arXiv subjects

Zi Li

Publications and source records attributed to Zi Li.

35 records · Page 2Linked to original sources

arXiv2023

Professional Network Matters: Connections Empower Person-Job Fit

Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions. While existing works leverage historical or contextual information, they often disregard a crucial aspect: job seekers' social relationships in professional networks. This paper emphasizes the importance of incorporating professional networks into the Person-Job Fit model. Our innovative approach consists of two stages: (1) defining a Workplace Heterogeneous Information Network (WHIN) to capture heterogeneous knowledge, including professional connections and pre-training representations of various entities using a heterogeneous graph neural network; (2) designing a Contextual Social Attention Graph Neural Network (CSAGNN) that supplements users' missing information with professional connections' contextual information. We introduce a job-specific attention mechanism in CSAGNN to handle noisy professional networks, leveraging pre-trained entity representations from WHIN. We demonstrate the effectiveness of our approach through experimental evaluations conducted across three real-world recruitment datasets from LinkedIn, showing superior performance compared to baseline models.

cs.SI↗

arXiv2023

Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions

Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures for the specific type of medical data. To tackle the aforementioned problems, this paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts, e.g., medical/clinical users, to conveniently find off-the-shelf registration algorithms for diverse scenarios. Specifically, we establish a triple-level framework to deduce registration network architectures and objectives with an auto-searching mechanism and cooperating optimization. We conduct image registration experiments on multi-site volume datasets and various registration tasks. Extensive results demonstrate that our AutoReg may automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance, also significantly improving computation efficiency than the mainstream UNet architectures (from 0.558 to 0.270 seconds for a 3D image pair on the same configuration).

cs.CV↗

arXiv2023

DRIVE: Dockerfile Rule Mining and Violation Detection

A Dockerfile defines a set of instructions to build Docker images, which can then be instantiated to support containerized applications. Recent studies have revealed a considerable amount of quality issues with Dockerfiles. In this paper, we propose a novel approach DRIVE (Dockerfiles Rule mIning and Violation dEtection) to mine implicit rules and detect potential violations of such rules in Dockerfiles. DRIVE firstly parses Dockerfiles and transforms them to an intermediate representation. It then leverages an efficient sequential pattern mining algorithm to extract potential patterns. With heuristic-based reduction and moderate human intervention, potential rules are identified, which can then be utilized to detect potential violations of Dockerfiles. DRIVE identifies 34 semantic rules and 19 syntactic rules including 9 new semantic rules which have not been reported elsewhere. Extensive experiments on real-world Dockerfiles demonstrate the efficacy of our approach.

cs.SE↗

arXiv2023

Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images

Radiotherapists require accurate registration of MR/CT images to effectively use information from both modalities. In a typical registration pipeline, rigid or affine transformations are applied to roughly align the fixed and moving images before proceeding with the deformation step. While recent learning-based methods have shown promising results in the rigid/affine step, these methods often require images with similar field-of-view (FOV) for successful alignment. As a result, aligning images with different FOVs remains a challenging task. Self-supervised landmark detection methods like self-supervised Anatomical eMbedding (SAM) have emerged as a useful tool for mapping and cropping images to similar FOVs. However, these methods are currently limited to intra-modality use only. To address this limitation and enable cross-modality matching, we propose a new approach called Cross-SAM. Our approach utilizes a novel iterative process that alternates between embedding learning and CT-MRI registration. We start by applying aggressive contrast augmentation on both CT and MRI images to train a SAM model. We then use this SAM to identify corresponding regions on paired images using robust grid-points matching, followed by a point-set based affine/rigid registration, and a deformable fine-tuning step to produce registered paired images. We use these registered pairs to enhance the matching ability of SAM, which is then processed iteratively. We use the final model for cross-modality matching tasks. We evaluated our approach on two CT-MRI affine registration datasets and found that Cross-SAM achieved robust affine registration on both datasets, significantly outperforming other methods and achieving state-of-the-art performance.

cs.CV↗

arXiv2023

Epitaxial growth and electronic structure of Ruddlesden-Popper nickelates ($ \mathrm{La}_{n+1}\mathrm{Ni}_{n}\mathrm{O}_{3n+1}, n=1-5 $)

