arXiv subjects

Bin Liu

Publications and source records attributed to Bin Liu.

At least 451 records · Page 25Linked to original sources

arXiv2019

Synthetic Oversampling of Multi-Label Data based on Local Label Distribution

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multilabel sampling methods alleviate the (global) imbalance of multi-label datasets. However, performance degradation is mainly due to rare subconcepts and overlapping of classes that could be analysed by looking at the local characteristics of the minority examples, rather than the imbalance of the whole dataset. We propose a new method for synthetic oversampling of multi-label data that focuses on local label distribution to generate more diverse and better labeled instances. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed approach in a variety of evaluation measures, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

cs.LG↗

arXiv2019

Robust Particle Filtering via Bayesian Nonparametric Outlier Modeling

This paper is concerned with the online estimation of a nonlinear dynamic system from a series of noisy measurements. The focus is on cases wherein outliers are present in-between normal noises. We assume that the outliers follow an unknown generating mechanism which deviates from that of normal noises, and then model the outliers using a Bayesian nonparametric model called Dirichlet process mixture (DPM). A sequential particle-based algorithm is derived for posterior inference for the outlier model as well as the state of the system to be estimated. The resulting algorithm is termed DPM based robust PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to "speak for itself" to determine the complexity and structure of the outlier model. Simulation results show that it performs remarkably better than two state-of-the-art methods especially when outliers appear frequently along time.

stat.CO↗

arXiv2019

Collective Dynamics and Control for Multiple Unmanned Surface Vessels

A multi-unmanned surface vessel (USV) formation control system is established on a novel platform composed of three 1.2 meter-long hydraulic jet propulsion surface vessels, a differential GPS reference station, and inter-vessel Zigbee communication modules. The system is also equipped with an upper level collective multi-USV protocol and a lower level vessel dynamics controller. The system is capable of chasing and surrounding a target vessel. The results are supported by rigorous theoretical analysis in terms of asymptotical surrounding behavior and trajectory regulation. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed hardware and software architectures.

eess.SY↗

arXiv2019

Complementary constraints on dark energy equation of state from strongly lensed gravitational wave

It has been shown that time delay of strong gravitational lensing is not only an effective cosmological probe to constrain the Hubble constant, the matter density and the curvature of the universe but also useful for breaking the degeneracy of the dark energy equation of state and thus provide complementarity with other popular probes, such as type Ia supernovae, baryon acoustic oscillations, and cosmic microwave background radiation. Interestingly, compared to traditional strong lensing systems where quasars act as sources, strongly lensed gravitational waves (GWs) from compact binary coalescence and their electromagnetic (EM) counterparts systems have been recently proposed to be more powerful for studying cosmology since GWs and their EM counterparts are transients. Short durations of GWs and their EM counterparts are very advantageous to time delay measurement and lens profile modeling. Here, in the framework of Chevalier-Polarski-Linder (CPL) parametrization, we investigate improvement of constraining power on dark energy equation of state due to including time delay measurements of strong lensed GW systems. It is suggested that, on the basis of the third generation ground-based detector, e.g. Einstein Telescope, adding time delay of only 30 strong lensed GW systems to type Ia supernovae and cosmic microwave background radiation can improve the dark energy figure of merit by a factor 2. For the traditional standard siren method where the uncertainties of luminosity distances of GWs are $\sim10\%$, a few $\times~10^4$ events are expected to present similar constraints. In the precision cosmology era, this progress is of great significance for studying the nature of dark energy.

astro-ph.CO↗

arXiv2019

Symmetry breaking of quantum droplets in a dual-core trap

We consider the dynamical model of a binary bosonic gas trapped in a symmetric dual-core cigar-shaped potential. The setting is modeled by a system of linearly-coupled one-dimensional Gross-Pitaevskii equations with the cubic self-repulsive terms and quadratic attractive ones,which represent the Lee-Huang-Yang corrections to the mean-field theory in this geometry. The main subject is spontaneous symmetry breaking (SSB) of quantum droplets (QDs), followed by restoration of the symmetry, with respect to the symmetric parallel-coupled trapping cores, following the increase of the QD's total norm. The SSB transition and inverse symmetry-restoring one form a bifurcation loop, whose shape in concave at small values of the inter-core coupling constant, $κ$, and convex at larger $κ$. The loop does not exist above a critical value of $κ$. At very large values of the norm, QDs do not break their symmetry, featuring a flat-top shape. Some results are obtained in an analytical form, including an exact front solution connecting constant zero and finite values of the wave function. Collisions between moving QDs are considered too, demonstrating a trend to merger into breathers.

