arXiv subjects

Zhijian Wang

Publications and source records attributed to Zhijian Wang.

At least 19 recordsLinked to original sources

arXiv2026

Data-driven moving-window Bayesian inference for transient CO2-temperature network models of buildings

In this work, we proposes a CO2-temperature network model that links multi-zone mass transport and thermal dynamics through shared latent drivers, airflow and occupancy. The thermal component is formulated as a resistance-capacitance (RC) network augmented with airflow-driven convective exchange, while the CO2 component is governed by inter-zonal convective transport. To calibrate the model and track time-varying operating conditions based on sparse sensing, we introduce a moving-window Bayesian inference procedure that jointly estimates thermal parameters, airflow and occupancy trajectories. The estimation also provides room-specific sensor noise levels, yielding posterior predictive forecasts with credible intervals. The framework is assessed using a controlled synthetic benchmark, and a scaled physical validation experiment using CO2 and temperature sensing. In both settings, the posterior accurately reconstructs trajectories within windows and delivers low forecast errors. When inference windows overlap abrupt regime transitions, the widened uncertainty bands and increased inferred noise levels provide an interpretable diagnostic of model-data mismatch, followed by rapid recovery once the new regime is observed. Overall, coupling CO2-informed airflow with thermal dynamics yields a robust approach for conductive and advective temperature prediction, supporting practical monitoring and energy-performance assessment under limited sensing.

math.NA↗

arXiv2025

Quasi-Maximum Likelihood Estimation for a Genuinely Unbalanced Dynamic Network Panel Data Model

This paper develops a quasi-maximum likelihood estimator for genuinely unbalanced dynamic network panel data models with individual fixed effects. We propose a model that accommodates contemporaneous and lagged network spillovers, temporal dependence, and a listing effect that activates upon a unit's first appearance in the panel. We establish the consistency of the QMLE as both $N$ and $T$ go to infinity, derive its asymptotic distribution, and identify an asymptotic bias arising from incidental parameters when $N$ is asymptotically large relative to $T$. Based on the asymptotic bias expression, we propose a bias-corrected estimator that is asymptotically unbiased and normally distributed under appropriate regularity conditions. Monte Carlo experiments examine the finite sample performance of the bias-corrected estimator across different criteria, including bias, RMSE, coverage probability, and the normality of the estimator. The empirical application to Airbnb listings from New Zealand and New York City reveals region-specific patterns in spatial and temporal price transmission, illustrating the importance of modeling genuine unbalancedness in dynamic network settings.

stat.ME↗

arXiv2025

Don't Just Search, Understand: Semantic Path Planning Agent for Spherical Tensegrity Robots in Unknown Environments

Endowed with inherent dynamical properties that grant them remarkable ruggedness and adaptability, spherical tensegrity robots stand as prototypical examples of hybrid softrigid designs and excellent mobile platforms. However, path planning for these robots in unknown environments presents a significant challenge, requiring a delicate balance between efficient exploration and robust planning. Traditional path planners, which treat the environment as a geometric grid, often suffer from redundant searches and are prone to failure in complex scenarios due to their lack of semantic understanding. To overcome these limitations, we reframe path planning in unknown environments as a semantic reasoning task. We introduce a Semantic Agent for Tensegrity robots (SATPlanner) driven by a Large Language Model (LLM). SATPlanner leverages high-level environmental comprehension to generate efficient and reliable planning strategies.At the core of SATPlanner is an Adaptive Observation Window mechanism, inspired by the "fast" and "slow" thinking paradigms of LLMs. This mechanism dynamically adjusts the perceptual field of the agent: it narrows for rapid traversal of open spaces and expands to reason about complex obstacle configurations. This allows the agent to construct a semantic belief of the environment, enabling the search space to grow only linearly with the path length (O(L)) while maintaining path quality. We extensively evaluate SATPlanner in 1,000 simulation trials, where it achieves a 100% success rate, outperforming other real-time planning algorithms. Critically, SATPlanner reduces the search space by 37.2% compared to the A* algorithm while achieving comparable, near-optimal path lengths. Finally, the practical feasibility of SATPlanner is validated on a physical spherical tensegrity robot prototype.

cs.RO↗

arXiv2025

mmEgoHand: Egocentric Hand Pose Estimation and Gesture Recognition with Head-mounted Millimeter-wave Radar and IMU

