arXiv subjects

Hong Qin

Publications and source records attributed to Hong Qin.

At least 55 records · Page 3Linked to original sources

arXiv2024

UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs

Numerical solvers of Partial Differential Equations (PDEs) are of fundamental significance to science and engineering. To date, the historical reliance on legacy techniques has circumscribed possible integration of big data knowledge and exhibits sub-optimal efficiency for certain PDE formulations, while data-driven neural methods typically lack mathematical guarantee of convergence and correctness. This paper articulates a mathematically rigorous neural solver for linear PDEs. The proposed UGrid solver, built upon the principled integration of U-Net and MultiGrid, manifests a mathematically rigorous proof of both convergence and correctness, and showcases high numerical accuracy, as well as strong generalization power to various input geometry/values and multiple PDE formulations. In addition, we devise a new residual loss metric, which enables unsupervised training and affords more stability and a larger solution space over the legacy losses.

math.NA↗

arXiv2024

A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure

Multilayer perception (MLP) has permeated various disciplinary domains, ranging from bioinformatics to financial analytics, where their application has become an indispensable facet of contemporary scientific research endeavors. However, MLP has obvious drawbacks. 1), The type of activation function is single and relatively fixed, which leads to poor `representation ability' of the network, and it is often to solve simple problems with complex networks; 2), the network structure is not adaptive, it is easy to cause network structure redundant or insufficient. In this work, we propose a novel neural network paradigm X-Net promising to replace MLPs. X-Net can dynamically learn activation functions individually based on derivative information during training to improve the network's representational ability for specific tasks. At the same time, X-Net can precisely adjust the network structure at the neuron level to accommodate tasks of varying complexity and reduce computational costs. We show that X-Net outperforms MLPs in terms of representational capability. X-Net can achieve comparable or even better performance than MLP with much smaller parameters on regression and classification tasks. Specifically, in terms of the number of parameters, X-Net is only 3% of MLP on average and only 1.1% under some tasks. We also demonstrate X-Net's ability to perform scientific discovery on data from various disciplines such as energy, environment, and aerospace, where X-Net is shown to help scientists discover new laws of mathematics or physics.

cs.AI↗

arXiv2024

The Emergent Aging Model: Aging as an Emergent Property of Biological Systems

Based on the study of cellular aging using the single-cell model organism of budding yeast and corroborated by other studies, we propose the Emergent Aging Model (EAM). EAM hypothesizes that aging is an emergent property of complex biological systems, exemplified by biological networks such as gene networks. An emergent property refers to traits that a system has at the system level but which its low-level components do not. EAM is based on a quantitative definition of aging using the mortality rate. A biological entity with a constant mortality rate is considered non-aging which is equivalent to a first-order chemical reaction. Aging can be quantitatively defined as an increasing mortality rate over time, corresponding to an organism's increasing chance of dying over time. EAM posits that biological aging can arise at the system level of an organism, even if the system is composed of only non-aging components. EAM is consistent with the observation that aging is largely stochastic, influenced by numerous genes and epigenetic factors, with no single gene or factor known as the bona fide cause of aging. A parsimonious version of EAM can predict the Gompertz model of biological aging, the Strehler-Mildvan correlation, and the trade-off between initial reproductive fitness (asexual reproductive fitness) and late-life survival. EAM has been applied to experimental results of the replicative lifespan of the budding yeast and can potentially offer new insights into the aging process of other biological species.

q-bio.QM↗

arXiv2024

Helicity is a topological invariant of massless particles: C=-2h

There is an elementary but indispensable relationship between the topology and geometry of massive particles. The geometric spin $s$ is related to the topological dimension of the internal space $V$ by $\dim V = 2s + 1$. This breaks down for massless particles, which are characterized by their helicity $h$, but all have 1D internal spaces. We show that a subtler relation exists between the topological and geometry of massless particles. Wave functions of massless particles are sections of nontrivial line bundles over the lightcone whose topology are completely characterized by their first Chern number $C$. We prove that in general $C = -2h$. In doing so, we also exhibit a method of generating all massless bundle representations via an abelian group structure of massless particles.

