arXiv subjects

Qiang Chen

Publications and source records attributed to Qiang Chen.

At least 127 records · Page 7Linked to original sources

arXiv2019

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR. Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.

cs.CL↗

arXiv2019

On-surface synthesis of antiaromatic and open-shell indeno[2,1-b]fluorene polymers and their lateral fusion into porous ribbons

Polycyclic hydrocarbons have received great attention due to their potential role in organic electronics and, for open-shell systems with unpaired electron densities, in spintronics and da-ta storage. However, the intrinsic instability of polyradical hydrocarbons severely limits de-tailed investigations of their electronic structure. Here, we report the on-surface synthesis of conjugated polymers consisting of indeno[2,1-b]fluorene units, which are antiaromatic and open-shell biradicaloids. The observed reaction products, which also include a non-benzenoid porous ribbon arising from lateral fusion of unprotected indeno[2,1-b]fluorene chains, have been characterized via low temperature scanning tunneling microscopy/spectroscopy and non-contact atomic force microscopy, complemented by density-functional theory calculations. These polymers present a low band gap when adsorbed on Au(111). Moreover, their pro-nounced antiaromaticity and radical character, elucidated by ab initio calculations, make them promising candidates for applications in electronics and spintronics. Further, they provide a rich playground to explore magnetism in low-dimensional organic nanomaterials.

cond-mat.mtrl-sci↗

arXiv2019

Symmetries and Local Conservation Laws of Variational Schemes for the Surface Plasmon Polaritons

The relation between symmetries and local conservation laws, known as Noether's theorem, plays an important role in modern theoretical physics. As a discrete analog of the differentiable physical system, a good numerical scheme should admit the discrete local conservation laws and inherent mathematical structures. A class of variational schemes constructed for the hydrodynamic-electrodynamic model of lossless free-electron gas in a quasi-neutral background shows good properties in secular simulations of surface plasmon polaritons [Q. Chen et al., Phys. Rev. E 99, 023313 (2019)]. We show the discrete local conservation laws admitted by these schemes. Based on the gauge symmetry of the discrete action functional, a discrete charge conservation law is realized locally, which is consistent with the discrete Euler-Lagrange equations obtained from the variational schemes. Based on the discrete Euler-Lagrange equations, discrete local momentum and energy conservation laws are derived directly, which are rigorous in theory. The preservation of the discrete local conservation laws and Lagrangian symplectic structure ensure that the numerical scheme is correct in physics.

physics.comp-ph↗

arXiv2019

Coexistence of metallic and nonmetallic properties in the pyrochlore Lu$_2$Rh$_2$O$_7$

Transition metal oxides of the $4d$ and $5d$ block have recently become the targets of materials discovery, largely due to their strong spin-orbit coupling that can generate exotic magnetic and electronic states. Here we report the high pressure synthesis of Lu$_2$Rh$_2$O$_7$, a new cubic pyrochlore oxide based on $4d^5$ Rh$^{4+}$ and characterizations via thermodynamic, electrical transport, and muon spin relaxation measurements. Magnetic susceptibility measurements reveal a large temperature-independent Pauli paramagnetic contribution, while heat capacity shows an enhanced Sommerfeld coefficient, $γ$ = 21.8(1) mJ/mol-Rh K$^2$. Muon spin relaxation measurements confirm that Lu$_2$Rh$_2$O$_7$ remains paramagnetic down to 2 K. Taken in combination, these three measurements suggest that Lu$_2$Rh$_2$O$_7$ is a correlated paramagnetic metal with a Wilson ratio of $R_W = 2.5$. However, electric transport measurements present a striking contradiction as the resistivity of Lu$_2$Rh$_2$O$_7$ is observed to monotonically increase with decreasing temperature, indicative of a nonmetallic state. Furthermore, although the magnitude of the resistivity is that of a semiconductor, the temperature dependence does not obey any conventional form. Thus, we propose that Lu$_2$Rh$_2$O$_7$ may belong to the same novel class of non-Fermi liquids as the nonmetallic metal FeCrAs.

cond-mat.str-el↗

arXiv2019

Variational Schemes and Geometric Simulations for a Hydrodynamic-Electrodynamic Model of Surface Plasmon Polaritons

