arXiv subjects

Jun Xu

Publications and source records attributed to Jun Xu.

At least 415 records · Page 23Linked to original sources

arXiv2019

User Donations in a Crowdsourced Video System

Crowdsourced video systems like YouTube and Twitch.tv have been a major internet phenomenon and are nowadays entertaining over a billion users. In addition to video sharing and viewing, over the years they have developed new features to boost the community engagement and some managed to attract users to donate, to the community as well as to other users. User donation directly reflects and influences user engagement in the community, and has a great impact on the success of such systems. Nevertheless, user donations in crowdsourced video systems remain trade secrets for most companies and to date are still unexplored. In this work, we attempt to fill this gap, and we obtain and provide a publicly available dataset on user donations in one crowdsourced video system named BiliBili. Based on information on nearly 40 thousand donators, we examine the dynamics of user donations and their social relationships, we quantitively reveal the factors that potentially impact user donation, and we adopt machine-learned classifiers and network representation learning models to timely and accurately predict the destinations of the majority and the individual donations.

cs.SI↗

arXiv2019

The properties of broad absorption line outflows based on a large sample of quasars

Quasar outflows carry mass, momentum and energy into the surrounding environment, and have long been considered a potential key factor in regulating the growth of supermassive black holes and the evolution of their host galaxies. A crucial parameter for understanding the origin of these outflows and measuring their influence on their host galaxies is the distance (R) between the outflow gas and the galaxy center. While R has been measured in a number of individual galaxies, its distribution remains unknown. Here we report the distributions of R and the kinetic luminosities of quasars outflows, using the statistical properties of broad absorption line variability in a sample of 915 quasars from the Sloan Digital Sky Surveys. The mean and standard deviation of the distribution of R are 10^{1.4+/-0.5} parsecs. The typical outflow distance in this sample is tens of parsec, which is beyond the theoretically predicted location (0.01 ~ 0.1 parsecs) where the accretion disc line-driven wind is launched, but is smaller than the scales of most outflows that are derived using the excited state absorption lines. The typical value of the mass-flow rate is of tens to a hundred solar masses per year, or several times the accretion rate. The typical kinetic-to-bolometric luminosity ratio is a few per cent, indicating that outflows are energetic enough to influence the evolution of their host galaxies.

astro-ph.GA↗

arXiv2019

Cryogenic Characerization and Modeling of Standard CMOS down to Liquid Helium Temperature for Quantum Computing

Cryogenic characterization and modeling of 0.18um CMOS technology (1.8V and 5V) are presented in this paper. Several PMOS and NMOS transistors with different width to length ratios(W/L) were extensively characterized under various bias conditions at temperatures ranging from 300K down to 4.2K. We extracted their fundamental physical parameters and developed a compact model based on BSIM3V3. In addition to their I-V characteristics, threshold voltage(Vth) values, on/off current ratio, transconductance of the MOS transistors, and resistors on chips are measured at temperatures from 300K down to 4.2K. A simple subcircuit was built to correct the kink effect. This work provides experimental evidence for implementation of cryogenic CMOS technology, a valid industrial tape-out process model, and romotes the application of integrated circuits in cryogenic environments, including quantum measurement and control systems for quantum chips at very low temperatures.

physics.app-ph↗

arXiv2019

HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering

This paper is concerned with open-domain question answering (i.e., OpenQA). Recently, some works have viewed this problem as a reading comprehension (RC) task, and directly applied successful RC models to it. However, the performances of such models are not so good as that in the RC task. In our opinion, the perspective of RC ignores three characteristics in OpenQA task: 1) many paragraphs without the answer span are included in the data collection; 2) multiple answer spans may exist within one given paragraph; 3) the end position of an answer span is dependent with the start position. In this paper, we first propose a new probabilistic formulation of OpenQA, based on a three-level hierarchical structure, i.e.,~the question level, the paragraph level and the answer span level. Then a Hierarchical Answer Spans Model (HAS-QA) is designed to capture each probability. HAS-QA has the ability to tackle the above three problems, and experiments on public OpenQA datasets show that it significantly outperforms traditional RC baselines and recent OpenQA baselines.

