Search arXiv⌕ Search

arXiv subjects

Xi Zhang

Publications and source records attributed to Xi Zhang.

At least 217 records · Page 12Linked to original sources
arXiv2024

Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b

Hot Jupiters are among the best-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5-12 $μ$m with JWST's Mid-Infrared Instrument (MIRI). The spectra reveal a large day-night temperature contrast (with average brightness temperatures of 1524$\pm$35 and 863$\pm$23 Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ~100 mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Contrary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2$σ$ upper limit of 1-6 parts per million, depending on model assumptions).

astro-ph.EP↗
arXiv2024

The Fate of Simple Organics on Titan's Surface: A Theoretical Perspective

Atmospheric photochemistry on Titan continuously transforms methane and nitrogen gases into various organic compounds. This study explores the fate of these molecules when they land on Titan's surface. Our analytical exploration reveals that most simple organics found in Titan's atmosphere, including all nitriles, triple-bonded hydrocarbons, and benzene, land as solids. Only a few compounds are in the liquid phase, while only ethylene remains gaseous. For the simple organics that land as solids, we further examine their interactions with Titan's lake liquids. Utilizing principles of buoyancy, we found that flotation can be achieved via porosity-induced (25-60% porosity) or capillary force-induced buoyancy for HCN ices on ethane-rich lakes. Otherwise, these ices would sink and become lakebed sediments. By evaluating the timescale of flotation, our findings suggest that porosity-induced flotation of millimeter-sized and larger sediments is the only plausible mechanism for floating solids to explain the transient "magic islands" phenomena on Titan's lakes.

astro-ph.EP↗
arXiv2023

Anomalous exchange bias effect in ferromagnetic VI3 flakes

The exchange bias (EB) effect, pivotal in magnetic data storage and sensing devices, has been observed not only in interfacial regions but also in intrinsic ferromagnetic materials. Here, we've uncovered a robust and stable exchange bias effect within the layered van der Waals (vdW) ferromagnet VI3 employing magnetic circular dichroism microscopy. At 10 K, we observed a significant exchange field of approximately 0.1 T, accompanied by random shifts (positive or negative relative to zero magnetic field) after zero-field cooling. Notably, this effect is effectively controllable after field cooling, with shift direction opposing the applied magnetic field. The presence of strong magnetic anisotropic energy within VI3 results in larger coercivity-bound magnetic domains. These domains dictate the neighboring ferromagnetic alignment and induce shifts in the hysteresis loop. Our study not only contributes to comprehending fundamental nanoscale magnetic interactions but also sheds light on emergent phenomena within layered van der Waals magnets.

cond-mat.mtrl-sci↗
arXiv2023

Detecting bulk carbon ferromagnetism in graphene multi-edge structure

The emergence of bulk carbon ferromagnetism is long-expected over years. At nanoscale, carbon ferromagnetism was detected by analyzing the magnetic edge states via scanning tunneling microscopy(STM), and its origin can be explained by local redistribution of electron wave function. In larger scale, carbon ferromagnetism can be created by deliberately producing defects in graphite, and detected by macroscopic technical magnetization. Meanwhile, it becomes crucial to determine that the detected magnetization is originated from carbon rather than from magnetic impurities. One solution is X-ray magnetic circular dichroism (XMCD). Nonetheless, a reproducible, full section of XMCD spectrum across C-1s absorption energy has not appeared yet, which should be decisive for assuring the indisputable existence of bulk carbon ferromagnetism. Besides, the lack of direct observation on the atomic structure of the ferromagnetic carbon leaves the structural origin of its ferromagnetism still in mist. In this work, for detecting bulk carbon ferromagnetism, we managed to grow all-carbon film consisting of vertically aligned graphene multi-edge (VGME), which wove into a three-dimensional hyperfine-porous network. Magnetization (M-H) curves and XMCD spectra co-confirmed bulk carbon ferromagnetism of VGME at room temperature, with the average unit magnetic momentum of ~0.0006 miuB/atom. The influence of magnetic impurities on magnetization was excluded by both absorption spectra and inductively coupled plasma mass spectrometry measurements. The spin transfer behavior also verified the long-range and robust feature of the bulk carbon ferromagnetism. Our work provides direct evidence of elementary resolved bulk carbon ferromagnetism at room temperature and clarifies its origin from pi-electrons at graphene edges.

cond-mat.mtrl-sci↗
arXiv2023

The Inhomogeneity Effect III: Weather Impacts on the Heat Flow of Hot Jupiters

The interior flux of a giant planet impacts atmospheric motion, and the atmosphere dictates the interior's cooling. Here we use a non-hydrostatic general circulation model (Simulating Nonhydrostatic Atmospheres on Planets, SNAP) coupled with a multi-stream multi-scattering radiative module (High-performance Atmospheric Radiation Package, HARP) to simulate the weather impacts on the heat flow of hot Jupiters. We found that the vertical heat flux is primarily transported by convection in the lower atmosphere and regulated by dynamics and radiation in the overlying ``radiation-circulation" zone. The temperature inversion occurs on the dayside and reduces the upward radiative flux. The atmospheric dynamics relay the vertical heat transport until the radiation becomes efficient in the upper atmosphere. The cooling flux increases with atmospheric drag due to increased day-night contrast and spatial inhomogeneity. The temperature dependence of the infrared opacity greatly amplifies the opacity inhomogeneity. Although atmospheric circulation could transport heat downward in a narrow region above the radiative-convective boundary, the opacity inhomogeneity effect overcomes the dynamical effect and leads to a larger overall interior cooling than the local simulations with the same interior entropy and stellar flux. The enhancement depends critically on the equilibrium temperature, drag, and atmospheric opacity. In a strong-drag atmosphere hotter than 1600 K, a significant inhomogeneity effect in three-dimensional (3D) models can boost interior cooling several-fold compared to the 1D radiative-convective equilibrium models. This study confirms the analytical argument of the inhomogeneity effect in Zhang (2023a,b). It highlights the importance of using 3D atmospheric models in understanding the inflation mechanisms of hot Jupiters and giant planet evolution in general.

astro-ph.EP↗
arXiv2023

Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk technique

Word embedding has become ubiquitous and is widely used in various natural language processing (NLP) tasks, such as web retrieval, web semantic analysis, and machine translation, and so on. Unfortunately, training the word embedding in a relatively large corpus is prohibitively expensive. We propose a graph-based word embedding algorithm, called Word-Graph2vec, which converts the large corpus into a word co-occurrence graph, then takes the word sequence samples from this graph by randomly traveling and trains the word embedding on this sampling corpus in the end. We posit that because of the limited vocabulary, huge idioms, and fixed expressions in English, the size and density of the word co-occurrence graph change slightly with the increase in the training corpus. So that Word-Graph2vec has stable runtime on the large-scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real-world datasets show that the proposed algorithm outperforms traditional Word2vec four to five times in terms of efficiency and two to three times than FastText, while the error generated by the random walk technique is small.

cs.CL↗
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↗