arXiv subjects

Zihan Yang

Publications and source records attributed to Zihan Yang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Carbon Layer Orientation and Closed-Pore Construction Achieving Ultra-Low Specific Surface Area Hard Carbon for High-Performance Na-ion Storage

Addressing the critical trade-off between initial Coulombic efficiency (ICE) and reversible capacity in hard carbon anodes for Na-ion batteries (NIBs), we introduce a novel coupling strategy that combines carbon layer orientation reconstruction with closed-pore construction to produce hard carbon with an ultra-low specific surface area. We demonstrate that the nanographite domains within the hard carbon precursor undergo entropy-driven orientation reconstruction through the synergistic regulation of heteroatom doping and medium-temperature carbonization. This process not only increases interlayer spacing and promotes structural disorder but also enables the formation of dense, closed pores and ultramicropores at domain boundaries via confined atomic migration, while simultaneously encapsulating surface open pores within internal closed ones. Due to this unique pore architecture, our hard carbon exhibits an ultra-low specific surface area of 1.89 m2 g-1 with a markedly higher proportion of closed pores. As a result, our hard carbon achieves a remarkable reversible capacity of 342.3 mAh g-1 at 20 mA g-1, with an exceptional ICE of 90.4% and a dominant plateau capacity of 262.3 mAh g-1 (76.6%) for NIBs. We believe this coupling strategy provides a new paradigm for the structural engineering of high-ICE anode materials in advanced NIBs.

cond-mat.mtrl-sci↗

arXiv2026

Who Is in Mind Matters: Attachment Representations in Early Childhood Synchronize Child-Adult Interacting Brains

Human attachment is distinguished by enduring internalized representations that shapes neurodevelopment and social-emotional functioning. However, as unobservable inner processes mixed with social cues and partner-specific factors, the neurocognitive mechanisms of these representations during real-time interaction remain unclear. Using a novel Remote Partner-Belief Manipulation paradigm in 40 child-mother-stranger trios, we experimentally isolated attachment representations in 3-4-year-olds by manipulating children's partner-belief during remote cooperation. The inner processes were captured from synchrony between partners' EEG, showing that children's mother-partner belief, regardless of the actual partner, significantly enhanced interbrain synchrony. This partner-belief modulation concentrated on children's P4 channel (overlaying the attachment-designated right temporoparietal junction), where synchrony strength correlated to attachment security and children's response acceleration due to mother-partner belief. These findings established attachment representations as an independent, endogenous driver of interbrain synchrony, potentially via children's heightened attention towards their attachment figure, implying the role of symbolic attachment activation when separation.

q-bio.NC↗

arXiv2026

Baton: Explicit Semantic Blueprints for Joint Video-Audio Generation

Current open-source diffusion models struggle to generate stable and synchronized audio-visual content, particularly in scenarios demanding complex semantic reasoning. The root cause is that existing methods rely on coarse text embeddings from off-the-shelf encoders to guide audio-video denoising, which discards fine-grained semantics and, critically, lacks a shared long-horizon plan, leading to uncoordinated denoising trajectories and fragile cross-modal alignment. We propose Baton, the first framework that introduces explicit semantic planning into joint video-audio generation. Our key insight is that complementing coarse text guidance with semantically rich, modality-aware planned tokens, jointly reasoned and mutually aligned before denoising, can simultaneously restore fine-grained semantic detail and establish a shared blueprint that coordinates both audio and video denoising trajectories. Concretely, Baton first introduces the VA-Planner, a multimodal language model equipped with dual semantic alignment towers, where learnable queries cross-attend to both video and audio features to produce a pair of semantically aligned video and audio planned tokens as keyframe-level blueprints. These planned tokens are injected into the diffusion backbone via cross-attention layers, providing temporally grounded guidance complementary to coarse text embeddings. Since planned tokens do not share one-to-one spatial-temporal correspondence with diffusion latents, we further propose Relative Semantic RoPE, a relative positional encoding that maps planned tokens and latents into a shared spatial-temporal coordinate frame, enabling each latent to accurately attend to its positionally corresponding semantic cues. Experiments on benchmarks show the effectiveness of Baton both qualitatively and quantitatively.

