arXiv subjects

Weijun Ren

Publications and source records attributed to Weijun Ren.

At least 19 recordsLinked to original sources

arXiv2023

Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.

cs.DB↗

arXiv2023

What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings

Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recent considerable research progress in embedding-based EA, how it works remains to be explored. In this paper, we provide a similarity flooding perspective to explain existing translation-based and aggregation-based EA models. We prove that the embedding learning process of these models actually seeks a fixpoint of pairwise similarities between entities. We also provide experimental evidence to support our theoretical analysis. We propose two simple but effective methods inspired by the fixpoint computation in similarity flooding, and demonstrate their effectiveness on benchmark datasets. Our work bridges the gap between recent embedding-based models and the conventional similarity flooding algorithm. It would improve our understanding of and increase our faith in embedding-based EA.

cs.LG↗

arXiv2023

Optical conductivity and vibrational spectra of the narrow-gap semiconductor FeGa$_3$

Intermetallic narrow-gap semiconductors have been intensively explored due to their large thermoelectric power at low temperatures and a possible role of strong electronic correlations in their unusual thermodynamic and transport properties. Here we study the optical spectra and vibrational properties of $\mathrm{FeGa_3}$ single crystal. The optical conductivity indicates that $\mathrm{FeGa_3}$ has a direct band gap of $\sim 0.7$\,eV, consistent with density functional theory (DFT) calculations. Most importantly, we find a substantial spectral weight also below 0.4~eV, which is the energy of the indirect (charge) gap found in resistivity measurements and ab initio calculations. We find that the spectral weight below the gap decreases with increasing temperature, which indicates that it originates from the impurity states and not from the electronic correlations. Interestingly, we did not find any signatures of the impurity states in vibrational spectra. The infrared and Raman vibrational lines are narrow and weakly temperature dependent. The vibrational frequencies are in excellent agreement with our DFT calculations, implying a modest role of electronic correlations. Narrow M\" ossbauer spectral lines also indicate high crystallinity of the sample.

cond-mat.str-el↗

arXiv2023

Magnetic phase diagram and multiple field-induced states in the intermetallic triangular-lattice antiferromagnet NdAuAl$_4$Ge$_2$ with Ising-like spins

Geometrical frustration and the enhancement of strong quantum fluctuations in two-dimensional triangular antiferromagnets can lead to various intriguing phenomena. Here, we studied the spin-1/2 triangular lattice antiferromagnet NdAuAl$_4$Ge$_2$. Thermodynamic and transport properties, such as magnetization and specific heat together with the resistivity measurements were performed. In zero field, two successive phase transitions were observed at $T_{\rm N1}=1.75\pm 0.02$ K and $T_{\rm N2}=0.49\pm 0.02$ K, respectively. Under magnetic field, $\rm XXZ$-type anisotropy was revealed, with the moments pointing along the easy $c$ axis. For $B\parallel c$, multiple field-induced states were observed, and the magnetic phase diagram was established based on the specific heat and magnetization data. The temperature-dependent resistivity measurements indicate that NdAuAl$_4$Ge$_2$ is a good metal. It is very likely that both the long-range RKKY interactions and the geometrical frustration play an important roles in this case.

cond-mat.str-el↗

arXiv2023

Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection

With the pandemic of COVID-19, relevant fake news is spreading all over the sky throughout the social media. Believing in them without discrimination can cause great trouble to people's life. However, universal language models may perform weakly in these fake news detection for lack of large-scale annotated data and sufficient semantic understanding of domain-specific knowledge. While the model trained on corresponding corpora is also mediocre for insufficient learning. In this paper, we propose a novel transformer-based language model fine-tuning approach for these fake news detection. First, the token vocabulary of individual model is expanded for the actual semantics of professional phrases. Second, we adapt the heated-up softmax loss to distinguish the hard-mining samples, which are common for fake news because of the disambiguation of short text. Then, we involve adversarial training to improve the model's robustness. Last, the predicted features extracted by universal language model RoBERTa and domain-specific model CT-BERT are fused by one multiple layer perception to integrate fine-grained and high-level specific representations. Quantitative experimental results evaluated on existing COVID-19 fake news dataset show its superior performances compared to the state-of-the-art methods among various evaluation metrics. Furthermore, the best weighted average F1 score achieves 99.02%.

