending on their many-body energy landscapes. These results open new avenues for all-electrical quantum manipulation in spin-based quantum processors in the strongly driven regime.

cond-mat.mes-hall↗

arXiv2025

Towards Practical Defect-Focused Automated Code Review

The complexity of code reviews has driven efforts to automate review comments, but prior approaches oversimplify this task by treating it as snippet-level code-to-text generation and relying on text similarity metrics like BLEU for evaluation. These methods overlook repository context, real-world merge request evaluation, and defect detection, limiting their practicality. To address these issues, we explore the full automation pipeline within the online recommendation service of a company with nearly 400 million daily active users, analyzing industry-grade C++ codebases comprising hundreds of thousands of lines of code. We identify four key challenges: 1) capturing relevant context, 2) improving key bug inclusion (KBI), 3) reducing false alarm rates (FAR), and 4) integrating human workflows. To tackle these, we propose 1) code slicing algorithms for context extraction, 2) a multi-role LLM framework for KBI, 3) a filtering mechanism for FAR reduction, and 4) a novel prompt design for better human interaction. Our approach, validated on real-world merge requests from historical fault reports, achieves a 2x improvement over standard LLMs and a 10x gain over previous baselines. While the presented results focus on C++, the underlying framework design leverages language-agnostic principles (e.g., AST-based analysis), suggesting potential for broader applicability.

cs.SE↗

arXiv2024

Construction of topological quantum magnets from atomic spins on surfaces

Artificial quantum systems have emerged as indispensable platforms to realize exotic topological matter in a well-controlled manner. Here, we demonstrate topological quantum Heisenberg spin lattices, engineered with spin chains and two-dimensional spin arrays using spin 1/2 atoms on insulating films in a scanning tunnelling microscope (STM). We engineered with atomic precision both topological and trivial phases of the quantum spin model, realizing first- and second-order topological quantum magnets. Their many-body excitations were probed by single-atom electron spin resonance with ultrahigh energy resolution. The atomically-localized magnetic field of the STM tip allows us to directly visualize various topological bound modes including topological edge states, topological defects, and higher-order corner modes. Our results provide an important bottom-up approach to simulating exotic quantum many-body phases of interacting spins.

cond-mat.mes-hall↗

arXiv2023

Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework called MGL4MEP that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators.

cs.LG↗

arXiv2023

Magnetic field stabilized Wigner crystal states in a graphene moiré superlattice

Wigner crystals are predicted as the crystallization of the dilute electron gas moving in a uniform background when the electron-electron Coulomb energy dominates the kinetic energy. The Wigner crystal has previously been observed in the ultraclean two-dimensional electron gas (2DEG) present on the surface of liquid helium and in semiconductor quantum wells at high magnetic field. More recently, Wigner crystals have also been reported in WS2/WSe2 moiré heterostructures. ABC-stacked trilayer graphene on boron nitride (ABC-TLG/hBN) moiré superlattices provide a unique tunable platform to explore Wigner crystal states where the electron correlation can be controlled by electric and magnetic field. Here we report the observation of magnetic field stabilized Wigner crystal states in a ABC-TLG/hBN moiré superlattice. We show that correlated insulating states emerge at multiple fractional and integer fillings corresponding to v = 1/3, 2/3, 1, 4/3, 5/3 and 2 electrons per moiré lattice site under a magnetic field. These correlated insulating states can be attributed to generalized Mott states for the integer fillings (v = 1, 2) and generalized Wigner crystal states for the fractional fillings (v = 1/3, 2/3, 4/3, 5/3). The generalized Wigner crystal states are stabilized by a vertical magnetic field, and they are strongest at one magnetic flux quantum per three moiré superlattices. The correlated insulating states at v = 2 persists up to 30 Tesla, which can be described by a Mott-Hofstadter transition at high magnetic field. The tunable Mott and Wigner crystal states in the ABC-TLG/hBN highlight the opportunities to discover new correlated quantum phases due to the interplay between the magnetic field and moiré flatbands.

