arXiv subjects

Meng Zhang

Publications and source records attributed to Meng Zhang.

At least 235 records · Page 13Linked to original sources

arXiv2019

How to Price Fresh Data

We introduce the concept of a fresh data market, in which a destination user requests, and pays for, fresh data updates from a source provider. Data freshness is captured by the {\it age of information} (AoI) metric, defined as the time elapsed since the latest update has reached the destination. The source incurs an operational cost, modeled as an increasing convex function of the number of updates. The destination incurs an age-related cost, modeled as an increasing convex function of the AoI. The source charges the destination for each update and designs a pricing mechanism to maximize its profit; the destination on the other hand chooses a data update schedule to minimize the summation of its payments to the source and its age-related cost. The interaction among the source and destination is hence game-theoretic. Motivated by the existing pricing literature, we first study a time-dependent pricing scheme, in which the price for each update depends on when it is requested. We show in this case that the game equilibrium leads to only one data update, which does not yield the maximum profit to the source. This motivates us to consider a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested. We show that among all pricing schemes in which the price of an update may vary according to both time and quantity, the quantity-based pricing scheme performs best: it maximizes the source's profit and minimizes the social cost of the system, defined as the aggregate source's operational cost and the destination's age-related cost. Numerical results show that the optimal quantity-based pricing can be 27% more profitable for the source and incurs 54% less social cost, compared with the optimal time-dependent pricing.

cs.NI↗

arXiv2019

Hybrid Pricing for Mobile Collaborative Internet Access

Mobile Collaborative Internet Access (MCA) enables mobile users to share their Internet through flexible tethering arrangements. This can potentially make better use of network resources. However, from a mobile network operator's (MNO's) viewpoint, it can either reduce revenue or increase congestion, and thus has been blocked by some MNOs in practice. We propose a hybrid pricing framework for MNOs who charge users separately for access and tethering. This scheme serves to coordinate the tethering decisions of mobile users with MNO network management objectives. We analyze the MNOs' equilibrium pricing strategies in both cooperative and competitive scenarios. In the cooperative scenario, at the equilibrium, each user's cost is independent of any chosen tethering links. We then characterize the optimal hybrid pricing strategies of MNOs in this scenario. For the competitive scenario, we formulate the MNOs' competitive interactions as a pricing game, and we show that MNO competition leads to equalized prices for users if an equilibrium exists but does not guarantee its existence. Both insights motivate a quantity competition game, which is shown to guarantee an equilibrium. Simulation results show that in scenarios of interest the proposed hybrid pricing schemes can double both MNOs' profit and users' payoff and such improvements increase with the degree of network heterogeneity.

cs.NI↗

arXiv2019

Interfacial superconductivity induced by single-quintuple-layer Bi$_2$Te$_3$ on top of FeTe forming van-der-Waals heterostructure

We report the first clear observation of interfacial superconductivity on top of FeTe(FT) covered by one quintuple-layer Bi$_2$Te$_3$(BT) forming van-der-Waals heterojunction. Both transport and scanning tunneling spectroscopy measurements confirm the occurrence of superconductivity at a transition temperature T$_c$ = 13~K, when a single-quintuple-layer BT is deposited on the non-superconducting FT surface. The superconductivity gap decays exponentially with the thickness of BT, suggesting it occurs at the BT-FT interface and the proximity length is above 5-6~nm. We also measure the work function's dependence on the thickness of BT, implying a charge transfer may occur at the BT/FT interface to introduce hole doping into the FT layer, which may serve as a possible candidate for the superconducting mechanism. Our BT/FT heterojunction provides a clean system to study the unconventional interfacial superconductivity.

cond-mat.supr-con↗

arXiv2019

Multi-Granularity Reasoning for Social Relation Recognition from Images

Discovering social relations in images can make machines better interpret the behavior of human beings. However, automatically recognizing social relations in images is a challenging task due to the significant gap between the domains of visual content and social relation. Existing studies separately process various features such as faces expressions, body appearance, and contextual objects, thus they cannot comprehensively capture the multi-granularity semantics, such as scenes, regional cues of persons, and interactions among persons and objects. To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images. The global knowledge and mid-level details are learned from the whole scene and the regions of persons and objects, respectively. Most importantly, we explore the fine-granularity pose keypoints of persons to discover the interactions among persons and objects. Specifically, the pose-guided Person-Object Graph and Person-Pose Graph are proposed to model the actions from persons to object and the interactions between paired persons, respectively. Based on the graphs, social relation reasoning is performed by graph convolutional networks. Finally, the global features and reasoned knowledge are integrated as a comprehensive representation for social relation recognition. Extensive experiments on two public datasets show the effectiveness of the proposed framework.

