text:
Alexander Wilhelm von Brill (1842-1935) va ser un matemàtic alemany especialitzat en l'estudi de les corbes algebraiques.

Vida i Obra
La mare de von Brill era germana del matemàtic, físic i filòsof Christian Wiener; i von Brill va estudiar a la universitat de Karlsruhe on eren professors el seu oncle i Alfred Clebsch. El 1863, en traslladar-se Clebsch a la universitat de Giessen, també ho va fer von Brill, que va obtenir el seu doctorat en aquesta universitat el 1864, sota la direcció de Clebsch.

El 1867 va aconseguir l'habilitació docent a la universitat de Giessen en la que va ser professor fins al 1869. Del 1869 al 1875 ho va ser al Politècnic de Darmstadt i del 1875 al 1884 al Politècnic de Munic on va coincidir amb Felix Klein amb qui establiria una relació molt productiva. Des de 1884 fins que es va retirar el 1918 va ser professor de la universitat de Tubinga.

El seu treball es va centrar fonamentalment en la teoria de los funcions algebraiques utilitzant mètodes geomètrics. El estudi sistemàtic de les propietats d'aquestes funcions que son invariants sota transformacions biracionals està en la seva obra principal, Ueber die algebraischen Functionen und ihre Anwendung in der Geometrie (Sobre les funcions algebraiques i la seva aplicació en geometria), escrita conjuntament amb Max Noether i publicada als Mathematische Annalen de 1874.

A partir dels seus anys a Munic i amb col·laboració amb Felix Klein, va dedicar-se a desenvolupar models tridimensionals de les superfícies generades per les funcions estudiades que van ser fabricats per Martin Shilling a partir de 1899 i es venien a totes les universitats alemanyes i europees com a eines d'estudi.

Referències

Bibliografia

Enllaços externs

Matemàtics del segle XIX
Matemàtics del segle XX
Persones de Darmstadt
Alumnes de la Universitat de Giessen
Morts a Tübingen
Matemàtics de Hessen


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text:
Mary Lou Harkness (August 19, 1925 – May 13, 2014) was a librarian and a university library director, the first woman to hold that title at any Florida university. She was the fourth employee hired by the University of South Florida.

Early life
She was born Mary Lou Barker in Denby, South Dakota, where her father was proprietor of a general store on the Oglala Sioux Indian Reservation. Mary and her brother would either walk or ride horseback to a two-room schoolhouse during her grade school years, but by her senior year she and her family had moved to Gordon, Nebraska to live with family friends.  She graduated high school as valedictorian. Following that, she earned her Bachelor of Science degree from Nebraska Wesleyan University. Next, she attended the University of Michigan for two semesters of post-graduate study. Finally, she earned the master's degree in library science from Columbia University.

Career
Her first employment in the field of librarianship was at the Georgia Institute of Technology, before her graduation from Columbia University. While at Columbia, she was recruited to work at Florida's newest state university, the University of South Florida. She was hired in 1958 as a catalog librarian, the fourth employee of the university. Harkness and other library employees worked at a house in Tampa's Hyde Park neighborhood, a house on campus, and the university student union ballroom before the library building opened in 1961.

In 1968, Harkness became the library's director. She was the first woman to hold such a position in the Florida university system, and at the time, one of a few to hold such a position nationwide.

Legacy
Her career at USF saw her help create a library collection that started from zero, and grew to approximately 800,000 by the time she retired as library director in 1988. During this time she helped to implement automation technology, and she fought to ensure that it received its fair share of funding, from Florida legislators who, she said, tended to favor older universities in the state.

In 1962 Harkness traveled to Nigeria to assist with the creation of national system of libraries. She felt strongly about women's rights and women in politics. She was member of the Athena Society, the USF Women's Club, and she also worked on the campaigns of former state Sen. Betty Castor (who was also USF's first female president), and former Tampa Mayor Pam Iorio.

References

1925 births
2014 deaths
American librarians
American women librarians
University of South Florida people
Nebraska Wesleyan University alumni
University of Michigan alumni
Columbia University School of Library Service alumni
People from Gordon, Nebraska
21st-century American women

redpajama_set_name: RedPajamaWikipedia


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Now that we've completed the first month of the year, we can take a look at the books I read during January. It's a doozy, even by my standards.
Disappointment of the Month: While I mostly read science fiction and fantasy, I'm following along with the short list for the Tournament of Books and will try to read as much of that as I can before the tournament begins in March. I wouldn't say that A Spool of Blue Thread is bad, and I will still read at least one more Anne Tyler novel (the Pulitzer Prize winning Breathing Lessons), but I was disinterested. So, why continue if I don't care?
Discovery of the Month: Planetfall. This could well have been The Fifth Season, because I have no idea how I've gotten by in life without having read Nora Jemisin's novels. But - The Fifth Season was also the best book of the month by a solid margin and I don't like to double up here. Emma Newman's Planetfall is likewise one of the top novels of the month and of 2015 and I wish I had read it last year.
Worth Noting: I'd have to do some digging, but I feel that 17 books in January my strongest month of reading in a long time. I'm not quite sure how that happened.
Gender Breakdown: Having finished 17 books this month, I'm happy to report that 11 of them were written by women. My year in reading opens with a 64.70% percentage. While I do not a have specific goal this year to read more books written by women than those written by men, I would like to at least keep the breakdown near a 50/50 split.


There is nothing for me to "correct" in your points. We are all here to learn/exchange views and we will keep it that way.
>disappearance/death also in vyavahAra only...can we close this issue >by saying this prabhuji??
>bhaktha-s in other temples have involved in high handed celebrations.
No comments here. But I thought you meant "we advaitins" by "us".
Definitely not. But it certainly shows that they dont affect him. I have been quite clear that facts are not to be denied. What is required is a correct interpretation of them. Dont you agree?
>But in vyavahAra we are celebrating birth days & death anniversaries for >the remembrance of our beloved ones.  Like that, we can celebrate >these auspicious events in the case of divine incarnations also by >calling it janana & mahA prasthAna.Here birth & death hardly matters, >point is we are creating more & more special opportunities to do >bhagavannAma saNkIrtana & upAsana. Anything wrong in this attitude >prabhuji??
Sorry to disagree. He who knows that Krishna is not affected by the time and place of his birth would not bother about it. May be, it would only amuse him as to why Krishna has chosen a prison of all places.
Just some quetions for you to think about. No need to answer. Do you really think "that method of leaving the body" was the case with all avatAra-s? Was his leaving the body a matter of destiny or choice?


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text:
\section{INTRODUCTION}

An accurate and real-time estimation of the vehicle sideslip angle is essential to strengthen the performance of active vehicle control systems. The state-of-the-art estimation of sideslip angle mostly relies on model-based approaches using an Unscented Kalman Filter (UKF). Despite its efficiency and robustness, the process and observation noise parameters must be accurately tuned to achieve good performance \cite{chen2018weak, abbeel2005discriminative}. The noise parameters selection is particularly relevant because they need to capture the following aspects:
\begin{itemize}
    \item Process and observation model mismatch with respect to the real vehicle dynamics.
    \item Discretisation error because the time-step in the Kalman filter influences the noise parameters to achieve the best state estimation \cite{chen2021time}.
    \item Different working conditions of the sensors installed in the vehicle.
\end{itemize}
If the tuning is obtained through numerical optimisation, the cost function is highly non-convex, non-smooth and nonlinear, calling for robust optimisation methods \cite{chen2018weak}.

Several optimisation algorithms for UKF tuning \cite{acosta2019optimized} have been studied: GA, Sequential Quadratic Programming, Nelder-Mead, Artificial Bee Colony, Fruit Fly Optimisation (FFO) and Differential Evolution. The comparison demonstrates that each algorithm leads to different optimum locations, illustrating the importance of the optimization method. However, a comparison regarding the amount of data and the optimisation time has been omitted. Furthermore, only stochastic optimisation algorithms have been compared that can be inefficient if the UKF takes a long simulation time. For vehicle sideslip angle estimation, large-scale training sets representing vehicle behaviour \cite{mazzilli2021benefit} make the cost function evaluation very time-consuming. In different engineering applications, several solutions have been proposed, e.g. Reinforcement Learning (RL) \cite{tang2021reinforcement} or Bayesian Optimisation (BO) \cite{chen2018weak}. However, the majority of these are tested only on simulation data or toy examples. A more detailed analysis of previous works is in Section II. \newline

\begin{figure}[t]
   \centering
   \setlength{\fboxrule}{0pt}
   \framebox{\parbox{3in}{\includegraphics[scale=0.35]{Pictures/Drawing1.jpg}}}
    \caption{Framework utilizing a Two-Stage Bayesian Optimisation (TSBO) to tune the process noise parameters of a UKF.}
    \label{Framework}
\end{figure}

This paper proposes a new UKF tuning methodology using Two-Stage Bayesian Optimisation (TSBO) \cite{torun2018global}, based on a t-Student Process (tSP) for vehicle sideslip angle estimation. The proposed methodology to tune the process noise parameters reduces the optimisation time and improves the optimum localisation. Both process and observation noise are assumed Gaussian, zero mean and uncorrelated, as for the majority of the model-based vehicle state estimation filters \cite{heidfeld2020ukf}. The methodology framework is represented in Figure \ref{Framework}, a more detailed explanation will be presented in Section IV. The observation noise parameters are tuned by performing a statistical analysis of the vehicle sensor measurements \cite{mazzilli2021benefit}. The process noise parameters' tuning is based on optimising a cost function defined as the sum of the Normalized Root Mean Squared Errors (NRMSE) of the vehicle states and measurements for 8 manoeuvres. The 8 manoeuvres which compose the training set and the 23 forming the test set are selected from a real-world experimental dataset composed of 216 manoeuvres recorded at the Automotive Testing Papenburg GmbH. The manoeuvres are chosen to cover a wide range of vehicle motions. The cost function is optimised using the modified TSBO
\cite{torun2018global}. It consists of a fast exploration and a pure exploitation stage to find the optimum, reducing the number of simulations required. Differently from the TSBO proposed in \cite{torun2018global}, the surrogate model is a tSP to enhance the robustness and the ability to capture heavy-tailed NRMSE. The methodology is experimentally validated using a UKF, but can also be applied to an Extended Kalman Filter (EKF). \looseness = -1

\section{PREVIOUS WORKS}

Despite the extensive use of UKF for vehicle sideslip angle estimation, the tuning of the process and observation noise parameters is rarely investigated in the literature. The observation noise parameters are usually tuned through statistical analysis, obtained after sensor calibration tests \cite{mazzilli2021benefit}. The only exception is when the observation noise parameters are tuned together with the process noise parameters \cite{heidfeld2021optimization}. This rarely happens because the filter performance depends mainly on the ratio between the eigenvalues of the observation and process covariance matrices. Thus, it is more convenient to fix the observation noise parameters, which can be determined through sensor calibration, and optimise the process noise parameters.The methodologies for process noise parameters tuning can be split into three different categories: \emph{manual tuning}, \emph{metaheuristic} optimisation and \emph{data-driven} techniques. An overview of optimisation algorithms for UKF tuning is shown in Table \ref{Table_Overview}. \looseness = -1

\emph{Manual tuning} is a trial-and-error approach driven by the user experience. The idea is to perform a grid search in the parameter space and reach the best tuning iteratively. Despite the oversimplicity of the concept, it is still very often used, especially for vehicle sideslip angle estimation \cite{van2018adaptive}, because the performance of Kalman filters is stable for a range of process noise parameters settings \cite{mazzilli2021benefit}.
The main drawback is the inefficiency of the approach.
Moreover, there is no proof to reach the optimal performance. This leads to an unreliable comparison when different filter architectures are analysed.

