text:
\section{Introduction}

The goal of this study is to provide a deep learning-based system that allows a robot to distinguish different affordances in different visual scenes.

The term affordances originates from cognitive psychology \cite{gibson79} and means possible interactions between an animal and its environment. Examples for affordances from the perspective of a cat are: A bush affords shelter, a mouse affords nutrition. Later, the term was adopted by the robotics community and extended to from animals robots. Essentially in robotics this term very often takes the meaning of: ``Which actions could a robot perform in a given situation (with some given objects)?''

In this work, we assess affordances from the perspective of a human, which implies that such affordance may also be useful for a humanoid robot. In  addition, even human affordances, which a robot cannot directly make use of, may nonetheless help the machine to understand human behavior.

We focus on a set of $15$ affordances, which are listed in Table \ref{aff_set}. The goal is to predict a dense visual map for each of these affordances labeling the pixels in the scene with $1$ if a certain affordance is present and $0$ otherwise. These affordance maps then indicate, which actions could be performed with the different objects and places in a scene.

Central to our approach is that we perform affordance prediction by just using single RGB images. This 2D-approach allows using un-calibrated images of any kind and, thus, makes the method applicable in a wide range of robotics applications and other scenarios.

\section{Related Work}

\noparagraph{Dense Labeling}
From a methodological point of view, work in dense labeling is particular relevant, especially approaches that are based on neural networks.
As one of the first approaches, \citet{long15}, enhance and retrain the VGG16 network \cite{simonyan14} to densely predict class labels, with averaging up-sampled ``skip'' branches being a core idea.
The DeepLab model of \citet{chen15} adds a conditional random field to improve the alignment between predictions and edges in the image.
\citet{eigen15a} jointly predict depth, normals and object class labels by alternating between convolutions and incorporating multi-scale skip connections.
Segnet \citet{badrinarayanan17} store the pooling indices of the encoder and pass them to the up-sampling layers to preserve spatial accuracy.

Most relevant for our model is the work of \citet{pinheiro16}, which introduces refinement modules that integrate high- and low-level activations. The whole architecture is based on ResNet \cite{he16}.

\noparagraph{Affordances}
Affordances have been addressed in several works in the past (see for example Table~\ref{aff_comp}), both in robotics as well as in computer vision. A straightforward setting involves estimating affordances of whole objects, which is applied in the work of \cite{stark08} and \cite{zhu14}. Akin to these is the work of \citet{ye16} who detect bounding boxes of affordances using a two-stage approach consisting of region proposal and CNN-feature-based affordance recognition.

Affordances can also be predicted in form of (human) poses. This scheme is adopted by \cite{gupta11} making use of scene geometry and in \citet{grabner11} specifically for chairs and by \citet{fouhey14a} using video.

The idea of ``action maps'' is similar to affordance segmentation. However, the former
tends to be more specific, e.g. by referring to concrete objects and the set of considered actions is fairly small. Examples of these approaches are
\citet{savva14} who generate seven different ``action maps'' by tracking people in RGB-D video footage and \citet{rhinehart16} who learn 6 action maps through analyzing egocentric videos recordings.

The method proposed by \citet{roy16} is very similar to ours as it also generates pixel-wise maps given an RGB image. Their model learns intermediate representations for depth, surface normals and object classes which are then employed to carry out the affordance map prediction. The learning of these representations is actively enforced during training, i.e. the method requires additional data during training, while our method only needs RGB images and affordance map ground truth. Another difference to our work is the set of considered affordances.

\begin{table}[t]
	\footnotesize
	\vspace{0.3cm}
	\centering
	\begin{tabular}{llll}
		\hline
		Approach & \# & input & output \\
		\hline
		\citet{grabner11}  & 1 & RGB-D & per voxel \\
		\citet{gupta11} & 4 & RGB & per pixel \\
		\citet{savva14} & 7 & Video & per voxel \\
		\citet{rhinehart16} & 6 & Video & per grid-cell \\
		\citet{roy16} & 5 & RGB & per pixel \\
		\hline
		our approach & 15 & RGB & per pixel \\
		\hline
	\end{tabular}
	\vspace{0.2cm}
	\caption{\label{aff_comp}Comparison of related algorithms with \# denoting the number of used affordances.}
	\hfill
\end{table}

\section{Methods}

\subsection{Part Labels for Affordance Definition}
\label{from_object}
We use real as well as simulated data for training (and testing) our system. In the following we will describe how to assign affordances to real scenes, which is the more complicated case. Generation of simulated scenes is described afterwards, where the same principles for affordance assignment are employed.

Our method relies on human-provided labels for the affordances of object parts for training. We use 15 types of affordances (Tab.~\ref{aff_set}) and define them using three guiding principles:

\begin{table}[t]
	\footnotesize
	\centering
	\setlength{\tabcolsep}{0.2em}
	\renewcommand{\arraystretch}{1.2}
	\begin{tabular}{l|lllllllllllll}
		object & \rotatebox{90}{obstruct} & \rotatebox{90}{pinch-pull} & \rotatebox{90}{break} & \rotatebox{90}{sit} & \rotatebox{90}{grasp} & \rotatebox{90}{illumination} & \rotatebox{90}{support} & \rotatebox{90}{place-on} & ...  \\
		\hline
		*/knob     & 1 & 1 & 0.5 & 0   & 1   & 0 & 0 & 0 & ...\\
		*/top      & 0 & 0 & 0 & 0.5   & 0   & 0 & 0 & 1 & ...\\
		pot        & 1 & 0  & 0.5 & 0   & 1   & 0 & 0 & 0 & ...\\
		\rotatebox{90}{...} & \rotatebox{90}{...} & \rotatebox{90}{...} & \rotatebox{90}{...} & \rotatebox{90}{...} & \rotatebox{90}{...} & \rotatebox{90}{...} & \rotatebox{90}{...} & \rotatebox{90}{...} & \rotatebox{135}{...} \\
	\end{tabular}
	\vspace{0.2cm}
	\caption{
		\label{transfer}
		Excerpt from the transfer table. (0/0.5/1) = affordance (not/partial/fully) present, * = joker, that matches any object.}
\end{table}

\noindent
\begin{enumerate}
    \item
Affordances should be \emph{valuable} (in some sense) for robots or humans.

\item
We require that affordance names are specific. For example, \emph{open} is a very unspecific multi-action. A \emph{pinch-pull}, on the other hand, can be used for opening a container (and also for doing other things). Hence, if an action is considered then we will try to use the corresponding most specific action descriptor (action word) to annotate the affordance.

\item
Actions can have a hierarchy, but lead to the same final outcome: E.g. a house can be entered, a door, which is a part of the house, can be opened and the door's handle, as a part of the door can be pulled. All of this will be done to enter the house, where the pulling of the door handle is here the action at the lowest semantic hierarchical level. Only this level will be considered to label affordances in this study. The other levels could possibly be addressed from there on using a reasoning- or inference-engine, which is not part of this work.
\end{enumerate}

Adhering to these principles we create affordances for network training using the following methods.

\noparagraph{Object Parts}
An affordance most often refers to only a part of an object. For example, it is the surface of the table that affords placing an object there, but not the table legs. Thus, we define affordances part-wise. The novel ADE20K dataset \cite{ade_20k} is currently the only one that resolves objects into their parts. Hence ADE20K will be used here.

\noparagraph{Transfer Table}
We use a manually defined look-up table for the mapping from object labels to affordance maps. The transfer table (Tab.~\ref{transfer}) uses the following annotation hierarchy:
object names (like ``cabinet''), paths to object-parts (like ``cabinet/drawer/handle'') or pure parts (like ``*/drawer''). These annotations are associated with different 15-dimensional affordance vectors, where every dimension corresponds to one affordance. Clearly, multiple affordances can be present simultaneously.

This annotation hierarchy is searched from specific to general to associate a concrete object or part to an affordance. Hence, in the example above, we first ask if ``cabinet/drawer'' is specified in the table and only if it this not found ``*/drawer'' is checked.

From the ADE20K dataset, we only use the most frequent 500 objects and parts.  For very large objects multiple affordances can exist; most, if not all, only apply to specific parts (and not to the whole object). In addition, for some parts of the image, no affordance may exist. This case is treated in the cost function (see section \ref{cost_function}), exploiting the fact that we know \emph{where} affordance-data is missing.

\begin{table}
	\setlength{\tabcolsep}{0.3em}
	\footnotesize
	\renewcommand{\arraystretch}{1.2}
	\begin{tabular}{|l|p{5.8cm}|}
		\hline
		\textbf{Affordance} & \textbf{Description}                                                                                \\ \hline
		obstruct           & vertical surface that prevents locomotion. \textit{e.g. wall}                                                              \\
		break              & detachable objects that can easily be damaged or destroyed \textit{e.g. vase }   \\
		sit                & surface a human can sit on while having the feet on the ground \textit{e.g.  seat cushion}                                                                    \\
		grasp              & detachable objects that can be encompassed with one hand or only few fingers and be moved with one arm.\textit{e.g.  vase)} \\
		pinch-pull         & surfaces that can be pulled through a pinch movement (all directions). \textit{e.g.  knob}                                                   \\
		hook-pull          & surfaces that can be pulled by hooking up fingers (all directions). \textit{e.g.  handle }  \\
		tip-push           & surfaces that trigger some action when being pushed. \textit{e.g.  button-panel }                                                  \\
		warmth             & surfaces that emit warmth. \textit{e.g. fireplace  }                                                                        \\
		illumination       & surfaces that emit visible light.\textit{e.g. bulb }                                                                      \\
		observe            & surfaces that present information or art, i.e. that can be read or watched. \textit{e.g.  display}                                                           \\
		support            & stable surfaces that provide support for standing (for the agent) except ground. \textit{e.g. wall}\\
		place-on           & raised surfaces where objects can be placed on (this excludes the ground). \textit{e.g.  tabletop}                                                             \\
		dry                & surfaces capable of soaking up water. \textit{e.g.  towel}                                                                      \\
		roll               & surfaces that can be used with wheels. \textit{e.g.  road}                                                                 \\
		walk               & surfaces a human can walk on. \textit{e.g. grass} \\
		\hline
	\end{tabular}
	\caption{\label{aff_set}Description of the used set of affordances.}
\end{table}

\noparagraph{Data Augmentation}
Scene quality in ADE20K substantially varies. This leads to a situation that only a rather small number of good-quality training samples can directly be generated from ADE20K. Therefore, we augment the dataset by cropping  out image patches from an original image where we then vary color and contrast within such a patch. How many crop one can obtains depends on the original image quality. Better images with many objects can be used to create more augmented data.