cs.CL↗

arXiv2022

Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data

Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this issue, many KG completion methods are proposed. Among them, open KG completion methods leverage the Web to find missing facts. However, noisy data collected from diverse sources may damage the completion accuracy. In this paper, we propose a new trustworthy method that exploits facts for a KG based on multi-sourced noisy data and existing facts in the KG. Specifically, we introduce a graph neural network with a holistic scoring function to judge the plausibility of facts with various value types. We design value alignment networks to resolve the heterogeneity between values and map them to entities even outside the KG. Furthermore, we present a truth inference model that incorporates data source qualities into the fact scoring function, and design a semi-supervised learning way to infer the truths from heterogeneous values. We conduct extensive experiments to compare our method with the state-of-the-arts. The results show that our method achieves superior accuracy not only in completing missing facts but also in discovering new facts.

cs.IR↗

arXiv2021

Ultrasensitive barocaloric material for room-temperature solid-state refrigeration

Solid-state refrigeration based on caloric effects is an energetically efficient and environmentally friendly technology, which is deemed as a potential alternative to the conventional vapor-compression technology. One of the greatest obstacles to the real application is the huge driving fields. Here, we report a giant barocaloric effect in inorganic NH4I with maximum entropy changes of ΔS_BCE^max ~89 J K-1 kg-1 around room temperature, associated with the orientationally order-disorder phase transition. The phase transition temperature, Tt, varies dramatically with pressure in a rate of dTt/dP ~0.81 K MPa-1, which leads to a very much small saturation driving pressure of ΔP ~20 MPa, an unprecedentedly large caloric strength of |ΔS_BCE^max/ΔP| ~4.45 J K-1 kg-1 MPa-1, as well as a broad temperature window of ~68 K under an 80 MPa driving pressure. Comprehensive characterization of the crystal structure and dynamics by neutron scattering measurements reveals a strong reorientation-vibration coupling that is responsible for the large pressure sensitivity of Tt. This work is expected to advance the practical application of barocaloric refrigeration.

cond-mat.mtrl-sci↗

arXiv2021

Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning

Most real-world datasets are inherently heterogeneous graphs, which involve a diversity of node and relation types. Heterogeneous graph embedding is to learn the structure and semantic information from the graph, and then embed it into the low-dimensional node representation. Existing methods usually capture the composite relation of a heterogeneous graph by defining metapath, which represent a semantic of the graph. However, these methods either ignore node attributes, or discard the local and global information of the graph, or only consider one metapath. To address these limitations, we propose a Metapaths-guided Neighbors-aggregated Heterogeneous Graph Neural Network(MHN) to improve performance. Specially, MHN employs node base embedding to encapsulate node attributes, BFS and DFS neighbors aggregation within a metapath to capture local and global information, and metapaths aggregation to combine different semantics of the heterogeneous graph. We conduct extensive experiments for the proposed MHN on three real-world heterogeneous graph datasets, including node classification, link prediction and online A/B test on Alibaba mobile application. Results demonstrate that MHN performs better than other state-of-the-art baselines.

cs.AI↗

arXiv2020

Ultrafast ferromagnetic fluctuations preceding magnetoelastic first-order transitions

First-order magnetic transitions are of both fundamental and technological interest given that a number of emergent phases and functionalities are thereby created. Of particular interest are giant magnetocaloric effects, which are attributed to first-order magnetic transitions and have attracted broad attention for solid-state refrigeration applications. While the conventional wisdom is that atomic lattices play an important role in first-order magnetic transitions, a coherent microscopic description of the lattice and spin degrees of freedom is still lacking. Here, we study the magnetic phase transition dynamics on the intermetallic LaFe13-xSix, which is one of the most classical giant magnetocaloric systems, in both frequency and time domains utilizing neutron scattering and ultrafast X-ray diffraction. We have observed a strong magnetic diffuse scattering in the paramagnetic state preceding the first-order magnetic transition, corresponding to picosecond ferromagnetic fluctuations. Upon photon-excitation, the ferromagnetic state is completely suppressed in 0.9 ps and recovered in 20 ps. The ultrafast dynamics suggest that the magnetic degree of freedom dominates this magnetoelastic transition and ferromagnetic fluctuations might be universally relevant for this kind of compounds.

