arXiv subjects

Feng Yang

Publications and source records attributed to Feng Yang.

At least 145 records · Page 8Linked to original sources

arXiv2020

Evidence for bosonic mode coupling in electron dynamics of LiFeAs superconductor

Super-high resolution laser-based angle-resolved photoemission measurements are carried out on LiFeAs superconductor to investigate its electron dynamics. Three energy scales at $\sim$20 meV, $\sim$34 meV and $\sim$55 meV are revealed for the first time in the electron self-energy both in the superconducting state and normal state. The $\sim$20 meV and $\sim$34 meV scales can be attributed to the coupling of electrons with sharp bosonic modes which are most likely phonons. These observations provide definitive evidence on the existence of mode coupling in iron-based superconductors.

cond-mat.supr-con↗

arXiv2020

Electronic Raman Scattering in Suspended Semiconducting Carbon Nanotubes

The electronic Raman scattering (ERS) features of single-walled carbon nanotubes (SWNTs) can reveal a wealth of information about their electronic structures, but have previously been thought to appear exclusively in metallic (M-) but not in semiconducting (S-) SWNTs. We report the experimental observation of the ERS features with an accuracy of 1 meV in suspended S-SWNTs, the processes of which are accomplished via the available high-energy electron-hole pairs. The ERS features can facilitate further systematic studies on the properties of SWNT, both metallic and semiconducting, with defined chirality.

cond-mat.mes-hall↗

arXiv2020

An Integrated Quadratic Reconstruction for Finite Volume Schemes to Scalar Conservation Laws in Multiple Dimensions

We proposed a piecewise quadratic reconstruction method in multiple dimensions, which is in an integrated style, for finite volume schemes to scalar conservation laws. This integrated quadratic reconstruction is parameter-free and applicable on flexible grids. We show that the finite volume schemes with the new reconstruction satisfy a local maximum principle with properly setup on time steplength. Numerical examples are presented to show that the proposed scheme attains a third-order accuracy for smooth solutions in both 2D and 3D cases. It is indicated by numerical results that the local maximum principle is helpful to prevent overshoots in numerical solutions.

math.NA↗

arXiv2020

The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

Handling digital images is almost always accompanied by a lossy compression in order to facilitate efficient transmission and storage. This introduces an unavoidable tension between the allocated bit-budget (rate) and the faithfulness of the resulting image to the original one (distortion). An additional complicating consideration is the effect of the compression on recognition performance by given classifiers (accuracy). This work aims to explore this rate-distortion-accuracy tradeoff. As a case study, we focus on the design of the quantization tables in the JPEG compression standard. We offer a novel optimal tuning of these tables via continuous optimization, leveraging a differential implementation of both the JPEG encoder-decoder and an entropy estimator. This enables us to offer a unified framework that considers the interplay between rate, distortion and classification accuracy. In all these fronts, we report a substantial boost in performance by a simple and easily implemented modification of these tables.

eess.IV↗

arXiv2020

GIFnets: Differentiable GIF Encoding Framework

Graphics Interchange Format (GIF) is a widely used image file format. Due to the limited number of palette colors, GIF encoding often introduces color banding artifacts. Traditionally, dithering is applied to reduce color banding, but introducing dotted-pattern artifacts. To reduce artifacts and provide a better and more efficient GIF encoding, we introduce a differentiable GIF encoding pipeline, which includes three novel neural networks: PaletteNet, DitherNet, and BandingNet. Each of these three networks provides an important functionality within the GIF encoding pipeline. PaletteNet predicts a near-optimal color palette given an input image. DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering. Finally, BandingNet is designed to detect color banding, and provides a new perceptual loss specifically for GIF images. As far as we know, this is the first fully differentiable GIF encoding pipeline based on deep neural networks and compatible with existing GIF decoders. User study shows that our algorithm is better than Floyd-Steinberg based GIF encoding.

eess.IV↗

arXiv2020

A comparative study on magnetic order and field induced magnetic transition on two series of double perovskite iridates RE2BIrO6 (RE=Pr,Nd,Sm,Eu,Gd B=Zn,Mg)

