text:
\section{Introduction}
	The ability to perceive and process quantity is undoubtedly one of the crucial skills for both humans and animals.

	There are many suggestions that there is a common mechanism for representing approximate numerosity in animals and humans \cite{Xu2000}. However, human quantification skills are much more advanced than those of animals. We not only estimate and perceive numerosity but we can count, i.e. enumerate objects by tagging them by numbers' names \cite{alibali1999function,wynn1992children}.

	The ability to count itself is not simply the recitation of numbers. Researchers found that children, although familiar with the numbers' names and their order, are not able to count objects \cite{wynn1990children,wynn1992children,le2007one}. There have been two tests introduced to investigate the development of number cognition: HM (How many) and GM (give me a number). The understanding of numbers is difficult to define; It has been assumed that, the child understands the meaning of the number when she passes a GM test \cite{wynn1992children,le2007one} and so provides number of objects that was asked for.

	A significant influence of gestures in the learning process of counting  has been demonstrated in \cite{alibali1999function,fischer2012finger,graham1999role}. There is evidence that pointing and touching gestures facilitate counting accuracy in children and non-human primates \cite{alibali1999function}. They are not only used for keeping track of counted objects, but may also help with the implementation of one-to-one correspondence by contributing to the individuation of items, and thereby to the segmentation of the counting task into smaller units \cite{alibali1999function}. Such a behaviour might also provide support for working memory in the one-to-one correspondence task \cite{alibali1999function}. Furthermore, finger counting is considered to be an essential stage in number cognition \cite{Vivian2014,goldin2014gestures,soylu2018you}. Thus, there is strong evidence that embodiment has an influence on cognition, including for abstract concepts such as numbers. More about developmental robotics and embodied cognition can be found in \cite{cangelosi2015developmental}.

	In the field of connectionist modelling of the enumerating process, one of the first models was created by Amit in 1988 \cite{amit1988neural}. It consisted of a neural network capable of counting clock chimes. The model shows that Elman network can develop the ability to count four symbols using supervised learning with backpropagation through time. In 1999 Rodriguez et al. \cite{rodriguez1999recurrent} presented a recurrent neural network that was capable of counting letters. Another important model can be found in \cite{ahmad2001simulation} and \cite{ahmad2002connectionist}. The main focus in this model is on the distinction between sequential enumeration and subitizing (i.e. the immediate apprehension of small numerosity).

	Although there have been several models of the enumeration process, not many of them have been implemented on a robot platform or robot simulator. In this field Ruci\'nski et al. \cite{rucinski2011embodied}, using the iCub humanoid robot \cite{metta2010icub}, developed a model capable of number comparison and parity judgement that took into account the SNARC (spatial-numerical association of response codes) effect. In another work \cite{rucinski2012robotic} they constructed a recurrent neural network that is first taught to recite the numbers in the counting order and then to count the presented objects with or without proprioceptive gesture input from iCub robot.

	Finally, the influence of finger counting on number word grounding has been documented in \cite{DiNuovo2014,DiNuovo2014a,DiNuovo2015,Vivian2014,di2017embodied,shu20884}. In these studies, the iCub platform (and its simulator) was used to provide embodied conditions as well. The results support the hypothesis that learning the number words in sequence along with finger configurations helps the fast building of the initial representation of number in the robot \cite{DiNuovo2014a}.

	To increase embodiment, Ruci\'nski's model of learning to count \cite{rucinski2012robotic,rucinski2014modelling} has been modified and extended in the work presented here. This makes the simulations more comparable with those obtained by psychologists (e.g. \cite{alibali1999function}).

	In Ruci\'nski's work, a cognitive model of learning to count uses a recurrent artificial neural network which has as input visual stimuli and proprioceptive gesture information. The network is first pre-trained to recite numbers from 1 to 10. After achieving this skill, the network learns to stop counting when the number of elements from the visual input is reached. In this way, the neural network (NN) is counting the elements from the visual stimuli. In his work, the behaviour and results of the training of the network were compared with respect to the proprioceptive gesture information. The results showed that the learning of counting procedure is better when the gesture input is included.

