arXiv subjects

Bin Liu

Publications and source records attributed to Bin Liu.

At least 379 records · Page 21Linked to original sources

arXiv2021

A Random Algorithm for Profit Maximization with Multiple Adoptions in Online Social Networks

Online social networks have been one of the most effective platforms for marketing and advertising. Through "word of mouth" effects, information or product adoption could spread from some influential individuals to millions of users in social networks. Given a social network $G$ and a constant $k$, the influence maximization problem seeks for $k$ nodes in $G$ that can influence the largest number of nodes. This problem has found important applications, and a large amount of works have been devoted to identifying the few most influential users. But most of existing works only focus on the diffusion of a single idea or product in social networks. However, in reality, one company may produce multiple kinds of products and one user may also have multiple adoptions. Given multiple kinds of different products with different activation costs and profits, it is crucial for the company to distribute the limited budget among multiple products in order to achieve profit maximization. Profit Maximization with Multiple Adoptions (PM$^{2}$A) problem aims to seek for a seed set within the budget to maximize the overall profit. In this paper, a Randomized Modified Greedy (RMG) algorithm based on the Reverse Influence Sampling (RIS) technique is presented for the PM$^{2}$A problem, which could achieve a $(1-1/e-\varepsilon)$-approximate solution with high probability. Compared with the algorithm proposed in [16] that achieves a $\frac{1}{2}(1-1/e^{2})$-approximate solution, our algorithm provides a better performance ratio which is also the best performance ratio of the PM$^{2}$A problem. Comprehensive experiments on three real-world social networks are conducted, and the results demonstrate that our RMG algorithm outperforms the algorithm proposed in [16] and other heuristics in terms of profit maximization, and could better allocate the budget.

cs.SI↗

arXiv2021

Enhanced Lidov-Kozai migration and the formation of the transiting giant planet WD1856+534b

We investigate the possible origin of the transiting giant planet WD1856+534b, the first strong exoplanet candidate orbiting a white dwarf, through high-eccentricity migration (HEM) driven by the Lidov-Kozai (LK) effect. The host system's overall architecture is an hierarchical quadruple in the '2+2' configuration, owing to the presence of a tertiary companion system of two M-dwarfs. We show that a secular inclination resonance in 2+2 systems can significantly broaden the LK window for extreme eccentricity excitation ($e \gtrsim 0.999$), allowing the giant planet to migrate for a wide range of initial orbital inclinations. Octupole effects can also contribute to the broadening of this 'extreme' LK window. By requiring that perturbations from the companion stars be able to overcome short-range forces and excite the planet's eccentricity to $e \simeq 1$, we obtain an absolute limit of $a_{1} \gtrsim 8 \, {\rm AU} \, (a_{3} / 1500 \, {\rm AU})^{6/7}$ for the planet's semi-major axis just before migration (where $a_{3}$ is the semi-major axis of the 'outer' orbit). We suggest that, to achieve a wide LK window through the 2+2 resonance, WD1856b likely migrated from $30 \, {\rm AU} \lesssim a_{1} \lesssim 60 \, {\rm AU}$, corresponding to $\sim 10$--$20 \, {\rm AU}$ during the host's main-sequence phase. We discuss possible difficulties of all flavours of HEM affecting the occurrence rate of short-period giant planets around white dwarfs.

astro-ph.EP↗

arXiv2021

Data Poisoning Attacks to Deep Learning Based Recommender Systems

Recommender systems play a crucial role in helping users to find their interested information in various web services such as Amazon, YouTube, and Google News. Various recommender systems, ranging from neighborhood-based, association-rule-based, matrix-factorization-based, to deep learning based, have been developed and deployed in industry. Among them, deep learning based recommender systems become increasingly popular due to their superior performance. In this work, we conduct the first systematic study on data poisoning attacks to deep learning based recommender systems. An attacker's goal is to manipulate a recommender system such that the attacker-chosen target items are recommended to many users. To achieve this goal, our attack injects fake users with carefully crafted ratings to a recommender system. Specifically, we formulate our attack as an optimization problem, such that the injected ratings would maximize the number of normal users to whom the target items are recommended. However, it is challenging to solve the optimization problem because it is a non-convex integer programming problem. To address the challenge, we develop multiple techniques to approximately solve the optimization problem. Our experimental results on three real-world datasets, including small and large datasets, show that our attack is effective and outperforms existing attacks. Moreover, we attempt to detect fake users via statistical analysis of the rating patterns of normal and fake users. Our results show that our attack is still effective and outperforms existing attacks even if such a detector is deployed.