nlin.PS↗

arXiv2019

Second Order Asymptotic Properties for the Tail Probability of the Number of Customers in the M/G/1 Retrial Queue

When an explicit expression for a probability distribution function $F(x)$ can not be found, asymptotic properties of the tail probability function $\bar{F}(x)=1-F(x)$ are very valuable, since they provide approximations or bounds for system performance, and approaches for computing probability distribution. In this paper, we study tail asymptotic properties for the number of the customers in the $M/G/1$ retrial queueing system. For queueing systems, studies on asymptotic tails have mainly concentrated on the first order asymptotic behavior. To best our knowledge, there is no second order tail asymptotic analysis for retrial queueing models with heavy-tailed service time. Second order asymptotic expansions provide the refined asymptotic results from the first order approximation, and are often more difficult to obtain as expected. The main contribution of this paper is the second order asymptotic analysis for the $M/G/1$ retrial queue.

math.PR↗

arXiv2019

Tail Asymptotics for a Retrial Queue with Bernoulli Schedule

In this paper, we study the asymptotic behavior of the tail probability of the number of customers in the steady-state $M/G/1$ retrial queue with Bernoulli schedule, under the assumption that the service time distribution has a regularly varying tail. Detailed tail asymptotic properties are obtained for the (conditional and unconditional) probability of the number of customers in the (priority) queue, orbit and system, respectively.

math.PR↗

arXiv2019

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features.

cs.IR↗

arXiv2019

Context and Attribute Grounded Dense Captioning

Dense captioning aims at simultaneously localizing semantic regions and describing these regions-of-interest (ROIs) with short phrases or sentences in natural language. Previous studies have shown remarkable progresses, but they are often vulnerable to the aperture problem that a caption generated by the features inside one ROI lacks contextual coherence with its surrounding context in the input image. In this work, we investigate contextual reasoning based on multi-scale message propagations from the neighboring contents to the target ROIs. To this end, we design a novel end-to-end context and attribute grounded dense captioning framework consisting of 1) a contextual visual mining module and 2) a multi-level attribute grounded description generation module. Knowing that captions often co-occur with the linguistic attributes (such as who, what and where), we also incorporate an auxiliary supervision from hierarchical linguistic attributes to augment the distinctiveness of the learned captions. Extensive experiments and ablation studies on Visual Genome dataset demonstrate the superiority of the proposed model in comparison to state-of-the-art methods.

cs.CV↗

arXiv2019

Semantics Disentangling for Text-to-Image Generation

Synthesizing photo-realistic images from text descriptions is a challenging problem. Previous studies have shown remarkable progresses on visual quality of the generated images. In this paper, we consider semantics from the input text descriptions in helping render photo-realistic images. However, diverse linguistic expressions pose challenges in extracting consistent semantics even they depict the same thing. To this end, we propose a novel photo-realistic text-to-image generation model that implicitly disentangles semantics to both fulfill the high-level semantic consistency and low-level semantic diversity. To be specific, we design (1) a Siamese mechanism in the discriminator to learn consistent high-level semantics, and (2) a visual-semantic embedding strategy by semantic-conditioned batch normalization to find diverse low-level semantics. Extensive experiments and ablation studies on CUB and MS-COCO datasets demonstrate the superiority of the proposed method in comparison to state-of-the-art methods.

cs.CV↗

arXiv2019

Two-dimensional composite solitons in Bose-Einstein condensates with spatially confined spin-orbit coupling

It was recently found that the spin-orbit (SO) coupling can help to create stable matter-wave solitons in spinor Bose-Einstein condensates in the two-dimensional (2D) free space. Being induced by external laser illumination, the effective SO coupling can be applied too in a spatially confined area. Using numerical methods and the variational approximation (VA), we build families of 2D solitons of the semi-vortex (SV) and mixed-mode (MM) types, and explore their stability, assuming that the SO-coupling strength is confined in the radial direction as a Gaussian. The most essential result is identification, by means of the VA and numerical methods, of the minimum size of the spatial confinement for which the 2D system maintains stable solitons of the SV and MM types.

