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Bin Gao

Bin Gao appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Machine Learningmath.OCComputation and Languagecond-mat.str-elcond-mat.mtrl-scicond-mat.supr-conInformation Retrievalmath.NAArtificial IntelligenceComputer Visioncond-mat.mes-hallmath.CAmath.DSmath.SPNeural and Evolutionary ComputingNumerical Analysisphysics.comp-ph

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Published work

21 published item(s)

preprint2026arXiv

A second-order method landing on the Stiefel manifold via Newton$\unicode{x2013}$Schulz iteration

Retraction-free approaches offer attractive low-cost alternatives to Riemannian methods on the Stiefel manifold, but they are often first-order, which may limit the efficiency under high-accuracy requirements. To this end, we propose a second-order method landing on the Stiefel manifold without invoking retractions, which is proved to enjoy local quadratic (or superlinear for its inexact variant) convergence. The update consists of the sum of (i) a component tangent to the level set of the constraint-defining function that aims to reduce the objective and (ii) a component normal to the same level set that reduces the infeasibility. Specifically, we construct the normal component via Newton$\unicode{x2013}$Schulz, a fixed-point iteration for orthogonalization. Moreover, we establish a geometric connection between the Newton$\unicode{x2013}$Schulz iteration and Stiefel manifolds, in which Newton$\unicode{x2013}$Schulz moves along the normal space. For the tangent component, we formulate a modified Newton equation that incorporates Newton$\unicode{x2013}$Schulz. Numerical experiments on the orthogonal Procrustes problem, principal component analysis, and real-data independent component analysis illustrate that the proposed method performs better than the existing methods.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Spectroscopic Demarcation of Emergent Photons and Spinons in a Dipolar-Octupolar Quantum Spin Liquid

The identification of fractionalized excitations in quantum spin liquids (QSLs) remains a central challenge in condensed matter physics. In dipolar-octupolar (DO) pyrochlores, such as $\text{Ce}_2\text{Zr}_2\text{O}_7$, the candidate $π$-flux quantum spin ice (QSI) state is predicted to host both gapless emergent photons and a continuum of spinons. However, resolving these modes at zero field is complicated by their spectral overlap and the presence of nonmagnetic scattering near zero energy. Here, we report neutron scattering experiments on $\text{Ce}_2\text{Zr}_2\text{O}_7$ under a magnetic field along the $[1,1,1]$ direction. In contrast to previous unpolarized studies at zero-field that relied on high-temperature subtraction, we use a same-temperature high-field subtraction protocol to isolate the photon mode. Leveraging the selective coupling of the magnetic field to the dipolar degrees of freedom, we demonstrate the spectroscopic demarcation of these excitations. We observe that weak fields ($\approx 0.15$ T) suppress the low-energy photon weight while leaving the high-energy spinon continuum robust, albeit hardened. Our results, supported by gauge mean-field theory and exact diagonalization calculations, provide strong evidence for the $π$-flux QSI state and introduce a powerful field-tuning protocol for investigating DO-QSLs.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Evidence for gapless quantum spin liquid in a honeycomb lattice

One main theme in current condensed matter physics is the search of quantum spin liquid (QSL), an exotic magnetic state with strongly-fluctuating and highly-entangled spins down to zero temperature without static order. However, there is no consensus on the existence of a QSL ground state in any real material so far. The disorders and competing exchange interactions may prevent the formation of an ideal QSL state on frustrated spin lattices. Here we report systematic heat transport measurements on a honeycomb-lattice compound BaCo2(AsO4)2, which manifests magnetic order in zero field. In a narrow field range after the magnetic order is nearly suppressed by an in-plane field, in both perpendicular and parallel to the zigzag direction, a finite residual linear term of thermal conductivity is clearly observed, which is attributed to the mobile fractionalized spinon excitations. This provides smoking-gun evidence for a gapless QSL state in BaCo2(AsO4)2. We discuss the underlying physics to form this exotic gapless QSL state in Co2+ honeycomb lattice.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Anisotropic magnon damping by zero-temperature quantum fluctuations in ferromagnetic CrGeTe$_3$

