arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 739 records · Page 41Linked to original sources

arXiv2018

Observation of topological superconductivity on the surface of an iron-based superconductor

Topological superconductors, whose edge hosts Majorana bound states or Majorana fermions that obey non-Abelian statistics, can be used for low-decoherence quantum computations. Most of the proposed topological superconductors are realized with spin-helical states through proximity effect to BCS superconductors. However, such approaches are difficult for further studies and applications because of the low transition temperatures and complicated hetero-structures. Here by using high-resolution spin-resolved and angle-resolved photoelectron spectroscopy, we discover that the iron-based superconductor FeTe1-xSex (x = 0.45, Tc = 14.5 K) hosts Dirac-cone type spin-helical surface states at Fermi level, which open an s-wave SC gap below Tc. Our study proves that the surface states of FeTe0.55Se0.45 are 2D topologically superconducting, and thus provides a simple and possibly high-Tc platform for realizing Majorana fermions.

cond-mat.supr-con↗

arXiv2018

Topological Dirac semimetal phase in the iron-based superconductor Fe(Te,Se)

Topological Dirac semimetals (TDSs) exhibit bulk Dirac cones protected by time reversal and crystal symmetry, as well as surface states originating from non-trivial topology. While there is a manifold possible onset of superconducting order in such systems, few observations of intrinsic superconductivity have so far been reported for TDSs. We observe evidence for a TDS phase in FeTe$_{1-x}$Se$_x$ ($x$ = 0.45), one of the high transition temperature ($T_c$) iron-based superconductors. In angle-resolved photoelectron spectroscopy (ARPES) and transport experiments, we find spin-polarized states overlapping with the bulk states on the (001) surface, and linear magnetoresistance (MR) starting from 6 T. Combined, this strongly suggests the existence of a TDS phase, which is confirmed by theoretical calculations. In total, the topological electronic states in Fe(Te,Se) provide a promising high $T_c$ platform to realize multiple topological superconducting phases.

cond-mat.supr-con↗

arXiv2018

Direct observation of multiple topological phases in the iron-based superconductor Li(Fe,Co)As

Topological insulators/semimetals and unconventional iron-based superconductors have attracted major attentions in condensed matter physics in the past 10 years. However, there is little overlap between these two fields, although the combination of topological states and superconducting states will produce more exotic topologically superconducting states and Majorana bound states (MBS), a promising candidate for realizing topological quantum computations. With the progress in laser-based spin-resolved and angle-resolved photoemission spectroscopy (ARPES) with very high energy- and momentum-resolution, we directly resolved the topological insulator (TI) phase and topological Dirac semimetal (TDS) phase near Fermi level ($E_F$) in the iron-based superconductor Li(Fe,Co)As. The TI and TDS phases can be separately tuned to $E_F$ by Co doping, allowing a detailed study of different superconducting topological states in the same material. Together with the topological states in Fe(Te,Se), our study shows the ubiquitous coexistence of superconductivity and multiple topological phases in iron-based superconductors, and opens a new age for the study of high-Tc iron-based superconductors and topological superconductivity.

cond-mat.supr-con↗

arXiv2018

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.

cs.IR↗

arXiv2018

SNC: A Cloud Service Platform for Symbolic-Numeric Computation using Just-In-Time Compilation

Cloud services have been widely employed in IT industry and scientific research. By using Cloud services users can move computing tasks and data away from local computers to remote datacenters. By accessing Internet-based services over lightweight and mobile devices, users deploy diversified Cloud applications on powerful machines. The key drivers towards this paradigm for the scientific computing field include the substantial computing capacity, on-demand provisioning and cross-platform interoperability. To fully harness the Cloud services for scientific computing, however, we need to design an application-specific platform to help the users efficiently migrate their applications. In this, we propose a Cloud service platform for symbolic-numeric computation - SNC. SNC allows the Cloud users to describe tasks as symbolic expressions through C/C++, Python, Java APIs and SNC script. Just-In-Time (JIT) compilation through using LLVM/JVM is used to compile the user code to the machine code. We implemented the SNC design and tested a wide range of symbolic-numeric computation applications (including nonlinear minimization, Monte Carlo integration, finite element assembly and multibody dynamics) on several popular cloud platforms (including the Google Compute Engine, Amazon EC2, Microsoft Azure, Rackspace, HP Helion and VMWare vCloud). These results demonstrate that our approach can work across multiple cloud platforms, support different languages and significantly improve the performance of symbolic-numeric computation using cloud platforms. This offered a way to stimulate the need for using the cloud computing for the symbolic-numeric computation in the field of scientific research.