math-ph↗

arXiv2024

Topological modes and spectral flows in inhomogeneous PT-symmetric continuous media

In classical Hermitian continuous media, the spectral-flow index of topological modes is linked to the bulk topology via index theorem. However, the interface between two bulks is usually non-Hermitian due to the inhomogeneities of system parameters. We show that the connection between topological modes and bulk topology still exists despite the non-Hermiticity at the interface if the system is endowed with PT symmetry. The theoretical framework developed is applied to the Hall magnetohydrodynamic model to identify a topological mode called topological Alfvén-sound wave in magnetized plasmas.

physics.plasm-ph↗

arXiv2024

An Empirical Study on Developers Shared Conversations with ChatGPT in GitHub Pull Requests and Issues

ChatGPT has significantly impacted software development practices, providing substantial assistance to developers in a variety of tasks, including coding, testing, and debugging. Despite its widespread adoption, the impact of ChatGPT as an assistant in collaborative coding remains largely unexplored. In this paper, we analyze a dataset of 210 and 370 developers shared conversations with ChatGPT in GitHub pull requests (PRs) and issues. We manually examined the content of the conversations and characterized the dynamics of the sharing behavior, i.e., understanding the rationale behind the sharing, identifying the locations where the conversations were shared, and determining the roles of the developers who shared them. Our main observations are: (1) Developers seek ChatGPT assistance across 16 types of software engineering inquiries. In both conversations shared in PRs and issues, the most frequently encountered inquiry categories include code generation, conceptual questions, how-to guides, issue resolution, and code review. (2) Developers frequently engage with ChatGPT via multi-turn conversations where each prompt can fulfill various roles, such as unveiling initial or new tasks, iterative follow-up, and prompt refinement. Multi-turn conversations account for 33.2% of the conversations shared in PRs and 36.9% in issues. (3) In collaborative coding, developers leverage shared conversations with ChatGPT to facilitate their role-specific contributions, whether as authors of PRs or issues, code reviewers, or collaborators on issues. Our work serves as the first step towards understanding the dynamics between developers and ChatGPT in collaborative software development and opens up new directions for future research on the topic.

cs.SE↗

arXiv2024

Algebraic discrete quantum harmonic oscillator with dynamic resolution scaling

We develop an algebraic formulation for the discrete quantum harmonic oscillator (DQHO) with a finite, equally-spaced energy spectrum and energy eigenfunctions defined on a discrete domain, which is known as the su(2) or Kravchuk oscillator. Unlike previous approaches, ours does not depend on the discretization of the Schrödinger equation and recurrence relations of special functions. This algebraic formulation is endowed with a natural su(2) algebra, each finite dimensional irreducible representation of which defines a distinct DQHO labeled by its resolution. In addition to energy ladder operators, the formulation allows for resolution ladder operators connecting all DQHOs with different resolutions. The resolution ladder operators thus enable the dynamic scaling of the resolution of finite degree-of-freedom quantum simulations. Using the algebraic DQHO formalism, we are able to rigorously derive the energy eigenstate wave functions of the QHO in a purely algebraic manner without using differential equations or differential operators, which is impossible in the continuous or infinite discrete setting. The coherent state of the DQHO is constructed, and its expected position is proven to oscillate as a classical harmonic oscillator. The DQHO coherent state recovers that of the quantum harmonic oscillator at large resolution. The algebraic formulation also predicts the existence of an inverse DQHO that has no known continuous counterpart.

quant-ph↗

arXiv2024

Advanced fuel fusion, phase space engineering, and structure-preserving geometric algorithms