Recent advancements in millimeter-wave (mmWave) radar have demonstrated its potential for human action recognition and pose estimation, offering privacy-preserving advantages over conventional cameras while maintaining occlusion robustness, with promising applications in human-computer interaction and wellness care. However, existing mmWave systems typically employ fixed-position configurations, restricting user mobility to predefined zones and limiting practical deployment scenarios. We introduce mmEgoHand, a head-mounted egocentric system for hand pose estimation to support applications such as gesture recognition, VR interaction, skill digitization and assessment, and robotic teleoperation. mmEgoHand synergistically integrates mmWave radar with inertial measurement units (IMUs) to enable dynamic perception. The IMUs actively compensate for radar interference induced by head movements, while our novel end-to-end Transformer architecture simultaneously estimates 3D hand keypoint coordinates through multi-modal sensor fusion. This dual-modality framework achieves spatial-temporal alignment of mmWave heatmaps with IMUs, overcoming viewpoint instability inherent in egocentric sensing scenarios. We further demonstrate that intermediate hand pose representations substantially improve performance in downstream task, e.g., VR gesture recognition. Extensive evaluations with 10 subjects performing 8 gestures across 3 distinct postures -- standing, sitting, lying -- achieve 90.8% recognition accuracy, outperforming state-of-the-art solutions by a large margin. Dataset and code are available at https://github.com/WhisperYi/mmVR.

cs.CV↗

arXiv2024

Real-time 3D human action recognition based on Hyperpoint sequence

Real-time 3D human action recognition has broad industrial applications, such as surveillance, human-computer interaction, and healthcare monitoring. By relying on complex spatio-temporal local encoding, most existing point cloud sequence networks capture spatio-temporal local structures to recognize 3D human actions. To simplify the point cloud sequence modeling task, we propose a lightweight and effective point cloud sequence network referred to as SequentialPointNet for real-time 3D action recognition. Instead of capturing spatio-temporal local structures, SequentialPointNet encodes the temporal evolution of static appearances to recognize human actions. Firstly, we define a novel type of point data, Hyperpoint, to better describe the temporally changing human appearances. A theoretical foundation is provided to clarify the information equivalence property for converting point cloud sequences into Hyperpoint sequences. Secondly, the point cloud sequence modeling task is decomposed into a Hyperpoint embedding task and a Hyperpoint sequence modeling task. Specifically, for Hyperpoint embedding, the static point cloud technology is employed to convert point cloud sequences into Hyperpoint sequences, which introduces inherent frame-level parallelism; for Hyperpoint sequence modeling, a Hyperpoint-Mixer module is designed as the basic building block to learning the spatio-temporal features of human actions. Extensive experiments on three widely-used 3D action recognition datasets demonstrate that the proposed SequentialPointNet achieves competitive classification performance with up to 10X faster than existing approaches.

cs.CV↗

arXiv2022

Eigen mode selection in human subject game experiment

Eigen mode selection ought to be a practical issue in some real game systems, as it is a practical issue in the dynamics behaviour of a building, bridge, or molecular, because of the mathematical similarity in theory. However, its reality and accuracy have not been known in real games. We design a 5-strategy game which, in the replicator dynamics theory, is predicted to exist two eigen modes. Further, in behaviour game theory, the game is predicted that the mode selection should depends on the game parameter. We conduct human subject game experiments by controlling the parameter. The data confirm that, the predictions on the mode existence as well as the mode selection are significantly supported. This finding suggests that, like the equilibrium selection concept in classical game theory, eigen mode selection is an issue in game dynamics theory.

econ.TH↗

arXiv2022

Dynamic Structure in Four-strategy Game: Theory and Experiment

Game dynamics theory, as a field of science, the consistency of theory and experiment is essential. In the past 10 years, important progress has been made in the merging of the theory and experiment in this field, in which dynamics cycle is the presentation. However, the merging works have not got rid of the constraints of Euclidean two-dimensional cycle so far. This paper uses a classic four-strategy game to study the dynamic structure (non-Euclidean superplane cycle). The consistency is in significant between the three ways: (1) the analytical results from evolutionary dynamics equations, (2) agent-based simulation results from learning models and (3) laboratory results from human subjects game experiments. The consistency suggests that, game dynamic structure could be quantitatively predictable, observable and controllable in general.

econ.TH↗

arXiv2019

Blind source separation using Fast-ICA with a novel nonlinear function

Blind source separation(BSS) is a hotspot in signal processing, and independent component analysis (ICA) is a very effective tool for solving the BSS problem. In order to improve the performance of the separation, a new nonlinear function sin was introduced. It can replace the commonly used classical functions (tanh, gauss and pow3) and does not need to select different nonlinear functions according to the Gauss property of signals. The two Matlab simulation results show that the improved Fast-ICA algorithm with the proposed nonlinearity can not only improve the separation accuracy but also speed up the convergence of blind source separation.

