text:
\section{Introduction}

There are many potential applications of machine learning in medicine, chemistry, small business, and human interaction where the practical costs of obtaining more data are high enough that there is no choice but to deal with small data. In many of these cases, there are plenty of examples of similar problems, but the details of the specific problem are important and should be captured. Model-free machine learning approaches suffer from overfitting in the small-data regime dependent on the regularization techniques which are applied, whereas inference-based approaches which perform well in the extremely low data regime require a particular hypothesis space or model of the generating process to be specified in advance. A compromise between these two extremes is so-called model-based meta-learning, in which a more general machine learning approach is used to learn a model space that can span a wide array of tasks. This can be framed as the meta-task'conditioned on a training set and its labels, what classification policy should we use to maximize generalization performance on test data?' which we can directly optimize in an end-to-end fashion. Rather than the input being a single data point, for this meta-task an input is considered to be an entire data set (similar to \cite{edwards2016towards} and \cite{zaheer2017deep}), split into training and test subsets. By optimizing the entire pipeline directly for generalization performance in the low-data regime, we can transfer knowledge from an ensemble of weakly similar tasks to obtain high performance on unseen tasks for which we do not have explicit models or hypothesis spaces.

In order to process datasets as inputs to a meta-task, Santoro et al. proposed a general class of model called memory-augmented neural networks\cite{santoro2016one}. In these networks, a Neural Turing Machine\cite{graves2014neural} is used to process incoming samples from the training set one by one. However, this requires inputting the data in sequence, and the order of such input sequences can have an impact on the results\cite{vinyals2015order}. We observe that in order to process the network's memory, it is not strictly necessary that the memory by loaded in this sort of serial fashion, but rather we can consider a memory which is already composed of the data which is to comprise the training set and then use attentional mechanisms to address and update this memory, as per the Transformer network architecture\cite{vaswani2017attention}. As such, we present an attention-based memory-augmented neural network which has guaranteed permutation invariance with respect to the order of presentation of its inputs.

While previous work has focused on the image domain, many of the opportunities for low-data models lie in structured data problems, in which there are some set of features which are potentially meaningful in different ways. In this paper, we focus on evaluating the degree to which this type of memory-augmented network is appropriate for such problems. The difficulty posed by this is that, since each feature may be meaningful in a distinct way, and each dataset will likely have different numbers of features, we cannot use a large corpus of related image data in order to learn a model over these types of datasets. We explore the use of synthetic datasets to bridge this gap.

In inference-based approaches, a model is specified by hand and generally the corresponding inference procedure is derived analytically or simulated in order to perform inference. Here, by formulating the meta-learning task, we can specify models by example and effectively ask the learned network to discover an appropriate parameterization as well as the inference procedure which should be used in order to estimate those parameters. Even when trained only on weakly related synthetic datasets, we obtain good performance in the 20-100 data point regime compared to standard algorithms\cite{pedregosa2011scikit} such as support vector classifiers (linear and RBF), k-nearest neighbors, random forests, and XGBoost\cite{chen2016xgboost}. As the amount of data increases, the relative accuracy gain narrows, but with this method consistently matching performance up to 100 training points.

Because the problem is formulated over training set/testing set pairs, we can explicitly target statistical effects we suspect may interfere with the robustness and reliability of continuing classification by putting those statistical effects explicitly into the tasks we use to train the classifier generator. For example, we can impose unbalanced classes, covariate shift, and nonstationarity while training the algorithm in order to find classification policies which will be more robust to those effects (to the extent that the specific form of these issues we impose generalizes to the actual problems we are interested in). We can also make use of ancillary problem knowledge such as the expected training set size or available unlabelled data to learn regularization strategies that are tuned to specific data regimes.

\section{Related work}

The general problem that this method seeks to solve is closely related to the problems of transfer learning\cite{pan2010survey} and one-shot/few-shot learning. In transfer learning, the goal is to take what a model has learned from other weakly related problems and have it apply to a new problem, often in the form of accelerated training or increased data efficiency, while in one-shot learning the concept of transfer often still applies, but there is a stringent constraint on the amount of extra data or training which the model will receive (e.g. it must achieve sufficient transfer with access only to a single representative set of examples of the new problem).

\paragraph{Meta-learning}
As our method involves learning the process of generating a classification protocol from data as an overarching task, it is related to other meta-learning approaches which broadly fall under the category of learning to learn. These approaches seek to encapsulate some elements of the overall model generation pipeline as tasks in their own right, which can be optimized via training. The approaches taken in this area can be divided into model-based, metric-based, and optimization-based meta-learning\cite{vinyals2017}.

Our approach belongs to the class of model-based meta-learning methods called memory-augmented neural networks\cite{santoro2016one}. We similarly use a memory containing the training data and maximize test set performance over the distribution of datasets, but rather than construct this memory one sample at a time through a Neural Turing Machine, we make use of a self-attention based architecture to process all of the training set and its internal relationships in parallel, in a strictly permutation-invariant fashion.

Metric-based meta-learning approaches have also been applied to the general problem of one-shot learning, with most of the results being in the image domain. This is a family of approaches which use a combination of an embedding transformation with interpretation of the embeddings in the context of an imposed metric space in order to achieve one-shot learning of similarity relationships in a shared domain (e.g. visual similarity). These approaches include matching networks\cite{vinyals2016matching}, siamese networks\cite{kumar2016learning}, and FaceNet\cite{schroff2015facenet}. The work ``Towards a Neural Statistician''\cite{edwards2016towards} learns latent spaces which explain the statistics of the input (it extracts summary statistics from domain examples), which naturally produces a space that has good properties for metric-based one-shot learning approaches on datasets.

In optimization-based meta-learning, the process of gradient descent can be formulated as a recurrently applied differentiable transform which should be optimized to reduce the final loss at the end of a period of training\cite{andrychowicz2016learning}. In MAML\cite{finn2017model}, this is cast as a form of transfer learning problem, and as such the parameters of the model used to solve the particular task are still the network parameters (rather than some latent representation of the task inferred directly from the training set), but for which the model as a whole has been adapted such that only a small number of gradient descent steps will be needed in order to obtain high performance. This sort of optimization-based approach has recently been explicitly applied to the problem of few-shot classification\cite{ren2018meta}.

\paragraph{Transfer learning}
Our method can be taken to be a form of inductive transfer learning (in contrast to domain adaptation approaches), where we use the statistics of the overarching set of problems combined with conditioning information about the particular problem we are currently attempting to solve. In our case, the conditioning information directly takes the form of a set of training data. Other related approaches involve  simultaneously training against multiple potential objectives under some constraints or protocols to prevent overfitting to any single object, then performing a degree of fine-tuning to the particular problem case: for example in Elastic Weight Consolidation\cite{kirkpatrick2017overcoming}. Another technique is to learn to stack models using a higher level structure which controls the connectivity of previously learned single-task solvers as in PathNet\cite{fernando2017pathnet}.

Attentive meta-learning\cite{mishra2017simple} is similar to our approach in that an attention mechanism is used to effectively make use of rich conditioning information which acts to specify the particular sub-task which must be solved in a given context.

\section{Method}

Ideally, the architecture we use to formulate the problem of generating classifiers should possess a number of invariances --- it should be invariant to the order of the data points, invariant to the order of the features, and also flexible enough to handle varying numbers of data points, features, and classes. There are a number of methods which explicitly implement permutation invariant or equivariant computations\cite{ravanbakhsh2016deep}. In general, attentional methods also have this property with respect to the data over which attention is applied\cite{vinyals2015order}. As such, we use attentional blocks based on the ``Attention is All You Need'' architecture\cite{vaswani2017attention} in order to obtain invariance over the ordering of the training set and test set data.

We must also deal with the fact that different datasets will have different feature counts. We tried using non-negative matrix factorization\cite{lee2001algorithms}, PCA, and random projections to provide fixed-length embeddings for the data, and found that random projections significantly outperformed the alternatives. As such, moving forward we simply use random projections followed by normalization to feature-wise zero mean and unit standard deviation in all our presented results.

In order to handle a variable number of classes, we train the classifier generator on a larger number of classes and then mask the probabilities assigned to impossible classes to zero, normalizing the remainder to one. It is also possible via preparing subsampled datasets to explicitly train this kind of generator to produce an 'unknown class' classification (for example, by randomly de-labelling different subsets of classes in one of the training problems and asking the network to assign points belonging to missing classes to this extra category).

\subsection{Network Structure}

\begin{figure}[h]
  \centering
  \fbox{\rule[-.5cm]{0cm}{4cm} \rule[-.5cm]{0cm}{0cm} \includegraphics[width=14cm]{network_architecture}}

  \caption{\label{Architecture}Network architecture for the classifier generator.}
\end{figure}

The classifier generator network takes as inputs a set of training points $X_{train}$ which have been randomly projected into a $D$ dimensional space (we present results for networks with $D=4$ and $D=128$) and normalized to have zero mean and unit standard deviation on each axis, a set of one-hot encoded training labels $Y_{train}$, and a set of test points $X_{test}$ projected and normalized according to the same transform as used for $X_{train}$. These are batched, meaning that each of these inputs is a rank-3 tensor. Both training inputs are stacked via concatenation and processed through two initial dense layers with ReLU nonlinearities to increase dimensionality to $384$, obtaining $Z_{train}$. Similarly, the test input is processed through the same structure (but with separate weights) to obtain $Z_{test}$. We find that it is helpful to scale these embeddings by a factor of 10 to accelerate initial training, as it causes there to be more initial variation in the output of the subsequent attentional layers.