cond-mat.mes-hall↗

arXiv2022

Few-Shot Nested Named Entity Recognition

While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data has been challenging, especially for entities with nested structures. Unlike flat entities, entities and their nested entities are more likely to have similar semantic feature representations, drastically increasing difficulties in classifying different entity categories in the few-shot setting. Although prior work has briefly discussed nested structures in the context of few-shot learning, to our best knowledge, this paper is the first one specifically dedicated to studying the few-shot nested NER task. Leveraging contextual dependency to distinguish nested entities, we propose a Biaffine-based Contrastive Learning (BCL) framework. We first design a Biaffine span representation module for learning the contextual span dependency representation for each entity span rather than only learning its semantic representation. We then merge these two representations by the residual connection to distinguish nested entities. Finally, we build a contrastive learning framework to adjust the representation distribution for larger margin boundaries and more generalized domain transfer learning ability. We conducted experimental studies on three English, German, and Russian nested NER datasets. The results show that the BCL outperformed three baseline models on the 1-shot and 5-shot tasks in terms of F1 score.

cs.CL↗

arXiv2022

Spectroscopy Signatures of Electron Correlations in a Trilayer Graphene/hBN Moiré Superlattice

ABC-stacked trilayer graphene/hBN moiré superlattice (TLG/hBN) has emerged as a playground for correlated electron physics. We report spectroscopy measurements of dual-gated TLG/hBN using Fourier transformed infrared photocurrent spectroscopy. We observed a strong optical transition between moiré mini-bands that narrows continuously as a bandgap is opened by gating, indicating a reduction of the single particle bandwidth. At half-filling of the valence flat band, a broad absorption peak emerges at ~18 meV, indicating direct optical excitation across an emerging Mott gap. Similar photocurrent spectra are observed in two other correlated insulating states at quarter- and half-filling of the first conduction band. Our findings provide key parameters of the Hubbard model for the understanding of electron correlation in TLG/hBN.

cond-mat.str-el↗

arXiv2020

Tunable ferromagnetism at non-integer filling of a moiré superlattice

The flat bands resulting from moiré superlattices in magic-angle twisted bilayer graphene (MATBG) and ABC-trilayer graphene aligned with hexagonal boron nitride (ABC-TLG/hBN) have been shown to give rise to fascinating correlated electron phenomena such as correlated insulators and superconductivity. More recently, orbital magnetism associated with correlated Chern insulators was found in this class of layered structures centered at integer multiples of n0, the density corresponding to one electron per moiré superlattice unit cell. Here we report the experimental observation of ferromagnetism at fractional filling of a flat Chern band in an ABC-TLG/hBN moirésuperlattice. The ferromagnetic state exhibits prominent ferromagnetic hysteresis behavior with large anomalous Hall resistivity in a broad region of densities, centered in the valence miniband at n = -2.3 n0. This ferromagnetism depends very sensitively on the control parameters in the moiré system: not only the magnitude of the anomalous Hall signal, but also the sign of the hysteretic ferromagnetic response can be modulated by tuning the carrier density and displacement field. Our discovery of electrically tunable ferromagnetism in a moiré Chern band at non-integer filling highlights the opportunities for exploring new correlated ferromagnetic states in moiré heterostructures.

cond-mat.mes-hall↗

arXiv2019

Visualization of the flat electronic band in twisted bilayer graphene near the magic angle twist

Bilayer graphene was theorized to host a moire miniband with flat dispersion if the layers are stacked at specific twist angles known as the magic angles. Recently, such twisted bilayer graphene (tBLG) with the first magic angle twist was reported to exhibit correlated insulating state and superconductivity, where the presence of the flat miniband in the system is thought to be essential for the emergence of these ordered phases in the transport measurements. Tunneling spectroscopy and electronic compressibility measurements in tBLG have revealed a van Hove singularity that is consistent with the presence of the flat miniband. However, a direct observation of the flat dispersion in the momentum-space of such moire miniband in tBLG is still elusive. Here, we report the visualization of the flat moire miniband by using angle-resolved photoemission spectroscopy with nanoscale resolution (nanoARPES). The high spatial resolution in nanoARPES enabled the measurement of the local electronic structure of the tBLG. We clearly demonstrate the existence of the flat moire band near the charge neutrality for tBLG close to the magic angle at room temperature.

