arXiv subjects

Wei Zheng

Publications and source records attributed to Wei Zheng.

At least 109 records · Page 6Linked to original sources

arXiv2020

Ensuring Privacy in Location-Based Services: A Model-based Approach

In recent years, the widespread of mobile devices equipped with GPS and communication chips has led to the growing use of location-based services (LBS) in which a user receives a service based on his current location. The disclosure of user's location, however, can raise serious concerns about user privacy in general, and location privacy in particular which led to the development of various location privacy-preserving mechanisms aiming to enhance the location privacy while using LBS applications. In this paper, we propose to model the user mobility pattern and utility of the LBS as a Markov decision process (MDP), and inspired by probabilistic current state opacity notation, we introduce a new location privacy metric, namely $ε-$privacy, that quantifies the adversary belief over the user's current location. We exploit this dynamic model to design a LPPM that while it ensures the utility of service is being fully utilized, independent of the adversary prior knowledge about the user, it can guarantee a user-specified privacy level can be achieved for an infinite time horizon. The overall privacy-preserving framework, including the construction of the user mobility model as a MDP, and design of the proposed LPPM, are demonstrated and validated with real-world experimental data.

cs.CR↗

arXiv2020

Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we re-analyzed the computational approaches and findings presented in two recent manuscripts by Ji et al. (https://doi.org/10.1002/jmv.25682) and by Pradhan et al. (https://doi.org/10.1101/2020.01.30.927871), which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. Results from our re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, do not support the conclusions proposed by these manuscripts. Based on our analyses and existing data of coronaviruses, we concluded that the intermediate hosts of 2019-nCoV are more likely to be mammals and birds than snakes, and that the "novel insertions" observed in the spike protein are naturally evolved from bat coronaviruses.

q-bio.GN↗

arXiv2019

KRM-based Dialogue Management

A KRM-based dialogue management (DM) is proposed using to implement human-computer dialogue system in complex scenarios. KRM-based DM has a well description ability and it can ensure the logic of the dialogue process. Then a complex application scenario in the Internet of Things (IOT) industry and a dialogue system implemented based on the KRM-based DM will be introduced, where the system allows enterprise customers to customize topics and adapts corresponding topics in the interaction process with users. The experimental results show that the system can complete the interactive tasks well, and can effectively solve the problems of topic switching, information inheritance between topics, change of dominance.

cs.HC↗

arXiv2019

A New Deep Learning Method for Image Deblurring in Optical Microscopic Systems

Deconvolution is the most commonly used image processing method to remove the blur caused by the point-spread-function (PSF) in optical imaging systems. While this method has been successful in deblurring, it suffers from several disadvantages including being slow, since it takes many iterations, suboptimal, in cases where experimental operator chosen to represent PSF is not optimal. In this paper, we are proposing a deep-learning-based deblurring method applicable to optical microscopic imaging systems. We tested the proposed method in database data, simulated data, and experimental data (include 2D optical microscopic data and 3D photoacoustic microscopic data), all of which showed much improved deblurred results compared to deconvolution. To quantify the improved performance, we compared our results against several deconvolution methods. Our results are better than conventional techniques and do not require multiple iterations or pre-determined experimental operator. Our method has the advantages of simple operation, short time to compute, good deblur results and wide application in all types of optical microscopic imaging systems. The deep learning approach opens up a new path for deblurring and can be applied in various biomedical imaging fields.

eess.IV↗

arXiv2019

A Random Interaction Forest for Prioritizing Predictive Biomarkers

Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system. Various statistical methodologies have been developed tackling problems in different aspects of this field, e.g., assessing treatment heterogeneity, identifying patient subgroups, or building treatment decision models. However, there is a lack of new tools devoted to selecting and prioritizing predictive biomarkers. We propose a novel tree-based ensemble method, random interaction forest (RIF), to generate predictive importance scores and prioritize candidate biomarkers for constructing refined treatment decision models. RIF was evaluated by comparing with the conventional random forest and univariable regression methods and showed favorable properties under various simulation scenarios. We applied the proposed RIF method to a biomarker dataset from two phase III clinical trials of bezlotoxumab on $\textit{Clostridium difficile}$ infection recurrence and obtained biologically meaningful results.