cs.CV↗

arXiv2018

Predicting Inpatient Discharge Prioritization With Electronic Health Records

Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care. We studied this problem using eight years of Electronic Health Records (EHR) data from Stanford Hospital. We fit models to predict 24 hour discharge across the entire inpatient population. The best performing models achieved an area under the receiver-operator characteristic curve (AUROC) of 0.85 and an AUPRC of 0.53 on a held out test set. This model was also well calibrated. Finally, we analyzed the utility of this model in a decision theoretic framework to identify regions of ROC space in which using the model increases expected utility compared to the trivial always negative or always positive classifiers.

cs.LG↗

arXiv2018

Hair-GANs: Recovering 3D Hair Structure from a Single Image

We introduce Hair-GANs, an architecture of generative adversarial networks, to recover the 3D hair structure from a single image. The goal of our networks is to build a parametric transformation from 2D hair maps to 3D hair structure. The 3D hair structure is represented as a 3D volumetric field which encodes both the occupancy and the orientation information of the hair strands. Given a single hair image, we first align it with a bust model and extract a set of 2D maps encoding the hair orientation information in 2D, along with the bust depth map to feed into our Hair-GANs. With our generator network, we compute the 3D volumetric field as the structure guidance for the final hair synthesis. The modeling results not only resemble the hair in the input image but also possesses many vivid details in other views. The efficacy of our method is demonstrated by using a variety of hairstyles and comparing with the prior art.

cs.GR↗

arXiv2018

Cooperative Communication based Connectivity Recovery for UAV Networks

UAV networks often partition into separated clusters due to the high node and link dynamic. As a result, network connectivity recovery is an important issue in this area. Existing solutions always need excessive movement of nodes and thus lead to low recovery efficiency in terms of the time and energy consumption. In this paper, we for the first time study the issue of how to utilize cooperative communication technology to improve the connectivity recovery efficiency in UAV networks. We propose a Cooperative Communication based Connectivity Recovery algorithm for UAV Networks, named C3RUN. The key novelty is C3RUN not only uses cooperative communication to enlarge node's communication range and thus achieve quick repair of network connectivity, but also enables nodes to proactively move to better places for ensuring the establishment of cooperative communication links. We conduct extensive simulations to evaluate the performance of C3RUN. The simulation results reveal that C3RUN can not only achieve connectivity recovery with less nodes and shorter distance to move, but also always finish recovery with less time, when comparing with existing work. Furthermore, C3RUN can achieve 100% success ratio for connectivity recovery.

cs.NI↗

arXiv2018

Superconductivity in a misfit layered compound (SnSe)$_{1.16}$(NbSe$_2$)

The large size single crystals of (SnSe)$_{1.16}$(NbSe$_2$) misfit layered compound were grown and superconductivity with $T_c$ of 3.4 K was first discovered in this system. Powder X-ray diffraction (XRD) and high resolution transmission electron microscopy (HRTEM) clearly display the misfit feature between SnSe and NbSe$_2$ subsystems. The Sommerfeld coefficient $γ$ inferred from specific-heat measurements is 16.73 mJ mol$^{-1}$ K$^{-2}$, slightly larger than the usual misfit compounds. The normalized specific heat jump $Δ$$C_e$/$γ$$T_{\rm c}$ is about 0.98, and the electron-phonon coupling constant $λ$$_{e-ph}$ is estimated to be 0.80. The estimated value of the in-plane upper critical magnetic field, $H_{c2}^{ab}$(0), is about 7.82 T, exceeding the Pauli paramagnetic limit slightly. Both the specific-heat and $H_{c2}$ data suggest that (SnSe)$_{1.16}$(NbSe$_2$) is a multi-band superconductor.

cond-mat.supr-con↗

arXiv2018

Recent Advances in Convolutional Neural Network Acceleration

In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression. Two of the feature properties, local connectivity and weight sharing, can reduce the number of parameters and increase processing speed during training and inference. However, as the dimension of data becomes higher and the CNN architecture becomes more complicated, the end-to-end approach or the combined manner of CNN is computationally intensive, which becomes limitation to CNN's further implementation. Therefore, it is necessary and urgent to implement CNN in a faster way. In this paper, we first summarize the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers. We propose a taxonomy in terms of three levels, i.e.~structure level, algorithm level, and implementation level, for acceleration methods. We also analyze the acceleration methods in terms of CNN architecture compression, algorithm optimization, and hardware-based improvement. At last, we give a discussion on different perspectives of these acceleration and optimization methods within each level. The discussion shows that the methods in each level still have large exploration space. By incorporating such a wide range of disciplines, we expect to provide a comprehensive reference for researchers who are interested in CNN acceleration.