Non-thermal advanced fuel fusion trades the requirement of a large amount of recirculating tritium in the system for that of large recirculating power. Phase space engineering technologies utilizing externally injected electromagnetic fields can be applied to meet the challenge of maintaining non-thermal particle distributions at a reasonable cost. The physical processes of the phase space engineering are studied from a theoretical and algorithmic perspective. It is emphasized that the operational space of phase space engineering is limited by the underpinning symplectic dynamics of charged particles. The phase space incompressibility according to the Liouville theorem is just one of many constraints, and Gromov's non-squeezing theorem determines the minimum footprints of the charged particles on every conjugate phase space plane. In this sense and level of sophistication, the mathematical abstraction of phase space engineering is symplectic topology. To simulate the processes of phase space engineering, such as the Maxwell demon and electromagnetic energy extraction, and to accurately calculate the minimum footprints of charged particles, recently developed structure-preserving geometric algorithms can be used. The family of algorithms conserves exactly, on discretized spacetime, symplecticity and thus incompressibility, non-squeezability, and symplectic capacities. The algorithms apply to the dynamics of charged particles under the influence of external electromagnetic fields as well as the charged particle-electromagnetic field system governed by the Vlasov-Maxwell equations.

physics.plasm-ph↗

arXiv2024

Photon topology

The topology of photons in vacuum is interesting because there are no photons with $\boldsymbol{k}=0$, creating a hole in momentum space. We show that while the set of all photons forms a trivial vector bundle $γ$ over this momentum space, the $R$- and $L$-photons form topologically nontrivial subbundles $γ_\pm$ with first Chern numbers $\mp2$. In contrast, $γ$ has no linearly polarized subbundles, and there is no Chern number associated with linear polarizations. It is a known difficulty that the standard version of Wigner's little group method produces singular representations of the Poincaré group for massless particles. By considering representations of the Poincaré group on vector bundles we obtain a version of Wigner's little group method for massless particles which avoids these singularities. We show that any massless bundle representation of the Poincaré group can be canonically decomposed into irreducible bundle representations labeled by helicity, which in turn can be associated to smooth irreducible Hilbert space representations. This proves that the $R$- and $L$-photons are globally well-defined as particles and that the photon wave function can be uniquely split into $R$- and $L$-components. This formalism offers a method of quantizing the EM field without invoking discontinuous polarization vectors as in the traditional scheme. We also demonstrate that the spin-Chern number of photons is not a purely topological quantity. Lastly, there has been an extended debate on whether photon angular momentum can be split into spin and orbital parts. Our work explains the precise issues that prevent this splitting. Photons do not admit a spin operator; instead, the angular momentum associated with photons' internal degree of freedom is described by a helicity-induced subalgebra corresponding to the translational symmetry of $γ$.

math-ph↗

arXiv2024

Robust Zero Level-Set Extraction from Unsigned Distance Fields Based on Double Covering

In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and a user-specified parameter $r$ (a small positive real number) as input and extracts an iso-surface with an iso-value $r$ using the conventional marching cubes algorithm. We show that the computed iso-surface is the boundary of the $r$-offset volume of the target zero level-set $S$, which is an orientable manifold, regardless of the topology of $S$. Next, the algorithm computes a covering map to project the boundary mesh onto $S$, preserving the mesh's topology and avoiding folding. If $S$ is an orientable manifold surface, our algorithm separates the double-layered mesh into a single layer using a robust minimum-cut post-processing step. Otherwise, it keeps the double-layered mesh as the output. We validate our algorithm by reconstructing 3D surfaces of open models and demonstrate its efficacy and effectiveness on synthetic models and benchmark datasets. Our experimental results confirm that our method is robust and produces meshes with better quality in terms of both visual evaluation and quantitative measures than existing UDF-based methods. The source code is available at https://github.com/jjjkkyz/DCUDF.

cs.CV↗

arXiv2024

Nonlinear solution of classical three-wave interaction via finite dimensional quantum model

The quantum three-wave interaction, the lowest order nonlinear interaction in plasma physics, describes energy-momentum transfer between three resonant waves in the quantum regime. We describe how it may also act as a finite-degree-of-freedom approximation to the classical three-wave interaction in certain circumstances. By promoting the field variables to operators, we quantize the classical system, show that the quantum system has more free parameters than the classical system, and explain how these parameters may be selected to optimize either initial or long-term correspondence. We then numerically compare the long-time quantum/classical correspondence far from the fixed point dynamics. We discuss the Poincare recurrence of the system and the mitigation of quantum scrambling.

