arXiv subjects

Yao Yang

Publications and source records attributed to Yao Yang.

34 records · Page 2Linked to original sources

arXiv2023

Three-dimensional atomic positions and local chemical order of medium- and high-entropy alloys

Medium- and high-entropy alloys (M/HEAs) mix multiple principal elements with near-equiatomic composition and represent a paradigm-shift strategy for designing new materials for metallurgy, catalysis, and other fields. One of the core hypotheses of M/HEAs is lattice distortion. However, experimentally determining the 3D local lattice distortion in M/HEAs remains a challenge. Additionally, the presumed random elemental mixing in M/HEAs has been questioned by atomistic simulations, energy dispersive x-ray spectroscopy (EDS), and electron diffraction, which suggest the existence of local chemical order in M/HEAs. However, the 3D local chemical order has eluded direct experimental observation since the EDS elemental maps integrate the composition of atomic columns along the zone axes, and the diffuse reflections/streaks in electron diffraction of M/HEAs may originate from planar defects. Here, we determine the 3D atomic positions of M/HEA nanocrystals using atomic electron tomography, and quantitatively characterize the local lattice distortion, strain tensor, twin boundaries, dislocation cores, and chemical short-range order (CSRO) with unprecedented 3D detail. We find that the local lattice distortion and strain tensor in the HEAs are larger and more heterogeneous than in the MEAs. We observe CSRO-mediated twinning in the MEAs. that is, twinning occurs in energetically unfavoured CSRO regions but not in energetically favoured CSRO ones. This observation confirms the atomistic simulation results of the bulk CrCoNi MEA and represents the first experimental evidence of correlating local chemical order with structural defects in any material system. We expect that this work will not only expand our fundamental understanding of this important class of materials, but also could provide the foundation for tailoring M/HEA properties through lattice distortion and local chemical order.

cond-mat.mtrl-sci↗

arXiv2023

Fraudulent User Detection Via Behavior Information Aggregation Network (BIAN) On Large-Scale Financial Social Network

Financial frauds cause billions of losses annually and yet it lacks efficient approaches in detecting frauds considering user profile and their behaviors simultaneously in social network . A social network forms a graph structure whilst Graph neural networks (GNN), a promising research domain in Deep Learning, can seamlessly process non-Euclidean graph data . In financial fraud detection, the modus operandi of criminals can be identified by analyzing user profile and their behaviors such as transaction, loaning etc. as well as their social connectivity. Currently, most GNNs are incapable of selecting important neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored. In this paper, we propose a novel behavior information aggregation network (BIAN) to combine the user behaviors with other user features. Different from its close "relatives" such as Graph Attention Networks (GAT) and Graph Transformer Networks (GTN), it aggregates neighbors based on neighboring edge attribute distribution, namely, user behaviors in financial social network. The experimental results on a real-world large-scale financial social network dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with the State-Of-The-Art models.

cs.SI↗

arXiv2022

Audio Tampering Detection Based on Shallow and Deep Feature Representation Learning

Digital audio tampering detection can be used to verify the authenticity of digital audio. However, most current methods use standard electronic network frequency (ENF) databases for visual comparison analysis of ENF continuity of digital audio or perform feature extraction for classification by machine learning methods. ENF databases are usually tricky to obtain, visual methods have weak feature representation, and machine learning methods have more information loss in features, resulting in low detection accuracy. This paper proposes a fusion method of shallow and deep features to fully use ENF information by exploiting the complementary nature of features at different levels to more accurately describe the changes in inconsistency produced by tampering operations to raw digital audio. The method achieves 97.03% accuracy on three classic databases: Carioca 1, Carioca 2, and New Spanish. In addition, we have achieved an accuracy of 88.31% on the newly constructed database GAUDI-DI. Experimental results show that the proposed method is superior to the state-of-the-art method.

