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This Pansy is hallmarked by flowers in purple with an orange face. The Jolly Joker Series cultivars produce bi-color flowers in summer. Pansies are biennial plant which are commonly used to enhance flower gardens during fall, winter and spring.
Swiss garden pansy with white blooms marked with a dark dot will constitute a great addition to flower arrangements. Garden pansies count among the most popular garden plants and stand out with long and abundant blooming.
Pink Geranium is a beautiful perennial which grows 40 to 60 centimetres in height. This outstanding decorative variety has a long flowering period, which spans from the spring to the first frost in the autumn.
White Geranium is a perennial plant that grows 40 to 60 centimetres in height. It is in flower from the spring all the way to the first frost in the autumn. White Geranium is an outstanding decorative plant with many white flowers.
Swiss garden pansy is a biennial plant that develops large, extraordinarily ornamental flowers. Small plants grow 15 - 25 cm tall, while their blooms reach up to 10 cm in diameter and delight with a multitude of bright, cheerful colours.
Garden pansies belong to biennial plants. They take on small habit, growing 15 - 20 cm tall. They are valued for their large, single flowers that consist of five rounded petals. The "Orange mit Auge" variety has orange petals.
Garden pansies are very popular spring flowers. The multitude of offered varieties, that differ in size and shape of the blooms, provide us with countless arrangement options. Garden pansies are used for planting on lower borders and flower beds.
Pansy 'Bride' is a fine example of an eye-catching decorative biennial plant; it grows 15 to 20 centimetres in height. Its beautiful large white flowers have a black blotch in the very centre, which significantly contributes to its ornamental value.
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Large flowered garden pansy is one of the most popular and commonly planted flowers not only in our part of Europe, but worldwide. The variety of cultivars and colours are the most important assets of this species.
Blooms attract butterflies and make superb cut flowers. Flowers appear in flushes throughout summer and into fall. Bushy, base-branching plants with grassy, blue-gray leaves grow to 20-24 in. tall and require little or no support.
Grow beautiful Carnations in your own flower garden easily from flower seed! In many parts of the world, the popularity of Carnations is more than any other type of flower including roses.
Sweet William 'Double Mix', as the name implies, is mix of plants of biennial type with gorgeous double flowers. The plants grows up to 25 centimetres in height, whereas the flowers create unique umbellate inflorescences.
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Pink Ball flossflower is an extremely valuable ornamental plant that blooms all summer long until first autumn frosts. This rather low-growing annual plant grows 15 - 20 cm tall. Numerous tiny blooms are gathered in large panicles or umbels.
White Ageratum is a relatively short perennial plant that grows around 10 to 20 centimetres in height. This ageratum variety develops distinctive white flowers, which form umbellate inflorescences.
Digitalis lanata has a beautiful colouration, from late spring to mid summer it produces tall spikes of densely packed, fawn-coloured flowers each with a pearl coloured lower lip. Each of the blooms is delicately patterned with dark brown veins.
This mix of flowering daisies is so impressive your neighbors will all be asking where you got this beautiful mix. You can tell them that you grew your own Gerbera Daisies from flower seed!
What a charmer for the flower garden! Beautiful white daisies cover Aster Alpinus early in the season. They are ideal for containers and rock gardens, and they are considered to be easy to establish from Aster flower seed.
Doronicum Orientale plants are uncommon, but they are wonderful spring blooming perennials with attractively toothed foliage and daisy-like flowers. It performs best in a partially shaded, consistently moist location.
Grown from Carnation seeds, this is an heirloom Dianthus flower found in many home gardens which is often used for a cut flower. King of Burgundy is the darkest of a reddish purple color and has the appearance of black velvet.
Carnations also make wonderful bedding and border plants and have a spicy-sweet fragrance and are attractive to bees and butterflies. Throughout the centuries the popularity of Carnation flowers has remained undiminished.
The Bellis English Daisy is great for early season color, and it is easily established by growing English Daisy seeds. The pompom-like flowers are bright red and are born on 3 - 5 inch stems.
This Bellis flower seed is a mixture of English Daisy seeds that creates a lovely arrangement of shades ranging from white to red. Large, fully double English Daisy blooms grow on compact uniform plants.
Bellis Perennis seeds can be started directly outdoors, and are so much more than the sweet little lawn flowers that are common low-growing daisies called English Daisy. This Bellis English Daisy cultivar is larger and much showier.
Grow a garden filled with the lovely Chabaud Rose Carnation, from freshly harvested Dianthus caryophyllus flower seeds. Clove Carnations are not your typical Carnation flower.
Originating from the Greek word for "heavenly flower," Carnations are sure to bring joy to your garden, and the "Grenadin Double Mix" Carnation seeds pack twice the delight!
Abutilon, is also called Flowering Maple or Parlor Maple. This Abutilon mixture makes a lovely house plant or is suitable for containers. Very free blooming and easy to care for, it asks for only a sunny window and a periodic dose of fertilizer.
Perennial Sweet Pea vine is also known as Everlasting Pea or Everlasting Peavine. It will grow 48 - 60 inches and will climb any adjacent vegetation or structure with its twining tendrils. In open areas it will spread along the ground.
For this evergreen Candytuft, start Iberis Gibraltarica seeds and add this lovely ground cover to your garden. This type of perennial Candytuft is a flowering plant of the genus Iberis and the family Brassicaceae.
From Western China, this rare and delightful species bears in July drumsticks of delicate rose-lilac flowers with dark red filaments. These honey-scented heads look to be made of pink velvet.
Once a humble American prairie perennial, Liatris (familiarly known as Blazing Star or Gayfeather) is now a celebrity in vases everywhere! Its tall spikes of flowers, irresistible to butterflies, have become a mainstay of the commercial cut-flower.
This rare, heavy blooming selection features large and stunning, blue, pink, or white, double, dense flower balls. Masses of bells are produced in an astonishing range. Flowers appear all summer long and are from 60-90cm high (2-3ft).
Missouri Evening Primrose has huge yellow flowers from late spring through the summer and attractive green foliage. Deep-rooted and xeric, this prairie native is a long-lived beauty. We grow an especially drought tolerant selection.
Arabis Wall Rock Cress White Snow Peak Arabis Alpina Caucasica is a dwarf, compact growing perennial. Other common names of this plant are Arabis Snow Cap or Mountain Rock Cress. This mounding, dense-growing perennial can be easily grown.


text:
\section{Introduction}
\label{sec:intro}
\noindent
DML has proven to be a relevant topic given its strategy of acting directly on the resulting embedding distances to capture the semantic similarity of the data, using the robustness of deep learning models. Over time, DML methods have been integrated to task such as zero-shot \cite{roth2021simultaneous, milbich2021characterizing}, few-shot \cite{tian2019contrastive, tian2020rethinking} and self-supervised learning \cite{rajasegaran2020self,ji2021power, milbich2020diva}.

Baseline DML methods have a variety of proposals with different loss functions \cite{kaya2019deep,deng2019arcface}. However, pair or triplet samples imply high complexity time in the training process. Thus, were introduced sample mining strategies \cite{kaya2019deep, harwood2017smart}. Still, these strategies do not generalize well to all architectures and can be counterproductive depending on the nature of the data or architecture \cite{wu2017sampling,musgrave2020metric}.
In addition, the experiments realized in \cite{roth2020revisiting} and \cite{musgrave2020metric} indicate overall flaws in the experimental setups in DML that lead to unfair comparison.

Most Machine Learning (ML) models usually assume that the train and test data are drawn from the same distribution (i.i.d. assumption). However, in a realistic scenario, distributional shifts between the train and test set can occur, where the test distribution is unknown and diverges from the train distribution. Precisely, Out-Of-Distribution (OOD) generalization address this problem \cite{shen2021towards}.

While there is still a vague definition of OOD in the literature and characterization of distributional shift is still an open problem \cite{shen2021towards}, we have opted to use these concepts using several references \cite{yang2021generalized,koh2021wilds,milbich2021characterizing,shen2021towards} that remain congruent in certain properties. For example, the distributional shifts can be caused by \textit{semantic shift} (or label shift), in which the OOD samples belong to new classes, or by \textit{covariate shift}, where the distribution of the data changes between the train and test scenarios while keeping the same labels. Our proposal focuses on solving for covariate shift.

Among the causes of covariate shift are included problems related to \textit{domain generalization}, i.e., when the train and test domains are disjoint but still share the same labels. As well as by problems related to \textit{sub-population shift}, i.e., when the train and test domains are the same, but their proportions are different. Ideally, a DML model learns an embedding space that generalizes well enough within the train data distribution, known as In-Distribution (ID), avoiding vulnerabilities to data difficulty (unspecific covariate shift). Some methods make use of diverse concepts such as \textit{knowledge distillation} \cite{furlanello2018born,kim2021embedding,roth2021simultaneous} and consider a probable distributional shift in the data \cite{milbich2021characterizing,du2022vos} to improve generalization.