eess.SP↗

arXiv2019

Blind separation of rotor vibration signals in high-noise environments

During the operation of the engine rotor, the vibration signal measured by the sensor is the mixed signal of each vibration source, and contains strong noise at the same time. In this paper, a new separation method for mixed vibration signals in strong noise environment(SNR=-5) is proposed. Firstly, the time-delay auto-correlation de-noising method is used to de-noise the mixed signals, and then the common blind separation algorithm (MSNR algorithm is used here) is used to separate the mixed vibration signals, which improves the separation performance. The simulation results verify the validity of the method. The proposed method provides a new idea for health monitoring and fault diagnosis of engine rotor vibration signals.

eess.SP↗

arXiv2019

Blind Source Separation of Optical Wireless Communications in Noisy Environments

Blind source separation is a research hotspot in the field of signal processing because it aims to separate unknown source signals from observed mixtures through an unknown transmission channel. A low computational complexity instantaneous linear mixture signals blind separation algorithm was introduced and improved. There is only one variable parameter named the length of moving average in the algorithm, which has a significant impact on the separation effect. This paper gives some suggestions on the reasonable value through experiments. The algorithm is extended to the separation of visible light communication signals in different noise environments, and has achieved certain results, which further expands the applicability of the algorithm.

eess.SP↗

arXiv2018

Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics

Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.

econ.EM↗

arXiv2017

Using experimental game theory to transit human values to ethical AI

Knowing the reflection of game theory and ethics, we develop a mathematical representation to bridge the gap between the concepts in moral philosophy (e.g., Kantian and Utilitarian) and AI ethics industry technology standard (e.g., IEEE P7000 standard series for Ethical AI). As an application, we demonstrate how human value can be obtained from the experimental game theory (e.g., trust game experiment) so as to build an ethical AI. Moreover, an approach to test the ethics (rightness or wrongness) of a given AI algorithm by using an iterated Prisoner's Dilemma Game experiment is discussed as an example. Compared with existing mathematical frameworks and testing method on AI ethics technology, the advantages of the proposed approach are analyzed.

cs.AI↗

arXiv2017

Testability of evolutionary game dynamics models based on experimental economics data

Understanding the dynamic processes of a real game system requires an appropriate dynamics model, and rigorously testing a dynamics model is non-trivial. In our methodological research, we develop an approach to testing the validity of game dynamics models that considers the dynamic patterns of angular momentum and speed as measurement variables. Using Rock-Paper-Scissors (RPS) games as an example, we illustrate the geometric patterns in the experiment data. We then derive the related theoretical patterns from a series of typical dynamics models. By testing the goodness-of-fit between the experimental and theoretical patterns, we show that the validity of these models can be evaluated quantitatively. Our approach establishes a link between dynamics models and experimental systems, which is, to the best of our knowledge, the most effective and rigorous strategy for ascertaining the testability of evolutionary game dynamics models.

physics.soc-ph↗

arXiv2016

Social Spiral Pattern in Experimental 2x2 Games

With evolutionary game theory, mathematicians, physicists and theoretical biologists usually show us beautiful figures of population dynamic patterns. 2x2 game (matching pennies game) is one of the classical cases. In this letter, we report our finding that, there exists a dynamical pattern, called as social spiral, in human subjects 2x2 experiment data. In a flow/velocity vector field method, we explore the data in the discrete lattices of the macro-level social strategy space in the games, and then above spiral pattern emergent. This finding hints that, there exists a macro-level order beyond the stochastic process in micro-level. We notice that, the vector pattern provides an interesting way to conceal evolutionary game theory models and experimental economics data. This lattice vector field method provides a novel way for models evaluating and experiment designing.