We perform a comparative magnetic study on two series of rare-earth (RE) based double perovskite iridates RE2BIrO6 (RE=Pr,Nd,Sm-Gd;B=Zn,Mg), which show Mott insulating state with tunable charge energy gap from ~330 meV to ~560 meV by changing RE cations. For nonmagnetic RE=Eu cations, Eu2MgIrO6 shows antiferromagnetic (AFM) order and field-induced spin-flop transitions below Néel temperature (TN) in comparison with the ferromagnetic (FM)-like behaviors of Eu2ZnIrO6 at low temperatures. For magnetic-moment-containing RE ions, Gd2BIrO6 show contrasting magnetic behaviors with FM-like transition (B=Zn) and AFM order (B=Mg), respectively. While, for RE=Pr, Nd and Sm ions, all members show AFM ground state and field-induced spin-flop transitions below TN irrespective of B=Zn or Mg cations. Moreover, two successive field-induced metamagnetic transitions are observed for RE2ZnIrO6 (RE=Pr,Nd) in high field up to 56 T, the resultant field temperature (H-T) phase diagrams are constructed. The diverse magnetic behaviors in RE2BIrO6 reveal that the 4f-Ir exchange interactions between the RE and Ir sublattices can mediate their magnetism.

cond-mat.str-el↗

arXiv2020

Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning

Countries across the globe have been pushing strict regulations on the protection of personal or private data collected. The traditional centralized machine learning method, where data is collected from end-users or IoT devices, so that it can discover insights behind real-world data, may not be feasible for many data-driven industry applications in light of such regulations. A new machine learning method, coined by Google as Federated Learning (FL) enables multiple participants to train a machine learning model collectively without directly exchanging data. However, recent studies have shown that there is still a possibility to exploit the shared models to extract personal or confidential data. In this paper, we propose to adopt Multi Party Computation (MPC) to achieve privacy-preserving model aggregation for FL. The MPC-enabled model aggregation in a peer-to-peer manner incurs high communication overhead with low scalability. To address this problem, the authors proposed to develop a two-phase mechanism by 1) electing a small committee and 2) providing MPC-enabled model aggregation service to a larger number of participants through the committee. The MPC enabled FL framework has been integrated in an IoT platform for smart manufacturing. It enables a set of companies to train high quality models collectively by leveraging their complementary data-sets on their own premises, without compromising privacy, model accuracy vis-a-vis traditional machine learning methods and execution efficiency in terms of communication cost and execution time.

cs.DC↗

arXiv2020

McKernel: A Library for Approximate Kernel Expansions in Log-linear Time

McKernel introduces a framework to use kernel approximates in the mini-batch setting with Stochastic Gradient Descent (SGD) as an alternative to Deep Learning. Based on Random Kitchen Sinks [Rahimi and Recht 2007], we provide a C++ library for Large-scale Machine Learning. It contains a CPU optimized implementation of the algorithm in [Le et al. 2013], that allows the computation of approximated kernel expansions in log-linear time. The algorithm requires to compute the product of matrices Walsh Hadamard. A cache friendly Fast Walsh Hadamard that achieves compelling speed and outperforms current state-of-the-art methods has been developed. McKernel establishes the foundation of a new architecture of learning that allows to obtain large-scale non-linear classification combining lightning kernel expansions and a linear classifier. It travails in the mini-batch setting working analogously to Neural Networks. We show the validity of our method through extensive experiments on MNIST and FASHION MNIST [Xiao et al. 2017].

cs.LG↗

arXiv2020

Super-Resolving Commercial Satellite Imagery Using Realistic Training Data

In machine learning based single image super-resolution, the degradation model is embedded in training data generation. However, most existing satellite image super-resolution methods use a simple down-sampling model with a fixed kernel to create training images. These methods work fine on synthetic data, but do not perform well on real satellite images. We propose a realistic training data generation model for commercial satellite imagery products, which includes not only the imaging process on satellites but also the post-process on the ground. We also propose a convolutional neural network optimized for satellite images. Experiments show that the proposed training data generation model is able to improve super-resolution performance on real satellite images.

cs.CV↗

arXiv2020

Distortion Agnostic Deep Watermarking

Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference from the original image. Recently, deep learning-based methods achieved impressive results in both visual quality and message payload under a wide variety of image distortions. However, these methods all require differentiable models for the image distortions at training time, and may generalize poorly to unknown distortions. This is undesirable since the types of distortions applied to watermarked images are usually unknown and non-differentiable. In this paper, we propose a new framework for distortion-agnostic watermarking, where the image distortion is not explicitly modeled during training. Instead, the robustness of our system comes from two sources: adversarial training and channel coding. Compared to training on a fixed set of distortions and noise levels, our method achieves comparable or better results on distortions available during training, and better performance on unknown distortions.

