text:
\section{Introduction}
\label{introduction}

The ILSVRC12 ImageNet dataset had an enormous impact on computer vision and its related fields in recent years. Convolutional neural networks built on ImageNet outperformed the hand-engineered feature descriptors \cite{bay2006surf, dalal2005histograms} and these pretrained CNNs were used for transfer learning. In this case, the intermediate latent features of the CNN are the input to train some additional layers for a certain new problem while the other layers are frozen. The latent features are powerful to encode natural images for various tasks not far from the ImageNet dataset distribution. Pretrained ImageNet models were successfully applied to robotic vision \cite{sunderhauf2018limits}, medical imaging \cite{raghu2019transfusion}, historical document analysis \cite{studer2019comprehensive}, classifying species \cite{van2018inaturalist}, cars \cite{krause20133d} or flowers \cite{nilsback2008automated}.

However, some challenges were uncovered for the ImageNet dataset with the image sampling, the labeling process and the learning methods. The dataset creation introduced several biases to the image distribution such as gender \cite{dulhanty2019auditing}, race \cite{stock2018convnets} and location \cite{shankar2017no}. The labeling process involved a human workforce to annotate the images that cannot guarantee the occasional errors. Regarding the learning methods, the models showed a lack of robustness to adversarial examples \cite{cisse2017houdini}.

Some attempts were made to correct the biases in human samples \cite{yang2020towards} and general labeling mistakes \cite{northcutt2019confident} in ImageNet, but their results were not analyzed for more CNNs and their proposed fixes were not published in a reusable form for other researchers.

This paper uses automated methods to clean up the ImageNet dataset and assess the effects of the modifications to deep learning training and validation performance.

\begin{figure*}
\centering
\subfloat[First][Label: polecat \\ \hspace*{0.14in} Real: mouse]{\label{fig1-a}\includegraphics[width = 1.3in]{figure1_mislabeled__val_00005085.jpg}}\qquad
\subfloat[Second][Label: laptop \\ \hspace*{0.14in} Candidate: notebook]{\label{fig1-b}\includegraphics[width = 1.2in]{figure1_categories__val_00002580.jpg}}\qquad
\subfloat[Third][Label: American\_egret]{\label{fig1-c}\includegraphics[width = 1.3in]{figure1_bad_image__val_00001871.jpg}}\qquad
\subfloat[Fourth][Label: siamang]{\label{fig1-d}\includegraphics[width = 1.3in]{figure1_bad_image__val_00000087.jpg}}\qquad
\subfloat[Fifth][Label: breakwater \\ \hspace*{0.13in} Candidate: seashore]{\label{fig1-e}\includegraphics[width = 1.4in]{figure1_multilabel__val_00000358.jpg}}\qquad
\subfloat[Sixth][Label: neck\_brace]{\label{fig1-f}\includegraphics[width = 1.3in]{figure1_small_object__val_00005600.jpg}}\qquad
\subfloat[Seventh][Label: plate]{\label{fig1-g}\includegraphics[width = 1.3in]{figure1_semantics__val_00005376.jpg}}\qquad
\subfloat[Eigth][Label: restaurant]{\label{fig1-h}\includegraphics[width = 1.2in]{figure1_semantics2__val_00012638.jpg}}
\caption{Labeling challenges in the ImageNet dataset: (a) incorrect label, (b) equivalent categories (c) miniscule objects, (d) bad quality photo, (e) multiple correct categories, (f) many objects, and (g-h) semantics.}
\label{fig1}
\end{figure*}

\section{Labeling Challenges in the ILSVRC12 ImageNet Dataset}
\label{dataset-challenges}

The ImageNet project annotated several million images into classes based on a synset hierarchy. The ILSVRC12 ImageNet corpus is a subset of this database and it contains over a million images for 1000 categories. One ground-truth class is assigned to each image in the database.

\cref{fig1} shows labeling problems from the ImageNet validation set. Although the labeling process evaluated every picture by more individuals on the Amazon Mechanical Turk service, wrong categories were sometimes assigned to the images. People did not recognize the content on the image, there were too many candidate categories present on the image or a similar, incorrect category was assigned. \cref{fig1-a} contains a vole and it was labeled as \emph{polecat}. The closest ILSVRC12 ImageNet category is \emph{hamster} while the closest ImageNet category is \emph{mouse}. Such images without matching ILSVRC12 ImageNet category should be removed from the dataset or changed to the closest category. \cref{fig1-b} displays a computer that is named as \emph{laptop} or \emph{notebook} in our everyday language. Nevertheless, these are different categories in the dataset what generates unnecessary noise for the training process. \cref{fig1-c,fig1-d} show two examples when the image content cannot be recognized by a human because either the subject is too small (\cref{fig1-c}) or it is too close to the camera with possible bad lightning conditions (Figure 1.d). Pictures with little information content can be removed from the dataset since they are not reasonable to be recognized without prior knowledge.

Even if a "correct" label is assigned, the image content can be ambiguous in many ways. \cref{fig1-e} shows when a scene has two equally important parts (a breakwater and a seashore). Both \emph{breakwater} and \emph{seashore} categories are correct and flipping the image label between these two categories are not wrong. However, too many categories are present on \cref{fig1-f} and the target object is small as well. The target label is \emph{neck\_brace}, but the image has additional objects for categories of \emph{pole}, \emph{terrace}, \emph{sunglasses}, \emph{car\_wheel}, \emph{car}, \emph{bracelet}. This diverse content can be confusing for trained models. In other cases, the whole scene is more important than a few smaller objects on the image. The dominant object on \cref{fig1-g} is a \emph{plate} though the correct category would change if the plate is almost fully covered by some food. Similarly, on \cref{fig1-h}, the interior of a \emph{restaurant} is more important meaning than labeling the image as \emph{desk}, \emph{plate} or \emph{bottle}.

These issues can be solved by fixing the labeling mistakes and assigning multiple classes to the images where it is applicable. Nonetheless, these corrections need tedious efforts and extensive funding to cover the costs. The author proposes an automated clean-up process by combining model consensus, confident learning and explainability to minimize human supervision.

\section{Clean-Up Methods}
\label{clean-up methods}

The labeling challenges in ImageNet were identified by the research community \cite{beyer2020imagenet, northcutt2019confident, shankar2020evaluating} and initial attempts were made for mitigation. Northcutt et al \cite{northcutt2019confident} removed wrong samples detected by confident learning up to 10 \% of the training set and they could achieve 0.5 \% accuracy improvement with ResNet-50 on the ILSVRC2012 validation set. Beyer et al \cite{beyer2020imagenet} used pretrained models and crowdsourcing to relabel the ImageNet validation set (ReaL) with more possible classes per image. They removed appr. 10 \% of the training set, and after training for 90 epochs, the accuracy of ResNet50 improved by 0.3-0.4 \%. Some works developed new validation sets to test out-of-distribution samples. In \cite{hendrycks2020many}, artistic images were collected to form ImageNet-R validation set, while in \cite{recht2019imagenet}. new natural pictures were gathered by replicating the original labeling process to create ImageNetV2 validation sets. All these researches involved extensive human supervision in their processes except \cite{northcutt2019confident}. When images were removed by automated clean-up methods from the training set during the experiments in \cite{northcutt2019confident} and \cite{beyer2020imagenet}, the improvements were 0.3-0.4 \% for the original and ReaL validation sets. The author implemented a new process with label fixes and image removal. The former is beneficial to correct wrong labels without human intervention, the latter targets the deletion of noisy pictures for the training process. Namely, images with minuscule objects (e.g. \cref{fig1-c}), quality issues (e.g. \cref{fig1-d}) and too many categories (e.g. \cref{fig1-f}).

Confident learning, model consensus and explainability were utilized to get a good suboptimal solution whilst managing the time and computational constraints. An image sample is denoted by $s$ later in the paper, a set of $n$ images by $S_n = \{s_0, s_1, ..., s_{n-1}\}$, a label to a sample $s$ by $l$, a new label candidate to a sample $s$ by $l'$, the corresponding labels to $S$ by $L$, a deep learning model by $m$ and a set of $n$ models by $M_n = \{m_0, m_1, ..., m_{n-1}\}$. The predictions of a sample set $S$ by a model $m$ are denoted by $P_n = \{p_0, p_1, ..., p_{n-1}\}$. The clean-up process results in two sets. $S_r$ is a set of samples collected for removal from $S$ and $S_f$ is a set of samples whose annotation should be fixed by label candidates.

\subsection{Confident Learning}
\label{confident learning}

The confident learning theorem was invented by Northcutt et al in \cite{northcutt2019confident} to find label errors in datasets. It uses model predictions to estimate the uncertainty of the annotated labels and proposes new label candidates. If a sample $s$ is identified as mislabeled, a new label $l'$ is suggested instead of the original $l$. Confident learning is defined here as a $CL$ function:

\begin{equation}
\label{eq1}
\begin{gathered}
N'_m = CL(S, L, m, P, fn), \quad fn \in [0, 1] \\
(s'_i; l'_i) \in N'_m, \quad i \in \{0, ..., k-1\}
\end{gathered}
\end{equation}

where $CL$ takes a set of samples ($S$), annotated labels ($L$) and predictions ($P$) from model $m$ to output $k$ noisy samples with candidate labels ($|N'_m|=k$). The fraction noise ($fn$) is a hyperparameter between $[0, 1]$ to control the returned fraction of noisy labels per class. The fixed hyperparameters of confident learning were the pruning by noise rate and sorting the label indices by a normalized margin.

\begin{figure}[tp]
\centering
\subfloat{\includegraphics[width = 3.2in]{figure2_gradcam__val_00001314.jpg}}
\caption{Explainability example. GradCAM heatmap visualizes the important regions for a CNN model to recognize the husky. The white rectangle is the annotated bounding box for the label. }
\label{fig2}
\end{figure}

For a set of models ($M$), the label candidates are gathered for each sample:

\begin{equation}
\label{eq2}
\begin{gathered}
(s_i, L'_i) \in N'_S, \quad \forall s \in S, \quad \exists (s, l') \in N'_m  \\
L'_i = (l'_0, l'_1, ..., l'_p), \quad 0 <= p < |M|
\end{gathered}
\end{equation}

where $N'_S$ contains the candidate label lists for each sample and the same label can occur multiple times in $L'_i$. The more times a sample is considered mislabeled by confident learning with the same candidate label the more likely the true label is this candidate. The annotation ($l_i \in L$) is fixed for a sample $s_i$ when the following conditions are met:

\begin{equation}
\label{eq3}
\begin{gathered}
|L'_i| >= h_1 \\
|unique\_items(L'_i)| < 3
\end{gathered}
\end{equation}

where $h_1$ number of models consider the sample mislabeled and there are less than three unique candidates. The sample $s_i$ and the most frequent candidate from $L'_i$ are added to $S_f$ in this case. A sample $s_i$ is added to $S_r$ for removal if the unique items in $L'_i$  are over a certain threshold $h_2$ ($|unique\_items(L'_i)| \geq h_2$). Samples marked for annotation fixes cannot be removed from $S$ ($(s_i, l'_i) \in S_f \Rightarrow s_i \notin S_r$)

To apply confident learning, the models $M$ and $fn$, $h_1$, $h_2$ must be defined. These latter hyperparameters can be found empirically after some initial experimentation with a dataset since confident learning is not a silver bullet. The author experienced that the success of confident learning depends on how representative is a training set and how good is a model built on it (see in \cref{validation-set}). In the upcoming \cref{cleaning-up-imagenet}, the official confident learning implementation (Cleanlab) was used from Github \footnote{URL: https://github.com/cgnorthcutt/cleanlab}.