nlin.AO↗

arXiv2015

Extortion outperforms generosity in iterated Prisoners' Dilemma

Promoting cooperation is an intellectual challenge in the social sciences, for which the iterated Prisoners' Dilemma (IPD) is a fundamental framework. The traditional view that there exists no simple ultimatum strategy whereby one player can unilaterally control the share of the surplus has been challenged by a new class of "zero-determinant" (ZD) strategies raised by Press and Dyson. In particular, the extortionate strategies can subdue the opponent and obtain higher scores. However, no empirical evidence has yet been found to support this theoretical finding. In a long-run laboratory experiment of the iterated Prisoners' Dilemma pairing each human subject with a computer co-player, we demonstrate that the extortionate strategy indeed outperforms the generous strategy against human subjects. Our results show that the extortionate strategy achieves higher scores than the generous strategy, the extortionate strategy promotes the cooperation rate to a similar level as the generous strategy does, and the human subjects' cooperation rates in both the extortionate and generous treatments are increasing over time. While our results imply that the human subjects cared about their earnings as well as fairness or reciprocity, we do observe that subjects learned to become increasingly cooperative over time to increase their own monetary payoffs. Our experiments provide the first laboratory evidence in support of the Press-Dyson theory.

physics.soc-ph↗

arXiv2014

Cycling in stochastic general equilibrium

By generalizing the measurements on the game experiments of mixed strategy Nash equilibrium, we study the dynamical pattern in a representative dynamic stochastic general equilibrium (DSGE). The DSGE model describes the entanglements of the three variables (output gap [$y$], inflation [$π$] and nominal interest rate [$r$]) which can be presented in 3D phase space. We find that, even though the trajectory of $π\!-\!y\!-\!r$ in phase space appears highly stochastic, it can be visualized and quantified. It exhibits as clockwise cycles, counterclockwise cycles and weak cycles, respectively, when projected onto $π\!-\!y$, $y\!-\!r$ and $r\!-\!π$ phase planes. We find also that empirical data of United State (1960-2013) significantly exhibit same cycles. The resemblance between the cycles in general equilibrium and the cycles in mixed strategy Nash equilibrium suggest that, there generally exists dynamical fine structures accompanying with equilibrium. The fine structure, describing the entanglement of the non-equilibrium (the constantly deviating from the equilibrium), displays as endless cycles.

nlin.AO↗

arXiv2014

Incentive and stability in the Rock-Paper-Scissors game: an experimental investigation

In a two-person Rock-Paper-Scissors (RPS) game, if we set a loss worth nothing and a tie worth 1, and the payoff of winning (the incentive a) as a variable, this game is called as generalized RPS game. The generalized RPS game is a representative mathematical model to illustrate the game dynamics, appearing widely in textbook. However, how actual motions in these games depend on the incentive has never been reported quantitatively. Using the data from 7 games with different incentives, including 84 groups of 6 subjects playing the game in 300-round, with random-pair tournaments and local information recorded, we find that, both on social and individual level, the actual motions are changing continuously with the incentive. More expressively, some representative findings are, (1) in social collective strategy transit views, the forward transition vector field is more and more centripetal as the stability of the system increasing; (2) In the individual behavior of strategy transit view, there exists a phase transformation as the stability of the systems increasing, and the phase transformation point being near the standard RPS; (3) Conditional response behaviors are structurally changing accompanied by the controlled incentive. As a whole, the best response behavior increases and the win-stay lose-shift (WSLS) behavior declines with the incentive. Further, the outcome of win, tie, and lose influence the best response behavior and WSLS behavior. Both as the best response behavior, the win-stay behavior declines with the incentive while the lose-left-shift behavior increase with the incentive. And both as the WSLS behavior, the lose-left-shift behavior increase with the incentive, but the lose-right-shift behaviors declines with the incentive. We hope to learn which one in tens of learning models can interpret the empirical observation above.

physics.soc-ph↗

arXiv2014

Social cycling and conditional responses in the Rock-Paper-Scissors game

How humans make decisions in non-cooperative strategic interactions is a challenging question. For the fundamental model system of Rock-Paper-Scissors (RPS) game, classic game theory of infinite rationality predicts the Nash equilibrium (NE) state with every player randomizing her choices to avoid being exploited, while evolutionary game theory of bounded rationality in general predicts persistent cyclic motions, especially for finite populations. However, as empirical studies on human subjects have been relatively sparse, it is still a controversial issue as to which theoretical framework is more appropriate to describe decision making of human subjects. Here we observe population-level cyclic motions in a laboratory experiment of the discrete-time iterated RPS game under the traditional random pairwise-matching protocol. The cycling direction and frequency are not sensitive to the payoff parameter a. This collective behavior contradicts with the NE theory but it is quantitatively explained by a microscopic model of win-lose-tie conditional response without any adjustable parameter. Our theoretical calculations reveal that this new strategy may offer higher payoffs to individual players in comparison with the NE mixed strategy, suggesting that high social efficiency is achievable through optimized conditional response.

physics.soc-ph↗