arXiv subjects

Yao Zhang

Publications and source records attributed to Yao Zhang.

At least 145 records · Page 8Linked to original sources

arXiv2024

DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning

Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry practitioners. The representation learning of DTDGs has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. Currently, DTDG representation learning predominantly relies on GNN+RNN architectures, which manifest the inherent limitations of both Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs). GNNs suffer from the over-smoothing issue as the models architecture goes deeper, while RNNs struggle to capture long-term dependencies effectively. GNN+RNN architectures also grapple with scaling to large graph sizes and long sequences. Additionally, these methods often compute node representations separately and focus solely on individual node characteristics, thereby overlooking the behavior intersections between the two nodes whose link is being predicted, such as instances where the two nodes appear together in the same context or share common neighbors. This paper introduces a novel representation learning method DTFormer for DTDGs, pivoting from the traditional GNN+RNN framework to a Transformer-based architecture. Our approach exploits the attention mechanism to concurrently process topological information within the graph at each timestamp and temporal dynamics of graphs along the timestamps, circumventing the aforementioned fundamental weakness of both GNNs and RNNs. Moreover, we enhance the model's expressive capability by incorporating the intersection relationships among nodes and integrating a multi-patching module. Extensive experiments conducted on six public dynamic graph benchmark datasets confirm our model's efficacy, achieving the SOTA performance.

cs.LG↗

arXiv2024

High-Resolution Spectroscopy of the Intermediate Impurity States near a Quantum Phase Transition

The intermediate behavior near a quantum phase transition is crucial for understanding the quantum criticality of various competing phases and their separate origins, yet remains unexplored for the multiple Yu-Shiba-Rusinov (YSR) states. Here, we investigated the detailed spectroscopic change of the exchange coupling-dependent YSR states near a quantum phase transition. The initially developed one pair of YSR states, induced by the Fe vacancy in monolayer Fe(Te,Se) superconductor, are clearly resolved with high resolution showing an evolution into two pairs of YSR peaks yet with dichotomy in their spectral features. Interestingly, while the lower-energy YSR branch enters the quantum phase transition region, the higher-lying one remains rigidly away from the lower-energy counterpart with a constant energy difference. Spectral weight analysis of the higher-energy branch yields an exponential dependence on the exchange coupling, which can be well rationalized by taking the two pairs of YSR states as a result of field splitting by the magnetic anisotropy. Our results unveil the intermediate region of a quantum phase transition with a magnetic anisotropy-induced splitting of the YSR resonance, and highlight a prospect for developing functional electronics based on the flexibly controllable multiple quantum states.

cond-mat.supr-con↗

arXiv2024

Charged hadron spectra and anisotropic flow from the AMPT model with shear viscous transport dynamics simulations at RHIC

We utilized the AMPT model to simulate the shear viscous transport dynamics of parton matter in Au+Au collisions at a constant specific shear viscosity and varying phase transition temperatures at 200 GeV. The resulting charged hadron spectra and anisotropic flow profiles correspond closely with experimental data. The transverse momentum spectra and longitudinal decorrelations are essentially unaffected by the phase transition temperature. An increase in the phase transition temperature leads to a rise in particle yields at midrapidity, accompanied by a decrease in both elliptic and triangular flows over a range of transverse momenta and pseudorapidities.

nucl-th↗

arXiv2024

When Large Language Models Meet Optical Networks: Paving the Way for Automation

Since the advent of GPT, large language models (LLMs) have brought about revolutionary advancements in all walks of life. As a superior natural language processing (NLP) technology, LLMs have consistently achieved state-of-the-art performance on numerous areas. However, LLMs are considered to be general-purpose models for NLP tasks, which may encounter challenges when applied to complex tasks in specialized fields such as optical networks. In this study, we propose a framework of LLM-empowered optical networks, facilitating intelligent control of the physical layer and efficient interaction with the application layer through an LLM-driven agent (AI-Agent) deployed in the control layer. The AI-Agent can leverage external tools and extract domain knowledge from a comprehensive resource library specifically established for optical networks. This is achieved through user input and well-crafted prompts, enabling the generation of control instructions and result representations for autonomous operation and maintenance in optical networks. To improve LLM's capability in professional fields and stimulate its potential on complex tasks, the details of performing prompt engineering, establishing domain knowledge library, and implementing complex tasks are illustrated in this study. Moreover, the proposed framework is verified on two typical tasks: network alarm analysis and network performance optimization. The good response accuracies and sematic similarities of 2,400 test situations exhibit the great potential of LLM in optical networks.

