Xia Ji, Xiaodong Liu, Bo Zhang
Similar to the obstacle or medium scattering problems, an important property of the phaseless far field patterns for source scattering problems is the translation invariance. Thus it is impossible to reconstruct the location of the underlying sources. Furthermore, the phaseless far field pattern is also invariant if the source is multiplied by any complex number with modulus one. Therefore, the source can not be uniquely determined, even the multifrequency phaseless far field patterns are considered. By adding a reference point source into the model, we propose a simple and stable phase retrieval method and establish several uniqueness results with phaseless far field data. We proceed to introduce a novel direct sampling method for shape and location reconstruction of the source by using broadband sparse phaseless data directly. We also propose a combination method with the novel phase retrieval algorithm and the classical direct sampling methods with phased data. Numerical examples in two dimensions are also presented to demonstrate their feasibility and effectiveness.
Hongwu Kuang, Xiaodong Liu, Jingwei Zhang, Zicheng Fang
Multi-modality fusion is the guarantee of the stability of autonomous driving systems. In this paper, we propose a general multi-modality cascaded fusion framework, exploiting the advantages of decision-level and feature-level fusion, utilizing target position, size, velocity, appearance and confidence to achieve accurate fusion results. In the fusion process, dynamic coordinate alignment(DCA) is conducted to reduce the error between sensors from different modalities. In addition, the calculation of affinity matrix is the core module of sensor fusion, we propose an affinity loss that improves the performance of deep affinity network(DAN). Last, the proposed step-by-step cascaded fusion framework is more interpretable and flexible compared to the end-toend fusion methods. Extensive experiments on Nuscenes [2] dataset show that our approach achieves the state-of-theart performance.dataset show that our approach achieves the state-of-the-art performance.
Tilo Arens, Xia Ji, Xiaodong Liu
This paper is dedicated to design a direct sampling method of inverse electromagnetic scattering problems, which uses multi-frequency sparse backscattering far field data for reconstructing the boundary of perfectly conducting obstacles. We show that a smallest strip containing the unknown object can be approximately determined by the multi-frequency backscattering far field data at two opposite observation directions. The proof is based on the Kirchhoff approximation and Fourier transform. Such a strip is then reconstructed by an indicator, which is the absolute value of an integral of the product of the data and some properly chosen function over the frequency interval. With the increase of the number of the backscattering data, the location and shape of the underlying object can be reconstructed. Numerical examples are conducted to show the validity and robustness of the proposed sampling method. The numerical examples also show that the concave part of the underlying object can be well reconstructed, and the different connected components of the underlying object can be well separated.
Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, Jianfeng Gao
Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning. However, these models are still vulnerable to adversarial attacks. In this paper, we show that adversarial pre-training can improve both generalization and robustness. We propose a general algorithm ALUM (Adversarial training for large neural LangUage Models), which regularizes the training objective by applying perturbations in the embedding space that maximizes the adversarial loss. We present the first comprehensive study of adversarial training in all stages, including pre-training from scratch, continual pre-training on a well-trained model, and task-specific fine-tuning. ALUM obtains substantial gains over BERT on a wide range of NLP tasks, in both regular and adversarial scenarios. Even for models that have been well trained on extremely large text corpora, such as RoBERTa, ALUM can still produce significant gains from continual pre-training, whereas conventional non-adversarial methods can not. ALUM can be further combined with task-specific fine-tuning to attain additional gains. The ALUM code is publicly available at https://github.com/namisan/mt-dnn.
Xiaodong Liu, Wei Li, Yuwei Fang, Aerin Kim, Kevin Duh, Jianfeng Gao
This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is unanswerable or not. The extended SAN contains two components: a span detector and a binary classifier for judging whether the question is unanswerable, and both components are jointly optimized. Experiments show that SAN achieves the results competitive to the state-of-the-art on Stanford Question Answering Dataset (SQuAD) 2.0. To facilitate the research on this field, we release our code: https://github.com/kevinduh/san_mrc.
Xia Ji, Xiaodong Liu, Bo Zhang
An important property of the phaseless far field patterns with incident plane waves is the translation invariance. Thus it is impossible to reconstruct the location of the underlying scatterers. By adding a reference point scatterer into the model, we design a novel direct sampling method using the phaseless data directly. The reference point technique not only overcomes the translation invariance, but also brings a practical phase retrieval algorithm. Based on this, we propose a hybrid method combining the novel phase retrieval algorithm and the classical direct sampling methods. Numerical examples in two dimensions are presented to demonstrate their effectiveness and robustness.