\subsection{Top-5 Model Consensus and Explainability}
\label{model-consensus}

\cref{fig1} presents why a single label cannot describe some images in the ImageNet dataset unequivocally when the top-1 accuracy is calculated. Nevertheless, the top-5 accuracy lets a model assign five potential labels for an input picture which can recognize a single image with a few categories. On the other hand, if an image is not predicted by the top-5 predictions correctly, that picture likely exhibits one challenge from \cref{fig1-c,fig1-d,fig1-f} (minuscule subject, bad image quality, more than five categories). These images would be beneficial to remove thus a process is proposed to find these pictures indirectly by the top-5 mispredictions and explainability.

Fort et al \cite{fort2020deep} showed that the deep learning models do not learn the same function after their training runs. They produce similar accuracy with the same dataset and hyperparameters, but they disagree about the actual misclassified samples. In other words, when models built on a training set achieve appr. 90 \% accuracy, each model misclassifies different samples in the validation set (10 \% error). Because of the misclassified samples change for the same model architecture after every training run, various topologies and model capacities must behave likewise. If most models in an $M$ miss certain images to classify correctly in their top-5 predictions, we can assume that those images are good candidates for removal. Let us denote a counter variable ($C_s$) for every sample ($s$) to sum all models which do not predict the label ($l$) in their top-5 predictions:

\begin{equation}
\label{eq4}
\begin{gathered}
 \\
c_s(m, s, l) = \begin{cases} 1 & \mbox{if } l \notin top_5(m(s)) \\0 & \mbox{otherwise} \end{cases} \\
C_s =  \sum^{|M|-1}_{i=0} c_s(m_i, s, l) \\
\end{gathered}
\end{equation}

Those images will be added to $S_r$ whose $C_s \geq h_3$. However, such an images can be exempted from removal if at least one model detects the target object with reasonable confidence by explainability.

There are various interpretability algorithms, but the author found only some class activation mappings (CAMs) reliable and quick enough for large-scale batch processing. GradCAM \cite{selvaraju2017grad}, GradCAM++ \cite{chattopadhay2018grad} and ScoreCAM \cite{wang2019score} were implemented based on a GitHub project \footnote{URL: https://github.com/tabayashi0117/Score-CAM}. These algorithms provide a heatmap to show which pixels contribute to the actual decision of a neural network (\cref{fig2}). The hottest areas (red pixels close to value 1) have a major contribution to the model output while the neutral pixels are close to zero values (blue areas). The criteria for an image to avoid the removal by insufficient top-5 predictions if at least 1 \% of the bounding box area of the target subject (white rectangle on \cref{fig2}) have 0.75 values on the heatmap when a good model prediction is analyzed. Visually, this condition can be translated so that at least 1 \% of the bounding box area is red. The \emph{explainability score} will mean the percentage of pixels in a bounding box whose value is at least 0.75 on the heatmap. The husky on \cref{fig2} has an explainability score 0.06 (6 \%) by GradCAM. Finally, three CAMs make the final decision on the images because these methods are not reliable enough alone, they produce varying results (see \cref{app-fig1} in Appendix), similar to the experiences in \cite{graziani2021evaluation}. Thus, at least two methods out of the three CAMs must met the 1 \%-red-area condition for the bounding box for a model to exempt the inclusion in $S_r$ if $C_s \geq h_3$. Since the explainability methods visualize a model decision, those images can be examined that have at least one successful model prediction in their top-5 results. If the bounding box is not available for a sample, the explainability step is omitted in the process.

\subsection{Merged Categories}
\label{merged-categories}

Some categories in ILSVRC12 ImageNet are interchangeable. Either two categories are synonyms or the difference in their meaning is so subtle that the images were highly intermixed during the labeling process. More studies identified problematic categories \cite{beyer2020imagenet, northcutt2019confident, tsipras2020imagenet} in the past, but a comprehensive solution was not applied to check the effects on the training and the model performance. The author gathered these past experiences and complemented them with new categories found during the initial experimentation (see \cref{app-table1} in Appendix). The categories were fixed by copying the samples of one category into a close/synonym category. After moving the samples, a placeholder black image was generated into the empty category to have one placeholder sample. This latter step kept the original 1000-class interface of the ImageNet models compatible with the newly-trained models after the clean-up process. The category merging does not have a foreseeable larger impact on the model performance since the affected samples are appr. 0.04 \% of the dataset, but the training is benefited by fewer overlapping categories and eliminated interclass variability between these confusing categories. The categories were merged after the image fixes and removal steps in the clean-up process.

\section{Cleaning Up ImageNet datasets}
\label{cleaning-up-imagenet}

ILSVRC2012 ImageNet dataset has two parts, the training set ($T$) contains 1281167 images and 523966 of them have bounding boxes specified for the image label. The original validation set ($V$) contains 50000 images and all of them have bounding box annotations. The ImageNetV2 validation sets were developed by Recht et al \cite{recht2019imagenet}, the matched frequency version ($V2$) was included in the experiments in this section. It contains 10000 images, 10 per category. They were collected by replicating the same labeling procedure of $T$ and $V$, but there are no bounding box annotations for these images. The datasets were cleaned up separately, the cleaned training set will be denoted by $T_c$, the cleaned original validation set by $V_c$ and the cleaned ImageNetV2 with matched frequency by $V2_c$.

\subsection{ILSVRC12 Validation Set}
\label{validation-set}

The author chose 10 pretrained ImageNet models to build a consensus on which images should be fixed or removed. The model set $M_{val}$ had lightweight CNN models from recent years: SqueezeNet, NASNet, MobileNet, MobilenetV2 and EfficientNet (B0, B1, B2 and their Noisy Student variants). These pretrained models were fetched from Keras (Tensorflow) except SqueezeNet \footnote{URL: https://github.com/rcmalli/keras-squeezenet} and EfficientNet \footnote{URL: https://github.com/qubvel/efficientnet}. There were three reasons why bigger models were not included in this set. First, explainability methods were executed on many images, and practically, it is not feasible to run them with bigger models on this scale. Secondly, the lighter models generalize better on mislabeled images as it was found in \cite{northcutt2020labelerrors} recently. Finally, the author had the intuition that the latest semi-supervised models reached the limits of top-1 accuracy on the ImageNet dataset because of its labeling problems described in \cref{dataset-challenges}. These big models might not really learn better abstractions, but guessing better by other shortcuts (e.g. using the background to bet on the foreground object). Beyer et al \cite{beyer2020imagenet} suggested in their analysis of co-occurring labels that the noisy student versions of EfficientNet exploit the biases in the original ImageNet labeling procedure.

\begin{table}[tp]
\caption{Label errors found by confident learning per class ($N'_m$) as well as pretrained model accuracies on the original ImageNet validation set ($V$) and the cleaned version ($V_c$). The last four rows have results for some large models that were not used in the clean-up process hence their label errors are empty.}
\label{table1}
\vskip 0.1in
\begin{center}
\begin{adjustbox}{width=\columnwidth,center}
\small
\begin{sc}
\begin{tabular}{lcccr}
\toprule
Model                & Label  & Top-1/Top-5 & Top-1/Top-5 \\
                     & errors & Original    & Cleaned    \\
\midrule
SqueezeNet           & 13716 & 56.15/79.38 & 63.78/85.66 \\
MobileNet            & 8550 & 68.88/88.60 & 78.14/94.45 \\
MobileNet V2         & 7341 & 69.70/89.35 & 79.26/95.09 \\
NASNet               & 7489 & 71.83/90.49 & 81.64/95.88 \\
ENet-B0              & 5422 & 76.22/92.96 & 86.61/98.00 \\
ENet-B0 (NS)         & 5597 & 76.82/93.65 & 87.09/98.40 \\
ENet-B1              & 5224 & 77.81/93.86 & 87.98/98.53 \\
ENet-B1 (NS)         & 4926 & 80.20/95.33 & 89.91/99.15 \\
ENet-B2              & 4921 & 79.06/94.54 & 89.26/98.89 \\
ENet-B2 (NS)         & 4658 & 81.41/95.94 & 90.73/99.37 \\
\midrule
ENet-B5 (NS)         & -    & 86.07/97.75 & 90.23/99.19 \\
ENet-B7              & -    & 84.93/97.20 & 90.11/99.10 \\
IG-ResNext101        & -    & 85.42/97.88 & 89.06/98.86 \\
ENet-L2 (NS)         & -    & 88.35/98.65 & 90.87/99.25 \\
\bottomrule
\end{tabular}
\end{sc}
\end{adjustbox}
\end{center}
\vskip -0.2in
\end{table}

Since these models in $M_{val}$ were pretrained on the whole ImageNet dataset and all images had bounding box annotations, the hyperparameters of the clean-up process were less strict. Half of the models were sufficient to fix a label by confident learning ($h_1 = |M_{val}| / 2$), all candidate labels were considered by $fn = 1$ and the removal was initiated over 2 label candidates ($h_2 = 3$). The top-5 model consensus hyperparameter for removal was $h_3 = |M_{val}|*2 / 3$.

The models made no mistake for 74.45 \% of $V$ regarding their top-5 predictions. This top-5 consensus error on the validation set (25.54 \%) was similar to the problematic ratio (29 \%) of multi-object and multicategorical images in \cite{beyer2020imagenet}. Eventually, 3565 labels were fixed by confident learning and 2471 images were removed. These changes involved 7.13 \% and 4.9 \% of the original validation set respectively.