nlin.PS↗

arXiv2019

Type-II superconductivity in W5Si3-type Nb5Sn2Al

We report the discovery of superconductivity in the ternary aluminide Nb$_{5}$Sn$_{2}$Al, which crystallizes in the W$_{5}$Si$_{3}$-type structure with one-dimensional Nb chains along the $c$-axis. It is found that the compound has a multiband nature and becomes a weakly coupled, type-II superconductor below 2.0 K. The bulk nature of superconductivity is confirmed by the specific heat jump, whose temperature dependence shows apparent deviation from a single isotropic gap behavior. The lower and upper critical fields are estimated to be 2.0 mT and 0.3 T, respectively. From these values, we derive the penetration depth, coherence length and Ginzburg-Landau parameter to be 516 nm, 32.8 nm and 15.6, respectively. By contrast, the isostructural compound Ti$_{5}$Sn$_{2}$Al dose not superconduct above 0.5 K. A comparison of these results with other W$_{5}$Si$_{3}$-type superconductors suggests that $T_{\rm c}$ of these compounds correlates with the average number of valence electrons per atom.

cond-mat.supr-con↗

arXiv2019

Deep Triplet Quantization

Deep hashing establishes efficient and effective image retrieval by end-to-end learning of deep representations and hash codes from similarity data. We present a compact coding solution, focusing on deep learning to quantization approach that has shown superior performance over hashing solutions for similarity retrieval. We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets. To enable more effective triplet training, we design a new triplet selection approach, Group Hard, that randomly selects hard triplets in each image group. To generate compact binary codes, we further apply a triplet quantization with weak orthogonality during triplet training. The quantization loss reduces the codebook redundancy and enhances the quantizability of deep representations through back-propagation. Extensive experiments demonstrate that DTQ can generate high-quality and compact binary codes, which yields state-of-the-art image retrieval performance on three benchmark datasets, NUS-WIDE, CIFAR-10, and MS-COCO.

cs.CV↗

arXiv2019

Survey of Ionized Gas of the Galaxy, Made with the Arecibo telescope (SIGGMA): Inner Galaxy Data Release

The Survey of Ionized Gas of the Galaxy, Made with the Arecibo telescope (SIGGMA) provides a fully-sampled view of the radio recombination line (RRL) emission from the portion of the Galactic plane visible by Arecibo. Observations use the Arecibo L-band Feed Array (ALFA), which has a FWHM beam size of 3.4 arcmin. Twelve hydrogen RRLs from H163$α$ to H174$α$ are located within the instantaneous bandpass from 1225 MHz to 1525 MHz. We provide here cubes of average ("stacked") RRL emission for the inner Galaxy region $32 \le \ell \le 70$ degrees, $|b|\le1.5$ degree, with an angular resolution of 6 arcmin. The stacked RRL rms at 5.1 km/s velocity resolution is $\sim0.65$ mJy beam$^{-1}$, making this the most sensitive large-scale fully-sampled RRL survey extant. We use SIGGMA data to catalogue 319 RRL detections in the direction of 244 known HII regions, and 108 new detections in the direction of 79 HII region candidates. We identify 11 Carbon RRL emission regions, all of which are spatially coincident with known HII regions. We detect RRL emission in the direction of 14 of the 32 supernova remnants (SNRs) found in the survey area. This RRL emission frequently has the same morphology as the SNRs. The RRL velocities give kinematic distances in agreement with those found in the literature, indicating that RRLs may provide an additional tool to constrain distances to SNRs. Finally, we analyze the two bright star-forming complexes: W49 and W51. We discuss the possible origins of the RRL emission in directions of SNRs W49B and W51C.

