arXiv subjects

Jing Lu

Publications and source records attributed to Jing Lu.

At least 55 records · Page 3Linked to original sources

arXiv2025

Bound states and the collective dynamics of Distant Quantum Emitters coupled to a chiral waveguide

We consider two two-level quantum emitters (QEs) with separations on the order of the wavelength which are chirally coupled to a one-dimensional (1D) waveguide, and the electromagnetic field of the 1D waveguide has a direction-dependent velocity, which produces two field propagation phases on the dynamics of QEs. Their spontaneous process is examined for QEs having unequal emission rates to the waveguide. It is found that radiation could be enhanced for both QEs, inhibited for both QEs, enhanced for one while inhibited for the other, completely suppressed for both QEs. In particular, the mechanism for radiation completely suppressed is the presence of a QE-photon bound state.

quant-ph↗

arXiv2025

$Λ$-Type Giant Atom Mediated Controllable Single-Photon Transport in a One-Dimensional Chiral Waveguide

We investigate the single-photon scattering spectrum of a driven $Λ$-type giant atom system chirally coupled to a one-dimensional (1D) waveguide. By employing a real-space scattering approach, we obtain analytical solutions for the scattering amplitudes that remain valid in both Markovian and non-Markovian regimes. We observe that an external driving field induces a splitting of the transmission spectrum's dip into double dips, with the distance between the two dips increasing as the strength of the driving field increases. The chiral nature of the coupling allows for controlled switching between complete transmission and perfect reflection of incident photons. In the Markovian limit, we predict robust perfect transmission at specific phase values, independent of the driving field parameters.Moreover, in the non-Markovian regime, as the size of the giant atom increases, the oscillatory behavior of the scattering spectrum becomes more pronounced. Adjusting the giant atom size enables control over the number of decoupling points as well as the number of complete reflection points.

quant-ph↗

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

A k-space approach to modeling multi-channel parametric array loudspeaker systems

Multi-channel parametric array loudspeaker (MCPAL) systems offer enhanced flexibility and promise for generating highly directional audio beams in real-world applications. However, efficient and accurate prediction of their generated sound fields remains a major challenge due to the complex nonlinear behavior and multi-channel signal processing involved. To overcome this obstacle, we propose a k-space approach for modeling arbitrary MCPAL systems arranged on a baffled planar surface. In our method, the linear ultrasound field is first solved using the angular spectrum approach, and the quasilinear audio sound field is subsequently computed efficiently in k-space. By leveraging three-dimensional fast Fourier transforms, our approach not only achieves high computational and memory efficiency but also maintains accuracy without relying on the paraxial approximation. For typical configurations studied, the proposed method demonstrates a speed-up of more than four orders of magnitude compared to the direct integration method. Our proposed approach paved the way for simulating and designing advanced MCPAL systems.

eess.AS↗

arXiv2025

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound

Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are inefficient, operator-dependent, and challenging to apply in cases of complex fetal anatomy. Existing deep learning methods are based on 2D standard ultrasound (US) images or videos that lack spatial information, limiting their prediction accuracy. In this study, we propose the first method for directly estimating FBW from 3D fetal US volumes. Our approach integrates a multi-scale feature fusion network (MFFN) and a synthetic sample-based learning framework (SSLF). The MFFN effectively extracts and fuses multi-scale features under sparse supervision by incorporating channel attention, spatial attention, and a ranking-based loss function. SSLF generates synthetic samples by simply combining fetal head and abdomen data from different fetuses, utilizing semi-supervised learning to improve prediction performance. Experimental results demonstrate that our method achieves superior performance, with a mean absolute error of $166.4\pm155.9$ $g$ and a mean absolute percentage error of $5.1\pm4.6$%, outperforming existing methods and approaching the accuracy of a senior doctor. Code is available at: https://github.com/Qioy-i/EFW.

eess.IV↗

arXiv2025

Computational Design of Two-Dimensional MoSi$_2$N$_4$ Family Field-Effect Transistor for Future Ångström-Scale CMOS Technology Nodes

