arXiv subjects

Xiaoguang Yang

Publications and source records attributed to Xiaoguang Yang.

At least 19 recordsLinked to original sources

arXiv2026

A Hybrid Quantum-Classical Coordination Architecture for Portfolio Optimization via Global Context Injection

Near-term quantum and quantum-inspired solvers for portfolio optimization rely on local subproblem execution under severe size constraints, but this locality can omit cross-cluster covariance essential for global risk coordination. We propose Context-Aware Folding (CAF), a lightweight hybrid quantum-classical coordination layer between one-shot static decomposition and full-matrix optimization. CAF injects a compressed global risk state into each local subproblem via a state-dependent linear bias, decouples local candidate generation from global commitment, and retains a sequential acceptance rule with a monotonic non-divergence guarantee. On a 2016 Russell 3000 subset (N=484) with Simulated Annealing (SA), CAF improves the scalarized mean-variance objective by 6.59\% over a static baseline (20/20 wins), and by 0.2579\% on an additional 2018 panel (N=1397, 17/20 wins). We further report matched folded N=40 compatibility studies with the Quantum Approximate Optimization Algorithm (QAOA) and simulated quantum annealing (SQA) as local solvers, together with a frozen hardware-in-the-loop run on Origin Quantum's Wukong 180 (\texttt{WK\_C180}) for one warm-started QAOA subproblem at n=5. These results support CAF as a coordination architecture with strong classical large-scale evidence, cross-backend compatibility, and local executability on real quantum hardware in the noisy intermediate-scale quantum (NISQ) era.

cs.ET↗

arXiv2026

On finite-horizon approximation of an infinite-horizon feedback Nash equilibrium in discrete-time LQ games

In infinite-horizon discrete-time linear-quadratic (LQ) dynamic games, computing feedback Nash equilibria (FNEs) remains computationally challenging. Motivated by this, we study a finite-horizon strategy for approximating one of the infinite-horizon FNEs. The finite-horizon strategy is as follows. Each player $i$ has an individual prediction horizon $T^i$. In the infinite-horizon game, at each stage, each player $i$ computes its control in the following way: player $i$ envisions an auxiliary $T^i$-stage game in which the same set of players play, computes the unique FNE of the auxiliary game using a standard method, and implements only the first-stage control. Our main result is, under suitable conditions, the total cost under these finite-horizon strategies converges to that under one of the infinite-horizon FNEs when all players' prediction horizons tend to infinity. Moreover, we derive an explicit cubic-polynomial upper bound on this cost gap with respect to the distance between the corresponding strategy matrices. This strategy is tractable and implementable, as it avoids the direct solution of the coupled algebraic Riccati equations (CARE) of infinite-horizon LQ games.

eess.SY↗

arXiv2025

A Unified Model for Multi-Task Drone Routing in Post-Disaster Road Assessment

Post-disaster road assessment (PDRA) is essential for emergency response, enabling rapid evaluation of infrastructure conditions and efficient allocation of resources. Although drones provide a flexible and effective tool for PDRA, routing them in large-scale networks remains challenging. Exact and heuristic optimization methods scale poorly and demand domain expertise, while existing deep reinforcement learning (DRL) approaches adopt a single-task paradigm, requiring separate models for each problem variant and lacking adaptability to evolving operational needs. This study proposes a unified model (UM) for drone routing that simultaneously addresses eight PDRA variants. By training a single neural network across multiple problem configurations, UM captures shared structural knowledge while adapting to variant-specific constraints through a modern transformer encoder-decoder architecture. A lightweight adapter mechanism further enables efficient finetuning to unseen attributes without retraining, enhancing deployment flexibility in dynamic disaster scenarios. Extensive experiments demonstrate that the UM reduces training time and parameters by a factor of eight compared with training separate models, while consistently outperforming single-task DRL methods by 6-14%, heuristic algorithms by 22-42%, and commercial solvers by 24-82% in terms of solution quality (total collected information value). The model achieves rapid solutions (1-10 seconds) across networks of up to 1,000 nodes, with robustness confirmed through sensitivity analyses. Moreover, finetuning experiments show that unseen attributes can be effectively incorporated with minimal cost while retaining high solution quality. The source code for UM is publicly available at https://github.com/PJ-HTU/UM_PDRA.

