Existing research has shown that political crisis events can directly impact the tourism industry. However, the current methods suffer from potential changes of unobserved variables, which poses challenges for a reliable evaluation of the political crisis impacts. This paper proposes a panel counterfactual approach with Internet search index, which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest. It also provides a tool to examine potential channels through which the crisis may affect tourist outflows. This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea. Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place. This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.

This paper considers the real-time estimation problem of vehicle mass, which has a significant impact on driving comfort and safety. A bilinear parameter identification algorithm is proposed for a type of nonlinear identification problems, which encompass vehicle mass estimation. The feature of this nonlinear model is that two parameters to be estimated are multiplied together, which brings great difficulties to identification compared to linear models. The main idea proposed in the algorithm design is to transform the original nonlinear model into two mutually dependent linear models, which are identified by the recursive algorithms. By constructing a combined Lyapunov function, it is theoretically proved that the algorithm converges under the input excitation condition, and the convergence rate $O(1/t)$ is achieved based on some extra mild conditions. Finally, the algorithm is verified through practical experiments, with the estimated vehicle mass error of $1.06%$ on average, which shows the feasibility of the algorithm.

With the development and applications of the Smart Court System (SCS) in China, the reliability and accuracy of legal artificial intelligence have become focal points in recent years. Notably, criminal sentencing prediction, a significant component of the SCS, has also garnered widespread attention. According to the Chinese criminal law, actual sentencing data exhibits a saturated property due to statutory penalty ranges, but this mechanism has been ignored by most existing studies. Given this, the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks. Building on the saturated structure of our model, a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the $L_1$ loss together with the corresponding gradient update strategy, a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge, the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure. An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.

This paper focuses on the disturbance suppression issue of hidden semi-Markov jump systems leveraging composite control. The system consists of a semi-Markov layer and an observed mode sequence layer, and it is subject to a matched disturbance generated by an exogenous system and a mismatched disturbance that is norm bounded. The proposal is to design a composite controller based on a disturbance observer to counteract and attenuate the disturbances effectively. By constructing a special Lyapunov function comparison point, the exponential stability is analyzed with the stability criterion in the form of linear matrix inequality is established. Two simulation examples are provided to demonstrate the practical merits of the composite controller relative to the single H_∞ control.

In this paper, the problem of identifying autoregressive-moving-average systems under random threshold binary-valued output measurements is considered. With the help of stochastic approximation algorithms with expanding truncations, the authors give the recursive estimates for the parameters of both the linear system and the binary sensor. Under reasonable conditions, all constructed estimates are proved to be convergent to the true values with probability one, and the convergence rates are also established. A simulation example is provided to justify the theoretical results.