	In the model presented in this paper, the gestures are produced by the network as an output, or there is an output-input feedback loop covering the produced gestures. Such a set-up corresponds more closely to the experiments with children in which active gestures were investigated.

	\begin{comment}After including the mentioned changes, the obtained model should provide possibility to carry out the experiments in a way more similar to children's psychological tests. Thus, we believe the results will give the significant input about how to simulate process of grounding the numbers' meaning by the robot. The results might also give some information about how the process of learning is conducted in a human brain and how external proprioceptive inputs influence it.

	In this paper we attempt not only to include proprioceptive gesture signal as an input but also we teach the network to produce the signal itself. One of the architectures uses output-input feedback loop of gestures as suggested in \cite{rucinski2014modelling}. This is not the only configuration that was considered. The variety of experimental conditions will be presented in the following sections. One of these network configurations was a hint to try more complex multi-stage training also presented in this article.
	\end{comment}

	The paper is organised as follows. First, the model with its architecture and evaluation is presented. Then, the first study presenting a variety of experimental conditions is described.
	This study will help us understand the dependencies in the model and how the model internal connections influence its performance.

	The second study describes multi-stage training where the network was separately pre-trained to produce the sequence of words and a pointing signal. The article finishes with conclusions and discussion of future work.

	\section{Model description}
	Similarly to \cite{rucinski2012robotic,rucinski2014modelling} a recurrent neural network model based on Elman architecture \cite{elman1990finding} was used to model the process of acquisition of the counting skills. Different structures of the neural network architecture were considered in order to compare their performance, and to make the training more similar to experiments with children.

	The most complex structure can be found in Fig.~\ref{fig:architecture} where all the possible layers and connections between them are shown.

	The environment that was used for creating the network is the Tensorflow library and Python.	As for the robotic platform, the iCub humanoid robot and its simulator was used. The iCub is an open source humanoid robot designed to support cognitive developmental robotics research \cite{metta2010icub}. In the current state the platform is a child-like robot with 53 degrees of freedom and is 1.05 m tall. The iCub simulator has been designed to reproduce the physics and the dynamics of the physical platform. It permits the creation of realistic physical scenarios in which the robot interacts with a virtual environment \cite{DiNuovo2014a}.
	\begin{figure}[tbp]
		\centerline{\includegraphics[width = 1\columnwidth]{iCub.png}}
		\caption{The iCub simulator. Robot performing pointing.}
		\label{fig:iCub}
	\end{figure}
		\subsection{Counting task}

		The main task of the model is to produce a correct sequence of number words and pointing gestures. These output words (described in section \ref{model output}) correspond to the numbers from 1 to 10. The expected result is that the NN will produce the sequence of number words and stop when it reaches the last object. For example, for four objects it should produce sequence of four words corresponding to: ``one'', ``two'', ``three'', ``four''. The model was trained and tested for number of objects ranging from 0 to 10.

		\subsection{Model Architecture}\label{model structure}
		\begin{figure}[tbp]
			\centerline{\includegraphics[width = 1\columnwidth]{Architecture.png}}
			\caption{Model architecture. Gray polygons represent all-to-all connections between the layers of neurons. Numbers in the rectangles corresponds to the number of neurons, ``nh'' represents the variable number of hidden units}
			\label{fig:architecture}
		\end{figure}
		The inputs and outputs of the network are organized in several blocks. Inputs: Visual and Trigger. Outputs: Gestures and Numbers (see Fig.~\ref{fig:architecture}). The blocks: Gestures\textsuperscript{-1} and Context are not considered as model inputs as they are part of the recurrent loops (they are not provided externally to the model). However, in some architectures (described in section \ref{exp_cond1}) the gestures are not produced by the model but only provided to it externally. In these cases, the gesture input will have the same structure as the one described in the section with model outputs (\ref{model output}).

		The network has two recurrent loops: Elman recurrent loop where the hidden units from previous time step are copied to the input units; and Jordan recurrent loop where the network copies the output block \cite{elman1990finding}.
		The architecture of  the network is very similar to the one described in \cite{rucinski2012robotic} with the difference of the additional output and second recurrent loop.