cs.DC↗

arXiv2018

Tuning Streamed Applications on Intel Xeon Phi: A Machine Learning Based Approach

Many-core accelerators, as represented by the XeonPhi coprocessors and GPGPUs, allow software to exploit spatial and temporal sharing of computing resources to improve the overall system performance. To unlock this performance potential requires software to effectively partition the hardware resource to maximize the overlap between hostdevice communication and accelerator computation, and to match the granularity of task parallelism to the resource partition. However, determining the right resource partition and task parallelism on a per program, per dataset basis is challenging. This is because the number of possible solutions is huge, and the benefit of choosing the right solution may be large, but mistakes can seriously hurt the performance. In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given application, targeting the Intel XeonPhi architecture. Instead of hand-crafting the heuristic for which the process will have to repeat for each hardware generation, we employ machine learning techniques to automatically learn it. We achieve this by first learning a predictive model offline using training programs; we then use the learned model to predict the resource partition and task granularity for any unseen programs at runtime. We apply our approach to 23 representative parallel applications and evaluate it on a CPU-XeonPhi mixed heterogenous many-core platform. Our approach achieves, on average, a 1.6x (upto 5.6x) speedup, which translates to 94.5% of the performance delivered by a theoretically perfect predictor.

cs.PF↗

arXiv2018

Uniform Recovery Bounds for Structured Random Matrices in Corrupted Compressed Sensing

We study the problem of recovering an $s$-sparse signal $\mathbf{x}^{\star}\in\mathbb{C}^n$ from corrupted measurements $\mathbf{y} = \mathbf{A}\mathbf{x}^{\star}+\mathbf{z}^{\star}+\mathbf{w}$, where $\mathbf{z}^{\star}\in\mathbb{C}^m$ is a $k$-sparse corruption vector whose nonzero entries may be arbitrarily large and $\mathbf{w}\in\mathbb{C}^m$ is a dense noise with bounded energy. The aim is to exactly and stably recover the sparse signal with tractable optimization programs. In this paper, we prove the uniform recovery guarantee of this problem for two classes of structured sensing matrices. The first class can be expressed as the product of a unit-norm tight frame (UTF), a random diagonal matrix and a bounded columnwise orthonormal matrix (e.g., partial random circulant matrix). When the UTF is bounded (i.e. $μ(\mathbf{U})\sim1/\sqrt{m}$), we prove that with high probability, one can recover an $s$-sparse signal exactly and stably by $l_1$ minimization programs even if the measurements are corrupted by a sparse vector, provided $m = \mathcal{O}(s \log^2 s \log^2 n)$ and the sparsity level $k$ of the corruption is a constant fraction of the total number of measurements. The second class considers randomly sub-sampled orthogonal matrix (e.g., random Fourier matrix). We prove the uniform recovery guarantee provided that the corruption is sparse on certain sparsifying domain. Numerous simulation results are also presented to verify and complement the theoretical results.

cs.IT↗

arXiv2018

Role of dimensional crossover on spin-orbit torque efficiency in magnetic insulator thin films

