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Xi Zhang

Publications and source records attributed to Xi Zhang.

At least 271 records · Page 15Linked to original sources
arXiv2021

Local-time Dependence of Chemical Species in the Venusian Mesosphere

Observed chemical species in the Venusian mesosphere show local-time variabilities. SO2 at the cloud top exhibits two local maxima over local time, H2O at the cloud top is uniformly distributed, and CO in the upper atmosphere shows a statistical difference between the two terminators. In this study, we investigated these local-time variabilities using a three-dimensional (3D) general circulation model (GCM) in combination with a two-dimensional (2D) chemical transport model (CTM). Our simulation results agree with the observed local-time patterns of SO2, H2O, and CO. The two-maximum pattern of SO2 at the cloud top is caused by the superposition of the semidiurnal thermal tide and the retrograde superrotating zonal (RSZ) flow. SO2 above 85 km shows a large day-night difference resulting from both photochemistry and the sub-solar to anti-solar (SS-AS) circulation. The transition from the RSZ flows to SS-AS circulation can explain the CO difference between two terminators and the displacement of the CO local-time maximum with respect to the anti-solar point. H2O is long-lived and exhibits very uniform distribution over space. We also present the local-time variations of HCl, ClO, OCS and SO simulated by our model and compare to the sparse observations of these species. This study highlights the importance of multidimensional CTMs for understanding the interaction between chemistry and dynamics in the Venusian mesosphere.

astro-ph.EP↗
arXiv2021

A Cross-Laboratory Comparison Study of Titan Haze Analogs: Surface Energy

In Titan's nitrogen-methane atmosphere, photochemistry leads to the production of complex organic particles, forming Titan's thick haze layers. Laboratory-produced aerosol analogs, or "tholins", are produced in a number of laboratories; however, most previous studies have investigated analogs produced by only one laboratory rather than a systematic, comparative analysis. In this study, we performed a comparative study of an important material property, the surface energy, of seven tholin samples produced in three independent laboratories under a broad range of experimental conditions, and explored their commonalities and differences. All seven tholin samples are found to have high surface energies, and are therefore highly cohesive. Thus, if the surface sediments on Titan are similar to tholins, future missions such as Dragonfly will likely encounter sticky sediments. We also identified a commonality between all the tholin samples: a high dispersive (non-polar) surface energy component of at least 30 mJ/m2. This common property could be shared by the actual haze particles on Titan as well. Given that the most abundant species interacting with the haze on Titan (methane, ethane, and nitrogen) are non-polar in nature, the dispersive surface energy component of the haze particles could be a determinant factor in condensate-haze and haze-lake liquids interactions on Titan. With this common trait of tholin samples, we confirmed the findings of a previous study by Yu et al. (2020) that haze particles are likely good cloud condensation nuclei (CCN) for methane and ethane clouds and would likely be completely wetted by the hydrocarbon lakes on Titan.

astro-ph.IM↗
arXiv2021

Multi-objective Explanations of GNN Predictions

Graph Neural Network (GNN) has achieved state-of-the-art performance in various high-stake prediction tasks, but multiple layers of aggregations on graphs with irregular structures make GNN a less interpretable model. Prior methods use simpler subgraphs to simulate the full model, or counterfactuals to identify the causes of a prediction. The two families of approaches aim at two distinct objectives, "simulatability" and "counterfactual relevance", but it is not clear how the objectives can jointly influence the human understanding of an explanation. We design a user study to investigate such joint effects and use the findings to design a multi-objective optimization (MOO) algorithm to find Pareto optimal explanations that are well-balanced in simulatability and counterfactual. Since the target model can be of any GNN variants and may not be accessible due to privacy concerns, we design a search algorithm using zeroth-order information without accessing the architecture and parameters of the target model. Quantitative experiments on nine graphs from four applications demonstrate that the Pareto efficient explanations dominate single-objective baselines that use first-order continuous optimization or discrete combinatorial search. The explanations are further evaluated in robustness and sensitivity to show their capability of revealing convincing causes while being cautious about the possible confounders. The diverse dominating counterfactuals can certify the feasibility of algorithmic recourse, that can potentially promote algorithmic fairness where humans are participating in the decision-making using GNN.

