arXiv subjects

Yubo Zhang

Publications and source records attributed to Yubo Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2022

Prediction of protected band edge states and dielectric tunable quasiparticle and excitonic properties of monolayer MoSi$_2$N$_4$

The electronic structure of two-dimensional (2D) materials are inherently prone to environmental perturbations, which may pose significant challenges to their applications in electronic or optoelectronic devices. A 2D material couples with its environment through two mechanisms: local chemical coupling and nonlocal dielectric screening effects. The local chemical coupling is often difficult to predict or control experimentally. Nonlocal dielectric screening, on the other hand, can be tuned by choosing the substrates or layer thickness in a controllable manner. Therefore, a compelling 2D electronic material should offer band edge states that are robust against local chemical coupling effects. Here it is demonstrated that the recently synthesized MoSi$_2$N$_4$ is an ideal 2D semiconductor with robust band edge states protected from capricious environmental chemical coupling effects. Detailed many-body perturbation theory calculations are carried out to illustrate how the band edge states of MoSi$_2$N$_4$ are shielded from the direct chemical coupling effects, but its quasiparticle and excitonic properties can be modulated through the nonlocal dielectric screening effects. This unique property, together with the moderate band gap and the thermodynamic and mechanical stability of this material, paves the way for a range of applications of MoSi$_2$N$_4$ in areas including energy, 2D electronics, and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2022

Critical role of magnetic moments in heavy-fermion materials: revisiting mysteries of SmB$_{6}$

Heavy-fermion family exhibits fascinating and often puzzling properties due to the presence of open-shell $f$ ions and the complexity of the associated charge, orbital, and spin degrees of freedom. SmB$_6 $ is a prototypical heavy-fermion compound that is electrically insulating but yet it displays quantum oscillations, which are a telltale signature of the metallic state. Adding to the enigma is the possibility that SmB$_6$ is a topological Kondo insulator. Here, by treating the spin degree of freedom on an equal footing with other degrees of freedom using the parameter-free strongly-constrained and appropriately-normed (SCAN) density functional, we explore the ground-state electronic structure of SmB$_{6}$. A number of competing magnetic phases lying very closely in energy are found, indicating the key role of spin fluctuations in the material. The computed band structure, crystal-field splittings in the $f$-electron complex, the heavy effective electron mass at the Fermi energy, and the large specific heat are all in good agreement with the corresponding experimental results. In particular, our predicted FS explains the experimentally observed bulk quantum oscillations as well as the low electrical conductivity of SmB$_{6}$. The topological Kondo state of SmB$_6$ is shown to be robust regardless of its magnetic configuration. The excellent performance of SCAN in heavy-fermion systems is explained in terms of its ability to treat self-interaction errors and symmetry breaking within the framework of the density functional theory. Our study provides a new approach for modeling heavy-fermion materials.

cond-mat.str-el↗

arXiv2022

Doctor Recommendation in Online Health Forums via Expertise Learning

Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task. To better help patients, this paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise. While most prior work in recommendation focuses on modeling target users from their past behavior, we can only rely on the limited words in a query to infer a patient's needs for privacy reasons. For doctor modeling, we study the joint effects of their profiles and previous dialogues with other patients and explore their interactions via self-learning. The learned doctor embeddings are further employed to estimate their capabilities of handling a patient query with a multi-head attention mechanism. For experiments, a large-scale dataset is collected from Chunyu Yisheng, a Chinese online health forum, where our model exhibits the state-of-the-art results, outperforming baselines only consider profiles and past dialogues to characterize a doctor.

cs.CL↗

arXiv2022

Sensitivity of the electronic and magnetic structures of cuprate superconductors to density functional approximations

We discuss the crystal, electronic, and magnetic structures of $\mathrm{La_{2-x}Sr_{x}CuO_{4}}$ (LSCO) for $x=0.0$ and $x=0.25$ employing 13 density functional approximations, representing the local, semi-local, and hybrid exchange-correlation approximations within the Perdew-Schmidt hierarchy. The meta-generalized gradient approximation (meta-GGA) class of functionals is found to perform well in capturing the key properties of LSCO, a prototypical high-temperature cuprate superconductor. In contrast, the local-spin-density approximation, GGA, and the hybrid density functional fail to capture the metal-insulator transition under doping.