cs.SD↗

arXiv2022

Learnable Privacy-Preserving Anonymization for Pedestrian Images

This paper studies a novel privacy-preserving anonymization problem for pedestrian images, which preserves personal identity information (PII) for authorized models and prevents PII from being recognized by third parties. Conventional anonymization methods unavoidably cause semantic information loss, leading to limited data utility. Besides, existing learned anonymization techniques, while retaining various identity-irrelevant utilities, will change the pedestrian identity, and thus are unsuitable for training robust re-identification models. To explore the privacy-utility trade-off for pedestrian images, we propose a joint learning reversible anonymization framework, which can reversibly generate full-body anonymous images with little performance drop on person re-identification tasks. The core idea is that we adopt desensitized images generated by conventional methods as the initial privacy-preserving supervision and jointly train an anonymization encoder with a recovery decoder and an identity-invariant model. We further propose a progressive training strategy to improve the performance, which iteratively upgrades the initial anonymization supervision. Experiments further demonstrate the effectiveness of our anonymized pedestrian images for privacy protection, which boosts the re-identification performance while preserving privacy. Code is available at \url{https://github.com/whuzjw/privacy-reid}.

cs.CV↗

arXiv2022

Atomic-scale identification of the active sites of nanocatalysts

Alloy nanocatalysts have found broad applications ranging from fuel cells to catalytic converters and hydrogenation reactions. Despite extensive studies, identifying the active sites of nanocatalysts remains a major challenge due to the heterogeneity of the local atomic environment. Here, we advance atomic electron tomography to determine the 3D local atomic structure, surface morphology and chemical composition of PtNi and Mo-doped PtNi nanocatalysts. Using machine learning trained by density functional theory calculations, we identify the catalytic active sites for the oxygen reduction reaction from experimental 3D atomic coordinates, which are corroborated by electrochemical measurements. By quantifying the structure-activity relationship, we discover a local environment descriptor to explain and predict the catalytic active sites at the atomic level. The ability to determine the 3D atomic structure and chemical species coupled with machine learning is expected to expand our fundamental understanding of a wide range of nanocatalysts.

physics.chem-ph↗

arXiv2022

Elucidating the Degradation Mechanism of Gd2Zr2O7 Waste Form under Multi-Energy He Ion Irradiation

We studied the microstructural and helium bubbling evolutions of Gd2Zr2O7 waste form with immobilized TRPO (50 wt%) under multi-energy He ion irradiation. Three structurally heterogeneous regions for the Gd2Zr2O7 waste form were found as a function of the depth from the He-irradiated surface. Specifically, at a depth less than 40 nm below the He-irradiated surface (Region I) the Gd2Zr2O7 waste form is completely amorphous with large spherical He bubbles (5-25 nm). In the intermediate region, Region II, (40-800 nm) partially amorphized Gd2Zr2O7 waste form accompanied with ribbon-like He bubbles that may lead to the formation of microcracks is observed. The crystallinity is not impacted in Region III for a depth of more than 800 nm. For the first time, we elucidated that the Gd2Zr2O7 waste form, which was considered to be structurally intact at 100 dpa, is completely amorphized at 6.5 dpa with the synergistic displacement damage, electronic energy loss, and He concentration enabled. This study leads to new physical insights into amorphization and He bubbles formation mechanisms of Gd2Zr2O7 waste form under multi-energy He irradiation, which is essential for the design and optimization of irradiation-resistant ceramic waste matrices.

cond-mat.mtrl-sci↗

arXiv2021

A framework for massive scale personalized promotion

Technology companies building consumer-facing platforms may have access to massive-scale user population. In recent years, promotion with quantifiable incentive has become a popular approach for increasing active users on such platforms. On one hand, increased user activities can introduce network effect, bring in advertisement audience, and produce other benefits. On the other hand, massive-scale promotion causes massive cost. Therefore making promotion campaigns efficient in terms of return-on-investment (ROI) is of great interest to many companies. This paper proposes a practical two-stage framework that can optimize the ROI of various massive-scale promotion campaigns. In the first stage, users' personal promotion-response curves are modeled by machine learning techniques. In the second stage, business objectives and resource constraints are formulated into an optimization problem, the decision variables of which are how much incentive to give to each user. In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage. We leverage existing counterfactual prediction techniques to correct treatment bias in data. We also introduce a novel deep neural network (DNN) architecture, the deep-isotonic-promotion-network (DIPN), to reduce noise in the promotion response curves. The DIPN architecture incorporates our prior knowledge of response curve shape, by enforcing isotonicity and smoothness. It out-performed regular DNN and other state-of-the-art shape-constrained models in our experiments.

