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Source author record

Yang He

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

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cond-mat.str-elphysics.plasm-phcond-mat.mtrl-scicond-mat.supr-conphysics.comp-phComputer Visioncond-mat.mes-hallMachine Learninghep-phInformation Retrievalphysics.chem-phphysics.opticsArtificial IntelligenceComputation and Languageeess.SPmath.NAnucl-thNumerical Analysisphysics.acc-phphysics.app-phquant-phSoftware Engineering

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Published work

42 published item(s)

preprint2026arXiv

FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agents

Text-to-SQL over large analytical databases requires navigating complex schemas, resolving ambiguous queries, and grounding decisions in actual data. Most current systems follow a fixed pipeline where schema elements are retrieved once upfront and the database is only revisited for post-hoc repair, limiting recovery from early mistakes. We present FlexSQL, a text-to-SQL agent whose core design principle is flexible database interaction: the agent can explore schema structure, inspect data values, and run verification queries at any point during reasoning. FlexSQL generates diverse execution plans to cover multiple query interpretations, implements each plan in either SQL or Python depending on the task, and uses a two-tiered repair mechanism that can backtrack from code-level errors to plan-level revisions. On Spider2-Snow, using gpt-oss-120b, FlexSQL achieves a 65.4\% score, outperforming strong open-source baselines that use stronger, larger models such as gpt-o3 and DeepSeek-R1. When integrated into a general-purpose coding agent (as skills in Claude Code), our approach yields over 10\% relative improvement on Spider2-Snow. Further analysis shows that flexible exploration and flexible execution jointly contribute to the effectiveness of our approach, highlighting flexibility as a key design principle. Our code is available at: https://github.com/StringNLPLAB/FlexSQL

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://xcodemind.github.io). At last, we also point out several challenging and promising directions for future research.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Effects of the formation time of parton shower on jet quenching in heavy-ion collisions

Jet quenching has successfully served as a hard probe to study the properties of Quark-Gluon Plasma (QGP). As a multi-particle system, jets take time to develop from a highly virtual parton to a group of partons close to mass shells. In this study, we present a systematical study on the effects of this formation time on jet quenching in relativistic nuclear collisions. Jets from initial hard scatterings were simulated with Pythia, and their interactions with QGP were described using a Linear Boltzmann Transport (LBT) model that incorporates both elastic and inelastic scatterings between jet partons and the thermal medium. Three different estimations of the jet formation time were implemented and compared, including instantaneous formation, formation from single splitting, and formation from sequential splittings, before which no jet-medium interaction was assumed. We found that deferring the jet-medium interaction with a longer formation time not only affects the overall magnitude of the nuclear modification factor of jets, but also its dependence on the jet transverse momentum.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework

We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alleviating over-fits caused by data-sparsity in two ways: learning probabilistic embedding, and incorporating trainable and regularized priors which utilize the rich side information of cold start users and advertisements (Ads). The two techniques are naturally integrated into a variational inference framework, forming an end-to-end training process. Abundant empirical tests on benchmark datasets well demonstrate the advantages of our proposed VELF. Besides, extended experiments confirmed that our parameterized and regularized priors provide more generalization capability than traditional fixed priors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization

Federated learning is a promising framework to mitigate data privacy and computation concerns. However, the communication cost between the server and clients has become the major bottleneck for successful deployment. Despite notable progress in gradient compression, the existing quantization methods require further improvement when low-bits compression is applied, especially the overall systems often degenerate a lot when quantization are applied in double directions to compress model weights and gradients. In this work, we propose a simple cosine-based nonlinear quantization and achieve impressive results in compressing round-trip communication costs. We are not only able to compress model weights and gradients at higher ratios than previous methods, but also achieve competing model performance at the same time. Further, our approach is highly suitable for federated learning problems since it has low computational complexity and requires only a little additional data to recover the compressed information. Extensive experiments have been conducted on image classification and brain tumor semantic segmentation using the CIFAR-10, and BraTS datasets where we show state-of-the-art effectiveness and impressive communication efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Filter Pruning by Switching to Neighboring CNNs with Good Attributes

Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pruning criterion remains the same. In addition, when evaluating the filter importance, only the magnitude information of the filters is considered. However, in neural networks, filters do not work individually, but they would affect other filters. As a result, the magnitude information of each filter, which merely reflects the information of an individual filter itself, is not enough to judge the filter importance. To solve the above problems, we propose Meta-attribute-based Filter Pruning (MFP). First, to expand the existing magnitude information based pruning criteria, we introduce a new set of criteria to consider the geometric distance of filters. Additionally, to explicitly assess the current state of the network, we adaptively select the most suitable criteria for pruning via a meta-attribute, a property of the neural network at the current state. Experiments on two image classification benchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reduce more than 50% FLOPs with only 0.44% top-5 accuracy loss.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction

The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction. However, the existing methods for exposure sequence modeling bring extensive computational burden and neglect noise problems, resulting in an excessively latency and the limited performance in online recommenders. In this paper, we propose to address the high latency and noise problems via Gating-adapted wavelet multiresolution analysis (Gama), which can effectively denoise the extremely long exposure sequence and adaptively capture the implied multi-dimension user interest with linear computational complexity. This is the first attempt to integrate non-parametric multiresolution analysis technique into deep neural networks to model user exposure sequence. Extensive experiments on large scale benchmark dataset and real production dataset confirm the effectiveness of Gama for exposure sequence modeling, especially in cold-start scenarios. Benefited from its low latency and high effecitveness, Gama has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hamiltonian Particle-in-Cell methods for Vlasov-Poisson equations

In this paper, Particle-in-Cell algorithms for the Vlasov-Poisson system are presented based on its Poisson bracket structure. The Poisson equation is solved by finite element methods, in which the appropriate finite element spaces are taken to guarantee that the semi-discretized system possesses a well defined discrete Poisson bracket structure. Then, splitting methods are applied to the semi-discretized system by decomposing the Hamiltonian function. The resulting discretizations are proved to be Poisson bracket preserving. Moreover, the conservative quantities of the system are also well preserved. In numerical experiments, we use the presented numerical methods to simulate various physical phenomena. Due to the huge computational effort of the practical computations, we employ the strategy of parallel computing. The numerical results verify the efficiency of the new derived numerical discretizations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Observation of biradical spin coupling through hydrogen bonds

Investigation of intermolecular electron spin interaction is of fundamental importance in both science and technology.Here, radical pairs of all-trans retinoic acid molecules on Au(111) are created using an ultra-low temperature scanning tunneling microscope. Antiferromagnetic coupling between two radicals is identified by magnetic-field dependent spectroscopy.The measured exchange energies are from 0.1 to 1.0 meV. The biradical spin coupling is mediated through O-H$\cdots$O hydrogen bonds, as elucidated from analysis combining density functional theory calculation and a modern version of valence bond theory.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PYTHIA 8 underlying event tune For RHIC energies

We report an underlying event tune for the PYTHIA 8 Monte Carlo event generator that is applicable for hadron collisions primarily at $\sqrt{s}$ ranges available at the Relativistic Heavy-Ion Collider (RHIC). We compare our new PYTHIA 8 tuned predictions to mid-rapidity inclusive $π^{\pm}$ spectra, jet sub-structure, Drell-Yan production, and underlying event measurements from RHIC and the Tevatron, as well as underlying event data from the Large Hadron Collider. With respect to the default PYTHIA 8 Monash Tune, the new `Detroit' tune shows significant improvements in the description of the experimental data. Additionally, we explore the validity of PYTHIA 8 predictions for forward rapidity $π$ in $\sqrt{s}$ = 200 GeV collisions, where neither tune is able to sufficiently describe the data. We advocate for the new tune to be used for PYTHIA 8 studies at current and future RHIC experiments, and discuss future tuning exercises at lower center-of-mass energies, where forward/backward kinematics are essential at the upcoming Electron-Ion collider.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Reliable and Broad-range Layer Identification of Au-assisted Exfoliated Large Area MoS$_2$ and WS$_2$ Using Reflection Spectroscopic Fingerprints

The emerging Au-assisted exfoliation technique provides a wealth of large-area and high-quality ultrathin two-dimensional (2D) materials compared with traditional tape-based exfoliation. Fast, damage-free, and reliable determination of the layer number of such 2D films is essential to study layer-dependent physics and promote device applications. Here, an optical method has been developed for simple, high throughput, and accurate determination of the layer number for Au-assisted exfoliated MoS$_2$ and WS$_2$ films in a broad thickness range. The method is based on quantitative analysis of layer-dependent white light reflection spectra, revealing that the reflection peak intensity can be used as a clear indicator for determining the layer number. The simple yet robust method will facilitate the fundamental study on layer-dependent optical, electrical, and thermal properties and device applications of 2D materials. The technique can also be readily combined with photoluminescence and Raman spectroscopies to study other layer-dependent physical properties of 2D materials.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Self-injection-locked second-harmonic integrated source