cs.MM↗

arXiv2020

Spectroscopic Evidence of Bilayer Splitting and Interlayer Pairing in an Iron Based Superconductor

In high temperature cuprate superconductors, the interlayer coupling between the CuO$_2$ planes plays an important role in dictating superconductivity, as indicated by the sensitive dependence of the critical temperature (T$_C$) on the number of CuO$_2$ planes in one structural unit. In Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ superconductor with two CuO$_2$ planes in one structural unit, the interaction between the two CuO$_2$ planes gives rise to band splitting into two Fermi surface sheets (bilayer splitting) that have distinct superconducting gap. The iron based superconductors are composed of stacking of the FeAs/FeSe layers; whether the interlayer coupling can cause similar band splitting and its effect on superconductivity remain unclear. Here we report high resolution laser-based angle-resolved photoemission spectroscopy (ARPES) measurements on a newly discovered iron based superconductor, KCa$_2$Fe$_4$As$_4$F$_2$ (T$_C$=33.5\,K) which consists of stacking FeAs blocks with two FeAs layers separated by insulating Ca$_2$F$_2$ blocks. Bilayer splitting effect is observed for the first time that gives rise to totally five hole-like Fermi surface sheets around the Brilliouin zone center. Band structure calculations reproduce the observed bilayer splitting by identifying interlayer interorbital interaction between the two FeAs layers within one FeAs block. All the hole-like pockets around the zone center exhibit Fermi surface-dependent and nodeless superconducting gap. The gap functions with short-range antiferromagetic fluctuations are proposed and the gap symmetry can be well understood when the interlayer pairing is considered. The particularly strong interlayer pairing is observed for one of the bands. Our observations provide key information on the interlayer coupling and interlayer pairing in understanding superconductivity in iron based superconductors.

cond-mat.supr-con↗

arXiv2019

CNN-Based Invertible Wavelet Scattering for the Investigation of Diffusion Properties of the In Vivo Human Heart in Diffusion Tensor Imaging

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing.

eess.IV↗

arXiv2019

Evidence for an Additional Symmetry Breaking from Direct Observation of Band Splitting in the Nematic State of FeSe Superconductor

The iron-based superconductor FeSe has attracted much recent attention because of its simple crystal structure, distinct electronic structure and rich physics exhibited by itself and its derivatives. Determination of its intrinsic electronic structure is crucial to understand its physical properties and superconductivity mechanism. Both theoretical and experimental studies so far have provided a picture that FeSe consists of one hole-like Fermi surface around the Brillouin zone center in its nematic state. Here we report direct observation of two hole-like Fermi surface sheets around the Brillouin zone center, and the splitting of the associated bands, in the nematic state of FeSe by taking high resolution laser-based angle-resolved photoemission measurements. These results indicate that, in addition to nematic order and spin-orbit coupling, there is an additional order in FeSe that breaks either inversion or time reversal symmetries. The new Fermi surface topology asks for reexamination of the existing theoretical and experimental understanding of FeSe and stimulates further efforts to identify the origin of the hidden order in its nematic state.

cond-mat.supr-con↗

arXiv2019

Selective Hybridization between Main Band and Superstructure Band in Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ Superconductor

High-resolution laser-based angle-resolved photoemission measurements have been carried out on Bi$_2$Sr$_2$CaCu$_2$O$_{8+δ}$ (Bi2212) and Bi$_2$Sr$_{2-x}$La$_x$CuO$_{6+δ}$ (Bi2201) superconductors. Unexpected hybridization between the main band and the superstructure band in Bi2212 is clearly revealed. In the momentum space where one main Fermi surface intersects with one superstructure Fermi surface, four bands are observed instead of two. The hybridization exists in both superconducting state and normal state, and in Bi2212 samples with different doping levels. Such a hybridization is not observed in Bi2201. This phenomenon can be understood by considering the bilayer splitting in Bi2212, the selective hybridization of two bands with peculiar combinations, and the altered matrix element effects of the hybridized bands. These observations provide strong evidence on the origin of the superstructure band which is intrinsic to the CuO$_2$ planes. Therefore, understanding physical properties and superconductivity mechanism in Bi2212 should consider the complete Fermi surface topology which involves the main bands, the superstructure bands and their interactions.

cond-mat.supr-con↗

arXiv2019

Estimating and decomposing most productive scale size in parallel DEA networks with shared inputs: A case of China's Five-Year Plans

