Integrated Task and Motion Planning

The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the objects, is known as task and motion planning (TAMP). TAMP problems contain elements of discrete task planning, discrete–continuous mathematical programming, and continuous motion planning and thus cannot be effectively addressed by any of these fields directly. In this article, we define a class of TAMP problems and survey algorithms for solving them, characterizing the solution methods in terms of their strategies for solving the continuous-space subproblems and their techniques for integrating the discrete and continuous components of the search. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Can the Baidu Index predict realized volatility in the Chinese stock market?

This paper incorporates the Baidu Index into various heterogeneous autoregressive type time series models and shows that the Baidu Index is a superior predictor of realized volatility in the SSE 50 Index. Furthermore, the predictability of the Baidu Index is found to rise as the forecasting horizon increases. We also find that continuous components enhance predictive power across all horizons, but that increases are only sustained in the short and medium terms, as the long-term impact on volatility is less persistent. Our findings should be expected to influence investors interested in constructing trading strategies based on realized volatility.

An imputation approach for fitting two-part mixed effects models for longitudinal semi-continuous data

Two-part mixed effects models are often used for analyzing longitudinal data with many zeros. Typically, these models are formulated with binary and continuous components separately with random effects that are correlated between the two components. Researchers have developed maximum-likelihood and Bayesian approaches for fitting these models that often require using particular software packages or very specialized software. We propose an imputation approach that will allow practitioners to separately use standard linear and generalized linear mixed models to estimate the fixed effects for two-part mixed effects models with complex random effects structures. An approximation to the conditional distribution of positive measurements given an individual’s pattern of non-zero measurements is proposed that can be easily estimated and then imputed from. We show that for a wide range of parameter values, the imputation approach results in nearly unbiased estimation and can be implemented with standard software. We illustrate the proposed imputation approach for the analysis of longitudinal clinical trial data with many zeros.

Continuous components of solar activity oscillation spectrum and forecasting of solar activity

<p>Spectrum of solar activity oscillations contains apart from the well-known 11-year activity cycle a continuous component, which includes, in particular, quasy-biennual oscillations as well as long-term oscillations including so-called Gleisberg cycle.  We suggest to consider the mid-term solar variability in terms of statistical dynamic of fully turbulent systems, where solid arguments are required to accept an isolated dominant frequency in a continuous (smooth) spectrum. What about the timescales longer than the Schwabe cycle, we consider them as a presence of long-term memory in solar dynamo and discuss statistical test for veryication of this interpretation. Sequences for statistical long-term forecast of solar activity are discussed.</p>

Analysis of responder-based endpoints: improving power through utilising continuous components

Background: Clinical trials and other studies commonly assess effectiveness of an intervention through use of responder-based endpoints. These classify patients based on whether they meet a number of criteria which often involve continuous variables categorised as being above or below a threshold. The proportion of patients who are responders is estimated and, where relevant, compared between groups. An alternative method called the augmented binary method keeps the definition of the endpoint the same but utilises information contained within the continuous component to increase the power considerably (equivalent to increasing the sample size by >30%). In this article we summarise the method and investigate the variety of clinical conditions that use endpoints to which it could be applied.Methods : We reviewed a database of Core Outcome Sets (COS) that covered physiological and mortality trial endpoints recommended for collection in clinical trials of different disorders. We identified responder-based endpoints where the augmented binary method would be useful for increasing power.Results: Out of the 287 COS reviewed, we identified 67 new clinical areas where endpoints were used that would be more efficiently analysed using the augmented binary method. Clinical areas that had particularly high numbers were rheumatology (11 clinical disorders identified), non-solid tumour oncology (10 identified), neurology (9 identified), and cardiovascular (8 identified)Conclusions: The augmented binary method can potentially provide large benefits in a vast array of clinical areas. Further methodological development is needed to account for some types of endpoint.