Advancing complementary metal-oxide-semiconductor (CMOS) technology into the sub-1-nm angström-scale technology nodes is expected to involve alternative semiconductor channel materials, as silicon transistors encounter severe performance degradation at physical gate lengths below 10 nm. Two-dimensional (2D) semiconductors have emerged as strong candidates for overcoming short-channel effects due to their atomically thin bodies, which inherently suppress electrostatic leakage and improve gate control in aggressively scaled field-effect transistors (FETs). Among the growing library of 2D materials, the MoSi$_2$N$_4$ family -- a synthetic septuple-layered materials -- has attracted increasing attention for its remarkable ambient stability, suitable bandgaps, and favorable carrier transport characteristics, making it a promising platform for next-generation transistors. While experimental realization of sub-10-nm 2D FETs remains technologically demanding, computational device simulation using first-principles density functional theory combined with nonequilibrium Green's function transport simulations provide a powerful and cost-effective route for exploring the performance limits and optimal design of ultrascaled FET. This review consolidates the current progress in the computational design of MoSi$_2$N$_4$ family FETs. We review the physical properties of MoSi$_2$N$_4$ that makes them compelling candidates for transistor applications, as well as the simulated device performance and optimization strategy of MoSi$_2$N$_4$ family FETs. Finally, we identify key challenges and research gaps, and outline future directions that could accelerate the practical deployment of MoSi$_2$N$_4$ family FET in the angström-scale CMOS era.

cond-mat.mtrl-sci↗

arXiv2025

Pressure-Driven Metallicity in Ångström-Thickness 2D Bismuth and Layer-Selective Ohmic Contact to MoS2

Recent fabrication of two-dimensional (2D) metallic bismuth (Bi) via van der Waals (vdW) squeezing method opens a new avenue to ultrascaling metallic materials into the ångström-thickness regime [Nature 639, 354 (2025)]. However, freestanding 2D Bi is typically known to exhibit a semiconducting phase [Nature 617, 67 (2023), Phys. Rev. Lett. 131, 236801 (2023)], which contradicts with the experimentally observed metallicity in vdW-squeezed 2D Bi. Here we show that such discrepancy originates from the pressure-induced buckled-to-flat structural transition in 2D Bi, which changes the electronic structure from semiconducting to metallic phases. Based on the experimentally fabricated MoS2-Bi-MoS2 trilayer heterostructure, we demonstrate the concept of layer-selective Ohmic contact in which one MoS2 layer forms Ohmic contact to the sandwiched Bi monolayer while the opposite MoS2 layer exhibits a Schottky barrier. The Ohmic contact can be switched between the two sandwiching MoS2 monolayers by changing the polarity of an external gate field, thus enabling charge to be spatially injected into different MoS2 layers. The layer-selective Ohmic contact proposed here represents a layertronic generalization of metal/semiconductor contact, paving a way towards layertronic device application.

cond-mat.mtrl-sci↗

arXiv2025

A Lightweight Hybrid Dual Channel Speech Enhancement System under Low-SNR Conditions

Although deep learning based multi-channel speech enhancement has achieved significant advancements, its practical deployment is often limited by constrained computational resources, particularly in low signal-to-noise ratio (SNR) conditions. In this paper, we propose a lightweight hybrid dual-channel speech enhancement system that combines independent vector analysis (IVA) with a modified version of the dual-channel grouped temporal convolutional recurrent network (GTCRN). IVA functions as a coarse estimator, providing auxiliary information for both speech and noise, while the modified GTCRN further refines the speech quality. We investigate several modifications to ensure the comprehensive utilization of both original and auxiliary information. Experimental results demonstrate the effectiveness of the proposed system, achieving enhanced speech with minimal parameters and low computational complexity.

eess.AS↗

arXiv2025

TS-URGENet: A Three-stage Universal Robust and Generalizable Speech Enhancement Network

Universal speech enhancement aims to handle input speech with different distortions and input formats. To tackle this challenge, we present TS-URGENet, a Three-Stage Universal, Robust, and Generalizable speech Enhancement Network. To address various distortions, the proposed system employs a novel three-stage architecture consisting of a filling stage, a separation stage, and a restoration stage. The filling stage mitigates packet loss by preliminarily filling lost regions under noise interference, ensuring signal continuity. The separation stage suppresses noise, reverberation, and clipping distortion to improve speech clarity. Finally, the restoration stage compensates for bandwidth limitation, codec artifacts, and residual packet loss distortion, refining the overall speech quality. Our proposed TS-URGENet achieved outstanding performance in the Interspeech 2025 URGENT Challenge, ranking 2nd in Track 1.

eess.AS↗

arXiv2025

WaveNet-Volterra Neural Networks for Active Noise Control: A Fully Causal Approach

Active Noise Control (ANC) systems are challenged by nonlinear distortions, which degrade the performance of traditional adaptive filters. While deep learning-based ANC algorithms have emerged to address nonlinearity, existing approaches often overlook critical limitations: (1) end-to-end Deep Neural Network (DNN) models frequently violate causality constraints inherent to real-time ANC applications; (2) many studies compare DNN-based methods against simplified or low-order adaptive filters rather than fully optimized high-order counterparts. In this letter, we propose a causality-preserving time-domain ANC framework that synergizes WaveNet with Volterra Neural Networks (VNNs), explicitly addressing system nonlinearity while ensuring strict causal operation. Unlike prior DNN-based approaches, our method is benchmarked against both state-of-the-art deep learning architectures and rigorously optimized high-order adaptive filters, including Wiener solutions. Simulations demonstrate that the proposed framework achieves superior performance over existing DNN methods and traditional algorithms, revealing that prior claims of DNN superiority stem from incomplete comparisons with suboptimal traditional baselines. Source code is available at https://github.com/Lu-Baihh/WaveNet-VNNs-for-ANC.git.