In remainder of this paper, the training-split of this dataset, involving 17955 samples, is referred to as \emph{ADE$^T$} and the evaluation-split which contains 1970 samples is named \emph{ADE$^E$}.

\subsection{Simulation Model}
Transferring labels from real-image object parts has some disadvantages: Maps are incomplete and some affordances occur rarely. We overcome this problem by generating a new dataset of simulated images. It relies on a probabilistic scene model of a living room and a kitchen with several constituents of the scene being randomized. Hence, we can generate strongly varying images of the scene. More precisely, the randomized variables in our model are object material, -position, -shape, scene illumination, and perspective.

\noparagraph{Object material}
Objects can have different materials. A table surface, for instance, can be composed of plastic, wood or glass. Glass can be transparent or opaque. During scene generation, every object in the scene gets a randomly assigned material, with possibilities being constrained based on the object name.

\noparagraph{Object positions}
Several objects are randomly positioned in the scene and relative to other objects. Examples are a plate on a table, which is dependent on the table's height and a fork and knife, which are positioned relative to the plate.

\noparagraph{Object shape}
For some objects we define key model shapes and interpolate between these key shapes when a scene is generated. E.g. we interpolate between a chair with rounded edges and a chair with sharp edges.

\noparagraph{Scene illumination}
The world during day-time looks entirely different than at night. We account for this by varying light from the outside as well the intensity of indoor and outdoor illumination.

\noparagraph{Perspective}
Having obtained a variable scene model, we still need to simulate the process of photography by projecting the 3D scene onto a 2D plane from many possible viewpoints. For this, it is desirable to use viewpoints that sample mostly interesting aspects of the scene (e.g. multiple objects and sufficient distance), while avoiding irrelevant projections (e.g. view of the ground only) or invalid perspectives (e.g. taking an image from behind a wall).
We address this challenge by sampling the camera's position randomly along a fixed heuristically assumed trajectory and introducing slight variances with respect to the position.

For each object or object part we manually define corresponding affordances and render the corresponding affordance maps in a second pass by changing the objects' materials.

This procedure allows us to generate an arbitrary number of training samples each providing consistent, fully covered affordance maps. This way, we can extend the training set by many additional images.

The simulation model is implemented in the open source 3D modeling and simulation software blender\footnote{https://blender.org} using it's scripting API and the unbiased, physics-based renderer cycles. Figure \ref{sim_samples} gives an impression of the variability of the simulated samples. Scenes obtained using this method are subsequently denoted by \emph{Sim$^T$}.

\begin{figure}
	\vspace{0.25cm}
    \centering
	\includegraphics[width=1.4cm]{sim_variation/000_rgb.png}
	\includegraphics[width=1.4cm]{sim_variation/001_rgb.png}
	\includegraphics[width=1.4cm]{sim_variation/002_rgb.png}
	\includegraphics[width=1.4cm]{sim_variation/008_rgb.png}
	\includegraphics[width=1.4cm]{sim_variation/005_rgb.png}
	\caption{\label{sim_samples}Multiple images generated from a fixed perspective while other variables were randomly sampled.}
\end{figure}

\subsection{CNN}

\begin{figure}
	\centering
	\vspace{0.3cm}
	\includegraphics[width=5cm]{network.pdf}
	\caption{\label{model}Architecture of our proposed Dense-Refine-ResNet. With the lowercase letters indicating sub-blocks within the ResNet50 architecture. }
\end{figure}

\noparagraph{General Definition}
Affordances are context dependent. An example makes this clear.  We could ask whether a surface is walk-able or suitable to place things? If we now compare the ground with a table surface, we find that, locally, both are flat and uniform. Only context may resolve the difference between them. Walk-able surfaces, for example, may be accompanied by cars and trees, a table surface, on which we would put things --- on the other hand --- is often flanked by e.g. chairs. This leads to the requirement that the receptive field of a pixel should, ideally, cover the whole image because even distant pixels might be decisive for a local affordance.

This could well be in conflict with the second essential requirement, which demands that image details must not get lost during the forward pass of the network. Hence, object- and part-boundaries should be preserved. For example, many affordances concern rather smaller image aspects (e.g. a knob for pulling) and these aspect should not be lost by the network's operation. With these requirements in mind, we propose a novel neural network architecture, depicted in Figure \ref{model},  that is based on ResNet50 \cite{he16} and adopts the idea of refinement modules \cite{pinheiro16}. It had been shown by these authors that ResNet50 together with refinement modules successfully generates object proposals, because refinement modules offer an elegant way for merging local with scene-level information. Thus, here we use a modified version of the architecture in \cite{pinheiro16}.

This architecture then integrates abstract information from deep layers with the spatially more accurate representations still present in less deep layers. Here both input layers will deliver maps of the same image size where they are then first stacked on top of each other (concatenated along depth) and subsequently convolved with the learned filters to obtain $15 k$ feature maps. The variable $k$ is a control parameter of our model (see Section~\ref{experiments}). To reduce training effort, we preserve the weights learned from ImageNet in the original ResNet encoder and only train the refinement modules (see ``encoder train'' parameter in Table \ref{quant}).

\noparagraph{Cost function}
\label{cost_function}
Here we propose a novel cost function we call masked binary cross entropy. This cost functions deals with two aspects: 1) affordances are often not unique and for a given pixel multiple affordances may exist. Hence, we imply a binary (present vs not present) probability distribution for each pixel and each affordance. 2) Some parts of the image may contain no (or indecisive) affordances. For those, we can not tell whether an affordance is present or not, because the corresponding object or part is not found in the transfer table. However, since we generated the affordance maps, we know the location of the invalid regions. The idea is to incorporate also this information into the cost function.

Both aspects from above lead to the fact that commonly-used cost functions for semantic segmentation cannot be employed here.

Subsequently, we will formally derive the here used \emph{masked binary cross entropy cost function}.

\def\mathbf{Y}{\mathbf{Y}}
\def\mathbf{\hat{Y}}{\mathbf{\hat{Y}}}
\def\sum_{i \in \mathcal{I}}{\sum_{i \in \mathcal{I}}}
\def\sum_{a \in \mathcal{A}}{\sum_{a \in \mathcal{A}}}
\defH(\a_{ai}, \b_{ai}){H(\mathbf{Y}_{ai}, \mathbf{\hat{Y}}_{ai})}
\def\mathbf{M}_{i}{\mathbf{M}_{i}}

\label{masked_loss}
We annotate the ground truth matrix of an image for affordance $a \in \mathcal{A}$ and pixel $i \in \mathcal{I}$ with $\mathbf{Y}_{ai}$ and the associated model prediction is given by $\mathbf{\hat{Y}}_{ai}$.

Then the binary cross entropy $H$ is defined by:
$ H(p,q) = - p \log{(q)}\ - \ (1\!-\!p)\log{(1\!-\!q)}$.
This is summed up to render a scalar loss (cost), which captures the average binary entropy over all affordances and the image.
\[
\mathcal{L}(\mathbf{\mathbf{Y},\mathbf{\hat{Y}}}) = (|\mathcal{A}||\mathcal{I}|)^{-1} \sum_{a \in \mathcal{A}} \sum_{i \in \mathcal{I}} H(\a_{ai}, \b_{ai})
\]
So far this definition is compatible with non-exclusive classes, but it does not yet account for incomplete data. To achieve this, we mask the cross entropy matrix, excluding all regions where no (or indecisive) affordance is present, before averaging. Masking is a very efficient and simple way for removing the incompleteness ambiguities and we get the following loss:
\[\mathcal{L}^{\text{m}}(\mathbf{\mathbf{Y},\mathbf{\hat{Y}}}) = (|\mathcal{A}| \sum_{i \in \mathcal{I}} \mathbf{M}_{i})^{-1} \sum_{a \in \mathcal{A}} \sum_{i \in \mathcal{I}} \mathbf{M}_{i} H(\a_{ai}, \b_{ai}) \]
with $\mathbf{M}_{i} \in \{0,1\}$ indicating if pixel $i$ is valid, i.e. if a corresponding entry is found in the transfer table.

\begin{table*}[h!]
		\vspace{0.25cm}
	\centering
	\footnotesize
	\setlength{\tabcolsep}{0.2em}
	\renewcommand{\arraystretch}{1.2}
	\begin{tabular}{|lcccccc|cc|cc|cc|}
		\hline
		& &  \bfseries encoder & \bfseries masked & & & \bfseries training & \multicolumn{2}{c|}{\bfseries mean IoU } & \multicolumn{2}{c|}{\bfseries mean accuracy } & \multicolumn{2}{c|}{\bfseries pixel accuracy } \\
		\bfseries Network  &
		\bfseries k &
		\bfseries train &
		\bfseries loss &
		\bfseries epoch &
		\bfseries val. error &
		\bfseries dataset &
		\bfseries \emph{Sim$^E$} & \bfseries \emph{ADE$^E$}
		& \bfseries \emph{Sim$^E$} & \bfseries \emph{ADE$^E$}
		& \bfseries \emph{Sim$^E$} & \bfseries \emph{ADE$^E$} \\
		\hline\hline
		\input{evaluation_final.log.tex}
		\hline
	\end{tabular}
	\caption{\label{quant}Quantitative Results of our method in comparison with state-of-the-art algorithms.}
\end{table*}

\section{Experimental Setup}
\label{experiments}

\noparagraph{Evaluation Datasets}
The training and validation samples of \emph{ADE$^T$} are generated from a 90\% portion of the ADE20K \emph{training} dataset. From the remaining 10\% we manually pick 50 images of good quality, transfer the annotations to affordances and let an expert manually correct this according to the definitions provided by Table \ref{aff_set}. Due to this manual correction, systematic errors of the part-to-affordance conversion procedure are punished during evaluation and we obtain a more realistic estimate of the error. Additionally, we test on simulated data which was generated the same way as the simulation training data.