cs.LG↗
arXiv2021

Explaining GNN over Evolving Graphs using Information Flow

Graphs are ubiquitous in many applications, such as social networks, knowledge graphs, smart grids, etc.. Graph neural networks (GNN) are the current state-of-the-art for these applications, and yet remain obscure to humans. Explaining the GNN predictions can add transparency. However, as many graphs are not static but continuously evolving, explaining changes in predictions between two graph snapshots is different but equally important. Prior methods only explain static predictions or generate coarse or irrelevant explanations for dynamic predictions. We define the problem of explaining evolving GNN predictions and propose an axiomatic attribution method to uniquely decompose the change in a prediction to paths on computation graphs. The attribution to many paths involving high-degree nodes is still not interpretable, while simply selecting the top important paths can be suboptimal in approximating the change. We formulate a novel convex optimization problem to optimally select the paths that explain the prediction evolution. Theoretically, we prove that the existing method based on Layer-Relevance-Propagation (LRP) is a special case of the proposed algorithm when an empty graph is compared with. Empirically, on seven graph datasets, with a novel metric designed for evaluating explanations of prediction change, we demonstrate the superiority of the proposed approach over existing methods, including LRP, DeepLIFT, and other path selection methods.

cs.LG↗
arXiv2021

Layer-controlled Ferromagnetism in Atomically Thin CrSiTe$_3$ Flakes

The research on two-dimensional (2D) van der Waals (vdW) ferromagnets has promoted the development of ultrahigh-density and nanoscale data storage. However, intrinsic ferromagnetism in layered magnets is always subject to many factors, such as stacking orders, interlayer couplings, and the number of layers. Here, we report a magnetic transition from soft to hard ferromagnetic behaviors as the thickness of CrSiTe$_3$ flakes decreases down to several nanometers. Phenomenally, in contrast to the negligible hysteresis loop in the bulk counterparts, atomically thin CrSiTe$_3$ shows a rectangular loop with finite magnetization and coercivity as thickness decreases down to ~8 nm, indicative of a single-domain and out-of-plane ferromagnetic order. We find that the stray field is weakened with decreasing thickness, which suppresses the formation of the domain wall. In addition, thickness-dependent ferromagnetic properties also reveal a crossover from 3 dimensional to 2 dimensional Ising ferromagnets at a ~7 nm thickness of CrSiTe$_3$, accompanied by a drop of the Curie temperature from 33 K for bulk to ~17 K for 4 nm sample.

cond-mat.mtrl-sci↗
arXiv2021

Influence Dynamics Among Narratives: A Case Study of the Venezuelan Presidential Crisis

It is widely understood that diffusion of and simultaneous interactions between narratives -- defined here as persistent point-of-view messaging -- significantly contributes to the shaping of political discourse and public opinion. In this work, we propose a methodology based on Multi-Variate Hawkes Processes and our newly-introduced Process Influence Measures for quantifying and assessing how such narratives influence (Granger-cause) each other. Such an approach may aid social scientists enhance their understanding of socio-geopolitical phenomena as they manifest themselves and evolve in the realm of social media. In order to show its merits, we apply our methodology on Twitter narratives during the 2019 Venezuelan presidential crisis. Our analysis indicates a nuanced, evolving influence structure between 8 distinct narratives, part of which could be explained by landmark historical events.

cs.SI↗
arXiv2021

RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View

In this paper, we present a new approach to estimate the layout of a room from its single image. While recent approaches for this task use robust features learnt from data, they resort to optimization for detecting the final layout. In addition to using learnt robust features, our approach learns an additional ranking function to estimate the final layout instead of using optimization. To learn this ranking function, we propose a framework to train a CNN using max-margin structure cost. Also, while most approaches aim at detecting cuboidal layouts, our approach detects non-cuboidal layouts for which we explicitly estimates layout complexity parameters. We use these parameters to propose layout candidates in a novel way. Our approach shows state-of-the-art results on standard datasets with mostly cuboidal layouts and also performs well on a dataset containing rooms with non-cuboidal layouts.

cs.CV↗
arXiv2021

Self-learn to Explain Siamese Networks Robustly

Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and imbalanced. As these applications make high-stake decisions and involve societal values like fairness and transparency, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradient-based explanations due to the additional compared object in SN, in contrast to architectures with a single input instance. We propose an optimization framework that derives global invariance from unlabeled data using self-learning to promote the stability of local explanations tailored for specific query-reference pairs. The optimization problems can be solved using gradient descent-ascent (GDA) for constrained optimization, or SGD for KL-divergence regularized unconstrained optimization, with convergence proofs, especially when the objective functions are nonconvex due to the Siamese architecture. Quantitative results and case studies on tabular and graph data from neuroscience and chemical engineering show that the framework respects the self-learned invariance while robustly optimizing the faithfulness and simplicity of the explanation. We further demonstrate the convergence of GDA experimentally.