eess.AS↗

arXiv2025

Introducing STARDIS: An Open and Modular Stellar Spectral Synthesis Code

We introduce a new 1D stellar spectral synthesis Python code called stardis. stardis is a modular, open-source radiative transfer code that is capable of spectral synthesis from near-UV to IR for FGK stars. We describe the structure, inputs, features, underlying physics, and assumptions of stardis as well as the radiative transfer scheme implemented. To validate our code, we show spectral comparisons between stardis and korg with the same input atmospheric structure models, and also compare qualitatively to phoenix for solar models. We find that stardis generally agrees well with korg for solar models on the few percent level or better, that the codes can diverge in the ultraviolet, with more extreme differences in cooler stars. stardis can be found at https://github.com/tardis-sn/stardis, and documentation can be found at https://tardis-sn.github.io/stardis/.

astro-ph.SR↗

arXiv2025

Generating Localized Audible Zones Using a Single-Channel Parametric Loudspeaker

Advanced sound zone control (SZC) techniques typically rely on massive multi-channel loudspeaker arrays to create high-contrast personal sound zones, making single-loudspeaker SZC seem impossible. In this Letter, we challenge this paradigm by introducing the multi-carrier parametric loudspeaker (MCPL), which enables SZC using only a single loudspeaker. In our approach, distinct audio signals are modulated onto separate ultrasonic carrier waves at different frequencies and combined into a single composite signal. This signal is emitted by a single-channel ultrasonic transducer, and through nonlinear demodulation in air, the audio signals interact to virtually form multi-channel outputs. This novel capability allows the application of existing SZC algorithms originally designed for multi-channel loudspeaker arrays. Simulations validate the effectiveness of our proposed single-channel MCPL, demonstrating its potential as a promising alternative to traditional multi-loudspeaker systems for achieving high-contrast SZC. Our work opens new avenues for simplifying SZC systems without compromising performance.

eess.AS↗

arXiv2025

FNSE-SBGAN: Far-field Speech Enhancement with Schrodinger Bridge and Generative Adversarial Networks

The prevailing method for neural speech enhancement predominantly utilizes fully-supervised deep learning with simulated pairs of far-field noisy-reverberant speech and clean speech. Nonetheless, these models frequently demonstrate restricted generalizability to mixtures recorded in real-world conditions. To address this issue, this study investigates training enhancement models directly on real mixtures. Specifically, we revisit the single-channel far-field to near-field speech enhancement (FNSE) task, focusing on real-world data characterized by low signal-to-noise ratio (SNR), high reverberation, and mid-to-high frequency attenuation. We propose FNSE-SBGAN, a framework that integrates a Schrodinger Bridge (SB)-based diffusion model with generative adversarial networks (GANs). Our approach achieves state-of-the-art performance across various metrics and subjective evaluations, significantly reducing the character error rate (CER) by up to 14.58% compared to far-field signals. Experimental results demonstrate that FNSE-SBGAN preserves superior subjective quality and establishes a new benchmark for real-world far-field speech enhancement. Additionally, we introduce an evaluation framework leveraging matrix rank analysis in the time-frequency domain, providing systematic insights into model performance and revealing the strengths and weaknesses of different generative methods.

eess.AS↗

arXiv2025

Interference trapping of populations in a semi-infinite coupled-resonator waveguide

We study the energy structure and dynamics of a two-level emitter (2LE) locally coupled to a semi-infinite one-dimensional (1D) coupled-resonator array (CRA). The energy spectrum in the single-excitation subspace features a continuous band with scattering states, discrete levels with bound states, and a quantum phase transition characterized by the change of the number of bound states. The number of bound states is revealed by the behavior of the excited-state population at long times with quantum beat, residual oscillation, a constant with either non-zero or zero.