cs.NI↗

arXiv2024

Permeability distribution of gas drainage of borehole with the different moisture content caused polar permeability effect

In order to study the penetration characteristics in areas with different water content and different stress distributions in the radial direction of the hole after hydraulicization measures, an improved LFTD1812 triaxial permeability meter was used to conduct a test to measure the polar permeability characteristics of coal with different water content combinations were measured by permeability instrument, and the porosity, permeability, pressure gradient and seepage velocity of different samples were analyzed. The relationship between sample porosity, permeability, pressure gradient and seepage velocity was discussed, the influence of moisture content on permeability was discussed, and the directionality and the directivity and polarization effect of permeability were found.. Result shows that The relationship between permeability and porosity shows two trends of exponential type and logarithmic type, and the porosity-permeability(ϕ-k) plane is divided into three influence regions: super index (I), index (II) and logarithm (III).

hep-ex↗

arXiv2024

Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling

Temporal Graph Networks (TGNs) have demonstrated their remarkable performance in modeling temporal interaction graphs. These works can generate temporal node representations by encoding the surrounding neighborhoods for the target node. However, an inherent limitation of existing TGNs is their reliance on fixed, hand-crafted rules for neighborhood encoding, overlooking the necessity for an adaptive and learnable neighborhood that can accommodate both personalization and temporal evolution across different timestamps. In this paper, we aim to enhance existing TGNs by introducing an adaptive neighborhood encoding mechanism. We present SEAN, a flexible plug-and-play model that can be seamlessly integrated with existing TGNs, effectively boosting their performance. To achieve this, we decompose the adaptive neighborhood encoding process into two phases: (i) representative neighbor selection, and (ii) temporal-aware neighborhood information aggregation. Specifically, we propose the Representative Neighbor Selector component, which automatically pinpoints the most important neighbors for the target node. It offers a tailored understanding of each node's unique surrounding context, facilitating personalization. Subsequently, we propose a Temporal-aware Aggregator, which synthesizes neighborhood aggregation by selectively determining the utilization of aggregation routes and decaying the outdated information, allowing our model to adaptively leverage both the contextually significant and current information during aggregation. We conduct extensive experiments by integrating SEAN into three representative TGNs, evaluating their performance on four public datasets and one financial benchmark dataset introduced in this paper. The results demonstrate that SEAN consistently leads to performance improvements across all models, achieving SOTA performance and exceptional robustness.

cs.SI↗

arXiv2024

Visualizing uniform lattice-scale pair density wave in single-layer FeSe/SrTiO3 films

Typical BCS superconductors are microscopically homogeneous in real space governed by the coherent Cooper pairs with high phase stiffness of superfluid density, which is characterized by a coherence length. However, a periodic oscillation of superconducting order parameter may develop driven by breaking the time-reversal or translational invariance. To date, such modulated orders were specific to each material systems, with a periodicity much larger than the lattice constant. Here we report the direct observation of a uniform lattice-scale pair density wave (PDW) in single-layer FeSe/SrTiO3 films, enforced by peculiar interfacial structure of crystal symmetries breaking. Our spectroscopic imaging scanning tunneling microscopy unravels a spatial modulation of Cooper-pairing gap within a single unit-cell, depending on inequivalent atomic sites. Prominent periodic variation of superfluid density is visualized via Josephson current by a superconducting tip, indicating a real-space oscillation of phase stiffness. Such a lattice-scale superconducting modulation, which coexists with a larger length scale of PDW order, indicates the lattice-scale variation of both pairing strength and phase stiffness. Our findings provide new insights into the intertwined density-wave orders of quasiparticle character in correlated electronic systems, and provoke future studies on the unconventional pairing interaction and phase stiffness in the two-dimensional limit.

cond-mat.supr-con↗

arXiv2024

Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge

The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical School's Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location.

eess.IV↗

arXiv2024

The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.

eess.IV↗

arXiv2024

The long-lasting effect of X-ray preheating in the post-reionization intergalactic medium