cs.LG↗

arXiv2025

Deep Reinforcement Learning for Drone Route Optimization in Post-Disaster Road Assessment

Rapid post-disaster road damage assessment is critical for effective emergency response, yet traditional optimization methods suffer from excessive computational time and require domain knowledge for algorithm design, making them unsuitable for time-sensitive disaster scenarios. This study proposes an attention-based encoder-decoder model (AEDM) for rapid drone routing decision in post-disaster road damage assessment. The method employs deep reinforcement learning to determine high-quality drone assessment routes without requiring algorithmic design knowledge. A network transformation method is developed to convert link-based routing problems into equivalent node-based formulations, while a synthetic road network generation technique addresses the scarcity of large-scale training datasets. The model is trained using policy optimization with multiple optima (POMO) with multi-task learning capabilities to handle diverse parameter combinations. Experimental results demonstrate two key strengths of AEDM: it outperforms commercial solvers by 20--71\% and traditional heuristics by 23--35\% in solution quality, while achieving rapid inference (1--2 seconds) versus 100--2,000 seconds for traditional methods. The model exhibits strong generalization across varying problem scales, drone numbers, and time constraints, consistently outperforming baseline methods on unseen parameter distributions and real-world road networks. The proposed method effectively balances computational efficiency with solution quality, making it particularly suitable for time-critical disaster response applications where rapid decision-making is essential for saving lives. The source code for AEDM is publicly available at https://github.com/PJ-HTU/AEDM-for-Post-disaster-road-assessment.

cs.LG↗

arXiv2025

Smartphone User Fingerprinting on Wireless Traffic

Due to the openness of the wireless medium, smartphone users are susceptible to user privacy attacks, where user privacy information is inferred from encrypted Wi-Fi wireless traffic. Existing attacks are limited to recognizing mobile apps and their actions and cannot infer the smartphone user identity, a fundamental part of user privacy. To overcome this limitation, we propose U-Print, a novel attack system that can passively recognize smartphone apps, actions, and users from over-the-air MAC-layer frames. We observe that smartphone users usually prefer different add-on apps and in-app actions, yielding different changing patterns in Wi-Fi traffic. U-Print first extracts multi-level traffic features and exploits customized temporal convolutional networks to recognize smartphone apps and actions, thus producing users' behavior sequences. Then, it leverages the silhouette coefficient method to determine the number of users and applies the k-means clustering to profile and identify smartphone users. We implement U-Print using a laptop with a Kali dual-band wireless network card and evaluate it in three real-world environments. U-Print achieves an overall accuracy of 98.4% and an F1 score of 0.983 for user inference. Moreover, it can correctly recognize up to 96% of apps and actions in the closed world and more than 86% in the open world.

cs.CR↗

arXiv2025

Linear-Quadratic Discrete-Time Dynamic Games with Unknown Dynamics

Considering linear-quadratic discrete-time games with unknown input/output/state (i/o/s) dynamics and state, we provide necessary and sufficient conditions for the existence and uniqueness of feedback Nash equilibria (FNE) in the finite-horizon game, based entirely on offline input/output data. We prove that the finite-horizon unknown-dynamics game and its corresponding known-dynamics game have the same FNEs, and provide detailed relationships between their respective FNE matrices. To simplify the computation of FNEs, we provide an invertibility condition and a corresponding algorithm that computes one FNE by solving a finite number of linear equation systems using offline data. For the infinite-horizon unknown-dynamics game, limited offline data restricts players to computing optimal strategies only over a finite horizon. We prove that the finite-horizon strategy ``watching $T$ steps into the future and moving one step now,'' which is commonly used in classical optimal control, exhibits convergence in both the FNE matrices and the total costs in the infinite-horizon unknown-dynamics game, and further provide an analysis of the convergence rate of the total cost. The corresponding algorithm for the infinite-horizon game is proposed and its efficacy is demonstrated through a non-scalar numerical example.

