arXiv subjects

Xiaodong Zhang

Publications and source records attributed to Xiaodong Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2021

Magnetic properties of a highly ordered single crystal of the layered perovskite YBaCuFe$_{0.95}$Mn$_{0.05}$O$_5$

The layered perovskite YBaCuFeO5 (YBCFO) is able to adopt chiral magnetic order up to unexpectedly high temperatures, paving the way to strong magnetoelectric coupling at room temperature. In this perovskite A-site cations are fully ordered whereas the occupancy of the B-sites strongly depend on the preparation process. Though the structure is not geometrically frustrated, the presence of partial Fe3+/Cu2+ disorder at the B-sites produces magnetic frustration. In an effort to increase the spin-orbit coupling in the system, we have synthesized and studied YBaCuFe0.95Mn0.05O5 in single crystal form, where the highly symmetric Fe3+ ions (3d5) are partially substituted with Jahn-Teller active 3d4 Mn3+ ions. We report the structural and magnetic properties of a highly ordered single crystal of this layered perovskite, which are presented in comparison with a polycrystalline specimen (three times more disordered). Single-crystal neutron diffraction measurements reveal two collinear magnetic phases and the lack of incommensurate spiral order. The magnetic phases and transitions found in the crystal grown by the traveling solvent floating zone (TSFZ) method are fully described and analyzed in the light of its high level of Fe/Cu cationic order (~90%).

cond-mat.mtrl-sci↗

arXiv2021

First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities. Existing methods are short on building correlation between target-opinion pairs, and neglect the mutual interference among different sentiment triplets. To address these issues, we utilize a two-stage framework to enhance the correlation between targets and opinions: at stage one, we extract targets and opinions through sequence tagging; then we append a group of artificial tags named Perceivable Pair, which indicate the span of a specific target-opinion tuple, to the input sentence to obtain closer correlated target-opinion pair representation. Meanwhile, we reduce the negative interference between triplets by restricting tokens' attention field. Finally, the polarity is identified according to the representation of the Perceivable Pair. We conduct experiments on four datasets, and the experimental results show the effectiveness of our model.

cs.CL↗

arXiv2021

MHD analysis on the physical designs of CFETR and HFRC

The China Fusion Engineering Test Reactor (CFETR) and the Huazhong Field Reversed Configuration (HFRC), currently both under intensive physical and engineering designs in China, are the two major projects representative of the low-density steady-state and high-density pulsed pathways to fusion. One of the primary tasks of the physics designs for both CFETR and HFRC is the assessment and analysis of the magnetohydrodynamic (MHD) stability of the proposed design schemes. Comprehensive efforts on the assessment of MHD stability of CFETR and HFRC baseline scenarios have led to preliminary progresses that may further benefit engineering designs.

physics.plasm-ph↗

arXiv2021

Ferromagnetic metallic Sr-rich Ln$_{1/2}$A$_{1/2}$CoO$_3$ cobaltites with spontaneous spin rotation

The Pr$_{0.50}$Sr$_{0.5}$0CoO$_3$ perovskite exhibits unique magnetostructural properties among the rest of ferromagnetic/metallic Ln$_{0.50}$Sr$_{0.50}$CoO$_3$ compounds. The sudden orthorhombic-tetragonal (Imma $\to$ I4/mcm) structural transition produces an unusual magnetic behavior versus temperature and external magnetic fields. In particular, the symmetry change is responsible for a spontaneous spin rotation in this metallic oxide. We have studied half-doped Ln$_{0.50}$(Sr$_{1-x}$A$_x$)$_{0.50}$CoO$_3$ cobaltites varying the ionic radius rA of A-site cations (divalent cations and lanthanides) in order to complete the T-rA phase diagram. The influence of the structural distortion and the A-cations size for the occurrence of a spontaneous spin reorientation in the metallic state has been investigated. As the reorientation of the magnetization is driven by the temperature induced collapse of the orthorhombic distortion, a careful investigation of the structural symmetry is presented varying the structural distortion of the Sr-rich half-doped cobaltites by means of both compositional and temperature changes. The region in the phase diagram of these perovskites where the phase of magnetic symmetry Fm'm'm replaces that of Im'm'a symmetry was determined in this family of ferromagnetic/metallic cobaltites. In that region the magnetization direction has rotated 45 degrees within the a-b plane with respect to the second.

