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The fact that digital trading options have fixed risks is the next advantage of choosing it. This is where as a trader, you already know in advance the risks as well as rewards which you are likely to make from this investment. Getting a risk higher than the amount invested in the business is not easy. The amount of money that is at risk is the one that you had traded with. There is a huge distinction between forex and digital trading platforms. You will be able to make approximated profits regardless of the direction in which the market graph moves. Whether the graph moves by a pip or 50 pips, you will still get instant payouts.
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I went to Big Woods in Holly Springs last Thursday and shot in a USPSA match. I had a good time and wow what an eye opener for me. I've shot thousands upon thousands of rounds in ranges but; its always been just standing there plunking away at a target. This was the first time I have moved while shooting and trying to acquire a target. If you have a chance to do any scenario shooting I highly recommend it.
Guns are bad,.....umh k .
Moving while trying to acquire a target is real world stuff.


text:
\section{Introduction}

Fine-grained visual categorization (FGVC) has received widespread attention and also gained great success in recent years, owing to the increasing popularity of deep learning methods and its powerful feature extraction ability. Unlike the classic classification tasks which usually identify objects that belong to different species like cars \cite{krause20133d}, birds \cite{wah2011caltech}, and aircrafts \cite{maji13fine-grained}, the FGVC is widely recognized due to its advantages in classifying objects in the same or closely related species, for example (e.g,) different types of birds \cite{DBLP:journals/corr/abs-2105-08788}. The challenge of FGVC tasks lies in how to distinguish different categories with high intra-class and small inter-class variance \cite{zhao2022learning,gao2002face,zhang2009local}.  To overcome this problem, various methods have been developed in the past years, especially the deep-learning-based methods, which dedicate to develop various convolutional neural networks (CNN) frameworks, have been successfully applied for increasing the feature representation abilities of the model and learning more discriminative and informative features for the FGVC tasks \cite{lecun2015deep, Chen_2019_CVPR, DBLP:journals/corr/abs-2105-08788}.

\begin{figure}[!t]
\centering
\includegraphics[width=0.45\textwidth]{./comparison.pdf}
\caption{Comparison between samples from normal fine-grained visual categorization task (aircraft) and ultra-fine-grained visual categorization task (leaf). The aircrafts/leaves in images all belong to different families (cultivars). It is clear that the Ultra-FGVC dataset has a much smaller inter-class variance compare to the FGVC dataset.}
\label{FGVC_comparison}
\end{figure}

However, FGVC methods often heavily rely on large-scale datasets for training, it is prone to fail when the training data is insufficient. Moreover, FGVC methods also tend to produce inferior performance when the inter-class variance among images is small. Yu~\textit{et.al}~\cite{yu2021maskcov} regarded these problems as Ultra-FGVC problems and first proposed the concept for Ultra-FGVC. The Ultra-FGVC tasks are more challenging than the FGVC tasks since the inter-class invariance of the former is much smaller than the latter, namely the difference between different classes is too subtle to identify, even human experts may fail to identify their visual differences \cite{YUICCV21}. Please refer to Fig. \ref{FGVC_comparison} for the comparison of the FGVC task (aircraft) and Ultra-FGVC task (leaf). Besides, the Ultra-FGVC tasks often have smaller sample amounts for training with which most FGVC models will encounter overfitting problems. In this paper, a mask-guided feature extraction and augmentation framework is proposed to solve these problems.

The proposed method mainly consists of two modules: feature detection module and feature augmentation module. An overview of the proposed framework is given in Fig. \ref{network}. In the feature detection module, to help the model quickly identify the most discriminative features from the training samples, we propose to take advantage of more annotations to guide the model training and obtain the detailed differences between categories. Specifically, it utilizes mask-guided attention features focusing on the discriminative regions to help the training process. During this process, YOLOv5 \cite{glenn_jocher_2020_4154370}, a state-of-the-art object detection model, is used to extract the regions which contain the most discriminative features of the objects. Since the objects in the Ultra-FGVC dataset are similar, YOLOv5 has superior ability in locating the desired regions under this circumstance with just a few supervised inputs. In the feature augmentation module, the feature map is generated based on original images and masked images of the selected regions. The selected regions generated by the feature detection module are used as ground truths of attention maps generation and enhance the original image. The attention mechanism has been widely used to search for informative regions in images \cite{zheng2017learning, peng2017object, wang2021mask,ZHAO2021107938}. However, most of them are used in the unsupervised learning tasks, which may not make full use of the ground truth information, resulting in incorrectly identifying the discriminative regions in Ultra-FGVC tasks. By contrast, the proposed work takes full advantage of the ground truth information learned from the feature detection module and forces the model to focus on the informative parts of the object rather than other general or non-related features.

The contributions of our work are summarized as follows:
\begin{itemize}
\item A feature detection module is developed to locate the informative parts of the objects and then generate different levels of feature maps for the classification model.
\item A feature augmentation module is developed to augment the original data based on the attention maps learned from the extracted features in the feature detection module.
\item The feature attention mechanism makes important progress to address the Ultra-FGVC problems and can be easily extended to general Ultra-FGVC tasks.
\end{itemize}

The remainder of this paper is organized as follows: Related works and Motivations are introduced in Section II. The proposed method is presented in Section III. The datasets, implementation details, experimental results, and ablation studies are given in Section IV. Conclusions are drawn in Section V.

\vspace{\baselineskip}
\section{Related Works and Motivations}

\subsection{Ultra-fine-grained visual classification}
Recently, some studies are conducted based on ultra-fine-grained visual classification tasks \cite{yu2021maskcov, wang2021mask, yu2020patchy, YUICCV21} due to its great potential for solving real-world problems by identifying objects with small inter-class variance. For example, there still remain challenges for the existing methods to distinguish different sub-class of plant cultivars which has great inter-class variances, even human experts can hardly identify different cultivars from their outward appearances. Larese \textit{et.al}~\cite{larese2014multiscale} first explored an Ultra-FGVC task using a soy leaf dataset that consists of 422 leaf images. They applied different machine learning methods (random forest, support vector machine, and penalized discriminant analysis) to classify leaves by using their vein-trait details and obtained promising results even compared with human experts. However, their dataset is not released to the public and it only contains three different cultivars of leaves, which is relatively simple to classify and cannot prove the robustness of their methods. What's more, they only extracted the vein-trait details for classification while other discriminative information was ignored including the leaf contours, colours, sizes, etc, which limited the classification performance. Recently, to solve these problems, Yu \textit{et.al}~\cite{yu2021maskcov} released a dataset and developed a MaskCOV method to address the Ultra-FGVC tasks on classifying cultivars of leaves. Specifically, they made full use of image (patch) level covariance features by splitting the images into equality sections and randomly masking or shuffling them to form new features which can help the network ignore the irrelevant parts in images and better focus on discriminative details. Thus, the performance of the MaskCOV method on these Ultra-FGVC datasets significantly outperforms that of the normal CNN methods like VGG-16 \cite{simonyan2014very} and ResNet-50 \cite{he2016deep}. In addition, both the explorations of Ultra-FGVC on leaf datasets mentioned above conclude that the main challenging of Ultra-FGVC comes from the limited number of samples for training, which means the model may encounter overfitting problems and cannot locate the most discriminative regions. Two research works mentioned above all used feature augmentation techniques to reduce the overfitting problem and achieved impressive classification improvements on Ultra-FGVC tasks. Thus, advancing feature augmentation methods are feasible solutions to address the Ultra-FGVC tasks. In the meantime, the lack of Ultra-FGVC datasets is another important factor that limits people to conduct more research works in this field.