cond-mat.mtrl-sci↗

arXiv2020

Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection

Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce platforms to attract users to place orders. E-coupons are the digital equivalent of traditional paper coupons which provide customers with discounts or gifts. One of the fundamental problems related is how to deliver e-coupons with minimal cost while users' willingness to place an order is maximized. We call this problem the coupon allocation problem. This is a non-trivial problem since the number of regular users on a mature e-platform often reaches hundreds of millions and the types of e-coupons to be allocated are often multiple. The policy space is extremely large and the online allocation has to satisfy a budget constraint. Besides, one can never observe the responses of one user under different policies which increases the uncertainty of the policy making process. Previous work fails to deal with these challenges. In this paper, we decompose the coupon allocation task into two subtasks: the user intent detection task and the allocation task. Accordingly, we propose a two-stage solution: at the first stage (detection stage), we put forward a novel Instantaneous Intent Detection Network (IIDN) which takes the user-coupon features as input and predicts user real-time intents; at the second stage (allocation stage), we model the allocation problem as a Multiple-Choice Knapsack Problem (MCKP) and provide a computational efficient allocation method using the intents predicted at the detection stage. We conduct extensive online and offline experiments and the results show the superiority of our proposed framework, which has brought great profits to the platform and continues to function online.

cs.AI↗

arXiv2020

Room-Temperature Skyrmion Thermopower in Fe3Sn2

We present first room-temperature thermoelectric signature of the skyrmion lattice. This was observed in Fe3Sn2, a Kagome Dirac crystal with massive Dirac fermions that features high-temperature skyrmion phase. The room-temperature skyrmion lattice shows magnetic-field dependence of the wavevector whereas thermopower is dominated by the electronic diffusion mechanism, allowing for the skyrmionic bubble lattice detection. Our results pave the way for the future skyrmion-based devices based on the manipulation of the thermal gradient.

cond-mat.mtrl-sci↗

arXiv2020

Dimer rattling mode induced low thermal conductivity in an excellent acoustic conductor

A solid with larger sound speeds exhibits higher lattice thermal conductivity (k_{lat}). Diamond is a prominent instance where its mean sound speed is 14400 m s-1 and k_{lat} is 2300 W m-1 K-1. Here, we report an extreme exception that CuP2 has quite large mean sound speeds of 4155 m s-1, comparable to GaAs, but the single crystals show a very low lattice thermal conductivity of about 4 W m-1 K-1 at room temperature, one order of magnitude smaller than GaAs. To understand such a puzzling thermal transport behavior, we have thoroughly investigated the atomic structure and lattice dynamics by combining neutron scattering techniques with first-principles simulations. Cu atoms form dimers sandwiched in between the layered P atomic networks and the dimers vibrate as a rattling mode with frequency around 11 meV. This mode is manifested to be remarkably anharmonic and strongly scatters acoustic phonons to achieve the low k_{lat}. Such a dimer rattling behavior in layered structures might offer an unprecedented strategy for suppressing thermal conduction without involving atomic disorder.

cond-mat.mtrl-sci↗

arXiv2019

Magnetic-field control of topological electronic response near room temperature in correlated Kagome magnets

Strongly correlated Kagome magnets are promising candidates for achieving controllable topological devices owing to the rich interplay between inherent Dirac fermions and correlation-driven magnetism. Here we report tunable local magnetism and its intriguing control of topological electronic response near room temperature in the Kagome magnet Fe3Sn2 using small angle neutron scattering, muon spin rotation, and magnetoresistivity measurement techniques. The average bulk spin direction and magnetic domain texture can be tuned effectively by small magnetic fields. Magnetoresistivity, in response, exhibits a measurable degree of anisotropic weak localization behavior, which allows the direct control of Dirac fermions with strong electron correlations. Our work points to a novel platform for manipulating emergent phenomena in strongly-correlated topological materials relevant to future applications.