q-bio.QM↗

arXiv2019

Many-Body Echo

In this letter we propose a protocol to reverse a quantum many-body dynamical process. We name it "many-body echo" because the underlying physics is closely related to the spin echo effect in nuclear magnetic resonance systems. We consider a periodical modulation of the interaction strength in a weakly interacting Bose condensate, which resonantly excites quasi-particles from the condensate. A dramatic phenomenon is that, after pausing the interaction modulation for half a period and then continuing on with the same modulation, nearly all the excited quasi-particles in the resonance modes will be absorbed back into the condensate. During the intermediate half period, the free evolution introduces a $π$ phase, which plays a role reminiscent of that played by the $π$-pulse in the spin echo. Comparing our protocol with another one implemented by the Chicago group in a recent experiment, we find that ours is more effective at reversing the many-body process. The difference between these two schemes manifests the physical effect of the micro-motion in the Floquet theory. Our scheme can be generalised to other periodically driven many-body systems.

cond-mat.quant-gas↗

arXiv2019

Ball: An R package for detecting distribution difference and association in metric spaces

The rapid development of modern technology facilitates the appearance of numerous unprecedented complex data which do not satisfy the axioms of Euclidean geometry, while most of the statistical hypothesis tests are available in Euclidean or Hilbert spaces. To properly analyze the data of more complicated structures, efforts have been made to solve the fundamental test problems in more general spaces. In this paper, a publicly available R package Ball is provided to implement Ball statistical test procedures for K-sample distribution comparison and test of mutual independence in metric spaces, which extend the test procedures for two sample distribution comparison and test of independence. The tailormade algorithms as well as engineering techniques are employed on the Ball package to speed up computation to the best of our ability. Two real data analyses and several numerical studies have been performed and the results certify the powerfulness of Ball package in analyzing complex data, e.g., spherical data and symmetric positive matrix data.

stat.CO↗

arXiv2019

Do the current astronomical observations exclude the existence of non-strange quark stars?

As is pointed out in a recent work (Phys. Rev. Lett. 120, 222001), quark matter may not be strange. Inspired by this argument, we use a new self-consistent mean field approximation method to study the equation of state of cold dense matter within the framework of the two-flavor NJL model. Then the mass-radius relationship of two-flavor pure quark stars is studied. In the new self-consistent mean field approximation method we introduced, there is a free parameter $α$, which reflects the weight of different interaction channels. In principal, $α$ should be determined by experiments rather than the self-consistent mean field approximation itself. In this paper, thus, the influence of the variation of $α$ on the critical chemical potential of chiral phase transition and the stiffness of the EOS are thoroughly explored. It is found that the stiffness of EOS can be increased greatly to support a two-solar-mass pure quark star when $α$ is greater than 0.95. This means that current theoretical calculations and astronomical observations cannot rule out the possibility of a two-flavor pure quark star.

nucl-th↗

arXiv2019

Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation

Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps across views. Here we propose a recurrent aggregation learning method to tackle this challenging task. By pyramid ConvBlocks, multi-level and multi-scale features are extracted efficiently. Hierarchical ConvLSTMs next fuse these features and capture spatial-temporal information in multi-level and multi-scale space. We further introduce a double-branch aggregation mechanism for segmentation and classification which are mutually promoted by deep aggregation of multi-level and multi-scale features. The segmentation branch provides information to guide the classification while the classification branch affords multi-view regularization to refine segmentations and further lessen gaps across views. Our method is built as an end-to-end framework for segmentation and classification. Adequate experiments on our multi-view dataset (9000 labeled images) and the CAMUS dataset (1800 labeled images) corroborate that our method achieves not only superior segmentation and classification accuracy but also prominent temporal stability.

eess.IV↗

arXiv2019

Vector Autoregressive POMDP Model Learning and Planning for Human-Robot Collaboration

Human-robot collaboration (HRC) has emerged as a hot research area at the intersection of control, robotics, and psychology in recent years. It is of critical importance to obtain an expressive but meanwhile tractable model for human beings in HRC. In this paper, we propose a model called Vector Autoregressive POMDP (VAR-POMDP) model which is an extension of the traditional POMDP model by considering the correlation among observations. The VAR-POMDP model is more powerful in the expressiveness of features than the traditional continuous observation POMDP since the traditional one is a special case of the VAR-POMDP model. Meanwhile, the proposed VAR-POMDP model is also tractable, as we show that it can be effectively learned from data and we can extend point-based value iteration (PBVI) to VAR-POMDP planning. Particularly, in this paper, we propose to use the Bayesian non-parametric learning to decide potential human states and learn a VAR-POMDP model using data collected from human demonstrations. Then, we consider planning with respect to PCTL which is widely used as safety and reachability requirement in robotics. Finally, the advantage of using the proposed model for HRC is validated by experimental results using data collected from a driver-assistance test-bed.

