text:
\section{Introduction}
\label{sec:intro}

In many real-world optimization challenges, we encounter optimization problems which are too complex to be explicitly modeled via mathematical functions, but which nonetheless need to be assessed and solved, more often than not requiring significant computational resources to do so. Explicit problem modeling is also an issue when the relationship between decision variables and solution quality cannot be established other than by simulations or experiments. A standard example for the latter is the design of (deep) neural networks. \emph{Black-box optimization algorithms (BBOA)} are algorithms designed to solve problems of the two types above. BBOA are usually iterative procedures, which actively steer the search by using information obtained from previous iterations, with the goal to eventually converge towards an estimated optimal solution. In each generation, a number of solutions candidates are generated and undergo evaluation.

Classically, BBOA were manually designed, based on users' experience. A plethora of algorithmic components exist from which users can choose to build their own algorithms, and the number of these components is growing every year.
Even though the basic underlying principles of these components can be considered similar in nature, their performances on different problem instances can greatly vary.
An important and challenging task is thus to select the most appropriate and efficient algorithm when presented with a new, unknown problem instance.  This research problem, formalized as the \emph{algorithm selection problem (ASP)}~\cite{RiceASproblem}, is one of \emph{the} core questions that evolutionary computation aims to answer.
The algorithm selection problem is classically tackled by relying on expert knowledge of both the problem instance and the algorithm's strengths and weaknesses.  In recent years, however, due to the significant progress in the machine learning (ML) field, there has been a shift towards an \emph{automated} selection~\cite{KerschkeKBHT18,KerschkeT19,MunozASSurvey,JD20} and configuration~\cite{BelkhirDSS17} of algorithms based on supervised learning approaches. The idea behind these approaches is in utilizing ML techniques to design and to train models to accurately predict the performance of different black-box algorithms on previously unseen problem instances, with the goal to use these performance predictions to select and to configure the best algorithm for the problem at hand, respectively.
In order to apply supervised learning, problem instances need to be represented in a convenient way via numerical values. That is, we need to quantify relevant characteristics of a problem instance through appropriate measures. These measures are referred to as \emph{features}. In the terminology used in evolutionary computation (EC), features are hence aimed at describing the \emph{fitness landscape} of a problem instance.

Fitness landscape analysis has a long tradition in EC. For practical use in black-box optimization, however, the fitness landscape properties can only be described via an informed guessing strategy. Concretely, we can only approximate the fitness landscapes, through the samples that we have evaluated and to which a solution quality has been assigned. Research addressing efficient ways to characterize problem instances via feature approximations is subsumed under the umbrella term \emph{exploratory landscape analysis (ELA)}~\cite{mersmann_exploratory_2011}. Common research questions in ELA concern the number of samples needed to accurately approximate feature values, the design and the selection of features that are descriptive and easy to approximate, and the possibility to use feature values to transfer learned policies from some instances to previously unseen ones.

A drawback of existing ELA-approaches are the resources needed to extract and to compute the feature values, the time required to train the models, and a lack of explainability. In this work, we focus on the first issues, the feature extraction. Most ELA-based studies perform a three-step selection/configuration: in the first step a number of search points are sampled (commonly using uniform sampling, Latin Hypercube Designs, or quasi-random sampling~\cite{RenauDDD20}), evaluated, and then plugged into a feature computation algorithm such as the R tool \emph{flacco}~\cite{flacco}. In the second step the model for the classification or regression task is built and an algorithm and/or its configuration is suggested. In the third step, this algorithm is then run on the problem instance under consideration. Clearly, the effort for steps 1 and 2 cannot be neglected, and can have a decisive influence on the usefulness of a per-instance algorithm selection/configuration approach, as its effort needs to pay off compared to the performance of a default solver. Even when neglecting the computational overhead of this approach and focusing on function evaluations only as performance measure (as is commonly done in evolutionary computation~\cite{cocoplat}), the evaluations needed for completing step 1 need to be taken into account. Much research has been done on determining a suitable number of samples, and typical recommendations vary between $30d$~\cite{BelkhirDSS16} and $50d$ samples~\cite{kerschke_low-budget_2016}, where $d$ denotes the dimension of the problem. This is hence a considerable investment.

Of course, one could use these samples to \emph{warm-start} the optimization heuristics, e.g., by initiating them in good regions and/or by calibrating their search behavior based on the information obtained from the samples used to compute the features.

A charming, yet straightforward alternative would be to integrate the first step of the ELA-based approach described above into the optimization routine, by computing the features based on the search points that a default algorithm would anyway perform. That is, one would use the search trajectory of such a default algorithm to predict and then to select and/or to configure a solver \emph{on the fly}, once or even several times during the optimization process.

Similar to parameter control~\cite{KarafotiasHE15,AletiM16,DoerrD20paramControlchapter}, such a dynamic selection would not only allow to \emph{identify} an efficient algorithm for the given problem instance, but could also benefit from \emph{tracking} the best choice while the optimization process (and the best response to its needs) evolves. Such a dynamic algorithm selection can therefore be seen as an ELA-based variant of hyper-heuristics~\cite{BurkeGHKOOQ13hyperheuristicssurvey}. The approach has previously been used in the context of constrained optimization, with the goal to have a dynamic, ELA-guided selection of a suitable constraint handling technique; see~\cite{Malan18DEconstraints} for examples and further references.

A key challenge in applying dynamic ELA-based algorithm selection is the fact that the feature values can vary drastically between different sampling strategies~\cite{RenauDDD20}. Since the distribution of points sampled on different problem instances can differ quite drastically even when using the same algorithm, it is not clear, a priori, if or how suitable ML models can be trained. This challenge was confirmed in~\cite{JD19}, where it was shown that the landscape which an algorithm sees locally during the optimization process (i.e., the partial landscape it is aware of at each step of the optimization process) usually differs a lot from the global fitness landscape of the problem it is solving.

\paragraph{Our Results.} With the long-term goal to obtain well-performing dynamic ELA-based algorithm selection and configuration techniques, we analyze in this work a first, rather cautious task: ELA-based performance prediction using the trajectory samples of the algorithm under investigation. More precisely, we
consider the Covariance Matrix Adaptation Evolution Strategy (CMA-ES~\cite{HansenO01CMAES}), and we aim at predicting its solution quality (measured as target precision, i.e., the difference to an optimal solution in quality space) after a fixed budget of function evaluations. Concretely, we use the first 250 samples evaluated by the CMA-ES and we aim at predicting its performance after additional 250 evaluations, doing so for 20 independent CMA-ES runs. The performance regression is done via a random forest model which takes as input the features computed from the trajectory data and which outputs an estimate for the final solution quality.

We then take into account that problem characteristics cannot only be described via classic ELA features, but that internal states of the search heuristics can also be used to derive information about the problem instance at hand. Such approaches have in the past been used, for example, for local surrogate-modelling~\cite{Holena}.  We analyze the accuracy gains when using the same state information as in~\cite{Holena}, that is, the values of the CMA-ES internal variables that mainly carry information about the current probability distribution from which the CMA-ES samples candidates for the new generation.  In our experiments, the advantage of using this state information over using ELA-features only, however, is only marginal. Concretely, the average
difference between true and predicted solution quality decreases from 14.4 to 12.1 when adding the state variables as features (where the average error
reported here is taken over all 24 benchmark problems from the BBOB suite of the COCO platform~\cite{cocoplat}, and over all performed CMA-ES runs).

We observe in the experiments above that some CMA-ES runs are drastic outliers in terms of performance, at times with the target precision differing from the target precision of all the other runs by up to 10 orders of magnitude.  We therefore also consider an intentionally more ``friendly'' setting, in which we analyze the regression quality only for the run achieving median performance on a given problem instance.
Conclusions for combining trajectory-based and state variable features remain almost identical to those stated above.

We then compare these median trajectory-based predictions to the classical approach using globally sampled features. Here, we pessimistically assume that the samples were computed for free. That is, we couple 2 separate sets of the global feature values approximated from 250 and 2000 uniformly sampled points each to the target precision achieved by the CMA-ES after 500 function evaluations.
Interestingly, the difference in prediction accuracy compared to our trajectory-based predictions is rather small.  The global predictions still remain, however, more accurate, with an average absolute prediction error of 4.7 vs. 6.2 for the trajectory approach (where again the average is taken over all 24 BBOB functions).

Furthermore, we also use this median setting to analyze the influence of feature selection on prediction accuracy.  Different state-of-the-art methods were applied, using a transfer learning scenario, to select features estimated to be the most important and to have highest discriminative power. Here again, the differences in prediction accuracy were small,  with feature selection surprisingly leading to an overall slightly worsened solution quality than the full feature portfolio.

As suggested in~\cite{JD20}, all our experiments are based on two independently trained models: one which aims to predict target precision after 500 evaluations, and one which predicts the logarithm of this target precision. While the former is better in guessing the broader ``ball park'', the latter is more suitable for fine-grained performance prediction, i.e., when the expected performance of the algorithm is very good. As in~\cite{JD20}, we also build a combined regression, which uses either one of the two models, depending on whether the predicted performance is better or worse than a certain threshold. The optimal thresholds differ quite drastically between different feature sets. However, a sensitivity analysis reveals that their influence on overall performance is rather small. Also, the ranking of the different feature portfolios remains almost unaffected by the choice of the threshold.
In line with the results in~\cite{JD20}, the combined models perform consistently better than any of the two standalone ones, albeit slightly.

\section{Supervised ML for Performance Regression}
\label{sec:supervisedml}

\textbf{The Experimental Setup.}
When it comes to landscape-aware performance prediction, supervised machine learning techniques such as regression and classification have been studied in a variety of settings.  Regression models, unlike classification ones, have an advantage of keeping track of the magnitude of differences between performances of different algorithms, as they measure concrete values for performances of all algorithms from the portfolio.

Among different supervised learning regressors in the literature, such as support vector machines, Gaussian processes or ridge regression to name a few, it has been empirically shown that random forests outperform other models in terms of prediction accuracy~\cite{HutterXHL14}.  A random forest is an ensemble-based meta-estimator that works by fitting multiple decision trees on subsamples of the original data set, then uses averaging as a way to control overfitting.  In our experimental setup, we used an off-the-shelf random forest regressor from the Python \emph{scikit-learn} package~\cite{scikit-learn}, without parameter tuning and using 1000 estimators.

