arXiv subjects

Jian Wang

Publications and source records attributed to Jian Wang.

At least 919 records · Page 51Linked to original sources

arXiv2019

Distribution of high-dimensional orbital angular momentum entanglement at telecom wavelength over 1km of optical fibre

High-dimensional entanglement has demonstrated potential for increasing channel capacity and resistance to noise in quantum information processing. However, its distribution is a challenging task, imposing a severe restriction on its application. Here we report the first distribution of three-dimensional orbital angular momentum (OAM) entanglement via a 1-km-long optical fibre. Using an actively-stabilizing phase pre-compensation technique, we successfully transport one photon of a three-dimensional OAM entangled photon pair through the fibre. The distributed OAM entangled state still shows a fidelity up to 71\% with respect to the three-dimensional maximal-entangled-state (MES). In addition, we certify that the high-dimensional quantum entanglement survives the transportation by violating a generalized Bell inequality, obtaining a violation of $\sim3$ standard deviations with $I_{3}=2.12\pm0.04$. The method we developed can be extended to higher OAM dimension and larger distances in principle. Our results make a significant step towards future OAM-based high-dimensional long-distance quantum communication.

quant-ph↗

arXiv2019

Leading-logarithmic threshold resummation of Higgs production in gluon fusion at next-to-leading power

We sum the leading logarithms $α_s^n \ln^{2 n-1}(1-z)$, n=1,2,... near the kinematic threshold $z=m_H^2/\hat{s}\to 1$ at next-to-leading power in the expansion in (1-z) for Higgs production in gluon fusion. We highlight the new contributions compared to Drell-Yan production in quark-antiquark annihilation and show that the final result can be obtained to all orders by the substitution of the colour factor $C_F\to C_A$, confirming previous fixed-order results and conjectures. We also provide a numerical analysis of the next-to-leading power leading logarithms, which indicates that they are numerically relevant.

hep-ph↗

arXiv2019

The scattering matrix for 0th order pseudodifferential operators

We use microlocal radial estimates to prove the full limiting absorption principle for $P$, a self-adjoint 0th order pseudodifferential operator satisfying hyperbolic dynamical assumptions as of Colin de Verdière and Saint-Raymond. We define the scattering matrix for $P-ω$ with generic $ω\in \mathbb R$ and show that the scattering matrix extends to a unitary operator on appropriate $L^2$ spaces. After conjugation with natural reference operators, the scattering matrix becomes a $0$th order Fourier integral operator with a canonical relation associated to the bicharacteristics of $P-ω$. The operator $P$ gives a microlocal model of internal waves in stratified fluids as illustrated in the paper of Colin de Verdière and Saint-Raymond.

math.AP↗

arXiv2019

TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation

In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story. This differs from traditional video highlight detection or video summarization problems in that each sub-video is required to maintain a coherent and integral story, which is becoming particularly important for resource-production video sharing platforms such as Youtube, Facebook, TikTok, Kwai, etc. To address the problem, we collect and annotate a new large video truncation dataset, named as TruNet, which contains 1470 videos with on average 11 short stories per video. With the new dataset, we further develop and train a neural architecture for video truncation that consists of two components: a Boundary Aware Network (BAN) and a Fast-Forward Long Short-Term Memory (FF-LSTM). We first use the BAN to generate high quality temporal proposals by jointly considering frame-level attractiveness and boundaryness. We then apply the FF-LSTM, which tends to capture high-order dependencies among a sequence of frames, to decide whether a temporal proposal is a coherent and integral story. We show that our proposed framework outperforms existing approaches for the story-preserving long video truncation problem in both quantitative measures and user-study. The dataset is available for public academic research usage at https://ai.baidu.com/broad/download.

cs.CV↗

arXiv2019

Preconditioned Multiple Orthogonal Least Squares and Applications in Ghost Imaging via Sparsity Constraint