		\subsubsection{Model inputs}\label{input}

		\begin{itemize}
			\item Trigger:
			This is a single unit input; its role is to indicate when the counting process should start. The network is supposed to produce zeros at the output whenever the trigger unit is off. The desired counting is produced when the trigger is set to one. The trigger input value remains constant throughout the whole time sequence in the training and testing data sets.
			\item Visual: This is a 1-dimensional vector (saliency map), which can be considered a simple model of retina (same as in \cite{rucinski2012robotic}). This input consists of 20 units corresponding to 20 spacial locations. Depending on the presence of an object in a given location, each unit of the visual input will be activated or not. The maximal number of objects assumed in presented experiments is 10, their locations are chosen randomly. The visual input is normalized to 1 in order to eliminate possibility of discrimination of cardinality based on summed activation of that input.
		\end{itemize}

		\subsubsection{Model outputs} \label{model output}

		\begin{itemize}
			\item Numbers: This is a 1-dimensional vector with 10 units. For simplification and to facilitate the output and performance analysis, the one-hot coding has been used.
			\begin{comment}
			Check performance for few examples of other codding
			\end{comment}
			This type of coding helps to calculate the percent accuracy of the network. It is, however, different from phonetic words representing the numbers, which are non-orthogonal.
			\item Gestures: The values of proprioceptive signal were collected using the iCub humanoid robot simulator. The robot was made to point to 20 locations in front of it, corresponding to 20 locations in the visual input. These locations were uniformly distributed in a line placed 15 cm above its hip and 30 cm in front of it. The length of the line was 30 cm. The right arm Cartesian controller was used to move the arm. The controlled kinematic chain consists of 6 degrees of freedom: torso yaw and pitch and the first 4 joints of the robot arm (shoulder and elbow). The torso roll angle was disabled to avoid unnatural postures. The joint angles were stored. Additionally, the arm base position (where the iCub arm is down aside its body) was also included. The Principle Component Analysis has been applied on the  data and the first 3 principal components were selected. This reduced the dimensionality of the original signal from 6 to 3. These 3 components carry more than 97\% of the total statistical information of the original data.

		\end{itemize}

		\subsection{Evaluation of the results}\label{evaluation}
		Similar to \cite{rucinski2012robotic}, the intention in the model design was to perform experiments inspired by those with child participants. More precisely, the study presented in \cite{alibali1999function} - where the function of gestures in learning to count was investigated - was used to build this model and counting task. In this text we present the training for a variety of network configurations and we discuss whether it can be compared to human learning.

		To obtain the results presented in this article for each type of configuration, at first a hyperparameter search was performed to chose the learning rate value and hidden layer size.

		It has been observed that very often the size of a hidden layer in some range did not influence the result much. Thus, the final choice of the learning rate and hidden layer size was made in such a way that those parameters where the same over different experimental conditions. The reason for this was to make the results comparable. Their values will be presented separately for each study (see sections \ref{combination-training} and \ref{double-training})

		After these parameters where chosen, the model was trained in 15 independent repetitions in order to verify the robustness of the results and obtain reliable standard deviation and mean value of them. For each of these 15 training runs, the network has been tested with 50 different test sets to obtain the mean value of the results for each of the training processes.
		The values of the training data and test data were generated as separate sets. A random generator has been used to obtain them: the training order of numbers was randomised (from 1 to 10) and the spacial locations of the objects, out of 20 possible positions (note that possible arrangement of the objects grows exponentially with their number up to 6.7E11 for 10 objects).

	\section{Study 1: combination of modalities} \label{experiment1}

	The intention of this study is to investigate a variety of the training configurations that can be obtain by the model. This will help to comprehend the dependencies in the model and what kind of inputs and outputs have significant influence to the model performance.

		\subsection{Experimental Conditions}\label{exp_cond1}

		All possible combinations of inputs and outputs together with their correlations with human trials are presented below. Several configurations were omitted as they had no psychological reference e.g. training with only gestures as an input and as output. The results for listed conditions will be presented in Table~\ref{tab1}.

