arXiv subjects

Mao Wang

Publications and source records attributed to Mao Wang.

At least 19 recordsLinked to original sources

arXiv2026

CMNIE: An Information Extraction Benchmark for Chinese Military News

Structured extraction from Chinese military news supports intelligence analysis, decision-making, and knowledge base construction. However, existing resources provide limited support for joint informa?tion extraction in this domain, especially when events, event arguments, entities, and relations must be modeled together. We present CMNIE, an information extraction benchmark for Chinese military news. Extend?ing military-domain resources beyond document-level event annotations, CMNIE jointly annotates event triggers, event arguments, named enti?ties, and entity relations under a unified domain schema. The dataset contains 13,000 instances collected from public Chinese military news, with manual annotations for 7 event types, 10 argument roles, 7 entity types, and 8 relation types. We evaluate supervised IE models, zero-shot large language models, and fine-tuned LLM-based extraction methods on a shared test set. Experimental results show that CMNIE remains chal?lenging, especially for relation extraction and exact matching of event?argument spans; zero-shot LLMs often identify relevant semantic units but fail to match gold span boundaries exactly. CMNIE provides a stan?dardized benchmark for studying schema adherence, exact span match?ing, and joint structured extraction in specialized Chinese news.

cs.CL↗

arXiv2026

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and assortative edges in disassortative graphs. This structural inversion creates structure-feature mismatches that disrupt neighborhood aggregation across different graph types. However, we find that existing defenses are limited, as they either treat neighborhoods as monolithic under fixed assortativity assumptions or rely on standard softmax classifiers that fail to account for perturbation-induced representation shifts. To further exploit this observation, we adopt a robustness perspective that jointly disentangles node representations and decision spaces, isolating perturbation effects while enforcing well-separated decision regions. Based on this principle, we propose Graph Joint Disentanglement Network (GJDNet), a unified framework for robust node classification across diverse graph assortativity regimes. GJDNet enhances robustness at both representation and decision levels: it employs feature-driven soft structural disentanglement with skewness-aware neighbor filtering to suppress perturbation-induced structure-feature mismatches, and introduces a Spherical Decision Boundary (SDB) to promote intra-class compactness and inter-class separation in the embedding space, thereby stabilizing decision boundaries under perturbations. Theoretical analysis provides insights into the effectiveness of the proposed disentangled representation and decision mechanisms, while extensive experiments demonstrate that GJDNet consistently achieves strong robustness across graphs with different connectivity regimes.

cs.LG↗

arXiv2025

Topotactic phase transition in epitaxial La0.7Sr0.3MnO3-{\delta} films induced by oxygen getter assisted thermal annealing

Oxygen vacancies play a crucial role in controlling the physical properties of complex oxides. In La0.7Sr0.3MnO3-{\delta}, the topotactic phase transition from Perovskite (PV) to Brownmillerite (BM) can be triggered e.g. via oxygen removal during thermal annealing. Here we report on a very efficient thermal vacuum annealing method using aluminum as an oxygen getter material. The topotactic phase transition is characterized by X-ray Diffraction which confirms a successful transition from PV to BM in La0.7Sr0.3MnO3-{\delta} thin films grown via physical vapor deposition. The efficiency of this method is confirmed using La0.7Sr0.3MnO3-{\delta} micron-sized bulk powder. The accompanying transition from the original Ferromagnetic (FM) to an Antiferromagnetic (AF) state and the simultaneous transition from a metallic to an insulating state is characterized using Superconducting Quantum Interference Device (SQUID)-magnetometry and Alternating Current (AC) resistivity measurements, respectively. The near surface manganese oxidation states are probed by synchrotron X-ray Absorption Spectroscopy. Moreover, X-ray Reflectivity, Atomic Force Microscopy and Scanning Transmission Electron Microscopy reveal surface segregation and cation redistribution during the oxygen getter assisted annealing process.

cond-mat.mtrl-sci↗

arXiv2025

Disentangled Graph Representation Based on Substructure-Aware Graph Optimal Matching Kernel Convolutional Networks

