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Researcher profile

Cong Chen

Cong Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-sciComputer VisionArtificial Intelligencecond-mat.mes-halleess.SYSystems and ControlComputer Science and Game TheoryMachine Learningphysics.app-phApplicationsComputation and LanguageCryptography and Securityecon.GNeess.IVnlin.AOphysics.opticsq-fin.EC

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Published work

24 published item(s)

preprint2026arXiv

AviationLMM: A Large Multimodal Foundation Model for Civil Aviation

Civil aviation is a cornerstone of global transportation and commerce, and ensuring its safety, efficiency and customer satisfaction is paramount. Yet conventional Artificial Intelligence (AI) solutions in aviation remain siloed and narrow, focusing on isolated tasks or single modalities. They struggle to integrate heterogeneous data such as voice communications, radar tracks, sensor streams and textual reports, which limits situational awareness, adaptability, and real-time decision support. This paper introduces the vision of AviationLMM, a Large Multimodal foundation Model for civil aviation, designed to unify the heterogeneous data streams of civil aviation and enable understanding, reasoning, generation and agentic applications. We firstly identify the gaps between existing AI solutions and requirements. Secondly, we describe the model architecture that ingests multimodal inputs such as air-ground voice, surveillance, on-board telemetry, video and structured texts, and performs cross-modal alignment and fusion, and produces flexible outputs ranging from situation summaries and risk alerts to predictive diagnostics and multimodal incident reconstructions. In order to fully realize this vision, we identify key research opportunities to address, including data acquisition, alignment and fusion, pretraining, reasoning, trustworthiness, privacy, robustness to missing modalities, and synthetic scenario generation. By articulating the design and challenges of AviationLMM, we aim to boost the civil aviation foundation model progress and catalyze coordinated research efforts toward an integrated, trustworthy and privacy-preserving aviation AI ecosystem.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

The Great Pretender: A Stochasticity Problem in LLM Jailbreak

"Oh-Oh, yes, I'm the great pretender. Pretending that I'm doing well. My need is such, I pretend too much..." summarizes the state in the area of jailbreak creation and evaluation. You find this method to generate adversarial attacks proposed by a reputable institution (e.g., BoN from Anthropic or Crescendo from Microsoft Research). However, this method does not deliver on the promise claimed in the paper despite having top ASR scores against industry-grade LLMs. You successfully generate the jailbreak prompts against your target (open) model. However, the generated jailbreak prompt works against the target model with a 50% consecutive success rate (5 out of 10 attempts) despite having an 80% ASR (on paper) on the latest closed-source model (with a guardrail system)! This observation leads us to think. First, Attack Success Rate (ASR), the primary metric for LLM jailbreak benchmarking, is not a stable quantity. Second, published ASR numbers are therefore systematically inflated and incomparable across papers. Therefore, we wonder "Why a successful jailbreak prompt does not perform consistently well against a target model on which the prompts have been optimized?". To answer this question, we study the impact of stochasticity not only during attack evaluation but also during attack generation. Our evaluation includes several jailbreak attacks, models (different sizes and providers), and judges. In addition, we propose a new metric and two new frameworks (CAS-eval and CAS-gen). Our evaluation framework, CAS-eval, shows that an attack can have an ASR drop of up to 30 percentage points when a jailbreak prompt needs to succeed on more than one attempt. Thankfully, our attack generation framework (CAS-gen) improves previous jailbreak methods and helps them recover this loss of 30 percentage points!

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Wholesale Market Participation of DERA: DSO-DERA-ISO Coordination

Distributed energy resource aggregators (DERAs) must share the distribution network together with the distribution utility in order to participate in the wholesale electricity markets that are operated by independent system operators (ISOs). We propose a forward auction that a distribution system operator (DSO) can utilize to allocate distribution network access limits to DERAs. As long as the DERAs operate within their acquired limits, these limits define operating envelopes that guarantee distribution network security, thus defining a mechanism that requires no real-time intervention from the DSOs for DERAs to participate in the wholesale markets. Our auctions take the form of robust and risk-sensitive markets with bids/offers from DERAs and utility's operational costs. Properties of the proposed auction, e.g., resulting surpluses of DSO and the DERAs, and the auction prices, along with empirical performance studies, are presented.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion

