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Ji Wang

Publications and source records attributed to Ji Wang.

At least 145 records · Page 8Linked to original sources
arXiv2023

Spectral Retrieval with JWST Photometric data: a Case Study for HIP 65426 b

Half of the JWST high-contrast imaging objects will only have photometric data {as of Cycle 2}. However, to better understand their atmospheric chemistry which informs formation origin, spectroscopic data are preferred. Using HIP 65426 b, we investigate to what extent planet properties and atmospheric chemical abundance can be retrieved with only JWST photometric data points (2.5-15.5 $μ$m) in conjunction with ground-based archival low-resolution spectral data (1.0-2.3 $μ$m). We find that the data is consistent with an atmosphere with solar metallicity and C/O ratios at 0.40 and 0.55. We rule out 10x solar metallicity and an atmosphere with C/O = 1.0. We also find strong evidence of silicate clouds but no sign of an enshrouding featureless {dust} extinction. This work offers guidance and cautionary tales on analyzing data in the absence of medium-to-high resolution spectral data.

astro-ph.EP↗
arXiv2023

The development of HISPEC for Keck and MODHIS for TMT: science cases and predicted sensitivities

HISPEC is a new, high-resolution near-infrared spectrograph being designed for the W.M. Keck II telescope. By offering single-shot, R=100,000 between 0.98 - 2.5 um, HISPEC will enable spectroscopy of transiting and non-transiting exoplanets in close orbits, direct high-contrast detection and spectroscopy of spatially separated substellar companions, and exoplanet dynamical mass and orbit measurements using precision radial velocity monitoring calibrated with a suite of state-of-the-art absolute and relative wavelength references. MODHIS is the counterpart to HISPEC for the Thirty Meter Telescope and is being developed in parallel with similar scientific goals. In this proceeding, we provide a brief overview of the current design of both instruments, and the requirements for the two spectrographs as guided by the scientific goals for each. We then outline the current science case for HISPEC and MODHIS, with focuses on the science enabled for exoplanet discovery and characterization. We also provide updated sensitivity curves for both instruments, in terms of both signal-to-noise ratio and predicted radial velocity precision.

astro-ph.IM↗
arXiv2023

Vortex Fiber Nulling for Exoplanet Observations: Implementation and First Light

Vortex fiber nulling (VFN) is a single-aperture interferometric technique for detecting and characterizing exoplanets separated from their host star by less than a diffracted beam width. VFN uses a vortex mask and single mode fiber to selectively reject starlight while coupling off-axis planet light with a simple optical design that can be readily implemented on existing direct imaging instruments that can feed light to an optical fiber. With its axially symmetric coupling region peaking within the inner working angle of conventional coronagraphs, VFN is more efficient at detecting new companions at small separations than conventional direct imaging, thereby increasing the yield of on-going exoplanet search campaigns. We deployed a VFN mode operating in K band ($2.0{-}2.5~μ$m) on the Keck Planet Imager and Characterizer (KPIC) instrument at the Keck II Telescope. In this paper we present the instrument design of this first on-sky demonstration of VFN and the results from on-sky commissioning, including planet and star throughput measurements and predicted flux-ratio detection limits for close-in companions. The instrument performance is shown to be sufficient for detecting a companion $10^3$ times fainter than a $5^{\mathrm{th}}$ magnitude host star in 1 hour at a separation of 50 mas (1.1$λ/D$). This makes the instrument capable of efficiently detecting substellar companions around young stars. We also discuss several routes for improvement that will reduce the required integration time for a detection by a factor ${>}$3.

