arXiv subjects

Hailong Li

Publications and source records attributed to Hailong Li.

33 records · Page 2Linked to original sources

arXiv2023

Transport theory in non-Hermitian systems

Non-Hermitian systems have garnered significant attention due to the emergence of novel topology of complex spectra and skin modes. However, investigating transport phenomena in such systems faces obstacles stemming from the non-unitary nature of time evolution. Here, we establish the continuity equation for a general non-Hermitian Hamiltonian in the Schrödinger picture. It attributes the universal non-conservativity to the anti-commutation relationship between particle number and non-Hermitian terms. Our work derives a comprehensive current formula for non-Hermitian systems using Green's function, applicable to both time-dependent and steady-state responses. To demonstrate the validity of our approach, we calculate the local current in models with one-dimensional and two-dimensional settings, incorporating scattering potentials. The spatial distribution of local current highlights the widespread non-Hermitian phenomena, including skin modes, non-reciprocal quantum dots, and corner states. Our findings offer valuable insights for advancing theoretical and experimental research in the transport of non-Hermitian systems.

cond-mat.mes-hall↗

arXiv2023

Self-supervised Multi-view Clustering in Computer Vision: A Survey

Multi-view clustering (MVC) has had significant implications in cross-modal representation learning and data-driven decision-making in recent years. It accomplishes this by leveraging the consistency and complementary information among multiple views to cluster samples into distinct groups. However, as contrastive learning continues to evolve within the field of computer vision, self-supervised learning has also made substantial research progress and is progressively becoming dominant in MVC methods. It guides the clustering process by designing proxy tasks to mine the representation of image and video data itself as supervisory information. Despite the rapid development of self-supervised MVC, there has yet to be a comprehensive survey to analyze and summarize the current state of research progress. Therefore, this paper explores the reasons and advantages of the emergence of self-supervised MVC and discusses the internal connections and classifications of common datasets, data issues, representation learning methods, and self-supervised learning methods. This paper does not only introduce the mechanisms for each category of methods but also gives a few examples of how these techniques are used. In the end, some open problems are pointed out for further investigation and development.

cs.CV↗

arXiv2023

Nanocrystallites Modulate Intermolecular Interactions in Cryoprotected Protein Solutions

Studying protein interactions at low temperatures has important implications for optimizing cryostorage processes of biological tissue, food, and protein-based drugs. One of the major challenges is related to the formation of ice nanocrystals which can occur even in the presence of cryoprotectants and can lead to protein denaturation. Here, using a combination of small- and wide-angle X-ray scattering (SAXS and WAXS), we investigate the structural evolution of concentrated Lysozyme solutions in a cryoprotected glycerol-water mixture upon cooling from room temperature (T=300 K) down to cryogenic temperatures (T=195 K). Upon cooling, we observe a transition near the melting temperature of the solution (T~245 K), which manifests both in the temperature dependence of the scattering intensity peak position reflecting protein-protein length scales (SAXS) and the interatomic distances within the solvent (WAXS). Upon thermal cycling, a hysteresis is observed in the scattering intensity, which is attributed to the formation of nanocrystallites in the order of 10 nm. The experimental data is well described by the two-Yukawa model, which indicates temperature-dependent changes in the short-range attraction of the protein-protein interaction potential. Our results demonstrate that the nanocrystal growth yields effectively stronger protein-protein attraction and influences the protein pair distribution function beyond the first coordination shell.

cond-mat.soft↗

arXiv2023

A Novel Collaborative Self-Supervised Learning Method for Radiomic Data

The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on annotating radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method shows the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.

eess.IV↗

arXiv2022

A Novel Ontology-guided Attribute Partitioning Ensemble Learning Model for Early Prediction of Cognitive Deficits using Quantitative Structural MRI in Very Preterm Infants

Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering the domain-specific relationship among features. With the better partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 years of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches.

cs.LG↗

arXiv2022

Quantum Anomalous Layer Hall Effect in the Topological Magnet MnBi2Te4

Recently, a type of Hall effect due to an unusual layer-locked Berry curvature called the layer Hall effect (LHE) has been reported in the even-layered two-dimensional antiferromagnetic (AFM) MnBi2Te4 [A. Gao et.al, Nature 595, 521 (2021)]. In this work, we report that the quantization of LHE, which we call the quantum anomalous layer Hall effect (QALHE), can be realized in MnBi2Te4. The QALHE originates from kicking a layer-locked Berry-curvature monopole out of the Fermi sea by a vertielectric cal field. Remarkably, we demonstrate that the electric-field reversal can switch the sign of the quantized Hall conductance of QALHE in the even-layered AFM phase. The QALHE can also be realized in the ferromagnetic phase. These results provide a promising way toward the electric engineering of the Berry curvature monopoles and quantized-layered transport in topological magnets.

