arXiv subjects

Jun Li

Publications and source records attributed to Jun Li.

At least 865 records · Page 48Linked to original sources

arXiv2017

Superconducting (Li, Fe)OHFeSe film of high quality and high critical parameters

The superconducting film of (Li1-xFex)OHFeSe is reported for the first time. The thin film exhibits a small in-plane crystal mosaic of 0.22 deg, in terms of the FWHM (full-width-at-half-maximum) of x-ray rocking curve, and an excellent out-of-plane orientation by x-ray phi-scan. Its bulk superconducting transition temperature (Tc) of 42.4 K is characterized by both zero electrical resistance and diamagnetization measurements. The upper critical field (Hc2) is estimated to be 79.5 T and 443 T, respectively, for the magnetic field perpendicular and parallel to the ab plane. Moreover, a large critical current density (Jc) of a value over 0.5 MA/cm2 is achieved at ~20 K. Such a (Li1-xFex)OHFeSe film is therefore not only important to the fundamental research for understanding the high-Tc mechanism, but also promising in the field of high-Tc superconductivity application, especially in high-performance electronic devices and large scientific facilities such as superconducting accelerator.

cond-mat.supr-con↗

arXiv2017

Promoting superconductivity in FeSe films via fine manipulation of crystal lattice

Stabilized FeSe thin films in ambient pressure with tunable superconductivity would be a promising candidate for superconducting electronic devices yet its superconducting transition temperature (Tc) is below 10 K in bulk materials. By carefully controlling the depositions on twelve kinds of substrates using pulsed laser deposition technique, high quality single crystalline FeSe samples were fabricated with full width of half maximum 0.515? in the rocking curve and clear four-fold symmetry in phi-scan from x-ray diractions. The films have a maximum Tc 15 K on the CaF2 substrate and do not show obvious decay in the air for more than half a year. Slightly tuning the stoichiometry of the FeSe targets, the Tc becomes adjustable from 15 to < 2 K with quite narrow transition widths less than 2 K, and shows a positive relation with the out-of-plane (c-axis) lattice parameter of the films. However, there is no clear relation between the Tc and the surface atomic distance of the substrates. By reducing the thickness of the films, the Tc decreases and fades away in samples of less than 10 nm, suggesting that the strain effect is not responsible for the enhancement of Tc in our experiments.

cond-mat.supr-con↗

arXiv2017

A Separability-Entanglement Classifier via Machine Learning

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach.

quant-ph↗

arXiv2017

Dense Transformer Networks

The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on natural and biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods.

cs.CV↗

arXiv2017

Spectrum-based deep neural networks for fraud detection

In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as spectral coordinate) in the low dimensional spectral space of the graph's adjacency matrix as input of deep neural networks. Spectral coordinates in the spectral space capture the most useful topology information of the network. Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real signed graph show that our spectrum based deep neural networks are effective in fraud detection.

cs.CR↗

arXiv2017

Transit and lifespan in neutrophil production: implications for drug intervention

We compare and contrast the transit compartment ordinary differential equation modelling approach with distributed and discrete delay differential equation models. We focus on Quartino's extension to the Friberg transit compartment model of myelosuppression, widely relied upon in the pharmaceutical sciences to predict the neutrophil response after chemotherapy, and on a QSP delay differential equation model of granulopoiesis. We extend the Quartino model by considering a general number of transit compartments and introduce an extra parameter which allows us to decouple the maturation time from the production rate of cells, and review the well established linear chain technique from the delay differential equation (DDE) literature which can be used to reformulate transit compartment models with constant transit rates as distributed delay DDEs. We perform a state-dependent time rescaling of the Quartino model in order to apply the linear chain technique and rewrite the Quartino model as a distributed delay DDE, which yields a discrete delay DDE model in a certain parameter limit. We then perform stability and bifurcation analyses on the models to situate such studies in a mathematical pharmacology context. We show that both the original Friberg and the Quartino extension model incorrectly define the mean maturation time, essentially treating the proliferative pool as an additional maturation compartment, which can have far reaching consequences on the development of future models of myelosuppression in PK/PD.