astro-ph.GA↗

arXiv2019

Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) aims to predict defects of projects lacking training data by using prediction models trained on historical defect data from other projects. However, since the distribution differences between datasets from different projects, it is still a challenge to build high-quality CPDP models. Unfortunately, class imbalanced nature of software defect datasets further increases the difficulty. In this paper, we propose a transferlearning oriented minority over-sampling technique (TOMO) based feature weighting transfer naive Bayes (FWTNB) approach (TOMOFWTNB) for CPDP by considering both classimbalance and feature importance problems. Differing from traditional over-sampling techniques, TOMO not only can balance the data but reduce the distribution difference. And then FWTNB is used to further increase the similarity of two distributions. Experiments are performed on 11 public defect datasets. The experimental results show that (1) TOMO improves the average G-Measure by 23.7\%$\sim$41.8\%, and the average MCC by 54.2\%$\sim$77.8\%. (2) feature weighting (FW) strategy improves the average G-Measure by 11\%, and the average MCC by 29.2\%. (3) TOMOFWTNB improves the average G-Measure value by at least 27.8\%, and the average MCC value by at least 71.5\%, compared with existing state-of-theart CPDP approaches. It can be concluded that (1) TOMO is very effective for addressing class-imbalance problem in CPDP scenario; (2) our FW strategy is helpful for CPDP; (3) TOMOFWTNB outperforms previous state-of-the-art CPDP approaches.

cs.SE↗

arXiv2019

Tail Asymptotics for the $M_1,M_2/G_1,G_2/1$ Retrial Queue with Priority

Stochastic networks with complex structures are key modelling tools for many important applications. In this paper, we consider a specific type of network: the retrial queueing systems with priority. This type of queueing system is important in various applications, including telecommunication and computer management networks with big data. For this type of system, we propose a detailed stochastic decomposition approach to study its asymptotic behaviour of the tail probability of the number of customers in the steady-state for retrial queues with two types (Type-1 and Type-2) of customers, in which Type-1 customers (in a queue) have non-preemptive priority to receive service over Type-2 customers (in an orbit). Under the assumption that the service times of Type-1 customers have a regularly varying tail and the service times of Type-2 customers have a tail lighter than Type-1 customers, we obtain tail asymptotic properties for the number of customers in the queue and in the orbit, respectively, conditional on the server's status, in terms of a detailed stochastic decomposition approach. Tail asymptotic properties are often used as key tools for approximating various performance metrics and constructing numerical algorithms for computations.

math.PR↗

arXiv2019

Anomalous relation between in-plane and out-of-plane stiffnesses in 2D networked materials

For thin networked materials, which are spatial discrete structures constructed by continuum components, a paradox on the effective thickness defined by the in-plane and out-of-plane stiffnesses is found, i.e. the effective thickness is not a constant but varies with loading modes. To reveal the mechanism underneath the paradox, we have established a micromechanical framework to investigate the deformation mechanism and predict the stiffness matrix of the networked materials. It is revealed that the networked materials can carry in-plane loads by axial stretching/compression of the components in the networks and resist out-of-plane loading by bending and torsion of the components. The bending deformation of components has a corresponding relation to the axial stretching/compression through the effective thickness, as the continuum plates do, while the torsion deformation has no relation to the axial stretching/compression. The isolated torsion deformation breaks the classical stiffness relation between the in-plane stiffness and the out-of-plane stiffness, which can even be further distorted by the stiffness threshold effect in randomly networked materials. Accordingly, a new formula is summarized to describe the anomalous stiffness relation. This network model can also apply in atomic scale 2D nanomaterials when combining with the molecular structural mechanics model. This work gives an insight into the understanding of the mechanical properties of discrete materials/structures ranging from atomic scale to macro scale.

physics.app-ph↗

arXiv2019

Edge states at nematic domain walls in FeSe films

Quantum spin Hall (QSH) effect is an intriguing phenomenon arising from the helical edge states in two-dimensional topological insulators. We use molecular beam epitaxy (MBE) to prepare FeSe films with atomically sharp nematic domain boundaries, where tensile strains, nematicity suppression and topological band inversion are simultaneously achieved. Using scanning tunneling microscopy (STM), we observe edge states at the Fermi level that spatially distribute as two distinct strips in the vicinity of the domain boundaries. At the endpoint of the boundaries, a bound state at the Fermi level is further observed. The topological origin of the edge states is supported by density functional theory calculations. Our findings not only demonstrate a candidate for QSH states, but also provide a new pathway to realize topological superconductivity in a single-component film.

cond-mat.supr-con↗