cond-mat.supr-con↗

arXiv2021

ColDE: A Depth Estimation Framework for Colonoscopy Reconstruction

One of the key elements of reconstructing a 3D mesh from a monocular video is generating every frame's depth map. However, in the application of colonoscopy video reconstruction, producing good-quality depth estimation is challenging. Neural networks can be easily fooled by photometric distractions or fail to capture the complex shape of the colon surface, predicting defective shapes that result in broken meshes. Aiming to fundamentally improve the depth estimation quality for colonoscopy 3D reconstruction, in this work we have designed a set of training losses to deal with the special challenges of colonoscopy data. For better training, a set of geometric consistency objectives was developed, using both depth and surface normal information. Also, the classic photometric loss was extended with feature matching to compensate for illumination noise. With the training losses powerful enough, our self-supervised framework named ColDE is able to produce better depth maps of colonoscopy data as compared to the previous work utilizing prior depth knowledge. Used in reconstruction, our network is able to reconstruct good-quality colon meshes in real-time without any post-processing, making it the first to be clinically applicable.

eess.IV↗

arXiv2021

Discovering Hidden Physics Behind Transport Dynamics

Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most commonly by injecting a tracer. An advection-diffusion equation is widely used to describe these transport phenomena. Our goal is estimating the underlying physics of advection-diffusion equations, expressed as velocity and diffusion tensor fields. We propose a learning framework (YETI) building on an auto-encoder structure between 2D and 3D image time-series, which incorporates the advection-diffusion model. To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields. Instead of directly learning these velocity and diffusion tensor fields, we introduce representations that assure incompressible flow and symmetric positive semi-definite diffusion fields and demonstrate the additional benefits of these representations on improving estimation accuracy. We further use transfer learning to apply YETI on a public brain magnetic resonance (MR) perfusion dataset of stroke patients and show its ability to successfully distinguish stroke lesions from normal brain regions via the estimated velocity and diffusion tensor fields.

eess.IV↗

arXiv2021

Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces

High screening coverage during colonoscopy is crucial to effectively prevent colon cancer. Previous work has allowed alerting the doctor to unsurveyed regions by reconstructing the 3D colonoscopic surface from colonoscopy videos in real-time. However, the lighting inconsistency of colonoscopy videos can cause a key component of the colonoscopic reconstruction system, the SLAM optimization, to fail. In this work we focus on the lighting problem in colonoscopy videos. To successfully improve the lighting consistency of colonoscopy videos, we have found necessary a lighting correction that adapts to the intensity distribution of recent video frames. To achieve this in real-time, we have designed and trained an RNN network. This network adapts the gamma value in a gamma-correction process. Applied in the colonoscopic surface reconstruction system, our light-weight model significantly boosts the reconstruction success rate, making a larger proportion of colonoscopy video segments reconstructable and improving the reconstruction quality of the already reconstructed segments.

cs.CV↗

arXiv2020

Generalized Sorting with Predictions

Generalized sorting problem, also known as sorting with forbidden comparisons, was first introduced by Huang et al. together with a randomized algorithm which requires $\tilde O(n^{3/2})$ probes. We study this problem with additional predictions for all pairs of allowed comparisons as input. We propose a randomized algorithm which uses $O(n \log n+w)$ probes with high probability and a deterministic algorithm which uses $O(nw)$ probes, where $w$ is the number of mistakes made by prediction.

cs.DS↗

arXiv2020

Spectral Clustering with Smooth Tiny Clusters

Spectral clustering is one of the most prominent clustering approaches. The distance-based similarity is the most widely used method for spectral clustering. However, people have already noticed that this is not suitable for multi-scale data, as the distance varies a lot for clusters with different densities. State of the art(ROSC and CAST ) addresses this limitation by taking the reachability similarity of objects into account. However, we observe that in real-world scenarios, data in the same cluster tend to present in a smooth manner, and previous algorithms never take this into account. Based on this observation, we propose a novel clustering algorithm, which con-siders the smoothness of data for the first time. We first divide objects into a great many tiny clusters. Our key idea is to cluster tiny clusters, whose centers constitute smooth graphs. Theoretical analysis and experimental results show that our clustering algorithm significantly outperforms state of the art. Although in this paper, we singly focus on multi-scale situations, the idea of data smoothness can certainly be extended to any clustering algorithms

cs.LG↗

arXiv2020

Remarkable band gap renormalization via dimensionality of the layered material C3B