cs.LG↗

arXiv2018

Stellar mass distribution and star formation history of the Galactic disk revealed by mono-age stellar populations from LAMOST

We present a detailed determination and analysis of 3D stellar mass distribution of the Galactic disk for mono-age populations using a sample of 0.93 million main-sequence turn-off and subgiant stars from the LAMOST Galactic Surveys. Our results show (1) all stellar populations younger than 10\,Gyr exhibit strong disk flaring, which is accompanied with a dumpy vertical density profile that is best described by a $sech^n$ function with index depending on both radius and age; (2) Asymmetries and wave-like oscillations are presented in both the radial and vertical direction, with strength varying with stellar populations; (3) As a contribution by the Local spiral arm, the mid-plane stellar mass density at solar radius but 400--800\,pc (3--6$^\circ$) away from the Sun in the azimuthal direction has a value of $0.0594\pm0.0008$\,$M_\odot$/pc$^3$, which is 0.0164\,$M_\odot$/pc$^3$ higher than previous estimates at the solar neighborhood. The result causes doubts on the current estimate of local dark matter density; (4) The radial distribution of surface mass density yields a disk scale length evolving from $\sim$4\,kpc for the young to $\sim$2\,kpc for the old populations. The overall population exhibits a disk scale length of $2.48\pm0.05$\,kpc, and a total stellar mass of $3.6(\pm0.1)\times10^{10}$\,$M_\odot$ assuming $R_{\odot}=8.0$\,kpc, and the value becomes $4.1(\pm0.1)\times10^{10}$\,$M_\odot$ if $R_{\odot}=8.3$\,kpc; (5) The disk has a peak star formation rate ({\rm SFR}) changing from 6--8\,Gyr at the inner to 4--6\,Gyr ago at the outer part, indicating an inside-out assemblage history. The 0--1\,Gyr population yields a recent disk total {\rm SFR} of $1.96\pm0.12$\,$M_\odot$/yr.

astro-ph.GA↗

arXiv2018

Origin of interfacial conductivity at complex oxide heterointerfaces: possibility of electron transfer from water chemistry at surface oxygen vacancies

Variety of conducting heterointerfaces have been made between SrTiO3 substrates and thin capping layers of distinctly different oxide materials that can be classified into polar band insulators (e.g. LaAlO3), polar Mott insulators (e.g. LaTiO3), apparently non-polar band insulators (e.g. γ-Al2O3), and amorphous oxides (e.g. amorphous SrTiO3). A fundamental question to ask is if there is a common mechanism that governs interfacial conductivity in all these heterointerfaces. Here, we examined the conductivity of different kinds of heterointerfaces by annealing in oxygen and surface treatment with water. It was found that the conductivity of all the heterointerfaces show a strong dependence on annealing, and can be universally tuned by surface treatment whose effect is determined by the annealing condition. These observations, together with ambient-pressure X-ray photoelectron spectroscopy measurements, suggest that water chemistry at surface oxygen vacancies is a common mechanism that supplies electrons to the interface.

cond-mat.mtrl-sci↗

arXiv2018

Bearing fault diagnosis based on domain adaptation using transferable features under different working conditions

Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The dataset of normal bearing and faulty bearings are obtained through the fast Fourier transformation(FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy(MMD) in feature space by refining pseudo test labels, which can be obtained by the Nearest-Neighbor(NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.

eess.SP↗

arXiv2018

Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition

Traditional intelligent fault diagnosis of rolling bearings work well only under a common assumption that the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. However, in many real-world applications, this assumption does not hold, especially when the working condition varies. In this paper, a new adversarial adaptive 1-D CNN called A2CNN is proposed to address this problem. A2CNN consists of four parts, namely, a source feature extractor, a target feature extractor, a label classifier and a domain discriminator. The layers between the source and target feature extractor are partially untied during the training stage to take both training efficiency and domain adaptation into consideration. Experiments show that A2CNN has strong fault-discriminative and domain-invariant capacity, and therefore can achieve high accuracy under different working conditions. We also visualize the learned features and the networks to explore the reasons behind the high performance of our proposed model.

eess.SP↗

arXiv2018

Binary Star Fractions from the LAMOST DR4

Stellar systems composed of single, double, triple or high-order systems are rightfully regarded as the fundamental building blocks of the Milky Way. Binary stars play an important role in formation and evolution of the Galaxy. Through comparing the radial velocity variations from multi-epoch observations, we analyze the binary fraction of dwarf stars observed with the LAMOST. Effects of different model assumptions such as orbital period distributions on the estimate of binary fractions, are investigated. The results based on log-normal distribution of orbital periods reproduce the previous complete analyses better than the power-law distribution. We find that the binary fraction increases with $T_{\rm eff}$ and decreases with [Fe/H]. We first investigate the relation between $α$-elements and binary fraction in such a large sample as the LAMOST. The old stars with high [$α$/Fe] dominate higher binary fraction than young stars with low [$α$/Fe]. At the same mass, former forming stars possess a higher binary fraction than newly forming ones, which may be related with the evolution of the Galaxy.

