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Xi Zhang

Publications and source records attributed to Xi Zhang.

At least 307 records · Page 17Linked to original sources
arXiv2020

Aerosol Composition of Hot Giant Exoplanets Dominated by Silicates and Hydrocarbon Hazes

Aerosols are common in the atmospheres of exoplanets across a wide swath of temperatures, masses, and ages. These aerosols strongly impact observations of transmitted, reflected, and emitted light from exoplanets, obfuscating our understanding of exoplanet thermal structure and composition. Knowing the dominant aerosol composition would facilitate interpretations of exoplanet observations and theoretical understanding of their atmospheres. A variety of compositions have been proposed, including metal oxides and sulphides, iron, chromium, sulphur, and hydrocarbons. However, the relative contributions of these species to exoplanet aerosol opacity is unknown. Here we show that the aerosol composition of giant exoplanets observed in transmission is dominated by silicates and hydrocarbons. By constraining an aerosol microphysics model with trends in giant exoplanet transmission spectra, we find that silicates dominate aerosol opacity above planetary equilibrium temperatures of 950 K due to low nucleation energy barriers and high elemental abundances, while hydrocarbon aerosols dominate below 950 K due to an increase in methane abundance. Our results are robust to variations in planet gravity and atmospheric metallicity within the range of most giant transiting exoplanets. We predict that spectral signatures of condensed silicates in the mid-infrared are most prominent for hot (>1600 K), low-gravity (<10 m s$^{-2}$) objects.

astro-ph.EP↗
arXiv2020

Challenge of Spatial Cognition for Deep Learning

Given the success of the deep convolutional neural networks (DCNNs) in applications of visual recognition and classification, it would be tantalizing to test if DCNNs can also learn spatial concepts, such as straightness, convexity, left/right, front/back, relative size, aspect ratio, polygons, etc., from varied visual examples of these concepts that are simple and yet vital for spatial reasoning. Much to our dismay, extensive experiments of the type of cognitive psychology demonstrate that the data-driven deep learning (DL) cannot see through superficial variations in visual representations and grasp the spatial concept in abstraction. The root cause of failure turns out to be the learning methodology, not the computational model of the neural network itself. By incorporating task-specific convolutional kernels, we are able to construct DCNNs for spatial cognition tasks that can generalize to input images not drawn from the same distribution of the training set. This work raises a precaution that without manually-incorporated priors or features DCCNs may fail spatial cognitive tasks at rudimentary level.

cs.CV↗
arXiv2020

Rigorous Explanation of Inference on Probabilistic Graphical Models

Probabilistic graphical models, such as Markov random fields (MRF), exploit dependencies among random variables to model a rich family of joint probability distributions. Sophisticated inference algorithms, such as belief propagation (BP), can effectively compute the marginal posteriors. Nonetheless, it is still difficult to interpret the inference outcomes for important human decision making. There is no existing method to rigorously attribute the inference outcomes to the contributing factors of the graphical models. Shapley values provide an axiomatic framework, but naively computing or even approximating the values on general graphical models is challenging and less studied. We propose GraphShapley to integrate the decomposability of Shapley values, the structure of MRFs, and the iterative nature of BP inference in a principled way for fast Shapley value computation, that 1) systematically enumerates the important contributions to the Shapley values of the explaining variables without duplicate; 2) incrementally compute the contributions without starting from scratches. We theoretically characterize GraphShapley regarding independence, equal contribution, and additivity. On nine graphs, we demonstrate that GraphShapley provides sensible and practical explanations.

cs.SI↗
arXiv2020

High Curie Temperature Ferromagnetism and High Hole Mobility in Tensile Strained Mn-doped SiGe Thin Films

Diluted magnetic semiconductors (DMSs) based on group-IV materials are desirable for spintronic devices compatible with current silicon technology. In this work, amorphous Mn-doped SiGe thin films were first fabricated on Ge substrates by radio frequency magnetron sputtering and then crystallized by rapid thermal annealing (RTA). After the RTA, the samples became ferromagnetic (FM) semiconductors, in which the Curie temperature increased with increasing Mn doping concentration and reached 280 K with 5% Mn concentration. The data suggest that the ferromagnetism came from the hole-mediated process and was enhanced by the tensile strain in the SiGe crystals. Meanwhile, the Hall effect measurement up to 33 T to eliminate the influence of anomalous Hall effect (AHE) reveals that the hole mobility of the annealed samples was greatly enhanced and the maximal value was ~1000 cm2/Vs, owing to the tensile strain-induced band structure modulation. The Mn-doped SiGe thin films with high Curie temperature ferromagnetism and high hole mobility may provide a promising platform for semiconductor spintronics.