Xiaodong Liu, Jürgen Schmidt
It is expected since the early 1970s that tenuous dust rings are formed by grains ejected from the Martian moons Phobos and Deimos by impacts of hypervelocity interplanetary projectiles. In this paper, we perform direct numerical integrations of a large number of dust particles originating from Phobos and Deimos. In the numerical simulations, the most relevant forces acting on dust are included: Martian gravity with spherical harmonics up to 5th degree and 5th order, gravitational perturbations from the Sun, Phobos, and Deimos, solar radiation pressure, as well as the Poynting-Robertson drag. In order to obtain the ring configuration, simulation results of various grain sizes ranging from submicron to 100 microns are averaged over a specified initial mass distribution of ejecta. We find that for the Phobos ring grains smaller than about 2 microns are dominant; while the Deimos ring is dominated by dust in the size range of about 5-20 microns. The asymmetries, number densities and geometrical optical depths of the rings are quantified from simulations. The results are compared with the upper limits of the optical depth inferred from Hubble observations. We compare to previous work and discuss the uncertainties of the models.
Xiaodong Liu, Xiangke Liao, Shanshan Li, Jingying Zhang, Lisong Shao, Chenlin Huang, Liquan Xiao
Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time, significantly affecting the efficiency. In this paper, we observe from real-world traces that the evolution of social network follows the preferential attachment rule and the influential nodes are mainly selected from high-degree nodes. Such observations shed light on the design of IncInf, an incremental approach that can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to effectively narrow the search space into nodes experiencing major increases or with high degrees. We carried out extensive experiments on real-world dynamic social networks including Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static heuristic, IncInf achieves as much as 21X speedup in execution time while maintaining matching performance in terms of influence spread.
Hongyu Liu, Xiaodong Liu
We consider an inverse acoustic scattering problem in simultaneously recovering an embedded obstacle and its surrounding inhomogeneous medium by formally determined far-field data. It is shown that the knowledge of the scattering amplitude with a fixed incident direction and all observation angles along with frequencies from an open interval can be used to uniquely identify the embedded obstacle, sound-soft or sound-hard disregarding the surrounding medium. Furthermore, if the surrounding inhomogeneous medium is from an admissible class (still general), then the medium can be recovered as well. Our argument is based on deriving certain integral identities involving the unknowns and then inverting them by certain harmonic analysis techniques. Finally, based on our theoretical study, a fast and robust sampling method is proposed to reconstruct the shape and location of the buried targets and the support of the surrounding inhomogeneities.
Zhenghan Chen, Kun Yang, Xiaodong Liu
The irregular satellites of Jupiter produce dust particles through the impact of interplanetary micrometeoroids. In this paper, the dynamics of these particles is studied by both high-accuracy numerical simulation and analytical theory, in order to learn their transport, final fate, and spatial distribution. The perturbation forces that are considered in our dynamical model include the solar radiation pressure, solar gravity, Poynting-Robertson drag, Jovian oblateness, and the Galilean satellites' gravity. The trajectories of different size particles are simulated until they hit Jupiter, the Galilean satellites, or escape from the Jovian system. The average dynamical lifetimes of dust with different grain sizes are calculated, and the final fate of dust particles is reported and analysed. The steady-state spatial number density of particles is estimated by integrating the trajectories of dust particles over their initial size distribution, and compared to the previous work. The impact sites of dust on Callisto's surface are recorded and provide an important clue for the study of the hemisphere asymmetry of Callisto. Besides, the mass accretion rate, cross-sectional area influx, and mass influx density of dust on Callisto are calculated. A ring outside the orbit of Callisto dominated by dust between 2 and 25 $μ$m from Jupiter's irregular satellites is suggested, with the average normal geometric optical depth of the order of $10^{-8}$ and the configuration of the ring ansae similar to Jupiter's gossamer rings.
Bin Liu, Xiaodong Liu
May 24, 2024·astro-ph.EP·PDF A morphological and photometric analysis of the naked-eye long-period comet C/2022 E3 (ZTF) before perihelion is presented in this study. The observation images taken by the Zwicky Transient Facility survey telescope from July 2022 to October 2022 show a gradually brightening dust coma and a tail with a clear structure. The morphology of the dust coma reveals nonsteady-state emission with an ejection velocity lower than 14 m s$^{-1}$ for particles larger than 100 um. According to the syndyne-synchrone analysis, dust particles larger than about 10 um contribute significantly to the observed tail. The model simulations of the 10 October 2022 image suggest that the radii of large particles lingering near the nucleus range from 0.1 mm to 1 mm. Assuming that the nucleus of comet E3 is a homogeneous sphere with an albedo of 0.1, the photometry analysis sets the lower and upper limits of the nucleus radius to be $0.81\pm0.07$ km and $2.79\pm0.01$ km, respectively. The dust production rates increased continuously from $241\pm3$ kg s$^{-1}$ in July to $476\pm9$ kg s$^{-1}$ in October. The dependence of the ejection velocity $v_{\perp}$ perpendicular to the orbital plane of comet E3 on the particle size $a$ can be simplified as $v_{\perp}\propto a^{-1/2}$, which indicates that the dust emission is likely driven by gas. The water-production rate is inferred as $\sim 368\pm72$ kg s$^{-1}$ in October 2022, which is sustained by an equilibrium-sublimating area of $8.2\times10^6$ m$^2$ at least. The comparative analysis of the characteristics of comet E3 with those of comets belonging to different types shows that the activity profile of long-period comet E3 surprisingly aligns more closely with those of short-period comets within a heliocentric distance range of about [1.7, 3.4] AU, where the images of comet E3 that we used in this study were taken.