Thus, Kalman filter tuning with numerical optimisation techniques are widely used. Gradient-based optimisations are the most straightforward algorithms which can be implemented \cite{kerst2019}, but the non-convex and nonlinear cost functions let them easily be trapped into local minima. Thus, gradient-free optimisation algorithms are introduced. One of the simplest algorithms is the coordinate descent algorithm \cite{abbeel2005discriminative} which consists of a cycling increase or decrease of each parameter of the process covariance.
A similar optimisation technique is the downhill simplex algorithm \cite{powell2002automated}, which consists of evaluating the cost function at the simplex vertices of the predefined sample space. After this, the algorithm should converge to the optimum through a series of movements: reflection, expansion, and contraction. Interested reader can find more information on both algorithms in \cite{abbeel2005discriminative} and \cite{powell2002automated} respectively. These algorithms may easily fall in local optima when the cost function is non-smooth, so more advanced metaheuristic optimisations have been proposed.

\emph{Metaheuristics} are procedures that can provide an acceptable solution to an optimisation problem with incomplete information about the cost function. Examples of metaheuristic algorithms are: the already cited downhill simplex algorithm \cite{powell2002automated}, GA \cite{oshman2000optimal} or the Multi-Objective GA \cite{mazzilli2021benefit}, SA \cite{heidfeld2021optimization} and FFO \cite{acosta2019optimized}. A comparison of different metaheuristic algorithms for Kalman filter tuning \cite{acosta2019optimized} shows that GA and FFO found the best cost function optimum. This explains why GA is the current state-of-the-art for UKF tuning for vehicle sideslip angle estimation, which is applied in many recent publications \cite{mazzilli2021benefit}. GA is an evolutionary algorithm that tries to emulate Darwin's theory of natural evolution.
Due to the high number of generations required, GA can be very time-consuming when the cost function is costly to evaluate.

\begin{table}[t]
    \caption{Overview of the optimisation algorithm most used to tune the noise parameters of nonlinear Kalman filter.}
    \label{Table_Overview}
    \begin{center}
    \begin{tabular}{| >{\centering\arraybackslash}m{1in} | >{\centering\arraybackslash}m{1in} | >{\centering\arraybackslash}m{0.6in} |}
    \hline
    \textbf{Authors} & \textbf{Process noise tuning} & \textbf{Dataset}\\
    \hline
    van Aalst, 2018 \cite{van2018adaptive} & Manual Tuning & Experimental \\
    \hline
    Abbeel, 2005 \cite{abbeel2005discriminative} & Coordinate Ascent Algorithm & Experimental \\
    \hline
    Powell, 2002 \cite{powell2002automated} & Downhill Simplex Algorithm & Simulated \\
    \hline
    Mazzilli, 2021 \cite{mazzilli2021benefit}, Oshman, 2000 \cite{oshman2000optimal} & Multi-Objective GA, GA & Experimental \\
    \hline
    Acosta, 2019 \cite{acosta2019optimized} & FFO & Simulated \\
    \hline
    Heidfeld, 2021 \cite{heidfeld2021optimization} & Simulated Annealing & Experimental \\
    \hline
    Tang, 2021 \cite{tang2021reinforcement} & RL & Experimental \\
    \hline
    Escoriza, 2021 \cite{Escoriza2021} & Recurrent Neural Network & Experimental \\
    \hline
    Chen, 2018 \cite{chen2018weak}, Chen, 2019 \cite{chen2019kalman} & BO based on Gaussian \& t-Student Process & Simulated \\
    \hline
    \textbf{Current Paper} & \textbf{TSBO with t-Student Process} & \textbf{Experimental} \\
    \hline
    \end{tabular}
    \end{center}
\end{table}

\emph{Data-driven} optimisation algorithms such as BO are based on the creation of a surrogate model, Gaussian Processes GP \cite{chen2018weak}, or tSP \cite{chen2019kalman}, using as inputs the tuning parameters and as output the value of the cost function. The surrogate model does not only approximate the cost function, but also it is associated with a model probability distribution. The acquisition function takes this information to find the new parameters with the highest probability of being the best new optimum. Standard BO fits the surrogate model on parameters randomly taken from the sample space, but this can lead to a non-optimal surrogate model. Furthermore, it will require many cost function evaluations if the sample space is broad.
Other data-driven approaches can be implemented; for instance, Reinforcement Learning RL can be applied to choose the parameters of the process noise, or RL can compensate for the error of the Kalman filter \cite{tang2021reinforcement} due to not-optimal process noise parameters. A similar concept is the so-called "KalmanNet" \cite{Escoriza2021} which uses a Recurrent Neural Network RNN to compute the Kalman gain, reducing the problems of non-optimal Kalman tuning. Despite the potential of RL and RNN, the amount of data required to train the Neural Networks makes them impractical for the tuning/improvement of Kalman filter for sideslip angle estimation. \looseness = -1

The main contributions of this paper are twofold. The first is the development of a new TSBO based on a tSP for tuning the process noise parameters of UKF for vehicle sideslip angle estimation. The TSBO in this paper differs from a recent TBSO related to electronic design \cite{torun2018global} because the surrogate model is based on a tSP to increase its robustness and ability to reach a better global optimum without increasing the required computational power.

The second contribution is that the developed TSBO reduces the number of simulations required to tune the Kalman filter with respect to the state-of-the-art GA \cite{mazzilli2021benefit,acosta2019optimized} and it improves the accuracy of the tuning respect GA. The tuning performance is tested and validated using an experimental dataset for vehicle  sideslip angle estimation.

\section{BAYESIAN OPTIMISATION}

Filter tuning consists of minimising a nonlinear, non-convex and non-smooth cost function due to the reasons already expressed in section I. A minimisation problem is:

\begin{equation}
    \begin{aligned}
        & \text{min}_{\;q\;\in\;Q}\;J\left(q\right) \\
        & \text{where} \;\; Q \subseteq\;\Re^d
    \end{aligned}
    \label{eq1}
\end{equation}

\noindent
where $q$ is the parameter vector of dimension $d$, $J\left(q\right)$ is the cost function, and $Q \subseteq\;\Re^d$ is the solution space. The function $J\left(q\right)$ is a "black-box" function because it is not accessible, but its outputs are observed based on some given inputs. Thus, "black-box" stochastic optimisation algorithms are considered. It is time-consuming to perform a dense sampling of the solution space $Q$, so the algorithm should focus on sampling the subset of $Q$ with the highest probability to contain the cost function optimum $q^*$. BO can deal with incomplete and sparse knowledge of the solution thanks to its probabilistic approach. It is based on the Bayes' theorem

\begin{equation}
    P\left(\text{beliefs}\; |\; \text{data}\right) = \dfrac{P\left(\text{beliefs}\right) \times P\left(\text{data}\; |\; \text{beliefs}\right) }{P\left(\text{data}\right) }
    \label{eq2}
\end{equation}

\noindent
where data are the available observations of the function $J\left(q\right)$, beliefs are the beliefs of the shape of $J\left(q\right)$, and $P\left(\text{beliefs}\; |\; \text{data}\right)$, $P\left(\text{data}\; |\; \text{beliefs}\right)$, $P\left(\text{beliefs}\right)$, and $P\left(\text{data}\right) $ are the posterior, likelihood, and the marginal probabilities respectively. Thus, BO aims to find $q^*$ learning the shape of $J\left(q\right)$ through Bayesian inference. The TSBO overcomes the standard BO due to its ability to define a better ratio between exploration (function shape learning) and exploitation (approaching the optimum).

BO can be split into two steps: creating a stochastic process of the cost function, called "Surrogate Model", and building an "Acquisition Function" that uses the surrogate model to approach the optimum $q^*$.

\subsection{Surrogate Model}

\begin{figure}[t]
   \centering
   \setlength{\fboxrule}{0pt}
   \framebox{\parbox{3in}{\includegraphics[scale=0.28]{Pictures/DEF_GPVSTP.JPG}}}
    \caption{GP and sTP model of the cost function varying the process noise parameter related with the vehicle lateral velocity. The tSP allows more outliers and it can follow the measurements better than the GP. The shadow area is the variance of the tSP and the uncertainty for the GP.}
    \label{tSPvsGP}
\end{figure}

BO approximates the "black-box" cost function through a stochastic process called the surrogate model. It represents a probabilistic prior over the space of functions. The prior is updated as soon as the cost function is evaluated thanks to the Bayes' Theorem. Different priors are eligible for being the surrogate model; a partial list comprises: GP, tSP, Bayesian neural networks and polynomial chaos expansion. GP are the current state-of-art because it is a non-parametric model \cite{torun2018global}, and the Bayesian update step is analytical. Despite this, in this paper a tSP prior is selected for the surrogate model due to two reasons. The first one is that the t-Student distribution allows defining the level of Kurtosis, so it allows much more likely outliers \cite{shah2014student,tracey2018upgrading} than GP. The second reason is that the observations directly influence the predictive covariance \cite{shah2014student,tracey2018upgrading}. Both properties are beneficial for the tuning of UKF because the cost function can have discontinuities, for instance, due to the noise. Figure \ref{tSPvsGP} shows the explained differences between tSP and GP modelling in an example that models the cost function varying the process noise parameter related with lateral velocity. It is visible how tSP reduces the influence of the outliers, allowing the mean to have a better fitting.