Layer-dependent electronic and structural properties of emerging graphitic carbon boron compound C3B are investigated using both density functional theory and the GW approximation. We discover that, in contrast to a moderate quasiparticle band gap of 2.55 eV for monolayer C3B, the calculated quasiparticle band gap of perfectly stacked bulk phase C3B is as small as 0.17 eV. Therefore, our results suggest that layered material C3B exhibits a remarkably large band gap renormalization of over 2.3 eV due to the interlayer coupling and screening effects, providing a single material with an extraordinary band gap tunability. The quasiparticle band gap of monolayer C3B is also over 1.0 eV larger than that of C3N, a closely related two-dimensional semiconductor. Detailed inspections of the near-edge electronic states reveal that the conduction and valence band edges of C3B are formed by out-of-plane and in-plane electronic states, respectively, suggesting an interesting possibility of tuning the band edges of such layered material separately by modulating the in-plane and out-of-plane interactions.

cond-mat.mtrl-sci↗

arXiv2020

Diagnosing the Environment Bias in Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-by-step navigational instructions are crucial when the agent is navigating new environments about which it has no prior knowledge. Most recent works that study VLN observe a significant performance drop when tested on unseen environments (i.e., environments not used in training), indicating that the neural agent models are highly biased towards training environments. Although this issue is considered as one of the major challenges in VLN research, it is still under-studied and needs a clearer explanation. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias. We observe that neither the language nor the underlying navigational graph, but the low-level visual appearance conveyed by ResNet features directly affects the agent model and contributes to this environment bias in results. According to this observation, we explore several kinds of semantic representations that contain less low-level visual information, hence the agent learned with these features could be better generalized to unseen testing environments. Without modifying the baseline agent model and its training method, our explored semantic features significantly decrease the performance gaps between seen and unseen on multiple datasets (i.e. R2R, R4R, and CVDN) and achieve competitive unseen results to previous state-of-the-art models. Our code and features are available at: https://github.com/zhangybzbo/EnvBiasVLN

cs.CL↗

arXiv2020

Symmetry-Breaking Polymorphous Descriptions for Complex Materials without Interelectronic U

Correlated materials with open-shell d- and f-ions having degenerate band edge states show a rich variety of interesting properties ranging from metal-insulator transition to unconventional superconductivity. The textbook view for the electronic structure of these materials is that mean-field approaches are inappropriate, as the interelectronic interaction U is required to open a band gap between the occupied and unoccupied degenerate states while retaining symmetry. We show that the latter scenario often defining what Mott insulators are, is in fact not needed for the 3d binary oxides MnO, FeO, CoO, and NiO. The mean-field band theory can indeed lift such degeneracies in the binaries when nontrivial unit cell representations (polymorphous networks) are allowed to break symmetries, in conjunction with a recently developed non-empirical exchange and correlation density-functional without an on-site interelectronic interaction U. We explain how density-functional theory (DFT) in the polymorphous representation achieves band gap opening in correlated materials through a separate mechanism to the Mott-Hubbard approach. We show the method predicts magnetic moments and gaps for the four binary monoxides in both the antiferromagnetic and paramagnetic phases, offering an effective alternative to symmetry-conserving approaches for studying a range of functionalities in open d- and f-shell complex materials.

cond-mat.str-el↗

arXiv2020

First-Principles Calculation of Spin and Orbital Contributions to Magnetically Ordered Moments in Sr$_{2}$IrO$_{4}$

We show how an accurate first-principles treatment of the canted-antiferromagnetic ground state of Sr$_2$IrO$_4$, a prototypical $5d$ correlated spin-orbit coupled material, can be obtained without invoking any free parameters such as the Hubbard U or tuning the spin-orbit coupling strength. Our theoretically predicted iridium magnetic moment of 0.250 $μ_B$, canted by 12.6$^{\circ}$ off the a-axis, is in accord with experimental results. By resolving the magnetic moments into their spin and orbital components, we show that our theoretically obtained variation of the magnetic scattering amplitude $ $ as a function of the polarization angle is consistent with recent non-resonant magnetic x-ray scattering measurements. The computed value of the band gap (55 meV) is also in line with the corresponding experimental values. A comparison of the band structure to that of the cuprates suggests the presence of incommensurate charge-density wave phases in Sr$_{2}$IrO$_{4}$.

cond-mat.str-el↗

arXiv2020

Exceptionally large anomalous Hall effect due to anticrossing of spin-split bands in the antiferromagnetic half-Heusler compound TbPtBi