Xiaodong Liu, Jiguang Sun
The inverse scattering problems have been popular for the past thirty years. While very successful in many cases, progress has lagged when only {\em limited-aperture} measurement is available. In this paper, we perform some elementary study to recover data that can not be measured directly. To be precise, we aim at recovering the {\em full-aperture} far field data from {\em limited-aperture} measurement. Due to the reciprocity relation, the multi-static response matrix (MSR) has a symmetric structure. Using the Green's formula and single layer potential, we propose two schemes to recover {\em full-aperture} MSR. The recovered data is tested by a recently proposed direct sampling method and the factorization method. The numerical results show the possibility to recover, at least partially, the missing data and consequently improve the reconstruction of the scatterer.
Xiaodong Liu
This paper presents a significant improvement on the previous conference paper known as DefSent. The prior study seeks to improve sentence embeddings of language models by projecting definition sentences into the vector space of dictionary entries. We discover that this approach is not fully explored due to the methodological limitation of using word embeddings of language models to represent dictionary entries. This leads to two hindrances. First, dictionary entries are constrained by the single-word vocabulary, and thus cannot be fully exploited. Second, semantic representations of language models are known to be anisotropic, but pre-processing word embeddings for DefSent is not allowed because its weight is frozen during training and tied to the prediction layer. In this paper, we propose a novel method to progressively build entry embeddings not subject to the limitations. As a result, definition sentences can be projected into a quasi-isotropic or isotropic vector space of unlimited dictionary entries, so that sentence embeddings of noticeably better quality are attainable. We abbreviate our approach as DefSent+ (a plus version of DefSent), involving the following strengths: 1) the task performance on measuring sentence similarities is significantly improved compared to DefSent; 2) when DefSent+ is used to further train data-augmented models like SIMCSE, SNCSE, and SynCSE, state-of-the-art performance on measuring sentence similarities can be achieved among the approaches without using manually labeled datasets; 3) DefSent+ is also competitive in feature-based transfer for NLP downstream tasks.
Xiaodong Liu, Kevin Duh, Jianfeng Gao
We propose a stochastic answer network (SAN) to explore multi-step inference strategies in Natural Language Inference. Rather than directly predicting the results given the inputs, the model maintains a state and iteratively refines its predictions. Our experiments show that SAN achieves the state-of-the-art results on three benchmarks: Stanford Natural Language Inference (SNLI) dataset, MultiGenre Natural Language Inference (MultiNLI) dataset and Quora Question Pairs dataset.
Youjun Deng, Hongyu Liu, Xiaodong Liu
In this paper, we are concerned with the inverse electromagnetic scattering problem of recovering a complex scatterer by the corresponding electric far-field data. The complex scatterer consists of an inhomogeneous medium and a possibly embedded perfectly electric conducting (PEC) obstacle. The far-field data are collected corresponding to incident plane waves with a fixed incident direction and a fixed polarisation, but frequencies from an open interval. It is shown that the embedded obstacle can be uniquely recovered by the aforementioned far-field data, independent of the surrounding medium. Furthermore, if the surrounding medium is piecewise homogeneous, then the medium can be recovered as well. Those unique recovery results are new to the literature. Our argument is based on low-frequency expansions of the electromagnetic fields and certain harmonic analysis techniques.
Xiaodong Liu, Hexi Baoyin, Xingrui Ma
Aug 23, 2011·astro-ph.EP·PDF In the current study, the existence of periodic orbits around a fixed homogeneous cube is investigated, and the results have powerful implications for examining periodic orbits around non-spherical celestial bodies. In the two different types of symmetry planes of the fixed cube, periodic orbits are obtained using the method of the Poincaré surface of section. While in general positions, periodic orbits are found by the homotopy method. The results show that periodic orbits exist extensively in symmetry planes of the fixed cube, and also exist near asymmetry planes that contain the regular Hex cross section. The stability of these periodic orbits is determined on the basis of the eigenvalues of the monodromy matrix. This paper proves that the homotopy method is effective to find periodic orbits in the gravity field of the cube, which provides a new thought of searching for periodic orbits around non-spherical celestial bodies. The investigation of orbits around the cube could be considered as the first step of the complicated cases, and helps to understand the dynamics of orbits around bodies with complicated shapes. The work is an extension of the previous research work about the dynamics of orbits around some simple shaped bodies, including a straight segment, a circular ring, an annulus disk, and simple planar plates.
