Bo Xiong
We investigate localized atomic matter waves in two-component Bose-Einstein condensates coupled by the two photon microwave field. Interestingly, the oscillations of localized atomic matter waves will gradually decay and finally become non-oscillating behavior even if existing coupling field. In particular, atom numbers occupied in two different hyperfine spin states will appear asymmetric occupations after some time evolution.
Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab
Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry. Recent works find that non-Euclidean Riemannian manifolds provide specific inductive biases for embedding hierarchical or spherical data. However, they cannot align well with data of mixed graph topologies. We consider a larger class of pseudo-Riemannian manifolds that generalize hyperboloid and sphere. We develop new geodesic tools that allow for extending neural network operations into geodesically disconnected pseudo-Riemannian manifolds. As a consequence, we derive a pseudo-Riemannian GCN that models data in pseudo-Riemannian manifolds of constant nonzero curvature in the context of graph neural networks. Our method provides a geometric inductive bias that is sufficiently flexible to model mixed heterogeneous topologies like hierarchical graphs with cycles. We demonstrate the representational capabilities of this method by applying it to the tasks of graph reconstruction, node classification and link prediction on a series of standard graphs with mixed topologies. Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
Bo Xiong, Mojtaba Nayyer, Shirui Pan, Steffen Staab
Link prediction on knowledge graphs (KGs) has been extensively studied on binary relational KGs, wherein each fact is represented by a triple. A significant amount of important knowledge, however, is represented by hyper-relational facts where each fact is composed of a primal triple and a set of qualifiers comprising a key-value pair that allows for expressing more complicated semantics. Although some recent works have proposed to embed hyper-relational KGs, these methods fail to capture essential inference patterns of hyper-relational facts such as qualifier monotonicity, qualifier implication, and qualifier mutual exclusion, limiting their generalization capability. To unlock this, we present \emph{ShrinkE}, a geometric hyper-relational KG embedding method aiming to explicitly model these patterns. ShrinkE models the primal triple as a spatial-functional transformation from the head into a relation-specific box. Each qualifier ``shrinks'' the box to narrow down the possible answer set and, thus, realizes qualifier monotonicity. The spatial relationships between the qualifier boxes allow for modeling core inference patterns of qualifiers such as implication and mutual exclusion. Experimental results demonstrate ShrinkE's superiority on three benchmarks of hyper-relational KGs.
Uwe R. Fischer, Bo Xiong
We consider a two-mode model describing scalar bosons with two-body interactions in a single trap, taking into account coherent pair-exchange between the modes. It is demonstrated that the resulting fragmented many-body states with continuous (nonsingular) Fock-space distribution amplitudes are robust against perturbations due to occupation number and relative phase fluctuations, Josephson-type tunneling between the modes, and weakly broken parity of orbitals, as well as against perturbations due to interaction with a third mode.
Bo Xiong, Jun-hui Zheng, Yu-Ju Lin, Daw-wei Wang
We investigate magnetic phase in the bilayer system of ultra-cold bosons in an optical lattice, which is involved with Raman-induced spin-orbit (SO) coupling and laser-assisted interlayer tunneling. It is shown that there exit a rich of spin textures such as hetero ferromagnet, heterochiral magnet, chiral magnet with interlayer antiferromagnet. In particular, heterochiral magnet induced by SO coupling occurs extremely rarely in real solid-state materials. We present detailed experimental setup of realizing such a model in cold atom system.
Bo Xiong
We show that how to generate propagation of spin degree in spin-symmetric exciton-polariton condensates in a semiconductor microcavity. Due to the stimulated spin-dependent scattering between hot excitons and condensates, exciton polaritons form a circular polarized condensate with spontaneous breaking of the spin rotation symmetry. The spin antiferromagnetic state is developed evidently from the density and spin flow pumped by localized laser source. The low energy spin current is identified where the steady state is characterized by the oscillating spin pattern. Finally, we predict via simulation how to dynamical generation of phase slip where ring-shape phase jump shows the behavior of splitting and joining together.
Bo Xiong, Yimin Huang, Hanrong Ye, Steffen Staab, Zhenguo Li
This paper presents a novel and lightweight hyperparameter optimization (HPO) method, MOdular FActorial Design (MOFA). MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter space by factorial design and exploitation of evaluation results by factorial analysis. Each round first explores the configuration space by constructing a low-discrepancy set of hyperparameters that cover this space well while de-correlating hyperparameters, and then exploits evaluation results through factorial analysis that determines which hyperparameters should be further explored and which should become fixed in the next round. We prove that the inference of MOFA achieves higher confidence than other sampling schemes. Each individual round is highly parallelizable and hence offers major improvements of efficiency compared to model-based methods. Empirical results show that MOFA achieves better effectiveness and efficiency compared with state-of-the-art methods.