\cref{table1} shows the label errors found by confident learning and the clean-up results with the pretrained models. Most label errors were found for SqueezeNet, the least for EfficientNet-B2 and a strong correlation can be identified between the decreasing error count and the growing model complexity. The better a model learns the abstractions in the training set the fewer label errors are detected by confident learning. Similar to these findings, Shaikhina et al \cite{shaikhina2021effects} found across various machine learning classifiers and datasets that the post-hoc techniques have degraded quality when the models are uncertain in their decisions. However, it is a good practice to avoid relying on the larger models exclusively since the models learn different prediction functions \cite{fort2020deep} and the smaller models generalize better on unseen mislabeled samples \cite{northcutt2020labelerrors}. The middle column of \cref{table1} contains the top-1/top-5 accuracies on the original $V$ and the top-1 results improved by 7-11 \% while the top-5 had a 4-6 \% increase. The 99 \% top-5 accuracies on $V_c$ suggest upper adaptability limits that are already reached by the smaller EfficientNet models for the original ImageNet validation set. Since multicategorical images are still present after the clean-up process, it cannot be expected to reach 100 \% top-1 accuracy. The author evaluated four large models from \cite{rw2019timm}, close to the latest state-of-the-art (SOTA) results on ImageNet (last rows of \cref{table1}). Their top-1 accuracies did not go above the EfficientNet-B2 performance (appr. 90 \% accuracy). Some examples label fixes and removed images are shown in the Appendix (\cref{app-fig2}).

\subsection{ILSVRC12 Training Set}
\label{training-set}

The training set had to be cleaned by another approach compared to the validation set because the confident learning requires unseen samples for the label error estimation. The training set was evaluated by 4-fold cross-validation in Cleanlab to get label corrections for the whole set based on the testing folds. When the cross-validation was run on $T$, three folds were used for training which is 25 \% fewer training images compared to the whole $T$. The decreased training set size impacts the model performance negatively during the cross-validation as Hernández-García and König shown in \cite{hernandez2018data}, the model accuracy can drop up to 12-15 \% after removing half of the ImageNet training set. The smaller model accuracy means more uncertain estimation of the label errors on the testing folds insomuch as this relationship was discovered on \cref{table1}. This effect can be minimized by increasing the fold count, but that solution inflates the computational complexity dramatically since the ImageNet training is resource demanding.

8 deep learning models ($M_{tr}$) were crossvalidated: SqueezeNet, NASNet, MobileNet, MobileNetV2, ResNet18, EfficientNet-B0, EfficientNet-B1 and EfficientNet-B2. Because of the decreased model confidence, stricter hyperparameters were defined. The fraction noise remained $fn = 1$, but all models had to agree in a label fix ($h_1 = |M_{tr}|$) and the removal was over more label candidates ($h_2 = |M_{tr}| / 2$). The top-5 model consensus hyperparameter for removal was tightened to $h_3 = |M_{tr}|$. The stricter hyperparameters for removal were needed because the initial experimentation shown that removing 8-15 \% problematic samples result in 1.5-2.5 \% performance loss. Northcutt et al reported a similar loss for ResNet18 after removing 15 \% of of the training set in \cite{northcutt2019confident}. The source of this phenomena is that even the problematic images contribute to feature learning despite their noisy labels. Finally, 110839 labels were fixed by confident learning and 48581 images were removed from the training set. These numbers correspond to 8.6 \% and 3.7 \% of the ImageNet training set respectively.

\subsection{ImageNetV2 Matched Frequency Set}
\label{imagenetv2-set}

The ImageNetV2 matched frequency set ($V2$) have 10000 new images (10 images/category) sampled 10 years after the original ImageNet release \cite{recht2019imagenet}. Since the sampling process was replicated to collect the images under similar conditions, the same limitations and labeling errors can be expected from $V2$. The state-of-the-art ImageNet models achieve a maximum of 80.50 \% top-1 accuracy on $V2$, therefore, the ImageNet models were less capable to estimate the labeling errors and the removable images compared to $V$. ImageNetV2 lacks annotated bounding boxes what prevents the use of interpretability for the top-5 model consensus. The pretrained models of $M_{val}$ were utilized to clean up this validation set. Considering these challenging conditions, the most strict hyperparameters were defined in the clean-up process. The fraction noise was decreased to $fn = 0.9$, but other parameters for confident learning and top-5 model consensus remained the same ($h_1 = |M_{val}|$, $h_2 = |M_{val}| / 2$, $h_3 = |M_{val}|$). These hyperparameters resulted in 292 label fixes and 524 image removals that are 2.9 \% and 5.2 \% of ImageNetV2 respectively. Some example label fixes and removed images are shown in the Appendix (\cref{app-fig3}) to demonstrate the repeated labeling mistakes in ImageNetV2 and the original ImageNet validation set.

\begin{table*}[th]
\caption{The effects of cleaning up the training and validation sets for ImageNet. The first three rows contain the results for SqueezeNet 1.1, ShuffleNetV2 x1.5 and EffecientNet-B0 models trained and evaluated in different settings. The column headers show the used training/validation sets and the numbers are top-1/top-5 accuracies. The last four rows show pretrained models built on the original ImageNet training set and evaluated on the clean validation sets. Therefore, the last two columns are special cases where the models in the upper three rows were trained on the cleaned training set while the other rows were trained on the original training set. ($T$ - ImageNet training set, $T_c$ - cleaned $T$, $V$ - ImageNet validation set, $V_c$ - cleaned $V$, $V2$ - ImageNetV2 matched frequency validation set, $V2_c$ - cleaned $V2$)}
\label{table2}
\vskip 0.10in
\begin{center}
\begin{adjustbox}{width=\columnwidth+2.3in,center}
\small
\begin{sc}
\begin{tabular}{lcccccr}
\toprule
Model                & Top-1/Top-5 & Top-1/Top-5 & Top-1/Top-5 & Top-1/Top-5 & Top-1/Top-5  & \\
                     & $T, V$      & $T,V_c$     & $T_c,V_c $  & $T_c/T,V2$  & $T_c/T,V2_c$ & \\
\midrule
SqueezeNet 1.1       & 55.07/78.19 & 60.99/83.98 & 63.15/84.71 & 42.69/65.15 & 47.72/71.38  & \\
ShuffleNetV2         & 70.39/89.25 & 77.94/94.56 & 78.31/94.50 & 56.64/77.81 & 63.07/84.32  & \\
ENet-B0              & 73.06/91.40 & 80.74/96.24 & 83.11/96.78 & 61.53/82.23 & 68.58/88.87  & \\
\midrule
ENet-B5 (NS)         & 86.07/97.75 & 90.23/99.19 & -           & 76.70/93.51 & 81.73/96.58  & \\
ENet-B7              & 84.93/97.20 & 90.11/99.10 & -           & 74.45/92.12 & 79.49/95.73  & \\
IG-ResNext101        & 85.42/97.88 & 89.06/98.86 & -           & 76.77/93.42 & 80.68/96.04  & \\
ENet-L2 (NS)         & 88.35/98.65 & 90.87/99.25 & -           & 80.10/95.86 & 84.12/97.81  & \\
\bottomrule
\end{tabular}
\end{sc}
\end{adjustbox}
\end{center}
\vskip -0.2in
\end{table*}

\begin{table}[th]
\caption{Testing SqueezeNet and EfficientNet-B0 models with different input resolutions on the cleaned ImageNet validation set ($V_c$). The first two resolutions are in landscape orientation and the rest are in portrait.}
\label{table3}
\vskip 0.10in
\begin{center}
\begin{adjustbox}{width=\columnwidth-0.3in,center}
\small
\begin{sc}
\begin{tabular}{lcccr}
\toprule
Resolution    & Top-1/Top-5 & Top-1/Top-5 & \\
              & SqueezeNet  & ENet-B0     & \\
\midrule
320x180 (L)   & \textcolor{red}{-0.56}/\textcolor{red}{-0.05} & \textcolor{blue}{+0.26}/\textcolor{blue}{+0.09} & \\
384x216 (L)   & \textcolor{blue}{+0.93}/\textcolor{blue}{+0.89} & \textcolor{blue}{+1.36}/\textcolor{blue}{+0.75} & \\
180x320 (P)   & \textcolor{red}{-1.03}/\textcolor{red}{-0.30} & \textcolor{blue}{+0.25}/\textcolor{blue}{+0.33} & \\
216x384 (P)   & \textcolor{blue}{+0.29}/\textcolor{blue}{+0.48} & \textcolor{blue}{+1.30}/\textcolor{blue}{+0.55} & \\
\bottomrule
\end{tabular}
\end{sc}
\end{adjustbox}
\end{center}
\vskip -0.2in
\end{table}

\section{New Models}
\label{new-models}

After the clean-up process was finished, new models were trained to examine the effects on the model performance. All training sessions had the same hyperparameters to avoid any influence by such changes when the results were compared to each other. The training was executed by pytorch-image-models \cite{rw2019timm} for 90 epochs with cosine scheduler, one warm-up epoch, 10 cool-down epochs, initial learning rate 0.1, random pixel erase probability 0.5 \cite{zhong2018randomerase}, SGD loss function and RandAugment \cite{cubuk2019randaugment} data augmentation (magnitude 7, the standard deviation of magnitude noise 0.5, increased severity with magnitude). These hyperparameters were not the most optimal to get accuracy close to the SOTA for any models, but they provided reasonably good results to compare the effects of the clean-up process. The default input resolutions were the standard square 224x224.

Three deep learning architectures were explored during the experiments, SqueezeNet 1.1, ShuffleNetV2 x1.5 and EfficientNet-B0. These models are small enough to train in many configurations, nevertheless, they scale in complexity and accuracy from SqueezeNet up to EfficientNet-B0. \cref{table2} shows the model performance for different training and validation set configurations. After cleaning up the ImageNet validation set, the third column contains the performance gains. The small models achieved +5-7.5 \% top-1/top-5 accuracies compared to $V$ while the big pretrained models hit accuracy limits, 90 \% for top-1 and 99 \% for top-5 after minimal gains. When the small models were trained on a cleaned training set ($T_c$), further +0.3-2.3 \% accuracies were reached in the fourth column. When Beyer et al \cite{beyer2020imagenet} tested a ResNet50 classifier built on their cleaned ImageNet training set, they experienced +0.3 \% accuracy improvement against their cleaned validation set (ImageNet-ReaL). SqueezeNet 1.1 and EfficientNet-B0 achieved a bigger, +2.16 \% and +2.37 \% gains respectively. The sixth and seventh columns show the impact of the cleaned ImageNetV2 validation set ($V2_c$). The small models got improved top-1 accuracies by +5-7 \% and better top-5 accuracies by +6-6.5 \%. The big pretrained models built on original $T$ had lower gains, +4-5 \% top-1 and +2-3 \% top-5 accuracies.

