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Bing Xu

Publications and source records attributed to Bing Xu.

At least 91 records · Page 5Linked to original sources
arXiv2019

Observation of topological nodal-line semimetal in YbMnSb2 through optical spectroscopy

The optical properties of YbMnSb2 have been measured in a broad frequency range from room temperature down to 7 K. With decreasing temperature, a flat region develops in the optical conductivity spectra at about 300cm-1, which can not be described by the well-known Drude-Lorentz model. A frequency-independent component has to be introduced to model the measured optical conductivity. Our first-principles calculations show that YbMnSb2 possesses a Dirac nodal line near the Fermi level. A comparison between the measured optical properties and calculated electronic band structures suggests that the frequency-independent optical conductivity component arises from interband transitions near the Dirac nodal line, thus demonstrating that YbMnSb2 is a Dirac nodal line semimetal.

cond-mat.mtrl-sci↗
arXiv2018

Infrared spectroscopic studies of the topological properties in CaMnSb2

We present temperature-dependent infrared spectroscopic studies of CaMnSb2, a proposed threedimensional topological material. The low plasma edge in the reflectivity spectrum and small Drude component in the optical conductivity indicate a very low carrier density. The low-frequency optical conductivity is well described by the superposition of a narrow and a broad Drude terms. Several linear components have been observed in the low-temperature optical conductivity, but none of them extrapolates to the origin, at odds with the optical response expected for three-dimensional Dirac fermions. A series of absorption peaks have been resolved in the high-frequency optical conductivity. The energy of these peaks agrees well with the interband transitions expected for the band structures from first-principles calculations. Intriguingly, the lowest band gap increases with decreasing temperature, mimic the temperature evolution of inverted bands. Furthermore, our theoretical calculations demonstrate the existence of weak coupling between two Sb-chains layers results in the topological trivial surface states in CaMnSb2.

physics.optics↗
arXiv2017

Observation of an Emergent Coherent State in the Iron-Based Superconductor KFe$_\mathbf{2}$As$_\mathbf{2}$

The optical properties of KFe$_2$As$_2$ have been measured for light polarized in the a-b planes over a wide temperature and frequency range. Below $T^\ast\simeq 155$ K, where this material undergoes an incoherent-coherent crossover, we observe a new coherent response emerging in the optical conductivity. A spectral weight analysis suggests that this new feature arises out of high-energy bound states. Below about $T_{\rm FL} \simeq 75$ K the scattering rate for this new feature is quadratic in temperature, indicating a Fermi-liquid response. Theory calculations suggest this crossover is dominated by the $d_{xy}$ orbital. Our results advocate for Kondo-type screening as the mechanism for the orbital-selective incoherent-coherent crossover in hole-overdoped KFe$_2$As$_2$.

cond-mat.supr-con↗
arXiv2016

Full Duplex Joint Sensing for Opportunistic Access in Spectrum-Heterogeneous Cognitive Radio Networks

In cognitive radio networks (CRN), secondary users (SUs) can share spectrum with licensed primary users (PUs). Because an SU receiver (SU-Rx) does not always share the same view of spectrum availability as the corresponding SU transmitter (SU-Tx), spectrum sensing conducted only by an SU transmitter tends to be overly sensitive to guarantee safe spectrum access at the price of SU inefficiency. In this letter, we propose a joint spectrum sensing mechanism, named Full-Duplex Joint Sensing (FJDS), to relax sensitivity of SU detection and improve SU throughput. FDJS employs instantaneous feedback enabled by in-band full duplex communication to facilitate the sharing of spectral information from SU-Rx to SU-Tx. The joint detection problem in FDJS is modeled as non-linear optimization and solved by a binary searching algorithm. Simulation results show that FDJS could improve SU throughput as well as meeting PU interruption constraints in a wide range of parameter settings.

cs.NI↗
arXiv2016

Three-dimensional chiral microstructures fabricated by structured optical vortices in isotropic material

