arXiv subjects

Wei Tian

Publications and source records attributed to Wei Tian.

At least 37 records · Page 2Linked to original sources

arXiv2025

Spin density wave and van Hove singularity in the kagome metal CeTi3Bi4

Kagome metals with van Hove singularities near the Fermi level can host intriguing quantum phenomena such as chiral loop currents, electronic nematicity, and unconventional superconductivity. However, to our best knowledge, unconventional magnetic states driven by van Hove singularities--like spin-density waves--have not been observed experimentally in kagome metals. Here, we report the magnetic and electronic structure of the layered kagome metal CeTi3Bi4, where Ti kagome electronic structure interacts with a magnetic sublattice of Ce3+ Jeff = 1/2 moments. Neutron diffraction reveals an incommensurate spin-density wave ground state of the Ce3+ moments, coexisting with commensurate antiferromagnetic order across most of the temperature-field phase diagram. The commensurate component is preferentially suppressed by thermal fluctuations and magnetic field, yielding a rich phase diagram involving an intermediate single-Q spin-density wave phase. First-principles calculations and angle-resolved photoemission spectroscopy identify van Hove singularities near the Fermi level, with the observed magnetic propagation vectors connecting their high density of states, strongly suggesting a van Hove singularity-assisted spin-density wave. These findings establish kagome metals LnTi3Bi4 as a model platform where the characteristic electronic structure of the kagome lattice plays a pivotal role in magnetic order.

cond-mat.str-el↗

arXiv2025

MuonSLab: A plastic scintillator based detector for muon measurement in the deep ocean

Atmospheric muons are important probes for studying primary cosmic rays and extensive air showers. Additionally, they constitute a significant background for many underground and deep-sea neutrino experiments, such as TRopIcal DEep-sea Neutrino Telescope (TRIDENT). Understanding the muon flux at various depths in the deep sea is essential for validating TRIDENT simulations and guiding the development of optimized trigger strategies. This paper introduces a novel device based on plastic scintillalors and silicon photomultipliers (SiPMs) named MuonSLab, which is designed to measure muon flux in the deep sea and has the potential to be extended to other atmospheric muon property measurements. We discuss the design and instrumentation of MuonSLab and present results from several muon flux measurements, demonstrating its sensitivity to muon detection and its stability during operations across multiple locations.

hep-ex↗

arXiv2025

The Light Source of the TRIDENT Pathfinder Experiment

In September 2021, a site scouting mission known as the TRIDENT pathfinder experiment (TRIDENT EXplorer, T-REX for short) was conducted in the South China Sea with the goal of envisaging a next-generation multi-cubic-kilometer neutrino telescope. One of the main tasks is to measure the in-situ optical properties of seawater at depths between $2800~\mathrm{m}$ and $3500~\mathrm{m}$, where the neutrino telescope will be instrumented. To achieve this, we have developed a light emitter module equipped with a clock synchronization system to serve as the light source, which could be operated in pulsing and steady modes. Two light receiver modules housing both photomultiplier tubes (PMTs) and cameras are employed to detect the photons emitted by the light source. This paper presents the instrumentation of the light source in T-REX, including its design, calibration, and performance.

astro-ph.IM↗

arXiv2025

Correlation between Complex Spin Textures and the Magnetocaloric and Hall Effects in Eu(Ga$_{1-x}$Al$_x$)$_4$ ($x$ = 0.9, 1)

Determining the electronic phase diagram of a quantum material as a function of temperature (T) and applied magnetic field (H) forms the basis for understanding the microscopic origin of transport properties, such as the anomalous Hall effect (AHE) and topological Hall effect (THE). For many magnetic quantum materials, including EuAl$_4$, a THE arises from a topologically protected magnetic skyrmion lattice with a non-zero scalar spin chirality. We identified a square skyrmion lattice (sSkL) peak in Eu(Ga$_{1-x}$Al$_x$)$_4$ ($x$ = 0.9) identical to the peak previously observed in EuAl$_4$ by performing neutron scattering measurements throughout the phase diagram. Comparing these neutron results with transport measurements, we found that in both compounds the maximal THE does not correspond to the sSkL area. Instead of the maximal THE, the maximal magnetocaloric effect (MCE) boundaries better identify the sSkL lattice phase observed by neutron scattering measurements. The maximal THE therefore arises from interactions of itinerant electrons with frustrated spin fluctuations in a topologically trivial magnetic state.