Spin and lattice are two fundamental degrees of freedom in a solid, and their fluctuations about the equilibrium values in a magnetic ordered crystalline lattice form quasiparticles termed magnons (spin waves) and phonons (lattice waves), respectively. In most materials with strong spin-lattice coupling (SLC), the interaction of spin and lattice induces energy gaps in the spin wave dispersion at the nominal intersections of magnon and phonon modes. Here we use neutron scattering to show that in the two-dimensional (2D) van der Waals honeycomb lattice ferromagnetic CrGeTe3, spin waves propagating within the 2D plane exhibit an anomalous dispersion, damping, and break-down of quasiparticle conservation, while magnons along the c axis behave as expected for a local moment ferromagnet. These results indicate the presence of dynamical SLC arising from the zero-temperature quantum fluctuations in CrGeTe3, suggesting that the observed in-plane spin waves are mixed spin and lattice quasiparticles fundamentally different from pure magnons and phonons.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Band-Mott mixing hybridizes the gap in Fe$_2$Mo$_3$O$_8$

We combined optical spectroscopy and first principles electronic structure calculations to reveal the charge gap in the polar magnet Fe$_2$Mo$_3$O$_8$. Iron occupation on the octahedral site draws the gap strongly downward compared to the Zn parent compound, and subsequent occupation of the tetrahedral site creates a narrow resonance near the Fermi energy that draws the gap downward even further. This resonance is a many-body effect that emanates from a flat valence band in a Mott-like state due to screening of the local moment - similar to expectations for a Zhang-Rice singlet, except that here, it appears in a semi-conductor. We discuss the unusual hybridization in terms of orbital occupation and character as well as the structure-property relationships that can be unveiled in various metal-substituted systems (Ni, Mn, Co, Zn).

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs

Generative adversarial networks (GANs) have proven successful in image generation tasks. However, GAN training is inherently unstable. Although many works try to stabilize it by manually modifying GAN architecture, it requires much expertise. Neural architecture search (NAS) has become an attractive solution to search GANs automatically. The early NAS-GANs search only generators to reduce search complexity but lead to a sub-optimal GAN. Some recent works try to search both generator (G) and discriminator (D), but they suffer from the instability of GAN training. To alleviate the instability, we propose an efficient two-stage evolutionary algorithm-based NAS framework to search GANs, namely EAGAN. We decouple the search of G and D into two stages, where stage-1 searches G with a fixed D and adopts the many-to-one training strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts the one-to-one training and weight-resetting strategies to enhance the stability of GAN training. Both stages use the non-dominated sorting method to produce Pareto-front architectures under multiple objectives (e.g., model size, Inception Score (IS), and Fréchet Inception Distance (FID)). EAGAN is applied to the unconditional image generation task and can efficiently finish the search on the CIFAR-10 dataset in 1.2 GPU days. Our searched GANs achieve competitive results (IS=8.81$\pm$0.10, FID=9.91) on the CIFAR-10 dataset and surpass prior NAS-GANs on the STL-10 dataset (IS=10.44$\pm$0.087, FID=22.18). Source code: https://github.com/marsggbo/EAGAN.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition

We propose new Riemannian preconditioned algorithms for low-rank tensor completion via the polyadic decomposition of a tensor. These algorithms exploit a non-Euclidean metric on the product space of the factor matrices of the low-rank tensor in the polyadic decomposition form. This new metric is designed using an approximation of the diagonal blocks of the Hessian of the tensor completion cost function, thus has a preconditioning effect on these algorithms. We prove that the proposed Riemannian gradient descent algorithm globally converges to a stationary point of the tensor completion problem, with convergence rate estimates using the $Ł$ojasiewicz property. Numerical results on synthetic and real-world data suggest that the proposed algorithms are more efficient in memory and time compared to state-of-the-art algorithms. Moreover, the proposed algorithms display a greater tolerance for overestimated rank parameters in terms of the tensor recovery performance, thus enable a flexible choice of the rank parameter.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Optimization flows landing on the Stiefel manifold

