arXiv subjects

Ying Zhao

Publications and source records attributed to Ying Zhao.

At least 37 records · Page 2Linked to original sources

arXiv2025

Subthreshold Swing Behavior in Amorphous Indium-Gallium-Zinc-Oxide Transistors from Room to Cryogenic Temperatures

While cryogenic-temperature subthreshold swing (SS) in crystalline semiconductors has been widely studied, a careful study on the temperature-dependent SS in amorphous oxide semiconductors remains lacking. In this paper, a comprehensive analysis of the SS in thin-film transistors with an amorphous indium gallium zinc oxide (IGZO) channel at temperatures from 300 K down to 4 K is presented. Main observations include: 1) At room temperature (300 K), the devices exhibit a SS of 61 mV/dec, and a low interface trap density (<1011 cm-2), among the best reported values for IGZO devices. 2) A SS saturation around 40 mV/dec is observed between 200 K and 100 K. It is well explained by the electron transport via band tail states with exponential decay (Wt) of 13 meV. 3) At deep-cryogenic temperature, SS increase significantly exceeding 200 mV/dec at 4 K. Such high SS values are actually limited by the measurement current range, confirmed by Id-Vg simulations based on the variable range hopping (VRH) model. This work not only elucidates the SS behavior in amorphous IGZO devices but also provides a deep understanding of the physical mechanisms of electron transport in amorphous semiconductors.

cond-mat.mtrl-sci↗

arXiv2025

Electronic Correlations Control Interlayer Coupling and Magnetic Transition in MnBi$_2$Te$_4$/MnBr$_3$ Heterostructure

Bulk MnBi$_2$Te$_4$ (MBT) is an intrinsic antiferromagnetic topological insulator. However, its low Néel temperature of $\sim 25\,\mathrm{K}$ severely restricts its practical applications. Here, we propose a van der Waals heterostructure composed of monolayer MBT (ML-MBT) and monolayer MnBr$_3$, an intrinsic Chern insulator possessing a high Curie temperature ($T_\mathrm{C} \sim 200\,\mathrm{K}$). By employing density functional theory calculations and Monte Carlo simulations, we demonstrate that interfacing ML-MBT with MnBr$_3$ significantly enhances the $T_\mathrm{C}$ of ML-MBT by a factor of four to five. Electronic correlations characterized by the Hubbard parameter $U_2$ for Mn-$d$ orbitals in MnBr$_3$ play a crucial role in governing magnetic coupling within the system. At a moderate correlation strength of $U_2 = 3.0\,\mathrm{eV}$, slight structural distortions in MnBr$_3$ break intralayer symmetry, enabling robust interlayer ferromagnetic coupling and yielding a single, unified magnetic transition. Increasing $U_2$ reduces these structural distortions, weakens interlayer coupling, and induces two distinct magnetic transitions, indicating interlayer magnetic decoupling. Thus, the MBT/MnBr$_3$ heterostructure offers a novel approach for controlling magnetic order and enhancing the performance of spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Polymer Data Challenges in the AI Era: Bridging Gaps for Next-Generation Energy Materials

The pursuit of advanced polymers for energy technologies, spanning photovoltaics, solid-state batteries, and hydrogen storage, is hindered by fragmented data ecosystems that fail to capture the hierarchical complexity of these materials. Polymer science lacks interoperable databases, forcing reliance on disconnected literature and legacy records riddled with unstructured formats and irreproducible testing protocols. This fragmentation stifles machine learning (ML) applications and delays the discovery of materials critical for global decarbonization. Three systemic barriers compound the challenge. First, academic-industrial data silos restrict access to proprietary industrial datasets, while academic publications often omit critical synthesis details. Second, inconsistent testing methods undermine cross-study comparability. Third, incomplete metadata in existing databases limits their utility for training reliable ML models. Emerging solutions address these gaps through technological and collaborative innovation. Natural language processing (NLP) tools extract structured polymer data from decades of literature, while high-throughput robotic platforms generate self-consistent datasets via autonomous experimentation. Central to these advances is the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles, adapted to polymer-specific ontologies, ensuring machine-readability and reproducibility. Future breakthroughs hinge on cultural shifts toward open science, accelerated by decentralized data markets and autonomous laboratories that merge robotic experimentation with real-time ML validation. By addressing data fragmentation through technological innovation, collaborative governance, and ethical stewardship, the polymer community can transform bottlenecks into accelerants.

