arXiv subjects

Yi Lu

Publications and source records attributed to Yi Lu.

At least 181 records · Page 10Linked to original sources

arXiv2022

Magnetism in doped infinite-layer NdNiO2 studied by combined density functional theory and dynamical mean-field theory

The recent observation of superconductivity in infinite-layer nickelates has brought intense debate on the established knowledge of unconventional superconductivity based on the cuprates. Despite many similarities, the nickelates differ from the cuprates in many characteristics, the most notable one among which is the magnetism. Instead of a canonical antiferromagnetic Mott insulator as the undoped cuprates, from which the superconductivity is generally believed to arise upon doping, the undoped nickelates show no sign of magnetic ordering in experiments. Through a combined density functional theory, dynamical mean-field theory, and model study, we show that although the increased energy splitting between O-$p$ orbital and Cu/Ni-$d$ orbital ($Δ_{dp}$) results in larger magnetic moment in nickelates, it also leads to stronger antiferromagnetism/ferromagnetism competition, and weaker magnetic exchange coupling. Meanwhile, the self-doping effect caused by Nd-$d$ orbital screens the magnetic moment of Ni. The Janus-faced effect of $Δ_{dp}$ and self-doping effect together give a systematic understanding of magnetic behavior in nickelates and explain recent experimental observations.

cond-mat.supr-con↗

arXiv2022

Matrix Syncer -- A Multi-chain Data Aggregator For Supporting Blockchain-based Metaverses

Due to the rising complexity of the metaverse's business logic and the low-latency nature of the metaverse, developers typically encounter the challenge of effectively reading, writing, and retrieving historical on-chain data in order to facilitate their functional implementations at scale. While it is true that accessing blockchain states is simple, more advanced real-world operations such as search, aggregation, and conditional filtering are not available when interacting directly with blockchain networks, particularly when dealing with requirements for on-chain event reflection. We offer Matrix Syncer, the ultimate middleware that bridges the data access gap between blockchains and end-user applications. Matrix Syncer is designed to facilitate the consolidation of on-chain information into a distributed data warehouse while also enabling customized on-chain state transformation for a scalable storage, access, and retrieval. It offers a unified layer for both on- and off-chain state, as well as a fast and flexible atomic query. Matrix Syncer is easily incorporated into any infrastructure to aggregate data from various blockchains concurrently, such as Ethereum and Flow. The system has been deployed to support several metaverse projects with a total value of more than $15 million USD.

cs.MM↗

arXiv2022

An Extended Halo-based Group/Cluster finder: application to the DESI legacy imaging surveys DR8

We extend the halo-based group finder developed by \citet[][]{Yang2005a} to use data {\it simultaneously} with either photometric or spectroscopic redshifts. A mock galaxy redshift survey constructed from a high-resolution N-body simulation is used to evaluate the performance of this extended group finder. For galaxies with magnitude ${\rm z\le 21}$ and redshift $0<z\le 1.0$ in the DESI legacy imaging surveys (the Legacy Surveys), our group finder successfully identifies more than 60\% of the members in about $90\%$ of halos with mass $\ga 10^{12.5}\msunh$. Detected groups with mass $\ga 10^{12.0}\msunh$ have a purity (the fraction of true groups) greater than 90\%. The halo mass assigned to each group has an uncertainty of about 0.2 dex at the high mass end $\ga 10^{13.5}\msunh$ and 0.40 dex at the low mass end. Groups with more than 10 members have a redshift accuracy of $\sim 0.008$. We apply this group finder to the Legacy Surveys DR8 and find 5.2 Million groups with at least 3 members. About 387,000 of these groups have at least 10 members. The resulting catalog containing 3D coordinates, richness, halo masses, and total group luminosities, is made publicly available.

astro-ph.GA↗

arXiv2022

DePS: An improved deep learning model for de novo peptide sequencing

De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of the represetative models. In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing even with missing signal peaks or large number of noisy peaks in tandem mass spectrometry data. It is showed that, for the same test set of DeepNovoV2, the DePS model achieved excellent results of 74.22%, 74.21% and 41.68% for amino acid recall, amino acid precision and peptide recall respectively. Furthermore, the results suggested that DePS outperforms DeepNovoV2 on the cross species dataset.