cond-mat.mes-hall↗

arXiv2022

Transport Theory of Half-quantized Hall Conductance in a Semi-magnetic Topological Insulator

Recently,a half-quantized Hall conductance (HQHC) plateau is experimentally observed in a semi-magnetic topological insulator heterostructure. However,the heterostructure is metallic with a nonzero longitudinal conductance, which contradicts the common belief that quantized Hall conductance is usually observed in insulators.In this work,we systematically study the surface transport of the semi-magnetic topological insulator with both gapped and gapless Dirac surfaces in the presence of dephasing process.In particular, we reveal that the HQHC is directly related to the half-quantized chiral current along the edge of a strongly dephasing metal. The Hall conductance keeps a half-quantized value for large dephasing strengths, while the longitudinal conductance varies with Fermi energies and dephasing strengths. Furthermore, we evaluate both the conductance and resistance as a function of the temperature, which is consistent with the experimental results.Our results not only provide the microscopic transport mechanism of the HQHC,but also are instructive for the probe of the HQHC in future experiments.

cond-mat.mes-hall↗

arXiv2021

Evolution with Magnetic Field of Discrete Scale Invariant Supercritical States in Graphene

We investigate the quasi-bound states of a Coulomb impurity in graphene in the presence of a magnetic field. \ These states exhibit the dramatic and rather rare property of discrete scale invariance when the Coulomb potential is supercritical. \ We show using both Wentzel-Kramers-Brillouin (WKB) approximation and numerical studies that the supercritical states are converted to subcritical states as the field is increased. \ The local density of states is calculated and it shows direct signatures of discrete scale invariance. \ In a magnetic field, these signatures are gradually destroyed in a systematic way. \ Hence the effect that we propose can be detected via scanning tunneling microscope experiments. \ The range of magnetic field and energy resolution required are compatible with existing experimental setups. \ These experiments can be performed in a single sample by changing the field; they do not involve changing the nuclear charge.

cond-mat.mes-hall↗

arXiv2021

SNAC: An Unbiased Metric Evaluating Topology Recognize Ability of Network Alignment

Network alignment is a problem of finding the node mapping between similar networks. It links the data from separate sources and is widely studied in bioinformation and social network fields. The critical difference between network alignment and exact graph matching is that the network alignment considers node mapping in non-isomorphic graphs with error tolerance. Researchers usually utilize AC (accuracy) to measure the performance of network alignments which comparing each output element with the benchmark directly. However, this metric neglects that some nodes are naturally indistinguishable even in single graphs (e.g., nodes have the same neighbors) and no need to distinguish across graphs. Such neglect leads to the underestimation of models. We propose an unbiased metric for network alignment that takes indistinguishable nodes into consideration to address this problem. Our detailed experiments with different scales on both synthetic and real-world datasets demonstrate that the proposed metric correctly reflects the deviation of result mapping from benchmark mapping as standard metric AC does. Comparing with the AC, the proposed metric effectively blocks the effect of indistinguishable nodes and retains stability under increasing indistinguishable nodes.

cs.SI↗

arXiv2021

Coexisting of Quantum Hall and Quantum Anomalous Hall phases in Disordered $\mathrm{MnBi_2Te_4}$

In most cases, to observe quantized Hall plateaux, an external magnetic field is applied in intrinsic magnetic topological insulators $\mathrm{MnBi_2Te_4}$. Nevertheless, whether the nonzero Chern number ($C\neq 0$) phase is a quantum anomalous Hall (QAH) state, or a quantum Hall (QH) state, or a mixing state of both is still a puzzle, especially for the recently observed $C=2$ phase [Deng \textit{et al}., Science \textbf{367}, 895 (2020)]. In this Letter, we propose a physical picture based on the Anderson localization to understand the observed Hall plateaux in disordered $\mathrm{MnBi_2Te_4}$. Rather good consistency between the experimental and numerical results confirms that the bulk states are localized in the absence of a magnetic field and a QAH edge state emerges with $C=1$. However, under a strong magnetic field, the lowest Landau band formed with the localized bulk states, survives disorder, together with the QAH edge state, leading to a $C=2$ phase. Eventually, we present a phase diagram of a disordered $\mathrm{MnBi_2Te_4}$ which indicates more coexistence states of QAH and QH to be verified by future experiments.

