arXiv subjects

Yu Jiang

Publications and source records attributed to Yu Jiang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Towards a general-purpose foundation model for fMRI analysis

Functional MRI (fMRI) is crucial for studying brain function and diagnosing neurological disorders. However, existing analysis methods suffer from reproducibility and transferability challenges due to complex preprocessing pipelines and task-specific model designs. In this work, we introduce NeuroSTORM (Neuroimaging Foundation Model with Spatial-Temporal Optimized Representation Modeling) that learns generalizable representations directly from 4D fMRI volumes and enables efficient transfer to diverse downstream applications. Specifically, NeuroSTORM is pre-trained on 28.65 million fMRI frames from over 50,000 subjects, spanning multiple centers and ages 5 to 100. It combines an efficient spatiotemporal modeling design and lightweight task adaptation to enable scalable pre-training and fast transfer to downstream applications. Here we show that NeuroSTORM consistently outperforms existing methods across five downstream tasks, including demographic prediction, phenotype prediction, disease diagnosis, re-identification, and state classification. On two multi-hospital clinical cohorts with 17 diagnoses, NeuroSTORM achieves the best diagnosis performance while remaining predictive of psychological and cognitive phenotypes. These results suggest that NeuroSTORM could become a standardized foundation model for reproducible and transferable fMRI analysis.

cs.CV↗

arXiv2026

Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning

Federated unlearning has become an attractive approach to address privacy concerns in collaborative machine learning, for situations when sensitive data is remembered by AI models during the machine learning process. It enables the removal of specific data influences from trained models, aligning with the growing emphasis on the "right to be forgotten." While extensively studied in horizontal federated learning, unlearning in vertical federated learning (VFL) remains challenging due to the distributed feature architecture. VFL unlearning includes sample unlearning that removes specific data points' influence and label unlearning that removes entire classes. Since different parties hold complementary features of the same samples, unlearning tasks require cross-party coordination, creating computational overhead and complexities from feature interdependencies. To address such challenges, we propose FedORA (Federated Optimization for data Removal via primal-dual Algorithm), designed for sample and label unlearning in VFL. FedORA formulates the removal of certain samples or labels as a constrained optimization problem solved using a primal-dual framework. Our approach introduces a new unlearning loss function that promotes classification uncertainty rather than misclassification. An adaptive step size enhances stability, while an asymmetric batch design, considering the prior influence of the remaining data on the model, handles unlearning and retained data differently to efficiently reduce computational costs. We provide theoretical analysis proving that the model difference between FedORA and Train-from-scratch is bounded, establishing guarantees for unlearning effectiveness. Experiments on tabular and image datasets demonstrate that FedORA achieves unlearning effectiveness and utility preservation comparable to Retrain with reduced computation and communication overhead.

cs.CR↗

arXiv2026

Efficient Emotion and Speaker Adaptation in LLM-Based TTS via Characteristic-Specific Partial Fine-Tuning

While LLM-based TTS models exhibit zero-shot emotion and speaker cloning, their cloning fidelity and pronunciation clarity degrade on unseen domains. Fine-tuning is essential for adaptation, yet uniform approaches overlook specific parameter contributions. Uniform tuning on limited data causes slow training and catastrophic forgetting, leading to degraded pronunciation accuracy. To address this, we propose CSP-FT, a characteristic-specific partial fine-tuning strategy. By dynamically analyzing layer contributions via a weighted sum, we selectively fine-tune only the two layers capturing the most and least emotion and speaker information, maximizing the utility of the former while explicitly strengthening the capacity of the latter. Experiments on a combined corpus of 11 datasets show CSP-FT matches or exceeds the fidelity and intelligibility of full fine-tuning while updating only ~8% of parameters, accelerating training by ~2x, and significantly mitigating catastrophic forgetting.

eess.AS↗

arXiv2026

LSPRAG: LSP-Guided RAG for Language-Agnostic Real-Time Unit Test Generation

Automated unit test generation is essential for robust software development, yet existing approaches struggle to generalize across multiple programming languages and operate within real-time development. While Large Language Models (LLMs) offer a promising solution, their ability to generate high coverage test code depends on prompting a concise context of the focal method. Current solutions, such as Retrieval-Augmented Generation, either rely on imprecise similarity-based searches or demand the creation of costly, language-specific static analysis pipelines. To address this gap, we present LSPRAG, a framework for concise-context retrieval tailored for real-time, language-agnostic unit test generation. LSPRAG leverages off-the-shelf Language Server Protocol (LSP) back-ends to supply LLMs with precise symbol definitions and references in real time. By reusing mature LSP servers, LSPRAG provides an LLM with language-aware context retrieval, requiring minimal per-language engineering effort. We evaluated LSPRAG on open-source projects spanning Java, Go, and Python. Compared to the best performance of baselines, LSPRAG increased line coverage by up to 174.55% for Golang, 213.31% for Java, and 31.57% for Python.