cond-mat.mtrl-sci↗
arXiv2020

Unexpected two-fold symmetric superconductivity in few-layer NbSe$_2$

Two-dimensional transition metal dichalcogenides (TMDs) have been attracting significant interest due to a range of properties, such as layer-dependent inversion symmetry, valley-contrasted Berry curvatures, and strong spin-orbit coupling (SOC). Of particular interest is niobium diselenide (NbSe2), whose superconducting state in few-layer samples is profoundly affected by an unusual type of SOC called Ising SOC. Combined with the reduced dimensionality, the latter stabilizes the superconducting state against magnetic fields up to ~35 T and could lead to other exotic properties such as nodal and crystalline topological superconductivity. Here, we report transport measurements of few-layer NbSe$_2$ under in-plane external magnetic fields, revealing an unexpected two-fold rotational symmetry of the superconducting state. In contrast to the three-fold symmetry of the lattice, we observe that the magnetoresistance and critical field exhibit a two-fold oscillation with respect to an applied in-plane magnetic field. We find similar two-fold oscillations deep inside the superconducting state in differential conductance measurements on NbSe$_2$/CrBr$_3$ superconductor-magnet junctions. In both cases, the anisotropy vanishes in the normal state, demonstrating that it is an intrinsic property of the superconducting phase. We attribute the behavior to the mixing between two closely competing pairing instabilities, namely, the conventional s-wave instability typical of bulk NbSe$_2$ and an unconventional d- or p-wave channel that emerges in few-layer NbSe2. Our results thus demonstrate the unconventional character of the pairing interaction in a few-layer TMD, opening a new avenue to search for exotic superconductivity in this family of 2D materials.

cond-mat.supr-con↗
arXiv2020

Two-Dimensional Si-Ge Monolayers: Stabilities, Structures and Electronic Properties

Si-Ge monolayers (SiGeM) with different elementary proportion x (0<x<1) were systematically studied for the first-time using ab initio calculations in this work. The structural stabilities of the Si1-xGexM with different symmetries were investigated using phonon spectra, and an infinite miscibility between Si and Ge elements were revealed in the 2D honeycomb structures. The simulated scanning tunneling microscope images and Raman and infrared active modes of the Si1-xGexM were then obtained for structural characterizations. Interestingly, the study of electronic properties revealed not previously reported oscillatory nonlinear dependence of band gap values on the elementary proportion x in the Si1-xGexM, which suggests an alternative way for tuning the band gaps of 2D materials. Additionally, low effective masses (0.008m0 ~ 0.021m0) of the carriers in the semiconducting Si1-xGexM were found, which has potentials for high-speed applications. Considering the advantage of their compatibility with current Si-based technology and the trend of miniature of electronic devices, the Si1-xGexM with stable structures and excellent properties would be important for 2D applications based on group IV materials.

cond-mat.mtrl-sci↗
arXiv2020

MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing

Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both model inference and update. Since data collection is costly in reality, Google's Federated Learning offers not only complete data privacy but also better model robustness based on multiple user data. However, personal mobile sensing applications are mostly user-specific and highly affected by environment. As a result, continuous local changes may seriously affect the performance of a global model generated by Federated Learning. In addition, deploying Federated Learning on a local server, e.g., edge server, may quickly reach the bottleneck due to resource constraint and serious failure by attacks. Towards pushing deep learning on devices, we present MDLdroid, a novel decentralized mobile deep learning framework to enable resource-aware on-device collaborative learning for personal mobile sensing applications. To address resource limitation, we propose a ChainSGD-reduce approach which includes a novel chain-directed Synchronous Stochastic Gradient Descent algorithm to effectively reduce overhead among multiple devices. We also design an agent-based multi-goal reinforcement learning mechanism to balance resources in a fair and efficient manner. Our evaluations show that our model training on off-the-shelf mobile devices achieves 2x to 3.5x faster than single-device training, and 1.5x faster than the master-slave approach.