We restricted this work to a single heuristic, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES~\cite{HansenO01CMAES}). The CMA-ES works by iteratively sampling a new population of candidate solutions from a shifted multivariate normal distribution, choosing the best offspring of the current population based on their respective fitness values, and then updating the parameters of the probability distribution according to the best candidates. For the purpose of our work, we used its standard version, available in the Python \emph{pycma} package~\cite{pycmaes}, which uses a fixed population size and no restarts during the optimization process.

As our benchmark, we used the first five instances of all 24 noiseless \emph{BBOB} functions of the \emph{COCO} platform~\cite{cocoplat}, an environment for comparison of algorithm performance in continuous black-box optimization.  The different instances of each function are generated by translating and rotating the function in the objective space. These transformations do not affect the performance of CMA-ES, but they do influence some of the feature values, especially those which are not transformation-invariant~\cite{TomeinvarianceASoC} (for the invariant features, the boundary handling can have an effect on the feature values). We focus on dimension $d=5$ here.

For our first experiments, we perform 20 independent runs of the CMA-ES on these 120 problem instances, while keeping track of the search trajectories and the internal state variables of the algorithm itself.  Throughout this work, we fix the budget of 500 function evaluations, after which we stop the optimization and record the target precision of the best found solution within the budget.  In order to predict those recorded target precisions after 500 function evaluations, we compute the trajectory-based landscape features using the first 250 sampled points and their evaluations from the beginning of each trajectory, and couple them with the values of the internal CMA-ES state variables extracted at the $250^{th}$ function evaluation.

\begin{figure*}[t]
\includegraphics[width=\textwidth]{geom_point_trajectory_true_target_precisions_logscale.pdf}
\caption{Target precision achieved by the CMA-ES with a budget of 500 function evaluations, for each of the first five instances of all 24 BBOB functions. Differently colored and shaped points represent 20 independent CMA-ES runs.} \label{fig:true_tp_20_runs}
\end{figure*}

Figure~\ref{fig:true_tp_20_runs} summarizes the target precision achieved by CMA-ES in each of the 20 runs. We see that the results are more or less homogeneous across different runs and across different instances of the same problem. However, we also observe several outliers, e.g., for functions 7 (outlier for all instances), function 10 (instance 4), function 12 (instance 1). It is important to keep in mind that the randomness of these performances are entirely caused by the randomness of the algorithm itself -- the problem instance does not change between different runs.

For landscape feature computation, we use the R package \emph{flacco}~\cite{flacco}. Following suggestions made in~\cite{KerschkeT19,BelkhirDSS17} we restrict ourselves to those feature sets that do not require additional function evaluations for computing the features.  Namely, in this work we use 2 original ELA feature sets (\emph{y-Distribution} and \emph{Meta-Model}), as well as \emph{Dispersion}, \emph{Nearest-Better Clustering} and \emph{Information Content} feature sets.  This gives us a total of 38 landscape features per problem instance.  In addition, we follow up on an idea previously used in~\cite{Holena} and consider a set of internal CMA-ES state variables as features:
\begin{itemize}
    \item Step-size: its value indicates how large is the approximated region from which the CMA-ES samples new candidate solutions.
    \item Mahalanobis mean distance: represents the measure of suitability of the current sampled population for model training from the point of view of the current state of the CMA-ES algorithm.
    \item $C$ evolution path length: indicates the similarity of landscapes among previous generations.
    \item $\sigma$ evolution path ratio: provides information about the changes in the probability distribution used to sample new candidates.
    \item CMA similarity likelihood: it is a log-likelihood of the set of candidate solutions with respect to the CMA-ES distribution and may also represent a measure of the set suitability for training.
\end{itemize}

As suggested in~\cite{JD20}, and using the elements described above, we establish two separate regression approaches.  One model is trained to predict the actual, true value of the target precision data (we refer to it as the \emph{unscaled model} in the remainder of the paper), while the other predicts the logarithm of the target precision data (the \emph{log-model}).  It is important to note that the target precision measure intuitively carries the information about the order of magnitude of the actual distance to the optimum, i.e., the \emph{distance level} to the optimum, which is effectively computed as the log-target precision.  For instance, if an algorithm reaches a target precision of $10^{-3}$ for one problem instance and $10^{-7}$ for another, it means that the algorithm found a solution which is 4 distance levels closer to the optimum in the latter scenario.  Moreover, to reduce variability, we estimate both models' prediction accuracy through performing a 5-fold \emph{leave-one-instance-out} cross-validation, making sure to train on 4 out of 5 instances per BBOB function, test on the remaining instance and combine the results over the rounds.

\textbf{Results.}
Adopting our two regression models, we trained them separately in the following three scenarios: using as predictor variables the landscape features only, using the internal CMA-ES state variables only, and using the combination of the two.  We trained the random forests 3 independent times and took a median of the 3 runs to ensure the robustness of the results.

\begin{figure*}[t]
\centering
    \includegraphics[trim= 1cm 12.5cm 6cm 1cm, clip, width=0.6\textwidth]{rf_format_all_models.pdf}
\caption{Absolute prediction errors for both regression models aggregated per BBOB function in 3 different scenarios depending on the feature set used. The \emph{SV} column stands for the CMA-ES state variables, the \emph{ELA} for the landscape features, and the third one is the combination of both.} \label{fig:traj_pred}
\end{figure*}

Figure~\ref{fig:traj_pred} highlights the absolute prediction errors per BBOB function using two regression models, the unscaled and the log- one, when trained with 3 different feature sets: using only the trajectory landscape data, only the CMA-ES state variable data, and the combination of the two.  For the majority of the functions, using the combination of the trajectory data and the state variable data seems to help in improving the performance prediction accuracy, compared to the scenarios which use only one of those two feature sets.

We also confirm that the log-model is indeed better at predicted fine-grained target precision (e.g., in the case of F1 (sphere function) or F6 (linear slope function), we know that those functions do not require many function evaluations to converge to the global optimum, and their recorded target precision values are already quite small as they are very near the optimal solution).  On the other hand, the unscaled model performs better where the target precision values are higher (e.g., for the functions such as F3, F15 (two versions of Rastrigin function), and also F24 (Lunacek bi-Rastigin), which are all highly multimodal, the number of function evaluations in our budget was not nearly enough to allow for finding a true optimum).

We also notice that using only the state variables for the unscaled model does not suffice for an accurate prediction in the most cases.  The reverse situation is nevertheless also possible: we see that for F12, using only the state variables yields the best accuracy in the unscaled model.  Furthermore, there are also exceptions where using only the landscape data results in a higher accuracy than using the combined features (e.g., F11 for both models, F5 for the unscaled model).

\section{Comparison with Global Feature Values}
\label{sec:compglobal}

We then proceeded to compare the differences in the prediction accuracy from the sets described in the Section~\ref{sec:supervisedml} with the prediction accuracy using the global feature data, both alone and combined with the same CMA-ES state variable data as above.  To be able to perform a fair comparison, for the trajectory data we selected from the 20 executed CMA-ES runs those runs with the median target precision value per problem instance and their corresponding features and re-trained the unscaled and the log-model.  Global features-wise, both models were also trained using features computed from 2000 and 250 globally uniformly sampled points (the median value of 50 independent feature computations) for each function and instance.

\begin{figure*}[t]
\centering
    \includegraphics[trim= 1.5cm 5cm 2cm 1cm, clip, width=\textwidth]{rf_median_runs_format_all_models.pdf}
\caption{Absolute prediction errors for both regression models for the median trajectory-based prediction (the first 3 columns of each block) and the median global feature prediction (the middle two columns of each block represent the errors when using the 2000-sample features, and the last two columns correspond to using the 250-sample features).} \label{fig:glob_pred}
\end{figure*}

Figure~\ref{fig:glob_pred} shows the absolute errors in prediction when the trajectory-based approach is compared with the results using the global features.  The highest accuracy is reported in cases when only the global landscape features were used, across almost all problems, with 2000-sample features yielding the best results.  Here, we do not observe a huge improvement when combining the global landscape features with the state variable data.  It seems that the number of samples used to compute the features can be crucial in reducing the errors in prediction, as global sampling could be linked to a potential higher discriminative power of features thus computed. Again, for certain functions such as F2 and F10 (both of which are different variants of the ellipsoidal function), we observe an overall low accuracy.

\section{Sensitivity Analyses}
\label{sec:sensitivity}

\textbf{Feature Selection.}
To provide a sensitivity analysis based on the features used for the performance regression, we performed feature selection in the scenario of transfer learning, i.e., between different supervised tasks, where the features selected for the problem classification task have been evaluated on the performance regression task.

To do this, we have explored four state-of-the-art feature selection techniques:
\textit{Boruta}~\cite{kursa2010boruta} is a feature selection and ranking algorithm based on random forests algorithm, which only selects features that are statistically significant.
\textit{Recursive feature elimination (rfe)}~\cite{granitto2006recursive} learns a model assessing different sets of features by recursively eliminating features per loop until a good model is learnt. It requires an ML algorithm for evaluation, and here we use a random forest.
\textit{Stepwise forward and backward selection (swfb)}~\cite{derksen1992backward} tries to fit the best regression model by iteratively selecting and removing features. In our experiments, we used it in both directions simultaneously.
\textit{Correlation analysis with different threshold values (cor)}~\cite{benesty2009pearson} is based on the correlation analysis done only using the features (i.e., excluding the target). The result is a feature set where highly correlated features are omitted. In our case, we tested three different correlation thresholds: 0.50, 0.75, and 0.90.
Note that while the first three feature selection methods require a supervised ML task, the last one is completely unsupervised and does not depend on the target.

