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Xi Zhang

Publications and source records attributed to Xi Zhang.

At least 217 records · Page 12Linked to original sources
arXiv2023

Acceleration or finite speed propagation in weakly monostable reaction-diffusion equations

This paper focuses on propagation phenomena in reaction-diffusion equations with a weaklymonostable nonlinearity. The reaction term can be seen as an intermediate between the classicallogistic one (or Fisher-KPP) and the standard weak Allee effect one. We investigate the effect ofthe decay rate of the initial data on the propagation rate. When the right tail of the initial datais sub-exponential, finite speed propagation and acceleration may happen and we derive the exactseparation between the two situations. When the initial data is sub-exponentially unbounded, accel-eration unconditionally occurs. Estimates for the locations of the level sets are expressed in termsof the decay of the initial data. In addition, sharp exponents of acceleration for initial data withsub-exponential and algebraic tails are given. Numerical simulations are presented to illustrate theabove findings.

math.AP↗
arXiv2023

Approaching the robust linearity in dual-floating van der Waals photodiode

Two-dimensional (2D) material photodetectors have gained great attention as potential elements for optoelectronic applications. However, the linearity of the photoresponse is often compromised by the carrier interaction, even in 2D photodiodes. In this study, we present a new device concept of dual-floating van der Waals heterostructures (vdWHs) photodiode by employing ambipolar MoTe2 and n-type MoS2 2D semiconductors. The presence of type II heterojunctions on both sides of channel layers effectively deplete carriers and restrict the photocarrier trapping within the channel layers. As a result, the device exhibits robust linear photoresponse under photovoltaic mode from the visible (405 nm) to near-infrared (1600 nm) band. With the built-in electric field of the vdWHs, we achieve a linear dynamic range of ~ 100 dB, responsivity of ~ 1.57 A/W, detectivity of ~ 4.28 * 10^11 Jones, and response speed of ~ 30 μs. Our results showcase a promising device concept with excellent linearity towards fast and low-loss detection, high-resolution imaging, and logic optoelectronics.

cond-mat.mes-hall↗
arXiv2023

Selective Manipulation and Tunneling Spectroscopy of Broken-Symmetry Quantum Hall States in a Hybrid-edge Quantum Point Contact

We present a device architecture of hybrid-edge and dual-gated quantum point contact. We demonstrate improved electrostatic control over the separation, position, and coupling of each broken-symmetry compressible strip in graphene. Via low-temperature magneto-transport measurement, we demonstrate selective manipulation over the evolution, hybridization, and transmission of arbitrarily chosen quantum Hall states in the channel. With gate-tunable tunneling spectroscopy, we characterize the energy gap of each symmetry-broken quantum Hall state with high resolution on the order of ~0.1 meV.

cond-mat.mes-hall↗
arXiv2023

Visual Commonsense based Heterogeneous Graph Contrastive Learning

How to select relevant key objects and reason about the complex relationships cross vision and linguistic domain are two key issues in many multi-modality applications such as visual question answering (VQA). In this work, we incorporate the visual commonsense information and propose a heterogeneous graph contrastive learning method to better finish the visual reasoning task. Our method is designed as a plug-and-play way, so that it can be quickly and easily combined with a wide range of representative methods. Specifically, our model contains two key components: the Commonsense-based Contrastive Learning and the Graph Relation Network. Using contrastive learning, we guide the model concentrate more on discriminative objects and relevant visual commonsense attributes. Besides, thanks to the introduction of the Graph Relation Network, the model reasons about the correlations between homogeneous edges and the similarities between heterogeneous edges, which makes information transmission more effective. Extensive experiments on four benchmarks show that our method greatly improves seven representative VQA models, demonstrating its effectiveness and generalizability.

cs.CV↗
arXiv2023

Heat-Flux Limited Cloud Activity and Vertical Mixing in Giant Planet Atmospheres with an Application to Uranus and Neptune