cs.LG↗
arXiv2020

Ultra High Fidelity Image Compression with $\ell_\infty$-constrained Encoding and Deep Decoding

In many professional fields, such as medicine, remote sensing and sciences, users often demand image compression methods to be mathematically lossless. But lossless image coding has a rather low compression ratio (around 2:1 for natural images). The only known technique to achieve significant compression while meeting the stringent fidelity requirements is the methodology of $\ell_\infty$-constrained coding that was developed and standardized in nineties. We make a major progress in $\ell_\infty$-constrained image coding after two decades, by developing a novel CNN-based soft $\ell_\infty$-constrained decoding method. The new method repairs compression defects by using a restoration CNN called the $\ell_\infty\mbox{-SDNet}$ to map a conventionally decoded image to the latent image. A unique strength of the $\ell_\infty\mbox{-SDNet}$ is its ability to enforce a tight error bound on a per pixel basis. As such, no small distinctive structures of the original image can be dropped or distorted, even if they are statistical outliers that are otherwise sacrificed by mainstream CNN restoration methods. More importantly, this research ushers in a new image compression system of $\ell_\infty$-constrained encoding and deep soft decoding ($\ell_\infty\mbox{-ED}^2$). The $\ell_\infty \mbox{-ED}^2$ approach beats the best of existing lossy image compression methods (e.g., BPG, WebP, etc.) not only in $\ell_\infty$ but also in $\ell_2$ error metric and perceptual quality, for bit rates near the threshold of perceptually transparent reconstruction. Operationally, the new compression system is practical, with a low-complexity real-time encoder and a cascade decoder consisting of a fast initial decoder and an optional CNN soft decoder.

eess.IV↗
arXiv2020

Deflating Super-Puffs: Impact of Photochemical Hazes on the Observed Mass-Radius Relationship of Low Mass Planets

The observed mass-radius relationship of low-mass planets informs our understanding of their composition and evolution. Recent discoveries of low mass, large radii objects ("super-puffs") have challenged theories of planet formation and atmospheric loss, as their high inferred gas masses make them vulnerable to runaway accretion and hydrodynamic escape. Here we propose that high altitude photochemical hazes could enhance the observed radii of low-mass planets and explain the nature of super-puffs. We construct model atmospheres in radiative-convective equilibrium and compute rates of atmospheric escape and haze distributions, taking into account haze coagulation, sedimentation, diffusion, and advection by an outflow wind. We develop mass-radius diagrams that include atmospheric lifetimes and haze opacity, which is enhanced by the outflow, such that young (~0.1-1 Gyr), warm (T$_{eq}$ $\geq$ 500 K), low mass objects ($M_c$ < 4M$_{\rm Earth}$) should experience the most apparent radius enhancement due to hazes, reaching factors of three. This reconciles the densities and ages of the most extreme super-puffs. For Kepler-51b, the inclusion of hazes reduces its inferred gas mass fraction to <10%, similar to that of planets on the large radius side of the sub-Neptune radius gap. This suggests that Kepler-51b may be evolving towards that population, and that some warm sub-Neptunes may have evolved from super-puffs. Hazes also render transmission spectra of super-puffs and sub-Neptunes featureless, consistent with recent measurements. Our hypothesis can be tested by future observations of super-puffs' transmission spectra at mid-infrared wavelengths, where we predict that the planet radius will be half of that observed in the near-infrared.

astro-ph.EP↗
arXiv2020

Near-lossless $\ell_\infty$-constrained Image Decompression via Deep Neural Network

Recently a number of CNN-based techniques were proposed to remove image compression artifacts. As in other restoration applications, these techniques all learn a mapping from decompressed patches to the original counterparts under the ubiquitous $\ell_\infty$ metric. However, this approach is incapable of restoring distinctive image details which may be statistical outliers but have high semantic importance (e.g., tiny lesions in medical images). To overcome this weakness, we propose to incorporate an $\ell_\infty$ fidelity criterion in the design of neural network so that no small, distinctive structures of the original image can be dropped or distorted. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in $\ell_\infty$ error metric and perceptual quality, while being competitive in $\ell_2$ error metric as well. It can restore subtle image details that are otherwise destroyed or missed by other algorithms. Our research suggests a new machine learning paradigm of ultra high fidelity image compression that is ideally suited for applications in medicine, space, and sciences.