cs.CR↗

arXiv2020

Adapting Active Reflector Technology for greater sensitivity and sky-coverage in FAST-like Telescopes

The Five-hundred-meter Aperture Spherical radio Telescope (FAST), the largest single dish radio telescope in the world, has implemented an innovative technology for its huge reflector, which changes the shape of the primary reflector from spherical to that of a paraboloid of 300 m aperture. Here we explore how the current FAST sensitivity can potentially be further improved by increasing the illuminated area (i.e., the aperture of the paraboloid embedded in the spherical surface). Alternatively, the maximum zenith angle can be increased to give greater sky coverage by decreasing the illuminated aperture.Different parabolic apertures within the FAST capability are analyzed in terms of how far the spherical surface would have to move to approximate a paraboloid. The sensitivity of FAST can be improved by approximately 10 % if the aperture of the paraboloid is increased from 300 m to 315 m. The parabolic aperture lies within the main spherical surface and does not extend beyond its edge. The maximum zenith angle can be increased to approximately 35 degrees from 26.4 degrees, if we decrease the aperture of the paraboloid to 220 m. This would still give a sensitivity similar to the Arecibo 305 m radio telescope. Radial deviations between paraboloids of different apertures and the spherical surfaces of differing radii are also investigated. Maximum zenith angles corresponding to different apertures of the paraboloid are further derived. A spherical surface with a different radius can provide a reference baseline for shape-changing applied through active reflector technology to FAST-like telescopes.

astro-ph.IM↗

arXiv2020

Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning

Recurrent neural networks (RNNs) have shown significant improvements in recent years for speech enhancement. However, the model complexity and inference time cost of RNNs are much higher than deep feed-forward neural networks (DNNs). Therefore, these limit the applications of speech enhancement. This paper proposes a deep time delay neural network (TDNN) for speech enhancement with full data learning. The TDNN has excellent potential for capturing long range temporal contexts, which utilizes a modular and incremental design. Besides, the TDNN preserves the feed-forward structure so that its inference cost is comparable to standard DNN. To make full use of the training data, we propose a full data learning method for speech enhancement. More specifically, we not only use the noisy-to-clean (input-to-target) to train the enhanced model, but also the clean-to-clean and noise-to-silence data. Therefore, all of the training data can be used to train the enhanced model. Our experiments are conducted on TIMIT dataset. Experimental results show that our proposed method could achieve a better performance than DNN and comparable even better performance than BLSTM. Meanwhile, compared with the BLSTM, the proposed method drastically reduce the inference time.

cs.SD↗

arXiv2020

Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition

The joint training framework for speech enhancement and recognition methods have obtained quite good performances for robust end-to-end automatic speech recognition (ASR). However, these methods only utilize the enhanced feature as the input of the speech recognition component, which are affected by the speech distortion problem. In order to address this problem, this paper proposes a gated recurrent fusion (GRF) method with joint training framework for robust end-to-end ASR. The GRF algorithm is used to dynamically combine the noisy and enhanced features. Therefore, the GRF can not only remove the noise signals from the enhanced features, but also learn the raw fine structures from the noisy features so that it can alleviate the speech distortion. The proposed method consists of speech enhancement, GRF and speech recognition. Firstly, the mask based speech enhancement network is applied to enhance the input speech. Secondly, the GRF is applied to address the speech distortion problem. Thirdly, to improve the performance of ASR, the state-of-the-art speech transformer algorithm is used as the speech recognition component. Finally, the joint training framework is utilized to optimize these three components, simultaneously. Our experiments are conducted on an open-source Mandarin speech corpus called AISHELL-1. Experimental results show that the proposed method achieves the relative character error rate (CER) reduction of 10.04\% over the conventional joint enhancement and transformer method only using the enhanced features. Especially for the low signal-to-noise ratio (0 dB), our proposed method can achieves better performances with 12.67\% CER reduction, which suggests the potential of our proposed method.

cs.SD↗

arXiv2020

The influence of stochastic forcing on strong solutions to the Incompressible Slice Model in 2D bounded domain