It is with great pleasure and admiration that we celebrate the 60th birthday of Professor Lihua Xie, a distinguished researcher and visionary leader in the field of robust control and estimation. Prof. Xie’s remarkable journey, marked by outstanding achievements and groundbreaking contributions, has left an indelible mark on the world of engineering and academia.
Prof. Xie’s academic odyssey began at Nanjing University of Science and Technology, where he earned his bachelor’s and master’s degrees in 1983 and 1986, respectively. His pursuit of knowledge led him to the University of Newcastle, Australia, where he obtained his PhD in 1992. Since 1992, he has been a cornerstone of Nanyang Technological University (NTU), Singapore, currently serving as a distinguished professor in the School of Electrical and Electronic Engineering and as the Director of the Centre for Advanced Robotics Technology Innovation (CARTIN), NTU.
One of Prof. Xie’s pivotal contributions lies in the realm of robust control and estimation. His early work in the early 1990s addressed robust solutions for systems with parametric uncertainties, providing a profound understanding of how uncertainty influences control system performance. His pioneering research not only illuminated the impact of uncertainty but also offered effective strategies, particularly for parametric uncertainty, ensuring the robustness of control systems. Prof. Xie was among the first to develop robust estimation techniques for systems grappling with parametric uncertainties, influencing researchers globally since the 1990s.
In the past two decades, Prof. Xie, alongside his co-author, established a groundbreaking equivalence between quantized feedback and robust control. This breakthrough extended the applicability of existing robust control theory to the analysis and design of control systems operating under quantized feedback. His work also unraveled the intricate interplay among data rate, network topology, and agent dynamics in multi-agent consensus - a fundamental challenge in cooperative control. Prof. Xie’s research provided answers to crucial questions, such as determining the minimal data rate and network topology for multi-agent consensus, along with corresponding coding and decoding schemes.
The spectrum of Prof. Xie’s impact extends to compressive sensing, where he and his student established a phase transition relationship between sparsity and recoverability for complex signals. Their continuous compressive sensing algorithms and Vandermonde decomposition theory for multi-level Toeplitz matrices have found applications in array signal processing, marking another significant milestone in his illustrious career.
Beyond theoretical endeavors, Prof. Xie’s practical innovations have revolutionized localization and unmanned systems. His research group’s developments include a WiFi-based indoor positioning system, multi-modality sensor fusion technology, and a fully integrated navigation solution for UAVs. These innovations have found applications in diverse fields, from structure inspection and delivery using UAVs to a low-cost universal navigation system for AGVs in logistics and manufacturing.
In the realm of research and development leadership, Prof. Xie’s impact is equally profound. He is the founding Director of the Delta-NTU Corporate Laboratory for Cyber-physical Systems, which focuses on the development of smart manufacturing and smart learning technologies for industry. Additionally, Prof. Xie established the Centre for Advanced Robotics Technology Innovation, where he currently serves as the Director. The center’s mission is to pioneer advanced sensing and perception technologies, as well as collaborative robotics technologies, with applications in logistics, manufacturing, and elderly care.
As an accomplished researcher, Prof. Xie has demonstrated unparalleled dedication to serving the research community. His extensive editorial roles, including a founding Editor-inChief for Unmanned Systems and Associate Editor for Sciences China - Information Science, showcase his commitment to advancing scientific knowledge. He has played pivotal roles in various editorial boards, such as IET Book Series in Control and esteemed journals like IEEE Transactions on Automatic Control and Automatica.
Prof. Xie’s impact extends beyond editorial responsibilities; he has been a distinguished IEEE Distinguished Lecturer, a Board of Governors member for the IEEE Control System Society, and Vice President since January 2024. His leadership roles also include serving as General Chair of significant conferences, including the 62nd IEEE Conference on Decision and Control in December 2023.
His professional achievements, recognized by peers worldwide, include fellowships in the Academy of Engineering Singapore, the Institute of Electrical and Electronics Engineers (IEEE), International Federation of Automatic Control (IFAC), and the Chinese Automation Association (CAA).
In celebration of Prof. Xie’s 60th birthday, we invited 17 papers from friends and colleagues for this special issue. As editors, we extend our deepest gratitude to all the authors for their invaluable contributions. Special thanks to the Journal of Systems Science & Complexity editorial office, including Prof. Xiao-Shan Gao (Editor-in-Chief), Prof. Yanlong Zhao (Managing Editor), and Ms. Guoyun Wu (Editorial Director), for their steadfast support from the conception to the publication of this special issue.
On this momentous occasion, we express our profound appreciation for Prof. Lihua Xie for his unwavering commitment to advancing knowledge and look forward to the continued brilliance and innovation in the next chapters of his illustrious career.
Happy Birthday, Prof. Lihua Xie!

The overuse and misuse of antibiotics has become a major problem for public health. People become resistant to antibiotics and because of this the anticipated therapeutic effect is never reached. In-hospital infections are often aggravated and large amounts of money are spent for treating complications in the patients' condition. In this paper a nonlinear optimal (H-infinity) control method is developed for the dynamic model of bacterial infections exhibiting resistance to antibiotics. First, differential flatness properties are proven for the associated state-space model. Next, the state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instance around a time-varying operating point which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. For the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed. To compute the controller's gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. The proposed method achieves stabilization and remedy for the bacterial infection under moderate use of antibiotics.

In this paper, several equivalent forms of the well-known Brockett's second example system are firstly presented. The stabilization of the system is then treated in the fully actuated system approach. A simple continuous time-invariant sub-stabilizing controller is designed, and the corresponding region of attraction is characterized. As a result, all trajectories of the system starting from the characterized region of attraction are driven exponentially to the origin. Since the region of attraction is very large, the designed sub-stabilizing controller can be directly useful in many practical situations. In cases where the initial values are indeed needed to be chosen out of the region of attraction, extremely simple pre-controllers can be designed, which drive the system trajectories into the designed region of attraction. A simulation of the designed control system is carried out to show the effect of the proposed approach.