cond-mat.soft↗

arXiv2025

Zebrafish Counting Using Event Stream Data

Zebrafish share a high degree of homology with human genes and are commonly used as model organism in biomedical research. For medical laboratories, counting zebrafish is a daily task. Due to the tiny size of zebrafish, manual visual counting is challenging. Existing counting methods are either not applicable to small fishes or have too many limitations. The paper proposed a zebrafish counting algorithm based on the event stream data. Firstly, an event camera is applied for data acquisition. Secondly, camera calibration and image fusion were preformed successively. Then, the trajectory information was used to improve the counting accuracy. Finally, the counting results were averaged over an empirical of period and rounded up to get the final results. To evaluate the accuracy of the algorithm, 20 zebrafish were put in a four-liter breeding tank. Among 100 counting trials, the average accuracy reached 97.95%. As compared with traditional algorithms, the proposed one offers a simpler implementation and achieves higher accuracy.

cs.CV↗

arXiv2025

AnomalyHybrid: A Domain-agnostic Generative Framework for General Anomaly Detection

Anomaly generation is an effective way to mitigate data scarcity for anomaly detection task. Most existing works shine at industrial anomaly generation with multiple specialists or large generative models, rarely generalizing to anomalies in other applications. In this paper, we present AnomalyHybrid, a domain-agnostic framework designed to generate authentic and diverse anomalies simply by combining the reference and target images. AnomalyHybrid is a Generative Adversarial Network(GAN)-based framework having two decoders that integrate the appearance of reference image into the depth and edge structures of target image respectively. With the help of depth decoders, AnomalyHybrid achieves authentic generation especially for the anomalies with depth values changing, such a s protrusion and dent. More, it relaxes the fine granularity structural control of the edge decoder and brings more diversity. Without using annotations, AnomalyHybrid is easily trained with sets of color, depth and edge of same images having different augmentations. Extensive experiments carried on HeliconiusButterfly, MVTecAD and MVTec3D datasets demonstrate that AnomalyHybrid surpasses the GAN-based state-of-the-art on anomaly generation and its downstream anomaly classification, detection and segmentation tasks. On MVTecAD dataset, AnomalyHybrid achieves 2.06/0.32 IS/LPIPS for anomaly generation, 52.6 Acc for anomaly classification with ResNet34, 97.3/72.9 AP for image/pixel-level anomaly detection with a simple UNet.

cs.CV↗

arXiv2025

Evaluation of tortuosity: A radical tessellation-based method in porous spherical particle packing systems

Estimating the tortuosity of porous electrodes is important for understanding the performance of lithium-ion batteries and optimizing the design of electrode microstructures. In this work, a new method for estimating the tortuosity of porous electrodes is proposed based on radical tessellation, and the results agree well with those calculated by empirical formulas and finite element simulations. Compared with empirical formulas, the proposed method can estimate tortuosity for more complicated microstructures, regardless of particle distribution and size dispersity; compared with finite element simulations, the proposed method offers a significant advantage in computational efficiency. Based on the method, the influence of different microstructure characters on the tortuosity is discussed. It is found that in addition to the porosity, the particle size and particle aggregation morphology are all critical in influencing the tortuosity of the porous electrode. Finally, the tortuosity obtained by this method is integrated into the Pseudo-2-dimensional (P2D) model for the calculation of effective properties, and the results show that this method offers an efficient way in improving the prediction accuracy of P2D models.

