arXiv subjects
Publications and source records attributed to Yue Zhao.
This paper is concerned with an inverse source problem for the three-dimensional Helmholtz equation by a single boundary measurement at a fixed frequency. We show the Lipschitz stability under the assumption that the source function is piecewise constant on a domain which is made of a union of disjoint convex polyhedral subdomains..
We perform a systematic analysis of models with GeV-scale dark matter coupled to baryons and leptons. Such theories provide a natural framework to explain the matter-antimatter asymmetry of the universe. We find that only a few baryonic dark matter models are free from tree-level proton decay without explicitly imposing baryon number conservation. We enumerate those cases and provide a brief overview of their phenomenology. We then focus on a leptonic dark matter model for a more detailed discussion of the baryon asymmetry generation via leptogenesis, the symmetry restoration in the dark sector and the expected dark matter annihilation signals in indirect detection experiments.
How can we spot money laundering in large-scale graph-like accounting datasets? How to identify the most suspicious period in a time-evolving accounting graph? What kind of accounts and events should practitioners prioritize under time constraints? To tackle these crucial challenges in accounting and auditing tasks, we propose a flexible system called AutoAudit, which can be valuable for auditors and risk management professionals. To sum up, there are four major advantages of the proposed system: (a) "Smurfing" Detection, spots nearly 100% of injected money laundering transactions automatically in real-world datasets. (b) Attention Routing, attends to the most suspicious part of time-evolving graphs and provides an intuitive interpretation. (c) Insight Discovery, identifies similar month-pair patterns proved by "success stories" and patterns following Power Laws in log-logistic scales. (d) Scalability and Generality, ensures AutoAudit scales linearly and can be easily extended to other real-world graph datasets. Experiments on various real-world datasets illustrate the effectiveness of our method. To facilitate reproducibility and accessibility, we make the code, figure, and results public at https://github.com/mengchillee/AutoAudit.
Boosted dark matter (BDM) is a well-motivated class of dark matter (DM) candidates in which a small component of DM is relativistic at the present time. We lay the foundation for BDM searches via hadronic interactions in large liquid-argon time-projection chambers (LArTPCs), such as DUNE. We investigate BDM-nucleus scattering in detail by developing new event generation techniques with a parameterized detector simulation. We study the discovery potential in a DUNE-like experiment using the low threshold and directionality of hadron detection in LArTPCs and compare with other experiments.
A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from multiple sources when direct collection is difficult or costly. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this paper, we study a specific synthetic data generation task called downscaling, a procedure to infer high-resolution, harder-to-collect information (e.g., individual level records) from many low-resolution, easy-to-collect sources, and propose a multi-stage framework called SYNC (Synthetic Data Generation via Gaussian Copula). For given low-resolution datasets, the central idea of SYNC is to fit Gaussian copula models to each of the low-resolution datasets in order to correctly capture dependencies and marginal distributions, and then sample from the fitted models to obtain the desired high-resolution subsets. Predictive models are then used to merge sampled subsets into one, and finally, sampled datasets are scaled according to low-resolution marginal constraints. We make four 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) perform simulation studies to validate SYNC's performance as a synthetic data generation algorithm, 3) 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, 4) release an easy-to-use framework implementation for reproducibility and scalability at the production level that easily incorporates new data.
Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of "extremeness". Intuitively, we think of this as calculating an anomalous p-value. This makes COPOD both parameter-free, highly interpretable, and computationally efficient. In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.
We present a deep ($\sim 330~\mathrm{ks}$) {\it Chandra} survey of the Galactic globular cluster M30 (NGC 7099). Combining the new Cycle 18 with the previous Cycle 3 observations we report a total of 10 new X-ray point sources within the $1.03$ arcmin half-light radius, compiling an extended X-ray catalogue of a total of 23 sources. We incorporate imaging observations by the {\it Hubble Space Telescope} and the {\it Karl G. Jansky Very Large Array} from the MAVERIC survey to search for optical and radio counterparts to the new and old sources. Two X-ray sources are found to have a radio counterpart, including the known millisecond pulsar PSR J2140$-$2310A, the radio position of which also matches a previously reported faint optical counterpart which is slightly redder than the main sequence. We found optical counterparts to $18$ of the $23$ X-ray sources, identifying $2$ new cataclysmic variables (CVs), $5$ new CV candidates, $2$ new candidates of RS CVn type of active binary (AB), and $2$ new candidates of BY Dra type of AB. The remaining unclassified X-ray sources are likely background active galactic nuclei (AGN), as their number is consistent with the expected number of AGN at our X-ray sensitivity. Finally, our analysis of radial profiles of different source classes suggests that bright CVs are more centrally distributed than faint CVs in M30, consistent with other core-collapsed globular clusters.