The Cotter-Holm Slice Model (CHSM) was introduced to study the behavior of whether and specifically the formulation of atmospheric fronts, whose prediction is fundamental in meteorology. Considered herein is the influence of stochastic forcing on the Incompressible Slice Model (ISM) in a smooth 2D bounded domain, which can be derived by adapting the Lagrangian function in Hamilton's principle for CHSM to the Euler-Boussinesq Eady incompressible case. First, we establish the existence and uniqueness of local pathwise solution (probability strong solution) to the ISM perturbed by nonlinear multiplicative stochastic forcing in Banach spaces $W^{k,p}(D)$ with $k>1+1/p$ and $p\geq 2$. The solution is obtained by introducing suitable cut-off operators applied to the $W^{1,\infty}$-norm of the velocity and temperature fields, using the stochastic compactness method and the Yamada-Watanabe type argument based on the Gyöngy-Krylov characterization of convergence in probability. Then, when the ISM is perturbed by linear multiplicative stochastic forcing and the potential temperature does not vary linearly on the $y$-direction, we prove that the associated Cauchy problem admits a unique global-in-time pathwise solution with high probability, provided that the initial data is sufficiently small or the diffusion parameter is large enough. The results partially answer the problems left open in Alonso-Or{á}n et al. (Physica D 392:99--118, 2019, pp. 117).

math.AP↗

arXiv2020

Doping-induced superconductivity in the topological semimetal Mo5Si3

Chemical doping of topological materials may provide a possible route for realizing topological superconductivity. However, all such cases known so far are based on chalcogenides. Here we report the discovery of superconductivity induced by Re doping in the topological semimetal Mo$_{5}$Si$_{3}$ with a tetragonal structure. Partial substitution of Re for Mo in Mo$_{5-x}$Re$_{x}$Si$_{3}$ results in an anisotropic shrinkage of the unit cell up to the solubility limit of approximately $x$ = 2. Over a wide doping range (0.5 $\leq$ $x$ $\leq$ 2), these silicides are found to be weakly coupled superconductors with a fully isotropic gap. $T_{\rm c}$ increases monotonically with $x$ from 1.67 K to 5.78 K, the latter of which is the highest among superconductors of the same structural type. This trend in $T_{\rm c}$ correlates well with the variation of the number of valence electrons, and is mainly ascribed to the enhancement of electron-phonon coupling. In addition, band structure calculations reveal that superconducting Mo$_{5-x}$Re$_{x}$Si$_{3}$ exhibits nontrivial band topology characterized by $Z_{2}$ invariants (1;000) or (1;111) depending on the Re doping level. Our results suggest that transition metal silicides are a fertile ground for the exploration of candidate topological superconductors.

cond-mat.supr-con↗

arXiv2020

A new form of liquid matter: quantum droplets

This brief review summarizes recent theoretical and experimental results which predict and establish the existence of quantum droplets (QDs), i.e., robust two- and three-dimensional (2D and 3D) self-trapped states in Bose-Einstein condensates (BECs), which are stabilized by effective selffirepulsion induced by quantum fluctuations around the mean-field (MF) states [alias the Lee-Huang--Yang (LHY) effect]. The basic models are presented, taking special care of the dimension crossover, 2D -> 3D. Recently reported experimental results, which exhibit stable 3D and quasi-2D QDs in binary BECs, with the inter-component attraction slightly exceeding the MF self-repulsion in each component, and in single-component condensates of atoms carrying permanent magnetic moments, are presented in some detail. The summary of theoretical results is focused, chiefly, on 3D and quasi-2D QDs with embedded vorticity, as the possibility to stabilize such states is a remarkable prediction. Stable vortex states are presented both for QDs in free space, and for singular but physically relevant 2D modes pulled to the center by the inverse-square potential, with the quantum collapse suppressed by the LHY effect.

cond-mat.quant-gas↗

arXiv2020

Ultrafast Thermal Modification of Strong Coupling in an Organic Microcavity

There is growing interest in using strongly coupled organic microcavities to tune molecular dynamics, including the electronic and vibrational properties of molecules. However, very little attention has been paid to the utility of cavity polaritons as sensors for out-of-equilibrium phenomena, including thermal excitations. Here, we demonstrate that non-resonant infrared excitation of an organic microcavity system induces a transient response in the visible spectral range near the cavity polariton resonances. We show how these optical response can be understood in terms of ultrafast heating of electrons in the metal cavity mirror, which modifies the effective refractive index and subsequently the strong coupling conditions. The temporal dynamics of the microcavity are strictly determined by carriers in the metal, including the cooling of electrons via electron-phonon coupling and excitation of propagating coherent acoustic modes in the lattice. We rule out multiphoton excitation processes and verify that no real polariton population exists despite their strong transient features. These results suggest the promise of cavity polaritons as sensitive probes of non-equilibrium phenomena.