		Some of these experimental conditions are similar to those presented in \cite{rucinski2012robotic}. There are however, some differences in the training procedure as described in section \ref{combination-training}. All of the presented configurations used trigger input.

			\subsubsection{Input: Visual; Output: Numbers}
			Similar to the training shown in \cite{rucinski2012robotic}. This configuration corresponds to the condition when a child is only counting the objects since is not allowed to point to them. This experimental condition could be obtained by the network as shown in Fig.~\ref{fig:architecture}, where Gestures output and input blocks (together with output-input feedback loop between them) were removed.
			\subsubsection{Input: Visual; Output: Gestures}
			The purpose of this condition was to check the hypothesis that gesture proprioceptive signals might be easier to learn in a separate dedicated training, when no counting is performed. This configuration corresponds to the situation when a child is asked to point to all objects from left to right. The network structure is very similar to the previous experimental condition with the only difference that the numbers' output is replaced by the gestures' one.
			Although training seems to be similar, there is a direct correlation between visual input and gestures (gesture signal exactly corresponds to randomly chosen visual position). There is no such direct correlation between number's tag and visual input e.g. the object in position 7 can be a second object from the left (have a number's tag 'two'). Thus, we presumed such training might be easier for the network and can give interesting results.

			\subsubsection{Input: Visual; Output: Numbers and Gestures}
			This training configuration is similar to the next one. However, the output-input feedback loop was not used (together with gesture input block, which was a part of that loop).

			Such training corresponds to the situation where the child is producing the gestures herself and at the same time she counts the presented objects.

			\subsubsection{Input: Visual (and Gestures from a loop); Output: Numbers and pointing}
			This configuration is depicted in Fig.~\ref{fig:architecture}. In this case the network uses all its inputs and outputs.

			We consider this training (as well as the previous one) to correspond to the situation where the child is producing the gestures herself while counting the presented objects.
			Pointing to the object is produced together with the number word. At the next step (next object), such proprioceptive gesture signal from the previous pointing is used as an input.
			Although, this condition seems more complete, it is also more complicated and might be more difficult to train. Moreover, the discrete character of time (causing the one time step difference between gesture input and output) might implicate that it is not as adequate to the child pointing case as we intended it to be.
			\begin{comment}Because of the discreet time character used in the model, the input gesture signal is always from the previous time step. There has been another variation of this configuration checked where the time step of the input was corresponding to the output number and the network was trained to point (output pointing) to the object from the following time step. The difference of the performance for such configuration compare to this one was not significant and so the results are not presented.
			\end{comment}

			\subsubsection{Input: Gestures; Output: Numbers}
			This training corresponds to a situation when the child's hand is guided by the teacher and they are pointing to the object, but the child eyes are covered so she cannot see the objects.
			\subsubsection{Input: Gestures; Output: Numbers and Gestures}
			This is very similar training to the previous condition, the only difference is that the gesture signal is additionally produced by the network (there is no output-input feedback loop in this case). This training is intend to show if such a passing of pointing signal through the network could give any benefits for the training (compare to gestures used as only input or only output cases).
			\subsubsection{Input: Visual and Gestures; Output: Numbers} \label{input_gest}
			A similar experimental configuration as used in \cite{rucinski2012robotic}. In this condition, the proprioceptive signal from gestures was given as an input to the network together with the visual input (and trigger). Ruci\'nski compared this to a situation where the pointing is performed by a puppet, as described in \cite{alibali1999function}, and presented to a child that is required to count (without performing pointing herself). As the nature of the gesture signal is proprioceptive and not visual, we would rather consider this case more similar to the situation where the child hand is held and the pointing is performed by an external teacher.
			\subsubsection{Input: Visual and Gestures; Output: Numbers and Gestures}
			This configuration is similar to the condition described previously with a gesture signal additionally produced by the network.

