arXiv subjects

Jingyuan Li

Publications and source records attributed to Jingyuan Li.

66 records · Page 4Linked to original sources

arXiv2023

Visualization of Oxygen Vacancies and Self-doped Ligand Holes in La3Ni2O7-δ

The recent discovery of superconductivity in La3Ni2O7-δ under high pressure with a transition temperature around 80 K has sparked extensive experimental and theoretical efforts. Several key questions regarding the pairing mechanism remain to be answered, such as the most relevant atomic orbitals and the role of atomic deficiencies. Here, we develop a new energy-filtered multislice electron ptychography technique, assisted with electron energy loss spectroscopy, to address these critical issues. Oxygen vacancies are directly visualized and are found to primarily occupy the inner apical sites, which have been proposed to be crucial to superconductivity. We precisely determine the nanoscale stoichiometry and its correlation to the oxygen K edge spectra, which reveals a significant inhomogeneity in the oxygen content and electronic structure within the sample. The spectroscopic results also unveil that stoichiometric La3Ni2O7 is in the charge-transfer regime, with holes that are self-doped from Ni sites into O sites. The outer apical oxygen is found to be less relevant to the low-energy physics and can be safely disregarded in theoretical models. These observations will assist in further development and understanding of superconducting nickelate materials. Our imaging technique for quantifying atomic deficiencies can also be widely applied in materials science and condensed matter physics.

cond-mat.supr-con↗

arXiv2023

An approximate operator-based learning method for the numerical solutions of stochastic differential equations

Stochastic differential equation (SDE in short) solvers find numerous applications across various fields. However, in practical simulations, we usually resort to using Ito-Taylor series-based methods like the Euler-Maruyama method. These methods often suffer from the limitation of fixed time scales and recalculations for different Brownian motions, which lead to computational inefficiency, especially in generative and sampling models. To address these issues, we propose a novel approach: learning a mapping between the solution of SDE and corresponding Brownian motion. This mapping exhibits versatility across different scales and requires minimal paths for training. Specifically, we employ the DeepONet method to learn a nonlinear mapping. And we also assess the efficiency of this method through simulations conducted at varying time scales. Additionally, we evaluate its generalization performance to verify its good versatility in different scenarios.

math.ST↗

arXiv2023

Superconductivity near 80 Kelvin in single crystals of La3Ni2O7 under pressure

High-transition-temperature (high-T_c) superconductivity in cuprates has been discovered for more than three decades, but the underlying mechanism remains a mystery. Cuprates are the only unconventional superconducting family that host bulk superconductivity with T_cs above the liquid nitrogen boiling temperature at 77 Kelvin. Here we report an observation of superconductivity in single crystals of La3Ni2O7 with a maximum T_c of 80 Kelvin at pressures between 14.0-43.5 gigapascals using high-pressure resistance and mutual inductive magnetic susceptibility measurements. The superconducting phase under high pressure exhibits an orthorhombic structure of Fmmm space group with the 3d_(x^2-y^2 ) and 3d_(z^2 ) orbitals of Ni cations strongly interacting with oxygen 2p orbitals. Our density functional theory calculations suggest the superconductivity emerges coincidently with the metallization of the σ-bonding bands under the Fermi level, consisting of the 3d_(z^2 ) orbitals with the apical oxygens connecting Ni-O bilayers. Thus, our discoveries not only reveal important clues for the high-T_c superconductivity in this Ruddlesden-Popper double-layered perovskite nickelates but also provide a new family of compounds to investigate the high-T_c superconductivity mechanism.

cond-mat.supr-con↗

arXiv2023

Anisotropic magnetism and electronic properties of the kagome metal SmV6Sn6

Kagome magnets are expected to feature emergent properties due to the interplays among geometry, magnetism, electronic correlation, and band topology. The magnetism and topological electronic states can be tuned via the rare earth engineering in RV6Sn6 kagome metals, where R is a rare earth element. Herein, we present the synthesis and characterization of SmV6Sn6, a metal with two-dimensional kagome nets of vanadium and frustrated triangular Sm lattice. Partial of the Sm atoms are shifted from the normal R positions by c/2 along the c axis. Magnetic measurements reveal obvious anisotropy, where the easy magnetic axis is within the ab plane. Electronic transports show multiband behaviors below 200 K. Density functional theory calculations find that the electronic structure of SmV6Sn6 hosts flat bands, Dirac cone, and saddle point arising from the V-3d electrons near the Fermi level. No evidence for the existence of charge density wave or magnetic order down to 2 K can be observed. Thus, SmV6Sn6 can be viewed as a modest disordered derivative of the RV6Sn6 structure, in which the disordered rare earth ions can suppress the magnetic order and charge density wave in the RV6Sn6 kagome family.

