arXiv subjects

Yue Zhao

Publications and source records attributed to Yue Zhao.

At least 415 records · Page 23Linked to original sources

arXiv2020

Spectroscopic realization of large surface gap in a doped magnetic topological insulator

Realization of the quantum anomalous Hall effect and axion electrodynamics in topological materials are among the paradigmatic phenomena in condensed matter physics. Recently, signatures of both phases are observed to exist in thin films of MnBi$_2$Te$_4$, a stoichiometric antiferromagnetic topological insulator. Direct evidence of the bulk topological magnetoelectric response in an axion insulator requires an energy gap at its topological surface state (TSS). However, independent spectroscopic experiments revealed that such a surface gap is absent, or much smaller than previously thought, in MnBi$_2$Te$_4$. Here, we utilize angle resolved photoemission spectroscopy and density functional theory calculations to demonstrate that a sizable TSS gap unexpectedly exists in Sb-doped MnBi$_2$Te$_4$. This gap is found to be topologically nontrivial, insensitive to the bulk antiferromagnetic-paramagnetic transition, while enlarges along with increasing Sb concentration. Our work shows that Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$ is a potential platform to observe the key features of the high-temperature axion insulator state, such as the topological magnetoelectric responses and half-integer quantum Hall effects.

cond-mat.mtrl-sci↗

arXiv2020

Spontaneous Surface Collapse and Reconstruction in Antiferromagnetic Topological Insulator MnBi$_2$Te$_4$

MnBi$_2$Te$_4$ is an antiferromagnetic topological insulator which stimulates intense interests due to the exotic quantum phenomena and promising device applications. Surface structure is a determinant factor to understand the novel magnetic and topological behavior of MnBi2Te4, yet its precise atomic structure remains elusive. Here, we discovered a spontaneous surface collapse and reconstruction in few-layer MnBi2Te4 exfoliated under delicate protection. Instead of the ideal septuple-layer structure in the bulk, the collapsed surface is shown to reconstruct as Mn-doped Bi$_2$Te$_3$ quintuple-layer and Mn$_x$Bi$_y$Te double-layer with a clear van der Waals gap in between. Combining with first-principles calculations, such spontaneous surface collapse is attributed to the abundant intrinsic Mn-Bi antisite defects and tellurium vacancy in the exfoliated surface, which is further supported by in-situ annealing and electron irradiation experiments. Our results shed light on the understanding of the intricate surface-bulk correspondence of MnBi$_2$Te$_4$, and provide insightful perspective of the surface-related quantum measurements in MnBi$_2$Te$_4$ few-layer devices.

cond-mat.mtrl-sci↗

arXiv2020

FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding

On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport analysis, which requires the capability of parsing an activity into phases and differentiating between subtly different actions, their performances remain far from being satisfactory. To take action recognition to a new level, we develop FineGym, a new dataset built on top of gymnastic videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity. In particular, it provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. For example, a "balance beam" event will be annotated as a sequence of elementary sub-actions derived from five sets: "leap-jump-hop", "beam-turns", "flight-salto", "flight-handspring", and "dismount", where the sub-action in each set will be further annotated with finely defined class labels. This new level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes. We systematically investigate representative methods on this dataset and obtain a number of interesting findings. We hope this dataset could advance research towards action understanding.

cs.CV↗

arXiv2020

A random-batch Monte Carlo method for many-body systems with singular kernels

We propose a fast potential splitting Markov Chain Monte Carlo method which costs $O(1)$ time each step for sampling from equilibrium distributions (Gibbs measures) corresponding to particle systems with singular interacting kernels. We decompose the interacting potential into two parts, one is of long range but is smooth, and the other one is of short range but may be singular. To displace a particle, we first evolve a selected particle using the stochastic differential equation (SDE) under the smooth part with the idea of random batches, as commonly used in stochastic gradient Langevin dynamics. Then, we use the short range part to do a Metropolis rejection. Different from the classical Langevin dynamics, we only run the SDE dynamics with random batch for a short duration of time so that the cost in the first step is $O(p)$, where $p$ is the batch size. The cost of the rejection step is $O(1)$ since the interaction used is of short range. We justify the proposed random-batch Monte Carlo method, which combines the random batch and splitting strategies, both in theory and with numerical experiments. While giving comparable results for typical examples of the Dyson Brownian motion and Lennard-Jones fluids, our method can save more time when compared to the classical Metropolis-Hastings algorithm.