Storms operated by moist convection and the condensation of $\rm CH_{4}$ or $\rm H_{2}S$ have been observed on Uranus and Neptune. However, the mechanism of cloud formation, thermal structure, and mixing efficiency of ice giant weather layers remains unclear. In this paper, we show that moist convection is limited by heat transport on giant planets, especially on ice giants where planetary heat flux is weak. Latent heat associated with condensation and evaporation can efficiently bring heat across the weather layer through precipitations. This effect was usually neglected in previous studies without a complete hydrological cycle. We first derive analytical theories and show the upper limit of cloud density is determined by the planetary heat flux and microphysics of clouds but independent of the atmospheric composition. The eddy diffusivity of moisture depends on the heat fluxes, atmospheric composition, and gravity of the planet but is not directly related to cloud microphysics. We then conduct convection- and cloud-resolving simulations with SNAP to validate our analytical theory. The simulated cloud density and eddy diffusivity are smaller than the results acquired from the equilibrium cloud condensation model and mixing length theory by several orders of magnitude but consistent with our analytical solutions. Meanwhile, the mass-loading effect of $\rm CH_{4}$ and $\rm H_{2}S$ leads to superadiabatic and stable weather layers. Our simulations produced three cloud layers that are qualitatively similar to recent observations. This study has important implications for cloud formation and eddy mixing in giant planet atmospheres in general and observations for future space missions and ground-based telescopes.

astro-ph.EP↗
arXiv2023

Structure and mechanical properties of monolayer amorphous carbon and boron nitride

Amorphous materials exhibit various characteristics that are not featured by crystals and can sometimes be tuned by their degree of disorder (DOD). Here, we report results on the mechanical properties of monolayer amorphous carbon (MAC) and monolayer amorphous boron nitride (maBN) with different DOD. The pertinent structures are obtained by kinetic-Monte-Carlo (kMC) simulations using machine-learning potentials (MLP) with density-functional-theory (DFT)-level accuracy. An intuitive order parameter, namely the areal fraction Fx occupied by crystallites within the continuous random network, is proposed to describe the DOD. We find that Fx captures the essence of the DOD: Samples with the same Fx but different sizes and distributions of crystallites have virtually identical radial distributions functions as well as bond-length and bond-angle distributions. Furthermore, by simulating the fracture process with molecular dynamics, we found that the mechanical responses of MAC and maBN before fracture are solely determined by Fx and are insensitive to the sizes and specific arrangements of the crystallites. The behavior of cracks in the two materials is analyzed and found to mainly propagate in meandering paths in the CRN region and to be influenced by crystallites in distinct ways that toughen the material. The present results reveal the relation between structure and mechanical properties in amorphous monolayers and may provide a universal toughening strategy for 2D materials.

cond-mat.mtrl-sci↗
arXiv2023

Constraining Thermal Emission of Pluto's Haze From Infrared Rotational Lightcurves

The rotational lightcurves of the Pluto-Charon system were previously believed to be solely attributed to their surfaces. However, a proposed scenario of haze cooling \citep{2017Natur.551..352Z} suggests that the atmospheric haze of Pluto could significantly contribute to mid-infrared emission, which calls for a revisit of previous analyses. In this study, we employ a Bayesian retrieval approach to constrain the haze emission from the rotational lightcurves of the Pluto-Charon system. The lightcurves were observed by the Spitzer and Herschel telescopes at 24 and 70 $μ$m, and were combined with the latest surface albedo maps of Pluto and Charon from the New Horizons spacecraft. Our results show that including the haze emission is consistent with all current observations, with the best-fit haze flux around 1.63 mJy. This is in agreement with the composition of Titan-like tholins. However, the ``surface only" scenario, which excludes the haze contribution, can still explain the observations. We conclude that the current data at 24 $μ$m cannot constrain Pluto's haze emission due to the degeneracy with Charon's surface emission. Regardless, some surface properties of Pluto are well constrained by the shape of the lightcurves, with a thermal inertia of approximately 8--10 MKS and a relatively low CH$_4$ emissivity of 0.3--0.5. We suggest that observations by the JWST telescope at 18 $μ$m, which can resolve Pluto from Charon, could directly probe the haze emission of Pluto due to the low surface emission at that wavelength.