physics.plasm-ph↗

arXiv2023

A Unified Particle-Based Solver for Non-Newtonian Behaviors Simulation

In this paper, we present a unified framework to simulate non-Newtonian behaviors. We combine viscous and elasto-plastic stress into a unified particle solver to achieve various non-Newtonian behaviors ranging from fluid-like to solid-like. Our constitutive model is based on a Generalized Maxwell model, which incorporates viscosity, elasticity and plasticity in one non-linear framework by a unified way. On the one hand, taking advantage of the viscous term, we construct a series of strain-rate dependent models for classical non-Newtonian behaviors such as shear-thickening, shear-thinning, Bingham plastic, etc. On the other hand, benefiting from the elasto-plastic model, we empower our framework with the ability to simulate solid-like non-Newtonian behaviors, i.e., visco-elasticity/plasticity. In addition, we enrich our method with a heat diffusion model to make our method flexible in simulating phase change. Through sufficient experiments, we demonstrate a wide range of non-Newtonian behaviors ranging from viscous fluid to deformable objects. We believe this non-Newtonian model will enhance the realism of physically-based animation, which has great potential for computer graphics.

cs.GR↗

arXiv2023

WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning

To date, the widely adopted way to perform fixation collection in panoptic video is based on a head-mounted display (HMD), where users' fixations are collected while wearing an HMD to explore the given panoptic scene freely. However, this widely-used data collection method is insufficient for training deep models to accurately predict which regions in a given panoptic are most important when it contains intermittent salient events. The main reason is that there always exist "blind zooms" when using HMD to collect fixations since the users cannot keep spinning their heads to explore the entire panoptic scene all the time. Consequently, the collected fixations tend to be trapped in some local views, leaving the remaining areas to be the "blind zooms". Therefore, fixation data collected using HMD-based methods that accumulate local views cannot accurately represent the overall global importance - the main purpose of fixations - of complex panoptic scenes. To conquer, this paper introduces the auxiliary window with a dynamic blurring (WinDB) fixation collection approach for panoptic video, which doesn't need HMD and is able to well reflect the regional-wise importance degree. Using our WinDB approach, we have released a new PanopticVideo-300 dataset, containing 300 panoptic clips covering over 225 categories. Specifically, since using WinDB to collect fixations is blind zoom free, there exists frequent and intensive "fixation shifting" - a very special phenomenon that has long been overlooked by the previous research - in our new set. Thus, we present an effective fixation shifting network (FishNet) to conquer it. All these new fixation collection tool, dataset, and network could be very potential to open a new age for fixation-related research and applications in 360o environments.

cs.CV↗

arXiv2023

Towards Trustworthy Artificial Intelligence for Equitable Global Health

Artificial intelligence (AI) can potentially transform global health, but algorithmic bias can exacerbate social inequities and disparity. Trustworthy AI entails the intentional design to ensure equity and mitigate potential biases. To advance trustworthy AI in global health, we convened a workshop on Fairness in Machine Intelligence for Global Health (FairMI4GH). The event brought together a global mix of experts from various disciplines, community health practitioners, policymakers, and more. Topics covered included managing AI bias in socio-technical systems, AI's potential impacts on global health, and balancing data privacy with transparency. Panel discussions examined the cultural, political, and ethical dimensions of AI in global health. FairMI4GH aimed to stimulate dialogue, facilitate knowledge transfer, and spark innovative solutions. Drawing from NIST's AI Risk Management Framework, it provided suggestions for handling AI risks and biases. The need to mitigate data biases from the research design stage, adopt a human-centered approach, and advocate for AI transparency was recognized. Challenges such as updating legal frameworks, managing cross-border data sharing, and motivating developers to reduce bias were acknowledged. The event emphasized the necessity of diverse viewpoints and multi-dimensional dialogue for creating a fair and ethical AI framework for equitable global health.