X-ray photons can penetrate deep into the intergalactic medium (IGM), leading to preheating of the IGM prior to cosmic reionization. X-ray preheating wipes out some of the small-scale structures that would otherwise be present prior to the passage of an ionization front. Accurate modeling of the small-scale structure is vital to the post-reionization IGM since the small-scale structure is ultimately the dominant source of long-lasting relics from hydrogen reionization. However, the precise impact of X-ray preheating in the fossils from hydrogen reionization is highly uncertain. In this work, we explore and establish for the first time, the long-lasting impact of X-ray preheating in the post-reionization IGM via hydrodynamic simulations with high-mass resolution. We find that the addition of X-ray preheating astrophysics leads to an overall lesser impact of the effect of inhomogeneous reionization in the Lyman-$α$ forest -- depending on specific X-ray prescription -- at low redshifts ($z \sim 2$) with respect to a model with no X-ray preheating. However, at high redshifts ($z \sim 4$), our results indicate a strengthening of the relics of reionization in the Lyman-$α$ forest because the IGM becomes more transparent compared to the scenario with no preheating. Thus, the absence of X-ray preheating in Lyman-$α$ modeling can lead to a biased inference of cosmological parameters. Nevertheless, optimistically, the inclusion of X-ray preheating emerges as a promising novel avenue to probe the astrophysics of cosmic dawn.

astro-ph.CO↗

arXiv2024

Record-high Anomalous Ettingshausen effect in a micron-sized magnetic Weyl semimetal on-chip cooler

Solid-state cooling devices offer compact, quiet, reliable and environmentally friendly solutions that currently rely primarily on the thermoelectric (TE) effect. Despite more than two centuries of research, classical thermoelectric coolers suffer from low efficiency which hampers wider application. In this study, the less researched Anomalous Ettingshausen effect (AEE), a transverse thermoelectric phenomenon, is presented as a new approach for on-chip cooling. This effect can be boosted in materials with non-trivial band topologies as demonstrated in the Heusler alloy $\text{Co}_2\text{MnGa}$. Enabled by the high quality of our material, in situ scanning thermal microscopy experiments reveal a record-breaking anomalous Ettingshausen coefficient of $-2.1$~mV in $μ$m-sized on-chip cooling devices at room temperature. A significant 44\% of the effect is contributed by the intrinsic topological properties, in particular the Berry curvature of $\text{Co}_2\text{MnGa}$, emphasising the unique potential of magnetic Weyl semimetals for high-performance spot cooling in nanostructures.

cond-mat.mes-hall↗

arXiv2024

Prompt Learning on Temporal Interaction Graphs

Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems. To facilitate representation learning on TIGs, researchers have proposed a series of TIG models. However, these models are still facing two tough gaps between the pre-training and downstream predictions in their ``pre-train, predict'' training paradigm. First, the temporal discrepancy between the pre-training and inference data severely undermines the models' applicability in distant future predictions on the dynamically evolving data. Second, the semantic divergence between pretext and downstream tasks hinders their practical applications, as they struggle to align with their learning and prediction capabilities across application scenarios. Recently, the ``pre-train, prompt'' paradigm has emerged as a lightweight mechanism for model generalization. Applying this paradigm is a potential solution to solve the aforementioned challenges. However, the adaptation of this paradigm to TIGs is not straightforward. The application of prompting in static graph contexts falls short in temporal settings due to a lack of consideration for time-sensitive dynamics and a deficiency in expressive power. To address this issue, we introduce Temporal Interaction Graph Prompting (TIGPrompt), a versatile framework that seamlessly integrates with TIG models, bridging both the temporal and semantic gaps. In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks. These prompts stand out for their minimalistic design, relying solely on the tuning of the prompt generator with very little supervision data. To cater to varying computational resource demands, we propose an extended ``pre-train, prompt-based fine-tune'' paradigm, offering greater flexibility. Through extensive experiments, the TIGPrompt demonstrates the SOTA performance and remarkable efficiency advantages.

cs.AI↗

arXiv2024

Three-dimensional atomic interface between metal and oxide in Zr-ZrO2 nanoparticles