cond-mat.str-el↗

arXiv2024

Tilted stripes origin in ${\mathrm{La}}_{1.88}{\mathrm{Sr}}_{0.12}{\mathrm{CuO}}_{4}$ revealed by anisotropic next-nearest neighbor hopping

Spin- and charge- stripe order has been extensively studied in the superconducting cuprates, among which underdoped ${\mathrm{La}}_{2-x}{\mathrm{Sr}}_{x}{\mathrm{CuO}}_{4}$ (LSCO) is an archetype with static spin stripes at low temperatures. An intriguing, but not completely understood, phenomenon in LSCO is that the stripes are tilted away from the high-symmetry Cu-Cu directions. Using high-resolution neutron scattering on LSCO with $x=0.12$, we find two coexisting phases at low temperatures, one with static spin stripes and the other with fluctuating ones, both sharing the same tilt angle. Our numerical calculations using the doped Hubbard model elucidate the tilting's origin, attributing it to anisotropic next-nearest neighbor hopping $t^{\prime}$, consistent with the material's slight orthorhombicity. Our results underscore the model's success in describing specific details of the ground state of this real material and highlight the role of $t^\prime$ in the Hamiltonian, revealing the delicate interplay between stripes and superconductivity across theoretical and experimental contexts.

cond-mat.str-el↗

arXiv2024

Vacancy-induced suppression of CDW order and its impact on magnetic order in kagome antiferromagnet FeGe

Two-dimensional (2D) kagome lattice metals are interesting because they display flat electronic bands, Dirac points, Van Hove singularities, and can have interplay between charge density wave (CDW), magnetic order, and superconductivity. In kagome lattice antiferromagnet FeGe, a short-range CDW order was found deep within an antiferromagnetically ordered state, interacting with the magnetic order. Surprisingly, post-growth annealing of FeGe at 560$^{\circ}$C can suppress the CDW order while annealing at 320$^{\circ}$C induces a long-range CDW order, with the ability to cycle between the states repeatedly by annealing. Here we perform transport, neutron scattering, scanning transmission electron microscopy (STEM), and muon spin rotation ($μ$SR) experiments to unveil the microscopic mechanism of the annealing process and its impact on magneto-transport, CDW, and magnetic properties of FeGe. We find that 560$^{\circ}$C annealing creates germanium vacancies uniformly distributed throughout the FeGe kagome lattice, which prevent the formation of Ge-Ge dimers necessary for the CDW order. Upon annealing at 320$^{\circ}$C, the system segregates into stoichiometric FeGe regions with long-range CDW order and regions with stacking faults that act as nucleation sites for the CDW. The presence or absence of CDW order greatly affects the anomalous Hall effect, incommensurate magnetic order, and spin-lattice coupling in FeGe, thus placing FeGe as the only known kagome lattice material with a tunable CDW and magnetic order, potentially useful for sensing and information transmission.

cond-mat.str-el↗

arXiv2024

Synthetic Controls with Multiple Outcomes

We generalize the synthetic control (SC) method to a multiple-outcome framework, where the conventional pre-treatment time dimension is supplemented with the extra dimension of related outcomes in computing the SC weights. This generalization improves the reliability of treatment effect estimation, and can be particularly useful for evaluating the effect of a treatment on multiple outcomes or when only a small number of pre-treatment periods are available. To illustrate our method, we provide a new perspective on the classic SC application to the 1990 German reunification.

econ.GN↗

arXiv2024

A camera system for real-time optical calibration of water-based neutrino telescopes