For example, \cite{milbich2021characterizing} show through exhaustive experiments how data splitting into train and test involves different distribution shifts that modify the difficulty of the data and proposes few-shot DML to improve generalization upon unknown shifts in test

This paper presents an approach that brings together adaptations of both Few-Shot Learning (FSL) and knowledge distillation that avoids the restrictions of classification layers. The proposal consists of two independent models: The first one is a multi-branch master model, called GEMINI, that exploits local and global information to generate a reduced hypothesis space based on prior knowledge from the source labeled dataset in the form of triplet samples. This model represents a fast-convergence compact model, with negligible train time cost and no change in test time cost, avoiding problems of large teacher networks and time consumption \cite{furlanello2018born}. Its architecture is an analogy to strategies based on parameter sharing in FSL \cite{wang2020generalizing}. The second model, a deep learning model, is a student model that learns an adequate embedding function guided by the mentioned reduced hypothesis space by using a similarity function $s(\cdot,\cdot)$, following the teacher-student concept from knowledge distillation \cite{hinton2015distilling,furlanello2018born}. Exploiting the tractability of the features space low-dimensionality to regularize the student decision bounds (Figure \ref{fig:propBlocks})

To test our proposal, we have followed some of the guidelines proposed by \cite{musgrave2020metric} to design an experimental protocol that allows us to compare different models under equal conditions as much as possible. Our proposal demostrate the following key contributions:
\begin{itemize}
  \item The experiments empirically show that the quality of the GEMINI embedding space is reliable given the consistency of the results with a random sample selection, reducing the dependence on the not-so-reliable sample mining strategies.
  \item Following \cite{milbich2021characterizing}, we further show that FSL adaptations in DML improve generalization with learning circumstances hindered by distributional shifts.

  \item The evaluation metrics suggest that our proposal can obtain better-delimited embedding spaces with compact clusters than with the compared models, giving less uncertainty between ID and OOD data
  \item The performance in low-dimensional (two-dimensional) embeddings positions the proposed model as useful for data visualization.
\end{itemize}

\begin{figure}[ht!]
\centering
\captionsetup{justification=justified}
\includegraphics[width=0.5\textwidth]{fig/Blockprop2.PNG}
\caption{Block diagram of our proposal. The reduced hypothesis space generated a priori by the GEMINI model is used to guide the training of the deep learning model.}
\label{fig:propBlocks}
\end{figure}

\section{Related works}
\label{sec:related}
\noindent
\textbf{Deep Metric Learning.} Usually, there are two fundamental DML models considered in the literature: The former is the \textit{siamese network} (contrastive loss) \cite{chopra2005learning,hadsell2006dimensionality}, a method based on pairwise samples, which encourages small positive pairwise distances and negative pairwise distances above a certain margin. The latter is the \textit{triplet network} (triplet loss) \cite{hoffer2015deep} that considers three types of samples: positive, negative, and an anchor. In this case, the distance between the anchor-negative samples should be greater than the distance between the anchor-positive samples by at least a margin. The triplet network improves over the siamese network by using intra-class and inter-class relationships \cite{musgrave2020metric}, allowing a better fit to the variance differences between classes and making the model less restrictive. In this way, using these same fundamental architectures, there are a variety of proposed loss functions, e.g., Angular loss \cite{wang2017deep}, Mixed loss \cite{chen2018dress}, Margin Loss \cite{wu2017sampling}, Multi-similarity loss \cite{wang2019multi}, N-Pairs loss \cite{sohn2016improved}, among others \cite{kaya2019deep,deng2019arcface}.

However, pairwise or triplet samples can involve high time complexity in the training process. Hence, \textit{sample mining} strategies to identify the most informative examples capable of increasing performance, as well as the training speed, can be used. Nevertheless, for instance, in the case of hard-negative mining, the siamese network generally converges faster. However, the case of the triplet network often leads to problems where all samples have the same embedding and produce noisy gradients \cite{wu2017sampling,musgrave2020metric}. Meanwhile, semi-hard negative mining is recommended for triplet network over hard-negative mining to avoid the risk of \textit{overfitting}. However, in some cases, it might converge quickly at first, but as the number of negative examples within the margin runs out, it drastically slows down its progress \cite{wu2017sampling}. These indicate that choosing an appropriate sampling strategy could be a difficult decision, as it seems to be sensitive to the properties of the underlying dataset or when architecture changes occur

Meanwhile, new approaches have extended DML methods to other topics and have shown improvements in leveraging data relationships. Such is the case of Zero-Shot Learning (ZSL) and Few-Shot Learning (FSL), paradigms that address applications hindered by a limited number of samples and where it uses prior knowledge to generalize quickly \cite{wang2020generalizing,milbich2021characterizing}.

The ZSL approach, where test and train classes are distinct, intends to learn representation spaces that capture and transfer visual similarity to unseen classes \cite{milbich2021characterizing,roth2021simultaneous,brattoli2020rethinking,oh2016deep}. Faces the challenge of constructing a priori unknown test distribution with an unspecified distributional shift from the train distribution. However, arbitrarily large distributional shifts may cause the captured knowledge from the training data to be less significant to the test data \cite{milbich2021characterizing}, i.e., ill-posed learned representations.
In the case of FSL, where at least a few samples of the test distribution are available during training, improve the quality of embeddings or prototype representations \cite{tian2020rethinking,rajasegaran2020self,sung2018learning,snell2017prototypical}. Specifically, in \cite{milbich2021characterizing} has been proven that adaptations of FSL can improve the generalization capability of DML since even the minor additional domain knowledge provided helps to adjust the learned representation space to achieve better OOD generalization (commonly referring to covariate shift).

Also related to our line of research, there are some approaches following FSL methods that learn by constraining the hypothesis space through prior knowledge using a \textit{parameter sharing} strategy \cite{wang2020generalizing}. Such is the case of fine-grained image classification \cite{zhang2018fine}, domain adaptation \cite{motiian2017few}, and cross-domain translation \cite{benaim2018one}, where some layers are for capturing global information and others for local information.\\

\noindent
\textbf{Knowledge Distillation.} Originally introduced as model compression and acceleration techniques, \textit{knowledge distillation} refers to an approach with teacher-student architecture \cite{hinton2015distilling,gou2021knowledge}. The main idea is to learn a student model (small network) from a teacher model (large network). As a result, it's obtained a small network trained to learn to replicate the behavior of the original network. This idea evolved beyond the goal of model compression and was subsequently used to improve performance in computer vision and language modeling tasks \cite{furlanello2018born}. Specifically, in the context of DML, the purpose is to take advantage of the knowledge captured by the master model to learn better embedding functions \cite{chen2018darkrank,tian2019contrastive,tian2020rethinking}.

In addition, there are variants within this approach with more specific configurations, such as \textit{Self-distillation} \cite{zhang2019your,roth2021simultaneous}, where the same network acts as both teacher and student models. As well as \textit{self-supervised learning} \cite{rajasegaran2020self,milbich2020diva}, oriented to help initialize a network where there is a lack of labeled data. In self-supervised, an initial (pretext) task is learned by a master model using a general content dataset (e.g., ImageNet), which provides the \say{supervision} to the student model to perform the actual (downstream) task. A common approach in self-supervised learning uses \textit{contrastive learning} to act over the similarity between the embeddings of pair samples \cite{ji2021power}.

\input{fig/CompleteModel}

\section{Method}
\noindent
In this section, we propose a novel architecture for the task of deep metric learning, called \textit{Guided deep metric learning}. This architecture has key concepts based on both FSL and Knowledge distillation. This architecture consists of two independent models (embedding functions). The first is called \textit{GEMINI}, a fast-convergence compact model, which is in charge of generating a reduced hypothesis space based on prior knowledge. The second one is a deep learning architecture (specifically, a ResNet-18) used to learn the embedding function guided by the hypothesis space generated by the GEMINI model.

\subsection{GEMINI Model}
\noindent
Analogous to parameter sharing strategies found on FSL, the GEMINI model consists of two main parts (Figure \ref{fig:complete}). The first component $f_k(\cdot)$ exploits the local information of each class, one stream layer \cite{aghamaleki2019multi,chenarlogh2019multi} per class. Then, the global fully connected component $g(\cdot)$ tries to exploit the global information of local representations by sharing some parameters between the different classes, producing a unique data representation and, thus, avoiding the strong restrictions of a classification layer, e.g., cross-entropy.

The model uses a triplet dataset $X$ generated from a training dataset $D_{train}$ with $c$ classes. We will use the notation ${\bm{x}}_{(k)}$ and ${\bm{x}}^{+}_{(k)}$ for the anchor and positive samples of class $k$ and ${\bm{x}}^{-}_{(l)}$ for the negative sample of class $l$, where $k \neq l$. Each sample is input to the network through its respective stream, depending on its class. The outputs are intermediate representations denoted by $f_i({\bm{x}}^{*}_{(i)})$ for each class $i = 1 \dots c$, regardless of the sample type ($^*$).

\begin{equation}
    f_{k}\left({\bm{x}}_{\left(k\right)}^{*}\right) = h_{\left(k\right)}^{L}\circ h_{\left(k\right)}^{L-1}\circ\cdots\circ h_{\left(k\right)}^{1}\left({\bm{x}}_{\left(k\right)}^{*}\right)
    \label{LayerStream}
\end{equation}

\noindent
The equation (Eq. \ref{LayerStream}) represents the layer composition performed at each stream. Here, the triplets $({\bm{x}}_{(k)},{\bm{x}}^{+}_{(k)},{\bm{x}}^{-}_{(l)})$ are selected in two steps: First, a permutation of length 2 (without replacement) is randomly selected from the classes, thus selecting two respective streams. Second, two samples of the first class (${\bm{x}}_{(k)},{\bm{x}}^{+}_{(k)}$) and one of the second class (${\bm{x}}^{-}_{(l)}$) are extracted from the selected permutation.