Magnetic insulators (MIs) attract tremendous interest for spintronic applications due to low Gilbert damping and absence of Ohmic loss. Magnetic order of MIs can be manipulated and even switched by spin-orbit torques (SOTs) generated through spin Hall effect and Rashba-Edelstein effect in heavy metal/MI bilayers. SOTs on MIs are more intriguing than magnetic metals since SOTs cannot be transferred to MIs through direct injection of electron spins. Understanding of SOTs on MIs remains elusive, especially how SOTs scale with the film thickness. Here, we observe the critical role of dimensionality on the SOT efficiency by systematically studying the MI layer thickness dependent SOT efficiency in tungsten/thulium iron garnet (W/TmIG) bilayers. We first show that the TmIG thin film evolves from two-dimensional to three-dimensional magnetic phase transitions as the thickness increases, due to the suppression of long-wavelength thermal fluctuation. Then, we report the significant enhancement of the measured SOT efficiency as the thickness increases. We attribute this effect to the increase of the magnetic moment density in concert with the suppression of thermal fluctuations. At last, we demonstrate the current-induced SOT switching in the W/TmIG bilayers with a TmIG thickness up to 15 nm. The switching current density is comparable with those of heavy metal/ferromagnetic metal cases. Our findings shed light on the understanding of SOTs in MIs, which is important for the future development of ultrathin MI-based low-power spintronics.

cond-mat.mes-hall↗

arXiv2018

Dynamic characterization of cellulose nanofibrils in sheared and extended semi-dilute dispersions

New materials made through controlled assembly of dispersed cellulose nanofibrils (CNF) has the potential to develop into biobased competitors to some of the highest performing materials today. The performance of these new cellulose materials depends on how easily CNF alignment can be controlled with hydrodynamic forces, which are always in competition with a different process driving the system towards isotropy, called rotary diffusion. In this work, we present a flow-stop experiment using polarized optical microscopy (POM) to study the rotary diffusion of CNF dispersions in process relevant flows and concentrations. This is combined with small angle X-ray scattering (SAXS) experiments to analyze the true orientation distribution function (ODF) of the flowing fibrils. It is found that the rotary diffusion process of CNF occurs at multiple time scales, where the fastest scale seems to be dependent on the deformation history of the dispersion before the stop. At the same time, the hypothesis that rotary diffusion is dependent on the initial ODF does not hold as the same distribution can result in different diffusion time scales. The rotary diffusion is found to be faster in flows dominated by shear compared to pure extensional flows. Furthermore, the experimental setup can be used to quickly characterize the dynamic properties of flowing CNF and thus aid in determining the quality of the dispersion and its usability in material processes.

cond-mat.soft↗

arXiv2018

Emergent interlayer nodal superfluidity of a dipolar fermi gas in bilayer optical lattices

Understanding the interplay between magnetism and superconductivity is one of the central issues in condensed matter physics. Such interplay induced nodal structure of superconducting gap is widely believed to be a signature of exotic pairing mechanism (not phonon mediated) to achieve unconventional superconductivity, such as in heavy fermion, high $T_c$, and organic superconductors. Here we report a new mechanism to drive the interplay between magnetism and superfluidity via the spatially anisotropic interaction. This scheme frees up the usual requirement of suppressing long-range magnetic order to access unconventional superconductivity like through doping or adding pressure in solids. Surprisingly, even for the half-filling case, such scheme can lead the coexistence of superfluidity and antiferromagnetism and interestingly an unexpected interlayer nodal superfluid emerges, which will be demonstrated through a cold atom system composed of a pseudospin-$1/2$ dipolar fermi gas in bilayer optical lattices. Our mechanism should pave an alternative way to unveil exotic pairing scheme resulting from the interplay between magnetism and superconductivity or superfluidity.

cond-mat.quant-gas↗

arXiv2018

Atom-dimer scattering in heteronuclear mixture with finite intra-species scattering length