\vspace{\baselineskip}
\subsection{Mask attention feature map}
Attention network has already demonstrated its great success in a variety of detection tasks. Sun \textit{et.al}~\cite{sun2018multi} applied mask attention mechanism on the features extracted by their one-squeeze multi-excitation module to enforce different correlation parts in the image are trained. Song \textit{et.al}~\cite{song2018mask} proposed a masked-guided attention method to assist person re-identification task. It was achieved by generating binary masks of people in the image to get rid of the image background clutters. Xie \textit{et.al}~\cite{xie2020mask} applied a mask-guided attention network to detect occluded pedestrians. In this method, the pedestrians are detected by the Faster R-CNN detector and extracted as feature maps to support the VGG-16 classification network. Wang \textit{et.al}~\cite{wang2021mask} adopted mask attention network on leaf dataset. Their method generated leaf vein structures as extra mask annotations to augment the vein features of leaf images, which improve the accuracy of Ultra-FGVC on leaf datasets by approximately 2\% compared with the baseline models. What's more notable is that there are relatively fewer studies based on the mask attention mechanism applying to Ultra-FGVC tasks in comparison with normal classification tasks. This gap is addressed by implementing a mask attention module to allow the model to focus on some target areas in the image. At the same time, the features from the original images can also be preserved.

\begin{figure*}[!t]
\centering

\includegraphics[width=\textwidth]{./network.pdf}

\caption{The Overview of the proposed network for Ultra-FGVC tasks. There are two main sections in this network. The first section is the feature detection module which is used to detect and extract features of the regions of interest. Then it creates mask images and generates attention feature maps as ground truth.
The second section is the feature augmentation module. It is used to aggregate all the attention maps to augment original features. The feature maps generated in the first section supervise the attention map generation and update the $1\times 1$ convolution layer.}
\label{network}
\end{figure*}

\vspace{\baselineskip}
\subsection{Motivation}
The proposed mask attention module is inspired by \cite{song2018mask} which demonstrated an effective way to combine the feature attention maps with the original data so that the important features can be emphasized when passing through the training network. However, they used fully convolutional networks (FCN) \cite{long2015fully} to locate desired areas in images and extracted the whole object (person) for generating mask images, which can not be adopted on Ultra-FGVC tasks. Instead of using FCN to extract target regions, our feature extraction method is more advance because it can identify different discriminative areas in the object and force the classification network learning from those regions.

Besides, from the information provided in Fig. 1 and Fig. \ref{network}, it is clear that the details of veins and outline structures in the leaf image are important to identify different leaf cultivars in the same species \cite{yu2016multiscale,yu2015leaf,gan2019automatic}. However, the existing methods in \cite{yu2021maskcov} and \cite{wang2021mask} ignored the whole contour structure of the leaf, to solve this problem, we propose to focus on both leaf and vein structures and treat them as discriminative parts. Considering that providing extra annotations for the samples will enhance the discriminative part features \cite{peng2017object,wang2021mask,yu2020patchy}, this work further explores the advantages of making using extra annotations details of leaf structures and outlines brought to the current backbone model. The annotations are used to create mask images as attention features with which the model can focus on useful regions and extract more discriminative information.

\vspace{\baselineskip}
\section{Methods}
The whole structure of the model can be divided into two parts as shown in Fig. \ref{network}. The first part is to obtain the regions with informative features. Original images are masked and only discriminative parts will be saved as extra annotations. They are used as ground truth to guide the attention maps generation. The ground truth images are used to guide the classification network and provide extra loss information so that the overfitting problem can be eased. In the following, we first introduce the feature detection module and then the feature augmentation module.

\vspace{\baselineskip}
\subsection{Feature detection module}
To obtain part-level features from the images, additional bounding boxes are used to mark the desire regions and train the feature detection model. Many strategies \cite{zhao2020mobilefan} have been proposed to detect similar objects (features) in images including YOLOv5 \cite{glenn_jocher_2020_4154370} and Fast R-CNN \cite{girshick2015fast}. The complete training process of the feature detection module consists of the following two steps.

\textit{Step 1:} Since for Ultra-FGVC tasks, the target objects always have small inter-class variations, we first manually mark out the target regions with which the detection model can easily learn the structure within the specified regions even with limited samples provided. Thus a small number of images with boundary boxes can produce promising feature detection results. The datasets used in this paper are different cultivars of cotton and soy leaves. Fig. \ref{Feature_extraction} demonstrates the results of four sample leaf images features extracted by the proposed feature detection module. Details of the leaf datasets will be introduced in Section IV. The feature detection accuracy is measured by the mean Average Precision (mAP) with the pre-defined Intersection over Union (IoU) value \cite{Redmon_2016_CVPR} being calculated by:

\begin{equation}
\begin{aligned}
& \emph{IoU} = \frac{\mathsf{Interaction \;area}}{\mathsf{Union \;area}}
\end{aligned}
\vspace{\baselineskip}
\end{equation}
where IoU is set to 0.5 when determining the mAP in this work.

\textit{Step 2:} The second step involves using the training model to find out the desired parts for all images in the same dataset. It's an efficient and labor saving way to quickly obtain informative parts from a large number of images. The masked images are converted from RGB images to binary format with only one channel. Instead of resizing the mask image to the same size as the original image, a ground truth feature map is extracted by average pooling and normalization to get rid of the noise during conversion. The final attention map can be obtained by training the subsequent classification task based on the ground truth map.

\begin{figure*}[!t]
\centering
\includegraphics[width=0.9\textwidth]{./feature_detection.pdf}
{\footnotesize \\~~~~~~~~Cotton Vein~~~~~~~~~~~Cotton Leaf~~~~~~~~~~Cotton Contour~~~~~~~~~~~~~~~~~~~~Soy Vein~~~~~~~~~~~~~~~Soy Leaf~~~~~~~~~~~~~~~Soy Contour~~~~~~\par}
\caption{Vein and contour regions extracted by the feature detection module from six different leaf samples. The left side of the image shows cotton leaves and the right side belongs to soy leaves.}
\label{Feature_extraction}
\end{figure*}

\vspace{\baselineskip}
\subsection{Feature augmentation module and classification}
As can been seen from Fig. \ref{network}, the classification network can run independently from the feature detection module. The ground truth is only used to guide attention maps generation in the training process. Given an image $\boldsymbol{\mathcal{I}}\in R^{C\times H \times W}$, its feature maps from the backbone network is denoted by $\boldsymbol{\mathcal{M}}_{img}\in R^{1\times C\times H\times W}$. The attention feature maps are obtained by first performing maximum pooling and average pooling to the original feature maps separately. The mean map $\boldsymbol{\mathcal{M}}_{mean}\in R^{1\times 1\times H\times W}$ and maximum map $\boldsymbol{\mathcal{M}}_{max}\in R^{1\times 1\times H\times W}$ from $\boldsymbol{\mathcal{M}}_{img}$ can be calculated by:
\vspace{\baselineskip}

\begin{equation}
\begin{aligned}
& \boldsymbol{\mathcal{M}}_{mean} = \frac{1}{C} \sum_{c=1}^{C}\boldsymbol{\mathcal{M}}_{img}^c,
\end{aligned}
\vspace{\baselineskip}
\end{equation}

\begin{equation}
\begin{aligned}
& \boldsymbol{\mathcal{M}}_{max} = \mathsf{argmax}\{\boldsymbol{\mathcal{M}}_{img}^c\}_{c=1}^{C},
\end{aligned}
\vspace{\baselineskip}
\end{equation}
where $\boldsymbol{\mathcal{M}}_{img}^c$ represents the feature map in the $c$-th channel of $\boldsymbol{\mathcal{M}}_{img}$, and  $C$ is the number of the image channels. Then $\boldsymbol{\mathcal{M}}_{mean}$ and $\boldsymbol{\mathcal{M}}_{max}$ are aggregated into the final attention map $\boldsymbol{\mathcal{M}}_{fea}$ which contains detailed features of the selected regions. The ground truth generated from the detection module are then used to train the final attention map and update the parameters of the convolution layer. The $\boldsymbol{\mathcal{M}}_{fea}$ can be calculated by:

\begin{equation}
\begin{aligned}
& \boldsymbol{\mathcal{M}}_{fea} = \mathsf{Softmax}(\mathsf{conv}_{1\times 1}(\boldsymbol{\mathcal{M}}_{max} | \boldsymbol{\mathcal{M}}_{mean}))
\end{aligned}
\vspace{\baselineskip}
\end{equation}