In this article, a fixed-time tracking control strategy is proposed for a quadrotor UAV (QUAV) with external disturbance and asymmetric output error constraints. Firstly, a dynamic model of the QUAV is transformed into a strict feedback system with external disturbance, and it is decoupled into attitude subsystem and position subsystem for simplifying controller design. Secondly, an asymmetric tangent barrier Lyapunov function (ATBLF) is applied to solve the tracking error constraints problem, and a fixed-time control law is designed. Meanwhile, a fixed-time disturbance observer (FTDO) is designed to cope with external disturbance. Then, it is proved that the designed controller guarantees the tracking error remains within the constraint ranges and converges to zero in fixed-time by Lyapunov stability theory. Finally, the effectiveness of the proposed control scheme is verified by numerical simulations.

This paper is devoted to stabilizing the high-order uncertain nonlinear system in a fixed time by output feedback control. First, a novel settling time solution method is proposed by establishing an indirect double system and using the comparison principle. Then a fixed-time observer and a neural networked based adaptive law are constructed to estimate the state and the unknown disturbance for the high-order uncertain nonlinear system. Furthermore, a fixed-time output feedback controller is proposed via the homogeneity technique. The upper bound of the settling time is analyzed for the closed-loop system under the proposed output feedback control. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.

This paper investigates the networked evolutionary games (NEGs) with profile-dependent delays, including modeling and stability analysis. Profile-dependent delay, which varies with the game profiles, slows the information transmission between participants. Firstly, the dynamics model is proposed for the profile-dependent delayed NEG, then the algebraic formulation is established using the algebraic state space approach. Secondly, the dynamic behavior of the game is discussed, involving general stability and evolutionarily stable profile analysis. Necessary and sufficient criteria are derived using the matrices, which can be easily verified by mathematical software. Finally, a numerical example is carried out to demonstrate the validity of the theoretical results.

This paper studies the periodic zero-dynamics attacks (ZDAs) in multi-agent systems without velocity measurements under directed graph. Specifically, two types of attack modes are addressed, i.e., infinite number and finite number of zero-dynamics attacks. For the former case, the authors show that the consensus of the considered system cannot be guaranteed. For the latter case, the dynamic evolution of the agents is investigated and it is found that only attacking the rooted agents can destroy the consensus. Then, a sufficient condition which quantifies whether or not the consensus value is destroyed is given, revealing the relationship among parameters of system model, filter and attack signal. Finally, simulations are carried out to verify the effectiveness of the theoretical findings.

Generalized linear models are usually adopted to model the discrete or nonnegative responses. In this paper, empirical likelihood inference for fixed design generalized linear models with longitudinal data is investigated. Under some mild conditions, the consistency and asymptotic normality of the maximum empirical likelihood estimator are established, and the asymptotic χ² distribution of the empirical log-likelihood ratio is also obtained. Compared with the existing results, the new conditions are more weak and easy to verify. Some simulations are presented to illustrate these asymptotic properties.

In this paper, a new stochastic analysis tool on semi-global stability is constructed, for nonlinear systems disturbed by stochastic processes with strongly bounded in probability. The definition of semi-global noise to state practical stability in probability and its Lyapunov criterion for random systems are presented. As a major application of stability, the semi-global practical tracking of random nonlinear systems based on dynamic surface control technique is considered. The trajectory tracking of manipulator robot driven by direct current motors is carried out in simulation to illustrate the effectiveness and feasibility of the control scheme.

In the process of coal mine drilling, controlling the rotary speed is important as it determines the efficiency and safety of drilling. In this paper, a linear extended state observer (LESO) based backstepping controller for rotary speed is proposed, which can overcome the impact of changes in coal seam hardness on rotary speed. Firstly, the influence of coal seam hardness on the drilling rig's rotary system is considered for the first time, which is reflected in the numerical variation of load torque, and a dynamic model for the design of rotary speed controller is established. Then an LESO is designed to observe the load torque, and feedforward compensation is carried out to overcome the influence of coal seam hardness. Based on the model of the compensated system, a backstepping method is used to design a controller to achieve tracking control of the rotary speed. Finally, the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments, the steady-state error of the rotary speed in field is 1 r/min, and the overshoot is reduced to 5.8$%$. This greatly improves the stability and security, which is exactly what the drilling process requires.