Consistent to the training datasets, the evaluation datasets are denoted as \emph{ADE$^E$} and \emph{Sim$^E$}.

\noparagraph{Metrics}
For quantification,  we use measures which are common in the field of semantic segmentation. We measure: \\
(1) pixel accuracy  {\small \begin{equation*}
\text{pixel Acc}(\mathbf{Y}, \mathbf{\hat{Y}}) = \frac{\sum_{a \in \mathcal{A}} \sum_{i \in \mathcal{I}} \mathbbm{1} [\mathbf{Y}_{ai} = \mathbf{\hat{Y}}_{ai}] }{\sum_{a \in \mathcal{A}} \sum_{i \in \mathcal{I}} \mathbbm{1} [\mathbf{Y}_{ai} = 1] }, \end{equation*} }
(2) mean class-wise accuracy  {\small\begin{equation*} \small \text{mean Acc}(\mathbf{Y}, \mathbf{\hat{Y}}) =\frac{1}{|\mathcal{A}|} \sum_{a \in \mathcal{A}} \frac{\sum_{i \in \mathcal{I}} \mathbbm{1}[\mathbf{Y}_{ai} = \mathbf{\hat{Y}}_{ai}]}{\sum_{i \in \mathcal{I}} \mathbbm{1} [\mathbf{Y}_{ai} = 1]} \text{ and} \end{equation*} }
(3) mean class-wise intersection over union (IoU) {\small \begin{equation*}
 \text{mean IoU}(\mathbf{Y}, \mathbf{\hat{Y}}) =\frac{1}{|\mathcal{A}|} \sum_{a \in \mathcal{A}} \frac{\sum_{i \in \mathcal{I}} \mathbbm{1}[\mathbf{Y}_{ai} = 1 \land \mathbf{\hat{Y}}_{ai} = 1]}{\sum_{i \in \mathcal{I}} \mathbbm{1} [\mathbf{Y}_{ai} = 1 \lor \mathbf{\hat{Y}}_{ai} = 1]},
\end{equation*}} following the notation introduced in section \ref{cost_function} with $\mathbf{Y}$ denoting ground truth and $\mathbf{\hat{Y}}$ a model's prediction.
$\mathbbm{1}[]$ is the indicator function.

Of central relevance in this study will be the IoU measure also known as Jaccard Index. Maximal IoU would be 1.0. It is important to note that IoU is measuring the overlap with the ground-truth image segment area and is punished for both, lack of overlap in the labeling as well as false positive outside-of-segment labeling.

\noparagraph{Output Binarization}
To compute metrics, the probabilistic predictions of the network must be binarized. We determine optimal thresholds for each affordance by considering IoU scores on the \emph{Sim$^T$} dataset and simply selecting the threshold with the best performance.

\noparagraph{Implementation Details}
For training and evaluations we employ Geforce Titan X, Geforce 1080 Ti and Geforce 1060 GTX GPUs and \cite{keras} with \cite{tensorflow} backend. Gradients are updated using the RMSprop method \cite{rmsprop} and training is stopping if the validation error did not improved for 5 epochs.

\noparagraph{Baselines}
To judge the performance of our model we compare against two baseline models: VGG16 + Upsamping is a simple encoder-decoder (or hourglass) architecture without skip connections.

Additionally, we re-implement the well-known SegNet \cite{badrinarayanan17} architecture and adapt it to affordance segmentation by using sigmoid activations instead of softmax. In contrast to the simple encoder-decoder architecture this method memorizes the pooling indices and makes use of this information during the upscaling-phase.

\section{Results and Discussion}
\subsection{Quantitative Analysis}

Table \ref{quant} presents the results of our evaluation on different configurations of the proposed method with baseline models. The columns of this table report on detailed configurations of the respective model, involving parameter $k$, indication if the encoder weights were trained (encoder train), the used loss function (masked or normal binary cross entropy), the epoch at which the respective model was chosen and the corresponding error on the validation dataset.

We experimented also with dropout, separately applied in the encoder and decoder as well as applied in both, but find that these variations do not lead to substantial improvements, while slowing down the training procedure. Therefore, dropout is not considered any further.

Note, we used the often applied approach of stopping in the training evaluating the validation error. If this does not improve for 5 epochs training is stopped because then the network has basically converged. This is the only fair point for model comparison.

We consider intersection-over-union (IoU) to be the most meaningful metric as it requires very good predictions (low false negative \textit{as well as} low false positives) to obtain high scores and consequently we focus on IoU during this discussion. Other metrics are reported in Table \ref{quant} for completeness.

Rows are grouped according to the training dataset used and best results are highlighted in bold.

\noparagraph{General Observations}
All values reported are statistically highly significant due to the large-enough database used. We took care to find for the baseline methods (VGG16, SegNet) the optimal performance parameters. Still, all baselines produces lower values than our approach. Furthermore, as expected, in all real data cases we get smaller IoUs , whereas for simulated data IoUs are larger. Cross-modality learning, shown by the red and blue entries, demonstrates the across-dataset generalization capabilities of the network, i.e. how well a network trained on one dataset performs on the other. Blue entries are for $\emph{Sim$^T$} ~\rightarrow~ \emph{ADE$^E$}$ and red ones for $\emph{ADE$^T$} ~\rightarrow~ \emph{Sim$^E$}$. Models trained using rendered images tend to perform badly on real data while the opposite direction works well. This is likely due to the variance being larger in real data and models that were trained on simulated data would have to extrapolate beyond their training distribution.

\noparagraph{Model Complexity Parameter $k$, Column 1}
Regarding model complexity, we do not see any difference between $k=3$ and $k=5$, where $k=1$ and $k=7$ in general produced worse results. This suggests that moderately small models have a sufficient number of parameters to fit the data well.

\noparagraph{Encoder Training, Column 2}
Furthermore, we found that it is better to train the decoder only and keep the encoder's weights fixed. This can be seen in Table \ref{quant} for models R8 and R9.

All in all, training without any ``bells and whistles'' leads to the best performance.

\noparagraph{Masking, Column 3}
The masked cost function introduced in \ref{masked_loss} does not have a big impact on the quality of the predictions but one can see that the IoU scores on \emph{ADE$^E$} when trained on  \emph{Sim$^T$} + \emph{ADE$^T$} tend to be higher if masked binary cross entropy was applied. This makes intuitively sense as the simulated data is complete anyway and masking can only improve training on \emph{ADE$^T$}.

\noparagraph{Joint Training, Rows R8-R11}
In addition to models trained on individual datasets, we also train models on both datasets conjointly: The datasets are first concatenated and then randomly mixed. This way, each mini-batch for training is composed of samples from both datasets and each update of the network weights through the gradient will reflect this. Hence, the network learns both datasets simultaneously.

In our experiment, we find that joint training improves performance. Scores on \emph{ADE$^E$} are on-par with scores of models trained on \emph{ADE$^T$} only while the performance on \emph{Sim$^E$} is, as expected, significantly better ($>0.5$ instead of $\approx 0.24$).

\begin{figure*}
		\vspace{0.25cm}
	\centering
	\includegraphics[width=2.4cm, height=2.4cm]{images/003_rgb.png}
	\includegraphics[width=2.4cm, height=2.4cm]{images/004_rgb_2.png}
	\includegraphics[width=2.4cm, height=2.4cm]{images/007_rgb.png}
	\includegraphics[width=2.4cm, height=2.4cm]{images/tables004.jpg}
	\includegraphics[width=2.4cm, height=2.4cm]{images/kit005.jpg}
	\includegraphics[width=2.4cm, height=2.4cm]{images/kit010.jpg} \\[0.1cm]
	\includegraphics[width=2.4cm]{images/003_rgb_hook_pull+sit+break_0.png}
	\includegraphics[width=2.4cm]{images/004_rgb_2_hook_pull+sit+break_0_mag.png}
	\includegraphics[width=2.4cm]{images/007_rgb_hook_pull+sit+break_0.png}
	\includegraphics[width=2.4cm]{images/tables004_hook_pull+sit+break_0.png}
	\includegraphics[width=2.4cm]{images/kit005_hook_pull+sit+break_0_mag.png}
	\includegraphics[width=2.4cm]{images/kit010_hook_pull+sit+break_0.png} \\[0.1cm]
	\includegraphics[width=2.4cm]{images/003_rgb_place_on+walk+grasp_0.png}
	\includegraphics[width=2.4cm]{images/004_rgb_2_place_on+walk+grasp_0.png}
	\includegraphics[width=2.4cm]{images/007_rgb_place_on+walk+grasp_0.png}
	\includegraphics[width=2.4cm]{images/tables004_place_on+walk+grasp_0.png}
	\includegraphics[width=2.4cm]{images/kit005_place_on+walk+grasp_0.png}			\includegraphics[width=2.4cm]{images/kit010_place_on+walk+grasp_0.png}
	\centering
	\caption{\label{quali}Qualitative assessment of model R9. The three columns on the left show simulated images, while the right columns use photographs. In some cases, details are magnified in insets with increased contrast.
		Top row: input image,
		middle: {\color{red} hook-pull}, {\color{OliveGreen} sit}, {\color{MidnightBlue} break},
		bottom: {\color{red} place-on}, {\color{OliveGreen} walk}, {\color{MidnightBlue} grasp}. Note, in case of simultaneous presence of two affordances the colors blend.
	}
\end{figure*}

