Search arXiv⌕ Search

arXiv subjects

Wei Zhu

Publications and source records attributed to Wei Zhu.

At least 109 records · Page 6Linked to original sources
arXiv2025

Robust non-Abelian even-denominator fractional Chern insulator in twisted bilayer MoTe$_2$

A recent experiment observes a series of quantum spin Hall effects in transition metal dichalcogenide moiré MoTe$_2$ [K. Kang, et. al, Nature 628, 522-526 (2024)]. Among them, the vanishing Hall signal at the filling factor $ν=3$ implies a possible realization of a time-reversal pair of the even-denominator fractional Chern insulators (FCIs). Inspired by this discovery, we investigate whether a robust incompressible quantum Hall liquid can be stabilized in the half-filled Chern band of twisted MoTe$_2$ bilayers. We use the continuum model with parameters relevant to twisted MoTe$_2$ bilayers and obtain three consecutive nearly flat Chern bands with the same Chern number. Crucially, when the second moiré miniband is half-filled,signatures of non-Abelian frctional quantum Hall state are found via exact diagonalization calculations, including the stable six-fold ground state degeneracy that grows more robust with the lattice size and is consistent with an even-denominator FCI state. We further perform flux insertion simulations to reveal a 1/2 quantized many-body Chern number as direct evidence of topological order. Furthermore, the ground state density structure factors show no sharp peak, indicating no charge density wave order. These evidences signal the potential of realizing the non-Abelian state at zero magnetic field in twisted bilayer MoTe$_2$ at the fractional hole filling 3/2.

cond-mat.str-el↗
arXiv2025

Learning Novel Transformer Architecture for Time-series Forecasting

Despite the success of Transformer-based models in the time-series prediction (TSP) tasks, the existing Transformer architecture still face limitations and the literature lacks comprehensive explorations into alternative architectures. To address these challenges, we propose AutoFormer-TS, a novel framework that leverages a comprehensive search space for Transformer architectures tailored to TSP tasks. Our framework introduces a differentiable neural architecture search (DNAS) method, AB-DARTS, which improves upon existing DNAS approaches by enhancing the identification of optimal operations within the architecture. AutoFormer-TS systematically explores alternative attention mechanisms, activation functions, and encoding operations, moving beyond the traditional Transformer design. Extensive experiments demonstrate that AutoFormer-TS consistently outperforms state-of-the-art baselines across various TSP benchmarks, achieving superior forecasting accuracy while maintaining reasonable training efficiency.

cs.LG↗
arXiv2025

Adapting Large Language Models for Time Series Modeling via a Novel Parameter-efficient Adaptation Method

Time series modeling holds significant importance in many real-world applications and has been extensively studied. While pre-trained foundation models have made impressive strides in the fields of natural language processing (NLP) and computer vision (CV), their development in time series domains has been constrained by data sparsity. A series of recent studies have demonstrated that large language models (LLMs) possess robust pattern recognition and reasoning abilities over complex sequences of tokens. However, the current literature have yet striked a high-quality balance between (a) effectively aligning the time series and natural language modalities, and (b) keeping the inference efficiency. To address the above issues, we now propose the Time-LlaMA framework. Time-LlaMA first converts the time series input into token embeddings through a linear tokenization mechanism. Second, the time series token embeddings are aligned with the text prompts. Third, to further adapt the LLM backbone for time series modeling, we have developed a dynamic low-rank adaptation technique (D-LoRA). D-LoRA dynamically chooses the most suitable LoRA modules at each layer of the Transformer backbone for each time series input, enhancing the model's predictive capabilities. Our experimental results on an extensive collection of challenging real-world time series tasks confirm that our proposed method achieves the state-of-the-art (SOTA) performance.

cs.CL↗
arXiv2025

A differentiable binary microlensing model using adaptive contour integration method

We present microlux, which is a Jax-based code that can compute the binary microlensing light curve and its derivatives both efficiently and accurately. The key feature of microlux is the implementation of a modified version of the adaptive sampling algorithm that was originally proposed by V. Bozza to account for the finite-source effect most efficiently. The efficiency and accuracy of microlux have been verified across the relevant parameter space for binary microlensing. As a differentiable code, microlux makes it possible to apply gradient-based algorithms to the search and posterior estimation of the microlensing modeling. As an example, we use microlux to model a real microlensing event and infer the model posterior via both Fisher information matrix and Hamiltonian Monte Carlo, neither of which would have been possible without the access to accurate model gradients.