Graphs effectively characterize relational data, driving graph representation learning methods that uncover underlying predictive information. As state-of-the-art approaches, Graph Neural Networks (GNNs) enable end-to-end learning for diverse tasks. Recent disentangled graph representation learning enhances interpretability by decoupling independent factors in graph data. However, existing methods often implicitly and coarsely characterize graph structures, limiting structural pattern analysis within the graph. This paper proposes the Graph Optimal Matching Kernel Convolutional Network (GOMKCN) to address this limitation. We view graphs as node-centric subgraphs, where each subgraph acts as a structural factor encoding position-specific information. This transforms graph prediction into structural pattern recognition. Inspired by CNNs, GOMKCN introduces the Graph Optimal Matching Kernel (GOMK) as a convolutional operator, computing similarities between subgraphs and learnable graph filters. Mathematically, GOMK maps subgraphs and filters into a Hilbert space, representing graphs as point sets. Disentangled representations emerge from projecting subgraphs onto task-optimized filters, which adaptively capture relevant structural patterns via gradient descent. Crucially, GOMK incorporates local correspondences in similarity measurement, resolving the trade-off between differentiability and accuracy in graph kernels. Experiments validate that GOMKCN achieves superior accuracy and interpretability in graph pattern mining and prediction. The framework advances the theoretical foundation for disentangled graph representation learning.

cs.LG↗

arXiv2024

EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents

In real life, many dynamic events, such as major disasters and large-scale sports events, evolve continuously over time. Obtaining an overview of these events can help people quickly understand the situation and respond more effectively. This is challenging because the key information of the event is often scattered across multiple documents, involving complex event knowledge understanding and reasoning, which is under-explored in previous work. Therefore, we proposed the Event-Centric Multi-Document Summarization (ECS) task, which aims to generate concise and comprehensive summaries of a given event based on multiple related news documents. Based on this, we constructed the EventSum dataset, which was constructed using Baidu Baike entries and underwent extensive human annotation, to facilitate relevant research. It is the first large scale Chinese multi-document summarization dataset, containing 5,100 events and a total of 57,984 news documents, with an average of 11.4 input news documents and 13,471 characters per event. To ensure data quality and mitigate potential data leakage, we adopted a multi-stage annotation approach for manually labeling the test set. Given the complexity of event-related information, existing metrics struggle to comprehensively assess the quality of generated summaries. We designed specific metrics including Event Recall, Argument Recall, Causal Recall, and Temporal Recall along with corresponding calculation methods for evaluation. We conducted comprehensive experiments on EventSum to evaluate the performance of advanced long-context Large Language Models (LLMs) on this task. Our experimental results indicate that: 1) The event-centric multi-document summarization task remains challenging for existing long-context LLMs; 2) The recall metrics we designed are crucial for evaluating the comprehensiveness of the summary information.

cs.CL↗

arXiv2024

ViViD: Video Virtual Try-on using Diffusion Models

Video virtual try-on aims to transfer a clothing item onto the video of a target person. Directly applying the technique of image-based try-on to the video domain in a frame-wise manner will cause temporal-inconsistent outcomes while previous video-based try-on solutions can only generate low visual quality and blurring results. In this work, we present ViViD, a novel framework employing powerful diffusion models to tackle the task of video virtual try-on. Specifically, we design the Garment Encoder to extract fine-grained clothing semantic features, guiding the model to capture garment details and inject them into the target video through the proposed attention feature fusion mechanism. To ensure spatial-temporal consistency, we introduce a lightweight Pose Encoder to encode pose signals, enabling the model to learn the interactions between clothing and human posture and insert hierarchical Temporal Modules into the text-to-image stable diffusion model for more coherent and lifelike video synthesis. Furthermore, we collect a new dataset, which is the largest, with the most diverse types of garments and the highest resolution for the task of video virtual try-on to date. Extensive experiments demonstrate that our approach is able to yield satisfactory video try-on results. The dataset, codes, and weights will be publicly available. Project page: https://becauseimbatman0.github.io/ViViD.

