arXiv subjects

Chenxi Xu

Publications and source records attributed to Chenxi Xu.

8 recordsLinked to original sources

arXiv2026

Competing Chern states revealed by quasiparticle charging in moir\'e rhombohedral graphene

Moir\'e materials realize a versatile platform for exploring the physics of fractional Chern insulators (FCIs). The recently observed evolution from FCIs to an extended quantum anomalous Hall background upon lowering the electronic temperature in moir\'e rhombohedral graphene (mRG)8 raises a fundamental question: Is it caused by a failure to equilibrate the edge states of an FCI or by a genuine phase transition in the bulk from an FCI to a generalized anomalous Hall crystal? Here we address this question by probing quasiparticle charging in a mesoscopic mRG antidot device and by bulk resistance measurements, both of which are bulk-sensitive and free from complications from edge states. Tunneling to the mRG antidot reveals quasiparticles carrying one electron charge for both Chern states at filling factors {\nu}=1 and 2/3 at low temperatures. Temperature dependence measurements of the bulk resistance near {\nu}=2/3 further suggest a thermodynamic phase transition from an FCI to a generalized anomalous Hall crystal at temperatures below about 150mK. The results clearly exclude the edge state equilibration scenario and favor the phase transition scenario. Our work establishes mesoscopic probes as a powerful approach to uncover competing ground states in moir\'e materials and provides a basis for probing fractionalized excitations in FCIs.

cond-mat.mes-hall↗

arXiv2025

VibraVerse: A Large-Scale Geometry-Acoustics Alignment Dataset for Physically-Consistent Multimodal Learning

Understanding the physical world requires perceptual models grounded in physical laws rather than mere statistical correlations. However, existing multimodal learning frameworks, focused on vision and language, lack physical consistency and overlook the intrinsic causal relationships among an object's geometry, material, vibration modes, and the sounds it produces. We introduce VibraVerse, a large-scale geometry-acoustics alignment dataset that explicitly bridges the causal chain from 3D geometry -> physical attributes -> modal parameters -> acoustic signals. Each 3D model has explicit physical properties (density, Young's modulus, Poisson's ratio) and volumetric geometry, from which modal eigenfrequencies and eigenvectors are computed for impact sound synthesis under controlled excitations. To establish this coherence, we introduce CLASP, a contrastive learning framework for cross-modal alignment that preserves the causal correspondence between an object's physical structure and its acoustic response. This framework enforces physically consistent alignment across modalities, ensuring that every sample is coherent, traceable to the governing equations, and embedded within a unified representation space spanning shape, image, and sound. Built upon VibraVerse, we define a suite of benchmark tasks for geometry-to-sound prediction, sound-guided shape reconstruction, and cross-modal representation learning. Extensive validations on these tasks demonstrate that models trained on VibraVerse exhibit superior accuracy, interpretability, and generalization across modalities. These results establish VibraVerse as a benchmark for physically consistent and causally interpretable multimodal learning, providing a foundation for sound-guided embodied perception and a deeper understanding of the physical world. The dataset will be open-sourced.

cs.AI↗

arXiv2025

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64.2% on SWE-bench Verified. With much fewer parameters than several competitors, GLM-4.5 ranks 3rd overall among all evaluated models and 2nd on agentic benchmarks. We release both GLM-4.5 (355B parameters) and a compact version, GLM-4.5-Air (106B parameters), to advance research in reasoning and agentic AI systems. Code, models, and more information are available at https://github.com/zai-org/GLM-4.5.