Our experimental design has been done using stratified 5-fold cross-validation. For a fair feature selection, we used the aforementioned methods on each training fold separately, then selected the intersection of the features returned by each training fold in the end. These features are further evaluated in the performance regression task.

\begin{table*}[t]
\centering
{\scriptsize{
\begin{tabular}{@{}lccccccc@{}}
\toprule
  & boruta & swfb & rfe & cor0.5 & cor0.75 & cor0.9 \\ \midrule
   \# selected ELA  features                   & 37     & 1    & 7   & 4      & 9       & 15     \\ \midrule
        \# selected state variable      features                   & 2      & 0    & 0   & 3      & 3       & 5      \\ \bottomrule
\end{tabular}
}}
\caption{Number of ELA and state variable features for each selected feature portfolio. Details are available in Table~\ref{tab:selectedfeaturesdetails}.}
\label{tab:selectedfeatures}
\end{table*}

Table~\ref{tab:selectedfeatures} summarizes how many features were selected per portfolio, from the whole set of 38 ELA landscape features and 5 CMA-ES state variable features.

\begin{table*}[t]
\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{@{}c|cc|ccc:cccc:cccccc@{}}
\toprule

               &  &  & \textbf{SV}    & \textbf{ELA}   & \textbf{ELA} & \textbf{GLOB2k} & \textbf{GLOB2k} & \textbf{GLOB250} & \textbf{GLOB250} & \textbf{boruta} & \textbf{cor} & \textbf{cor} & \textbf{cor} & \textbf{rfe}   & \textbf{swfb}  \\
                              & &  & &   & \textbf{+SV} &  & \textbf{+SV} &  & \textbf{+SV} &  & \textbf{0.5}  & \textbf{0.75} & \textbf{0.9} &  & \\ \midrule
        &&& \multicolumn{13}{c}{\textbf{Best threshold $\tau$}}      \\
\textbf{FID}            &  \textbf{min\_tp }      &  \textbf{ max\_tp}  &   \textbf{1.336} & \textbf{3.99}  & \textbf{4.742}   & \textbf{14.497}        & \textbf{9.46}              & \textbf{0.694}        & \textbf{2.605}            & \textbf{3.63}   & \textbf{1.813}  & \textbf{4.901}   & \textbf{1.717}  & \textbf{7.388} & \textbf{20}    \\  \midrule
1              & 0       & 0       & 0.43  & 0     & 0       & 0             & 0                 & 0            & 0                & 0      & 0      & 0       & 0      & 0     & 0.21  \\
2              & 9.25    & 87.47   & 44.69 & 25.47 & 24.46   & 24.49         & 23.65             & 28.96        & 27.98            & 25.16  & 36.78  & 35.28   & 34.62  & 24.51 & 53.85 \\
3              & 10.34   & 14.63   & 9.73  & 6.62  & 7.02    & 5.62          & 6.25              & 4.43         & 9.39             & 6.73   & 8.08   & 7.7     & 5.63   & 7.95  & 8.82  \\
4              & 10.29   & 14.53   & 11.86 & 5.08  & 5.08    & 4.81          & 5.73              & 5.1          & 7.49             & 5.18   & 9.26   & 6.71    & 5.59   & 7.53  & 8.41  \\
5              & 8.53    & 11.34   & 4.59  & 8.24  & 8.15    & 6.02          & 5.52              & 1.48         & 1.06             & 8.21   & 2.36   & 6.76    & 4.91   & 4.52  & 7.74  \\
6              & 0.04    & 0.11    & 0.78  & 3.87  & 2.15    & 0.24          & 0.27              & 0.33         & 0.37             & 4.46   & 1.02   & 1.19    & 1.65   & 0.89  & 1.43  \\
7              & 0.13    & 1.83    & 3.84  & 0.36  & 0.39    & 4.49          & 4.07              & 4.62         & 1.13             & 0.42   & 0.86   & 0.6     & 0.65   & 0.67  & 7.94  \\
8              & 1.22    & 2.59    & 3.02  & 1.06  & 1.1     & 1.25          & 1.22              & 2.03         & 0.84             & 0.95   & 2.02   & 0.93    & 1.04   & 1.42  & 4.88  \\
9              & 0.68    & 1.36    & 3.05  & 0.34  & 0.34    & 0.59          & 0.53              & 0.77         & 0.48             & 0.33   & 0.52   & 0.26    & 0.29   & 0.86  & 3.47  \\
10             & 8.44    & 84.69   & 43.33 & 32.06 & 32.85   & 21.39         & 23.16             & 29.29        & 33.35            & 32.13  & 42.18  & 32.73   & 35.01  & 29.64 & 47.68 \\
11             & 4.97    & 8.83    & 17.48 & 10.37 & 12.63   & 3.46          & 2.99              & 8.26         & 10.18            & 11.17  & 25.11  & 21.61   & 20.86  & 2.43  & 4.77  \\
12             & 2.53    & 6.32    & 4.76  & 19.34 & 15.89   & 4.03          & 4.16              & 5.76         & 4.13             & 19.45  & 4.38   & 2.31    & 9.69   & 19.32 & 3.56  \\
13             & 1.09    & 5.92    & 13.62 & 2.07  & 2.13    & 2.29          & 2.32              & 3.51         & 2.21             & 2.11   & 2.45   & 2.04    & 5.37   & 2.03  & 3.5   \\
14             & 0       & 0       & 1.65  & 0.01  & 0.01    & 0.07          & 0.08              & 1.89         & 0.37             & 0.01   & 0.61   & 0       & 0      & 0     & 5.18  \\
15             & 8.49    & 12.93   & 7.44  & 6.79  & 6.66    & 6.08          & 6.81              & 6.24         & 8.12             & 5.77   & 7.07   & 6.11    & 5.88   & 6.73  & 9.12  \\
16             & 0.18    & 0.83    & 0.87  & 6.31  & 5.85    & 0.32          & 0.35              & 0.25         & 0.25             & 6.45   & 0.48   & 4.42    & 4.77   & 2.43  & 1.21  \\
17             & 0.03    & 0.64    & 2.63  & 0.36  & 0.33    & 0.32          & 0.33              & 0.28         & 0.31             & 0.38   & 4.25   & 0.41    & 0.38   & 0.27  & 2.75  \\
18             & 0.16    & 0.69    & 1.54  & 0.34  & 0.34    & 0.33          & 0.3               & 4.96         & 0.45             & 0.34   & 0.54   & 0.28    & 0.3    & 0.54  & 1.24  \\
19             & 0.75    & 1.19    & 18.75 & 0.34  & 0.36    & 0.65          & 0.67              & 1.66         & 0.49             & 0.38   & 9.95   & 0.59    & 0.5    & 0.46  & 8.54  \\
20             & 1.7     & 1.79    & 3.77  & 0.76  & 0.84    & 0.99          & 1.17              & 0.99         & 0.93             & 0.69   & 3.2    & 1.22    & 3.16   & 1.77  & 5.16  \\
21             & 0       & 8.12    & 4.86  & 4.55  & 4.52    & 5.02          & 5.02              & 5.2          & 4.95             & 4.53   & 4.72   & 4.84    & 4.83   & 4.83  & 3.72  \\
22             & 0       & 25.48   & 12.04 & 11.91 & 11.91   & 11.9          & 11.91             & 12.88        & 11.94            & 11.91  & 11.89  & 11.93   & 11.7   & 11.92 & 5.34  \\
23             & 1.99    & 2.35    & 7.49  & 3.67  & 3.61    & 0.69          & 0.77              & 0.64         & 0.91             & 3.72   & 7.89   & 2.73    & 4.53   & 2.46  & 3.04  \\
24             & 15.73   & 20.73   & 10.06 & 4.76  & 4.94    & 5.15          & 6.52              & 2.95         & 4.29             & 4.81   & 11.13  & 9.49    & 7.22   & 7.43  & 11.65 \\ \midrule
\textbf{Overall RMSE, combined}             &    &   	 & \textbf{15.05} 	 & \textbf{10.41} 	 & \textbf{10.25} 	& \textbf{7.74} 	 & \textbf{7.92} 	& \textbf{9.48} 	 & \textbf{10.05} 	 & \textbf{10.43} 	& \textbf{13.66} 	 & \textbf{11.67} 	 & \textbf{11.86}   & \textbf{9.77} 	 & \textbf{15.73}  \\
Overall RMSE, unscaled             &    &    	& 15.08 	& 11.18 	&  10.88 	& 9.21 	 & 9.30 	 & 9.58 	& 10.19 	& 11.16 	&  14.11 	& 11.80 	&  12.00 & 10.93 	& 17.03  \\
Overall RMSE, log             &    &      	& 15.63 	& 13.05 	& 13.21 	 & 11.46 	& 11.88 	& 12.05 	& 12.87 & 13.14 	& 14.65 	& 13.89 	& 14.29	& 12.61 	& 15.73
 \\\bottomrule
\end{tabular}}
\caption{RMSE values of the combined selector in three scenarios: when the prediction is based on the search trajectory landscape features and state variables (first 3 columns), on global features (next 4 columns), and finally on selected feature portfolios (last 6 columns).}
\label{tab:RMSEpermodel}
\end{table*}

\textbf{Combined Selector Model and Sensitivity Analysis.}

As common in ML, we measure the regression accuracy in terms of \emph{Root Mean Squared Error} (RMSE). Table~\ref{tab:RMSEpermodel} summarizes the RMSE values for the different feature portfolios when using (1) the unscaled model, (2) the scaled model, and (3) a combination of unscaled and the log-model (see last three rows of Table~\ref{tab:RMSEpermodel}). The threshold $\tau$ at which the predictive model changes is optimized for each feature portfolio individually, the obtained thresholds are summarized at the top of Table~\ref{tab:RMSEpermodel}. That is,
we select the prediction of the log-model when the predicted precision (according to the log-model) is smaller than the threshold value $\tau$, and we use the prediction of the unscaled model otherwise. Note that the optimal threshold value $\tau$ varies significantly between the different feature portfolios.

When comparing all the different portfolios (initial trajectory-based, global and selected trajectory-based ones), the good performance of the global feature sets is not surprising. Differences from the initial trajectory-based predictions are marginal for sets such as \emph{boruta}, \emph{cor0.75} and \emph{cor0.9}, whereas \emph{swbf} and \emph{cor0.5} perform constantly worse than \emph{ELA+SV}. Using the \emph{rfe} set, on the other hand, led to better results than using the original feature set.  \emph{SV} alone does not achieve good accuracy, but its contribution to ELA-only feature portfolio is around 3\% at the best threshold for the combined model, which is $\tau=4.901$.
The absolute errors per instance are plotted in Figure~\ref{fig:abs_error_details}.

\begin{figure}[t]
    \centering
    \includegraphics[trim= 3cm 4cm 8cm 3cm, clip, width=\textwidth]{Carols-Boxplots-CombinedModel-perInstance-BestThreshold.pdf}
    \caption{Absolute prediction errors of the combined models using portfolio-specific optimal thresholds $\tau$.}
    \label{fig:abs_error_details}
\end{figure}

\section{Conclusions and Future Work}
\label{sec:conclusions}

We analyzed in this paper the accuracy of predicting the CMA-ES solution quality after given budget based on the features computed from the samples on the CMA-ES search trajectory using two complementary regression models, the unscaled and the log-model. Adding information obtained from the CMA-ES internal state variables does not improve the prediction accuracy drastically compared to the trajectory-based data only.  Those results were then contrasted to the regression using the global features, where using the latter ones, especially those computed using a higher number of samples, yielded a consistently better accuracy.