astro-ph.IM↗
arXiv2025

Ultra-sensitive integrated circuit sensors based on high-order nonHermitian topological physics

High-precision sensors are of fundamental importance in modern society and technology.Although numerous sensors have been developed, obtaining sensors with higher levels of sensitivity and stronger robustness has always been expected. Here, we propose theoretically and demonstrate experimentally a novel class of sensors with superior performances based on exotic properties of highorder non-Hermitian topological physics. The frequency shift induced by perturbations for these sensors can show an exponential growth with respect to the size of the device, which can well beyond the limitations of conventional sensors. The fully integrated circuit chips have been designed and fabricated in a standard 65nm complementary metal oxide semiconductor process technology. The sensitivity of systems not only less than 0.001fF has been experimentally verified, they are also robust against disorders.Our proposed ultra-sensitive integrated circuit sensors can possess a wide range of applications in various fields and show an exciting prospect for next-generation sensing technologies.

quant-ph↗
arXiv2025

Short-Period Small Planets with High Mutual Inclinations are more Common around Metal-Rich Stars

We present a correlation between the stellar metallicities and the mutual inclinations of multi-planet systems hosting short-period small planets (a/Rs<12, Rp<4Re). We analyzed 89 multi-planet systems discovered by Kepler, K2, and TESS, where the innermost planets have periods shorter than 10 days. We found that the mutual inclinations of the innermost two planets are higher and more diverse around metal-rich stars. The mutual inclinations are calculated as the absolute differences between the best-fit inclinations of the innermost two planets from transit modeling, which represent the lower limits of the true mutual inclinations. The mean and variance of the mutual inclination distribution of the metal-rich systems are 3.1+-0.5 and 3.1+-0.4 degrees, while for the metal-poor systems they are 1.3+-0.2 and 1.0+-0.2 degrees. This finding suggests that inner planetary systems around metal-rich stars are dynamically hotter. We summarized the theories that could plausibly explain this correlation, including the influence of giant planets, higher solid densities in protoplanetary disks around metal-rich stars, or secular chaos coupled with an excess of angular momentum deficits. Planet formation and population synthesis models tracking the mutual inclination evolution would be essential to fully understand this correlation.

astro-ph.EP↗
arXiv2025

PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment

In the realm of parameter-efficient fine-tuning (PEFT) methods, while options like LoRA are available, there is a persistent demand in the industry for a PEFT approach that excels in both efficiency and performance within the context of single-backbone multi-tenant applications. This paper introduces a new and straightforward PEFT technique, termed \underline{P}rompt \underline{A}ware \underline{R}epresentation \underline{A}djustment (PARA). The core of our proposal is to integrate a lightweight vector generator within each Transformer layer. This generator produces vectors that are responsive to input prompts, thereby adjusting the hidden representations accordingly. Our extensive experimentation across diverse tasks has yielded promising results. Firstly, the PARA method has been shown to surpass current PEFT benchmarks in terms of performance, despite having a similar number of adjustable parameters. Secondly, it has proven to be more efficient than LoRA in the single-backbone multi-tenant scenario, highlighting its significant potential for industrial adoption.

cs.CL↗
arXiv2025

Electric-field switchable chirality in rhombohedral graphene Chern insulators stabilized by tungsten diselenide

Chern insulators host topologically protected chiral edge currents with quantized conductance characterized by their Chern number. Switching the chirality of a Chern insulator, namely, the direction of the edge current, is highly challenging due to topologically forbidden backscattering but is of considerable importance for the design of topological devices. Nevertheless, this can be achieved by reversing the sign of the Chern number through a topological phase transition. Here, we report electrically switchable chirality in rhombohedral multilayer graphene-based Chern insulators. By introducing moire superlattices in rhombohedral heptalayer graphene, we observed a cascade of topological phase transitions at quarter electron filling of a moire band with the Chern number tunable from -1, 1 to 2. Furthermore, integrating monolayer tungsten diselenide at the moireless interface of rhombohedral decalayer graphene/h-BN superlattices stabilizes the Chern insulators, enabling quantized anomalous Hall resistance of h/2e^2. Remarkably, the Chern number can be switched from -1 to 2 using displacement fields. Our work establishes rhombohedral multilayer graphene moire superlattices as a versatile platform for topological engineering, with switchable chirality offering significant promise for integrating chiral edge currents into topological electronic circuits.