cs.CV↗
arXiv2020

Synthesis and temperature-dependent photoluminescence of high density GeSe triangular nanoplate arrays on Si substrates

We have grown germanium selenide (GeSe) triangular nanoplate arrays (TNAs) with a high density (3.82E+6 / mm2) on the Si (111) substrate using a simple thermal evaporation method. The thickness and trilateral lengths of a single triangular nanoplate were statistically estimated by atomic force microscopy (AFM) as 44 nm, 365 nm, 458 nm and 605 nm, respectively. Transmission electron microscopy (TEM) images and X-ray diffraction (XRD) patterns show that the TNAs were composed of single crystalline GeSe phase. The Se-related defects in the lattice were also revealed by TEM images and Raman vibration modes. Unlike previously reported GeSe compounds, the GeSe TNAs exhibited temperature-dependent photoluminescence (PL). In addition, not previously reported PL peak (1.25 eV) of the 44 nm thick TNAs at 5 K was in the gaps between those of GeSe monolayers (1.5 nm) and thin films (400 nm), revealing a close relationship between the PL peak and the thickness of GeSe. The high-density structure and temperature-dependent PL of the TNAs on the Si substrate may be useful for temperature controllable semiconductor nanodevices.

cond-mat.mes-hall↗
arXiv2020

Rapid Synthesis of Thermoelectric SnSe Thin Films by MPCVD

Microwave plasma chemical vapor deposition (MPCVD) has been traditionally used to synthesize carbon-based materials such as diamonds, carbon nanotubes and graphene. Here we report that a rapid and catalyst-free growth of SnSe thin films can be achieved by using single-mode MPCVD with appropriate source materials. The analysis combing microscope images, X-ray diffraction patterns and lattice vibration modes shows that the grown thin films were composed of orthorhombic structured SnSe polycrystals mainly along the (111) direction. Further thermoelectric (TE) characterizations reveal that the power factor of the SnSe films reached 3.98 μW cm-1K-2 at 600 K, comparable to the highest reported values of SnSe thin films. Our results may open an avenue for rapid synthesis of new types of materials such as IV-VI compounds and be useful for TE application of these materials.

cond-mat.mtrl-sci↗
arXiv2019

Finding Route Hotspots in Large Labeled Networks

In many advanced network analysis applications, like social networks, e-commerce, and network security, hotspots are generally considered as a group of vertices that are tightly connected owing to the similar characteristics, such as common habits and location proximity. In this paper, we investigate the formation of hotspots from an alternative perspective that considers the routes along the network paths as the auxiliary information, and attempt to find the route hotspots in large labeled networks. A route hotspot is a cohesive subgraph that is covered by a set of routes, and these routes correspond to the same sequential pattern consisting of vertices' labels. To the best of our knowledge, the problem of Finding Route Hotspots in Large Labeled Networks has not been tackled in the literature. However, it is challenging as counting the number of hotspots in a network is #P-hard. Inspired by the observation that the sizes of hotspots decrease with the increasing lengths of patterns, we prove several anti-monotonicity properties of hotspots, and then develop a scalable algorithm called FastRH that can use these properties to effectively prune the patterns that cannot form any hotspots. In addition, to avoid the duplicate computation overhead, we judiciously design an effective index structure called RH-Index for storing the hotspot and pattern information collectively, which also enables incremental updating and efficient query processing. Our experimental results on real-world datasets clearly demonstrate the effectiveness and scalability of our proposed methods.

cs.DS↗
arXiv2019

Transit Signatures of Inhomogeneous Clouds on Hot Jupiters: Insights From Microphysical Cloud Modeling