cs.LG↗

arXiv2020

Direct observation of 3D atomic packing in monatomic amorphous materials

Liquids and solids are two fundamental states of matter. However, due to the lack of direct experimental determination, our understanding of the 3D atomic structure of liquids and amorphous solids remained speculative. Here we advance atomic electron tomography to determine for the first time the 3D atomic positions in monatomic amorphous materials, including a Ta thin film and two Pd nanoparticles. We observe that pentagonal bipyramids are the most abundant atomic motifs in these amorphous materials. Instead of forming icosahedra, the majority of pentagonal bipyramids arrange into networks that extend to medium-range scale. Molecular dynamic simulations further reveal that pentagonal bipyramid networks are prevalent in monatomic amorphous liquids, which rapidly grow in size and form icosahedra during the quench from the liquid state to glass state. The experimental method and results are expected to advance the study of the amorphous-crystalline phase transition and glass transition at the single-atom level.

cond-mat.mtrl-sci↗

arXiv2020

Resource-Aware Pareto-Optimal Automated Machine Learning Platform

In this study, we introduce a novel platform Resource-Aware AutoML (RA-AutoML) which enables flexible and generalized algorithms to build machine learning models subjected to multiple objectives, as well as resource and hard-ware constraints. RA-AutoML intelligently conducts Hyper-Parameter Search(HPS) as well as Neural Architecture Search (NAS) to build models optimizing predefined objectives. RA-AutoML is a versatile framework that allows user to prescribe many resource/hardware constraints along with objectives demanded by the problem at hand or business requirements. At its core, RA-AutoML relies on our in-house search-engine algorithm,MOBOGA, which combines a modified constraint-aware Bayesian Optimization and Genetic Algorithm to construct Pareto optimal candidates. Our experiments on CIFAR-10 dataset shows very good accuracy compared to results obtained by state-of-art neural network models, while subjected to resource constraints in the form of model size.

cs.LG↗

arXiv2020

Determining the three-dimensional atomic structure of a metallic glass

Amorphous solids such as glass are ubiquitous in our daily life and have found broad applications ranging from window glass and solar cells to telecommunications and transformer cores. However, due to the lack of long-range order, the three-dimensional (3D) atomic structure of amorphous solids have thus far defied any direct experimental determination without model fitting. Here, using a multi-component metallic glass as a proof-of-principle, we advance atomic electron tomography to determine the 3D atomic positions in an amorphous solid for the first time. We quantitatively characterize the short-range order (SRO) and medium-range order (MRO) of the 3D atomic arrangement. We find that although the 3D atomic packing of the SRO is geometrically disordered, some SRO connect with each other to form crystal-like networks and give rise to MRO. We identify four crystal-like MRO networks - face-centred cubic, hexagonal close-packed, body-centered cubic and simple cubic - coexisting in the sample, which show translational but no orientational order. These observations confirm that the 3D atomic structure in some parts of the sample is consistent with the efficient cluster packing model. Looking forward, we anticipate this experiment will open the door to determining the 3D atomic coordinates of various amorphous solids, whose impact on non-crystalline solids may be comparable to the first 3D crystal structure solved by x-ray crystallography over a century ago.

cond-mat.dis-nn↗

arXiv2019

Correlating 3D atomic defects and electronic properties of 2D materials with picometer precision

The exceptional electronic, optical and chemical properties of two-dimensional materials strongly depend on the 3D atomic structure and crystal defects. Using Re-doped MoS2 as a model, here we develop scanning atomic electron tomography (sAET) to determine the 3D atomic positions and crystal defects such as dopants, vacancies and ripples with a precision down to 4 picometers. We measure the 3D bond distortion and local strain tensor induced by single dopants for the first time. By directly providing experimental 3D atomic coordinates to density functional theory (DFT), we obtain more truthful electronic band structures than those derived from conventional DFT calculations relying on relaxed 3D atomic models, which is confirmed by photoluminescence measurements. We anticipate that sAET is not only generally applicable to the determination of the 3D atomic coordinates of 2D materials, heterostructures and thin films, but also could transform ab initio calculations by using experimental 3D atomic coordinates as direct input to better predict and discover new physical, chemical and electronic properties.