cs.SE↗

arXiv2026

Hierarchical self-organization of highly-ordered granular ensemble of optical solitons through collective motions

Self-organizations of ordered patterns in far-from-equilibrium many-body systems host fundamental importance in many disciplines. Meanwhile, complex systems often feature hierarchical structures with distinct scales for different layers, enabling high-level effective dynamics without exhaustive tracking of all possible degrees of freedoms. In this work, we report a study of the self-organization dynamics of highly-ordered soliton ensembles in a high-harmonic mode-locked fiber lasers through collective motions driven by nonlocal optomechanical interactions and local collisions, which exhibit a series of universal characteristics reminiscent of phase transitions. Moreover, the multi-soliton laser-field can be coarsely grained as a granular ensemble of limit-cycle oscillators with simple interaction rules derived from fine-scale physics. The self-organization of the multitude of solitons in the mode-locked laser cavity can then be mapped into a low-dimensional dynamic model that essentially reproduced the emergent process. Our work affords a conceptual framework for understanding the complex structure formation in nonlinear laser systems, and may help to design ultrafast lasers by exploiting universal principles of collective motions.

physics.optics↗

arXiv2025

Rare Word Recognition and Translation Without Fine-Tuning via Task Vector in Speech Models

Rare words remain a critical bottleneck for speech-to-text systems. While direct fine-tuning improves recognition of target words, it often incurs high cost, catastrophic forgetting, and limited scalability. To address these challenges, we propose a training-free paradigm based on task vectors for rare word recognition and translation. By defining task vectors as parameter differences and introducing word-level task vector arithmetic, our approach enables flexible composition of rare-word capabilities, greatly enhancing scalability and reusability. Extensive experiments across multiple domains show that the proposed method matches or surpasses fine-tuned models on target words, improves general performance by about 5 BLEU, and mitigates catastrophic forgetting.

eess.AS↗

arXiv2025

Thin-film magnomechanics in the low gigahertz regime

The coherent interaction between magnons and phonons in the low-GHz regime represents an unexplored frontier in hybrid magnonics, critical for quantum information processing and microwave-to-acoustic transduction. While previous studies have focused on higher frequencies (>5 GHz), we demonstrate magnon-phonon coupling near 2 GHz using spoof surface plasmon polariton (SSPP) waveguides integrated with yttrium iron garnet (YIG) thin films of varying thicknesses. SSPP waveguides provide strong slow-wave enhancement, enabling efficient magnon readout in this challenging regime. Systematic measurements reveal the dependence of coupling strength on YIG thickness and phonon wavelength matching, achieving cooperativity C = 1 for a 3 $μ$m film at 2 GHz. Angle-dependent studies uncover coupling to both transverse and longitudinal phonon modes. Further investigation shows that thicker films exhibit rich multimode dynamics between high-order magnons and HBAR phonons. These results establish a robust low-GHz magnomechanical platform, opening pathways for multimode quantum transduction and hardware-efficient quantum technologies.

physics.app-ph↗

arXiv2025

Generation of Ultra-Broadband Frequency Comb in Strongly Bistable Nonlinear Magnonic Resonator

Magnonic frequency combs (MFCs) offer a promising route to compact, energy-efficient platforms for on-chip coherent microwave signal generation and processing. Conventional on-chip comb generation typically relies on nonlinear resonators supporting a series of equidistant, low-loss resonances driven by a strong monochromatic signal, resulting in fixed comb spacing defined by the resonator's free spectral range (FSR). Here we introduce and experimentally demonstrate a fundamentally different mechanism for ultrabroadband MFC generation using a highly nonlinear miniaturized magnonic resonator. The small resonator volume, combined with a slow-wave transducer, yields high intra-resonator power density, driving the system deep into the bistable regime where parametric excitation of propagating spin waves facilitates comb formation. Our approach yields more than 350 comb lines spanning a 450 MHz bandwidth, with spacing continuously tunable via a two-tone external drive, representing an order-of-magnitude enhancement over prior reports while operating at relatively low power. The platform is ultra-compact (4-6 orders of magnitude smaller in size than conventional YIG sphere resonators), fully scalable, and highly tunable, enabling precise control of comb properties through magnetic bias and pump manipulation. These results establish a new paradigm for frequency comb technology, unlocking transformative opportunities in microwave signal processing, neuromorphic computing, and precision sensing.