Each mini-batch contain a certain number of triplet samples, given by a hyper-parameter, of one permutation of the classes, this is denoted by $X_{b}$ where $b = 1, \dots, \frac{\left|c\right|!}{(\left|c\right|-2)!}$. This means that, given that there are only two different classes in each mini-batch, only two stream are used (activated) in each mini-batch. Thus, the activation of the streams are control by the given indicator function $\textbf{1}_{ij}$, where $i$ and $j$ are the stream index and the sample class respectively,
\begin{equation} \label{indfunc}
\textbf{1}_{ij} = \left\{
\begin{array}{ll}
      1, & \textrm{if }  i = j \\
      0, & \textrm{Otherwise}\\
\end{array}
\right.
\end{equation}

\noindent
In order that, during the training process, the following condition is enforced,
\begin{equation} \label{streamsfunc}
    \textbf{1}_{ij} \cdot || f_{i}( {\bm{x}}_{(j)}) - f_{i}( {\bm{x}}^{+}_{(j)} ) || < \epsilon \textrm{,} \quad \forall \left({\bm{x}}_{(j)},{\bm{x}}^{+}_{(j)}\right) \in X_{b}
\end{equation}
\begin{equation} \label{streamsfunc2}
    \textbf{1}_{il} \cdot f_{i}({\bm{x}}^{-}_{(l)}) \textrm{,} \quad \forall {\bm{x}}^{-}_{(l)} \in X_{b}
\end{equation}

\noindent
where $\epsilon$ is a small positive number. This is the expected behaviour in each update of the model parameters after each mini-batch, in accordance with the proposed cost function (Eq. \ref{costfunc}). However, once they passed through the local component $f_k(\cdot)$, they all pass through the global component $g(\cdot)$ where the samples in the mini-batch share the layer parameters. The embedded representation of the network is denoted by $g({\bm{x}}^{*}_{(i)}) = g(f_i({\bm{x}}^{*}_{(i)}))$. Thus, once the intermediate and final representations are obtained for each sample in the triplet sample, both components of the network are coupled through the proposed cost function (Eq. \ref{costfunc}).
\begin{align}
L\left(f, g, d, \beta, M\right) & =  \frac{1}{\left| X_{b} \right|} \sum_{\left({\bm{x}}_{(k)},{\bm{x}}^{+}_{(k)},{\bm{x}}^{-}_{(l)}\right) \in X_{b}} \nonumber \\
& \beta \cdot d\left( f_k \left({\bm{x}}_{(k)}\right), f_k \left({\bm{x}}^{+}_{(k)}\right)\right) + ... \nonumber \\
&  (1-\beta) \left[ M - d\left(g\left({\bm{x}}_{(k)}\right), g\left({\bm{x}}^{-}_{(l)}\right)\right)\right]_{+}
\label{costfunc}
\end{align}
\noindent
where the term $\beta \in \left[0,1\right]$ is a weighting parameter, the term $[x]_{+}=max(0,x)$ is the hinge loss, and $M$ is a margin. The first term of the cost function evaluates the closeness of the similar samples, using the local information of the class to emphasize the closeness of the intermediate representations.

Note that using this first term alone may certainly lead to a trivial solution, i.e., a mapping of all points to a single point in the embedded space. To prevent this type of solution, a second term has been added which sets away points belonging to different classes. Thus, as the training progress, only the pairs that satisfy $ d(g({\bm{x}}_{(k)}), g({\bm{x}}^{-}_{(l)})) < M $ will produce a cost value. In this way, the first term minimizes intra-class distances, and the second term prevents trivial solutions by maximizing the distances between classes, inter-class distances.

The term $M$ can be defined as $M = d(g({\bm{x}}_{(k)}), g({\bm{x}}^{+}_{(k)})) + m$, where $m$ is a margin value. This makes the second term of the cost function resembles the triplet loss function, but with an added term that keeps the similar samples together using the local information. To balance both effects in the loss function there is a $\beta$ term which, experimentally, its value is usually small ($\approx 0.005$), but significant to  improve the resulting reduced hypothesis space.\\

\input{fig/AlgGEMINI}

\noindent
The model works by decreasing the distances between anchor and positive samples and, simultaneously, increasing the distance between anchor and  negative samples (Figure \ref{fig:propBlocks}). Rather than engaging with sample mining techniques, which, as mentioned before, have been shown not to work well for all possible scenarios, restricting the generality. An alternative approach arises when the cost function (Eq. \ref{costfunc}) is more generalized with a small change in the second term (Eq. \ref{modcostfunc}).
\begin{equation} \label{modcostfunc}
    \left[ M - d\left(g\left({\bm{x}}_{(k)}\right), {\bm{x}}^{-}_{(l)} \right)\right]_{+}
\end{equation}
\noindent
In this rewriting of the second term, the transformation $g({\bm{x}}^{-}_{(l)})$ is changed by ${\bm{x}}^{-}_{(l)}$. This implies that the input ${\bm{x}}^{-}_{(l)}$, in this generalization, can be interpreted as a simple vector that imposes a limitation or restriction on the system. These constraints can be samples from another class, proposed by the user or even coming from another model. Thus, it can be considered that the model can shape the distribution of each class according to these specific points.

\subsection{Complete Architecture}
\noindent
\noindent
The complete architecture follows the offline knowledge distillation guidelines. In our proposal, the student network is a deep learning model of choice; in this case, we opted for the PyTorch ResNet-18 implementation. We say that this model is \say{guided} to replicate the behavior of the master network (GEMINI model).

This approach has the advantage that GEMINI has already searched the space and arrived at a hypothesis space constrained by prior knowledge. With such a reduced hypothesis space, the ResNet model is expected to need fewer samples to converge to a suitable hypothesis, closer to the optimum, and have a lower risk of overfitting. Both models are coupled by a similarity function, $s(\cdot,\cdot)$, which measures the deviation of the ResNet hypothesis from the reduced hypothesis space (Figure \ref{fig:complete}).

The complete architecture is described in two steps: First, acquire the low-dimensionality embeddings of the reduced hypothesis space $\hat{z}_i \in Z \subseteq \mathbb{R}^{m}$ corresponding to each sample in the original training dataset $x_i \in D_{train} \subseteq \mathbb{R}^{d}$ where $m \ll d$, from a previously trained GEMINI model. Second, the ResNet model takes as input the original training dataset $D_{train}$, directly embedding each sample $x_i \in D_{train}$ into a lower-dimensional space $z_i \in Z$ without a classification layer. The resulting embeddings $z_i$ are compared with the ones obtained from the GEMINI model $\hat{z}_i$ through a similarity measure $s(z_i,\hat{z}_i)$ in the low-dimensionality space $Z \subseteq \mathbb{R}^{m}$. Our chosen similarity measure is the $\l_{2}$-distance. The discrepancy is then used as feedback for updating the ResNet parameters.

\input{fig/AlgGen}

\section{Results}
\noindent
This section evaluates, analyzes, and compares the performance of our proposed method with other approaches. The evaluation of the performance follows a few guidelines suggested by \cite{musgrave2020metric} to perform a more fair and realistic comparison. To claim that an algorithm outperforms other methods, the conditions to which the models compare must remain as similar as possible. The rationale for this approach is to ensure that it is the algorithm (not any external design choices) the one improving performance. Therefore, the designed experimental setup keeps consistency in parameter choices and avoids some commonly used techniques that could interfere with the results. For example, the use of different pre-trained network architectures (leading to differences in initial accuracies), the choice of the data augmentation strategy, and choice of the optimizer (e.g., SGD, Adam, RMSprop), among other design choices that remain inconsistent (variable) throughout the literature.

\subsection{Experimental setup}
\noindent
All of the experiments have been implemented using Python 3.8 and Pytorch 1.6 on an NVIDIA RTX 3060 super 12 GB. In addition, no data augmentation nor pre-processing (besides global normalization to zero mean and unit variance) were applied. For all the networks, the dimensionality of the output embedding space is two, gradient clipping with a factor of 0.1 and a weight decay with a decay factor of 0.0001. Further, the backbone of all networks (including our proposed method) is a ResNet-18, where the batches were constructed randomly (assuming a uniform distribution) without any sample miner. We used the repository by \cite{musgrave2020pytorch} for some losses, reducers, and DML metrics

The networks to be compared have been trained with a learning range of 0.1-0.5, using an SGD optimizer and a batch size range of 32-512 samples. In the specific case of our proposed model, the GEMINI model has been trained at a learning rate of 0.001, margin value of 3.0, SGD optimizer with a batch size range of 32-64 triplet samples, and takes between 10 to 15 epochs to converge to a satisfactory solution. The complementary deep learning model (ResNet-18) has been trained at a learning rate of 0.1, SGD optimizer, and a batch size range of 32-128 samples.\footnote{Our source code is available at https://github.com/G-DML.

For base performance comparison purposes, we considered the baseline models: Siamese and Triplet network, described in section 2. Additionally, recent models such as Multi-Similarity Loss \cite{wang2019multi} and Margin Loss \cite{wu2017sampling} were considered. The metrics to evaluate the performance of all architectures were the following: Recall@K, F1-score, and Normalized Mutual Information (NMI). In addition, we have used the metrics proposed by \cite{musgrave2020metric}: R-Precision (RP) and Mean Average Precision at R (MAP@R). All these metrics were obtained using the kNN classifier ($k=1$) of scikit-learn in the test embedding space (specifically, ResNet-18 output, the student model in our proposal)

The experiments were on well-known datasets: MNIST \cite{lecun1998gradient}, Fashion-MNIST \cite{xiao2017fashion}, and CIFAR10 \cite{krizhevsky2009learning}. We chose these because they are easily comparable benchmark datasets. Tables \ref{tab:comparison-stateoftheart11}-\ref{tab:comparison-stateoftheart31} show the mean performance across training runs with a 95\% confidence interval. The bold type represents the best result

\input{tab/ResultTables}
\noindent
Given an average performance of 10 experiments for the proposed method and the other methods. As shown, in general, our proposal is superior to the others. MNIST (Table \ref{tab:comparison-stateoftheart11}) shows on average a marginal improvement. Compared to Margin loss our proposal improves by 0.19\% in Recall@1, and up to 1.3\% with respect to the Triplet network. In Fashion-MNIST (Table \ref{tab:comparison-stateoftheart21}), compared to Multi-similarity loss our proposal improves by 1.7\% and up to 9.6\% in Recall@1 compared to the Triplet network. Finally, in CIFAR10 (Table \ref{tab:comparison-stateoftheart31}) there is a clear significant improvement. Compared to Multi-similarity loss, there is an improvement of about 40\% in Recall@1 and about 3 times better compared to the Siamese network.