We study a continuous-time system that solves optimization problems over the set of orthonormal matrices, which is also known as the Stiefel manifold. The resulting optimization flow follows a path that is not always on the manifold but asymptotically lands on the manifold. We introduce a generalized Stiefel manifold to which we extend the canonical metric of the Stiefel manifold. We show that the vector field of the proposed flow can be interpreted as the sum of a Riemannian gradient on a generalized Stiefel manifold and a normal vector. Moreover, we prove that the proposed flow globally converges to the set of critical points, and any local minimum and isolated critical point is asymptotically stable.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Stabilization of layered perovskite structures via strontium substitution in Ca$_3$Ti$_2$O$_7$ revealed via elemental mapping

Extensive studies have been performed on layered compounds, ranging from layered cuprates to van der Waals materials with critical issues of intergrowths and stacking faults. However, such structures have been studied less because of experimental difficulty. We present characteristic defect structures of intergrowths in the Ruddlesden-Popper Ca$_{2.46}$Sr$_{0.54}$Ti$_2$O$_7$, which is known to exhibit hybrid improper ferroelectricity. Transmission electron microscopy reveals that numerous intergrowths composed of 7 and 15 layers are introduced in the ferroelectric domains. Elemental maps demonstrate that Sr ions are selectively substituted into the perovskite layers of intergrowths. Density functional theory calculations support the site-selective substitution of Sr ions, favorably located in the intergrowths. The stabilization of the Ruddlesden-Popper phase and intergrowths via Sr substitution can be explained by the ionic-radius difference between Ca and Sr ions. The study reveals detailed defect structures originating from the layered perovskite structure of Ca$_{2.46}$Sr$_{0.54}$Ti$_2$O$_7$, and shows the usefulness of elemental mapping in probing the substitution effects in oxides.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Riemannian rank-adaptive method for low-rank matrix completion

The low-rank matrix completion problem can be solved by Riemannian optimization on a fixed-rank manifold. However, a drawback of the known approaches is that the rank parameter has to be fixed a priori. In this paper, we consider the optimization problem on the set of bounded-rank matrices. We propose a Riemannian rank-adaptive method, which consists of fixed-rank optimization, rank increase step and rank reduction step. We explore its performance applied to the low-rank matrix completion problem. Numerical experiments on synthetic and real-world datasets illustrate that the proposed rank-adaptive method compares favorably with state-of-the-art algorithms. In addition, it shows that one can incorporate each aspect of this rank-adaptive framework separately into existing algorithms for the purpose of improving performance.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

On the Łojasiewicz Exponent of the Quadratic Sphere Constrained Optimization Problem

In this paper, we prove that the global version of the $Ł$ojasiewicz gradient inequality holds for quadratic sphere constrained optimization problem with exponent $θ=\frac{3}{4}$. An example from Ting Kei Pong shows that $θ=\frac{3}{4}$ is tight. This is the first $Ł$ojasiewicz gradient inequality established for the sphere constrained optimization problem with a linear term.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Symplectic eigenvalue problem via trace minimization and Riemannian optimization

We address the problem of computing the smallest symplectic eigenvalues and the corresponding eigenvectors of symmetric positive-definite matrices in the sense of Williamson's theorem. It is formulated as minimizing a trace cost function over the symplectic Stiefel manifold. We first investigate various theoretical aspects of this optimization problem such as characterizing the sets of critical points, saddle points, and global minimizers as well as proving that non-global local minimizers do not exist. Based on our recent results on constructing Riemannian structures on the symplectic Stiefel manifold and the associated optimization algorithms, we then propose solving the symplectic eigenvalue problem in the framework of Riemannian optimization. Moreover, a connection of the sought solution with the eigenvalues of a special class of Hamiltonian matrices is discussed. Numerical examples are presented.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An orthogonalization-free parallelizable framework for all-electron calculations in density functional theory