High coherence visible and near-visible laser sources are centrally important to the operation of advanced position/navigation/timing systems as well as classical/quantum sensing systems. However, the complexity and size of these bench-top lasers is an impediment to their transitioning beyond the laboratory. Here, a system-on-a-chip that emits high-coherence visible and near-visible lightwaves is demonstrated. The devices rely upon a new approach wherein wavelength conversion and coherence increase by self-injection-locking are combined within in a single nonlinear resonator. This simplified approach is demonstrated in a hybridly-integrated device and provides a short-term linewidth around 10-30 kHz. On-chip, converted optical power over 2 mW is also obtained. Moreover, measurements show that heterogeneous integration can result in conversion efficiency higher than 25% with output power over 11 mW. Because the approach uses mature III-V pump lasers in combination with thin-film lithium niobate, it can be scaled for low-cost manufacturing of high-coherence visible emitters. Also, the coherence generation process can be transferred to other frequency conversion processes including optical parametric oscillation, sum/difference frequency generation, and third-harmonic generation.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Consistency between ARPES and STM measurements on SmB$_6$

Strongly correlated topological surface states are promising platforms for next-generation quantum applications, but they remain elusive in real materials. The correlated Kondo insulator SmB$_6$ is one of the most promising candidates, with theoretically predicted heavy Dirac surface states supported by transport and scanning tunneling microscopy (STM) experiments. However, a puzzling discrepancy appears between STM and angle-resolved photoemission (ARPES) experiments on SmB$_6$. Although ARPES detects spin-textured surface states, their velocity is an order of magnitude higher than expected, while the Dirac point -- the hallmark of any topological system -- can only be inferred deep within the bulk valence band. A significant challenge is that SmB$_6$ lacks a natural cleavage plane, resulting in ordered surface domains limited to 10s of nanometers. Here we use STM to show that surface band bending can shift energy features by 10s of meV between domains. Starting from our STM spectra, we simulate the full spectral function as an average over multiple domains with different surface potentials. Our simulation shows excellent agreement with ARPES data, and thus resolves the apparent discrepancy between large-area measurements that average over multiple band-shifted domains and atomically-resolved measurements within a single domain.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Lithium niobate photonic-crystal electro-optic modulator

Modern advanced photonic integrated circuits require dense integration of high-speed electro-optic functional elements on a compact chip that consumes only moderate power. Energy efficiency, operation speed, and device dimension are thus crucial metrics underlying almost all current developments of photonic signal processing units. Recently, thin-film lithium niobate (LN) emerges as a promising platform for photonic integrated circuits. Here we make an important step towards miniaturizing functional components on this platform, reporting probably the smallest high-speed LN electro-optic modulators, based upon photonic crystal nanobeam resonators. The devices exhibit a significant tuning efficiency up to 1.98 GHz/V, a broad modulation bandwidth of 17.5 GHz, while with a tiny electro-optic modal volume of only 0.58 $μ{\rm m}^3$. The modulators enable efficient electro-optic driving of high-Q photonic cavity modes in both adiabatic and non-adiabatic regimes, and allow us to achieve electro-optic switching at 11 Gb/s with a bit-switching energy as low as 22 fJ. The demonstration of energy efficient and high-speed electro-optic modulation at the wavelength scale paves a crucial foundation for realizing large-scale LN photonic integrated circuits that are of immense importance for broad applications in data communication, microwave photonics, and quantum photonics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Probing Image Potential States on Topological Semimetal Antimony Surface

A point charge near the surface of a topological insulator (TI) with broken time-reversal symmetry is predicted to generate an image magnetic charge in addition to an image electric charge. We use scanning tunneling spectroscopy to study the image potential states (IPS) of the topological semimetal Sb(111) surface. We observe five IPS with discrete energy levels that are well described by a one-dimensional model. The spatial variation of the IPS energies and lifetimes near surface step edges shows the first local signature of resonant interband scattering between IPS, which suggests that image charges too may interact. Our work motivates the exploration of the TI surface geometry necessary to realize and manipulate a magnetic charge.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Progressive Local Filter Pruning for Image Retrieval Acceleration