astro-ph.IM↗
arXiv2023

Delay-Adaptive Boundary Control of Coupled Hyperbolic PDE-ODE Cascade Systems

This paper presents a delay-adaptive boundary control scheme for a $2\times 2$ coupled linear hyperbolic PDE-ODE cascade system with an unknown and arbitrarily long input delay. To construct a nominal delay-compensated control law, assuming a known input delay, a three-step backstepping design is used. Based on the certainty equivalence principle, the nominal control action is fed with the estimate of the unknown delay, which is generated from a batch least-squares identifier that is updated by an event-triggering mechanism that evaluates the growth of the norm of the system states. As a result of the closed-loop system, the actuator and plant states can be regulated exponentially while avoiding Zeno occurrences. A finite-time exact identification of the unknown delay is also achieved except for the case that all initial states of the plant are zero. As far as we know, this is the first delay-adaptive control result for systems governed by heterodirectional hyperbolic PDEs. The effectiveness of the proposed design is demonstrated in the control application of a deep-sea construction vessel with cable-payload oscillations and subject to input delay.

math.OC↗
arXiv2023

StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning

Emotion distribution learning has gained increasing attention with the tendency to express emotions through images. As for emotion ambiguity arising from humans' subjectivity, substantial previous methods generally focused on learning appropriate representations from the holistic or significant part of images. However, they rarely consider establishing connections with the stylistic information although it can lead to a better understanding of images. In this paper, we propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL, which interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. Specifically, we consider exploring the intra- and inter-layer correlations among GRAM-based stylistic representations, and meanwhile exploit an adversary-constrained high-order attention mechanism to capture potential interactions between subtle visual parts. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations to benefit the final emotion distribution learning. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness of our proposed StyleEDL compared to state-of-the-art methods. The implementation is released at: https://github.com/liuxianyi/StyleEDL.

cs.CV↗
arXiv2023

An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks

Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with little accuracy degradation by quantizing floating-point numbers to low-width fixed-point representations. Quantized neural networks (QNNs) have been developed, with binarized neural networks (BNNs) restricted to binary values as a special case. Another concern about neural networks is their vulnerability and lack of interpretability. Despite the active research on trustworthy of DNNs, few approaches have been proposed to QNNs. To this end, this paper presents an automata-theoretic approach to synthesizing BNNs that meet designated properties. More specifically, we define a temporal logic, called BLTL, as the specification language. We show that each BLTL formula can be transformed into an automaton on finite words. To deal with the state-explosion problem, we provide a tableau-based approach in real implementation. For the synthesis procedure, we utilize SMT solvers to detect the existence of a model (i.e., a BNN) in the construction process. Notably, synthesis provides a way to determine the hyper-parameters of the network before training.Moreover, we experimentally evaluate our approach and demonstrate its effectiveness in improving the individual fairness and local robustness of BNNs while maintaining accuracy to a great extent.

cs.LG↗
arXiv2023

Fizzy Super-Earths: Impacts of Magma Composition on the Bulk Density and Structure of Lava Worlds

Lava worlds are a potential emerging population of Super-Earths that are on close-in orbits around their host stars with likely partially molten mantles. To date, few studies address the impact of magma on the observed properties of a planet. At ambient conditions magma is less dense than solid rock; however, it is also more compressible with increasing pressure. Therefore, it is unclear how large-scale magma oceans affect planet observables, such as bulk density. We update ExoPlex, a thermodynamically self-consistent planet interior software, to include anhydrous, hydrous (2.2 wt \% H_2O), and carbonated magmas (5.2 wt\% CO_2). We find that Earth-like planets with magma oceans larger than \sim 1.5 R_{\oplus} and \sim 3.2 M_{\oplus} are modestly denser than an equivalent mass solid planet. From our model, three classes of mantle structures emerge for magma ocean planets: (1) mantle magma ocean, (2) surface magma ocean, and (3) one consisting of a surface magma ocean, solid rock layer, and a basal magma ocean. The class of planets in which a basal magma ocean is present may sequester dissolved volatiles on billion-year timescales, in which a 4 M_{\oplus} mass planet can trap more than 130 times the mass of water than in Earth's present-day oceans and 1000 times the carbon in the Earth's surface and crust.