Ghost imaging via sparsity constraint (GISC) can recover objects from the intensity fluctuation of light fields even when the sampling rate is far below the Nyquist sampling rate. In this paper, we develop an efficient algorithm called the preconditioned multiple orthogonal least squares (PmOLS) for solving the GISC reconstruction problem. Our analysis shows that the PmOLS algorithm perfectly recovers any $n$-dimensional $K$-sparse signal from $m$ random linear samples of the signal with probability exceeding $1-3n^2 e^{-cm/K^2}$. Simulations and experiments demonstrate that the proposed algorithm has very competitive imaging quality compared to the state-ofthe-art methods.

cs.IT↗

arXiv2019

Node similarity distribution of complex networks and its application in link prediction

Over the years, quantifying the similarity of nodes has been a hot topic in complex networks, yet little has been known about the distributions of node-similarity. In this paper, we consider a typical measure of node-similarity called the common neighbor based similarity (CNS). By means of the generating function, we propose a general framework for calculating the CNS distributions of node sets in various complex networks. In particular, we show that for the Erdös-Rényi (ER) random network, the CNS distribution of node sets of any particular size obeys the Poisson law. We also connect the node-similarity distribution to the link prediction problem. We found that the performance of link prediction depends solely on the CNS distributions of the connected and unconnected node pairs in the network. Furthermore, we derive theoretical solutions of two key evaluation metrics in link prediction: i) precision and ii) area under the receiver operating characteristic curve (AUC). We show that for any link prediction method, if the similarity distributions of the connected and unconnected node pairs are identical, the AUC will be $0.5$. The theoretical solutions are elegant alternatives of the traditional experimental evaluation methods with nevertheless much lower computational cost.

cs.SI↗

arXiv2019

Towards Good Practices for Video Object Segmentation

Semi-supervised video object segmentation is an interesting yet challenging task in machine learning. In this work, we conduct a series of refinements with the propagation-based video object segmentation method and empirically evaluate their impact on the final model performance through ablation study. By taking all the refinements, we improve the space-time memory networks to achieve a Overall of 79.1 on the Youtube-VOS Challenge 2019.

cs.CV↗

arXiv2019

Identifying significant edges via neighborhood information

Heterogeneous nature of real networks implies that different edges play different roles in network structure and functions, and thus to identify significant edges is of high value in both theoretical studies and practical applications. We propose the so-called second-order neighborhood (SN) index to quantify an edge's significance in a network. We compare SN index with many other benchmark methods based on 15 real networks via edge percolation. Results show that the proposed SN index outperforms other well-known methods.

physics.soc-ph↗

arXiv2019

Exponential ergodicity for general continuous-state nonlinear branching processes

By using the coupling technique, we present sufficient conditions for the exponential ergodicity of general continuous-state nonlinear branching processes in both the $L^1$-Wasserstein distance and the total variation norm, where the drift term is dissipative only for large distance, and either diffusion noise or jump noise is allowed to be vanished. Sufficient conditions for the corresponding strong ergodicity are also established.

math.PR↗

arXiv2019

Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning

Aspect category detection is an essential task for sentiment analysis and opinion mining. However, the cost of categorical data labeling, e.g., label the review aspect information for a large number of product domains, can be inevitable but unaffordable. In this study, we propose a novel problem, cross-domain aspect category transfer and detection, which faces three challenges: various feature spaces, different data distributions, and diverse output spaces. To address these problems, we propose an innovative solution, Traceable Heterogeneous Graph Representation Learning (THGRL). Unlike prior text-based aspect detection works, THGRL explores latent domain aspect category connections via massive user behavior information on a heterogeneous graph. Moreover, an innovative latent variable "Walker Tracer" is introduced to characterize the global semantic/aspect dependencies and capture the informative vertexes on the random walk paths. By using THGRL, we project different domains' feature spaces into a common one, while allowing data distributions and output spaces stay differently. Experiment results show that the proposed method outperforms a series of state-of-the-art baseline models.

