arXiv subjects

Jing Lu

Publications and source records attributed to Jing Lu.

At least 37 records · Page 2Linked to original sources

arXiv2026

The Search for Stable Nickel: Investigating the Origins of Type Ia Supernovae with Late-time NIR Spectroscopy from the Carnegie Supernova Project-II

Producing stable $^{58}$Ni in Type Ia supernovae (SNe Ia) requires sufficiently high density conditions that are not predicted for all origin scenarios, so examining the distribution of $^{58}$Ni using the NIR [Ni II] 1.939 $μ$m line may observationally distinguish between possible progenitors and explosion mechanisms. We present 79 telluric-corrected NIR spectra of 22 low-redshift SNe Ia from the Carnegie Supernova Project-II ranging from +50 to +505 days, including 31 previously unpublished spectra. We introduce the Gaussian Peak Ratio, a detection parameter that confirms the presence of the NIR [Ni II] 1.939 $μ$m line in 8 SNe in our sample. Non-detections occur at earlier phases when the NIR Ni line has not emerged yet or in low signal-to-noise spectra yielding inconclusive results. Subluminous 86G-like SNe Ia show the earliest NIR Ni features around ~+50 days, whereas normal-bright SNe Ia do not exhibit NIR Ni until ~+150 days. NIR Ni features detected in our sample have low peak velocities ($v$~1200 km/s) and narrow line widths ($\leq$ 3500 km/s), indicating stable $^{58}$Ni is centrally located. This implies high density burning conditions in the innermost regions of SNe Ia and could be due to higher mass progenitors (i.e. near-$M_{ch}$). NIR spectra of the nearly two dozen SNe Ia in our sample are compared to various model predictions and paired with early-time properties to identify ideal observation windows for future SNe Ia discovered by upcoming surveys with Rubin-LSST or the Roman Space Telescope.

astro-ph.HE↗

arXiv2026

WhispSynth: Scaling Multilingual Whisper Corpus through Real Data Curation and A Novel Pitch-free Generative Framework

Whisper generation is constrained by the difficulty of data collection. Because whispered speech has low acoustic amplitude, high-fidelity recording is challenging. In this paper, we introduce WhispSynth, a large-scale multilingual corpus constructed via a novel high-fidelity generative framework. Specifically, we propose a pipeline integrating Differentiable Digital Signal Processing (DDSP)-based pitch-free method with Text-to-Speech (TTS) models. This framework refines a comprehensive collection of resources, including our newly constructed WhispNJU dataset, into 118 hours of high-fidelity whispered speech from 479 speakers. Unlike standard synthetic or noisy real data, our data engine faithfully preserves source vocal timbre and linguistic content while ensuring acoustic consistency, providing a robust foundation for text-to-whisper research. Experimental results demonstrate that WhispSynth exhibits significantly higher quality than existing corpora. Moreover, our CosyWhisper, tuned with WhispSynth, achieves speech naturalness on par with ground-truth samples. The official implementation and related resources are available at https://github.com/tan90xx/cosywhisper.

cs.SD↗

arXiv2026

Altermagnetic Flatband-Driven Fermi Surface Geometry for Giant Tunneling Magnetoresistance

Altermagnetism, characterized by zero net magnetization and symmetry-protected spin-split band structures, has recently emerged as a promising platform for spintronics. In altermagnetic tunnel junctions (AMTJs), the suppression of tunneling in the antiparallel configuration relies on the mismatch between spin-polarized conduction channels in momentum space. However, ideal nonoverlapping spin-polarized Fermi surfaces are rarely found in bulk altermagnets. Motivated by the critical influence of Fermi surface geometry on tunneling magnetoresistance (TMR), we investigate three experimentally synthesized altermagnets -- bulk $\mathrm{V_2Te_2O}$, $\mathrm{RbV_2Te_2O}$, and $\mathrm{KV_2Se_2O}$ -- to elucidate how flatband-driven Fermi surfaces minimize spin-channel overlap and boost AMTJ performance. Notably, $\mathrm{RbV_2Te_2O}$ and $\mathrm{KV_2Se_2O}$ host flat altermagnetic Fermi sheets, which confine spin degeneracy to minimal arc-like or nodal-like regions. Such Fermi surface geometry drastically reduces spin overlap, resulting in an unprecedented intrinsic TMR well over $10^3\%$ in the $\mathrm{KV_2Se_2O}$-based AMTJ. Incorporating an insulating barrier further enhances the TMR to $\sim10^6\%$, surpassing most conventional MTJs. These results not only establish $\mathrm{KV_2Se_2O}$ as a compelling candidate AMTJ material, but also highlight the critical role of flatband Fermi surface geometry in achieving high-performance altermagnetic-spintronic device technology.