cond-mat.str-el↗

arXiv2021

Pressure-tuned intralayer exchange in superlattice-like MnBi2Te4/(Bi2Te3)n topological insulators

The magnetic structures of MnBi2Te4(Bi2Te3)n can be manipulated by tuning the interlayer coupling via the number of Bi2Te3 spacer layers n, while the intralayer ferromagnetic (FM) exchange coupling is considered too robust to control. By applying hydrostatic pressure up to 3.5 GPa, we discover opposite responses of magnetic properties for n = 1 and 2. MnBi4Te7 stays at A-type antiferromagnetic (AFM) phase with a decreasing Néel temperature and an increasing saturation field. In sharp contrast, MnBi6Te10 experiences a phase transition from A-type AFM to a quasi-two-dimensional FM state with a suppressed saturation field under pressure. First-principles calculations reveal the essential role of intralayer exchange coupling from lattice compression in determining these magnetic properties. Such magnetic phase transition is also observed in 20% Sb-doped MnBi6Te10 due to the in-plane lattice compression.

cond-mat.mtrl-sci↗

arXiv2020

Sparse Semi-Supervised Action Recognition with Active Learning

Current state-of-the-art methods for skeleton-based action recognition are supervised and rely on labels. The reliance is limiting the performance due to the challenges involved in annotation and mislabeled data. Unsupervised methods have been introduced, however, they organize sequences into clusters and still require labels to associate clusters with actions. In this paper, we propose a novel approach for skeleton-based action recognition, called SESAR, that connects these approaches. SESAR leverages the information from both unlabeled data and a handful of sequences actively selected for labeling, combining unsupervised training with sparsely supervised guidance. SESAR is composed of two main components, where the first component learns a latent representation for unlabeled action sequences through an Encoder-Decoder RNN which reconstructs the sequences, and the second component performs active learning to select sequences to be labeled based on cluster and classification uncertainty. When the two components are simultaneously trained on skeleton-based action sequences, they correspond to a robust system for action recognition with only a handful of labeled samples. We evaluate our system on common datasets with multiple sequences and actions, such as NW UCLA, NTU RGB+D 60, and UWA3D. Our results outperform standalone skeleton-based supervised, unsupervised with cluster identification, and active-learning methods for action recognition when applied to sparse labeled samples, as low as 1% of the data.

cs.CV↗

arXiv2020

Recurrent Feature Reasoning for Image Inpainting

Existing inpainting methods have achieved promising performance for recovering regular or small image defects. However, filling in large continuous holes remains difficult due to the lack of constraints for the hole center. In this paper, we devise a Recurrent Feature Reasoning (RFR) network which is mainly constructed by a plug-and-play Recurrent Feature Reasoning module and a Knowledge Consistent Attention (KCA) module. Analogous to how humans solve puzzles (i.e., first solve the easier parts and then use the results as additional information to solve difficult parts), the RFR module recurrently infers the hole boundaries of the convolutional feature maps and then uses them as clues for further inference. The module progressively strengthens the constraints for the hole center and the results become explicit. To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR. Empirically, we first compare the proposed RFR-Net with existing backbones, demonstrating that RFR-Net is more efficient (e.g., a 4\% SSIM improvement for the same model size). We then place the network in the context of the current state-of-the-art, where it exhibits improved performance. The corresponding source code is available at: https://github.com/jingyuanli001/RFR-Inpainting