We report the epitaxial growth of Ruddlesden-Popper nickelates, $ \mathrm{La}_{n+1}\mathrm{Ni}_{n}\mathrm{O}_{3n+1} $, with $ n $ up to 5 by reactive molecular beam epitaxy (MBE). X-ray diffractions indicate high crystalline quality of these films and transport measurements show strong dependence on the $ n $ values. Angle-resolved photoemission spectroscopy (ARPES) reveals the electronic structure of $ \mathrm{La}_{5}\mathrm{Ni}_{4}\mathrm{O}_{13} $, showing a large hole-like pocket centered around the Brillouin zone corner with a $ (π, π) $ back-folded copy.

cond-mat.supr-con↗

arXiv2023

Ab initio calculated dynamic structure factor and optical properties of beryllium along the Hugoniot

Beryllium is an ablator material in the inertial-confinement fusion and hypervelocity impact studies. The thermoelastic properties, structure factors, and optical properties of beryllium are important in these studies. In this paper, the static structure factors, ion-ion dynamic structure factors, adiabatic velocity, and optical properties of beryllium along the Hugoniot were calculated by ab initio simulations. The static structure factors show that beryllium atoms were randomly distributed. The dynamic structure factors via the scattering function have extracted the dispersion relation for the collective excitations. By collecting the peak position of dynamic structure factors, the dispersion relation and adiabatic sound velocity were derived by definitions. By the calculated equation of state, the thermoelastic properties and adiabatic sound velocity have been derived. The two calculated methods about adiabatic sound velocity were verified to be equivalent.

physics.plasm-ph↗

arXiv2023

Thermoelastic properties and thermal evolution of the Martian core from ab initio calculated ferromagnetic Fe-S liquid

The accurate thermoelastic properties and thermal conductivity are crucial in understanding the thermal evolution of the Martian core. A fitting method based on the ab initio calculated pressure-volume-temperature data is proposed in the formulation of the equation of state with high accuracy, by which the pressure and temperature dependent thermoelastic properties can be directly calculated by definitions. The ab initio results show that the liquid Fe0.75S0.25 under Martian core condition is thoroughly in the ferromagnetic state, without existing spin crossover. The liquid Fe0.75S0.25 in magnetic calculation owns a low thermal conductivity (21~23 W/m/K) when compared with non-magnetic calculation at the same state. Based on the Insight estimated and ab initio calculated properties of the Martian core, the iron snow model is verified when the current temperature at the core-mantle boundary is below the core melting temperature, and the simply secular cooling model is verified on the contrary.

astro-ph.EP↗

arXiv2023

Discrete scale invariance of the quasi-bound states at atomic vacancies in a topological material

Recently, log-periodic quantum oscillations have been detected in topological materials zirconium pentatelluride (ZrTe5) and hafnium pentatelluride (HfTe5), displaying intriguing discrete scale invariance (DSI) characteristic. In condensed materials, the DSI is considered to be related to the quasi-bound states formed by massless Dirac fermions with strong Coulomb attraction, offering a feasible platform to study the long-pursued atomic-collapse phenomenon. Here, we demonstrate that a variety of atomic vacancies in the topological material HfTe5 can host the geometric quasi-bound states with DSI feature, resembling the artificial supercritical atom collapse. The density of states of these quasi-bound states are enhanced and the quasi-bound states are spatially distributed in the "orbitals" surrounding the vacancy sites, which are detected and visualized by low-temperature scanning tunneling microscope/spectroscopy (STM/S). By applying the perpendicular magnetic fields, the quasi-bound states at lower energies become wider and eventually invisible, meanwhile the energies of quasi-bound states move gradually towards the Fermi energy (EF). These features are consistent with the theoretical prediction of a magnetic-field-induced transition from supercritical to subcritical states. The direct observation of geometric quasi-bound states sheds light on the deep understanding of the DSI in quantum materials.