\noparagraph{Affordance-wise Evaluation}
Table \ref{indiv} reports individual scores for the model R10 of Table \ref{quant}.

\begin{table}
	\centering
	\setlength{\tabcolsep}{0.2em}
	\footnotesize
	\renewcommand{\arraystretch}{1.2}
	\begin{minipage}[t][][c]{3.5cm}
	\begin{tabular}{|l|cc|}
		\hline
		& \multicolumn{2}{c|}{ \bfseries IoU} \\
		\bfseries affordance &  \bfseries \emph{Sim$^E$} & \bfseries \emph{ADE$^E$} \\
		\hline
            obstruct & 0.929 & 0.826  \\
            break & 0.660 & 0.405 \\
            sit & 0.455 & 0.182 \\
            grasp & 0.563 & 0.153 \\
            pinch\_pull & 0.046 & 0.003 \\
            hook\_pull & 0.084 & 0.050 \\
            tip/push & 0.000 & 0.002 \\
            warmth & 0.220 & 0.009 \\
		\hline
	\end{tabular}
	\end{minipage}
	\begin{minipage}[t][][c]{3.5cm}
		\begin{tabular}{|l|cc|}
			\hline
			& \multicolumn{2}{c|}{ \bfseries IoU} \\
			\bfseries affordance &  \bfseries \emph{Sim$^E$} & \bfseries \emph{ADE$^E$} \\
			\hline
            illumination & 0.609 & 0.455 \\
            read/watch & 0.828 & 0.147 \\
            support & 0.760 & 0.623 \\
            place\_on & 0.689 & 0.112 \\
            dry & 0.026 & 0.238 \\
            roll & 0.850 & 0.611 \\
            walk & 0.850 & 0.608 \\
			\hline
		\end{tabular}
	\end{minipage}
	\caption{\label{indiv}Performance of individual affordances.}
\end{table}

This shows that affordances that often occur and cover large areas tend to be learned more reliably. This is also the reason for the poor performance of some rare, small affordances (pull).

\subsection{Qualitative Evaluation}
In Figure \ref{quali} we show predictions of network R10 in Table \ref{quant} after training for 21 epochs on both datasets. For these results we do not use binarization. All colored pixels encode the probability for the corresponding affordance by color intensity. This renders an assessment of the degree of confidence the model attains for any given pixel's affordance. Mixed colors indicate the presence of mixed affordances. The here-used images challenge the network with difficult situations like front-lighting and transparent materials.  It is remarkable that, indeed, all affordance predictions appear to be reasonable. Furthermore, we find that the performance on novel images is good, although the model's confidence is in these cases lower.  In particular for small and rare structures like the knobs in the 5th column: There is only a very weak signal, which is barely recognizable in the image. This is likely due to the cost function weighting all (or all valid) pixels equally.

Summed up, the qualitative samples confirm the observations from the quantitative evaluation: The method works well as long as enough training data is available.

\section{Conclusion}
In this paper we have described a method that labels a comparatively large set of 15 affordances pixel-wise given only single RGB images. We show that state-of-the art semantic segmentation methods can be adapted to this new task with simple modifications only and learn to produce good affordance maps if enough data of good quality is provided.

In all cases trained on real images we found that at least a best-performing IoU of more than 0.2. Note that these numbers depend on the binarization threshold. For example, a lower threshold will increase the labeled pixel percentage but at the cost of increasing false positives, too. A higher threshold leads to less false positives but all to a lower correctly labeled set of pixels. Both would lead to a reduced IoU.

From a robotic perspective it might, however be advisable to increase the threshold (going away from an optimal IoU), because this way one obtains a labeling, which is sparse but highly reliable and avoids false positives. Why would this be better for robot action selection? Avoiding/reducing false positives reduces the danger of potentially damaging action-choices by a robot. Different from this, a low number of reliably labeled pixels will not negatively influence the actions of a machine. This is due to the fact that semantic 3D part segmentation algorithms exist that extract parts with a single action relevant meaning (like "handle", "blade", etc., \cite{schoeler16}). The here presented affordance prediction could, thus, be combined semantic part-segmentation and the robot can try perform the action on the segmented part (or area) where the affordance-guaranteeing fraction of pixels had been found.

A strength of our method is that it operates on 2D images and can be applied on all kinds of scenes, even in presence of light-absorbing, transparent and reflecting materials where structured light can not be used. Future work have to focus on improving the prediction of small structures, possibly by accounting for them in the cost function.

\printbibliography

\end{document}

redpajama_set_name: RedPajamaArXiv


text:
Update: These dogs are currently available for adoption in Greenville
by: Stacker.com
Posted: Dec 9, 2021 / 03:23 PM EST / Updated: Jan 11, 2022 / 12:52 PM EST
Editor's note: Stacker.com has been providing details about dogs and cats you can adopt in the Greenville area through Petfinder.com. The list changes often so be sure to click the Petfinder.com links to get more details on these and other animals up for adoption around Eastern NC.
GREENVILLE, N.C. (Stacker.com) When looking at the makeup of American families today, it's rare to see a home where a pet isn't part of the family. According to the most recent data from the APPA National Pet Owners Survey, 67% of households—or around 85 million homes—own a pet. Of these households, dogs, and cats top the list of most popular pets, with 63.4 million and 42.7 million households owning dogs and cats, respectively.
In exploring the history of animal shelters and rescue organizations in the country, nothing proves as problematic as the lack of a centralized reporting system to collect data on these organizations. Most of the shelter statistics that are available and accepted today are estimates based on several period surveys, including the aforementioned APPA National Pet Owners Survey along with the AVMA U.S. Pet Ownership & Demographics Sourcebook. Without a nationally codified and streamlined process for collecting, organizing, and reporting on shelter data, there has been a grave lack of transparency around the country's shelters. That makes it difficult not only to pinpoint accurate statistics regarding sheltered animals in America but also to understand the true state of animal welfare in the country, which is the first step to improving that welfare.
If you've been considering bringing home a furry companion, be sure to keep reading. Stacker compiled a list of dogs available for adoption in Greenville on Petfinder, ranging in age, breed composition, temperament, and needs. It's important to note that pet adoption is a huge responsibility and requires available savings for emergency and routine veterinary care, a day-to-day routine, and a game plan for coverage if the pup needs to be left at home during the day (i.e. hiring a dog walker), and time and attention devoted to training your newest family member. Be sure to do due diligence on breed research to see what kind of dog will be the best fit for your lifestyle.
1 / 8Petfinder
– Gender: Male
– Age: Baby
– Breed: Mixed Breed
– Read more on Petfinder
Binx
– Age: Young
– Gender: Female
– Age: Adult
– Breed: Pit Bull Terrier
– Breed: Labrador Retriever
Fact check: Are you reading this COVID mask chart the wrong way?

redpajama_set_name: RedPajamaCommonCrawl


text:
TN 5 (11-15)
GN 03305.030 Disclosure of ADAP Records
A. Definition of ADAP record and programs
The U.S. Public Health Service (PHS) has published regulations at 42 CFR Part 2 governing the disclosure of Alcoholism and Drug Abuse Patient (ADAP) records. An ADAP record is any record of identification, diagnosis, prognosis, or treatment of a patient maintained in connection with the performance of an alcohol or drug abuse prevention function, directly or indirectly, funded by the Federal government. A diagnostic code derived from or based on an ADAP record is also an ADAP record subject to the PHS regulation. The PHS defines an ADAP program as any program under which a person or entity holds itself out as a provider of alcohol or drug abuse diagnosis, treatment, or treatment referral services. An ADAP program exists in a general medical care facility if the facility:
Has an identified unit holding itself out as a provider and provides alcohol or drug abuse diagnosis, treatment, or treatment referral; or
Has medical or other staff whose primary function is the provision of alcohol or drug abuse diagnosis, treatment, or referral for treatment and who are identified as such providers.
B. SSA policy for handling ADAP records
The existence of an alcohol or drug problem is not an adequate basis for invoking the requirements of the PHS regulations. For example, a diagnosis of alcoholism or drug abuse recorded in the social history of a patient's medical report is not sufficient to classify the report as an ADAP record. An ADAP record will be classified and bear a label or other marking identifying it as an ADAP record when we receive it from the source. We may disclose medical records that are not classified or labeled as ADAP records in accordance with the Privacy Act, FOIA, and other applicable disclosure laws.
C. Consent requirements for disclosing ADAP records
The PHS regulation at 42 CFR 2.31 requires a special consent document to authorize the disclosure of ADAP records. The consent document must contain:
The specific name or general designation of the program or person permitted to make the disclosure;
The name or title of the individual or the name of the organization to which disclosure is to be made;
The name of the patient;
The purpose of the disclosure;
How much and what kind of information is to be disclosed;
The signature of the patient and, when required for a patient who is a minor, the signature of a person authorized to give consent under State law, or legal guardian or other person authorized under State law to act on behalf of the patient, or if the patient has been adjudged incompetent or is deceased, the signature of a person authorized to sign in lieu of the patient (see GN 00502.023);
The date on which the authorized person signs the consent;
A statement that the consent is subject to revocation at any time except to the extent that the program or person that is to make the disclosure has already acted, relying on the consent. This includes the provision of treatment services relying on a valid consent to disclose information to a third party payer; and
The date, event, or condition upon which the consent will expire if not revoked before; this date, event, or condition must be set to ensure that the consent will last no longer than reasonably necessary to serve the purpose for which it is given.
A consent document to disclose an ADAP record must meet the above PHS and SSA requirements before we can disclose any information. If any of the required information is illegible, missing, or incomplete, return the consent to the requester with an explanation of why we cannot honor it. Inform the requester of the information we need in order to process a request.
NOTE: If the patient has died, the executor or administrator of the estate, or if there is none, the spouse or closest other relative of the deceased patient may sign the consent. (See 42 CFR 2.15(b)(2))
D. Prohibition on redisclosure of ADAP records
PHS regulation 42 CFR 2.32 requires us to include the text below in our notice with each consent-based disclosure of an ADAP record.
"This information has been disclosed to you from records protected by Federal confidentiality rules (42 CFR, Part 2). The Federal rules prohibit you from making any further disclosure of this information unless further disclosure is expressly permitted by the written consent of the person to whom it pertains or as otherwise permitted by 42 CFR, Part 2. A general authorization for the release of medical or other information is not sufficient for this purpose. The Federal rules restrict any use of the information to criminally investigate or prosecute any alcohol or drug abuse patient."
GN 03305.030 - Disclosure of ADAP Records - 11/16/2015


