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Researcher profile

Shijie Wang

Shijie Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.EPComputer Visionastro-ph.SReess.SYgr-qcSystems and Control

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Published work

9 published item(s)

preprint2026arXiv

Converse Barrier Certificates for Finite-time Safety Verification of Continuous-time Perturbed Deterministic Systems

In this paper, we investigate the problem of verifying the finite-time safety of continuous-time perturbed deterministic systems represented by ordinary differential equations in the presence of measurable disturbances. Given a finite-time horizon, if the system is safe, it, starting from a compact initial set, will remain within an open and bounded safe region throughout the specified time horizon, regardless of the disturbances. The main contribution of this work is a converse theorem: we prove that a continuously differentiable, time-dependent barrier certificate exists if and only if the system is safe over the finite-time horizon. The existence problem is explored by finding a continuously differentiable approximation of a unique Lipschitz viscosity solution to a Hamilton-Jacobi equation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Learning to Align Generative Appearance Priors for Fine-grained Image Retrieval

Fine-grained image retrieval (FGIR) typically relies on supervision from seen categories to learn discriminative embeddings for retrieving unseen categories. However, such supervision often biases retrieval models toward the semantics of seen categories rather than the underlying appearance characteristics that generalize across categories, thereby limiting retrieval performance on unseen categories. To tackle this, we propose GAPan, a Generative Appearance Prior alignment network that reformulates the learning objective from category prediction toward appearance modeling. Technically, GAPan treats retrieval features with an invertible density model based on normalizing flows. In the forward direction, the flow maps all instance features into a latent density space, where each seen category is modeled by a class-conditional Gaussian prior and optimized via exact likelihood estimation. This formulation preserves richer appearance details by leveraging the invertible property of the flows. In the reverse direction, samples from the high-density regions of these learned priors are mapped back to the feature space to produce appearance-aware anchors that reflect intra-category variation. These anchors supervise a prior-driven alignment objective that aligns retrieval embeddings with category-specific appearance distributions, thereby improving generalization to unseen categories. Evaluations demonstrate that our GAPan achieves state-of-the-art performance on both widely-used fine- and coarse-grained benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Architecture of planetary systems predicted from protoplanetary disks observed with ALMA II: evolution outcomes and dynamical stability

Recent ALMA observations on disk substructures suggest the presence of embedded protoplanets in a large number disks. The primordial configurations of these planetary systems can be deduced from the morphology of the disk substructure and serve as initial conditions for numerical investigation of their future evolution. Starting from the initial configurations of 12 multi-planetary systems deduced from ALMA disks, we carried out two-stage N-body simulation to investigate the evolution of the planetary systems at the disk stage as well as the long term orbital stability after the disk dispersal. At the disk stage, our simulation includes both the orbital migration and pebble/gas accretion effects. We found a variety of planetary systems are produced and can be categorised into distant giant planets, Jupiter-like planets, Neptune-like planets and distant small planets. We found the disk stage evolution as well as the final configurations are sensitive to both the initial mass assignments and viscosity. After the disk stage, we implement only mutual gravity between star and planets and introduce stochastic perturbative forces. All systems are integrated for up to 10 Gyr to test their orbital stability. Most planetary systems are found to be stable for at least 10 Gyr with perturbative force in a reasonable range. Our result implies that a strong perturbation source such as stellar flybys is required to drive the planetary system unstable. We discuss the implications of our results on both the disk and planet observation, which may be confirmed by the next generation telescopes such as JWST and ngVLA.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection

We present an approach to efficiently and effectively adapt a masked image modeling (MIM) pre-trained vanilla Vision Transformer (ViT) for object detection, which is based on our two novel observations: (i) A MIM pre-trained vanilla ViT encoder can work surprisingly well in the challenging object-level recognition scenario even with randomly sampled partial observations, e.g., only 25% $\sim$ 50% of the input embeddings. (ii) In order to construct multi-scale representations for object detection from single-scale ViT, a randomly initialized compact convolutional stem supplants the pre-trained large kernel patchify stem, and its intermediate features can naturally serve as the higher resolution inputs of a feature pyramid network without further upsampling or other manipulations. While the pre-trained ViT is only regarded as the 3$^{rd}$-stage of our detector's backbone instead of the whole feature extractor. This results in a ConvNet-ViT hybrid feature extractor. The proposed detector, named MIMDet, enables a MIM pre-trained vanilla ViT to outperform hierarchical Swin Transformer by 2.5 box AP and 2.6 mask AP on COCO, and achieves better results compared with the previous best adapted vanilla ViT detector using a more modest fine-tuning recipe while converging 2.8$\times$ faster. Code and pre-trained models are available at https://github.com/hustvl/MIMDet.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing

To boost the object grabbing capability of underwater robots for open-sea farming, we propose a new dataset (UDD) consisting of three categories (seacucumber, seaurchin, and scallop) with 2,227 images. To the best of our knowledge, it is the first 4K HD dataset collected in a real open-sea farm. We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively. Specifically, Poisson GAN combines Poisson blending into its generator and employs a new loss called Dual Restriction loss (DR loss), which supervises both implicit space features and image-level features during training to generate more realistic images. By utilizing Poisson GAN, objects of minority class like seacucumber or scallop could be added into an image naturally and annotated automatically, which could increase the loss of minority classes during training detectors to eliminate the class-imbalance problem; AquaNet is a high-efficiency detector to address the problem of detecting mass small objects from cloudy underwater pictures. Within it, we design two efficient components: a depth-wise-convolution-based Multi-scale Contextual Features Fusion (MFF) block and a Multi-scale Blursampling (MBP) module to reduce the parameters of the network to 1.3 million. Both two components could provide multi-scale features of small objects under a short backbone configuration without any loss of accuracy. In addition, we construct a large-scale augmented dataset (AUDD) and a pre-training dataset via Poisson GAN from UDD. Extensive experiments show the effectiveness of the proposed Poisson GAN, AquaNet, UDD, AUDD, and pre-training dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Architecture of planetary systems predicted from protoplanetary disks observed with ALMA I: mass of the possible planets embedded in the dust gap

Recent ALMA observations have identified a variety of dust gaps in protoplanetary disks, which are commonly interpreted to be generated by unobserved planets. Predicting mass of such embedded planets is of fundamental importance in comparing those disk architectures with the observed diversity of exoplanets. The prediction, however, depends on the assumption that whether the same gap structure exists in the dust component alone or in the gas component as well. We assume a planet can only open a gap in the gas component when its mass exceeds the pebble isolation mass by considering the core accretion scenario. We then propose two criteria to distinguish if a gap is opened in the dust disk alone or the gas gap as well when observation data on the gas profile is not available. We apply the criteria to 35 disk systems with a total of 55 gaps compiled from previous studies, and classify each gap into four different groups. The classification of the observed gaps allows us to predict the mass of embedded planets in a consistent manner with the pebble isolation mass. We find that outer gaps are mostly dust alone, while inner gaps are more likely to be associated with a gas gap as well. The distribution of such embedded planets is very different from the architecture of the observed planetary systems, suggesting that the significant inward migration is required in their evolution.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A strategy to search for an inner binary black hole from the motion of the tertiary star

There are several on-going projects to search for stars orbiting around an invisible companion. A fraction of such candidates may be a triple, instead of a binary, consisting of an inner binary black hole (BBH) and an outer orbiting star. In this paper, we propose a methodology to search for a signature of such an inner BBH, possibly a progenitor of gravitational-wave sources discovered by {\it LIGO}, from the precise radial velocity (RV) follow-up of the outer star. We first describe a methodology using an existing approximate RV formula for coplanar circular triples. We apply this method and constrain the parameters of a possible inner binary objects in 2M05215658+4359220, which consists of a red giant and an unseen companion. Next we consider co-planar but non-circular triples. We compute numerically the RV variation of a tertiary star orbiting around an inner BBH, generate mock RV curves, and examine the feasibility of the BBH detection for our fiducial models. We conclude that the short-cadence RV monitoring of a star-BH binary provides an interesting and realistic method to constrain and/or search for possible inner BBHs. Indeed a recent discovery of a star--BH binary system LB-1 may imply that there are a large number of such unknown objects in our Galaxy, which are ideal targets for the methodology proposed here.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Architecture of three-planet systems predicted from the observed protoplanetary disk of HL Tau

A number of protoplanetary disks observed with ALMA potentially provide direct examples of initial conditions for planetary systems. In particular, the HL Tau disk has been intensively studied, and its rings/gaps are conventionally interpreted to be a result of unseen massive planets embedded in the gaps. Based on this interpretation, we carried out N-body simulations to investigate orbital evolution of planets within the protoplanetary disk and after the disk dispersal. Before the disk dispersal, our N-body simulations include both migration and mass-growth of the planet coupled with evolution of the disk. By varying the disk parameters, we produce a variety of widely-separated planetary systems consisting of three super-Jupiters at the end of disk dispersal. We found the outer planet is more massive than the inner one, and the migration of the innermost planet is inefficient due to the accretion of outer planet(s). We also showed how the final configuration and the final planetary mass depend on disk parameters. The migration is found to be convergent and no planet-pair has a period ratio less than 2. After the disk dispersal, we switch to pure gravitational N-body simulations and integrate the orbits up to 10 Gyr. Most simulated systems remain stable for at least 10 Gyr. We discuss implications of our result in terms of the observed widely-separated planetary systems HR 8799 and PDS 70.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Graph Edit Distance Reward: Learning to Edit Scene Graph

Scene Graph, as a vital tool to bridge the gap between language domain and image domain, has been widely adopted in the cross-modality task like VQA. In this paper, we propose a new method to edit the scene graph according to the user instructions, which has never been explored. To be specific, in order to learn editing scene graphs as the semantics given by texts, we propose a Graph Edit Distance Reward, which is based on the Policy Gradient and Graph Matching algorithm, to optimize neural symbolic model. In the context of text-editing image retrieval, we validate the effectiveness of our method in CSS and CRIR dataset. Besides, CRIR is a new synthetic dataset generated by us, which we will publish it soon for future use.

0 citations0 reviewsNo code linkedOpen access