Existing low-rank tensor completion (LRTC) approaches aim at restoring a partially observed tensor by imposing a global low-rank constraint on the underlying completed tensor. However, such a global rank assumption suffers the trade-off between restoring the originally details-lacking parts and neglecting the potentially complex objects, making the completion performance unsatisfactory on both sides. To address this problem, we propose a novel and practical strategy for image restoration that restores the partially observed tensor in a coarse-to-fine (C2F) manner, which gets rid of such trade-off by searching proper local ranks for both low- and high-rank parts. Extensive experiments are conducted to demonstrate the superiority of the proposed C2F scheme. The codes are available at: https://github.com/RuiLin0212/C2FLRTC.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Competitive DER Aggregation for Participation in Wholesale Markets

The problem of the large-scale aggregation of the behind-the-meter demand and generation resources by a distributed-energy-resource aggregator (DERA) is considered. As a profit-seeking wholesale market participant, a DERA maximizes its profit while providing competitive services to its customers with higher consumer/prosumer surpluses than those offered by the distribution utilities or community choice aggregators. A constrained profit maximization program for aggregating behind-the-meter generation and consumption resources is formulated, from which payment functions for the behind-the-meter consumptions and generations are derived. Also obtained are DERA's bid and offer curves for its participation in the wholesale energy market and the optimal schedule of behind-the-meter resources. It is shown that the proposed DERA's aggregation model can achieve market efficiency equivalent to that when its customers participate individually directly in the wholesale market.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High-Performance Flexible All-Perovskite Tandem Solar Cells with Reduced VOC-Deficit in Wide-Bandgap Subcell

Among various types of perovskite-based tandem solar cells (TSCs), all-perovskite TSCs are of particular attractiveness for building- and vehicle-integrated photovoltaics, or space energy areas as they can be fabricated on flexible and lightweight substrates with a very high power-to-weight ratio. However, the efficiency of flexible all-perovskite tandems is lagging far behind their rigid counterparts primarily due to the challenges in developing efficient wide-bandgap (WBG) perovskite solar cells on the flexible substrates as well as the low open-circuit voltage (VOC) in the WBG perovskite subcell. Here, we report that the use of self-assembled monolayers as hole-selective contact effectively suppresses the interfacial recombination and allows the subsequent uniform growth of a 1.77 eV WBG perovskite with superior optoelectronic quality. In addition, we employ a post-deposition treatment with 2-thiopheneethylammonium chloride to further suppress the bulk and interfacial recombination, boosting the VOC of the WBG top cell to 1.29 V. Based on this, we present the first proof-of-concept four-terminal all-perovskite flexible TSC with a PCE of 22.6%. When integrating into two-terminal flexible tandems, we achieved 23.8% flexible all-perovskite TSCs with a superior VOC of 2.1 V, which is on par with the VOC reported on the 28% all-perovskite tandems grown on the rigid substrate.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

High-quality femtosecond laser surface micro/nano-structuring assisted by a thin frost layer

Femtosecond laser ablation has been demonstrated to be a versatile tool to produce micro/nanoscale features with high precision and accuracy. However, the use of high laser fluence to increase the ablation efficiency usually results in unwanted effects, such as redeposition of debris, formation of recast layer and heat-affected zone in or around the ablation craters. Here we circumvent this limitation by exploiting a thin frost layer with a thickness of tens of microns, which can be directly formed by the condensation of water vapor from the air onto the exposed surface whose temperature is below the freezing point. When femtosecond laser beam is focused onto the target surface covered with a thin frost layer, only the local frost layer around the laser-irradiated spot melts into water, helping to boost ablation efficiency, suppress the recast layer and reduce the heat-affect zone, while the remaining frost layer can prevent ablation debris from adhering to the target surface. By this frost-assisted strategy, high-quality surface micro/nano-structures are successfully achieved on both plane and curved surfaces at high laser fluences, and the mechanism behind the formation of high-spatial-frequency (HSF) laser induced periodic surface structures (LIPSSs) on silicon is discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Second-order and real Chern topological insulator in twisted bilayer $α$-graphyne