In this paper, the authors design a reinforcement learning algorithm to solve the adaptive linear-quadratic stochastic $n$-players non-zero sum differential game with completely unknown dynamics. For each player, a critic network is used to estimate the Q-function, and an actor network is used to estimate the control input. A model-free online Q-learning algorithm is obtained for solving this kind of problems. It is proved that under some mild conditions the system state and weight estimation errors can be uniformly ultimately bounded. A simulation with five players is given to verify the effectiveness of the algorithm.

The issue of stability and group consensus tracking is investigated for the discrete-time heterogeneous networked multi-agent systems with communication constraints (e.g., time delays and data loss) in this paper. Firstly, the couple-group consensus tracking control is analyzed theoretically, the communication constraints are compensated by the prediction method, and the factor of leaders is introduced to make the system not lose generality. Secondly, the necessary and sufficient condition is given to ensure the stability of the system and achieve the couple-group consensus tracking control, and relax the topology constraint of in-degrees balance by cooperative-competitive interactions. In addition, the result of couple groups is extended to multiple groups based on the predictive control protocol. Numerical simulations with Matlab show that the proposed networked predictive control can effectively overcome the network constraints, the dynamic performance and control effect are better than the general control without the prediction.

Trivium is an international standard of lightweight stream ciphers (ISO/IEC 29192-3: 2012). In this paper, the Trivium-like NFSRs, a class of Galois NFSRs generalized from the Galois NFSR of Trivium, are studied from the perspective of Fibonacci NFSRs. It is shown that an $n$-stage Trivium-like NFSR cannot be equivalent to an $n$-stage Fibonacci NFSR, which is proved by showing the existence of \textquotedblleft collision initial states\textquotedblright. As an intermediate conclusion, a necessary and sufficient condition for a kind of linear degeneracy of a Trivium-like NFSR is obtained from the persepective of interleaved sequences. Moreover, the smallest stage number of a Fibonacci NFSR that can generate all the output sequences of an $n$-stage Trivium-like NFSR is shown to be greater than $n-7$ and this value is no less than $371=287+\min\{93,84,111\}$ specifically for the 288-stage Galois NFSR used in Trivium. These results contradict the existence of a equivalent Fibonacci model of Trivium NFSR of small stage, which implies that Trivium algorithm possesses a fair degree of immunity against "structure attack".

In this paper, the authors propose a two-stage online debiased lasso estimation and statistical inference method for high-dimensional quantile regression (QR) models in the presence of streaming data. In the first stage, the authors modify the QR score function based on kernel smoothing and obtain the online lasso smoothed QR estimator through iterative algorithms. The estimation process only involves the current data batch and specific historical summary statistics, which perfectly accommodates to the special structure of streaming data. In the second stage, an online debiasing procedure is carried out to eliminate biases caused by the lasso penalty as well as the accumulative approximation error so that the asymptotic normality of the resulting estimator can be established. The authors conduct extensive numerical experiments to evaluate the performance of the proposed method. These experiments demonstrate the effectiveness of the proposed method and support the theoretical results. An application to the Beijing PM2.5 Dataset is also presented.

This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL), where agents communicating over a network aim to find an optimal policy that maximizes the average of all the agents' local returns. To address the challenges of high variance and bias in stochastic policy gradients for MARL, this paper proposes a distributed policy gradient method with variance reduction, combined with gradient tracking to correct the bias resulting from the difference between local and global gradients. The authors also utilize importance sampling to solve the distribution shift problem in the sampling process. The authors then show that the proposed algorithm finds an $\epsilon$-approximate stationary point, where the convergence depends on the number of iterations, the mini-batch size, the epoch size, the problem parameters, and the network topology. The authors further establish the sample and communication complexity to obtain an $\epsilon$-approximate stationary point. Finally, numerical experiments are performed to validate the effectiveness of the proposed algorithm.

Let $f(t,y,y')=\sum_{i=0}^n a_i(t,y)y'^i=0$ be an irreducible first order ordinary differential equation with polynomial coefficients. Eremenko in 1998 proved that there exists a constant $C$ such that every rational solution of $f(t,y,y')=0$ is of degree not greater than $C$. Examples show that this degree bound $C$ depends not only on the degrees of $f$ in $t,y,y'$ but also on the coefficients of $f$ viewed as the polynomial in $t,y,y'$. In this paper, the authors show that if $f$ satisfies $\deg(f,y)<\deg(f,y')$ or $$ \max_{i=0}^n \{ \deg(a_i,y)-2(n-i)\}>0, $$ then the degree bound $C$ only depends on the degrees of $f$ in $t,y,y'$, and furthermore we present an explicit expression for $C$ in terms of the degrees of $f$ in $t,y,y'$.