physics.comp-ph↗

arXiv2020

On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks

While deep learning in 3D domain has achieved revolutionary performance in many tasks, the robustness of these models has not been sufficiently studied or explored. Regarding the 3D adversarial samples, most existing works focus on manipulation of local points, which may fail to invoke the global geometry properties, like robustness under linear projection that preserves the Euclidean distance, i.e., isometry. In this work, we show that existing state-of-the-art deep 3D models are extremely vulnerable to isometry transformations. Armed with the Thompson Sampling, we develop a black-box attack with success rate over 95% on ModelNet40 data set. Incorporating with the Restricted Isometry Property, we propose a novel framework of white-box attack on top of spectral norm based perturbation. In contrast to previous works, our adversarial samples are experimentally shown to be strongly transferable. Evaluated on a sequence of prevailing 3D models, our white-box attack achieves success rates from 98.88% to 100%. It maintains a successful attack rate over 95% even within an imperceptible rotation range $[\pm 2.81^{\circ}]$.

cs.LG↗

arXiv2020

The MAVERIC survey: A hidden pulsar and a black hole candidate in ATCA radio imaging of the globular cluster NGC 6397

Using a 16.2 hr radio observation by the Australia Telescope Compact Array (ATCA) and archival Chandra data, we found $>5σ$ radio counterparts to 4 known and 3 new X-ray sources within the half-light radius ($r_\mathrm{h}$) of the Galactic globular cluster NGC 6397. The previously suggested millisecond pulsar (MSP) candidate, U18, is a steep-spectrum ($S_ν\propto ν^α$; $α=-2.0^{+0.4}_{-0.5}$) radio source with a 5.5 GHz flux density of $54.7\pm 4.3~\mathrm{μJy}$. We argue that U18 is most likely a "hidden" MSP that is continuously hidden by plasma shocked at the collision between the winds from the pulsar and companion star. The nondetection of radio pulsations so far is probably the result of enhanced scattering in this shocked wind. On the other hand, we observed 5.5 GHz flux of the known MSP PSR J1740-5340 (U12) to decrease by a factor of $>2.8$ during epochs of 1.4 GHz eclipse, indicating that the radio flux is absorbed in its shocked wind. If U18 is indeed a pulsar whose pulsations are scattered, we note the contrast with U12's flux decrease in eclipse, which argues for two different eclipse mechanisms at the same radio frequency. In addition to U12 and U18, we also found radio associations for 5 other Chandra X-ray sources, four of which are likely background galaxies. The last, U97, which shows strong H$α$ variability, is mysterious; it may be either a quiescent black hole low-mass X-ray binary, or something more unusual.

astro-ph.HE↗

arXiv2020

Probing Axions with Event Horizon Telescope Polarimetric Measurements

With high spatial resolution, polarimetric imaging of a supermassive black hole, like M87$^\star$ or Sgr A$^\star$, by the Event Horizon Telescope can be used to probe the existence of ultralight bosonic particles, such as axions. Such particles can accumulate around a rotating black hole through the superradiance mechanism, forming an axion cloud. When linearly polarized photons are emitted from an accretion disk near the horizon, their position angles oscillate due to the birefringent effect when traveling through the axion background. In particular, the observations of supermassive black holes M87$^\star$ (Sgr A$^\star$) can probe the dimensionless axion-photon coupling $c = 2 πg_{a γ} f_a$ for axions with mass around $O(10^{-20})$~eV ($O( 10^{-17}$)~eV) and decay constant $f_a < O(10^{16})$ GeV, which is complimentary to other axion measurements.

hep-ph↗

arXiv2020

SUOD: Toward Scalable Unsupervised Outlier Detection

Outlier detection is a key field of machine learning for identifying abnormal data objects. Due to the high expense of acquiring ground truth, unsupervised models are often chosen in practice. To compensate for the unstable nature of unsupervised algorithms, practitioners from high-stakes fields like finance, health, and security, prefer to build a large number of models for further combination and analysis. However, this poses scalability challenges in high-dimensional large datasets. In this study, we propose a three-module acceleration framework called SUOD to expedite the training and prediction with a large number of unsupervised detection models. SUOD's Random Projection module can generate lower subspaces for high-dimensional datasets while reserving their distance relationship. Balanced Parallel Scheduling module can forecast the training and prediction cost of models with high confidence---so the task scheduler could assign nearly equal amount of taskload among workers for efficient parallelization. SUOD also comes with a Pseudo-supervised Approximation module, which can approximate fitted unsupervised models by lower time complexity supervised regressors for fast prediction on unseen data. It may be considered as an unsupervised model knowledge distillation process. Notably, all three modules are independent with great flexibility to "mix and match"; a combination of modules can be chosen based on use cases. Extensive experiments on more than 30 benchmark datasets have shown the efficacy of SUOD, and a comprehensive future development plan is also presented.