cond-mat.mes-hall↗
arXiv2025

Detecting Planetary Oblateness in the Era of JWST: A Case Study of Kepler-167e

Planets may be rotationally flattened, and their oblateness thus provide useful information on their formation and evolution. Here we develop a new algorithm that can compute the transit light curve due to an oblate planet very efficiently and use it to study the detectability of planet oblateness (and spin obliquity) with the James Webb Space Telescope (JWST). Using the Jupiter analog, Kepler-167e, as an example, we show that observations of a single transit with JWST are able to detect a Saturn-like oblateness ($f=0.1$) with high confidence, or set a stringent upper limit on the oblateness parameter, as long as the planetary spin is slightly misaligned ($\gtrsim 20^\circ$) with respect to its orbital direction. Based on known obliquity measurements and theoretical arguments, it is reasonable to believe that this level of misalignment may be common. We estimate the sensitivity limit of JWST in oblateness detections and highlight the importance of better characterizations of cold planets in planning future JWST transit observations. The potential to detect rings, moons, and atmospheric species of the cold giants with JWST is also discussed.

astro-ph.EP↗
arXiv2025

TS-HTFA: Advancing Time Series Forecasting via Hierarchical Text-Free Alignment with Large Language Models

Given the significant potential of large language models (LLMs) in sequence modeling, emerging studies have begun applying them to time-series forecasting. Despite notable progress, existing methods still face two critical challenges: 1) their reliance on large amounts of paired text data, limiting the model applicability, and 2) a substantial modality gap between text and time series, leading to insufficient alignment and suboptimal performance. In this paper, we introduce \textbf{H}ierarchical \textbf{T}ext-\textbf{F}ree \textbf{A}lignment (\textbf{TS-HTFA}), a novel method that leverages hierarchical alignment to fully exploit the representation capacity of LLMs while eliminating the dependence on text data. Specifically, we replace paired text data with adaptive virtual text based on QR decomposition word embeddings and learnable prompt. Furthermore, we establish comprehensive cross-modal alignment at three levels: input, feature, and output. Extensive experiments on multiple time-series benchmarks demonstrate that HTFA achieves state-of-the-art performance, significantly improving prediction accuracy and generalization.

cs.AI↗
arXiv2025

A possible evidence of pion condensation

This work demonstrates that once a large number of pion is condensed in a high-energy hadron collision, the gamma-ray spectrum from $π^0$ decay takes on a typical broken power-law shape, which has been documented in many astronomical observations, but we have not yet recognized it. We show that this pion condensation is caused by a large number of soft gluons condensed in protons.

hep-ph↗
arXiv2024

Single Trajectory Distillation for Accelerating Image and Video Style Transfer

Diffusion-based stylization methods typically denoise from a specific partial noise state for image-to-image and video-to-video tasks. This multi-step diffusion process is computationally expensive and hinders real-world application. A promising solution to speed up the process is to obtain few-step consistency models through trajectory distillation. However, current consistency models only force the initial-step alignment between the probability flow ODE (PF-ODE) trajectories of the student and the imperfect teacher models. This training strategy can not ensure the consistency of whole trajectories. To address this issue, we propose single trajectory distillation (STD) starting from a specific partial noise state. We introduce a trajectory bank to store the teacher model's trajectory states, mitigating the time cost during training. Besides, we use an asymmetric adversarial loss to enhance the style and quality of the generated images. Extensive experiments on image and video stylization demonstrate that our method surpasses existing acceleration models in terms of style similarity and aesthetic evaluations. Our code and results will be available on the project page: https://single-trajectory-distillation.github.io.

cs.CV↗
arXiv2024

Neural Networks for Threshold Dynamics Reconstruction

We introduce two convolutional neural network (CNN) architectures, inspired by the Merriman-Bence-Osher (MBO) algorithm and by cellular automatons, to model and learn threshold dynamics for front evolution from video data. The first model, termed the (single-dynamics) MBO network, learns a specific kernel and threshold for each input video without adapting to new dynamics, while the second, a meta-learning MBO network, generalizes across diverse threshold dynamics by adapting its parameters per input. Both models are evaluated on synthetic and real-world videos (ice melting and fire front propagation), with performance metrics indicating effective reconstruction and extrapolation of evolving boundaries, even under noisy conditions. Empirical results highlight the robustness of both networks across varied synthetic and real-world dynamics.