Topological magnets are a new family of quantum materials providing great potential to realize emergent phenomena, such as quantum anomalous Hall effect and axion-insulator state. Here we present our discovery that stoichiometric ferromagnet MnBi8Te13 with natural heterostructure MnBi2Te4-(Bi2Te3)3 is an unprecedented half-magnetic topological insulator, with the magnetization existing at the MnBi2Te4 surface but not at the opposite surface terminated by triple Bi2Te3 layers. Our angle-resolved photoemission spectroscopy measurements unveil a massive Dirac gap at the MnBi2Te4 surface, and gapless Dirac cone on the other side. Remarkably, the Dirac gap (~28 meV) at MnBi2Te4 surface decreases monotonically with increasing temperature and closes right at the Curie temperature, thereby representing the first smoking-gun spectroscopic evidence of magnetization-induced topological surface gap among all known magnetic topological materials. We further demonstrate theoretically that the half-magnetic topological insulator is desirable to realize the half-quantized surface anomalous Hall effect, which serves as a direct proof of the general concept of axion electrodynamics in condensed matter systems.
We introduce OmniSource, a novel framework for leveraging web data to train video recognition models. OmniSource overcomes the barriers between data formats, such as images, short videos, and long untrimmed videos for webly-supervised learning. First, data samples with multiple formats, curated by task-specific data collection and automatically filtered by a teacher model, are transformed into a unified form. Then a joint-training strategy is proposed to deal with the domain gaps between multiple data sources and formats in webly-supervised learning. Several good practices, including data balancing, resampling, and cross-dataset mixup are adopted in joint training. Experiments show that by utilizing data from multiple sources and formats, OmniSource is more data-efficient in training. With only 3.5M images and 800K minutes videos crawled from the internet without human labeling (less than 2% of prior works), our models learned with OmniSource improve Top-1 accuracy of 2D- and 3D-ConvNet baseline models by 3.0% and 3.9%, respectively, on the Kinetics-400 benchmark. With OmniSource, we establish new records with different pretraining strategies for video recognition. Our best models achieve 80.4%, 80.5%, and 83.6 Top-1 accuracies on the Kinetics-400 benchmark respectively for training-from-scratch, ImageNet pre-training and IG-65M pre-training.
This technical report summarizes submissions and compiles from Actor-Action video classification challenge held as a final project in CSC 249/449 Machine Vision course (Spring 2020) at University of Rochester
Numerous astrophysical and cosmological observations are best explained by the existence of dark matter, a mass density which interacts only very weakly with visible, baryonic matter. Searching for the extremely weak signals produced by this dark matter strongly motivate the development of new, ultra-sensitive detector technologies. Paradigmatic advances in the control and readout of massive mechanical systems, in both the classical and quantum regimes, have enabled unprecedented levels of sensitivity. In this white paper, we outline recent ideas in the potential use of a range of solid-state mechanical sensing technologies to aid in the search for dark matter in a number of energy scales and with a variety of coupling mechanisms.
The epitaxial growth of technically-important $β$-Ga$_2$O$_3$ semiconductor thin films have not been realized on flexible substrates due to limitations by the high-temperature crystallization conditions and the lattice-matching requirements. In this report, for the first time single crystal $β$-Ga$_2$O$_3$(-201) thin films is epitaxially grown on the flexible CeO2 (001)-buffered hastelloy tape. The results indicate that CeO$_2$ (001) has a small bi-axial lattice mismatch with $β$-Ga$_2$O$_3$ (-201), thus inducing a simultaneous double-domain epitaxial growth. Flexible photodetectors are fabricated based on the epitaxial $β$-Ga$_2$O$_3$ coated tapes. Measurements show that the obtained photodetectors have a responsivity of 40 mA/W, with an on/off ratio reaching 1000 under 250 nm incident light and 5 V bias voltage. Such photoelectrical performance is already within the mainstream level of the $β$-Ga$_2$O$_3$ based photodetectors by using the conventional rigid single crystal substrates; and more importantly remained robust against more than 1000 cycles of bending tests. In addition, the epitaxy technique described in the report also paves the way for the fabrication of a wide range of flexible epitaxial film devices that utilize the materials with lattice parameters similar to $β$-Ga$_2$O$_3$, including GaN, AlN and SiC.
Large scale implementation of electrochemical water splitting for hydrogen evolution requires cheap and efficient catalysts to replace expensive platinum. Molybdenum disulfide is one of the most promising alternative catalysts but its intrinsic activity is still inferior to platinum. There is therefore a need to explore new active site origins in molybdenum disulfide with ultrafast reaction kinetics and to understand their mechanisms. Here, we report a universal cold hydrogen plasma reduction method for synthesizing different single atoms sitting on two-dimensional monolayers. In case of molybdenum disulfide, we design and identify a new type of active site, i.e., unsaturated Mo single atoms on cogenetic monolayer molybdenum disulfide. The catalyst shows exceptional intrinsic activity with a Tafel slope of 35.1 mV dec-1 and a turnover frequency of ~10^3 s-1 at 100 mV, based on single flake microcell measurements. Theoretical studies indicate that coordinately unsaturated Mo single atoms sitting on molybdenum disulfide increase the bond strength between adsorbed hydrogen atoms and the substrates through hybridization, leading to fast hydrogen adsorption/desorption kinetics and superior hydrogen evolution activity. This work shines fresh light on preparing highly-efficient electrocatalysts for water splitting and other electrochemical processes, as well as provides a general method to synthesize single atoms on two-dimensional monolayers.