In this article, the authors investigate and derive adaptive strategies for the pursuit-evasion problem where both players lack knowledge of the opponent’s cost function parameters, which has rarely been investigated in the existing literature. To address this challenge, the authors consider a basic information structure that assumes that the evader will use an adaptive learning algorithm to estimate the unknown parameters and update its adaptive strategy piecewise, whereas the pursuer will adopt a strategy based on the opponent’s choices at each time instant. By employing methods of diminishing excitation and random switching, the authors establish certain excitation conditions for signals of the closed-loop game system to ensure the strong consistency of the parameter estimates. Moreover, the authors demonstrate that the adaptive game system can asymptotically reach the Nash equilibrium, which is the same equilibrium achieved in pursuit-evasion games when all game parameters are known.

Feature correspondence is a crucial aspect of various computer vision and robot vision tasks. Unlike traditional optimization-based matching techniques, researchers have recently adopted a learning-based approach for matching, but these methods face challenges in dealing with outlier features. This paper presents an outlier robust feature correspondence method that employs a pruned attentional graph neural network and a matching layer to address the outlier issue. Additionally, the authors introduce a modified cross-entropy matching loss to handle the outlier problem. As a result, the proposed method significantly enhances the performance of learning-based matching algorithms in the presence of outlier features. Benchmark experiments confirm the effectiveness of the proposed approach.

The authors consider an M/M/1 queue with two types of customers, where customers are classified into two categories according to their psychological feelings when facing uncertainty about queue information. In the unobservable queue, experienced customers could accurately calculate their expected utilities, while first-time customers are loss-averse and the psychological feelings could incur additional gain-loss utilities. By defining customers’ willingness to pay, the authors derive the equilibrium joining-balking behaviors for each type of customer and obtain the service provider’s optimal pricing decision. The authors also classify the implications of the obtained results.

In many application fields of regression analysis, prior information about how explanatory variables affect response variable of interest is often available and can be formulated as constraints on regression coefficients. In this paper, the authors consider statistical inference of partially linear spatial autoregressive model under constraint conditions. By combining series approximation method, twostage least squares method and Lagrange multiplier method, the authors obtain constrained estimators of the parameters and function in the partially linear spatial autoregressive model and investigate their asymptotic properties. Furthermore, the authors propose a testing method to check whether the parameters in the parametric component of the partially linear spatial autoregressive model satisfy linear constraint conditions, and derive asymptotic distributions of the resulting test statistic under both null and alternative hypotheses. Simulation results show that the proposed constrained estimators have better finite sample performance than the unconstrained estimators and the proposed testing method performs well in finite samples. Furthermore, a real example is provided to illustrate the application of the proposed estimation and testing methods.

This paper focuses on linear-quadratic (LQ) optimal control for a class of systems governed by first-order hyperbolic partial differential equations (PDEs). Different from most of the previous works, an approach of discretization-then-continuousization is proposed in this paper to cope with the infinite-dimensional nature of PDE systems. The contributions of this paper consist of the following aspects: 1) The differential Riccati equations and the solvability condition of the LQ optimal control problems are obtained via the discretization-then-continuousization method. 2) A numerical calculation way of the differential Riccati equations and a practical design way of the optimal controller are proposed. Meanwhile, the relationship between the optimal costate and the optimal state is established by solving a set of forward and backward partial difference equations (FBPDEs). 3) The correctness of the method used in this paper is verified by a complementary continuous method and the comparative analysis with the existing operator results is presented. It is shown that the proposed results not only contain the classic results of the standard LQ control problem of systems governed by ordinary differential equations as a special case, but also support the existing operator results and give a more convenient form of computation.