Optical vortices, as a kind of structured beam with helical phase wavefronts and doughnut shape intensity distribution, have been used for fabricating chiral structures in metal and spiral patterns in anisotropic polarization-dependent azobenzene polymer. However, in isotropic polymer, the fabricated microstructures are typically confined to non-chiral cylindrical geometry due to two-dimensional doughnut intensity profile of optical vortices. Here we develop a powerful strategy for realizing chiral microstructures in isotropic material by coaxial interference of a vortex beam and a plane wave, which produces three-dimensional (3D) spiral optical fields. This coaxial interference beams are creatively produced by designing the contrivable holograms consisting of azimuthal phase and equiphase loaded on liquid-crystal spatial light modulator. Then, in isotropic polymer, 3D chiral microstructures are achieved under illumination of the coaxial interference femtosecond laser beams with their chirality controlled by the topological charge. Our further investigation reveals that the spiral lobes and chirality are caused by the interfering patterns and helical phase wavefronts, respectively. This technique is simple, stable, and easy-operation, and offers broad applications in optical tweezers, optical communications and fast metamaterial fabrication.

physics.optics↗
arXiv2016

Optical study of the antiferromagnetic ordered state in electron-overdoped Ca0.77Nd0.23FeAs2

In Ca1-xRExFeAs2 (RE= rare earth), an antiferromagnetic (AFM) phase as well as a structural transition has been reported, even in the electron-overdoped regime. Here we investigated the temperature-dependent in-plane optical spectroscopy of overdoped Ca0.77Nd0.23FeAs2. Upon entering the AFM state, we found an abrupt reduction of low-frequency (500-2 000 cm-1) spectral weight in the optical conductivity. In sharp contrast to the parent compounds of 122 system, where spin-density-wave gaps have been clearly observed in the AFM state, a gap signature is absent in Ca0.77Nd0.23FeAs2. This may be a consequence of the poor nesting condition between hole and electron pockets. However, a spectral weight analysis shows that the reduced spectral weight at low frequency is transferred to the high frequency range (> 4 000 cm-1), pointing to a localization effect. These observations suggest that the AFM order in Ca0.77Nd0.23FeAs2 is most likely to originate from a localized nature rather than Fermi surface nesting.

cond-mat.supr-con↗
arXiv2016

Revise Saturated Activation Functions

In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh). We point out that, besides the well-known non-zero centered property, slope of the activation function near the origin is another possible reason making training deep networks with the logistic function difficult to train. We demonstrate that, with proper rescaling, the logistic sigmoid achieves comparable results with tanh. Then following the same argument, we improve tahn by penalizing in the negative part. We show that "penalized tanh" is comparable and even outperforms the state-of-the-art non-saturated functions including ReLU and leaky ReLU on deep convolution neural networks. Our results contradict to the conclusion of previous works that the saturation property causes the slow convergence. It suggests further investigation is necessary to better understand activation functions in deep architectures.

cs.LG↗
arXiv2016

Training Deep Nets with Sublinear Memory Cost

We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O(sqrt(n)) memory to train a n layer network, with only the computational cost of an extra forward pass per mini-batch. As many of the state-of-the-art models hit the upper bound of the GPU memory, our algorithm allows deeper and more complex models to be explored, and helps advance the innovations in deep learning research. We focus on reducing the memory cost to store the intermediate feature maps and gradients during training. Computation graph analysis is used for automatic in-place operation and memory sharing optimizations. We show that it is possible to trade computation for memory - giving a more memory efficient training algorithm with a little extra computation cost. In the extreme case, our analysis also shows that the memory consumption can be reduced to O(log n) with as little as O(n log n) extra cost for forward computation. Our experiments show that we can reduce the memory cost of a 1,000-layer deep residual network from 48G to 7G with only 30 percent additional running time cost on ImageNet problems. Similarly, significant memory cost reduction is observed in training complex recurrent neural networks on very long sequences.

cs.LG↗
arXiv2016

Learning with a Strong Adversary

The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, \emph{learning with a strong adversary}, that learns robust classifiers from supervised data. The proposed method takes finding adversarial examples as an intermediate step. A new and simple way of finding adversarial examples is presented and experimentally shown to be efficient. Experimental results demonstrate that resulting learning method greatly improves the robustness of the classification models produced.

cs.LG↗
arXiv2015

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines.

