arXiv subjects

Xi Zhang

Publications and source records attributed to Xi Zhang.

At least 253 records · Page 14Linked to original sources

arXiv2022

LAMOST meets Gaia: The Galactic Open Clusters

Open Clusters are born and evolve along the Milky Way plane, on them is imprinted the history of the Galactic disc, including the chemical and dynamical evolution. Chemical and dynamical properties of open clusters can be derived from photometric, spectroscopic, and astrometric data of their member stars. Based on the photometric and astrometric data from the Gaia mission, the membership of stars in more than 2000 Galactic clusters has been identified in the literature. The chemical and kinematical properties, however, are still poorly known for many of these clusters. In synergy with the large spectroscopic survey LAMOST (data release 8) and Gaia (data release 2), we report a new comprehensive catalogue of 386 open clusters. This catalogue has homogeneous parameter determinations of radial velocity, metallicity, and dynamical properties, such as orbit, eccentricity, angular momenta, total energy, and 3D Galactic velocity. These parameters allow the first radial velocity determination and the first spectroscopic [Fe/H] determination for 44 and 137 clusters, respectively. The metallicity distribution of majority clusters shows falling trends in the parameter space of the Galactocentric radius, the total energy, and the Z component of angular momentum -- except for two old groups that show flat tails in their own parameter planes. Cluster populations of ages younger and older than 500 Myrs distribute diversely on the disc. The latter has a spatial consistency with the Galactic disc flare. The 3-D spatial comparison between very young clusters (< 100 Myr) and nearby molecular clouds revealed a wide range of metallicity distribution along the Radcliffe gas cloud wave, indicating a possible inhomogeneous mixing or fast star formation along the wave. This catalogue would serve the community as a useful tool to trace the chemical and dynamical evolution of the Milky Way.

astro-ph.GA↗

arXiv2022

Cleaning our Hazy Lens: Exploring Trends in Transmission Spectra of Warm Exoplanets

Relatively little is understood about the atmospheric composition of temperate to warm exoplanets (equilibrium temperature $T_{\rm eq}<$ 1000 K), as many of them are found to have uncharacteristically flat transmission spectra. Their flattened spectra are likely due to atmospheric opacity sources such as planet-wide photochemical hazes and condensation clouds. We compile the transmission spectra of 25 warm exoplanets previously observed by the Hubble Space Telescope and quantify the haziness of each exoplanet using a normalized amplitude of the water absorption feature ($A_{\rm H}$). By examining the relationships between $A_{\rm H}$ and various planetary and stellar forcing parameters, we endeavor to find correlations of haziness associated with planetary properties. We adopt new statistical correlation tests that are more suitable for the small, non-normally distributed warm exoplanet sample. Our analysis shows that none of the parameters hold statistically significant correlation with $A_{\rm H}$ ($p \le 0.01$) with the addition of new exoplanet data, including the previously identified linear trends between $A_{\rm H}$ and $T_{\rm{eq}}$ or hydrogen-helium envelope mass fraction (f$_{\rm{HHe}}$). This suggests that haziness in warm exoplanets is not simply controlled by any single planetary/stellar parameter. Among all the parameters we investigated, planet gravity ($g_{\rm p}$), atmospheric scale height ($H$), planet density ($ρ_{\rm p}$), orbital eccentricity ($e$), and age of the star ($t_{\rm age}$) hold tentative correlations with $A_{\rm H}$. Specifically, lower $H$, higher $g_{\rm p}$, $ρ_{\rm p}$, $e$, or $t_{\rm age}$ may lead to clearer atmospheres. We still need more observations and laboratory experiments to fully understand the complex physics and chemistry involved in creating hazy warm exoplanets.

astro-ph.EP↗

arXiv2022

ABO: Dataset and Benchmarks for Real-World 3D Object Understanding

We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects. We derive challenging benchmarks that exploit the unique properties of ABO and measure the current limits of the state-of-the-art on three open problems for real-world 3D object understanding: single-view 3D reconstruction, material estimation, and cross-domain multi-view object retrieval.