cs.CV↗

arXiv2024

CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military News

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 event types and 11 argument role types. We designed a two-stage, multi-turns annotation strategy to ensure the quality of CMNEE and reproduced several state-of-the-art event extraction models with a systematic evaluation. The experimental results on CMNEE fall shorter than those on other domain datasets obviously, which demonstrates that event extraction for military domain poses unique challenges and requires further research efforts. Our code and data can be obtained from https://github.com/Mzzzhu/CMNEE.

cs.CL↗

arXiv2023

Active Sites of Te-hyperdoped Silicon by Hard X-ray Photoelectron Spectroscopy

Multiple dopant configurations of Te impurities in close vicinity in silicon are investigated using photoelectron spectroscopy, photoelectron diffraction, and Bloch wave calculations. The samples are prepared by ion implantation followed by pulsed laser annealing. The dopant concentration is variable and high above the solubility limit of Te in silicon. The configurations in question are distinguished from isolated Te impurities by a strong chemical core level shift. While Te clusters are found to form only in very small concentrations, multi-Te configurations of type dimer or up to four Te ions surrounding a vacancy are clearly identified. For these configurations a substitutional site location of Te is found to match the data best in all cases. For isolated Te ions this matches the expectations. For multi-Te configurations the results contribute to understanding the exceptional activation of free charge carriers in hyperdoping of chalcogens in silicon.

cond-mat.mtrl-sci↗

arXiv2023

On-chip lateral Si:Te PIN photodiodes for room-temperature detection in the telecom optical wavelength bands

Photonic integrated circuits require photodetectors that operate at room temperature with sensitivity at telecom wavelengths and are suitable for integration with planar complementary-metal-oxide-semiconductor (CMOS) technology. Silicon hyperdoped with deep-level impurities is a promising material for silicon infrared detectors because of its strong room-temperature photoresponse in the short-wavelength infrared region caused by the creation of an impurity band within the silicon band gap. In this work, we present the first experimental demonstration of lateral Te-hyperdoped Si PIN photodetectors operating at room temperature in the optical telecom bands. We provide a detailed description of the fabrication process, working principle, and performance of the photodiodes, including their key figure of merits. Our results are promising for the integration of active and passive photonic elements on a single Si chip, leveraging the advantages of planar CMOS technology.

physics.optics↗

arXiv2022

Mid- and far-infrared localized surface plasmon resonances in chalcogen-hyperdoped silicon

Plasmonic sensing in the infrared region employs the direct interaction of the vibrational fingerprints of molecules with the plasmonic resonances, creating surface-enhanced sensing platforms that are superior than the traditional spectroscopy. However, the standard noble metals used for plasmonic resonances suffer from high radiative losses as well as fabrication challenges, such as tuning the spectral resonance positions into mid- to far-infrared regions, and the compatibility issue with the existing complementary metal-oxide-semiconductor (CMOS) manufacturing platform. Here, we demonstrate the occurrence of mid-infrared localized surface plasmon resonances (LSPR) in thin Si films hyperdoped with the known deep-level impurity tellurium. We show that the mid-infrared LSPR can be further enhanced and spectrally extended to the far-infrared range by fabricating two-dimensional arrays of micrometer-sized antennas in a Te-hyperdoped Si chip. Since Te-hyperdoped Si can also work as an infrared photodetector, we believe that our results will unlock the route toward the direct integration of plasmonic sensors with the one-chip CMOS platform, greatly advancing the possibility of mass manufacturing of high-performance plasmonic sensing systems.

cond-mat.mtrl-sci↗

arXiv2021

Membrane fouling: microscopic insights into the effects of surface chemistry and roughness