The tSP is defined as:

\begin{equation}
    f\left(q\right) = tSP\left(m\left(q\right),\;k\left(q,q'\right),\; \nu \right)
    \label{eq3}
\end{equation}

\noindent
where $m\left(q\right)$ represents the mean which is assumed equal to constant zero mean function because there is no prior \cite{torun2018bayesian}, $\nu$ are the degrees of freedom of the t-Student distribution, which defines the level of Kurtosis, and $k\left(q,q'\right)$ is the Kernel function between the inputs pair $q$ and $q'$. The choice of the Kernel is essential to have a reliable model fitting. This paper chooses the automatic relevance determination Matérn $5/2$ function, due to its generalisation capabilities and interpretability properties:

\begin{equation}
    k_{q,\;q'} = k(q,\;q')=\sigma_f^2\left(1+\frac{\sqrt{5}r}{\sigma_d}+\frac{5\,r^2}{3\,\sigma_d^2}\right)e^{-\frac{\sqrt{5}r}{\sigma_d}}
    \label{eq4}
\end{equation}

\noindent
where $r$ is the Euclidean distance between the two points $q$ and $q'$ calculated as:
\begin{equation}
    r = \sqrt{\left(q-q'\right)^T\left(q-q'\right)}
    \label{eq5}
\end{equation}

\noindent
where $\sigma_f$ and $\sigma_d$ are two hyperparamters, which are trained to minimize the negative log marginal likelihood of the tSP. It is essential to highlight that tSP should be accurate only when there are higher chances of finding the cost function optimum. Thus, every time a new observation is available, the surrogate model is retrained. tSP are implemented using the GPML Matlab Code version 4.2 \cite{williams2006gaussian}.

\subsection{Acquisition Function}

The Acquisition Function AF is responsible for moving the BO towards the optimum region, choosing the next sample point to be evaluated. Thus, it is responsible for defining the ratio between exploration, moving the search towards the area where the surrogate model is uncertain, and exploiting, moving the search towards the area with a higher chance of improving the current optimum. Ideally, the more focus is given to the exploration stage, the higher the chances to find the global optimum; however, it increases the computational time due to a larger number of cost function evaluations.

The next point to be evaluated ($q_{t+1}$) is usually obtained through an additional optimisation of the AF. This paper evaluates the AF on some candidate points obtained through the process that will be explained in Section IV to avoid further optimisation. The AF is evaluated on the candidate points \cite{williams2006gaussian, tracey2018upgrading} according to:

\begin{equation}
    \hat{\mu}_{\,tSP}\left(q_{t+1}\right) = k_{q,\,q}^T\,K_{\tilde{q},\;\tilde{q}}^{-1}\,f\left(\tilde{q}\right)
    \label{eq6}
\end{equation}

\noindent
where $\tilde{q}$ are the sampling points already evaluated, $\hat{\mu}_{\,tSP}$ is the posterior mean of a tSP for a new sampling point $q$ and $k$ is defined in Eq. \ref{eq4}.

\begin{multline}
     \hat{\sigma}^2_{\,tSP}\left(q_{t+1}\right) = \left(\frac{\nu+f\left(\tilde{q}\right)^T\,K_{\tilde{q},\;\tilde{q}}^{-1}\,J\left(\tilde{q}\right)-2}{\nu + |D| -2}\right)\times(k\left(q,q\right) + \\-k_{q,\;\tilde{q}}^T\,K_{\tilde{q},\;\tilde{q}}^{-1}\,k_{q,\;\tilde{q}})
    \label{eq7}
\end{multline}

\noindent
where $\hat{\sigma}_{\,tSP}$ is the posterior covariance, and $D$ is the set of samples $D=\left[\left(\tilde{q}_1,J\left(\tilde{q}_1\right)\right),\; \left(\tilde{q}_2,J\left(\tilde{q}_2\right)\right), \; \left(\tilde{q}_3,J\left(\tilde{q}_3\right)\right),\;...\,\right]$. \newline

Two AFs are selected: the Expected Improvement (EI) and the Confidence Bound Minimisation (CBM) \cite{clare2020expected}.

The EI is defined as:

\begin{multline}
    EI_{\,tSP}\left(q\right)= \hat{\sigma}_{\,tSP}\left(q\right)\,\left(\frac{\nu}{\nu-1}\right)\,\left(1+\frac{z_s^2}{\nu}\right)\,\phi\left(z_s\right)+\\
    + \left[ \hat{y} - \hat{\mu}_{\,tSP}\right]\,\Phi\left(z_s\right)
    \label{eq8}
\end{multline}

\noindent
where $\phi\left(z_s\right)$ and $\Phi\left(z_s\right)$ are the probability density function and the cumulative distribution function of an univariate standard t-Student's random variable, $z_s$,  and $\hat{y}$ is the current optimum sampled by the BO.

The CBM is formulated as:

\begin{equation}
    CBM_{\,tSP}\left(q\right)= \hat{\sigma}_{\,tSP}\left(q\right)\,\sqrt{\beta} + \left[f^*- \hat{\mu}_{\,tSP}\right]
    \label{eq9}
\end{equation}

\noindent
where the $\beta$ defines the ratio between exploitation and exploration, and $f^*$ is the best known optimum, representing the prior knowledge of the optimum.

\section{TWO-STAGE BAYESIAN OPTIMISATION}

This paper proposes an evolution of the BO to tune the process noise parameters of the UKF. It is called TSBO, code by \cite{torun2018global}, and aims to reduce the number of function evaluations, and improve the ratio between exploitation and exploration. The trade-off improvement is obtained thanks to the subdivisions of the BO in two stages. The first, "fast exploration", aims to find the sample space region where the optimum is contained. The second, "pure exploitation", aims to fine-tune the restricted sample space obtained from the first stage. The first stage allows starting the surrogate model training from a single evaluation at the centre of the sample space. This allows the user not to evaluate a random set of process noises to fit the first surrogate model.

\subsection{Fast Exploration}

The fast exploration aims to narrow the sample space $Q\subseteq \Re^d$ where the BO search the optimum as fast as possible. It consists of subdividing the sample space into $2^d$ hyper-rectangles.  Every hyper-rectangle centre point is a candidate point, which the AF evaluates to choose the next sampling point $q_{\,t+1}$ where the cost function is evaluated. The following sampling point is not obtained as in the standard BO through an auxiliary optimisation of the AF, but it is the candidate point with the maximum value of the AF between the other candidate points. The cost function is only evaluated at points $q_{\,t+1}$. A new set of hyper-rectangles is generated starting from the hyper-rectangle enclosing the best candidate point. The two available AFs, EI and CBM, are alternatively used to evaluate the candidate points. After a predefined number of iterations $MAX_{AF}$, the AF which would have had the greatest gain to the optimisation is selected as final AF.

The fast exploration stage is repeated until the euclidean distance between the new best sampling point $q_{t+1,\;max}$ and the previous best sampling point $q_{t\;max}$ is below a user-defined threshold $TR_{FE}$ for a number of times $MAX_{FE}$:

\begin{equation}
    n = \begin{cases}
            n+1\,, \;\;\;\;\;\;\;& \text{if} \;\;\; ||q_{\,t+1,\,max} - q_{\,t,\,max}|| < TR_{FE}\\
            0\,, \;\;\;\;\;\;\; & \text{otherwise} \\
        \end{cases}
    \label{eq10}
\end{equation}

Every time the cost function is evaluated and a new measurement is available; the surrogate model is retrained to improve the fitting with the black-box function.

Algorithm \ref{alg1} sums up the fast exploration stage of the TSBO.

\begin{algorithm}
    \caption{Fast Exploration stage of the TSBO}
    \label{alg1}
    \begin{algorithmic}
        \Require A sample space $Q$ and an evaluated sampling point at the center of $Q$
        \State Fit the tSP Surrogate model
        \State Subdivide $Q$ in $2^d$ hyper-rectangles
        \While{$n \le MAX_{FE}$}
            \State Compute candidate points at the center of hyper-rectangles
            \State Evaluate AF at the candidate points
            \State Pick the candidate points with the maximum AF
            \If{$||q_{\,t+1\,max} - q_{\,t\,max}|| < TR_{FE}$}
                \State $n+1 \gets n$
            \ElsIf{$||q_{\,t+1\,max} - q_{\,t\,max}|| \geq TR_{FE}$}
                \State $n \gets n$
            \EndIf
        \State Evaluate the cost function in the sampling points
        \State Train the tSP of the Surrogate with the new observation
        \State Subdivide the hyper-rectangle which were enclosing the best candidate points in $2^d$ hyper-rectangles
        \EndWhile
    \end{algorithmic}
\end{algorithm}

\begin{algorithm}
    \caption{Pure Exploitation stage of the TSBO}
    \label{alg2}
    \begin{algorithmic}
        \Require $n = MAX_{FE}$
        \While{$n_{iter} \le MAX_{PE}$, and the output from Algorithm 1}
            \State Compute candidate points at the center of hyper-rectangles
            \State Evaluate AF at the candidate points
            \State Pick the candidate points with the maximum AF
            \State Evaluate the cost function in the sampling points
            \If{$n_{iter} \le MAX_{SM}$}
                \State Train the tSP of the Surrogate with the new observation
            \EndIf
        \State Subdivide the hyper-rectangle which were enclosing the best candidate points in $3^d$ hyper-rectangles
        \EndWhile
    \end{algorithmic}
\end{algorithm}

\subsection{Pure Exploitation}

The pure exploitation stage follows a procedure similar to the fast exploration, but it specialises in refining the optimum position. The first step is the computation of the sample space $\tilde{Q}^d$ around the optimum computed in the fast exploration stage:

\begin{equation}
    \tilde{Q}^d = \begin{bmatrix}
                        \left(1-\alpha\right)\,q^*_{\,1} & \left(1+\alpha\right)\,q^*_{\,1}\\
                        \vdots & \vdots\\
                        \left(1-\alpha\right)\,q^*_{\,d} & \left(1+\alpha\right)\,q^*_{\,d}
                    \end{bmatrix}
    \label{eq11}
\end{equation}

\noindent
where $\alpha$ is a hyper-parameter that defines how wide the sample space should be, and $q^*_i$ is the optimum sampling point obtained in the fast exploration stage. The new sample space $\tilde{Q}^d$ is divided into $3^d$ hyper-rectangles to have more candidate points to evaluate. The higher number of candidate points helps the pure exploitation stage to converge faster to the global optimum of the function. The rest of the procedure is the same of the fast exploration. The only difference is that when the number of evaluated sampling points $n_{iter}$ is higher than a predefined threshold $MAX_{SM}$, the tSP is no more retrained.