q-bio.TO↗

arXiv2017

ARABIS: an Asynchronous Acoustic Indoor Positioning System for Mobile Devices

Acoustic ranging based indoor positioning solutions have the advantage of higher ranging accuracy and better compatibility with commercial-off-the-self consumer devices. However, similar to other time-domain based approaches using Time-of-Arrival and Time-Difference-of-Arrival, they suffer from performance degradation in presence of multi-path propagation and low received signal-to-noise ratio (SNR) in indoor environments. In this paper, we improve upon our previous work on asynchronous acoustic indoor positioning and develop ARABIS, a robust and low-cost acoustic positioning system (IPS) for mobile devices. We develop a low-cost acoustic board custom-designed to support large operational ranges and extensibility. To mitigate the effects of low SNR and multi-path propagation, we devise a robust algorithm that iteratively removes possible outliers by taking advantage of redundant TDoA estimates. Experiments have been carried in two testbeds of sizes 10.67m*7.76m and 15m*15m, one in an academic building and one in a convention center. The proposed system achieves average and 95% quantile localization errors of 7.4cm and 16.0cm in the first testbed with 8 anchor nodes and average and 95% quantile localization errors of 20.4cm and 40.0cm in the second testbed with 4 anchor nodes only.

cs.NI↗

arXiv2017

GRASS: Generative Recursive Autoencoders for Shape Structures

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.

cs.GR↗

arXiv2017

Optimal experiment design for quantum state tomography

Quantum state tomography is an indispensable but costly part of many quantum experiments. Typically, it requires measurements to be carried in a number of different settings on a fixed experimental setup. The collected data is often informationally overcomplete, with the amount of information redundancy depending on the particular set of measurement settings chosen. This raises a question about how should one optimally take data so that the number of measurement settings necessary can be reduced. Here, we cast this problem in terms of integer programming. For a given experimental setup, standard integer programming algorithms allow us to find the minimum set of readout operations that can realize a target tomographic task. We apply the method to certain basic and practical state tomographic problems in nuclear magnetic resonance experimental systems. The results show that, considerably less readout operations can be found using our technique than it was by using the previous greedy search strategy. Therefore, our method could be helpful for simplifying measurement schemes so as to minimize the experimental effort.

quant-ph↗

arXiv2017

Optimized spin-injection efficiency and spin MOSFET operation based on low-barrier ferromagnet/insulator/n-Si tunnel contact

We theoretically investigate the spin injection in different FM/I/n-Si tunnel contacts by using the lattice NEGF method. We find that the tunnel contacts with low barrier materials such as TiO$_2$ and Ta$_{2}$O$_{5}$, have much lower resistances than the conventional barrier materials, resulting in a wider and attainable optimum parameters window for improving the spin injection efficiency and MR ratio of a vertical spin MOSFET. Additionally, we find the spin asymmetry coefficient of TiO$_2$ tunnel contact has a negative value, while that of Ta$_{2}$O$_{5}$ contact can be tuned between positive and negative values, by changing the parameters.

cond-mat.mes-hall↗

arXiv2017

Sum-MSE performance gain of DFT-based channel estimator over frequency-domain LS one in full-duplex OFDM systems with colored interference

In this paper, we make an investigation on the sum-mean-square-error (sum-MSE) performance gain achieved by DFT-based least-square (LS) channel estimator over frequency-domain LS one in full-duplex OFDM system in the presence of colored interference and noise. The closed-form expression of the sum-MSE performance gain is given. Its simple upper and lower bounds are derived by using inequalities of matrix eigen-values. By simulation and analysis, the upper lower bound is shown to be close to the exact value of MSE gain as the ratio of the number N of total subcarriers to the cyclic prefix length L grows and the correlation factor of colored interference increases. More importantly, we also find that the MSE gain varies from one to N/L as the correlation among colored interferences decreases gradually. According to theoretical analysis, we also find the MSE gain has very simple forms in two extreme scenarios. In the first extreme case that the colored interferences over all subchannels are fully correlated, i.e., their covariance matrix is a matrix of all-ones, the sum-MSE gain reduces to 1. In other words, there is no performance gain. In the second extreme case that the colored-interference covariance matrix is an identity matrix, i.e, they are mutually independent, the achievable sum-MSE performance gain is N/L. A large ratio N/L will achieve a significant sum-MSE gain.

