arXiv subjects

Yang Xu

Publications and source records attributed to Yang Xu.

At least 289 records · Page 16Linked to original sources

arXiv2023

Numerical study of the splashing wave induced by a seaplane using mesh-based and particle-based methods

In recent years, forest fires and maritime accidents have occurred frequently, which have had a bad impact on human production and life. Thus, the development of seaplanes is an increasingly urgent demand. It is important to study the taxiing process of seaplanes for the development of seaplanes, which is a strong nonlinear fluid-structure interaction problem. In this paper, the Smoothed Particle Hydrodynamics (SPH) method based on the Lagrangian framework is utilized to simulate the taxiing process of seaplanes, and the SPH results are compared with those of the Finite Volume Method (FVM) based on the Eulerian method. The results show that the SPH method can not only give the same accuracy as the FVM but also have a strong ability to capture the splashing waves in the taxiing process, which is quite meaningful for the subsequent study of the effect of a splash on other parts of the seaplane.

physics.flu-dyn↗

arXiv2023

Collaborative Optimization of Multi-microgrids System with Shared Energy Storage Based on Multi-agent Stochastic Game and Reinforcement Learning

Achieving the economical and stable operation of Multi-microgrids (MMG) systems is vital. However, there are still some challenging problems to be solved. Firstly, from the perspective of stable operation, it is necessary to minimize the energy fluctuation of the main grid. Secondly, the characteristics of energy conversion equipment need to be considered. Finally, privacy protection while reducing the operating cost of an MMG system is crucial. To address these challenges, a Data-driven strategy for MMG systems with Shared Energy Storage (SES) is proposed. The Mixed-Attention is applied to fit the conditions of the equipment, additionally, Multi-Agent Soft Actor-Critic(MA-SAC) and (Multi-Agent Win or Learn Fast Policy Hill-Climbing)MA-WoLF-PHC are proposed to solve the partially observable dynamic stochastic game problem. By testing the operation data of the MMG system in Northwest China, following conclusions are drawn: the R-Square (R2) values of results reach 0.999, indicating the neural network effectively models the nonlinear conditions. The proposed MMG system framework can reduce energy fluctuations in the main grid by 1746.5kW in 24 hours and achieve a cost reduction of 16.21% in the test. Finally, the superiority of the proposed algorithms is verified through their fast convergence speed and excellent optimization performance.

eess.SY↗

arXiv2023

Scale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs)

The weight-sharing mechanism of convolutional kernels ensures translation-equivariance of convolution neural networks (CNNs). Recently, rotation-equivariance has been investigated. However, research on scale-equivariance or simultaneous scale-rotation-equivariance is insufficient. This study proposes a Lie group-CNN, which can keep scale-rotation-equivariance for image classification tasks. The Lie group-CNN includes a lifting module, a series of group convolution modules, a global pooling layer, and a classification layer. The lifting module transfers the input image from Euclidean space to Lie group space, and the group convolution is parameterized through a fully connected network using Lie-algebra of Lie-group elements as inputs to achieve scale-rotation-equivariance. The Lie group SIM(2) is utilized to establish the Lie group-CNN with scale-rotation-equivariance. Scale-rotation-equivariance of Lie group-CNN is verified and achieves the best recognition accuracy on the blood cell dataset (97.50%) and the HAM10000 dataset (77.90%) superior to Lie algebra convolution network, dilation convolution, spatial transformer network, and scale-equivariant steerable network. In addition, the generalization ability of the Lie group-CNN on SIM(2) on rotation-equivariance is verified on rotated-MNIST and rotated-CIFAR10, and the robustness of the network is verified on SO(2) and SE(2). Therefore, the Lie group-CNN can successfully extract geometric features and performs equivariant recognition on images with rotation and scale transformations.

cs.CV↗

arXiv2023

Word sense extension

Humans often make creative use of words to express novel senses. A long-standing effort in natural language processing has been focusing on word sense disambiguation (WSD), but little has been explored about how the sense inventory of a word may be extended toward novel meanings. We present a paradigm of word sense extension (WSE) that enables words to spawn new senses toward novel context. We develop a framework that simulates novel word sense extension by first partitioning a polysemous word type into two pseudo-tokens that mark its different senses, and then inferring whether the meaning of a pseudo-token can be extended to convey the sense denoted by the token partitioned from the same word type. Our framework combines cognitive models of chaining with a learning scheme that transforms a language model embedding space to support various types of word sense extension. We evaluate our framework against several competitive baselines and show that it is superior in predicting plausible novel senses for over 7,500 English words. Furthermore, we show that our WSE framework improves performance over a range of transformer-based WSD models in predicting rare word senses with few or zero mentions in the training data.