cs.CV↗

arXiv2026

Unveiling hidden millihertz quasi-periodic oscillations in 1A 0535+262

Be/X-ray binary pulsars show transient outbursts and complex timing behaviour, including millihertz quasi-periodic oscillations (QPOs), whose physical origin and energy dependence remain poorly understood. We aim to characterise the temporal evolution and energy-dependent properties of the mHz QPO during the 2020 giant outburst of 1A 0535+262. We use the multi-Lorentzian fitting framework to jointly model the power spectra and the real and imaginary parts of the cross-spectrum, incorporating simultaneous broadband X-ray observations from NICER and Insight-HXMT (0.2-120 keV). We report the first detection of weak, but significant, mHz QPOs at low X-ray energies (below 27 keV), extending their detection to a new energy regime. The centroid frequency evolves from 41 to 93 mHz, with the peak root-mean-square (rms) amplitude detected in the 50-65 keV. Throughout the outburst, the QPOs generally exhibit a hard lag between 0.12 pi rad and 0.9 pi rad. However, at the outburst peak, the higher-energy bands (above 35 keV) display a soft lag of up to -0.93 pi rad. We propose that interactions between soft seed photons and an extended outflow located outside the magnetosphere can account for the observed hard lags. Furthermore, we detect a double-peaked mHz QPO only at high energies (E above 35 keV) near peak luminosity. The two peaks maintain an approximately constant separation of 2*nu_spin and exhibit anti-correlated phase evolution. Our results indicate that the mHz QPOs in 1A 0535+262 are closely linked to the coupled evolution of a soft-photon source and a Comptonizing outflow or corona. The joint cross-spectral framework provides a complementary probe of mHz QPOs beyond traditional power-spectral analyses.

astro-ph.HE↗

arXiv2026

Deep Learning for Blood-Brain Barrier Permeability Prediction: From Discriminative Models to Mechanism-Aware Design

Predicting whether a molecule can cross the blood-brain barrier (BBB) is a key step in early-stage neuro-pharmaceutical design, directly influencing the efficiency and success rate of drug development. Traditional methods based on physicochemical properties are prone to systematic misjudgements due to their reliance on previous empirical evidence. Early machine learning (ML) models, although data-driven, often suffer from limited capacity, poor generalization, and insufficient interpretability. In recent years, more advanced models have become essential tools for predicting BBB permeability and guiding related drug design, owing to their ability to simulate molecular structures and capture complex biological mechanisms. This article systematically reviews the evolution of this field-from deep neural networks to graph-based structural modelling-highlighting the advantages of multi-task and multimodal learning strategies in identifying mechanism-related features. We further explore the emerging potential of generative models and causal inference methods for integrating permeability prediction with mechanism-aware drug design. Nowadays, ML-based BBB crossing prediction is in the critical transition from mere discriminative classification toward structure-function modelling from a mechanistic perspective. This paradigm shift provides a methodological progression and future roadmap for the integration of AI into neuropharmacological development.

q-bio.QM↗

arXiv2026

NavSpace: How Navigation Agents Follow Spatial Intelligence Instructions

Instruction-following navigation is a key step toward embodied intelligence. Prior benchmarks mainly focus on semantic understanding but overlook systematically evaluating navigation agents' spatial perception and reasoning capabilities. In this work, we introduce the NavSpace benchmark, which contains six task categories and 1,228 trajectory-instruction pairs designed to probe the spatial intelligence of navigation agents. On this benchmark, we comprehensively evaluate 22 navigation agents, including state-of-the-art navigation models and multimodal large language models. The evaluation results lift the veil on spatial intelligence in embodied navigation. Furthermore, we propose SNav, a new spatially intelligent navigation model. SNav outperforms existing navigation agents on NavSpace and real robot tests, establishing a strong baseline for future work.

cs.RO↗

arXiv2026

ArcFlow: Unleashing 2-Step Text-to-Image Generation via High-Precision Non-Linear Flow Distillation

