arXiv subjects

Qian Yang

Publications and source records attributed to Qian Yang.

At least 37 records · Page 2Linked to original sources

arXiv2025

LongCat-Flash-Omni Technical Report

We introduce LongCat-Flash-Omni, a state-of-the-art open-source omni-modal model with 560 billion parameters, excelling at real-time audio-visual interaction. By adopting a curriculum-inspired progressive training strategy that transitions from simpler to increasingly complex modality sequence modeling tasks, LongCat-Flash-Omni attains comprehensive multimodal capabilities while maintaining strong unimodal capability. Building upon LongCat-Flash, which adopts a high-performance Shortcut-connected Mixture-of-Experts (MoE) architecture with zero-computation experts, LongCat-Flash-Omni integrates efficient multimodal perception and speech reconstruction modules. Despite its immense size of 560B parameters (with 27B activated), LongCat-Flash-Omni achieves low-latency real-time audio-visual interaction. For training infrastructure, we developed a modality-decoupled parallelism scheme specifically designed to manage the data and model heterogeneity inherent in large-scale multimodal training. This innovative approach demonstrates exceptional efficiency by sustaining over 90% of the throughput achieved by text-only training. Extensive evaluations show that LongCat-Flash-Omni achieves state-of-the-art performance on omni-modal benchmarks among open-source models. Furthermore, it delivers highly competitive results across a wide range of modality-specific tasks, including text, image, and video understanding, as well as audio understanding and generation. We provide a comprehensive overview of the model architecture design, training procedures, and data strategies, and open-source the model to foster future research and development in the community.

cs.MM↗

arXiv2025

Rapid fabrication of clean van der Waals nanochannels using Mask and Stack method

Two-dimensional (2D) nanochannels have emerged as a pivotal platform for exploring nanoscale hydrodynamics and electrokinetics. Conventional fabrication methods to make nanochannels often introduce polymer contamination and require lengthy processing, limiting device performance and scalability. Here we introduce the Mask & Stack method, employing silicon nitride stencil mask combined with dry transfer stacking to rapidly fabricate ultraclean vdW nanochannels within hours. This polymer-free approach preserves pristine interfaces, confirmed by atomic force microscopy and Raman spectroscopy, and yields nanochannel devices exhibiting reproducible ionic transport and long-term stability. The streamlined process is compatible with diverse 2D materials and promising for upscale production. Our method advances the fabrication of nanofluidic and 2D heterostructure devices, facilitating applications in quantum transport, photonics, energy harvesting, and sensing technologies requiring high-throughput, contamination-free heterostructure architectures.

cond-mat.mes-hall↗

arXiv2025

Systematic Analysis of Changing-look AGN Variability Using ZTF Light Curves

Changing-look active galactic nuclei (CLAGNs) are a unique population of AGNs that exhibit the appearance (turn-on) or disappearance (turn-off) of broad emission lines. This study aims to explore the intrinsic mechanisms of CLAGNs by investigating their photometric variability using data from the Zwicky Transient Facility (ZTF), which has provided high-cadence observations over the past five years. By visual inspections, we construct a sample of 152 CLAGNs from the literature, all of which show spectral transitions and large optical variability in their ZTF light curves. By analyzing 90 of these CLAGNs and the control samples of Type 1 AGNs, Type 2 AGNs, and extremely variable quasars (EVQs), matched in redshift ($0.2<z<0.8$) and supermassive black hole mass, we compare the color variability, structure function (SF), and variability metric $σ_{\mathrm{QSO}}$, which quantifies how closely the light curves resemble a damped random walk (DRW) model. We find that while CLAGNs and EVQs differ from typical Type 1 and Type 2 AGNs in bolometric luminosity and Eddington ratio, the on/off-state CLAGNs share similar variability patterns with the overall CLAGN population, and distinct from EVQ, Type 1 and Type 2 AGNs. This suggests that 'on' and 'off' CLAGNs are not simply equivalent to Type 1 and Type 2 AGNs, respectively. Instead of undergoing genuine transitions between two AGN types, CLAGNs may inhabit a critical state where moderate fluctuations in accretion rate lead to the temporary spectral changes.