		\subsection{Model Training} \label{combination-training}
		The network is trained to count the objects presented in the form of visual input and/or in the form of proprioceptive gesture signal. The training uses supervised learning by backpropagation through time. Differently to \cite{rucinski2012robotic}, the results were obtained using one stage training - no pre-training was used.
		\begin{comment}In fact, similar training was peforem together with pre-training as described in \cite{rucinski2012robotic} where network was trained to recite the numbers from 1 to 10 (see Fig.~\ref{fig:architecture}, Stage 1A). The results were not significant from point of view of this paper and there are not presented.
		\end{comment}
		The training data set consists of two sequences of length 12.

		The visual input for both these sequences was the same; it was constant over the 12 time steps and it was representing the positions of objects in front of the robot. In the first sequence, the trigger input, all number target outputs and target and/or input gestures were equal to zero throughout all time steps. For the second sequence the trigger input was equal to one, the number targets consisted of a sequence of words corresponding  to the numbers (from 1 to up the number of objects presented to the model) and the remaining words of the sequence were set to zero. Similarly, target and/or input gesture sequence consisted of proprioceptive signal corresponding to the locations of presented objects, from left to right. The remaining positions in the sequence were consequent repetition of the last pointing value. This approach for gestures is reasonable when they are given as an input; when a zero value or base position is set in the remaining elements of the sequence, the NN easily learns to distinguish it and stop counting when that value appears (reaching 100\% accuracy).

		Such a collection of two sequences was constructed for each of 11 possible numbers (possible amount of objects) from 0 to 10 (giving 22 sequences, each with 12 times steps). We will call this set a sub-epoch set, the order of numbers (represented by pair of sequences) was chosen randomly with an alternating trigger. Training sub-epoch and test sub-epoch sets were randomly generated; each of them consists of all the same numbers from 0 to 10 but each of these numbers have objects in random, different spacial locations (for visual input and gestures).

		The Adam optimizer was used for the training. Each training cycle consists of 20000 sub-epochs, using batch training (the batch consists a full 22 element sub-epoch set). A starting learning rate was chosen to be 0.005 and a hidden layer size was set to 68 (chosen in a way described in section \ref{evaluation}). An exception was made for a condition 2 (Inputs: V, Outputs: G) and the learning rate was set to 0.02 in order to obtain good results. The weights were initialized using Glorot uniform initializer and biases were initialized with zero values.

		The counting numbers are considered as correct when the whole sequence for a given number of objects was fully correct.
		For the gesture output, the sequence could be partially correct when some of the sequence elements were correct (e.g when counting 10 elements and the pointing was performed correctly to 3 of them, the performance for that sequence would be 30\%) . As the remaining elements of the sequence are constant and are less important from the point of view of the correct counting (as they are repetition of the last element) we will only consider n+1 first elements of the sequence, where n is the number of counted objects.
		\begin{comment}
		learning of the sequence of constant elements was easier for the network and to make the accuracy fair for each number of objects, only the first n+1 number of elements of the sequence were checked for correctness, where n corresponds to number of presented elements. To explain that we will use the example: when 1 object is presented to the network, the network is suppose to produce gestures pointing to that object through the whole sequence, if we consider the mean value of correctness for whole sequence then even if the network did not pointed correctly at the beginning but started to point to proper location from second or third time step the accuracy of such output would be 91.7\% or 83\% respectively (when using definition described above: 50\% or 0\%).
		\end{comment}

		\subsection{Results}
		\begin{table}[htbp]
			\caption{Collected results for Study 1}
			\begin{center}
				\begin{tabular}{>{\centering}p{0.1\columnwidth}
						>{\centering}p{0.14\columnwidth}
						>{\centering}p{0.14\columnwidth}
						>{\centering}p{0.2\columnwidth}
						>{\centering\arraybackslash}p{0.2\columnwidth}}
					\hline
					\multicolumn{3}{c}{\textbf{Experimental Conditions}}&\multicolumn{2}{c}{\textbf{\% of correct responses$^{\mathrm{a}}$}}\\