cs.CV↗

arXiv2020

Iterate & Cluster: Iterative Semi-Supervised Action Recognition

We propose a novel system for active semi-supervised feature-based action recognition. Given time sequences of features tracked during movements our system clusters the sequences into actions. Our system is based on encoder-decoder unsupervised methods shown to perform clustering by self-organization of their latent representation through the auto-regression task. These methods were tested on human action recognition benchmarks and outperformed non-feature based unsupervised methods and achieved comparable accuracy to skeleton-based supervised methods. However, such methods rely on K-Nearest Neighbours (KNN) associating sequences to actions, and general features with no annotated data would correspond to approximate clusters which could be further enhanced. Our system proposes an iterative semi-supervised method to address this challenge and to actively learn the association of clusters and actions. The method utilizes latent space embedding and clustering of the unsupervised encoder-decoder to guide the selection of sequences to be annotated in each iteration. Each iteration, the selection aims to enhance action recognition accuracy while choosing a small number of sequences for annotation. We test the approach on human skeleton-based action recognition benchmarks assuming that only annotations chosen by our method are available and on mouse movements videos recorded in lab experiments. We show that our system can boost recognition performance with only a small percentage of annotations. The system can be used as an interactive annotation tool to guide labeling efforts for 'in the wild' videos of various objects and actions to reach robust recognition.

cs.CV↗

arXiv2018

A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation

Traditional neural language models tend to generate generic replies with poor logic and no emotion. In this paper, a syntactically constrained bidirectional-asynchronous approach for emotional conversation generation (E-SCBA) is proposed to address this issue. In our model, pre-generated emotion keywords and topic keywords are asynchronously introduced into the process of decoding. It is much different from most existing methods which generate replies from the first word to the last. Through experiments, the results indicate that our approach not only improves the diversity of replies, but gains a boost on both logic and emotion compared with baselines.

cs.CL↗

arXiv2015

Computational Studies about the Interactions of Nanomaterials with Proteins and their Impacts

Intensive concerns about the biosafety of nanomaterials demand the systematic study of the mechanisms about their biological effects. Many biological effects can be attributed to the interaction of nanomaterials with protein and their impacts on protein function. On the other hand, nanomaterials exhibit the potential in a variety of biomedical applications, many of which also involve the direct interaction with protein. In this paper, we review some recent computational studies about this subject, especially the interaction of carbon and gold nanomaterials. Besides the hydrophobic and π-stacking interactions, the interaction mode of carbon nanomaterials can be regulated by their functional groups. And the coating of gold nanomaterials also adjusts their interaction mode, in addition to the coordination interaction with cysteine's sulfur group and histidine's imidazole group. Moreover, nanomaterials can interact with multiple proteins and the impacts on protein activity are attributed to a wide spectrum of mechanisms. The findings about the mechanisms of nanomaterial-protein interaction can further guide the design and development of nanomaterial to realize the applications in disease diagnosis and treatment.

physics.bio-ph↗

arXiv2012

Are hydrodynamic interactions important in the kinetics of hydrophobic collapse?

We study the kinetics of assembly of two plates of varying hydrophobicity, including cases where drying occurs and water strongly solvates the plate surfaces. The potential of mean force and molecular-scale hydrodynamics are computed from molecular dynamics simulations in explicit solvent as a function of particle separation. In agreement with our recent work on nanospheres [J. Phys. Chem. B 116, 378 (2012)] regions of high friction are found to be engendered by large and slow solvent fluctuations. These slow fluctuations can be due to either drying or confinement. The mean first passage times for assembly are computed by means of molecular dynamics simulations in explicit solvent and by Brownian dynamics simulations along the reaction path. Brownian dynamics makes use of the potential of mean force and hydrodynamic profile that we determined. Surprisingly, we find reasonable agreement between full scale molecular dynamics and Brownian dynamics, despite the role of slow solvent relaxation in the assembly process. We found that molecular scale hydrodynamic interactions are essential in describing the kinetics of assembly.

cond-mat.soft↗

arXiv2011

Interplay between hydrodynamics and the free energy surface in the assembly of nanoscale hydrophobes

Solvent plays an important role in the relative motion of nanoscopic bodies, and the study of such phenomena can help elucidate the mechanism of hydrophobic assembly, as well as the influence of solvent-mediated effects on in vivo motion in crowded cellular environments. Here we study important aspects of this problem within the framework of Brownian dynamics. We compute the free energy surface that the Brownian particles experience and their hydrodynamic interactions from molecular dynamics simulations in explicit solvent. We find that molecular scale effects dominate at short distances, thus giving rise to deviations from the predictions of continuum hydrodynamic theory. Drying phenomena, solvent layering, and fluctuations engender distinct signatures of the molecular scale. The rate of assembly in the diffusion-controlled limit is found to decrease from molecular scale hydrodynamic interactions, in opposition to the free energy driving force for hydrophobic assembly, and act to reinforce the influence of the free energy surface on the association of more hydrophilic bodies.

cond-mat.stat-mech↗