cond-mat.mtrl-sci↗

arXiv2020

Quantum droplets in two-dimensional optical lattices

We study the stability of zero-vorticity and vortex lattice quantum droplets (LQDs), which are described by a two-dimensional (2D) Gross-Pitaevskii (GP) equation with a periodic potential and Lee-Huang-Yang (LHY) term. The LQDs are divided in two types: onsite-centered and offsitecentered LQDs, the centers of which are located at the minimum and the maximum of the potential, respectively. The stability areas of these two types of LQDs with different number of sites for zerovorticity and vorticity with S = 1 are given. We found that the u-N relationship of the stable LQDs with a fixed number of sites can violate the Vakhitov-Kolokolov (VK) criterion, which is a necessary stability condition for nonlinear modes with an attractive interaction. Moreover, the u-N relationship shows that two types of vortex LQDs with the same number of sites are degenerated, while the zero-vorticity LQDs are not degenerated. It is worth mentioning that the offsite-centered LQDs with zero-vorticity and vortex LQDs with S = 1 are heterogeneous.

nlin.PS↗

arXiv2020

Two-dimensional vortex quantum droplets get thick

We study two-dimensional (2D) vortex quantum droplets (QDs) trapped by a thicker transverse confinement with a>1um. Under this circumstance, the Lee-Huang-Yang (LHY) term should be described by its original form in the three-dimensional (3D) configuration. Previous studies have demonstrated that stable 2D vortex QDs can be supported by a thin transverse confinement with a<<1um. In this case, the LHY term is described by a logarithm. Hence, two kinds of confinement features result in different mechanisms of the vortex QDs. The stabilities and characteristics of the vortex QDs must be re-identified. In the current system, we find that stable 2D vortex QDs can be supported with topological charge number up to at least 4. We reformulated their density profile, chemical potential and threshold norm for supporting the stable vortex QDs according to the new condition. Unlike the QDs under thin confinement, the QDs in the current system strongly repel each other because the LHY term features a higher-order repulsion than that of the thin confinement system. Moreover, elastic and inelastic collisions between two moving vortex QDs are studied throughout the paper. Two kinds of collisions can be characterized by exerting different values of related speed. The dynamics of the stable nested vortex QD, which is constructed by embedding one vortex QD with a smaller topological number into another vortex QD with a larger number of topological charge, can be supported by the system.

cond-mat.quant-gas↗

arXiv2020

Generalized weighted composition operators on Bergman spaces induced by doubling weights

Bounded and compact generalized weighted composition operators acting from the weighted Bergman space $A^p_ω$, where $0<p<\infty$ and $ω$ belongs to the class $\mathcal{D}$ of radial weights satisfying a two-sided doubling condition, to a Lebesgue space $L^q_ν$ are characterized. On the way to the proofs a new embedding theorem on weighted Bergman spaces $A^p_ω$ is established. This last-mentioned result generalizes the well-known characterization of the boundedness of the differentiation operator $D^n(f)=f^{(n)}$ from the classical weighted Bergman space $A^p_α$ to the Lebesgue space $L^q_μ$, induced by a positive Borel measure $μ$, to the setting of doubling weights.

math.CV↗

arXiv2020

Refined Tail Asymptotic Properties for the $M^X/G/1$ Retrial Queue

In the literature, retrial queues with batch arrivals and heavy service times have been studied and the so-called equivalence theorem has been established under the condition that the service time is heavier than the batch size. The equivalence theorem provides the distribution (or tail) equivalence between the total number of customers in the system for the retrial queue and the total number of customers in the corresponding standard (non-retrial) queue. In this paper, under the assumption of regularly varying tails, we eliminate this condition by allowing that the service time can be either heavier or lighter than the batch size. The main contribution made in this paper is an asymptotic characterization of the difference between two tail probabilities: the probability of the total number of customers in the system for the $M^X/G/1$ retrial queue and the probability of the total number of customers in the corresponding standard (non-retrial) queue. The equivalence theorem by allowing a heavier batch size is another contribution in this paper.