quant-ph↗

arXiv2025

Supersonic wave propagation in active non-Hermitian acoustic metamaterials

Obtaining a group velocity higher than the speed of sound in a waveguide is a challenging task in acoustic wave engineering. Even more challenging is to achieve this velocity increase without any intervention with the waveguide profile, such as narrowing or widening, and particularly without interfering with the passage by flexible inclusions, either passive or active. Here, we approach this problem by invoking concepts from non- Hermitian physics, and imposing them using active elements that are smoothly sealed within the waveguide wall. In a real-time feedback operation, the elements induce local pressure gain and loss, as well as non-local pressure integration couplings. We employ a dedicated balancing between the control parameters, derived from lattice theory and adjusted to the waveguide system, to drive the dynamics into a stable parity-time-symmetric regime. We demonstrate the accelerated propagation of a wave packet both numerically and experimentally in an air-filled waveguide and discuss the trade-off between stabilization and the achievable velocity increase. Our work prepares the grounds for advanced forms of wave transmission in continuous media, enabled by short and long range active couplings, created via embedded real-time feedback control.

physics.app-ph↗

arXiv2025

Experimentally Probing Non-Hermitian Spectral Transition and Eigenstate Skewness

Non-Hermitian (NH) systems exhibit intricate spectral topology arising from complex-valued eigenenergies, with positive/negative imaginary parts representing gain/loss. Unlike the orthogonal eigenstates of Hermitian systems, NH systems feature left and right eigenstates that form a biorthogonal basis and can differ significantly, showcasing pronounced skewness between them. These characteristics give rise to unique properties absent in Hermitian systems, such as the NH skin effect and ultra spectral sensitivity. However, conventional experimental techniques are inadequate for directly measuring the complex-valued spectra and left and right eigenstates -- key elements for enhancing our knowledge of NH physics. This challenge is particularly acute in higher-dimensional NH systems, where the spectra and eigenstates are highly sensitive to macroscopic shapes, lattice geometry, and boundary conditions, posing greater experimental demands compared to one-dimensional systems. Here, we present a Green's function-based method that enables the direct measurement and characterization of both complex-valued energy spectra and the left and right eigenstates in arbitrary NH lattices. Using active acoustic crystals as the experimental platform, we observe spectral transitions and eigenstate skewness in two-dimensional NH lattices under both nonreciprocal and reciprocal conditions, with varied geometries and boundary conditions. Our approach renders complex spectral topology and left eigenstates experimentally accessible and practically meaningful, providing new insights into these quantities. The results not only confirm recent theoretical predictions of higher-dimensional NH systems but also establish a universal and versatile framework for investigating complex spectral properties and NH dynamics across a wide range of physical platforms.

cond-mat.mes-hall↗

arXiv2025

Multi-class Decoding of Attended Speaker Direction Using Electroencephalogram and Audio Spatial Spectrum

Decoding the directional focus of an attended speaker from listeners' electroencephalogram (EEG) signals is essential for developing brain-computer interfaces to improve the quality of life for individuals with hearing impairment. Previous works have concentrated on binary directional focus decoding, i.e., determining whether the attended speaker is on the left or right side of the listener. However, a more precise decoding of the exact direction of the attended speaker is necessary for effective speech processing. Additionally, audio spatial information has not been effectively leveraged, resulting in suboptimal decoding results. In this paper, it is found that on the recently presented dataset with 14-class directional focus, models relying exclusively on EEG inputs exhibit significantly lower accuracy when decoding the directional focus in both leave-one-subject-out and leave-one-trial-out scenarios. By integrating audio spatial spectra with EEG features, the decoding accuracy can be effectively improved. The CNN, LSM-CNN, and Deformer models are employed to decode the directional focus from listeners' EEG signals and audio spatial spectra. The proposed Sp-EEG-Deformer model achieves notable 14-class decoding accuracies of 55.35% and 57.19% in leave-one-subject-out and leave-one-trial-out scenarios with a decision window of 1 second, respectively. Experiment results indicate increased decoding accuracy as the number of alternative directions reduces. These findings suggest the efficacy of our proposed dual modal directional focus decoding strategy.

cs.SD↗

arXiv2025

Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging

Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) abnormalities and result in unnecessary patient examinations. We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies to aid prenatal diagnosis. In our collected multi-center dataset of fetal craniocerebral anomalies covering four typical anomalies of the fetal central nervous system (CNS): anencephaly, encephalocele (including meningocele), holoprosencephaly, and rachischisis, patient-level prediction accuracy reaches 94.5%, with an AUROC value of 99.3%. In the subgroup analyzes, our model is applicable to the entire gestational period, with good identification of fetal anomaly types for any gestational period. Heatmaps superimposed on the ultrasound images not only provide a visual interpretation for the algorithm but also provide an intuitive visual aid to the physician by highlighting key areas that need to be reviewed, helping the physician to quickly identify and validate key areas. Finally, the retrospective reader study demonstrates that by combining the automatic prediction of the DL system with the professional judgment of the radiologist, the diagnostic accuracy and efficiency can be effectively improved and the misdiagnosis rate can be reduced, which has an important clinical application prospect.

eess.IV↗