arXiv subjects

Yinuo Zhang

Publications and source records attributed to Yinuo Zhang.

At least 19 recordsLinked to original sources

arXiv2026

CyberSelf: Embodied Self-Distancing for Emotional Support in Virtual Reality

Self-distancing is an effective emotion regulation strategy; however, it may fail during personal crises due to its cognitive demands. Virtual Reality (VR) provides a novel approach to externalizing psychological distance by enabling embodied self-representation. In this paper, we present CyberSelf, a VR system for emotional support that integrates a visually self-resembling avatar, a cloned self-voice, and Large Language Model (LLM)-driven real-time dialogue. The system enables users to engage in multi-turn conversations with their self-representations in immersive VR, enabling embodied self-distancing while maintaining a strong sense of self-relevance. We evaluated CyberSelf in a short-term study that compares three levels of self-representation richness (Text, Text+Voice, and Text+Voice+Appearance). The results demonstrated robust pre-post improvements across affective and coping measures, specifically increased valence, arousal, hope, and resilience, as well as reduced anxiety and simulator sickness. Richer representations increased conversational engagement, and full embodiment produced the strongest physiological indicators of emotional regulation. A subsequent four-week long-term study demonstrated that these benefits are both sustainable and cumulative. Additionally, users rated the reconstructed avatar and the cloned voice as highly recognizable and acceptable. Collectively, these findings suggest that embodied, self-resembling conversational agents provide a viable mechanism for externalizing self-distancing and supporting emotional regulation in VR.

cs.HC↗

arXiv2026

UniRED: Unified RGB-D Video Frame Interpolation with Event Guidance

High frame-rate RGB-D videos are crucial for a variety of downstream tasks, including motion analysis, dynamic scene understanding, and 3D reconstruction. However, due to hardware and sensing constraints, practical RGB-D cameras are typically limited to low frame rates, making it difficult to capture rapid scene dynamics. Existing video interpolation methods have achieved strong performance on RGB data, but they are not readily applicable to RGB-D scenarios, where they often yield blurry boundaries, visible artifacts, and degraded geometric consistency. Furthermore, motion estimation from only two boundary frames is inherently under-constrained in complex dynamic scenes. Event cameras, by contrast, provide asynchronous measurements with ultra-high temporal resolution, offering dense motion cues. In this paper, we propose a unified multimodal framework for RGB-D video interpolation that jointly exploits RGB appearance, depth geometry, and event-based temporal cues. Specifically, it first extracts and fuses RGB, depth and event cues, then estimates bidirectional flow with motion basis refinement for RGB and Z-axial refinement for depth, and finally synthesizes the target RGB-D frame via bidirectional warping and soft blending. In addition, we construct a new RGB-D-Event dataset to alleviate the scarcity of tri-modal training data. Extensive experiments on a public benchmark and the proposed dataset demonstrate that our method achieves superior photometric fidelity for RGB interpolation and stronger geometric accuracy for depth interpolation than existing approaches.

cs.CV↗

arXiv2026

mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties

Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regions (UTRs), and their coupling jointly determine stability, translation efficiency, and protein expression. Here, we present mRNA generation via unrolled trajectories and informed latent updates (mRNAutilus), a framework for simultaneous codon optimization and de novo UTR design directly from sequence. mRNAutilus combines a masked discrete diffusion model trained on millions of full-length mRNAs with Monte Carlo Tree Guidance to generate Pareto-efficient sequences under multiple functional objectives, using lightweight regressors over model embeddings to predict half-life, translation efficiency, and protein abundance. Unlike recent methods that design coding sequences and UTRs separately or rely on post hoc assembly and screening, mRNAutilus generates complete transcripts in a single process optimized across properties. Across diverse targets, zero-shot mRNAs encoding P. pyralis luciferase achieve over 400-fold higher expression than wild-type and outperform commercial and machine learning-designed baselines, including zero-shot generative approaches. Zero-shot SARS-CoV-2 Spike mRNAs exceed clinically used and commercial constructs and match or surpass lab-optimized designs with improved durability. We further demonstrate generality in therapeutic settings, including prime editing (PEMax) and programmable proteome modulation, where mRNAutilus-designed constructs enhance expression of peptide-guided E3 ligases (uAbs) for beta-catenin degradation. These results establish a sequence-based, multi-objective framework for generating functional mRNAs tailored to diverse biological applications.