This paper focuses on network pruning for image retrieval acceleration. Prevailing image retrieval works target at the discriminative feature learning, while little attention is paid to how to accelerate the model inference, which should be taken into consideration in real-world practice. The challenge of pruning image retrieval models is that the middle-level feature should be preserved as much as possible. Such different requirements of the retrieval and classification model make the traditional pruning methods not that suitable for our task. To solve the problem, we propose a new Progressive Local Filter Pruning (PLFP) method for image retrieval acceleration. Specifically, layer by layer, we analyze the local geometric properties of each filter and select the one that can be replaced by the neighbors. Then we progressively prune the filter by gradually changing the filter weights. In this way, the representation ability of the model is preserved. To verify this, we evaluate our method on two widely-used image retrieval datasets,i.e., Oxford5k and Paris6K, and one person re-identification dataset,i.e., Market-1501. The proposed method arrives with superior performance to the conventional pruning methods, suggesting the effectiveness of the proposed method for image retrieval.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation

Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collection and annotation of such datasets comes at significant efforts and associated costs. In addition, visual data might contain private or sensitive information, that makes it equally unsuited for public release. Unfortunately, recent work on membership inference in the broader area of adversarial machine learning and inference attacks on machine learning models has shown that even black box classifiers leak information on the dataset that they were trained on. We show that such membership inference attacks can be successfully carried out on complex, state of the art models for semantic segmentation. In order to mitigate the associated risks, we also study a series of defenses against such membership inference attacks and find effective counter measures against the existing risks with little effect on the utility of the segmentation method. Finally, we extensively evaluate our attacks and defenses on a range of relevant real-world datasets: Cityscapes, BDD100K, and Mapillary Vistas.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Stochastic Model Pruning via Weight Dropping Away and Back

Deep neural networks have dramatically achieved great success on a variety of challenging tasks. However, most successful DNNs have an extremely complex structure, leading to extensive research on model compression.As a significant area of progress in model compression, traditional gradual pruning approaches involve an iterative prune-retrain procedure and may suffer from two critical issues: local importance judgment, where the pruned weights are merely unimportant in the current model; and an irretrievable pruning process, where the pruned weights have no chance to come back. Addressing these two issues, this paper proposes the Drop Pruning approach, which leverages stochastic optimization in the pruning process by introducing a drop strategy at each pruning step, namely, drop away, which stochastically deletes some unimportant weights, and drop back, which stochastically recovers some pruned weights. The suitable choice of drop probabilities decreases the model size during the pruning process and helps it flow to the target sparsity. Compared to the Bayesian approaches that stochastically train a compact model for pruning, we directly aim at stochastic gradual pruning. We provide a detailed analysis showing that the drop away and drop back approaches have individual contributions. Moreover, Drop Pruning can achieve competitive compression performance and accuracy on many benchmark tasks compared with state-of-the-art weights pruning and Bayesian training approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Synthetic Convolutional Features for Improved Semantic Segmentation

Recently, learning-based image synthesis has enabled to generate high-resolution images, either applying popular adversarial training or a powerful perceptual loss. However, it remains challenging to successfully leverage synthetic data for improving semantic segmentation with additional synthetic images. Therefore, we suggest to generate intermediate convolutional features and propose the first synthesis approach that is catered to such intermediate convolutional features. This allows us to generate new features from label masks and include them successfully into the training procedure in order to improve the performance of semantic segmentation. Experimental results and analysis on two challenging datasets Cityscapes and ADE20K show that our generated feature improves performance on segmentation tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Imaging emergent heavy Dirac fermions of a topological Kondo insulator

Kondo insulators are primary candidates in the search for strongly correlated topological quantum phases, which may host topological order, fractionalization, and non-Abelian statistics. Within some Kondo insulators, the hybridization gap is predicted to protect a nontrivial topological invariant and to harbor emergent heavy Dirac fermion surface modes. We use high-energy-resolution spectroscopic imaging in real and momentum space on the Kondo insulator, SmB$_6$. On cooling through $T^*_Δ\approx$ 35 K we observe the opening of an insulating gap that expands to $Δ\approx$ 10 meV at 2 K. Within the gap, we image the formation of linearly dispersing surface states with effective masses reaching $m^* = (410\pm20)m_e$. We thus demonstrate existence of a strongly correlated topological Kondo insulator phase hosting the heaviest known Dirac fermions.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Explicit high-order non-canonical symplectic particle-in-cell algorithms for Vlasov-Maxwell systems