cond-mat.mtrl-sci↗

arXiv2019

Demonstration of beta-Ga2O3 Optical Waveguides and the Analysis of Their Propagation Losses in the UV-Visible Spectra

This paper reports the first demonstration of beta-phase gallium oxide as optical waveguides on sapphire substrates grown by metal-organic chemical vapor deposition (MOCVD). The propagation losses from visible to ultraviolet spectra were comprehensively studied. By optimizing the fabrication processes, minimum propagation loss was identified to be 3.7 dB/cm at the wavelength of 810 nm, which is comparable to other wide bandgap materials within the III-N family (GaN, AlN). To further reveal the underlying loss mechanisms, several physical mechanisms such as two-photon absorption, sidewall scattering, top surface scattering, and bulk scattering were taken into consideration. The results obtained from this work suggest that beta-Ga2O3 is promising for ultraviolet-visible spectrum integrated photonic applications.

physics.optics↗

arXiv2019

Text Level Graph Neural Network for Text Classification

Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information. However, previous methods based on GNN are mainly faced with the practical problems of fixed corpus level graph structure which do not support online testing and high memory consumption. To tackle the problems, we propose a new GNN based model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. This method removes the burden of dependence between an individual text and entire corpus which support online testing, but still preserve global information. Besides, we build graphs by much smaller windows in the text, which not only extract more local features but also significantly reduce the edge numbers as well as memory consumption. Experiments show that our model outperforms existing models on several text classification datasets even with consuming less memory.

cs.CL↗

arXiv2019

Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning

The purpose of this work is to develop a deep learning-based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones magnetic resonance (MR) imaging, and to automatically determine MR relaxation times, namely the T1, T1$ρ$, and T2* parameters, which can be used to assess knee osteoarthritis (OA). Whole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by two experienced radiologists based on subtracted T1$ρ$-weighted MR images. Transfer learning was applied to develop 2D attention U-Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1$ρ$, T2* relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared.The models developed using ROIs provided by two radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists' manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1$ρ$, and T2* relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter-observer variability of two radiologists. The proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.

eess.IV↗

arXiv2019

Carrier free long-range magnetism in Mo doped one quintuple layer Bi2Te3 and Sb2Te3

One of the keys to the realization of Quantum Anomalous Hall effect (QAHE) is long range ferromagnetism, which is only experimentally realized in Cr or V doped (Bi,Sb)2Te3 system. Both elements are 3d transition metals and 4d transition metals are found to be ineffective to produce long range ferromagnetism in Bi2Se3. Still, whether long range ferromagnetism can be realized by magnetic doping of 4d elements is an open question. Based on density functional theory calculations, we predict that long range ferromagnetism can be realized in Mo doped Bi2Te3 and Sb2Te3, which are semiconducting. The coupling strength is comparable with that of Cr doped Bi2Te3 and Sb2Te3. Therefore, Mo doped Bi2Te3 and Sb2Te3 or their alloys can be new systems to realize diluted magnetic semiconductors and QAHE.

cond-mat.mtrl-sci↗

arXiv2019

A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem

A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce. An online customer's order usually contains several items and needs to be packed as a whole before shipping. In particular, 5% of tens of millions of packages are using plastic wrapping as outer packaging every day, which brings pressure on the plastic surface minimization to save traditional logistics costs. Because of the huge practical significance, we focus on the issue of packing cuboid-shaped items orthogonally into a least-surface-area bin. The existing heuristic methods for classic 3D bin packing don't work well for this particular NP-hard problem and designing a good problem-specific heuristic is non-trivial. In this paper, rather than designing heuristics, we propose a novel multi-task framework based on Selected Learning to learn a heuristic-like policy that generates the sequence and orientations of items to be packed simultaneously. Through comprehensive experiments on a large scale real-world transaction order dataset and online AB tests, we show: 1) our selected learning method trades off the imbalance and correlation among the tasks and significantly outperforms the single task Pointer Network and the multi-task network without selected learning; 2) our method obtains an average 5.47% cost reduction than the well-designed greedy algorithm which is previously used in our online production system.