eess.SY↗

arXiv2025

Discovering Mechanistic Causality from Time Series: A Behavioral-System Approach

Identifying ``true causality'' is a fundamental challenge in complex systems research. Widely adopted methods, like the Granger causality test, capture statistical dependencies between variables rather than genuine driver-response mechanisms. This critical gap stems from the absence of mathematical tools that reliably reconstruct underlying system dynamics from observational time-series data. In this paper, we introduce a new control-based method for causality discovery through the behavior-system theory, which represents dynamical systems via trajectory spaces and has been widely used in data-driven control. Our core contribution is the \textbf{B}ehavior-\textbf{e}nabled \textbf{Caus}ality test (the BeCaus test), which transforms causality discovery into solving fictitious control problems. By exploiting the intrinsic asymmetry between system inputs and outputs, the proposed method operationalizes our conceptualization of mechanistic causality: variable $X$ is a cause of $Y$ if $X$ (partially) drives the evolution of $Y$. We establish conditions for linear time-invariant systems to be causality-discoverable, i.e., conditions for the BeCaus test to distinguish four basic causal structures (independence, full causality, partial causality, and latent-common-cause relation). Notably, our approach accommodates open systems with unobserved inputs. Moreover, an exploratory case study indicates the new method's potential extensibility to nonlinear systems.

math.OC↗

arXiv2025

Mechanism Design in Max-Flows

This paper studies allocation mechanisms in max-flow games with players' capacities as private information. We first show that no core-selection mechanism is truthful: there may exist a player whose payoff increases if she under-reports her capacity when a core-section mechanism is adopted. We then introduce five desirable properties for mechanisms in max-flow games: DSIC (truthful reporting is a dominant strategy), SIR (individual rationality and positive payoff for each player contributing positively to at least one coalition), SP (no edge has an incentive to split into parallel edges), MP (no parallel edges have incentives to merge), and CM (a player's payoff does not decrease as another player's capacity and max-flow increase). While the Shapley value mechanism satisfies DSIC and SIR, it fails to meet SP, MP and CM. We propose a new mechanism based on minimal cuts that satisfies all five properties.

cs.GT↗

arXiv2024

On the convergence of fictitious play algorithm in repeated games via the geometrical approach

As the earliest and one of the most fundamental learning dynamics for computing NE, fictitious play (FP) has being receiving incessant research attention and finding games where FP would converge (games with FPP) is one central question in related fields. In this paper, we identify a new class of games with FPP, i.e., $3\times3$ games without IIP, based on the geometrical approach by leveraging the location of NE and the partition of best response region. During the process, we devise a new projection mapping to reduce a high-dimensional dynamical system to a planar system. And to overcome the non-smoothness of the systems, we redefine the concepts of saddle and sink NE, which are proven to exist and help prove the convergence of CFP by separating the projected space into two parts. Furthermore, we show that our projection mapping can be extended to higher-dimensional and degenerate games.

math.OC↗

arXiv2024

Eavesdropping Mobile Apps and Actions through Wireless Traffic in the Open World

While smartphones and WiFi networks are bringing many positive changes to people's lives, they are susceptible to traffic analysis attacks, which infer user's private information from encrypted traffic. Existing traffic analysis attacks mainly target TCP/IP layers or are limited to the closed-world assumption, where all possible apps and actions have been involved in the model training. To overcome these limitations, we propose MACPrint, a novel system that infers mobile apps and in-app actions based on WiFi MAC layer traffic in the open-world setting. MACPrint first extracts rich statistical and contextual features of encrypted wireless traffic. Then, we develop Label Recorder, an automatic traffic labeling app, to improve labeling accuracy in the training phase. Finally, TCN models with OpenMax functions are used to recognize mobile apps and actions in the open world accurately. To evaluate our system, we collect MAC layer traffic data over 125 hours from more than 40 apps. The experimental results show that MAC-Print can achieve an accuracy of over 96% for recognizing apps and actions in the closed-world setting, and obtains an accuracy of over 86% in the open-world setting.