Next, we tested whether we would achieve further gains in accuracy through feature selection. Although the overall results are comparable to the ones from initial trajectory-based portfolios, several selected feature sets resulted in worse accuracy than in the initial approach.  We ultimately pointed out the advantages of using our combined selector model over relying separately on predictions of the standalone unscaled or log-model across all different feature portfolios in all 3 scenarios.

In terms of \textbf{future work,} we plan on continuing this research by considering the following questions and tasks: \\
(0) Performance prediction of \textbf{other solvers}: How accurately can we use trajectory-based features of one algorithm to predict the performance of another algorithm?  In this work, we have only tried to predict performance for the same algorithm from whose trajectory the feature values have been computed. A next step would be to test if models for configuring the same algorithm can be trained. When this is successful, transfer learning from one algorithm to another one can be considered.   \\
(1) How can we more efficiently capture the \textbf{temporal component}, i.e., the information which sample was evaluated \emph{when} during the search? Using such longitudinal data, both in terms of extracted feature values and in terms of state variable evolution could possibly be done using recurrent neural networks~\cite{zhao2019learning}.\\
(2) \textbf{Combining global and trajectory-based sampling:} In our work, we only considered the case in which \emph{either} global sampling \emph{or} trajectory-based sampling is used. The accuracy of the models based on global sampling was better than that of the trajectory-based features. Even if we keep in mind that this comparison was unfair in that we provided the global feature values ``for free'', the results nevertheless suggest that a combination of global and trajectory-based feature computations could be worthwhile to investigate. How we can optimally balance the budget between global sampling, trajectory-based sampling, and remaining optimization budget is a challenging question in this context. \\
(3) \textbf{Warm-starting} the CMA-ES such that it starts the optimization process with the covariance matrix and other parameters that are extrapolated from the (uniformly or otherwise) distributed global samples might significantly improve the overall accuracy, as the CMA-ES will have a better overview of the whole problem instance "from the get-go". A similar approach has been suggested in~\cite{MohammadiRT15BOCMAES} when switching from a Bayesian optimization algorithm to CMA-ES. \\
(4) \textbf{Feature selection and ranking:} Instead of using transfer learning for feature selection between two different supervised ML tasks, feature selection within the same supervised task has not been considered in this paper. We also plan on making better use of variable importance estimations provided by feature ranking algorithms such as those based on ensemble of predictive clustering trees~\cite{petkovic2020feature} and those based on ReliefF and RReliefF~\cite{robnik2003theoretical}. \\
(5) \textbf{Feature design:} The work~\cite{DerbelLVAT19} suggests several algorithm-specific features for the SOO tree algorithm~\cite{munos2011SOOtree}. Such specific features can much more explicitly capture the characteristics of the algorithm-problem instance interaction. It could be worthwhile to study whether, possibly in addition to the longitudinal data mentioned in (1), such specific features can be identified for other common solvers, such as the CMA-ES. \\
(6) \textbf{Feature portfolio:} We note that our work above is based on the features available in the \emph{flacco} package~\cite{flacco}. Since the design of \emph{flacco}, however, several new feature sets have been suggested. Another straightforward way to extend our analyses would be in the inclusion of these feature sets, with the hope to improve the overall regression accuracy. In this respect, we find in particular the Search Trajectory Networks suggested in~\cite{OchoaM020continupusLON} worth investigating. \\
(7) \textbf{Representation learning of landscapes:} The  feature data will be additionally explored by applying representation learning methods that automatically learn new data representations by reducing the dimension of the data, automatically detecting correlations, and removing bias and redundancies presented in the feature data. The work presented in~\cite{eftimov2020linear} showed that linear matrix factorization representations of the ELA features values significantly detects better correlation between different problem instances.\\
(8) \textbf{Hyperparameter tuning of regression models: } Last, but not least, we are planning to explore algorithm portfolio that consists of different regression methods in order to find the most suitable one, together with finding its best hyperparameters for achieving better performance. In this study, we have used random forest for regression without tuning its parameters, since we have been interested in the contribution of different feature portfolios.

\vspace{1ex}
\textbf{Acknowledgments.}
This research benefited from the support of the Paris Ile-de-France region and of a public grant as part of the
Investissement d'avenir project, reference ANR-11-LABX-0056-LMH,
LabEx LMH. This work was also supported by projects from the Slovenian Research Agency: research core funding No. P2-0098 and project No. Z2-1867. We also acknowledge support by COST Action CA15140 ``Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO)''.

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redpajama_set_name: RedPajamaArXiv