cond-mat.mtrl-sci↗

arXiv2025

Enhanced deep-freezing magneto- and elasto-caloric effects by modifying lattice anharmonicity and electronic structures

Designing the high performance magneto or elastocaloric effect in NiMnIn alloys with spin-lattice coupling in a deep freezing temperature range of 200 K to 255 K is challenging due to the limited lattice entropy change and large negative contribution of magnetic entropy change during phase transitions. In this work, we systematically study the first order magneto-structural transition in NiMnIn based alloys by in-situ microstructural characterizations, physical property measurements, and first principles calculations. A multi element alloying strategy involving Cu and Ga co doping is proposed to manipulate the phase transition. The co doping reduces the lattice anharmonicity and thermal expansion coefficient of the martensitic phase, leading to an increase in the unit cell volume change and lattice entropy change. It also modifies the electronic density of states, causing a decrease in the magnetization change .The relief of the lattice mismatch reduces hysteresis losses in the refrigeration cycle. These synergetic effects yield excellent magneto and elastocaloric effects,with the effective magnetocaloric refrigeration capacity reaching up to 182 J/kg under the magnetic field of 5 T or an adiabatic temperature change of -4 K under a low field of 1.5 T and the elastocaloric coefficient of performance to 30 or an adiabatic temperature change of -7 K with the strain of 5% at 230 K, offering a potential solution for solid-state deep-freezing refrigeration.

cond-mat.mtrl-sci↗

arXiv2025

Closed universes, factorization, and ensemble averaging

We study closed universes in holographic theories of quantum gravity. We argue that within any fixed theory, factorization implies there is one unique closed universe state. The wave function of any state that can be prepared by the path integral is proportional to the Hartle-Hawking wave function. This unique wave function depends on the properties of the underlying holographic theory such as the energy spectrum. We show these properties explicitly in JT gravity, which is known to be dual to an ensemble of random Hamiltonians. For each member of the ensemble, the corresponding wave function is erratic as a result of the spectrum being chaotic. After ensemble averaging, we obtain smooth semi-classical wave functions as well as different closed universe states.

hep-th↗

arXiv2025

Quantum mechanics and observers for gravity in a closed universe

Recent arguments based on the quantum extremal surface formula or the gravitational path integral have given fairly compelling evidence that the Hilbert space of quantum gravity in a closed universe is one-dimensional and real. How can this be consistent with the complexity of our own experiences? In this paper we propose that the experiences of any observer $Ob$ in a closed universe can be approximately described by a quantum mechanical theory with a Hilbert space whose dimension is roughly $e^{S_{Ob}}$, where $S_{Ob}$ is the number of degrees of freedom of $Ob$. Moreover we argue that the errors in this description are exponentially small in $S_{Ob}$. We give evidence for this proposal using the gravitational path integral and the coding interpretation of holography, and we explain how similar effects arise in black hole physics in appropriate circumstances.

hep-th↗

arXiv2024

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks

Federated Learning (FL), as a privacy-preserving machine learning paradigm, trains a global model across devices without exposing local data. However, resource heterogeneity and inevitable stragglers in wireless networks severely impact the efficiency and accuracy of FL training. In this paper, we propose a novel Dynamic Cross-Tier Federated Learning framework (FedDCT). Firstly, we design a dynamic tiering strategy that dynamically partitions devices into different tiers based on their response times and assigns specific timeout thresholds to each tier to reduce single-round training time. Then, we propose a cross-tier device selection algorithm that selects devices that respond quickly and are conducive to model convergence to improve convergence efficiency and accuracy. Experimental results demonstrate that the proposed approach under wireless networks outperforms the baseline approach, with an average reduction of 54.7\% in convergence time and an average improvement of 1.83\% in convergence accuracy.

