arXiv subjects

Yi Qin

Publications and source records attributed to Yi Qin.

At least 37 records · Page 2Linked to original sources

arXiv2024

Improved ICNN-LSTM Model Classification Based on Attitude Sensor Data for Hazardous State Assessment of Magnetic Adhesion Climbing Wall Robots

Magnetic adhesion tracked climbing robots are widely utilized in high-altitude inspection, welding, and cleaning tasks due to their ability to perform various operations against gravity on vertical or inclined walls. However, during operation, the robot may experience overturning torque caused by its own weight and load, which can lead to the detachment of magnetic plates and subsequently pose safety risks. This paper proposes an improved ICNN-LSTM network classification method based on Micro-Electro-Mechanical Systems (MEMS) attitude sensor data for real-time monitoring and assessment of hazardous states in magnetic adhesion tracked climbing robots. Firstly, a data acquisition strategy for attitude sensors capable of capturing minute vibrations is designed. Secondly, a feature extraction and classification model combining an Improved Convolutional Neural Network (ICNN) with a Long Short-Term Memory (LSTM) network is proposed. Experimental validation demonstrates that the proposed minute vibration sensing method achieves significant results, and the proposed classification model consistently exhibits high accuracy compared to other models. The research findings provide effective technical support for the safe operation of climbing robots

cs.RO↗

arXiv2024

Dynamical suppression of many-body non-Hermitian skin effect in Anyonic systems

The non-Hermitian skin effect (NHSE) is a fascinating phenomenon in nonequilibrium systems where eigenstates massively localize at the systems' boundaries, pumping (quasi-)particles loaded in these systems unidirectionally to the boundaries. Its interplay with many-body effects have been vigorously studied recently, and inter-particle repulsion or Fermi degeneracy pressure have been shown to limit the boundary accumulation induced by the NHSE both in their eigensolutions and dynamics. However, in this work we found that anyonic statistics can even more profoundly affect the NHSE dynamics, suppressing or even reversing the state dynamicss against the localizing direction of the NHSE. This phenomenon is found to be more pronounced when more particles are involved.The spreading of quantum information in this system shows even more exotic phenomena, where NHSE affects only the information dynamics for a thermal ensemble, but not that for a single initial state. Our results open up a new avenue on exploring novel non-Hermitian phenomena arisen from the interplay between NHSE and anyonic statistics, and can potentially be demonstrated in ultracold atomic quantum simulators and quantum computers.

quant-ph↗

arXiv2024

Analysis of a FEM-MCM Discretization for the 2D/3D stochastic closed-loop geothermal system

This paper develops a new 2D/3D stochastic closed-loop geothermal system with a random hydraulic conductivity tensor. We use the finite element method (FEM) and the Monte Carlo method (MCM) to discrete physical and probability spaces, respectively. This FEM-MCM method is effective. The stability for velocity and temperature is rigorously proved. Compared with the deterministic closed-loop geothermal system, a same optimal error estimate for approximate velocity and temperature is obtained. Furthermore, a series of numerical experiments were carried out to show this method has better stability and accuracy results.

math.NA↗

arXiv2024

A high-order, high-efficiency adaptive time filter algorithm for shale reservoir model based on coupled fluid flow with porous media flow

In this paper, a third-order time adaptive algorithm with less computation, low complexity is provided for shale reservoir model based on coupled fluid flow with porous media flow. The algorithm combines the three-step linear time filters method for simple post-processing and the second-order backward differential formula (BDF2), is third-order accurate and provides, at no extra computational complexity. At the same time, the time filter method can also be used to damp non-physical oscillations inherent in the BDF2 method, ensuring stability. We proves the variable time stepsize second-order backward differential formula plus time filter (BDF2-TF) algorithm's stability and the convergence properties of the fluid velocity u and hydraulic head $ϕ$ in the $L^2$ norm with an order of $O(k_{n+1}^3 + h^3)$. In the experiments, the adaptive algorithm automatically adjusts the time step in response to the varying characteristics of different models, ensuring that errors are maintained within acceptable limits. This algorithm addresses the issue that high-order algorithms may select inappropriate time steps, resulting in instability or reduced precision of the numerical solution, thereby enhancing calculation accuracy and efficiency. We perform three-dimensional numerical experiments to verify the BDF2-TF algorithm's effectiveness, stability, and third-order convergence. Simultaneously, a simplified model is employed to simulate the process of shale oil extraction from reservoirs, further demonstrating the algorithm's practical applicability.