Following the initial embedding, we apply a series of attentional blocks (as per \cite{vaswani2017attention}). An attentional block $Attn(x,y)$ here consists of four attention heads, a query/key space of size $32$, and a value space of size $96$ (such that stacking the results maintains the overall dimension of $384$). The first argument is linearly projected into the key and value spaces, while the second argument is projected into the query space to determine the weighting over $x$. As such, $Attn(x,y)$ can be read as '$y$ searches and returns values from $x$'. We modify the training set embedding via two self-attentional passes $Attn(Z_{train}, Z_{train})$, have the test set attend to the training set over two additional passes $Attn(Z_{train}, Z_{test})$, and then finally apply a series of four dense layers with the final dense layer having a SoftMax nonlinearity with the target number of classes (to be trained against the categorical cross-entropy loss). The overall network structure is shown in Figure \ref{Architecture}.

PyTorch\cite{paszke2017automatic} code for all experiments in this paper is available at \url{https://github.com/arayabrain/ClassifierGenerators}.

\subsection{Synthetic Tasks}

In order to train these networks, we need large datasets of datasets. In this paper, we focus on the use of synthetic datasets to provide this. Normally, synthetic data is a poor test for machine learning performance, but here we are in essence using the synthetic data generator to implicitly specify a hypothesis space which will be learned by the method. Further elaborations on this approach would be to pretrain on synthetic data and then use a smaller set of real related tasks in addition to extensive data augmentation via feature subsampling to fine-tune, but for the purposes of introducing the method we observe that even using just synthetic data for training we can obtain good performance on a variety of real datasets.

The synthetic problem space we use consists of classifying a set comprised of $N_C$ different $N_F$-dimensional Gaussians with random means and covariance matrices. The means are distributed according to a unit Gaussian, and the covariance matrices for each class are each separate random matrices with unit Gaussian components, scaled by an overall problem difficulty factor $\sigma$. While the number of projected input features of the classifier generator is held constant, we vary $N_F$ uniformly between $0.5$ to $3$ times this number during training so that the network will adapt to the type of structure imposed by the random projection operation. We additionally vary $N_C$ uniformly between $2$ and the maximum number of classes for the given classifier generator. We use a statistically uniform balanced set of classes during training, but a given training set is not guaranteed to contain an equal number of examples of each class (and in fact may occasionally contain no members of some classes).

Training these networks becomes difficult when the number of input features is large, as there is usually a strong initial saddle point associated with the high degree of symmetry of the feature orderings and meanings (e.g. for every problem where a given class is positively correlated with a feature, there will be a problem where it is negatively correlated with that same feature). In order to accelerate the training process, we use a curriculum successively more difficult sets of synthetic problems. This is constructed by doubling the problem difficulty ($\sigma$ range) every time the cross-entropy drops below $0.7$ up to a particular target difficulty level.

In all cases we use we use Adam\cite{kingma2014adam} with a learning rate $10^{-5}$, and minibatches comprised of 200 datasets. Networks are trained over $1\times 10^{5}$ batches. We refer to two particular models which we compare throughout: $CG_{128,16}^{100}$, which uses $128$ features, $16$ classes, $100$ training points, and a final difficulty $\sigma = 0.1 - 0.3$; and $CG_{32,16}^{100}$, which instead uses $32$ features and a final difficulty $\sigma = 0.2 - 0.6$ but is otherwise the same.

\section{Results}

Here we compare the classifier generator to a number of baseline algorithms on synthetic tasks and real data. In many cases, the dimensions of the problem that the classifier generator is being tested on will be different than the dimensions on which it was trained, and so we use random projections to standardize the problem shape. We found that by ensembling the results of the model over multiple sets of random projections consistently gave significantly better predictions than any single random projection. Additionally, we found that this was true for the baseline algorithms. As such, to standardize the comparison to focus primarily on the differences in the underlying algorithms rather than in the augmentation strategy, in all cases we show results obtained by ensembling over $30$ random projections (to $128$ features, in the case of the baseline classifiers; otherwise, to the input dimension of the classifier generator).

\subsection{Synthetic data}

We will make use of synthetic data generated in the same fashion as the training problems in order to examine the generalization properties of the trained classifier generators to different amounts of data, different numbers of features, different numbers of classes, and different difficulties. For results in this section, we use $20$ randomly sampled problems with $400$ test points each, for each combination of parameter values. We ensure that the training set and test set have at least one member of each class, and compare models based on AUC.

\begin{figure}[h]
  \centering
  \fbox{\rule[-.5cm]{0cm}{4cm} \rule[-.5cm]{0cm}{0cm} \includegraphics[width=14cm]{sweeps}}

  \caption{\label{NSweep} Effect of varying amount of training data, relative to the target against which the network was trained.}
\end{figure}

We vary the training set size, feature count, and problem difficulty ($\sigma$) and compare the performance of the $CG_{128,16}^{100}$ and $CG_{32,16}^{100}$ models against Scikit-Learn baselines in Fig.~\ref{NSweep}. Despite the specific classifier generators being tuned to particular numbers of features, data points, or difficulties, their performance is fairly consistent even when those quantities are changed significantly. Furthermore, changing the number of input features for the classifier generator itself has a strong effect regardless of the number of features in the actual testing task, which may indicate that the behavior of ensembling random projections is more important there than any kinds of fine-tuning to the specific dimensionality of the problem.

\paragraph{Visualization of decision boundaries} As there are a number of different factors involved in selecting the synthetic dataset size, train/test split, and difficulty, we would like to obtain an intuitive understanding of how these different considerations influence the type of decision boundaries that the model learns to produce. Since this is difficult in high dimensional spaces, we train a set of classifier generators on 2d tasks with 4 classes. We vary the amount of expected training data, the problem difficulty, and the dispersion of problem difficulty in the synthetic training sets. Each model is trained for $40000$ epochs. Since the 2d problems are relatively easy to learn, we do not need to make use of a staged curriculum of increasingly difficult instances of the problem. In Fig.~\ref{DecisionBoundary} we visualize the decision boundaries for different generators given two synthetic problems from the same space, as well as a swiss-roll dataset to look at how the method may generalize.

\begin{figure}[h]
  \centering
  \fbox{\rule[-.5cm]{0cm}{4cm} \rule[-.5cm]{0cm}{0cm} \includegraphics[width=14cm]{decision}}

  \caption{\label{DecisionBoundary}Decision boundaries on problems of different difficulty, with generators trained to expect $20$, $100$, and $400$ points of training data at $\sigma=1$, a generator trained on a harder version of the task ($\sigma=4$), and a model trained over uniformly randomly varied values of both the training data and task difficulty. }
\end{figure}

\subsection{Small datasets}

We collected a number of small datasets from the UCI repository\cite{Lichman:2013} in order to test the classifier generator on actual data different from its training distribution. We subsample the training data of each dataset down to a variable number $N$ of points, and cross-validate across $100$ train-test splits at each value of $N$. Some datasets supplied an explicit train-test split, but since we subsample the data to control the amount available we only use data from the training set in those cases and report cross validation scores. In all cases, the classifier generator models receive no additional training updates with respect to the particular datasets or any of the other datasets in this section.

In most cases, some data cleaning was necessary in order to standardize the problem. We one-hot encoded categorical values, treating missing values as a unique category. In the case of numerical values, we replaced all instances of missing values with the mean over the dataset. The reported number of features for each dataset ($N_F$) is the final number of columns after one-hot encoding and other preprocessing. We provide the preprocessed datasets with the source code distribution of this paper.

These datasets exhibit some range of class imbalance, with the most extreme case being the cervical cancer dataset which is 94\% composed of a single class (non-detection). Even if some significant features are discovered, in terms of raw accuracy often all classifiers will perform near chance level on these datasets when the amount of data becomes small. However, there are still noticeable differences in AUC score. Results of the classifier generator against the baseline methods are shown in table \ref{UCIResults}. Each point in the table corresponds to $100$ random subsamplings of the dataset (using the same subsamplings for each classifier).