cs.CL↗

arXiv2025

NAT: Neural Acoustic Transfer for Interactive Scenes in Real Time

Previous acoustic transfer methods rely on extensive precomputation and storage of data to enable real-time interaction and auditory feedback. However, these methods struggle with complex scenes, especially when dynamic changes in object position, material, and size significantly alter sound effects. These continuous variations lead to fluctuating acoustic transfer distributions, making it challenging to represent with basic data structures and render efficiently in real time. To address this challenge, we present Neural Acoustic Transfer, a novel approach that utilizes an implicit neural representation to encode precomputed acoustic transfer and its variations, allowing for real-time prediction of sound fields under varying conditions. To efficiently generate the training data required for the neural acoustic field, we developed a fast Monte-Carlo-based boundary element method (BEM) approximation for general scenarios with smooth Neumann conditions. Additionally, we implemented a GPU-accelerated version of standard BEM for scenarios requiring higher precision. These methods provide the necessary training data, enabling our neural network to accurately model the sound radiation space. We demonstrate our method's numerical accuracy and runtime efficiency (within several milliseconds for 30s audio) through comprehensive validation and comparisons in diverse acoustic transfer scenarios. Our approach allows for efficient and accurate modeling of sound behavior in dynamically changing environments, which can benefit a wide range of interactive applications such as virtual reality, augmented reality, and advanced audio production.

cs.SD↗

arXiv2025

Quantifying Argon Concentration within Insulating Glass Units using Low Frequency Ultrasonic Technique

Insulating glass units (IGUs) account for over 30% of thermal transmission losses in building envelopes. To mitigate this, IGUs are often filled with low-conductivity gases like Argon. However, Argon concentration decreases over time due to IGU aging and manufacturing processes, which lessens their insulating effectiveness. This study presents a novel nondestructive methodology to quantify Argon concentration in IGUs using ultrasonic technique. The ultrasonic energy transmitted through the IGU is correlated with Argon concentration, validated through both experimental measurements and numerical models using COMSOL Multiphysics. The models simulate acoustic-structure interaction by adjusting gas density to reflect Argon presence, showing increased ultrasonic energy with higher Argon concentrations. Experimental measurements on two IGU samples with twenty Argon-air mixtures (ranging from 100% to 25% Argon) show that the proposed ultrasonic technique achieves a mean absolute error of 0.13, outperforming Spark Emission Spectroscopy and Helantec ISO-GAS-Control, which have errors of 2.31 and 0.33, respectively.

physics.app-ph↗

arXiv2024

DiffSound: Differentiable Modal Sound Rendering and Inverse Rendering for Diverse Inference Tasks

Accurately estimating and simulating the physical properties of objects from real-world sound recordings is of great practical importance in the fields of vision, graphics, and robotics. However, the progress in these directions has been limited -- prior differentiable rigid or soft body simulation techniques cannot be directly applied to modal sound synthesis due to the high sampling rate of audio, while previous audio synthesizers often do not fully model the accurate physical properties of the sounding objects. We propose DiffSound, a differentiable sound rendering framework for physics-based modal sound synthesis, which is based on an implicit shape representation, a new high-order finite element analysis module, and a differentiable audio synthesizer. Our framework can solve a wide range of inverse problems thanks to the differentiability of the entire pipeline, including physical parameter estimation, geometric shape reasoning, and impact position prediction. Experimental results demonstrate the effectiveness of our approach, highlighting its ability to accurately reproduce the target sound in a physics-based manner. DiffSound serves as a valuable tool for various sound synthesis and analysis applications.

cs.SD↗

arXiv2021

Real-Time Ground-Plane Refined LiDAR SLAM

SLAM system using only point cloud has been proven successful in recent years. In most of these systems, they extract features for tracking after ground removal, which causes large variance on the z-axis. Ground actually provides robust information to obtain [t_z, θ_{roll}, θ_{pitch}]$. In this project, we followed the LeGO-LOAM, a light-weighted real-time SLAM system that extracts and registers ground as an addition to the original LOAM, and we proposed a new clustering-based method to refine the planar extraction algorithm for ground such that the system can handle much more noisy or dynamic environments. We implemented this method and compared it with LeGo-LOAM on our collected data of CMU campus, as well as a collected dataset for ATV (All-Terrain Vehicle) for off-road self-driving. Both visualization and evaluation results show obvious improvement of our algorithm.

cs.RO↗

arXiv2018

Exposure: A White-Box Photo Post-Processing Framework

Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this paper a deep learning approach that is instead trained on unpaired data, namely a set of photographs that exhibits a retouching style the user likes, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits, rather than just a "black-box" result. Through quantitative comparisons and user studies, we show that this technique generates retouching results consistent with the provided photo set.

cs.GR↗