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TOP 10 The Backpacking Sleeping Bags to Buy on Amazon aug. 2019
See Price on Amazon
The Kelty Ignite DriDown is a good runner-up pick if you still want a traditional mummy bag, but our main pick jumps in price or becomes unavailable.
1.Kelty Ignite Down 16
2.Lost Ranger 15
3.Backcountry Bed 600 3-Season
4.KELTY COSMIC 20
5.THERM-A-REST HYPERION 32
6.NEMO SONIC
7.MOUNTAIN HARDWEAR LAMINA 30
Why you should trust us
Who is this guide for?
For more legroom
For more sleeping freedom
After about 40 hours of research and testing, we're not quite where we'd ideally like to be when it comes to picking a "best" sleeping bag for most people. But with the typical camping season ending soon and end-of-season sales ramping up, now's a good time to make a preliminary pick. The $200 REI Radiant is the sleeping bag we like (so far) for most campers and backpackers because it's a classic, proven design that fits most people and sleeping styles. Plus, it has waterproof down (like our other picks), making it a comfortable choice that won't get or stay moist. In a field of otherwise good bags, the Radiant's value makes it great.
Kelty Ignite Down 16 Sleeping Bag
Kelty Ignite Down 16
The Sleeping Bags We Like (so far)
We know that some people find a mummy bag's fit constrictive—the Radiant's standard 72-inch length, 62-inch shoulder, and 58-inch waist measurements are about in line with what you'd expect from a typical mummy bag. Hell, some of us have never had a perfect night's sleep in a mummy bag. Luckily, there are great bags for those who need more space, starting with … drumroll … the Radiant Long Wide (same product page). It's identical to our main pick, but the added 6 inches of length, 8 inches of girth at the shoulders, and 8 inches at the waist are great for folks over 6 feet and 190 lbs. Similarly, taller sleepers who don't need the extra width can opt for the Long option, which only adds 6 inches to the length.
If the Radiant is sold out and you want a traditional mummy bag fit, go with the $240 Marmot Sawtooth or $220 Kelty Ignite DriDown 16. There are minor differences between them: The Sawtooth has a foot pocket for adding a hot water bottle, while the Ignite has loops on the back for securing a sleeping pad. But at their core, they're virtually the same bags as the Radiant down to the fit—they just haven't been around as long and tend to be a bit more expensive than the Radiant on any given day. They both feature water-resistant down, weigh about 2 lbs. 10 oz., and are still cheaper than competing bags of similar offerings from other big-name brands like The North Face or Mountain Hardwear.
Lost Ranger 15
If you need more space to stretch your legs, the Big Agnes Lost Ranger is a great option. The bag-and-pad weight remains low and it has an impressively roomy cut.
Check at Amazon
The $260 Big Agnes Lost Ranger 15 is a good choice for people who need to stretch out their legs. It combines the best traits of a mummy bag—light weight and portability—with the best trait of a rectangular bag—a super spacious interior. Adding roughly a foot of extra girth to a typical mummy's dimensions, it comes in at 70 inches at the shoulders, 66 inches at the hips, and 54 inches at the feet—great for sleepers who like to roll around or snooze with their gams spread out. Big Agnes forgoes insulation on the bottom of the bag, instead including a full-length sleeve for an insulated sleeping pad. The design keeps the overall bag-and-pad weight low, prevents sleepers from rolling off their pads in the middle of the night, and allows for the impressively roomy cut. But note that it also means you pretty much always need a sleeping pad (roughly $90, not included) to stay warm and comfy.
Backcountry Bed 600 3-Season
Those who want the most versatile bag should pay up for the the $300 Sierra Designs Backcountry Bed 600 3-Season. Rather than a typical zippered design, it features a wide circular opening over the torso, covered by a quilted "comforter" that you pull over your chest and neck. It might sound like a gimmick, but it's been widely lauded by every major outdoor gear publication and thoroughly impressed one of our testers. The spacious opening allowed him to easily lay both arms out of the bag on warmer nights instead of comfortably extending just one arm, as is the case with zippered bags. And he could just as easily snuggle up, pulling the comforter to his chin when temps dropped. The shoulder girth is a typical 61 inches and the hips are a slightly wide 60 inches, but the flexible design of the comforter made it feel bigger.
You can also stick your feet through the footbox vent to allow your feet to stay cool on warmer nights and even walk around while still in your bag. The idea is that it will feel less like a restrictive bag and more like your bed at home, but your mileage may vary so we definitely recommend trying it out in store or buying from somewhere with a generous return policy before committing.
KELTY COSMIC 20 Amazon's Choice
The Kelty Cosmic 20 Degree sleeping bag is ideal for 3-season backpacking and camping. Water-resistant down insulation and an array of features make this sleeping bag a great combination of value and comfort.
KELTY COSMIC 20
THERM-A-REST HYPERION 32
NEMO SONIC
MOUNTAIN HARDWEAR LAMINA 30
Thermal.Q synthetic insulation
We had two of our most senior outdoor gear writers tackle this one in tandem. Eric Hansen has spent fifteen years filing stories from some of the most remote places on the planet, and he knows a thing or two about outdoor gear.
Andrew Matranga has reviewed gear for ten years as a technology writer and outdoor editor. Between them, they've crashed out in dozens of sleeping bags, ranging from Walmart clunkers to 900-power fill ultralight bags, so they have a good idea of what a sleeping bag should be and how it should fit. Both spent extensive hands-on time with all the main selections. Andrew spent a night in each of the three final bags in order to better judge overall overnight comfort.
In researching this guide, we tried to keep in mind the everyman camper: Someone who spends a few weekends a year in the wilderness, someone who doesn't backpack on every trip but wants the flexibility to do so.
This guide isn't tailored for ultralight backpackers planning a thru-hike of the Appalachian Trail. Nor is it aimed at RV campers for whom weight is no issue. The picks are for the rest of us, and they're light enough to satisfy most backpackers' needs and comfortable enough for even the pickiest luxury campers.
Sleeping bags, like bedding, wear down over time. And while down is extremely durable, repeated stuffing reduces loft, which in turn reduces the bag's insulating abilities. As such, what started as a 20-degree bag several years ago may only be comfortable down to 30 or 40 degrees today.
Typically, a down bag can last as long as 15 years…whereas a synthetic bag will last around five years before needing to be replaced.
Typically, a down bag can last as long as 15 years, depending on how often it's used and how it's stored and maintained, whereas a synthetic bag will last around five years before needing to be replaced. While it may be difficult to say goodbye to a longtime backcountry companion, the fact is, you'll be much warmer and sleep better in a newer bag.
Similarly, if you have a sleeping bag that you just don't like or bought because it was cheap, only to find that you wanted something warmer, lighter, and more packable, upgrading to one of our picks will help you sleep better.
However, if you invested a couple hundred dollars in a new bag within the past few years and are happy with it, there's no reason to get a new one. Not too much has changed, other than the popularization of water-resistant down (which might be worth upgrading for if you live in a wetter climate). Just keep using it until it wears out. Even the occasional tear or leak can be easily and cheaply patched with minimal detriment to a bag's performance.
A couple reviewers and one store clerk complained that the zipper can catch on the nylon if you try to zip it up quickly. The neck baffle lacks some loft compared to the other two bags in the test. There's also nothing in the way of bells and whistles: Glow-in-the-dark zipper pulls, a livelier color, and touches of fleece around the hood would be nice, but it's hard to demand perks when you're already getting more for your money.
Shorter women might find it a bit large despite its unisex design. We will eventually do a women-specific sleeping bag recommendation, but for now, if you're shorter than 5 ft. 6 in., consider trying out the REI Serrana before committing to the Radiant. It's basically the same bag, but a bit narrower around the shoulders and wider around the hips. The regular women's bag is 66 inches long, while the women's long is 72 inches. Both are $20 more than their Radiant counterparts because women sleep slightly colder than men, so female-specific bags like the Serrana require more insulation to achieve similar comfort ratings.
If our top pick is sold out, and you prefer the tight fit of a traditional mummy bag, both the $240 Marmot Sawtooth and $220 Kelty Ignite DriDown 16 are good alternatives. They're virtually the same design as the Radiant with minor differences in features and just a bit more expensive (usually, though sales can change this). We didn't spend a full night in either of these because they were so similar in design to the Radiant, but there's sufficient editorial and user review data backing both to warrant a runner-up designation.
The Sawtooth has a very similar fit to the Radiant, give or take an inch along the shoulders and hips, but uses slightly higher-grade 650-fill water-resistant down (though the whole package still weighs the same, so it's not really a selling point). The only main differentiating feature is that there's a pocket in the footbox for a hot water bottle when it gets really chilly. OutdoorGearLab took a look at the Sawtooth in their most recent roundup of backpacking sleeping bags and concluded, "We recommend the Sawtooth to budget conscious backpackers who are looking for a warm sleeping bag."
While it didn't earn their best buy award, the $160 Kelty Cosmic Down 21 they chose uses lower-quality, non-water-resistant 550-fill down. That makes it less versatile than our pick and the Sawtooth as well. In other words, it's cheap, but that's only because it's not as good. The Sawtooth is well-liked by customers as well, garnering a 4.8-star average rating on REI across four reviews.
The $220 Kelty Ignite is again very similar to the Radiant. It has the same cut and the same 600-fill down insulation—the only major point of differentiation is a set of straps on the back for securing a sleeping pad. It received Outside's prestigious Gear of the Year award in 2013 for its combination of affordability and quality of materials. They wrote, "Two hundred bucks for an ordinary down sleeping bag would be a killer deal. It's even better when that bag is made of water-resistant down, weighs less than three pounds, and packs to the size of a football. That, in a stuffsack, is why the Ignite DriDown is our favorite bag this year."
We'd take the football-sized packing with a grain of salt though. Other reviewers, like the Gear Institute, cite the Ignite's compressibility as one its major drawbacks. They still picked it for their value pick of 2013 though, saying that, "Every member of team praised the fit and comfort of the Ignite, and all thought it one of the warmest in this class." It's also one of the most popularly reviewed bags on REI, garnering a solid 4.6-star average over 24 reviews.
The Big Agnes Lost Ranger 15 has been around for a while as a part of the Big Agnes "Classic Series," and by all accounts, it appears to have earned its keep—some of the positive reviews date back seven or more years. The main thing that sets this sleeping bag apart is that hugely generous fit—70 inches of girth at the shoulders, 66 inches at the hips, and 54 inches at the feet for the regular, compared to a typical mummy's mere 62, 58, and 40 inches. At the very least, it's enough for anyone to lay their arms at their sides without touching their body.
To create that fit without upping the weight, Big Agnes trades an insulated bottom (which usually gets flattened and doesn't do much to keep you warm anyway) for an uninsulated sleeping-pad pocket. This design not only cuts weight, but allows you to increase or decrease the bag's warmth by buying a thickly or thinly insulated sleeping pad. You can use another company's pad, like Therm-a-Rest, or you can use one of the nine 20-inch-wide rectangular pads from Big Agnes, most of which are between $80 and $140—not cheap, but not exorbitant either. The pad sleeve also ensures you never, ever fall off your pad in the middle of the night and wake up cold.
The regular bag ($260) and the long ($290) are packed with 650-fill, water-resistant DownTek, which leads to weights of 2 lbs. 11 oz. for the regular and 3 lbs. 1 oz. for the long. That's just a couple ounces heavier than the Radiant.
Eric loved the ample room, calling it a revelation.
Since there is no insulation on the bottom of the bag, EN tests cannot independently confirm the temperature ratings. But given Big Agnes's great reputation and the fact that reviewers have been happy with it for years, we'd be surprised if the claimed 15°F rating wasn't accurate. (We'll keep testing through the fall.)
Eric loved the ample room, calling it a revelation. He was quick to recommend this bag for folks like him who don't naturally sleep like King Tut but prefer to stretch out their gams or sleep on their sides.
Because of the full sleeping pad pocket locking in a fully inflated pad, Andrew naturally found himself rolling up against the side of the bag at few times in the night, a much better problem to have than constantly re-adjusting a sleeping pad that's sliding all over the tent. With a normal bag and a separate sleeping pad, like the Radiant, you just flop off if you're not using pad straps. That said, when it comes to overall spaciousness, this bag had it in spades and was easily the largest in this price range. If you've always wanted the insulative value of a mummy style bag but treasure room to wiggle, this bag will fit the bill.
…when it comes to overall spaciousness, this bag had it in spades and was easily the largest in this price range.
As for other differentiating features, this bag nails 'em. It has a built-in pillow pocket that can hold balled-up clothes or a camping pillow. Unlike our other picks, the baffles (the stitched tubes that are stuffed with down) run up and down the bag, as opposed to side to side, ostensibly allowing heat to flow from your core to your feet and head, but we didn't notice any big benefit. It's definitely nice that you can pick which side you want the zipper on when you buy it. Many unisex bags, like the Radiant, are sold with the zipper on just one side, usually the left. Many women's bags come with the zipper on the right. So the ability to buy the Ranger with a zipper on either side increases the odds that you'll be able to mate it to another bag.
Also, for those who like to use a sleeping bag liner to extend the life of their bag or add extra warmth, the Lost Ranger has loops inside that allow you to attach the liner to the bag, thus preventing the liner from wrapping around your body. Like in the Radiant, a full-length draft tube lines the zipper, while a draft collar seals in warm air around the hood opening.
Five years ago, Backpacker raved about the "serious space–great for XL frames, restless sleepers, and storing extra winter clothes–without extra weight," and noted that it was a "staff favorite [that] has a tapered rectangular shape, yet it's rated conservatively. The 15°F bag kept our cold-sleeping gear editor warm when temps dipped below freezing."
Back in 2002, Outside praised the warmth-to-weight ratio while also giving high marks to the (then) innovative sleeping pad sleeve and the compressibility that left plenty of pack space and reduced bulk. A few years later, Outside recommended the Lost Ranger for those that want more girth in a bag.
The Sierra Designs Backcountry