Algorithm \ref{alg2} summarises the pure exploitation stage of the TSBO, which is iterated until $n_{iter}$ is lower than a user-defined threshold $MAX_{PE}$.

\section{APPLICATION: UKF TUNING}

The proposed TSBO based on tSP is tested on the tuning of the process noise parameters of a UKF for vehicle sideslip angle estimation. In this paper, the single-track model with tyre axle forces computed by the Dugoff tyre is chosen as the vehicle model. The states are the longitudinal velocity at the Center-of-Gravity CoG $V_x$, the lateral velocity at the CoG $V_y$ and the yaw rate $\dot{\psi}$ while the vehicle measurements are the $V_x$, the lateral acceleration at the CoG $a_y$ and the $\dot{\psi}$. The vehicle sideslip angle $\beta_s$ is obtained as $\beta_s = \arctan{\frac{V_y}{V_x}}$:

\subsection{Dataset}

All the manoeuvres that compose the training set and test set are taken from a real-world experimental dataset composed of 216 manoeuvres which correspond to 2 hrs of driving. It puts together standard vehicle dynamics manoeuvres, e.g. double lane change, slalom, random steer, J-turn, spiral, braking in a turn, and steady-state circular tests, together with recorded laps at the Papenburg track. All manoeuvres were performed on dry asphalt with tyres inflated according to the vehicle specifications.

All manoeuvres are recorded using the BMW test platform instrumented by the inertial measurement unit, wheel force transducers for all four wheels, GPS and an optical sensor from Corrsys-Datron to measure the sideslip angle. The high-end optical speed sensor is not present in consumer vehicles and will be used as a reference for TBSO tuning and validation.

The UKF should be tuned for different working conditions, so various vehicle maneuvers are jointly considered in the training set. The training set is composed of 8 manoeuvres: 1 braking in a turn, 1 skidpad, 2 J-turn, 2 slaloms and 2 lance change.

A test set composed of 23 manoeuvres is taken to prove the generalization capability of the approach. All 23 maneuvers of the test set are different from the training set even if, some of them have similar characteristics. The manoeuvres are: 2 braking in a turn, 2 skidpad, 5 J-turn, 4 slalom, 4 lane change, 2 random steer, 1 lap track and 3 spiral. The characteristics of the manoeuvres cover a wide range of vehicle driving behaviours and different settings of the Electronic Stability Control.

\subsection{Cost Function Formulation}

In this paper, the UKF tuning aims to improve the accuracy of the vehicle state estimation, therefore, this paper focuses on an objective function defined by minimising the Normalised Root Mean Squared Error NRMSE rather than maximising the negative log-likelihood \cite{abbeel2005discriminative}.

The minimisation problem is described as:

\begin{equation}
    \begin{aligned}
        & \text{min}_{\;q\;\in\;Q} \;J\left(q\right) \\
        & \text{where} \;\; Q =\;\begin{bmatrix}
                                        q_{\,1,\, min} & q_{\,1,\, max}\\
                                        \vdots & \vdots\\
                                        q_{\,d,\, min} & q_{\,d,\, max}
                                    \end{bmatrix}
    \end{aligned}
    \label{eq13}
\end{equation}

\noindent
where $J$ is the cost function defined as:

\begin{equation}
    \begin{aligned}
        & J = W_1\,E_{\beta} + W_2\,E_{\dot{\psi}} + W_3\,E_{a_y} \\
        &\text{where}\;\; E_j = \sqrt{\frac{\sum_{i=1}^{N{\,man}}\left(NRMSE_{j,\;i}\right)^2\,N_{s,\;i}}{\sum_{i=1}^{N_{\,man}}N_{s,\;i}}}
    \end{aligned}
    \label{eq14}
\end{equation}

\noindent
where $W_1$, $W_2$, $W_3$ are the weights of the cost function, $N_s$ is the length of the manoeuvre measured in sampling points, $N_{man}$ is the number of manoeuvres considered in the training set, and $NRMSE$ is computed as follow:.

\begin{equation}
    NRMSE = \sqrt{\frac{\sum_{k=1}^{N_s}\left(\hat{X}_k-X_k\right)^2}{N_s}}\frac{1}{\text{max}\left(|X|\right)}
    \label{eq15}
\end{equation}

\noindent
where $X$ and $\hat{X}$ are the vectors of the measured and estimated states respectively.

\subsection{Key Performance Indicator}

The accuracy of the estimation of sideslip angle is evaluated through the use of the root mean squared error RMSE, maximum absolute error MAE, the RMSE and the MAE when the absolute vehicle acceleration $a_y$ is greater than \SI{4}{m/s^2}, which are respectively abbreviated with RMSE\textsubscript{NON} and MAE\textsubscript{NON}. The latter is used to analyse how the UKF tuning is especially relevant when the vehicle behaves in a nonlinear way.

\section{RESULTS}

 The performance in terms of minimisation time and optimum localisation of the TSBO based on tSP is compared with the current state-of-the-art technique GA and with a TSBO based on GP. All optimisations perform in the same sample space $Q$ where all the process noise parameters for $V_x$, $V_y$ and $\dot{\psi}$ vary from $10^{-10}$ to 1.

The optimisation parameters for TSBO based on tSP and GP are summarised in Table \ref{par_TSBO}. The parameters for GA are summarized in Table \ref{par_GA}.

\begin{table}[b]
    \caption{User-defined parameters for TSBO optimisations.}
    \label{par_TSBO}
    \begin{center}
    \begin{tabular}{| >{\centering\arraybackslash}m{0.9in} | >{\centering\arraybackslash}m{0.7in} | >{\centering\arraybackslash}m{0.7in} |}
    \hline
    \textbf{Parameters} & \textbf{TSBO - tSP} & \textbf{TSBO - GP} \\
    \hline
    Likelihood function  & t-Student 's & Gaussian \\
    \hline
    Inference  & Variational Bayes & Exact \\
    \hline
    Kernel  function & Matérn ARD $5/2$ & Matérn ARD $5/2$ \\
    \hline
    Mean function  & Constant & Constant \\
    \hline
    $\nu$  & 15 & NA \\
    \hline
    $MAX_{FE}$  & 15 & 15 \\
    \hline
    $MAX_{PE}$  & 40 & 40 \\
    \hline
    $MAX_{SM}$  & 38 & 38 \\
    \hline
    $TR_{FE}$  & $0.01\parallel \;q^* \parallel$ & $0.01\parallel \;q^* \parallel$ \\
    \hline
    $\beta$  & 0.01 & 0.01 \\
    \hline
    $\alpha$  & 0.15 & 0.15 \\
    \hline
    $W_1$, $W_2$, $W_3$  & 5, 1, 1 & 5, 1, 1\\
    \hline
    \end{tabular}
    \end{center}
\end{table}

\begin{table}[t]
    \caption{User-defined parameters for GA optimisation.}
    \label{par_GA}
    \begin{center}
    \begin{tabular}{| >{\centering\arraybackslash}m{0.9in} | >{\centering\arraybackslash}m{0.9in} |}
    \hline
    \textbf{Parameters} & \textbf{GA} \\
    \hline
    Population size  & 15 \\
    \hline
    Max generations  & 15\\
    \hline
    Élite count & 0.75 \\
    \hline
    Crossover fraction & 0.8 \\
    \hline
    $W_1$, $W_2$, $W_3$  & 5, 1, 1 \\
    \hline
    \end{tabular}
    \end{center}
\end{table}

The optimum cost and optimisation time for the TSBO based on tSP, TSBO based on GP and GA are summarised in Table \ref{res_com_tab} and Figure \ref{res_com_pic}.

\begin{table}[t]
    \caption{Numerical results of the optimum cost function and optimisation time using different algorithms for UKF tuning.}
    \label{res_com_tab}
    \begin{center}
    \begin{tabular}{| >{\centering\arraybackslash}m{0.9in} | >{\centering\arraybackslash}m{0.9in} | >{\centering\arraybackslash}m{0.9in} |}
    \hline
    \textbf{Algorithm} &  \textbf{Total cost [-] }  &  \textbf{Optim. time [s]}\\
    \hline
    Manual tuning &   1.714             & NA\\
    \hline
    GA            &   0.378             & 1599\\
    \hline
    TSBO - GP     &   0.400             & \textbf{306}\\
    \hline
    TSBO - tSP    &   \textbf{0.316}    & 320\\
    \hline
    \end{tabular}
    \end{center}
\end{table}

\begin{figure}[b]
   \centering
   \setlength{\fboxrule}{0pt}
   \framebox{\parbox{3in}{\includegraphics[scale=0.3]{Pictures/Number_of.JPG}}}
    \caption{Performance comparison of TSBO based on tSP, TSBO based on GP and GA.}
    \label{res_com_pic}
\end{figure}

Figure \ref{res_com_pic} shows how both TSBOs approach the global optimum faster than GA. Where TSBO converges after 40 evaluations of the cost function, the GA needs at least 220 evaluations, resulting in TSBO being on average \SI{79}{\%} faster than GA. Thus, TSBOs are particularly convenient when the cost function evaluation is computationally heavy, for instance, due to numerous manoeuvres considered in the training set. Despite the faster optimisation, TSBO based on tSP reaches an optimum \SI{16.4}{\%} lower than the GA optimum. The proposed algorithm outperforms the state-of-the-art approach both in terms of optimisation time and optimisation accuracy. TSBO based on GP is \SI{4.4}{\%} faster than TSBO based on tSP. The rise in optimisation time is due to the surrogate model's training phase, which is slower for the tSP due to the more optimisation parameters. The better optimisation accuracy, \SI{21}{\%}, of the tSP than the GP is due to the higher Kurtosis of the t-Student's distribution which makes the non-parametric model capable of dealing with outliers. A much higher optimisation accuracy compensates for the slight higher computational time. \newline

Table \ref{Trai} presents the RMSE, MAE, RMSE\textsubscript{NON} and MAE\textsubscript{NON} of the sideslip angle for the various optimised UKFs. The reported values are the average of all manoeuvres composing the training set. Table \ref{Trai} highlights how the cost function's minimisation is related to the KPIs used to compare the estimation accuracy because TSBO based on tSP outperforms the other algorithms. On average, the RMSE of the sideslip angle optimised with tSBO based on tSP is \SI{15.4}{\%} lower than the one with GA, and the MAE is \SI{15}{\%} lower. Particularly important is the improvement of the RMSE\textsubscript{NON} and of the MAE\textsubscript{NON} because when the $|a_y| \ge$ \SI{4}{m/s^2} the sideslip angle estimation becomes more critical for vehicle stability control. The UKF optimised by the TSBO based on tSP reduces the RMSE\textsubscript{NON}, and the MAE\textsubscript{NON} of the UKF optimised by GA of \SI{24.6}{\%} and \SI{12.5}{\%}, respectively. The accuracy of the UKF tuned by GA is higher than the one optimised by TSBO based on GP.

