Switching Framework Research Articles

Trust-Based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence. In this article, distributed symbolic motion planning for multi-robot systems is developed to address scalability. More specifically, compositional reasoning approaches are developed to decompose the global planning problem, and atomic propositions for observation, communication, and control are proposed to address inter-robot collision avoidance. To improve solution quality and adaptability, a hypothetical dynamic, quantitative, and probabilistic human-to-robot trust model is developed to aid this decomposition. Furthermore, a trust-based real-time switching framework is proposed to switch between autonomous and manual motion planning for tradeoffs between task safety and efficiency. Deadlock- and livelock-free algorithms are designed to guarantee reachability of goals with a human-in-the-loop. A set of nontrivial multi-robot simulations with direct human inputs and trust evaluation is provided, demonstrating the successful implementation of the trust-based multi-robot symbolic motion planning methods.

On the effect of herding behavior on dependence structure between stock markets: Evidence from GCC countries

This paper aims to test whether herding behavior affects the dependence structure between stock markets in the Gulf Cooperation Council (GCC) countries, by taking account of different market conditions. Based on the cross sectional absolute deviation (CSAD) approach, herding behavior is tested in both static and regime switching framework. The dependence between GCC stock markets is assessed by the DCC-GARCH model. By using weekly data covering the period 2003–2017, empirical results show the presence of herding behavior in all GCC markets except of Bahraini and Kuwaiti markets. Furthermore, the effect of this behavior on the dependence structure is statistically significant and positive. In addition, based on the switching regime framework, results suggest the evidence of a negative effect in low herding regimes and a positive effect in high herding periods. Our findings are of interest and allow for formulating a reliable portfolios design framework for investors and risk managers.

Robust stability of switched positive linear systems with interval uncertainties via multiple time-varying linear copositive Lyapunov functions

This paper studies the robust stability analysis of a class of switched positive linear systems with uncertainties in the framework of dwell time switching. The uncertainties refer to interval uncertainties. Two classes of dwell time switching signals are considered in this paper: (i) the first class is confined by a certain pair of upper and lower bounds; (ii) the other is the minimum dwell time. First, a class of multiple time-varying linear copositive Lyapunov functions is constructed to analyze the robust stability of the studied switched system. Then, under the pre-given dwell time switching, the sufficient conditions are obtained by restricting the decay of the Lyapunov functions of the active subsystem and forcing “energy” of the overall switched system to decrease at switching instants by the proposed Lyapunov functions. All present conditions are solvable in terms of linear programming. An example is considered in order to emphasize the effectiveness of the proposed approach.

Public policy and financial stability: The impact of PCA and TARP on U.S. bank non‐performing loans

AbstractThis study explores the implications of prompt corrective action and the Troubled Asset Relief Program on the behaviour of non‐performing loans (NPLs) and real estate non‐performing loans (RELs) in the United States over 1984–2015 using a Markov switching framework. We find that NPLs and RELs exhibit pronounced episodic behaviour switching between non‐stationary and stationary regimes. Prompt corrective action and the Troubled Asset Relief Program have a significant impact on banking sector stability by influencing the probability of switching from non‐stationary regimes to stationary regimes and by reducing the level of NPLs and RELs. These results are robust to various model specifications and have important implications for bank regulation as well as for the formulation of macro stress testing.

Time consistent multi-period robust risk measures and portfolio selection models with regime-switching

To better describe the time-varying property of the dynamic investment risk and the ambiguity of the random return process, we propose two multi-period robust risk measures under the regime switching framework. Using regime-dependent dynamic uncertainty sets, we show that the multi-period robust portfolio selection problems under the two multi-period robust risk measures with regime switching can be transformed into second order cone programs, which can thus be efficiently solved in polynomial time. To show the generality of the dynamic uncertainty sets under the regime switching framework, we further consider multi-period robust risk measures under time-varying uncertainty sets with moments uncertainty and discuss the tractability of the corresponding multi-period robust portfolio selection problems. A series of empirical results demonstrate that the robust portfolio selection models with regime switching can flexibly help the investor make superior and robust investment strategies according to the switching of the market environment.