Analysis of responder-based endpoints: improving power through utilising continuous components

Background Clinical trials and other studies commonly assess effectiveness of an intervention through use of responder-based endpoints. These classify patients based on whether they meet a number of criteria which often involve continuous variables categorised as being above or below a threshold. The proportion of patients who are responders is estimated and, where relevant, compared between groups. An alternative method called the augmented binary method keeps the definition of the endpoint the same but utilises information contained within the continuous component to increase the power considerably (equivalent to increasing the sample size by >30%). In this article we summarise the method and investigate the variety of clinical conditions that use endpoints to which it could be applied. Methods We reviewed a database of Core Outcome Sets (COS) that covered physiological and mortality trial endpoints recommended for collection in clinical trials of different disorders. We identified responder-based endpoints where the augmented binary method would be useful for increasing power. Results Out of the 287 COS reviewed, we identified 67 new clinical areas where endpoints were used that would be more efficiently analysed using the augmented binary method. Clinical areas that had particularly high numbers were rheumatology (11 clinical disorders identified), non-solid tumour oncology (10 identified), neurology (9 identified), and cardiovascular (8 identified) Conclusions The augmented binary method can potentially provide large benefits in a vast array of clinical areas. Further methodological development is needed to account for some types of endpoint.

Analysis of responder-based endpoints: improving power through utilising continuous components

Background Clinical trials and other studies commonly assess effectiveness of an intervention through use of responder-based endpoints. These classify patients based on whether they meet a number of criteria which often involve continuous variables categorised as being above or below a threshold. The proportion of patients who are responders is estimated and, where relevant, compared between groups. An alternative method called the augmented binary method keeps the definition of the endpoint the same but utilises information contained within the continuous component to increase the power considerably (equivalent to increasing the sample size by >30%). In this article we summarise the method and investigate the variety of clinical conditions that use endpoints to which it could be applied. Methods We reviewed a database of physiological and mortality trial endpoints recommended for collection in clinical trials of different disorders. We identified responder-based endpoints where the augmented binary method would be useful for increasing power. Results We identified 68 new clinical areas where endpoints were used that would be more efficiently analysed using the augmented binary method. Conclusions The augmented binary method can potentially provide large benefits in a vast array of clinical areas. Further methodological development is needed to account for some types of endpoint.

Predicting B.Tech student admission decisions by data mining algorithms

In learning calculations affiliation govern mining is the most intense capacity in information mining. The age of principles includes two stages in which the primary stage finds the arrangements of continuous components and the second stage creates the run the show. Numerous calculations are determined to discover sets of incessant components from successive examples. In our exploration work an imperative perception is made in the information digging calculations for the informational index of the designing understudies. By discovering relationship between qualities, we can discover the potential outcomes for affirmation and anticipate understudy confirmation choices. To discover solid and substantial affiliation rules, distinctive measures are thought about lift, support, cost, confidence and conviction. The gauge is come to with the utilization of the imperative as needs be amid the age of the affiliation rules. As we move towards the objective, to give an examination the affiliation runs, the understudies who pick the branch have utilized the calculations specified to demonstrate the guidelines and the aftereffects of the affiliation in light of the past database of the records of confirmation.

A Theoretical Approach of the Time Cycle Optimisation Based Control of a Mechatronics Line Served by Mobile Robot

In this paper a theoretical approach of the generalized hybrid model and time cycle optimization of a mechatronics line served by mobile robot with manipulator is presented. Thus, the considered global model is a hybrid one as it is based on the dynamics of discrete and continuous components which interact on a basic level. The generalized Synchronized Hybrid Petri Nets (SHPN) model describes this hybrid system with N degree of repetitive tasks. The advantage of this generalized approach consists in the optimization of the process parameters. Consequently, a time cycle optimization which is based on the minimization of temporal duration events is proposed. The proposal is exemplified by the assembly/disassembly process of a mechatronics line (A/DML), served by a wheeled mobile robot (WMR) equipped with a robotic manipulator (RM).