Table \ref{Vali} reports the same information as Table \ref{Trai}, but for the test set, to check that the numerical optimisation algorithms do not overfit the training set leading to worse performance in other manoeuvres. Table \ref{Vali} shows that TSBO based on tSP still behaves better than all other algorithms for the 4 KPIs, proving better performance in tuning the process noise UKF parameters.
On average, the RMSE of the sideslip angle optimised with tSBO based on tSP is \SI{9.9}{\%} lower than the one with GA, and the MAE is \SI{17.6}{\%} lower. The improvement of the RMSE\textsubscript{NON} and of the MAE\textsubscript{NON} is particularly significant because when the $|a_y| \ge$ \SI{4}{m/s^2}, the sideslip angle estimation becomes more critical for vehicle stability control. The UKF optimised with the TSBO based on tSP reduces the RMSE\textsubscript{NON}, and the MAE\textsubscript{NON} of the UKF optimised with GA of \SI{10.6}{\%} and \SI{9.8}{\%}, respectively. The improvement in the test set is lower than the one obtained in the training set but still particularly relevant, especially for what concerns the MAE. \newline

\begin{table}[t]
    \caption{Comparison of the average KPIs for the UKF tuned by manual tuning, GA, TSBO based on GP and TSBO based on sTP. Results of the training set.}
    \label{Trai}
    \begin{center}
    \begin{tabular}{| >{\centering\arraybackslash}m{0.5in} | >{\centering\arraybackslash}m{0.5in} | >{\centering\arraybackslash}m{0.5in} | >{\centering\arraybackslash}m{0.5in} | >{\centering\arraybackslash}m{0.5in} |}

    \hline
    \textbf{KPI} &  \textbf{Manual}  &  \textbf{GA} &  \textbf{TSBO - GP}&  \textbf{TSBO - tSP}\\
    \hline
    RMSE [deg]                        & 1.028  & 0.500 & 0.634  & \textbf{0.423} \\
    \hline
    MAE [deg]                         & 1.196  & 0.900 & 1.252  & \textbf{0.765} \\
    \hline
    RMSE\textsubscript{NON} [deg]     & 0.716  & 0.568 & 0.855  & \textbf{0.428} \\
    \hline
    MAE\textsubscript{NON} [deg]      & 0.818  & 0.671 & 0.961  & \textbf{0.587} \\
    \hline
    \end{tabular}
    \end{center}
\end{table}

\begin{table}[b]
    \caption{Comparison of the average KPIs for the UKF tuned by manual tuning, GA, TSBO based on GP and TSBO based on sTP. Results of the test set.}
    \label{Vali}
    \begin{center}
    \begin{tabular}{| >{\centering\arraybackslash}m{0.5in} | >{\centering\arraybackslash}m{0.5in} | >{\centering\arraybackslash}m{0.5in} | >{\centering\arraybackslash}m{0.5in} | >{\centering\arraybackslash}m{0.5in} |}
    \hline
    \textbf{KPI} &  \textbf{Manual}  &  \textbf{GA} &  \textbf{TSBO - GP}&  \textbf{TSBO - tSP}\\
    \hline
    RMSE [deg]                        & 0.577 & 0.383 & 0.431 & \textbf{0.345}  \\
    \hline
    RMSE [deg]                        & 0.672 & 0.529 & 0.689 & \textbf{0.436}  \\
    \hline
    RMSE\textsubscript{NON} [deg]     & 1.328  & 0.860 & 1.030  & \textbf{0.769}  \\
    \hline
    MAE\textsubscript{NON} [deg]      & 1.047  & 0.746 & 0.902 & \textbf{0.673} \\
    \hline
    \end{tabular}
    \end{center}
\end{table}

\begin{figure}[t]
   \centering
   \setlength{\fboxrule}{0pt}
   \framebox{\parbox{3in}{\includegraphics[scale=0.27]{Pictures/BAR.JPG}}}
    \caption{Relative improvement of the UKF tuned by TSBO based on tSP with respect to the UKF tuned by the state-of-the-art GA. The improvement is measured through the sideslip angle's RMSE for 6 different maneuver chosen from the test set.}
    \label{bar}
\end{figure}

\begin{figure}[t]
   \centering
   \setlength{\fboxrule}{0pt}
   \framebox{\parbox{3in}{\includegraphics[scale=0.12]{Pictures/SKIDPAD.jpg}}}
    \caption{Sideslip angle estimation in a skidpad maneuver.}
    \label{SKIDPAD}
\end{figure}

\begin{figure}[t]
   \centering
   \setlength{\fboxrule}{0pt}
   \framebox{\parbox{3in}{\includegraphics[scale=0.12]{Pictures/SLALOM.jpg}}}
    \caption{Sideslip angle estimation in a slalom maneuver.}
    \label{SLALOM}
\end{figure}

\begin{figure}[ht]
   \centering
   \setlength{\fboxrule}{0pt}
   \framebox{\parbox{3in}{\includegraphics[scale=0.12]{Pictures/JTURN.jpg}}}
    \caption{Sideslip angle estimation in a J-turn maneuver.}
    \label{JTURN}
\end{figure}

Figure \ref{bar} shows the relative improvement of the UKF tuned by TSBO based on tSP and GA in 6 different maneuvers of the test set. The relative improvement is measured with the RMSE of the sideslip angle, $a_y$ and $\dot{\psi}$. In 5 out of 6 maneuvers the TSBO reaches higher performance than the GA. The spiral maneuver is the only one which shows a degraded accuracy in the RMSE of sideslip angle  of \SI{8}{\%}. Performance degradation in this particular maneuver is considered acceptable due to the various driving conditions of the test set. The relative improvement is significant especially for the sideslip angle and for the $a_y$, while the $\dot{\psi}$ does not have this. The reason is that the $\dot{\psi}$ has lower maximum absolute error compared with the other variables, so the relative improvement is less noticeable. Figure \ref{bar} shows how TSBO can enhance the performance of the UKF for both transient (J-turn, slalom) and steady state (skidpad) maneuvers. Significant is the improvement in the skidpad, see also Figure \ref{SKIDPAD}. The reason is that during a steady-state maneuver the vehicle yaw acceleration is almost null so the difference between estimated and measured tyre forces becomes essential for the $V_y$ estimation. The best way to reduce the tyre force error is to optimise the process noise parameters.

Despite this, the improvement in sideslip angle estimation is noticeable also for a J-turn, see Figure \ref{JTURN}, and a slalom manoeuvre, see Figure \ref{SLALOM}.
Figure \ref{SLALOM} shows how the improvement in the tuning of the UKF is noticeable at the sideslip angle peaks of the slalom maneuver. The improvement at the peaks is more remarkable when the sideslip angle is above \SI{3}{deg} while when it is between 0 and \SI{3}{deg} the difference is not visible due to the lower absolute error of both UKFs.
Figure \ref{JTURN} shows the sideslip angle estimation in J-turn and it highlights another time how the UKF tuned by TSBO based on tSP reduces the estimation error especially when the sideslip angle is above \SI{4}{deg}.

\section{CONCLUSIONS}

This paper presented a two-stage Bayesian optimisation approach based on t-Student process to tune the process noise parameters of a unscented Kalman filter for sideslip angle estimation. The proposed approach reaches the cost function optimum \SI{79.9}{\%} faster than the state-of-the-art GA and the optimum is \SI{16.4}{\%} better than the one obtained by GA. The obtained results are tested on an experimental test set composed by transient and quasi-steady state maneuvers. Furthermore, two-stage Bayesian optimisation creates a physical representation of the black-box cost function thanks to surrogate model training. The performance of the two-stage Bayesian optimisation based on t-Student process is also compared with the two-stage Bayesian optimisation based on Gaussian process. The algorithm based on t-Student process is \SI{4.4}{\%} slower than the one based on Gaussian process but it reaches a \SI{21}{\%} better optimum. As future research, the plan is to tune not only the process noise parameters of the unscented Kalman filter but also the parameters defining the location of the sigma points.

\addtolength{\textheight}{-15cm}

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


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Meanwhile, KTXers can also relive the sold-out concert "Ogie and the Hurados," held last 2019. Catch the spectacular performances by Ogie ans the hurados of "It's Showtime's 'Tawag ng Tanghalan,' including the Divine Diva Zsa Zsa Padilla, Randy Santiago, K Brosas, Karylle, Jed Madela, and Rey Valera.
Watch it online starting December 11 (Friday) up to December 13 (Sunday) for P750.
These theatrical productions follow previous successful KTX.PH offerings - Jed Madela's "New Normal," JaMill's "Tayo Hanggang Dulo," K Brosas' "20k20," "Hello Stranger: Finale Fancon," "Apollo: A Daniel Padilla Digital Experience," "The House Arrest Of Us," "U-Turn" and other special exclusive events.
Watch out for more exciting experiences on KTX.PH.

redpajama_set_name: RedPajamaCommonCrawl


You're all set for your trip to Brazil. You can't wait to experience the Amazon, Rio de Janeiro, the beaches, and the vibe of Latin America as the country gets ready for the 2014 World Cup and 2016 Summer Olympics. It's going to be a great trip!
"You need a valid visa to travel to Brazil," they explain.
"Unfortunately, you can't fly until you have a valid passport and visa to enter Brazil," says the agent in a monotone voice. They've obviously confronted this situation before.
"No, I'm afraid not. You have to renew your passport online and then apply by mail for the visa through the Brazilian Embassy. It can take quite a while," they tell you and hand back your passport and ticket.
You stand dumbfounded with your bag and travel documents as the ticket agent helps another customer.
Not only are you going to miss your flight, but you'll have to postpone your trip until you update your travel documents. It could take weeks until you can head to Brazil. What a disastrous start to what could have been a great trip!
There is a rule of thumb: If a Brazilian needs a visa to enter you country, you need a visa to enter Brazil. So, a Colombian can entre Brazil without a visa (and even a passport, the Colombian personal id is enough) while North Americans and Japanese need one.


text:
Et Fredningsnævn er et nævn nedsat af miljøministeren i medfør af naturbeskyttelsesloven. Miljøministeren nedsætter to til tre Fredningsnævn i hver af Danmarks regioner. Fredningsnævnene har til opgave at tage stilling i fredningssager indenfor landskabs- og naturfredning.