Globular clusters host a variety of lower-luminosity ($L_X<10^{35}$ erg s$^{-1}$) X-ray sources, including accreting neutron stars and black holes, millisecond pulsars, cataclysmic variables, and chromospherically active binaries. In this paper, we provide a comprehensive catalog of more than 1100 X-ray sources in 38 Galactic globular clusters observed by the Chandra X-ray Observatory's ACIS detector. The targets are selected to complement the MAVERIC survey's deep radio continuum maps of Galactic globular clusters. We perform photometry and spectral analysis for each source, determine a best-fit model, and assess the possibility of it being a foreground/background source based on its spectral properties and location in the cluster. We also provide basic assessments of variability. We discuss the distribution of X-ray binaries in globular clusters, their X-ray luminosity function, and carefully analyze systems with $L_X > 10^{33}$ erg s$^{-1}$. Among these moderately bright systems, we discover a new source in NGC 6539 that may be a candidate accreting stellar-mass black hole or a transitional millisecond pulsar. We show that quiescent neutron star LMXBs in globular clusters may spend ~2% of their lifetimes as transitional millisecond pulsars in their active ($L_X>10^{33}$ erg s$^{-1}$) state. Finally, we identify a substantial under-abundance of bright ($L_X>10^{33}$ erg s$^{-1}$) intermediate polars in globular clusters compared to the Galactic field, in contrast with the literature of the past two decades.
We propose boosted dark matter (BDM) as a possible explanation for the excess of keV electron recoil events observed by XENON1T. BDM particles have velocities much larger than those typical of virialized dark matter, and, as such, BDM-electron scattering can naturally produce keV electron recoils. We show that the required BDM-electron scattering cross sections can be easily realized in a simple model with a heavy vector mediator. Though these cross sections are too large for BDM to escape from the Sun, the BDM flux can originate from the Galactic Center or from halo dark matter annihilations. Furthermore, a daily modulation of the BDM signal will be present, which could not only be used to differentiate it from various backgrounds, but would also provide important directional information for the BDM flux.
We present a recent Chandra observation of the quiescent low-mass X-ray binary containing a neutron star, located in the globular cluster M30. We fit the thermal emission from the neutron star to extract its mass and radius. We find no evidence of flux variability between the two observations taken in 2001 and 2017, nor between individual 2017 observations, so we analyse them together to increase the signal to noise. We perform simultaneous spectral fits using standard light-element composition atmosphere models (hydrogen or helium), including absorption by the interstellar medium, correction for pile-up of X-ray photons on the detector, and a power-law for count excesses at high photon energy. Using a Markov-chain Monte Carlo approach, we extract mass and radius credible intervals for both chemical compositions of the atmosphere: $R_{\textrm{NS}}=7.94^{+0.76}_{-1.21}$ km and $M_{\textrm{NS}}<1.19$ M$_{\odot}$ assuming pure hydrogen, and $R_{\textrm{NS}}=10.50^{+2.88}_{-2.03}$ km and $M_{\textrm{NS}}<1.78$ M$_{\odot}$ for helium, where the uncertainties represent the 90% credible regions. For H, the small radius is difficult to reconcile with most current nuclear physics models (especially for nucleonic equations of state) and with other measurements of neutron star radii, with recent preferred values generally in the 11-14 km range. Whereas for He, the measured radius is consistent with this range. We discuss possible sources of systematic uncertainty that may result in an underestimation of the radius, identifying the presence of surface temperature inhomogeneities as the most relevant bias. According to this, we conclude that either the atmosphere is composed of He, or it is a H atmosphere with a significant contribution of hot spots to the observed radiation.
Current methods for action recognition primarily rely on deep convolutional networks to derive feature embeddings of visual and motion features. While these methods have demonstrated remarkable performance on standard benchmarks, we are still in need of a better understanding as to how the videos, in particular their internal structures, relate to high-level semantics, which may lead to benefits in multiple aspects, e.g. interpretable predictions and even new methods that can take the recognition performances to a next level. Towards this goal, we construct TAPOS, a new dataset developed on sport videos with manual annotations of sub-actions, and conduct a study on temporal action parsing on top. Our study shows that a sport activity usually consists of multiple sub-actions and that the awareness of such temporal structures is beneficial to action recognition. We also investigate a number of temporal parsing methods, and thereon devise an improved method that is capable of mining sub-actions from training data without knowing the labels of them. On the constructed TAPOS, the proposed method is shown to reveal intra-action information, i.e. how action instances are made of sub-actions, and inter-action information, i.e. one specific sub-action may commonly appear in various actions.
We propose a new strategy to search for a particular type of dark matter via nuclear capture. If the dark matter particle carries baryon number, as motivated by a class of theoretical explanations of the matter-antimatter asymmetry of the universe, it can mix with the neutron and be captured by an atomic nucleus. The resulting state de-excites by emitting a single photon or a cascade of photons with a total energy of up to several MeV. The exact value of this energy depends on the dark matter mass. We investigate the prospects for detecting dark matter capture signals in current and future neutrino and dark matter direct detection experiments.