The $300 Sierra Designs Backcountry Bed 600 3-Season turned out to be the sleeping bag that Andrew, a fidgety sleeper, had long sought. And he's not alone. The Backcountry Bed has earned awards from just about every mainstay outdoor publication including Outside magazine, Men's Journal, Climbing magazine, Backpacker magazine, Gear Institute, and Gear Junkie. Instead of your typical zip-up design, the Backcountry Bed has a large opening over the chest. Attached at the bottom of the opening, near your waistline, is a "blanket" that extends to the hood. The result is a sleeping bag that feels about as restrictive as your bed and comforter at home and can double a comfy blanket either on trail or around the house.
The bag comes in three sizes, each a tweaked version of the classic mummy bag silhouette: a longish men's regular (80 inches long, 61 inches at the shoulders, 60 inches wide at the hips, 42 inches at the foot; $300), a very long men's long (86 in. long, 65 in. at the shoulders, 64 in. at the hips, 44 in. at the foot; $320) and women's regular (76 in. long, 58 in. wide at the shoulders, and 40 in. wide at the hips; $340). These are fairly standard widths, but the blanket-style opening on top makes it sleep much wider than it actually is. It's comfortable for all sorts of sleeping positions but especially good for face sleepers, who will appreciate not being suffocated in a traditional mummy bag's hood or having their arms pinned at their sides by a traditional bag's single-sided opening.
Andrew noted that the innovative features were also good for maintaining a comfortable temperature. Early in the evening, when temps were still in the mid 60s, he folded down the quilt-style blanket, leaving the egg-shaped opening exposed and venting the upper section of the bag, while his feet stuck out of the open footbox. "One quirk I have is that my feet are very active at night, and often a classic mummy keeps me boxed in and really uncomfortable," he said. "This feature, odd at first, turned out to help me immensely as I tried to pass out." When temps dropped, he slipped his feet into the bag and hands into the insulated hand pockets of the blanket, keeping his extremities warm and his chest open to the air.
Once temps hit their low in the mid 50s, he drew the quilt over his head. "With my old mummy bags, I'd fiddle with drawstrings and neck baffles to keep out drafts," he said. "But with this design, I'd tuck my hands in and then secure the quilt under the tension of the hood–and I experienced no major draft." In the morning, as he woke, he was able to comfortably exit the bag, happy to avoid any zipper fiddling. EN rated it for 28°F for comfort with a 17° lower limit.
The sleeping pad sleeve on the regular bag supports a 20-inch sleeping pad. Andrew used the Big Agnes Air Core Insulated pad, which fit snugly into the sleeve without stretching the bag too tight across the chest.
Eric (call him Bigfoot) didn't appreciate the narrow bottom of the bag, which kept his feet tight together, and found it harder to roll onto his side than in the Lost Ranger. He also missed the ability to mate it to another bag. But all in all, he had to agree with Andrew and the editorial reviews. The Backcountry Bed was a rare innovation in time-honored mummy bag design and proof that novelty can sometimes trump tradition if executed thoughtfully and properly.
Five reviewers on REI give it an average of 4.8 stars, and three reviewers on Amazon give it a 4.7-star average, calling it a "gem."
REI has some important tips and tricks aimed at keeping your bag in good form. They cover how to sleep in it, clean it, treat it, and store it. We'll break down the basics for you here as well.
When on the trail, try to sleep as cleanly as possible to keep dirt and body oils from collecting in your bag, necessitating more frequent washing. This is harder to do on long expeditions, but it can be done. Even on a four-week backpacking trip, our tester was able to bring a sleep-specific set of shorts and shirt and wash his hands, feet, and head to minimize the amount of dirt and grime rubbing all over the inside of the bag. Certainly avoid eating in your bag and your tent, especially in bear country.
A sleeping bag liner not only keeps your bag clean but also adds warmth. Whether you use a liner or not, make sure to fluff your bag after it has sat compressed at the bottom of your pack all day. This helps you get all the loft, and therefore warmth, you expect.
When you're not using it, store your bag unstuffed or in the oversize sack that comes with the bag. This will help it maintain its loft and last longer.
The safest way to wash a down bag is by hand in a bathtub, or you can use a front-loading washer (top-loaders will rip the stitching and should be avoided). Air dry your bag when done, or insert a couple tennis balls and dry it without heat in your clothes dryer—standard practice for down jackets and anything that shouldn't be treated with fabric softeners, which can strip the natural oils of the down and reduce insulation value.
As for re-treating your down bag's DWR repellent, there are many products, like Nikwax Down Wash, that are made for just that task.
After doing our initial research, it became clear that we didn't have to call in every bag under the sun and compare them head-to-head. Objective, lab-measured EN temperature ratings, once rare, are now standard, so the info they've compiled about a bag's warmth is better than any tests that we, or virtually anyone else outside of MIT, could design. Same with a bag's dimensions. Most manufacturers list the bag's shoulder and hip widths and its compressed size and weight. Even a sleeping bag's features are pretty easy to read up on.
The big challenge was deciding what traits mattered most to most people. To figure this out, we spoke with quite a few experts—the editors of Backpacker magazine's 2014 and 2013 gear guides Will Rocheford and Kelly Bastone; Sam Moulton, the executive editor of Outside magazine, who has written or edited their sleeping bag reviews for over a decade; Richard Bangs, the founder of esteemed, four-decade-old adventure-travel outfitter Mountain Travel Sobek; Ben Cargill, an employee at the AMC Huts in New Hampshire's exceedingly popular White Mountains; and others.
Except for one person, they all recommended down due to its being lightweight, warm, compressible, and durable. Down does lose the ability to trap heat when wet, but few people knew anyone who stayed out camping after a deluge. And anyway, the recent introduction of water-resistant down—most bags use DriDown from American Recreation Products and DownTek from Down Decor—means that down bags repel dew and light moisture for hours, making them suitable for every excursion short of consecutive soakings in, say, the Pacific Northwest.
Bangs favors the synthetic North Face Cat's Meow that he has used for years, but recent reviews discount it, so we did too. (OutdoorGearLab also used to love the Cat's Meow, but they hate this year's version: "The North Face Cat's Meow is an ultra classic and over priced synthetic bag that walks the car camping/backpacking line," they say.)
Experts also noted that even though the price of down has spiked, it's still a good value compared to synthetic bags. Take the 15-degree Montbell bags. The $280 down version weighs 2 lbs. 14 oz. and supposedly compresses to 8.6 liters. The $185 synthetic version weighs 4 lbs. 2 oz., more than a pound heavier, and compresses to only 13 liters, making it the size of a small daypack. If you're hitting the trail with the synthetic bag, that means you might be tempted to leave behind that cushy camp chair or tasty fruit or proper first aid kit—no fun. Add the fact that the down bag will last many years longer, and the $90 cheaper synthetic bag is no real savings. Down is clearly what most people want.
Experts also agreed that a three-season bag was the most comfortable in the widest range of temperatures and thus the essential bag. So for the most part, each bag had to have an EN lower limit rating of at least 25 degrees for men and 30 degrees for women—a pretty standard cutoff for a three-season bag. The reason for the male-female temperature difference according to Scott Kaier, public relations manager at American Recreation Products, is that "women traditionally sleep colder than men, so we add insulation and rate differently to help ensure everyone is most comfortable."
Third, we looked for a comfortable fit in a mummy-style sleeping bag. Sleeping bags come in different shapes and sizes, usually mummy or rectangle, but Will Rochford, Backpacker magazine's current sleeping bag test director, said, "If you're going to go backpacking at all, get a mummy." Why? Because a shapely mummy bag can be comfortable to sleep in and carry, but a rectangle bag will never be less than huge and heavy, no matter how comfortable it is. The NEMO Strato Loft rectangular bag probably comes the closest to a mummy in weight and compressed size, but is still a pound heavier than the humble REI Radiant mummy bag, roughly three times the size when compressed, and $370 instead of $200. A mummy clearly offers the most versatility and value. That said, within the mummy design, we preferred roomier dimensions. We figured that space to wiggle and turn is more important than a couple ounces of weight savings.
The fourth criteria were the size, weight, and extra features, like glow-in-the-dark zippers, chest zip pockets for headlamps, foot boxes that unzip for ventilation, and the availability of women's versions.
So our criteria for choosing the best three-season bag came down (no pun intended) to this list:
All regular size (up to 6-foot length)
Street price
Fill power
Stuffed size
Dimensions: shoulder x hips x footbox
Zipper length and/or mateability
Zipper baffle
Collar yoke
Sleeping pad sleeve
Women's version
Extras: waterproof down, glow in the dark zippers, choose left or right zips
Setting a $300 price limit, the most we figured a casual outdoorsperson would want to spend, we considered 20 three-season down bags featuring various editorial reviews from the last two years, plus comments from eight websites (Amazon, OutdoorGearLab, REI, Sierra Trading Post, Campmor, EMS, Dick's Sporting Goods, and Moosejaw). Based on that, we placed them roughly in order of best to worst. Then we cut them for one reason or another (price, material, old model, too warm/cold). Not surprisingly, the best values were coming from REI, The North Face, Marmot, Big Agnes, and Kelty, with prices ranging from $170 to $250.We were more concerned with the fit and other features, acknowledging that nearly every bag would be subjected to the same standards in rigorous lab tests in research labs like one found at the Institute for Environmental Research at Kansas State University, a leading facility that uses thermal mannequins and conducts military-grade thermal insulation tests, and that the down within the bag would meet down and feather industry standards for loft and fill power.Eric filtered through the criteria to select three final bags to highlight (e.g., the Eddie Bauer First Ascent Karakorum 20 was eliminated because of its extremely narrow 33-inch footbox; the Millet Dreamer Composite was very hard to find in stock in the US), and then he visited two REI stores to see if there was something that the specs didn't capture. At both stores, he compared roughly a dozen models side-by-side to make a rough check of the dimensions and then rolled them out, unzipped them on REI's sleeping platform, and fake-slept in them. There were a few small surprises (e.g., the Marmot Sawtooth, supposedly just an inch narrower through the hips than the Radiant, felt tighter than that), but most of the bags were as described in the specs, differing most significantly on price.Once we picked the final bags, Andrew spent a few nights in each bag to understand overnight comfort and fit on a pad, which is the basic use for these bags. The general testing conditions were overnights in the high 50s on Colorado's Front Range, and he used a regular-sized (20 by 72 by 3.25 inches) Big Agnes Insulated Air Core Sleeping Pad. Each night was in a three-season tent, and as a control for apparel and since it was late summer, he wore a pair of non-cotton boxer shorts and a non-cotton t-shirt.
TOP 10 The Best Backpacking Sleeping Bags to Buy on Amazon aug. 2019
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Filter by: Review (4169); No Filter
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Dreaming the Future: The Fantastic Story of Prediction
.), a polymathic researcher and professional observer of the weird, would eventually write about the mysteries of divination; his 20-some popular books cover subjects as diverse as aliens, fractals and the nature of time.
Rachel's Journal: The Story of a Pioneer Girl
Moss extends the format she perfected in Amelia's Notebook and Amelia Writes Again to cover historical fiction in this solidly researched and wholly captivating illustrated diary "by" a 10-year-old girl who travels with her family along the Oregon Trail in 1850.
WAR STORIES: Operation Iraqi Freedom
So it is hardly surprising that he does excellent work covering a Marine aviation unit, one appointed to transport assault troops and evacuate wounded in aging helicopters—never without risk and sometimes with bloody incidents.
NO STONE UNTURNED: The Story of NecroSearch International
The book covers the group's quirky beginnings and digs into its most important cases suspensefully; Jackson's sharp eye misses nothing in the painstakingly rendered details.
SCHOOL: The Story of American Public Education
Chronologically arranged in four sections (1770–1890, 1900–1950, 1950–1980, 1980–2000), this anthology covers much ground (charter, common, frontier and dame schools) at a brisk, engaging pace.
The Spark: A Mother's Story of Nurturing Genius
With passion and grace, Barnett covers Jake's transformation, how close he came to being "lost" in a special ed system focused on what children "can't do," and her own journey as the mother of three sons and as advocate for Jake and other autistic children.
A PITCHER'S STORY: Innings with David Cone
Although Angell's focal point is Cone's last year with the Yankees, he covers all of Cone's life and career, tracking his baseball journey from his days as a star athlete in Kansas City to his stops with the Mets, Blue Jays, Royals and Yankees.
SCIENCE FICTIONS: A Scientific Mystery, a Massive Cover-up, and the Dark Legacy of Robert Gallo
Although the basic facts of the story have been documented before, largely via Crewdson's reporting in the Chicago Tribune, the level of detail and drama here is unprecedented.
The River of Lost Voices: Stories from Guatemala
The physical distance these 10 stories cover is short, but the geography of human spirit it traverses is vast.
Alien Archives: Eighteen Stories of Extraterrestrial Encounters
Perhaps most revealing for longtime fans are the forewords that detail Silverberg's dealings (courteous and/or contentious) with other famous SF writers and editors such as Harlan Ellison and Frederik Pohl, along with glimpses of the era when stories were commissioned for previously purchased magazine cov
Liquid Jade: The Story of Tea from East to West
Trailing tea over continents and centuries as it grows in popularity and becomes a power unto itself-in the form of the East India Tea Company-Hohenegger covers an interesting mix of topics, including Zen Buddhism, the Opium Wars, the first and only "tea spy," and, of course,
The Rhino Records Story: The Revenge of the Music Nerds
Like a proud father, Rhino Records co-founder Bronson (Hey, Hey, We're The Monkees) tells the story of the label's birth and growth.
Deadmonton: Crime Stories from Canada's Murder City
In one chapter, she covers the unsolved 1983 murder of 18-year-old Charlotte Bass, who perished when an arsonist burned her home and whose mother has spent decades waiting for the killer to be brought to justice.
Maybe You Never Cry Again: A True Story
Mac's first book, I Ain't Scared of You (2001), was a comedy riff of sorts, covering everything from sex and marriage to professional athletes and religion, interspersing tidbits about Mac's own life.
Short Stories by Jesus: The Enigmatic Parables of a Controversial Rabbi
As the text enlightens, it also emboldens critical application of Jesus's ancient stories to modern hearts and minds.