cs.CL↗

arXiv2023

Language Anisotropic Cross-Lingual Model Editing

Multilingual pre-trained language models can learn task-specific abilities or memorize facts across multiple languages but inevitably make undesired predictions with specific inputs. Under similar observation, model editing aims to post-hoc calibrate a model targeted to specific inputs with keeping the model's raw behavior. However, existing work only studies the monolingual scenario, which lacks the cross-lingual transferability to perform editing simultaneously across languages. In this work, we focus on cross-lingual model editing. Firstly, we define the cross-lingual model editing task and corresponding metrics, where an edit in one language propagates to the others. Next, we propose a framework to naturally adapt monolingual model editing approaches to the cross-lingual scenario using parallel corpus. Further, we propose language anisotropic editing to improve cross-lingual editing by amplifying different subsets of parameters for each language. On the newly defined cross-lingual model editing task, we empirically demonstrate the failure of monolingual baselines in propagating the edit to multiple languages and the effectiveness of the proposed language anisotropic model editing. Our code is publicly available at https://github.com/franklear/LiME.

cs.CL↗

arXiv2023

Knowledge of cultural moral norms in large language models

Moral norms vary across cultures. A recent line of work suggests that English large language models contain human-like moral biases, but these studies typically do not examine moral variation in a diverse cultural setting. We investigate the extent to which monolingual English language models contain knowledge about moral norms in different countries. We consider two levels of analysis: 1) whether language models capture fine-grained moral variation across countries over a variety of topics such as ``homosexuality'' and ``divorce''; 2) whether language models capture cultural diversity and shared tendencies in which topics people around the globe tend to diverge or agree on in their moral judgment. We perform our analyses with two public datasets from the World Values Survey (across 55 countries) and PEW global surveys (across 40 countries) on morality. We find that pre-trained English language models predict empirical moral norms across countries worse than the English moral norms reported previously. However, fine-tuning language models on the survey data improves inference across countries at the expense of a less accurate estimate of the English moral norms. We discuss the relevance and challenges of incorporating cultural knowledge into the automated inference of moral norms.

cs.CL↗

arXiv2023

Marriage Matching-based Instant Parking Spot Sharing in Internet of Vehicles

The rapid development and integration of automotive manufacturing, sensor, and communication technologies have facilitated the emergence of the Internet of Vehicles (IoV). However, the explosive growing demand for parking spots has become a challenging issue to be addressed in IoV. In this paper, we propose a novel Smart Parking System (SPS) for IoV by applying the matching game approach. Specifically, the proposed SPS consists of three types of entities: drivers, parking spot owners (PSOs), and a certificate authority (CA). Drivers and PSOs send parking requests and parking spot information to the CA, respectively. The CA is responsible for identifying legitimate system users, matching drivers with PSOs, and recording their transaction information. We first design rational utility functions for drivers and PSOs, and then establish preference lists for them based on real-time conditions. With these information, we further develop an improved marriage matching (MM) algorithm to achieve stable matching results for drivers' requests and parking spot allocation. Simulation results demonstrate that the proposed MM-based SPS not only ensures stable matching results with the objective of distance minimization but also achieves an overall utility close to that of the optimal algorithm.

cs.SI↗

arXiv2023

PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy

AI-Generated Content (AIGC) has recently gained a surge in popularity, powered by its high efficiency and consistency in production, and its capability of being customized and diversified. The cross-modality nature of the representation learning mechanism in most AIGC technology allows for more freedom and flexibility in exploring new types of art that would be impossible in the past. Inspired by the pictogram subset of Chinese characters, we proposed PaCaNet, a CycleGAN-based pipeline for producing novel artworks that fuse two different art types, traditional Chinese painting and calligraphy. In an effort to produce stable and diversified output, we adopted three main technical innovations: 1. Using one-shot learning to increase the creativity of pre-trained models and diversify the content of the fused images. 2. Controlling the preference over generated Chinese calligraphy by freezing randomly sampled parameters in pre-trained models. 3. Using a regularization method to encourage the models to produce images similar to Chinese paintings. Furthermore, we conducted a systematic study to explore the performance of PaCaNet in diversifying fused Chinese painting and calligraphy, which showed satisfying results. In conclusion, we provide a new direction of creating arts by fusing the visual information in paintings and the stroke features in Chinese calligraphy. Our approach creates a unique aesthetic experience rooted in the origination of Chinese hieroglyph characters. It is also a unique opportunity to delve deeper into traditional artwork and, in doing so, to create a meaningful impact on preserving and revitalizing traditional heritage.