\begin{table}[t]
  \caption{AUC Results on UCI datasets.}
  \label{UCIResults}
  \centering
  \begin{tabular}{llllllllll}
    \toprule
Dataset & N & $\sigma$ & LSVC & SVC & RF & XGB & KNN & CG & FTCG \\
\midrule
\multirow{2}{*}{Immunotherapy\cite{khozeimeh2017expert, khozeimeh2017intralesional}} & 10 & 0.004 & 0.553 & 0.454 & \bf{0.578} & 0.55 & 0.563 & \bf{0.579} & \bf{0.578} \\
& 50 & 0.00399 & 0.536 & 0.641 & 0.674 & 0.674 & 0.668 & \bf{0.682} & \bf{0.678} \\
\midrule
\multirow{2}{*}{Forest type\cite{johnson2012using}} & 10 & 0.00274 & 0.681 & 0.351 & 0.94 & 0.922 & 0.887 & \bf{0.948} & 0.943 \\
& 50 & 0.000168 & 0.993 & 0.993 & \bf{0.994} & 0.993 & 0.99 & 0.992 & 0.992 \\
\midrule
\multirow{2}{*}{Wine type\cite{forina1990parvus}} & 10 & 0.00241 & 0.863 & 0.733 & 0.984 & 0.981 & 0.965 & \bf{0.99} & \bf{0.989} \\
& 50 & 6.56e-05 & 0.996 & \bf{0.999} & 0.998 & \bf{0.999} & 0.997 & 0.998 & 0.998 \\
\midrule
\multirow{2}{*}{Cryotherapy\cite{khozeimeh2017expert, khozeimeh2017intralesional}} & 10 & 0.00416 & 0.834 & 0.661 & \bf{0.898} & 0.886 & 0.851 & 0.89 & 0.891 \\
& 50 & 0.00105 & 0.953 & 0.955 & \bf{0.967} & \bf{0.968} & 0.943 & 0.965 & 0.965 \\
\midrule
\multirow{2}{*}{Chronic kidney\cite{chronickidney}} & 10 & 0.00367 & 0.885 & 0.889 & 0.951 & 0.937 & 0.927 & \bf{0.968} & \bf{0.969} \\
& 50 & 0.000246 & 0.994 & 0.98 & 0.995 & \bf{0.995} & 0.971 & 0.988 & 0.987 \\
\midrule
\multirow{2}{*}{Wine quality (red)\cite{cortez2009modeling}} & 10 & 0.00156 & 0.407 & 0.366 & \bf{0.637} & 0.593 & 0.62 & 0.601 & 0.602 \\
& 50 & 0.00145 & 0.663 & 0.556 & \bf{0.721} & 0.7 & 0.643 & 0.652 & 0.668 \\
\midrule
\multirow{2}{*}{Echocardiogram\cite{echocardiogram}} & 10 & 0.00426 & 0.578 & 0.453 & \bf{0.681} & 0.62 & 0.659 & 0.655 & 0.658 \\
& 50 & 0.00403 & 0.579 & \bf{0.75} & 0.716 & 0.688 & 0.647 & 0.723 & 0.733 \\
\midrule
\multirow{2}{*}{Haberman\cite{haberman1976generalized}} & 10 & 0.00345 & 0.542 & 0.46 & \bf{0.592} & 0.576 & 0.568 & 0.586 & 0.586 \\
& 50 & 0.00255 & 0.573 & 0.584 & 0.645 & 0.642 & 0.616 & 0.645 & \bf{0.648} \\
\midrule
\multirow{2}{*}{Iris\cite{fisher1936use}} & 10 & 0.0025 & 0.917 & 0.757 & \bf{0.967} & 0.927 & 0.883 & 0.931 & 0.938 \\
& 50 & 0.0003 & 0.995 & 0.993 & \bf{0.996} & 0.993 & 0.985 & 0.966 & 0.974 \\
\midrule
\multirow{2}{*}{HCC Survival\cite{santos2015new}} & 10 & 0.00294 & 0.551 & 0.376 & \bf{0.65} & 0.625 & 0.625 & 0.643 & 0.643 \\
& 50 & 0.00152 & 0.714 & 0.763 & 0.778 & 0.78 & 0.738 & \bf{0.789} & 0.787 \\
\midrule
\multirow{2}{*}{Wine quality (white)\cite{cortez2009modeling}} & 10 & 0.00134 & 0.427 & 0.395 & \bf{0.592} & 0.562 & 0.58 & 0.572 & 0.565 \\
& 50 & 0.00133 & 0.619 & 0.494 & \bf{0.656} & 0.654 & 0.61 & 0.607 & 0.603 \\
\midrule
\multirow{2}{*}{Horse Colic\cite{horsecolic}} & 10 & 0.00177 & 0.507 & 0.446 & 0.548 & 0.551 & 0.544 & \bf{0.553} & 0.551 \\
& 50 & 0.00157 & 0.639 & 0.639 & 0.708 & 0.712 & 0.685 & 0.711 & \bf{0.718} \\
\midrule
\multirow{1}{*}{Lung cancer\cite{hong1991optimal}} & 10 & 0.00347 & 0.54 & 0.427 & 0.592 & 0.575 & 0.579 & 0.588 & \bf{0.598} \\
\midrule
\multirow{2}{*}{Hepatitis\cite{hepatitis}} & 10 & 0.0032 & 0.538 & 0.437 & \bf{0.611} & 0.594 & 0.607 & 0.605 & 0.606 \\
& 50 & 0.00197 & 0.528 & 0.656 & 0.68 & 0.659 & 0.649 & \bf{0.691} & \bf{0.69} \\
\midrule
\multirow{2}{*}{Dermatology} & 10 & 0.00245 & 0.391 & 0.138 & 0.944 & 0.907 & 0.901 & \bf{0.961} & \bf{0.959} \\
& 50 & 0.000456 & 0.992 & 0.965 & 0.995 & 0.995 & 0.993 & \bf{0.996} & \bf{0.996} \\
\midrule
\multirow{2}{*}{Blood transfusion\cite{yeh2009knowledge}} & 10 & 0.00413 & 0.567 & 0.469 & 0.625 & 0.608 & 0.607 & \bf{0.63} & \bf{0.63} \\
& 50 & 0.00263 & 0.648 & 0.581 & 0.673 & 0.676 & 0.665 & \bf{0.704} & \bf{0.704} \\
\midrule
\multirow{2}{*}{Autism\cite{thabtah2017autism}} & 10 & 0.0032 & 0.643 & 0.426 & 0.653 & 0.646 & 0.637 & \bf{0.66} & \bf{0.658} \\
& 50 & 0.00162 & 0.869 & 0.834 & 0.852 & \bf{0.878} & 0.793 & 0.854 & 0.856 \\
\midrule
\multirow{2}{*}{Cervical cancer\cite{fernandes2017transfer}} & 10 & 0.00211 & 0.587 & 0.424 & \bf{0.618} & 0.516 & 0.531 & 0.565 & 0.572 \\
& 50 & 0.00188 & 0.554 & 0.507 & \bf{0.652} & 0.619 & 0.571 & 0.597 & 0.606 \\
\midrule
\midrule
\multirow{2}{*}{Average} & 10 & 0.000516 & 0.612 & 0.481 & \bf{0.726} & 0.699 & 0.696 & 0.718 & 0.719 \\
 & 50 & 0.000286 & 0.755 & 0.758 & \bf{0.806} & 0.801 & 0.774 & 0.798 & 0.8 \\
\bottomrule
  \end{tabular}
\end{table}

The baselines we compare against use Scikit-Learn implementations\cite{pedregosa2011scikit} with default parameters. LSVC indicates LinearSVC with $C=1$, SVC is a radial basis function support vector classifier with $C=1$, RF is a RandomForestClassifier with the default number of estimators, XGB is XGBoost, and KNN is KNeighborsClassifier set to use 5 neighbors. We tried varying the regularization in the case of the linear and RBF models and found that while in a few cases a smaller $C$ value can obtain improvements on these datasets, the lower $C$ values were significantly worse on average, and so we omit those results from the table.

In 12 of the 18 datasets, the classifier generator (either fine-tuned or purely from synthetic data) has a top performing entry at either $N=10$ or $N=50$ or both, with the random forest classifier having a top performing entry on 11 of the datasets, XGBoost on 4, and rbf-SVC on 2. However, the random forest classifier obtained a better AUC on average.

\paragraph{Fine-tuning to real data} In addition to the classifier generators trained purely on synthetic data, we examine using the synthetic data as a pretraining step followed by fine-tuning on similar datasets. To do this, we use a leave-one-out cross-validation strategy, taking the same initial classifier generator and fine-tuning to all but the particular dataset we are evaluating on. In addition to the random projection operation, we perform additional data augmentation by randomly permuting the class labels, feature indices, and by subsampling the training data. We train for an additional $20$ batches, where each batch involves examples randomly sampled from the training set of datasets.

These results are obtained starting from the $CG_{128,16}^{100}$ model, and are labeled as $FTCG_{128,16}^{100}$. Surprisingly, we find very little additional benefit can be extracted from tuning (at least, on this amount of data) as compared to just using the synthetic tasks. The improvements we do see depend on a fairly short fine-tuning period, disappearing when using much more than $50$ batches of additional training. Furthermore, this is despite a significant change in the cross-entropy of the model over this period of time (generally dropping from around $2$ at the start of fine-tuning to $1.3$ at $20$ batches, and as low as $0.7$ for longer training periods. As such, it seems that this type of model would need to be trained on a very large number of real datasets to maintain it's generalization behavior without overfitting in some fashion.

\paragraph{Architecture hyperparameters and overfitting} We tried variations around the basic architecture --- network layer sizes of $256$ and $512$, using four attentional blocks for processing $x$ and $y$ rather than two, and adding a side-path for deriving summary statistics over the entire dataset (similar to \cite{parisotto2017neural}) by pooling over $x$ and stacking with each $y$ representation. In general, we found that final trained cross-entropies on the same problem set could vary from $0.73$ to $0.86$ on the synthetic tasks, with larger networks and more attention blocks systematically corresponding to lower cross-entropies. However, when applying these networks to the small real datasets, we found a performance peak corresponding to the architecture reported in this paper (layer sizes of $384$, with two attention blocks for $x$ and for $y$), with other architectural choices degrading the average AUC for $N=10$ to as low as $0.674$ (will falloffs being more severe for overly large networks than for overly small networks). On the other hand, training a given network longer generally did not decrease the performance on real data. We interpret these observations as detecting a kind of overfitting.

Because the classifier generators are trained against infinite sets of synthetic data, the sense in which they overfit is a bit different from the usual sense of memorizing the training data. Instead, if the family of training tasks as representing a particular precisely-defined hypothesis space (such as the set of Gaussian classification tasks), and as the networks are  given higher capacity, they more closely adopt this prior. However, there is a gap between capturing the broad elements of the hypothesis space (such as nearby points tending to be similar) and treating points as evidence for inference using the full probability distributions of the training tasks.