q-bio.BM↗

arXiv2026

TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation

Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This is especially important for clinically relevant G protein-coupled receptors (GPCRs), where therapeutic efficacy depends on functional directionality. Structure-based design methods optimize binding to static conformations and cannot represent non-reversible, directional effects or systematically distinguish agonist from antagonist behavior. To address this gap, we introduce Transition-Directed Discrete Diffusion for Allosteric Binder Design (TD3B), a sequence-based generative framework that designs binders with specified agonist or antagonist behavior via a directional transition control objective. TD3B combines a target-aware Direction Oracle, a soft binding-affinity gate, and amortized fine-tuning of a pre-trained discrete diffusion model, enabling targeted agonist and antagonist generation decoupled from binding affinity and unattainable by equilibrium-based or inference-only guidance baselines. The code and checkpoints are available at https://huggingface.co/ChatterjeeLab/TD3B.

q-bio.BM↗

arXiv2026

CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models

World action models (WAMs) provide a powerful generative framework for embodied control, yet transferring knowledge across heterogeneous WAMs remains challenging due to mismatched latent interfaces, high adaptation cost, and the rigidity of conventional distillation objectives. We propose \textbf{CKT-WAM}, a parameter-efficient \textbf{C}ontext \textbf{K}nowledge \textbf{T}ransfer framework that transfers teacher WAM's knowledge into a student WAM through a compact context in the text embedding space, rather than output imitation or dense hidden-state matching. Specifically, CKT-WAM extracts intermediate teacher hidden states, reduces the number of tokens via compressors' learnable-query cross attention (LQCA), and transforms them through an always-on generalized adapter, a lightweight router, and sparsely activated specialized adapters. The resulting context is then appended to the student's conditioning textual embeddings, thereby injecting the transferred knowledge into the student with minimal architectural modification. Experiments show that CKT-WAM consistently improves zero-shot generalization and achieves the best overall performance on LIBERO-Plus, reaching 86.1\% total success rate with only 1.17\% trainable parameters, while approaching full fine-tuning performance. Beyond simulation, CKT-WAM also demonstrates strong real-world long-horizon manipulation ability, achieving the best average success rate of 83.3\% across four multi-step and long-horizon tasks. Code is available at https://github.com/YuhuaJiang2002/CKT-WAM.

cs.RO↗

arXiv2026

EchoReview: Learning Peer Review from the Echoes of Scientific Citations

As the volume of scientific submissions continues to grow rapidly, traditional peer review systems are facing unprecedented scalability pressures, highlighting the urgent need for automated reviewing methods that are both scalable and reliable. Existing supervised fine-tuning approaches based on real review data are fundamentally constrained by single-source of data as well as the inherent subjectivity and inconsistency of human reviews, limiting their ability to support high-quality automated reviewers. To address these issues, we propose EchoReview, a citation-context-driven data synthesis framework that systematically mines implicit collective evaluative signals from academic citations and transforms scientific community's long-term judgments into structured review-style data. Based on this pipeline, we construct EchoReview-16K, the first large-scale, cross-conference, and cross-year citation-driven review dataset, and train an automated reviewer, EchoReviewer-7B. Experimental results demonstrate that EchoReviewer-7B can achieve significant and stable improvements on core review dimensions such as evidence support and review comprehensiveness, validating citation context as a robust and effective data paradigm for reliable automated peer review.

cs.CL↗

arXiv2026

Weighted Graph Clustering via Scale Contraction and Graph Structure Learning

Graph clustering aims to partition nodes into distinct clusters based on their similarity, thereby revealing relationships among nodes. Nevertheless, most existing methods do not fully utilize these edge weights. Leveraging edge weights in graph clustering tasks faces two critical challenges. (1) The introduction of edge weights may significantly increase storage space and training time, making it essential to reduce the graph scale while preserving nodes that are beneficial for the clustering task. (2) Edge weight information may inherently contain noise that negatively impacts clustering results. However, few studies can jointly optimize clustering and edge weights, which is crucial for mitigating the negative impact of noisy edges on clustering task. To address these challenges, we propose a contractile edge-weight-aware graph clustering network. Specifically, a cluster-oriented graph contraction module is designed to reduce the graph scale while preserving important nodes. An edge-weight-aware attention network is designed to identify and weaken noisy connections. In this way, we can more easily identify and mitigate the impact of noisy edges during the clustering process, thus enhancing clustering effectiveness. We conducted extensive experiments on three real-world weighted graph datasets. In particular, our model outperforms the best baseline, demonstrating its superior performance. Furthermore, experiments also show that the proposed graph contraction module can significantly reduce training time and storage space.