cond-mat.mtrl-sci↗

arXiv2018

Colossal barocaloric effects in plastic crystals

Solid-state refrigeration technology based on caloric effects are promising to replace the currently used vapor compression cycles. However, their application is restricted due to limited performances of caloric materials. Here, we have identified colossal barocaloric effects (CBCEs) in a class of disordered solids called plastic crystals. The obtained entropy changes are about 380 J kg-1 K-1 in the representative neopentylglycol around room temperature. Inelastic neutron scattering reveals that the CBCEs in plastic crystals are attributed to the combination of the vast molecular orientational disorder, giant compressibility, and high anharmonic lattice dynamics. Our study establishes the microscopic scenario for CBCEs in plastic crystals and paves a new route to the next-generation solid-state refrigeration technology.

cond-mat.mtrl-sci↗

arXiv2018

Absence of Dirac states in BaZnBi$_{2}$ induced by spin-orbit coupling

We report magnetotransport properties of BaZnBi$_{2}$ single crystals. Whereas electronic structure features Dirac states, such states are removed from the Fermi level by spin-orbit coupling (SOC) and consequently electronic transport is dominated by the small hole and electron pockets. Our results are consistent with three dimensional (3D) but also with quasi two dimensional (2D) portions of the Fermi surface. The spin-orbit coupling-induced gap in Dirac states is much larger when compared to isostructural SrMnBi$_{2}$. This suggests that not only long range magnetic order but also mass of the alkaline earth atoms A in ABX$_{2}$ (A = alkaine earth, B = transition metal and X=Bi/Sb) are important for the presence of low-energy states obeying the relativistic Dirac equation at the Fermi surface

cond-mat.mes-hall↗

arXiv2017

Observation of Various and Spontaneous Magnetic Skyrmionic Bubbles at Room-Temperature in a Frustrated Kagome Magnet with Uniaxial Magnetic Anisotropy

Various and spontaneous magnetic skyrmionic bubbles are experimentally observed for the first time, at room temperature in a frustrated kagome magnet Fe3Sn2 with unixial magnetic anisotropy. The magnetization dynamics were investigated using in-situ Lorentz transmission electron microscopy, revealing that the transformation between different magnetic bubbles and domains are via the motion of Bloch lines driven by applied external magnetic field. The results demonstrate that Fe3Sn2 facilitates a unique magnetic control of topological spin textures at room temperature, making it a promising candidate for further skyrmion-based spintronic devices.

cond-mat.mtrl-sci↗

arXiv2016

Two-dimensional Dirac fermions in YbMnBi2 antiferromagnet

We report two-dimensional quantum transport and Dirac fermions in YbMnBi2 single crystals. YbMnBi2 is a layered material with anisotropic conductivity and magnetic order below 290 K. Magnetotransport properties, nonzero Berry phase and small cyclotron mass indicate the presence of quasi two dimensional Dirac fermions. Quantum oscillations in Hall resistivity suggest the presence of both electron and hole parts of the Fermi surface whereas the Berry phase suggests spin-orbit coupling.

cond-mat.mes-hall↗

arXiv2014

Microwave-driven Topological Resonant Excitations of Coupled Skyrmions

We study nonlinear dynamics of coupled Skyrmions and present a method for manipulating topological resonant excitations by a dual-frequency microwave field. Thiele's equation is extended by introducing a new effective mass associated with time derivative of topological density. Two coupled resonant modes endowed with the new effective mass are found in the coupled Skyrmions. Polygon-like resonant excitations are observed and modulated when the two modes are activated simultaneously by microwave field with commensurate frequency ratio. Quasiperiodic behavior of excitations are related to an incommensurate ratio. Numerical solutions based on the extended Thiele's equation for Skyrmion under a dual-frequency field agree well with the micromagnetic simulation results and clarify the importance of frequency value, frequency ratio of an external field and the effective mass to the polygon-like dynamics of Skyrmion. We also show the coupling between two Skyrmions can dominate their topological resonant excitations.

cond-mat.mtrl-sci↗