Fredningsnævnene er uafhængige domstolslignende organer, der behandler fredningssager og træffer afgørelser om hvorvidt  de skal gennemføres.

Fredningsnævnet er nedsat efter Naturbeskyttelseslovens § 35.
Efter loven består nævnet af en formand, der skal være dommer og 2 andre medlemmer; Dommeren og det  ene medlem udpeges af miljøministeren, mens nævnets tredje medlem vælges af kommunalbestyrelsen i den kommune, hvori det areal, sagen omhandler er beliggende. Der er 13 Fredningsnævn, fordelt med 2 i Region Nordjylland og i Region Midtjylland samt 3 fredningsnævn i hver af de øvrige regioner. Klager over afgørelser om fredninger og om dispensationer fra disse behandles af Naturklagenævnet.

Kilder/henvisninger

Naturbeskyttelse
Danske nævn


text:
ABLAZE - 2018 Summer Tour!
Ohio Christian University's ABLAZE student ministry minstrel group will be performing in Indiana, Georgia, Pennsylvania, and Ohio on these dates at the following CCCU churches and summer camps, where OCU students are also serving as camp counselors:
June 10: Community Wesleyan Church, Athens, OH
June 17: Hartsville Church of Christ, Harstville, IN
June 17: Chillicothe Paints Baseball Game, Chillicothe, OH (singing the national anthem at 6:05 p.m.)
June 19: General CCCU Council, Circleville, OH
June 20-23: Teens of Praise Camp, Circleville, OH
June 24-28: Bible Camp, Maysville, GA
July 1: The Grove Level Church, Maysville, GA
July 4-12: Taylor County Camp, Butler, GA
July 8: Rock Springs Church, Milner, GA
July 15-21: Teens in Action Camp, Nipgen, OH
July 21: Cincinnati Reds Baseball Game, Cincinnati, OH (singing the national anthem)
July 23-27: Appalachian Youth Camp, Orrstown, PA
August 3-12: Roxbury Holiness Camp, Orrstown, PA
This music group is comprised of seven talented students and travels the region year-round, bringing its distinctive sound to churches, summer camps, youth events and more.
C.S. Lewis is Coming to Ohio Christian University
Ohio Christian University is excited to bring "The Most Reluctant Convert" to its Circleville Campus. There will be one show only, on January 15, 2022, at 1:00 PM in the Ministry and Performing Arts Center (MPAC). This Fellowship for Performing Arts production has toured the United States from New York to California and played on campuses such as Princeton, Vanderbilt, Wheaton, UNC-Chapel Hill, and many other esteemed institutions of higher learning. Using C.S. Lewis' own words, award-winning actor Max McLean brings the brilliant Oxford fellow to life, taking us on his extraordinary
Ohio Christian University is excited to bring "The Most Reluctant Convert" to its Circleville Campus. There will be one show only, on January 15, 2022, at 1:00 PM in the Ministry and Performing Arts Center (MPAC). This Fellowship for Performing Arts production has toured the United States from New York to California and played on campuses such as Princeton, Vanderbilt, Wheaton, UNC-Chapel
Ohio Christian University is excited to bring "The Most Reluctant Convert" to its Circleville Campus. There will be one show only, on January 15, 2022, at 1:00 PM in the Ministry and Performing
Columbus Symphony to Perform in Ministry & Performing Arts Center
The beloved Columbus Symphony holiday tradition of Holiday Pops returns in 2021 with spectacular performances of popular holiday songs and classic carols. Conductor Ron Jenkins will lead the Columbus Symphony in performances of "Winter Wonderland," "We Wish You a Merry Christmas," "Sleigh Ride," "All I Want for Christmas is You," and many more. About The Columbus Symphony Orchestra: Since 1951, the Columbus Symphony (CSO) has been proud to serve as central Ohio's flagship music performance organization. The CSO's musicians, conductors, chorus, and world-renowned guest artists
The beloved Columbus Symphony holiday tradition of Holiday Pops returns in 2021 with spectacular performances of popular holiday songs and classic carols. Conductor Ron Jenkins will lead the Columbus Symphony in performances of "Winter Wonderland," "We Wish You a Merry Christmas," "Sleigh Ride," "All I Want for Christmas is You," and many more. About The Columbus Symphony Orchestra:
The beloved Columbus Symphony holiday tradition of Holiday Pops returns in 2021 with spectacular performances of popular holiday songs and classic carols. Conductor Ron Jenkins will lead the
Drama Team Presents Fall Production: Dark Road
Ohio Christian University's Drama Team is honored to present this year's fall production, Dark Road, written by Laura Lundgren Smith, on Friday, Nov. 12th at 7:00 PM in the Ministry and Performing Arts Center's auditorium. Admission is free and open to the public. This play may not be suitable for some younger audiences who may find the subject matter disturbing. Parental discretion is advised. The play focuses on the story of a female concentration camp guard, Greta, who has been given a death sentence for her crimes committed under the Nazi regime. Given one last chance to tell
Ohio Christian University's Drama Team is honored to present this year's fall production, Dark Road, written by Laura Lundgren Smith, on Friday, Nov. 12th at 7:00 PM in the Ministry and Performing Arts Center's auditorium. Admission is free and open to the public. This play may not be suitable for some younger audiences who may find the subject matter disturbing. Parental discretion is
Ohio Christian University's Drama Team is honored to present this year's fall production, Dark Road, written by Laura Lundgren Smith, on Friday, Nov. 12th at 7:00 PM in the Ministry and Performing
2021 Summer Celebration Picnic
Ohio Christian University held its first ever Summer Celebration Picnic on Friday, July 16, 2021. This was a time for OCU Alumni, donor partners and community members to get together and enjoy Food, Friends and Fellowship. In spite of some scattered storms, many people of all ages gathered together. Matt Hunnell, VP of University Advancement commented: "Despite a little rain, we had a great turn out. It was wonderful to see smiling faces on campus, and our friends enjoying music, a good meal, and time together. We look forward to many more events and gatherings on campus in the
Ohio Christian University held its first ever Summer Celebration Picnic on Friday, July 16, 2021. This was a time for OCU Alumni, donor partners and community members to get together and enjoy Food, Friends and Fellowship. In spite of some scattered storms, many people of all ages gathered together. Matt Hunnell, VP of University Advancement commented: "Despite a little rain, we had a great
Ohio Christian University held its first ever Summer Celebration Picnic on Friday, July 16, 2021. This was a time for OCU Alumni, donor partners and community members to get together and enjoy Food
2021 Refer-A-Friend Program Announced
Ohio Christian University (OCU) is pleased to announce an ongoing Refer-A-Friend Program where individuals can invite a friend to learn more about OCU. A monthly drawing for referrers and a scholarship for prospective students make this an excellent opportunity to refer a friend. The referrer should enter his or her friend's information at ohiochristian.edu/referafriend. This will automatically qualify the referrer to enter that month's drawing for a $50 e-gift card of the winner's choice and qualify the referred student for a Refer-A-Friend Scholarship. Refer A Friend Program
Ohio Christian University (OCU) is pleased to announce an ongoing Refer-A-Friend Program where individuals can invite a friend to learn more about OCU. A monthly drawing for referrers and a scholarship for prospective students make this an excellent opportunity to refer a friend. The referrer should enter his or her friend's information at ohiochristian.edu/referafriend. This will
Ohio Christian University (OCU) is pleased to announce an ongoing Refer-A-Friend Program where individuals can invite a friend to learn more about OCU. A monthly drawing for referrers and a
Youth Groups Invited to Pizza and Points
Ohio Christian University is hosting Pizza and Points for youth groups and prospective students to cheer on the Ohio Christian University Trailblazers during the Men's Basketball game against the Point Park University Pioneers on Saturday, January 18th at 3 p.m. The free event includes admission to the game, pizza, giveaways and face painting. In addition, high school juniors and seniors can enter to shoot a half court shot for a chance to win a $12,000 scholarship! Watch our video from a previous Pizza & Points: REGISTER A STUDENT REGISTER A GROUP
Ohio Christian University is hosting Pizza and Points for youth groups and prospective students to cheer on the Ohio Christian University Trailblazers during the Men's Basketball game against the Point Park University Pioneers on Saturday, January 18th at 3 p.m. The free event includes admission to the game, pizza, giveaways and face painting. In addition, high school juniors and seniors can
Ohio Christian University is hosting Pizza and Points for youth groups and prospective students to cheer on the Ohio Christian University Trailblazers during the Men's Basketball game against the
Ohio Christian Welcomes New Students for Spring 2020
Ohio Christian University is working hard to prepare for the start of the Spring 2020 semester, which begins Jan. 8. This semester, the campus welcomes over 20 new students, 10 of whom are transferring from Cincinnati Christian University following the closing of its doors. The university is welcoming double the number of new students as last spring, so it is fundamental that each individual feels comfortable as the semester starts. "We have prepared the new student orientation schedule and have worked with partners on campus to ensure that new students will receive the information
Ohio Christian University is working hard to prepare for the start of the Spring 2020 semester, which begins Jan. 8. This semester, the campus welcomes over 20 new students, 10 of whom are transferring from Cincinnati Christian University following the closing of its doors. The university is welcoming double the number of new students as last spring, so it is fundamental that each individual
Ohio Christian University is working hard to prepare for the start of the Spring 2020 semester, which begins Jan. 8. This semester, the campus welcomes over 20 new students, 10 of whom are
Festival of Carols Shares Message of Christ
The Ohio Christian University Music Department shared an impactful message of Christ's birth in its annual Christmas concert, Festival of Carols. The concert presented this story of glory to audiences of almost 500 on both Dec. 7 and 8 in the Ministry
The Ohio Christian University Music Department shared an impactful message of Christ's birth in its annual Christmas concert, Festival of Carols. The concert presented this story of glory to
Ohio Christian Works to Enhance Graduates' Financial Security
Over recent years college debt has increased at a growing rate with the Federal Reserve currently reporting the average amount of college debt to be $32,731. Of those who owe education debt, 10.3% are reported to be behind on their payments. The level of debt and negative impact to credit for those who default is a major burden to student's financial security after graduation and their piece of mind. Ohio Christian University, as a Christian institution, has a goal not only of education but also to instill principles of biblical stewardship into our students. Additionally, the
Over recent years college debt has increased at a growing rate with the Federal Reserve currently reporting the average amount of college debt to be $32,731. Of those who owe education debt, 10.3% are reported to be behind on their payments. The level of debt and negative impact to credit for those who default is a major burden to student's financial security after graduation and their piece
Over recent years college debt has increased at a growing rate with the Federal Reserve currently reporting the average amount of college debt to be $32,731. Of those who owe education debt, 10.3%
Education Summit Draws Local and State Representatives
On Friday Nov. 15, teacher education students and professionals gathered on the campus of Ohio Christian University where they discussed Ohio's new "Each Child Our Future" education plan. The event featured government and education professionals with insight on the new plan and how it will affect education now and in the future. The Ohio Department of Education's vision for the plan is for each child to be challenged to discover and learn, prepared to pursue a fulfilling post-high school path, and empowered to become a resilient, lifelong learner who contributes to society. The plan
On Friday Nov. 15, teacher education students and professionals gathered on the campus of Ohio Christian University where they discussed Ohio's new "Each Child Our Future" education plan. The event featured government and education professionals with insight on the new plan and how it will affect education now and in the future. The Ohio Department of Education's vision for the plan is for
On Friday Nov. 15, teacher education students and professionals gathered on the campus of Ohio Christian University where they discussed Ohio's new "Each Child Our Future" education plan. The event