We have investigated magnetotransport properties and the topological electronic structure of the half-Heusler compound TbPtBi. Our experiments reveal an exceptionally large anomalous Hall effect (AHE) in the canted antiferromagnetic state of TbPtBi with the anomalous Hall angle (AHA) reaching ~0.68-0.76, which is a few times larger than the previously reported record in GdPtBi. First-principles electronic structure and the associated anomalous Hall conductivity were computed in order to interpret the experimental results. Our analysis shows that the AHE in TbPtBi does not originate from the Weyl points but that it is driven by the large net Berry curvature produced by the anticrossing of spin-split bands near the Fermi level in TbPtBi.

cond-mat.mes-hall↗

arXiv2020

Spinon Excitations in the Quasi-1D S = 1/2 Chain Cs4CuSb2Cl12

The spin-1/2 Heisenberg antiferromagnetic chain is ideal for realizing one of the simplest gapless quantum spin-liquids (QSLs), supporting a many-body ground state whose elementary excitations are fractional fermionic excitations called spinons. Here we report the discovery of such a 1D QSL in Cs4CuSb2Cl12. Compared to previously reported S = 1/2 1D chains, this material possesses a wider temperature range over which the QSL state is stabilized. We identify spinon excitations extending at T > 0.8 K, with a large T-linear contribution to the specific heat, gamma = 31.5(2) mJ mol-1 K-2 which contribute itinerantly to thermal transport up to temperatures as high as T = 35 K. At T = 0.7 K, we find a second-order phase transition, suggesting a weak spin-Peierls transition that is unchanged by a 5 T magnetic field. Cs4CuSb2Cl12 reveals new phenomenology deep in the 1D QSL regime, supporting a gapped QSL phase over a wide temperature range compared to many other experimental realizations.

cond-mat.str-el↗

arXiv2019

Observation of a Room Temperature Two-dimensional Ferroelectric Metal

Materials with reduced dimensions have been shown to host a wide variety of exotic properties and novel quantum states that often defy textbook wisdom1-5. Ferroelectric polarization and metallicity are well-known examples of mutually exclusive properties that cannot coexist in bulk solids because the net electric field in a metal can be fully screened by free electrons6. An atomically thin metallic layer capped by insulating layers has shown decent conductivity at room temperature7. Moreover, a penetrating polarization field can be employed to induce an ion displacement and create an intrinsic polarization in the metallic layer. Here we demonstrate that a ferroelectric metal can be artificially synthesized through imposing a strong polarization field in the form of ferroelectric/unit-cell-thin metal superlattices. In this way the symmetry of an atomically thin conductive layer can be broken and manipulated by a neighboring polar field, thereby forming a two-dimensional (2D) ferroelectric metal. The fabricated of (SrRuO3)1/(BaTiO3)10 superlattices exhibit ferroelectric polarization in an atomically thin layer with metallic conductivity at room temperature. A multipronged investigation combining structural analyses, electrical measurements, and first-principles electronic structure calculations unravels the coexistence of 2D electrical conductivity in the SrRuO3 monolayer accompanied by the electric polarization. Such 2D ferroelectric metal paves a novel way to engineer a quantum multi-state with unusual coexisting properties, such as ferroelectrics, ferromagnetics and metals, manipulated by external fields8,9.

cond-mat.mtrl-sci↗

arXiv2019

A first-principles-based study of the thermodynamics of competing low-energy states in correlated materials: Example of cuprates

We demonstrate how first-principles calculations of many competing low-energy states of a correlated material, here a cuprate, can be used to develop a thermodynamic model of Mott and pseudogap transitions in terms of magnetic short-range order. Mott physics is found in this picture to be driven by an unbinding of the antiphase domain walls, while the pseudogap phenomenon represents local moment formation. We provide explanations for nematicity and Fermi arc formation, and find a striking correspondence with many-body perturbation theory predictions.

cond-mat.str-el↗

arXiv2019

A Study on Action Detection in the Wild

The recent introduction of the AVA dataset for action detection has caused a renewed interest to this problem. Several approaches have been recently proposed that improved the performance. However, all of them have ignored the main difficulty of the AVA dataset - its realistic distribution of training and test examples. This dataset was collected by exhaustive annotation of human action in uncurated videos. As a result, the most common categories, such as `stand' or `sit', contain tens of thousands of examples, whereas rare ones have only dozens. In this work we study the problem of action detection in a highly-imbalanced dataset. Differently from previous work on handling long-tail category distributions, we begin by analyzing the imbalance in the test set. We demonstrate that the standard AP metric is not informative for the categories in the tail, and propose an alternative one - Sampled AP. Armed with this new measure, we study the problem of transferring representations from the data-rich head to the rare tail categories and propose a simple but effective approach.

cs.CV↗