All-electron calculations play an important role in density functional theory, in which improving computational efficiency is one of the most needed and challenging tasks. In the model formulations, both nonlinear eigenvalue problem and total energy minimization problem pursue orthogonal solutions. Most existing algorithms for solving these two models invoke orthogonalization process either explicitly or implicitly in each iteration. Their efficiency suffers from this process in view of its cubic complexity and low parallel scalability in terms of the number of electrons for large scale systems. To break through this bottleneck, we propose an orthogonalization-free algorithm framework based on the total energy minimization problem. It is shown that the desired orthogonality can be gradually achieved without invoking orthogonalization in each iteration. Moreover, this framework fully consists of Basic Linear Algebra Subprograms (BLAS) operations and thus can be naturally parallelized. The global convergence of the proposed algorithm is established. We also present a precondition technique which can dramatically accelerate the convergence of the algorithm. The numerical experiments on all-electron calculations show the efficiency and high scalability of the proposed algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Anisotropic effect of a magnetic field on the neutron spin resonance in FeSe

We use inelastic neutron scattering to study the effect of a magnetic field on the neutron spin resonance (Er = 3.6 meV) of superconducting FeSe (Tc = 9 K). While a field aligned along the in-plane direction broadens and suppresses the resonance, a c-axis aligned field does so much more efficiently, consistent with the anisotropic field-induced suppression of the superfluid density from the heat capacity measurements. These results suggest that the resonance in FeSe is associated with the superconducting electrons arising from orbital selective quasi-particle excitations between the hole and electron Fermi surfaces.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves

We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model that assumes the generation of each word within a sentence to depend on both the topic of the sentence and the whole history of its preceding words in the sentence. Different from conventional topic models that largely ignore the sequential order of words or their topic coherence, SLRTM gives full characterization to them by using a Recurrent Neural Networks (RNN) based framework. Experimental results have shown that SLRTM outperforms several strong baselines on various tasks. Furthermore, SLRTM can automatically generate sentences given a topic (i.e., topics to sentences), which is a key technology for real world applications such as personalized short text conversation.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding

Intelligence Quotient (IQ) Test is a set of standardized questions designed to evaluate human intelligence. Verbal comprehension questions appear very frequently in IQ tests, which measure human's verbal ability including the understanding of the words with multiple senses, the synonyms and antonyms, and the analogies among words. In this work, we explore whether such tests can be solved automatically by artificial intelligence technologies, especially the deep learning technologies that are recently developed and successfully applied in a number of fields. However, we found that the task was quite challenging, and simply applying existing technologies (e.g., word embedding) could not achieve a good performance, mainly due to the multiple senses of words and the complex relations among words. To tackle these challenges, we propose a novel framework consisting of three components. First, we build a classifier to recognize the specific type of a verbal question (e.g., analogy, classification, synonym, or antonym). Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries. Third, for each type of questions, we propose a specific solver based on the obtained distributed word representations and relation representations. Experimental results have shown that the proposed framework can not only outperform existing methods for solving verbal comprehension questions but also exceed the average performance of the Amazon Mechanical Turk workers involved in the study. The results indicate that with appropriate uses of the deep learning technologies we might be a further step closer to the human intelligence.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Strain enhanced superconductivity of Mo$X_2$ ($X$=S or Se) bilayers with Na intercalation

Mo$X_2$ ($X$=S or Se) is a semiconductor family with two-dimensional structure. And a recent calculation predicted the superconductivity in electron doped MoS$_2$ monolayer. In this work, the electronic structure and lattice dynamics of Mo$X_2$ bilayers with monolayer Na intercalated, have been calculated. According to the electron-phonon interaction, it is predicted that these bilayers can be transformed from indirect-gap semiconductors to a superconductors by Na intercalation. More interestingly, the biaxial tensile strain can significantly enhance the superconducting temperature up to $\sim10$ K in Na-intercalated MoS$_2$. In addition, the phonon mean free path at room-temperature is also greatly improved in Na intercalated MoSe$_2$, which is advantaged for related applications.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph

Word embedding, which refers to low-dimensional dense vector representations of natural words, has demonstrated its power in many natural language processing tasks. However, it may suffer from the inaccurate and incomplete information contained in the free text corpus as training data. To tackle this challenge, there have been quite a few works that leverage knowledge graphs as an additional information source to improve the quality of word embedding. Although these works have achieved certain success, they have neglected some important facts about knowledge graphs: (i) many relationships in knowledge graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many}, rather than simply \emph{one-to-one}; (ii) most head entities and tail entities in knowledge graphs come from very different semantic spaces. To address these issues, in this paper, we propose a new algorithm named ProjectNet. ProjecNet models the relationships between head and tail entities after transforming them with different low-rank projection matrices. The low-rank projection can allow non \emph{one-to-one} relationships between entities, while different projection matrices for head and tail entities allow them to originate in different semantic spaces. The experimental results demonstrate that ProjectNet yields more accurate word embedding than previous works, thus leads to clear improvements in various natural language processing tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

KNET: A General Framework for Learning Word Embedding using Morphological Knowledge

Neural network techniques are widely applied to obtain high-quality distributed representations of words, i.e., word embeddings, to address text mining, information retrieval, and natural language processing tasks. Recently, efficient methods have been proposed to learn word embeddings from context that captures both semantic and syntactic relationships between words. However, it is challenging to handle unseen words or rare words with insufficient context. In this paper, inspired by the study on word recognition process in cognitive psychology, we propose to take advantage of seemingly less obvious but essentially important morphological knowledge to address these challenges. In particular, we introduce a novel neural network architecture called KNET that leverages both contextual information and morphological word similarity built based on morphological knowledge to learn word embeddings. Meanwhile, the learning architecture is also able to refine the pre-defined morphological knowledge and obtain more accurate word similarity. Experiments on an analogical reasoning task and a word similarity task both demonstrate that the proposed KNET framework can greatly enhance the effectiveness of word embeddings.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

WordRep: A Benchmark for Research on Learning Word Representations

WordRep is a benchmark collection for the research on learning distributed word representations (or word embeddings), released by Microsoft Research. In this paper, we describe the details of the WordRep collection and show how to use it in different types of machine learning research related to word embedding. Specifically, we describe how the evaluation tasks in WordRep are selected, how the data are sampled, and how the evaluation tool is built. We then compare several state-of-the-art word representations on WordRep, report their evaluation performance, and make discussions on the results. After that, we discuss new potential research topics that can be supported by WordRep, in addition to algorithm comparison. We hope that this paper can help people gain deeper understanding of WordRep, and enable more interesting research on learning distributed word representations and related topics.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Limit Cycle Bifurcations from Centers of Symmetric Hamiltonian Systems Perturbing by Cubic Polynomials

In this paper, we consider some cubic near-Hamiltonian systems obtained from perturbing the symmetric cubic Hamiltonian system with two symmetric singular points by cubic polynomials. First, following Han [2012] we develop a method to study the analytical property of the Melnikov function near the origin for near-Hamiltonian system having the origin as its elementary center or nilpotent center. Based on the method, a computationally efficient algorithm is established to systematically compute the coefficients of Melnikov function. Then, we consider the symmetric singular points and present the conditions for one of them to be elementary center or nilpotent center. Under the condition for the singular point to be a center, we obtain the normal form of the Hamiltonian systems near the center. Moreover, perturbing the symmetric cubic Hamiltonian systems by cubic polynomials, we consider limit cycles bifurcating from the center using the algorithm to compute the coefficients of Melnikov function. Finally, perturbing the symmetric hamiltonian system by symmetric cubic polynomials, we consider the number of limit cycles near one of the symmetric centers of the symmetric near-Hamiltonian system, which is same to that of another center.

0 citations0 reviewsNo code linkedOpen access