astro-ph.GA↗

arXiv2025

A Neural Network Approach to Multi-radionuclide TDCR Beta Spectroscopy

Liquid scintillation triple-to-doubly coincident ratio (TDCR) spectroscopy is widely adopted as a standard method for radionuclide quantification because of its inherent advantages such as high precision, self-calibrating capability, and independence from radioactive reference sources. However, multiradionuclide analysis via TDCR faces the challenges of limited automation and reliance on mixture-specific standards, which may not be easily available. Here, we present an Artificial Intelligence (AI) framework that combines numerical spectral simulation and deep learning for standard-free automated analysis. $β$ spectra for model training were generated using Geant4 simulations coupled with statistically modeled detector response sampling. A tailored neural network architecture, trained on this dataset covering various nuclei mix ratio and quenching scenarios, enables autonomous resolution of individual radionuclide activities and detecting efficiency through end-to-end learning paradigms. The model delivers consistent high accuracy across tasks: activity proportions (mean absolute error = 0.009), detection efficiencies (mean absolute error = 0.002), and spectral reconstruction (Structural Similarity Index = 0.9998), validating its physical plausibility for quenched $β$ spectroscopy. This AI-driven methodology exhibits significant potential for automated safety-compliant multiradionuclide analysis with robust generalization, real-time processing capabilities, and engineering feasibility, particularly in scenarios where reference materials are unavailable or rapid field analysis is required.

cs.LG↗

arXiv2025

Facilitating Longitudinal Interaction Studies of AI Systems

UIST researchers develop tools to address user challenges. However, user interactions with AI evolve over time through learning, adaptation, and repurposing, making one time evaluations insufficient. Capturing these dynamics requires longer-term studies, but challenges in deployment, evaluation design, and data collection have made such longitudinal research difficult to implement. Our workshop aims to tackle these challenges and prepare researchers with practical strategies for longitudinal studies. The workshop includes a keynote, panel discussions, and interactive breakout groups for discussion and hands-on protocol design and tool prototyping sessions. We seek to foster a community around longitudinal system research and promote it as a more embraced method for designing, building, and evaluating UIST tools.

cs.HC↗

arXiv2025

MultiTaskDeltaNet: Change Detection-based Image Segmentation for Operando ETEM with Application to Carbon Gasification Kinetics

Transforming in-situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often encounter limitations due to the scarcity of labeled data, visually ambiguous features of interest, and small-object scenarios. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively utilizes minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to leverage correlations between physical features of interest. In an evaluation using data from in-situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges several key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

eess.IV↗

arXiv2025

Sleeping Giants Arise: Monitoring the Return of Three Changing-Look Quasars to Their High States

Changing-look quasars challenge many models of the quasar central engine. Their extreme variability in both the continuum and broad emission-line fluxes on timescales on the order of years is difficult to explain. To investigate the cause of the observed transitions, we present new contemporaneous optical and X-ray observations of three faded changing-look quasars as they return to a state of high optical luminosity. Two of these three remained in a quiescent state for more than ten years before returning to a new high state. We find that before, during, and after transition, the spectral energy distributions of all three follow predictions for quasars based on X-ray binary outbursts, suggesting that the underlying mechanism is likely a changing accretion rate causing changes in the accretion flow structure. In two of the three cases, the transition between the initial high and low state and the transition between the low and new high state took nearly identical amounts of time, on the order of hundreds of days. This transition timescale is a useful constraint on models of the accretion state changes. The behavior of the broad line profiles suggests that the BLR structure is changing during the transition.

astro-ph.GA↗

arXiv2025

MolProphecy: Bridging Medicinal Chemists' Knowledge and Molecular Pre-Trained Models via a Multi-Modal Framework