cond-mat.mes-hall↗

arXiv2025

Ripple-assisted adsorption of noble gases on graphene at room temperature

Controllable gas adsorption is critical for both scientific and industrial fields, and high-capacity adsorption of gases on solid surfaces provides a significant promise due to its high-safety and low-energy consumption. However, the adsorption of nonpolar gases, particularly noble gases, poses a considerable challenge under atmospheric pressure and room temperature (RT). Here, we theoretically simulate and experimentally realize the stable adsorption of noble gases like xenon (Xe), krypton (Kr), argon (Ar), and helium (He) on highly rippled graphene at RT. The elemental characteristics of adsorbed Xe are confirmed by electron energy loss spectroscopy and X-ray photoelectron spectroscopy. The adsorbed gas atoms are crystalized with periodic arrangements. These adsorbed noble gases on graphene exhibit high stability at RT and can be completely desorbed at approximately 350 °C without damaging the intrinsic lattice of graphene. The structural and physical properties of graphene are significantly influenced by the adsorbed gas, and they fully recover after desorption. Additionally, this controllable adsorption could be generalized to other layered adsorbents such as NbSe2, MoS2 and carbon nanotubes. We anticipate that this ripple-assisted adsorption will not only re-define the theoretical framework of gas adsorption, but also accelerate advancements in gas storage and separation technologies, as well as enhance the applications in catalysis, surface modification, and other related fields.

cond-mat.mtrl-sci↗

arXiv2025

FineSkiing: A Fine-grained Benchmark for Skiing Action Quality Assessment

Action Quality Assessment (AQA) aims to evaluate and score sports actions, which has attracted widespread interest in recent years. Existing AQA methods primarily predict scores based on features extracted from the entire video, resulting in limited interpretability and reliability. Meanwhile, existing AQA datasets also lack fine-grained annotations for action scores, especially for deduction items and sub-score annotations. In this paper, we construct the first AQA dataset containing fine-grained sub-score and deduction annotations for aerial skiing, which will be released as a new benchmark. For the technical challenges, we propose a novel AQA method, named JudgeMind, which significantly enhances performance and reliability by simulating the judgment and scoring mindset of professional referees. Our method segments the input action video into different stages and scores each stage to enhance accuracy. Then, we propose a stage-aware feature enhancement and fusion module to boost the perception of stage-specific key regions and enhance the robustness to visual changes caused by frequent camera viewpoints switching. In addition, we propose a knowledge-based grade-aware decoder to incorporate possible deduction items as prior knowledge to predict more accurate and reliable scores. Experimental results demonstrate that our method achieves state-of-the-art performance.

cs.CV↗

arXiv2025

On Terwilliger $\mathbb{F}$-algebras of factorial association schemes

The Terwilliger algebras of association schemes over an arbitrary field $\mathbb{F}$ were called the Terwilliger $\mathbb{F}$-algebras of association schemes in [9]. In this paper, we study the Terwilliger $\mathbb{F}$-algebras of factorial association schemes. We determine the centers, the semisimplicity, the Jacobson radicals and their nilpotent indices, the Wedderburn-Artin decompositions of the Terwilliger $\mathbb{F}$-algebras of factorial association schemes. Moreover, we determine all Terwilliger $\mathbb{F}$-algebras of factorial association schemes that are the symmetric $\mathbb{F}$-algebras or the Frobenius $\mathbb{F}$-algebras.

math.CO↗

arXiv2025

PlantSegNeRF: A few-shot, cross-species method for plant 3D instance point cloud reconstruction via joint-channel NeRF with multi-view image instance matching