Some widescreen resolutions were investigated during the experiments since ImageNet models are always trained in square input shape, but the webcams and phone cameras have a widescreen aspect ratio. The results for two portrait and landscape resolutions are shown on \cref{table3} after SqueezeNet 1.1 and EfficientNet-B0 models were trained on $T_c$, tested on $V_c$. The smaller resolutions (320x180, 180x320) have approximately the same amount of pixels as the standard input resolution (224x224). The bigger resolutions have 1.65x more pixels than the standard input. The EfficientNet-B0 models had a positive gain in every configuration, appr. +0.25 \% top-1 accuracy for the smaller resolutions and +1.3 \%  for the bigger. For SqueezeNet model, the smaller resolutions caused performance degradation while the bigger resulted in +0.2-0.9 \% gains. The increased pixel count always improved the model performance, but the resolutions 320x180/180x320 caused problems for the SqueezeNet model. All negative accuracy changes originated in the different cropping mechanisms during the training and evaluation compared to the usual square cropping. Nevertheless, the availability of widescreen models is an obvious advantage to use the camera pictures without resizing or cropping to square aspect ratio in real-world applications. The other takeaway of \cref{table3} is the accuracy increase by raising the input resolution for the existing architectures. In this case, the internal model parameter count does not change and the model will have minimal runtime cost. The bigger widescreen resolution changed the inference times by +10-20 msec on an Intel i7-7500U CPU (SqueezeNet: 22 msec$\rightarrow$34 msec, EfficientNet-B0: 100 msec$\rightarrow$121 msec) and the difference could not be measured with GPU inference.

\begin{table}[tp]
\caption{Testing models on ImageNet-R and cleaned ImageNetV2 validation sets after adding cleaned EDSR and CAE samples to the training set.}
\label{table4}
\vskip 0.10in
\begin{center}
\begin{adjustbox}{width=\columnwidth-0.2in,center}
\small
\begin{sc}
\begin{tabular}{lcccr}
\toprule
Model                & Top-1/Top-5 & Top-1/Top-5 & \\
                     & ImageNetV2  & ImageNet-R  & \\
\midrule
SqueezeNet 1.1       & \textcolor{blue}{+0.76}/\textcolor{blue}{+0.47} & \textcolor{blue}{+5.39}/\textcolor{blue}{+6.90} & \\
ShuffleNetV2         & \textcolor{red}{-0.65}/\textcolor{red}{-0.66} & \textcolor{blue}{+5.96}/\textcolor{blue}{+6.33} & \\
ENet-B0              & \textcolor{red}{-1.49}/\textcolor{red}{-1.02} & \textcolor{blue}{+5.86}/\textcolor{blue}{+5.73} & \\
\bottomrule
\end{tabular}
\end{sc}
\end{adjustbox}
\end{center}
\vskip -0.2in
\end{table}

Hendrycks et al \cite{hendrycks2020many} introduced a new validation set (ImageNet-R) to test out-of-sample distribution and they published modified ImageNet training sets augmented by their DeepAugment method. They distorted $T$ by two models (EDSR \cite{lim2017enhanced} and CAE \cite{theis2017lossy}) which resulted three times more training data in the end. \cref{table4} shows the accuracies when the models were trained on this big training set and validated on ImageNet-R and cleaned ImageNetV2 ($V2_c$). Surprisingly, the increased training set improves the performance by appr. +6 \% accuracy on ImageNet-R for all models, but it was counterproductive on $V2_c$ in the function of the model complexity. The worse the performance on ImageNetV2 the bigger the model. The reason is that ImageNet-R contains \emph{artistic} out-of-distribution images (sketches, cartoons, paint etc.) while ImageNetV2 has \emph{natural} out-of-distribution images. CAE and EDSR modified the original ImageNet training set ($T$) thus the new images were relatively close to the originals in the feature subspace. They can be considered like local perturbations of the original images. As \cite{fort2020deep} showed the near solutions around a local, suboptimal minimum on the loss landscape are functionally close to each other. DeepAugment helps to learn the \emph{artistic representations} of $T$, but it does not improve the generalization to natural out-of-distribution images. It facilitates a better local solution around a minimum on the loss landscape by modeling the local perturbations similar to Bayesian networks instead of increasing the generalization power in the global feature space \cite{fort2020deep}.

\section{Discussion}
\label{discussion}

Northcutt et al \cite{northcutt2020labelerrors} examined ten machine learning datasets from different domains (computer vision, natural language processing) to discover the labeling errors. They found smaller capacity models performed worse on the test sets because of the lack of their learning skills while they generalized better on mislabeled samples in the validation set. Models with more capacity behaved in the opposite way. They proposed that larger models might overfit the noise in the training data in order to explain their findings and they suggested the performance on mislabeled samples as a measure for overfitting. However, the author believes their results were caused by a more straightforward reason. The sample collection process for the training and validation sets in a certain dataset is identical, therefore, similar labeling errors will be present in both sets (see \cref{app-fig2} and \cref{app-fig3} in the Appendix). The lower capacity models generalize on mislabeled, unseen samples better because they cannot learn the fine-grained patterns in the training set. The larger models do not overfit to the mislabeled samples, but they \emph{learn} them. In \cite{graziani2019interpreting}, the deep learning models learned the real data distributions in the earlier epochs during the training process while the mislabeled samples were fit in later stages. These findings make sense because the mislabeled samples act as outliers for a category in the feature space and a larger model learns the wrong samples better with its bigger model capacity.

Cleaning up the training datasets is a necessary thing for the larger models to learn good generalization power instead of the labeling errors and clean validation sets can verify this power. However, the hidden benefits of mislabeled samples cannot be overlooked. Although these samples have wrong labels, they contribute to the feature learning in the deep learning models. When 10 \% of labeling error candidates were removed from the ImageNet training set in \cite{northcutt2019confident}, a degradation in the validation performance was observed. The author found similar effects during the initial experimentation when 5-15 \% wrong sample candidates were removed from the training set, the result was 0.5-1.5 \% accuracy loss on the validation set. These results suggested for the author that the labeling fixes and the sample removal are necessary for a better dataset, but these methods must be balanced. Too many fixes can change false positive candidates to a wrong label more likely while too many samples cannot be removed from the training set to avoid negative effects of losing generalization power.

Using the top-1 accuracy to measure the model performance on ImageNet is challenging. The ImageNet dataset assigns one category to each image and the top-1 accuracy tests the model prediction against this label. Although the top-1 and top-5 accuracies are reported for the ImageNet models, the top-1 accuracy is the common measure to rank the models and determine the state-of-the-art. Beyer et al \cite{beyer2020imagenet} found 29 \% of their examined ImageNet samples contained multiple objects or they could be assigned to synonym categories. Because of so many ImageNet images have ambiguous labels, the top-1 accuracy cannot be a true measure for the models. If the mislabeled images are additionally considered in this equation, it is questionable how much the top-1 accuracies over 80 \% accuracy can mean a valid rank for the models. The author agrees with the opinion in \cite{stock2018convnets}, the top-5 accuracy is a more valid measure for the models until ImageNet is properly fixed and gets multilabel annotations.

Since the original training set in ImageNet has many mislabeled samples, the teacher transfers this knowledge to the learners in semi-supervised training. The validation set has similar mislabeled images which implicitly rewards the students to learn these inaccuracies from the training set. A cleaned training set benefits the semi-supervised learning by teaching better generalization on an unlabeled dataset and a cleaned validation set can verity the potential gains from the massive training dataset. Currently, the big model capacities in semi-supervised learning might be used to learn the local mislabeling strategies of ImageNet in the feature space instead of helping the generalization. An iterated clean-up process on the ImageNet dataset would benefit better semi-supervised models for the future.

\subsection{Limitations}
\label{limitations}

There are a number of limitations in this research. First of all, the clean-up process was executed once, but this automated procedure should be repeated more times to improve the training and validation sets incrementally. The more the trained models are built and evaluated on clean sets the more improvements can be gained in feature learning. Another improvement would be the human verification before removing the images since the current approach eliminates problematic images, but these steps can remove out-of-sample images as well.

\section{Acknowledgements}
\label{acknowledgements}

The author thanks the Finnish supercomputing provider (CSC - IT Center for Science) to give access to on-demand GPU and CPU resources for this research.

redpajama_set_name: RedPajamaArXiv


Best Protocol To Beat Chronic Lymphocytic Leukemia?
No doubt, there's some smart minds around here!
About 8 months ago I (52 year old male-live in the U.S.) was diagnosed with CLL, stage 0, with NO symptoms (no night sweats, spleen & liver normal, lymph nodes normal, etc). However, have intermediate risk factors: CD19 87%+ of these ZAP70 1% & CD38 1%+, del(13q14) by FISH, IgVH mutated, VH2-5 family, beta 2 microglobulin elevated at 2.68 mg/L.
White blood cell count 23.4 (should be 2.8-10.8), absolute lymphocytes 18,603 (should be 850-3900), RDW 16.0 (should be 11.0-15.0), hs-CRP is 2.1, IGF 1 is 165 (in middle range), and Vitamin D 25 Hydroxy is 44 (normal but in low middle range). Everything else is normal.
I'm on Watch and Wait, which means my Oncologist monitors my blood every 6 months to see how I'm progressing.
Although CLL is a slow moving cancer there is no cure for it. In fact, there is only a 1% cure rate! Which stinks! From what I've been reading its tough to get these little buggers (they always come back after knocking them down), because they protect themselves in tissue and in bone marrow (stromal protection).
What mix would be the best bet to win the battle here?
Thanks for your nice words and welcome on this website!
Should I understand that in the Watch and Wait mode you are not taking any drug or supplement?
I am asking this so that I know what is the direction we have to think.
Yes, I am not taking any drug or supplement.
I can monitor my blood anytime I would like so whatever I decide to do, if it looks like something is going in the wrong direction, I could always stop or make an adjustment. The flip side is if something is going in the right direction, to continue on.
And obviously you guys and gals are just giving opinions, it ultimately comes down to decisions that I and my doctor/s make.
First, thank you for your donation. I very much appreciate that and will do my best to convert that into value for patients. I appreciate even more such gesture in the context in which that happens rarely (it's the second donation since March) while I am contacted by many people on e-mail asking for information on oncology. I do that as I can, with most pleasure and for free. Some I even help with money. But in order to be able to answer questions and do research full time (not only in my free time as I do now) I will have to search for an alternative source of income to my current full time job. I am actually busy with this and hope to find a sustainable way forward soon.
Ritonavir and metformin https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868500/ Based on this study, a strategy that combines glycolysis inhibition (here with GLUT4 inhibitor Ritonavir) with mitochondria suppression (here using Metformin) works well in CLL. This can be achieved with other drugs and supplements as well and could be most effective with intravenous administration of the drugs.
All of the above can be easily implemented.
Please note that recently, Verastem Oncology has demonstrated good activity against CLL with their drug Duvelisib http://www.verastem.com/pipeline/duvelisib-pi3k-delta-pi3k-gamma-inhibitor/ . It is highly likely that this drug will be approved by FDA in the second part of this year and by the end of the year is expected to be available to the patients. Technical details can be found here http://investor.verastem.com/phoenix.zhtml?c=250749&p=irol-calendar I hope this will not be needed but is good to see progress in the field.
I hope this helps and if there are questions please let me know.
You have a gift my friend! It would be terrible for you NOT to be able to do this full-time.
There is a wealth of information here. Impressive to say the least! Wow!
It's going to take me a little time to go through all of this. Lol. Will be in touch.
Like you said, its nice to see progress in the field.
After going everything, I must say, it's a very interesting approach. None of which I've come accross anywhere else. That being said, (if it was you) what would be your list: drug or supplement with dosses.
Also, what's your opinion on lifting weights? I'd like to get back into shape like when I was in my 20's & 30's and put some muscle back on, but I certainly don't want to help my CLL.