The study of higher-order and real topological states as well as the material realization have become a research forefront of topological condensed matter physics in recent years. Twisted bilayer graphene (tbG) is proved to have higher-order and real topology. However whether this conclusion can be extended to other two-dimensional twisted bilayer carbon materials and the mechanism behind it lack explorations. In this paper, we identify the twisted bilayer $α$-graphyne (tbGPY) at large twisting angle as a real Chern insulator (also known as Stiefel-Whitney insulator) and a second-order topological insulator. Our first-principles calculations suggest that the tbGPY at 21.78$^\circ$ is stable at 100 K with a larger bulk gap than the tbG. The non-trivial topological indicators, including the real Chern number and a fractional charge, and the localized in-gap corner states are demonstrated from first-principles and tight-binding calculations. Moreover, with $\mathcal C_{6z}$ symmetry, we prove the equivalence between the two indicators, and explain the existence of the corner states. To decipher the real and higher-order topology inherited from the Moiré heterostructure, we construct an effective four band tight-binding model capturing the topology and dispersion of the tbGPY at large twisting angle. A topological phase transition to a trivial insulator is demonstrated by breaking the $\mathcal C_{2y}$ symmetry of the effective model, which gives insights on the trivialization of the tbGPY as reducing the twisting angle to 9.43$^\circ$ suggested by our first-principles calculations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Topological hinge modes in Dirac semimetals

Dirac semimetals (DSMs) are an important class of topological states of matter. Here, focusing on DSMs of band inversion type, we investigate their boundary modes from the effective model perspective. We show that in order to properly capture the boundary modes, $k$-cubic terms must be included in the effective model, which would drive an evolution of surface degeneracy manifold from a nodal line to a nodal point. Using first-principles calculations, we demonstrate that this feature and the topological hinge modes can be clearly exhibited in $β$-CuI. We further extend the discussion to magnetic DSMs and show that the time-reversal symmetry breaking can gap out the surface bands and hence help to expose the hinge modes in the spectrum, which could be beneficial for the experimental detection of hinge modes.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Independent Action Models and Prediction of Combination Treatment Effects for Response Rate, Duration of Response and Tumor Size Change in Oncology Drug Development

An unprecedented number of new cancer targets are in development, and most are being developed in combination therapies. Early oncology development is strategically challenged in choosing the best combinations to move forward to late stage development. The most common early endpoints to be assessed in such decision-making include objective response rate, duration of response and tumor size change. In this paper, using independent-drug-action and Bliss-drug-independence concepts as a foundation, we introduce simple models to predict combination therapy efficacy for duration of response and tumor size change. These models complement previous publications using the independent action models (Palmer 2017, Schmidt 2020) to predict progression-free survival and objective response rate and serve as new predictive models to understand drug combinations for early endpoints. The models can be applied to predict the combination treatment effect for early endpoints given monotherapy data, or to estimate the possible effect of one monotherapy in the combination if data are available from the combination therapy and the other monotherapy. Such quantitative work facilitates efficient oncology drug development.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Phononic real Chern insulator with protected corner modes in graphynes

Higher-order topological insulators have attracted great research interest recently. Different from conventional topological insulators, higher-order topological insulators do not necessarily require spin-orbit coupling, which makes it possible to realize them in spinless systems. Here, we study phonons in 2D graphyne family materials. By using first-principle calculations and topology/symmetry analysis, we find that phonons in both graphdiyne and $γ$-graphyne exhibit a second-order topology, which belongs to the specific case known as real Chern insulator. We identify the nontrivial phononic band gaps, which are characterized by nontrivial real Chern numbers enabled by the spacetime inversion symmetry. The protected phonon corner modes are verified by the calculation on a finite-size nanodisk. Our study extends the scope of higher-order topology to phonons in real materials. The spatially localized phonon modes could be useful for novel phononic applications.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Pricing Energy Storage in Real-time Market