astro-ph.SR↗

arXiv2017

Index Modulation for 5G: Striving to Do More with Less

The fifth generation (5G) wireless communications brag both high spectrum efficiency and high energy efficiency. To meet the requirements, various new techniques have been proposed. Among these, the recently-emerging index modulation has attracted significant interests. By judiciously activating a subset of certain communication {building blocks, such as} antenna, subcarrier and time slot, index modulation is claimed to have the potential to meet the challenging 5G needs. In this article, we will discuss index modulation and its general and specific representations, enhancements, and potential applications in various 5G scenarios. The objective is to reveal whether, and how, index modulation may strive for more performance gains with less medium resource occupation.

cs.IT↗

arXiv2017

High brightness fully coherent X-ray amplifier seeded by a free-electron laser oscillator

X-ray free-electron laser oscillator (XFELO) is expected to be a cutting edge tool for fully coherent X-ray laser generation, and undulator taper technique is well-known for considerably increasing the efficiency of free-electron lasers (FELs). In order to combine the advantages of these two schemes, FEL amplifier seeded by XFELO is proposed by simply using a chirped electron beam. With the right choice of the beam parameters, the bunch tail is within the gain bandwidth of XFELO, and lase to saturation, which will be served as a seeding for further amplification. Meanwhile, the bunch head which is outside the gain bandwidth of XFELO, is preserved and used in the following FEL amplifier. It is found that the natural "double-horn" beam current as well as residual energy chirp from chicane compressor are quite suitable for the new scheme. Inheriting the advantages from XFELO seeding and undulator tapering, it is feasible to generate nearly terawatt level, fully coherent X-ray pulses with unprecedented shot-to-shot stability, which might open up new scientific opportunities in various research fields.

physics.acc-ph↗

arXiv2017

Bearing fault diagnosis under varying working condition based on domain adaptation

Traditional intelligent fault diagnosis of rolling bearings work well only under a common assumption that the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. When the distribution changes, most fault diagnosis models need to be rebuilt from scratch using newly recollected labeled training data. However, it is expensive or impossible to annotate huge amount of training data to rebuild such new model. Meanwhile, large amounts of labeled training data have not been fully utilized yet, which is apparently a waste of resources. As one of the important research directions of transfer learning, domain adaptation (DA) typically aims at minimizing the differences between distributions of different domains in order to minimize the cross-domain prediction error by taking full advantage of information coming from both source and target domains. In this paper, we present one of the first studies on unsupervised DA in the field of fault diagnosis of rolling bearings under varying working conditions and a novel diagnosis strategy based on unsupervised DA using subspace alignment (SA) is proposed. After processed by unsupervised DA with SA, the distributions of training data and testing data become close and the classifier trained on training data can be used to classify the testing data. Experimental results on the 60 domain adaptation diagnosis problems under varying working condition in Case Western Reserve benchmark data and 12 domain adaptation diagnosis problems under varying working conditions in our new data are given to demonstrate the effectiveness of the proposed method. The proposed methods can effectively distinguish not only bearing faults categories but also fault severities.

cs.SD↗

arXiv2017

Creatism: A deep-learning photographer capable of creating professional work

Machine-learning excels in many areas with well-defined goals. However, a clear goal is usually not available in art forms, such as photography. The success of a photograph is measured by its aesthetic value, a very subjective concept. This adds to the challenge for a machine learning approach. We introduce Creatism, a deep-learning system for artistic content creation. In our system, we break down aesthetics into multiple aspects, each can be learned individually from a shared dataset of professional examples. Each aspect corresponds to an image operation that can be optimized efficiently. A novel editing tool, dramatic mask, is introduced as one operation that improves dramatic lighting for a photo. Our training does not require a dataset with before/after image pairs, or any additional labels to indicate different aspects in aesthetics. Using our system, we mimic the workflow of a landscape photographer, from framing for the best composition to carrying out various post-processing operations. The environment for our virtual photographer is simulated by a collection of panorama images from Google Street View. We design a "Turing-test"-like experiment to objectively measure quality of its creations, where professional photographers rate a mixture of photographs from different sources blindly. Experiments show that a portion of our robot's creation can be confused with professional work.

cs.CV↗