cs.CL↗

arXiv2019

NIL: Learning Nonlinear Interpolants

Nonlinear interpolants have been shown useful for the verification of programs and hybrid systems in contexts of theorem proving, model checking, abstract interpretation, etc. The underlying synthesis problem, however, is challenging and existing methods have limitations on the form of formulae to be interpolated. We leverage classification techniques with space transformations and kernel tricks as established in the realm of machine learning, and present a counterexample-guided method named NIL for synthesizing polynomial interpolants, thereby yielding a unified framework tackling the interpolation problem for the general quantifier-free theory of nonlinear arithmetic, possibly involving transcendental functions. We prove the soundness of NIL and propose sufficient conditions under which NIL is guaranteed to converge, i.e., the derived sequence of candidate interpolants converges to an actual interpolant, and is complete, namely the algorithm terminates by producing an interpolant if there exists one. The applicability and effectiveness of our technique are demonstrated experimentally on a collection of representative benchmarks from the literature, where in particular, our method suffices to address more interpolation tasks, including those with perturbations in parameters, and in many cases synthesizes simpler interpolants compared with existing approaches.

cs.LO↗

arXiv2019

Heat kernel estimates for general symmetric pure jump Dirichlet forms

In this paper, we consider the following symmetric non-local Dirichlet forms of pure jump type on metric measure space $(M,d,μ)$: $$\mathcal{E}(f,g)=\int_{M\times M} (f(x)-f(y))(g(x)-g(y))\,J(dx,dy),$$ where $J(dx,dy)$ is a symmetric Radon measure on $M\times M\setminus {\rm diag}$ that may have different scalings for small jumps and large jumps. Under general volume doubling condition on $(M,d,μ)$ and some mild quantitative assumptions on $J(dx, dy)$ that are allowed to have light tails of polynomial decay at infinity, we establish stability results for two-sided heat kernel estimates as well as heat kernel upper bound estimates in terms of jumping kernel bounds, the cut-off Sobolev inequalities, and the Faber-Krahn inequalities (resp.\ the Poincaré inequalities). We also give stable characterizations of the corresponding parabolic Harnack inequalities.

math.PR↗

arXiv2019

Heat kernel estimates and parabolic Harnack inequalities for symmetric Dirichlet forms

In this paper, we consider the following symmetric Dirichlet forms on a metric measure space $(M,d,μ)$: $$\mathcal{E}(f,g) = \mathcal{E}(^{(c)}(f,g)+\int_{M\times M} (f(x)-f(y))(g(x)-g(y))\,J(dx,dy),$$ where $\mathcal{E}(^{(c)}$ is a strongly local symmetric bilinear form and $J(dx,dy)$ is a symmetric Random measure on $M\times M$. Under general volume doubling condition on $(M,d,μ)$ and some mild assumptions on scaling functions, we establish stability results for upper bounds of heat kernel (resp.\ two-sided heat kernel estimates) in terms of the jumping kernels, the cut-off Sobolev inequalities, and the Faber-Krahn inequalities (resp.\ the Poincaré inequalities). We also obtain characterizations of parabolic Harnack inequalities. Our results apply to symmetric diffusions with jumps even when the underlying spaces have walk dimensions larger than $2$.

math.PR↗

arXiv2019

Optimization of light fields in ghost imaging using dictionary learning

Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often reconstruct objects with unsatisfactory quality. To improve the imaging results, many reconstruction methods have been developed, yet the reconstruction quality is still fundamentally restricted by the modulated light fields. In this paper, we propose to improve the imaging quality of GI by optimizing the light fields, which is realized via matrix optimization for a learned dictionary incorporating the sparsity prior of objects. A closed-form solution of the sampling matrix, which enables successive sampling, is derived. Through simulation and experimental results, it is shown that the proposed scheme leads to better imaging quality compared to the state-of-the-art optimization methods for light fields, especially at a low sampling rate.