cs.CV↗

arXiv2023

Gate-tunable multiband transport in ZrTe5 thin devices

Interest in ZrTe5 has been reinvigorated in recent years owing to its potential for hosting versatile topological electronic states and intriguing experimental discoveries. However, the mechanism of many of its unusual transport behaviors remains controversial, for example, the characteristic peak in the temperature-dependent resistivity and the anomalous Hall effect. Here, through employing a clean dry-transfer fabrication method under inert environment, we successfully obtain high-quality ZrTe5 thin devices that exhibit clear dual-gate tunability and ambipolar field effects. Such devices allow us to systematically study the resistance peak as well as the Hall effect at various doping densities and temperatures, revealing the contribution from electron-hole asymmetry and multiple-carrier transport. By comparing with theoretical calculations, we suggest a simplified semiclassical two-band model to explain the experimental observations. Our work helps to resolve the long-standing puzzles on ZrTe5 and could potentially pave the way for realizing novel topological states in the two-dimensional limit.

cond-mat.mes-hall↗

arXiv2023

Boosting Distributed Machine Learning Training Through Loss-tolerant Transmission Protocol

Distributed Machine Learning (DML) systems are utilized to enhance the speed of model training in data centers (DCs) and edge nodes. The Parameter Server (PS) communication architecture is commonly employed, but it faces severe long-tail latency caused by many-to-one "incast" traffic patterns, negatively impacting training throughput. To address this challenge, we design the \textbf{L}oss-tolerant \textbf{T}ransmission \textbf{P}rotocol (LTP), which permits partial loss of gradients during synchronization to avoid unneeded retransmission and contributes to faster synchronization per iteration. LTP implements loss-tolerant transmission through \textit{out-of-order transmission} and \textit{out-of-order Acknowledges (ACKs)}. LTP employs \textit{Early Close} to adjust the loss-tolerant threshold based on network conditions and \textit{Bubble Filling} for data correction to maintain training accuracy. LTP is implemented by C++ and integrated into PyTorch. Evaluations on a testbed of 8 worker nodes and one PS node demonstrate that LTP can significantly improve DML training task throughput by up to 30x compared to traditional TCP congestion controls, with no sacrifice to final accuracy.

cs.DC↗

arXiv2023

Evaluating Racialized Economic Segregation in the Presence of Spatial Autocorrelation

Research on residential segregation has been active since the 1950s and originated in a desire to quantify the level of racial/ethnic segregation in the United States. The Index of Concentration at the Extremes (ICE), an operationalization of racialized economic segregation that simultaneously captures spatial, racial, and income polarization, has been a popular topic in public health research, with a particular focus on social epidemiology. However, the construction of the ICE metric usually ignores the spatial autocorrelation that may be present in the data, and it is usually presented without indicating its degree of statistical and spatial uncertainty. To address these issues, we propose reformulating the ICE metric using Bayesian modeling methodologies. We use a simulation study to evaluate the performance of each method by considering various segregation scenarios. The application is based on racialized economic segregation in Georgia, and the proposed modeling approach will help determine whether racialized economic segregation has changed over two non-overlapping time points.

stat.AP↗

arXiv2023

A Gröbner Basis Approach to Combinatorial Nullstellensatz

In this paper, using some conditions that arise naturally in Alon's combinatorial Nullstellensatz as well as its various extensions and generalizations, we characterize Gröbner bases consisting of monic polynomials, which helps us to establish a Nullstellensatz from a Gröbner basis perspective. As corollaries of this general Nullstellensatz, we establish four special Nullstellensatz, which, among others, include a common generalization of the Nullstellensatz for multisets established in Kós, Rónyai and Mészáros \cite{23,24} and the Nullstellensatz with multiplicity established in Ball and Serra \cite{9}, and include a punctured Nullstellensatz, generalizing several existing results in the literature. As applications of our punctured Nullstellensatz, we extend some results on hyperplane covering in \cite{9,23,24} to wider settings, and give an alternative proof of the generalized Alon-Füredi theorem established in Bishnoi, Clark, Potukuchi and Schmitt \cite{12}. Unless specified otherwise, all our results are established over an arbitrary commutative ring $R$.

math.CO↗

arXiv2023

Pressure tuning of optical reflectivity in LuH2

Recently, the claim of room-temperature superconductivity in nitrogen-doped lutetium hydride at near-ambient conditions has attracted tremendous attention. Criticism of the work rises shortly, while further explorations are needed to settle the dispute. One of the intriguing observations is the pressured-induced color change, which has been reproduced in the lutetium dihydride LuH2 while its mechanism remains unclear. Through optical reflectivity measurements of LuH2 in the visible to near-infrared region, we observe strong light absorption next to the sharp plasmon resonance, which continuously shifts to higher energies with increasing pressure. It gives rise to the increased reflection of red light and suppressed reflection of blue light. Our work sheds light on resolving the puzzles regarding the pressure induced color change in LuH2.

cond-mat.supr-con↗

arXiv2023

A Two-stage Bayesian Model for Assessing the Geography of Racialized Economic Segregation and Premature Mortality Across US Counties