A class of variational schemes for the hydrodynamic-electrodynamic model of lossless free-electron gas in a quasineutral background is developed for high-quality simulations of surface plasmon polaritons. The Lagrangian density of lossless free-electron gas with a self-consistent electromagnetic field is established, and the dynamical equations with the associated constraints are obtained via a variational principle. Based on discrete exterior calculus, the action functional of this system is discretized and minimized to obtain the discrete dynamics. Newton-Raphson iteration and the biconjugate gradient stabilized method are equipped as a hybrid nonlinear-linear algebraic solver. Instead of discretizing the partial differential equations, the variational schemes have better numerical properties in secular simulations, as they preserve the discrete Lagrangian symplectic structure, gauge symmetry, and general energy-momentum density. Two numerical experiments were performed. The numerical results reproduce characteristic dispersion relations of bulk plasmons and surface plasmon polaritons, and the numerical errors of conserved quantities in all experiments are bounded by a small value after long-term simulations.

physics.comp-ph↗

arXiv2018

Pump-Push-Probe for Ultrafast All-Optical Switching: The Case of a Nanographene Molecule

In the last two decades, the three-beams pump-push-probe (PPP) technique has become a well-established tool for investigating the multidimensional configurational space of a molecule, as it permits to disclose precious information about the multiple and often complex deactivation pathways of the excited molecule. From the spectroscopic point of view, such a tool has revealed details about the efficiency of charge pairs generation and conformational relaxation in p-conjugated molecules and macromolecules. In addition, PPP has been effectively utilised for modulating the gain signal in conjugated materials by taking advantage of the spectral overlap between stimulated emission and charge absorption in those systems. However, the relatively low stability of conjugated polymers under intense photoexcitation has been a crucial limitation for their real employment in plastic optical fibres (POFs) and for signal control applications. Here, we highlight the role of PPP for achieving ultrafast all-optical switching in p-conjugated systems. Furthermore, we report new experimental data on optical switching of a newly synthesised graphene molecule, namely dibenzo[hi,st]ovalene (DBOV). The superior environmental and photostability of DBOV and, in general, of graphene nanostructures can represent a great advantage for their effective applications in POFs and information and communications technology.

physics.chem-ph↗

arXiv2017

Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images

In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images. The network takes a pair of arbitrary length sequence images as inputs and extracts features for each silhouette independently. Then a feature map pooling strategy is adopted to aggregate sequence features. Subsequently, a network which is similar to Siamese network is designed to perform recognition. The proposed network is simple and easy to implement and can be trained in an end-to-end manner. Cross-view gait recognition experiments are conducted on OU-ISIR large population dataset. The results demonstrate that our network can extract and aggregate features from silhouette sequence effectively. It also achieves significant equal error rates and comparable identification rates when compared with the state of the art.

cs.CV↗

arXiv2017

On the quantification of the dissolved hydroxyl radicals in the plasma-liquid system using the molecular probe method

Hydroxyl (OH) radical is the most important reactive species produced by the plasma-liquid interactions, and the OH in the liquid phase (dissolved OH radical, OHdis) takes effect in many plasma-based applications due to its high reactivity. Therefore, the quantification of the OHdis in the plasma-liquid system is of great importance, and a molecular probe method usually used for the OHdis detection might be applied. Herein we investigate the validity of using the molecular probe method to estimate the [OHdis] in the plasma-liquid system. Dimethyl sulfoxide is used as the molecular probe to estimate the [OHdis] in an air plasma-liquid system, and the partial OHdis is related to the formed formaldehyde (HCHO) which is the OHdis-induced derivative. The analysis indicates that the true concentration of the OHdis should be estimated from the sum of three terms: the formed HCHO, the existing OH scavengers, and the OHdis generated H2O2. The results show that the measured [HCHO] needs to be corrected since the HCHO destruction is not negligible in the plasma-liquid system. We conclude from the results and the analysis that the molecular probe method generally underestimates the [OHdis] in the plasma-liquid system. If one wants to obtain the true concentration of the OHdis in the plasma-liquid system, one needs to know the destruction behavior of the OHdis-induced derivatives, the information of the OH scavengers (such as hydrated electron, atomic hydrogen besides the molecular probe), and also the knowledge of the OHdis generated H2O2.