cs.CY↗

arXiv2023

Spin Hall effect of radiofrequency waves in magnetized plasmas

In inhomogeneous media, electromagnetic-wave rays deviate from the trajectories predicted by the leading-order geometrical optics. This effect, called the spin Hall effect of light, is typically neglected in ray-tracing codes used for modeling waves in plasmas. Here, we demonstrate that the spin Hall effect can be significant for radiofrequency waves in toroidal magnetized plasmas whose parameters are in the ballpark of those used in fusion experiments. For example, an electron-cyclotron wave beam can deviate by as large as ten wavelengths ($\sim 0.1\,\text{m}$) relative to the lowest-order ray trajectory in the poloidal direction. We calculate this displacement using gauge-invariant ray equations of extended geometrical optics, and we also compare our theoretical predictions with full-wave simulations.

physics.plasm-ph↗

arXiv2023

Resonant instabilities mediated by drag and electrostatic interactions in laboratory and astrophysical dusty plasmas

Dusty plasmas are known to support a diverse range of instabilities, including both generalizations of standard plasma instabilities and ones caused by effects specific to dusty systems. It has been recently demonstrated that a novel broad class of streaming instabilities, termed resonant drag instabilities (RDIs), can be attributed to a particular resonance phenomenon, manifested by defective eigenvalues of the linearized dust/fluid system. In this work, it is demonstrated that this resonance phenomenon is not unique to RDIs and can be used as a framework to understand a wider range of instabilities, termed resonant instabilities. Particular attention is given to the filamentary ionization instability seen in laboratory dusty plasmas and to the two-stream instability. It is shown that, due to the commonalities in underlying physics between the dust-ion-acoustic two-stream instability and the acoustic RDI, these instabilities should be relevant in strongly overlapping regimes in astrophysical dusty plasmas. It is proposed that a similar overlap in the experimental accessibility of these modes (and of the filamentary instability) allows for the possibility of experimental investigation in the laboratory of complex and astrophysically relevant instability dynamics.

physics.plasm-ph↗

arXiv2023

MPMNet: A Data-Driven MPM Framework for Dynamic Fluid-Solid Interaction

High-accuracy, high-efficiency physics-based fluid-solid interaction is essential for reality modeling and computer animation in online games or real-time Virtual Reality (VR) systems. However, the large-scale simulation of incompressible fluid and its interaction with the surrounding solid environment is either time-consuming or suffering from the reduced time/space resolution due to the complicated iterative nature pertinent to numerical computations of involved Partial Differential Equations (PDEs). In recent years, we have witnessed significant growth in exploring a different, alternative data-driven approach to addressing some of the existing technical challenges in conventional model-centric graphics and animation methods. This paper showcases some of our exploratory efforts in this direction. One technical concern of our research is to address the central key challenge of how to best construct the numerical solver effectively and how to best integrate spatiotemporal/dimensional neural networks with the available MPM's pressure solvers.

cs.GR↗

arXiv2022

Salient Object Detection via Dynamic Scale Routing

Recent research advances in salient object detection (SOD) could largely be attributed to ever-stronger multi-scale feature representation empowered by the deep learning technologies. The existing SOD deep models extract multi-scale features via the off-the-shelf encoders and combine them smartly via various delicate decoders. However, the kernel sizes in this commonly-used thread are usually "fixed". In our new experiments, we have observed that kernels of small size are preferable in scenarios containing tiny salient objects. In contrast, large kernel sizes could perform better for images with large salient objects. Inspired by this observation, we advocate the "dynamic" scale routing (as a brand-new idea) in this paper. It will result in a generic plug-in that could directly fit the existing feature backbone. This paper's key technical innovations are two-fold. First, instead of using the vanilla convolution with fixed kernel sizes for the encoder design, we propose the dynamic pyramid convolution (DPConv), which dynamically selects the best-suited kernel sizes w.r.t. the given input. Second, we provide a self-adaptive bidirectional decoder design to accommodate the DPConv-based encoder best. The most significant highlight is its capability of routing between feature scales and their dynamic collection, making the inference process scale-aware. As a result, this paper continues to enhance the current SOTA performance. Both the code and dataset are publicly available at https://github.com/wuzhenyubuaa/DPNet.

cs.CV↗