As shown in Fig. \ref{network}, the proposed network produces two different attention maps focusing on different levels of features on leaf vein $\boldsymbol{\mathcal{M}}_{vein}$ and $\boldsymbol{\mathcal{M}}_{con}$ respectively during training. Then both of them are used as attention maps to augment with the original map in a different proportion. The final feature map can be calculated by:

\begin{equation}
\begin{aligned}
& \boldsymbol{\mathcal{F}}_{final} =
\alpha\boldsymbol{\mathcal{M}}_{img} +
\beta\boldsymbol{\mathcal{M}}_{vein}\times\boldsymbol{\mathcal{M}}_{img} + \gamma\boldsymbol{\mathcal{M}}_{con}\times\boldsymbol{\mathcal{M}}_{img}
\end{aligned}
\end{equation}

\begin{equation}
\begin{aligned}
& \alpha + \beta + \gamma = 1
\end{aligned}
\vspace{\baselineskip}
\end{equation}
where $\alpha$, $\beta$, and $\gamma$ control the tradeoff among different levels of features.
The proportion can be adjusted according to the contribution of different regions. In this paper, the parameters are empirically set to $\alpha=0.3$, $\beta=0.5$, and $\gamma=0.2$ for all our experiments.

\vspace{\baselineskip}
\subsection{Proposed loss function}
The Cross-Entropy Loss $\boldsymbol{\mathcal{L}}_{ce}$ is used as the loss for classification in this paper. Additionally, the loss between ground truth features and the generated mask image are denoted as $\boldsymbol{\mathcal{L}}_{vein}$ and $\boldsymbol{\mathcal{L}}_{con}$. These two losses aim to guide the model to generate better feature maps and reduce overfitting. The Mean Square Loss function $\boldsymbol{\mathcal{L}}_{mse}$ is used to find the loss between ground truth features $\boldsymbol{\mathcal{M}}_{gt}$ and feature maps $\boldsymbol{\mathcal{M}}_{fea}$ generated from the classification network. It can be obtained by:

\begin{equation}
\begin{aligned}
& \boldsymbol{\mathcal{L}}_{mse} = \frac{1}{H\times W} \sum_{x=1}^{H-1}\sum_{y=1}^{W-1}[\boldsymbol{\mathcal{M}}_{gt}^{x,y} - \boldsymbol{\mathcal{M}}_{fea}^{x,y}]^{2},
\end{aligned}
\vspace{\baselineskip}
\end{equation}
where $x$, $y$ represent the pixel location, and $H$, $W$ indicate the height and width of the feature maps respectively. Then we obtain the following overall loss $\boldsymbol{\mathcal{L}}$:

\begin{equation}
\begin{aligned}
& \boldsymbol{\mathcal{L}} = \delta\boldsymbol{\mathcal{L}}_{vein} + \lambda\boldsymbol{\mathcal{L}}_{con} + \mu\boldsymbol{\mathcal{L}}_{ce}
\end{aligned}
\vspace{\baselineskip}
\end{equation}
where the parameters $\delta$, $\lambda$, and $\mu$ control the tradeoff among different types of loss, in this paper, these parameters are empirically set to $\delta=0.1$, $\lambda=0.1$, and $\mu=1$, respectively.

\vspace{\baselineskip}
\section{Experimental results}
In this section, we carry out experiments on publicly available databases, including CottonCultivar~\cite{yu2019multiscale} and SoyCultivarLocal~ \cite{yu2019multiscale}, for the Ultra-FGVC tasks, which serve both to demonstrate the efficacy of the proposed mask-guided feature extraction and augmentation framework and to verify the theoretical viewpoints mentioned in the previous sections. Experiments for mask detection, classifications, and ablation studies are successively implemented to evaluate the effectiveness of the proposed framework, comparing performance across various evaluation measurements, and comparing with ten recent benchmark methods including AlexNet \cite{DBLP:journals/corr/Krizhevsky14}, VGG-16 \cite{simonyan2014very}, MobileNetV2 \cite{sandler2018mobilenetv2}, InceptionV3 \cite{szegedy2016rethinking}, ResNet-50 \cite{he2016deep}, NTS-NET \cite{yang2018learning}, DCL \cite{Chen_2019_CVPR}, fast-MPN-COV \cite{8578203}, B-CNN \cite{lin2017improved}, and MaskCOV \cite{yu2021maskcov}.

\vspace{\baselineskip}
\subsection{Datasets}
Two public leaf datasets are used in this experiment. The training and testing sets are split with a ratio of 1:1 for model evaluation.

\subsubsection{CottonCultivar}
The CottonCultivar dataset \cite{yu2019multiscale} contains 80 cultivars of cotton leaf images with 6 samples per category. So there are $80\times 6$ = 480 images in total. For the vein and contour feature detection tasks, 30 images are annotated manually of target regions using bounding boxes to train the detection model. The training set for the classification task has 720 images including 240 original images from different categories and their 240 vein and contour masked counterparts. The rest 240 images are used as the testing set.

\subsubsection{SoyCultivarLocal}
There are 200 categories in the SoyCultivarLocal dataset \cite{yu2019multiscale} with each category containing six soy leaf sample images. 70 images are used for the feature detection task, while 1800 images are used for training the classification task and another 600 images are used for testing.

\vspace{\baselineskip}
\subsection{Implementation Details}
All the experimental results are implemented based on the PyTorch framework with stochastic gradient descent (SGD) being the optimizer. YOLOv5 is used as the backbone for feature detection with the size of the input image being $480\times 480$ and the same other experiment settings as \cite{glenn_jocher_2020_4154370}.

Regarding the classification model, ResNet-50 is employed as the backbone network and pre-trained on the ImageNet \cite{yang2019fairer} dataset with almost the same parameter settings of the work in \cite{Chen_2019_CVPR} except for SGD momentum of 0.938 and initial learning rate of 0.003 with a decrease factor of 10 every 100 epochs. In the training process, the masked images extracted by the feature detection module are used as the ground truth to help the model generate correct feature maps from the original images. The input images are resized to $512\times 512$ and randomly cropped to $448\times 448$. On top of that, the images may also be randomly horizontal flipped with 0.5 probability. In the testing stage, the images are resized to $448\times 448$ directly.

\vspace{\baselineskip}
\subsection{Mask Detection Results}
The performance of feature detection is measured by the mAP value. The accuracy of the detection process is listed in Table \ref{YOLOv5_performance} from which we can clearly see that the model can correctly identify most of the target regions from the leaf images and provide correct ground truth information for the classification task. The feature detection results can be clearly seen from Fig. \ref{Feature_extraction}.
\vspace{\baselineskip}

\begin{table}[!htbp]
\centering
\caption{Feature region detection accuracy.}
\begin{tabular}{lll}
\Xhline{2\arrayrulewidth}

         & CottonCultivar & SoyCultivarLocal \\
\hline
mAP 0.5   & 0.963          & 0.995            \\
\Xhline{2\arrayrulewidth}

\end{tabular}
\label{YOLOv5_performance}
\end{table}

\subsection{Classification Performance}
To evaluate the performance of the classification network with extra augmented feature information, this paper compares the classification result with ten state-of-the-art methods following the work of \cite{yu2021maskcov}. Five of those belong to normal CNN methods, including AlexNet \cite{DBLP:journals/corr/Krizhevsky14}, VGG-16 \cite{simonyan2014very}, MobileNetV2 \cite{sandler2018mobilenetv2}, InceptionV3 \cite{szegedy2016rethinking}, and ResNet-50 \cite{he2016deep}. The other five are designed for FGVC or Ultra-FGVC methods: NTS-NET \cite{yang2018learning}, DCL \cite{Chen_2019_CVPR}, fast-MPN-COV \cite{8578203}, B-CNN \cite{lin2017improved}, and MaskCOV \cite{yu2021maskcov}. The accuracy details of different methods are shown in Table \ref{performances_comparison} from which we can see that the proposed method has better prediction accuracy than any of these methods.