Explicit high-order non-canonical symplectic particle-in-cell algorithms for classical particle-field systems governed by the Vlasov-Maxwell equations are developed. The algorithm conserves a discrete non-canonical symplectic structure derived from the Lagrangian of the particle-field system, which is naturally discrete in particles. The electromagnetic field is spatially-discretized using the method of discrete exterior calculus with high-order interpolating differential forms for a cubic grid. The resulting time-domain Lagrangian assumes a non-canonical symplectic structure. It is also gauge invariant and conserves charge. The system is then solved using a splitting method discovered by He et al., which produces five exactly-soluable sub-systems, and high-order structure- preserving algorithms follow by combinations. The explicit, high-order, and conservative nature of the algorithms is especially suitable for long-term simulations of particle-field systems with extremely large number of degrees of freedom on massively parallel supercomputers. The algorithms have been tested and verified by the two physics problems, i.e., the nonlinear Landau damping and the electron Bernstein wave.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Explicit high-order noncanonical symplectic algorithms for ideal two-fluid systems

An explicit high-order noncanonical symplectic algorithm for ideal two-fluid systems is developed. The fluid is discretized as particles in the Lagrangian description, while the electromagnetic fields and internal energy are treated as discrete differential form fields on a fixed mesh. With the assistance of Whitney interpolating forms, this scheme preserves the gauge symmetry of the electromagnetic field, and the pressure field is naturally derived from the discrete internal energy. The whole system is solved using the Hamiltonian splitting method discovered by He et al., which was been successfully adopted in constructing symplectic particle-in-cell schemes. Because of its structure preserving and explicit nature, this algorithm is especially suitable for large-scale simulations for physics problems that are multi-scale and require long-term fidelity and accuracy. The algorithm is verified via two tests: studies of the dispersion relation of waves in a two-fluid plasma system and the oscillating two-stream instability.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Explicit symplectic algorithms based on generating functions for charged particle dynamics

Dynamics of a charged particle in the canonical coordinates is a Hamiltonian system, and the well-known symplectic algorithm has been regarded as the de facto method for numerical integration of Hamiltonian systems due to its long-term accuracy and fidelity. For long-term simulations with high efficiency, explicit symplectic algorithms are desirable. However, it is widely accepted that explicit symplectic algorithms are only available for sum-separable Hamiltonians, and that this restriction severely limits the application of explicit symplectic algorithms to charged particle dynamics. To overcome this difficulty, we combine the familiar sum-split method and a generating function method to construct second and third order explicit symplectic algorithms for dynamics of charged particle. The generating function method is designed to generate explicit symplectic algorithms for product-separable Hamiltonian with form of $H(\mathbf{p},\mathbf{q})=\mathbf{p}_{i}f(\mathbf{q})$ or $H(\mathbf{p},\mathbf{q})=\mathbf{q}_{i}f(\mathbf{p})$. Applied to the simulations of charged particle dynamics, the explicit symplectic algorithms based on generating functions demonstrate superiorities in conservation and efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Friedel Oscillations as a Probe of Fermionic Quasiparticles

When immersed in a see of cold electrons, local impurities give rise to density modulations known as Friedel oscillations. In spite of the generality of this phenomenon, the exact shape of these modulations is usually computed only for non-interacting electrons with a quadratic dispersion relation. In actual materials, one needs to take into account several additional factors, such as (i) the details of the band structure, (ii) the lifetime of quasiparticles, (iii) in superconductors, the presence of a pairing gap. Studying how these effects influence Friedel oscillations is a viable way to access the properties of fermionic excitations in strongly-correlated materials. In this work we analyze the signatures of Friedel oscillations in STM and X-ray scattering experiments, focusing on the concrete example of cuprates superconductors. A detailed comparison with recent experiments reveals a rich interplay between local modulation of the chemical potential and of the pairing gap.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Hamiltonian particle-in-cell methods for Vlasov-Maxwell equations

In this paper, we develop Hamiltonian particle-in-cell methods for Vlasov-Maxwell equations by applying conforming finite element methods in space and splitting methods in time. For the spatial discretisation, the criteria for choosing finite element spaces are presented such that the semi-discrete system possesses a discrete non-canonical Poisson structure. We apply a Hamiltonian splitting method to the semi-discrete system in time, then the resulting algorithm is Poisson preserving and explicit. The conservative properties of the algorithm guarantee the efficient and accurate numerical simulation of the Vlasov-Maxwell equations over long-time.