astro-ph.EP↗
arXiv2023

The Inhomogeneity Effect I: Inhomogeneous Surface and Atmosphere Accelerate Planetary Cooling

We propose a general principle that under the radiative-convective equilibrium, the spatial and temporal variations in a planet's surface and atmosphere tend to increase its cooling. This principle is based on Jensen's inequality and the curvature of the response functions of surface temperature and outgoing cooling flux to changes in incoming stellar flux and atmospheric opacity. We use an analytical model to demonstrate that this principle holds for various planet types: (1) on an airless planet, the mean surface temperature is lower than its equilibrium temperature; (2) on terrestrial planets with atmospheres, the inhomogeneity of incoming stellar flux and atmospheric opacity reduces the mean surface temperature; (3) on giant planets, inhomogeneously distributed stellar flux and atmospheric opacity increase the outgoing infrared flux, cooling the interior. Although the inhomogeneity of visible opacity might sometimes heat the atmosphere, the effect is generally much smaller than the inhomogeneous cooling effect of infrared opacity. Compared with the homogeneous case, the mean surface temperature on inhomogeneous terrestrial planets can decrease by more than 20\%, and the internal heat flux on giant planets can increase by over an order of magnitude. Despite simplifications in our analytical framework, the effect of stellar flux inhomogeneity appears to be robust, while further research is needed to fully understand the effects of opacity inhomogeneity in more realistic situations. This principle impacts our understanding of planetary habitability and the evolution of giant planets using low-resolution and one-dimensional frameworks that may have previously overlooked the role of inhomogeneity.

astro-ph.EP↗
arXiv2023

The Inhomogeneity Effect II: Rotational and Orbital States Impact Planetary Cooling

We generalize the theory of the inhomogeneity effect to enable comparison among different inhomogeneous planets. A metric of inhomogeneity based on the cumulative distribution function is applied to investigate the dependence of planetary cooling on previously overlooked parameters. The mean surface temperature of airless planets increases with rotational rate and surface thermal inertia, which bounds the value in the tidally locked configuration and the equilibrium temperature. Using an analytical model, we demonstrate that the internal heat flux of giant planets exhibits significant spatial variability, primarily emitted from the nightside and high-latitude regions acting as ``radiator fins." Given a horizontally uniform interior temperature in the convective zone, the outgoing internal flux increases up to several folds as the inhomogeneity of the incoming stellar flux increases. The enhancement decreases with increasing heat redistribution through planetary dynamics or rotation. The outgoing internal flux on rapidly rotating planets generally increases with planetary obliquity and orbital eccentricity. The radiative timescale and true anomaly of the vernal equinox also play significant roles. If the radiative timescale is long, the outgoing internal flux shows a slightly decreasing but nonlinear trend with obliquity. Our findings indicate that rotational and orbital states greatly influence the cooling of planets and impact the interior evolution of giant planets, particularly for tidally locked planets and planets with high eccentricity and obliquity (such as Uranus), as well as the spatial and temporal variations of their cooling fluxes.