For the CottonCultivar dataset, the highest accuracy of the proposed method achieves 62.08\%, which is over 3.33\% $\sim$ 39.16\% higher than that of other methods. With regards to the SoyCultivarLocal dataset, the highest accuracy from the proposed method is 49.67\% with over 3.50\% $\sim$ 30.17\% increase than the other methods, which demonstrates the effectiveness of the proposed method.
\vspace{\baselineskip}

\begin{table}[!h]
    \setlength{\tabcolsep}{2.8pt}
\centering
\caption{The classification accuracies from different methods on the CottonCultivar and SoyCultivarLocal datasets. The results of benchmark methods are from a published paper \cite{yu2021maskcov}. The results of the proposed method are highlighted in bold and the best accuracy among the rest methods is marked in italics.}
\renewcommand{\arraystretch}{1.3}
\begin{tabular}{ l c c c c c}

\Xhline{2\arrayrulewidth}
 \multirow{2}{*}{Method} & \multirow{2}{*}{Backbone} & \multicolumn{2}{c}{Top 1 Accuracy (\%)}\\  \cline{3-4}
&  & CottonCultivar. & SoyCultivarLocal. \\
\hline
Alexnet & Alexnet & 22.92	& 19.50 \\
VGG-16 & VGG-16 & 50.83 & 39.33	 \\
ResNet-50 & ResNet-50  & 52.50 & 38.83\\
InceptionV3 & GoogleNet  & 37.50 & 23.00\\
MobileNetV2 & MobileNet & 49.58 & 34.67\\
\hline
Improved B-CNN & VGG-16 & 45.00 & 33.33 \\
NTS-NET & ResNet-50 & 51.67 & 42.67 \\
fast-MPN-COV & ResNet-50  & 50.00 &	38.17 \\
DCL  & ResNet-50   & 53.75 &	45.33 \\
MaskCOV  & ResNet-50   & \emph{58.75} &	\emph{46.17} \\
\textbf{Proposed Method} & \textbf{ResNet-50} & \textbf{62.08} &	\textbf{49.67} \\
\Xhline{2\arrayrulewidth}

\end{tabular}
\label{performances_comparison}
\end{table}

\newpage
\subsection{Ablation studies}
An ablation study of the method is made to further investigate the effectiveness of the proposed method on both datasets in terms of classification accuracy. Since the proposed method takes advantage of two different types of features: vein feature and contour structure, the ablation studies are performed by successively using merely vein feature, contour structure, and the combination of both of them. The performance on the backbone ResNet-50 is used as the baseline. Table \ref{ablation_comparison} shows the quantitative results of the study.

\subsubsection{Backbone+vein feature} As mentioned before, the proposed method utilizes the vein and contour details for data augmentation. The first ablation study investigates the performance of only applying vein feature map $\boldsymbol{\mathcal{M}}_{vein}$ on the classification network. Compared with the performance with the normal ResNet-50 model, the proposed method has a significant improvement in the accuracy from 53.75\% to 58.33\% on the CottonCultivar dataset, which demonstrates that the information in the vein regions can help to identify different cultivars among leaves.
\vspace{\baselineskip}

\begin{table}[!htb]
    \setlength{\tabcolsep}{2.8pt}
\centering
\caption{Ablation studies based on different combinations of attention feature maps on CottonCultivar and SoyCultivarLocal datasets.}
\renewcommand{\arraystretch}{1.3}
\begin{tabular}{  l c c c}
\Xhline{2\arrayrulewidth}
 \multirow{2}{*}{Method}  & \multicolumn{2}{c}{Top 1 Accuracy (\%)}\\  \cline{2-3}
& CottonCultivar. & SoyCultivarLocal. \\
\Xhline{2\arrayrulewidth}
ResNet-50 & 53.75	& 45.33 \\
ResNet-50 + $\boldsymbol{\mathcal{M}}_{vein}$ & 58.33 & 46.16	 \\
ResNet-50 + $\boldsymbol{\mathcal{M}}_{con}$ & 60.60 & 47.83\\
ResNet-50 + $\boldsymbol{\mathcal{M}}_{vein}$ + $\boldsymbol{\mathcal{M}}_{con}$ & 62.08 & 49.67\\
\Xhline{2\arrayrulewidth}

\end{tabular}
\label{ablation_comparison}
\end{table}
\vspace{\baselineskip}

\subsubsection{Backbone+contour structure} The second ablation study use contour structure $\boldsymbol{\mathcal{M}}_{con}$ as augmentation details to train the model. The accuracy increases to 60.60\% on the CottonCultivar dataset. The performance on the SoyCultivarLocal dataset also has a similar trend as that on CottonCultivar in the above studies. It can be seen from these analyses that the contours of leaves may have more discriminative information compared with vein structure.

\begin{figure}[!htb]
\centering

    \includegraphics[width=.48\textwidth]{./cam.pdf}

{\footnotesize ~~~(a)~~~~~~~~~~~~~~(b)~~~~~~~~~~~~~~~(c)~~~~~~~~~~~~~~~(d)~~~~~~~~~~~~~~(e)~~\par}
\caption{Class activation maps (CAM) under different conditions of ablation studies on two datasets. Applying feature augmentation to the specific region helps the model focus on that area. (a) Original images; (b) CAM by only using the backbone; (c) CAM of applying both the backbone and vein feature maps; (d) CAM of applying the backbone and contour feature maps, and (e) CAM of applying backbone, contour, and vein feature maps.}
\label{CAM}
\end{figure}

\subsubsection{Backbone+vein feature+contour structure} By combining both leaf contour and vein structure, the classification accuracy achieves 62.08\%  on the CottonCultivar dataset and 49.67\% on the SoyCultivarLocal dataset.

In addition to the numerical results, the class activation maps (CAM) \cite{zhou2016learning} under different conditions of ablation studies on two datasets are also given to clearly show what kind of features are really used for classification, as shown in Fig. \ref{CAM}. It is clear that augmenting the vein and contour structure does help the model focus on the discriminative regions, which verifies the feasibility and superiority of the proposed method.

\section{Conclusion}
In this paper, a new feature extraction and augmentation model is developed to support the Ultra-FGVC training task, which overcomes the vulnerability of the existing methods in solving problems of the overfitting and small inter-class variance. The proposed method provides extra detail and location information as masked images based on original leaf images to the classification task. These mask features augment the original feature map so that the classification network can better focus on the most discriminative part of the images. The whole process only requires a small amount of human annotation and it does not require much computational power. The performance of the feature augmentation method has a great improvement compared to other state-of-the-art CNN or FGVC methods based on the evaluation experiments on two Ultra-FGVC leaf datasets. This feature detection and augmentation strategy can also be applied to other Ultra-FGVC problems as a promising solution to improve prediction accuracy in the future.
\vspace{\baselineskip}

\newpage
\bibliographystyle{ieeetr}

redpajama_set_name: RedPajamaArXiv


In the conditions of long atmospheric drought the СО2-exchange of seedlings of silver birch (Betula pendula var. pendula) and Karelian birch (Betula pendula var. сarelica) was investigated. Values of photosynthesis of the Karelian birch and silver birch were similar. A substantial difference was found in the parameters of the light response curves of photosynthesis during a soil drought. The Karelian birch more intensively took up CO2 in condition of soil water deficit during an atmospheric drought, and the low light intensity, and this was reflected in the higher slopes of the light response curves of photosynthesis. Effective absorption of CO2 early in the morning and in cloudy and dark weather in the conditions of atmospheric drought allows to support carbon balance at the acceptable level and increase competitive ability of Karelian birch.