q-bio.QM↗

arXiv2022

Active Learning Over Multiple Domains in Natural Language Tasks

Studies of active learning traditionally assume the target and source data stem from a single domain. However, in realistic applications, practitioners often require active learning with multiple sources of out-of-distribution data, where it is unclear a priori which data sources will help or hurt the target domain. We survey a wide variety of techniques in active learning (AL), domain shift detection (DS), and multi-domain sampling to examine this challenging setting for question answering and sentiment analysis. We ask (1) what family of methods are effective for this task? And, (2) what properties of selected examples and domains achieve strong results? Among 18 acquisition functions from 4 families of methods, we find H-Divergence methods, and particularly our proposed variant DAL-E, yield effective results, averaging 2-3% improvements over the random baseline. We also show the importance of a diverse allocation of domains, as well as room-for-improvement of existing methods on both domain and example selection. Our findings yield the first comprehensive analysis of both existing and novel methods for practitioners faced with multi-domain active learning for natural language tasks.

cs.CL↗

arXiv2022

Stabilization of three-dimensional charge order through interplanar orbital hybridization in Pr$_x$Y$_{1-x}$Ba$_2$Cu$_3$O$_{6+δ}$

The shape of 3$d$-orbitals often governs the electronic and magnetic properties of correlated transition metal oxides. In the superconducting cuprates, the planar confinement of the $d_{x^2-y^2}$ orbital dictates the two-dimensional nature of the unconventional superconductivity and a competing charge order. Achieving orbital-specific control of the electronic structure to allow coupling pathways across adjacent planes would enable direct assessment of the role of dimensionality in the intertwined orders. Using Cu-$L_3$ and Pr-$M_5$ resonant x-ray scattering and first-principles calculations, we report a highly correlated three-dimensional charge order in Pr-substituted YBa$_2$Cu$_3$O$_{7}$, where the Pr $f$-electrons create a direct orbital bridge between CuO$_2$ planes. With this, we demonstrate that interplanar orbital engineering can be used to surgically control electronic phases in correlated oxides and other layered materials.

cond-mat.str-el↗

arXiv2022

Nuclear states projected from a pair condensate

Atomic nuclei exhibit deformation, pairing correlations, and rotational symmetries. To meet these competing demands in a computationally tractable formalism, we revisit the use of general pair condensates with good particle number as a trial wave function for even-even nuclei. After minimizing the energy of the condensate, we project out states with good angular momentum with a fast projection technique, allowing for general triaxial deformations. To show applicability, we present example calculations from pair condensates in several model spaces, and compare against projected Hartree-Fock and full configuration-interaction shell model calculations. This approach successfully generates spherical, vibrational and rotational spectra, demonstrating potential for modeling medium- to heavy-mass nuclei.

nucl-th↗

arXiv2021

Chemical Freeze-out Parameters via a Non-perturbative QCD Approach

By analyzing the calculated baryon number susceptibility ratios ${χ_{1}^{B}}/{χ_{2}^{B}}$ and ${χ_{3}^{B}}/{χ_{1}^{B}}$ in two-flavor system via the Dyson-Schwinger equation approach of QCD, we determine the chemical freeze-out temperature and baryon chemical potential in cases of both thermodynamic limit and finite size. We calculate the center-of-mass energy dependence of the ${χ_{4}^{B}}/{χ_{2}^{B}}\, (κσ^{2})$ at the freeze-out line and find an excellent agreement with experimental data when taking into account the finite size effect. Our calculations indicate that the $κσ^{2}$ exhibits a nonmonotonic behavior in lower collision energy region. We also predict that the collision energy dependence of ${χ_{6}^{B}}/{χ_{2}^{B}}$ is nonmonotonic.

hep-ph↗

arXiv2021

MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets. We introduce Multilingual Knowledge Questions and Answers (MKQA), an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages (260k question-answer pairs in total). Answers are based on a heavily curated, language-independent data representation, making results comparable across languages and independent of language-specific passages. With 26 languages, this dataset supplies the widest range of languages to-date for evaluating question answering. We benchmark a variety of state-of-the-art methods and baselines for generative and extractive question answering, trained on Natural Questions, in zero shot and translation settings. Results indicate this dataset is challenging even in English, but especially in low-resource languages

cs.CL↗

arXiv2021

Joint Positioning and Tracking via NR Sidelink in 5G-Empowered Industrial IoT: Releasing the Potential of V2X Technology