math.PR↗

arXiv2020

A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis

Multiple-object tracking and behavior analysis have been the essential parts of surveillance video analysis for public security and urban management. With billions of surveillance video captured all over the world, multiple-object tracking and behavior analysis by manual labor are cumbersome and cost expensive. Due to the rapid development of deep learning algorithms in recent years, automatic object tracking and behavior analysis put forward an urgent demand on a large scale well-annotated surveillance video dataset that can reflect the diverse, congested, and complicated scenarios in real applications. This paper introduces an urban surveillance video dataset (USVD) which is by far the largest and most comprehensive. The dataset consists of 16 scenes captured in 7 typical outdoor scenarios: street, crossroads, hospital entrance, school gate, park, pedestrian mall, and public square. Over 200k video frames are annotated carefully, resulting in more than 3:7 million object bounding boxes and about 7:1 thousand trajectories. We further use this dataset to evaluate the performance of typical algorithms for multiple-object tracking and anomaly behavior analysis and explore the robustness of these methods in urban congested scenarios.

cs.CV↗

arXiv2020

Hydrodynamic and thermal characteristics of a freely-vibrating circular cylinder in mixed convection flow

The hydrodynamic and thermal characteristics of a freely-vibrating circular cylinder in mixed convection flow are numerically investigated at low Reynolds numbers. The numerical investigations are conducted for a range of parameters, Ur = [2.0, 10], Pr = [0.7, 10] and Ri = [0.5, 2.0]. Whereas the Reynolds number, the mass ratio and the damping ratio are fixed. A secondary VIV lock-in region is found in the cases of high Richardson number Ri=2.0 for high reduced velocity values, in which the buoyancy-driven flow is non-trivial. A wide VIV lock-in region is formed with tremendous energy transfer between fluid and structure, which is extremely meaningful for hydropower harvesting. The influences of Prandtl and Richardson numbers on the hydrodynamics, structural dynamics and heat transfer are discussed in detail. The temperature contours are concentrated around cylinder for the cases of high Prandtl number, which are associated with high mean Nusselt values. The influence on heat transfer efficiency over the cylinder's surface is quantified via the calculation of mean Nusselt number and its fluctuation for different circumstances. The energy transfer coefficient is employed to quantify the energy transfer between fluid and structure in mixed convection flow. The phase angle difference between the transverse displacement of cylinder and the lift force is used to support the discussions of energy transfer. A stabilized finite element formulation in Arbitrary Lagrangian-Eulerian description is derived. The structural dynamics and vortex-induced vibration are documented for various environments, e.g., different reduced velocity, Prandtl numbers and Richardson numbers. The influence of structural dynamics on the heat transfer efficiency over a heated cylinder is recorded and discussed as well. The obtained numerical results match well with literature and the established empirical formula.

physics.flu-dyn↗

arXiv2020

Merging Compact Binaries Near a Rotating Supermassive Black Hole: Eccentricity Excitation due to Apsidal Precession Resonance

We study the dynamics of merging compact binaries near a rotating supermassive black hole (SMBH) in a hierarchical triple configuration. We include various general relativistic effects that couple the inner orbit, the outer orbit and the spin of the SMBH. During the orbital decay due to gravitational radiation, the inner binary can encounter an "apsidal precession resonance" and experience eccentricity excitation. This resonance occurs when the apsidal precession rate of the inner binary matches that of the outer binary, with the precessions driven by both Newtonian interactions and various post-Newtonian effects. The eccentricity excitation requires the outer orbit to have a finite eccentricity, and is most effective for triples with small mutual inclinations, in contrast to the well-studied Lidov-Kozai effect. The resonance and the associated eccentricity growth may occur while the binary emits gravitational waves in the low-frequency band, and may be detectable by future space-based gravitational wave detectors.

astro-ph.HE↗

arXiv2020

Compact differences of composition operators on Bergman spaces induced by doubling weights

Bounded and compact differences of two composition operators acting from the weighted Bergman space $A^p_ω$ to the Lebesgue space $L^q_ν$, where $0 q$ and $ω\in\mathcal{D}$, involving pseudohyperbolic discs is established. This last-mentioned result generalizes the well-known characterization of $q$-Carleson measures for the classical weighted Bergman space $A^p_α$ with $-1<α<\infty$ to the setting of doubling weights. The case $ω\in\widehat{\mathcal{D}}$ is also briefly discussed and an open problem concerning this case is posed.

math.CV↗