MolProphecy is a human-in-the-loop (HITL) multi-modal framework designed to integrate chemists' domain knowledge into molecular property prediction models. While molecular pre-trained models have enabled significant gains in predictive accuracy, they often fail to capture the tacit, interpretive reasoning central to expert-driven molecular design. To address this, MolProphecy employs ChatGPT as a virtual chemist to simulate expert-level reasoning and decision-making. The generated chemist knowledge is embedded by the large language model (LLM) as a dedicated knowledge representation and then fused with graph-based molecular features through a gated cross-attention mechanism, enabling joint reasoning over human-derived and structural features. Evaluated on four benchmark datasets (FreeSolv, BACE, SIDER, and ClinTox), MolProphecy outperforms state-of-the-art (SOTA) models, achieving a 15.0 percent reduction in RMSE on FreeSolv and a 5.39 percent improvement in AUROC on BACE. Analysis reveals that chemist knowledge and structural features provide complementary contributions, improving both accuracy and interpretability. MolProphecy offers a practical and generalizable approach for collaborative drug discovery, with the flexibility to incorporate real chemist input in place of the current simulated proxy--without the need for model retraining. The implementation is publicly available at https://github.com/zhangruochi/MolProphecy.

cs.LG↗

arXiv2025

DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data

The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses. While conventional machine learning models have proven effective, they often lack robust representation learning and depend heavily on expert-crafted features. Although deep learning offers powerful solutions, it is often criticized for its lack of interpretability. To address these challenges, we propose DeepSelective, a novel end to end deep learning framework for predicting patient prognosis using EHR data, with a strong emphasis on enhancing model interpretability. DeepSelective combines data compression techniques with an innovative feature selection approach, integrating custom-designed modules that work together to improve both accuracy and interpretability. Our experiments demonstrate that DeepSelective not only enhances predictive accuracy but also significantly improves interpretability, making it a valuable tool for clinical decision-making. The source code is freely available at http://www.healthinformaticslab.org/supp/resources.php .

cs.LG↗

arXiv2025

Structure-Informed Learning of Flat Band 2D Materials

Flat electronic bands enhance electron-electron interactions and give rise to correlated states such as unconventional superconductivity or fractional topological phases. However, most current efforts towards flat-band materials discovery rely on density functional theory (DFT) calculations and manual band structures inspection, restraining their applicability to vast unexplored material spaces. While data-driven methods offer a scalable alternative, most existing models either depend on band structure inputs or focus on scalar properties like bandgap, which fail to capture flat-band characteristics. Here, we report a structure-informed framework for the discovery of previously unrecognized flat-band two-dimensional (2D) materials, which combines a data-driven flatness score capturing both band dispersion and density-of-states characteristics with multi-modal learning from atomic structure inputs. The framework successfully identified multiple flat-band candidates, with DFT validation of kagome-based systems confirming both band flatness and topological character. Our results show that the flatness score provides a physically meaningful signal for identifying flat bands from atomic geometry. The framework uncovers multiple new candidates with topologically nontrivial flat bands from unlabeled data, with consistent model performance across structurally diverse materials. By eliminating the need for precomputed electronic structures, our method enables large-scale screening of flat-band materials and expands the search space for discovering strongly correlated quantum materials.

cond-mat.mtrl-sci↗

arXiv2025

NEXUS: A Spectroscopic Census of Broad-line AGNs and Little Red Dots at $3\lesssim z\lesssim 6$

We present a spectroscopic sample of 23 broad-line AGNs (BLAGNs) at $3\lesssim z\lesssim 6$ selected using F322W2+F444W NIRCam/WFSS grism spectroscopy of the central 100 ${\rm arcmin^2}$ area of the NEXUS survey. Among these BLAGNs, 15 are classified as Little Red Dots (LRDs) based on their rest-frame UV-optical spectral slopes and compact morphology. The number density of LRDs is $\sim 10^{-5}\,{\rm cMpc^{-3}}$, with a hint of declining towards the lower end of the probed redshift range. These BLAGNs and LRDs span broad H$α$ luminosities of $\sim 10^{42.2}-10^{43.7}\,{\rm erg\,s^{-1}}$, black hole masses of $\sim 10^{6.3}-10^{8.4}\,M_\odot$, and Eddington ratios of $\sim 0.1-1$ (median value 0.4), though the black hole mass and Eddington ratio estimates carry large systematic uncertainties. Half of the LRDs show strong Balmer absorption, suggesting high-density gas surrounding the line-emitting region. We detect extended (hundreds of parsec) rest-frame UV-optical emission from the host galaxy in the majority of these LRDs, which contributes significantly or even dominantly to their total UV emission. This host emission largely accounts for the peculiar UV upturn of the LRD spectral energy distribution. We also measure the small-scale ($\lesssim 1\,{\rm cMpc}$) clustering of these BLAGNs and LRDs by cross-correlating with a photometric galaxy sample. Extrapolating the power-law two-point correlation function model to large linear scales, we infer a linear bias of $3.30_{-2.04}^{+2.88}$ and typical halo masses of a few $\times 10^{11}\,h^{-1}M_\odot$ for BLAGNs at the sample median redshift of $z\sim 4.5$. However, the inferred linear bias and halo masses of LRDs, while formally consistent with those for BLAGNs at $\sim 1.5σ$, appear too large to be compatible with their space density, suggesting LRDs may have strong excess clustering on small scales.