The fifth generation (5G) mobile networks with enhanced connectivity and positioning capabilities play an increasingly important role in the development of automated vehicle-to-everything (V2X) and other advanced industrial Internet of Things (IoT) systems. In this article, we address the prospects of 5G New Radio (NR) sidelink based V2X networks and their applicability for increasing the situational awareness, in terms of continuous tracking of moving connected machines and vehicles, in industrial systems. For increased system flexibility and fast deployments, we assume that the locations of the so-called anchor nodes are unknown, and describe an extended Kalman filter-based joint positioning and tracking framework in which the locations of both the anchor nodes and the target nodes can be estimated simultaneously. We assess and demonstrate the achievable 3D positioning and tracking performance in the context of a realistic industrial warehouse facility, through extensive ray-tracing based evaluations at the 26 GHz NR band. Our findings show that when both angle-based and time-based measurements are utilized, reaching sub-1 meter accuracy is realistic and that the system is also relatively robust against different node geometries. Finally, several research challenges towards achieving robust, high-performance and cost-efficient positioning solutions are outlined and discussed, identifying various potential directions for future work.

eess.SP↗

arXiv2021

On the Transferability of Minimal Prediction Preserving Inputs in Question Answering

Recent work (Feng et al., 2018) establishes the presence of short, uninterpretable input fragments that yield high confidence and accuracy in neural models. We refer to these as Minimal Prediction Preserving Inputs (MPPIs). In the context of question answering, we investigate competing hypotheses for the existence of MPPIs, including poor posterior calibration of neural models, lack of pretraining, and "dataset bias" (where a model learns to attend to spurious, non-generalizable cues in the training data). We discover a perplexing invariance of MPPIs to random training seed, model architecture, pretraining, and training domain. MPPIs demonstrate remarkable transferability across domains achieving significantly higher performance than comparably short queries. Additionally, penalizing over-confidence on MPPIs fails to improve either generalization or adversarial robustness. These results suggest the interpretability of MPPIs is insufficient to characterize generalization capacity of these models. We hope this focused investigation encourages more systematic analysis of model behavior outside of the human interpretable distribution of examples.

cs.CL↗

arXiv2020

The Detection of Thoracic Abnormalities ChestX-Det10 Challenge Results

The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab. The challenge is divided into two rounds. In this paper, we present the results of 6 teams which reach the second round. The challenge adopts the ChestX-Det10 dateset proposed by the Deepwise AI Lab. ChestX-Det10 is the first chest X-Ray dataset with instance-level annotations, including 10 categories of disease/abnormality of 3,543 images. The annotations are located at https://github.com/Deepwise-AILab/ChestX-Det10-Dataset. In the challenge, we randomly split all data into 3001 images for training and 542 images for testing.

eess.IV↗

arXiv2020

Exact sum rules with approximate ground states

Electromagnetic and weak transitions tell us a great deal about the structure of atomic nuclei. Yet modeling transitions can be difficult: it is often easier to compute the ground state, if only as an approximation, than excited states. One alternative is through transition sum rules, in particular the non-energy-weighted and energy-weighted sum rules, which can be computed as expectation values of operators. We investigate by computing sum rules for a variety of nuclei, comparing the numerically exact full configuration-interaction shell model, as a reference, to Hartree-Fock, projected Hartree-Fock, and the nucleon pair approximation. These approximations yield reasonable agreement, which we explain by prior work on the systematics of transition moments.

nucl-th↗

arXiv2020

Populating HI gas in dark matter halos: I. method

We combine data from the Sloan Digital Sky Survey (SDSS) and the Arecibo Legacy Fast ALFA Survey (ALFALFA) to establish an empirical model for the HI gas content within dark matter halos. A cross-match between our SDSS DR7 galaxy group sample and the ALFALFA HI sources provides a catalog of 16,520 HI-galaxy pairs within 14,270 galaxy groups (halos). Using these matched pairs, we model the HI gas mass distributions within halos using two components: 1) {\it in situ} galaxy relations that involve the HI masses, colors $({\rm g-r})$ and stellar masses 2) an {\it ex situ} dependence of the HI mass on the halo mass/environment. We find that if we solely use galaxy associated scaling relations to predict the HI gas distribution (solely component 1), the number of HI detections is significantly over-predicted with respect the ALFALFA observations. We introduce a concept for the survival of the HI masses/members within halos of different masses labelled as the `efficiency' factor, in order to describe the probability that a halo has in retaining its HI detections. Taking the above consideration into account we construct a `halo based HI mass model' which does not only predict the HI masses of galaxies, but also yields similar number, stellar, halo mass and satellite fraction distributions to the HI detections retrieved from observational data.