cond-mat.mes-hall↗

arXiv2019

ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task

Given many recent advanced embedding models, selecting pre-trained word embedding (a.k.a., word representation) models best fit for a specific downstream task is non-trivial. In this paper, we propose a systematic approach, called ETNLP, for extracting, evaluating, and visualizing multiple sets of pre-trained word embeddings to determine which embeddings should be used in a downstream task. For extraction, we provide a method to extract subsets of the embeddings to be used in the downstream task. For evaluation, we analyse the quality of pre-trained embeddings using an input word analogy list. Finally, we visualize the word representations in the embedding space to explore the embedded words interactively. We demonstrate the effectiveness of the proposed approach on our pre-trained word embedding models in Vietnamese to select which models are suitable for a named entity recognition (NER) task. Specifically, we create a large Vietnamese word analogy list to evaluate and select the pre-trained embedding models for the task. We then utilize the selected embeddings for the NER task and achieve the new state-of-the-art results on the task benchmark dataset. We also apply the approach to another downstream task of privacy-guaranteed embedding selection, and show that it helps users quickly select the most suitable embeddings. In addition, we create an open-source system using the proposed systematic approach to facilitate similar studies on other NLP tasks. The source code and data are available at https://github.com/vietnlp/etnlp.

cs.CL↗

arXiv2019

Generic Multilayer Network Data Analysis with the Fusion of Content and Structure

Multi-feature data analysis (e.g., on Facebook, LinkedIn) is challenging especially if one wants to do it efficiently and retain the flexibility by choosing features of interest for analysis. Features (e.g., age, gender, relationship, political view etc.) can be explicitly given from datasets, but also can be derived from content (e.g., political view based on Facebook posts). Analysis from multiple perspectives is needed to understand the datasets (or subsets of it) and to infer meaningful knowledge. For example, the influence of age, location, and marital status on political views may need to be inferred separately (or in combination). In this paper, we adapt multilayer network (MLN) analysis, a nontraditional approach, to model the Facebook datasets, integrate content analysis, and conduct analysis, which is driven by a list of desired application based queries. Our experimental analysis shows the flexibility and efficiency of the proposed approach when modeling and analyzing datasets with multiple features.

cs.CL↗

arXiv2019

Tunable Correlated Chern Insulator and Ferromagnetism in Trilayer Graphene/Boron Nitride Moiré Superlattice

Studies on two-dimensional electron systems in a strong magnetic field first revealed the quantum Hall (QH) effect, a topological state of matter featuring a finite Chern number (C) and chiral edge states. Haldane later theorized that Chern insulators with integer QH effects could appear in lattice models with complex hopping parameters even at zero magnetic field. The ABC-trilayer graphene/hexagonal boron nitride (TLG/hBN) moiré superlattice provides an attractive platform to explore Chern insulators because it features nearly flat moiré minibands with a valley-dependent electrically tunable Chern number. Here we report the experimental observation of a correlated Chern insulator in a TLG/hBN moiré superlattice. We show that reversing the direction of the applied vertical electric field switches TLG/hBN's moiré minibands between zero and finite Chern numbers, as revealed by dramatic changes in magneto-transport behavior. For topological hole minibands tuned to have a finite Chern number, we focus on 1/4 filling, corresponding to one hole per moiré unit cell. The Hall resistance is well quantized at h/2e2, i.e. C = 2, for |B| > 0.4 T. The correlated Chern insulator is ferromagnetic, exhibiting significant magnetic hysteresis and a large anomalous Hall signal at zero magnetic field. Our discovery of a C = 2 Chern insulator at zero magnetic field should open up exciting opportunities for discovering novel correlated topological states, possibly with novel topological excitations, in nearly flat and topologically nontrivial moiré minibands.

cond-mat.mes-hall↗

arXiv2019

dpUGC: Learn Differentially Private Representation for User Generated Contents

This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing. The proposed approaches protect the privacy of the individual from re-identification, especially provide better trade-off of privacy and data utility on UGC data for sharing. The experimental results show that the trained embedding models are applicable for the classic text analysis tasks (e.g., regression). Moreover, the proposed approaches of learning differentially private embedding models are both framework- and data- independent, which facilitates the deployment and sharing. The source code is available at https://github.com/sonvx/dpText.

cs.CL↗

arXiv2019

Evidence of Gate-Tunable Mott Insulator in Trilayer Graphene-Boron Nitride Moiré Superlattice