cs.LG↗

arXiv2026

Direct Detection of Type II-P Supernova Progenitors with the $\textit{Euclid}$ and CSST Surveys

Identifying and characterizing supernova (SN) progenitor stars remains a central yet difficult goal in SN research, limited by archival images lacking sufficient depth or spatial resolution and circumstellar dust biasing intrinsic parameter estimates. This field will be revolutionized by $\textit{Euclid}$ and the upcoming Chinese Space-station Survey Telescope (CSST), which conduct deep, wide-field, high-resolution and multi-band imaging surveys. We evaluate their detection capability by comparing model magnitudes of RSG progenitors with detection limits, finding their optical and near-infrared filters highly effective. Monte-Carlo simulations predict that completed $\textit{Euclid}$ and CSST surveys will enable $\lesssim$13 (or 24) progenitor detections per year within the mass range of 8--16 (or 8--25)\,$M_\odot$, an order of magnitude higher than the current detection rate of $\sim$1 per year (primarily based on HST). With the circumstellar dust, the emerging spectral energy distribution (SED) of the SN progenitor is mainly affected by the optical depth and is almost independent of dust temperature in their survey filters. Mock tests demonstrate that the progenitor mass and dust optical depth can be derived simultaneously by fitting the observed SED over 11 survey filters while fixing dust temperature to a typical value. $\textit{Euclid}$ and CSST will significantly enlarge the sample of direct progenitor detections with accurate mass measurements, crucial for resolving the long-standing RSG problem.

astro-ph.SR↗

arXiv2025

Tuning Breakdown-to-Coercive Field Ratio in Ultra-Thin Al1-xScxN Films via Reactive Nitrogen Atmosphere

Al1-xScxN has attracted significant interest due to its large remnant polarization and low processing temperature when compared to other ferroelectric material systems. However, device dielectric failure before ferroelectric switching remains a critical limitation for AlScN-based memory devices. With the continuing trend toward device miniaturization, expanding the operating window is essential for next-generation memory development. In this work, we optimized the breakdown field (EBD) and coercive field (EC) in ultra-thin Al1-xScxN films by controlling defect density via adjustment of nitrogen gas flow during sputter deposition. The characteristic breakdown field, EBD, was evaluated using Weibull statistics, yielding optimal characteristic breakdown fields of 12.47 MV/cm (EBD+) and -12.63 MV/cm (EBD-) for samples deposited under 27.5 sccm N2 flow. The minimum EC was achieved at a nitrogen flow of 25 sccm and increased for higher gas flows, a trend that is opposite to previous reports in much thicker films. The highest EBD over EC ratio of 2.25 occurred at 27.5 sccm, effectively expanding the operational window. Using a combination of X-ray diffraction and photoluminescence spectroscopy to study changes in crystal orientation and defects, device performance can be tuned by controlling the point defect concentration in the ultra-thin film via adjusting the sputtering N2 process gas flow rate.

cond-mat.mtrl-sci↗

arXiv2025

Entangled Schr\"odinger Bridge Matching

Simulating trajectories of multi-particle systems on complex energy landscapes is a central task in molecular dynamics (MD) and drug discovery, but remains challenging at scale due to computationally expensive and long simulations. Previous approaches leverage techniques such as flow or Schr\"odinger bridge matching to implicitly learn joint trajectories through data snapshots. However, many systems, including biomolecular systems and heterogeneous cell populations, undergo dynamic interactions that evolve over their trajectory and cannot be captured through static snapshots. To close this gap, we introduce Entangled Schr\"odinger Bridge Matching (EntangledSBM), a framework that learns the first- and second-order stochastic dynamics of interacting, multi-particle systems where the direction and magnitude of each particle's path depend dynamically on the paths of the other particles. We define the Entangled Schr\"odinger Bridge (EntangledSB) problem as solving a coupled system of bias forces that entangle particle velocities. We show that our framework accurately simulates heterogeneous cell populations under perturbations and rare transitions in high-dimensional biomolecular systems.

cs.LG↗

arXiv2025

Artificial intelligence as a surrogate brain: Bridging neural dynamical models and data

Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of ``surrogate brains''. In contrast to conventional hypothesis-driven biophysical models, the AI-based surrogate brain encompasses a broad spectrum of data-driven approaches to solve the inverse problem, with the primary objective of accurately predicting future whole-brain dynamics with historical data. Here, we introduce a unified framework of constructing an AI-based surrogate brain that integrates forward modeling, inverse problem solving, and model evaluation. Leveraging the expressive power of AI models and large-scale brain data, surrogate brains open a new window for decoding neural systems and forecasting complex dynamics with high dimensionality, nonlinearity, and adaptability. We highlight that the learned surrogate brain serves as a simulation platform for dynamical systems analysis, virtual perturbation, and model-guided neurostimulation. We envision that the AI-based surrogate brain will provide a functional bridge between theoretical neuroscience and translational neuroengineering.