text:
The Veda
What is the Veda?
The Veda & Science
The Veda & Religion/Tradition
The Veda & Consciousness
The Veda &
The Veda: Look Deeper
Vedic Technology
What is Vedic Technology?
The Power of Vedic Technology
Vedic Temples
Vedic Ceremonies
Vedic Mantras
The Ideal Vedic Community
Vedic Science
What is Vedic Science?
The Structure of the Veda
Rishi, Devata, Chhandas
Upangas & Vedangas –
Upanga & Vedanga Terms
Agnim
Smriti and Sarvadharma
Vedic Arts
What are the Vedic Arts?
Finding the Gods
Lord Karthikeya
Uma Parvathi Devi
Sri Nagadevatha
Sri Hanuman
Nine Planets (Navagrahas)
Introduction to the Veda
Embodying Knowledge
Freedom from Desire
What is a Myth?
Legend of the Crow
Linga with No End
Use of the Veda
What is a Linga and Yagya?
Michael Mamas
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World Peace, Vedic Technology and Mount Soma
by Michael Mamas | Sunday, February 17, 2019 | Vedic Technology | 6 comments
I have confidence in the technology of Vedic science. Studies at universities have shown that on a small scale, the technology works. Statistical p-values make its validity undeniable. Yet, because Vedic technology is not consistent with the way people have been indoctrinated into thinking, it is usually ignored. Instead, people cling to their current perspectives, even when those perspectives are limited or even flawed.
For example, in spite of the contradictions, many physicists cling to their current perspective on the Big Bang. Many biologists realize that evolution of species happened too fast to stand alone as valid, yet they do not update their theories. Vedic knowledge offers the solution to these contradictions. Yet, people have a hard time changing their beliefs, even when the proof is there.
The Vedic technology to bring about world peace is available. It is a technology as precise as any scientific technology in the world. Mount Soma (like everything else) can be viewed from different perspectives. Though it is so much more, Mount Soma can be viewed as a research 'laboratory' which adheres to the precise principles of Vedic technology to create a source that radiates peace and harmony to the world.
People marvel over the fabulous ancient ruins around the world: civilizations with phenomenal technologies we today cannot comprehend. They are technologies that have been lost to time. However, what perhaps makes Vedic Knowledge unique is that its knowledge has been passed on in the Vedic Literature and preserved by the Vedic Pandits.
Mount Soma is a place where we intend to complete the implementation of the Vedic technology for world peace. It is not based upon superstition, blind faith, or dogma. It is developed with the same (or even more) rigor as seen in Western science.
The technology is there, available in the Vedic literature. So, why would we not research it? Why would we not try it out? Even great physicists in the West, like Einstein, Oppenheimer, Schrodinger, Heisenberg, etc., studied it. So let's build it and see!
A Vedic expert (Sthapati) from India visited Mount Soma. He began to cry with tears of joy about what we are building here. He said that such a place exists nowhere else in the world.
At first I questioned him saying, "How could that be? There are so many magnificent temples in India!"
He insisted that the aspect of Vedic technology we are employing was not used there.
I thought for a moment, and then with confidence, responded, "Do you know why?"
He looked at me inquisitively.
Then I said, "Kali Yuga."
He smiled and nodded.
Kali Yuga is the Age of Ignorance.
The science was there, but nobody would believe that Marconi's radio could work until he built it. Scientific evidence supports that the technology to build what we are building at Mount Soma will work. Together we can build it. We have only to make it happen!! Why would we not give it our best?!
© Michael Mamas. All rights reserved.
Share this blog...
Matt	on February 17, 2019 at 7:26 pm
Powerful!
lori	on February 18, 2019 at 9:37 am
Mary	on February 18, 2019 at 12:17 pm
Gail	on February 18, 2019 at 1:56 pm
Such a great timely reminder
eri	on February 19, 2019 at 6:41 pm
But why enlightened gurus didnt make these in India?
And about the ancient ruins we cant comprehend… yes like in Ethiopia, underground carved from stone church, how did they do that.
The pyramids etc etc
Michael Brandon	on February 23, 2019 at 5:06 pm
A true blessing to live in this time..with this knowledge..with this teacher..with this awareness.
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redpajama_set_name: RedPajamaCommonCrawl


text:
"For the most part you are a goodly distance - and I guess a goodly distance was ten metres. How long does it take a tiger to do ten metres? " -- Russell Crowe on making Gladiator
INDISCREET: DVD
Anna Kaplan (Ingrid Bergman) may be a successful stage actress, but her love life is a flop. When she meets her brother-in-law's good looking, suave colleague Philip Adams (Cary Grant), there is an instant attraction. But there is a snag. Philip tells Anna early in their relationship that although he has split from his wife, she will never give him a divorce. When Anna discovers that Philip is not married at all and that his pretence is simply to protect his bachelor status, she decides to teach him a lesson.
'How dare he make love to me and not be a married man,' Ingrid Bergman's Anna retorts when she discovers her lover is not married man after all. It might be a little dated, but Indiscreet, with its delightful pairing of Cary Grant and Ingrid Bergman, remains a witty, sophisticated romantic comedy with timeless appeal. Love and revenge are the main juicy ingredients and Bergman and Grant play their parts with panache and elegance.
The story is loosely based on Norman Krasna's stage play Kind Sir, and here the New York setting has been replaced by London, with Grant's diplomat commuting each weekend from Paris. Back onscreen together for the first time since Notorious with roles that are worldly and mature, there's a playfulness that is a joy to watch. Love makes for an alluring tug of war, but revenge is the piece de resistance.
Strong supporting performances from Cecil Parker and Phyllis Calvert as Anna's brother in law and sister, who are her confidantes in matters of the heart. How could we forget Anna's shock when Calvert's Margaret drops the bombshell, nor the carefully planned theatrical scenario when Anna plays out her revenge. Watch out for David Kossoff as the chauffeur who agrees to play the part of the make-believe lover and Megs Jenkins as the loyal maid. Stanley Donen (who later directed Grant in Charade) makes the most of the material and the result is a genuinely funny, breezy classic romance for grown ups. The haunting title tune by Sammy Cahn and Jimmy Van Heusen (recorded by Frank Sinatra four years later) acts as a reminder of the bitter sweetness of the film's theme.
INDISCREET: DVD (PG)
CAST: Ingrid Bergman, Cary Grant, Cecil Parker, Phyllis Calvert, David Kossoff, Megs Jenkins
PRODUCER: Stanley Donen
DIRECTOR: Stanley Donen
SCRIPT: Norman Krasna
CINEMATOGRAPHER: Freddie Young
EDITOR: Jack Harris
MUSIC: Richard Rodney Bennett
PRODUCTION DESIGN: Donald M. Ashton
PRESENTATION: 16x9 Anamorphic 1.78-1
DVD DISTRIBUTOR: Paramount
DVD RELEASE: April 19, 2007


Susan Clark has been in the multi-million dollar sale tier and can show you what over 25 years in the business has to offer.
Susan has successfully sold millions of dollars in real estate in Pennsylvania and Delaware; she is a real estate veteran looking for clients who are motivated to sell. Her expertise in the resale market is very extensive and she has been visiting the Bethany Beach area for well over 30 years.
She has been awarded numerous honors in her home market of Pennsylvania including being in the top 5% Sales Tier in both Lebanon and Dauphin County as well as being in the top 1% in home sales at Shaffer & Son, Remax & Jack Gaughen.
Lighthouse Realty Group uses internet marketing strategies to get your home in front of thousands of buyers every single day. Your home will be on Zillow, Realtor, Trulia, Facebook & of course on Susan's website as well.
Susan Clark has a reputation of trustworthiness, adherence to all local laws and self motivation. She doesn't shy away from hard work no matter the situation or level of difficulty. She acts proactively to help hold deals together by staying in communication with her sellers, the listing agent, the home inspectors and the bank approving the buyers loan.
NO LONG TERM CONTRACTS. Lighthouse Realty doesn't believe in making people stay where they aren't happy. If you feel you're not being represented to your satisfaction, you will not be held to any kind of binding agreement and will be released within 48 hours of submitting your request for release. However, we're sure you will be completely happy with the service and attention we pay to you and your property.
Looking to sell? Contact Susan Clark today!


text:
Frondiculina es un género de foraminífero bentónico considerado un subgénero de Frondicularia, es decir, Frondicularia (Frondiculina), cuyo nombre fue sustituido por el de Frondiculinita de la familia Ichthyolariidae, de la superfamilia Robuloidoidea, del suborden Lagenina y del orden Lagenida. Fue considerado como sinónimo posterior de Lingulina de la Subfamilia Frondiculariinae, de la Familia Nodosariidae, de la Superfamilia Nodosarioidea. Su especie-tipo era Frondicularia (Frondiculina) dubiella. Su rango cronoestratigráfico abarcaba el Liásico (Jurásico inferior).

Clasificación
Frondiculina incluía a la siguiente especie:

 Frondiculina dubiella †, también considerado como Frondicularia (Frondiculina) dubiella †, y aceptado como Frondiculinita dubiella †

Bibliografía

Géneros de Lagenida
Géneros de foraminíferos bentónicos

redpajama_set_name: RedPajamaWikipedia


Calling all pizza lovers in the Kingdom of Cambodia! Dominos Pizza is now open in Phnom Penh!
January 22, 2015 marks the grand opening of Dominos Pizza located at BKK1 on Street 51 (Pasteur) corner of Street 288. We came there past 8PM and boy, the store was crowded with a lot of people! The queue was long and the wait was pretty much the same too. But what can you expect, the pizzas are 50% off!
We ordered 2 boxes of Pepperoni, 1 box of Margherita, 1 box of Hawaiian, 2 boxes of Meat Lover, and 1 box of Meatzza, and they all tasted great. I think Pizza Company and Pizza World will get a tougher competitor now with Dominos Pizza in town.
By the way, Nestle and Rex are doing a sideline job at night. They do mascots too if you like hahahaha.
Little kids have a special place to watch the pizza makers inside Dominos kitchen. Aiken loves watching how the doughs are being flipped.
Wondering how long it will take your pizza to make? Just look at the LCD screen located near the counter and you'll know exactly when yours will arrive.
To order Dominos Pizza, call 1-800-2020. By the way, their 50% off promo is going to last till January 31, 2015.