cs.RO↗

arXiv2019

Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

Environmental air quality affects people's life, obtaining real-time and accurate environmental air quality has a profound guiding significance for the development of social activities. At present, environmental air quality measurement mainly adopts the method that setting air quality detector at specific monitoring points in cities and timing sampling analysis, which is easy to be restricted by time and space factors. Some air quality measurement algorithms related to deep learning mostly adopt a single convolutional neural network to train the whole image, which will ignore the difference of different parts of the image. In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental images. Our method mainly includes two aspects: ensemble learning of double-channel convolutional neural network and self-learning weighted feature fusion. We constructed a double-channel convolutional neural network, used each channel to train different parts of the environment images for feature extraction. We propose a feature weight self-learning method, which weights and concatenates the extracted feature vectors, and uses the fused feature vectors to measure air quality. Our method can be applied to the two tasks of air quality grade measurement and air quality index (AQI) measurement. Moreover, we build an environmental image dataset of random time and location condition. The experiments show that our method can achieve nearly 82% accuracy and a small mean absolute error (MAE) on our test dataset. At the same time, through comparative experiment, we proved that our proposed method gained considerable improvement in performance compared with single channel convolutional neural network air quality measurements.

cs.CV↗

arXiv2018

Probing signatures of bounce inflation with current observations

The aim of this paper is to probe the features of the bouncing cosmology with the current observational data. Basing on bounce inflation model, with high derivative term, we propose a general parametrization of primordial power spectrum which includes the typical bouncing parameters, such as bouncing time-scale, and energy scale. By applying Markov Chain Monto Carlo analysis with current data combination of Planck 2015, BAO and JLA, we report the posterior probability distributions of the parameters. We find that, bouncing models can well explain CMB observations, especially the deficit and oscillation on large scale in TT power spectrum.

astro-ph.CO↗

arXiv2018

The onset of star formation 250 million years after the Big Bang

A fundamental quest of modern astronomy is to locate the earliest galaxies and study how they influenced the intergalactic medium a few hundred million years after the Big Bang. The abundance of star-forming galaxies is known to decline from redshifts of about 6 to 10, but a key question is the extent of star formation at even earlier times, corresponding to the period when the first galaxies might have emerged. Here we present spectroscopic observations of MACS1149-JD1, a gravitationally lensed galaxy observed when the Universe was less than four per cent of its present age. We detect an emission line of doubly ionized oxygen at a redshift of $9.1096\pm0.0006$, with an uncertainty of one standard deviation. This precisely determined redshift indicates that the red rest-frame optical colour arises from a dominant stellar component that formed about 250 million years after the Big Bang, corresponding to a redshift of about 15. Our results indicate the it may be possible to detect such early episodes of star formation in similar galaxies with future telescopes.

astro-ph.GA↗

arXiv2018

POMDP Model Learning for Human Robot Collaboration

Recent years have seen human robot collaboration (HRC) quickly emerged as a hot research area at the intersection of control, robotics, and psychology. While most of the existing work in HRC focused on either low-level human-aware motion planning or HRC interface design, we are particularly interested in a formal design of HRC with respect to high-level complex missions, where it is of critical importance to obtain an accurate and meanwhile tractable human model. Instead of assuming the human model is given, we ask whether it is reasonable to learn human models from observed perception data, such as the gesture, eye movements, head motions of the human in concern. As our initial step, we adopt a partially observable Markov decision process (POMDP) model in this work as mounting evidences have suggested Markovian properties of human behaviors from psychology studies. In addition, POMDP provides a general modeling framework for sequential decision making where states are hidden and actions have stochastic outcomes. Distinct from the majority of POMDP model learning literature, we do not assume that the state, the transition structure or the bound of the number of states in POMDP model is given. Instead, we use a Bayesian non-parametric learning approach to decide the potential human states from data. Then we adopt an approach inspired by probably approximately correct (PAC) learning to obtain not only an estimation of the transition probability but also a confidence interval associated to the estimation. Then, the performance of applying the control policy derived from the estimated model is guaranteed to be sufficiently close to the true model. Finally, data collected from a driver-assistance test-bed are used to train the model, which illustrates the effectiveness of the proposed learning method.