Racialized economic segregation, a key metric that simultaneously accounts for spatial, social and income polarization, has been linked to adverse health outcomes, including morbidity and mortality; however, statistical methods for measuring the association between racialized economic segregation and health outcomes are not well-developed and are usually studied at the individual level. In this paper we propose a two-stage Bayesian statistical framework that provides a broad, flexible approach to studying the spatially varying association between premature mortality and racialized economic segregation, while accounting for neighborhood-level latent health factors across US counties. We apply our method by using data from three sources: (1) the CDC WONDER, (2) the County Health Rankings, and (3) the Public Health Disparities Geocoding Project. Findings from our study show that the posterior estimates of latent health factors clearly demonstrate geographical patterning across US counties. Additionally, our results highlight the importance of accounting for the presence of spatial autocorrelation in racialized economic segregation measures, in health equity focused settings.

stat.AP↗

arXiv2023

Tunable positions of Weyl nodes via magnetism and pressure in the ferromagnetic Weyl semimetal CeAlSi

The noncentrosymmetric ferromagnetic Weyl semimetal CeAlSi with simultaneous space-inversion (SI) and time-reversal (TR) symmetry breaking provides a unique platform for the exploration of novel topological states. Here, by employing electrical and thermoelectrical transport, angle-resolved photoemission spectroscopy (ARPES), high-pressure techniques, and band calculations, we demonstrate that magnetism and pressure can serve as efficient parameters to tune the positions of Weyl nodes in CeAlSi. At ambient pressure, an anomalous Hall effect (AHE) and an anomalous Nernst effect (ANE) arise in the paramagnetic state, and then are enhanced when temperature approaches the ferromagnetic ordering temperature, evidencing magnetism facilitates the AHE/ANE. Such an enhancement of AHE/ANE can be ascribed to the tuning of the positions of Weyl nodes via magnetism. The ARPES measurements reveal that the ferromagnetism serves as a pivotal knob to tune the band structure of CeAlSi both in the bulk and on the surface. Such magnetism-tunable electronic structure has hitherto not been reported in other magnetic $R$Al$Pn$ ($R$ = rare earth elements, $Pn$ = Si, Ge) siblings, suggesting the great potential of controlling Weyl node positions in CeAlSi. Under pressure, an enhancement and a sign change of AHE are discovered. Based on band calculations, the evolution of AHE may root in the tuning of Weyl nodes via pressure. Moreover, multiple pressure-induced phase transitions are uncovered. These findings indicate that CeAlSi provides a unique and tunable platform for exploring exotic topological physics and electron correlations, as well as catering to an array of potential applications, such as spintronics and thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2023

Observation of Rydberg moiré excitons

Rydberg excitons, the solid-state counterparts of Rydberg atoms, have sparked considerable interest in harnessing their quantum application potentials, whereas a major challenge is realizing their spatial confinement and manipulation. Lately, the rise of two-dimensional moiré superlattices with highly tunable periodic potentials provides a possible pathway. Here, we experimentally demonstrate this capability through the observation of Rydberg moiré excitons (XRM), which are moiré trapped Rydberg excitons in monolayer semiconductor WSe2 adjacent to twisted bilayer graphene. In the strong coupling regime, the XRM manifest as multiple energy splittings, pronounced redshift, and narrowed linewidth in the reflectance spectra, highlighting their charge-transfer character where electron-hole separation is enforced by the strongly asymmetric interlayer Coulomb interactions. Our findings pave the way for pursuing novel physics and quantum technology exploitation based on the excitonic Rydberg states.

cond-mat.mes-hall↗

arXiv2023

Dataset Management Platform for Machine Learning

The quality of the data in a dataset can have a substantial impact on the performance of a machine learning model that is trained and/or evaluated using the dataset. Effective dataset management, including tasks such as data cleanup, versioning, access control, dataset transformation, automation, integrity and security, etc., can help improve the efficiency and speed of the machine learning process. Currently, engineers spend a substantial amount of manual effort and time to manage dataset versions or to prepare datasets for machine learning tasks. This disclosure describes a platform to manage and use datasets effectively. The techniques integrate dataset management and dataset transformation mechanisms. A storage engine is described that acts as a source of truth for all data and handles versioning, access control etc. The dataset transformation mechanism is a key part to generate a dataset (snapshot) to serve different purposes. The described techniques can support different workflows, pipelines, or data orchestration needs, e.g., for training and/or evaluation of machine learning models.

cs.DB↗

arXiv2023

Analysis and experimental study on the Jumping Chain

A freely falling chain from a cup at certain height can jump. The process can be divided into two parts: a stable suspension and an accelerating procedure. Variational principle and force analysis demonstrate that the shape of stable suspension is an inverted catenary. The requirement of the jumping and the parameters to describe the jumping catenary have been studied in detail, and experiments have been conducted to verify the theoretical analysis. The physical picture of the falling chain could be useful in certain falling systems, providing valuable insight into the dynamical system.

physics.class-ph↗