math.NA↗

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

Non-orthogonal cavity modes near exceptional points in the far field

Non-orthogonal eigenstates are a fundamental feature of non-Hermitian systems and are accompanied by the emergence of nontrivial features. However, the platforms to explore non-Hermitian mode couplings mainly measure near-field effects, and the far-field behaviour remain mostly unexplored. Here, we study how a microcavity with non-Hermitian mode coupling exhibits eigenstate non-orthogonality by investigating the spatial field and the far-field polarization of cavity modes. The non-Hermiticity arises from asymmetric backscattering, which is controlled by integrating two scatterers of different size and location into a microdisk. We observe that the spatial field overlaps of two modes increases abruptly to its maximum value, whilst different far-field elliptical polarizations of two modes coalesce when approaching an exceptional point. We demonstrate such features experimentally by measuring the far-field polarization from the fabricated microdisks. Our work reveals the non-orthogonality in the far-field degree of freedom, and the integrability of the microdisks paves a way to integrate more non-Hermitian optical properties into nanophotonic systems.

physics.optics↗

arXiv2023

A Review of Intelligent Music Generation Systems

With the introduction of ChatGPT, the public's perception of AI-generated content (AIGC) has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects.

cs.SD↗

arXiv2023

Occupation-dependent particle separation in one-dimensional non-Hermitian lattices

We unveil an exotic phenomenon arising from the intricate interplay between non-Hermiticity and many-body physics, namely an occupation-dependent particle separation for hardcore bosons in a one-dimensional lattice driven by uni-directional non-Hermitian pumping. Taking hardcore bosons as an example, we find that a pair of particles occupying the same unit cell exhibit an opposite non-Hermitian pumping direction to that of unpaired ones occupying different unit cells. By turning on an intracell interaction, many-body eigenstates split in their real energies, forming separable clusters in the complex energy plane with either left-, right-, or bipolar-types of non-Hermitian skin effect (NHSE). The dependency of skin accumulating directions on particle occupation is further justified with local sublattice correlation and entanglement entropy of many-body eigenstates. Dynamically, this occupation-dependent NHSE manifests as uni- or bi-directional pumping for many-body initial states, allowing for spatially separating paired and unpaired particles. Similar phenomena also apply to fermionic systems, unveiling the possibility of designing and exploring novel non-Hermitian phases originated from particle non-conservation in subsystems (e.g., orbitals, sublattices, or spin species) and their spatial configurations.

cond-mat.quant-gas↗

arXiv2023

FSDiffReg: Feature-wise and Score-wise Diffusion-guided Unsupervised Deformable Image Registration for Cardiac Images

Unsupervised deformable image registration is one of the challenging tasks in medical imaging. Obtaining a high-quality deformation field while preserving deformation topology remains demanding amid a series of deep-learning-based solutions. Meanwhile, the diffusion model's latent feature space shows potential in modeling the deformation semantics. To fully exploit the diffusion model's ability to guide the registration task, we present two modules: Feature-wise Diffusion-Guided Module (FDG) and Score-wise Diffusion-Guided Module (SDG). Specifically, FDG uses the diffusion model's multi-scale semantic features to guide the generation of the deformation field. SDG uses the diffusion score to guide the optimization process for preserving deformation topology with barely any additional computation. Experiment results on the 3D medical cardiac image registration task validate our model's ability to provide refined deformation fields with preserved topology effectively. Code is available at: https://github.com/xmed-lab/FSDiffReg.git.

cs.CV↗

arXiv2023

Geometry-dependent skin effect and anisotropic Bloch oscillations in a non-Hermitian optical lattice