In addition to better performance, our proposal consistently demonstrates stable performances (narrow confidence intervals) during the different datasets. Meanwhile, the other models showed more unstable performances, especially in the most difficult dataset (Table \ref{tab:comparison-stateoftheart31}). Thus, our proposal can be considered, in general, less sensitive to datasets with certain difficulty (distributional shift).

\begin{figure}[H]
\centering
\captionsetup{justification=justified}
\includegraphics[width=0.496\textwidth]{experiments/Complete-MNIST.PNG}
\caption{Embedding spaces using the MNIST dataset.}
\label{fig:comparison4}
\end{figure}

\begin{figure}[h]
\centering
\captionsetup{justification=justified}
\includegraphics[width=0.496\textwidth]{experiments/Complete-Fashion.PNG}
\caption{Embedding spaces using the Fashion-MNIST dataset.}
\label{fig:comparison5}
\end{figure}

\begin{figure}[h]
\centering
\captionsetup{justification=justified}
\includegraphics[width=0.496\textwidth]{experiments/Complete-Cifar10.PNG}
\caption{Embedding spaces using the CIFAR10 dataset.}
\label{fig:comparison6}
\end{figure}

The choice of the two-dimensional embedding size was motivated to push the proposed method to a more restrictive condition, such as producing low-dimensional outputs, and consequently, to its potential for tasks such as data visualization or more data insight.

In order to provide more information, figures \ref{fig:comparison4}-\ref{fig:comparison6} show the test set in the embedding spaces learned by the different methods and data sets. We can generally observe that our proposal achieves better compactness and separation of the different classes on the different datasets. It is appreciated particularly in the most difficult dataset (Figure \ref{fig:comparison6}).

\section{Conclusions}
\noindent
In this paper, we analyze Deep Metric Learning (DML) models for embedding learning, the relationships and implementations that connect them to Few-Shot Learning (FSL), and how some models found in FSL have an architecture analogous to knowledge distillation with a change in their approach.

We proposed a DML model that integrates FSL and knowledge distillation concepts to develop an architecture that uses local and global information for better manifold generalization and data representation capabilities, providing better performance and stability compared to the compared methods. It also demonstrates that FSL adaptations boost generalization performance in DML models but also meaningful embeddings can be learned without a strict sample selection phase.

Our approach and the models to which it was compared have been implemented in a careful experimental setup. Seeking the conditions were as similar as possible to avoid unfair comparisons, and we have used metrics that provide a completer picture of the generated embedding space. Though our results were based only on simple datasets, we will perform additional experiments on more closely related datasets with DML or FSL methods and out-of-distribution generalization metrics to further verify the performance.

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text:
Fédération nationale du tourisme (FNT) a été créée le , elle regroupe en son sein toutes les professions liées au tourisme. Sa principale mission première est de fédérer tous les métiers touristiques à la vision 2010. Son président est Othman Cherif Alami. Elle est affiliée à la Confédération générale des entreprises du Maroc (CGEM).

Ses composants
La FNT se compose de 184 membres adhérents, constitués par FNIH (Fédération Nationale de l'Industrie Hôtelière), FNAVM (Fédération Nationale des Agences de Voyages Marocaines), STT (Syndicat des Transporteurs Touristiques), FNR (Fédération Nationale des Restaurateurs) et la FLV (Fédération des Loueurs de Voitures).

Articles connexes
 Tourisme au Maroc
 Trophée du tourisme responsable

Liens externes
  Site officiel de la FNT

Tourisme au Maroc
Organisme fondé en 1995

redpajama_set_name: RedPajamaWikipedia


text:
Q: Remove max value from simply-connected list How can I can remove max value from a Simply-Connected list?
Two of the solutions I tried produce wrong results. Please explain to me what am I doing wrong. With code, if not difficult.
Stack:
struct Stack
{
    int info;
    Stack *next;
} *top;

Wrong solution 1:
void delMaxValue(Stack **stck, int maxValue){
Stack *tmp = NULL;
do {
    if ((*stck)->info != maxValue)
        tmp = *stck;
        cout << tmp->info << endl;
        tmp = tmp->next;
    *stck = (*stck)->next;
} while ((*stck)->next != NULL);
while (tmp != NULL)
{
    *stck = tmp;
    *stck = (*stck)->next;
    tmp = tmp->next;
}

Wrong solution 2:
Stack* deleteMaxValue(Stack *begin) {
Stack *t = begin, *p = begin->next;
for (; p; p = p->next)
    if (p->info > t->info)  t = p;
p = begin;
if (p != t) {
    while (p->next != t)   p = p->next;
    p->next = t->next;
}
else
    begin = t->next;
delete t;
return begin;}

A: Your first solution takes maximum value as a parameter, while the second one doesn't. I am assuming we don't have the maximum value and will calculate it while processing the stack.
The basic approach should be to think of a logic first.
Step 1.) We need to pop all the elements to find the maximum element in the stack. Also, we need to store all the values we popped in another stack(say, auxiliary). Now, we are aware of the maximum value(say MAX).
Step 2.) Note we would have the stack in reverse now. Pop all elements from the auxiliary stack and if the value is not max, push them in the original stack.
Data Initially,
Original Stack: 1->2->3->4->100->5->7->NULL
Auxiliary Stack: NULL

Data after first Step,
Original Stack: NULL
Auxiliary Stack: 7->5->100->4->3->2->1->NULL
MAX: 100

Finally,
Original Stack: 1->2->3->4->5->7->NULL
Auxiliary Stack: NULL

Try to code for this. Your both solutions are doing things way differently than expected.

A: I hope It will be helpful.
#include <iostream>

struct LList
{
    int info;
    LList *next;

    //constructer
    LList(int info_) :info(info_) {
        next = nullptr;
    }
};
void removeMaxValue(LList *&root) {
    int max = 0;

    LList *temp = root;
    //Searching for max value
    while (temp!=nullptr)
    {
        if (temp->info > max)
            max = temp->info;

        temp = temp->next;
    }
    temp = root;
    //Find max value and remove
    while (temp->next->info != max)
        temp = temp->next;

    LList *maxNode = temp->next;

    temp->next = temp->next->next;

    delete maxNode;
}
void print(const LList *root)
{
    while (root!=nullptr)
    {
        std::cout << root->info << " ";
        root = root->next;
    }
    std::cout << std::endl;
}

int main() {

    LList *root = new LList(15);
    root->next= new LList(10);
    root->next->next= new LList(45);
    root->next->next->next = new LList(85);
    root->next->next->next->next = new LList(5);

    //before removing
    print(root);

    removeMaxValue(root);

    //After removing
    print(root);

    std::cin.get();
}

A: #include <cstdio>
#include <iostream>

struct Stack
{
    int info;
    Stack *next;
    // added just to easy initialization
    Stack(int _info, Stack *_next) : info(_info), next(_next) {}
} *top;

void delMaxValue(Stack *&head)
{
    // first - find MaxValue in the list
    // as you can see, i save pointer to the previous element in the list
    Stack* max_prev = nullptr;
    Stack* max = head;
    for(Stack *i_prev = nullptr, *i = head; i; i_prev = i, i = i->next) {
         if (max->info < i->info) {
             max_prev = i_prev;
             max = i;
         }
    }
    // max has the maximum value and max_prev is the element before max in the list
    // now we remove max
    if (max_prev == nullptr) {
        // max has no prev, so max is the head of the list. We assign the new head
        head = max->next;
     } else {
        max_prev->next = max->next;
        max->next = NULL;
     }
}

void printStack(Stack *head) {
    std::cout << "Priting " << head << std::endl;
    for(Stack *i = head; i; i = i->next) {
        std::cout << i << " " << i->info << std::endl;
    }
}

int main()
{
    Stack *head = new Stack(1, new Stack(15, new Stack(10, nullptr)));
    printStack(head);
    delMaxValue(head);
    printStack(head);
    return 0;
}

You may interest yourself in list helping macros from bsd, now available in glibc, newlib, openbsd etc., see here.