q-bio.NC↗

arXiv2025

AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance

Designing sequences that satisfy multiple, often conflicting, objectives is a central challenge in therapeutic and biomolecular engineering. Existing generative frameworks largely operate in continuous spaces with single-objective guidance, while discrete approaches lack guarantees for multi-objective Pareto optimality. We introduce AReUReDi (Annealed Rectified Updates for Refining Discrete Flows), a discrete optimization algorithm with theoretical guarantees of convergence to the Pareto front. Building on Rectified Discrete Flows (ReDi), AReUReDi combines Tchebycheff scalarization, locally balanced proposals, and annealed Metropolis-Hastings updates to bias sampling toward Pareto-optimal states while preserving distributional invariance. Applied to peptide and SMILES sequence design, AReUReDi simultaneously optimizes up to five therapeutic properties (including affinity, solubility, hemolysis, half-life, and non-fouling) and outperforms both evolutionary and diffusion-based baselines. These results establish AReUReDi as a powerful, sequence-based framework for multi-property biomolecule generation.

cs.LG↗

arXiv2025

Coercive Field Reduction in Ultra-thin Al1-XScXN via Interfacial Engineering with a Scandium Electrode

Aluminum scandium nitride (AlScN) ferroelectrics are promising for next-generation non-volatile memory applications due to high remnant polarization compared with Pb(ZrxTi1-x)O3 and doped-HfO2 material systems, as well as their fast switching and scalability to nanometer thicknesses. As AlScN films are thinned to 10 nm thickness, coercive field has been shown to substantially increase, which hinders low voltage operation. We demonstrate that interfacial engineering through bottom electrode selection and strain management reduces this coercive field increase with scaling and improves ferroelectric performance. Here, we demonstrate robust ferroelectricity in ultra-thin AlScN capacitors deposited on a Sc bottom electrode under both alternating current and direct current conditions. The coercive field is reduced by over 20 percent compared to capacitors with an Al bottom electrode. Furthermore, the difference in dynamic switching behavior over a decade of frequency was evaluated by applying the KAI model. At frequencies lower than 16.7 kHz, the capacitors with Sc and Al bottom electrodes exhibit comparable KAI exponents of 0.030 and 0.028, indicating similar switching kinetics. However, at higher frequencies, the capacitor with an Al bottom electrode shows a significantly higher exponent of 0.063, indicating a stronger frequency dependence, whereas the capacitor with a Sc bottom electrode maintains a stable exponent of 0.030, suggesting a lower frequency dependence during faster switching scenarios. The Scanning Electron Nanobeam Diffraction technique was selected to measure the strain difference in AlScN thin films grown on templates with different lattice mismatch, providing a correlation between lattice mismatch, film strain and switching behavior in ultra-thin film systems.

cond-mat.mtrl-sci↗

arXiv2025

Branched Schr\"odinger Bridge Matching

Predicting the intermediate trajectories between an initial and target distribution is a central problem in generative modeling. Existing approaches, such as flow matching and Schr\"odinger bridge matching, effectively learn mappings between two distributions by modeling a single stochastic path. However, these methods are inherently limited to unimodal transitions and cannot capture branched or divergent evolution from a common origin to multiple distinct modes. To address this, we introduce Branched Schr\"odinger Bridge Matching (BranchSBM), a novel framework that learns branched Schr\"odinger bridges. BranchSBM parameterizes multiple time-dependent velocity fields and growth processes, enabling the representation of population-level divergence into multiple terminal distributions. We show that BranchSBM is not only more expressive but also essential for tasks involving multi-path surface navigation, modeling cell fate bifurcations from homogeneous progenitor states, and simulating diverging cellular responses to perturbations.