astro-ph.EP↗
arXiv2023

Verifying Safety of Neural Networks from Topological Perspectives

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper, we propose a set-boundary reachability method to investigate the safety verification problem of NNs from a topological perspective. Given an NN with an input set and a safe set, the safety verification problem is to determine whether all outputs of the NN resulting from the input set fall within the safe set. In our method, the homeomorphism property and the open map property of NNs are mainly exploited, which establish rigorous guarantees between the boundaries of the input set and the boundaries of the output set. The exploitation of these two properties facilitates reachability computations via extracting subsets of the input set rather than the entire input set, thus controlling the wrapping effect in reachability analysis and facilitating the reduction of computation burdens for safety verification. The homeomorphism property exists in some widely used NNs such as invertible residual networks (i-ResNets) and Neural ordinary differential equations (Neural ODEs), and the open map is a less strict property and easier to satisfy compared with the homeomorphism property. For NNs establishing either of these properties, our set-boundary reachability method only needs to perform reachability analysis on the boundary of the input set. Moreover, for NNs that do not feature these properties with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations. Finally, some examples demonstrate the performance of the proposed method.

cs.LG↗
arXiv2023

Keck/KPIC Emission Spectroscopy of WASP-33b

We present Keck/KPIC high-resolution ($R\sim35,000$) $K$-band thermal emission spectroscopy of the ultra-hot Jupiter WASP-33b. The use of KPIC's single-mode fibers greatly improves both blaze and line-spread stabilities relative to slit spectrographs, enhancing the cross-correlation detection strength. We retrieve the dayside emission spectrum with a nested sampling pipeline which fits for orbital parameters, the atmospheric pressure-temperature profile, and molecular abundances.We strongly detect the thermally-inverted dayside and measure mass-mixing ratios for CO ($\log\rm CO_{MMR} = -1.1^{+0.4}_{-0.6}$), H$_2$O ($\log\rm H_2O_{MMR} = -4.1^{+0.7}_{-0.9}$) and OH ($\log\rm OH_{MMR} = -2.1^{+0.5}_{-1.1}$), suggesting near-complete dayside photodissociation of H$_2$O. The retrieved abundances suggest a carbon- and possibly metal-enriched atmosphere, with a gas-phase C/O ratio of $0.8^{+0.1}_{-0.2}$, consistent with the accretion of high-metallicity gas near the CO$_2$ snow line and post-disk migration or with accretion between the soot and H$_2$O snow lines. We also find tentative evidence for $\rm ^{12}CO/^{13}CO \sim 50$, consistent with values expected in protoplanetary disks, as well as tentative evidence for a metal-enriched atmosphere (2--15$\times$ solar). These observations demonstrate KPIC's ability to characterize close-in planets and the utility of KPIC's improved instrumental stability for cross-correlation techniques.

astro-ph.EP↗
arXiv2023

Repairing Deep Neural Networks Based on Behavior Imitation

The increasing use of deep neural networks (DNNs) in safety-critical systems has raised concerns about their potential for exhibiting ill-behaviors. While DNN verification and testing provide post hoc conclusions regarding unexpected behaviors, they do not prevent the erroneous behaviors from occurring. To address this issue, DNN repair/patch aims to eliminate unexpected predictions generated by defective DNNs. Two typical DNN repair paradigms are retraining and fine-tuning. However, existing methods focus on the high-level abstract interpretation or inference of state spaces, ignoring the underlying neurons' outputs. This renders patch processes computationally prohibitive and limited to piecewise linear (PWL) activation functions to great extent. To address these shortcomings, we propose a behavior-imitation based repair framework, BIRDNN, which integrates the two repair paradigms for the first time. BIRDNN corrects incorrect predictions of negative samples by imitating the closest expected behaviors of positive samples during the retraining repair procedure. For the fine-tuning repair process, BIRDNN analyzes the behavior differences of neurons on positive and negative samples to identify the most responsible neurons for the erroneous behaviors. To tackle more challenging domain-wise repair problems (DRPs), we synthesize BIRDNN with a domain behavior characterization technique to repair buggy DNNs in a probably approximated correct style. We also implement a prototype tool based on BIRDNN and evaluate it on ACAS Xu DNNs. Our experimental results show that BIRDNN can successfully repair buggy DNNs with significantly higher efficiency than state-of-the-art repair tools. Additionally, BIRDNN is highly compatible with different activation functions.

