arXiv subjects

Hongyu Shi

Publications and source records attributed to Hongyu Shi.

10 recordsLinked to original sources

arXiv2026

Multidimensional Infinite-Horizon Markovian Backward Stochastic Differential Equations with Continuous Coefficients and Nonzero-Sum Differential Games

This paper addresses the solvability of multidimensional infinite-horizon Markovian backward stochastic differential equations with continuous coefficients. We do not require the Lipschitz property or other quantitative controls of differences, and each component of the generator is allowed to depend simultaneously on all components of the multidimensional backward variables. A local L^q-domination framework adapted to the infinite-horizon setting is developed and combined with our linear flow conjugacy analysis to obtain the required distributional control in the presence of unbounded linear drift. We also derive a common Markov representation valid for the entire family of initial states. The analytical obstacles arising from weak regularity and the multidimensional structure are thereby overcome. Furthermore, we consider a class of infinite-horizon nonzero-sum differential games with a general non linear structure, allowing player controls to be fully coupled and permitting payoff criteria that are not restricted to an exponential discount form, establish an equivalence between the game and the corresponding multidimensional infinite-horizon backward stochastic differential equations, and prove the existence of a Nash equilibrium.

math.PR↗

arXiv2026

Recompose and Refine Latent Reasoning Flows for Vision-Language-Action Models

Latent reasoning enables vision-language-action (VLA) models to transform multimodal observations into task-relevant internal states before generating continuous robot actions. While existing methods learn to generate or refine such states for each policy query, they discard successful reasoning after execution and therefore reconstruct similar computation from scratch. We present Reasoning and Flow Memory (FLOWMEM), a unified VLA model that turns successful latent computation into reusable reasoning experience. Rather than appending a fixed retrieved context, FLOWMEM dynamically retrieves and recomposes compatible latent fragments as the embodied context evolves, forming a reasoning route that follows the temporal structure and progress of successful computation. The route is then refined using current visual and proprioceptive evidence before it conditions action generation. Experiments on RoboMME and LIBERO-Plus show that FLOWMEM attains 48.0% and 77.3% success, outperforming memory-free policies by 1.7 and 4.1 percentage points, respectively. These results demonstrate the value of reusing successful latent computation for closed-loop VLA control.

cs.AI↗

arXiv2026

Mean-variance portfolio selection: connections between mean-field systems and forward-backward stochastic systems under initial-terminal cost coupling

This paper investigates the connection between mean-field systems and forward-backward stochastic systems arising from the mean-variance portfolio selection problem. By introducing the conditional expectation process of terminal wealth, the mean-field term is represented by a backward state, leading to a class of control problems for forward-backward stochastic systems with coupled initial-terminal values. The coupling between the terminal value of the forward state and the initial value of the backward state in the cost functional prevents classical methods for forward-backward stochastic optimal control from being directly applicable. To overcome this difficulty, we establish a stochastic maximum principle for control problems with running costs and coupled initial-terminal values, and provide sufficient conditions for optimality. Furthermore, we investigate a class of generalized stochastic linear-quadratic control problems with initial-terminal coupling, allowing the control weight in the running cost to be indefinite, and derive the optimal feedback representation via a Riccati equation. The classical mean-variance portfolio selection model is shown to be a special case, for which the corresponding efficient investment strategy and efficient frontier are obtained explicitly, and numerical simulations are presented to illustrate the results and examine parameter sensitivity and its financial implications.

math.OC↗

arXiv2024

Photoelectron -- residual-ion entanglement in streaked shake-up ionization of helium

Streaked photoelectron emission spectra access the correlated dynamics of photoelectrons and residual target electrons with attosecond temporal resolution. We calculated ab initio single-ionization spectra for photoemission from helium atoms by co-linearly polarized ultrashort XUV and assisting few-femtosecond IR pulses. Distinguishing direct and shake-up ionization resulting in ground-state and excited (n=2,3) He+ residual ions, respectively, we examined the effects of the correlated photoemission dynamics on the photoelectron phase-accumulation as a function of the observable photoelectron detection direction and kinetic energy, and XUV-IR pulse delay. We tracked the dynamical evolution of the residual ion in relative streaked photoemission delays and found dominant contributions for shake-up emission from the residual ion - photoelectron interaction. These are in very good and fair agreement, respectively, for n=2 and n=3 shake-up photoemission along the pulse-polarization directions, with previous experimental and theoretical investigations [Ossiander et. al. 2017] and reveal a strong photoemission-direction dependence for shake-up ionization due to the coupling between the photoelectron and evolving residual-ion charge distribution in the IR-laser field.

physics.atom-ph↗

arXiv2024

DSGNN: A Dual-View Supergrid-Aware Graph Neural Network for Regional Air Quality Estimation