physics.chem-ph↗

arXiv2017

Strong Exciton-Photon Coupling in a Nanographene Filled Microcavity

Dibenzo[\emph{hi,st}]ovalene (DBOV) - a quasi-zero-dimensional `nanographene' - displays strong, narrow, and well-defined optical-absorption transitions at room temperature. On placing a DBOV-doped polymer film into an optical microcavity, we demonstrate strong coupling of the \textbf{0 $\rightarrow$ 0'} electronic and \textbf{0 $\rightarrow$ 1'} vibrational transitions to a confined cavity mode, with coupling energies of 104 meV and 40 meV, respectively. Photoluminescence measurements indicate that the polariton population is distributed between the lower and middle polariton branches at energies approximately coincident with the emission of the DBOV, indicating polariton population via an optical pumping mechanism.

physics.optics↗

arXiv2017

Canonical symplectic structure and structure-preserving geometric algorithms for Schrödinger-Maxwell systems

An infinite dimensional canonical symplectic structure and structure-preserving geometric algorithms are developed for the photon-matter interactions described by the Schrödinger-Maxwell equations. The algorithms preserve the symplectic structure of the system and the unitary nature of the wavefunctions, and bound the energy error of the simulation for all time-steps. This new numerical capability enables us to carry out first-principle based simulation study of important photon-matter interactions, such as the high harmonic generation and stabilization of ionization, with long-term accuracy and fidelity.

quant-ph↗

arXiv2017

FoveaNet: Perspective-aware Urban Scene Parsing

Parsing urban scene images benefits many applications, especially self-driving. Most of the current solutions employ generic image parsing models that treat all scales and locations in the images equally and do not consider the geometry property of car-captured urban scene images. Thus, they suffer from heterogeneous object scales caused by perspective projection of cameras on actual scenes and inevitably encounter parsing failures on distant objects as well as other boundary and recognition errors. In this work, we propose a new FoveaNet model to fully exploit the perspective geometry of scene images and address the common failures of generic parsing models. FoveaNet estimates the perspective geometry of a scene image through a convolutional network which integrates supportive evidence from contextual objects within the image. Based on the perspective geometry information, FoveaNet "undoes" the camera perspective projection analyzing regions in the space of the actual scene, and thus provides much more reliable parsing results. Furthermore, to effectively address the recognition errors, FoveaNet introduces a new dense CRFs model that takes the perspective geometry as a prior potential. We evaluate FoveaNet on two urban scene parsing datasets, Cityspaces and CamVid, which demonstrates that FoveaNet can outperform all the well-established baselines and provide new state-of-the-art performance.

cs.CV↗

arXiv2017

Impedance Analysis of Modular Multilevel Converter Based on Harmonic State-Space Modeling Method

The small-signal impedance modeling of modular multilevel converter (MMC) is the key for analyzing resonance and stability of MMC-based ac power electronics systems. MMC is a converter system with a typical multi-frequency response due to its significant steady-state harmonic components in the arm currents, capacitor voltages, and control signals. Therefore, traditional small-signal modeling methods for 2-level voltage-source converters (VSCs) cannot be directly applied to the MMC. In this paper, the harmonic state-space (HSS) modeling approach is introduced to characterize the harmonic coupling behavior of the MMC. On this basis, the small-signal impedance models of the MMC are developed according to the harmonic linearization principle, which can include all the steady-state harmonic effects of the state variables, leading to the accurate impedance models. Furthermore, in order to reveal the impact of the internal dynamics and closed-loop control on the small-signal impedance of the MMC, three cases are considered in this paper, i.e., open-loop control, ac voltage closed-loop control, and circulating current closed-loop control. Finally, the analytical impedance models are verified by both simulation and experimental results.

eess.SY↗

arXiv2016

A Fast Factorization-based Approach to Robust PCA

Robust principal component analysis (RPCA) has been widely used for recovering low-rank matrices in many data mining and machine learning problems. It separates a data matrix into a low-rank part and a sparse part. The convex approach has been well studied in the literature. However, state-of-the-art algorithms for the convex approach usually have relatively high complexity due to the need of solving (partial) singular value decompositions of large matrices. A non-convex approach, AltProj, has also been proposed with lighter complexity and better scalability. Given the true rank $r$ of the underlying low rank matrix, AltProj has a complexity of $O(r^2dn)$, where $d\times n$ is the size of data matrix. In this paper, we propose a novel factorization-based model of RPCA, which has a complexity of $O(kdn)$, where $k$ is an upper bound of the true rank. Our method does not need the precise value of the true rank. From extensive experiments, we observe that AltProj can work only when $r$ is precisely known in advance; however, when the needed rank parameter $r$ is specified to a value different from the true rank, AltProj cannot fully separate the two parts while our method succeeds. Even when both work, our method is about 4 times faster than AltProj. Our method can be used as a light-weight, scalable tool for RPCA in the absence of the precise value of the true rank.