cs.LG↗

arXiv2019

Intrinsic long range antiferromagnetic coupling in dilutely V doped CuInTe$_2$

Despite the various magnetic orders mediated by superexchange mechanism, the existence of a long range antiferromagnetic (AFM) coupling is unknown. Based on DFT calculations, we discovered an intrinsic long range AFM coupling in V doped CuInTe$_2$. The AFM coupling is mainly due to the $p-d$ coupling and electron redistribution along the interacting chains. The relatively small energy difference between $p$ and $d$ orbitals and the small energy difference between d orbitals of the dopants and that of stepping stone sites can enhance the stability of this AFM configuration. A multi-bands Hubbard model was proposed to provide fundamental understanding to the long range AFM coupling in chalcopyrite diluted magnetic semiconductors(DMS).

cond-mat.mtrl-sci↗

arXiv2018

Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach

The goal of sentiment-to-sentiment "translation" is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.

cs.CL↗

arXiv2018

Long Range Magnetic order stabilized by acceptors

Tuning magnetic order in magnetic semiconductors is a long sought goal. A proper concentration of acceptors can dramatically suppress local magnetic order in favor of the long one. Using Mn and an acceptor codoped LiZnAs as an example, we demonstrate, by first-principles calculation, the emergence of a long-range magnetic order. This intriguing phenomenon can be understood from an interplay between an acceptor-free magnetism and a band coupling magnetism. Our observation thus lays the ground for a precise control of the magnetic order in future spintronic devices.

cond-mat.mtrl-sci↗

arXiv2017

Interactive Attention Networks for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.

cs.AI↗

arXiv2017

Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method

In this paper, a new type of 3D bin packing problem (BPP) is proposed, in which a number of cuboid-shaped items must be put into a bin one by one orthogonally. The objective is to find a way to place these items that can minimize the surface area of the bin. This problem is based on the fact that there is no fixed-sized bin in many real business scenarios and the cost of a bin is proportional to its surface area. Our research shows that this problem is NP-hard. Based on previous research on 3D BPP, the surface area is determined by the sequence, spatial locations and orientations of items. Among these factors, the sequence of items plays a key role in minimizing the surface area. Inspired by recent achievements of deep reinforcement learning (DRL) techniques, especially Pointer Network, on combinatorial optimization problems such as TSP, a DRL-based method is applied to optimize the sequence of items to be packed into the bin. Numerical results show that the method proposed in this paper achieve about 5% improvement than heuristic method.

cs.AI↗

arXiv2017

Step Stone Effect: A sp anti-bonding Mediated Long-Range Ferromagnetism in Cr-doped Carrier-Free Bi2Te3

Despite the recent success in the realization of the quantum anomalous Hall effect, the underlying physical mechanism of the long range Ferromagnetism is still unclear. Based on our density functional theory calculations, we discovered an intriguing long-range ferromagnetic order in Cr-doped, carrier-free Bi2Te3, with the separation between dopants more than 8 Å. We found that this magnetic coupling is facilitated by an anti-bonding state originated from the lone pair of a Te 5p state and a Bi 6s state, despite this anti-bonding state lies below the valence band maximum. Such a state serves as a hidden step stone merged in the electron sea and mediates the long-range magnetic order. An effective electron hopping model is proposed to explain this mechanism. This novel mechanism sheds light on the understanding of long-range ferromagnetism in insulators and may lead to the realization of the long-range magnetic order in dilute magnetic semiconductors.