The interplay between the non-Hermiticity and dimensionality gives rise to exotic characteristics in higher dimensions, with one representative phenomenon known as the geometry-dependent skin effect (GDSE), which refers to that the localization of extensive eigenstates depends on the system's geometry under open boundary conditions. In this paper, we demonstrate the emergence of GDSE in a two-dimensional $sp$ optical ladder lattice with on-site atom loss, which can be manifested by anisotropic dynamics of Bloch oscillations in the bulk of the system. By applying a static force in different directions, the wave-packet dynamics retrieve the complex energy spectra with either nonzero or zero spectral winding number, indicating the presence or absence of skin accumulation in the corresponding directions, respectively. Our results reveal that the GDSE has an intrinsic anisotropic bulk dynamics independent of boundary conditions, and offer its realization and detection in quantum systems.

quant-ph↗

arXiv2023

Topological superconductor candidates PdBi$_2$Te$_4$ and PdBi$_2$Te$_5$ from a generic ab initio strategy

Superconducting topological metals (SCTMs) have recently emerged as a promising platform of topological superconductivity (TSC) and Majorana zero modes(MZMs) for quantum computation. Despite their importance in both fundamental research and applications, SCTMs are very rare in nature. In addition, some superconductors with topological electronic structures have been reported recently, but a feasible program to determine their TSC properties is still lacking. Here, we propose a new strategy to design SCTMs by intercalating the superconducting units into the topological insulators. A program that characterizes the superconducting BdG Chern number of 2D BdG Hamiltonian from ab initio calculations is also developed. Following this strategy, PdBi$_2$Te$_5$ and PdBi$_2$Te$_4$ are found to be experimentally synthesizable and ideal SCTMs. Chiral TSC could be realized in such SCTMs by incorporating topological surface states with Zeeman effect, which can be realized by an external magnetic field or in proximity to ferromagnetic (FM) insulator. Our strategy provides a new method for identifying the SCTMs and TSC candidates, and the program makes it possible to design and modulate the TSC candidates from ab initio calculations.

cond-mat.mtrl-sci↗

arXiv2022

Analysis of A New Adaptive Time Filter Algorithm for The Unsteady Stokes/Darcy Model

In this report, we propose a new adaptive time filter algorithm for the unsteady Stokes/Darcy model. First we present a first order $θ$-scheme with the variable time step which is one parameter family of Linear Multi-step methods and use a time filter algorithm to increase the convergence order to second order with almost no increasing the amount of computation. Furthermore, we construct coupled and decoupled adaptive algorithms. Then we analyze stabilities and the second-order accuracy of variable time-stepping algorithm for Linear Multi-step methods plus time filter, respectively. Finally, we use two numerical experiments to verify theoretical results including effectiveness, convergence and efficiency with adaptive method.

math.NA↗

arXiv2020

A variable timestepping algorithm for the unsteady Stokes/Darcy model

This report considers a variable step time discretization algorithm proposed by Dahlquist, Liniger and Nevanlinna and applies the algorithm to the unsteady Stokes/Darcy model. Although long-time forgotten and little explored, the algorithm performs advantages in variable timestep analysis of various fluid flow systems, including the coupled Stokes/Darcy model. The paper proves that the approximate solutions to the unsteady Stokes/Darcy model are unconditionally stable due to the G-stability of the algorithm. Also variable time stepping error analysis follows from the combination of G-stability and consistency of the algorithm. Numerical experiments further verify the theoretical results, demonstrating the accuracy and stability of the algorithm for time-dependent Stokes/Darcy model.

math.NA↗

arXiv2020

Analysis of the variable step method of Dahlquist, Liniger and Nevanlinna for fluid flow

The two-step time discretization proposed by Dahlquist, Liniger and Nevanlinna is variable step $G$-stable. (In contrast, for increasing time steps, the BDF2 method loses $A$-stability and suffers non-physical energy growth in the approximate solution.) While unexplored, it is thus ideal for time accurate approximation of the Navier-Stokes equations. This report presents an analysis, for variable time-steps, of the method's stability and convergence rates when applied to the NSE. It is proven that the method is variable step, unconditionally, long time stable and second order accurate. Variable step error estimates are also proven. The results are supported by several numerical tests.