cs.HC↗

arXiv2018

Rotation in [CII]-emitting gas in two galaxies at a redshift of 6.8

The earliest galaxies are expected to emerge in the first billion years of the Universe during the Epoch of Reionization. However, both the spectroscopic confirmation of photometrically-selected galaxies at this epoch and the characterization of their early dynamical state has been hindered by the lack of bright, accessible lines to probe the velocity structure of their interstellar medium. We present the first ALMA spectroscopic confirmation of such sources at z > 6 using the far-infrared [C II]λ157.74μm emission line, and, for the first time, measurement of the velocity structure, for two galaxies at z = 6.8540+/-0.0003 and z = 6.8076+/-0.0002. Remarkably, the [C II] line luminosity from these galaxies is higher than previously found in `normal' star-forming galaxies at z > 6.5. This suggests that we are sampling a part of the galaxy population different from the galaxies found through detection of the Lyα line. The luminous and extended [C II] detections reveal clear velocity gradients that, if interpreted as rotation, would suggest these galaxies have turbulent, yet rotation-dominated disks, with similar stellar-to-dynamical mass fractions as observed for Hα emitting galaxies 2 Gyr later at cosmic noon. Our novel approach for confirming galaxies during Reionization paves the way for larger studies of distant galaxies with spectroscopic redshifts from ALMA. Particularly important, this opens up opportunities for high angular-resolution [C II] dynamics in galaxies less than one billion years after the Big Bang.

astro-ph.GA↗

arXiv2018

Magnetic orders in a Fermi gas induced by cavity-field fluctuations

We study magnetic orders of fermions under cavity-assisted Raman couplings in a one-dimensional lattice at half filling. The cavity-enhanced atom-photon coupling introduces a dynamic long-range interaction between the fermions, which competes with the short-range on-site interaction and leads to a variety of magnetic orders. Adopting a numerical density-matrix-renormalization-group method, we investigate the various magnetic orders and map out the steady-state phase diagram. Interestingly, as all the phase transitions take place outside the superradiant regime, the magnetic orders are associated with cavity-field fluctuations with a vanishing number of photons on the mean-field level.

quant-ph↗

arXiv2017

Direct Investigation of the Birefringent Optical Properties of Black Phosphorus with Picosecond Interferometry

Black phosphorus (BP) is an emerging two-dimensional semiconducting material with great potential for nanoelectronic and nanophotonic applications, especially owing to its unique anisotropic electrical and optical properties. Many theoretical studies have predicted the anisotropic optical properties of BP, but the direct experimental quantification remains challenging. The difficulties stem from the ease of BP's degradation when exposed to air in ambient conditions, and from the indirect nature of conventional approaches that are subject to large measurement uncertainties. This work reports a direct investigation of the birefringent optical constants of micrometer-thick BP samples with picosecond (ps) interferometry, over the wavelength range from 780 to 890 nm. In this ps-interferometry approach, an ultrathin (5 nm) platinum layer for launching acoustic waves naturally protects the BP flake from degradation. The birefringent optical constants of BP for light polarization along the two primary crystalline orientations, zigzag and armchair, are directly obtained via fitting the attenuated Brillouin scattering signals. A bi-exponential model is further proposed to analyze the BS signals for a random incident light polarization. The BP experimental results and the associated measurement sensitivity analysis demonstrate the reliability and accuracy of the ps-interferometry approach for capturing the polarization-dependent optical properties of birefringent materials.

physics.optics↗

arXiv2017

Anomalous Diffusion in a Dynamical Optical Lattice

Motivated by experimental progress in strongly coupled atom-photon systems in optical cavities, we study theoretically the quantum dynamics of atoms coupled to a one-dimensional dynamical optical lattice. The dynamical lattice is chosen to have a period that is incommensurate with that of an underlying static lattice, leading to a dynamical version of the Aubry-André model which can cause localization of single-particle wavefunctions. We show that atomic wavepackets in this dynamical lattice generically spread via anomalous diffusion, which can be tuned between super-diffusive and sub-diffusive regimes. This anomalous diffusion arises from an interplay between quantum localization and quantum fluctuations of the cavity field.

cond-mat.quant-gas↗