cs.LG↗
arXiv2021

Poisson metrics and Higgs bundles over noncompact Kähler manifolds

In this paper, we study the existence of Poisson metrics on flat vector bundles over noncompact Riemannian manifolds and discuss related consequence, specially on the applications in Higgs bundles, towards generalizing Corlette-Donaldson-Hitchin-Simpson's nonabelian Hodge correspondence to noncompact Kähler manifolds setting.

math.DG↗
arXiv2021

Haze Evolution in Temperate Exoplanet Atmospheres Through Surface Energy Measurements

Photochemical hazes are important opacity sources in temperate exoplanet atmospheres, hindering current observations from characterizing exoplanet atmospheric compositions. The haziness of an atmosphere is determined by the balance between haze production and removal. However, the material-dependent removal physics of the haze particles is currently unknown under exoplanetary conditions. Here we provide experimentally-measured surface energies for a grid of temperate exoplanet hazes to characterize haze removal in exoplanetary atmospheres. We found large variations of surface energies for hazes produced under different energy sources, atmospheric compositions, and temperatures. The surface energies of the hazes were found to be the lowest around 400 K for the cold plasma samples, leading to the lowest removal rates. We show a suggestive correlation between haze surface energy and atmospheric haziness with planetary equilibrium temperature. We hypothesize that habitable zone exoplanets could be less hazy, as they would possess high-surface-energy hazes which can be removed efficiently.

astro-ph.EP↗
arXiv2021

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps in understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

cs.SI↗
arXiv2021

An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks

Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge pruning. We propose a novel data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. In this framework, we design a hybrid causal graph learning method (HPCI), which combines Hawkes Process with Conditional Independence tests, as well as propose a novel Causal Propagation-Based Embedding algorithm (CPBE) to infer edge weights. We subsequently discover root cause alarms in a real-time data stream by applying an influence maximization algorithm on the weighted graph. We evaluate our method on artificial data and real-world telecom data, showing a significant improvement over the best baselines.

cs.LG↗
arXiv2021

How to identify exoplanet surfaces using atmospheric trace species in hydrogen-dominated atmospheres

Sub-Neptunes (Rp~1.25-4 REarth) remain the most commonly detected exoplanets to date. However, it remains difficult for observations to tell whether these intermediate-sized exoplanets have surfaces and where their surfaces are located. Here we propose that the abundances of trace species in the visible atmospheres of these sub-Neptunes can be used as proxies for determining the existence of surfaces and approximate surface conditions. As an example, we used a state-of-the-art photochemical model to simulate the atmospheric evolution of K2-18b and investigate its final steady-state composition with surfaces located at different pressures levels (Psurf). We find the surface location has a significant impact on the atmospheric abundances of trace species, making them deviate significantly from their thermochemical equilibrium and "no-surface" conditions. This result arises primarily because the pressure-temperature conditions at the surface determine whether photochemically-produced species can be recycled back to their favored thermochemical-equilibrium forms and transported back to the upper atmosphere. For an assumed H2-rich atmosphere for K2-18b, we identify seven chemical species that are most sensitive to the existence of surfaces: ammonia (NH3), methane (CH4), hydrogen cyanide (HCN), acetylene (C2H2), ethane (C2H6), carbon monoxide (CO), and carbon dioxide (CO2). The ratio between the observed and the no-surface abundances of these species, can help distinguish the existence of a shallow surface (Psurf < 10 bar), an intermediate surface (10 bar < Psurf < 100 bar), and a deep surface (Psurf > 100 bar). This framework can be applied together with future observations to other sub-Neptunes of interest.