astro-ph.GA↗

arXiv2025

Silver Electrodeposition from Ag/AgCl Electrodes: Implications for Nanoscience

With the advancement of nanoscience, silver/silver chloride (Ag/AgCl) electrodes have become widely utilised in microscale and nanoscale fluidic experiments, because of their stability. However, our findings reveal that the dissolution of AgCl from the electrode in \ch{Cl-}-rich solutions can lead to significant silver contamination, through the formation of silver complexes, \ch{[AgCl_{n+1}]^{n-}}. We demonstrate the electrodeposition of silver particles on graphene in KCl aqueous solution, with AgCl dissolution from the electrode as the sole source of silver. This unexpected electrodeposition process offers a more plausible interpretation of the recently reported ``ionic flow-induced current in graphene''. That is, the measured electronic current in graphene is due to the electrodeposition of silver, challenging the previously claimed ``ionic Coulomb drag''. More caution is called for when using Ag/AgCl electrodes in microfluidic, and especially nanofluidic systems, because AgCl dissolution should not be neglected.

cond-mat.mes-hall↗

arXiv2025

Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models

Additive manufacturing enables the fabrication of complex designs while minimizing waste, but faces challenges related to defects and process anomalies. This study presents a novel multimodal Retrieval-Augmented Generation-based framework that automates anomaly detection across various Additive Manufacturing processes leveraging retrieved information from literature, including images and descriptive text, rather than training datasets. This framework integrates text and image retrieval from scientific literature and multimodal generation models to perform zero-shot anomaly identification, classification, and explanation generation in a Laser Powder Bed Fusion setting. The proposed framework is evaluated on four L-PBF manufacturing datasets from Oak Ridge National Laboratory, featuring various printer makes, models, and materials. This evaluation demonstrates the framework's adaptability and generalizability across diverse images without requiring additional training. Comparative analysis using Qwen2-VL-2B and GPT-4o-mini as MLLM within the proposed framework highlights that GPT-4o-mini outperforms Qwen2-VL-2B and proportional random baseline in manufacturing anomalies classification. Additionally, the evaluation of the RAG system confirms that incorporating retrieval mechanisms improves average accuracy by 12% by reducing the risk of hallucination and providing additional information. The proposed framework can be continuously updated by integrating emerging research, allowing seamless adaptation to the evolving landscape of AM technologies. This scalable, automated, and zero-shot-capable framework streamlines AM anomaly analysis, enhancing efficiency and accuracy.

cs.AI↗

arXiv2025

Searching for True Muonium in Relativistic Heavy Ion Collisions

We investigate the production of the as-yet-undetected true muonium within the quark-gluon plasma formed in relativistic heavy-ion collisions, employing a relativistic Boltzmann transport framework coupled to viscous hydrodynamic simulations. The obtained effective cross sections for central collisions are 1.23~$μb$ in AuAu collisions with $\sqrt{s_{\rm NN}}=200$~GeV and 14.2~$μb$ in PbPb collisions with $\sqrt{s_{\rm NN}}=5.02$~TeV, resulting in a yield of $\mathcal{O}(10^4)$ and $\mathcal{O}(10^5)$ true muonium per billion $AA$ collisions at RHIC and the LHC, respectively. This establishes heavy-ion collisions as a promising process for detecting true muonium.