All films come with a ten year guarantee!
Whether you need window films for privacy and insulation purposes or to highlight your business's brand, Safety First Window Films are guaranteed to find the right product for you.
Having been established within the window film industry since 1988, Safety First can provide window film that can be applied to 99% of all glass types in Scotland, fitting all films onto your existing windows efficiently and professionally.
"Although Edinburgh is not known for bright light, it actually has very long bright days in the summer. This will fade fabric and wood furniture in a short time. It is very important to install UV film on windows and Safety First is a fabulous company. The installers, Frank and Darren, are consummate professionals with years of expertise and experience. It is obvious they are proud of their craft, and rightly so. It was a pleasure working with them. They make a terrific team. Also, John and Gwen who give the estimates and schedule the work are very friendly and helpful. John, in particular, knows the regulations for Grade I historic buildings. For protecting furniture, carpets, pictures, and fabrics, I highly recommend installing UV film on windows and using Safety First as the company to do it."
As always, Darren and Frank discussed and advised us on our requirements then carried out a full professional job on the glass and tided up behind themselves. I will be happy to recommend them and of course your Company to my successor Mr John Boag who takes over as of the end of March this year when I retire. Thank you for your help in the past.
"This film is helping to maintain a more normal environment for the children to grow up in."


Harrison Murray Lettings are very pleased to bring to the market this spacious 4 bedroom property in the popular area of East Hunsbury. Hunsbury has ample ameneties on the doorstep including shops, schools, leisure centre, doctors and dentist and easy access to M1.
AVAILABLE 11th March 2019 and offered UNFURNISHED!!!Property briefly comprises:Entrance hall leading to all downstairs rooms and staircase. Large lounge with decorative fireplace with mirror above and large french doors onto garden. Bay fronted window to front aspect.
Newly installed Shaker style kitchen in cream with complimentary brick effect spalsh back tiles and oak effect work tops. Gas hob and electric double oven included. Space for fridge/ freezer and plumbing for washing machine and dishwasher.
Upstairs there are 4 double bedrooms. master bedroom has fitted dressing table with light and wardrobes either side. Two matching portable bedside cabinets. White en suite with towel radiator, wc, basin and shower cublicle.
Second and third bedrooms also generous size doubles and the fourth is a smaller double. Family bathroom also with a neutral suite with electric shower over bath, towel radiator and wall cabinet.
Neutral carpet and decor throughout the property. Outside there is a private enclosed garden to the rear with patio areas, pathways and grass areas. There is also a pond. Side access to the front of the property.
To the front there is a large shared gravel driveway. Unfortunately no commercial vehicles or caravan's to be parked on the drive due to a covenant in the deeds.
To arrange a viewing please call 01604634890.
See hmlettings.co.uk for full information or contact your nearest branch.


Are you a person that wants to get into the administrative field? If so, try looking into becoming a new business specialist. A new business specialist works for a whole sale brokerage that works for insurance agents. These agents call and provide information for their clients and the new business specialist finds the best product for the client. Then, once they've found the appropriate agent to place the client with, they complete and submit the application for the agent.


A river-friendly future for New Savannah Bluff Lock & Dam.
Rock The Dam from Savannah Riverkeeper on Vimeo.
What is Rock the Dam?
Rock the Dam is a campaign organized by concerned and informed citizens to encourage a responsible, forward-thinking future for the New Savannah Bluff Lock & Dam on the Savannah River.
This aging structure has long outlived its original purpose and is in a critical state of disrepair, and fixing it would put local taxpayers on the hook for millions. The dam impedes fishing, swimming, recreation and travel on the river, all things that would become possible or drastically improve once it's gone. We've got a solution that will save taxpayers money, help the fish and health of the river, and preserve the pool.
Dam removals and innovative replacements all across the country have successfully restored river systems and given them back to the people. The time has come to free the mighty Savannah River and with it, unleash an incredible future for everyone in our community. We think it's a once-in-a-lifetime opportunity to change our area for the better. Learn more and join us!
MAY 2018: The City of Augusta hires McLaughlin Whitewater Group for the whitewater park design at Lock & Dam.
The Corps of Engineers have published this webpage featuring helpful info and FAQ about this quickly-evolving issue. Click to read all about the SHEP Fish Passage requirements at Lock & Dam.


Timeshare Forums > Timeshare Feedback and Experiences - UNITED STATES > Hawaii > OK, I have to ask - Centipedes??
View Full Version : OK, I have to ask - Centipedes??
So, I was reading the Maui Revealed book (which is great!! thanks for the tip on the books), and in the "things to watch out for" section, there is a mention of centipedes on Maui.
Yes, up close and personal !!!
When I was 22 and lived on Oahu, I was awakened one night by something I felt around my wrist. Thinking that it was the lace edging on the sleeve edge of my nightie, I just half-awakedly (is that a word), tried to brush it off. I ended up standing up on my bed screaming like a banshee until my housemates came in and killed it. They sprayed liquid freon on it (before it was banned - 1974). Horrible, awful, took me a looooong time to sleep peacefully ever again.
It was a centipede. I ended up with an arm about the size of a football. The bite was just small-fanged, but the swelling is what I remember most. Saw the doc, Rx'd with antibiotics and a couple of lasix (fluid) to set me right.
True story. My roomies still like to remind me of the blood-curdling scream.
I lived in leeward Oahu, right across from the cane fields, but it wasn't the centipedes that were hideous. No, it was the cane spiders which got into the house when they harvested the cane. To harvest the cane, they burn the fields which leaves the stocks or sugar cane. Unfortunately, it drives all the cane spiders out and most of them seemed to like my little house. The centipedes must've told them .
Oh yuck! My husband saw something in Maui Revealed about cane spiders when we were there in Sept. Luckily we didn't see any, but he was just mentioning them again just yesterday.
Here (http://www.whatsthatbug.com/cent_2.html) is a large picture of one in HI. Scroll down a little bit.
We were staying at the Kauai Coast Resort Sept of 05. We had a two bedroom unit and my daughter and her husband were staying with us. After we finished breakfast my son in law was in the shower and my daughter was making the bed in their room when she started screaming. I ran into the room and she said something was under the covers. I pulled the covers down and a centipede fell out of them. I picked it up with my hand wrapped in the blanket I pulled off the bed and threw it out over the lanai. Luckily no one got bitten. I was fairly large and if I remember correctly it was green in color with other colors running through it.
We have centipedes in Denver and see them often in our back yard, especially in the hot part of summer, but I have never been bit by one and they don't see to move fast. I notice them in my flower pots. I guess I should be more careful, but perhaps they are not aggressive and not poisonous. I have yet to have one on me.
I am not scared of spiders or bugs of any kind. Snakes are just a little bit scary, but we have garter snakes in our yard all the time. They like our flagstone rocks and sun themselves around our pond. I went out to pull weeds around the pond and saw four snakes on the rocks. They like the water, too. My stepdad totally freaks out when he sees a snake and I told him not to look by the pond when he takes care of our yard while we are gone. As long as he cannot see them, he seems to be okay. There are always snakes back there, with all those giant rocks and the hot sun. Our neighbor killed two rattle snakes about 20 years ago that the kids all heard when they were outside playing. We have scary stuff around here. We also have ticks that hang out by the older parts of our fence. I would hate to have to worry about a dog. Our cats stay indoors.
I'm not afraid of snakes, bugs or most other critters either. I've learned to identify poisonous vs non-poisonous snakes, since I'm an avid gardener. Most snakes are gardeners' allies. I also like bats because they eat mosquitoe, but unfortunately none of the bat houses I've installed in yards attracted bats to make them home. I don't like mosquitos or those pesky sand fleas or no-see-ums in Mexico...thank goodness for SkinSoSoft and my fast-action swatting maneuvers.
I am very, very afraid of spiders and any insects, so now I am rethinking every going to Hawaii (or anywhere very tropical for that matter!). I already had some fears b/c my friend grew up in Hawaii and had this weird bump on her arm when she was a kid. Finally, the doctor lanced it and guess what was inside -- something had bitten her, laid eggs and they hatched....so add this to my Hawaii bug stories!
Probably the most revolting thing I've ever seen. Move fast as hell, and are big and creepy enough to scare even the most macho of folks. I looked 'em up online coz I had no idea what they were, and apparently they have a painful bite as well. I don't know if they are similar to the Maui ones, but god if I never see one again, that's quite alright by me.
now I am going to dream of bugs tonight--ewe!!!!
Oppps, hit the send button a little too soon. Someone mentioned the cane spiders--oh my, those things are huge! I still chuckle about a posting on another Hawaii board. This gentleman had taken his girl friend to Kauai to propose. The second day they were there, a cane spider walked out in the middle of the living room. Being the protective male, he immediately screamed like a girl and hid in the bathroom until she killed it! My mental piciture of that still amuses me. Happily, the guy did pop the question and despite his rather unprotective spider response, she did accept. And, how many of you know that there are scorpions in Hawaii? Friends of our saw one at Hapuna Beach on the Big Island.