\subsection{Computational costs}

Compared to classic machine learning techniques, neural network based techniques tend to be much heavier computationally. Even when there may be some gain in accuracy, in many cases it's a serious consideration as to whether or not the increased computational burden pays for that gain. In the case of the classifier generator model, the initial training required about two days time and 3GB of memory on a single Titan-X GPU. However, once trained, the entire process of fitting a new dataset and rendering classifications operates as a single instance of inference for the neural network, with additional ensembling easily parallelizable in the batch dimension. The result is that, despite the neural network itself being much more complex than the final trained forms of the other baseline classifiers, the actual process of applying it to a new dataset is relatively fast.

\begin{table}[t]
  \caption{Training and inference time for different classifiers (seconds)}
  \label{TimingResults}
  \centering
  \begin{tabular}{lllllllll}
    \toprule
    $N_{train}$ & $N_{test}$ & LSVC & SVC & RF & XGB & KNN & CG (CPU) & CG (GPU) \\
    \midrule
    100 & 400 & 0.948 & 1.07 & 0.682 & 9.5 & 0.268 & 1.18 & 0.16 \\
    200 & 400 & 2.41 & 2.78 & 0.912 & 17.7 & 0.473 & 1.59 & 0.133 \\
    400 & 400 & 7.97 & 8.43 & 1.45 & 42.9 & 0.928 & 2.94 & 0.167 \\
    100 & 800 & 1.25 & 1.4 & 0.719 & 8.83 & 0.499 & 2.37 & 0.155 \\
    100 & 1600 & 1.88 & 2.08 & 0.805 & 9.02 & 0.899 & 4.43 & 0.232 \\
    100 & 3200 & 3.13 & 3.39 & 0.95 & 9.51 & 1.72 & 8.4 & 0.383 \\
    100 & 6400 & 5.54 & 6.01 & 1.26 & 10.6 & 3.43 & 15.8 & 0.728 \\
    \bottomrule
   \end{tabular}
\end{table}

To compare the practical costs of using the classifier generator, we ran the network in inference mode on CPU and GPU on synthetic problems of different sizes and looked at the time cost per dataset processed when compared to the Scikit-Learn baselines (which all ran on CPU). All problems had 128 features and 16 classes, but varied in the number of training and test data points. These experiments were run on a system with two Intel Xeon E5-2690 v3 CPUs (totaling 48 virtual cores) and a Titan-X GPU (although in practice only the XGBoost benchmark takes advantage of that parallelism and performed optimally at 8 threads). Timings were averaged over 10 datasets each, randomly projecting to 128 features and ensembling 30 times in all cases. The results are in table \ref{TimingResults}.

Despite being costly to train via gradient descent, the fact that the classifier generator model directly 'trains' on new datasets by performing conditional inference rather than via an iterative process means that it manages to perform comparably to the other reference methods which did require training. On the other hand, k-nearest neighbors, which also works directly from data, consistently performed 3-4 times faster. On GPU, the classifier generator was faster across the board for the tested problem sizes, though presumably GPU implementations of the baseline algorithms would also receive comparable speed-ups. The reference methods tend to scale better when the size of the test set is large relative to the size of the training set, suggesting that  at least in terms of pure computational efficiency, this type of method would best fit a use case where retraining on quickly evolving data distributions is a necessity.

\section{Conclusion}

We have demonstrated a method for learning strategies to classify datasets so as to directly target test-time performance. This allowed us to construct classifier generators tuned to specific families of data distributions as well as specific use-cases including very small datasets. The generated classifiers generally performed well, having a top-performing entry on  12 out of the 18 datasets, and having stable performance comparable with other baseline methods in other cases.

In the larger sense, by framing the overall task of determining a classification policy as correctly predicting test labels conditioned on the training set and test coordinates, we can use gradient descent to discover strategies against statistical difficulties such as limited data, the need for regularization, covariate shift, nonstationarity, and the use of unlabelled data. Rather than having to design a specific pathway for that information to be used, it is only necessary to design a task set which exemplifies the statistical problem in question.

\subsubsection*{Acknowledgements}

We would like to acknowledge Martin Biehl and Nathaniel Virgo for discussion regarding the contents of this manuscript.

redpajama_set_name: RedPajamaArXiv


text:
January 20, 2021 in Motorsport
Familiar faces in new places for WeatherTech SportsCar Championship
You might need a scorecard to keep track of the first few rounds of the 2021 IMSA WeatherTech SportsCar Championship.
Changes in affiliations between teams and manufacturers produced more than the usual amount of shuffling in driver lineups. And since this has been a shorter offseason than usual, it may take a bit longer to remember who has gone where.
Barely 60 days have passed since the checkered flag flew on the 2020 Mobil 1 Twelve Hours of Sebring Presented by Advance Auto Parts, precious little time for teams to button up contracts. With so many faces in new places, the annual Roar Before the Rolex 24 test – which includes a 100-minute qualifying race for the first time this year – will be more important than ever.
Acura's decision to split its pair of Daytona Prototype international (DPi) entries between Wayne Taylor Racing and Meyer Shank Racing with Curb-Agajanian was the key domino in the offseason movement.
Ricky Taylor and Dane Cameron, incumbents from Acura's 2018-20 DPi affiliation with Team Penske, will continue to anchor the marque's lineup. Taylor returns "home" to the operation run by his father to team with Filipe Albuquerque, who most recently raced Cadillacs for Action Express Racing. Cameron will share MSR's full-time entry with Olivier Pla, a veteran of the GT Le Mans (GTLM) Ford GT program and Mazda Motorsports' DPi effort.
Team Penske's other previously full-time DPi drivers will provide continuity to the 2021 Acura program by competing in the endurance races, starting with the season opener, the Rolex 24 At Daytona. Juan Pablo Montoya will drive for MSR and Helio Castroneves for WTR. Alexander Rossi, an endurance driver for Team Penske, maintains his Acura links with WTR, while Simon Pagenaud has jumped to the Cadillac ranks.
Pagenaud will be part of the Action Express "star" car at Daytona, the No. 48 Ally Racing Cadillac, along with two-time defending Rolex 24 co-champion Kamui Kobayashi, NASCAR champion turned IndyCar rookie Jimmie Johnson and Mike Rockenfeller, who returns to Prototypes after spending recent years as an Audi factory driver in the DTM touring car series and being an add-on for Corvette Racing in WeatherTech Championship GTLM endurance races.
AXR's full-time entry piloted by Pipo Derani and Felipe Nasr, the No. 31 Whelen Engineering Cadillac, has a star co-driver of its own: 2020 NASCAR Cup Series champion Chase Elliott will make his Rolex 24 debut, along with 2019-20 FIA World Endurance Championship champion Mike Conway, making his fourth Rolex start in the No. 31.
While Cadillac lost WTR to Acura, the American marque picked up a worthy substitute: Seven-time IMSA champion Chip Ganassi Racing returns to sports car racing after a one-year sabbatical, stepping up from the GTLM category into DPi with drivers Kevin Magnussen and Renger van der Zande. The latter comes to CGR from WTR, where he joined Kobayashi as the Rolex 24 winner in 2019 and '20.
In GT racing, Nick Tandy has been synonymous with Porsche for more than a decade. His accomplishments with the German marque include overall victories in the 24 Hours of Le Mans, the Nurburgring 24 Hours and the Spa 24 Hours, plus 13 wins in WeatherTech Championship GTLM competition since 2014.
But for 2021, the 36-year-old native of Bedford, England, has moved to Corvette Racing, teaming with incumbent Tommy Milner in the No. 4 Corvette C8.R for the full season. For endurance races including the Rolex 24, they will be joined by Alexander Sims, who served as a factory BMW driver the last four years.
Given that he's staying in GTLM and that Corvette switched to a mid-engine layout last year, Tandy's learning curve isn't as steep as it could be. But he still faces a tall task getting up to speed quickly with a new car, team procedures and personnel.
Sims competed full time for BMW Team RLL in GTLM in 2017 and '18, winning five times and finishing second in the standings in 2017. For the last two years, BMW utilized Sims in Formula E, the electric-powered championship for semi-open-wheel cars.
The 32-year-old London native has run just one sports car race in the last two years, in a front-engine BMW M6 GTLM, so his adjustment from the comparatively low-power Formula E single-seater will be much greater.
Over the last two months, Sims and Tandy spent plenty of time in the GM Racing simulator in North Carolina, and Corvette Racing conducted a private test at Sebring International Raceway to get them seat time in the C8.R.
Other changes of note regarding familiar WeatherTech Championship drivers include:
WeatherTech Racing and Cooper MacNeil stepping up from GTD to the GTLM class to field the No. 79 Porsche 911 RSR-19 in partnership with Proton Competition. MacNeil will share the car with a rotating cast of Porsche factory drivers throughout the 2021 season, opening the year alongside Richard Lietz, Kevin Estre and Gianmaria Bruni at the Rolex 24;
Ryan Briscoe, also a Rolex 24 DPi winner last year with WTR, hopping into the Scuderia Corsa Ferrari GTD entry for the Rolex;
Earl Bamber, who won 25 races and the 2019 GTLM title with Porsche, forming his own team with Rob Ferriol (Team Hardpoint/EBM) for a full-season GTD entry;
Patrick Pilet, who picked up 12 wins and the 2015 crown while a member of Porsche's GTLM program from 2014-2019, co-driving the Black Swan Racing Porsche this year at the Rolex;
Joao Barbosa moving from the Mustang Sampling Racing DPi to the Sean Creech Motorsport entry in the new Le Mans Prototype 3 (LMP3) class;
Long-time Corvette Racing driver Oliver Gavin heading to Vasser Sullivan Racing for its GT Daytona (GTD) run at the Rolex 24;
Mario Farnbacher, the two-time reigning GTD champion with the Meyer Shank Acura NSX GT3 program that was halted in favor of the DPi effort, joining Magnus with Archangel in an Acura for the Rolex 24.
The Roar Before the Rolex 24 runs Jan. 22-24 on the Daytona International Speedway 3.56-mile road course, with the Motul Pole Award 100 qualifying race on the final day. Rolex 24 At Daytona action runs Jan. 27-31. Live coverage of the 24-hour race begins at 3:30 p.m. ET Saturday, Jan. 30 on NBC.tishGT
Rolex 24 2021 has 49 Entries including 50 Past Race Winners
Wayne Taylor Racing Acura Leads Rolex 24 at Halfway Point
Mike Conway takes the chequered flag for Toyota in WEC 6 Hours of Monza
Toyota 1-2 in 6 Hours of Spa final practice
Twelve Hours of Sebring Fact Sheet
Rolex 24 At Daytona: GTLM and GTD Team-By-Team
Featured Car for Sale
Hendon Way Motors
Porsche 993 C2 Cabriolet
motorsport news motorsport Mike Conway Dane Cameron Simon Pagenaud Team Penske Nick Tandy Kevin Magnussen Kamui Kobayashi Felipe Nasr Racecar Daytona Prototype Rolex 24 Ricky Taylor Wayne Taylor Racing Chip Ganassi Racing Cadillac BMW Team RLL WeatherTech SportsCar Championship DPi Meyer Shank Racing Porsche 911 RSR-19 Corvette C8.R web site design classic car dealer web site design automotive web site design marine web site design boat dealer web site design