Attaining the optimal scale size of production systems is an issue frequently found in the priority questions on management agendas of various types of organizations. Determining the most productive scale size (MPSS) allows the decision makers not only to know the best scale size that their systems can achieve but also to tell the decision makers how to move the inefficient systems onto the MPSS region. This paper investigates the MPSS concept for production systems consisting of multiple subsystems connected in parallel. First, we propose a relational model where the MPSS of the whole system and the internal subsystems are measured in a single DEA implementation. Then, it is proved that the MPSS of the system can be decomposed as the weighted sum of the MPSS of the individual subsystems. The main result is that the system is overall MPSS if and only if it is MPSS in each subsystem. MPSS decomposition allows the decision makers to target the non-MPSS subsystems so that the necessary improvements can be readily suggested. An application of China's Five-Year Plans (FYPs) with shared inputs is used to show the applicability of the proposed model for estimating and decomposing MPSS in parallel network DEA. Industry and Agriculture sectors are selected as two parallel subsystems in the FYPs. Interesting findings have been noticed. Using the same amount of resources, the Industry sector had a better economic scale than the Agriculture sector. Furthermore, the last two FYPs, 11th and 12th, were the perfect two FYPs among the others.

econ.GN↗

arXiv2019

Most productive scale size of China's regional R&D value chain: A mixed structure network

This paper offers new mathematical models to measure the most productive scale size (MPSS) of production systems with mixed structure networks (mixed of series and parallel). In the first property, we deal with a general multi-stage network which can be transformed, using dummy processes, into a series of parallel networks. In the second property, we consider a direct network combined with series and parallel structure. In this paper, we propose new models to measure the overall MPSS of the production systems and their internal processes. MPSS decomposition is discussed and examined. As a real-life application, this study measures the efficiency and MPSS of research and development (R&D) activities of Chinese provinces within an R&D value chain network. In the R&D value chain, profitability and marketability stages are connected in series, where the profitability stage is composed of operation and R&D efforts connected in parallel. The MPSS network model provides not only the MPSS measurement but also values that indicate the appropriate degree of intermediate measures for the two stages. Improvement strategy is given for each region based on the gap between the current and the appropriate level of intermediate measures. Our findings show that the marketability efficiency values of Chinese R&D regions were low, and no regions are operated under the MPSS. As a result, most Chinese regions performed inefficiently regarding both profitability and marketability. This finding provides initial evidence that the generally lower profitability and marketability efficiency of Chinese regions is a severe problem that may be due to wasted resources on production and R&D.

econ.GN↗

arXiv2019

Analysis and Optimization of Successful Symbol Transmission Rate for Grant-free Massive Access with Massive MIMO

Grant-free massive access is an important technique for supporting massive machine-type communications (mMTC) for Internet-of-Things (IoT). Two important features in grant-free massive access are low-complexity devices and short-packet data transmission, making the traditional performance metric, achievable rate, unsuitable in this case. In this letter, we investigate grant-free massive access in a massive multiple-input multiple-output (MIMO) system. We consider random access control, and adopt approximate message passing (AMP) for user activity detection and channel estimation in the pilot transmission phase and small phase-shift-keying (PSK) modulation in the data transmission phase. We propose a more reasonable performance metric, namely successful symbol transmission rate (SSTR), for grant-free massive access. We obtain closed-form approximate expressions for the asymptotic SSTR in the cases of maximal ratio combining (MRC) and zero forcing (ZF) beamforming at the base station (BS), respectively. We also maximize the asymptotic SSTR with respect to the access parameter and pilot length.

cs.IT↗

arXiv2019

Analysis and Optimization of Outage Probability in Multi-Intelligent Reflecting Surface-Assisted Systems

Intelligent reflecting surface (IRS) is envisioned to be a promising solution for designing spectral and energy efficient wireless systems. In this letter, we study a multi-IRS-assisted system under Rician fading where the phase shifts adapt to only the line of sight (LoS) components. First, we analyze and optimize the outage probability of the multi-IRS-assisted system in the slow fading scenario for the non-LoS (NLoS) components. We also show that the optimal outage probability decreases with the numbers of IRSs and elements of each IRS when the LoS components are stronger than the NLoS ones. Then, we characterize the asymptotically optimal outage probability in the high signal-to-noise ratio (SNR) regime, and show that it decreases with the powers of the LoS components. To the best of our knowledge, this is the first work that studies the outage probability in multi-IRS-assisted systems.

cs.IT↗