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preprint2016arXiv

High order volume-preserving algorithms for relativistic charged particles in general electromagnetic fields

We construct high order symmetric volume-preserving methods for the relativistic dynamics of a charged particle by the splitting technique with processing. Via expanding the phase space to include time $t$, we give a more general construction of volume-preserving methods that can be applied to systems with time-dependent electromagnetic fields. The newly derived methods provide numerical solutions with good accuracy and conservative properties over long time of simulation. Furthermore, because of the use of processing technique the high order methods are explicit, and cost less than the methods derived from standard compositions, thus are more efficient. The results are verified by the numerical experiments. Linear stability analysis of the methods show that the high order processed method allows larger time step size during integration.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Atomic Conversion Reaction Mechanism of WO3 in Secondary Ion Batteries

Conversion reaction is one of the most important chemical processes in energy storage such as lithium ion batteries. While it is generally assumed that the conversion reaction is initiated by ion intercalation into the electrode material, solid evidence of intercalation and the subsequent transition mechanism to conversion remain elusive. Here, using well-defined WO3 single crystalline thin films grown on Nb doped SrTiO3(001) as a model electrode, we elucidate the conversion reaction mechanisms during Li+, Na+ and Ca2+ insertion into WO3 by in situ transmission electron microscopy studies. Intercalation reactions are explicitly revealed for all ion insertions. With corroboration from first principle molecular simulations, it is found that, beyond intercalation, ion-oxygen bonding destabilize the W framework, which gradually collapses to pseudo-amorphous structure. In addition, we show the interfacial tensile strain imposed by the SrTiO3 substrate can preserve the structure of an ultra-thin layer of WO3, offering a possible engineering solution to improve the cyclability of electrode materials. This study provides a detailed atomistic picture on the conversion-type electrodes in secondary ion batteries.

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preprint2015arXiv

Canonical symplectic particle-in-cell method for long-term large-scale simulations of the Vlasov-Maxwell system

Particle-in-Cell (PIC) simulation is the most important numerical tool in plasma physics. However, its long-term accuracy has not been established. To overcome this difficulty, we developed a canonical symplectic PIC method for the Vlasov-Maxwell system by discretizing its canonical Poisson bracket. A fast local algorithm to solve the symplectic implicit time advance is discovered without root searching or global matrix inversion, enabling applications of the proposed method to very large-scale plasma simulations with many, e.g., $10^{9}$, degrees of freedom. The long-term accuracy and fidelity of the algorithm enables us to numerically confirm Mouhot and Villani's theory and conjecture on nonlinear Landau damping over several orders of magnitude using the PIC method, and to calculate the nonlinear evolution of the reflectivity during the mode conversion process from extraordinary waves to Bernstein waves.

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preprint2015arXiv

Comment on "Hamiltonian splitting for the Vlasov-Maxwell equations"

The paper [1] by Crouseilles, Einkemmer, and Faou used an incorrect Poisson bracket for the Vlasov-Maxwell equations. If the correct Poisson bracket is used, the solution of one of the subsystems cannot be computed exactly in general. As a result, one cannot construct a symplectic scheme for the Vlasov-Maxwell equations using the splitting Hamiltonian method proposed in Ref [1].

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preprint2015arXiv

Explicit non-canonical symplectic algorithms for charged particle dynamics

We study the non-canonical symplectic structure, or K-symplectic structure inherited by the charged particle dynamics. Based on the splitting technique, we construct non-canonical symplectic methods which is explicit and stable for the long-term simulation. The key point of splitting is to decompose the Hamiltonian as four parts, so that the resulting four subsystems have the same structure and can be solved exactly. This guarantees the K-symplectic preservation of the numerical methods constructed by composing the exact solutions of the subsystems. The error convergency of numerical solutions is analyzed by means of the Darboux transformation. The numerical experiment display the long-term stability and efficiency for these methods.

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preprint2015arXiv

Hamiltonian integration methods for Vlasov-Maxwell equations

Hamiltonian integration methods for the Vlasov-Maxwell equations are developed by a Hamiltonian splitting technique. The Hamiltonian functional is split into five parts, i.e., the electrical energy, the magnetic energy, and the kinetic energy in three Cartesian components. Each of the subsystems is a Hamiltonian system with respect to the Morrison-Marsden-Weinstein Poisson bracket and can be solved exactly. Compositions of the exact solutions yield Poisson structure preserving, or Hamiltonian, integration methods for the Vlasov-Maxwell equations, which have superior long-term fidelity and accuracy.