nucl-th↗

arXiv2025

Assessing and Learning Alignment of Unimodal Vision and Language Models

How well are unimodal vision and language models aligned? Although prior work have approached answering this question, their assessment methods do not directly translate to how these models are used in practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to assess vision-language alignment. We identify that the degree of alignment of the SSL vision models depends on their SSL training objective, and we find that the clustering quality of SSL representations has a stronger impact on alignment performance than their linear separability. Next, we introduce Swift Alignment of Image and Language (SAIL), a efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream vision-language tasks. Since SAIL leverages the strengths of pretrained unimodal models, it requires significantly fewer (6%) paired image-text data for the multimodal alignment compared to models like CLIP which are trained from scratch. SAIL training only requires a single A100 GPU, 5 hours of training and can accommodate a batch size up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (vs. CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. Additionally, SAIL improves the language-compatibility of vision encoders that in turn enhance the performance of multimodal large language models. The entire codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/

cs.CV↗

arXiv2025

Stacking-induced ferroelectricity in tetralayer graphene

Recent studies have reported emergent ferroelectric behavior in twisted or moiré-engineered graphene-based van der Waals heterostructures, yet the microscopic origin of this effect remains under debate. Pristine mono- or few-layer graphene lacks a permanent dipole due to its centrosymmetric lattice, making the emergence of ferroelectricity unlikely. However, mixed-stacked graphene, such as the ABCB tetralayer configuration, breaks both inversion and mirror symmetry and has been theoretically predicted to support electrically switchable dipoles. ABCB graphene represents the simplest natural graphene polytype exhibiting intrinsic out-of-plane polarization, arising from asymmetric charge carrier distribution across its layers. Here, we report robust ferroelectric behavior in dual-gated, non-aligned ABCB tetralayer graphene encapsulated in hexagonal boron nitride. The device exhibits pronounced hysteresis in resistance under both top and bottom gate modulation, with the effect persisting up to room temperature. This hysteresis originates from reversible layer-polarized charge reordering, driven by gate-induced transitions between ABCB and BCBA stacking configurations -- without requiring moiré superlattices. Our findings establish stacking-order-induced symmetry breaking as a fundamental route to electronic ferroelectricity in graphene and open pathways for non-volatile memory applications based on naturally occurring mixed-stacked multilayer graphene.

cond-mat.mes-hall↗

arXiv2025

My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data

Safety-critical data, such as crash and near-crash records, are crucial to improving autonomous vehicle (AV) design and development. Sharing such data across AV companies, academic researchers, regulators, and the public can help make all AVs safer. However, AV companies rarely share safety-critical data externally. This paper aims to pinpoint why AV companies are reluctant to share safety-critical data, with an eye on how these barriers can inform new approaches to promote sharing. We interviewed twelve AV company employees who actively work with such data in their day-to-day work. Findings suggest two key, previously unknown barriers to data sharing: (1) Datasets inherently embed salient knowledge that is key to improving AV safety and are resource-intensive. Therefore, data sharing, even within a company, is fraught with politics. (2) Interviewees believed AV safety knowledge is private knowledge that brings competitive edges to their companies, rather than public knowledge for social good. We discuss the implications of these findings for incentivizing and enabling safety-critical AV data sharing, specifically, implications for new approaches to (1) debating and stratifying public and private AV safety knowledge, (2) innovating data tools and data sharing pipelines that enable easier sharing of public AV safety data and knowledge; (3) offsetting costs of curating safety-critical data and incentivizing data sharing.

cs.HC↗

arXiv2025

How Problematic Writer-AI Interactions (Rather than Problematic AI) Hinder Writers' Idea Generation

Writing about a subject enriches writers' understanding of that subject. This cognitive benefit of writing -- known as constructive learning -- is essential to how students learn in various disciplines. However, does this benefit persist when students write with generative AI writing assistants? Prior research suggests the answer varies based on the type of AI, e.g., auto-complete systems tend to hinder ideation, while assistants that pose Socratic questions facilitate it. This paper adds an additional perspective. Through a case study, we demonstrate that the impact of genAI on students' idea development depends not only on the AI but also on the students and, crucially, their interactions in between. Students who proactively explored ideas gained new ideas from writing, regardless of whether they used auto-complete or Socratic AI assistants. Those who engaged in prolonged, mindless copyediting developed few ideas even with a Socratic AI. These findings suggest opportunities in designing AI writing assistants, not merely by creating more thought-provoking AI, but also by fostering more thought-provoking writer-AI interactions.

cs.HC↗