EQA (Ireland) is an independent certification body providing third party verification of Management System, Products and Services in accordance with Irish, European and International standards. Encom have achieved the following quality and industry awards demonstrating to clients that quality is a key objective of the company.
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text:
Als Salinität (von ) bezeichnet man (vereinfachend) den Salzgehalt eines Gewässers, Wasserkörpers bzw. Wassers. Im einfachsten Fall wird sie als Massenanteil in g/kg (Gramm Salz pro Kilogramm Salzwasser bzw. Lösung) oder in Prozent angegeben (1 % entspricht 10 g/kg).

Die Salinität kann mit Hilfe eines Salinometers bestimmt werden. Dabei wird ausgenutzt, dass die elektrische Leitfähigkeit des Wassers proportional zum Salzgehalt ist.
In der Ozeanographie ist die Salinität eine wichtige Kenngröße bei der Bestimmung von Wassermassen und Meeresströmungen.

Geschichte der Salinitätsbestimmung
Mit der Salinitätsbestimmung will man herausfinden, wie viel Salz in einer Menge von Wasser gelöst ist. Der Salzgehalt ist zusammen mit dem Druck und der Temperatur für die Dichte beziehungsweise die potentielle Dichte des Wassers verantwortlich. Damit haben unterschiedliche Salzgehalte großen Einfluss auf die Meeresströmungen.

Verdunstung
Die ursprünglichste Methode ist die Verdunstung von Wasser und die Wägung dessen, was nicht verdunstet ist.
Das Problem ist, dass Wasser dabei in Salzkristallen eingeschlossen werden kann. Um dieses Wasser aus den Salzkristallen zu lösen, sind hohe Temperaturen notwendig. Durch die hohe Temperatur zerfallen jedoch einige Salze (z. B. MgCO3 → MgO + CO2).
Schon Robert Boyle stellte im 17. Jahrhundert fest, dass die Trocknung und Wägung von Meerwasser zu wenig reproduzierbaren Ergebnissen für die Konzentration von gelösten Substanzen führte.

Im 19. Jahrhundert wurde das Marcet-Prinzip oder auch das Gesetz der konstanten Proportionen formuliert. Es besagt: Ungeachtet wie stark sich die Salinität von Ort zu Ort unterscheidet, die Anteile der wichtigsten Ionen (engl. Major Ions) im Wasser des offenen Ozeans sind fast konstant.

Anfang des 20. Jahrhunderts kamen neue Methoden zur Bestimmung des Salzgehaltes auf.

Salinität, S‰ (Knudsen, 1902)
Um reproduzierbare Ergebnisse zu bekommen, wurde eine erste strikte Definition eingeführt.
"Salinität ist definiert als das Gewicht in Gramm der gelösten anorganischen Stoffe in einem Kilogramm Seewassers, nachdem alle Bromide und Iodide mit der gleichen Menge an Chloriden ersetzt wurden und alle Kohlenstoff-Verbindungen oxidiert wurden." D. h. alle Kohlenstoffverbindungen gasen als CO2 aus.
Dazu wurde das Seewasser, HCl und gesättigtes Chlorwasser getrocknet, für 72 Stunden bei 480 °C erhitzt und schließlich das rückständige Chlorid titriert.

Diese Methode brachte nun zwar reproduzierbare Ergebnisse, war aber sehr umständlich und zur Anwendung auf See nur sehr bedingt geeignet.

Chlorinität, Cl‰ (Sørensen und Knudsen, 1902)
Durch das Gesetz der konstanten Proportionen ist es möglich, aus dem Gehalt eines Salzes den Gehalt der restlichen Salze recht genau abzuschätzen.
Mit Hilfe der Mohr-Titration konnte die Menge der Halogeniden in Seewasser bestimmt werden. Bei der Anwendung mit Seewasser fällt neben Silberchlorid noch Silberbromid und Silberiodid aus, welches nun gewogen werden kann. Damit die Ergebnisse präzise sind, wird die Silbernitrat-Lösung gegen sogenanntes Standard-Seewasser mit bekannter Chlorinität geeicht.
Um nun aus der ermittelten Chlorinität die Salinität zu berechnen, maß Sørensen die Salinität von neun Seewasserproben direkt und bestimmte außerdem die Chlorinität. Die daraus abgeleitete Korrelation war: Salinität [‰] = 1,805 · Chlorinität [‰] + 0,030

Diese Methode ist sehr genau und auch auf See wesentlich besser anwendbar als die von 1902.

Chlorinität, Cl‰ (UNESCO, 1962)
Wie die Addition eines konstanten Wertes in der obigen Korrelation schon vermuten lässt, gab es jedoch ein Problem. Die neun Proben stammten zum Teil aus der Ostsee. Die Ostsee hat jedoch eine andere Ionenkomposition als der offene Ozean. Dieser Fehler wurde durch eine neue Kalibrierung nach nun 60 Jahren korrigiert:
Salinität [‰] = 1,80655 · Chlorinität [‰]

Practical Salinity Scale, S (1978)

Die heute verwendete Practical Salinity Scale (PSS-78) beruht auf der Proportionalität von Salzgehalt und elektrolytischer Leitfähigkeit und ist dimensionslos. Häufig findet man jedoch zur Angabe der Salinität nachfolgend ein PSU, welches für Practical Salinity Unit steht. Dies ist keine physikalische Einheit.

Zur Errechnung des Salzgehaltes aus der Leitfähigkeit wird folgende Formel benutzt:

Dabei gibt K15 das Verhältnis von der gemessenen Leitfähigkeit zur Leitfähigkeit einer Kalium-Chlorid-Lösung von 32.4356 g/kg bei 15 °C und bei einem Druck von einem Bar an. Ist das Verhältnis K15 gleich eins, ist S = 35.

Die Messung der Leitfähigkeit erfolgt heutzutage meist automatisiert mit Hilfe einer Conductivity Temperature Depth-Sonde (CTD), auf deutsch einer Leitfähigkeit-Temperatur-Tiefensonde.

Schwankungen der Salinität in Gewässern

Kurzfristige Schwankungen
Durch den Einfluss von Wetter und Gezeiten unterliegt die Salinität natürlichen Schwankungen. Zum einen das Ansteigen der Salinität durch Verdunstung bei Ebbe in ufernahen Bereichen (Wattenmeer, Gezeitentümpel) bis hin zur Ausbildung von Salzwiesen, zum Beispiel durch längere Sonnenperioden nach Sturmfluten. Bei Vorliegen einer wasserundurchlässigen Bodenschicht kann sich in Marschgebieten stark salzhaltiges flaches Grundwasser bilden, das auf der Insel Læsø im Kattegat eine Salinität von bis zu 15 Prozent erreicht. Eine Herabsetzung der Salinität kann sich durch Süßwassereintrag in Flussmündungen, in Schmelzwasserzonen und bei starkem Regen ergeben.

Auch wenn Meerwasser gefriert, bleibt Salz zurück. Zwar befindet sich das Salz nach dem Gefrieren zunächst im Eis, ein Großteil diffundiert aber heraus und gelangt wieder ins Meerwasser, dessen Salzgehalt dadurch steigt.[Beleg?]

Großräumige Unterschiede
Der Salzgehalt an der Wasseroberfläche unterscheidet sich typischerweise von Meer zu Meer. Im Ostseeraum, wo die Niederschläge (mit eingerechnet die Landfläche, aus der die Ostsee gespeist wird) wesentlich größer sind als die Verdunstung, wird das Oberflächenwasser verdünnt. Je weiter man nach Osten kommt, desto geringer ist die Oberflächensalinität. Die Verdünnung des Oberflächenwassers führt zu einer stabilen Wasserschichtung, wo salzarmes Ostseewasser über salzreicherem, aus der Nordsee stammendem Wasser liegt.

Das Gegenteil passiert im Mittelmeer – dort ist die Verdunstung größer als die Niederschlagsmenge. Das Salz bleibt im Wasser zurück, sein Gehalt steigt. Tritt das Mittelmeerwasser bei Gibraltar aus dem Mittelmeer aus, sinkt es trotz seiner hohen Temperatur in mittlere Tiefen ab. Es kann anhand seines hohen Salzgehaltes quasi im ganzen Nordatlantik identifiziert werden.

Salzgehalte

Die wichtigsten gelösten Salzionen des Meerwassers haben in den Weltmeeren den gleichen Anteil an der Salinität, sie sind konservativ. Das heißt, auch bei unterschiedlicher Salinität ist der Anteil der Ionen zueinander der gleiche. Dies liegt daran, dass sie nach ihrem Eintrag ins Meer nicht mehr nennenswert von biologischen oder geochemischen Prozessen beeinflusst werden.
Eine einfache begriffliche Einteilung lautet: "Wasser kann polyhalin sein (über 10 Promille Salze – nur Meerestiere), mesohalin (1,0 bis 10 Promille Salze – spezielle Brackwasserfauna) oder oligohalin (0,1 bis 1,0 Promille Salze – schon Süßwassertiere)"

Einordnung
 Süßwasser weist eine Salinität von unter 0,1 % auf (d. h. unter 1 g/kg.)
 Beim Brackwasser liegt die Salinität zwischen 0,1 % und 1,0 %.
 Ab einer Salinität über 1,0 % spricht man von Salzwasser.

Zum Vergleich:
 Der durchschnittliche Salzgehalt der Ozeane liegt bei ca. 3,5 % (35 g/kg).
 Eine isotonische Kochsalzlösung enthält 0,9 % (9 g/kg) Kochsalz.
 Eine gesättigte Kochsalzlösung enthält 35,6 % (356 g/kg) Natriumchlorid bei 0 °C (359 g/kg bei 25 °C).