cond-mat.mtrl-sci↗

arXiv2026

Rethinking Flow and Diffusion Bridge Models for Speech Enhancement

Flow matching and diffusion bridge models have emerged as leading paradigms in generative speech enhancement, modeling stochastic processes between paired noisy and clean speech signals based on principles such as flow matching, score matching, and Schrödinger bridge. In this paper, we present a framework that unifies existing flow and diffusion bridge models by interpreting them as constructions of Gaussian probability paths with varying means and variances between paired data. Furthermore, we investigate the underlying consistency between the training/inference procedures of these generative models and conventional predictive models. Our analysis reveals that each sampling step of a well-trained flow or diffusion bridge model optimized with a data prediction loss is theoretically analogous to executing predictive speech enhancement. Motivated by this insight, we introduce an enhanced bridge model that integrates an effective probability path design with key elements from predictive paradigms, including improved network architecture, tailored loss functions, and optimized training strategies. Experiments on denoising and dereverberation tasks demonstrate that the proposed method outperforms existing flow and diffusion baselines with fewer parameters and reduced computational complexity. The results also highlight that the inherently predictive nature of this generative framework imposes limitations on its achievable upper-bound performance.

eess.AS↗

arXiv2026

UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture Search

Lightweight models are essential for real-time speech enhancement applications. In recent years, there has been a growing trend toward developing increasingly compact models for speech enhancement. In this paper, we propose an Ultra-Lightweight U-net optimized by Network Architecture Search (UL-UNAS), which is suitable for implementation in low-footprint devices. Firstly, we explore the application of various efficient convolutional blocks within the U-Net framework to identify the most promising candidates. Secondly, we introduce two boosting components to enhance the capacity of these convolutional blocks: a novel activation function named affine PReLU and a causal time-frequency attention module. Furthermore, we leverage neural architecture search to discover an optimal architecture within our carefully designed search space. By integrating the above strategies, UL-UNAS not only significantly outperforms the latest ultra-lightweight models with the same or lower computational complexity, but also delivers competitive performance compared to recent baseline models that require substantially higher computational resources. Source code and audio demos are available at https://github.com/Xiaobin-Rong/ul-unas.

eess.AS↗

arXiv2026

Supernova Rates and Luminosity Functions from ASAS-SN III: Over a Decade of Type Ia SNe and Their Subtypes

We present volumetric rates and luminosity functions (LFs) of Type Ia supernovae (SNe Ia) from the All-Sky Automated Survey for Supernovae (ASAS-SN), covering the 11-year period from 2014 to 2024. By combining the 2014--2017 $V$-band sample with the 2018--2024 $g$-band sample, we construct a large statistical dataset of $1776$ SNe Ia. We compute completeness corrections based on injection-recovery simulations of the ASAS-SN light curves, taking into account the variations in light curve shapes. For our standard sample ($M_{g,\mathrm{peak}}<-16.0$ mag), we extract a total volumetric SN Ia rate of $R_{\mathrm{tot}} = (2.55 \pm 0.12) \times 10^4\,\mathrm{yr}^{-1}\,\mathrm{Gpc}^{-3}\,h_{70}^3$ at a median redshift of $z=0.029$. With a statistical uncertainty of $4.7\%$, this is the most precise local measurement to date. While the "normal" SNe Ia account for $(92.7 \pm 1.9)\%$ of this rate, the total LF reveals immense diversity, with $M_{g,\mathrm{peak}}$ spanning over five magnitudes. The LF of SNe Iax is also broad and rises toward lower luminosities, resulting in a likely lower limit of $(4.3 \pm 1.8)\%$ of the total rate. We place strong constraints on the rate of SNe Ia-CSM, finding they account for only $(0.036 \pm 0.017)\%$ of the total local rate. Finally, we find that the low-luminosity 02es-like SNe are $7 \pm 5$ times more common than the luminous 03fg-like SNe. This places demographic constraints on models proposing a physical continuum for these two subtypes, implying that any common channel for the two classes must strongly favor lower-luminosity explosions.

astro-ph.HE↗

arXiv2026

From Struggle to Success: Context-Aware Guidance for Screen Reader Users in Computer Use