cs.IT↗

arXiv2017

Resolving Knudsen Layer by High Order Moment Expansion

We model the Knudsen layer in Kramers' problem by linearized high order hyperbolic moment system. Due to the hyperbolicity, the boundary conditions of the moment system is properly reduced from the kinetic boundary condition. For Kramers' problem, we give the analytical solutions of moment systems. With the order increasing of the moment model, the solutions are approaching to the solution of the linearized BGK kinetic equation. The velocity profile in the Knudsen layer is captured with improved accuracy for a wide range of accommodation coefficients.

math.NA↗

arXiv2017

Doping Mn into (Li1-xFex)OHFe1-ySe superconducting crystals via ion-exchange and ion-release/introduction syntheses

We report the success in introducing Mn into (Li1-xFex)OHFe1-ySe superconducting crystals by applying two different hydrothermal routes, ion exchange (1-Step) and ion release/introduction (2-Step). The micro-region x-ray diffraction and energy dispersive x-ray spectroscopy analyses indicate that the Mn has been doped into the lattice, and its content in the 1-Step fabricated sample is higher than that in the 2-Step one. Magnetic susceptibility and electric transport properties reveal that Mn doping influences little on the superconducting transition, regardless of 1-Step or 2-Step routes. By contrast, the characteristic temperature, T*, where the negative Hall coefficient reaches its minimum, is significantly reduced by Mn doping. This implies that the reduction of the hole carriers contribution is obviously modified, and hence the hole band might have no direct relationship with the superconductivity in (Li1-xFex)OHFe1-ySe superconductors. Our present hydrothermal methods of ion exchange and ion release/introduction provide an efficient way for elements substitution/doping into (Li1-xFex)OHFe1-ySe superconductors, which will promote the in-depth investigations on the role of multiple electron and hole bands and their interplay with the high-temperature superconductivity in the FeSe-based superconductors.

cond-mat.supr-con↗

arXiv2017

S-shaped suppression of the superconducting transition temperature in Cu-intercalated NbSe2

2H-NbSe2 is the prototype and most frequently studied of the well-known transition metal dichalcogenide (TMDC) superconductors. Widely acknowledged to be a conventional superconductor, its transition temperature to the superconducting state (Tc) is 7.3 K - a Tc that is substantially higher than those seen for the majority of TMDCs, where Tcs between 2 and 4 K are the norm. Here we report the intercalation of Cu into 2H-NbSe2 to make CuxNbSe2. As is typically found when chemically altering an optimal superconductor, Tc decreases with increasing x, but the way that Tc is suppressed in this case is unusual - an S-shaped character is observed, with an inflection point near x = 0.03 and, at higher x, a leveling off of the Tc near 3 K - down to the usual value for a layered TMDC. Electronic characterization reveals corresponding S-like behavior for many of the materials parameters that influence Tc. To illustrate its character, the superconducting phase diagram for CuxNbSe2 is contrasted to those of FexNbSe2 and NbSe2-xSx.

cond-mat.supr-con↗

arXiv2017

Enhanced circular photogalvanic effect in HgTe quantum wells in the heavily inverted regime