cs.LG↗

arXiv2020

Distinct Topological Surface States on the Two Terminations of MnBi$_4$Te$_7$

The recent discovered intrinsic magnetic topological insulator MnBi2Te4 have been met with unusual success in hosting emergent phenomena such as the quantum anomalous Hall effect and the axion insulator states. However, the surface-bulk correspondence of the Mn-Bi-Te family, composed by the superlattice-like MnBi2Te4/(Bi2Te3)n (n = 0, 1, 2, 3 ...) layered structure, remains intriguing but elusive. Here, by using scanning tunneling microscopy (STM) and angle-resolved photoemission spectroscopy (ARPES) techniques, we unambiguously assign the two distinct surface states of MnBi4Te7 (n = 1) to the quintuple-layer (QL) Bi2Te3 termination and the septuple-layer (SL) MnBi2Te4 termination, respectively. A comparison of the experimental observations with theoretical calculations reveals the diverging topological behaviors, especially the hybridization effect between magnetic and nonmagnetic layers, on the two terminations: a gap on the QL termination originating from the topological surface states of the QL hybridizing with the bands of the beneath SL, and a gapless Dirac-cone band structure on the SL termination with time-reversal symmetry. The quasi-particle interference patterns further confirm the topological nature of the surface states for both terminations, continuing far above the Fermi energy. The QL termination carries a spin-helical Dirac state with hexagonal warping, while at the SL termination, a strongly canted helical state from the surface lies between a pair of Rashba-split states from its neighboring layer. Our work elucidates an unprecedented hybridization effect between the building blocks of the topological surface states, and also reveals the termination-dependent time-reversal symmetry breaking in a magnetic topological insulator, rendering an ideal platform to realize the half-integer quantum Hall effect and relevant quantum phenomena.

cond-mat.mtrl-sci↗

arXiv2019

Hybridization-Induced Gapped and Gapless States on the Surfaces of Magnetic Topological Insulators

The layered MnBi2nTe3n+1 family represents the first intrinsic antiferromagnetic topological insulator (AFM TI, protected by a combination symmetry ) ever discovered, providing an ideal platform to explore novel physics such as quantum anomalous Hall effect at elevated temperature and axion electrodynamics. Recent angle-resolved photoemission spectroscopy (ARPES) experiments on this family have revealed that all terminations exhibit (nearly) gapless topological surface states (TSSs) within the AFM state, violating the definition of the AFM TI, as the surfaces being studied should be -breaking and opening a gap. Here we explain this curious paradox using a surface-bulk band hybridization picture. Combining ARPES and first-principles calculations, we prove that only an apparent gap is opened by hybridization between TSSs and bulk bands. The observed (nearly) gapless features are consistently reproduced by tight-binding simulations where TSSs are coupled to a Rashba-split bulk band. The Dirac-cone-like spectral features are actually of bulk origin, thus not sensitive to the-breaking at the AFM surfaces. This picture explains the (nearly) gapless behaviour found in both Bi2Te3- and MnBi2Te4-terminated surfaces and is applicable to all terminations of MnBi2nTe3n+1 family. Our findings highlight the role of band hybridization, superior to magnetism in this case, in shaping the general surface band structure in magnetic topological materials for the first time.

cond-mat.str-el↗

arXiv2019

Extracting clinical concepts from user queries

Clinical concept extraction often begins with clinical Named Entity Recognition (NER). Often trained on annotated clinical notes, clinical NER models tend to struggle with tagging clinical entities in user queries because of the structural differences between clinical notes and user queries. User queries, unlike clinical notes, are often ungrammatical and incoherent. In many cases, user queries are compounded of multiple clinical entities, without comma or conjunction words separating them. By using as dataset a mixture of annotated clinical notes and synthesized user queries, we adapt a clinical NER model based on the BiLSTM-CRF architecture for tagging clinical entities in user queries. Our contribution are the following: 1) We found that when trained on a mixture of synthesized user queries and clinical notes, the NER model performs better on both user queries and clinical notes. 2) We provide an end-to-end and easy-to-implement framework for clinical concept extraction from user queries.

cs.IR↗

arXiv2019

Searching for Dark Photon Dark Matter in LIGO O1 Data

Dark matter exists in our Universe, but its nature remains mysterious. The remarkable sensitivity of the Laser Interferometer Gravitational-Wave Observatory (LIGO) may be able to solve this mystery. A good dark matter candidate is the ultralight dark photon. Because of its interaction with ordinary matter, it induces displacements on LIGO mirrors that can lead to an observable signal. In a study that bridges gravitational wave science and particle physics, we perform a direct dark matter search using data from LIGO's first (O1) data run, as opposed to an indirect search for dark matter via its production of gravitational waves. We demonstrate an achieved sensitivity on squared coupling as $\sim 4 \times 10^{-45}$, in a $U(1)_{\rm B}$ dark photon dark matter mass band around $m_{\rm A} \sim 4 \times 10^{-13}$eV. Substantially improved search sensitivity is expected during the coming years of continued data taking by LIGO and other gravitational wave detectors in a growing global network.