Equal access to digital technologies is critical for education, employment, and social participation. However, mainstream interfaces are visually oriented, creating steep learning curves and frequent obstacles for screen reader users, and limiting their independence and opportunities. Existing support is inadequate -- tutorials mainly target sighted users, while human assistance lacks real-time availability. We introduce AskEase, an on-demand AI assistant that provides step-by-step, screen reader user-friendly guidance for computer use. AskEase manages multiple sources of context to infer user intent and deliver precise, situation-specific guidance. Its seamless interaction design minimizes disruption and reduces the effort of seeking help. We demonstrated its effectiveness through representative usage scenarios and robustness tests. In a within-subjects study with 12 screen reader users, AskEase significantly improved task success while reducing perceived workload, including physical demand, effort, and frustration. These results demonstrate the potential of LLM-powered assistants to promote accessible computing and expand opportunities for users with visual impairments.

cs.HC↗

arXiv2026

VoCodec: An Efficient Lightweight Low-Bitrate Speech Codec

Recent advancements in end-to-end neural speech codecs enable compressing audio at extremely low bitrates while maintaining high-fidelity reconstruction. Meanwhile, low computational complexity and low latency are crucial for real-time communication. In this paper, we propose VoCodec, a speech codec model featuring a computational complexity of only 349.29M multiply-accumulate operations per second (MACs/s) and a latency of 30 ms. With the competitive vocoder Vocos as its backbone, the proposed model ranked fourth on Track 1 in the 2025 LRAC Challenge and achieved the highest subjective evaluation score (MUSHRA) on the clean speech test set. Additionally, we cascade a lightweight neural network at the front end to extend its capability of speech enhancement. Experimental results demonstrate that the two systems achieve competitive performance across multiple evaluation metrics. Speech samples can be found at https://acceleration123.github.io/.

eess.AS↗

arXiv2025

SPECTRE: Spectral Pre-training Embeddings with Cylindrical Temporal Rotary Position Encoding for Fine-Grained sEMG-Based Movement Decoding

Decoding fine-grained movement from non-invasive surface Electromyography (sEMG) is a challenge for prosthetic control due to signal non-stationarity and low signal-to-noise ratios. Generic self-supervised learning (SSL) frameworks often yield suboptimal results on sEMG as they attempt to reconstruct noisy raw signals and lack the inductive bias to model the cylindrical topology of electrode arrays. To overcome these limitations, we introduce SPECTRE, a domain-specific SSL framework. SPECTRE features two primary contributions: a physiologically-grounded pre-training task and a novel positional encoding. The pre-training involves masked prediction of discrete pseudo-labels from clustered Short-Time Fourier Transform (STFT) representations, compelling the model to learn robust, physiologically relevant frequency patterns. Additionally, our Cylindrical Rotary Position Embedding (CyRoPE) factorizes embeddings along linear temporal and annular spatial dimensions, explicitly modeling the forearm sensor topology to capture muscle synergies. Evaluations on multiple datasets, including challenging data from individuals with amputation, demonstrate that SPECTRE establishes a new state-of-the-art for movement decoding, significantly outperforming both supervised baselines and generic SSL approaches. Ablation studies validate the critical roles of both spectral pre-training and CyRoPE. SPECTRE provides a robust foundation for practical myoelectric interfaces capable of handling real-world sEMG complexities.

cs.HC↗

arXiv2025

Resonator-assisted single-photon frequency convertion in a conventional waveguide with a giant V-type atom

We propose a scheme to achieve efficient frequency conversion for a single photon propagating in a 1D conventional waveguide by exploiting the quantum interference induced by the scale of a V-type giant atom (GA) characterized by the distance between the two coupling points as well as single-photon transition pathways originated from the coupling between the GA and the resonator. The presence of photons in the resonator triggers the frequency conversion of photons. The scattering spectra and the conversion contrast are studied in both the Markovian and the non-Markovian regimes. The disappearance of frequency conversion is rooted in the complete suppression of the emission from the excited state to either of lower states in the $n+1$ subspace where $n$ is the photon number of the resonator, and the non-Markovicity-induced nonreciprocity is found under specific conditions. Altering the photon number $n$ induces the non-reciprocal transmission of single photons in the waveguide, hence, enhance the conversion probability.

quant-ph↗

arXiv2025

Tomato, Tomahto, Tomate: Do Multilingual Language Models Understand Based on Subword-Level Semantic Concepts?