astro-ph.EP↗
arXiv2023

Balancing Approach for Causal Inference at Scale

With the modern software and online platforms to collect massive amount of data, there is an increasing demand of applying causal inference methods at large scale when randomized experimentation is not viable. Weighting methods that directly incorporate covariate balancing have recently gained popularity for estimating causal effects in observational studies. These methods reduce the manual efforts required by researchers to iterate between propensity score modeling and balance checking until a satisfied covariate balance result. However, conventional solvers for determining weights lack the scalability to apply such methods on large scale datasets in companies like Snap Inc. To address the limitations and improve computational efficiency, in this paper we present scalable algorithms, DistEB and DistMS, for two balancing approaches: entropy balancing and MicroSynth. The solvers have linear time complexity and can be conveniently implemented in distributed computing frameworks such as Spark, Hive, etc. We study the properties of balancing approaches at different scales up to 1 million treated units and 487 covariates. We find that with larger sample size, both bias and variance in the causal effect estimation are significantly reduced. The results emphasize the importance of applying balancing approaches on large scale datasets. We combine the balancing approach with a synthetic control framework and deploy an end-to-end system for causal impact estimation at Snap Inc.

stat.ME↗
arXiv2023

Longitudinal Data Clustering with a Copula Kernel Mixture Model

Many common clustering methods cannot be used for clustering multivariate longitudinal data in cases where variables exhibit high autocorrelations. In this article, a copula kernel mixture model (CKMM) is proposed for clustering data of this type. The CKMM is a finite mixture model which decomposes each mixture component's joint density function into its copula and marginal distribution functions. In this decomposition, the Gaussian copula is used due to its mathematical tractability and Gaussian kernel functions are used to estimate the marginal distributions. A generalized expectation-maximization algorithm is used to estimate the model parameters. The performance of the proposed model is assessed in a simulation study and on two real datasets. The proposed model is shown to have effective performance in comparison to standard methods, such as K-means with dynamic time warping clustering and latent growth models.

stat.ME↗
arXiv2023

Robust Ranking Explanations

Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using $\ell_p$-norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the \textit{R2ET} algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy.

cs.LG↗
arXiv2023

The Edge Sensor of Segmented Mirror Based on Fringes of Equal Thickness

Co-phase and co-focus detection is one of the key technologies for large-aperture segmented mirror telescopes. In this paper, a new edge sensor based on fringes of equal thickness is developed, which can detect each segment's relative piston, tilt, and tip errors from the interferograms. Based on the co-focus demand for many ground-based seeing limited segmented mirror telescopes, an edge sensor prototype based on such a principle is built and applied in the indoor segmented mirror experiment system in the lab. According to the co-focus requirement of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, many simulations and experiments are carried out for co-focus error detection of the segmented mirror system. Experiment results show that the co-focus accuracy is better than 0."02 rms, which can meet the co-focus requirements of most large or extremely large segmented mirror astronomical telescopes.

astro-ph.IM↗
arXiv2023

Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent semantics between images and texts, and rarely spot the inconsistency among the post contents and background knowledge. In addition, they commonly assume the completeness of multiple modalities and thus are incapable of handling handle missing modalities in real-life scenarios. Motivated by the intuition that rumors in social media are more likely to have inconsistent semantics, a novel Knowledge-guided Dual-consistency Network is proposed to detect rumors with multimedia contents. It uses two consistency detection subnetworks to capture the inconsistency at the cross-modal level and the content-knowledge level simultaneously. It also enables robust multi-modal representation learning under different missing visual modality conditions, using a special token to discriminate between posts with visual modality and posts without visual modality. Extensive experiments on three public real-world multimedia datasets demonstrate that our framework can outperform the state-of-the-art baselines under both complete and incomplete modality conditions. Our codes are available at https://github.com/MengzSun/KDCN.

cs.LG↗
arXiv2023

Generation and modulation of multiple 2D bulk photovoltaic effects in space-time reversal asymmetric 2H-FeCl2