Diffusion models have achieved remarkable generation quality, but they suffer from significant inference cost due to their reliance on multiple sequential denoising steps, motivating recent efforts to distill this inference process into a few-step regime. However, existing distillation methods typically approximate the teacher trajectory by using linear shortcuts, which makes it difficult to match its constantly changing tangent directions as velocities evolve across timesteps, thereby leading to quality degradation. To address this limitation, we propose ArcFlow, a few-step distillation framework that explicitly employs non-linear flow trajectories to approximate pre-trained teacher trajectories. Concretely, ArcFlow parameterizes the velocity field underlying the inference trajectory as a mixture of continuous momentum processes. This enables ArcFlow to capture velocity evolution and extrapolate coherent velocities to form a continuous non-linear trajectory within each denoising step. Importantly, this parameterization admits an analytical integration of this non-linear trajectory, which circumvents numerical discretization errors and results in high-precision approximation of the teacher trajectory. To train this parameterization into a few-step generator, we implement ArcFlow via trajectory distillation on pre-trained teacher models using lightweight adapters. This strategy ensures fast, stable convergence while preserving generative diversity and quality. Built on large-scale models (Qwen-Image-20B and FLUX.1-dev), ArcFlow only fine-tunes on less than 5% of original parameters and achieves a 40x speedup with 2 NFEs over the original multi-step teachers without significant quality degradation. Experiments on benchmarks show the effectiveness of ArcFlow both qualitatively and quantitatively.

cs.CV↗

arXiv2026

Learning-based Observer for Coupled Disturbance

Achieving high-precision control for robotic systems is hindered by the low-fidelity dynamical model and external disturbances. Especially, the intricate coupling between internal uncertainties and external disturbances further exacerbates this challenge. This study introduces an effective and convergent algorithm enabling accurate estimation of the coupled disturbance via combining control and learning philosophies. Concretely, by resorting to Chebyshev series expansion, the coupled disturbance is firstly decomposed into an unknown parameter matrix and two known structures dependent on system state and external disturbance respectively. A regularized least squares algorithm is subsequently formalized to learn the parameter matrix using historical time-series data. Finally, a polynomial disturbance observer is specifically devised to achieve a high-precision estimation of the coupled disturbance by utilizing the learned portion. The proposed algorithm is evaluated through extensive simulations and real flight tests. We believe this work can offer a new pathway to integrate learning approaches into control frameworks for addressing longstanding challenges in robotic applications.

cs.RO↗

arXiv2026

Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning

Real-world physics can only be analytically modeled with a certain level of precision for modern intricate robotic systems. As a result, tracking aggressive trajectories accurately could be challenging due to the existence of residual physics during controller synthesis. This paper presents a self-supervised residual learning and trajectory optimization framework to address the aforementioned challenges. At first, unknown dynamic effects on the closed-loop model are learned and treated as residuals of the nominal dynamics, jointly forming a hybrid model. We show that learning with analytic gradients can be achieved using only trajectory-level data while enjoying accurate long-horizon prediction with an arbitrary integration step size. Subsequently, a trajectory optimizer is developed to compute the optimal reference trajectory with the residual physics along it minimized. It ends up with trajectories that are friendly to the following control level. The agile flight of quadrotors illustrates that by utilizing the hybrid dynamics, the proposed optimizer outputs aggressive motions that can be precisely tracked.

cs.RO↗

arXiv2026

Unified Meta-Representation and Feedback Calibration for General Disturbance Estimation

Precise control in modern robotic applications is always an open issue due to unknown time-varying disturbances. Existing meta-learning-based approaches require a shared representation of environmental structures, which lack flexibility for realistic non-structural disturbances. Besides, representation error and the distribution shifts can lead to heavy degradation in prediction accuracy. This work presents a generalizable disturbance estimation framework that builds on meta-learning and feedback-calibrated online adaptation. By extracting features from a finite time window of past observations, a unified representation that effectively captures general non-structural disturbances can be learned without predefined structural assumptions. The online adaptation process is subsequently calibrated by a state-feedback mechanism to attenuate the learning residual originating from the representation and generalizability limitations. Theoretical analysis shows that simultaneous convergence of both the online learning error and the disturbance estimation error can be achieved. Through the unified meta-representation, our framework effectively estimates multiple rapidly changing disturbances, as demonstrated by quadrotor flight experiments. See the project page for video, supplementary material and code: https://nonstructural-metalearn.github.io.

cs.RO↗

arXiv2025

Data-Augmented Machine Learning for Predicting Biomass-Derived Hard Carbon Anode Performance in Sodium-Ion Batteries