Calibrating the optical properties within the detection medium of a neutrino telescope is crucial for determining its angular resolution and energy scale. For the next generation of neutrino telescopes planned to be constructed in deep water, such as the TRopIcal DEep-sea Neutrino Telescope (TRIDENT), there are additional challenges due to the dynamic nature and potential non-uniformity of the water medium. This necessitates a real-time optical calibration system distributed throughout the large detector array. This study introduces a custom-designed CMOS camera system equipped with rapid image processing algorithms, providing a real-time optical calibration method for TRIDENT and other similar projects worldwide. In September 2021, the TRIDENT Pathfinder experiment (TRIDENT Explorer, T-REX for short) successfully deployed this camera system in the West Pacific Ocean at a depth of 3420 meters. Within 30 minutes, about 3000 images of the T-REX light source were captured, allowing for the in-situ measurement of seawater attenuation and absorption lengths under three wavelengths. This deep-sea experiment for the first time showcased a technical demonstration of a functioning camera calibration system in a dynamic neutrino telescope site, solidifying a substantial part of the calibration strategies for the future TRIDENT project.

physics.ins-det↗

arXiv2024

Pulling order back from the brink of disorder: Observation of a nodal line spin liquid and fluctuation stabilized order in K$_2$IrCl$_6$

Competing interactions in frustrated magnets can give rise to highly degenerate ground states from which correlated liquid-like states of matter often emerge. The scaling of this degeneracy influences the ultimate ground state, with extensive degeneracies potentially yielding quantum spin liquids, while sub-extensive or smaller degeneracies yield static orders. A longstanding problem is to understand how ordered states precipitate from this degenerate manifold and what echoes of the degeneracy survive ordering. Here, we use neutron scattering to experimentally demonstrate a new "nodal line" spin liquid, where spins collectively fluctuate within a sub-extensive manifold spanning one-dimensional lines in reciprocal space. Realized in the spin-orbit coupled, face-centered cubic iridate K$_2$IrCl$_6$, we show that the sub-extensive degeneracy is robust, but remains susceptible to fluctuations or longer range interactions which cooperate to select a magnetic order at low temperatures. Proximity to the nodal line spin liquid influences the ordered state, enhancing the effects of quantum fluctuations and stabilizing it through the opening of a large spin-wave gap. Our results demonstrate quantum fluctuations can act counter-intuitively in frustrated materials: instead of destabilizing ordering, at the brink of the nodal spin liquid they can act to stabilize it and dictate its low-energy physics.

cond-mat.str-el↗

arXiv2024

New insight into tuning magnetic phases of $R$Mn$_6$Sn$_6$ kagome metals

Predicting magnetic ordering in kagome compounds offers the possibility of harnessing topological or flat-band physical properties through tuning of the magnetism. Here, we examine the magnetic interactions and phases of ErMn$_6$Sn$_6$ which belongs to a family of $R$Mn$_6$Sn$_6$, $R=$ Sc, Y, Gd--Lu, compounds with magnetic kagome Mn layers, triangular $R$ layers, and signatures of topological properties. Using results from single-crystal neutron diffraction and mean-field analysis, we find that ErMn$_6$Sn$_6$ sits close to the critical boundary separating the spiral-magnetic and ferrimagnetic ordered states typical for nonmagnetic versus magnetic $R$ layers, respectively. Finding interlayer magnetic interactions and easy-plane Mn magnetic anisotropy consistent with other members of the family, we predict the existence of a number of temperature and field dependent collinear, noncollinear, and noncoplanar magnetic phases. We show that thermal fluctuations of the Er magnetic moment, which act to weaken the Mn-Er interlayer magnetic interaction and quench the Er magnetic anisotropy, dictate magnetic phase stability. Our results provide a starting point and outline a multitude of possibilities for studying the behavior of Dirac fermions in $R$Mn$_6$Sn$_6$ compounds with control of the Mn spin orientation and real-space spin chirality.