redpajama_set_name: RedPajamaCommonCrawl


Do you clean your refrigerator regularly, or only after you've run out of space? If your answer is the latter, your refrigerator may be harboring dangerous bacteria that can make you, your family, and your house guests ill.
Refrigerators should be cleaned monthly, inside and out, ideally. The process involves removing all foods, cleaning all shelves, and wiping down drawers. Spoiled food is removed and old containers reclaimed.
The process takes 20 minutes. Here's how to do it.
First, prepare your cleaning stations, including filling a sink with soapy water, and having drying towels handy. Complete this step before you start to remove food from the refrigerator. This limits the amount of time that food has available to reach room temperature.
Next, remove all food from the appliance. Throw out old food past its expiration, and leftovers which have been in containers for more than a few days. Check dairy products for expiration dates — especially cheeses and creams. Toss fruits and vegetables that have spoiled. Wipe down condiment jars and bottles with a damp towel.
Next, with the refrigerator empty, remove all shelves and drawers and wash them in the water-filled sink. Scrub to remove any caked-on foods and spills. Rinse off the soapy water and dry the part on your drying towels.
Then, while the shelves and drawers are drying, using a mixture of baking soda and water, wipe down the interior surfaces of your appliance. The mixture should be roughly 2 tablespoons of baking soda for every 1 gallon of water. Wipe the mixture off with a clean towel.
Lastly, move the shelves and drawers back into the refrigerator and replace all of the food that's "good".
Refrigerators can be a dirty place. We rarely wash our hands before handling food in a refrigerator and that can contribute to a bacteria-heavy environment. A good cleaning, though, can keep our foods — and our home — healthy.


Texpack is a little bundle of scripts to create documented LaTeX style, class files and docs. Texpack provides useful scripts for the automated creation of the skeleton required by *.dtx documented TeX files. It is useful both for creating packages (*.sty) and new classes (*.cls). Texpack also provides a command to automate the creation of commonly needed LaTeX skeleton files. This feature is mostly useful for the Italian author, but also provides the fast creation of a conditional preamble for handling dvi/pdf compilation.


text:
Q: Convert Tensor to numpy array in TF 2.x I am trying to load Universal Sentence Encoder and this is my code snippet:
import tensorflow as tf
import tensorflow_hub as hub
import os, requests, tarfile

def extractUSEEmbeddings(words):
    # Extracts USE embeddings
    # Replace `USE_folder` with any directory in your machine, where you want USE to be downloaded
    try:
        embed = hub.KerasLayer(USE_folder)
    except Exception as e:
        print ("Downloading USE embeddings...")
        r = requests.get("https://tfhub.dev/google/universal-sentence-encoder-large/5?tf-hub-format=compressed")
        open("USE.tar.gz", "wb").write(r.content)

        tar = tarfile.open("USE.tar.gz", "r:gz")
        tar.extractall(path=USE_folder)
        tar.close()

        os.remove("USE.tar.gz")
        embed = hub.KerasLayer(USE_folder)
        pass
    word_embeddings = embed(words)
    return word_embeddings.numpy()

I get the error 'Tensor' object has no attribute 'numpy'. When I run the same code on Jupyter notebook, with the same versions of tensorflow (2.2.0) and tensorflow-hub (0.9.0), I do not get any error and it works perfectly fine.
I printed the type of Tensor in both cases, and realized that this is because I get an Eager Tensor (tensorflow.python.framework.ops.EagerTensor) in Jupyter, which has a numpy method whereas in my script, the Tensor is of type tensorflow.python.framework.ops.Tensor. However, I am now unable to figure out how to switch on Eager Execution in my script, since in TF 2.x it is supposed to be enabled by default.
I have tried all the solutions given in this thread, but none of them work for me.
Why am I not getting an Eager Tensor when run through the terminal, but get it through Jupyter? Does my problem have anything to do with the fact that I am using tensorflow-hub here, and is that why none of the solutions are working for me? Most importantly, how do I convert Tensor in tf 2.x to a numpy array?

redpajama_set_name: RedPajamaStackExchange


75% of mainstream mutual fund companies routinely oppose shareholder resolutions addressing issues such as pesticides, asthma, and toxic chemicals in toys and cosmetics. Citing substantial evidence that shareholders are concerned about toxics, the report urges mutual funds to adopt policies that support shareholders' efforts for greater disclosure of toxic risk. Part of the Environmental Fiduciary Report Series.


We now have a Faceplant page where we list all manner of old stuff nobody would ever want to buy.
We add more mouldy stuff here frequently, so if you're into that sort of thing, check back often.
how to find the most comfortable and efficient riding position for you. That's right, YOU, not anybody else.
Click here to read about Schmidt hub dynamos and various battery powered lights.
Our SON hub dynamo powered light systems are very efficient, environmentally clean, provide lots of light, and work in all weather conditions. Your bicycle can replace an automobile for most of your day to day transportation needs.
Stronglight chainrings, headsets, fenders and bottom brackets.
Pitlock Is Here! Quick release skewers on your wheels make them easy to remove, which is great when you have a flat, not so great when a thief targets your bike. Pitlock locking skewers will make a thief look elsewhere.
TA Carmina cranksets in a wide range of arm lengths and chainring options. We have these in Single, Double and Triple sets.
If you want to mix Campy and Shimano derailleurs, try a ShiftMate.
Beautiful touring bags, carbon fiber fenders and stainless steel fenders from Gilles Berthoud.
Under no circumstances should you place an order without reading this first.
This web site can only be viewed with a computer. So, if you're having trouble viewing this site, and don't have a computer, that could be the reason.
Peter White Cycles LLC is a small custom shop specializing in wheelbuilding, lighting systems, touring and commuting bikes and equipment for tandeming, touring, commuting and randoneuring. We import bicycle lighting products from Germany and France. We are also a wholesale distributor for several manufacturers of fine bicycle components for racing, touring and commuting, selling them to other retail shops in the US and Canada. We also build wheels for other bicycle shops and manufacturers. When you call, you'll either talk to me, Peter, my wife Linda, or one of our employees, either Sheena or Penny. If anyone else answers, it's party time. I've been in the bicycle business since 1975, started Peter White Cycles in 1988, and got onto the web in 1997. Linda joined the business in 2002. Sheena and Penny joined us in 2006.
In the US and Canada, Peter White Cycles represents Wilfried Schmidt Maschinenbau, Busch & Müller, Spécialités TA, Stronglight SA, Tout Terrain, Pitlock, Rohloff AG, Sanyo, Supernova and Spanninga, Velocity, Wheelsmith, Wippermann and Sapim. Bicycle shops and bicycle manufacturers can order products from these companies through us. See this page for details. Consumers can find a list of bike shops across the US and Canada that purchase these products from us here.
While we specialize in the touring and commuting arena, we also cater to road racing and cross country off road cyclists. Much of our own cycling is off road, though Linda and I both enjoy road riding on our singles as well as on our tandem.
We offer products that make the practical use of the bicycle easier and safer. Many people don't know that good lights are available to make riding at night much safer and more fun. That's why we're here.
We keep a large inventory of components and parts on hand from Wilfried Schmidt Maschinenbau, Spécialités TA, Carradice, Busch & Müller, Stronglight, Velocity, Pitlock, Tout Terrain, Gilles Berthoud, Supernova, Rohloff, Paul Components, Campagnolo, Shimano, Phil Wood, Mavic, White Industries, Ortlieb, Tubus, King, Schwalbe, Nokian, Wheelsmith, Brooks, Camelbak, Zefal, SKS, Nitto and many others.
We don't have parts for BMX or other children's bikes. We don't have parts for Huffys or any bikes sold through department stores. Nor do we have specialized components for "Invalid Bikes", you know, for those poor unfortunate people with green or pink hair who have to be carried up the mountain on a ski lift so they can ride down yelling "Yo Dude!"
Yeah, I know, that violates the Americans with Disabilities Act. I'm a heartless scoundrel. Make the world a better place and turn me in by clicking here.
We do a brisk business building Tout Terrain bicycles and wheelbuilding. We don't offer general service on bicycles.
The shop is in Hillsborough, New Hampshire, a small town midway between Concord and Keene.
From the east, Concord, take Interstate 89 North to Exit 5. It's a left lane exit. Don't ask why. Follow Route 9 West to Route 31 and turn right. You're now heading north towards Washington. Hall Rd. is a dirt road on the left, 1.3 miles north of Route 9. If you go past William's Store on the left, you have gone too far, by about one hundred yards.
From the west, take Route 9 eastbound from Keene. Watch for Route 31 to merge with Route 9 from the south. About 2 miles later, Route 31 will leave Route 9. It's a left turn, heading north towards Washington. Hall Rd is 1.3 miles on the left. If you're coming from White River Junction, VT. follow Interstate 89 south to exit 5, pick up Route 9 west and then follow the directions as if coming from the east.
From the south, take Route 202 North from Peterborough until it intersects Route 9 in Hillsborough. Then take Route 9 West as if you came from Concord. Route 31 north is just 1.5 miles away.
From the north, well, hardly anybody ever comes from the north. There's just polar bears, tundra, and a few old fur trappers who've perhaps spent too much time in the wilderness. But, after crossing the glacier (watch out for that last Bergschrund, it's a whopper), take Route 10 south from Newport, (Don't ask where the mansions are. People will look at you funny.) bear left onto Route 31 south, and after you cross the Hillsborough town line watch for Hall Rd. on the right just past William's Store.
Please call for an appointment, in particular if you need to speak with me, the guy with the grey beard.
Since the main highway (Route 9) through town was re-routed in 2002, many maps even now are not accurate, including some on-line mapping software. Follow these directions, not your map. Besides, the maps never show the Bergschrunds. Also, some maps show Hall Rd. going all the way from Route 31 and connecting directly to Route 9, west of the point where Route 31 goes north from Route 9. Unless you're on a mountain bike or are hiking the AT and gotten lost, don't try to come up Hall Rd. from the south. The middle of Hall Rd. is barely navigable in a tracked vehicle, but lots of fun on full suspension mountain bike.
Yahoo Maps is up to date. Click here for a map.
Here's the old shop on a busy day.
If you'll be dropping in by hot air balloon (no jet packs please, you'll disturb the wildlife), here are the coordinates.
N 43∞ 7' 29.5" - W 71∞ 58' 9.0"
Peter keeps banker's hours so he's typically in the shop from 10AM to 4PM Monday through Friday, eastern time. Linda, Sheena and Penny are in the shop from 9AM until 5:30PM. Late in the day we're rushing to get Fedex orders packed for the driver, so it's best to call before 4PM.
We're not open regular hours on weekends. But I often make appointments to see people. It's best to set a time a day or two in advance if you want to come by on Saturday or Sunday.
By the way, I received an email a while back from a prospective customer who says he doesn't like the way I write, and therefore he's not going to buy anything from us. So I'm going to try to improve my writing and I've decided to use the following paragraph as a guide. I'm hoping it helps us get more business. This was written by a really smart college professor from Berkeley.