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preprint2015arXiv

Holographic Maps of Quasiparticle Interference

The analysis of Fourier-transformed scanning-tunneling-microscopy (STM) images with subatomic resolution is a common tool for studying properties of quasiparticle excitations in strongly correlated materials. While Fourier amplitudes are generally complex valued, earlier analysis mostly considered only their absolute values. Their complex phases were deemed random, and thus irrelevant, due to the unknown positions of impurities in the sample. Here we show how to factor out these random phases by analysing overlaps between Fourier amplitudes that differ by reciprocal lattice vectors. The resulting holographic maps provide important and previously-unknown information about the electronic structures of materials. When applied to superconducting cuprates, our method solves a long-standing puzzle of the dichotomy between equivalent wavevectors. We show that $d$-wave Wannier functions of the conduction band provide a natural explanation for experimental results that were interpreted as evidence for competing unconventional charge modulations. Our work opens a new pathway to identify the nature of electronic states in STM measurements.

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preprint2015arXiv

Spin Manipulation by Creation of Single-Molecule Radical Cations

All-trans-retinoic acid (ReA), a closed-shell organic molecule comprising only C, H, and O atoms, is investigated on a Au(111) substrate using scanning tunneling microscopy and spectroscopy. In dense arrays single ReA molecules are switched to a number of states, three of which carry a localized spin as evidenced by conductance spectroscopy in high magnetic fields. The spin of a single molecule may be reversibly switched on and off without affecting its neighbors. We suggest that ReA on Au is readily converted to a radical by the abstraction of an electron.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Spin-Polarized Quantum Well States on Bi$_{2-x}$Fe$_x$Se$_3$

Low temperature scanning tunneling microscopy is used to image the doped topological insulator Bi$_{2-x}$Fe$_x$Se$_3$. Interstitial Fe defects allow the detection of quasiparticle interference (QPI), and the reconstruction of the empty state band structure. Quantitative comparison between measured data and density functional theory calculations reveals the unexpected coexistence of quantum well states (QWS) with topological surface states (TSS) on the atomically clean surface of Bi$_{2-x}$Fe$_x$Se$_3$. Spectroscopic measurements quantify the breakdown of linear dispersion due to hexagonal warping. Nonetheless, both QWS and TSS remain spin-polarized and protected from backscattering to almost 1 eV above the Dirac point, suggesting their utility for spin-based applications.

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preprint2014arXiv

Charge order driven by Fermi-arc instability in Bi2201

The understanding of the origin of superconductivity in cuprates has been hindered by the apparent diversity of intertwining electronic orders in these materials. We combined resonant x-ray scattering (REXS), scanning-tunneling microscopy (STM), and angle-resolved photoemission spectroscopy (ARPES) to observe a charge order that appears consistently in surface and bulk, and in momentum and real space within one cuprate family, Bi2201. The observed wave vectors rule out simple antinodal nesting in the single-particle limit but match well with a phenomenological model of a many-body instability of the Fermi arcs. Combined with earlier observations of electronic order in other cuprate families, these findings suggest the existence of a generic charge-ordered state in underdoped cuprates and uncover its intimate connection to the pseudogap regime.

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preprint2014arXiv

Fermi Surface and Pseudogap Evolution in a Cuprate Superconductor

The unclear relationship between cuprate superconductivity and the pseudogap state remains an impediment to understanding the high transition temperature (Tc) superconducting mechanism. Here we employ magnetic-field-dependent scanning tunneling microscopy to provide phase-sensitive proof that d-wave superconductivity coexists with the pseudogap on the antinodal Fermi surface of an overdoped cuprate. Furthermore, by tracking the hole doping (p) dependence of the quasiparticle interference pattern within a single Bi-based cuprate family, we observe a Fermi surface reconstruction slightly below optimal doping, indicating a zero-field quantum phase transition in notable proximity to the maximum superconducting Tc. Surprisingly, this major reorganization of the system's underlying electronic structure has no effect on the smoothly evolving pseudogap.

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preprint2014arXiv

Nanoscale interplay of strain and doping in a high-temperature superconductor

The highest temperature superconductors are electronically inhomogeneous at the nanoscale, suggesting the existence of a local variable which could be harnessed to enhance the superconducting pairing. Here we report the relationship between local doping and local strain in the cuprate superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$. We use scanning tunneling microscopy to discover that the crucial oxygen dopants are periodically distributed, in correlation with local strain. Our picoscale investigation of the intra-unit-cell positions of all oxygen dopants provides essential structural input for a complete microscopic theory.