We determine the observability in transmission of inhomogeneous cloud cover on the limbs of hot Jupiters through post processing a general circulation model to include cloud distributions computed using a cloud microphysics model. We find that both the east and west limb often form clouds, but that the different properties of these clouds enhances the limb to limb differences compared to the clear case. Using JWST it should be possible to detect the presence of cloud inhomogeneities by comparing the shape of the transit lightcurve at multiple wavelengths because inhomogeneous clouds impart a characteristic, wavelength dependent signature. This method is statistically robust even with limited wavelength coverage, uncertainty on limb darkening coefficients, and imprecise transit times. We predict that the short wavelength slope varies strongly with temperature. The hot limb of the hottest planets form higher altitude clouds composed of smaller particles leading to a strong rayleigh slope. The near infrared spectral features of clouds are almost always detectable, even when no spectral slope is visible in the optical. In some of our models a spectral window between 5 and 9 microns can be used to probe through the clouds and detect chemical spectral features. Our cloud particle size distributions are not log-normal and differ from species to species. Using the area or mass weighted particle size significantly alters the relative strength of the cloud spectral features compared to using the predicted size distribution. Finally, the cloud content of a given planet is sensitive to a species' desorption energy and contact angle, two parameters that could be constrained experimentally in the future.

astro-ph.EP↗
arXiv2019

Scalable Explanation of Inferences on Large Graphs

Probabilistic inferences distill knowledge from graphs to aid human make important decisions. Due to the inherent uncertainty in the model and the complexity of the knowledge, it is desirable to help the end-users understand the inference outcomes. Different from deep or high-dimensional parametric models, the lack of interpretability in graphical models is due to the cyclic and long-range dependencies and the byzantine inference procedures. Prior works did not tackle cycles and make \textit{the} inferences interpretable. To close the gap, we formulate the problem of explaining probabilistic inferences as a constrained cross-entropy minimization problem to find simple subgraphs that faithfully approximate the inferences to be explained. We prove that the optimization is NP-hard, while the objective is not monotonic and submodular to guarantee efficient greedy approximation. We propose a general beam search algorithm to find simple trees to enhance the interpretability and diversity in the explanations, with parallelization and a pruning strategy to allow efficient search on large and dense graphs without hurting faithfulness. We demonstrate superior performance on 10 networks from 4 distinct applications, comparing favorably to other explanation methods. Regarding the usability of the explanation, we visualize the explanation in an interface that allows the end-users to explore the diverse search results and find more personalized and sensible explanations.

cs.SI↗
arXiv2019

Atmospheric Circulation of Brown Dwarfs and Jupiter and Saturn-like Planets: Zonal Jets, Long-term Variability, and QBO-type Oscillations

Brown dwarfs and directly imaged giant planets exhibit significant evidence for active atmospheric circulation, which induces a large-scale patchiness in the cloud structure that evolves significantly over time, as evidenced by infrared light curves and Doppler maps. These observations raise critical questions about the fundamental nature of the circulation, its time variability, and the overall relationship to the circulation on Jupiter and Saturn. Jupiter and Saturn themselves exhibit numerous robust zonal (east-west) jet streams at the cloud level; moreover, both planets exhibit long-term stratospheric oscillations involving perturbations of zonal wind and temperature that propagate downward over time on timescales of ~4 years (Jupiter) and ~15 years (Saturn). These oscillations, dubbed the Quasi Quadrennial Oscillation (QQO) for Jupiter and the Semi-Annual Oscillation (SAO) on Saturn, are thought to be analogous to the Quasi-Biennial Oscillation (QBO) on Earth, which is driven by upward propagation of equatorial waves from the troposphere. To investigate these issues, we here present global, three-dimensional, high-resolution numerical simulations of the flow in the stratified atmosphere--overlying the convective interior--of brown dwarfs and Jupiter-like planets. The effect of interior convection is parameterized by inducing small-scale, randomly varying perturbations in the radiative-convective boundary at the base of the model. In the simulations, the convective perturbations generate atmospheric waves and turbulence that interact with the rotation to produce numerous zonal jets. Moreover, the equatorial stratosphere exhibits stacked eastward and westward jets that migrate downward over time, exactly as occurs in the terrestrial QBO, Jovian QQO, and Saturnian SAO. This is the first demonstration of a QBO-like phenomenon in 3D numerical simulations of a giant planet.

astro-ph.EP↗