text:
Q: Fatal error: Call to private MyObject::__construct() from invalid context When creating a new object in PHP, I get the following error message:
Fatal error: Call to private MyObject::__construct() from invalid context
I just create the new object and do not try to call the constructor explicitly.  Does anyone know what's going on?

A: Your MyObject class has a protected or private constructor, which means that the class cannot be instantiated.  __construct() functions are always called when an object is instantiated, so trying to do something like $x = new MyObject() will cause a fatal error with a private construction function.  (If you do not specifically declare a __construct() function, the parent constructor will be called).
Private constructors are often used in Singleton classes to prevent direct instantiation of an object.  If it's not a class that you built, it might have a getInstance() function available (or something similar) to return an instance of itself.

A: Instead of $x = new MyObject() you could use
$x = MyObject::getInstance();

assuming that MyObject is a Singleton and getInstance() function is available.

A: For me its was that the name of the CLASS was the same name as one of the methods
that was private...
for example...
class myClass {

   public function __construct() {

   }

   private function myClass() {

   }
}

redpajama_set_name: RedPajamaStackExchange


DOVER — They were former high school basketball teammates whose current relationship was headed to a different kind of court.
While a Dover Police detective volunteered to interview Raymond Ward during a murder investigation last year because of their familiarity, according to a judge's opinion and order, the defendant later claimed he invoked Miranda rights and the questioning should have stopped.
In Kent County Superior Court Tuesday, a judge deemed that assertion partially correct in a 14-page ruling on an appeal submitted by defense attorneys Thomas D. Donovan and Zachary A. George on June 27.
Everything Mr. Ward said after "Nah bro" can't be used as the case continues, Judge Jeffrey J. Clark determined.
Mr. Ward is charged with first-degree murder in the shooting death of Dequan Dukes in Dover on June 27, 2017, and scheduled for trial on Aug. 20. Other charges include first-degree attempted robbery, second-degree conspiracy and possession of a firearm during the commission of a felony. A final case review is set for Tuesday.
Mr. Ward answered "oh yeah" in response to the detective's question "having these rights in mind, do you wish to talk to me now? Just about what, what, like I said downstairs, about today" according to police, the court order said.
The interrogation commenced with the detective talking about "Mr. Ward's recent marriage and other routine matters," the Court noted before turning to the defendant arrest. Mr. Ward "readily answered" questions about a firearm police said was found in his home. The defendant claimed he got it for "his protection, and that it had not been used for any murders," video indicated.
While Deputy Attorneys General Jason C. Cohee and Stephen E. Smith conceded "that Mr. Ward ambiguously asserted his Miranda rights" twice during the interview.
Mr. Ward answered "Nah bro" after the detective said "You can talk to me to help yourself out," according to the judge's review.
The detective testified he took the response "to mean that Mr. Ward did not want to delay talking to the detective because that would not help Mr. Ward's case," according to the opinion.
Judge Clark referenced the continued questioning and answer session that was deemed inadmissible to future proceedings. In reviewing audio and video recordings, the Court found that the detective "asked no further clarifying questions" when the second supposed assertion was in question.
Mr. Ward also responded with "I'm not trying to get quizzed," the order said the recording showed.


text:
well ok then
Uchiha
Well Ok
still-love-you
Dick, Vagina, and Red: Roses are red, Leaves are green, REDUCTRESS.COM 4 Exercises That'll Make Your Vagina So Tight It Becomes a Dick Guillotine Ummm, well ok then
Ummm, well ok then
Inside Out, Life, and Water: LIP INSIDE OUT AND HAND WASH COLD WATER SO YOU DON'T RUIN IT LIKE EVERYHING ELSE IN YOUR LIFE RN# 148916 Well, ok then
Well, ok then
Funny, Inside Out, and Life: LIP INSIDE OUT AND HAND WASH COLD WATER SO YOU DON'T RUIN IT LIKE EVERYHING ELSE IN YOUR LIFE RN# 148916 Well, ok then via /r/funny https://ift.tt/2FxtvHm
Well, ok then via /r/funny https://ift.tt/2FxtvHm
Animals, Books, and Children: purple-ladys-stuff Question... An anguished question from a Trump supporter: "Why do liberals think Trump supporters are stupid?" The serious answer: Heres what we really think about Trump supporters- the rich, the poor, the malignant and the innocently well-meaning, the ones who think and the ones who dont.. That when you saw a man who had owned a fraudulent University, intent on scamming poor people, you thought Fine." That when you saw a man who had made it his business practice to stiff his creditors, you said, "Okay." That when you heard him proudly brag about his own history of sexual abuse, you said, "No problem. That when he made up stories about seeing muslim- Americans in the thousands cheering the destruction of the World Trade Center, you said, "Not an issue." That when you saw him brag that he could shoot a man on Fifth Avenue and you wouldn't care, you chirped, "He sure Knows me That when you heard him illustrate his own character by telling that cute story about the elderly guest bleeding on the floor at his country club, the story about how he turned his back and how it was all an imposition on him, you said, "That's cool!" That when you saw him mock the disabled, you thought it was the funniest thing you ever saw That when you heard him brag that he doesn't read books, you said, "Well, who has time? That when the Central Park Five were compensated as innocent men convicted of a crime they didn't commit, and he angrily said that they should still be in prison, you said, "That makes sense." That when you heard him tell his supporters to beat up protesters and that he would hire attorneys, you thought, "Yes!" That when you heard him tell one rally to confiscate a man's coat before throwing him out into the freezing cold, you said, "What a great guy!" That you have watched the parade of neo-Nazis and white supremacists with whom he curries favor, while refusing to condemn outright Nazis, and you have said, "Thumbs up" That you hear him unable to talk to foreign dignitaries without insulting their countries and demanding that they praise his electoral win, you said, "That's the way want my President to be." That you have watched him remove expertise from all layers of government in favor of people who make money off of eliminating protections in the industries they're supposed to be regulating and you have said, "What a genius!" That you have heard him continue to profit from his businesses, in part by leveraging his position as President, to the point of overcharging the Secret Service for space in the properties he owns, and you have said, "That's smart!" That you have heard him say that it was difficult to help Puerto Rico because it was the middle of water and you have said, "That makes sense." That you have seen him start fights with every country from Canada to New Zealand while praising Russia and quote, "falling in love" with the dictator of North Korea, and you have said, "That's statesmanship!" That Trump separated children from their families and put them in cages, managed to lose track of 1500 kids has opened a tent city incarceration camp in the desert in Texas - he explains that they're just "animals"- and you say, "well, ok then. That you have witnessed all the thousand and one other manifestations of corruption and low moral character and outright animalistic rudeness and contempt for you, the working American voter, and you still show up grinning and wearing your MAGA hats and threatening to beat up anybody who says otherwise What you don't get, Trump supporters in 2018, is that succumbing to frustration and thinking of you as stupid may be wrong and unhelpful, but it's also...hear me.. charitable Because if you're NOT stupid, we must turn to other explanations, and most of them are less* flattering Fuente: purple-ladys-stuff 25,271 notas A message to red caps
A message to red caps
Memes, Hair, and 🤖: When she hops into your bed and puts her hair in a ponytail douggiehouse Look at me. I'm gonna blow you now. Well ok then
Memes, Weed, and Cancer: me: hi weed smoker: it's just a plant. honestly. its used for cancer treatment as well as chronic pain. it calms me and helps me sleep. douggiehouse Well ok then
Memes, 🤖, and Server: When it's slow because it's raining and your manager says "stay busy" IG: @SERVER LIFE Well, ok then... S-O to @bayneebugg and her team at Oggi's in Upland, CA
Well, ok then... S-O to @bayneebugg and her team at Oggi's in Upland, CA
Memes, 🤖, and Class: When you're in class and your crush accidentally touches your leg @why hesavage Well ok then
Memes, 🤖, and Sport: Don't mess with the mascot sviness @Sports Well ok then... follow (me) @dm_kpsz
Well ok then... follow (me) @dm_kpsz
Well, Gyms, and Ok Then: es official WHEN YOU DECIDE TO TAKE IS NOTHING BUT GYM POSTS Well ok then.
Well ok then.