We study the three-body problem of two ultracold identical bosonic atoms (denoted by $B$) and one extra atom (denoted by $X$), where the scattering length $a_{BX}$ between each bosonic atom and atom $X$ is resonantly large and positive. We calculate the scattering length $a_{\rm ad}$ between one bosonic atom and the shallow dimer formed by the other bosonic atom and atom $X$, and investigate the effect induced by the interaction between the two bosonic atoms. We find that even if this interaction is weak (i.e., the corresponding scattering length $a_{BB}$ is of the same order of the van der Waals length $r_{\rm vdW}$ or even smaller), it can still induce a significant effect for the atom--dimer scattering length $a_{\rm ad}$. Explicitly, an atom--dimer scattering resonance can always occur when the value of $a_{BB}$ varies in the region with $|a_{BB}|\lesssim r_{\rm vdW}$. As a result, both the sign and the absolute value of $a_{\rm ad}$, as well as the behavior of the $a_{\rm ad}$-$a_{BX}$ function, depends sensitively on the exact value of $a_{BB}$. Our results show that, for a good quantitative theory, the intra-species interaction is required to be taken into account for this heteronuclear system, even if this interaction is weak.

cond-mat.quant-gas↗

arXiv2017

Two transient X-ray Quasi-Periodic Oscillations separated by an intermediate state in 1H 0707-495

In the narrow-line Seyfert 1 galaxy 1H 0707-495, recently a transient quasi-periodic oscillation (QPO) signal with a frequency of $\sim 2.6\times 10^{-4}$ Hz has been detected at a high statistical significance. Here, we reanalyze the same set of XMM-Newton data observed on 2008 February 4 with the Weighted-Wavelet Z-transform (WWZ) method. In addition to confirming the previous findings, we also find another QPO signal with a frequency of $\sim 1.2\times 10^{-4}$ Hz in a separated X-ray emission phase at the significance level of $\sim 3.7σ$. The signal is also found fitting an auto-regressive model though at a lower significance. The frequency ratio between these two signals is $\sim 2:1$. The analysis of other XMM-Newton measurements of 1H 0707-495 also reveals the presence of the $\sim 2.6\times 10^{-4}$ Hz ($\sim 1.2\times 10^{-4}$ Hz) QPO signal on 2007 May 14 (2010 September 17) at the significance level of $\sim 4.2σ$ ($\sim 3.5σ$). The QPO frequency found in this work follows the $f_{QPO}-M_{BH}$ relation reported in previous works spanning from stellar-mass to supermassive black holes. This is the first time to observe two separated transient X-ray QPO signals in active galactic nuclei (AGNs), which sheds new light on the physics of accreting supermassive black holes.

astro-ph.HE↗

arXiv2017

Strongly Interacting Bose Gases near a $d$-wave Shape Resonance

Many unconventional quantum matters, such as fractional quantum Hall effect and $d$-wave high-Tc superconductor, are discovered in strongly interacting systems. Understanding quantum many-body systems with strong interaction and the unconventional phases therein is one of the most challenging problems in physics nowadays. Cold atom systems possess a natural way to create strong interaction by bringing the system to the vicinity of a scattering resonance. Although this has been a focused topic in cold atom physics for more than a decade, these studies have so far mostly been limited for $s$-wave resonance. Here we report the experimental observation of a broad $d$-wave shape resonance in degenerate ${}^{41}$K gas. We further measure the molecular binding energy that splits into three branches as a hallmark of $d$-wave molecules, and find that the lifetime of this many-body system is reasonably long at strongly interacting regime. From analyzing the breathing mode excited by ramping through this resonance, it suggests that a quite stable low-temperature atom and molecule mixture is produced. Putting all the evidence together, our system offers great promise to reach a $d$-wave molecular superfluid.

cond-mat.quant-gas↗

arXiv2017

Universal Feature in Optical Control of a p-wave Feshbach Resonance

In this Letter we report the experimental results on optical control of a p-wave Feshbach resonance, by utilizing a laser driven bound-to-bound transition to shift the energy of closed channel molecule. The magnetic field location for p-wave resonance as a function of laser detuning can be captured by a simple formula with essentially one parameter, which describes how sensitive the resonance depends on the laser detuning. The key result of this work is to demonstrate, both experimentally and theoretically, that the ratio between this parameter for $m=0$ resonance and that for $m=\pm 1$ resonance, to large extent, is universal. We also show that this optical control can create intriguing situations where interesting few- and many-body physics can occurs, such as a p-wave resonance overlapping with an s-wave resonance or three p-wave resonances being degenerate.

