arXiv subjects

Shuqin Wang

Publications and source records attributed to Shuqin Wang.

5 recordsLinked to original sources

arXiv2026

Observable Social Life Spaces: Exploring User Interpretations of agent-side life context in human-agent interaction

Many AI agents are organized around instrumental "command-execution" interactions, where users primarily encounter agents through task requests and responses. Recent work on generative agents and agent life worlds has drawn attention to agents that maintain social contexts beyond direct user commands. In this paper, we study how observable social life spaces shape users' subjective experience, relational interpretations, and perceived equality during human-agent interaction. We introduce the \textit{Observable Social Life Spaces} paradigm, where agents inhabit a continuous virtual environment, engage in daily activities, and form social relationships that users can directly observe. Through an exploratory mixed-methods study ($N=24$), we found that the Observable condition yielded higher perceived-equality ratings and more frequent equality-related role descriptions than the Baseline and Unobservable conditions, but participant-level analysis suggests that the quantitative effect should be interpreted cautiously. We discuss perceived equality as a user-perception signal shaped by this design, with attention to boundary conditions including visual richness, novelty, and person-like attribution from visible agent cues.

cs.HC↗

arXiv2017

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

cs.LG↗

arXiv2015

A hybridized discontinuous Galerkin method for 2D fractional convection-diffusion equations

A hybridized discontinuous Galerkin method is proposed for solving 2D fractional convection-diffusion equations containing derivatives of fractional order in space on a finite domain. The Riemann-Liouville derivative is used for the spatial derivative. Combining the characteristic method and the hybridized discontinuous Galerkin method, the symmetric variational formulation is constructed. The stability of the presented scheme is proved. Theoretically, the order of $\mathcal{O}(h^{k+1/2}+Δt)$ is established for the corresponding models and numerically the better convergence rates are detected by carefully choosing the numerical fluxes. Extensive numerical experiments are performed to illustrate the performance of the proposed schemes. The first numerical example is to display the convergence orders, while the second one justifies the benefits of the schemes. Both are tested with triangular meshes.

math.NA↗

arXiv2014

Characteristic local discontinuous Galerkin methods for solving time-dependent convection-dominated Navier-Stokes equations

Combining the characteristic method and the local discontinuous Galerkin method with carefully constructing numerical fluxes, we design the variational formulations for the time-dependent convection-dominated Navier-Stokes equations in $\mathbb{R}^2$. The proposed symmetric variational formulation is strictly proved to be unconditionally stable; and the scheme has the striking benefit that the conditional number of the matrix of the corresponding matrix equation does not increase with the refining of the meshes. The presented scheme works well for a wide range of Reynolds numbers, e.g., the scheme still has good error convergence when $Re=0.5 e+005$ or $1.0 e+ 008$. Extensive numerical experiments are performed to show the optimal convergence orders and the contours of the solutions of the equation with given initial and boundary conditions.

physics.comp-ph↗

arXiv1999

On Z-graded associative algebras and their N-graded modules

Let $A$ be a $Z$-graded associative algebra and let $ρ$ be an irreducible $N$-graded representation of $A$ on $W$ with finite-dimensional homogeneous subspaces. Then it is proved that $ρ(\tilde{A})=gl_{J}(W)$, where $\tilde{A}$ is the completion of $A$ with respect to a certain topology and $gl_{J}(W)$ is the subalgebra of $\End W$, generated by homogeneous endomorphisms. It is also proved that an $N$-graded vector space $W$ with finite-dimensional homogeneous spaces is the only continuous irreducible $N$-graded $gl_{J}(W)$-module up to equivalence, where $gl_{J}(W)$ is considered as a topological algebra in a certain natural way, and that any continuous $N$-graded $gl_{J}(W)$-module is a direct sum of some copies of $W$. A duality for certain subalgebras of $gl_{J}(W)$ is also obtained.

math.QA↗