Human understanding of text depends on general semantic concepts of words rather than their superficial forms. To what extent does our human intuition transfer to language models? In this work, we study the degree to which current multilingual language models (mLMs) understand based on subword-level semantic concepts. To this end, we form "semantic tokens" by merging the semantically similar subwords and their embeddings, and evaluate the updated mLMs on five heterogeneous multilingual downstream tasks. Results show that the general shared semantics could get the models a long way in making the predictions on mLMs with different tokenizers and model sizes. Inspections of the grouped subwords show that they exhibit a wide range of semantic similarities, including synonyms and translations across many languages and scripts. Lastly, we find that the zero-shot results with semantic tokens are on par with or even better than the original models on certain classification tasks, suggesting that the shared subword-level semantics may serve as the anchors for cross-lingual transfer.

cs.CL↗

arXiv2025

PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement

Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative approaches. However, existing generative SE approaches often overlook the risk of hallucination under severe noise, leading to incorrect spoken content or inconsistent speaker characteristics, which we term linguistic and acoustic hallucinations, respectively. We argue that linguistic hallucination stems from models' failure to constrain valid phonological structures and it is a more fundamental challenge. While language models (LMs) are well-suited for capturing the underlying speech structure through modeling the distribution of discrete tokens, existing approaches are limited in learning from noise-corrupted representations, which can lead to contaminated priors and hallucinations. To overcome these limitations, we propose the Phonologically Anchored Speech Enhancer (PASE), a generative SE framework that leverages the robust phonological prior embedded in the pre-trained WavLM model to mitigate hallucinations. First, we adapt WavLM into a denoising expert via representation distillation to clean its final-layer features. Guided by the model's intrinsic phonological prior, this process enables robust denoising while minimizing linguistic hallucinations. To further reduce acoustic hallucinations, we train the vocoder with a dual-stream representation: the high-level phonetic representation provides clean linguistic content, while a low-level acoustic representation retains speaker identity and prosody. Experimental results demonstrate that PASE not only surpasses state-of-the-art discriminative models in perceptual quality, but also significantly outperforms prior generative models with substantially lower linguistic and acoustic hallucinations.

eess.AS↗

arXiv2025

Tunable single-photon frequency converter in a waveguide with a giant V-type atom

We study the single-photon scattering in a one-dimensional (1D) waveguide coupled to one transition of a $V$-type giant atom (GA), whose other transition is coherently driven by an classical field. The inelastic scattering of single photons by the GA realizes the single-photon frequency conversion. By applying the Lippmann-Schwinger equation, the scattering coefficients for single photons incident from different directions are obtained, which present different scattering spectra in the Markovian and the non-Markovian regimes. The conversion contrast characterizing the nonreciprocity is also analyzed in both regimes. It is found that the probability of the frequency up- or down-conversion vanishes as long as the emission from either transition pathways for single photons is suppressed, but it is enhanced and even reach unity by introducing the nonreciprocity. It is the quantum self-interference induced by the scale of this two-legged GA and the phase difference between the GA-waveguide couplings that tune the probability of the frequency up- or down-conversion.

quant-ph↗

arXiv2025

Adaptive Convolution for CNN-based Speech Enhancement Models

Deep learning-based speech enhancement methods have significantly improved speech quality and intelligibility. Convolutional neural networks (CNNs) have been proven to be essential components of many high-performance models. In this paper, we introduce adaptive convolution, an efficient and versatile convolutional module that enhances the model's capability to adaptively represent speech signals. Adaptive convolution performs frame-wise causal dynamic convolution, generating time-varying kernels for each frame by assembling multiple parallel candidate kernels. A lightweight attention mechanism is proposed for adaptive convolution, leveraging both current and historical information to assign adaptive weights to each candidate kernel. This enables the convolution operation to adapt to frame-level speech spectral features, leading to more efficient extraction and reconstruction. We integrate adaptive convolution into various CNN-based models, highlighting its generalizability. Experimental results demonstrate that adaptive convolution significantly improves the performance with negligible increases in computational complexity, especially for lightweight models. Moreover, we present an intuitive analysis revealing a strong correlation between kernel selection and signal characteristics. Furthermore, we propose the adaptive convolutional recurrent network (AdaptCRN), an ultra-lightweight model that incorporates adaptive convolution and an efficient encoder-decoder design, achieving superior performance compared to models with similar or even higher computational costs.