White Adjustable Measuring Spoon Unique event give-a-way.How does your business measure against the competition? Promote on this measuring spoon and find out! Measures 5 different amounts: 1/8, 1/4, 1/2, 3/4 and 1 teaspoon.


Using the modern bathroom tile is an advantage for the owners. Not only does it look nice, but it is waterproof and easy to clean. Tile comes in a variety of colors, textures, and styles. No matter how modern or classic your style, you will find a tab to match. Consider these ideas when you remodel your bathroom if you are adding accessories or stripping the whole room and starting over.
Create a fake carpet by arranging small tiles on the floor in a square or circular pattern with repeated designs in the middle. When combined with other, more neutral colored tiles, these additions will make a room more dynamic. Try to alternate between two colors, one light, and one darkness, to create more contrast. This look will wake up in the morning when falling from the bed and the head of the shower.


The Czech Republic is known for a solid infrastructure, a highly educated and skilled workforce, and relatively low wage costs. No wonder that many interesting companies were born here – AVG, Avast, Apiary to name just a few. This month we took a close look at the Czech startup scene and created a list of 10 promising startups to watch in 2018.
All of the companies we're listing below were founded within the previous 2-3 years and all of them have a interesting as well as promising business model.
T.Koláčný, Brno Deputy Mayor: "The primary aim of Brno is to attract students & professionals who are interested & motivated to become part of innovations."


text:
#OWATONNAPROUD
New High School Updates
Build a new high school for 1,600 students
Our vision has not changed since the May 2019 referendum. From the research completed by the Community Task Force on Facilities, the design plan would include:
New learning spaces that propel our students into 21st century opportunities
300,000 square foot building designed to accommodate 1600 students
70 classrooms, labs and shops outfitted with modern technology to support learning
Lunchroom space adequate to allow all students to get a seat during lunch (567 students per lunch), it will also double as a community commons
Flexible media center to support a variety of learning opportunities
Main gymnasium and auxiliary gym to provide 4 courts for physical education and athletics
Modern career education spaces with updated vocational labs with modern industrial equipment, and flexibility to accommodate changes
900 seat auditorium will be paired with an improved music area containing rooms more appropriately sized to accommodate our very popular music programs
Adequate on site parking for students, staff and visitors as well as better accommodations for event parking - at least 1,000 parking stalls
Up to date building systems including modern and energy efficient mechanical and electrical systems
Temperature control for both winter and summer building use
Building systems designed to allow flexibility for future changes as technology evolves in the future
*Site plan design concept is shown above. The final plan will be developed after careful consideration by our stakeholders.
Following a successful vote on November 5, the District will involve stakeholders and community members in the design of the new high school. A new high school will become an important community space; therefore, it is critical the eventual design be created by and for our community.
Architects created design concepts (shown above) to best meet the needs of our students, however, the final plan will be developed after careful consideration by our stakeholders. Members of our community will be welcomed into the design process including citizens, staff and student representatives. All efforts will be made to ensure the plan will meet the needs of our students and incorporate functional designs that fit our Owatonna sensibilities and prepare our future workforce in our community and beyond.
The Owatonna School District has identified an approximately 88-acre parcel of land suitable for a new high school, following an extensive review of land options done in partnership with city and county officials. Federated Insurance has signed a letter of intent with the landowner to purchase the land and donate it to the school district, if voters approve the November 5 ballot question requesting funding for a new high school.
The land is located at 1945 SE 18th St, near the intersection of Highways 14 and 218 in the southern part of the community. The parcel rose to the top of other options that were explored based on criteria that city, county and school district planners used to assess available land including traffic flow, proximity to utilities and soil conditions.
Repurpose the current Owatonna High School for district and / or community use
If voters approve Question 1 and 2, it will provide a total of $11M to be used to repurpose the current Owatonna High School site.
Plans to repurpose the current high school
Review the current high school to utilize the best parts of the building for district services and potential community usage. If Question 2 is successful, the final design would be a collaboration with the community and will likely include partial demolition, renovation and systems updates. This proposal allows the site to stay in School District control and explore the opportunity to sell other district-owned properties, thus consolidating many operations into one efficient location.
If voters approve only Question 1 and not Question 2, the current high school location would still stay in School District control- however, most of the current high school would be demolished, as the district would not have enough funds to maintain or renovate it.
*The above photo is one concept the community can explore.
The final design would be a collaboration with the community.
www.owatonna.k12.mn.us
Prepared and paid for by Owatonna Public Schools. This publication is not circulated on behalf of any candidate or ballot question.


text:
Ranking the Villains of the Marvel Cinematic Universe
Posted by Anton Reyes | Apr 30, 2015 | Lists | 0 |
Everyone loves a good supervillain. They can make or break any comic book movie, and while the Marvel Cinematic Universe is totally a series run by heroes, let's not forget all these grand, charismatic villains we've been given over the years.
For this list, I decided to only focus on the main antagonists of the 11 films and the Daredevil series, so none of those cannon fodder villains like Laufey or Raza. Also, no lackeys (James Wesley, the Other) or villains who still have more to offer (Nebula, Crossbones). And sorry, Agents of S.H.I.E.L.D. and Agent Carter, but I won't be including any of your villains.
From monsters to rich businessmen, terrorists to disfigured faces, genocidal maniacs to… more genocidal maniacs, let's rank the Marvel Cinematic Universe villains.
*SPOILERS FOR AGE OF ULTRON AHEAD.
16.) Malekith – Thor: The Dark World
Truth be told, Malekith was at a real disadvantage. He's the unlucky baddie who had to share a movie with Tom Hiddleston's Loki. That aside, he's a pretty lousy villain. He's just another villain who wants plunge the universe into darkness because… plot, I guess. True, Marvel has had villains who functioned just as those types, but Malekith doesn't have any personality of any kind. He's just punch-shout-Elvish dialogue.
15.) Baron Wolfgang von Strucker – Avengers: Age of Ultron
Pros are he's the main focus of my favorite Marvel end credits scene and he created Scarlet Witch and Quicksilver. His cons are that's literally about all he does. After being teased in a couple episodes of Agents of S.H.I.E.L.D. and at the end of Captain America: The Winter Soldier, he's taken out in the Bond-esque opening scene of Avengers: Age of Ultron. At least, Whedon was able to give him a few good lines before he was taken out.
14.) Dr. Arnim Zola – Captain America duology
Dr. Exposition should be his name. I'm kidding. I actually like Zola. Toby Jones is fun to watch on-screen, and his interactions with the Red Skull are cute. He does get a few minus points for that exposition dump in Captain America: The Winter Soldier, but kudos to the team on making that scene a treat for comic book fans by making him resemble his comic book counterpart.
13.) Ronan the Accuser – Guardians of the Galaxy
The blue meanie serves his purpose as the antagonist in the film. He's pretty menacing, but there's really nothing to him other than his politics, and I agree with Thanos; his politics do bore me. Had he accepted that dance battle with Quill, however, he'd probably be much higher on this list.
12.) Iron Monger (Obadiah Stane) – Iron Man
Jon Favreau nailed it with the MCU's first villain. Power-hungry, cruel, and full of personality, his back-and-forth with Tony Stark is pretty fun to watch, and he's not even acting as a villain most of the time. Him turning into Iron Monger at the end really comes out nowhere and all of a sudden, he's a total whackjob, but I'm willing to let that slide.
11.) Whiplash (Ivan Vanko) – Iron Man 2
Typical revenge story villain, but Mickey Rourke made him enjoyable. He goes through with his revenge plan with arrogance and rage, but he carries himself with so much swagger. That's the real common trait behind Iron Man's film villains – even Loki & Ultron. They really do need that bravado to face-off against Downey, Jr., and in this case, it works.
10.) Justin Hammer – Iron Man 2
Probably Iron Man 2's most redeeming factor, Sam Rockwell brought some fun and levity to the sequel with the aforementioned swagger aspect of Iron Man villains. He isn't really menacing or a real threat to Iron Man, but I'd be lying if I said I didn't enjoy his scenes amidst all the multiple pointless plot threads.
09.) Alexander Pierce – Captain America: The Winter Soldier
Nothing scarier than a villain who thinks he/she's doing the right thing, espescially when he/she thinks that killing millions of people is "the right thing". Robert Redford tackles a genre he's well-versed in, and proves himself a pretty decent threat to Captain America and his allies. Hail Hydra.
08. & 07.) The Mandarin (Trevor Slattery) & Aldrich Killian – Iron Man 3
This is combined, because I feel one wouldn't work without the other, and their rankings are interchangeable anyway.
Yup, I'm a Trevor Slattery fan. I thought the Mandarin twist was brilliant. Firstly, director Shane Black took the roots of the character and was able to turn them into something fruitful for the film. The comics Mandarin was created during a time when tensions were high between the US and China, so he was created as a Fu Manchu stereotype villain to feed into that fear/hate. The film iteration of the character is able to play on that by having Aldrich Killian create the Mandarin as a Middle Eastern stereotype villain to feed into the fear/hate in-movie. It's satirical, you believed into it, and no one saw it coming.
I also like the play on the previous Iron Man villain formula of how the rich business rival of Stark hires a terrorist to do his bidding, by having the one person embody both.
06.) Abomination (Emil Blonsky) – The Incredible Hulk
I went on about why I think Abomination is an underrated villain in my Incredible Hulk defense, so I'll try to keep it short here. Blonsky's downfall from dedicated soldier to a monster with a lust for power plays out like a rock star getting addicted to drugs, it's tragic. Or maybe it's another prime example of what Dr. Abaraham Erskine said in The First Avenger, "good becomes great, bad becomes worse". Maybe it was in Blonsky's heart all along to turn to the dark side. Oh, and speaking of The First Avenger…
05.) Red Skull (Johann Schmidt) – Captain America: The First Avenger
This is Marvel's best example of a villain who just wants to rule the world. I love how they made Red Skull the anti-Captain America, he's the power-hungry dictator who rules with fear, and he believes he's doing the right thing. Everything you love about Captain America is why you feel hatred for the Red Skull, it's great villain vs hero material. Plus, Hugo Weaving kills it.
04.) Winter Soldier (Bucky Barnes) – Captain America: The Winter Soldier
The Winter Soldier kicked ass for the first half of Captain America 2. The action sequences featuring him were really intense and he quickly became Cap's most dangerous threat, but it turned out that the killing machine hired to assassinate him was his closest friend. Bucky died when he fell of that train and the Winter Soldier was born. Sebastian Stan greatly portrayed this physically dangerous character full of rage, but he also managed to make us feel for him when we discovered his loss. Also, I love the final moments of the finale of the film was hinging on Steve trying to save Bucky.
03.) Wilson Fisk – Daredevil
Fisk is probably Marvel's most fleshed out villain, getting a little under 10 hours to develop him as a character. His origin story episode still gives me chills. It's quite clear that his childhood traumas have prevented him from maturing, and there's something very intimidating about this unpredictable child running organized crime. He's even convinced himself that he's there to save his city and that he's not a monster being cruel just to be cruel, which is somewhat partly true. He'd be considered a villain, but I'm not sure that's entirely true. He's almost exactly like Matt Murdock, only he has a pretty different moral standpoint and takes steps to achieve his goals very differently, which makes his interactions with Daredevil all the more fascinating to watch.
02.) Loki – Thor duology, The Avengers
There was no doubt that Loki was going to end up in either of the Top 2 slots. Tom Hiddleston just oozes charisma. I love the first Thor film mainly because of Loki's (literal) fall from grace. The neglected, jealous, immature yet intelligent son, who sought power only to have it denied to his face. True, he does possess the qualities of a child on a tantrum, but we understand and sympathize for Loki. Even Thor gets it, but he still can't trust him. Loki's popularity just blew up all of a sudden after The Avengers because he was the charming bad guy that we already dove into in the film prior. In many ways, he's the most "Marvel Studios" out of all these villains, but there's just one that beats him.
01.) Ultron – Avengers: Age of Ultron
One of my first reactions to the film was "Ultron is the perfect Marvel villain," and that's because I felt he captured the personality and spirit of the Marvel Cinematic Universe, while also proving himself as the foil for our heroes, the Avengers. James Spader really captured all of that with just his voice. He tears the Avengers apart, beats them, and then laughs in their faces. He's the maniacal villain that actually made it feel like there was failure in the Marvel Cinematic Universe. His goal is to exterminate the world, and you actually kind of get it (or was that just me? I'm not genocidal, I promise). There are no strings on this villain, because he proved himself to be the baddest of the bad.
Now that you know my rankings, be sure to rank yours as well, and check out Audiences Everywhere's rankings of the MCU Heroes as well.
Featured Image: Walt Disney Studios Motion Pictures
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Anton Reyes
Anton is a lover of film and a writer of thoughts. He hopes to make you smile. Rating: Awesome.
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Q: Changing the position of a search bar when it is clicked My idea is to change the position of a search bar which is in the center of a page to the upper right corner when the user clicks or places cursor in the search bar thereby making area in the center part to show the search result.
Also the cursor should remain in the search bar during transition.
this is my html, and css for the search bar