Fouling is a major obstacle and challenge in membrane-based separation processes. Caused by the sophisticated interactions between foulant and membrane surface, fouling strongly depends on membrane surface chemistry and morphology. Current studies in the field have been largely focused on polymer membranes. Herein, we report a molecular simulation study for fouling on alumina and graphene membrane surfaces during water treatment. For two foulants (sucralose and bisphenol A), the fouling on alumina surfaces is reduced with increasing surface roughness; however, the fouling on graphene surfaces is enhanced by roughness. It is unravelled that the foulant-surface interaction becomes weaker in the ridge region of a rough alumina surface, thus allowing foulant to leave the surface and reducing fouling. Such behavior is not observed on a rough graphene surface because of the strong foulant-graphene interaction. Moreover, with increasing roughness, the hydrogen bonds formed between water and alumina surfaces are found to increase in number as well as stability. By scaling the atomic charges of alumina, fouling behavior on alumina surfaces is shifted to the one on graphene surfaces. This simulation study reveals that surface chemistry and roughness play a crucial role in membrane fouling, and the microscopic insights are useful for the design of new membranes towards high-performance water treatment.

cond-mat.soft↗

arXiv2021

Organic charged polaritons in the ultrastrong coupling regime

We embedded an all-hydrocarbon-based carbocation in a metallic microcavity that was tuned to resonance with an electronic transition of the carbocation. The measured Rabi splitting was 41% of the excitation energy, putting the system well into the ultrastrong coupling regime. Importantly, due to the intrinsic charge on the carbocation, the polaritons that form carry a significant charge fraction $0.55 e_0$ and a large charge-to-mass ratio ($\sim 2400 e_0 / m_0$). Moreover, the ground state of the ultrastrongly coupled system is calculated to carry about one percent of one elementary charge, implying a potentially enhanced charge transport across the molecules. These unique properties of our system, together with its convenient preparation, provide a practical platform to study charged polaritons in the ultrastrong coupling regime.

quant-ph↗

arXiv2020

Silicon-based Intermediate-band Infrared Photodetector realized by Te Hyperdoping

Si-based photodetectors satisfy the criteria of low-cost and environmental-friendly, and can enable the development of on-chip complementary metal-oxide-semiconductor (CMOS)-compatible photonic systems. However, extending their room-temperature photoresponse into the mid-wavelength infrared (MWIR) regime remains challenging due to the intrinsic bandgap of Si. Here, we report on a comprehensive study of a room-temperature MWIR photodetector based on Si hyperdoped with Te. The demonstrated MWIR p-n photodiode exhibits a spectral photoresponse up to 5 μm and a slightly lower detector performance than the commercial devices in the wavelength range of 1.0-1.9 μm. We also investigate the correlation between the background noise and the sensitivity of the Te-hyperdoped Si photodiode, where the maximum room-temperature specific detectivity is found to be 3.2 x 10^12 cmHz^{1/2}W^{-1} and 9.2 x 10^8 cmHz^{1/2}W^{-1} at 1 μm and 1.55 μm, respectively. This work contributes to pave the way towards establishing a Si-based broadband infrared photonic system operating at room temperature.

physics.app-ph↗

arXiv2020

Dissolution of donor-vacancy clusters in heavily doped n-type germanium

The n-type doping of Ge is a self-limiting process due to the formation of vacancy-donor complexes (DnV with n <= 4) that deactivate the donors. This work unambiguously demonstrates that the dissolution of the dominating P4V clusters in heavily phosphorus-doped Ge epilayers can be achieved by millisecond-flash lamp annealing at about 1050 K. The P4V cluster dissolution increases the carrier concentration by more than three-fold together with a suppression of phosphorus diffusion. Electrochemical capacitance-voltage measurements in conjunction with secondary ion mass spectrometry, positron annihilation lifetime spectroscopy and theoretical calculations enabled us to address and understand a fundamental problem that has hindered so far the full integration of Ge with complementary-metal-oxide-semiconductor technology.

cond-mat.mtrl-sci↗

arXiv2020

Critical behavior of the insulator-to-metal transition in Te-hyperdoped Si

Hyperdoping Si with chalcogens is a topic of great interest due to the strong sub-bandgap absorption exhibited by the resulting material, which can be exploited to develop broadband room-temperature infrared photodetectors using fully Si-compatible technology. Here, we report on the critical behavior of the impurity-driven insulator-to-metal transition in Te-hyperdoped Si layers fabricated via ion implantation followed by nanosecond pulsed-laser melting. Electrical transport measurements reveal an insulator-to-metal transition, which is also confirmed and understood by density functional theory calculations. We demonstrate that the metallic phase is governed by a power law dependence of the conductivity at temperatures below 25 K, whereas the conductivity in the insulating phase is well described by a variable-range hopping mechanism with a Coulomb gap at temperatures in the range of 2-50 K. These results show that the electron wave-function in the vicinity of the transition is strongly affected by the disorder and the electron-electron interaction.