eess.AS↗

arXiv2025

FlightKooba: A Fast Interpretable FTP Model

Flight trajectory prediction (FTP) and similar time series tasks typically require capturing smooth latent dynamics hidden within noisy signals. However, existing deep learning models face significant challenges of high computational cost and insufficient interpretability due to their complex black-box nature. This paper introduces FlightKooba, a novel modeling approach designed to extract such underlying dynamics analytically. Our framework uniquely integrates HiPPO theory, Koopman operator theory, and control theory. By leveraging Legendre polynomial bases, it constructs Koopman operators analytically, thereby avoiding large-scale parameter training. The method's core strengths lie in its exceptional computational efficiency and inherent interpretability. Experiments on multiple public datasets validate our design philosophy: for signals exhibiting strong periodicity or clear physical laws (e.g., in aviation, meteorology, and traffic flow), FlightKooba delivers competitive prediction accuracy while reducing trainable parameters by several orders of magnitude and achieving the fastest training speed. Furthermore, we analyze the model's theoretical boundaries, clarifying its inherent low-pass filtering characteristics that render it unsuitable for sequences dominated by high-frequency noise. In summary, FlightKooba offers a powerful, efficient, and interpretable new alternative for time series analysis, particularly in resource-constrained environments.

cs.LG↗

arXiv2025

Unveiling non-Hermitian band structures with non-Bloch supercells

Real-valued band structures are foundational to analyzing periodic systems within the Hermitian description and have been experimentally well-established over recent decades. In contrast, non-Hermitian systems exhibit complex band structures where both energy and momentum have imaginary parts, underpinning phenomena like the non-Hermitian skin effect and anomalous bulk-boundary correspondence that defy conventional Bloch theory. Experimentally mapping these complex bands-relating complex momentum to complex energy-and identifying their associated eigenstates is crucial for understanding these systems but remains a significant challenge. Here, we introduce a non-Bloch supercell framework designed to overcome this challenge by decoupling Bloch phase control from the imaginary part of momentum. Our method combines an exponent-flattening protocol with twisted boundary conditions, enabling system-size-independent control of imaginary momentum while preserving high-resolution Bloch phase sampling. Implemented in programmable one- and two-dimensional acoustic crystals, our approach acquires momentum-resolved complex energy surfaces and biorthogonal eigenmodes by Green's function measurements.Data obtained using this framework accurately predict open-boundary spectra and eigenstates, findings we verify through separate open-geometry experiments. Our work provides a broadly applicable experimental toolkit for exploring non-Hermitian band geometry and topology in diverse engineered classical and quantum platforms.

quant-ph↗

arXiv2025

Auditory Attention Decoding from Ear-EEG Signals: A Dataset with Dynamic Attention Switching and Rigorous Cross-Validation

Recent promising results in auditory attention decoding (AAD) using scalp electroencephalography (EEG) have motivated the exploration of cEEGrid, a flexible and portable ear-EEG system. While prior cEEGrid-based studies have confirmed the feasibility of AAD, they often neglect the dynamic nature of attentional states in real-world contexts. To address this gap, a novel cEEGrid dataset featuring three concurrent speakers distributed across three of five distinct spatial locations is introduced. The novel dataset is designed to probe attentional tracking and switching in realistic scenarios. Nested leave-one-out validation-an approach more rigorous than conventional single-loop leave-one-out validation-is employed to reduce biases stemming from EEG's intricate temporal dynamics. Four rule-based models are evaluated: Wiener filter (WF), canonical component analysis (CCA), common spatial pattern (CSP) and Riemannian Geometry-based classifier (RGC). With a 30-second decision window, WF and CCA models achieve decoding accuracies of 41.5% and 41.4%, respectively, while CSP and RGC models yield 37.8% and 37.6% accuracies using a 10-second window. Notably, both WF and CCA successfully track attentional state switches across all experimental tasks. Additionally, higher decoding accuracies are observed for electrodes positioned at the upper cEEGrid layout and near the listener's right ear. These findings underscore the utility of dynamic, ecologically valid paradigms and rigorous validation in advancing AAD research with cEEGrid.

eess.AS↗

arXiv2025

Unconventional tunnel magnetoresistance scaling with altermagnets

In conventional magnetic tunnel junctions (MTJs), the tunnel magnetoresistance (TMR) typically increases with barrier thickness as electron transmission in the antiparallel configuration decays faster than that of the parallel configuration. In this work, we reveal an anomalous scaling effect in altermagnetic tunnel junctions (AMTJs), where the TMR decreases anomalously with an increasing barrier thickness. The anomalous scaling originates from the overlapping spin-split branches forming a transmission path that cannot be suppressed in the antiparallel state. Such phenomenon is explained by a double-barrier model and is further demonstrated using ab initio quantum transport simulations in 2D V2Te2O/Cr2Se2O/V2Te2O and V2Te2O/ZnSe/V2Te2O AMTJs. Our work identifies a peculiar unexpected transport characteristic of AMTJ, providing a fundamental limit on AMTJ device design and illustrating the potential optimal design of AMTJ at the ultrascaled monolayer limit.

cond-mat.mes-hall↗