cs.DC↗

arXiv2024

AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localization

Recent advances in anomaly generation approaches alleviate the effect of data insufficiency on task of anomaly localization. While effective, most of them learn multiple large generative models on different datasets and cumbersome anomaly prediction models for different classes. To address the limitations, we propose a novel scalable framework, named AnomalyFactory, that unifies unsupervised anomaly generation and localization with same network architecture. It starts with a BootGenerator that combines structure of a target edge map and appearance of a reference color image with the guidance of a learned heatmap. Then, it proceeds with a FlareGenerator that receives supervision signals from the BootGenerator and reforms the heatmap to indicate anomaly locations in the generated image. Finally, it easily transforms the same network architecture to a BlazeDetector that localizes anomaly pixels with the learned heatmap by converting the anomaly images generated by the FlareGenerator to normal images. By manipulating the target edge maps and combining them with various reference images, AnomalyFactory generates authentic and diversity samples cross domains. Comprehensive experiments carried on 5 datasets, including MVTecAD, VisA, MVTecLOCO, MADSim and RealIAD, demonstrate that our approach is superior to competitors in generation capability and scalability.

cs.CV↗

arXiv2024

Closed universes in two dimensional gravity

We study closed universes in simple models of two dimensional gravity, such as Jackiw-Teiteilboim (JT) gravity coupled to matter, and a toy topological model that captures the key features of the former. We find there is a stark contrast, as well as some connections, between the perturbative and non-perturbative aspects of the theory. We find rich semi-classical physics. However, when non-perturbative effects are included there is a unique closed universe state in each theory. We discuss possible meanings and interpretations of this observation.

hep-th↗

arXiv2024

GRUtopia: Dream General Robots in a City at Scale

Recent works have been exploring the scaling laws in the field of Embodied AI. Given the prohibitive costs of collecting real-world data, we believe the Simulation-to-Real (Sim2Real) paradigm is a crucial step for scaling the learning of embodied models. This paper introduces project GRUtopia, the first simulated interactive 3D society designed for various robots. It features several advancements: (a) The scene dataset, GRScenes, includes 100k interactive, finely annotated scenes, which can be freely combined into city-scale environments. In contrast to previous works mainly focusing on home, GRScenes covers 89 diverse scene categories, bridging the gap of service-oriented environments where general robots would be initially deployed. (b) GRResidents, a Large Language Model (LLM) driven Non-Player Character (NPC) system that is responsible for social interaction, task generation, and task assignment, thus simulating social scenarios for embodied AI applications. (c) The benchmark, GRBench, supports various robots but focuses on legged robots as primary agents and poses moderately challenging tasks involving Object Loco-Navigation, Social Loco-Navigation, and Loco-Manipulation. We hope that this work can alleviate the scarcity of high-quality data in this field and provide a more comprehensive assessment of Embodied AI research. The project is available at https://github.com/OpenRobotLab/GRUtopia.

cs.RO↗

arXiv2024

LogicAL: Towards logical anomaly synthesis for unsupervised anomaly localization

Anomaly localization is a practical technology for improving industrial production line efficiency. Due to anomalies are manifold and hard to be collected, existing unsupervised researches are usually equipped with anomaly synthesis methods. However, most of them are biased towards structural defects synthesis while ignoring the underlying logical constraints. To fill the gap and boost anomaly localization performance, we propose an edge manipulation based anomaly synthesis framework, named LogicAL, that produces photo-realistic both logical and structural anomalies. We introduce a logical anomaly generation strategy that is adept at breaking logical constraints and a structural anomaly generation strategy that complements to the structural defects synthesis. We further improve the anomaly localization performance by introducing edge reconstruction into the network structure. Extensive experiments on the challenge MVTecLOCO, MVTecAD, VisA and MADsim datasets verify the advantage of proposed LogicAL on both logical and structural anomaly localization.

cs.CV↗

arXiv2024

The Quasinormal Modes and Isospectrality of Bardeen (Anti-) de Sitter Black Holes