text:
Running blade donated to boy through New Hampshire Paralympian's foundation
Noelle Lambert's foundation gifts running prosthesis
Updated: 11:04 PM EST Feb 13, 2020
Jean Mackin
News Anchor/Reporter
BORN TO RUN FOUNDATION GAVE OUT ITS SEVENTH PROSTHETIC TONIGHT TO A BOY WAITING TO TRY IT OUT. <XXXX MELROSE BOOY RUNNING BLADE BROLL 2:16 (NAT SOUND SOT) - VIDEO SHOWS ONLY FEET "HE'S THE EAGLES EVERYBODY GO GET HIM" XXXXXX> FROM ACROSS THE COURT - IT'S TOUGH TO TELL WHO STANDS OUT ON THIS TEAM... (GO TO 'MELROSE BOY RUNNING BLADE BROLL AT 2:40) AND THAT'S EXACTLY THE POINT 9 YEAR OLD RYAN FEENEY IS SPORTING HIS BRAND NEW RUNNING BLADE AT BASKETBALL PRACTICE IN MELROSE, MASSACHUSETTS <XXXX MELROSE BOY RUNNING BLADE BROLL 1:00 HANDS OVER DONATED LEG WITH APPLAUSE XXXX> - A DONATION FROM THE BORN TO RUN FOUNDATION STARTED BY NEW HAMPSHIRE'S NOELLE LAMBERT <XXXXX MELROSE BOY RUNNING BLADE RYAN 4:45 I THINK IT WAS REALLY NICE OF HER TO DONATE THIS LEG TO ME AND I CAN'T WAIT UNTIL I CAN PLAY AND BE REALLY FAST WITH IT 4:54 XXXXX> (PHOTOS WITH VNR) RYAN WAS BORN WITH NF-1 - HIS LEG WAS AMPUTATED BEFORE THE AGE OF 2 (áááWIPE OR DISSOLVE) THE DIVISION 1 ATHLETE LOST áHER LEG IN A MOPED CRASH ON MARTHA'S VINEYARD IN 2016. NOELLE NOW HELPS OTHERS GET BACK IN THE GAME WITH LIFE- CHANGING PROSTHETIC DONATIONS (MELROSE BOY RUNNING BLADE BROLL 1:15 SHOT OF WALKING LEG ON FLOOR, BLADE ON HIS LEG) SO RYAN CAN SET ASIDE HIS WALKING LEG FOR A LIGHTWEIGHT BLADE <XXXX MELROSE BOOY RUNNING BLADE BROLL 1:20 MAKES BASKET XXXX> <XXXXX MELROSE BOY RUNNING BLADE JILL FEENEY / MOTHER MOM - JILL 5:07 WE DIDNT KNOW HOW TO AFFORD ONE BECAUSE INSURANCE DOESN'T COVER IT UNTIL THEY'RE A LITTLE OLDER AND NOELLE AND HER FOUNDATION HAS REALLY BEEN AMAZING 5:17 XXXXX> <XXXXX MELROSE BOY RUNNING BLADE NOELLE 7:35 SEEING RYAN TODAY RUNNING WITH ALL HIS FRIENDS AND TEAMMATES AND BEING ABLE TO PLAY BASKETBALL LIKE HE DOESNT EVEN HAVE AN AMPUTATION LIKE HE HAS TWO LEGS I MEAN THAT'S WHAT'S MOST IMPORTANT IN LIFE 7:45 XXXXX> NOELLE ALSO GIVES THE GIFT OF INSPIRATION - SHE'S A MEMBER OF THE U.S. PARALYMPIC TEAM TRAINING IN TRACK AND FIELD FOR THE 2020 TOKYO GAMES AND SHE'S SHARING WHAT IT TAKES TO BE A CHAMPION <XXXXX MELROSE BOY RUNNING BLADE NOELLE 8:51 IT'LL GIVE HIM REASSUR
Noelle Lambert's 'Born to Run' foundation gave out its seventh prosthetic on Thursday night to a boy waiting to try it out.>> Download the FREE WMUR appRyan Feeney, 9, sported his brand-new running blade at basketball practice in Melrose, Massachusetts on Thursday. The blade was a donation from the Lambert's foundation. Feeney was certainly excited about the act of kindness."I think it was really nice of her to donate this leg to me and I can't wait until I can play and be really fast with it," Feeny told New 9.Feeney was born with NF-1 and his leg was amputated before he turned 2.Division 1 athlete Lambert lost her leg in a moped crash on Martha's Vineyard in 2016. She now helps others get back in the game with life-changing prosthetic donations."We didn't know how to afford one because insurance doesn't cover it until they're a little older," Feeney's mother Jill said. "Noelle and her foundation has really been amazing."Lambert also gives the gift of inspiration. She is a member of the U.S. Paralympic team training in track and field for the 2020 Tokyo games."Seeing Ryan today, running with all his friends and teammates and being able to play basketball like he doesn't even have an amputation, like he has two legs. I mean, that's what's most important in life," Lambert said. "It'll give him reassurance he can do whatever he wants to do with his life."
MANCHESTER, N.H. —
Noelle Lambert's 'Born to Run' foundation gave out its seventh prosthetic on Thursday night to a boy waiting to try it out.
>> Download the FREE WMUR app
Ryan Feeney, 9, sported his brand-new running blade at basketball practice in Melrose, Massachusetts on Thursday. The blade was a donation from the Lambert's foundation.
Feeney was certainly excited about the act of kindness.
"I think it was really nice of her to donate this leg to me and I can't wait until I can play and be really fast with it," Feeny told New 9.
Feeney was born with NF-1 and his leg was amputated before he turned 2.
Division 1 athlete Lambert lost her leg in a moped crash on Martha's Vineyard in 2016. She now helps others get back in the game with life-changing prosthetic donations.
Wednesday, February 5th: Noelle Lambert: From Tragedy to Triumph
"We didn't know how to afford one because insurance doesn't cover it until they're a little older," Feeney's mother Jill said. "Noelle and her foundation has really been amazing."
Lambert also gives the gift of inspiration. She is a member of the U.S. Paralympic team training in track and field for the 2020 Tokyo games.
"Seeing Ryan today, running with all his friends and teammates and being able to play basketball like he doesn't even have an amputation, like he has two legs. I mean, that's what's most important in life," Lambert said. "It'll give him reassurance he can do whatever he wants to do with his life."


Just say the damn thing!!!
I seem to be recalling that statement from the instructor and the quote from Mark Twain all the time. We live in a world where it seems like most communication is happening about things that don't truly matter and we're using very few words wrapped in very little punctuation, syntax, or even proper spelling. (Evidence: most texting and twitter).
Because we seem to be drowning in the brief and unimportant, it makes it really difficult to say the things clearly that are truly important. We are helping a lot of people refine their message, their mission, and even the expectations of their employees. Simple, powerful, and direct language is an increasingly rare commodity.
The temptation, in a very noisy and superficial world, is to speak louder and more excessively. Ironically, the answer is the opposite. We need to simplify and be even more clear.
No conditions, no qualifications. No need to describe what it looks like, how it might apply in different situations, or if it was intended for only certain people. Basically, He just said the dang thing.
As the world gets more complicated and overwhelming, keeping your company messaging, your internal/external communications, and even employee expectations clear, is going to become even more essential.
Having a clear, specific, and powerful purpose statement has never been more important.
Being anchored is values that define and guide your work and team are more valuable than ever.
Having an inspiring vision that motivates and focus your team has never been more crucial to survival and future success.
Building a detailed strategic plan with accountability is no longer a luxury, but a necessity.
Is your purpose, values, and vision clear?
Do your employees really know what is expected of them, how they are supposed to achieve those results, and how they will be measured?
Is your messaging clear both internally and externally?
What is it costing you to not be clear on those things and how much more will it cost you as the world gets more noisy?