					\textbf{Cond.}&\textbf{Inputs}&\textbf{Outputs}&\textbf{Counting$^{\mathrm{b}}$}&\textbf{Gestures} \\
					\hline
					1)&V&N&90.3 (4.9)&-\\
					\hline
					2)&V&G&-&60.4 (8.7)\\
					\hline
					3)&V&N, G&86.8 (6.8)&34.2 (2.9)\\
					\hline
					4)&V (+ G$^{\mathrm{c}}$)&N, G&84.9 (8.1)&32.5 (4.0)\\
					\hline
					5)&G&N&97.5 (1.8)&-\\
					\hline
					6)&G&N, G&96.9 (1.6)&97.7 (1.9)\\
					\hline
					7)&V, G&N&96.8 (3.8)&-\\
					\hline
					8)&V, G&N, G&98.6 (1.2)&92.0 (4.4)\\
					\hline
					\multicolumn{5}{l}{$^{\mathrm{a}}$Values are presented in a form: mean (SD).}\\
					\multicolumn{5}{l}{$^{\mathrm{b}}$\% of correct counting responses for 1 to 10 items.}\\
					\multicolumn{5}{l}{$^{\mathrm{c}}$Gestures from a loop.}
				\end{tabular}
				\label{tab1}
			\end{center}
		\end{table}

		The results of all simulations are presented in Table~\ref{tab1}. The results for counting are higher than those presented in \cite{rucinski2012robotic}. This is especially visible in case of experimental conditions 1 (with visual input and numbers as an output). There are several reasons for this. The main one depends on the usage of the Adam optimizer, which was able to increase the performance of the network and learn counting much better even when only visual input was given. Additionally, the improvement of the training was achieved by using batch training (with sub-epochs sets used as batches). When gradient descent optimizer was used we obtained results much more similar to \cite{rucinski2012robotic}

		In our study the results were not much improved when pre-training of number recitation was used (as it was in \cite{rucinski2012robotic}). The reason for this might be that they were already very high. For example for the configuration 7 (inputs: V,G and outputs: N) we obtained 96.8\% (Table~\ref{tab1}) and when pre-training of numbers (Stage 1A from Study 2) was used, additionally, we got a result of 97.6\%. Thus, those results were not presented.

		All of the repetitions were separate simulations giving a final neural network tested with test data. A one-way ANOVA was used between  groups of results for statistical analysis.

			\subsubsection{Counting results}
			As expected, and described in \cite{rucinski2012robotic}, the results with gesture input were significantly higher than those without them.
			When we compare the corresponding results where the gestures were added as an input: the analysis showed a strong significant difference when condition 1 with 7 and 3 with 8 were compared (${p<0.001}$ in both cases). It was also visible that using gesture input instead of visual, gave strong significant improvement as well (comparing 1 with 5 and 3 with 6 gave ${p<0.001}$).
			Surprisingly, there was no improvement of counting observed when the network was trained to produce gestures and used them in an output-input feedback loop. The results for such training were worse than those where only visual input was provided (see condition 1 and 4 in Table~\ref{tab1}). What was also unexpected, the configuration with the output-input feedback loop (4\textsuperscript{th} condition) performed worse than the one without the additional gesture loop (3\textsuperscript{rd} condition).
			However, the difference was not statistically significant ($p = 0.5$).
			As mentioned before, the reason why results with a loop are not better might be that the model is more complicated and needs more training time or it might be because of time step delay appearing in the output-input loop. When gestures were used as an independent input and output their influence on counting (compare to the case where gestures were only the input) was not visible.

			\subsubsection{Gestures results}
			Important observation from the results is that the pointing performance was much better when the network was trained only to produce gestures without being trained to count - the percentage of correct responses was almost doubled (see conditions 2,3 and 4 in the Table~\ref{tab1}). Obviously, the configuration with gesture inputs gave much better gesture results, as the network have the full information at the input (condition 6 and 8).

	\section{Study 2: double pre-training}\label{double}
	As the results from the Study 1 did not provide some of the expected improvement of the network performance, another experiment was proposed where a more complex multi-stage training was introduced. In this study we check the performance of learning to count and to produce gestures when one or two pre-training stages were added (for number recitation like in \cite{rucinski2012robotic} and for pointing).