This entry was posted on Monday, June 30th, 2014 at 09:30 and is filed under Board Talk.	You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.
I agree with everything that Ed talks about. Being a part of the IGDA community gives me a sense that I am part of a larger, worldwide community of like-minded professionals.
Beyond this, the IGDA has to provide tangible benefits for its members. Discounts, industry events, and the recent restart of Wednesday Webinars are good examples of this.
I still think there can be more. For example, my wife is a licensed counselor. The relevant professional society for her field is the American Counseling Association. Although membership isn't mandatory for her to practice her field, membership adds to her credibility as a counselor.
The IGDA needs to find a way to add this type of credibility for its members. I recall that when I worked in previous studios, they took almost no notice of the IGDA. As a newbie in the industry, and longtime member of the IGDA, I found this surprising. Why wouldn't every studio see the IGDA as an essential organization? Nevertheless, it didn't seem like the IGDA was seen as a credible organization.
I think the IGDA has come to be seen more as a hobbyist organization, relevant to newcomers and students, but not really serving the acting professional. I think a lot of this is because the organization allows free members (unheard of in most professional societies). I'm not saying that we should remove free membership…I'm just making an observation.
It could also be true that the IGDA is seen as a threat by many studios. The IGDA is actively involved in advocacy that supports better working conditions, proper credits for those who work on games, equitable treatment of women and LGBT members, and a push for diversity.
* Studio membership: member studios would agree to a code of ethics, receive discounts for employee membership. The cost of a studio membership could be reduced based on the number of employees enrolled, offering an incentive for individual membership. Member studios could be prominently displayed on the IGDA web site and relevant publications. Promotional opportunities could be offered at a discount.
* Adjudication: individual members could file grievances that would be reviewed by an ethics committee (of course, only really effective if the individual is a member of a member studio). Studios who failed to resolve legitimate grievances could be sanctioned or lose their membership.
* Publication: the IGDA should be seen as THE go to source for information about our industry. Right now, that title is held by Gamasutra. I love Gamasutra, and I'm not necessarily advocating direct competition. But the IGDA could, for example, publish an online professional journal. This would especially fill the hole since Game Developer Magazine is no longer in print.
So there are some of my ideas. Let's make the IGDA an essential resource to our industry rather than a nice-to-have card in my wallet.
First off apologies for not getting your comment approved quicker – predictably the notification went to my spam folder.
I think you make some pretty good points in this, though one thing I would highlight is that we don't currently have a free tier of membership. That said, at a lot of our events and for a number of our resources, membership is not required to participate, which could be where that perception has come from?
Thanks for taking the time to comment! I'll be sure to make sure that these ideas are passed on!


text:
Introduction & Quick Facts
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Researcher's Note
Watch Adolf Hitler's campaign for chancellor and Joseph Goebbels's role in promoting his propaganda and terror
Adolf Hitler's campaign for chancellor is aided by Joseph Goebbels's promotion of propaganda and terror.
Contunico © ZDF Enterprises GmbH, Mainz See all videos for this article
Understand the reasons for the demise of the Weimar Republic
Overview of the Weimar Republic's demise.
An unintended effect of the anti-Young Plan campaign was to give widespread public exposure to Hitler, who used his access to the Hugenberg-owned press empire and to its weekly movie newsreels to give himself and his Nazi movement national publicity. An additional assist to Hitler's career came on October 29, 1929, with the stock market crash on Wall Street, an event that signaled the onset of what quickly became a worldwide depression. The crash had an immediate effect in Germany as American investors, anxious about their financial position, began withdrawing their loans to Germany. German indebtedness to these investors had by 1929 reached nearly 15 billion marks. Prices on the German stock exchanges fell drastically during the last month of the year. Business failures multiplied. Early in 1930 Germany's second largest insurance firm collapsed. Unemployment rose to three million during the course of the year. By the winter of 1932 it reached six million. Germany's industry was working at no more than 50 percent of its capacity, and the volume of German foreign trade fell by two-thirds between 1929 and 1932.
The first critically important political effect of the economic crisis came in March 1930 when the government coalition fell apart over the rising cost of maintaining the unemployment program adopted in 1927. The Social Democratic Party, representing labour, and the Peoples' Party, representing business, were unable to agree on the size of the government's contribution to the fund, and their coalition dissolved. When a new coalition could not be formed, parliamentary democracy in Germany came to an end.
Political instability forced President Hindenburg to invoke his emergency powers (Article 48), which he used to appoint Heinrich Brüning of the Catholic Centre Party as chancellor. For the next two years, until May 30, 1932, Brüning governed without a parliamentary majority, deriving his authority from the powers residing in the office of President Hindenburg. However well-intentioned, Brüning's deflationary economic policies were unable to stem the tide either of the depression or of its social and political ravages. His fateful decision to call for Reichstag elections in September 1930, moreover, inadvertently opened the door for the enemies of Weimar democracy. Together the Nazis and Communists gained nearly one of every three votes cast. In comparison to 1928, the Nazis increased their share of the vote sevenfold to 18 percent, while the Communists won 13 percent, a slight gain from their 10 percent share.
Although bitterly opposed to each other, during the next two years the Nazis and Communists succeeded in mobilizing the political and economic resentments generated by the depression. Hitler's charismatic appeal and the youthful energies of his movement were attractive to large segments of a populace fearful of being ruined by economic and social disaster. Hitler's record as a war veteran lent authority to the hypernationalism he expressed in racist terms. His identification of the Jew as the enemy responsible for all of Germany's ills, be it the defeat of 1918, the Treaty of Versailles, the reparations, the inflation, or now the depression, seemed plausible to many eager to find a scapegoat. The power of Hitler's appeal was reflected in the party's growing membership lists—from 170,000 members in 1929 to 1,378,000 in 1932—and in the swelling ranks of the Nazi Party's paramilitary SA (Sturmabteilung), the infamous storm troopers.
Unlike Hitler, the Communists found it difficult to extend their support beyond the German working classes. Moreover, Stalin's increasing control over the Communists limited their political flexibility. Nonetheless, their self-confidence rose substantially because the depression seemingly confirmed their prediction of the inevitable collapse of capitalism. To the Communists, Hitler and National Socialism were perceived merely as products of the last phase of capitalism.
The depression reached its depths in the winter of 1931–32. Unemployment was still rising; the succession of business failures resembled rows of falling dominoes. Brüning, helpless in the face of these problems, was dubbed "the hunger chancellor" by his critics. Some hope of breaking the political impasse came with the series of critical state legislative elections scheduled for the spring of 1932 and with the presidential election required at the expiration of Hindenburg's first term. Hitler's opponents recognized that the 84-year-old Hindenburg, now fading into senility, was their only hope to prevent Hitler from winning the presidency, and, with great difficulty, they convinced Hindenburg, who wanted to retire, to seek a second term. The year 1932 was to be one of continuous election campaigning.
Although Hindenburg was eventually reelected, a runoff was necessary, and Hitler won 37 percent of the popular vote. His larger aim, however, had been to make himself the leading, or only, candidate for Brüning's position as chancellor. Hindenburg did choose to replace Brüning in May 1932 but named the political dilettante Franz von Papen rather than Hitler. Desperate to find a base in parliament, Papen called for Reichstag elections in July. The result was a disaster for Papen and another triumph for the Nazis, who took 37 percent of the vote, the largest total they were ever to acquire in a free election. The Communists won 15 percent of the vote. Thus the two parties dedicated to destroying German democracy held a majority in the Reichstag. Still, Hitler was not appointed chancellor. In November Papen called for another Reichstag election in the hope of gaining parliamentary backing. Again he failed, although the Nazi vote fell by 4 percent. By contrast, the Communist vote rose to nearly 17 percent. In early December, when Hindenburg decided to replace Papen, he again ignored Hitler, choosing instead a friend from the army, General Kurt von Schleicher.
In the Nazi camp there was bitter frustration at the end of 1932. The party was deeply in debt and demoralized by the year's endless campaigning. Putschist elements in the party, never persuaded that elections could bring the party to power, were growing increasingly restive. So deep was the frustration that on December 7 Gregor Strasser, second only to Hitler in the party, broke with the Nazis and retired from politics.