cs.CV↗

arXiv2022

Tunable linearity of high-performance vertical dual-gate vdW phototransistor

Layered two-dimensional (2D) semiconductors have been widely exploited in photodetectors due to their excellent electronic and optoelectronic properties. To improve their performance, photogating, photoconductive, photovoltaic, photothermoelectric, and other effects have been used in phototransistors and photodiodes made with 2D semiconductors or hybrid structures. However, it is difficult to achieve the desired high responsivity and linear photoresponse simultaneously in a monopolar conduction channel or a p-n junction. Here we present dual-channel conduction with ambipolar multilayer WSe2 by employing the device concept of dual-gate phototransistor, where p-type and n-type channels are produced in the same semiconductor using opposite dual-gating. It is possible to tune the photoconductive gain using a vertical electric field, so that the gain is constant with respect to the light intensity-a linear photoresponse, with a high responsivity of ~2.5*10^4 A/W. Additionally, the 1/f noise of the device is kept at a low level under the opposite dual-gating due to the reduction of current and carrier fluctuation, resulting in a high detectivity of ~2*10^13 Jones in the linear photoresponse regime. The linear photoresponse and high performance of our dual-gate WSe2 phototransistor offer the possibility of achieving high-resolution and quantitative light detection with layered 2D semiconductors.

cond-mat.mtrl-sci↗

arXiv2022

THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequences

Learning causal structure among event types on multi-type event sequences is an important but challenging task. Existing methods, such as the Multivariate Hawkes processes, mostly assumed that each sequence is independent and identically distributed. However, in many real-world applications, it is commonplace to encounter a topological network behind the event sequences such that an event is excited or inhibited not only by its history but also by its topological neighbors. Consequently, the failure in describing the topological dependency among the event sequences leads to the error detection of the causal structure. By considering the Hawkes processes from the view of temporal convolution, we propose a Topological Hawkes process (THP) to draw a connection between the graph convolution in the topology domain and the temporal convolution in time domains. We further propose a causal structure learning method on THP in a likelihood framework. The proposed method is featured with the graph convolution-based likelihood function of THP and a sparse optimization scheme with an Expectation-Maximization of the likelihood function. Theoretical analysis and experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed method

cs.LG↗

arXiv2022

Mechanism-based Tuning of Room-temperature Ferromagnetism in Mn-doped \b{eta}-Ga2O3 by Annealing Atmospher

Mn-doped \b{eta}-Ga2O3 (GMO) films with room-temperature ferromagnetism (RTFM) are synthesized by polymer-assisted deposition and the effects of annealing atmosphere (air or pure O2 gas) on their structures and physical properties are investigated. The characterizations show that the concentrations of vacancy defects and Mn dopants in various valence states and lattice constants of the samples are all modulated by the annealing atmosphere. Notably, the samples annealed in air (GMO-air) exhibit a saturation magnetization as strong as 170% times that of the samples annealed in pure O2 gas (GMO-O2), which can be quantitatively explained by oxygen vacancy (VO) controlled ferromagnetism due to bound magnetic polarons established between delocalized hydrogenic electrons of VOs and local magnetic moments of Mn2+, Mn3+, and Mn4+ ions in the samples. Our results provide insights into mechanism-based tuning of RTFM in Ga2O3 and may be useful for design, fabrication, and application of related spintronic materials.

cond-mat.mtrl-sci↗

arXiv2022

Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users

Risk assessment is a substantial problem for financial institutions that has been extensively studied both for its methodological richness and its various practical applications. With the expansion of inclusive finance, recent attentions are paid to micro and small-sized enterprises (MSEs). Compared with large companies, MSEs present a higher exposure rate to default owing to their insecure financial stability. Conventional efforts learn classifiers from historical data with elaborate feature engineering. However, the main obstacle for MSEs involves severe deficiency in credit-related information, which may degrade the performance of prediction. Besides, financial activities have diverse explicit and implicit relations, which have not been fully exploited for risk judgement in commercial banks. In particular, the observations on real data show that various relationships between company users have additional power in financial risk analysis. In this paper, we consider a graph of banking data, and propose a novel HIDAM model for the purpose. Specifically, we attempt to incorporate heterogeneous information network with rich attributes on multi-typed nodes and links for modeling the scenario of business banking service. To enhance feature representation of MSEs, we extract interactive information through meta-paths and fully exploit path information. Furthermore, we devise a hierarchical attention mechanism respectively to learn the importance of contents inside each meta-path and the importance of different metapahs. Experimental results verify that HIDAM outperforms state-of-the-art competitors on real-world banking data.