cond-mat.mes-hall↗

arXiv2020

Critical Behavior and Universal Signature of an Axion Insulator State

Recently, the search for an axion insulator state in the ferromagnet-3D topological insulator (TI) heterostructure and $\mathrm{MnBi_2Te_4}$ has attracted intensive interest. However, its detection remains difficult in experiments. We systematically investigate the disorder-induced phase transition of the axion insulator state in a 3D TI with antiparallel magnetization alignment surfaces. It is found that there exists a 2D disorder-induced phase transition which shares the same universality class with the quantum Hall plateau to plateau transition. Then, we provide a phenomenological theory which maps the random mass Dirac Hamiltonian of the axion insulator state into the Chalker-Coddington network model. Therefore, we propose to probe the axion insulator state by investigating the universal signature of such a phase transition in the ferromagnet-3D TI heterostructure and $\mathrm{MnBi_2Te_4}$. Our findings not only show a global phase diagram of the axion insulator state, but also provide a new experimental routine to probe it.

cond-mat.mes-hall↗

arXiv2020

3D Quantum Hall Effect Manipulated by Chiral Landau Levels in Weyl Semimetals

We investigate the 3D quantum Hall effect in Weyl semimetals and elucidate a global picture of the edge states. The edge states hosting 3D quantum Hall effect are combinations of Fermi arcs and chiral bulk Landau levels parallel to the magnetic field. The Hall conductance, $σ_{xz}^H$, shows quantized plateaus at Weyl nodes while tuning the magnetic field. However, the chiral Landau levels manipulate the quantization of Weyl orbits, especially under a tilted magnetic field, and the resulting edge states lead to distinctive Hall transport phenomena. A tilted magnetic field contributes an intrinsic initial value to $σ_{xz}^H$ and such initial value is determined by the tilting angle $θ$. Particularly, even if the perpendicular magnetic field is fixed, $σ_{xz}^H$ will change its sign with an abrupt spatial shift of edge states when $θ$ exceeds a critical angle $θ_c$ in an experiment. Our work uncovers the unique edge-state nature of 3D quantum Hall effect in Weyl semimetals.

cond-mat.mes-hall↗

arXiv2018

SEA: A Combined Model for Heat Demand Prediction

Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decompose the series into seasonal and trend components. Then, predicting the seasonal and trend components respectively, and combining their predictions together as the heat demand prediction is a possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a combined model, was proposed based on the combination of the Elman neural network (ENN) and the autoregressive integrated moving average (ARIMA) model, which are commonly applied to heat demand prediction. ENN and ARIMA are used to predict seasonal and trend components, respectively. Experimental results demonstrate that the proposed SEA model has a promising performance.

cs.LG↗

arXiv2017

Salt-induced microheterogeneities in binary liquid mixtures

The salt-induced microheterogeneity (MH) formation in binary liquid mixtures is studied by small-angle X-ray scattering (SAXS) and liquid state theory. Previous experiments have shown that this phenomenon occurs for antagonistic salts, whose cations and anions prefer different components of the solvent mixture. However, so far the precise mechanism leading to the characteristic length scale of MHs remained unclear. Here, it is shown that MHs can be generated by the competition of short-ranged interactions and long-ranged monopole-dipole interactions. The experimental SAXS patterns can be quantitatively reproduced by fitting to the derived correlation functions without assuming any specific model. The dependency of the MH structure with respect to ionic strength and temperature is analyzed. Close to the demixing phase transition, critical-like behavior occurs with respect to the spinodal line in the phase diagram.

cond-mat.soft↗

arXiv2017

The Role of Data Analysis in the Development of Intelligent Energy Networks

Data analysis plays an important role in the development of intelligent energy networks (IENs). This article reviews and discusses the application of data analysis methods for energy big data. The installation of smart energy meters has provided a huge volume of data at different time resolutions, suggesting data analysis is required for clustering, demand forecasting, energy generation optimization, energy pricing, monitoring and diagnostics. The currently adopted data analysis technologies for IENs include pattern recognition, machine learning, data mining, statistics methods, etc. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by the IENs and, therefore, more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information.

cs.OH↗