Metal-oxide interfaces with poor coherency have unique properties comparing to the bulk materials and offer broad applications in the fields of heterogeneous catalysis, battery, and electronics. However, current understanding of the three-dimensional (3D) atomic metal-oxide interfaces remains limited because of their inherent structural complexity and limitations of conventional two-dimensional imaging techniques. Here, we determine the 3D atomic structure of metal-oxide interfaces in zirconium-zirconia nanoparticles using atomic-resolution electron tomography. We quantitatively analyze the atomic concentration and the degree of oxidation, and find the coherency and translational symmetry of the interfaces are broken. Moreover, we observe porous structures such as Zr vacancies and nano-pores and investigate their distribution. Our findings provide a clear 3D atomic picture of metal-oxide interface with direct experimental evidence. We anticipate this work could encourage future studies on fundamental problems of oxides such as interfacial structures in semiconductor and atomic motion during oxidation process.

cond-mat.mtrl-sci↗

arXiv2024

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models. Although VFL enables collaborative machine learning without sharing raw data, it is still susceptible to various privacy threats. In this paper, we conduct the first comprehensive survey of the state-of-the-art in privacy attacks and defenses in VFL. We provide taxonomies for both attacks and defenses, based on their characterizations, and discuss open challenges and future research directions. Specifically, our discussion is structured around the model's life cycle, by delving into the privacy threats encountered during different stages of machine learning and their corresponding countermeasures. This survey not only serves as a resource for the research community but also offers clear guidance and actionable insights for practitioners to safeguard data privacy throughout the model's life cycle.

cs.CR↗

arXiv2024

Graph Attention Transformer Network for Multi-Label Image Classification

Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize such inter-label correlations or dependencies. Current methods often use the co-occurrence probability of labels based on the training set as the adjacency matrix to model this correlation, which is greatly limited by the dataset and affects the model's generalization ability. In this paper, we propose a Graph Attention Transformer Network (GATN), a general framework for multi-label image classification that can effectively mine complex inter-label relationships. First, we use the cosine similarity based on the label word embedding as the initial correlation matrix, which can represent rich semantic information. Subsequently, we design the graph attention transformer layer to transfer this adjacency matrix to adapt to the current domain. Our extensive experiments have demonstrated that our proposed methods can achieve state-of-the-art performance on three datasets.

cs.CV↗

arXiv2023

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedback

cs.CV↗

arXiv2023

EndWatch: A Practical Method for Detecting Non-Termination in Real-World Software

Detecting non-termination is crucial for ensuring program correctness and security, such as preventing denial-of-service attacks. While termination analysis has been studied for many years, existing methods have limited scalability and are only effective on small programs. To address this issue, we propose a practical termination checking technique, called EndWatch, for detecting non-termination caused by infinite loops through testing. Specifically, we introduce two methods to generate non-termination oracles based on checking state revisits, i.e., if the program returns to a previously visited state at the same program location, it does not terminate. The non-termination oracles can be incorporated into testing tools (e.g., AFL used in this paper) to detect non-termination in large programs. For linear loops, we perform symbolic execution on individual loops to infer State Revisit Conditions (SRCs) and instrument SRCs into target loops. For non-linear loops, we instrument target loops for checking concrete state revisits during execution. We evaluated EndWatch on standard benchmarks with small-sized programs and real-world projects with large-sized programs. The evaluation results show that EndWatch is more effective than the state-of-the-art tools on standard benchmarks (detecting 87% of non-terminating programs while the best baseline detects only 67%), and useful in detecting non-termination in real-world projects (detecting 90% of known non-termination CVEs and 4 unknown bugs).

cs.SE↗

arXiv2023

Implementing Digital Twin in Field-Deployed Optical Networks: Uncertain Factors, Operational Guidance, and Field-Trial Demonstration

Digital twin has revolutionized optical communication networks by enabling their full life-cycle management, including design, troubleshooting, optimization, upgrade, and prediction. While extensive literature exists on frameworks, standards, and applications of digital twin, there is a pressing need in implementing digital twin in field-deployed optical networks operating in real-world environments, as opposed to controlled laboratory settings. This paper addresses this challenge by examining the uncertain factors behind the inaccuracy of digital twin in field-deployed optical networks from three main challenges and proposing operational guidance for implementing accurate digital twin in field-deployed optical networks. Through the proposed guidance, we demonstrate the effective implementation of digital twin in a field-trial C+L-band optical transmission link, showcasing its capabilities in performance recovery in a fiber cut scenario.

cs.NI↗