A: Your two functions take two different approaches. I chose the one where the function doesn't know what actual max value is so it has to find it first.
First, the function just iterates through the elements and chooses the max value.
Then it searches for the first node that contains this value and removes the node.
void stackRemoveMaxValue(Stack*& top) {
    if(top == nullptr) {
        return;
    }

    // Find max value.
    int maxValue = top->info;
    Stack* node = top->next;
    for(; node != nullptr; node = node->next) {
        if(maxValue < node->info) {
            maxValue = node->info;
        }
    }

    // Remove first node that contains maxValue.
    Stack* previous = nullptr;
    Stack* current = top;
    do {
        if(current->info != maxValue) {
            previous = current;
            current = current->next;
        } else {
            if(previous != nullptr) {
                previous->next = current->next;
            } else {
                top = current->next;
            }
            delete current;
            return;
        }
    } while(current != nullptr);
}

redpajama_set_name: RedPajamaStackExchange


Everyone knows Sundays and Mondays are the worst days of the week so, as these are the evenings I need cheering up most, I like to fill my diary with dinner in a new restaurant, catch ups with friends or the theatre so that there's something to look forward to at the end of a bleak beginning-of-the-week day.
Monday was a particularly grim evening; rainy, cold and dark after I left the office at 6.30pm. Oh, joy. Where's that late, balmy Indian summer we were promised? I cycled over to West London in the drizzle as I'd booked to try out No 35, the restaurant in The Hempel Hotel nestled between Lancaster Gate and Paddington, which cheered me up a treat.
The five star boutique hotel was created in 1996 by renowned designer Anouska Hempel as London's first minimialist hotel and the décor in every room, from the sandy low-lit lobby to the glamorous No. 31 Lounge Bar, is sleek, stylish and understated.
Housed in five Georgian townhouses with a private garden square, Hempel celebrates Zen minimalism by incorporating the traditional earth, wood, metal, fire and water in every room. Warm lighting with fire and candles and natural materials like Portland stone, Birch wood, silk wallpaper and Belgian limestone come in a warm palette of cream, honey, brown and slate. Hotels designed by interior designers really are a cut above the rest.
The Head Chef is Michael Carter (of Arbutus fame) who offers an imaginative menu of British and European ingredients from independent producers. I started with the Trio of Organic Duck of cured breast, potted leg, poached egg and pickled vegetables. It was delicious and therefore difficult to share. My BF ordered the Teruel D.O.P Ham which came with a suitably seasonal pumpkin mousse, manchego cheese, truffle, sage and gremolata. I liked that both starters were fairly unconventional; the strong flavour of the pickled veg really cut through the duck and the pumpkin mousse was jostling for centre stage with the ham. Who says British food is bland?
For mains I had the line caught wild sea bass which came with lots of little Scottish girolle mushrooms, grilled leeks and an amazing crab and sweetcorn risotto (another scene-stealer) and my BF had the organic Angus flat iron steak which came with punchy English onions, roasted carrots and creamed potatoes.
For dessert, I recommend the refreshing pineapple carpaccio with lemongrass syrup and passion fruit or, if you're not under the pretense of ordering "the healthy option", the warm Valrhona chocolate pudding with poached pears and chestnut ice cream.
The maître d' was a total dude, ensured the delicious Picpoul de Pinet was topped up in our glasses and served us with a smile. You should be proud, Ms Hempel – 16 years on and still a seriously impressive venue.


text:
Q: Refactor SQL statement with lots of common INNER JOINS and columns I need suggestions on how to re-factor the following SQL expression. As you can see all the selected columns except col_N are same. Plus all the inner joins except the last one in the 2 sub-queries are the same. This is just a snippet of my code so I am not including the WHERE clause I have in my query. FYI-This is part of a stored procedure which is used by a SSRS report and performance is BIG for me due to thousands of records:
SELECT col_A
       , col_B, col_C,...
       , '' As[col_N]
FROM table_A
INNER JOIN table_B
INNER JOIN table_C
INNER JOIN table_D1

UNION

SELECT col_A
       , col_B, col_C,...
       , (select E.field_2 from table_E AS E where D2.field_1 = E.field_1 AND A.field_1 = E.field_2)     AS [col_N]

FROM table_A as A
INNER JOIN table_B
INNER JOIN table_C
INNER JOIN table_D2 as D2

A: Jean's first suggestion of creating a view by joining A, B and C worked. I created a temp table by joining ABC and then used it to achieve significant performance improvement (query time reduces to half for couple thousand records)!


text:
By Ed Barrett on January 5, 2015
Music 1971 bikers Hell's Angels The Rolling Stones
Bike Shed Library: No. 1: Chopper by Pete Cave (NEL, 1971)
Bike Shed Library
An occasional feature devoted to those classroom classics, passed from hand to grubby hand, that were definitely NOT in the English syllabus.
No. 1: Chopper by Pate Cave (NEL, 1971)
In the days when Ron 'Chopper' Harris was bestriding Stamford Bridge, another Chopper Harris was enthralling the juvenile delinquents of Great Britain with a different brand of violence.
This Chopper was the number two in a London-based chapter of the Hell's Angels. Or should that be 'NEL's Angels'? Because, make no mistake, this bears the classic New English Library hallmarks of lurid sensationalism and dubious authenticity.
English bikers at the time were a relatively cheerful and almost innocent bunch, as demonstrated in this contemporaneous documentary.
Those English Angels entrusted with organising the 'security' for the Rolling Stones' 1969 Hyde Park concert took their responsibilities very seriously…
…and could even be spotted helping members of the audience who found themselves in distress…
By contrast their American counterparts who assumed the same role at the Stones' Altamont concert later that year used it as an excuse to indulge in an orgy of violence that culminated in murder…
Pete Cave's book goes for the latter angle, but transports the mechanical sex, functional drugs and brutal 'bundles' to the Smoke, where the main enemy is the Old Bill and… er… teenage skinheads. Oh, and a power struggle that is spread over two further volumes – Mama and The Run – knocked off quickly to cash in on the success of Chopper, and both published the following year.
The tone throughout is bleak and oddly contemptuous of its subject matter, in a way that NEL's notorious Skinhead series was not. The bikers are portrayed as losers and social inadequates, huddling together for warmth in a cold world that they are incapable of dealing with. The story is nothing special, and neither is the prose, but at a mere 120-odd pages it doesn't outstay its welcome. To cap it off nicely there is also a superbly crap cover picture which is embarrassingly ridiculous even by NEL standards.
In short, what's not to like?
At Home with the Rolling Stones in 1965
At the cusp of mega-fame photographs of the Rolling Stones in their respective homes by Danish photographer Bent Rej. ...
61 Sensational Soviet Space Posters
In the 1950s and 1960s the Soviet Union assured the great unwashed that ploughing fortunes into the space race was good for them ...
27 Snapshots of Manchester In The 1960s
37 Snapshots of Manchester In The 1970s Via: MMU
Post Views: 230,441
1980s Teenagers and Their Bedroom Walls
"Advice my father gave me: never take liquor into the bedroom. Don't stick anything in your ears. Be anything but an architect" - Kurt Vonnegut ...
Feed Sack Fashions And Patterns of Depression Era America
During the Depression people used cotton flour bags and feed sacks to make clothes, curtains, diapers, awnings and other household items. Manufacturers got wind of their bags' other uses and began decorating them. Color and patterns added a little style and joy to the common sack dress. Of course, it wasn't all altruistic. ...
British Ice-Lollies And Ice-Creams 1960-1990
YUMMY ice-lollies and ice-creams from the 1960s to the 1980s. What do you miss? What did you hanker for? Did the cider and Brandy Alexander lollies give you a taste for booze? Did you ever win that hovercraft..? (more…)
8 Reasons Children of the 1970s Should All Be Dead
The way things are going, every kid is going to go to school wearing bubble wrap and a helmet. Back in the 1970s (and earlier), parents didn't stress about our health and safety as much as they do today. It's not that they cared less – they just didn't worry ...
Northern Ireland Troubles 1971: Belfast In 50 Photos
In 1971, the Troubles in Belfast and the rest of Northern Ireland were in full cry. TIMELINE 10 January 1971 - Members of the Irish Republican Army (IRA) carried out an early form of 'punishment attack' by tarring and feathering four men who were accused of criminal activities in ...
Ronald McDonald Adventures in Scouting : A Troubling 1970s Magazine
Kissing On A Rainy Beach: Bruce Davidson's Photographs of 1960s Britain
The Great White Silence: Herbert Ponting's Portraits from Captain Scott's Antarctic Expedition
Hotpants, Cigarettes, Harp Lager and Guinness – A Belfast Student Party in 1970
Share Your Flashbaks
What is your story?*
Accepted file types: jpg, png, gif.
Christmas Gifts Sorted In The Flashbak Shop
George Mayerle's Eye Test charts, Schmidt Litho Co., San Francisco , ca. 1907 – Buy this great image here.
The Story Of 'Punk'
"Dope Down the Troll" – Photos of The Rolling Stones and Fans In Oslo (1965)
Sherlock Holmes and the Extraordinary Baker Street Bank Heist of 1971
Gimme Shelter From Hell's Angels: Meredith Hunter, Alan Passaro And The Rolling Stones Horror Show

redpajama_set_name: RedPajamaCommonCrawl


Personally, I prefer to stay away from stores and do all my shopping on my smartphone or tablet while reclining on my couch with a pussycat across my chest, watching an old movie on TCM. Sometimes, though, I'm forced into the abyss—I have to go into a store.
It was then that I was struck by an epiphany. I pulled out my iPhone, searched for Gethsemane Garden Center near my home (more expensive, but smaller and always teeming with store associates), touched the phone number in the Google results, and called. I asked the associate who answered the phone if they had pre-made flowerboxes in-stock and ready to go. She said yes.
I immediately left the home improvement giant's store, drove to Gethsemane, was in and out in 10 minutes tops, and was back home in no time.
What could have saved the sale for the more affordable home improvement retailer? Mobile technology in-store. In this case, beacons.