		\subsection{Experimental Conditions}\label{exp_cond2}
		There are only two types of experimental conditions considered in this study (both discribed in section \ref{exp_cond1}). They both can correspond to a situation where a child is producing gestures herself and counts presented objects at the same time:
		\begin{itemize}
			\item Input: Visual (and Gestures from a loop); Output: Numbers and pointing
			\item Input: Visual; Output: Numbers and Gestures

		\end{itemize}

		\subsection{Model Training}\label{double-training}
		The training of this model can be divided into three stages, two pre-trainings (Stage 1A and Stage 1B) and the main training (Stage 2). The architecture of the trained networks from both pre-trainings can be found in Fig.~\ref{fig:double}. These two stages could be run in parallel as they use different weights (different parts of the NN).
		We considered and compared simulations without pre-training, with one or with both pre-trainings.

		All the training stages used a supervised learning by backpropagation through time with the Adam optimizer.

		\begin{figure}[tbp]
			\centerline{\includegraphics[width = 1\columnwidth]{Architecture2.png}}
			\caption{Double pre-training. Polygons represent all-to-all connections between the layers of neurons. Numbers in the rectangles corresponds to the number of neurons, ``nh'' represents their number in the hidden layer}
			\label{fig:double}
		\end{figure}

		The training described below was performed in two training conditions where a sequence of pointing proprioceptive signals was constructed in a different way. The first condition was the same as described in Study 1:
		\begin{itemize}
			\item ``Stay at the last one'': For the trigger input set to zero, the input gestures were equal to zero throughout all time steps. For the trigger input set to one, input gestures consisted of a sequence corresponding to the locations of the presented objects, from left to right, and the remaining gestures in the sequence were consequent repetition of the last pointing value. As mentioned before, this approach is reasonable when gestures are given as an input. This condition was used in the second study to make the results comparable with Study 1.
			\item ``Go to base'' (introduced one): When the trigger was equal to zero, the input gestures were set to the base position signal throughout all time steps. The base position was obtained when the robot arm was directed down the robot body (the iCub ``home position'' for the robot simulator).
			When the trigger was equal to one, the input gestures consisted of sequence corresponding to the locations of presented objects, from left to right (as before). The remaining gestures were set to the base position value. This condition seems to be much more similar to a human condition, where a child simply stops pointing after the counting is finished.
		\end{itemize}
			\subsubsection{Stage 1A number pre-training}
			Psychological studies shows that human children can recite numbers, so they acquire a list of tags (numbers' names), prior to acquisition of counting skills \cite{gelman1980young}. In this training stage the network is taught to recite the numbers' names from 1 to 10 (same type of pre-training as in \cite{rucinski2012robotic}). In this stage, neural network uses only the trigger as an input (see Fig.~\ref{fig:double}). Similarly as in Study 1, the training data set consist of two sequences of length 12. In the first sequence, the trigger input and all number target outputs were set to zero throughout all the time steps. In the second sequence, the trigger input was one and the number targets consist of a sequence of words corresponding  to the recited numbers, from 1 to 10 and the remaining 2 words were set to zero.

			The training was run for 7000 epochs with a learning rate of 0.01 and a hidden layer with 20 neurons and the weights were updated using batch training. The size of the hidden layer and learning rate were chosen using the same approach as described in section \ref{evaluation}.
			\subsubsection{Stage 1B gesture pre-training}
			When children start to count (around 2-3 years old \cite{le2007one}) they are already capable of complex motor skills (children successfully perform reaching task before their age of 28 months \cite{bertenthal1998eye}). To reflect this in our studies, we decided to add another pre-training where the network is taught to point correctly to the locations indicated by the objects in the visual input.  In this stage, the neural network uses visual and trigger inputs and produces a gesture sequence as an output (see Fig.~\ref{fig:double}).
			This training stage is conducted in the same manner as the training in the experimental condition number 2 in the first studies (with additional ``go to base'' condition being applied).
			\begin{comment}
			Pottentially 1.5 paragraph above can be removed
			\end{comment}