Biomass-derived hard carbon has become the most promising anode material for sodium-ion batteries (SIBs) due to its high capacity and excellent cycling stability. However, the effects of synthesis parameters and structural features on hard carbon's (HC) electrochemical performance are still unclear, requiring time-consuming and resource-intensive experimental investigations. Machine learning (ML) offers a promising solution by training on large datasets to predict hard carbon performance more efficiently, saving time and resources. In this study, four ML models were used to predict the capacity and initial Coulombic efficiency (ICE) of HC. Data augmentation based on the TabPFN technique was employed to improve model robustness under limited data conditions, and the relationships between features and electrochemical performance were examined. Notably, the XGBoost model achieved an R2 of 0.854 and an RMSE of 23.290 mAh g-1 for capacity prediction, and an R2 of 0.868 and an RMSE of 3.813% for ICE prediction. Shapley Additive Explanations (SHAP) and Partial Dependence Plot (PDP) analyses identified carbonization temperature (Temperature_2) as the most important factor influencing both capacity and ICE. Furthermore, we used bamboo as the precursor to synthesize four hard carbons based on the predictive approach. The electrochemical performance of these samples closely matched our predictions. By leveraging machine-learning approach, this study provides an efficient framework for accelerating the screening process of biomass-derived hard carbon candidates.

physics.chem-ph↗

arXiv2025

CorrectNav: Self-Correction Flywheel Empowers Vision-Language-Action Navigation Model

Existing vision-and-language navigation models often deviate from the correct trajectory when executing instructions. However, these models lack effective error correction capability, hindering their recovery from errors. To address this challenge, we propose Self-correction Flywheel, a novel post-training paradigm. Instead of considering the model's error trajectories on the training set as a drawback, our paradigm emphasizes their significance as a valuable data source. We have developed a method to identify deviations in these error trajectories and devised innovative techniques to automatically generate self-correction data for perception and action. These self-correction data serve as fuel to power the model's continued training. The brilliance of our paradigm is revealed when we re-evaluate the model on the training set, uncovering new error trajectories. At this time, the self-correction flywheel begins to spin. Through multiple flywheel iterations, we progressively enhance our monocular RGB-based VLA navigation model CorrectNav. Experiments on R2R-CE and RxR-CE benchmarks show CorrectNav achieves new state-of-the-art success rates of 65.1% and 69.3%, surpassing prior best VLA navigation models by 8.2% and 16.4%. Real robot tests in various indoor and outdoor environments demonstrate \method's superior capability of error correction, dynamic obstacle avoidance, and long instruction following.

cs.RO↗

arXiv2025

Accurate Control under Voltage Drop for Rotor Drones

This letter proposes an anti-disturbance control scheme for rotor drones to counteract voltage drop (VD) disturbance caused by voltage drop of the battery, which is a common case for long-time flight or aggressive maneuvers. Firstly, the refined dynamics of rotor drones considering VD disturbance are presented. Based on the dynamics, a voltage drop observer (VDO) is developed to accurately estimate the VD disturbance by decoupling the disturbance and state information of the drone, reducing the conservativeness of conventional disturbance observers. Subsequently, the control scheme integrates the VDO within the translational loop and a fixed-time sliding mode observer (SMO) within the rotational loop, enabling it to address force and torque disturbances caused by voltage drop of the battery. Sufficient real flight experiments are conducted to demonstrate the effectiveness of the proposed control scheme under VD disturbance.

eess.SY↗

arXiv2025

FpgaHub: Fpga-centric Hyper-heterogeneous Computing Platform for Big Data Analytics

Modern data analytics requires a huge amount of computing power and processes a massive amount of data. At the same time, the underlying computing platform is becoming much more heterogeneous on both hardware and software. Even though specialized hardware, e.g., FPGA- or GPU- or TPU-based systems, often achieves better performance than a CPU-only system due to the slowing of Moore's law, such systems are limited in what they can do. For example, GPU-only approaches suffer from severe IO limitations. To truly exploit the potential of hardware heterogeneity, we present FpgaHub, an FPGA-centric hyper-heterogeneous computing platform for big data analytics. The key idea of FpgaHub is to use reconfigurable computing to implement a versatile hub complementing other processors (CPUs, GPUs, DPUs, programmable switches, computational storage, etc.). Using an FPGA as the basis, we can take advantage of its highly reconfigurable nature and rich IO interfaces such as PCIe, networking, and on-board memory, to place it at the center of the architecture and use it as a data and control plane for data movement, scheduling, pre-processing, etc. FpgaHub enables architectural flexibility to allow exploring the rich design space of heterogeneous computing platforms.