The lantern was designed by Hunt to serve as a model for the lantern held by Christ in Hunt's painting 'The Light of the World'.
It is physical evidence of the meticulous working methods of the artist. Manchester owns the second version of the painting.
Thomas Combe, 1797-1872; his wife Martha Combe, 1806-1893; sold by J R Mallam and Son, Oxford, 1894 to John Crossley; then by descent to present owner.


text:
Back to 2009 NCAA Tournament coverage
Bracket Contest
View Bracket
Printable Bracket
Current scores
Despite record, Big 12 feels it gets no respect
After fuming for years that an East Coast bias hurts their league, Big 12 Conference coaches have picked up a credible ally.
Jeff Capel, who played at Duke and coached in Virginia before becoming head coach at Oklahoma, says they're right.
"Many people look at this part of the country as football," said Capel, whose Sooners are seeded No. 2 in the South Region. "I was there until I came here, and that's what I thought when I lived there."
The Big 12 got six teams into the NCAA field this year, as many as the Pac-10 and only one fewer than the ACC, Big East and Big Ten.
But it's their seeding and bracketing that has some Big 12 teams upset. For example, ACC powers Duke and North Carolina get to play their opening-round games in Greensboro, N.C. But neither Kansas nor Missouri, the Big 12's regular-season and postseason champions, respectively, were assigned to Kansas City for their first games. The Jayhawks are in Minneapolis, while Missouri is at Boise, Idaho.
Oklahoma, which lost to both Kansas and Missouri head-to-head, is the Big 12 team that gets to open in Kansas City's Sprint Center.
"You can't sit there and cry about it," Missouri coach Mike Anderson said. "We're excited to be a part of it, and the seeds are what they are. It would have been nice to have been in Kansas City, no question about it, being closer to home. But Oklahoma got the nod and now we've just got to move forward."
The Big East got three No. 1 seeds. But Oklahoma, as a No. 2, is the highest among Big 12 teams. Kansas, the defending NCAA champion and regular-season Big 12 winner, is a No. 3 seed in the Midwest, while Missouri is a No. 3 in the West. Texas A&M; is seeded No. 9 in the West Region. In the East, Oklahoma State is No. 8 and Texas is No. 7.
Nobody is more unhappy than Kansas State's Frank Martin, and not just because his Wildcats, who were seeded No. 4 in the Big 12, were left out of the NCAA field.
"The seedings that Kansas, Missouri, Texas, and those other teams have gotten, it's kind of unfortunate they've gotten the lower seeds and been sent places that are so far away from home, unlike the winners of those (other) leagues," Martin said.
"It continues to be the lack of respect this league receives on a national basis. We have to figure out a way to be more forceful in how we sell our league. If you look at the history of the NCAA Tournament over the last nine or 10 years, there's not another league that's been better than ours. And for us to continue to get disrespected the way we do is a crying shame."
The Big 12 has plenty of arguments in its favor. Six of its teams were in the top 41 of RPI, more than any other league but the Big East. In addition, the Big 12 was second in the nation with 137 nonconference victories and has eight teams with 20 or more wins, the most of any other conference.
Capel and others think one problem facing the Big 12 is the East Coast background of many ESPN and other national commentators.
The passion East Coast residents feel for basketball is another reason, Capel figures, that national commentators may overlook the Big 12's basketball prowess.
"And I can say this, because I was like that: Until you come out here, until you see a game here, until you go to an Iowa State and see the passion there, until you go to a Kansas State and see the passion there ... you won't appreciate it," he said.
"This region is known for football. In the East, it's all basketball. That's really all anyone cares about."
Kansas winning the Big 12's first national championship last year didn't do much to help the league's image, Capel said, because "the only school in this region that people think of as basketball is Kansas."
Big 12 lobbies for seeds
KU, Mizzou in good position entering matchup
Big 12 still looking for first national title
craigers 10 years, 10 months ago
Respect or not, a good Big XII team will give any school a run for their money. The Big XII slugs out games. A good example is what KU did to UNC last year and what Oklahoma will do to UNC when they meet in the Elite 8. UNC will not win the championship this year. Too many physical teams out there for them.
maclothier 10 years, 10 months ago
Clearly, none of the Big XII teams were worthy of a #1 seed, and who out there believes KU, MU, or OU are more worthy of a #2 seed than Memphis, Duke, or Michigan State? Okay, so that leaves one spot in KC, based on the pod system, for a #2 seed, and a Big XII team got it. How is that East Coast bias? I could see if another Big East team got the #2 seed, but they didn't. It may not have been the Big XII team people in this area wanted to see, but that's hardly East Coast bias.
And Capel is clearly wrong when he says basketball is "all anyone cares about" on the East Coast. That may be true at Duke and North Carolina, whose football programs have been mediocre to horrible over the past few decades, but to characterize the whole East Coast as basketball-centric to the detriment of football is laughable. Penn State? Miami? Virginia Tech? Georgia? West Virginia? Ohio State? If Louisville falls under the umbrella of benefiting from East Coast bias (and only caring about basketball), then Ohio State must as well, and probably South Bend, which sits almost due north of Louisville. Exactly where does this bias stop? Any schools east of the Mississippi?
Perhaps the Big XII is viewed as Kansas and eleven other teams (and I say this as an Oklahoma State grad) because Kansas is the only team to have won a championship in the past 63 years. Oklahoma State is the only other Big XII member to have won an NCAA basketball title, and that was in 1946. The Big XII has sent a lot of teams to the Final Four, but have struggled until last year to break through, and even then it was by the usual suspect. Until the Big XII can list two teams as champions in the modern era, as the Big East, Pac 10, SEC, ACC and Big 10 all can, then perhaps the Big XII isn't deserving of the respect Frank Martin and his hapless Cats so desperately clamor for.
Annette Lee 10 years, 10 months ago
maclothier, you make a good point. Last year Texas had a good bracket as a 2. Ku should have focused more in okc.. Now it's harder but the key to this team is energy and focus so just play your A game and then move on.. I think it will be a gut check but they will still make sweet16.. after that A game !!!!
Ryan Wood 10 years, 10 months ago
Respect is for self-absorbed egomaniacs. Just win.
wlbjayhawk 10 years, 10 months ago
Clearly, maclothier does not live in the east, and does not read the stories written by the eastern sports writers.
While I am a KU grad, I have lived in NJ all of my life, and I read several NJ and NY papers each day. The sports writers have no clue about any college team west of Penn State unless they are the number on ranked team and that team loses.
College basketball is no different. Trying to get information about Big 12, Big Ten, Pac 10 or for that matter any other conference in the NY press is very difficult. At best you get two column inches that was written by the AP the night before. Good sports stories about teams other than Seton Hall, St. John's, UNC, Duke, Maryland and other Big East, ACC, or local colleges are typically not included in the local papers.
Only one team can be the national champion each year. For one to equate the number of Big 12 teams that have been national champions as the barometer of college basketball success it what is laughable.
Lets go back to 1988. The national championship game was between two then Big 8 teams, KU and OU, and only one of them could end up the winner. The caliber of this years Big 12 teams can be measured by how many are included in the road to the final four (6). And yes, there are several conferences with just as many.
The bottom line is, that the east coast, and especially the NYC sportscasters, commentators, and writers are bias. I was listening to WFAN (NY radio station) yesterday, and the commentator was talking about various college teams, mostly the #1 seeds. He was clueless about those teams outside of the greater New York region. I read the NY press everyday, I would rather read the sports pages of midwestern papers about basketball than those that are delivered to my home every day.
Rock Chalk Jayhawk.
Steve Gantz 10 years, 10 months ago
I agree that there's east coast bias. I don't think it comes into play in the NCAA tourney often however. As far as NY local writers being biased, well aren't all local writers and papers? This paper sure is. The Chicago Tribune that I read sure is.
Now the national media, yes it's there in so many ways, not just sports. Every thing that happens out east is bigger than if it happens somewhere else.
Jacobpaul81 10 years, 10 months ago
Just to play around a bit with some numbers:
National Championship Finals Appearences:
West of Mississippi: 44 East of Mississippi: 96 - includes Wisconsin and Memphis.
Now that seems like a large descepency until you compare how many division 1 programs are east vs. west of the mississippi. The west has actually outperformed the East, grabbing about 30% of the finals appearences in the 70 years of the tourney, when they have less than 30% of the qualified schools.
Here's a list of western teams to have played in the final game: Kansas, UCLA, Arizona, Oklahoma St. (A&M), Oregon, Kansas St., UTEP (Texas Western), Washington, Washington St., Baylor, Wyoming, Oklahoma, Stanford, San Fransisco, California, Arkansas, and Utah.
5 Big XII Teams have played for the title and 8 of the 12 have made the Final Four adding: Texas, Iowa State & Colorado to the mix.
The top 2 west teams have the same amount of final four appearances as the top 2 East teams:
UCLA & Kansas = 31 North Carolina & Duke = 31 The next three teams come from the central region: Kentucky, Indiana, and Lousiville, and combine for only 29 tourney appearences.
One more fun stat: Final Four Apperances:
104 - West of Mississippi 176 - East of Mississippi
The West is kicking the East's butt if you base that on percentage of actual schools.
Just to follow that up to explain my "kicking butt" reasoning, there are only 2 major conferences west of the Mississippi: The Big XII and Pac 10. The other 4 are east of the Mississippi: Big 10, SEC, ACC, Big East:
There are 22 Major conference teams West of the Mississippi There are 51 Major conference teams East of the Mississippi
Slight Discrepency. There are less than 1/3 of the Major conference teams in the west, yet the West accounts for nearly 40% of the Final Four appearences and 31% of the finals appearences.
I think the east coast bias is justified due to percentage of east coast teams, but in overall performance, the West is actually better.
RockChalkGumbyHawk 10 years, 10 months ago
This is going to sound odd coming from a diehard KU fan, but I thought it was very odd that K-State didn't get in given that they were the 4th best team in the Big XII regular season (9-7) and 21-11 record overall. Their first round loss in the Big XII tourney was a 3 point loss to a solid Texas team. I guess it's the Kansas native in me thinking that if they aren't getting any respect, it diminishes our performances in Allen Fieldhouse West (Bramlage)!
Go Jayhawks! Win it all!!!
Dan Pawlowski 10 years, 10 months ago
There is definitely an East Coast Bias in basketball. I lived on the East Coast for 15 years and it was not until the internet came of age that you could actually get any kind of information about Big 8 and later Big 12 basketball before the NCAA tournament rolled around. If you were lucky you might hear something about some west coast teams. To get anything before the season started you had to pick up Street and Smiths College Basketball. During the season you had to hope for some Sports Illustrated coverage of other teams. When the bracket was chosen you could then count on the USA Today special tournament section to give you some info on the non East Coast teams that made the tournament. The only consistent basketball coverage you got was on the ACC , The Big East and sometimes the Atlantic 10. It took quite a few years of the Big East having down years before you actually heard east coast media admit that the Big East was not what it used to be. If you followed a non East Coast team that was on a roll or having a much better year then they usually had you might be able to catch an ESPN story about them. Caple is dead on. I witnessed basketball coverage that definitely overshadowed football where I lived (DC/Northern Va). Football was lower on the totem pole. Yes there was Penn State Football but even then Basketball cast a far bigger shadow.
Lone_salina_kufan 10 years, 10 months ago
This is good news, and exactly why I picked the Big 12 teams in my bracket to do better than most people think. The whole conference is going in with a chip on its shoulder. And most teams, our Hawks included, play better when they're angry.
wlbjayhawk - I was actually hoping my East Coast roots didn't come through in my post - I spent the first 18 years of my life growing up in Delaware, so I have read East Coast reporting, and yes, the majority of the reporting focuses on those issues, teams, topics most pressing for the readers of the stories. I think it would be strange if the Lawrence Journal World was constantly running sports headlines about random Providence basketball games or Arizona State football games. How much has the Midwestern media covered the Pac-10 this year? Is that the dreaded "Midwestern Bias" coming in to play?
I-AA Delaware routinely sells out their 22,000 seat football stadium. Their D-1 basketball counterparts struggle to fill the 5,000 seat Bob Carpenter Center. Football rules at the University of Delaware.
Which professional team do Philadelphians care more about - the Sixers or the Eagles?
Capel might be right about the place of basketball in Virginia and North Carolina, but his characterization of the ENTIRE EAST COAST as dismissive of football in favor of basketball is pure hyperbole.
And I ask once again, where does the "East Coast" end when talking about "East Coast bias"? The Mississippi River?
Thanks for the great stats, Jacobpaul! But, in the last 50 years, teams east of the Mississippi have won 32 championships, compared to 18 for teams west. Take out UCLA from those west championships, and you have only 7 championships for the west. The most any team from the east has won in that time is 3. But we're still talking about the "east" as if it extends from the Atlantic to the Mississippi, which is crazy!
Lin Rahardja 10 years, 10 months ago
Just win baby, and Big-12 will get the respect it deserves. If OU can blast Syracuse & UNC this year, and KU can reach final-4 through Louisville, we will get the respect for the next few years.
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text:
Guillen's XI struggle in trial
Hope, Powell named in WI ODI squad for Zimbabwe...
14th October 2016 Comments Off on Hard Work Pays Off For Spinner Fletcher Views: 1025 News
Hard Work Pays Off For Spinner Fletcher
Rising West Indies leg-spin bowler Afy Fletcher has credited her improved form to more time spent working on her game.
The Grenadian, a fringe player for quite a while, made the remarks following her standout performances in the team's ongoing home series against England.
The diminutive 29-year-old has so far claimed five wickets in the series following figures of three for 20 off 10 overs and two for 21 off 10 overs in the first two games, which were played at the Trelawny Stadium.
The five-match series is level at 1-1 heading into Game Three, which will be played today at Sabina Park.
"It's just a whole lot of hard work that has got me to this stage," said Fletcher.
"I've spent a lot of time working on bowling with both my coaches back at home and with the West Indies team, and it's good to see it paying off."
Fletcher, who has been used as first-change bowler thus far, has also expressed satisfaction at being referred to as a "go-to" bowler by captain Stafanie Taylor.
"I see it as quite a compliment to be referred to as to a go-to player," she said.
"I guess the captain has a lot of confidence in my abilities, and it is just for me to repay her the best way I can.
Fletcher has so far taken 21 wickets in 21 matches at an average of 19.52.
Meanwhile, looking ahead of the third game of the series, Fletcher also believes the West Indies are in a good position, having assessed what the visitors have brought to the table so far.
"The game should again be close, but we are very confident of our chances," she said. "We are much more aware of their game and know our strengths, and if we should play to our strengths we should come out on top."
The series is scheduled to climax with the playing of the fourth and fifth matches on Sunday and next Wednesday at Sabina Park.