The problem of pricing utility-scale energy storage resources (ESRs) in the real-time electricity market is considered. Under a rolling-window dispatch model where the operator centrally dispatches generation and consumption under forecasting uncertainty, it is shown that almost all uniform pricing schemes, including the standard locational marginal pricing (LMP), result in lost opportunity costs that require out-of-the-market settlements. It is also shown that such settlements give rise to disincentives for generating firms and storage participants to bid truthfully, even when these market participants are rational price-takers in a competitive market. Temporal locational marginal pricing (TLMP) is proposed for ESRs as a generalization of LMP to an in-market discriminative form. TLMP is a sum of the system-wide energy price, LMP, and the individual state-of-charge price. It is shown that, under arbitrary forecasting errors, the rolling-window implementation of TLMP eliminates the lost opportunity costs and provides incentives to price-taking firms to bid truthfully with their marginal costs. Numerical examples show insights into the effects of uniform and non-uniform pricing mechanisms on dispatch following and truthful bidding incentives.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Second-Order Real Nodal-Line Semimetal in Three-Dimensional Graphdiyne

Real topological phases featuring real Chern numbers and second-order boundary modes have been a focus of current research, but finding their material realization remains a challenge. Here, based on first-principles calculations and theoretical analysis, we reveal the already experimentally synthesized three-dimensional (3D) graphdiyne as the first realistic example of the recently proposed second-order real nodal-line semimetal. We show that the material hosts a pair of real nodal rings, each protected by two topological charges: a real Chern number and a 1D winding number. The two charges generate distinct topological boundary modes at distinct boundaries. The real Chern number leads to a pair of hinge Fermi arcs, whereas the winding number protects a double drumhead surface bands. We develop a low-energy model for 3D graphdiyne which captures the essential topological physics. Experimental aspects and possible topological transition to a 3D real Chern insulator phase are discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading

Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients. However, vision loss occurs primarily in the late stages of DR, and the symptoms of visual impairment, ranging from mild to severe, can vary greatly, adding to the burden of diagnosis and treatment in clinical practice. Deep learning methods based on retinal images have achieved remarkable success in automatic DR grading, but most of them neglect that the presence of diabetes usually affects both eyes, and ophthalmologists usually compare both eyes concurrently for DR diagnosis, leaving correlations between left and right eyes unexploited. In this study, simulating the diagnostic process, we propose a two-stream binocular network to capture the subtle correlations between left and right eyes, in which, paired images of eyes are fed into two identical subnetworks separately during training. We design a contrastive grading loss to learn binocular correlation for five-class DR detection, which maximizes inter-class dissimilarity while minimizing the intra-class difference. Experimental results on the EyePACS dataset show the superiority of the proposed binocular model, outperforming monocular methods by a large margin.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fermi liquid behavior and colossal magnetoresistance in layered MoOCl2

A characteristic of a Fermi liquid is the T^2 dependence of its resistivity, sometimes referred to as the Baber law. However, for most metals, this behavior is only restricted to very low temperatures, usually below 20 K. Here, we experimentally demonstrate that for the single-crystal van der Waals layered material MoOCl2, the Baber law holds in a wide temperature range up to ~120 K, indicating that the electron-electron scattering plays a dominant role in this material. Combining with the specific heat measurement, we find that the modified Kadowaki-Woods ratio of the material agrees well with many other strongly correlated metals. Furthermore, in the magneto-transport measurement, a colossal magneto-resistance is observed, which reaches ~350% at 9 T and displays no sign of saturation. With the help of first-principles calculations, we attribute this behavior to the presence of open orbits on the Fermi surface. We also suggest that the dominance of electron-electron scattering is related to an incipient charge density wave state of the material. Our results establish MoOCl2 as a strongly correlated metal and shed light on the underlying physical mechanism, which may open a new path for exploring the effects of electron-electron interaction in van der Waals layered structures.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression

The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy. In this regard, low-rank tensor decomposition constitutes a powerful tool to compress convolutional neural networks (CNNs) by decomposing the 4-way kernel tensor into multi-stage smaller ones. Building on top of Tucker-2 decomposition, we propose a generalized Higher Order Tucker Articulated Kernels (HOTCAKE) scheme comprising four steps: input channel decomposition, guided Tucker rank selection, higher order Tucker decomposition and fine-tuning. By subjecting each CONV layer to HOTCAKE, a highly compressed CNN model with graceful accuracy trade-off is obtained. Experiments show HOTCAKE can compress even pre-compressed models and produce state-of-the-art lightweight networks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Kernelized Support Tensor Train Machines