redpajama_set_name: RedPajamaCommonCrawl


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Playful, exciting and creative classes that set a foundation of wellness, movement and mindfulness at a young age.
A deep, slow flowing practice allowing for mindfulness in poses and transitions. The slower pace invites you to fully explore your practice from the inside out. An excellent complement to more high intensity fitness, this class will allow the body and mind to focus on release, while preventing injury. Class concludes with a combination of relaxation, breath work and meditation. Suitable for all levels.
At Well Fed Yogi I believe it should not be hard to be healthy. My philosophy is that a healthy lifestyle is as unique as you are, and there is no cookie cutter approach. I can help you figure out what "wellness" means in your life.
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redpajama_set_name: RedPajamaGithub


text:
[Top 10] Minecraft Best Amplified Seeds
Updated: 11 Nov 2020 9:14 am
Experience this kind of terrain and so much more in an amplified seed.
BY: Cadie Mecteau
Bored of the regular lay of the land in Minecraft?
An amplified seed is a specific version of a normal seed that can be created by activating "amplified" when generating your world. Amplified seeds overexaggerate a world's natural proportions, creating insane mountains, valleys, and floating structures. These types of worlds are fun because they pose a challenge and test the true strength of your survival and building skills. Do you have what it takes to thrive in an amplified seed?
Keep in mind that all of these seeds are only available on the Java version of Minecraft for PC!
10. Shadow Mountains - Seed -615404404
A perfect introduction to amplified seeds
Found by Reddit User u/Mythsqueue
The structures in this seed look like they haven't even finished generating yet and there is a variety of biomes just within spawn distance, which is a perfect example of what to expect when venturing into an amplified world!
What makes this seed awesome:
The sudden drop-offs and random floating structures are something you would never see in a regular seed, and they cast some awesome shadows, too.
Right at the base of one of the mountains is a small birch forest, and then right next to it is a dark oak forest!
You can also easily access and explore the ocean as it surrounds the mountain.
This seed is rich with interesting build opportunities because of the strange generation pattern. Test your creativity here!
Compatible with version(s):
9. Snowy Village Spawn - Seed 1151862873
If you're looking for easy access to a village, this is your seed
This seed is perfect if you're looking to get into the winter spirit at any time of the year. You spawn into a scenic tundra with snowy taiga biomes connecting in the distance. Doesn't it just make you want to curl up near a campfire on top of one of those dangerous cliffs?
What's awesome about this seed:
The spawn point is right in front of a huge mountain in the middle of a massive snowy biome.
Perhaps the most immediately rewarding trait is that right behind the mountain at spawn you can find a small village!
This seed is very scenic, with several waterfalls and flowing rivers!
You do not need to search high and low for building materials, because this tundra biome connects with a snowy taiga biome in the distance if you're up for the trek!
Compatible on version(s):
8. Water Heavy - Seed 2484405433649801586
Get your potion of Water Breathing ready
Found by Reddit User u/Chubbychaser03
With this enormous ocean who knows what treasures you may find with a little exploring. This seed is only for those who aren't afraid of confronting a little bit of water, and just to be safe I'd put the Respiration enchantment on your helmet as well.
There are multiple coral reefs and who knows what else at the bottom of this ocean!
The amount of water in this world makes the towering mountains look even more dramatic.
This seed provides a new challenge for even the most experienced player and survivalist.
This seed is perfect for building an underwater base while still enjoying the amplified features of the world.
7. Sky High Mountains - Seed -5707472292548279773
Be prepared to climb
Found by Unstoppable Cam
If you are looking for more common biomes with a completely uncommon generation this is your seed. It may not look very "amplified" when you first spawn in, but you will quickly notice the towering mountains and steep valleys.
It provides your basic woods like oak and spruce directly near the spawn point for easy access to building materials right away.
However, just because the biomes are common it does not mean that it denotes the crazy structures generated around the spawn point. There are plateaus, waterfalls, lava falls, mountains, hills, and floating islands galore!
Right in front of the spawn point, you will discover a snaking river cutting low through the enormous mountains!
This world provides a really fun twist to generic seeds and biomes to those who are just starting to introduce themselves to amplified worlds.
6. Village and Pillage - Seed 8857522626057459026
The total explorer's package
Found by NowoCraft
This seed has almost everything to offer and more with multiple biomes, villages, and other goodies to be discovered. If you're looking for a high risk yet high reward challenge, this is the seed you're going to want to use.
This seed has no shortage of steep mountains and floating islands! Be ready to craft a lot of stairways if you're planning on living atop any of these extremely unnatural generations.
It provides a generous mix of biomes including desert, swamp, plains, and more, all very close to the spawn point.
It also has a regular ocean and deep ocean, if you're more into underwater exploration.
The biomes are not the only satisfying parts of this seed. There are also villages, shipwrecks, pillager outposts, and other profitable aspects to be found not far from spawn!
5. Mooshroom Island - Seed -3809413073267378935
In need of some mushroom soup?
Found by Reddit User u/lurkbne
Mushroom biomes are hard to come by, but what if I told you that you could combine the rarity of the mushroom biome with the drama of an amplified seed? Well, now you can!
The most obvious thing that makes this seed awesome is the mushroom biome that is within only 200 blocks of the original spawn! It's rather expansive as well and extends over various hill and mountains
This mushroom biome is also surrounded by a huge ocean!
Despite the massive amount of water that this seed has, on land you encounter a dramatic lava fall.
This is another seed to try out if you're looking to put your survival skills to the test.
4. Mesa Spawn - Seed -7787406471712364301
This seed defies environmental laws
Found by Reddit User u/Killstepz
Get ready to be challenged, intrigued, and even a little confused by this next seed. It's an interesting mixture of sand, clay, ice, and a lot of stone.
You spawn in a mesa biome, which is one of the rarer biomes in the game. Be ready to stock up on clay!
A snowy taiga biome connects with the mesa biome, which also connects to an ice biome with icy spikes scattered along the mountains. There even seems to be a little bit of a desert biome incorporated into this mixture!
There is a mountain top village so high up that I'm sure at least one villager has fallen to its death before.
Speaking of falling, if you do fall from these towering structures you have the chance of being caught by one of the various extravagant waterfalls that are spilling from the mountains.
3. Woodland Mansion Spawn - Seed -7295218833530769400
Conquer a Woodland Mansion early in the game by spawning near it!
This seed is a true treasure. It combines the challenge of surviving in the extreme conditions of an amplified world, along with the dare of a Woodland Mansion! It's sort of a dream for any adventurer in a few different ways.
No more searching endlessly for your next battle in a Woodland Mansion, this seed provides you with one right at spawn.
As if the promise of a Woodland Mansion wasn't enough, you are also right near a gorgeous mesa biome with scenic waterfalls!
Speaking of biomes, the Woodland Mansion is nestled into a dark oak forest and a savanna biome creeps into the mesa!
There is a large ocean surrounding these generations, with several pronounced waterfalls pouring into it from the mountains.
2. Craziest Generated Structures - Seed 1616352388
Watch out for the floating sand!
Found by Reddit User u/WonderfulYam69
If you're a fan of totally unnatural and outrageous structures this is the seed for you. There are several floating islands, steep drop-offs, towering cliffs, and… floating sand?! Be careful where you step and what you mine in this world.
As soon as you exit the spawn point you are met by one of the most unlikely structure generations. Build your base on one of the floating islands or random plateaus that cut unexpectedly into the mountains! Just make sure you don't fall.
Only a short distance away from the spawn point you will encounter a desert like you've never seen before. Stacks of sand defy the laws of gravity as they hang over you!
Adventure around the seed a bit more to discover a plethora of biomes such as an ice biome, a jungle, mushroom fields, and more!
This is the perfect seed for a nomad or adventurer that is interested in seeing all the truly amazing generated structures that the amplified setting is capable of.
Compatible version(s):
1. Ice Chain Biome - Seed 555040716
You've never seen ice generations like these