Do oil shocks predict economic policy uncertainty?

Oil price fluctuations have influential role in global economic policies for developed as well as emerging countries. I investigate the role of international oil prices disintegrated into structural (i) oil supply shock, (ii) aggregate demand shock and (iii) oil market specific demand shocks, based on the work of Kilian (2009) using structural VAR framework on economic policies uncertainty of sampled markets. Economic policy uncertainty, due to its non-linear behavior is modeled in a regime switching framework with disintegrated structural oil shocks. Our results highlight that Indian, Spain and Japanese economic policy uncertainty responds to the global oil price shocks, however aggregate demand shocks fail to induce any change. Oil specific demand shocks are significant only for China and India in high volatility state.

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

Leverage effect, economic policy uncertainty and realized volatility with regime switching

In this study, we first investigate the impacts of leverage effect and economic policy uncertainty (EPU) on future volatility in the framework of regime switching. Out-of-sample results show that the HAR-RV including the leverage effect and economic policy uncertainty with regimes can achieve higher forecast accuracy than RV-type and GARCH-class models. Our robustness results further imply that these factors in the framework of regime switching can substantially improve the HAR-RV’s forecast performance.

Improving Markov switching models using realized variance

SummaryThis paper proposes a class of models that jointly model returns and ex post variance measures under a Markov switching framework. Both univariate and multivariate return versions of the model are introduced. Estimation can be conducted under a fixed dimension state space or an infinite one. The proposed models can be seen as nonlinear common factor models subject to Markov switching and are able to exploit the information content in both returns and ex post volatility measures. Applications to equity returns compare the proposed models to existing alternatives. The empirical results show that the joint models improve density forecasts for returns and point predictions of return variance. Using the information in ex post volatility measures can increase the precision of parameter estimates, sharpen the inference on the latent state variable, and improve portfolio decisions.

Price linkages between Australian housing and stock markets

PurposeThe purpose of this study is to examine the linkages between Australian house prices and stock prices under the Toda and Yamamoto test framework. Specifically, it investigated whether there is a capital switching effect between house prices and stock prices.Design/methodology/approachThis study examined the linkages between house prices and stock prices under the Toda and Yamamoto test framework. To accommodate the impact of the global financial crisis (GFC), a sub-period analysis was undertaken. To assess the impact of investor structure, the tests were also performed for small cap stocks and large cap stocks individually.FindingsThe empirical results reveal a negative lead–lag relationship between house prices and stock prices in Australia, suggesting the existence of capital switching activities between housing and stocks. The impact of the GFC on the lead–lag relationship between house prices and stock prices is also documented. Before the crisis, a causality transmission was running from house prices to stock prices, whilst stock prices appeared to lead house prices after the crisis. The capital switching activities between housing and stocks are more evident for small cap stocks.Originality/valueThis study is the first to examine the linkages between house prices and stock prices under the Toda and Yamamoto test framework. This is the first study to explore the impacts of the GFC on the lead–lag relationship between the two asset prices under the capital switching framework. This study is also the first to provide empirical evidence regarding the existence of capital switching activities between housing and stocks. In addition, the impact of investor structure on the interrelationship between the two asset prices is examined for the first time under the capital switching framework.

A note on the macroeconomic consequences of ethnic/racial tension

While many studies stressed the importance of ethnic fractionalization on long-term economic growth, neither ethnic fractionalization always leads to ethnic conflict nor the intensity of conflict is constant over time. To address this potential bias, we construct an ethnic/racial tension index by using the number of US news articles that contain certain keywords. Utilizing this index we test the predictions of a simple theoretical model in a Markov Switching framework which allows to identify the impact of ethnic/racial tension in different states of the economy. Consistent with our theoretical predictions, results show that the magnitude of the impact of ethnic/racial tension is larger during low-growth periods.