cond-mat.str-el↗

arXiv2024

AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy

We propose Adjustable Molecular Representation (AdaMR), a new large-scale uniform pre-training strategy for small-molecule drugs, as a novel unified pre-training strategy. AdaMR utilizes a granularity-adjustable molecular encoding strategy, which is accomplished through a pre-training job termed molecular canonicalization, setting it apart from recent large-scale molecular models. This adaptability in granularity enriches the model's learning capability at multiple levels and improves its performance in multi-task scenarios. Specifically, the substructure-level molecular representation preserves information about specific atom groups or arrangements, influencing chemical properties and functionalities. This proves advantageous for tasks such as property prediction. Simultaneously, the atomic-level representation, combined with generative molecular canonicalization pre-training tasks, enhances validity, novelty, and uniqueness in generative tasks. All of these features work together to give AdaMR outstanding performance on a range of downstream tasks. We fine-tuned our proposed pre-trained model on six molecular property prediction tasks (MoleculeNet datasets) and two generative tasks (ZINC250K datasets), achieving state-of-the-art (SOTA) results on five out of eight tasks.

q-bio.BM↗

arXiv2024

LAiW: A Chinese Legal Large Language Models Benchmark

General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI. However, the current evaluations of these LLMs in LegalAI are defined by the experts of computer science, lacking consistency with the logic of legal practice, making it difficult to judge their practical capabilities. To address this challenge, we are the first to build the Chinese legal LLMs benchmark LAiW, based on the logic of legal practice. To align with the thinking process of legal experts and legal practice (syllogism), we divide the legal capabilities of LLMs from easy to difficult into three levels: basic information retrieval, legal foundation inference, and complex legal application. Each level contains multiple tasks to ensure a comprehensive evaluation. Through automated evaluation of current general and legal domain LLMs on our benchmark, we indicate that these LLMs may not align with the logic of legal practice. LLMs seem to be able to directly acquire complex legal application capabilities but perform poorly in some basic tasks, which may pose obstacles to their practical application and acceptance by legal experts. To further confirm the complex legal application capabilities of current LLMs in legal application scenarios, we also incorporate human evaluation with legal experts. The results indicate that while LLMs may demonstrate strong performance, they still require reinforcement of legal logic.

cs.CL↗

arXiv2023

The PMT System of the TRIDENT Pathfinder Experiment

Next generation neutrino telescopes are highly anticipated to boost the development of neutrino astronomy. A multi-cubic-kilometer neutrino telescope, TRopIcal DEep-sea Neutrino Telescope (TRIDENT), was proposed to be built in the South China Sea. The detector aims to achieve ~ 0.1 degree angular resolution for track-like events at energy above 100 TeV by using hybrid digital optical modules, opening new opportunities for neutrino astronomy. In order to measure the water optical properties and marine environment of the proposed TRIDENT site, a pathfinder experiment was conducted, in which a 100-meter-long string consisting of three optical modules was deployed at a depth of 3420 m to perform in-situ measurements. The central module emits light by housing LEDs, whereas the other two modules detect light with two independent and complementary systems: the PMT and the camera systems. By counting the number of detected photons and analyzing the photon arrival time distribution, the PMT system can measure the absorption and scattering lengths of sea water, which serve as the basic inputs for designing the neutrino telescope. In this paper, we present the design concept, calibration and performance of the PMT system in the pathfinder experiment.

hep-ex↗

arXiv2023

EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Locating 3D objects from a single RGB image via Perspective-n-Point (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, allowing for partial learning of 2D-3D point correspondences by backpropagating the gradients of pose loss. Yet, learning the entire correspondences from scratch is highly challenging, particularly for ambiguous pose solutions, where the globally optimal pose is theoretically non-differentiable w.r.t. the points. In this paper, we propose the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose with differentiable probability density on the SE(3) manifold. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle generalizes previous approaches, and resembles the attention mechanism. EPro-PnP can enhance existing correspondence networks, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation benchmark. Furthermore, EPro-PnP helps to explore new possibilities of network design, as we demonstrate a novel deformable correspondence network with the state-of-the-art pose accuracy on the nuScenes 3D object detection benchmark. Our code is available at https://github.com/tjiiv-cprg/EPro-PnP-v2.