cond-mat.mtrl-sci↗

arXiv2023

DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms

User engagement prediction plays a critical role for designing interaction strategies to grow user engagement and increase revenue in online social platforms. Through the in-depth analysis of the real-world data from the world's largest professional social platforms, i.e., LinkedIn, we find that users expose diverse engagement patterns, and a major reason for the differences in user engagement patterns is that users have different intents. That is, people have different intents when using LinkedIn, e.g., applying for jobs, building connections, or checking notifications, which shows quite different engagement patterns. Meanwhile, user intents and the corresponding engagement patterns may change over time. Although such pattern differences and dynamics are essential for user engagement prediction, differentiating user engagement patterns based on user dynamic intents for better user engagement forecasting has not received enough attention in previous works. In this paper, we proposed a Dynamic Intent Guided Meta Network (DIGMN), which can explicitly model user intent varying with time and perform differentiated user engagement forecasting. Specifically, we derive some interpretable basic user intents as prior knowledge from data mining and introduce prior intents in explicitly modeling dynamic user intent. Furthermore, based on the dynamic user intent representations, we propose a meta predictor to perform differentiated user engagement forecasting. Through a comprehensive evaluation on LinkedIn anonymous user data, our method outperforms state-of-the-art baselines significantly, i.e., 2.96% and 3.48% absolute error reduction, on coarse-grained and fine-grained user engagement prediction tasks, respectively, demonstrating the effectiveness of our method.

cs.LG↗

arXiv2022

A scheme for excitation of thorium-229 nuclei based on the electronic bridge excitaion

Thorium-229 possesses the lowest nuclear first excited state with an energy of about 8 eV. The extremely narrow linewidth of the nuclear first excited state with the uncertainty of 53 THz prevents the direct laser excitation and the realization of the nuclear clock. We present a proposal using the Coulomb crystal of a linear chain formed by the $^{229}$Th$^{3+}$ ions, the nuclei of $^{229}$Th$^{3+}$ ions in the ion trap are excited by the electronic bridge (EB) process. The 7$P_{1/2}$ state of the thorium-229 nuclear ground state is chosen for the EB excitation. Using the two-level optical Bloch equation under experimental conditions, we calculate that 2 out of 36 prepared thorium ions in the Coulomb crystal can be excited to the nuclear first excited state, and it takes about 2 hours to scan over the uncertainty of 0.22 eV. Taking the advantage of transition enhancement of the EB and the long stability of the Coulomb crystal, the energy uncertainty of the first excited state can be limited to the order of 1 GHz.

physics.atom-ph↗

arXiv2022

Mixed Chiral and Achiral Character in Substituted Ethane: A Next Generation QTAIM Perspective

We use the newly introduced spanning stress tensor trajectory $U_σ$-space construction within next generation quantum theory of atoms in molecules (NG-QTAIM) for a chirality investigation of singly and doubly substituted ethane with halogen substituents: F, Cl, Br. A lack of achiral character in $U_σ$-space was discovered for singly substituted ethane. The resultant axial bond critical point (BCP) sliding responded more strongly to the increase in atomic number of the substituted halogen than the chirality. The presence of the very light F atom was found responsible for a very high degree of achiral character of the doubly substituted ethane.

physics.chem-ph↗

arXiv2022

Chiral and Steric Effects in Ethane: A Next Generation QTAIM Interpretation

We introduce a development of next generation quantum theory of atoms in molecules (NG-QTAIM) for an investigation of the chirality of ethane and discover a $Q_σ$ isomer in addition to $S_σ$ and $R_σ$ stereoisomers in the stress tensor trajectory $U_σ$-space. The $Q_σ$ isomer is defined to be a 'null-isomer' since the value of the chirality-helicity function $\approx 0$. The presence of chiral contributions suggests that steric effects, rather than hyper-conjugation, explain the staggered geometry of ethane. The steric effects, within the NG-QTAIM interpretation, are reduced by a factor of two using an applied electric-field directed down a C-H bond.

physics.chem-ph↗

arXiv2021

Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond

Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric constraints, e.g., topology-preserving, which are indispensable to generate plausible deformations. We design a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation in order to integrate advantages and avoid limitations of these two categories of approaches. Specifically, we introduce a generic optimization model to formulate diffeomorphic registration and develop a series of learnable architectures to obtain propagative updating in the coarse-to-fine feature space. Moreover, we propose a novel bilevel self-tuned training strategy, allowing efficient search of task-specific hyper-parameters. This training strategy increases the flexibility to various types of data while reduces computational and human burdens. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data. Extensive results demonstrate the state-of-the-art performance of the proposed method with diffeomorphic guarantee and extreme efficiency. We also apply our framework to challenging multi-modal image registration, and investigate how our registration to support the down-streaming tasks for medical image analysis including multi-modal fusion and image segmentation.