Mott insulator plays a central role in strongly correlated physics, where the repulsive Coulomb interaction dominates over the electron kinetic energy and leads to insulating states with one electron occupying each unit cell. Doped Mott insulator is often described by the Hubbard model3, which can give rise to other correlated phenomena such as unusual magnetism and even high-temperature superconductivity. A tunable Mott insulator, where the competition between the Coulomb interaction and the kinetic energy can be varied in situ, can provide an invaluable model system for the study of Mott physics. Here we report the realization of such a tunable Mott insulator in the ABC trilayer graphene (TLG) and hexagonal boron nitride (hBN) heterostructure with a moiré superlattice. Unlike massless Dirac electrons in monolayer graphene, electrons in pristine ABC TLG are characterized by quartic energy dispersion and large effective mass that are conducive for strongly correlated phenomena. The moiré superlattice in TLG/hBN heterostructures leads to narrow electronic minibands that are gate tunable. Each filled miniband contains 4 electrons in one moiré lattice site due to the spin and valley degeneracy of graphene. The Mott insulator states emerge at 1/4 and 1/2 fillings, corresponding to one electron and two electrons per site, respectively. Moreover, the Mott states in the ABC TLG/hBN heterostructure exhibit unprecedented tunability: the Mott gap can be modulated in situ by a vertical electrical field, and at the meantime, the electron doping can be gate-tuned to fill the band from one Mott insulating state to another. Our observation of a tunable Mott insulator opens up exciting opportunities to explore novel strongly correlated phenomena in two-dimensional moiré superlattice heterostructures.

cond-mat.mes-hall↗

arXiv2019

Signatures of Gate-Tunable Superconductivity in Trilayer Graphene/Boron Nitride Moiré Superlattice

Understanding the mechanism of high temperature (high Tc) superconductivity is a central problem in condensed matter physics. It is often speculated that high Tc superconductivity arises from a doped Mott insulator as described by the Hubbard model. An exact solution of the Hubbard model, however, is extremely challenging due to the strong electron-electron correlation. Therefore, it is highly desirable to experimentally study a model Hubbard system in which the unconventional superconductivity can be continuously tuned by varying the Hubbard parameters. Here we report signatures of tunable superconductivity in ABC-trilayer graphene (TLG) / boron nitride (hBN) moiré superlattice. Unlike "magic angle" twisted bilayer graphene, theoretical calculations show that under a vertical displacement field the ABC-TLG/hBN heterostructure features an isolated flat valence miniband associated with a Hubbard model on a triangular superlattice. Upon applying such a displacement field we find experimentally that the ABC-TLG/hBN superlattice displays Mott insulating states below 20 Kelvin at 1/4 and 1/2 fillings, corresponding to 1 and 2 holes per unit cell, respectively. Upon further cooling, signatures of superconducting domes emerge below 1 kelvin for the electron- and hole-doped sides of the 1/4 filling Mott state. The electronic behavior in the TLG/hBN superlattice is expected to depend sensitively on the interplay between the electron-electron interaction and the miniband bandwidth, which can be tuned continuously with the displacement field D. By simply varying the D field, we demonstrate transitions from the candidate superconductor to Mott insulator and metallic phases. Our study shows that TLG/hBN heterostructures offer an attractive model system to explore rich correlated behavior emerging in the tunable triangular Hubbard model.

cond-mat.supr-con↗

arXiv2018

Self-adaptive Privacy Concern Detection for User-generated Content

To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual's sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).

cs.CR↗

arXiv2018

Black Arsenic: A Layered Semiconductor with Extreme in-plane Anisotropy

Two-dimensional (2D) layered materials emerge in recent years as a new platform to host novel electronic, optical or excitonic physics and develop unprecedented nanoelectronic and energy applications. By definition, these materials are strongly anisotropic between within the basal plane and cross the plane. The structural and property anisotropies inside their basal plane, however, are much less investigated. Herein, we report a rare chemical form of arsenic, called black-arsenic (b-As), as an extremely anisotropic layered semiconductor. We have performed systematic characterization on the structural, electronic, thermal and electrical properties of b-As single crystals, with particular focus on its anisotropies along two in-plane principle axes, armchair (AC) and zigzag (ZZ). Our analysis shows that b-As exhibits higher or comparable electronic, thermal and electric transport anisotropies between the AC and ZZ directions than any other known 2D crystals. Such extreme in-plane anisotropies are able to potentially implement novel ideas for scientific research and device applications.

cond-mat.mtrl-sci↗