October 2018 – Let's Paint Nature!
Let's Paint Mushroom at Work – Speed VIDEO!
https://youtu.be/H61lgvD4Itk It's so much fun to hike in the woods and find mushroom treasures! You never know when you'll find them or what types you'll find...that makes it exciting!! When you do find them, why not paint them? Spend a few minutes with me as I show you how to make a quick watercolor sketch … Continue reading Let's Paint Mushroom at Work – Speed VIDEO!


text:
module TextExtractor
  module Formats
    module Doc
      extend ActiveSupport::Concern

      def extract_text_from_doc(original_file_path)
        run_shell(%{antiword #{ to_shell(original_file_path) } > #{ to_shell(text_file_path) }})
        text = extract_text_from_txt(text_file_path)

        if text.length < TextExtractor.configuration.min_text_length && text.include?('[pic]')
          extract_text_with_docsplit(original_file_path)
        else
          text
        end
      end

      def self.formats
        {
          '.doc' => 'doc'
        }
      end
    end
  end
end

redpajama_set_name: RedPajamaGithub


text:
myKPS
Contact The Ethics Institute
Directions to The Ethics Institute
Ethics Institutearrow
About Ethicsarrow
The Ethics Institute Mission
History of The Ethics Institute
Visit The Ethics Institute
Student Programsarrow
Ethics Student Programs
National High School Ethics Bowl
Middle School Ethics Bowl
Ethics Summer Programsarrow
Ethical Entrepreneurship
Ethics in the Arts
The Bioethics Projectarrow
Bioethics Project Topics
Parent and Community Eventsarrow
Training and Curriculumarrow
Workshops at KPS
School Programming and Consulting
About Ethics
Ethics Summer Programs
The Bioethics Project
Parent and Community Events
Training and Curriculum
The mission of the Ethics in the Arts program is to explore ethical issues in the arts, through art making and art viewing. This program encourages ethical dialogue and reflection around ethical questions that arise organically when interacting with art. Through hearing from visiting artists and exploring art through dynamic activities, participants engage in meaningful exploration of fundamental concepts that enrich their understanding of themselves, the world, and their relationships with others.
The Ethics and the Arts program supports participants as they broaden their awarness of ethical issues and use their unique voice to explore ideas through art and dialogue. Some questions that often arise are:
Identity: What role does a person's identity have in their art and our viewing of their art?
Risk-Taking: What does taking risks look like for artists, and when does this cross an ethical line?
Power: What is the power of art? How should artists use this power? What kind of power do consumers and viewers of artwork have? What role do museums and society have in legitmatizing art?
Responsibility: What obligation, if any, do artists have to others, like their subjects, viewers, and the general public? How should we engage with an artwork? Can there be plagiarism within art?
Intention vs. Impact: How should we navigate tensions between the impact of the art and intention of the artist?
Ownership: Who should own art? How should art be treated?
Concepts: How can art explore ethical themes such as justice, empathy, and freedom?
Experience: What types of experiences should art capture? Are there any topics or experiences that should not be represented through art?
Function & Purpose: What is the value of art? What is art? How should we judge or evaluate art?
About our Summer Program
The Ethical Leadership in the Arts Summer Program invites young women to engage with various art forms through the lense of ethics and leadership, with the guidance of expert artists and ethicists. This two-week program will partner with women artists who will share their experiences of navigating ethical issues within the artworld, finding their unique voice, and developing their craft. The visiting artists will also introduce participants to their art form and guide participants through creating their own original art using that particular art medium.
Program participants will explore their own identity and values through in-depth discussions around questions that naturally arise when engaging with art and the artistic process.There will also be a field trip to New York City, where participants will visit art galleries and discuss ethical and leadership issues inspired by art. At the end of the program, participants will select one artwork to prepare for presentation that captures themselves as ethical leaders or explores the ethical and unethical dimensions of leadership.
This program will allow students to:
As part of the ethics and the arts program, participants can expect to:
Explore the intersections between art and ethics
Examine their personal identity and values
Develop a robust understanding of ethical leadership and what it means to be a woman in the arts
Experience different art media, with guidance from expert artists
Engage in various artistic expressions that encourage positive risk taking
Feel empowered as ethical leaders and artists
Develop critical and creative thinking
Enhance collaboration and communication skills
Ethics in the Arts Summer Program
Center for Innovation, Kent Place School
The program is open to rising 9th through 12th-grade girls, both KPS, and non-KPS students. This is an all-girls program.
$2195 includes all art materials for the program, lunch, and field trip. Scholarships are available.
Participants who enroll are asked to commit to the full program
2020 Facilitators
Dr. Karen Rezach
Karen Rezach joined the Kent Place community in 2000, and is the Director of the Middle School and the Director of The Ethics Institute. She has a master's in divinity from Yale University, an EdD from Seton Hall University and is an ordained Episcopal priest. Her doctoral research, "Spiritual Leadership as a Model of Effective Leadership in Independent School," explores the ways in which educators can bring values and moral awareness in non-religious ways into a variety of school settings. Dr. Rezach has over 20 years of experience as an independent school administrator, including director of the middle school at Kent Place and assistant head of school at St. Philip's Academy in Newark, NJ. She has been teaching and facilitating workshops on ethics for more than a decade.
Ms. Ariel Sykes
42 Norwood Avenue, Summit, NJ 07901 | (908) 273-0900
The Ethics Institute at Kent Place School is a first-of-its-kind institute at the primary and secondary school level. We believe that promoting the process and practice of ethical thinking and decision-making prepares people of all ages to be effective leaders and compassionate citizens.


Economic crisis is identified as the variability in income segment and streams figures because of unknown modifications in the asset price tags, rates of interest and change premiums (Kaen, 2005). The present economic crisis has started in August 2007 and also been taken into account the toughest financial crisis from the Cool Despair by George Soros, Alan Greenspan, Joseph Stiglitz, Jean Claude Trichet, as well as World wide Monetary Account. This financial crisis which set out in industrialized locations very quickly distributed to appearing target market and having economies. Buyers drawn funds from different countries, even people who have small to medium sized levels of defined possibility, and triggered ideals of stocks and shares and household currencies to jump. Also, slumping exports and commodity selling prices have combined with the woes and forced financial systems global choose to into recession or into a time period of less quickly economical growing. Amongst the components that offered to the current financial disaster are mentioned: increasing creativity in cash equipment and expanding complexity; unacceptable control and administration of stock markets; unhealthy or lax threat administration tactics at bankers in addition to financial institutions; extra intricacy of personal technologies; income promote speculation; predatory loaning practices; a mix of cyclical and structural conditions (Dianu and Lungu, 2008). But the existing financial disaster is global in nature, the seeds happen to be planted on the politics and affordable areas of the us of The us. One of the initial seeds was the deregulation of the usa financial arena in 1999.
The Usa Congress handed down the Gramm-Leach-Bliley Personal Suppliers Modernization Act of 1999 which detached a final barriers between these industrial and cost financial institutions. This induced competition inside loaning promotes to help increase greatly. The banks began to contend on issuing increasingly lending options by providing lower statistics and different home mortgage sorts (P. 114, Moffett, Stonehill And Eiteman 2011). Another seed from the up-to-date financial crisis was put in 2000 should the dot-com bubble broken. Technologies organisations had in your previous ages come across their segment principles escalate as more and more brokers jumped regarding the bandwagon. In 2000, the technological advances manufacturers possessed grown to be so treasured, that neither their the latest nor long run commercial foundation would be able to sustain our prime segment ideals.
Simply because earliest buyers begun to drop self esteem, they begun to cash money out. This produced a snowball result. Absolutely everyone began to retail their offers together with the current market beliefs plummeted, inducing the whole entire United States Of America economic system to initiate a economic downturn. The Federal Hold begun to low the interest to bring back the economy and quickly after 18 months it was subsequently around 1% - a levels which was continued until the summer time of 2004 (Plesner 2007). Following a dot-com bubble broken in 2000, shareholders set out to watch out for alternate choice market sectors where they could commit their money. Quite a few investors chosen the us real estate market together with the capital very quickly started to circulate toward simultaneously residential and commercial real estate market (P. 114, Moffett, Stonehill And Eiteman 2011).
The Usa housing market was a wonderful alternative option to the engineering community, as it experienced provided once-a-year increase deals of 6% simply because the very first quarter of 2000 - a progression rate that ended after the initially quarter of 2006 (Plesner 2007). The amalgamation of minimized interest charges, large amounts of investment capital along with deregulation of the United States bank area meant the usa housing pricing started to soar. America 1dayessay.com consumer banking segment distributed large numbers of funds, but some people even obtained a new home while others used out a 2nd mortgage loan. Right after the new deregulation, North America banks will no longer were forced to maintain your financial products on his or her steadiness sheet. Actually they had the opportunity of selling the financial loans off of, by bundling them into an alternate consumer credit derivative, and selling it to pension money among other institutional brokers with cost lenders (Plesner 2007). This exercise meant banking institutions could create funds and resell them, making revenue at the costs it will cost. Having the separation of issuing funds also, the associated potential for go into default, Usa banks have already been never as truly interested in only delivering financial products to the people folks with the highest credit score. They as a result commenced, to a even higher college degree, to make subprime home mortgages, which might be personal loans to those with minimized earnings or that normally you should not get a leading mortgage (P. 77, Ostrup 2010).
Even though it comes with an some other potential risk connected with a subprime mortgage loan compared to a major house loan, United States Of America lenders have no obstacles in locating shareholders wanting to spend money on. Even when subprime home mortgages conventionally made up 7-8Percent of the impressive mortgages by 2007, they constituted exceeding 65% of the a bankruptcy proceeding filings by individuals ultimately of 2008 (P. 115, Moffett, Stonehill And Eiteman 2011). Across the globe, the bank arena was frequently stricken by the 2008 financial crisis. From presenting log net income in their a long time previous the economic crisis, the field often times though begun to suffer from document deficits all through the financial disaster. There initially were nonetheless variations in the magnitude to which banking institutions were actually influenced by the financial crisis. Some bankers collapsed, some merged with other people to live and some suffered a reducing productivity.
But even one of the lenders that only dealt with a diminishing results, can tremendous variances be found. This universal capital and financial crisis has brought around the open public consciousness a lot of different arcane economic stipulations ordinarily restricted to the web address of traders and regulators. These stipulations lay in the middle of each of those figuring out and solving this financial crisis and include: Endemic chance: The risk the disaster of just one or a pair of sector contributors, for example, main financial institutions, will reverberate via the economical purpose and scheme significant difficulties for individuals in other areas. Deleveraging: The unwinding of debts. Enterprises get to order possessions that enhance their growing opportunity or elevate dividends on investment. Deleveraging lessens potential risk of default on financial obligation and mitigates damages, but when it is completed by marketing and advertising possessions at a discount, it might just depress advantage and secureness values and result significant cutbacks. Hedge financial resources can be remarkably leveraged. Procyclicality: The possibility for field individuals to bring behaviors over the operation spiral that improves the increase and bust issues.
Popular home equity: A cross connecting frequent financial obligation and investment. It allows the holder claims, just before those of standard stockholders, on earnings in addition, on assets in the eventuality of liquidation. Most chosen inventory compensates a fixed dividend. Owing to the strain studies at the begining of 2009, some banks will boost their capital starting point by changing preferred value to widely used stock options. Collateralized credit debt responsibilities: a type of organized asset-supported basic safety in whose significance and obligations are based on a collection of preset-income hidden possessions. Collateralized loan commitments influenced by sub-major financial loans have been at the heart to the international financial crisis Credit ratings go into default switch: In cases where a confident consumer credit party takes place maybe a default inside of an actual personal financial musical instrument. The worldwide crisis causes immense dislocation and failures into the industrialized locations on the planet, however in the majority of the creating locations it is really pressing men or women strong into poverty, a credit history derivative agreement concerning two counterparties in which the purchaser works to make recurrent funds into your owner and then in go back gets a sum of cash. The catastrophe has been carried in the poorer locations over decreasing exports, going down investment deals, stop migration, and shrinking remittances from citizens earning a living in another country.
The decline in taxation income as a result of the slowdown in financial physical activity also is expanding opponents throughout nations for hard to find price range income and disturbing steps concerning the allocation of federal tools. This spending budget constraint relates locally to being able to pay for standard improvement help to poorer countries along with software applications focused on alleviating poverty. The financial disaster of the past several years has highlighted the demand for upgraded endeavor ample associated risk management methods. With the intention to deal with the biggest shortages outlined from the actual financial crisis, the finance companies must lay a bit more increased exposure of anxiousness evaluation techniques and rethink the pay plan. If you want to improve with the revolutionary endemic risks, in order to guarantee the budgetary consistency and also to contribute to the international cash governance, in the mean time, the progression of world-wide financial markets enforces an modernizing within the actual physical legislation and arrangement equipment. Just one particular regulatory company in each and every one land may be accountable for overseeing the security and well-being of our altogether money unit. The role for this systemic regulator ought to include getting, considering, and reporting more knowledge about significant connections between the two and hazards between finance companies; using and constructing systemically reactive ordinances, among them investment capital criteria; and coordinating within the monetary experts besides other government departments in managing endemic crises.