cond-mat.mtrl-sci↗

arXiv2020

Arnold: an eFPGA-Augmented RISC-V SoC for Flexible and Low-Power IoT End-Nodes

A wide range of Internet of Things (IoT) applications require powerful, energy-efficient and flexible end-nodes to acquire data from multiple sources, process and distill the sensed data through near-sensor data analytics algorithms, and transmit it wirelessly. This work presents Arnold: a 0.5 V to 0.8 V, 46.83 uW/MHz, 600 MOPS fully programmable RISC-V Microcontroller unit (MCU) fabricated in 22 nm Globalfoundries GF22FDX (GF22FDX) technology, coupled with a stateof-the-art (SoA) microcontroller to an embedded Field Programmable Gate Array (FPGA). We demonstrate the flexibility of the System-OnChip (SoC) to tackle the challenges of many emerging IoT applications, such as (i) interfacing sensors and accelerators with non-standard interfaces, (ii) performing on-the-fly pre-processing tasks on data streamed from peripherals, and (iii) accelerating near-sensor analytics, encryption, and machine learning tasks. A unique feature of the proposed SoC is the exploitation of body-biasing to reduce leakage power of the embedded FPGA (eFPGA) fabric by up to 18x at 0.5 V, achieving SoA state bitstream-retentive sleep power for the eFPGA fabric, as low as 20.5 uW. The proposed SoC provides 3.4x better performance and 2.9x better energy efficiency than other fabricated heterogeneous re-configurable SoCs of the same class.

cs.AR↗

arXiv2019

Vertically-Oriented Graphene Oxide Membranes for High-Performance Osmotic Energy Conversion

Reverse electrodialysis is a promising method to harvest the osmotic energy stored between seawater and freshwater, but it has been a long-standing challenge to fabricate permselective membranes with the power density surpassing the industry benchmark of 5.0 W $m^{-2}$ for half a century. Herein, we report a vertically-oriented graphene oxide membranes (V-GOMs) with the combination of high ion selectivity and ultrafast ion permeation, whose permeation is three orders of magnitude higher than the extensively studied horizontally stacked GOMs (H-GOMs). By mixing artificial seawater and river water, we obtained an unprecedented high output power density of 10.6 W $m^{-2}$, outperforming all existing materials. Molecular dynamics (MD) simulations reveal the mechanism of the ultrafast transport in V-GOMs results from the quick entering of ions and the large accessible area as well as the apparent short diffusion paths in V-GOMs. These results will facilitate the practical application of osmotic energy and bring innovative design strategy for various systems involving ultrafast transport, such as filtration and catalysis.

physics.app-ph↗

arXiv2019

p-type codoping effect in (Ga,Mn)As: Mn lattice location versus magnetic properties

In the present work, we perform a systematic investigation on p-type codoping in (Ga,Mn)As. Through gradually increasing Zn doping concentration, the hole concentration increases, which should theoretically lead to an increase of the Curie temperature (TC) according to the p-d Zener model. Unexpectedly, although the film keeps its epitaxial structure, both TC and the magnetization decrease. The samples present a phase transition from ferromagnetism to paramagnetism upon increasing hole concentration. In the intermediate regime, we observe a signature of antiferromagnetism. By using channeling Rutherford backscattering spectrometry and particle-induced x-ray emission, the substitutional Mn atoms are observed to shift to interstitial sites, while more Zn atoms occupy Ga sites, which explains the observed behavior. This is also consistent with first-principles calculations, showing that the complex of substitutional Zn and interstitial Mn has the lowest formation energy.

cond-mat.mtrl-sci↗