Xinjiang’s agriculture is a typical irrigated agriculture for its agriculture water consumption accounts for 96% of the total water use. As a typical resource-deficient area, the key to Xinjiang’s agricultural development is saving water. This paper takes the high-efficient water-saving irrigation technology of 41 regions along the Tarim River from 2002 to 2013 as the research object, adopts spatial stochastic frontier model to measure the space efficiency of high-efficient water-saving irrigation technology, and analyzes the effect of water-saving irrigation technology on agricultural development. Results show that the water-saving irrigation technology has a spatial effect, if neglecting it, the error of missing variables will occur, and the average loss will be 6.98 percentage points. The spatial correlation effect promotes the improvement of the efficiency of water-saving irrigation technology. The spatial heterogeneity leads to the spatial imbalance of the efficiency of water-saving irrigation technology. The promotion of agricultural water-saving irrigation technology can increase production and the efficiency of agricultural development. Due to the technical heterogeneity of different types of water-saving irrigation technology, the contribution to the development of agriculture is also different. The study finds that water-saving irrigation technology of drip irrigation in the Tarim River contributes more to agricultural development.

The tracking problem of uncertain nonstrict-feedback nonlinear systems (UNFNS) is examined to develop a novel adaptive neural control scheme to ensure fixed-time convergence. In particular, the challenge associated with the unknown nonlinear function can be overcome through neural network (NN) based estimation. Therefore, an NN-based adaptive fixed-time control scheme is established with only one parameter, using the property of the basis function vector to address the algebraic loop problem. Furthermore, the singularity problem can be solved by incorporating a smooth switching function. A rigorous theoretical analysis is performed to demonstrate that the output signal can track the reference signal within a fixed time and that the signals in the control systems are bounded. Finally, numerical simulations are performed to validate the feasibility of the proposed methodology.

In this paper, physics-informed liquid networks (PILNs) are proposed based on liquid timeconstant networks (LTC) for solving nonlinear partial differential equations (PDEs). In this approach, the network state is controlled via ordinary differential equations (ODEs). The significant advantage is that neurons controlled by ODEs are more expressive compared to simple activation functions. In addition, the PILNs use difference schemes instead of automatic differentiation to construct the residuals of PDEs, which avoid information loss in the neighborhood of sampling points. As this method draws on both the traveling wave method and physics-informed neural networks (PINNs), it has a better physical interpretation. Finally, the KdV equation and the nonlinear Schrödinger equation are solved to test the generalization ability of the PILNs. To the best of the authors’ knowledge, this is the first deep learning method that uses ODEs to simulate the numerical solutions of PDEs.

In matrix completion, additional covariates often provide valuable information for completing the unobserved entries of a high-dimensional low-rank matrix ${A}$. In this paper, the authors consider the matrix recovery problem when there are multiple structural breaks in the coefficient matrix $\beta$ under the column-space-decomposition model $A= X \beta+ B$. A cumulative sum (CUSUM) statistic is constructed based on the penalized estimation of $\beta$. Then the CUSUM is incorporated into the Wild Binary Segmentation (WBS) algorithm to consistently estimate the location of breaks. Consequently, a nearly-optimal recovery of ${A}$ is fulfilled. Theoretical findings are further corroborated via numerical experiments and a real-data application.

Wan and Zhang (2021) obtained a nontrivial lower bound for the number of zeros of complete symmetric polynomials over finite fields, and proposed a problem whether their bound can be improved. In this paper, the author improves Wan-Zhang's bound from three aspects. The proposed results are based on the estimates related to the number of certain permutations and the value sets of non-permutation polynomials associated to the complete symmetric polynomial. And the author believes that there are still possibilities to improve the bounds and hence Wan-Zhang's bound.

The estimates of the high-dimensional volatility matrix based on high-frequency data play a pivotal role in many financial applications. However, most existing studies have been built on the sub-Gaussian and cross-sectional independence assumptions of microstructure noise, which are typically violated in the financial markets. In this paper, the authors proposed a new robust volatility matrix estimator, with very mild assumptions on the cross-sectional dependence and tail behaviors of the noises, and demonstrated that it can achieve the optimal convergence rate n^-1/4. Furthermore, the proposed model offered better explanatory and predictive powers by decomposing the estimator into low-rank and sparse components, using an appropriate regularization procedure. Simulation studies demonstrated that the proposed estimator outperforms its competitors under various dependence structures of microstructure noise. Additionally, an extensive analysis of the high-frequency data for stocks in the Shenzhen Stock Exchange of China demonstrated the practical effectiveness of the estimator.

The cross-dimensional dynamical systems have received increasing research attention in recent years. This paper characterizes the structure features of the cross-dimensional vector space. Specifically, it is proved that the completion of cross-dimensional vector space is an infinite-dimensional separable Hilbert space. Hence, it means that one can isometrically and linearly embed the cross-dimensional vector space into the $\ell^{2}$, which is known as the space of square summable sequences. This result will be helpful in the modeling and analyzing the dynamics of cross-dimensional dynamical systems.