hep-ph↗

arXiv2019

Inverse scattering for the one-dimensional Helmholtz equation with piecewise constant wave speed

This paper analyzes inverse scattering for the one-dimensional Helmholtz equation in the case where the wave speed is piecewise constant. Scattering data recorded for an arbitrarily small interval of frequencies is shown to determine the wave speed uniquely, and a direct reconstruction algorithm is presented. The algorithm is exact provided data is recorded for a sufficiently wide range of frequencies and the jump points of the wave speed are equally spaced with respect to travel time. Numerical examples show that the algorithm works also in the general case of arbitrary wave speed (either with jumps or continuously varying etc.) giving progressively more accurate approximations as the range of recorded frequencies increases. A key underlying theoretical insight is to associate scattering data to compositions of automorphisms of the unit disk, which are in turn related to orthogonal polynomials on the unit circle. The algorithm exploits the three-term recurrence of orthogonal polynomials to reduce the required computation.

math.AP↗

arXiv2019

XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed framework combines the strengths of both supervised and unsupervised machine learning methods by creating a hybrid approach that exploits each of their individual performance capabilities in outlier detection. XGBOD uses multiple unsupervised outlier mining algorithms to extract useful representations from the underlying data that augment the predictive capabilities of an embedded supervised classifier on an improved feature space. The novel approach is shown to provide superior performance in comparison to competing individual detectors, the full ensemble and two existing representation learning based algorithms across seven outlier datasets.

cs.LG↗

arXiv2019

On the Equilibria and Efficiency of Electricity Markets with Renewable Power Producers and Congestion Constraints

With increasing renewable penetration in power systems, a prominent challenge in efficient and reliable power system operation is handling the uncertainties inherent in the renewable generation. In this paper, we propose a simple two-settlement market mechanism in which renewable power producers (RPPs) participate, so that a) the independent system operator (ISO) does not need to consider the uncertainties of the renewables in its economic dispatch, and yet b) the market equilibrium is shown to approach social efficiency as if the ISO solves a stochastic optimization taking into account all the uncertainties. In showing this result, a key innovation is a new approach of efficiently computing the Nash equilibrium (NE) among the strategic RPPs in congestion-constrained power networks. In particular, the proposed approach decouples finding an NE into searching over congestion patterns and computing an NE candidate assuming a congestion pattern. As such, the computational complexity of finding an NE grows only cubically with the number of RPPs in the market. We demonstrate our results in the IEEE 14-bus system and show that the NE approaches social efficiency as the number of RPPs grows.

cs.GT↗

arXiv2019

DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth. In this paper, an unsupervised outlier detector combination framework called DCSO is proposed, demonstrated and assessed for the dynamic selection of most competent base detectors, with an emphasis on data locality. The proposed DCSO framework first defines the local region of a test instance by its k nearest neighbors and then identifies the top-performing base detectors within the local region. Experimental results on ten benchmark datasets demonstrate that DCSO provides consistent performance improvement over existing static combination approaches in mining outlying objects. To facilitate interpretability and reliability of the proposed method, DCSO is analyzed using both theoretical frameworks and visualization techniques, and presented alongside empirical parameter setting instructions that can be used to improve the overall performance.

cs.LG↗

arXiv2019

Combining Machine Learning Models using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.

cs.LG↗

arXiv2019

SynC: A Unified Framework for Generating Synthetic Population with Gaussian Copula

Synthetic population generation is the process of combining multiple socioeconomic and demographic datasets from different sources and/or granularity levels, and downscaling them to an individual level. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this study, we propose a multi-stage framework called SynC (Synthetic Population via Gaussian Copula) to fill the gap. SynC first removes potential outliers in the data and then fits the filtered data with a Gaussian copula model to correctly capture dependencies and marginal distributions of sampled survey data. Finally, SynC leverages predictive models to merge datasets into one and then scales them accordingly to match the marginal constraints. We make three key contributions in this work: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-of-the-art machine learning and statistical techniques, 2) demonstrate its value as a feature engineering tool, as well as an alternative to data collection in situations where gathering is difficult through two real-world datasets, 3) release an easy-to-use framework implementation for reproducibility, and 4) ensure the methodology is scalable at the production level and can easily incorporate new data.

cs.LG↗