cs.DC↗

arXiv2025

Feedback Favors the Generalization of Neural ODEs

The well-known generalization problem hinders the application of artificial neural networks in continuous-time prediction tasks with varying latent dynamics. In sharp contrast, biological systems can neatly adapt to evolving environments benefiting from real-time feedback mechanisms. Inspired by the feedback philosophy, we present feedback neural networks, showing that a feedback loop can flexibly correct the learned latent dynamics of neural ordinary differential equations (neural ODEs), leading to a prominent generalization improvement. The feedback neural network is a novel two-DOF neural network, which possesses robust performance in unseen scenarios with no loss of accuracy performance on previous tasks.} A linear feedback form is presented to correct the learned latent dynamics firstly, with a convergence guarantee. Then, domain randomization is utilized to learn a nonlinear neural feedback form. Finally, extensive tests including trajectory prediction of a real irregular object and model predictive control of a quadrotor with various uncertainties, are implemented, indicating significant improvements over state-of-the-art model-based and learning-based methods.

cs.LG↗

arXiv2024

LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU

Nowadays, AI researchers become more and more interested in fine-tuning a pre-trained LLM, whose size has grown to up to over 100B parameters, for their downstream tasks. One approach to fine-tune such huge models is to aggregate device memory from many GPUs. However, this approach introduces prohibitive costs for most data scientists with a limited budget for high-end GPU servers. In this paper, we focus on LLM fine-tuning on a single consumer-grade GPU in a commodity server with limited main memory capacity, which is accessible to most AI researchers. In such a scenario, existing offloading-based methods fail to fine-tune an LLM efficiently due to a lack of holistic intra-server tensor movement management. To this end, we present LoHan, a low-cost, high-performance deep learning training framework that enables efficient 100B-scale model fine-tuning on a commodity server with a consumer-grade GPU and limited main memory capacity. The key idea is to add holistic offloading traffic as an optimization dimension for 1)active gradient offloading, and 2)holistic traffic-aware activation swapping mechanism. The experimental results show that 1)LoHan is the first to fine-tune a 175B model on an RTX 4090 and 256 GB main memory, 2)LoHan achieves 2.32x throughput than the state-of-the-art baselines when fine-tuning a small 13B model, and 3)LoHan enables a cheap low-end consumer GPU to have higher cost-effectiveness than a DGX-A100 cluster when fine-tuning a 175B model.

cs.DC↗

arXiv2024

Pressure induced superconducting dome in LaNiGa2

LaNiGa2 is a time-reversal symmetry breaking superconductor with symmetry protected band crossings, making it an ideal platform for investigating the interplay between unconventional superconductivity and electronic structure topology. Here we present a transport study of LaNiGa2 under pressure. The application of pressure to LaNiGa2 induces a significant enhancement of the superconducting transition temperature Tc at a pressure of 7 GPa. In contrast, powder X-ray diffraction (XRD) results show no evidence of structural phase transitions up to 26.3 GPa. Moreover, the ratio of band diffusivity shows a sudden increase at around 7 GPa, suggesting possible pressure-induced changes in the electronic structure that are closely linked to the evolution of superconductivity.

cond-mat.supr-con↗

arXiv2024

High-temperature superconductivity with zero-resistance and strange metal behavior in La$_{3}$Ni$_{2}$O$_{7-δ}$

Recently signatures of superconductivity were observed close to 80 K in \LN\ under pressure. This discovery positions \LN\ as the first bulk nickelate with high-temperature superconductivity, but the lack of zero resistance presents a significant drawback for validating the findings. Here we report pressure measurements up to over 30 GPa using a liquid pressure medium and show that single crystals of \LNO\ do exhibit zero resistance. We find that \LNO\ remains metallic under applied pressures, suggesting the absence of a metal-insulator transition proximate to the superconductivity. Analysis of the normal state $T$-linear resistance suggests an intricate link between this strange metal behaviour and superconductivity, whereby at high pressures both the linear resistance coefficient and superconducting transition are slowly suppressed by pressure, while at intermediate pressures both the superconductivity and strange metal behaviour appear disrupted, possibly due to a nearby structural instability. The association between strange metal behaviour and high-temperature superconductivity is very much in line with diverse classes of unconventional superconductors, including the cuprates and Fe-based superconductors. Understanding the superconductivity of \LNO\ evidently requires further revealing the interplay of strange metal behaviour, superconductivity, as well as possible competing electronic or structural phases.

cond-mat.supr-con↗