cs.LG↗
arXiv2023

Mapping the Skies of Ultracool Worlds: Detecting Storms and Spots with Extremely Large Telescopes

Extremely large telescopes (ELTs) present an unparalleled opportunity to study the magnetism, atmospheric dynamics, and chemistry of very low mass stars (VLMs), brown dwarfs, and exoplanets. Instruments such as the Giant Magellan Telescope - Consortium Large Earth Finder (GMT/GCLEF), the Thirty Meter Telescope's Multi-Objective Diffraction-limited High-Resolution Infrared Spectrograph (TMT/MODHIS), and the European Southern Observatory's Mid-Infrared ELT Imager and Spectrograph (ELT/METIS) provide the spectral resolution and signal-to-noise (S/N) necessary to Doppler image ultracool targets' surfaces based on temporal spectral variations due to surface inhomogeneities. Using our publicly-available code, $Imber$, developed and validated in Plummer & Wang (2022), we evaluate these instruments' abilities to discern magnetic star spots and cloud systems on a VLM star (TRAPPIST-1); two L/T transition ultracool dwarfs (VHS J1256$-$1257 b and SIMP J0136+0933); and three exoplanets (Beta Pic b and HR 8799 d and e). We find that TMT/MODHIS and ELT/METIS are suitable for Doppler imaging the ultracool dwarfs and Beta Pic b over a single rotation. Uncertainties for longitude and radius are typically $\lesssim 10^{\circ}$, and latitude uncertainties range from $\sim 10^{\circ} \ \rm{to} \ 30^{\circ}$. TRAPPIST-1's edge-on inclination and low $\upsilon \sin i$ provide a challenge for all three instruments while GMT/GCLEF and the HR 8799 planets may require observations over multiple rotations. We compare the spectroscopic technique, photometry-only inference, and the combination of the two. We find combining spectroscopic and photometric observations can lead to improved Bayesian inference of surface inhomogeneities and offers insight into whether ultracool atmospheres are dominated by spotted or banded features.

astro-ph.EP↗
arXiv2023

Scalable Spectral Clustering with Group Fairness Constraints

There are synergies of research interests and industrial efforts in modeling fairness and correcting algorithmic bias in machine learning. In this paper, we present a scalable algorithm for spectral clustering (SC) with group fairness constraints. Group fairness is also known as statistical parity where in each cluster, each protected group is represented with the same proportion as in the entirety. While FairSC algorithm (Kleindessner et al., 2019) is able to find the fairer clustering, it is compromised by high costs due to the kernels of computing nullspaces and the square roots of dense matrices explicitly. We present a new formulation of underlying spectral computation by incorporating nullspace projection and Hotelling's deflation such that the resulting algorithm, called s-FairSC, only involves the sparse matrix-vector products and is able to fully exploit the sparsity of the fair SC model. The experimental results on the modified stochastic block model demonstrate that s-FairSC is comparable with FairSC in recovering fair clustering. Meanwhile, it is sped up by a factor of 12 for moderate model sizes. s-FairSC is further demonstrated to be scalable in the sense that the computational costs of s-FairSC only increase marginally compared to the SC without fairness constraints.

cs.LG↗
arXiv2023

Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models

Recent research has revealed that deep generative models including flow-based models and Variational Autoencoders may assign higher likelihoods to out-of-distribution (OOD) data than in-distribution (ID) data. However, we cannot sample OOD data from the model. This counterintuitive phenomenon has not been satisfactorily explained and brings obstacles to OOD detection with flow-based models. In this paper, we prove theorems to investigate the Kullback-Leibler divergence in flow-based model and give two explanations for the above phenomenon. Based on our theoretical analysis, we propose a new method \PADmethod\ to leverage KL divergence and local pixel dependence of representations to perform anomaly detection. Experimental results on prevalent benchmarks demonstrate the effectiveness and robustness of our method. For group anomaly detection, our method achieves 98.1\% AUROC on average with a small batch size of 5. On the contrary, the baseline typicality test-based method only achieves 64.6\% AUROC on average due to its failure on challenging problems. Our method also outperforms the state-of-the-art method by 9.1\% AUROC. For point-wise anomaly detection, our method achieves 90.7\% AUROC on average and outperforms the baseline by 5.2\% AUROC. Besides, our method has the least notable failures and is the most robust one.