cs.CL↗

arXiv2018

Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising

Most of the existing denoising algorithms are developed for grayscale images, while it is not a trivial work to extend them for color image denoising because the noise statistics in R, G, B channels can be very different for real noisy images. In this paper, we propose a multi-channel (MC) optimization model for real color image denoising under the weighted nuclear norm minimization (WNNM) framework. We concatenate the RGB patches to make use of the channel redundancy, and introduce a weight matrix to balance the data fidelity of the three channels in consideration of their different noise statistics. The proposed MC-WNNM model does not have an analytical solution. We reformulate it into a linear equality-constrained problem and solve it with the alternating direction method of multipliers. Each alternative updating step has closed-form solution and the convergence can be guaranteed. Extensive experiments on both synthetic and real noisy image datasets demonstrate the superiority of the proposed MC-WNNM over state-of-the-art denoising methods.

cs.CV↗

arXiv2018

Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?

The use of sparse representation (SR) and collaborative representation (CR) for pattern classification has been widely studied in tasks such as face recognition and object categorization. Despite the success of SR/CR based classifiers, it is still arguable whether it is the $\ell_{1}$-norm sparsity or the $\ell_{2}$-norm collaborative property that brings the success of SR/CR based classification. In this paper, we investigate the use of nonnegative representation (NR) for pattern classification, which is largely ignored by previous work. Our analyses reveal that NR can boost the representation power of homogeneous samples while limiting the representation power of heterogeneous samples, making the representation sparse and discriminative simultaneously and thus providing a more effective solution to representation based classification than SR/CR. Our experiments demonstrate that the proposed NR based classifier (NRC) outperforms previous representation based classifiers. With deep features as inputs, it also achieves state-of-the-art performance on various visual classification tasks.

cs.CV↗

arXiv2018

Characterization and Modeling of 0.18μm CMOS Technology at sub-Kelvin Temperature

Previous cryogenic electronics studies are most above 4.2K. In this paper we present the cryogenic characterization of a 0.18μm standard bulk CMOS technology(1.8V and 5V) at sub-kelvin temperature around 270mK. PMOS and NMOS devices with different width to length ratios(W/L) are tested and characterized under various bias conditions at temperatures from 300K to 270mK. It is shown that the 0.18μm standard bulk CMOS technology is still working at sub-kelvin temperature. The kink effect and current overshoot phenomenon are observed at sub-kelvin temperature. Especially, current overshoot phenomenon in PMOS devices at sub-kelvin temperature is shown for the first time. The transfer characteristics of large and thin-oxide devices at sub-kelvin temperature are modeled using the simplified EKV model. This work facilitates the CMOS circuits design and the integration of CMOS circuits with silicon-based quantum chips at extremely low temperatures.

physics.app-ph↗

arXiv2018

MOSFET Characterization and Modeling at Cryogenic Temperatures

Cryogenic CMOS technology (cryo-CMOS) offers a scalable solution for quantum device interface fabrication. Several previous works have studied the characterization of CMOS technology at cryogenic temperatures for various process nodes. However, CMOS characteristics for various width/length (W/L) ratios and under different bias conditions still require further research. In addition, no previous works have produced an integrated modeling process for cryo-CMOS technology. In this paper, the results of characterization of Semiconductor Manufacturing International Corporation (SMIC) 0.18 μm CMOS technology at cryogenic temperatures (varying from 300 K to 4.2 K) are presented. Measurements of thin- and thick-oxide NMOS and PMOS devices with different W/L ratios are taken under four distinct bias conditions and at different temperatures. The temperature-dependent parameters are revised and an advanced CMOS model is proposed based on BSIM3v3 at the liquid nitrogen temperature (LNT). The proposed model ensures precision at the LNT and is valid for use in an industrial tape-out process. The proposed method presents a calibration approach for BSIM3v3 that is available at different temperature intervals.

physics.app-ph↗

arXiv2018

Neuromorphic MoS2 memtransistors fabricated by localised helium ion beam irradiation

