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Xi Zhang

Publications and source records attributed to Xi Zhang.

At least 271 records · Page 15Linked to original sources
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↗
arXiv2020

Correlated Insulating States and Transport Signature of Superconductivity in Twisted Trilayer Graphene Moiré of Moiré Superlattices

Layers of two-dimensional materials stacked with a small twist-angle give rise to beating periodic patterns on a scale much larger than the original lattice, referred to as a moiré superlattice. When the stacking involves more than two layers with independent twist angles between adjacent layers, it generates moiré of moiré superlattices, with multiple length scales that control the system's behavior. Here we demonstrate these effects of a high-order moiré superlattice in twisted trilayer graphene with two consecutive small twist angles. We report correlated insulating states near the half filling of the moiré of moiré superlattice at an extremely low carrier density (~1010 cm-2), near which we also report a zero-resistance transport behavior typically expected in a 2D superconductor. Moreover, the temperature dependence of the measured resistances at full-occupancy (v = -4 and v = 4) states are semi-metallic, distinct from the insulating behavior of twisted bilayer systems, providing the first demonstration of emergent correlated transport behaviors from continuous, non-isolated higher-order moiré flat bands. Our findings shed new insights into the microscopic mechanisms of moiré correlated states and provide the impetus for future studies on this material platform, such as the demonstration of phase coherence and Meissner-like effect.

cond-mat.mes-hall↗
arXiv2020

The deformed Hermitian-Yang-Mills equation on almost Hermitian manifolds

In this paper, we consider the deformed Hermitian-Yang-Mills equation on closed almost Hermitian manifolds. In the case of hypercritical phase, we derive a priori estimates under the existence of an admissible $\mathcal{C}$-subsolution. As an application, we prove the existence of solutions for the deformed Hermitian-Yang-Mills equation under the condition of existence of a supersolution.

math.DG↗
arXiv2020

On Numerosity of Deep Neural Networks

Recently, a provocative claim was published that number sense spontaneously emerges in a deep neural network trained merely for visual object recognition. This has, if true, far reaching significance to the fields of machine learning and cognitive science alike. In this paper, we prove the above claim to be unfortunately incorrect. The statistical analysis to support the claim is flawed in that the sample set used to identify number-aware neurons is too small, compared to the huge number of neurons in the object recognition network. By this flawed analysis one could mistakenly identify number-sensing neurons in any randomly initialized deep neural networks that are not trained at all. With the above critique we ask the question what if a deep convolutional neural network is carefully trained for numerosity? Our findings are mixed. Even after being trained with number-depicting images, the deep learning approach still has difficulties to acquire the abstract concept of numbers, a cognitive task that preschoolers perform with ease. But on the other hand, we do find some encouraging evidences suggesting that deep neural networks are more robust to distribution shift for small numbers than for large numbers.

cs.LG↗
arXiv2020

CONQ: CONtinuous Quantile Treatment Effects for Large-Scale Online Controlled Experiments

In many industry settings, online controlled experimentation (A/B test) has been broadly adopted as the gold standard to measure product or feature impacts. Most research has primarily focused on user engagement type metrics, specifically measuring treatment effects at mean (average treatment effects, ATE), and only a few have been focusing on performance metrics (e.g. latency), where treatment effects are measured at quantiles. Measuring quantile treatment effects (QTE) is challenging due to the myriad difficulties such as dependency introduced by clustered samples, scalability issues, density bandwidth choices, etc. In addition, previous literature has mainly focused on QTE at some pre-defined locations, such as P50 or P90, which doesn't always convey the full picture. In this paper, we propose a novel scalable non-parametric solution, which can provide a continuous range of QTE with point-wise confidence intervals while circumventing the density estimation altogether. Numerical results show high consistency with traditional methods utilizing asymptotic normality. An end-to-end pipeline has been implemented at Snap Inc., providing daily insights on key performance metrics at a distributional level.

stat.ME↗
arXiv2020

Surface Energy of the Titan Aerosol Analog "Tholin"

The photochemical haze produced in the upper atmosphere of Titan plays a key role in various atmospheric and surface processes on Titan. The surface energy, one important physical properties of the haze, is crucial for understanding the growth of the haze particles and can be used to predict their wetting behavior with solid and liquid species on Titan. We produced Titan analog haze materials, so-called "tholin", with different energy sources and measured their surface energies through contact angle and direct force measurements. From the contact angle measurement, we found that the tholins produced by cold plasma and UV irradiation have total surface energy around 60-70 mJ/m2. The direct force measurement yields a total surface energy of ~66 mJ/m2 for plasma tholin. The surface energy of tholin is relatively high compared to common polymers, indicating its high cohesiveness. Therefore, the Titan haze particles would likely coagulate easily to form bigger particles, while the haze-derived surface sand particles would need higher wind speed to be mobilized because of the high interparticle cohesion. The high surface energy of tholins also makes them easily wettable by Titan's atmospheric hydrocarbon condensates and surface liquids. Thus, the hazes particles are likely good cloud condensation nuclei (CCN) for hydrocarbon clouds (methane and ethane) to nucleate and grow. And if the hazes particles are denser compared to the lake liquids, they would likely sink into the lakes instead of forming a floating film to dampen the lake surface waves.

astro-ph.EP↗