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Home Arts & Leisure Appliance maker goes green
Appliance maker goes green
July 1, 2021 | 12:03 am
ONE wouldn't normally think of appliances as weapons against climate change and pollution, but here we are, about to extol the green virtues of some.
During last week's Beko Global Sustainability Virtual Launch, Beko presented its BioCoffee Espresso Machine, AutoDose Dishwasher, EcoTub Washing Machine & Washer Dryer, GreenDry Tumble Dryer, EcoFiber Oven, and BioCycle Refrigerator. In a speech prior to the presentation, Hakan Bulgurlu, CEO of Beko parent Arçelik, discussed their environmental thrust. "You can see our vision statement behind me: 'respecting the world; respected worldwide.'"
"We live by this statement in every area of our business and our personal lives," he said.
He talked about the commitments they have made to reduce their emissions: these include a commitment to reduce its Scope 1 and Scope 2 emissions by 30% by 2030, and reducing Scope 3 emissions by 15%. Others include using 40% recycled plastic, recycling 99% of manufacturing waste, and reducing water consumption by 45%. They have also been carbon-neutral for the past two years.
"Many of my contemporary counterparts claim that they will become carbon-neutral by 2030, 2040, some even 2050. All I'm saying is that it's too late. We have to act today and we have to put our responsibility towards the planet before anything else," said Mr. Bulgurlu.
The espresso machine, for example, is developed with bio-composite technology, and each Espresso Machine is made of five cups of coffee residue, reducing its carbon footprint by 5% in bio-composite parts. The dishwasher, meanwhile, dispenses the right amount of detergent, and in each wash, up to 28% less detergent is used.
The fridge is made from durable components composed of bio-based plastics, and the egg trays are made, appropriately, from eggshell wastes and bioplastics (each egg tray boasts of using eggshell waste from five eggs). The fridge's fan cover is made of 100% bio-based plastics from sustainable resources such as corn starch or sugarcane, and the door seal is made from 25% bio-based (soybean oil) materials.
The oven, meanwhile, uses a sustainable material from recycled fishnet waste and industrial thread waste, and 50% of the BI Oven inner display cover is made from industrial thread waste. The dryer uses recycled plastic in 15% of its plastic parts on average, including in the water tank housing and back cover.
Finally, the washing machine contains approximately up to 60 recycled 0.5L PET bottles, particularly in the tubs of the washing machines and washer dryers on certain models.
Why do they do it?
Mr. Bulgurlu says that some companies believe that going green costs money. He counters: "There are many, many ways where you can do the right thing, without any cost."
"Doing the right thing is also profitable, or can make you more cost-competitive. We have lived this example, and we want to share this example with many companies."
Adapting their methods and products for a pro-environment policy is also an adaptive measure, he said. "I believe that the companies that do the right things by the planet will get ahead, and the ones that don't will disappear. Sustainability doesn't only mean doing the right thing by the planet and the environment — it also means making your business more sustainable.
"By doing right, you will win; by doing wrong, you will lose."
The brand has been in the Philippine market for five years and is currently available in select SM Appliance Centers, Robinsons, All Home, Anson's, Savers, Great World, and Manila Imperial Appliances Stores, and Asian Home Appliance Center in Cebu, and in e-commerce partners: Lazada and Household Appliances Trading (hat.com.ph). For information, follow @bekoph on Facebook and Instagram or go to http://www.beko.ph. — J.L. Garcia
Eagles' D faces ultimate test in Super Bowl: Chiefs' Patrick Mahomes February 9, 2023 | 6:07 pm
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Australia orders checks on Chinese-made cameras in defense offices February 9, 2023 | 5:16 pm
Biden says he sees no recession in 2023 or 2024 February 9, 2023 | 5:15 pm
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More public funds seen needed for cancer care in PHL February 9, 2023 | 5:08 pm
IP waiver for COVID goods seen affecting trade deals
Carpenter versus plunderer
8990 Holdings launches high-end property brand


Block paving is designed to be both attractive and flexible, allowing you to make your choice of design.
With their range of subtle colour and interesting laying patterns, paving blocks not only enhance your home, but can increase the value of your property.
Virtually maintenance free, the dense aggregate block used by Interlay Driveways Limited are designed to withstand extremes of temperature and vehicle loading.
Unlike many conventional surfacing materials such as asphalt or concrete, paving blocks will last for many decades under normal domestic use.
Because they are dry laid, individual blocks are easily lifted and replaced for access to underground services whenever necessary.


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Mahalo Haunani Kay Trask
"Our culture has to be the core of our mana." Haunani-Kay Trask
We invite you to take this time to remember and uplift the work of Dr Haunani-Kay Trask: Native Hawaiian, Mana Wahine, Activist, Author, Kumu, Poet, Scholar, and Visionary.
Haunani-Kay was a person whose clear understanding of our history moved her to her own form of personal action. Now that we are all in possession of our own levels of understanding, what can we do with this knowledge and insight?
Ideally, we let our leo be heard, understood, shared. We work together with insight, compassion, and understanding. We have evolved from our own perception of ourselves as angry to reasonable people resolute in truths that are steady and will prevail.
We continue to do what Haunani-Kay did in her own way: we work to heal intergenerational cultural trauma through steadfast truth and aloha.
Her Legacy
Born in 1949 to Haunani and Bernard Trask, Haunani is a descendant from Aliʻi Nui lineages of Māui & Kauʻi. Nurtured and surrounded by a family of Hawaiian politicians, Trask would go on to champion the movement for indigenous rights, Hawaiian Sovereignty, and the rights of the Hawaiian people in many ways. A student of knowledge, she earned her BA, MA, and PhD in Political Science from the University of Wisconsin-Madison. She became a Professor at the University of Hawaiʻi Mānoa, and would eventually become a founding member and prominent visionary for the Kamakakūokalani Center for Hawaiian Studies.
Learn more about Dr. Traskʻs academic accolades & work here:
In memoriam: Haunani-Kay Trask, exemplary Native Hawaiian scholar
Outside of her academic work, Dr. Haunani-Kay Trask's work also includes, but is not limited to: fighting against gender-based violence, opposing gentrification of Waimānalo, fighting evictions of Native Hawaiians from Sand Island, and being a critical voice in challenging colonization of Indigenous land.
Locally-owned news-site Civil Beat shares a beautiful tribute to Dr Traskʻs work and legacy here:
The Passing Of Haunani-Kay Trask And The Uplifting Of A Nation
Below are a list of her selected works:
Eros and Power: The Promise of Feminist Theory (1986)
From a Native Daughter: Colonialism and Sovereignty in Hawaiʻi (1993)
Light in the Crevice Never Seen (1994)
Night Is a Sharkskin Drum (2002)
Kūʻē: Thirty Years of Land Struggle in Hawaiʻi (2004)
Act of War: The Overthrow of the Hawaiian Nation (documentary film, 1993)
Haunani-Kay Trask: We Are Not Happy Natives (educational CD, 2002)
Her Poetry
Of poetry, Trask explains that "The genre of poetry can use everyday statements for political analysis. A metaphor can be more political than a declarative statement because it gets to the heart of the feeling, and a metaphor says many things in a small moment." Below is her poem entitled Pūowaina: Flag Day from her book "Night Is A Sharkskin Drum":
Feel free to download this poem as well as her pieces Nā ʻŌiwi & At Punaluʻu here.
Her Leo
Trask will forever be remembered as a fierce warrior at the forefront of the fight for Hawaiian Sovereignty & the self-determination for the Hawaiian people. She was never one to stay quiet, evident in her always articulate and strong speeches- speeches that will touch the hearts and minds for many years to come.
Amongst many of her speeches, there is one in particular that has and will continue to transcend time. Trask, standing tall at the rotunda at ʻIolani Palace on January 17, 1993, shouts:
"We are not American! We will die as Hawaiians, we will never be American!"
Please join us in celebrating the power of her leo with just a few of her numerous interviews, talks, and rousing speeches:
ʻWe Are Not Americanʻ ʻIolani Palace 1993
Journey to Justice : A Conversation With Dr. Haunani-Kay Trask
Haunani Kay Trask - Powerful Speech at University of Hawaiʻi November 1990
Haunani Kay Trask On the Word ʻHaoleʻ - Source: Island Issues: Racism and Academic Freedom. VHS, 1990
The Nā Mea and Native Books ʻOhana has proudly housed three of Traskʻs work: From a Native Daughter and Night is a Sharkskin Drum and Kūʻē: Thirty Years of Land Struggle In Hawaiʻi. While the first two titles are currently sold out, we are hoping to have re-stocks in the near future. Until then, please enjoy the Kūʻē: Thirty Years of Land Struggle in Hawaiʻi. as well as resources above in remembrance of our fearless advocate.
July 05, 2021 — Mahina Choy-Ellis
Tags: Haunani Kay Trask Hawaiian Practitioner Hawaiian Workshops Hawaiiana Kumu Na Mea Hawaiʻi Trask


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Tom Brady says he 'probably knew' before last season that 2019 would be final year with Patriots
Chris Bumbaca
The writing was on the wall for the eventual Tom Brady-New England Patriots breakup. And the former Patriots quarterback saw it as early as the beginning of last season, he said Wednesday on "The Howard Stern Show."
"I don't think there was a final, final decision until it happened," Brady said of his decision to leave New England and sign with the Tampa Bay Buccanneers. "But I would say I probably knew before the start of last season that it was my last year. I knew that it was just — our time was coming to an end."
Prior to last season, Brady and the Patriots reworked his contract so that for the first time in his career he could become a free agent at the end of the season, as it restricted the use of the franchise tag.
New England struggled offensively as the season went on, exacerbated by the lack of weapons at Brady's disposal. The three-time MVP finished with his lowest passer rating since 2013, and the Patriots made their earliest playoff exit in a decade.
Brady will certainly have upgrades in Tampa Bay with receivers Chris Godwin and Mike Evans, but it wasn't the deciding factor in his decision to pick Tampa Bay. Over the course of his two-hour interview, he mentioned that the weather and a new challenge were other components — on a list that reached almost 20, Brady said — behind the move.
Follow every game: Latest NFL Scores and Schedules
Ultimately, it was "time to move on," Brady said.
"I don't know what to say other than that," Brady said. "I accomplished everything I could in two decades with an incredible organization, an incredible group of people, and that will never change. No one can ever take that away from me, no one can ever take those experiences or Super Bowl championships away from us."