cond-mat.mtrl-sci↗

arXiv2016

System Combination for Short Utterance Speaker Recognition

For text-independent short-utterance speaker recognition (SUSR), the performance often degrades dramatically. This paper presents a combination approach to the SUSR tasks with two phonetic-aware systems: one is the DNN-based i-vector system and the other is our recently proposed subregion-based GMM-UBM system. The former employs phone posteriors to construct an i-vector model in which the shared statistics offers stronger robustness against limited test data, while the latter establishes a phone-dependent GMM-UBM system which represents speaker characteristics with more details. A score-level fusion is implemented to integrate the respective advantages from the two systems. Experimental results show that for the text-independent SUSR task, both the DNN-based i-vector system and the subregion-based GMM-UBM system outperform their respective baselines, and the score-level system combination delivers performance improvement.

cs.CL↗

arXiv2016

Monte Carlo simulation of a very high resolution thermal neutron detector composed of glass scintillator microfibers

In order to develop a high spatial resolution (micron level) thermal neutron detector, a detector assembly composed of cerium doped lithium glass microfibers, each with a diameter of 1\,$μ$m, is proposed, where the neutron absorption location is reconstructed from the observed charged particle products that result from neutron absorption. To suppress the cross talk of the scintillation light, each scintillating fiber is surrounded by air-filled glass capillaries with the same diameter as the fiber. This pattern is repeated to form a bulk microfiber detector. On one end, the surface of the detector is painted with a thin optical reflector to increase the light collection efficiency at the other end. Then the scintillation light emitted by any neutron interaction is transmitted to one end, magnified, and recorded by an intensified CCD camera. A simulation based on the Geant4 toolkit was developed to model this detector. All the relevant physics processes including neutron interaction, scintillation, and optical boundary behaviors are si\-mulated. This simulation was first validated through measurements of neutron response from lithium glass cylinders. With good expected light collection, an algorithm based upon the features inherent to alpha and triton particle tracks is proposed to reconstruct the neutron reaction position in the glass fiber array. Given a 1\,$μ$m fiber diameter and 0.1\,mm detector thickness, the neutron spatial resolution is expected to reach $σ\sim 1\, μ$m with a Gaussian fit in each lateral dimension. The detection efficiency was estimated to be 3.7\% for a glass fiber assembly with thickness of 0.1\,mm. When the detector thickness increases from 0.1\,mm to 1\,mm, the position resolution is not expected to vary much, while the detection efficiency is expected to increase by about a factor of ten.

physics.ins-det↗

arXiv2016

Re-enabling high-speed caching for LSM-trees

LSM-tree has been widely used in cloud computing systems by Google, Facebook, and Amazon, to achieve high performance for write-intensive workloads. However, in LSM-tree, random key-value queries can experience long latency and low throughput due to the interference from the compaction, a basic operation in the algorithm, to caching. LSM-tree relies on frequent compaction operations to merge data into a sorted structure. After a compaction, the original data are reorganized and written to other locations on the disk. As a result, the cached data are invalidated since their referencing addresses are changed, causing serious performance degradations. We propose dLSM in order to re-enable high-speed caching during intensive writes. dLSM is an LSM-tree with a compaction buffer on the disk, working as a cushion to minimize the cache invalidation caused by compactions. The compaction buffer maintains a series of snapshots of the frequently compacted data, which represent a consistent view of the corresponding data in the underlying LSM-tree. Being updated in a much lower rate than that of compactions, data in the compaction buffer are almost stationary. In dLSM, an object is referenced by the disk address of the corresponding block either in the compaction buffer for frequently compacted data, or in the underlying LSM-tree for infrequently compacted data. Thus, hot objects can be effectively kept in the cache without harmful invalidations. With the help of a small on-disk compaction buffer, dLSM achieves a high query performance by enabling effective caching, while retaining all merits of LSM-tree for write-intensive data processing. We have implemented dLSM based on LevelDB. Our evaluations show that with a standard DRAM cache, dLSM can achieve 5--8x performance improvement over LSM with the same cache on HDD storage.

cs.DS↗