astro-ph.EP↗
arXiv2021

Haze Formation on Triton

The largest moon of Neptune, Triton, possess a cold and hazy atmosphere. Since the discovery of near-surface haze layer during the Voyager fly in 1989, the haze formation mechanism has not been investigated in detail. Here, we provide the first haze microphysical model on Triton. Our model solves the evolution of both size and porosity distributions of haze particles in a self-consistent manner. We simulated the formation of sphere and aggregate hazes with and without condensation of the C$_2$H$_4$ ice. The haze particles can grow into fractal aggregates with mass-equivalent sphere sizes of $\sim0.1$--$1~{\rm μm}$ and fractal dimension of $D_{\rm f} = 1.8$--$2.2$. The ice-free hazes cannot simultaneously explain both UV and visible observations of Voyager 2, while including the condensation of C$_2$H$_4$ ices provides two better solutions. For ice aggregates, the required total haze mass flux is $\sim2\times{10}^{-15}~{\rm g~{cm}^{-2}~s^{-1}}$. For the icy sphere scenario, the column integrated C$_2$H$_4$ production rate is $\sim8\times{10}^{-15}~{\rm g~{cm}^{-2}~s^{-1}}$, and the ice-free mass flux of $\sim6\times{10}^{-17}~{\rm g~{cm}^{-2}~s^{-1}}$. The UV occultation observations at short wavelength $<0.15~{\rm μm}$ may slightly favor the icy aggregates. Observations of the haze optical depth and the degree of forward scattering in UV and visible should be able to distinguish whether Triton's hazes are icy spheres or ice aggregates in future Triton missions.

astro-ph.EP↗
arXiv2021

Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton

We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an attention-guided refinement layer. Unlike the existing ROI image compression methods that spend an extra bit budget equally on all pixels in ROI, AGDL employs a CNN module to predict those pixels on and near a saliency sketch within ROI that are critical to perceptual quality. Only the critical pixels are further sampled by compressive sensing (CS) to form a very compact refinement layer. Another novel CNN method is developed to jointly decode the two compression layers for a much refined reconstruction, while strictly satisfying the transmitted CS constraints on perceptually critical pixels. Extensive experiments demonstrate that the proposed AGDL system advances the state of the art in perception-aware image compression.

cs.CV↗
arXiv2021

Fluence Adaptation for Task-based Dose Optimization in X-ray Phase-Contrast Imaging

Purpose: Grating-based imaging (GBI) and edge-illumination (EI) are two promising types of XPCI as the conventional x-ray sources can be directly utilized. For GBI and EI systems, the phase-stepping acquisition with multiple exposures at a constant fluence is usually adopted in the literature. This work, however, attempts to challenge such a constant fluence concept during the phase-stepping process and proposes a fluence adaptation mechanism for dose reduction. Method: Recently, analytic multi-order moment analysis has been proposed to improve the computing efficiency. In these algorithms, multiple contrasts can be calculated by summing together the weighted phase-stepping curves (PSCs) with some kernel functions, which suggests us that the raw data at different steps have different contributions for the noise in retrieved contrasts. Based on analytic retrieval formulas and the Gaussian noise model for detected signals, we derived an optimal adaptive fluence distribution, which is proportional to the absolute weighting kernel functions and the root of original sample PSCs acquired under the constant fluence. Results: To validate our analyses, simulations and experiments are conducted for GBI and EI systems. Simulated results demonstrate that the dose reduction ratio between our proposed fluence distributions and the typical constant one can be about 20% for the phase contrast, which is consistent with our theoretical predictions. Although the experimental noise reduction ratios are a little smaller than the theoretical ones, synthetic and real experiments both observe better noise performance by our proposed method. Our simulated results also give out the effective ranges of the parameters of the PSCs, such as the visibility in GBI, the standard deviation and the mean value in EI, providing a guidance for the use of our proposed approach in practice.

physics.med-ph↗
arXiv2021

Probing quasi-long-range ordering by magnetostriction in monolayer CoPS3

Mermin-Wagner-Coleman theorem predicts no long-range magnetic order at finite temperature in the two-dimensional (2D) isotropic systems, but a quasi-long-range order with a divergent correlation length at the Kosterlitz-Thouless (KT) transition for planar magnets. As a representative of two-dimensional planar antiferromagnets, single-layer CoPS3 carries the promise of monolayer antiferromagnetic platforms for the ultimately thin spintronics. Here, with the aid of magnetostriction which is sensitive to the local magnetic order, we observe the signatured phonon mode splitting of below TKT in monolayer CoPS3, revealing the presence of quasi-long-range ordering in XY-type antiferromagnet. Moreover, the ratio (J'/J) between the interlayer and intralayer interactions, which characterizes the 2D behaviors, is evaluated to be around 0.03 for the first time. Our results provide an efficient method to detect the quasi-long-range antiferromagnetic ordering in the two-dimensional magnets down to monolayer limit.

cond-mat.mes-hall↗