Tensor, a multi-dimensional data structure, has been exploited recently in the machine learning community. Traditional machine learning approaches are vector- or matrix-based, and cannot handle tensorial data directly. In this paper, we propose a tensor train (TT)-based kernel technique for the first time, and apply it to the conventional support vector machine (SVM) for image classification. Specifically, we propose a kernelized support tensor train machine that accepts tensorial input and preserves the intrinsic kernel property. The main contributions are threefold. First, we propose a TT-based feature mapping procedure that maintains the TT structure in the feature space. Second, we demonstrate two ways to construct the TT-based kernel function while considering consistency with the TT inner product and preservation of information. Third, we show that it is possible to apply different kernel functions on different data modes. In principle, our method tensorizes the standard SVM on its input structure and kernel mapping scheme. Extensive experiments are performed on real-world tensor data, which demonstrates the superiority of the proposed scheme under few-sample high-dimensional inputs.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Nonsymmorphic nodal-line metals in the two-dimensional rare earth monochalcogenides MX (M = Sc, Y; X = S, Se, Te)

We predict a new family of two-dimensional (2D) rare earth monochalcogenide materials MX (M = Sc, Y; X = S, Se, Te). Based on first-principles calculations, we confirm their stability and systematically investigate their mechanical properties. We find that these materials are metallic and interestingly, they possess nodal lines in the low-energy band structure surrounding the whole Brillouin zone, protected by nonsymmorphic crystal symmetries in the absence of spin-orbit coupling (SOC). SOC opens small energy gaps at the nodal line, except for two high-symmetry points, at which fourfold degenerate 2D spin-orbit Dirac points are obtained. We show that these topological band features are robust under uniaxial and biaxial strains, but can be lifted by the shear strain. We also investigate the optical conductivities of these materials, and show that the transformation of the band structure under strain can be inferred from the optical absorption spectrum. Our work reveals a new family of 2D topological metal materials with interesting mechanical and electronic properties, which will facilitate the study of nonsymmorphic symmetry enabled nodal features in 2D.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Sequential Solutions in Machine Scheduling Games

We consider the classical machine scheduling, where $n$ jobs need to be scheduled on $m$ machines, and where job $j$ scheduled on machine $i$ contributes $p_{i,j}\in \mathbb{R}$ to the load of machine $i$, with the goal of minimizing the makespan, i.e., the maximum load of any machine in the schedule. We study inefficiency of schedules that are obtained when jobs arrive sequentially one by one, and the jobs choose themselves the machine on which they will be scheduled, aiming at being scheduled on a machine with small load. We measure the inefficiency of a schedule as the ratio of the makespan obtained in the worst-case equilibrium schedule, and of the optimum makespan. This ratio is known as the \emph{sequential price of anarchy}. We also introduce two alternative inefficiency measures, which allow for a favorable choice of the order in which the jobs make their decisions. As our first result, we disprove the conjecture of Hassin and Yovel claiming that the sequential price of anarchy for $m=2$ machines is at most 3. We show that the sequential price of anarchy grows at least linearly with the number $n$ of players, i.e., we show that $SPoA = Ω(n)$. Furthermore, we show that there exists an order of the jobs, resulting in makespan that is at most linearly larger than the optimum makespan. To the end, we show that if an authority can change the order of the jobs adaptively to the decisions made by the jobs so far (but cannot influence the decisions of the jobs), then there exists an adaptive ordering in which the jobs end up in an optimum schedule.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection

This paper focuses on Semi-Supervised Object Detection (SSOD). Knowledge Distillation (KD) has been widely used for semi-supervised image classification. However, adapting these methods for SSOD has the following obstacles. (1) The teacher model serves a dual role as a teacher and a student, such that the teacher predictions on unlabeled images may be very close to those of student, which limits the upper-bound of the student. (2) The class imbalance issue in SSOD hinders an efficient knowledge transfer from teacher to student. To address these problems, we propose a novel method Temporal Self-Ensembling Teacher (TSE-T) for SSOD. Differently from previous KD based methods, we devise a temporally evolved teacher model. First, our teacher model ensembles its temporal predictions for unlabeled images under stochastic perturbations. Second, our teacher model ensembles its temporal model weights with the student model weights by an exponential moving average (EMA) which allows the teacher gradually learn from the student. These self-ensembling strategies increase data and model diversity, thus improving teacher predictions on unlabeled images. Finally, we use focal loss to formulate consistency regularization term to handle the data imbalance problem, which is a more efficient manner to utilize the useful information from unlabeled images than a simple hard-thresholding method which solely preserves confident predictions. Evaluated on the widely used VOC and COCO benchmarks, the mAP of our method has achieved 80.73% and 40.52% on the VOC2007 test set and the COCO2014 minval5k set respectively, which outperforms a strong fully-supervised detector by 2.37% and 1.49%. Furthermore, our method sets the new state-of-the-art in SSOD on VOC2007 test set which outperforms the baseline SSOD method by 1.44%. The source code of this work is publicly available at http://github.com/syangdong/tse-t.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The curse of rationality in sequential scheduling games

Despite the emphases on computability issues in research of algorithmic game theory, the limited computational capacity of players have received far less attention. This work examines how different levels of players' computational ability (or "rationality") impact the outcomes of sequential scheduling games. Surprisingly, our results show that a lower level of rationality of players may lead to better equilibria. More specifically, we characterize the sequential price of anarchy (SPoA) under two different models of bounded rationality, namely, players with $k$-lookahead and simple-minded players. The model in which players have $k$-lookahead interpolates between the "perfect rationality" ($k=n-1$) and "online greedy" ($k=0$). Our results show that the inefficiency of equilibria (SPoA) increases in $k$ the degree of lookahead: $\mathrm{SPoA} = O (k^2)$ for two machines and $\mathrm{SPoA} = O\left(2^k \min \{mk,n\}\right)$ for $m$ machines, where $n$ is the number of players. Moreover, when players are simple-minded, the SPoA is exactly $m$, which coincides with the performance of "online greedy".

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study

Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases. We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Two-dimensional second-order topological insulator in graphdiyne

A second-order topological insulator (SOTI) in $d$ spatial dimensions features topologically protected gapless states at its $(d-2)$-dimensional boundary at the intersection of two crystal faces, but is gapped otherwise. As a novel topological state, it has been attracting great interest, but it remains a challenge to identify a realistic SOTI material in two dimensions (2D). Here, based on combined first-principles calculations and theoretical analysis, we reveal the already experimentally synthesized 2D material graphdiyne as the first realistic example of a 2D SOTI, with topologically protected 0D corner states. The role of crystalline symmetry, the robustness against symmetry-breaking, and the possible experimental characterization are discussed. Our results uncover a hidden topological character of graphdiyne and promote it as a concrete material platform for exploring the intriguing physics of higher-order topological phases.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Universal Approach to Magnetic Second-Order Topological Insulator

We propose a universal practical approach to realize magnetic second-order topological insulator (SOTI) materials, based on properly breaking the time reversal symmetry in conventional (first-order) topological insulators. The approach works for both three dimensions (3D) and two dimensions (2D), and is particularly suitable for 2D, where it can be achieved by coupling a quantum spin Hall insulator with a magnetic substrate. Using first-principles calculations, we predict bismuthene on EuO(111) surface as the first realistic system for a 2D magnetic SOTI. We explicitly demonstrate the existence of the protected corner states. Benefited from the large spin-orbit coupling and sizable magnetic proximity effect, these corner states are located in a boundary gap $\sim 83$ meV, hence can be readily probed in experiment. By controlling the magnetic phase transition, a topological phase transition between a first-order TI and a SOTI can be simultaneously achieved in the system. The effect of symmetry breaking, the connection with filling anomaly, and the experimental detection are discussed.

0 citations0 reviewsNo code linkedOpen access