Two-dimensional layered semiconductors have recently emerged as attractive building blocks for next-generation low-power non-volatile memories. However, challenges remain in the controllable sub-micron fabrication of bipolar resistively switching circuit components from these novel materials. Here we report on the scalable experimental realisation of lateral on-dielectric memtransistors from monolayer single-crystal molybdenum disulfide (MoS2) utilising a focused helium ion beam. Site-specific irradiation with the probe of a helium ion microscope (HIM) allows for the creation of charged defects in the MoS2 lattice. The reversible drift of these locally seeded defects in the applied electric field modulates the resistance of the semiconducting channel, enabling versatile memristive functionality on the nanoscale. We find the device can reliably retain its resistance ratios and set biases for hundreds of switching cycles at sweep frequencies of up to 2.9 V/s with relatively low drain-source biases. We also demonstrate long-term potentiation and depression with sharp habituation that promises application in future neuromorphic architectures. This work advances the down-scaling progress of memristive devices without sacrificing key performance parameters such as power consumption or its applicability for synaptic emulation.

cond-mat.mtrl-sci↗

arXiv2018

Revisiting the isospin relaxation time in intermediate-energy heavy-ion collisions

Isospin relaxation times characterizing isospin transport processes between the projectile and the target with different $N/Z$ ratios and that between the neck and the spectator with different isospin asymmetries and densities in intermediate-energy heavy-ion collisions are studied within an isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model using the lattice Hamiltonian approach. The respective roles and time scales of the isospin diffusion and drift as the major mechanisms of isospin transport in intermediate-energy heavy-ion collisions are discussed. Effects of nuclear symmetry energy and neutron-proton effective mass splitting on the isospin relaxation times are examined.

nucl-th↗

arXiv2018

External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising

Most of existing image denoising methods learn image priors from either external data or the noisy image itself to remove noise. However, priors learned from external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real-world noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real-world noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real-world noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real-world noisy images.

cs.CV↗

arXiv2018

Simulating the Chiral Magnetic Wave in a Box System

The chiral magnetic wave from the interplay between the chiral magnetic effect and the chiral separation effect is simulated in a box system with the periodic boundary condition based on the chiral kinetic equations of motion. Simulation results are compared with available limits from theoretical derivations, and effects of the temperature, the magnetic field, and the specific shear viscosity on the key properties of the chiral magnetic wave are discussed. Our study serves as a baseline for further simulations of chiral anomalies in relativistic heavy-ion collisions.

nucl-th↗

arXiv2018

Directed flow in an extended multiphase transport model

We have studied the rapidity-odd directed flow in $^{197}$Au+$^{197}$Au collisions in the beam energy range from $\sqrt{s_{NN}}$ = 7.7 to 39 GeV within the framework of an extended multiphase transport model with both partonic and hadronic mean-field potentials incorporated. Effects of the partonic scatterings, mean-field potentials, hadronization, and hadronic evolution on the directed flow are investigated, and it is found that the final directed flow is mostly sensitive to the partonic scatterings and the hadronization mechanism. Our study shows that a negative slope of the proton directed flow does not necessarily need the equation of state with a first-order phase transition.

nucl-th↗

arXiv2018

Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations

Vocabulary mismatch is a central problem in information retrieval (IR), i.e., the relevant documents may not contain the same (symbolic) terms of the query. Recently, neural representations have shown great success in capturing semantic relatedness, leading to new possibilities to alleviate the vocabulary mismatch problem in IR. However, most existing efforts in this direction have been devoted to the re-ranking stage. That is to leverage neural representations to help re-rank a set of candidate documents, which are typically obtained from an initial retrieval stage based on some symbolic index and search scheme (e.g., BM25 over the inverted index). This naturally raises a question: if the relevant documents have not been found in the initial retrieval stage due to vocabulary mismatch, there would be no chance to re-rank them to the top positions later. Therefore, in this paper, we study the problem how to employ neural representations to improve the recall of relevant documents in the initial retrieval stage. Specifically, to meet the efficiency requirement of the initial stage, we introduce a neural index for the neural representations of documents, and propose two hybrid search schemes based on both neural and symbolic indices, namely the parallel search scheme and the sequential search scheme. Our experiments show that both hybrid index and search schemes can improve the recall of the initial retrieval stage with small overhead.