cs.CV↗

arXiv2016

Photons, phonons, and plasmons with orbital angular momentum in plasmas

Exact eigen modes with orbital angular momentum (OAM) in the complex media of unmagnetized homogeneous plasma are studied. Three exact eigen modes with OAM are discovered, i.e., photons, phonons, and plasmons. It is found that an OAM photon can be excited by two familiar Bessel modes without OAM. For the phonons and plasmons, their OAM are carried by the electrons and ions. The OAM modes in plasmas and their characteristics can be explored for various potential applications in plasma physics and accelerator physics.

physics.plasm-ph↗

arXiv2016

Criticality-Enhanced Magnetocaloric Effect in Quantum Spin Chain Material Copper Nitrate

Low-dimensional quantum magnets, due to the existence of abundant exotic quantum phases therein and experimental feasibilities in laboratories, continues intriguing people in condensed matter physics. In this work, a comprehensive study of Cu(NO$_3$)$_2$ $\cdot$ 2.5H$_2$O (copper nitrate hemipentahydrate, CN), a spin chain material, is performed with multi-technique approach including thermal tensor network (TTN) simulations, first-principles calculations, as well as magnetization measurements in experiments. Employing a cutting-edge TTN method developed in the present work, we determine the couplings $J=5.13$ K, $α=0.23(1)$ and Landé factors $g_{\parallel}=2.31$, $g_{\perp}=2.14$ in an alternating Heisenberg antiferromagnetic chain model, with which one can fit strikingly well the magnetothermodynamic properties. Part of the fitted experimental data are measured on the single-crystal CN specimens synthesized by us. Based on first-principles calculations, we reveal explicitly the spin chain scenario in CN by displaying the calculated electron density distributions, from which the distinct superexchange paths are visualized. On top of that, we investigated the magnetocaloric effect (MCE) in CN by calculating its isentropes and magnetic Grüeisen parameter (GP). Prominent quantum-criticality-enhanced MCE was uncovered, the TTN simulations are in good agreements with measured isentropic lines in the sub-Kelvin region. We propose that CN is potentially a very promising quantum critical coolant, due to the remarkably enhanced MCE near both critical fields of moderate strengths as 2.87 and 4.08 T, respectively.

cond-mat.str-el↗

arXiv2015

A review of plasma liquid interactions for nanomaterial synthesis

In this review, we have summarized the recent advances and present conditions of the nanomaterials synthesis from the plasma-liquid interactions. A theoretical analysis for the nanomaterials synthesis process is presented by analyzing the experimental data. Besides the theoretical analysis, the practical applications in several nanomaterials syntheses of the the plasma-liquid interactions are also presented.

physics.plasm-ph↗

arXiv2015

LogDet Rank Minimization with Application to Subspace Clustering

Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose to use a log-determinant (LogDet) function as a smooth and closer, though non-convex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based non-convex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.

cs.CV↗

arXiv2015

Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network

We address the problem of cross-domain image retrieval, considering the following practical application: given a user photo depicting a clothing image, our goal is to retrieve the same or attribute-similar clothing items from online shopping stores. This is a challenging problem due to the large discrepancy between online shopping images, usually taken in ideal lighting/pose/background conditions, and user photos captured in uncontrolled conditions. To address this problem, we propose a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning. More specifically, DARN consists of two sub-networks, one for each domain, whose retrieval feature representations are driven by semantic attribute learning. We show that this attribute-guided learning is a key factor for retrieval accuracy improvement. In addition, to further align with the nature of the retrieval problem, we impose a triplet visual similarity constraint for learning to rank across the two sub-networks. Another contribution of our work is a large-scale dataset which makes the network learning feasible. We exploit customer review websites to crawl a large set of online shopping images and corresponding offline user photos with fine-grained clothing attributes, i.e., around 450,000 online shopping images and about 90,000 exact offline counterpart images of those online ones. All these images are collected from real-world consumer websites reflecting the diversity of the data modality, which makes this dataset unique and rare in the academic community. We extensively evaluate the retrieval performance of networks in different configurations. The top-20 retrieval accuracy is doubled when using the proposed DARN other than the current popular solution using pre-trained CNN features only (0.570 vs. 0.268).

cs.CV↗