cs.LG↗
arXiv2023

Direct Imaging Explorations for Companions around Mid-Late M Stars from the Subaru/IRD Strategic Program

The Subaru telescope is currently performing a strategic program (SSP) using the high-precision near-infrared (NIR) spectrometer IRD to search for exoplanets around nearby mid/late-M~dwarfs via radial velocity (RV) monitoring. As part of the observing strategy for the exoplanet survey, signatures of massive companions such as RV trends are used to reduce the priority of those stars. However, this RV information remains useful for studying the stellar multiplicity of nearby M~dwarfs. To search for companions around such ``deprioritized" M~dwarfs, we observed 14 IRD-SSP targets using Keck/NIRC2 observations with pyramid wavefront sensing at NIR wavelengths, leading to high sensitivity to substellar-mass companions within a few arcseconds. We detected two new companions (LSPM~J1002+1459~B and LSPM~J2204+1505~B) and two new candidates that are likely companions (LSPM~J0825+6902~B and LSPM~J1645+0444~B) as well as one known companion. Including two known companions resolved by the IRD fiber injection module camera, we detected seven (four new) companions at projected separations between $\sim2-20$~au in total. A comparison of the colors with the spectral library suggests that LSPM~J2204+1505~B and LSPM~J0825+6902~B are located at the boundary between late-M and early-L spectral types. Our deep high-contrast imaging for targets where no bright companions were resolved did not reveal any additional companion candidates. The NIRC2 detection limits could constrain potential substellar-mass companions ($\sim10-75\ M_{\rm Jup}$) at 10~au or further. The failure with Keck/NIRC2 around the IRD-SSP stars having significant RV trends makes these objects promising targets for further RV monitoring or deeper imaging with JWST to search for smaller-mass companions below the NIRC2 detection limits.

astro-ph.SR↗
arXiv2023

Detecting exomoons from radial velocity measurements of self-luminous planets: application to observations of HR 7672 B and future prospects

The detection of satellites around extrasolar planets, so called exomoons, remains a largely unexplored territory. In this work, we study the potential of detecting these elusive objects from radial velocity monitoring of self-luminous directly imaged planets. This technique is now possible thanks to the development of dedicated instruments combining the power of high-resolution spectroscopy and high-contrast imaging. First, we demonstrate a sensitivity to satellites with a mass ratio of 1-4% at separations similar to the Galilean moons from observations of a brown-dwarf companion (HR 7672 B; Kmag=13; 0.7" separation) with the Keck Planet Imager and Characterizer (KPIC; R~35,000 in K band) at the W. M. Keck Observatory. Current instrumentation is therefore already sensitive to large unresolved satellites that could be forming from gravitational instability akin to binary star formation. Using end-to-end simulations, we then estimate that future instruments such as MODHIS, planned for the Thirty Meter Telescope, should be sensitive to satellites with mass ratios of ~1e-4. Such small moons would likely form in a circumplanetary disk similar to the Jovian satellites in the solar system. Looking for the Rossiter-McLaughlin effect could also be an interesting pathway to detecting the smallest moons on short orbital periods. Future exomoon discoveries will allow precise mass measurements of the substellar companions that they orbit and provide key insight into the formation of exoplanets. They would also help constrain the population of habitable Earth-sized moons orbiting gas giants in the habitable zone of their stars.

astro-ph.EP↗
arXiv2023

The PEPSI Exoplanet Transit Survey (PETS). II. A Deep Search for Thermal Inversion Agents in KELT-20 b/MASCARA-2 b with Emission and Transmission Spectroscopy