cs.CR↗

arXiv2024

Periodicity in Hedge-myopic system and an asymmetric NE-solving paradigm for two-player zero-sum games

In this paper, we consider the $n \times n$ two-payer zero-sum repeated game in which one player (player X) employs the popular Hedge (also called multiplicative weights update) learning algorithm while the other player (player Y) adopts the myopic best response. We investigate the dynamics of such Hedge-myopic system by defining a metric $Q(\textbf{x}_t)$, which measures the distance between the stage strategy $\textbf{x}_t$ and Nash Equilibrium (NE) strategy of player X. We analyze the trend of $Q(\textbf{x}_t)$ and prove that it is bounded and can only take finite values on the evolutionary path when the payoff matrix is rational and the game has an interior NE. Based on this, we prove that the stage strategy sequence of both players are periodic after finite stages and the time-averaged strategy of player Y within one period is an exact NE strategy. Accordingly, we propose an asymmetric paradigm for solving two-player zero-sum games. For the special game with rational payoff matrix and an interior NE, the paradigm can output the precise NE strategy; for any general games we prove that the time-averaged strategy can converge to an approximate NE. In comparison to the NE-solving method via Hedge self-play, this HBR paradigm exhibits faster computation/convergence, better stability and can attain precise NE convergence in most real cases.

math.DS↗

arXiv2022

Impact of the political risk on food reserve ratio: evidence across countries

Using an unbalanced panel data covering 75 countries from 1991 to 2019, we explore how the political risk impacts on food reserve ratio. The empirical findings show that an increasing political risk negatively affect food reserve ratio, and same effects hold for both internal risk and external risk. Moreover, we find that the increasing external or internal risks both negatively affect production and exports, but external risk does not significantly impact on imports and it positively impacts on consumption, while internal risk negatively impacts on imports and consumption. The results suggest that most of governments have difficulty to raise subsequent food reserve ratio in face of an increasing political risk, no matter it is an internal risk or an external risk although the mechanisms behind the impacts are different.

econ.GN↗

arXiv2020

Evolution of the Chinese Guarantee Network under Financial Crisis and Stimulus Program

Our knowledge about the evolution of guarantee network in downturn period is limited due to the lack of comprehensive data of the whole credit system. Here we analyze the dynamic Chinese guarantee network constructed from a comprehensive bank loan dataset that accounts for nearly 80% total loans in China, during 01/2007-03/2012. The results show that, first, during the 2007-2008 global financial crisis, the guarantee network became smaller, less connected and more stable because of many bankruptcies; second, the stimulus program encouraged mutual guarantee behaviors, resulting in highly reciprocal and fragile network structure; third, the following monetary policy adjustment enhanced the resilience of the guarantee network by reducing mutual guarantees. Interestingly, our work reveals that the financial crisis made the network more resilient, and conversely, the government bailout degenerated network resilience. These counterintuitive findings can provide new insight into the resilience of real-world credit system under external shocks or rescues.

q-fin.RM↗

arXiv2019

Network Subgraphs of the heterogeneous Chinese credit system

In this study, we investigate the evolution of Chinese guarantee networks from the angle of sub-patterns. First, we find that the mutual, 2-out-stars and triangle sub-patterns are motifs in 2- and 3-node subgraphs. Considering the heterogeneous financial characteristics of nodes, we find that small firms tend to form a mutual guarantee relationship and large firms are likely to be the guarantors in 2-out-stars sub-patterns.

cs.CE↗

arXiv2018

Asymmetric response to PMI announcements in China's stock returns

Considered an important macroeconomic indicator, the Purchasing Managers' Index (PMI) on Manufacturing generally assumes that PMI announcements will produce an impact on stock markets. International experience suggests that stock markets react to negative PMI news. In this research, we empirically investigate the stock market reaction towards PMI in China. The asymmetric effects of PMI announcements on the stock market are observed: no market reaction is generated towards negative PMI announcements, while a positive reaction is generally generated for positive PMI news. We further find that the positive reaction towards the positive PMI news occurs 1 day before the announcement and lasts for nearly 3 days, and the positive reaction is observed in the context of expanding economic conditions. By contrast, the negative reaction towards negative PMI news is prevalent during downward economic conditions for stocks with low market value, low institutional shareholding ratios or high price earnings. Our study implies that China's stock market favors risk to a certain extent given the vast number of individual investors in the country, and there may exist information leakage in the market.