Dwell-time-based stabilization of switched linear singular systems with all unstable-mode subsystems

The global stabilization design of a class of switched linear singular systems via a novel dwell-time switching is investigated and solved in this work. The distinguishing feature of the proposed method is that stability of all subsystems of the switched systems is not necessarily required. A time-varying coordinate transformation is introduced first in order to convert the problem into an equivalent one of reduced-order switched conventional linear system with state jumps. Then, by constructing certain new multiple time-varying Lyapunov functions, computable sufficient conditions for the global stabilization task are proposed within the framework of dwell-time switching. Given the assumed instability of individual subsystems, the stabilization of the switched system is achieved under the condition of confining the dwell time by a certain pair of upper and lower bounds, which restrict the growth of Lyapunov function for the actively operating subsystem, thus decrease the energy of the Lyapunov function of the overall switched system at switching times. In addition, the multiple time-varying Lyapunov functions method is also used to analyze the stability analysis of a class of switched linear singular systems with stable subsystems. Two illustrative examples are presented to demonstrate the effectiveness of the proposed method.

How to Predict Financial Stress? An Assessment of Markov Switching Models

This paper predicts phases of the financial cycle by using a continuous financial stress measure in a Markov switching framework. The debt service ratio and property market variables signal a transition to a high financial stress regime, while economic sentiment indicators provide signals for a transition to a tranquil state. Whereas the in-sample analysis suggests that these indicators can provide an early warning signal up to several quarters prior to the respective regime change, the out-of-sample findings indicate that most of this performance is owing to the data gathered during the global financial crisis. Comparing the prediction performance with a standard binary early warning model reveals that the Markov switching model is outperforming the vast majority of model specifications for a horizon up to three quarters prior to the onset of financial stress.

Adaptive traffic parameter prediction: Effect of number of states and transferability of models

Traffic parameters can show shifts due to factors such as weather, accidents, and driving characteristics. This study develops a model for predicting traffic speeds under these abrupt changes within regime switching framework. The proposed approach utilizes Hidden Markov, Expectation Maximization, Recursive Least Squares Filtering, and ARIMA methods for an adaptive forecasting method. The method is compared with naive and mean updating linear and nonlinear time series models. The model is fitted and tested extensively using 1993 I-880 loop data from California and January 2014 INRIX data from Virginia. Analysis for number of states, impact of number of states on forecasting, prediction scope, and transferability of the model to different locations are investigated. A 5-state model is found to be providing best results. Developed model is tested for 1-step to 45-step forecasts. The accuracy of predictions are improved until 15-step over nonadaptive and mean adaptive models. Except 1-step predictions, the model is found to be transferable to different locations. Even if the developed model is not retrained on different datasets, it is able to provide better or close results with nonadaptive and adaptive models that are retrained on the corresponding dataset.

A laminate‐based framework for switching and microstructure evolution in polycrystalline ferroelectrics

AbstractThis contribution deals with the numerical modelling of polycrystalline ferroelectric materials considering a sequential laminate‐based approach established for tetragonal single‐crystal ferroelectrics. The particular model [1] is considered and extended to predict the material behaviour of poly‐crystal tetragonal ferroelectric ceramics. The derived laminate‐based model is implemented in a finite element environment to simulate the time‐dependent domain evolution and switching response of a bulk polycrystalline ferroelectric ceramic. © 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Modeling and Design Space Exploration for Bit-Cells Based on Voltage-Assisted Switching of Magnetic Tunnel Junctions