This seed welcomes you into an icy wonderland of dramatic and shocking structures. Even the most talented ice sculptor pales at the view you encounter when entering this world.
As you enter this world you are greeted with giant chain-like ice generations!
Basic environmental laws do not apply in this seed as there are a couple of huge lava falls right in the center of the ice biome!
Ice spikes litter the mountains!
If you are not looking to live in the ice biome you can live near it in the taiga biome on a closely neighboring hill. That way you can collect wood and other materials more easily while also keeping the luxury of the icy view!
[Top 10] Minecraft Best Castle Seeds
[Top 10] Minecraft Best Cave Seeds
Minecraft: How To Breed Villagers
[Top 10] Minecraft Best Farms
More on this topic:
Cadie Mecteau
Cadie Mecteau is from the small state of Connecticut, thoroughly enjoys video games and baking, and will sometimes get caught transitioning between English to Simlish. Sul sul, frabanage shig vadou?!
Gamer Since: 2010
Favorite Genre: RPG
Currently Playing: Phasmophobia
Top 3 Favorite Games:The Sims 4, The Elder Scrolls V: Skyrim - Dragonborn, Call of Duty 4: Modern Warfare
Ice Spikes
Ice spikes generate more dramatically in an amplified world.
[Top 15] Minecraft Best Seeds (2020 Edition)
Are you craving a new Minecraft challenge? Do you have a storyline in mind that you'd like to pursue? Maybe you're just not a fan of surprises. Use a Minecraft seed to generate the specific world you want to live in and play out your favorite scenario. The code will start your game just where...
Minecraft Best Enchantments for Every Gear Piece
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Top 15 Minecraft Best Builds of All Time
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[Top 10] Minecraft Best Fuel
Are you smelting your ingots efficiently? Finally, you have a full stack of iron ready to be smelted into ingots. Just imagine the things you will be able to craft with it all! As you stare into the animated flames on the furnace you begin to wonder, "Is there more to life than coal? Are there...
Top 10 Best Minecraft Multiplayer Maps
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[Top 10] Minecraft Best Dimension Mods
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[Top 10] Minecraft Best Mods For Forge
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[Top 15] New Best Minecraft Seeds (1.15 +1.14) 2020 Edition
What Are The New Best Minecraft Seeds? If you've never experimented with Minecraft seeds before, you're missing out. Minecraft worlds are procedurally generated, meaning each new world you create is randomly generated from a world building algorithm. A seed is a string of numbers that...
[Top 15] Minecraft Best Biomes
An aspect of Minecraft that has always been a fan favorite is its diverse pool of biomes. Before the large biome update in version 1.8, Minecraft only had 10 biomes in the Overworld. Today, there are over 60 variations of biomes in-game version 1.16. Biomes are the different regions within the...
[Top 10] Minecraft Best QOL Mods You Must Have!
Mojang does a great job of updating and upgrading Minecraft, but there are always a few little things that could be better. Fans of vanilla, rejoice! These QOL mods are just the ticket. From helpful organization mods to smarter maps, any one of these tiny tweaks will improve your experience....
10 More Awesome Minecraft Seeds You Should Try Out!
Sometimes, you just need the right seed to give you that sense of adventure This article isn't about your average wheat seed (though there are some very good things to say about growing wheat), we're talking about your Minecraft world seed; a randomly generated code that determines your spawn...
[Top 10] Minecraft Best Cracked Servers
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[Top 10] Best Minecraft Horror Adventure Maps
Multiplayer has become a gaming standard and Minecraft is no different when it comes to playing with your friends. As a sandbox game, there is naturally no end to the things you can create and do in Minecraft. As a result, some creators have designated their time and effort to create...
Top 15 Minecraft Best Skins That Look Freakin' Awesome
Have you ever found a skin you're super proud of, then met 4 other people that look the exact same? It's happened to everyone (I'm looking at you emo boy skins and colorful girl skins from 2012). These skins are perfect for making you stand out. 15. You - Kindergarten You by Chasie-Poo is...
Minecraft is known for its diversity and seemingly randomized worlds. Upon the creation of a Minecraft save, players are given a unique series of numbers known as a seed. Seeds can be used individually and can be given to other players if the seed number is distributed. Seeds allow for...
10 Sandbox Games You'll Love
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[Top 10] Minecraft Best Mods For Animals
Too long have we lived in a world where the craziest Minecraft animal is the Mooshroom. By now we've all become pretty well familiarized with the classic Minecraft animals. The cow, pig, sheep, and chicken have become passive icons in our beloved game, providing us with all the leather, wool,...
Top 10 Best Minecraft Armor Enchantments
In 2019, Minecraft is making the ultimate comeback in popularity. For those returning to the game after a long hiatus, the development team has made some monumental changes to the game. If you're a Minecraft veteran trying to catch up to the modern version, or a new player looking to learn the...
[Top 15] Best Minecraft Adventure Maps Loved by Millions Worldwide
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[Top 10] Minecraft Best Potions And How To Make Them
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15 Best Minecraft Mods That Make Everything More Awesome
The Best Minecraft Mods That Make The Game a Lot More Fun Long time players of Minecraft know that in itself, it's a very basic game. The open-world, block-based sandbox game only has so much you can do before it becomes a chore game after game. You build your first dirt hut, and then it folds...
What is Minecraft Dungeons? Everything You Need To Know
Wondering what this "Minecraft Dungeons" is all about? Well look no further! I'm here to tell you all about one of Microsoft's newest and most exciting games. Minecraft Dungeons is an upcoming RPG, first announced at E3 this past year. It's scheduled for release in May 2020, so luckily for all my...
[Top 10] Minecraft Best Horror Resource Packs We Love!
10. Survivalture Survivalture revamps the entire appearance of Minecraft, transforming its blocky and kid friendly textures into something darker and more natural. This gives the game a much more satisfying look that truly drives home Minecraft is a game about survival in a harsh world. How...
Top 15 Best Minecraft Hacked Servers 2019
Hacked Minecraft Servers are completely free and allow everyone to play! Whether you're fed up with having to pay for all these awesome texture and skin packs that come with the server or you're too bothered to pay for the monthly fees. Some of the most popular Minecraft servers might be "free"...
[Top 15] Minecraft Best Servers That Are Fun (2020 Edition)
Undoubtedly one of the best features of Minecraft is its multiplayer servers. There are so many outstanding servers out there that provide vastly different experiences for players. Today, I'm going to be counting down some of the best Minecraft servers that are fun as of May 2020. Please...
[Top 5] Minecraft Zombie Apocalypse Mods That Are Awesome
What if you could experience an apocalyptic zombie game within Minecraft? It's all but possible with the addition of a few mods, and some determination. Before turning your game into a desolate wasteland filled with the undead, you will need to install forge or another client that will...
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[Top 15] Minecraft Best Commands To Use
Why no, these aren't cheats. How dare you accuse me of cheating. These are commands. Commands allow you to access features of the game by typing out certain keywords in a specific order. Use them to enhance your game, get past that one boss fight, or just make life a little easier. All...
[Top 25] Best Minecraft Mods That Are Amazing
Minecraft is a great game on its own. With endless possibilities in this blocky sandbox, you always have more to discover and experiment with. But if you think you've experienced all of Minecraft has to offer, you're sorely mistaken. Today we will rank the top 25 Minecraft mods. These mods will...
[Top 10] Minecraft Best Village Seeds
If you're looking for the perfect place to set down roots, then look no further than these awesome villages. Seeds are an important aspect when it comes to Minecraft. They are responsible for generating the world and what you will encounter when exploring. One of the most important things when...
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On May 17, 2009, a ground-breaking game was released to the PC world: Minecraft. Minecraft quickly gathered steam through its infancy, from Indev to Infdev, and then to the official Alpha release. At this time, the game ran for roughly $14 USD, and was gaining a larger following every single day....
Top 10 Best Minecraft Axe Enchantments
If you're going to build anything fast, your best bet is going to be enchanting some axes before you collect materials. Not only can axe enchantments save you from mobs, they'll also save you hours of time. 10. Curse of Vanishing Let's say you're on a PvP server, and you really...