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Tom Lee: the bullish mood has returned to the cryptocurrency market
Tom Lee | Fundstrat Global Advisor Managing Partner
Fundstrat Global Advisor Managing Partner and cryptocurrency enthusiast Tom Lee believes that after the recent strong growth in Bitcoin, the bulls have again begun to dominate the market.
Last Saturday, the rate of the first cryptocurrency grew by 30%, showing a record growth over the past few years. The price of Bitcoin even exceeded $ 10,000. Now trading is at around $ 9,400, but according to Tom Lee, in the near future the rate may again go up.
Heath Tarbert: Ethereum futures will appear in 2020
Heath Tarbert | Head of CFTC
Heath Tarbert, chairman of the U.S. Commodity Futures Trading Commission (CFTC), believes that Ethereum futures will be launched as early as next year.
During his speech at Georgetown University, Tarbert expressed the view that air futures will appear within 6-12 months, however, the launch of such products does not mean an immediate growth in the cryptocurrency industry.
ico news Cryptocurrency ICO blockchain CFTC USA Ethereum
John McAfee: What's the main issue with decentralized exchanges?
John McAfee |
Well-known American entrepreneur and cryptocurrency enthusiast John McAfee believes that stablecoins are only an intermediate solution for decentralized exchanges.
McAfee mentioned the main problems associated with the conversion of fiat and cryptocurrencies on exchanges. According to him, stablecoins are not quite suitable for use on decentralized exchanges, given that such exchanges do not collect any information about users, including their names, addresses, insurance number, email, etc.
ico news Cryptocurrency ICO blockchain John McAfee
The TON launch date has been postponed to April 30, 2020
Telegram sent a new letter to its investors, in which it says that the launch of the TON blockchain is postponed for six months. The launch deadline is now set for April 30, 2020. Investors can exit the project by taking 77% of their investments.
In the letter, the company refers to a proceeding with the US Securities and Exchange Commission (SEC), which considered Gram tokens to be unregistered securities. The regulator managed to achieve a time limit on their sale and use, which interferes with Telegram's plans.
blockchain Cryptocurrency news Telegram TON SEC
Brian Kelly: I'm less concerned that you need a Bitcoin ETF at this point in time
Brian Kelly | The founder of BKCM
Founder and CEO of BKCM cryptocurrency investment company Brian Kelly believes that the industry does not currently need Bitcoin traded exchange traded funds (ETFs).
Kelly emphasized that Bitcoin ETFs are not needed for the development of the industry, since the first cryptocurrency is already available for trading on regulated platforms such as Fidelity and TD Ameritrade.
blockchain Cryptocurrency news Bitcoin ETF Fidelity
TON under attack: SEC accuses Telegram of illegal ICO
SEC accuses Telegram of illegal ICO
The American regulator dealt an unexpected blow to the upcoming TON blockchain, accusing Telegram of unregistered sale of tokens a year and a half after the ICO.
The U.S. government seems to consider corporate cryptocurrencies more dangerous than public blockchains. Until now, all the claims of regulators and politicians have been streaming on Facebook for the Libra project, and a similar development by Telegram, which began selling $ 1.7 billion in tokens in January and March 2018, has remained in the background. However, this silence was deceiving.
blockchain Cryptocurrency news SEC Telegram TON
Telegram publishes "Gram" cryptocurrency wallet rules
Telegram introduced the first edition of the rules for using the "Gram" cryptocurrency wallet, which was developed by the company to store the tokens of the same name.
The rules say that the Gram wallet is provided by Telegram FZ-LLC and will be built into the Telegram messenger, and is also available as a separate application. One of the main points in the rules is the age limit - only people over 18 years old can use the wallet. In addition, only citizens of countries in which it is not prohibited by law can use the Gram.
blockchain Cryptocurrency news Telegram ICO TON Gram
Former U.S. Attorney General: Blockchain transactions - "digital bread crumbs"
Kathryn Haun | Former U.S. Attorney General and partner at Andreessen Horowitz
Former U.S. Attorney General and current partner at Andreessen Horowitz investment company Kathryn Haun believes open blockchains help solve crimes.
Hon called blockchain transactions "digital bread crumbs," which, one by one, lead to a real criminal. In addition, law enforcement agencies are much more effective at combating crimes committed with bitcoin than monitoring traditional financial flows.
David Marcus: Existing payment systems are very limited
David Marcus | The head of the blockchain division of Facebook
The head of the blockchain division of Facebook, David Markus, in his blog spoke about the shortcomings of traditional payment systems and why you need to switch to systems based on the blockchain.
"As I've been traveling around the world meeting with key stakeholders, it became clear to me that the advantages of digital currency transmitted over a blockchain — notably when it comes to broadening access and lowering costs of financial services — weren't as evident to others as I assumed they were," Marcus began his story.
ico news Cryptocurrency ICO blockchain Facebook LIBRA
Hester Peirce: Digital assets become much more the money of the internet
Hester Peirce | SEC Commissioner
Speaking at a cryptocurrency summit hosted by Solidus Labs in New York, SEC Commissioner Hester Peirce expressed confidence that digital assets will continue to grow.
Peirce said that she looks at cryptocurrencies and other digital assets as "transactional mechanisms." At the same time, she believes that the function of preserving wealth is one of the most important for this kind of asset.
"Store of value, I think, is a really important function. I do think that we'll see as technology changes, that they become much more the money of the internet," said a member of the US Securities and Exchange Commission.
ico news Cryptocurrency ICO blockchain USA
Watford FC will add a Bitcoin symbol to players' uniforms
The symbol of Bitcoin will appear on the form of players of the English professional football club Watford. In addition, the team will begin to sell souvenirs for cryptocurrency.
The football club announced on its website that the Bitcoin logo will appear on the players form as part of the club's partnership with the Sportsbet.io betting platform. The team will also start accepting Bitcoin as payment for their souvenirs.
Changpeng Zhao: $ 1 billion cryptocurrency transaction shows bank failure
Changpeng Zhao | Binance CEO
Binance cryptocurrency exchange CEO Changpeng Zhao said a recent $ 1 billion cryptocurrency transaction shows bank failure.
He expressed his opinion on the social network Twitter in a commentary on the post of cryptocurrency enthusiast and entrepreneur Charlie Shrem. Shrem noted that the people who conducted the transaction paid $ 600 for the transfer of $ 1 billion in Bitcoin and, in his opinion, greatly overpaid.
ico news Cryptocurrency ICO blockchain BINANCE Bitcoin Changpeng Zhao
Apple Pay Vice President: We are Watching Cryptocurrencies
Jennifer Bailey | Apple Pay Vice President
While many giants from the technology sector, such as Samsung and IBM, are working intensively with blockchain technology, Apple is in no hurry to show interest in this direction.
However, this does not mean that the company is not interested in the new industry. Apple Pay vice president Jennifer Bailey told CNN that the company is eyeing the industry.
ico news Cryptocurrency ICO blockchain Apple Pay
Jack Dorsey: Bitcoin is functional, but does not work like a currency yet
Jack Dorsey | CEO of Square
Jack Dorsey, CEO of Square's payment startup and founder of Twitter, said that Bitcoin will continue to spread, but it's too early to consider currency.
In an interview with the Australian Financial Review, Dorsey said that, in his opinion, the first cryptocurrency will continue to be widely used, but so far, Bitcoin does not work as a currency.
ico news Cryptocurrency ICO blockchain Square Bitcoin
Telegram plans to begin a public test of its blockchain on September 1
According to one of the project's investors, on September 1, Telegram plans to begin a public test of its blockchain - TON (Telegram open network).
During the public test, the TON network node code and instructions for installing it should appear in the public domain. Sharding (the process of breaking transactions into separate pieces to ensure scaling) and consensus (a mechanism for confirming new blocks in the blockchain) should also be available, which will allow testing the entire blockchain.
blockchain Cryptocurrency news Telegram TON
Nigel Green: Bitcoin's new normal bottom price of $10,000
Nigel Green | Founder and CEO of deVere Group
The founder and CEO of the consulting company deVere Group, Nigel Green, believes that the new "bottom" for Bitcoin is now at around $ 10,000.
"Looking at its performance this year, I believe that the new normal bottom price for Bitcoin is $10,000. It bounces at this price. If it fluctuates below this level, it shoots back up again. We have seen this in action on Monday when Bitcoin hit $10,500 in a matter of minutes," Green said.
Marcus Swanepoel: Bitcoin is bought for investment, not for payments
Marcus Swanepoel | CEO of the LUNO
Luno cryptocurrency exchange CEO Marcus Swanepoel expressed the opinion that 90% of users use Bitcoin for investment or speculation, not for payments.
In an interview with CNBC, Swanepoel said that only a small part of the first cryptocurrency users use it for transactions and payments in the real world.
ico news Cryptocurrency ICO blockchain Bitcoin Luno
BabelFinance Research: Chinese investors await the Bitcoin growth
A survey conducted by the Chinese cryptocurrency company BabelFinance showed that local investors are waiting for the growth of Bitcoin, and according to 30% of respondents, the Bitcoin exchange rate for the year will exceed $ 30,000.
The study showed that the average Chinese cryptocurrency investor is a young, well-educated man with above-average income. More than 76.3% of male respondents have a college degree or higher.
blockchain Cryptocurrency news Bitcoin China
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