q-fin.ST↗

arXiv2013

Analyzing user behavior of the micro-blogging website Sinaweibo during hot social events

The spread and resonance of users' opinions on SinaWeibo, the most popular micro-blogging website in China, are tremendously influential, having significantly affected the processes of many real-world hot social events. We select 21 hot events that were widely discussed on SinaWeibo in 2011, and do some statistical analyses. Our main findings are that (i) male users are more likely to be involved, (ii) messages that contain pictures and those posted by verified users are more likely to be reposted, while those with URLs are less likely, (iii) gender factor, for most events, presents no significant difference in reposting likelihood.

cs.SI↗

arXiv2013

What are Chinese Talking about in Hot Weibos?

SinaWeibo is a Twitter-like social network service emerging in China in recent years. People can post weibos (microblogs) and communicate with others on it. Based on a dataset of 650 million weibos from August 2009 to January 2012 crawled from APIs of SinaWeibo, we study the hot ones that have been reposted for at least 1000 times. We find that hot weibos can be roughly classified into eight categories, i.e. Entertainment & Fashion, Hot Social Events, Leisure & Mood, Life & Health, Seeking for Help, Sales Promotion, Fengshui & Fortune and Deleted Weibos. In particular, Leisure & Mood and Hot Social Events account for almost 65% of all the hot weibos. This reflects very well the fundamental dual-structure of the current society of China: On the one hand, economy has made a great progress and quite a part of people are now living a relatively prosperous and fairly easy life. On the other hand, there still exist quite a lot of serious social problems, such as government corruptions and environmental pollutions. It is also shown that users' posting and reposting behaviors are greatly affected by their identity factors (gender, verification status, and regional location). For instance, (1) Two thirds of the hot weibos are created by male users. (2) Although verified users account for only 0.1% in SinaWeibo, 46.5% of the hot weibos are contributed by them. Very interestingly, 39.2% are written by SPA users. A more or less pathetic fact is that only 14.4% of the hot weibos are created by grassroots (individual users that are neither SPA nor verified). (3) Users from different areas of China have distinct posting and reposting behaviors which usually reflect very their local cultures. Homophily is also examined for people's reposting behaviors.

cs.SI↗

arXiv2012

Fashion, Cooperation, and Social Interactions

Fashion plays such a crucial rule in the evolution of culture and society that it is regarded as a second nature to the human being. Also, its impact on economy is quite nontrivial. On what is fashionable, interestingly, there are two viewpoints that are both extremely widespread but almost opposite: conformists think that what is popular is fashionable, while rebels believe that being different is the essence. Fashion color is fashionable in the first sense, and Lady Gaga in the second. We investigate a model where the population consists of the afore-mentioned two groups of people that are located on social networks (a spatial cellular automata network and small-world networks). This model captures two fundamental kinds of social interactions (coordination and anti-coordination) simultaneously, and also has its own interest to game theory: it is a hybrid model of pure competition and pure cooperation. This is true because when a conformist meets a rebel, they play the zero sum matching pennies game, which is pure competition. When two conformists (rebels) meet, they play the (anti-) coordination game, which is pure cooperation. Simulation shows that simple social interactions greatly promote cooperation: in most cases people can reach an extraordinarily high level of cooperation, through a selfish, myopic, naive, and local interacting dynamic (the best response dynamic). We find that degree of synchronization also plays a critical role, but mostly on the negative side. Four indices, namely cooperation degree, average satisfaction degree, equilibrium ratio and complete ratio, are defined and applied to measure people's cooperation levels from various angles. Phase transition, as well as emergence of many interesting geographic patterns in the cellular automata network, is also observed.

cs.MA↗