astro-ph.GA↗

arXiv2020

ContourRend: A Segmentation Method for Improving Contours by Rendering

A good object segmentation should contain clear contours and complete regions. However, mask-based segmentation can not handle contour features well on a coarse prediction grid, thus causing problems of blurry edges. While contour-based segmentation provides contours directly, but misses contours' details. In order to obtain fine contours, we propose a segmentation method named ContourRend which adopts a contour renderer to refine segmentation contours. And we implement our method on a segmentation model based on graph convolutional network (GCN). For the single object segmentation task on cityscapes dataset, the GCN-based segmentation con-tour is used to generate a contour of a single object, then our contour renderer focuses on the pixels around the contour and predicts the category at high resolution. By rendering the contour result, our method reaches 72.41% mean intersection over union (IoU) and surpasses baseline Polygon-GCN by 1.22%.

cs.CV↗

arXiv2020

Polarity induced electronic and atomic reconstruction at NdNiO2/SrTiO3 interfaces

Superconductivity has recently been observed in Sr-doped NdNiO2 films grown on SrTiO3. Whether it is caused by or related to the interface remains an open question. To address this issue, we use density functional theory calculation and charge transfer self-consistent model to study the effects of polar discontinuity on the electronic and atomic reconstruction at the NdNiO2/SrTiO3 interface. We find that sharp interface with pure electronic reconstruction only is energetically unfavorable, and atomic reconstruction is unavoidable. We further propose a possible interface configuration that contain residual apical oxygen. These oxygen atoms lead to hybrids of dz2 and dx2-y2 states at the Fermi level, which weaken the single-band feature and may be detrimental to superconductivity.

cond-mat.mtrl-sci↗

arXiv2020

BAlN alloy for enhanced two-dimensional electron gas characteristics of GaN-based high electron mobility transistor

The emerging wide bandgap BAlN alloys have potentials for improved III-nitride power devices including high electron mobility transistor (HEMT). Yet few relevant studies have been carried. In this work, we have investigated the use of the B0.14Al0.86N alloy as part or entirety of the interlayer between the GaN buffer and the AlGaN barrier in the conventional GaN-based high electron mobility transistor (HEMT). The numerical results show considerable improvement of the two-dimensional electron gas (2DEG) concentration with small 2DEG leakage into the ternary layer by replacing the conventional AlN interlayer by either the B0.14Al0.86N interlayer or the B0.14Al0.86N/AlN hybrid interlayer. Consequently, the transfer characteristics can be improved. The saturation current can be enhanced as well. For instance, the saturation currents for HEMTs with the 0.5 nm B0.14Al0.86N/0.5 nm AlN hybrid interlayer and the 1 nm B0.14Al0.86N interlayer are 5.8% and 2.2% higher than that for the AlN interlayer when VGS-Vth= +3 V.

physics.app-ph↗

arXiv2020

BAlN for III-nitride UV light emitting diodes: undoped electron blocking layer

The undoped BAlN electron-blocking layer (EBL) is investigated to replace the conventional AlGaN EBL in light-emitting diodes (LEDs). Numerical studies of the impact of variously doped EBLs on the output characteristics of LEDs demonstrate that the LED performance shows heavy dependence on the p-doping level in the case of the AlGaN EBL, while it shows less dependence on the p-doping level for the BAlN EBL. As a result, we propose an undoped BAlN EBL for LEDs to avoid the p-doping issues, which a major technical challenge in the AlGaN EBL. Without doping, the proposed BAlN EBL structure still possesses a superior capacity in blocking electrons and improving hole injection compared with the AlGaN EBL having high doping. This study provides a feasible route to addressing electron leakage and insufficient hole injection issues when designing UV LED structures.

physics.app-ph↗