[Top 10] Minecraft Best Armor Enchantments
10. Depth Strider This enchantment is tied to boots only, thus allowing you to swim faster in water. Not only does the enchantment increase your swimming speed, but it also reduces the rate at which water currents push you. This is an absolute must when it comes to traversing the ocean....
Top 10 Best Minecraft Texture Packs That Are Awesome
These resource packs are packs are only the best. Packs range from goofy to Witcher 3 levels of beauty. You are guaranteed to find something that fits your style. Remember these are not in any specific order, they're all great! 10. Lithos Core 32x This is an amazing resource pack if...
Best Minecraft Crossbow Enchantments
Introduced in Minecraft update 1.8, Crossbows are a ranged weapon that can be crafted, dropped from pillagers or piglins, and naturally generated at several structures. Created to give Minecraft another range weapon, crossbows are stronger than bows but have a slower draw time. With the...
[Top 5] Best Minecraft Kingdom Mods
Have you ever wanted to play as a heroic knight in Minecraft? What about a king, leading his kingdom to glory? Minecraft is limitless, and with the addition of mods and modpacks, there is an unending amount of ways to play. The following mods change Minecraft to a game centered around these...
What does Microsoft's talk with Minecraft mean for Windows 10?
In September of 2014 the sale of Mojangs Minecraft to Microsoft happened, and at first there were no significant changes to the game. That was until a few days after this years Xbox E3 conference, when the Redmond Team, that had been behind the collaboration of Minecafts beta Windows 10...
[Top 10] Minecraft Best Mods For Cities
Minecraft isn't necessarily known for modern skyscrapers and bustling metropolises, but more for their quaint villages and quiet residents. Wouldn't it be great if you could upgrade these little villages to something larger and more complex like the cities and towns we see today, whether you're...
[Top 10] Minecraft Best Horror Maps
When gamers think of Minecraft, 'horror' is the last thing to come to mind. Creepypastas such as Herobrine have brought about a sense of unease to the beloved game, but for the most part, Minecraft is an enjoyable survival game where the sky's the limit. The player is free to let their...
[Top 10] Minecraft Best Overhaul Mods That Are Awesome
10. Optifine Optifine provides Minecraft with the facelift it didn't know it needed. This is the most crucial mod you'll need if you plan to dive into the modding scene. If you are interested in the mod scene for Minecraft, Optifine is an absolute must for your mod list. This mod is...
Top 10 Must-Have Minecraft Texture Packs for Every Minecraft Player
If you are playing Minecraft, then you must get a resource pack. They are amazing additions that change the way all the blocks look in game. These texture packs will make your jaw drop at how great your builds look! Resource packs have been a necessary companion to Minecraft playthroughs for a...
[Top 10] Minecraft Horror Seeds
Get some suspenders handy, because these Minecraft seeds will scare your pants off! Long-time fans of the sandbox construction game, Minecraft, know of the Creepypasta rumors floating around the Internet. Cursed world seeds that generate real-world spirits, haunted worlds that can only be...
[Top 5] Minecraft Best Horror Texture Packs We Love
5. The Asphyxious Pack The Asphyxious CustomPack strives to bring a spooky and scary atmosphere into Minecraft and does so by retexturing the world into a more terrifying version of itself. Nearly everything was redesigned with the idea to turn Minecraft into a horror game through subtle changes...
Top 10 Minecraft Most XP Mob (Minecraft Highest XP Mobs)
These mobs are the best if you're grinding for enchantments or just trying to show off. This list will be in order from lowest to highest from 1 to 10. This will be a perfect guide for grinding for that efficiency IV enchantment. 10. Magma Cube This is an important mob for alchemy. The...
The Best Minecraft Chestplate Enchantments
No suit of armor is complete without a chestplate. On its own, chestplates can extend a player's health during combat. However, with enchantments both durability and a player's overall life can be extended. Today I'm going to countdown all 9 chestplate enchantments from worst to best. This...
[Top 15] Minecraft Best Ideas For Building
[Top 15] Minecraft Best Ideas For Building Let the inspiration begin Erebor from The Lord Of The Rings We all have that urge in Minecraft to build the coolest buildings, make our houses look the most intricate, and fill up our world with the best structures of all Minecraft....
Next-gen RPGs: 5 Things To Expect
Better graphics, do they actually make a better game? With the advent of next gen technologies and developers now having time to really tinker with the new tech and develop (or enhance) their game engines, it's no surprise when the graphics quality of the games they produce are usually their...
[Top 10] Minecraft Best Underground Base
Create the perfect hideaway! Maybe you're not the type of person who enjoys creating dramatic buildings that can be seen from miles away. Or maybe you want a secret headquarters hidden away from your friends and safe from all mobs. Either way, this is the place to find unique underground bases...
[Top 10] Minecraft Best Horror Mods
Who said Minecraft couldn't be terrifying? Fans of Minecraft know that when it comes to developing an exciting and open-ended world, the sky's the limit. It's even possible to build structures in the sky if you're creative enough to find a way! Despite the players' ability to build and create...
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The Best Funny Horror Movies To Watch Right Now 50. Shaun of the Dead Shaun of the Dead Official Trailer When a zombie apocalypse takes over London, Shaun must step up to save...
[Top 10] Rust Best Items to Recycle for Scrap
10. Metal Pipe Metal pipes are recyclable items that can also be used for crafting various items and weapons. What's good about Metal Pipes: They can be found at any monument or along any...
[Top 15] Minecraft Best Horror Mods We Love!
15. Herobrine https://www.youtube.com/watch?v=gpQalRXSOz0 What list would this be if the most frightening and viral Minecraft creepypasta wasn't included? This internet myth will come...
FFXIV White Mage Rotation Guide
Calling what any healer does a "rotation" might be a bit of a stretch. Unlike DPS and tank classes, healers have a more elastic role in their party composition. Instead of a set rotation, healers...
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Let's face it, most zombie movies and movies based off of video games are terrible. That's what made the Resident Evil movie so surprising! A zombie video game-based movie that we actually enjoyed...
Top 35 Best Chun Li Cosplays We've Ever Seen
Anyone who knows Capcom remembers playing one of their earliest games, Street Fighter. A now popular game where you and other characters in the game fight at different places until one of you K....
Top 10 Ultimate Best Horror Games to Play in 2019
Here are the 10 best horror games you can play today. 10. Resident Evil 2 Remake Resident Evil 2 Remake gameplay Resident Evil 2 is set two months after the events of the...
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The first person shooter has been a staple of gaming since the early days of Doom and Duke Nukem. What are the best games of the genre? Well, fortunately there is a definitive list available right...
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There are no cosplays like these! Have you been looking for Mass Effect cosplays? There are a lot of cosplays online, but if you want to see the 50 hottest Mass Effect cosplays, this list is for...
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Looking for the best Cammy cosplays? The beautiful, British, Cammy from Street Fighter is never far from fan's minds. From her blue eyes to her athletic build and immersive backstory, we are...
The 37 Hottest Mercy Cosplays Ever
Angela Ziegler – Or Mercy, was born in Switzerland. She lost her parents when she was a little child, and as a result, she started to hate war. She decided to devote her life to saving human lives,...
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As a beloved World of Warcraft character, the amount of Sylvanas Windrunner cosplays out there is insane. But which ones are the best? From the simple graphics of Warcraft III, to the...
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Skeptics question the existence of the ghosts, hauntings and the paranormal. Perhaps science does have an explanation for everything, and maybe 'it's all in the mind', but the numerous claims and...
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A survivor with sexy killer style, Jill Valentine is a cosplay must! Jill Valentine is one of the most iconic characters from the Resident Evil franchise. Mainly because she is tough and...
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Tifa Lockhart is the iconic vixen from Final Fantasy VII that can rip boys' hearts out with her fists of fury. Next to Cloud, she is one of the most beloved characters from the game, so she's a...
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There can only be one original on the Mortal Kombat women's roster. When Mortal Kombat was first released, it had a very small fighting roster. You could only choose from a list of 7 characters....
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