cond-mat.quant-gas↗

arXiv2017

Chiral orbital magnetism of $p$-orbital bosons in optical lattices

Chiral magnetism is a fascinating quantum phenomena that has been found in low-dimensional magnetic materials. It is not only interesting for understanding the concept of chirality, but also important for potential applications in spintronics. Past studies show that chiral magnets require both lack of the inversion symmetry and spin-orbit coupling to induce the Dzyaloshinskii-Moriya (DM) interaction. Here we report that the combination of inversion symmetry breaking and quantum degeneracy of orbital degrees of freedom will provide a new paradigm to achieve the chiral orbital magnetism. By means of the density matrix renormalization group (DMRG) calculation, we demonstrate that the chiral orbital magnetism can be found when considering bosonic atoms loaded in the $p$-band of an optical lattice in the Mott regime. The high tunability of our scheme is also illustrated through simply manipulating the inversion symmetry of the system for the cold atom experimental conditions.

cond-mat.quant-gas↗

arXiv2017

Hardness Results for Structured Linear Systems

We show that if the nearly-linear time solvers for Laplacian matrices and their generalizations can be extended to solve just slightly larger families of linear systems, then they can be used to quickly solve all systems of linear equations over the reals. This result can be viewed either positively or negatively: either we will develop nearly-linear time algorithms for solving all systems of linear equations over the reals, or progress on the families we can solve in nearly-linear time will soon halt.

cs.CC↗

arXiv2017

Atomic Norm Denoising-Based Joint Channel Estimation and Faulty Antenna Detection for Massive MIMO

We consider joint channel estimation and faulty antenna detection for massive multiple-input multiple-output (MIMO) systems operating in time-division duplexing (TDD) mode. For systems with faulty antennas, we show that the impact of faulty antennas on uplink (UL) data transmission does not vanish even with unlimited number of antennas. However, the signal detection performance can be improved with a priori knowledge on the indices of faulty antennas. This motivates us to propose the approach for simultaneous channel estimation and faulty antenna detection. By exploiting the fact that the degrees of freedom of the physical channel matrix are smaller than the number of free parameters, the channel estimation and faulty antenna detection can be formulated as an extended atomic norm denoising problem and solved efficiently via the alternating direction method of multipliers (ADMM). Furthermore, we improve the computational efficiency by proposing a fast algorithm and show that it is a good approximation of the corresponding extended atomic norm minimization method. Numerical simulations are provided to compare the performances of the proposed algorithms with several existing approaches and demonstrate the performance gains of detecting the indices of faulty antennas.

cs.IT↗

arXiv2017

An X-ray periodicity of $\sim$1.8 hours in a narrow-line Seyfert 1 galaxy Mrk 766

In the narrow-line Seyfert 1 galaxy Mrk 766, a Quasi-Periodic Oscillation (QPO) signal with a period of $\sim6450$ s is detected in the \emph{XMM-Newton} data collected on 2005 May 31. This QPO signal is highly statistical significant at the confidence level at $\sim5σ$ with the quality factor of $Q=f/Δf>13.6$. The X-ray intensity changed by a factor of 3 with root mean square fractional variability of $14.3\%$. Furthermore, this QPO signal presents in the data of all three EPIC detectors and two RGS cameras and its frequency follows the $f_{\rm QPO}$-$M_{\rm BH}$ relation spanning from stellar-mass to supermassive black holes. Interestingly, a possible QPO signal with a period of $\sim4200$ s had been reported in the literature. The frequency ratio of these two QPO signals is $\sim$ 3:2. Our result is also in support of the hypothesis that the QPO signals can be just transient. The spectral analysis reveals that the contribution of the soft excess component below $\sim$ 1 keV is different between epochs with and without QPO, this property as well as the former frequency-ratio are well detected in X-ray BH binaries, which may have shed some lights on the physical origin of our event.

astro-ph.HE↗