q-fin.RM↗

arXiv2022

Values of Coordinated Residential Space Heating in Demand Response Provision

Demand-side response from space heating in residential buildings can potentially provide a huge amount of flexibility for the power system, particularly with deep electrification of the heat sector. In this context, this paper presents a novel distributed control strategy to coordinate space heating across numerous residential households with diversified thermal parameters. By employing an iterative algorithm under the game-theoretical framework, each household adjusts its own heating schedule through demand shift and thermal comfort compensation with the purpose of achieving individual cost savings, whereas the aggregate peak demand is effectively shaved on the system level. Additionally, an innovative thermal comfort model which considers both the temporal and spatial differences in customised thermal comfort requirements is proposed. Through a series of case studies, it is demonstrated that the proposed space heating coordination strategy can facilitate effective energy arbitrage for individual buildings, driving a 13.96% reduction in system operational cost and 28.22% peak shaving. Moreover, the superiority of the proposed approach in thermal comfort maintenance is numerically analysed based on the proposed thermal comfort quantification model.

eess.SY↗

arXiv2022

New Open Cluster candidates Found in Galactic Disk Using Gaia DR2/EDR3 Data

We report 541 new open cluster candidates in Gaia EDR3 through revisiting the cluster results from an earlier analysis of the Gaia DR2, which revealed nearly a thousand open cluster candidates in the solar neighborhood (mostly d < 3 kpc) resideing at Galactic latitudes |b| < 20 degrees. A subsequent comparison with lists of known clusters shows a large increases of the cluster samples within 2 kpc from the Sun. We assign membership probabilities to the stars through the open source pyUPMASK algorithm, and also estimate the physical parameters through isochrone fitting for each candidate. Most of the new candidates show small total proper motion dispersions and clear features in the color-magnitude diagrams. Besides, the metallicity gradient of the new candidates is consistent with those found in the literature. The cluster parameters and member stars are available at CDS via anonymous ftp to cdsarc.u-strasbg.fr(130.79.128.5) or via https://cdsarc.unistra.fr/viz-bin/cat/J/ApJS. The discovery of these new objects shows that the open cluster samples in Gaia data is still not complete, and more discoveries are expected in the future researches.

astro-ph.GA↗

arXiv2022

Multi-modality Deep Restoration of Extremely Compressed Face Videos

Arguably the most common and salient object in daily video communications is the talking head, as encountered in social media, virtual classrooms, teleconferences, news broadcasting, talk shows, etc. When communication bandwidth is limited by network congestions or cost effectiveness, compression artifacts in talking head videos are inevitable. The resulting video quality degradation is highly visible and objectionable due to high acuity of human visual system to faces. To solve this problem, we develop a multi-modality deep convolutional neural network method for restoring face videos that are aggressively compressed. The main innovation is a new DCNN architecture that incorporates known priors of multiple modalities: the video-synchronized speech signal and semantic elements of the compression code stream, including motion vectors, code partition map and quantization parameters. These priors strongly correlate with the latent video and hence they are able to enhance the capability of deep learning to remove compression artifacts. Ample empirical evidences are presented to validate the superior performance of the proposed DCNN method on face videos over the existing state-of-the-art methods.

cs.CV↗

arXiv2022

Deep Decoding of $\ell_\infty$-coded Light Field Images

To enrich the functionalities of traditional cameras, light field cameras record both the intensity and direction of light rays, so that images can be rendered with user-defined camera parameters via computations. The added capability and flexibility are gained at the cost of gathering typically more than $100\times$ greater amount of information than conventional images. To cope with this issue, several light field compression schemes have been introduced. However, their ways of exploiting correlations of multidimensional light field data are complex and are hence not suited for inexpensive light field cameras. In this work, we propose a novel $\ell_\infty$-constrained light-field image compression system that has a very low-complexity DPCM encoder and a CNN-based deep decoder. Targeting high-fidelity reconstruction, the CNN decoder capitalizes on the $\ell_\infty$-constraint and light field properties to remove the compression artifacts and achieves significantly better performance than existing state-of-the-art $\ell_2$-based light field compression methods.

eess.IV↗

arXiv2022

A bimodal distribution of haze in Pluto's atmosphere

Pluto, Titan, and Triton make up a unique class of solar system bodies, with icy surfaces and chemically reducing atmospheres rich in organic photochemistry and haze formation. Hazes play important roles in these atmospheres, with physical and chemical processes highly dependent on particle sizes, but the haze size distribution in reducing atmospheres is currently poorly understood. Here we report observational evidence that Pluto's haze particles are bimodally distributed, which successfully reproduces the full phase scattering observations from New Horizons. Combined with previous simulations of Titan's haze, this result suggests that haze particles in reducing atmospheres undergo rapid shape change near pressure levels ~0.5Pa and favors a photochemical rather than a dynamical origin for the formation of Titan's detached haze. It also demonstrates that both oxidizing and reducing atmospheres can produce multi-modal hazes, and encourages reanalysis of observations of hazes on Titan and Triton.

astro-ph.EP↗

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↗