Air quality estimation can provide air quality for target regions without air quality stations, which is useful for the public. Existing air quality estimation methods divide the study area into disjointed grid regions, and apply 2D convolution to model the spatial dependencies of adjacent grid regions based on the first law of geography, failing to model the spatial dependencies of distant grid regions. To this end, we propose a Dual-view Supergrid-aware Graph Neural Network (DSGNN) for regional air quality estimation, which can model the spatial dependencies of distant grid regions from dual views (i.e., satellite-derived aerosol optical depth (AOD) and meteorology). Specifically, images are utilized to represent the regional data (i.e., AOD data and meteorology data). The dual-view supergrid learning module is introduced to generate supergrids in a parameterized way. Based on the dual-view supergrids, the dual-view implicit correlation encoding module is introduced to learn the correlations between pairwise supergrids. In addition, the dual-view message passing network is introduced to implement the information interaction on the supergrid graphs and images. Extensive experiments on two real-world datasets demonstrate that DSGNN achieves the state-of-the-art performances on the air quality estimation task, outperforming the best baseline by an average of 19.64% in MAE.

cs.LG↗

arXiv2023

SHA-SCP: A UI Element Spatial Hierarchy Aware Smartphone User Click Behavior Prediction Method

Predicting user click behavior and making relevant recommendations based on the user's historical click behavior are critical to simplifying operations and improving user experience. Modeling UI elements is essential to user click behavior prediction, while the complexity and variety of the UI make it difficult to adequately capture the information of different scales. In addition, the lack of relevant datasets also presents difficulties for such studies. In response to these challenges, we construct a fine-grained smartphone usage behavior dataset containing 3,664,325 clicks of 100 users and propose a UI element spatial hierarchy aware smartphone user click behavior prediction method (SHA-SCP). SHA-SCP builds element groups by clustering the elements according to their spatial positions and uses attention mechanisms to perceive the UI at the element level and the element group level to fully capture the information of different scales. Experiments are conducted on the fine-grained smartphone usage behavior dataset, and the results show that our method outperforms the best baseline by an average of 10.52%, 11.34%, and 10.42% in Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, respectively.

cs.HC↗

arXiv2021

DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine Model

Predicting user positive response (e.g., purchases and clicks) probability is a critical task in Web applications. To identify predictive features from raw data, the state-of-the-art extreme deep factorization machine model (xDeepFM) introduces a new interaction network to leverage feature interactions at the vector-wise level explicitly. However, since each hidden layer in the interaction network is a collection of feature maps, it can be viewed essentially as an ensemble of different feature maps. In this case, only using a single objective to minimize the prediction loss may lead to overfitting and generate correlated errors. In this paper, an ensemble diversity enhanced extreme deep factorization machine model (DexDeepFM) is proposed, which designs the ensemble diversity measure in each hidden layer and considers both ensemble diversity and prediction accuracy in the objective function. In addition, the attention mechanism is introduced to discriminate the importance of ensemble diversity measures with different feature interaction orders. Extensive experiments on three public real-world datasets are conducted to show the effectiveness of the proposed model.

cs.LG↗

arXiv2021

TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation

Evolving temporal networks serve as the abstractions of many real-life dynamic systems, e.g., social network and e-commerce. The purpose of temporal network embedding is to map each node to a time-evolving low-dimension vector for downstream tasks, e.g., link prediction and node classification. The difficulty of temporal network embedding lies in how to utilize the topology and time information jointly to capture the evolution of a temporal network. In response to this challenge, we propose a temporal motif-preserving network embedding method with bicomponent neighbor aggregation, named TME-BNA. Considering that temporal motifs are essential to the understanding of topology laws and functional properties of a temporal network, TME-BNA constructs additional edge features based on temporal motifs to explicitly utilize complex topology with time information. In order to capture the topology dynamics of nodes, TME-BNA utilizes Graph Neural Networks (GNNs) to aggregate the historical and current neighbors respectively according to the timestamps of connected edges. Experiments are conducted on three public temporal network datasets, and the results show the effectiveness of TME-BNA.

cs.SI↗

arXiv2021

Unraveling Ultrafast Photoionization in Hexagonal Boron Nitride

The non-linear response of dielectrics to intense, ultrashort electric fields has been a sustained topic of interest for decades with one of its most important applications being femtosecond laser micro/nano-machining. More recently, renewed interests in strong field physics of solids were raised with the advent of mid-infrared femtosecond laser pulses, such as high-order harmonic generation, optical-field-induced currents, etc. All these processes are underpinned by photoionization (PI), namely the electron transfer from the valence to the conduction bands, on a time scale too short for phononic motion to be of relevance. Here, in hexagonal boron nitride, we reveal that the bandgap can be finely manipulated by femtosecond laser pulses as a function of field polarization direction with respect to the lattice, in addition to the field's intensity. It is the modification of bandgap that enables the ultrafast PI processes to take place in dielectrics. We further demonstrate the validity of the Keldysh theory in describing PI in dielectrics in the few TW/cm2 regime.

physics.optics↗

arXiv2019

Orbital Angular Momentum Multiplexing in Highly Reverberant Environments

Previous studies on orbital angular momentum (OAM) communication mainly considered line-of-sight environments. In this letter, however, it is found that OAM communication with high-order modulation can be achieved in highly reverberant environments by combining the OAM multiplexing with a spatial equalizer. The OAM multiplexing exhibits comparable performance of conventional multiple-input multiple-output (MIMO) system.

eess.SP↗