cs.LG↗
arXiv2024

An Eccentric Binary with a Misaligned Circumbinary Disk

We present spectroscopic and photometric observations of Bernhard-2, which was previously identified as a candidate system to host a misaligned circumbinary disk. Our spectroscopic measurements confirm that Bernhard-2 indeed contains an eccentric ($e=0.69 \pm 0.08$) binary and thus that the periodic variability in the photometric light curve is best explained by the occultation by the misaligned circumbinary disk. By modeling the spectral energy distributions at different phases, we infer the masses of the two binary components to be $\sim 1.1\,M_\odot$ and $\sim 0.9\,M_\odot$, respectively. The system age is determined to be $\lesssim$ 20 Myr by combining the stellar isochrone model with lithium abundance. Our new photometric observations show clear deviations from the model prediction based on the archival data, suggesting ongoing precession of the circumbinary disk. The H$α$ line of Bernhard-2 also shows an inverse P-Cygni profile at epochs close to the pericenter passage, which could be attributed to the pulsed accretion around the pericenter. Bernhard-2 therefore closely resembles the well studied KH 15D system. Further detailed observations and studies of such rare systems can provide useful information about disk physics and evolution.

astro-ph.SR↗
arXiv2024

DRUM: Learning Demonstration Retriever for Large MUlti-modal Models

Recently, large language models (LLMs) have demonstrated impressive capabilities in dealing with new tasks with the help of in-context learning (ICL). In the study of Large Vision-Language Models (LVLMs), when implementing ICL, researchers usually adopts the naive strategies like fixed demonstrations across different samples, or selecting demonstrations directly via a visual-language embedding model. These methods does not guarantee the configured demonstrations fit the need of the LVLMs. To address this issue, we now propose a novel framework, \underline{d}emonstration \underline{r}etriever for large m\underline{u}lti-modal \underline{m}odel (DRUM), which fine-tunes the visual-language embedding model to better meet the LVLM's needs. First, we discuss the retrieval strategies for a visual-language task, assuming an embedding model is given. And we propose to concate the image and text embeddings to enhance the retrieval performance. Second, we propose to re-rank the demonstrations retrieved by the embedding model via the LVLM's feedbacks, and calculate a list-wise ranking loss for training the embedding model. Third, we propose an iterative demonstration mining strategy to improve the training of the embedding model. Through extensive experiments on 3 types of visual-language tasks, 7 benchmark datasets, our DRUM framework is proven to be effective in boosting the LVLM's in-context learning performance via retrieving more proper demonstrations.

cs.CL↗
arXiv2024

EM-based Fast Uncertainty Quantification for Bayesian Multi-setup Operational Modal Analysis

The current Bayesian FFT algorithm relies on direct differentiation to obtain the posterior covariance matrix (PCM), which is time-consuming, memory-intensive, and hard to code, especially for the multi-setup operational modal analysis (OMA). Aiming at accelerating the uncertainty quantification in multi-setup OMA, an expectation-maximization (EM)-based algorithm is proposed by reformulating the Hessian matrix of the negative log-likelihood function (NLLF) as a sum of simplified components corresponding to the complete-data NLLF. Matrix calculus is employed to derive these components in a compact manner, resulting in expressions similar to those in the single-setup case. This similarity allows for the reuse of existing Bayesian single-setup OMA codes, simplifying implementation. The singularity caused by mode shape norm constraints is addressed through null space projection, eliminating potential numerical errors from the conventional pseudoinverse operation. A sparse assembly strategy is further adopted, avoiding unnecessary calculations and storage of predominant zero elements in the Hessian matrix. The proposed method is then validated through a comprehensive parametric study and applied to a multi-setup OMA of a high-rise building. Results demonstrate that the proposed method efficiently calculates the PCM within seconds, even for cases with hundreds of parameters. This represents an efficiency improvement of at least one order of magnitude over the state-of-the-art method. Such performance paves the way for a real-time modal identification of large-scale structures, including those with closely-spaced modes.

stat.CO↗
arXiv2024

Sample Complexity of Probability Divergences under Group Symmetry

We rigorously quantify the improvement in the sample complexity of variational divergence estimations for group-invariant distributions. In the cases of the Wasserstein-1 metric and the Lipschitz-regularized $α$-divergences, the reduction of sample complexity is proportional to the group size if the group is finite. In addition to the published version at ICML 2023, our proof indeed has included the case when the group is infinite such as compact Lie groups, the convergence rate can be further improved and depends on the intrinsic dimension of the fundamental domain characterized by the scaling of its covering number. Our approach is different from that in [Tahmasebi & Jegelka, ICML 2024] and our work also applies to asymmetric divergences, such as the Lipschitz-regularized $α$-divergences. For the maximum mean discrepancy (MMD), the improvement of sample complexity is more nuanced, as it depends on not only the group size but also the choice of kernel. Numerical simulations verify our theories.

math.ST↗