Bo Xiong, Yannis Kalantidis, Deepti Ghadiyaram, Kristen Grauman
Highlight detection has the potential to significantly ease video browsing, but existing methods often suffer from expensive supervision requirements, where human viewers must manually identify highlights in training videos. We propose a scalable unsupervised solution that exploits video duration as an implicit supervision signal. Our key insight is that video segments from shorter user-generated videos are more likely to be highlights than those from longer videos, since users tend to be more selective about the content when capturing shorter videos. Leveraging this insight, we introduce a novel ranking framework that prefers segments from shorter videos, while properly accounting for the inherent noise in the (unlabeled) training data. We use it to train a highlight detector with 10M hashtagged Instagram videos. In experiments on two challenging public video highlight detection benchmarks, our method substantially improves the state-of-the-art for unsupervised highlight detection.
Bo Xiong, Tao Yang, Yu-Ju Lin, Daw-wei Wang
We study the dynamics of a doubly quantized vortex (DQV), created by releasing a ring-shaped Bose-Einstein condensate with quantized circulation into harmonic potential traps. It is shown that a DQV can be generated and exists stably in the middle of the ring-shaped condensate with the initial circulation $s = 2$ after released into the rotationally symmetric trap potential. For an asymmetric trap with a small degree of anisotropy the DQV initially splits into two singly quantized vortices and revives again but eventually evolves into two unit vortices due to the dynamic instability. For the degree of anisotropy above a critical value, the DQV is extremely unstably and decays rapidly into two singlet vortices. The geometry-dependent lifetime of the DQV and vortex-induced excitations are also discussed intensively.
Bo Xiong, Mojtaba Nayyeri, Linhao Luo, Zihao Wang, Shirui Pan, Steffen Staab
Reasoning with knowledge graphs (KGs) has primarily focused on triple-shaped facts. Recent advancements have been explored to enhance the semantics of these facts by incorporating more potent representations, such as hyper-relational facts. However, these approaches are limited to \emph{atomic facts}, which describe a single piece of information. This paper extends beyond \emph{atomic facts} and delves into \emph{nested facts}, represented by quoted triples where subjects and objects are triples themselves (e.g., ((\emph{BarackObama}, \emph{holds\_position}, \emph{President}), \emph{succeed\_by}, (\emph{DonaldTrump}, \emph{holds\_position}, \emph{President}))). These nested facts enable the expression of complex semantics like \emph{situations} over time and \emph{logical patterns} over entities and relations. In response, we introduce NestE, a novel KG embedding approach that captures the semantics of both atomic and nested factual knowledge. NestE represents each atomic fact as a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix that rotates the $1\times3$ atomic fact matrix through matrix multiplication. Each element of the matrix is represented as a complex number in the generalized 4D hypercomplex space, including (spherical) quaternions, hyperbolic quaternions, and split-quaternions. Through thorough analysis, we demonstrate the embedding's efficacy in capturing diverse logical patterns over nested facts, surpassing the confines of first-order logic-like expressions. Our experimental results showcase NestE's significant performance gains over current baselines in triple prediction and conditional link prediction. The code and pre-trained models are open available at https://github.com/xiongbo010/NestE.
Uwe R. Fischer, Kang-Soo Lee, Bo Xiong
We investigate the dynamical mode population statistics and associated first- and second-order coherence of an interacting bosonic two-mode model when the pair-exchange coupling is quenched from negative to positive values. It is shown that for moderately rapid second-order transitions, a new pair-coherent phase emerges on the positive coupling side in an excited state, which is not fragmented as the ground-state single-particle density matrix would prescribe it to be.
Bo Xiong, Kristen Grauman
360$^{\circ}$ panoramas are a rich medium, yet notoriously difficult to visualize in the 2D image plane. We explore how intelligent rotations of a spherical image may enable content-aware projection with fewer perceptible distortions. Whereas existing approaches assume the viewpoint is fixed, intuitively some viewing angles within the sphere preserve high-level objects better than others. To discover the relationship between these optimal snap angles and the spherical panorama's content, we develop a reinforcement learning approach for the cubemap projection model. Implemented as a deep recurrent neural network, our method selects a sequence of rotation actions and receives reward for avoiding cube boundaries that overlap with important foreground objects. We show our approach creates more visually pleasing panoramas while using 5x less computation than the baseline.
Zhenya Yan, Bo Xiong, Wu-Ming Liu
We report explicitly a novel family of exact PT-symmetric solitons and further study their spontaneous PT symmetry breaking, stabilities and collisions in Bose-Einstein condensates trapped in a PT-symmetric harmonic trap and a Hermite-Gaussian gain/loss potential. We observe the significant effects of mean-field interaction by modifying the threshold point of spontaneous PT symmetry breaking in Bose-Einstein condensates. Our scenario provides a promising approach to study PT-related universal behaviors in non-Hermitian quantum system based on the manipulation of gain/loss potential in Bose-Einstein condensates.