Thanks to the strong spin-orbit interaction (SOI), HgTe-based quantum wells (QWs) exhibit very rich spin-related properties. But the full descriptions of them are beyond the simple parabolic band models and conventional Rashba and Dresselhaus SOI terms, as a result of the strong interband coupling of the narrow gap band structures. Here, we develop a theoretical method to calculate the circular photogalvanic effect (CPGE) in Hg$_{0.3}$Cd$_{0.7}$Te/HgTe/Hg$_{0.3}$Cd$_{0.7}$Te quantum wells (HgTe QWs) based on the realistic eight-band $\mathbf{k}\cdot\mathbf{p}$ model with density matrix formalism. Our method could take account of the unusual band structures and SOIs of HgTe QWs, therefore can be used to calculate the CPGE currents in HgTe QWs with non-parabolic, Dirac-like and inverted energy dispersions. The microscopic origin of CPGE and the interplay effect of structure inversion asymmetry (SIA) and bulk inversion asymmetry (BIA) is also investigated. In addition, this method is extended to study the pure spin currents (PSCs) in HgTe QWs injected by linearly polarized light at normal incidence. Our calculation results support the following findings: (i) In the inverted phase regime, the energy dispersion of heavily inverted HgTe QWs could be strongly distorted, lead to a significant enhancement of CPGE at a certain range of energy spectrum. (ii) The interplay of SIA and BIA could lead to the CPGE currents anisotropically dependent on the azimuth angle of oblique incident light. (iii) The PSC $j_{y'}^{x'}$ ($x'\parallel[110]$ and $y'\parallel[\bar{1}10]$) produced by [110]-linearly-polarized light could change sign with HgTe QW transformed from normal phase to inverted phase. These findings might be utilized in developing the HgTe-based infrared/terahertz optoelectronic and spintronic devices.

cond-mat.mes-hall↗

arXiv2017

Hypothesis Testing For Network Data in Functional Neuroimaging

In recent years, it has become common practice in neuroscience to use networks to summarize relational information in a set of measurements, typically assumed to be reflective of either functional or structural relationships between regions of interest in the brain. One of the most basic tasks of interest in the analysis of such data is the testing of hypotheses, in answer to questions such as "Is there a difference between the networks of these two groups of subjects?" In the classical setting, where the unit of interest is a scalar or a vector, such questions are answered through the use of familiar two-sample testing strategies. Networks, however, are not Euclidean objects, and hence classical methods do not directly apply. We address this challenge by drawing on concepts and techniques from geometry, and high-dimensional statistical inference. Our work is based on a precise geometric characterization of the space of graph Laplacian matrices and a nonparametric notion of averaging due to Fréchet. We motivate and illustrate our resulting methodologies for testing in the context of networks derived from functional neuroimaging data on human subjects from the 1000 Functional Connectomes Project. In particular, we show that this global test is more statistical powerful, than a mass-univariate approach. In addition, we have also provided a method for visualizing the individual contribution of each edge to the overall test statistic.

stat.AP↗

arXiv2017

Heat-Bath Algorithmic Cooling with correlated qubit-environment interactions

Controlled preparation of highly pure quantum states is at the core of practical applications of quantum information science, from the state initialization of most quantum algorithms to a reliable supply of ancilla qubits that satisfy the fault-tolerance threshold for quantum error correction. Heat-bath algorithmic cooling has been shown to purify qubits by controlled redistribution of entropy and multiple contact with a bath, not only for ensemble implementations but also for technologies with strong but imperfect measurements. However, an implicit restriction about the interaction with the bath has been assumed in previous work. In this paper, we show that better purification can be achieved by removing that restriction. More concretely, we include correlations between the system and the bath, and we take advantage of these correlations to pump entropy out of the system into the bath. We introduce a tool for cooling algorithms, which we call "state-reset", obtained when the coupling to the environment is generalized from individual-qubits relaxation to correlated-qubit relaxation. We present improved cooling algorithms which lead to an increase of purity beyond all the previous work, and relate our results to the Nuclear Overhauser Effect.

quant-ph↗

arXiv2017

The first result on 76Ge neutrinoless double beta decay from CDEX-1 experiment

We report the first result on Ge-76 neutrinoless double beta decay from CDEX-1 experiment at China Jinping Underground Laboratory. A mass of 994 g p-type point-contact high purity germanium detector has been installed to search the neutrinoless double beta decay events, as well as to directly detect dark matter particles. An exposure of 304 kg*day has been analyzed. The wideband spectrum from 500 keV to 3 MeV was obtained and the average event rate at the 2.039 MeV energy range is about 0.012 count per keV per kg per day. The half-life of Ge-76 neutrinoless double beta decay has been derived based on this result as: T 1/2 > 6.4*10^22 yr (90% C.L.). An upper limit on the effective Majorana-neutrino mass of 5.0 eV has been achieved. The possible methods to further decrease the background level have been discussed and will be pursued in the next stage of CDEX experiment.

hep-ex↗