cs.LG↗

arXiv2025

Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design

Designing biological sequences that satisfy multiple, often conflicting, functional and biophysical criteria remains a central challenge in biomolecule engineering. While discrete flow matching models have recently shown promise for efficient sampling in high-dimensional sequence spaces, existing approaches address only single objectives or require continuous embeddings that can distort discrete distributions. We present Multi-Objective-Guided Discrete Flow Matching (MOG-DFM), a general framework to steer any pretrained discrete flow matching generator toward Pareto-efficient trade-offs across multiple scalar objectives. At each sampling step, MOG-DFM computes a hybrid rank-directional score for candidate transitions and applies an adaptive hypercone filter to enforce consistent multi-objective progression. We also trained two unconditional discrete flow matching models, PepDFM for diverse peptide generation and EnhancerDFM for functional enhancer DNA generation, as base generation models for MOG-DFM. We demonstrate MOG-DFM's effectiveness in generating peptide binders optimized across five properties (hemolysis, non-fouling, solubility, half-life, and binding affinity), and in designing DNA sequences with specific enhancer classes and DNA shapes. In total, MOG-DFM proves to be a powerful tool for multi-property-guided biomolecule sequence design.

cs.LG↗

arXiv2025

Demonstration of highly scaled AlScN ferroelectric diode memory with storage density > 100 Mbit/mm$^2$

Wurtzite nitride ferroelectric materials have emerged as promising candidates for next-generation memory applications due to their exceptional polarization properties and compatibility with conventional semiconductor processing techniques. Here, we demonstrate the first successful areal scaling of Aluminum Scandium Nitride (AlScN) ferroelectric diode (FeDiode) memory down to 40 nm device diameters while maintaining ON/OFF > 60. Using a 20 nm thick Al0.64Sc0.36N ferroelectric layer, we evaluate both metal-insulator-ferroelectric-metal (MIFM) and metal-ferroelectric-metal (MFM) architectures for scaled resistive memory devices. Our scaled devices exhibit an enhanced breakdown-to-coercive field ratio exceeding 2.6 due to increased breakdown field. The MIFM devices demonstrate stable 3-bit non-volatile multistate behavior with clearly distinguishable resistance states and retention exceeding 4*10^4 seconds at 85 C. By achieving more than a million-fold areal scaling with enhanced performance metrics, this work establishes AlScN-based FeDiode memory as a highly promising platform for non-volatile storage with potential for direct integration into CMOS technology.

cond-mat.mes-hall↗

arXiv2025

Write Cycling Endurance Exceeding 1010 in Sub-50 nm Ferroelectric AlScN

Wurtzite ferroelectrics, particularly aluminum scandium nitride (AlScN), have emerged as a promising materials platform for nonvolatile memories, offering high polarization values exceeding 100 uC/cm2. However, their high coercive fields (>3 MV/cm) have limited cycling endurance to ~107 cycles in previous reports. Here, we demonstrate unprecedented control of polarization switching in AlScN, achieving write cycling endurance exceeding 1010 cycles a thousand fold improvement over previous wurtzite ferroelectric benchmarks. Through precise voltage modulation in 45 nm thick Al0.64Sc0.36N capacitors, we show that while complete polarization reversal (2Pr ~ 200 uC/cm2) sustains ~108 cycles, partial switching extends endurance beyond 1010 cycles while maintaining a substantial polarization (>30 uC/cm2 for 2Pr). This exceptional endurance, combined with breakdown fields approaching 10 MV/cm in optimized 10 um diameter devices, represents the highest reported values for any wurtzite ferroelectric. Our findings establish a new paradigm for reliability in nitride ferroelectrics, demonstrating that controlled partial polarization and size scaling enables both high endurance and energy efficient operation.

cond-mat.mtrl-sci↗

arXiv2025

Low-voltage Ferroelectric Field-Effect Transistors with Ultrathin Aluminum Scandium Nitride and 2D channels

The continued evolution of CMOS technology demands materials and architectures that emphasize low power consumption, particularly for computations involving large scale data processing and multivariable optimization. Ferroelectric materials offer promising solutions through enabling dual-purpose memory units capable of performing both storage and logic operations. In this study, we demonstrate ferroelectric field effect transistors (FeFETs) with MoS2 monolayer channels fabricated on ultrathin 5 nm and 10 nm ferroelectric Aluminum Scandium Nitride (AlScN) films. By decreasing the thickness of the ferroelectric film, we achieve significantly reduced gate voltages (<3V) required to switch the conductance of the devices, enabling operation at low voltages compatible with advanced CMOS. We observe a characteristic crossover in hysteresis behavior that varies with film thickness, channel fabrication method, and environmental conditions. Through systematic investigation of multiple parameters including channel fabrication methods, dimensional scaling, and environmental effects, we provide pathways to improve device performance. While our devices demonstrate clear ferroelectric switching behavior, further optimization is required to enhance the ON/OFF ratio at zero gate voltage while continuing to reduce the coercive field of these ultrathin films.

physics.app-ph↗