What was the primary COLOR of the Upland-ground-like bird you saw in Minnesota?
Common Poorwill: Small, stocky nightjar with pale brown mottled body and white collar separating black throat from narrowly barred underparts. Wings are rounded and tail is short and fan-shaped with outer tail feathers tipped white. Pink-brown legs and feet. Silent flight on quick shallow wing beats.


Efficient boilers are only the starting point — Steven Evans.
Cutting back on energy use and carbon emissions should be an important priority for any business. With the non-domestic sector contributing to 18% of the UK's total carbon emissions, this market has a major part to play if the country is to reach its carbon reduction targets(See 2nd link below).
In an effort to encourage businesses to take their carbon-cutting commitments seriously, the Government has introduced a number of initiatives that will help organisations implement energy-saving upgrades and encourage the installation of more efficient products.
One of these is the Energy Savings Opportunity Scheme (ESOS), which is a mandatory requirement for large undertakings, helping them identify opportunities to make energy and cost savings. According to a sample of the assessments, the cost reduction achievable through the implementation of energy saving measures identified by ESOS audits could be much higher than first predicted, at nearly 25% (See 3rd link below).
And while ESOS is not a requirement for smaller businesses, the heightened focus on climate change and energy policy is likely to still filter across the wider commercial sector.
There has been no better time to consider ways to review energy consumption — and with a large proportion of energy use resulting from the heating system of a building, it makes sense to make this a priority.
Recent advances in the heating sector mean that it is now easier for building managers to select the most effective solution. Product efficiencies have become more visible and easily understood thanks to the Ecodesign and Energy Labelling Directive, commonly referred to as ErP, which was introduced in September 2015.
While the legislation covers a number of points, two of the main changes are that energy labels are required for boilers up to 70 kW, and manufacturers have to offer extra information on their websites, helping to compare products in the same energy banding.
When it comes to replacement, taking the opportunity to install a condensing boiler and updating system controls could save a substantial amount on existing energy costs.
Heating appliances offer the most potential for reducing cost and energy consumption when they are closely matched to the demand of the building in question. By considering how and when a building is used in the early stages of the project, building managers and contractors can ensure that the heating system has been correctly sized to the predicted demand of the building and can deliver to its performance capabilities.
However, there is more to cutting emissions and energy costs than just selecting an efficient and correctly sized boiler. It's important to look at the bigger picture, taking into account how the system is operated and maintained to ensure it continues to run at optimum performance.
Many commercial building managers will expect a system that is flexible enough to respond swiftly, accurately and efficiently to changes in heating and hot-water demand, making the specification of correct controls essential.
Zone controls are ideal for commercial applications as they can be programmed to allow for the fact that different areas within a building don't always need to be heated in the same way. Some newer commercial boilers also come fitted with weather-compensation controls as standard — enabling the boiler to react according to the outdoor temperature.
A scheduled maintenance and service regime must also be implemented from the installation stage of any heating system to safeguard long-term energy efficiency and performance. An important element of this is chemical water treatment, as heating-system efficiency and longevity can be severely reduced with un-treated water.
This is because water contains dissolved atmospheric gases and minerals that can affect a heating system's components. Failing to invest in appropriate water treatment can result in corrosion and the build-up of lime scale, leading to inefficiencies, poor performance and potential boiler failure after a relatively short period of time.
Although an efficient boiler should be at the heart of a heating system, energy efficiency is achieved by implementing a combination of key factors. These include correct installation from a suitably qualified tradesperson, weather compensation and adequate chemical water treatment – which can not only help save energy, but also extend the life of a boiler and improve reliability.
Anyone looking to change a boiler to improve the efficiency of a heating system should also consider other key variables to ensure key performance is maintained alongside important cost and energy savings for the end user.


Most Traditional IRA owners are familiar with the concept of a required minimum distribution (RMD)—and if not, they should be. Starting at age 70.5, account holders are required to remove a portion of assets from their IRAs each year as a distribution. It's important to note that in most cases RMDs apply to Traditional IRAs, not their Roth IRA counterparts.
Given that there are significant penalties for not fulfilling your RMD requirement during the correct timeframe, the following is a look at the rules for taking your RMD at 70.5 and beyond—especially if you hold alternative assets in a self-directed IRA.
Your first RMD must be taken by April 1st of the year following the year in which you turn 70.5. For all subsequent years, required minimum distributions must be taken by Dec. 31st. For example, if you turned 70.5 in 2017, you have until April 1st, 2018 to take your first distribution and December 31st, 2018, to take your next one. If you do not take your RMD during the correct timeframe, the non-distributed portion of your RMD could potentially be taxed at 50%.
For IRAs, your distribution is determined based on your age, your IRA's previous year account balance, and a "withdrawal factor" that is primarily based on life expectancy. There are many RMD calculators that can help you predict your next distribution, but the institution(s) where you hold your account(s) will also do it for you. However, institutions can't see all the IRAs you may hold in other places, so it's ultimately your responsibility to determine the total RMD amount for all of your accounts.
Investors typically satisfy their RMDs using cash from an IRA. However, RMDs and distributions in general can be trickier for investors who own a traditional self-directed IRA that is invested in an illiquid asset such as real estate or private stock. One solution is to take the distribution in-kind.
For example, let's say you own 100% of a piece of property in your IRA that's worth $100,000, and your RMD is $10,000. You can distribute $10,000 worth of said property to yourself via grant deed or quitclaim deed. Essentially, your self-directed IRA custodian assigns that portion of the property to you personally, and that would satisfy your required minimum distribution for the year. This can work for mortgage-backed notes as well, where the custodian writes a partial Assignment of Deed of Trust/Mortgage to the account holder personally for the amount of the RMD.
Whether you hold a traditional or self-directed IRA (or both), RMDs are a fact of life and RMD requirements need to be taken seriously. The good news is that there are a few options available that can allow you to meet the IRS regulations without compromising your investment strategy. At PENSCO, we have a number of resources to help you prepare and understand RMDs once you reach age 70.5.
Your IRA Required Minimum Distribution: You Asked. We Answered.
If you have questions about starting your RMDs at age 70.5 or taking an IRA withdrawal from your PENSCO self-directed IRA, you can contact us at 800.962.4238. The IRS also has an FAQ page addressing IRA withdrawals.
Editor's Note: This is an updated version of a post we originally published in February 2014. We welcome new comments and questions below.