Bin Liu, Man-To Hui, Xiaodong Liu
Jul 17, 2025·astro-ph.EP·PDF In this study, the dust loss of comet C/2023 A3 (Tsuchinshan-ATLAS) is investigated through the analysis of archival images. By measuring the surface brightness profile of the coma, we determined that the comet maintained nearly in a steady state during the observations. Analysis of the dust distribution perpendicular to the orbital plane reveals that the ejection velocity is $v_{\perp}\sim(65\pm5)\,β^{1/2}$ m s$^{-1}$, where $β$ is inversely proportional to the size of the dust grains. From the dust scattering cross-section measurement, we estimated the upper limit of the nucleus radius to be $\sim\!5.9\pm0.2$ km, assuming a geometric albedo of 0.04. Based on the extrapolation of the scattering cross-section over time, the onset time of significant dust activity is estimated to be 25 July 2022, corresponding to a heliocentric distance of 9.1 au, with the activity mechanism at this distance likely being the phase transition from amorphous to crystalline ice. Our simulation reveals that the minimum dust size is \SI{20}{\micro\meter} and the size distribution index is $s = 3.4$ in tail. The dust loss rate is determined to be $(1.7 \pm 0.8) \times 10^2$ kg s$^{-1}$, based on the derived average size of the particles and the rate of change of the scattering cross-section over time. Through a simplistic model, we evaluate that the nucleus of the comet remains stable against tidal effects, sublimation, and rotational instability, and disfavour the fate of disintegration. The result is consistent with observations that the nucleus has survived.
Bin Liu, Cunhui Li, Zhongcheng Mu, Xiaodong Liu
Jul 17, 2025·astro-ph.EP·PDF Main-belt asteroid 2010 LH$_{15}$ has been classified as an active asteroid, based on the recent discovery of dust activity from the archival images observed in 2010 and 2019. In this study, we perform measurements and dynamical modeling of the dust tail of the active asteroid 2010 LH$_{15}$ using ZTF archival data from July 26 to August 31, 2019, with the derived physical properties from these relatively independent methods being compatible. The photometric results show that the radius of the nucleus is $1.11\pm0.02$ km with assumed geometric albedo of $p_r = 0.05$, and the color index of the nucleus is relatively close to that of the ejecta around the nucleus, with a value of $H_g - H_r = 0.44\pm0.07$. The effective scattering cross-section increases at an average rate of $0.28\pm0.02$ km$^2$ day$^{-1}$ throughout the observation period, indicating that the activity of LH$_{15}$ is likely driven by mechanisms capable of causing a sustained process like sublimation. Further dust dynamics modeling indicates that the dust activity initiates as early as about 26 June 2019, with the ejected dust particles having a radius ranging from 0.03 mm to 3 mm. The dependence of the terminal velocity on dust size is consistent with a sublimation-driven mechanism. If the orbit of LH$_{15}$ is stable, its sublimation origin will extend the inner boundary of the water-ice-bearing region in the main asteroid belt inward by approximately 0.1 AU.
Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao
We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn.
Xia Ji, Xiaodong Liu
This paper is concerned with uniqueness, phase retrieval and shape reconstruction methods for inverse elastic scattering problems with phaseless far field data. Systematically, we study two basic models, i.e., inverse scattering of plane waves by rigid bodies and inverse scattering of sources with compact support. For both models, we show that the location of the objects can not be uniquely recovered by the data. To solve this problem, we consider simultaneously the incident point sources with one fixed source point and at most three scattering strengths. We then establish some uniqueness results for source scattering problem with multi-frequency phaseless far field data. Furthermore, a fast and stable phase retrieval approach is proposed based on a simple geometric result which provides a stable reconstruction of a point in the plane from three distances to given points. Difficulties arise for inverse scattering by rigid bodies due to the additional unknown far field pattern of the point sources. To overcome this difficulty, we introduce an artificial rigid body into the system and show that the underlying rigid bodies can be uniquely determined by the corresponding phaseless far field data at a fixed frequency. Noting that the far field pattern of the scattered field corresponding to point sources is very small if the source point is far away from the scatterers, we propose an appropriate phase retrieval method for obstacle scattering problems, without using the artificial rigid body. Finally, we propose several sampling methods for shape reconstruction with phaseless far field data. Extended numerical examples in two dimensions are conducted with noisy data, and the results further verify the effectiveness and robustness of the proposed phase retrieval techniques and sampling methods.