Independent directors’ board networks and controlling shareholders’ tunneling behavior

AbstractAs one of the channels by which board directors build important relationships, board networks can affect the governance role of independent directors. Defining director board networks as their connections based on direct ties they establish when serving on at least one common board, this paper explores the role of the network centrality of independent directors in restraining tunneling behavior by controlling shareholders in the Chinese capital market. Our empirical evidence shows that tunneling behavior by controlling shareholders is negatively related to the network centrality of independent directors and that this relationship is stronger when non-operating fund occupation is used as the measure of tunneling. The results of our study show that board networks can help independent directors to restrain tunneling behavior by large shareholders, which plays a positive role in corporate governance

SeDR: Segment Representation Learning for Long Documents Dense Retrieval

Recently, Dense Retrieval (DR) has become a promising solution to document retrieval, where document representations are used to perform effective and efficient semantic search. However, DR remains challenging on long documents, due to the quadratic complexity of its Transformer-based encoder and the finite capacity of a low-dimension embedding. Current DR models use suboptimal strategies such as truncating or splitting-and-pooling to long documents leading to poor utilization of whole document information. In this work, to tackle this problem, we propose Segment representation learning for long documents Dense Retrieval (SeDR). In SeDR, Segment-Interaction Transformer is proposed to encode long documents into document-aware and segment-sensitive representations, while it holds the complexity of splitting-and-pooling and outperforms other segment-interaction patterns on DR. Since GPU memory requirements for long document encoding causes insufficient negatives for DR training, Late-Cache Negative is further proposed to provide additional cache negatives for optimizing representation learning. Experiments on MS MARCO and TREC-DL datasets show that SeDR achieves superior performance among DR models, and confirm the effectiveness of SeDR on long document retrieval

The Fundamental Nature of Age of Incorrect Information

Age of Incorrect Information (AoII) is a newly introduced metric that captures not only the freshness of information but also the information content of the trans- mitted packets and the knowledge at the monitor. It overcomes the shortcomings of Age of Information (AoI) in many applications that involve the problem of remotely estimating an event in real-time. However, the fundamental nature of AoII has been elusive so far. This thesis considers a system in which a transmitter sends updates about a Markovian source to a remote monitor through an unreliable channel. By leveraging the notion of Markov Decision Process (MDP), it is shown that a simple ”always update” policy minimizes the AoII. The performances of ”always update” policy as well as a more general transmission policy - ”threshold update” policy are analyzed in this thesis. Lastly, numerical results that highlight the effects of the parameters on the performances of these two transmission policies are provided

Algorithms and Applications for Nonlinear Model Predictive Control with Long Prediction Horizon

Fast implementations of NMPC are important when addressing real-time control of systems exhibiting features like fast dynamics, large dimension, and long prediction horizon, as in such situations the computational burden of the NMPC may limit the achievable control bandwidth. For that purpose, this thesis addresses both algorithms and applications. First, fast NMPC algorithms for controlling continuous-time dynamic systems using a long prediction horizon have been developed. A bridge between linear and nonlinear MPC is built using partial linearizations or sensitivity update. In order to update the sensitivities only when necessary, a Curvature-like measure of nonlinearity (CMoN) for dynamic systems has been introduced and applied to existing NMPC algorithms. Based on CMoN, intuitive and advanced updating logic have been developed for different numerical and control performance. Thus, the CMoN, together with the updating logic, formulates a partial sensitivity updating scheme for fast NMPC, named CMoN-RTI. Simulation examples are used to demonstrate the effectiveness and efficiency of CMoN-RTI. In addition, a rigorous analysis on the optimality and local convergence of CMoN-RTI is given and illustrated using numerical examples. Partial condensing algorithms have been developed when using the proposed partial sensitivity update scheme. The computational complexity has been reduced since part of the condensing information are exploited from previous sampling instants. A sensitivity updating logic together with partial condensing is proposed with a complexity linear in prediction length, leading to a speed up by a factor of ten. Partial matrix factorization algorithms are also proposed to exploit partial sensitivity update. By applying splitting methods to multi-stage problems, only part of the resulting KKT system need to be updated, which is computationally dominant in on-line optimization. Significant improvement has been proved by giving floating point operations (flops). Second, efficient implementations of NMPC have been achieved by developing a Matlab based package named MATMPC. MATMPC has two working modes: the one completely relies on Matlab and the other employs the MATLAB C language API. The advantages of MATMPC are that algorithms are easy to develop and debug thanks to Matlab, and libraries and toolboxes from Matlab can be directly used. When working in the second mode, the computational efficiency of MATMPC is comparable with those software using optimized code generation. Real-time implementations are achieved for a nine degree of freedom dynamic driving simulator and for multi-sensory motion cueing with active seat

Consensus seeking in multi-agent systems with an active leader and communication delays

summary:In this paper, we consider a multi-agent consensus problem with an active leader and variable interconnection topology. The dynamics of the active leader is given in a general form of linear system. The switching interconnection topology with communication delay among the agents is taken into consideration. A neighbor-based estimator is designed for each agent to obtain the unmeasurable state variables of the dynamic leader, and then a distributed feedback control law is developed to achieve consensus. The feedback parameters are obtained by solving a Riccati equation. By constructing a common Lyapunov function, some sufficient conditions are established to guarantee that each agent can track the active leader by assumption that interconnection topology is undirected and connected. We also point out that some results can be generalized to a class of directed interaction topologies. Moreover, the input-to-state stability (ISS) is obtained for multi-agent system with variable interconnection topology and communication delays in a disturbed environment

An empirical study on predicting defect numbers

Abstract-Defect prediction is an important activity to make software testing processes more targeted and efficient. Many methods have been proposed to predict the defect-proneness of software components using supervised classification techniques in within-and cross-project scenarios. However, very few prior studies address the above issue from the perspective of predictive analytics. How to make an appropriate decision among different prediction approaches in a given scenario remains unclear. In this paper, we empirically investigate the feasibility of defect numbers prediction with typical regression models in different scenarios. The experiments on six open-source software projects in PROMISE repository show that the prediction model built with Decision Tree Regression seems to be the best estimator in both of the scenarios, and that for all the prediction models, the results yielded in the cross-project scenario can be comparable to (or sometimes better than) those in the within-project scenario when choosing suitable training data. Therefore, the findings provide a useful insight into defect numbers prediction for those new and inactive projects

Numerical simulation on structural safety and dynamic response of coal mine rescue ball with gas explosion load using Arbitrary Lagrangian-Eulerian (ALE) algorithm

Coal mine rescue devices, which can supply miners underground with fundamental shelters after gas explosion, are essential for safety production of coal mines. In this paper, a novel and composite structure-rescue antiknock ball for coal mine rescue is designed. Further, the structural safety and dynamic response under gas explosion of the antiknock ball is investigated by ALE algorithm. To achieve this goal, the ALE finite element method is described in dynamic form, and governing equations and the finite element expressions of the ALE algorithm are derived. 3 balls with different structures are designed and dynamic response analysis has been conducted in a semi-closed tunnel with explosive load of pre-mixed gas/air mixture by using ALE algorithm based on explicit nonlinear dynamic program LS-DYNA. Displacement field, stress field and energy transmission laws are analyzed and compared via theoretical calculations. Results show that the cabin door, emergency door and spherical shell are important components of the rescue ball. The 3# composite ball is the optimization structure that can delay the shock effect of the gas explosion load on a coal mine rescue system; the simulation results can provide reference data for coal mine rescue system design