Recent observations have shown that the atmospheres of ultra hot Jupiters (UHJs) commonly possess temperature inversions, where the temperature increases with increasing altitude. Nonetheless, which opacity sources are responsible for the presence of these inversions remains largely observationally unconstrained. We used LBT/PEPSI to observe the atmosphere of the UHJ KELT-20 b in both transmission and emission in order to search for molecular agents which could be responsible for the temperature inversion. We validate our methodology by confirming previous detections of Fe I in emission at $16.9σ$. Our search for the inversion agents TiO, VO, FeH, and CaH results in non-detections. Using injection-recovery testing we set $4σ$ upper limits upon the volume mixing ratios for these constituents as low as $\sim1\times10^{-9}$ for TiO. For TiO, VO, and CaH, our limits are much lower than expectations from an equilibrium chemical model, while we cannot set constraining limits on FeH with our data. We thus rule out TiO and CaH as the source of the temperature inversion in KELT-20 b, and VO only if the line lists are sufficiently accurate.

astro-ph.EP↗
arXiv2023

On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions

Kullback-Leibler (KL) divergence is one of the most important divergence measures between probability distributions. In this paper, we prove several properties of KL divergence between multivariate Gaussian distributions. First, for any two $n$-dimensional Gaussian distributions $\mathcal{N}_1$ and $\mathcal{N}_2$, we give the supremum of $KL(\mathcal{N}_1||\mathcal{N}_2)$ when $KL(\mathcal{N}_2||\mathcal{N}_1)\leq \varepsilon\ (\varepsilon>0)$. For small $\varepsilon$, we show that the supremum is $\varepsilon + 2\varepsilon^{1.5} + O(\varepsilon^2)$. This quantifies the approximate symmetry of small KL divergence between Gaussians. We also find the infimum of $KL(\mathcal{N}_1||\mathcal{N}_2)$ when $KL(\mathcal{N}_2||\mathcal{N}_1)\geq M\ (M>0)$. We give the conditions when the supremum and infimum can be attained. Second, for any three $n$-dimensional Gaussians $\mathcal{N}_1$, $\mathcal{N}_2$, and $\mathcal{N}_3$, we find an upper bound of $KL(\mathcal{N}_1||\mathcal{N}_3)$ if $KL(\mathcal{N}_1||\mathcal{N}_2)\leq \varepsilon_1$ and $KL(\mathcal{N}_2||\mathcal{N}_3)\leq \varepsilon_2$ for $\varepsilon_1,\varepsilon_2\ge 0$. For small $\varepsilon_1$ and $\varepsilon_2$, we show the upper bound is $3\varepsilon_1+3\varepsilon_2+2\sqrt{\varepsilon_1\varepsilon_2}+o(\varepsilon_1)+o(\varepsilon_2)$. This reveals that KL divergence between Gaussians follows a relaxed triangle inequality. Importantly, all the bounds in the theorems presented in this paper are independent of the dimension $n$. Finally, We discuss the applications of our theorems in explaining counterintuitive phenomenon of flow-based model, deriving deep anomaly detection algorithm, and extending one-step robustness guarantee to multiple steps in safe reinforcement learning.

cs.IT↗
arXiv2022

AI Empowered Net-RCA for 6G

6G is envisioned to offer higher data rate, improved reliability, ubiquitous AI services, and support massive scale of connected devices. As a consequence, 6G will be much more complex than its predecessors. The growth of the system scale and complexity as well as the coexistence with the legacy networks and the diversified service requirements will inevitably incur huge maintenance cost and efforts for future 6G networks. Network Root Cause Analysis (Net-RCA) plays a critical role in identifying root causes of network faults. In this article, we first give an introduction about the envisioned 6G networks. Next, we discuss the challenges and potential solutions of 6G network operation and management, and comprehensively survey existing RCA methods. Then we propose an artificial intelligence (AI)-empowered Net-RCA framework for 6G. Performance comparisons on both synthetic and real-world network data are carried out to demonstrate that the proposed method outperforms the existing method considerably.

cs.AI↗