eess.IV↗

arXiv2019

Anomalous energy diffusion in two-dimensional nonlinear lattices

Heat transport in one-dimensional (1D) momentum-conserving lattices is generally assumed to be anomalous, thus yielding a power-law divergence of thermal conductivity with system length. However, whether heat transport in two-dimensional (2D) system is anomalous or not is still on debate because of the difficulties involved in experimental measurements or due to the insufficiently large simulation size. Here, we simulate energy and momentum diffusion in the 2D nonlinear lattices using the method of fluctuation correlation functions. Our simulations confirm that energy diffusion in the 2D momentum-conserving lattices is anomalous and can be well described by the Lévy-stable distribution. We also find that the disappear of side peaks of heat mode may suggest a weak coupling between heat mode and sound mode in the 2D nonlinear system. It is also observed that the harmonic interactions in the 2D nonlinear lattices can accelerate the energy diffusion. Contrary to the hypothesis of 1D system, we clarify that anomalous heat transport in the 2D momentum-conserving system cannot be corroborated by the momentum superdiffusion any more. Moreover, as is expected, lattices with a nonlinear on-site potential exhibit normal energy diffusion, independent of the dimension. Our findings offer some valuable insights into the mechanism of thermal transport in 2D system.

cond-mat.stat-mech↗

arXiv2019

Deep Reinforcement Learning for Scheduling in Cellular Networks

Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia. A common solution is to replace partial or even all modules in the conventional systems, which is often lack of efficiency and robustness due to their ignoring of expert knowledge. In this paper, we take deep reinforcement learning (DRL) based scheduling as an example to investigate how expert knowledge can help with AI module in cellular networks. A simulation platform, which has considered link adaption, feedback and other practical mechanisms, is developed to facilitate the investigation. Besides the traditional way, which is learning directly from the environment, for training DRL agent, we propose two novel methods, i.e., learning from a dual AI module and learning from the expert solution. The results show that, for the considering scheduling problem, DRL training procedure can be improved on both performance and convergence speed by involving the expert knowledge. Hence, instead of replacing conventional scheduling module in the system, adding a newly introduced AI module, which is capable to interact with the conventional module and provide more flexibility, is a more feasible solution.

cs.IT↗

arXiv2019

Realistic Channel Models Pre-training

In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models. To facilitate this realistic channel modeling, a multi-domain channel embedding method combined with self-attention mechanism is proposed to extract channel features from multiple domains simultaneously. This 'one model to fit them all' solution employs available wireless channel data as the only data set for self-supervised pre-training. With the permission of users, network operators or other organizations can make use of some available user specific data to fine-tune this pre-trained realistic channel model for applications on channel-related downstream tasks. Moreover, even without fine-tuning, we show that the pre-trained realistic channel model itself is a great tool with its understanding of wireless channel.

eess.SP↗

arXiv2019

Heterostructural one-unit-cell FeSe/SrTiO3: from high-temperature superconductivity to topological states

High temperature superconductivity in one unit cell (1 UC) FeSe on SrTiO3 heterostructure has become a research frontier in condensed matter physics and material science. The superconducting transition temperature (Tc) of ultrathin FeSe film is significantly enhanced compared to its bulk counterpart and possibly approaches the liquid nitrogen region according to in situ spectroscopic measurements. Particularly, the Fermi surface topology of 1 UC FeSe consists of no hole pockets at Brillouin zone center, which poses a great challenge to the well accepted s+- wave pairing nesting the sign different electron hole Fermi pockets in iron based superconductors. In this review, we present the explorations of Tc enhancement, electron pairing and topological phases in 1 UC FeSe on SrTiO3. The potential coexistence of high temperature superconductivity and topological electronic states promotes such two dimensional heterostructure as the candidate of next generation connate high temperature topological superconductor and (or) topological quantum computation platform.

cond-mat.supr-con↗