Organ segmentation of plant point clouds is a prerequisite for the high-resolution and accurate extraction of organ-level phenotypic traits. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, the existing techniques for organ segmentation still face limitations in resolution, segmentation accuracy, and generalizability across various plant species. In this study, we proposed a novel approach called plant segmentation neural radiance fields (PlantSegNeRF), aiming to directly generate high-precision instance point clouds from multi-view RGB image sequences for a wide range of plant species. PlantSegNeRF performed 2D instance segmentation on the multi-view images to generate instance masks for each organ with a corresponding ID. The multi-view instance IDs corresponding to the same plant organ were then matched and refined using a specially designed instance matching module. The instance NeRF was developed to render an implicit scene, containing color, density, semantic and instance information. The implicit scene was ultimately converted into high-precision plant instance point clouds based on the volume density. The results proved that in semantic segmentation of point clouds, PlantSegNeRF outperformed the commonly used methods, demonstrating an average improvement of 16.1%, 18.3%, 17.8%, and 24.2% in precision, recall, F1-score, and IoU compared to the second-best results on structurally complex species. More importantly, PlantSegNeRF exhibited significant advantages in plant point cloud instance segmentation tasks. Across all plant species, it achieved average improvements of 11.7%, 38.2%, 32.2% and 25.3% in mPrec, mRec, mCov, mWCov, respectively. This study extends the organ-level plant phenotyping and provides a high-throughput way to supply high-quality 3D data for the development of large-scale models in plant science.

cs.CV↗

arXiv2025

LinkedIn Post Embeddings: Industrial Scale Embedding Generation and Usage across LinkedIn

A post embedding (representation of text in embedding space that effectively captures semantic meaning) is a foundational component of LinkedIn that is consumed by product surfaces in retrieval and ranking (e.g., ranking posts in the feed or video tab). This paper presents the post embeddings used at LinkedIn, where a pre-trained transformer-based large language model (LLM) is taken as input and fine-tuned using multi-task learning across a diverse set of semantic labeling tasks. We observe positive transfer, leading to improved performance across all tasks, compared to training them independently. The generated post embeddings outperform baseline models in zero-shot learning, demonstrating its potential for broader applicability. Furthermore, the generated post embeddings' performance surpasses that of OpenAI's ADA-001 and ADA-002 embeddings on LinkedIn specific datasets and tasks. We also describe the offline evaluation methodology and the deployment to our near-line infrastructure, which makes the post embedding available for use within minutes of post creation for any downstream application. We present how the embeddings were applied in the Feed product surface, in both ranking and retrieval stages, and showcase the real world online impact to demonstrate the superior performance of these embeddings. Finally, we also share the results of applying the embeddings to the retrieval system of our video ranking product surface in LinkedIn. These embeddings have been battle-tested in production at LinkedIn for over two years, consistently powering multiple products.

cs.LG↗

arXiv2025

Perturbation Self-Supervised Representations for Cross-Lingual Emotion TTS: Stage-Wise Modeling of Emotion and Speaker

Cross-lingual emotional text-to-speech (TTS) aims to produce speech in one language that captures the emotion of a speaker from another language while maintaining the target voice's timbre. This process of cross-lingual emotional speech synthesis presents a complex challenge, necessitating flexible control over emotion, timbre, and language. However, emotion and timbre are highly entangled in speech signals, making fine-grained control challenging. To address this issue, we propose EMM-TTS, a novel two-stage cross-lingual emotional speech synthesis framework based on perturbed self-supervised learning (SSL) representations. In the first stage, the model explicitly and implicitly encodes prosodic cues to capture emotional expressiveness, while the second stage restores the timbre from perturbed SSL representations. We further investigate the effect of different speaker perturbation strategies-formant shifting and speaker anonymization-on the disentanglement of emotion and timbre. To strengthen speaker preservation and expressive control, we introduce Speaker Consistency Loss (SCL) and Speaker-Emotion Adaptive Layer Normalization (SEALN) modules. Additionally, we find that incorporating explicit acoustic features (e.g., F0, energy, and duration) alongside pretrained latent features improves voice cloning performance. Comprehensive multi-metric evaluations, including both subjective and objective measures, demonstrate that EMM-TTS achieves superior naturalness, emotion transferability, and timbre consistency across languages.

cs.SD↗

arXiv2025

Investigations of MWISP Clumps: 13CO Clump Source Catalog and Physical Properties