math.NA↗

arXiv2019

An Efficient Solver for Cumulative Density Function-based Solutions of Uncertain Kinematic Wave Models

We develop a numerical framework to implement the cumulative density function (CDF) method for obtaining the probability distribution of the system state described by a kinematic wave model. The approach relies on Monte Carlo Simulations (MCS) of the fine-grained CDF equation of system state, as derived by the CDF method. This fine-grained CDF equation is solved via the method of characteristics. Each method of characteristics solution is far more computationally efficient than the direct solution of the kinematic wave model, and the MCS estimator of the CDF converges relatively quickly. We verify the accuracy and robustness of our procedure via comparison with direct MCS of a particular kinematic wave system, the Saint-Venant equation.

math.NA↗

arXiv2018

Historical shear deformation of rock fractures derived from digital outcrop models and its implications on the development of fracture systems

The initiation and development of fractures in rocks is the key part of many problems from academic to industrial, such as faulting, folding, rock mass engineering, reservoir characterization, etc. Conventional ways of evaluating the fracture historical deformations depend on the geologists' visual interpretation of indicating structures such as fault striations, fault steps, plumose structures, etc. on the fracture surface produced by previous deformations, and hence suffer from problems like subjectivity and the absence of obvious indicating structures. In this study, we propose a quantitative method to derive historical shear deformations of rock fractures from digital outcrop models (DOMs) based on the analysis of effects of fault striations and fault steps on the shear strength parameter of the fracture surface. A theoretical model that combines effects of fault striations, fault steps and isotropic base shear strength is fitted to the shear strength parameter. The amount of fault striations and fault steps and their occurrences are estimated, and the historical shear deformations can be inferred. The validity and the effectiveness of the proposed method was proved by testing it on a constructed fracture surface with idealized striations and a fracture surface with clear fault steps. The application of this method on an example outcrop shows an intuitive idea of how the rock mass was deformed and that the distribution, occurrence and mode of new fractures are strictly controlled by preexisting fractures, and hence emphasizes the importance of preexisting fractures in modeling the development of fracture systems.

physics.geo-ph↗

arXiv2018

Democratizing Production-Scale Distributed Deep Learning

The interest and demand for training deep neural networks have been experiencing rapid growth, spanning a wide range of applications in both academia and industry. However, training them distributed and at scale remains difficult due to the complex ecosystem of tools and hardware involved. One consequence is that the responsibility of orchestrating these complex components is often left to one-off scripts and glue code customized for specific problems. To address these restrictions, we introduce \emph{Alchemist} - an internal service built at Apple from the ground up for \emph{easy}, \emph{fast}, and \emph{scalable} distributed training. We discuss its design, implementation, and examples of running different flavors of distributed training. We also present case studies of its internal adoption in the development of autonomous systems, where training times have been reduced by 10x to keep up with the ever-growing data collection.

cs.CV↗

arXiv2018

CoMID: Context-based Multi-Invariant Detection for Monitoring Cyber-Physical Software

Cyber-physical software continually interacts with its physical environment for adaptation in order to deliver smart services. However, the interactions can be subject to various errors when the software's assumption on its environment no longer holds, thus leading to unexpected misbehavior or even failure. To address this problem, one promising way is to conduct runtime monitoring of invariants, so as to prevent cyber-physical software from entering such errors (a.k.a. abnormal states). To effectively detect abnormal states, we in this article present an approach, named Context-based Multi-Invariant Detection (CoMID), which consists of two techniques: context-based trace grouping and multi-invariant detection. The former infers contexts to distinguish different effective scopes for CoMID's derived invariants, and the latter conducts ensemble evaluation of multiple invariants to detect abnormal states. We experimentally evaluate CoMID on real-world cyber-physical software. The results show that CoMID achieves a 5.7-28.2% higher true-positive rate and a 6.8-37.6% lower false-positive rate in detecting abnormal states, as compared with state-of-the-art approaches (i.e., Daikon and ZoomIn). When deployed in field tests, CoMID's runtime monitoring improves the success rate of cyber-physical software in its task executions by 15.3-31.7%.

cs.SE↗