Black holes (BHs) exhibiting coordinate singularities but lacking essential singularities throughout the entire spacetime are referred to as regular black holes (RBHs). The initial formulation of RBHs was presented by Bardeen, who considered the Einstein equation coupled with a nonlinear electromagnetic field. In this study, we investigate the gravitational perturbations, including the axial and polar sectors, of the Bardeen (Anti-) de Sitter black holes. We derive the master equations with source terms for both axial and polar perturbations, and subsequently compute the quasinormal modes (QNMs) through numerical methods. For the Bardeen de Sitter black hole, we employ the 6th-order WKB approach. The numerical results reveal that the isospectrality is broken in this case. Conversely, for Bardeen Anti-de Sitter black holes, the QNM frequencies are calculated by using the HH method.

gr-qc↗

arXiv2024

A Dataset of Open-Domain Question Answering with Multiple-Span Answers

Multi-span answer extraction, also known as the task of multi-span question answering (MSQA), is critical for real-world applications, as it requires extracting multiple pieces of information from a text to answer complex questions. Despite the active studies and rapid progress in English MSQA research, there is a notable lack of publicly available MSQA benchmark in Chinese. Previous efforts for constructing MSQA datasets predominantly emphasized entity-centric contextualization, resulting in a bias towards collecting factoid questions and potentially overlooking questions requiring more detailed descriptive responses. To overcome these limitations, we present CLEAN, a comprehensive Chinese multi-span question answering dataset that involves a wide range of open-domain subjects with a substantial number of instances requiring descriptive answers. Additionally, we provide established models from relevant literature as baselines for CLEAN. Experimental results and analysis show the characteristics and challenge of the newly proposed CLEAN dataset for the community. Our dataset, CLEAN, will be publicly released at zhiyiluo.site/misc/clean_v1.0_ sample.json.

cs.CL↗

arXiv2024

Boundary signature of singularity in the presence of a shock wave

Matter falling into a Schwarzschild-AdS black hole from the left causes increased focussing of ingoing geodesics from the right, and, as a consequence, they reach the singularity sooner. In a standard Penrose diagram, the singularity "bends down". We show how to detect this feature of the singularity holographically, using a boundary two-point function. We model the matter with a shock wave, and show that this bending down of the singularity can be read off from a novel analytic continuation of the boundary two-point function. Along the way, we obtain a generalization of the recently proposed thermal product formula for two-point correlators.

hep-th↗

arXiv2023

Finite-temperature ductility-brittleness and electronic structures of Al$_{n}$Sc (n=1, 2 and 3)

Finite-temperature ductility-brittleness and electronic structures of Al$_3$Sc, Al$_2$Sc and AlSc are studied comparatively by first-principles calculations and ab-initio molecular dynamics. Results show that Al$_3$Sc and Al$_2$Sc are inherently brittle at both ground state and finite temperatures. By contrast, AlSc possesses a significantly superior ductility evaluated from all Pugh's, Pettifor's and Poisson's ductility-brittleness criteria. At ground state, AlSc meets the criteria of ductile according to Pugh's and Poisson's theories, while it is categorized as the brittle in the frame of Pettifor's picture. With the increasing temperature, the ductility of all the studied compounds exhibits a noticeable improvement. In particular, as the temperature rises, the Cauchy pressure of AlSc undergoes a transition from negative to positive. Thus, at high temperatures (T > 600 K), AlSc is unequivocally classified as the ductile from all criteria considered. In all Al$_3$Sc, Al$_2$Sc and AlSc, the Al-Al bond, originated from s-p and p-p orbital hybridizations, and the Al-Sc bond, dominated by p-d covalent hybridization, are the first and second strongest chemical bonds, respectively. To explain the difference in mechanical properties of the studied compounds, the mean bond strength (MBS) is evaluated. The weaker Al-Al bond in AlSc, leading to a smaller MBS, could be the origin for the softer elastic stiffness and superior intrinsic ductility. The longer length of the Al-Al bond in AlSc is responsible for its weaker bond strength. Furthermore, the enhanced metallicity of the Al-Al bond in AlSc would also contribute to its exceptional ductility.

cond-mat.mtrl-sci↗