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Muslim leaders denounce enquiry order against AMU VC
By TwoCircles.net
New Delhi: Top Indian Muslim community leaders and heads of leading socio-religious organizations have denounced the Union HRD Minister Kapil Sibal's enquriy order against the Vice Chancellor of Aligarh Muslim University, calling the move uncalled for and demanding its revocation immediately.
Putting their weight behind AMU VC Prof P K Abdul Azis the Muslim leaders have said the allegations made against the VC are from some vested interest groups who felt their interests were not safe under his leadership.
"It is shocking that the Hon'ble HRD Minister has seen it fit to order an inquiry against Prof. P.K. Abdul Azis, the Vice Chancellor of the Aligarh Muslim University in respect of certain allegations levelled by some vested interest groups who have their self interest and self aggrandisement agenda. They have been using the university as their private fiefdom deriving great financial benefit from the building construction and uniform contracts etc. If any VC comes in their way, they oppose him as they earlier opposed the former VC Mr M N Farooqui and forced Mr Nasim Ahmad, the then VC, to flee from the university. They are against the present VC because he gave the building contracts to the CPWD and uniform contracts to the Khadi Gramudyog. They also terrorised the VC by instigating a group of unruly students accompanied by outside bad elements who burnt the VC residence to ashes," the community leaders said in a joint statement.
While making it clear that they are not against an enquiry if in the interest of the university they said this enquiry at the behest of some vested interest groups will prove detrimental to the AMU which has been put on the right track.
"Nobody interested in the affairs and the welfare of the AMU, including the Vice Chancellor, should be scared of an inquiry as nothing cognizable is to be found against the Vice Chancellor, but an inquiry at this crucial juncture at the behest of some vested interests who are out to pressurize and blackmail the Vice Chancellor for their ulterior motives, will prove detrimental to the AMU which has been put on the right track and a very congenial academic atmosphere is prevailing on the campus. The examinations are being conducted on time, sessions are going on as per schedule, for the first time in the history of the university, 75 per cent attendance of the students has been ensured and the students are busy in their studies with a complete sense of security etc. Such an inquiry, if carried forward, may embolden this vested interest group and demoralise this and future VCs, apart from disturbing the peaceful atmosphere at the campus."
They said the enquiry into the allegations of financial irregularites against the VC is uncalled for as the UP audit body has cleared him of any such chargeas.
"The allegations of financial irregularities against the VC lose their significance altogether in view of the report of the Principal Accountant General (Civil Audit, UP) on behalf of the Comptroller and Auditor General of India which had categorically reported that there were no financial irregularities committed by the AMU," they said.
The leaders have demanded the ministry to revoke the enquiry order as it will hamper the university's expansion plan. If not revoked, they have decided to meet PM and UPA Chairperson.
"If the present unproductive and uncalled for inquiry, which will do a great harm to the AMU particularly at a time when its branch is being opened in Murshidabad (West Bengal) and Malappuram (Kerala) is not stopped, the community leaders have decided to meet the Prime Minister Dr. Manmohan Singh and the UPA Chairperson Smt. Sonia Gandhi and apprise them about the futility of the said inquiry."
Signatories to the statement are: Dr M Manzoor Alam, Chairman, Institute of Objective Studies and Secretary General, All India Milli Council, Kamal Farooqi, Chairman, Delhi State Minorities Commission, Dr Zafarul Islam Khan, President, All India Muslim Majlis-e Mushawarat & Editor, Milli Gazette, Mahmood Madni, Secretary, Jamiat Ulema-I-Hind and Nusrat Ali, Secretary General, Jamaat-e-Islami Hind, Dr Ozair Khan, Secretary General, Markazi Jamiat-e-Ulema Hind and Maulana Arshad Madni, President of a faction of Jamiat Ulema-I-Hind.
Labels: AMU, AMU VC, Indian Muslims - Current Affairs, Minority Institutions, PK Abdul Azis
azfar faizan August 19, 2009 at 8:13 AM
I feel that there is nothing wrong in setting up an enquiry by HRD ministry It is possible that motives of some members of exutive council may not be sincere.Once the allegations are enquired by the committee .The matter will settle down for ever.There must be no hue and cry on this matter
Shah Azfar Faizan
an Aligarian
Reader Head Department of Maths.Shibli college zamgarh
Abdulla January 27, 2010 at 4:02 PM
8 EC members gave complaint against VC. No value for that ? If the construction is an avenue for making money, removing construction to other firms wont make an avenue for corruption? Is CPWD corruption free ? Who does the work of CPWD ? Private contractors. If Azis's hands are clean, why cant he prove it to the EC members instead of raising a fight with them ? MUSLIM leaders are trying to inflame communal spirits here. Please do not do that. Whoever had done a crime, it is a crime and should be dealt with positive measures and not by communal politics. To the knowledge og Muslim leaders, Dr. Aziz is not a muslim party member, He is a CONGRESS MAN.


Mary Joy Perez is one of the Reporters of NE-TV48. And she is the segment producer and host of "PERA AT NEGOSYO" in "Balitang Unang Sigaw". She joined TV48 News Team since September 2011. Mary Joy finds that working at NE-TV48 satisfies her passions of performing and writing.
Her interest in journalism began in grade school as an EDITORIAL WRITER. She also love singing and dancing.


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Preventing speed loss at the end of the season - The National Association of Speed and Explosion (NASE) Inc.
A major challenge for strength and conditioning coaches is minimizing decrements in performance (i.e., sprinting speed) as the season trudges on. In the sport of soccer, there's been reports of nearly 5% reductions in sprinting speed at the end of the season. This is obviously problematic considering this is right around the time of the post-season, the most important time of the year where we want performance to be optimal. A big part of maintaining or even improving performance qualities throughout the season certainly has to do with maintaining an effective in-season training program. Simply put, if the athletes aren't lifting, they will get weaker. This leads to reduced force production on the field, consequently resulting in less height when jumping and more time to get from A to B. Therefore, coaches are strongly interested in implementing effective training strategies to prevent the drop-off in performance toward seasons end.
A new study published ahead of print of Science and Sports compared the effectiveness of a 2 different seven-week in season training interventions on sprinting speed and repeat sprint ability in twenty U-17 soccer players. The subjects were split into a plyometric training group who performed 4 jump exercise twice per week and a resisted sprint training group who performed 4 sprint exercises with 10-13% of body mass twice per week. Before and after the training period, the subjects performed a 30 m sprint test and a repeat sprint ability test.
The results showed that the resisted sprint training group experienced a small improvement (ES = 0.37) in sprint times at the end of the season whereas the plyometric training group saw no improvement (ES = -0.17). For the repeated sprint ability test, the resisted sprint training group again outperformed the plyometric training group, demonstrating moderate (ES = 0.73, 0.95) improvements vs small and trivial changes (ES = -0.21, 0.16) in peak and mean repeated sprint ability, respectively. Thus, it appears the including resisted sprinting twice per week throughout the competitive seen may attenuate decrements in both maximal and repeated sprinting speed. However, don't rule out plyometric training as it is unclear how performance in other tasks like jumping were effected.
Borges, J. H., Conceição, M. S., Vechin, F. C., Pascoal, E. H. F., Silva, R. P., & Borin, J. P. (2016). The effects of resisted sprint vs. plyometric training on sprint performance and repeated sprint ability during the final weeks of the youth soccer season. Science & Sports.


Banana Bobby Gets Bifocals | The Fresnel Prism and Lens Co.
Doctors have observed that children with bifocals in their glasses tend to use them incorrectly. In addition to that, parents find they don't know if their child is using the bifocal, or even how to tell. "Banana Bobby Gets Bifocals" addresses these concerns in a fun and innovative way.
The book is designed so that if you follow it correctly, you will not only know when your child is using their bifocals well, but also will learn how to recognize what proper bifocal usage looks like. You will see how normal head movements track and be able to see when your child is not using the bifocals properly, or later on if he or she might be developing bad habits due to poor fitting.
When you receive your book, start reading the story about how Banana Bobby gets his new bifocals. When you reach the Banana Bobby's Bifocal Adventure section in the middle of the book, put the blue stickers on right over the bifocal in your child's lenses. The stickers are specially made to not harm the lenses.
Since the film filters out the blue part of the spectrum, your child will soon realize that there are images hidden all through the blue parts in the book! Now he or she will be able to find the hidden images in the book, but only when they look are looking through their bifocals properly. You can join in on the fun by using the enclosed spy glass (this is for moms, dads and sibs only).
By completing the adventure, you will be teaching your child how to use their bifocals without them ever even knowing it! Just as important, you will begin to see the natural head movements associated with using their bifocals correctly. We recommend using this book every day for the first week when your child gets their bifocals. After that, get the book out from time to time whenever you are concerned that they may have stopped using the bifocals well.