Suyog Dutt Jain, Bo Xiong, Kristen Grauman
We propose an end-to-end learning framework for segmenting generic objects in videos. Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos. We formulate this task as a structured prediction problem and design a two-stream fully convolutional neural network which fuses together motion and appearance in a unified framework. Since large-scale video datasets with pixel level segmentations are problematic, we show how to bootstrap weakly annotated videos together with existing image recognition datasets for training. Through experiments on three challenging video segmentation benchmarks, our method substantially improves the state-of-the-art for segmenting generic (unseen) objects. Code and pre-trained models are available on the project website.
Uwe R. Fischer, Bo Xiong
We consider the rapid quench of a one-dimensional strongly correlated supersolid to a localized density wave (checkerboard) phase, and calculate the first-order coherence signal following the quench. It is shown that unique coherence oscillations between the even and odd sublattice sites of the checkerboard are created by the quench, which are absent when the initial state is described by a Gutzwiller product state. This is a striking manifestation of the versatility of the far-from-equilbrium and nonperturbative collapse and revival phenomenon as a microscope for quantum correlations in complex many-body states. For the present example, this opens up the possibility to discriminate experimentally between mean-field and many-body origins of supersolidity.
Bo Xiong, Tao Yang, Keith A. Benedict
We investigate the effects of interatomic interactions and expansion on the distortion of interference fringes of a pair of initially well-separated, but coherent, condensate clouds trapped in a harmonic trap. The distortion of interference fringes, which can lead to the spontaneous formation of vortices in the atom clouds, depends crucially on two relevant parameters: the center-of-mass velocity and peak density of the initial state. We identify three qualitatively distinct regimes for the interfering condensates: collision, expansion, and merging, by the spatial and temporal features of the fringe spacings. Using a comprehensive set of numerical simulations based on the Gross-Pitaevskii equation, we specify the cross-overs between these regimes and propose the optimal the system parameters required for dynamical instabilities and vortex creation.
Qun Wang, Bo Xiong
We investigate the low energy excitations of a dilute atomic Bose gas confined in a anharmonic trap interacting with repulsive forces. The dispersion law of both surface and compression modes are derived and analyzed for large numbers of atoms in the trap, which show two branches of excitation and appear a two critical value. For a upper limit, BEC can be unstable with respect to some specific collective excitation, while for the lower limit, the frequency of collective excitation under anharmonic influence can be effectively lower than that without anharmonicity. Our work reveals the key role played by the anharmonicity and interatomic forces which introduce a rich structure in the dynamic behavior of these new many-body systems.
Bo Xiong
Relational representation learning transforms relational data into continuous and low-dimensional vector representations. However, vector-based representations fall short in capturing crucial properties of relational data that are complex and symbolic. We propose geometric relational embeddings, a paradigm of relational embeddings that respect the underlying symbolic structures. Specifically, this dissertation introduces various geometric relational embedding models capable of capturing: 1) complex structured patterns like hierarchies and cycles in networks and knowledge graphs; 2) logical structures in ontologies and logical constraints applicable for constraining machine learning model outputs; and 3) high-order structures between entities and relations. Our results obtained from benchmark and real-world datasets demonstrate the efficacy of geometric relational embeddings in adeptly capturing these discrete, symbolic, and structured properties inherent in relational data.
Dongmei Wang, Bo Xiong, Tao Yang
We study the dynamic behavior of a Bose-Einstein condensate (BEC) containing a dark soliton separately reflected from potential drops and potential barriers. It is shown that for a rapidly varying potential and in a certain regime of incident velocity, the quantum reflection probability displays the cosine of the deflection angle between the incident soliton and the reflected soliton, i.e., $R(θ) \sim \cos 2θ$. For a potential drop, $R(θ)$ is susceptible to the widths of potential drop up to the length of the dark soliton and the difference of the reflection rates between the orientation angle of the soliton $θ=0$ and $θ=π/2$, $δR_s$, displays oscillating exponential decay with increasing potential widths. However, for a barrier potential, $R(θ)$ is insensitive for the potential width less than the decay length of the matter wave and $δR_s$ presents an exponential trend. This discrepancy of the reflectances in two systems is arisen from the different behaviors of matter waves in the region of potential variation.
Xin Wang, Bo Yang, Bo Zhang, Bo Xiong
We study the buildup of antiferromagnetic (AF) correlation in the dynamically tuned Ising models which are realized by the Rydberg atomic system. In short-time scale, we apply Magnus expansion (ME) to derive the high-order analytic expression of the connected correlation functions and compare it with exactly numerical results for the different lattice geometries, e.g., 1D chain, $2 \times n$ lattice, and $n \times n$ lattice. It is shown that the high-order expansion is required to describe accurately the buildup of AF correlation in the quench dynamics. Moreover, through a 2D square lattice, we find that the magnitude of AF correlation for the same Manhattan distance is proportional to the number of the shortest paths in a sufficiently long time until long and distinct paths are involved significantly with the buildup of the correlation. Finally, we propose an applicable experimental setup to realize our findings.