cs.CV↗

arXiv2020

Ab initio determination on the thermal evolution of the Earth's core

Earth's magnetic field is generated by the liquid outer core and sensitively depends on the thermal conductivity of the core. The dominant component of the Earth's core is Fe and Ni. However, current estimates on FeNi mixtures have not been previously tested at high pressures. In this paper, ab initio simulations were first applied to calculations of the thermal and electrical conductivities of FeNi mixtures at Earth's outer core conditions. Compared with the results for pure Fe, the addition of Ni decreases the thermal conductivity (12.30 W/m/K on average) along the adiabatic curve in the outer core. Based on the restriction of the entropy production rate or Joule losses, the existence of Ni prolongs the age of the inner core. The age of the inner core is 0.66 Ga with pure Fe and 0.67 Ga with an FeNi mixture when heat flow at the core-mantle boundary is 12 TW. In contrast, we observe that Ni decreases the thickness of thermal stratification in the outer core by analyzing the effective temperature gradient. After inner core solidification, the thickness of thermal stratification is 417.02 km with pure Fe and 320.12 km with an FeNi mixture when the cooling rate at the core-mantle boundary is 126 K/Ga.

physics.geo-ph↗

arXiv2020

Mesoscale investigations of fluid-solid interaction: Liquid slip flow in a parallel-plate microchannel

Liquid slip flow with a Knudsen number Kn = 0.001-0.1 plays a dominant role in confined flow channels. Its physical origin can be attributed to the intermolecular fluid-solid (F-S) interaction force. To this end, we propose herein continuous force functions (decaying either exponentially or by a power law) between fluid particles and two confined flat walls in the framework of the mesoscopic lattice Boltzmann model (LBM). The analytical solutions for density profile, velocity profile, slip length, and permeability ratio are derived and related to the mesoscale F-S interaction parameters and the size of the gap of the flow channel. Through nondimensionalization of the analytical solutions, we obtain the dimensionless numbers that indicate the key feature of slip-flow systems for each of the proposed force functions. The analytical solutions are strictly consistent with the LBM solutions. We suggest reasonable ranges for the F-S interaction parameters based on the observed range of density ratio (film fluid to bulk fluid) and the increasing permeability ratios with narrowing gap size. Within the given range of interaction parameters, simple relationships between permeability ratios and dimensionless numbers are provided by fitting. The curves for continuous F-S interaction force with two free parameters are calibrated for a hydrophobic surface by using LBM simulations, which were validated a priori by comparison with the slip velocity profile measured in a benchmark flow experiment. The mesoscopic LBM model based on the proposed F-S interaction force functions provides a robust framework to elucidate the physical process of liquid slip flow.

physics.flu-dyn↗

arXiv2016

A Shifting Bloom Filter Framework for Set Queries

Set queries are fundamental operations in computer systems and applications.This paper addresses the fundamental problem of designing a probabilistic data structure that can quickly process set queries using a small amount of memory. We propose a Shifting Bloom Filter (ShBF) framework for representing and querying sets. We demonstrate the effectiveness of ShBF using three types of popular set queries: membership, association, and multiplicity queries. The key novelty of ShBF is on encoding the auxiliary information of a set element in a location offset. In contrast, prior BF based set data structures allocate additional memory to store auxiliary information. To evaluate ShBF in comparison with prior art, we conducted experiments using real-world network traces. Results show that ShBF significantly advances the state-of-the-art on all three types of set queries.

cs.DS↗

arXiv2015

First-Principles Calculation of Principal Hugoniot and K-Shell X-ray Absorption Spectra for Warm Dense KCl

Principal Hugoniot and K-shell X-ray absorption spectra of warm dense KCl are calculated using the first-principles molecular dynamics method. Evolution of electronic structures as well as the influence of the approximate description of ionization on pressure (caused by the underestimation of the energy gap between conduction bands and valence bands) in the first-principles method are illustrated by the calculation. Pressure ionization and thermal smearing are shown as the major factors to prevent the deviation of pressure from global accumulation along the Hugoniot. In addition, cancellation between electronic kinetic pressure and virial pressure further reduces the deviation. The calculation of X-ray absorption spectra shows that the band gap of KCl persists after the pressure ionization of the $3p$ electrons of Cl and K taking place at lower energy, which provides a detailed understanding to the evolution of electronic structures of warm dense matter.

physics.plasm-ph↗