The two-dimensional (2D) bulk photovoltaic effect (BPVE) is a cornerstone for future highly efficient 2D solar cells and optoelectronics. The ferromagnetic semiconductor 2H-FeCl2 is shown to realize a new type of BPVE in which spatial inversion (P), time reversal (T), and space-time reversal (PT) symmetries are broken (PT-broken). Using density functional theory and perturbation theory, we show that 2H-FeCl2 exhibits giant photocurrents, photo-spin-currents, and photo-orbital-currents under illumination by linearly polarized light. The injection-like and shift-like photocurrents coexist and propagate in different directions. The material also demonstrates substantial photoconductance, photo-spin-conductance, and photo-orbital-conductance, with magnitudes up to 4650 (nm{\cdot}μA/V2), 4620 (nm{\cdot}μA/V2 {\hbar}/2e), and 6450 (nm{\cdot}μA/V2 {\hbar}/e), respectively. Furthermore, the injection-currents, shift-spin-currents, and shift-orbital-currents can be readily switched via rotating the magnetizations of 2H-FeCl2. These results demonstrate the superior performance and intriguing control of a new type of BPVE in 2H-FeCl2.

cond-mat.mtrl-sci↗
arXiv2023

Photochemical hazes dramatically alter temperature structure and atmospheric circulation in 3D simulations of hot Jupiters

Photochemical hazes are expected to form in hot Jupiter atmospheres and may explain the strong scattering slopes and muted spectral features observed in the transmission spectra of many hot Jupiters. Absorption and scattering by photochemical hazes have the potential to drastically alter temperature structure and atmospheric circulation of these planets but have previously been neglected in general circulation models (GCMs). We present GCM simulations of hot Jupiter HD 189733b that include photochemical hazes as a radiatively active tracer fully coupled to atmospheric dynamics. The influence of haze radiative feedback strongly depends on the assumed haze optical properties. For soot hazes, two distinct thermal inversions form, separated by a local temperature minimum around 10$^{-5}$ bar caused by upwelling on the dayside mixing air with low haze abundance upwards. The equatorial jet broadens and slows down. The horizontal distribution of hazes remains relatively similar to simulations with radiatively passive tracers. For Titan-type hazes, the equatorial jet accelerates and extends to much lower pressures, resulting in a dramatically different 3D distribution of hazes compared to radiatively passive or soot hazes. Further experimental and observational studies to constrain the optical properties of photochemical hazes will therefore be crucial for understanding the role of hazes in exoplanet atmospheres. In the dayside emission spectrum, for both types of hazes the amplitude of near-infrared features is reduced, while the emitted flux at longer wavelengths ($>$4 $μ$m) increases. Haze radiative feedback leads to increased phase curve amplitudes in many infrared wavelength regions, mostly due to stronger dayside emission.

astro-ph.EP↗
arXiv2023

Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection

User-generated reviews of products are vital assets of online commerce, such as Amazon and Yelp, while fake reviews are prevalent to mislead customers. GNN is the state-of-the-art method that detects suspicious reviewers by exploiting the topologies of the graph connecting reviewers, reviews, and target products. However, the discrepancy in the detection accuracy over different groups of reviewers can degrade reviewer engagement and customer trust in the review websites. Unlike the previous belief that the difference between the groups causes unfairness, we study the subgroup structures within the groups that can also cause discrepancies in treating different groups. This paper addresses the challenges of defining, approximating, and utilizing a new subgroup structure for fair spam detection. We first identify subgroup structures in the review graph that lead to discrepant accuracy in the groups. The complex dependencies over the review graph create difficulties in teasing out subgroups hidden within larger groups. We design a model that can be trained to jointly infer the hidden subgroup memberships and exploits the membership for calibrating the detection accuracy across groups. Comprehensive comparisons against baselines on three large Yelp review datasets demonstrate that the subgroup membership can be identified and exploited for group fairness.

cs.LG↗
arXiv2023

Prescribing Chern scalar curvatures on noncompact manifolds

In this paper, we investigate the noncompact prescribed Chern scalar curvature problem which reduces to solve a Kazdan-Warner type equation on noncompact non-Kähler manifolds. By introducing an analytic condition on noncompact manifolds, we establish related existence results. As its another application, we further give a new proof of a classical multiplicity theorem of W.M. Ni.

math.DG↗