text:
Saanich police Sgt. Dean Jantzen sits with a replica M16 Bushmaster propped up on the table in front of him. The assault rifle and another 158 guns were seized by police from the home of man
Long-gun registry helps bust Saanich man who embezzled 159 firearms
Semi-automatic rifles, shotguns, pistols were seized from the home of a man who worked as a manager at Island Outfitters.
Feb. 6, 2012 1:00 p.m.
Semi-automatic rifles, shotguns, pistols – 159 firearms in total – were seized from the home of a man who worked as a manager at Island Outfitters.
Saanich police were contacted Thursday (Feb. 2) after more than $250,000 worth of merchandise, including the guns, 400 boxes of ammunition and a crossbow, as well as hunting and fishing equipment, went missing from the store's inventory.
A 40-year-old Saanich resident, a manager in charge of inventory and ordering, was arrested Thursday at the Douglas Street shop. Police say he was legally ordering the items on the store's behalf, but then added them to his personal collection rather than putting them out for sale.
"This employee is a gun enthusiast," Jantzen said, calling him a "prominent member" of the local firearms community. "Many of these weapons have no legitimate (hunting) purpose."
The man legally registered all the firearms with the Canadian Firearms Registry after doctoring paperwork and receipts, Jantzen said.
"Long-gun registry checks were at the forefront of this investigation," he said. "We support these accessible databases … when we're investigating instances like this."
Police seized the items late Thursday night from the man's home in the 4200-block of Shelbourne St. Most of the guns, Jantzen said, were stored properly, and those that weren't had been rendered safe.
Many of the guns had never been shot, or even taken out of the packaging, Jantzen added.
"Our detectives are confident that we have everything (that was) outstanding," he said.
Police believe no guns were ever diverted to "illicit use," but a great concern to police was the potential for the weapons to have been stolen from the man's home.
"This is an unscrupulous person … a well-placed employee who was trusted entirely and took advantage of that trust," Jantzen said.
The 40-year-old employee has not been charged, but police are recommending a charge of theft over $5,000. He was released on a promise to appear.
Camosun College sees influx of international students
Citizens' group wants non-lethal solution to Greater Victoria deer problem


Have you heard the news? In the not too distant future, next Sunday A.D., Mystery Science Theater 3000 will be returning! But, of course, the whole story is complicated.
Shout Factory has purchased Mystery Science Theater 3000 and has licensed Joel Hodgson to restart the show. Joel has launched a Kickstarter campaign to do just that.
There will be new on-screen talent. Joel won't be the host, there will be a new villain, and new actors will portray Crow and Servo. None of the RiffTrax crew (Mike Nelson, Kevin Murphy, Bill Corbett, who played Mike, Servo, and Crow in the later years) will be involved.
Confused? Just be thankful I didn't give you the whole story about all the cast changes and network changes over the years. I could, you know. I'm that obnoxious. But I spared you of that. I'm that nice. You're welcome.
Anyway, MST3K is coming back! Yay!
Which brings up the Question of the Day. Who should be the new host?
If you want to be a killjoy and be serious about it, go ahead. Or, you could play comedian and come up with a really clever idea that will make everyone else just laugh and laugh. Go for that.
Again, who would be your choice for the new host for the reboot of Mystery Science Theater 3000?
That's easy..... Killary Klinton.... how she has survived without going to prison so far is a Mystery, her view of us minions is down to a Science, her entire campaign has been Theater, and since she is immortal, she will live to be 3000.
Fear the truth, it is much scarier than you could possibly imagine.
Villain: Bernie Sanders. Not so much the bad guy who is doing this on purpose but an incompetent memory addled geezer who keeps forgetting where he left his glasses.
The person in the satellite can pretty much be anyone except a liberal. They just don't have a sense of humor and it would all eventually devolve into them just shouting "racism" at the screen.
Ryan Stiles as the host and Colin Mochrie as the mad.
Biden as the clueless but lovable evil sidekick "TV's Joe"
Muhammad Saeed al-Sahhaf seems to be available.
I can't be a killjoy or a comedian - because I am not "minimally-cultured," especially in cable TV shows.
...or Christopher Walken - either one.
Gary Busey, obviously. You wouldn't even need to do any writing for him.
Neil De Grass Tyson and the first movie An Inconvenient Truth.
One has to wonder if modern Hollywood movies have gone beyond parody.
Obviously, Jar Jar Binks and co-host BB-8. Al Gore could guest host for laughs.
The host should be Andrew Heaton.
Frank J. should definitely be one of the villains, perhaps patterned after Rico (Superego).


Welcome to "Prayag Developers & Builders".We have 24 successful projects to our credit over past more than 20 years & yet, you, our customer, are our most valuable asset. We believe that high-class living doesn't mean high prices.
And that real emotions are more important than real estate. We believe building trust precedes building structures and a foundation of faith can support relationships for a lifetime. With our dedicated team of some of the best professionals in the business, you can rest assured that your home is every inch like you thought it would be.


Every Roman has its own favorite gelateria and they could spend hours arguing about it.
You will find a gelato shop around every corner in Rome, however, remember that the most impressive display or most colorful ice cream rarely guarantee an equally amazing taste. Therefore for you not to get lost, we have prepared for you our little guidebook on where to taste the best gelato in the city.
My personal favorite. "No tricks, just nature" – Fatamorgana's motto – perfectly summarizes this place. Nice, simple gelateria offering a wide range of heavenly tasting ice cream. Great selection of dairy free, vegan ice cream.
There are always some surprises in the daily-changing list. Be ready to stand in the line but we guarantee – it is worth the wait!
A local favorite – small gelateria close to San Giovanni. The name refers to the old way of making gelato and slushes, using snow from the mountains. Small display offers around 20 different flavors all made with fresh and seasonal products.
We sincerely hope this little guidebook will help you start your wander around the city searching for the perfect gelateria. Happy tastings!
Filed under Blogg, Italian Food, Tips on holiday.
Tagged gelateria, gelato, icecream, italia, Italy.


If you told me that someone had tried to combine metal, progressive rock, post-rock, dark folk, ambient, and electroacoustics all with symphonic and avant-garde stylings, I would have been interested. Skeptical, but interested.
Enter The Soundbyte, a solo effort from composer and guitarist Trond Engum. Teaming up with Third and the Mortal bandmate and drummer Rune Hoemsnes, Engum leads this effort through seven tracks that all exhibit a gothic and foreboding tone, but are varied nonetheless.
Additional instrumentation, all from guest musicians, provide atmospheric layers to the album. Included are organ, ethereal female voices, electronics, horns, and cello. While deliberately paced, Engum eschews conventional song structures. The melodies are clearly set forth, but accompanied by unusual percussion, chanting, and effects. The riffing is heavy at times, but not in a speed-metal fashion.
As an example, the second track, Descending, opens with dark electronics, whispered voices, and processed organ and cello. All of this is accompanied by erratic drumming, before morphing into a tension-building prog-metal riff overlaid with mournful vocals. The track eventually evolves into a few seconds of walled noise, then fades.
Despite my initial doubt, The Soundbyte has largely succeeded in combining a diverse array of musical styles in a compelling and cohesive fashion. Solitary IV gets a solid thumbs-up, and it looks like I've got another back catalog to explore.
If Barack Obama spoke in paragraphs, as was often said, then Steve Coleman speaks in philosophical treatises—long ones. "My middle name is Tangent," the alto-saxophonist and MacArthur fellow said at one point in our conversation (which, here, has been heavily edited from more than an hour of dialogue). But it's only because Coleman is full of ideas. His body of music, which now spans over 30 years, is elaborate, layered, and draws on a deep reservoir of influences, from human biology to Asian mysticism.
Even by his own standards, Coleman's new album, Morphogenesis, is incredibly dense. It combines jazz improvisation (including members of his primary improvising ensemble) with written arrangements for chamber musicians, including violin and clarinet. We spoke to Coleman about his creative processes, the tension between readers and improvisers, and the role of the audience in developing music.
Meditation / Resurrection is a double-album presenting a bounty of beautiful new William Parker compositions performed by two of his flagship ensembles, the Quartet -&- In Order To Survive.
It was recorded & mixed live during a one-day studio session in late 2016. With the tone of the pieces & performances reflecting our current moment, this is Parker & the groups' follow-up release to 2013's monumental Wood Flute Songs box set.
At the core of both groups is Parker's foundational bass together with rhythm twin, drummer Hamid Drake. Their partnership–two decades deep now–is one of the great rhythm sections in recorded history. Alto saxophonist Rob Brown is likewise featured throughout. Brown is one of the all-time great improvisers on alto, and the springboard of Parker's bands in which he has been perennially featured always brings out his best.
Live in New York, 2010 presents a tremendous one-night engagement by saxophone colossus David S. Ware and his latter day Trio in an intimate club setting – at Blue Note on October 4, 2010. This double-album features the complete night of music – one set per CD. Featuring his perennially steadfast musical partner William Parker on bass, and equally incomparable Warren Smith on drums, they were celebrating the recent release of the studio album, Onecept. Having played an electrifying set at Vision Festival in June, this extended night in October was the third and final magnificent time on which this trio convened.
Appearances at jazz clubs in New York were rare for Ware. The two hour+ sets, with repast in-between, allowed him and group to inhabit the space; afforded more time to stretch out and breathe with the music. As Ware was exclusively dealing with spontaneous forms during this period in his creative life, the six extended suites here were newly created on that night. None of these fantastic multi-track recordings have been previously released.