cs.CV↗

arXiv2023

Static and dynamical properties of the spin-5/2 nearly ideal triangular lattice antiferromagnet Ba3MnSb2O9

We study the ground state and spin excitations in Ba3MnSb2O9, an easy-plane S = 5/2 triangular lattice antiferromagnet. By combining single-crystal neutron scattering, electric spin resonance (ESR), and spin wave calculations, we determine the frustrated quasi-two-dimensional spin Hamiltonian parameters describing the material. While the material has a slight monoclinic structural distortion, which could allow for isosceles-triangular exchanges and biaxial anisotropy by symmetry, we observe no deviation from the behavior expected for spin waves in the in-plane 120o state. Even the easy-plane anisotropy is so small that it can only be detected by ESR in our study. In conjunction with the quasi-two-dimensionality, our study establishes that Ba3MnSb2O9 is a nearly ideal triangular lattice antiferromagnet with the quasi-classical spin S = 5/2, which suggests that it has the potential for an experimental study of Z- or Z2-vortex excitations.

cond-mat.str-el↗

arXiv2023

Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural radiance fields (NeRF) from multi-view images of diverse objects. Previous studies have used two-stage approaches that rely on pretrained NeRFs as real data to train diffusion models. In contrast, we propose a new single-stage training paradigm with an end-to-end objective that jointly optimizes a NeRF auto-decoder and a latent diffusion model, enabling simultaneous 3D reconstruction and prior learning, even from sparsely available views. At test time, we can directly sample the diffusion prior for unconditional generation, or combine it with arbitrary observations of unseen objects for NeRF reconstruction. SSDNeRF demonstrates robust results comparable to or better than leading task-specific methods in unconditional generation and single/sparse-view 3D reconstruction.

cs.CV↗

arXiv2023

The Synthetic Control Method with Nonlinear Outcomes: Estimating the Impact of the 2019 Anti-Extradition Law Amendments Bill Protests on Hong Kong's Economy

The synthetic control estimator (Abadie et al., 2010) is asymptotically unbiased assuming that the outcome is a linear function of the underlying predictors and that the treated unit can be well approximated by the synthetic control before the treatment. When the outcome is nonlinear, the bias of the synthetic control estimator can be severe. In this paper, we provide conditions for the synthetic control estimator to be asymptotically unbiased when the outcome is nonlinear, and propose a flexible and data-driven method to choose the synthetic control weights. Monte Carlo simulations show that compared with the competing methods, the nonlinear synthetic control method has similar or better performance when the outcome is linear, and better performance when the outcome is nonlinear, and that the confidence intervals have good coverage probabilities across settings. In the empirical application, we illustrate the method by estimating the impact of the 2019 anti-extradition law amendments bill protests on Hong Kong's economy, and find that the year-long protests reduced real GDP per capita in Hong Kong by 11.27% in the first quarter of 2020, which was larger in magnitude than the economic decline during the 1997 Asian financial crisis or the 2008 global financial crisis.

econ.EM↗

arXiv2023

Individual Causal Inference Using Panel Data With Multiple Outcomes

Policy evaluation in empirical microeconomics has been focusing on estimating the average treatment effect and more recently the heterogeneous treatment effects, often relying on the unconfoundedness assumption. We propose a method based on the interactive fixed effects model to estimate treatment effects at the individual level, which allows both the treatment assignment and the potential outcomes to be correlated with the unobserved individual characteristics. This method is suitable for panel datasets where multiple related outcomes are observed for a large number of individuals over a small number of time periods. Monte Carlo simulations show that our method outperforms related methods. To illustrate our method, we provide an example of estimating the effect of health insurance coverage on individual usage of hospital emergency departments using the Oregon Health Insurance Experiment data.

econ.EM↗