cond-mat.mtrl-sci↗

arXiv2019

Capturing Nucleation at 4D Atomic Resolution

Nucleation plays a critical role in many physical and biological phenomena ranging from crystallization, melting and evaporation to the formation of clouds and the initiation of neurodegenerative diseases. However, nucleation is a challenging process to study in experiments especially in the early stage when several atoms/molecules start to form a new phase from its parent phase. Here, we advance atomic electron tomography to study early stage nucleation at 4D atomic resolution. Using FePt nanoparticles as a model system, we reveal that early stage nuclei are irregularly shaped, each has a core of one to few atoms with the maximum order parameter, and the order parameter gradient points from the core to the boundary of the nucleus. We capture the structure and dynamics of the same nuclei undergoing growth, fluctuation, dissolution, merging and/or division, which are regulated by the order parameter distribution and its gradient. These experimental observations differ from classical nucleation theory (CNT) and to explain them we propose the order parameter gradient (OPG) model. We show the OPG model generalizes CNT and energetically favours diffuse interfaces for small nuclei and sharp interfaces for large nuclei. We further corroborate this model using molecular dynamics simulations of heterogeneous and homogeneous nucleation in liquid-solid phase transitions of Pt. We anticipate that the OPG model is applicable to different nucleation processes and our experimental method opens the door to study the structure and dynamics of materials with 4D atomic resolution.

cond-mat.mtrl-sci↗

arXiv2018

End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNN

In this work, we use a deep learning framework for simultaneous classification and regression of Parkinson disease diagnosis based on MR-Images and personal information (i.e. age, gender). We intend to facilitate and increase the confidence in Parkinson disease diagnosis through our deep learning framework.

cs.CV↗

arXiv2017

Experimental Study of residual radioactivity induced in SFC Electrostatic Deflector

Electrostatic Deflector, one of the key components of SFC accelerator at IMP, is easily activated in the long-term running of HIRFL due to the bombardment with losing ions. The level of residual radioactivity induced in main components and dust contained in the electrostatic deflector enclosure have been investigated using gamma-ray spectrometry and Fluke 451p ionization chamber. The results indicate that the activated electrostatic deflector represent a significant radiological hazard for workers during maintenance operation, and the waste disposal difficulties should be encountered after decommissioning.

physics.ins-det↗

arXiv2016

Generation and Detection of Surface Plasmon Polaritons by Transition Metal Dichalcogenides for Chip-level Electronic-Photonic Integrated Circuits

The monolithic integration of electronics and photonics has attracted enormous attention due to its potential applications. However, the realization of such hybrid circuits has remained a challenge because it requires optical communication at nanometer scales. A major challenge to this integration is the identification of a suitable material. After discussing the material aspect of the challenge, we identified atomically thin transition metal dichalcogenides (TMDs) as a perfect material platform to implement the circuit. The selection of TMDs is based on their very distinct property: monolayer TMDs are able to emit and absorb light at the same wavelength determined by direct exciton transitions. To prove the concept, we fabricated simple devices consisting of silver nanowires as plasmonic waveguides and monolayer TMDs as active optoelectronic media. Using photoexcitation, direct optical imaging and spectral analysis, we demonstrated generation and detection of surface plasmon polaritons by monolayer TMDs. Regarded as novel materials for electronics and photonics, transition metal dichalcogenides are expected to find new applications in next generation integrated circuits.

cond-mat.mes-hall↗

arXiv2015

Detecting Potential Instabilities of Numerical Algorithms

It has been the standard teaching of today that backward stability analysis is taught as absolute, just as in Newtonian physics time is taught absolute time. We will prove it is not true in general. It depends on algorithms. We will prove that forward and mixed stability anlaysis are absolutely invalid stability analysis in the sense that they have absolutely wrong reference points for detecting huge element growth of any algoritms(if any), even an "ideal" or "desirable" backward stability analysis is not so "ideal" or "desirable" in general. Any of forward stable, backward stable and mixed stable algorihms as in Demmel, Kahan , Parlett and other's papers and text books, see Demmel(6) and Higham(8)may not be really stable at all because they may fail to detect and expose any potential instabilities of the algorithm in corresponding stability analysis. Therefore, it is impossible to prove an algorithm is stable according to the standard teachin of today, just as it is impossible to prove a mathematical equuation(Maxwell's) is a law of physics according to the standard teaching in Newtonian physics.

math.NA↗