Motivated by applications in advanced cryptographic protocols, research on arithmetizationoriented symmetric primitives has been rising in the field of symmetric cryptography in recent years. In this paper, the authors focus on on the collision attacks for a family of arithmetization-oriented symmetric ciphers GMiMCHash. The authors firstly enhance the algebraically controlled differential attacks proposed by introducing more variables. Then, combining algebraic attacks and differential attacks, the authors propose algebraic-differential attacks on GMiMCHash. This attack method is shown to be effective by experiments on toy versions of GMiMCHash. The authors further introduce some tricks to reduce the complexities of algebraic-differential attacks and improve the success probability of finding collisions.

In order to build a low-risk Fund of Funds (FOF), from the perspective of correlation, the principal component factor is used to improve the traditional risk parity model. Principal component analysis is used to decompose the underlying assets and generate unrelated principal component factors, and then the authors can construct a principal component risk parity portfolio. The proposed empirical results based on China’s mutual fund market show that the performance of principal component risk parity portfolio (PCRPP) is better than that of equal weight portfolio (EWP) and traditional risk parity portfolio (RPP). That is to say, not only the PCRPP in this paper has much lower risk than EWP and RPP, but also slightly better than EWP and RPP in terms of average return. Moreover, the study of dividing the underlying assets shows that the PCRPP in this paper is not sensitive to the underlying assets. The PCRPP in this paper is better than EWP and RPP for both the better performing funds and the worse performing funds. In addition, the empirical results on dynamic portfolio adjustments show that it is not appropriate to adjust asset allocation too frequently when the expected rate of return is calculated using the arithmetic mean.

This study investigates and compares the effects of the Coronavirus disease 2019 (COVID- 19) pandemic, the Chicago mercantile exchange (CME)’s negative price suggestion on prices and trading activities in the crude oil futures market to discuss the cause of negative crude oil futures prices. Through event studies, the empirical results show that the COVID-19 pandemic no longer impacts crude oil futures prices in April, 2020 after controlled market risk, while the CME’s negative prices suggestion can explain the crude oil futures price changes around and even after April 8, 2020 to some degree. Moreover, this study uncovers anomalies in prices and trading activities by analyzing returns, trading volume, open interest, and illiquidity measures using vector autoregressive (VAR) models. The results imply that CME’s allowing negative prices strengthens the price impact on trading volume and makes illiquidity risk matter. This study’s results coincide with the following lawsuit evidence of market manipulation.

This paper focuses on optimizing an unknown cost function through extremum seeking (ES) control in the presence of a slow nonlinear dynamic sensor responsible for measuring the cost. In contrast to traditional perturbation-based ES control, which often suffers from sluggish convergence, the proposed method eliminates the time-scale separation between sensor dynamics and ES control by using the relative degree of the nonlinear sensor system. To improve the convergence rate, the authors incorporate high-frequency dither signals and a differentiator. To enhance the robustness with the existence of rapid disturbances, an off-the-shelf linear high-gain differentiator is applied. The first result demonstrates that, for any desired convergence rate, with properly tuned parameters for the proposed ES algorithm, the input of the cost function can converge to an arbitrarily small neighborhood of the optimal solution, starting from any initial condition within any given compact set. Furthermore, the second result shows the robustness of the proposed ES control in the presence of sufficiently fast, zero-mean periodic disturbances. Simulation results substantiate these theoretical findings.

Curve interpolation with B-spline is widely used in various areas. This problem is classic and recently raised in application scenario with new requirements such as path planning following the tangential vector field under certified error in CNC machining. This paper proposes an algorithm framework to solve Hausdorff distance certified cubic B-spline interpolation problem with or without tangential direction constraints. The algorithm has two stages: The first stage is to find the initial cubic B-spine fitting curve which satisfies the Hausdorff distance constraint; the second stage is to set up and solve the optimization models with certain constraints. Especially, the sufficient conditions of the global Hausdorff distance control for any error bound are discussed, which can be expressed as a series of linear and quadratic constraints. A simple numerical algorithm to compute the Hausdorff distance between a polyline and its B-spline interpolation curve is proposed to reduce our computation. Experimental results are presented to show the advantages of the proposed algorithms.