When you start to search for a new car insurance policy, you will soon realize that there are a multitude of companies in Saguache, CO to choose from. Many people believe that they can only get good rates from well-known national companies, but there are actually many other businesses that do not do have a national advertising that can actually offer exceptional rates. Of course, you do need to verify the authenticity of each company that you find as they may not have the best overall rating. However, if you are able to use websites that can connect you with different companies that have already been verified, you simply need to choose one of them that will offer you the lowest price. Here are a few of the cheap car insurance online tips that you can use when searching for your coverage in Colorado, regardless of the type of vehicle that you drive or how much coverage you actually need.
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Unlike that delicate, sensitive Mississippi flower Haley Barbour or creationist and cream puff-loving minister of cheeseburgers Mike Huckabee, Sarah Palin has got what it takes (a low IQ?) to run for president of the United States of Jesus. For one thing, she is more than prepared for the many challenges that lie ahead, not to mention, has tons of experience coming in second place. In fact, it's the one thing she excels at!
But most importantly, Sarah Louise Palin has that proverbial fire in her belly, meaning there is either some sort of glowing ember of heat burning inside her, desperately making her want to run for president, or First Dude Todd deposited another precious li'l bun in her oven.
"I am so adamantly supportive of the good, traditional things about America. … I want to make sure America is put back on the right track and we will do that by defeating Obama in 2012. I have that fire in my belly," Sarah Palin told Greta van Susteren.
Like no more unprotected sexytime in the back of Papa's pickup, got it Bristy? Same goes for you Piper!
But it's not just the pesky kids who are getting in the way of SarBear's White House dreams. It's all the haters in the lamestream media always tryin' to bait her and her family by askin' tough, non-yes-or-no-questions, reportin' on Bristol jivin' in a gorilla costume on national teevee, and all the other "little, petty, stupid things" they're always doin.
This makes Mama Grizzly very angry! And as evidenced in the video, tweakier than a methhead in her hometown Wasilla.
Unlike, say, the dime-a-dozen barrels of petroleum fun begging desperately to be freed from their frozen underground cages by some patriot with the skills and sense to wanna drill, baby drill (and spill, baby spill!) all over the Earth's crust.
Because, even though Mama Grizz wants nothing more than to run for president of the Lower 48, and stick it to all the haters, she has trouble "not taking the bait" from the terrible, awful, no-good, meany liberal lamestream media she's always ranting about for making her and her ever-growing brood rich and famous, with as many moose piles of money as their frozen hearts desire.
Of course, it's easy for a nobody like Greta Van Susteren to simply say, "then don't take the bait" when it's not her litter of terrible kids getting knocked up in high school, crusading for abstinence via poorly made after school special-like infomercials, joining the Army to avoid jail time for vandalizing a school bus, or getting chin jobs for medical reasons, not because their God-given face wasn't up to snuff.
Good thing, Sarah and her white hot belly-fire are prepared for that kind of thing.
It's called a semiautomatic machine gun with an extra magazine clip for good measure.
Now all of America can feel the burning in their belly, too. Except theirs will be the kind that comes as a result of having Sarah Palin as leader of the free world.


text:
Feb 13, 2018 4 min read Featured
Cavs Top Thunder in Hard-Fought Affair, 120-112
The Thunder and Cavaliers had a playoff-like match-up on Tuesday night in Oklahoma City, with each team having moments of dominance in front of a raucous crowd at Chesapeake Energy Arena. Despite the Thunder getting 92 combined points out of Russell Westbrook, Paul George, Steven Adams, and Carmelo Anthony, the Cavs simply had too much firepower — escaping OKC with a 120-112 victory.
LeBron James led the way for Cleveland with 37 points, 8 rebounds, and 8 assists — with 14 of his 37 coming in the first five minutes of the third quarter. His scoring outburst put the Cavs up 10 with 7:06 remaining in the third, but the Thunder used a 14-3 run over the next three minutes to briefly take a one-point advantage. Cleveland would re-take the lead shortly thereafter, holding a 91-87 lead headed into the fourth quarter.
With all superstars off the floor to begin the fourth, the Cavs bench went to work — outscoring the Thunder reserves 9-3 in the opening minutes of the quarter. James checked back in at the 8:12 mark with a 100-90 lead, and, despite OKC clawing to within five in the final minute, Cleveland was able to comfortably navigate the final frame with its lead intact.
Paul George led the Thunder with 25 points, Carmelo Anthony added 24, Steven Adams had 22 points and 17 rebounds, with Russell Westbrook tallying 21 points, 12 assists, and 7 rebounds. However, Cleveland shot 16/36 (44.4 percent) from downtown and was able to survive due to that three-point shooting and a massive advantage in bench effectiveness.
Full Highlights:
The loss drops the Thunder to 32-26 on the season ahead of tomorrow night's tilt against the Grizzlies. The All-Star break begins immediately after that game — right on time for an OKC squad that's just 4-6 over the last 10 contests.
OKC 28 29 30 25 112
CLE 29 33 29 29 120
51-20: The Cavs bench outscored Thunder reserves by a 51-20 margin. The opening minutes of the fourth quarter was the back-breaker.
51.1: The Cavs shot 51.1 percent from the floor, compared to 44.4 percent for OKC.
11 & 16: The Cavs hit 16 three-pointers at a 44.4 percent clip. OKC hit 11 on 31.4 percent shooting.
51-41: The Thunder won 51-41 on the glass, including 18-10 on the offensive boards. That last stat was buoyed by 12 offensive rebounds for Steven Adams alone.
36-46: The Cavs outscored OKC 46-36 in the paint.
70: The Thunder left nine points at the free throw line, going 21/30 (70 percent) from the stripe.
9 & 9: There were nine ties and nine lead changes.
Thunder Core Four
The Thunder's core four of Westbrook, George, Adams and Anthony combined for 92 points on 33/71 (46.5 percent) shooting. They dominated basically every touch, with the rest of the team shooting just 7/16 (43.8 percent) on the night.
Their individual lines:
George: 25 pts, 6 ast, 8/19 FG, 5/13 3P, -4
George hit five or more three-pointers for the fourth-consecutive game and led the Thunder in scoring as a product of it. It's hard to complain too much — he played very well — but you'd like to see him get more than three buckets from inside the arc.
Anthony: 24 pts, 7 reb, 10/22 FG, 3/9 3P, +3
Melo returned from injury and had his best scoring effort since January 20 — the last time OKC faced the Cavs. The numbers are fine and he hit some big/tough shots, no question. However, he shouldn't lead the team in field goal attempts basically ever. For every big shot he made, it gave him the confidence to take another — which usually doesn't work out.
Adams: 22 pts, 17 reb, 12 OReb, 2 stl, 8/12 FG, -1
Adams got off to a blistering start, scoring 11 points and grabbing 5 rebounds (all offensive) in the first quarter. He did so on 5/7 shooting and the Cavs looked completely incapable of slowing him down — a trend that continued all night. His 12 offensive rebounds is a new team-record and he was the best player on the floor tonight for OKC. The only problem? After scoring 15 points on 7/9 shooting in the first half, he only got three shots in the second half. Gotta keep feeding the big man.
Westbrook: 21 pts, 12 ast, 7 reb, 7/18 FG, -3
Westbrook shook off a 3/11 first half, going 4/7 in the final two quarters and helping the Thunder keep the game close. He looked rusty to start and certainly wasn't his normal self — but he only turned the ball over once and was generally effective after a two game absence. He'll look to continue rounding into form tomorrow night in Memphis.
The Cleveland Bench. The Cavs don't win this game without the absurd play of their reserves. The Cleveland second unit had four members in double-figure scoring: Rodney Hood (14), Jordan Clarkson (14), Larry Nance Jr. (13), and Jeff Green (10). When you factor in Nance Jr's eight offensive rebounds — all of which seemed to matter — the new-look Cavs appear quite formidable. Not one player in the Cavaliers' starting five finished with a positive plus-minus, but the reserves took care of that. Clarkson (+16), Nance (+14), Green (+14), Hood (+8).
Bad Alex/Good Jerami. Alex Abrines returned to earth after a solid performance on Sunday night. He got the start at shooting guard and turned in 0 points on 0/3 shooting in 23 minutes of action. Jerami Grant had 14 points and six rebounds — his third double-digit scoring effort in the last four games.
2Pat Goes Missing. Over the three games prior to tonight versus Cleveland, Patrick Patterson logged 30, 26, and 35 minutes. He hit Memphis for a season-high 14 points on Sunday night — and then drew just 10 minutes of action tonight. Melo was back from injury, which certainly cut into his minutes dramatically….but 10 minutes is a joke. He's a streaky shooter that was coming off a solid performance — he should have seen more run.
JR Smith. JR went for 18 points on 6/9 shooting from downtown. The Thunder led by seven in the first quarter, but he sank three long balls in the opening frame to eliminate said lead.
LeBron James. There's not much to say about LeBron James that hasn't already been said. His 37-8-8 on 12/17 shooting looked easy and he was 9/13 from the floor in the second half alone. Josh Huestis did a decent (?) job on him in stretches but he got whatever he wanted all night. This happens.