.search-container{
    position: absolute;
    width: 630px;
    height: 100px;
    z-index: 15;
    top: 50%;
    left: 37%;
    margin: -100px 0 0 -140px;
}
.search-wrapper{
    position: relative;
    background-color: #b2dfdb;
    width: auto;
    height: 15em;
    text-align: center;
    border: 1px solid #fff;
    box-shadow: 1px 1px rgba(0,0,0,0.35);
}
.searchbar-wrapper{
    vertical-align: middle;
    width: auto;
    margin: 20px;
    margin-top: 70px;
}
.search-bar{
    float: left;
    width: 400px;
    line-height: 30px;

}

                        <form class="form">

<input class="search-bar" type="text" placeholder="Search for Songs"/>

<input type="submit" id="submit-query" value="Submit">

                        </form>

How can I achieve the transition?

A: This is the way to do it with jQuery(a JavaScript library), if that is what you meant by moving it to the top.

$(document).ready(function(){ //when the document is fully loaded

  $('.search-bar').click(function(){ //select your searchbar by class and onclick create a new function
    var my_searchbar = $(this); //my_searchbar is how you can access your searchbar
    var my_btn = $('.btn'); //get your btn

    my_btn.css('display','none'); //set the css property of the submit button to "none"

    //create animation -60px top and 200px left
    //time is set to 1sec(1000ms)
    //after the animation is finishied create new function
    //in that functin make the submit button visible and animate it 500px to the left
    my_searchbar.animate({ marginTop: '-60px', marginLeft: '200px'}, 1000, function() {
    my_btn.css('display','block');
    $('.btn').animate({marginLeft: '500px'}, 1000);
    });
  });

});
.search-container{
    position: absolute;
    width: 630px;
    height: 100px;
    z-index: 15;
    top: 50%;
    left: 37%;
    margin: -100px 0 0 -140px;
}
.search-wrapper{
    position: relative;
    background-color: #b2dfdb;
    width: auto;
    height: 15em;
    text-align: center;
    border: 1px solid #fff;
    box-shadow: 1px 1px rgba(0,0,0,0.35);
}
.searchbar-wrapper{
    vertical-align: middle;
    width: auto;
    margin: 20px;
    margin-top: 70px;
}
.search-bar{
    float: left;
    width: 400px;
    line-height: 30px;

}

                        <form class="form">

<input class="search-bar" type="text" placeholder="Search for Songs"/>

<input type="submit" id="submit-query" value="Submit">

                        </form>


The 15th annual Loop the Lagoon race brought in nearly $7,000 for the Vacaville Public Education Foundation (VPEF).
More than 800 people participated in the April 28 event in Lagoon Valley, a partnership between Fleet Feet Sports Vacaville and VPEF.
Carol Gilpin, owner of Fleet Feet and race director, and Nolan Sullivan, VPEF board president, led this effort with more than 100 volunteers. The day brought in a total of $6,902.
ACE Charter School had the most attendees per population of school, so they received 100 percent of their entry fees, nearly $900.
This entry was posted in Financial aid, Vacaville Unified School District by lmreyes. Bookmark the permalink.


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Men In Black: International Incident
26-Dec-2019, 4:13 AM
This edit is based off of ideas by Hadam10Rose (mainly just the ending with T being the good guy) and a lot of other tweeks by me.
The edit is 1 hour, 23 minutes (plus credits) and includes no mention of the mole sub plot, now the weapon is kept from everyone because of M's inexperience as an agent, and she learns to trust MIB only at the end (this stems from her childhood when she kept the alien safe), I also removed any scenes with her as a civilian and we're introduced to her as an adult when she is recruited into MIB.
Hemsworth's character is a lot different, his bungling has been scaled back quite a bit, now he's more of an egotist who got lucky that one time he saved the world from the Hive, and needs to prove himself. Liam Neeson is now merely a long-suffering mentor who's faith in H is tested, and then has his fears quashed the more the mission goes on. Lastly, there is a brand new ending that still involves the flying car (the scene where they first use it, in the original cut they're on their way to stop T, but in this edit they're using it for yet another mission)
I wanted to use footage from the NBA promo trailers but they proved to be too long, unfunny and made H look really dense.


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Home Books Once Upon a Time — a Larger Page Count for Kids
Once Upon a Time — a Larger Page Count for Kids
admin March 7, 2016 March 7, 2016 Books, The Publishing Process
Everywhere, kids are seen gripping the latest media device, mesmerized by whatever image displays itself there. In fact, the younger the child, the more adept he or she appears with the tablet. Does this mean kids are turning from books?
Some would say so … a definite "Yes!" But interestingly, books themselves suggest otherwise. In fact, book sizes have increased as page counts grow for children's books.
How could that happen if kids don't read?
Thank J.K. Rowling for saving children's books and urging the popularity that kid's books are cool — even with high page counts.
According to The Guardian, J.K. Rowling is credited for keeping children's books competitive with hefty page counts designed to keep the reader engaged in the story:
… The success of Harry Potter as a major reason for the increase in length of children's novels, with the first book in Rowling's record-breaking series, Harry Potter and the Philosopher's Stone, published in 1997. Booklist contributing editor and author Ilene Cooper said the popularity of the fantasy genre itself also skews the average. "Fantasies tend to be really long," Cooper said. "Authors are building another world. Readers of fantasy want to get lost in those worlds."
This is great news for kids! As authors work to create stories that pull the child into the magic of books, page count will become less important as imaginations fly.
Caroline Horn, editor of the children's books resource, The Reading Zone, had this to say about lengthier children's books:
The belief was that children had quite short attention spans and wouldn't cope with longer stories," she said. "Harry Potter opened publishers' eyes a bit. The pagination increase was down to "the Harry Potter effect".
How awesome is that?
School children using digital tablet outside
Reading for fun is not a thing of the past, and solid stories with strong characters and gripping plots have a place in the hearts of our children. Authors have indeed become better writers, particularly of children's stories. And kids' exposure to all forms of the written word has expanded. Tablets filled with good books are a child's best tool, particularly with the child's interest in technology's newest gadgets.
Let them have 'em, we say, but fill 'em with books … and lots of 'em. Let's support great books together — let's read children's books!
children's books, eBooks, eReaders, kids, tablets, Time to Read. Bookmark.
It's Super Tuesday and Schools are Closing — Now What?
Erin Go Bragh This Day!