We present the first comprehensive catalogs of $^{13}$CO clumps from the Milky Way Imaging Scroll Painting (MWISP) project. By developing an equivalent global detection scheme integrated with the FacetClumps algorithm, we successfully extract 71,661 molecular clumps across a high-resolution $^{13}$CO data cube spanning 2310 deg$^2$ from the MWISP Phase I survey. To determine accurate distances, we design an automatic hierarchical distance decision method using signal regions as fundamental objects, effectively resolving the kinematic distance ambiguity problem and obtaining reliable measurements for 97.94% of the sample. Statistical analysis reveals that 65.3% of clumps are gravitationally bound, accounting for approximately 96.3% of the statistical total mass. Scaling relation analysis across multiple surveys reveals universal power-law behaviors in clump populations. Maser-associated clumps exhibit modified parameter distributions and scaling relations, revealing how active star formation alters clump dynamics and structure. These extensive catalogs establish a foundation for investigating molecular clump properties, star formation processes, and Galactic evolution.

astro-ph.GA↗

arXiv2025

Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap

The precise characterization of plant morphology provides valuable insights into plant environment interactions and genetic evolution. A key technology for extracting this information is 3D segmentation, which delineates individual plant organs from complex point clouds. Despite significant progress in general 3D computer vision domains, the adoption of 3D segmentation for plant phenotyping remains limited by three major challenges: i) the scarcity of large-scale annotated datasets, ii) technical difficulties in adapting advanced deep neural networks to plant point clouds, and iii) the lack of standardized benchmarks and evaluation protocols tailored to plant science. This review systematically addresses these barriers by: i) providing an overview of existing 3D plant datasets in the context of general 3D segmentation domains, ii) systematically summarizing deep learning-based methods for point cloud semantic and instance segmentation, iii) introducing Plant Segmentation Studio (PSS), an open-source framework for reproducible benchmarking, and iv) conducting extensive quantitative experiments to evaluate representative networks and sim-to-real learning strategies. Our findings highlight the efficacy of sparse convolutional backbones and transformer-based instance segmentation, while also emphasizing the complementary role of modeling-based and augmentation-based synthetic data generation for sim-to-real learning in reducing annotation demands. In general, this study bridges the gap between algorithmic advances and practical deployment, providing immediate tools for researchers and a roadmap for developing data-efficient and generalizable deep learning solutions in 3D plant phenotyping. Data and code are available at https://github.com/perrydoremi/PlantSegStudio.

cs.CV↗

arXiv2025

DATR: Diffusion-based 3D Apple Tree Reconstruction Framework with Sparse-View

Digital twin applications offered transformative potential by enabling real-time monitoring and robotic simulation through accurate virtual replicas of physical assets. The key to these systems is 3D reconstruction with high geometrical fidelity. However, existing methods struggled under field conditions, especially with sparse and occluded views. This study developed a two-stage framework (DATR) for the reconstruction of apple trees from sparse views. The first stage leverages onboard sensors and foundation models to semi-automatically generate tree masks from complex field images. Tree masks are used to filter out background information in multi-modal data for the single-image-to-3D reconstruction at the second stage. This stage consists of a diffusion model and a large reconstruction model for respective multi view and implicit neural field generation. The training of the diffusion model and LRM was achieved by using realistic synthetic apple trees generated by a Real2Sim data generator. The framework was evaluated on both field and synthetic datasets. The field dataset includes six apple trees with field-measured ground truth, while the synthetic dataset featured structurally diverse trees. Evaluation results showed that our DATR framework outperformed existing 3D reconstruction methods across both datasets and achieved domain-trait estimation comparable to industrial-grade stationary laser scanners while improving the throughput by $\sim$360 times, demonstrating strong potential for scalable agricultural digital twin systems.

cs.RO↗

arXiv2025

Aleks: AI powered Multi Agent System for Autonomous Scientific Discovery via Data-Driven Approaches in Plant Science

Modern plant science increasingly relies on large, heterogeneous datasets, but challenges in experimental design, data preprocessing, and reproducibility hinder research throughput. Here we introduce Aleks, an AI-powered multi-agent system that integrates domain knowledge, data analysis, and machine learning within a structured framework to autonomously conduct data-driven scientific discovery. Once provided with a research question and dataset, Aleks iteratively formulated problems, explored alternative modeling strategies, and refined solutions across multiple cycles without human intervention. In a case study on grapevine red blotch disease, Aleks progressively identified biologically meaningful features and converged on interpretable models with robust performance. Ablation studies underscored the importance of domain knowledge and memory for coherent outcomes. This exploratory work highlights the promise of agentic AI as an autonomous collaborator for accelerating scientific discovery in plant sciences.

cs.AI↗