text:
Writing about chronic illness is 'super interesting for a poet' but 'awful' as a person
By Rege Behe
Shannon Sankey grew up in various communities in the South Hills, moving frequently, never staying long enough anywhere to feel comfortable. Suffering from Crohn's disease — she underwent chemotherapy for five years as part of her treatment regimen — added another layer of discomfort. Sankey tackles these issues in her recently published chapbook from TAR Chapbook Series, We Ran Rapturous.
"For this work especially, but in a larger sense in all my work, I think a lot about containers, both in content and in form," Sankey says. "So, you'll see a lot of the poems are prose containers. For this work, I'm dealing with chronic illness, I'm dealing with an ancestral meditation, and all of it is very serial and dreamlike, so you need to use rooting, grounding images."
A graduate of Chatham University's Master of Fine Arts program, Sankey works as a copywriter at Penn State Fayette in Lemont Furnace, Fayette County, and teaches creative writing and sustainability in community programs. Writing about Crohn's disease, as well as her mother, who suffers from seven autoimmune afflictions, wasn't a priority.
When she finally decided to write about those illnesses, Sankey had "a lot of darkness and power and magic to draw on. ... For a long time, I resisted writing about it. I didn't want it to even inhabit my body, let alone write from it."
While in the MFA program at Chatham, Sankey noticed that many of her peers were examining their own traumas. That gave her the permission she needed to write about her disease. The first poem in the chapbook, "Disabled," illustrates the first tentative exploration of her affliction:
I don't choke on the word.
It is a red spice
from my mother's cabinet.
"It took the better part of the last decade to allow myself the accommodations that were necessary to even arrive at this chapbook," Sankey says. "In ["Disabled"], the speaker gifts herself all the darkness, the terror, the magic, of disabled as an identity. It's a reclaiming of power. … It feels amazing to hold the book in my hands and outside of myself, because it's so heady to write from the body, especially when it's one of chronic illness. It feels like you're standing on a really precarious foundation."
In addition to being physically debilitating, Crohn's disease also is a mental battle, sufferers always on edge about when symptoms will appear. Exploring the mental state of chronic illness, and Crohn's specifically, became the raw material for Sankey's poems.
"Crohn's changes your relationship to food," she says. "It changes your relationship to community, to sustenance, to survival. And some of the poems address that, the speaker grappling with `every time I feed myself, I'm feeding the thing that destroys me.' It causes a huge psychic dissonance, but it can be super interesting for a poet. But as a person, it's awful."
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This is my personal website that contains various original intellectual property ranging from writings to music.
The content produced and published here is original and the ownership of this author.
I have spent well over 16 years developing software, leading teams, researching technology, and herding cats all while continually attempting to learn and stay up to speed within this incredible industry.
As a drummer, I have been fortunate enough to play and collaborate with some amazing musicians. Whether it's tracking an album, filling in for a session, or eating baloney on hand while on tour - I have been through a couple rodeos.
I am an avid photographer who is more interested in capturing an image and the moment than obsessing about hardware specs. I simply enjoy taking photos.


text:
Pacific Edge shares gain 4% on fourth US deal
By Suze Metherell
Shares in Pacific Edge [NZX: PEB] gained 4 percent after the biotech company said it had signed a deal with US healthcare provider MultiPlan for the use of its non-invasive bladder cancer test, Cxbladder.
The contract is the largest of the four US healthcare provider Pacific Edge has signed up since October last year, with MultiPlan a preferred provider organisation securing cheaper health rates for insurers for more than 900,000 healthcare providers covering an estimated 68 million people. Its previous three deals covered 62.6 million people.
"Our sales and marketing teams are now focussed on the urologists and clinicians who are treating the largest number of bladder cancer patients to ensure they fully understand the value and benefits that Cxbladder provides to them and their patients" Jackie Walker, chief executive of subsidiary Pacific Edge Diagnostics USA, said in a statement.
It is also "advancing" its relationship with state funded insurer, Centre for Medicare and Medicaid Services, which could potentially give it access to a third of the US population, Walker said.
Pacific Edge shares rose 4 cents to $1.03 in early trading. The stock gained 150 percent last year on news of its US deals, and raised some $20 million to drive sales. Since its March debut on the NZX 50 Index the shares have dropped 58 percent amid a global sell off of growth-orientated tech stocks.
The non-invasive bladder cancer test was adopted by New Zealand district health boards earlier this year.
Pacific Edge
Shares in Pacific Edge [NZX: PEB] gained 4 percent after the biotec


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Even with a healthy diet, we are often times nutrient-deficient. The combination of many things such as our depleted soil, the storage and transportation of our food, genetic alterations to our food, and increased stress make it difficult for us to get the vitamins and minerals our bodies require solely from the foods we eat.
Make sure that you are getting the ingredients that you want without contaminants. Read the ingredient labels and look for descriptions like non-GMO, certified organic, no wheat, no corn, no soy, no dairy, gluten free, no ingredients of animal origin, and vegan friendly.
If you are in Singapore and you'd like to learn more about healthy nutrition for a healthy mind and body, make an appointment with our consultant (First appointment is FREE). For more tips, you may also wish to join our Ayurveda Facebook Group.


text:
Fall Choral Concert: Enjoyable Academic and Cultural Credit
In Jones Chapel on Sunday, Oct. 6, all of Meredith's vocal ensembles performed various songs selected to match the Arts and Humanities theme of the semester: activism. All ensemble performances by Meredith students count as Academic and Cultural events and occur twice every semester. This is a most enjoyable way to gain academic and cultural credits while supporting student performers.
The three performing ensembles were Chorus, Encore! and Chorale. Made up of music majors and non-musical majors alike, the three provide students with different singing opportunities. Chorus is the group that tends to require the least amount of outside class time and is, according to Choral Director Shannon Gravelle, "the heart of the vocal ensembles." Encore! is Meredith's vocal jazz group that works with early music and deals more with body percussion which results in a bit of a show-choir vibe. All the performers in Encore! sing across the vocal range; one is never just an alto or only a soprano. Chorale is the most well-known of the vocal ensembles as it is the group that deals with the most technically complicated music and tours over the summer and during some semesters.
The majority of songs performed at this concert were acapella, meaning it was purely vocals without any musical accompaniment. The songs that did have accompaniment were accompanied by a piano, played by Ms. Catherine Hamner.
All of the pieces were performed remarkably well, with an even balance of sound between all the altos and sopranos. At no point was the piano loud enough to overtake the vocals and all the high notes were hit without a screechy, strained quality. Overall, the performance was great and it was a privilege to be present for those students' midterm (especially because a few students won't be returning next semester to sing in the Chapel).
By Lilly R. Wood, A&E Editor


text:
Q: c++ objects of same class with different initial condition after constructed I create a class with name well, and it has a function to print its state; after I construct them writing as "well w1, w2, w3 ....., w10;" 8 or 9 of them prints same state but 1 or 2 of them prints another state (there are total 4 states I defined and initially it has a default state which 8-9 of the objects prints correctly).
I use CodeBlocks and I added the well class by " #include "well.h" " .
So in short; i define a class and create objects by just writing their name and some of them behave different than others.
enter code here

#include <iostream>
#include "myQueue.h"
#include "well.cpp"

using namespace std;

int main(){

Well w1,w2,w3,w4,w5,w6,w7,w8,w9,w10;
w1.checkState();
w1.printState();
w2.checkState();
w2.printState();
w3.checkState();
w3.printState();
w4.checkState();
w4.printState();
w5.checkState();
w5.printState();
w6.checkState();
w6.printState();
w7.checkState();
w7.printState();
w8.checkState();
w8.printState();
w9.checkState();
w9.printState();
w10.checkState();
w10.printState();

return 0;
}

    class implementation

include "well.h"
#include <iostream>
#include <stdlib.h>

using namespace std;

Well::Well()
{
    int state = 4;
    int day = 1;
    float storage = 2.0;
    int offday = 0;
    int useCount = 0;
}

int Well::checkState()
{
    if (storage==0)
        {if (useCount==2) state = 3; // dry and broken
         else state = 1;}    //dry
    else if (useCount==2) state = 2;   //broken
    else state = 4;         // available
}

void Well::printState()
{
    if (state==1) cout << "d" << endl;
    else if (state==2) cout << "b" << endl;
    else if (state==3) cout << "db" << endl;
    else if(state==4) cout << "avai"<< endl;
}

void Well::GFG()

{
    checkState();
    if (state !=2 ) {storage= min(5.0,storage + 0.5 );
        checkState(); };
}

void Well::SP()
{
    if (state == 4 )
    {
        storage = max(0.0, storage-1.0);
        useCount++;
        checkState();
    }
}

float Well::getStorage() const
{
    return storage;
}
void Well::newDay()
{
    ++day;
    GFG();
    --offday;
    useCount=0;
    checkState();
}

     on screen

avai
avai
d
avai
avai
avai
avai
d
avai
avai

A: This code:
Well::Well()
{
    int state = 4;
    int day = 1;
    float storage = 2.0;
    int offday = 0;
    int useCount = 0;
}

doesn't initialize member of the class. It initializes local variables in the constructor. Up your compiler's warning level to get warnings about such unused variables.
Here's a variant that initializes the members:
Well::Well()
    : state( 4 )
    , day( 1 )
    , storage( 2.0 )
    , offday( 0 )
    , useCount( 0 )
{}

Check your favorite C++ textbook for the initialization syntax.


text:
Moschino X H&M
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Television's most prestigious actors and actresses brought the glamour — and a stark political resistance to the 70th Emmy Awards this year. Amongst the best…
Shantay You Stay! RuPaul's Drag Race, a Must-Watch
By Maive Bellios
Fashion, makeup, entertainment and most importantly drama: "RuPaul's Drag Race" has it all. This Emmy Award-winning reality television show has been running consistently for 10…


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