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preprint2013arXiv

Exploring Quasiparticles in High-Tc Cuprates Through Photoemission, Tunneling, and X-ray Scattering Experiments

One of the key challenges in the field of high-temperature superconductivity is understanding the nature of fermionic quasiparticles. Experiments consistently demonstrate the existence of a second energy scale, distinct from the d-wave superconducting gap, that persists above the transition temperature into the "pseudogap" phase. One common class of models relates this energy scale to the quasiparticle gap due to a competing order, such as the incommensurate "checkerboard" order observed in scanning tunneling microscopy (STM) and resonant elastic X-ray scattering (REXS). In this paper we show that these experiments are better described by identifying the second energy scale with the inverse lifetime of quasiparticles. We develop a minimal phenomenological model that allows us to quantitatively describe STM and REXS experiments and discuss their relation with photoemission spectroscopy. Our study refocuses questions about the nature of the pseudogap phase to the study of the origin of inelastic scattering.

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preprint2013arXiv

Imaging the Kondo Insulating Gap on SmB6

Topological insulators host spin-polarized surface states which robustly span the band gap and hold promise for novel applications. Recent theoretical predictions have suggested that topologically protected surface states may similarly span the hybridization gap in some strongly correlated heavy fermion materials, particularly SmB6. However, the process by which the Sm 4f electrons hybridize with the 5d electrons on the surface of SmB6, and the expected Fermi-level gap in the density of states out of which the predicted topological surface states must arise, have not been directly measured. We use scanning tunneling microscopy to conduct the first atomic resolution spectroscopic study of the cleaved surface of SmB6, and to reveal a robust hybridization gap which universally spans the Fermi level on four distinct surface morphologies despite shifts in the f band energy. Using a cotunneling model, we separate the density of states of the hybridized bands from which the predicted topological surface states must be disentangled. On all surfaces we observe residual spectral weight spanning the hybridization gap down to the lowest T, which is consistent with a topological surface state.

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preprint2013arXiv

Imaging the Nanoscale Band Structure of Topological Sb

Many promising building blocks of future electronic technology - including non-stoichiometric compounds, strongly correlated oxides, and strained or patterned films - are inhomogeneous on the nanometer length scale. Exploiting the inhomogeneity of such materials to design next-generation nanodevices requires a band structure probe with nanoscale spatial resolution. To address this demand, we report the first simultaneous observation and quantitative reconciliation of two candidate probes - Landau level spectroscopy and quasiparticle interference imaging - which we employ here to reconstruct the multi-component surface state band structure of the topological semimetal antimony(Sb). We thus establish the technique of band structure tunneling microscopy (BSTM), whose unique advantages include nanoscale access to non-rigid band structure deformation, empty state dispersion, and magnetic field dependent states. We use BSTM to elucidate the relationship between bulk conductivity and surface state robustness in topological materials, and to quantify essential metrics for spintronics applications.

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preprint2012arXiv

A quantum phase transition from triangular to stripe charge order in NbSe$_{2}$

The competition between proximate electronic phases produces a complex phenomenology in strongly correlated systems. In particular, fluctuations associated with periodic charge or spin modulations, known as density waves, may lead to exotic superconductivity in several correlated materials. However, density waves have been difficult to isolate in the presence of chemical disorder, and the suspected causal link between competing density wave orders and high temperature superconductivity is not understood. Here we use scanning tunneling microscopy to image a previously unknown unidirectional (stripe) charge density wave (CDW) smoothly interfacing with the familiar tri-directional (triangular) CDW on the surface of the stoichiometric superconductor NbSe$_2$. Our low temperature measurements rule out thermal fluctuations, and point to local strain as the tuning parameter for this quantum phase transition. We use this discovery to resolve two longstanding debates about the anomalous spectroscopic gap and the role of Fermi surface nesting in the CDW phase of NbSe$_2$. Our results highlight the importance of local strain in governing phase transitions and competing phenomena, and suggest a new direction of inquiry for resolving similarly longstanding debates in cuprate superconductors and other strongly correlated materials.

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preprint2011arXiv

Creating and modulating electronic states on noble metal surfaces: ultrathin Ag islands on Si(111)-7$\times$7 as a prototype

Various-thickness Ag islands were prepared on Si(111)-7$\times$7 using the one-step deposition at a high substrate temperature. An electronic state centered at -0.40$\sim$-0.15eV versus E$_{Fermi}$, detectable on the surface of the Ag islands thinner than 9 layers, was created by the electronic hybridization between Ag and Si at the Ag-Si interface. Scanning tunneling microscopy/spectroscopy and density functional theory revealed that the thickness of Ag islands determined the strength of the hybridization, leading to a modulation to the energy and intensity of the state on the surface.

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