cs.DC↗
arXiv2015

Empirical Evaluation of Rectified Activations in Convolutional Network

In this paper we investigate the performance of different types of rectified activation functions in convolutional neural network: standard rectified linear unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU). We evaluate these activation function on standard image classification task. Our experiments suggest that incorporating a non-zero slope for negative part in rectified activation units could consistently improve the results. Thus our findings are negative on the common belief that sparsity is the key of good performance in ReLU. Moreover, on small scale dataset, using deterministic negative slope or learning it are both prone to overfitting. They are not as effective as using their randomized counterpart. By using RReLU, we achieved 75.68\% accuracy on CIFAR-100 test set without multiple test or ensemble.

cs.LG↗
arXiv2015

Formation of As-As Bond and Its Effect on Absence of Superconductivity in Collapsed Tetragonal Phase of Ca0.86Pr0.14Fe2As2 : An Optical Spectroscopy Study

The temperature dependence of in plane optical conductivity has been investigated for Ca0.86Pr0.14Fe2As2 which shows a structural transition from tetragonal (T) to collapsed tetragonal(cT) phase at TcT=73 K. Upon entering the cT phase, drastic change characterized by the formation of a midinfrared peak near 3200 cm-1(0.4 eV) in the optical conductivity is observed.Analysis of the spectral weight reveals reduced electron correlation after the cT phase transition.Based on the calculated band structure and simulated optical conductivity, we attribute the new feature around 0.4 eV to the formation of interlayer As-As bond. The As-As bond strongly affects the Fe-As hybridizations, and in turn, drastically changes the Ca0.86Pr0.14Fe2As2 into a nonmagnetic Fermi liquid system without bulk superconductivity in the cT phase.

cond-mat.supr-con↗
arXiv2014

Generative Adversarial Networks

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.

stat.ML↗
arXiv2014

Origin of Multi-band Emission from the Microquasar Cygnus X--1

We study the origin of non-thermal emissions from the Galactic black hole X-ray binary Cygnus X--1 which is a confirmed high mass microquasar. By analogy with those methods used in studies of active galactic nuclei, we propose a two-dimensional, time-dependent radiation model from the microquasar Cygnus X--1. In this model, the evolution equation for relativistic electrons in a conical jet are numerically solved by including escape, adiabatic and various radiative losses. The radiative processes involved are synchrotron emission, its self-Compton scattering, and inverse Compton scatterings of an accretion disk and its surrounding stellar companion. This model also includes an electromagnetic cascade process of an anisotropic $γ$-$γ$ interaction. We study the spectral properties of electron evolution and its emission spectral characteristic at different heights of the emission region located in the jet. We find that radio data from Cygnus X--1 are reproduced by the synchrotron emission, the \emph{Fermi} LAT measurements by the synchrotron emission and Comptonization of photons of the stellar companion, the TeV band emission fluxes by the Comptonization of the stellar photons. Our results show that: (1) Radio emission region extends from the binary system scales to the termination of the jet. (2) The GeV band emissions should originate from the distance close to the binary system scales. (3) The TeV band emissions could be inside the binary system, and these emissions could be probed by the upcoming CTA telescope. (4) The MeV tail emissions, which produces a strongly linearly polarized signal, are emitted inside the binary system. The location of the emissions is very close to the inner region of the jet.

astro-ph.HE↗
arXiv2013

Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches

The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice. This paper describes the solution of team "binghsu & MLRush & BrickMover". We conduct simple feature engineering work and train different models by each individual team member. Afterwards, we use listwise ensemble method to combine each model's output. Besides describing effective model and features, we will discuss about the lessons we learned while using deep learning in this competition.

cs.LG↗
arXiv2013

Challenges in Representation Learning: A report on three machine learning contests

The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions.

stat.ML↗
arXiv2013

Horizontal and Vertical Ensemble with Deep Representation for Classification

Representation learning, especially which by using deep learning, has been widely applied in classification. However, how to use limited size of labeled data to achieve good classification performance with deep neural network, and how can the learned features further improve classification remain indefinite. In this paper, we propose Horizontal Voting Vertical Voting and Horizontal Stacked Ensemble methods to improve the classification performance of deep neural networks. In the ICML 2013 Black Box Challenge, via using these methods independently, Bing Xu achieved 3rd in public leaderboard, and 7th in private leaderboard; Jingjing Xie achieved 4th in public leaderboard, and 5th in private leaderboard.

cs.LG↗