The effect of voltage on the anisotropy of a magnetic tunnel junction (MTJ) is of substantial interest for low-power nonvolatile memory applications. In this paper, we develop a device-to-bit-cell level simulation framework for voltage-assisted switching of the MTJs, which can satisfactorily reproduce the published experimental data. Our simulation framework is based on a coupled Landau–Lifshitz–Gilbert–Slonczewski equation and nonequilibrium greens function formalism. Using this simulation framework, we investigate the effect of scaling the oxide thickness of an MTJ to enhance the electric field effect. Although it seems attractive for an isolated device, yet, in a bit-cell configuration, reducing the oxide thickness leads to an increase in the supply voltage. In addition, we demonstrate that the unipolar characteristic of the voltage-assisted switching leads to write failures depending on the initial state stored in the MTJ. We, therefore, suggest a read before write scheme exhibiting approximately $2\times $ improvement in the write energy consumption at a bit-cell level compared with a standard STT-MRAM for iso-oxide thickness. However, the latency overhead associated with voltage-assisted MTJs results in a $1.55\times $ degradation of the write speed. Moreover, our layout analysis indicates that the bit-cell area reduction is constrained by the metal pitch, in spite of smaller access transistors.

Behavioral heterogeneity in the Australian housing market

ABSTRACTWe propose a heterogeneous agent model (HAM) of four groups of investors with Markov chain regime-dependent beliefs for the housing market. Within the Markov switching framework, we take into account how heterogeneous investors shift their trading behaviour in response to changes in housing market conditions. The model is estimated and compared with the benchmark rational expectation models using the Australian housing market data from 1982Q1 to 2013Q2. We find evidence of within- and between-group heterogeneity in the Australian housing market. We show that HAM with Markov switching beliefs provides a better in-sample estimation efficiency and outperforms the conventional rational expectation models in terms of out-of-sample prediction.

Control strategy switching for humanoid robots based on maximal output admissible set

Human-like bipedal walking is a goal of humanoid robotics. It is especially important to provide a robust falling prevention capability by imitating the human ability to switch between control strategies in response to disturbances, e.g., standing balancing and stepping motion. However, the motion control of a humanoid robot is challenging because the contact forces are constrained. This paper proposes a novel framework for control strategy switching based on the maximal output admissible (MOA) set, which is a set of initial states that satisfy the constraints. This makes it possible to determine whether the robot might fall down due to a constraint violation. The MOA set is extended to a trajectory tracking controller with a time-variant reference and constraint. In this extension, the motion of the vertical center of gravity is also considered, which has often been neglected in previous studies. Utilizing the MOA set, an example is shown of the falling prevention control by switching the standing balance control and trajectory tracking control to a stepping and hopping motion. Moreover, a method is presented for applying the MOA set framework to a position-controlled humanoid robot. The validity of the MOA set framework is verified based on simulations and experimental results.

Simultaneous control and fault detection for discrete‐time switched delay systems under the improved persistent dwell time switching

This study is devoted to simultaneous control and fault detection for discrete-time delay switched systems. In the persistent dwell time (PDT) switching framework, a more general switching law is designed, where the resulting PDT is dependent on the state delay. For delay switched systems, the result shows the relation between PDT and state delay. More importantly, it also includes the previous delay-independent results in special case. Next, under the switching law, the time-varying output-feedback-based controller/residual generators are provided such that the closed-loop system is with the non-weighted H _ index for fault and l 2-gain for disturbance. Compared with the existing method under the average dwell time switching, the method proposed here leads to better fault sensitivity. Finally, a simulation example is exploited to demonstrate the effectiveness of the proposed results.

Optimal hedging in carbon emission markets using Markov regime switching models

This paper proposes a Markov regime switching framework for modeling carbon emission (CO2) allowances that combines a regime switching behavior and disequilibrium adjustments in the mean process, along with a state-dependent dynamic volatility process. We find that all regime switching based hedging strategies significantly outperform single regime hedging strategies (both in-sample and out-of-sample), with the newly proposed framework providing the greatest variance reduction and the best hedging performance. Our results indicate that risk managers using state-dependent hedge ratios to manage portfolio risks in carbon emission markets will achieve superior hedging returns.