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Manaki Brothers
Only ten years after the birth of the film in world cultural centers, the brothers Janaki and Milton Manaki bought the Bioscope 300 film camera and shot the first film shots in the Balkans.
Their appearance in Bitola was not accidental and their marks right here were more mission and chronology of a more significant and comprehensive move of history. They were not just cinematographers, photographers, filmmakers, they were a kind of historical archivists, collectors of all the endless manifestations of life. Their fate, predetermined and set by the overall genesis of this city was in fact the fate of two dreamers who preserved the total immortality of humanity seen through all their photographic and film paintings with light.
Janaki and Milton Manaki with the masters who built the photo studio in Bitola (1904)
The Manaki family
Janaki Manaki in a student uniform at the Romanian high school in Bitola (1897)
The Manaki family comes from the village of Avdela in Grevena, today the Republic of Greece. Chifliksaibia Dimitrie and his wife Lusha (Lucia, Rusija) had two sons Janaki and Manaki and three daughters Evantija, Vasiliki and Sterjana.
Janaki was born on May 18, 1878 and after finishing primary school in Avdela, he enrolled in high school in Bitola, which he completed in the school year 1896/97. In 1898 he opened his own photography studio in Ioannina, where in the same year he was appointed professor of calligraphy and drawing at the Romanian Trade Gymnasium.
Younger brother Milton was born on September 9, 1882. After finishing primary school in his village, he continued his education at the Romanian Trade Gymnasium in Ioannina, which he soon left with an unfinished second grade. He started working in his brother's newly opened studio and soon became a master of photography.
Arrival in Bitola
Between 1898 and 1905, the Manaki brothers frequently traveled and photographed more than 40 settlements.
In Bitola, which at that time was an important socio-political, trade and cultural center, Janaki had a wide circle of friends from the time of his schooling. In 1904, the Manaki brothers bought a shop on the main street "Shirok Sokak" and hired builders to adapt it to a photo studio, which they opened in December 1905.
Wedding procession recorded on today's st. Solunska, Bitola 1904/05
In 1906, at the Grand Exhibition of Photographs in Bucharest, they received gold and silver medals and were honored with the title of court photographers of King Carol I of Romania.
During their visit to Constantinople, Janaki photographed Mehmed Reshad, who became sultan replacing his brother Abdul Hamid II after the Young Turk Revolution.
The first filmmakers in the Balkans
Educated and professional in his field, Janaki photographed many other important people and had the opportunity to travel to many European cities. During his visit to London (or Paris), he bought the Bioscope No.300 camera produced by Charles Urban Trading Co., with which in their village Avdela the brothers shot their first "live pictures" of the Balkans. Their 113-year-old grandmother Despina and a group of women preparing wool for spinning will be remembered forever.
In addition to photography, in Bitola and the surrounding area, the Manaki Brothers were also involved in the filming activity and left a lasting record of a number of fairs, weddings and religious ceremonies.
Parade on the occasion of the Young Turk Revolution, Bitola 1908
In 1908 they filmed and photographed the demonstrations on the occasion of the Young Turk Revolution. Insurgents and various groups that were involved in the demonstrations were photographed, as well as Niyazi Bey, the leader of the Young Turk Revolution.
The most significant year for the film opus of the Manaki brothers was 1911 when they created three video reports: the visit of Sultan Mehmed V Reshad to Thessaloniki and Bitola, the visit of the Romanian delegation to Bitola and the funeral of Metropolitan Emilianos of Grevena.
Sultan Mehmed Reshad V in Bitola, 1911
Balkan Wars and World War I
In 1912, during the Balkan Wars, the Serbian army led by Crown Prince Aleksandar Karadzhordzhevikj was photographed entering Bitola.
Shortly after, World War I began, and in 1915 numerous photographs were taken showing horrific scenes from the war. After occupying Bitola by the Bulgarian army, Janaki was imprisoned and interned in Bulgaria, where he later opened a photography studio in Plovdiv. In difficult conditions, Milton stayed in Bitola and continued to work, but during the bombing of the city, their atelier was damaged and their photographic equipment was destroyed.
Mother with starving children, Bitola 1915/16
Between the two world wars
After the end of the First World War, Bitola was practically destroyed by the two-year bombing, with a large number of human casualties. In 1919 Janaki returned to Bitola and together with Milton they renewed the work of the photography studio.
Poverty reigned in Bitola at that time and although at first their work was not going well, things slowly stabilized, and at the same time the brothers considered expanding their business.
In 1921, they bought a yard on Shirok Sokak with the intention of building a cinema hall and founded the Manaki Cinema. The first cinema screening was shown in the summer garden in August, and in the spring of 1922 the construction of the building began.
Passengers from Germany who traveled five continents with their car. Milton Manaki is leaning on the car on the right. Tumbe Cafe Bitola 1920/1921.
Manaki Cinema
The project for the new cinema building was simple, but even during the construction, two walls collapsed, which created new financial difficulties for the Manaki Brothers, who had already spent all their money. With a loan from the Mortgage Bank and an investment from their new partners Kosta Chomu and Dimitrie Georgievski, they managed to complete the facility and bought the necessary equipment for the cinema functioning.
They already had a good cooperation with Kosta Chomu, especially in the period from 1921 to 1922 when they organized numerous cinema performances at three places in the city – in the halls of the hotels "Bosnia" and "America" and in the cinema garden "Manaki".
The newly built facility of the "Manaki" cinema provided all the conditions for uninterrupted work. It was well equipped with the necessary equipment and there were total 373 seats in the cinema hall. In the beginning, the work went normally and innovative efforts were made to attract the audience, but over time the number of visitors to the cinema screenings began to decline, which in 1925 brought the question of profitability working. The partner owners soon withdrew and in 1927 the cinema was wholly owned by the Manaki Brothers, who were already financially exhausted and in debt. During that period, both in the world and in Bitola, the economic crisis was felt more and more, as well as the pressure of the competition, due to which the work in the photography studio was slowly decreasing.
It was really difficult to attract an audience and it took special effort and innovation to animate it. Special posters were printed, cinema screenings were advertised in an innovative way, and the screenings themselves were enriched with additional content. Screenings of silent films were often accompanied by music orchestras.
The world in that period was in an increasingly severe economic crisis, the competition was increasing, and the handling of the equipment was especially risky, which further increased the price of cinema tickets. The audience was constantly shrinking, and the annuities from the Mortgage Bank were a huge financial pressure.
In the period 1928-1930, the cinema was rented by Risto Zerde from Prilep, a film actor, who at the beginning managed to improve the work a little, but still the desired results were not achieved.
Due to the irregular payment of annuities to the Mortgage Bank, the financial problems of the Manaki Brothers became huge and in 1933 a bankruptcy of the cinema was announced, which gave the bank the right to manage with it.
In 1937, the rented cinema was taken over again by Risto Zerde, who signed a 10-year contract with the Mortgage Bank, with an obligation to renovate the building and modernize the equipment. At the beginning things were slowly improving but in 1939 the building burnt to the ground.
The debts of all the creditors were covered by the insurance received, but the biggest losers were the Manaki brothers who had previously sold a good part of their inherited property to build the cinema.
Manaki Cinema after the fire, 1939
Before the outbreak of World War II, Janaki moved to Thessaloniki, where his son Dimitrie was educated.
Milton remained in Bitola and in 1940 he photographed the consequences of the Italian bombing, and during the fascist occupation and various people and events.
In 1944 he photographed the withdrawal of the Bulgarian army, and on November 4, 1944 the entry of the fighters from the 7th Macedonian Brigade in liberated Bitola, the reception organized by the citizens and the events that followed.
After the liberation
Manifestations on the occasion of the liberation of Bitola, November 4, 1944
Janaki did not return to Bitola this time and remained in Thessaloniki where his son died on December 14, 1947, and seven years later, in 1954, Janaki Manaki also died on an unknown date.
Milton continued to work and create in Bitola and was an honorary photographer of many important people who visited the city such as the President of Yugoslavia – Josip Broz Tito. Although weakened by diabetes and old, he was actively involved in deciphering the photo material in the regional department of the State Archives in Bitola.
He died on March 5, 1964.
Known as the first Balkan cinematographers, Janaki and Milton Manaki incorporated all their knowledge and selfless love into the recording of a wide range of events and people.
In a period of sixty-six years, during which important historical events took place, as true pioneers in the field of photo and video documentary, the Manaki Brothers left a rich photo and video material, with great archival and artistic value.
In their honour, the International Film Camera Festival "Manaki Brothers'' has been held since 1979 in Bitola, which is the first and oldest world film festival dedicated to the creativity of world cinematographers.
Their predestination, their appearance through "painting with the light" to reach all the similarities in the Balkans and Europe was a rare example of courage, faith and full commitment. The two brothers titanically faced the epic contrasts of life and death that met in this enlightened European point, depicting Sisyphus' eternal struggle to reach the light in the sky against all the darkness that came as floods with the centuries that suffocated the Balkan cruelly, unjustly and mercilessly.
They have left a rich and significant film and photographic opus of over 7,715 negative glass plates, 2087 plan films, 8711 roll films and 17854 photographs, which together with their Camera 300 are kept in the Archive of Bitola.
The entire saved video material, which is grouped in 42 titles, is kept in the Cinematheque of the Republic of Northern Macedonia.
This webpage has been produced with the financial assistance of the European Union. The contents of the webpage are the sole responsibility of Municipality Bitola and can in no way be taken to reflect the views of the European Union, the participating countries and the Managing Authority