Ozeane

 Atlantischer Ozean: 3,54 %
 Indischer Ozean: 3,48 %
 Pazifischer Ozean: 3,45 %

Weitere Meere
 Ostsee: 0,8 % im Durchschnitt (westliche Ostsee 1,3 %, Finnischer und Bottnischer Meerbusen < 0,5 %)
 Kieler Bucht: 1,5 %
 Finnischer und Bottnischer Meerbusen: 0,1 %
 Nordsee: 3,5 %
 Flussmündungen: 1,5 % – 2,5 %
 Nördliche Nordsee: 3,2 % – 3,5 %
 Mittelmeer: 3,74 %
 Schwarzes Meer 1,7 % – 1,8 %
 Persischer Golf: 4 %
 Rotes Meer: 4 %
 Asowsches Meer: 0,2 % – 1,1 %
Bei den anderen Nebenmeeren liegt die Salinität zwischen 3 und 4 %.

Binnenseen
 Kaspisches Meer: 1,3 %
 Mono Lake: 7,3 %
 Qarunsee: 11,8 %
 Totes Meer: 28 % (Durchschnitt)
 Totes Meer: 32,66 % in einer Tiefe von 50 Metern
 Aralsee: 0,9 % (1960)
 Kleiner Aralsee: 2 % (2003)
 Großer Aralsee: 7,5 % (2003)
 Assalsee: 34,8 % (Durchschnitt)
 Assalsee: 38,8 % in einer Tiefe von 20 Metern
 Don-Juan-See: 44,2 % (welthöchster Wert für ein Gewässer)

Siehe auch

 Abiotische Umweltfaktoren, Ökologie
 Isotonische Kochsalzlösung (physiologische Kochsalzlösung)
 SMOS
 Wasserhärte

Weblinks

 International oceanographic tables. (PDF; 7,3 MB) UNESCO (englisch)
 Salinometry.com – Website mit Literaturangaben, Methodensammlung, Rechenhilfen und weiteren Informationen
 Gleichungen und Algorithmen für fundamentale Eigenschaften von Seewasser nach UNESCO Standard.
 Salzkarte der Meere

Einzelnachweise

Meereskunde
Limnologie
Chemische Größe

redpajama_set_name: RedPajamaWikipedia


The Islamic State and Jabhat al-Nusra compete to win over Arsal communities as the Syrian army tightens control of Zabadani.
Lebanese security forces carry the coffin of Lebanese soldier Ali al-Bazzal, who was killed by Jabhat al-Nusra in December, during his funeral after receiving his body in his hometown Bazalia village, April 3, 2015.
After 315 days, Lebanon remains without a president. The Ain el-Tineh meeting ended as parties emphasized the need to adhere to dialogue as the Easter holiday starts. Yet the unfolding events in the area between Arsal and its hinterland in the Anti-Lebanon mountains, and between Zabadani and the western mountain range in Rif Dimashq [in Syria], remain the focus of those following the situation there.
The fate of Hussein Seifudin from the town of Hosh al-Rafika, who was kidnapped from Arsal at the beginning of the week, remains in the hands of his captors, the Islamic State (IS) militants and the people of Arsal. While the former has been escalating the situation, the latter continue to insist on freeing him in return for an exclusive release of the kidnapped Syrians in the town. In the meantime, the flaring front in Zabadani has relatively calmed, "as the military operations that kicked off about two weeks ago achieved their target," according to an informed security source in the area.
Yesterday's [April 3] confrontations in Zabadani ended with the Syrian army and Hezbollah's control over the area's western mountain range, thus achieving three major goals: securing the Damascus-Beirut highway permanently; preventing militants from infiltrating Lebanon from Anjar; and tightening the military grip on the hills overlooking Zabadani city, cracking down on the militants' movement.
The Syrian army also managed to seize control of Radar Hill at the end of the Anti-Lebanon Mountains on the border between Syria and Lebanon, after dealing a blow to Jabhat al-Nusra, killing a number of its militants including the area's commander.
On the Qalamoun front, the Syrian army and its allies controlled the hills of Hamra and Shaaba al-Khasha in the Flita desolate areas. These hills separate Arsal's hinterlands from Flita's.
This was the result of clashes with Jabhat al-Nusra, which withdrew from Hankal Hill following heavy fire fighting.
Controlling these hills "will help secure Flita on one hand, and detecting the militants' movement in a large part of Arsal's desolate areas, on the other," according to the informed security sources.
Ali al-Hujairi, Arsal's mayor, told As-Safir that IS informed him that the group does not wish to negotiate with him regarding the release of Seifudin. "IS asked for $150,000 and three trucks loaded with supplies for the release of Seifudin," Hujairi said.
The people of Arsal, however, are determined not to pay anything for the captive's release. "We will stand our ground from now on," the people said, according to Hujairi.
As-Safir also learned that IS approached another of Arsal's mayors to negotiate the terms of Seifudin's release. The mayor came back with good news that IS intended to release him in the following hours. A well-informed source said that IS inquired about the town's stance and asked about the situation's details.
Those following up on the situation note that there has been a competition between Jabhat al-Nusra and IS to win over the people of Arsal. In fact, Jabhat al-Nusra handing over of the body of the martyr Ali al-Bazal occurs within this context and does not relate to the ongoing negotiations on the kidnapped soldiers.
It should also be noted that the town of Bazaliah held a mass funeral yesterday [April 3] for the martyr Sergeant Ali al-Bazal.
First, through the northeast side of the Kfir Yabous village, the closest to Lebanon after Jdeidat Yabous, all the way to Shrin, Shir al-Taka and Shir al-Joba, overlooking Zabadani city. All these areas have come under control.
Second, from the north of the Maadar town and the adjacent hills down to Dahrat al-Howa — the strategic stronghold hill of Jabhat al-Nusra. Most of the militants have been killed after after destroying their sites.
Third, from Bir Kasoor and Ain al-Ramleh down to Fathat al-Janzir and Dahr al-Baydar, where many of the militants who were deployed there have been killed. Also, one of their tanks has been destroyed.
Accordingly, sources said that the Zabadani "has become secured from the eastern side from the highland of Ayat al-Kursi, and from the western side, from Dahrat al-Howa, Shir al-Joba, the Syriatel Tower, all the way to Dahr al-Baydar, Fathat al-Janzir, Bir Kasoor and Ain al-Ramleh. Zabadani is now within the range of Syrian army fire and its allies, therefore movement of the insurgents there has been relatively paralyzed.
Mohammed Youssef al-Maghribi, known as "the griffin," a commander from Kfir Yaboos, and another commander, Abdullah Ammar, were killed along with 13 other Jabhat-al-Nusra-affiliated insurgents who tried to infiltrate to Dahrat al-Howa, and Shir al-Joba to the side of Zabadani.
"No field commander could tell when is 'zero hour.' The battle will take place but its time is known to the key stakeholders only," the military expert said.
The people of Arsal have also called on the Lebanese army and Hezbollah's leadership to make the necessary arrangements for them to reach their orchards in the hinterland that encloses 5 million fruit-giving trees, to take care of their agricultural business with the onset of spring.
The people's demands come within the context of the severe economic crisis affecting the town at all levels.
Are Russia, Iran engaged in tug of war over Syria?


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Robert James (1703 – 23 March 1776) was an English physician who is best known as the author of A Medicinal Dictionary, as the inventor of a popular "fever powder", and as a friend of Samuel Johnson.

Life
James was born in 1703, at Kinvaston in Staffordshire, to Edward James, a major in the English army, and his wife Frances, a sister of Sir Robert Clarke. His early education was at Lichfield Grammar School, where he became acquainted with his fellow student Samuel Johnson. He then attended St John's College, Oxford, from which he received the degree of A.B. on 5 July 1726. He was admitted as an extra-licentiate of the Royal College of Physicians on 12 January 1727/8, and in May of the same year was created doctor of medicine at Cambridge by royal mandate. He practised at Sheffield, Lichfield, and Birmingham before moving to London, where he was admitted as a licentiate of the Royal College on 25 June 1765. He died on 23 March 1776, aged seventy-three.

James's most notable publication was his three-volume Medicinal Dictionary (1743–1745), for which his friend Samuel Johnson wrote the "proposals", as well as several of the dictionary's articles (mainly at the beginning of the alphabet), including those for actuarius and Aretaeus. This work was immediately translated into French (as Dictionnaire universel de médecine, 1746–1748) by the team of Denis Diderot, François-Vincent Toussaint, and Marc-Antoine Eidous; and it retained its popularity for so long that Mark Twain felt justified in writing a scathing critique of it nearly 150 years later, in 1890.

Quackery

His fever powder, which he patented in 1747, was one of the most successful of 18th-century patent medicines,
though he is said to have "tarnished his image by patenting his powders, and falsifying their specification". (It was considered unbecomingly mercenary to patent a medicine, and his falsification of the ingredients in the patent documentation would have been designed to prevent others from replicating his formulation.) The use of this preparation, a compound of antimony and phosphate of lime, has been cited as a contributing factor in the death of Oliver Goldsmith. James' fever powder has been cited as an example of quackery.

Selected writings

Translations
Dissertation on Endemical Diseases [by Friedrich Hoffmann] and Treatise on the Diseases of Tradesmen [by Bernardino Ramazzini], 1746
The Presages of Life and Death in Diseases [by Prospero Alpini], 1746
Health's Improvement [by Thomas Muffet], 1746
A Treatise on Tobacco, Tea, Coffee and Chocolate [by Simon Paulli], 1746
The Modern Practice of Physick [by Herman Boerhaave, with annotations by Gerard van Swieten and additions from Friedrich Hoffmann], 1746

Original works
A Medicinal Dictionary, Including Physic, Surgery, Anatomy, Chymistry, and Botany, in All Their Branches Relative to Medicine; Together with a History of Drugs, an Account of Their Various Preparations, Combinations, and Uses; and an Introductory Preface, Tracing the Progress of Physic and Explaining the Theories Which Have Principally Prevail'd in All Ages of the World, 1743–45
A Treatise on the Gout and Rheumatism, 1745
A Dissertation on Fevers and Inflammatory Distempers, 1748
A Treatise on Canine Madness, 1760
A Vindication of the Fever Powder, with a Short Treatise on the Disorders of Children, 1778

See also
 History of masturbation (James's Medicinal Dictionary described masturbation as "productive of the most deplorable and generally incurable disorders".)

References

Further reading

1703 births
1776 deaths
18th-century English medical doctors
Alumni of St John's College, Oxford
English translators
People educated at King Edward VI School, Lichfield
18th-century British translators


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