cs.IR↗

arXiv2018

A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising

Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much more complex than AWGN, and is hard to be modelled by simple analytical distributions. As a result, many state-of-the-art denoising methods in literature become much less effective when applied to real-world noisy images captured by CCD or CMOS cameras. In this paper, we develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world image denoising. Specifically, we introduce three weight matrices into the data and regularisation terms of the sparse coding framework to characterise the statistics of realistic noise and image priors. TWSC can be reformulated as a linear equality-constrained problem and can be solved by the alternating direction method of multipliers. The existence and uniqueness of the solution and convergence of the proposed algorithm are analysed. Extensive experiments demonstrate that the proposed TWSC scheme outperforms state-of-the-art denoising methods on removing realistic noise.

cs.CV↗

arXiv2018

A Tree Search Algorithm for Sequence Labeling

In this paper we propose a novel reinforcement learning based model for sequence tagging, referred to as MM-Tag. Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word. Two long short-term memory networks (LSTM) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of LSTMs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of the whole sentence are produced. The policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a better search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Our work is the first to apply the MCTS enhanced MDP model to the sequence tagging task. We show that MM-Tag can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. Experimental results show based on a chunking benchmark showed that MM-Tag outperformed the state-of-the-art sequence tagging baselines including CRF and CRF with LSTM.

cs.CL↗

arXiv2018

Modeling Diverse Relevance Patterns in Ad-hoc Retrieval

Assessing relevance between a query and a document is challenging in ad-hoc retrieval due to its diverse patterns, i.e., a document could be relevant to a query as a whole or partially as long as it provides sufficient information for users' need. Such diverse relevance patterns require an ideal retrieval model to be able to assess relevance in the right granularity adaptively. Unfortunately, most existing retrieval models compute relevance at a single granularity, either document-wide or passage-level, or use fixed combination strategy, restricting their ability in capturing diverse relevance patterns. In this work, we propose a data-driven method to allow relevance signals at different granularities to compete with each other for final relevance assessment. Specifically, we propose a HIerarchical Neural maTching model (HiNT) which consists of two stacked components, namely local matching layer and global decision layer. The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document. The global decision layer accumulates local signals into different granularities and allows them to compete with each other to decide the final relevance score. Experimental results demonstrate that our HiNT model outperforms existing state-of-the-art retrieval models significantly on benchmark ad-hoc retrieval datasets.

cs.IR↗

arXiv2018

Combined Constraints on the Equation of State of Dense Neutron-Rich Matter from Terrestrial Experiments and Observations of Neutron Stars

Within the parameter space of equation of state (EOS) of dense neutron-rich matter limited by existing constraints mainly from terrestrial nuclear experiments, we investigate how the neutron star maximum mass $M_{\rm{max}}>2.01\pm0.04$ M$_\odot$, radius $10.62<R_{\rm{1.4}}< 12.83$ km and tidal deformability $Λ_{1.4}\leq800$ of canonical neutron stars all together constrain the EOS of dense neutron-rich nucleonic matter. While the 3-D parameter space of $K_{\rm{sym}}$ (curvature of nuclear symmetry energy), $J_{\rm{sym}}$ and $J_0$ (skewness of the symmetry energy and EOS of symmetric nuclear matter, respectively) are narrowed down significantly by the observational constraints, more data are needed to pin down the individual values of $K_{\rm{sym}}$, $J_{\rm{sym}}$ and $J_0$ with quantified uncertainties. The $J_0$ largely controls the maximum mass of neutron stars. While the EOS with $J_0=0$ is sufficiently stiff to support neutron stars as massive as 2.37 M$_{\odot}$, to support the hyperthetical ones as massive as 2.74 M$_{\odot}$ (composite mass of GW170817) requires $J_0$ to be larger than its currently known maximum value of about 400 MeV and beyond the causality limit. The upper limit on the tidal deformability of $Λ_{1.4}=800$ from the recent observation of GW170817 is found to provide upper limits on some EOS parameters consistent with but far less restrictive than the existing constraints of other observables studied.

nucl-th↗