			\subsubsection{Stage 2 learning to count}
			This training is run in exactly the same manner as Study 1's trainings. With the remark that two training conditions were considered.  However, the learning rate was again chosen in a manner described in section \ref{evaluation} and set to 0.001. The hidden layer was a sum of hidden layer neurons from Stage 1A and 1B. We found that results for Stage 1A were practically perfect for a wide range of hidden layer size, so the size used in that stage (20 neurons) was chosen to optimize the performance of the whole multi-stage training. As mentioned before this training was conducted for two configurations with the output-input loop and without it (see section \ref{exp_cond2}). The network is fully connected, thus, it can be found that some of the weights were not pre-trained. Their values were initialized using Glorot uniform initializer.

		\subsection{Results}
		\begin{table}[tbp]
			\caption{Collected results for Study 2 - Counting}
			\begin{center}
				\begin{tabular}{>{\centering}p{0.05\columnwidth}
						>{\centering}p{0.05\columnwidth}
						>{\centering}p{0.15\columnwidth}
						>{\centering}p{0.15\columnwidth}
						>{\centering}p{0.15\columnwidth}
						>{\centering\arraybackslash}p{0.15\columnwidth}}
					\hline
					&&\multicolumn{4}{c}{\textbf{Pre-trainings used$^{\mathrm{a}}$}}\\
					&&\textbf{No}&\textbf{Stage 1A}&\textbf{Stage 1B}&\textbf{Both}\\
					\hline
					\multirow{2}{*}{\textbf{S$^{\mathrm{b}}$}}&\textbf{L$^{\mathrm{d}}$}&84.9 (8.1)&87.9 (5.9)&95.0 (2.4)&97.5 (1.3)\\
					\cline{2-6}
					&\textbf{NL$^{\mathrm{e}}$}&86.8 (6.8)&88.5 (5.2)&95.9 (2.6)&97.8 (1.0)\\
					\hline
					\multirow{2}{*}{\textbf{B$^{\mathrm{c}}$}}&\textbf{L}&85.0 (11.7)&89.7 (5.4)&99.1 (1.4)&99.6 (0.4)\\
					\cline{2-6}
					&\textbf{NL}&87.4 (7.9)&87.4 (6.4)&99.0 (0.9)&99.5 (0.6)\\
					\hline
					\multicolumn{6}{l}{$^{\mathrm{a}}$\% of correct responses for 1 to 10 items, in a form: mean (SD).}\\
					\multicolumn{6}{l}{$^{\mathrm{b}}$``Stay at the last one'' training approach.}\\
					\multicolumn{6}{l}{$^{\mathrm{c}}$``Go to the base'' training approach.}\\
					\multicolumn{6}{l}{$^{\mathrm{d}}$Training with output-input gesture loop.}\\
					\multicolumn{6}{l}{$^{\mathrm{e}}$Training with no output-input gesture loop.}

				\end{tabular}
				\label{tab2}
			\end{center}
		\end{table}
	\begin{table}[tbp]
		\caption{Collected results for Study 2 - Gestures}
		\begin{center}
			\begin{tabular}{>{\centering}p{0.05\columnwidth}
					>{\centering}p{0.05\columnwidth}
					>{\centering}p{0.15\columnwidth}
					>{\centering}p{0.15\columnwidth}
					>{\centering}p{0.15\columnwidth}
					>{\centering\arraybackslash}p{0.15\columnwidth}}
				\hline
				&&\multicolumn{4}{c}{\textbf{Pre-trainings used}}\\
				&&\textbf{No}&\textbf{Stage 1A}&\textbf{Stage 1B}&\textbf{Both}\\
				\hline
				\multirow{2}{*}{\textbf{S}}&\textbf{L}&32.5 (4.0)&33.0 (2.9)&43.0 (2.9)&64.8 (2.3)\\
				\cline{2-6}
				&\textbf{NL}&34.2 (2.9)&34.8 (2.1)&45.3 (2.4)&65.1 (3.1)\\
				\hline
				\multirow{2}{*}{\textbf{B}}&\textbf{L}&24.2 (2.2)&25.2 (2.5)&44.2 (2.4)&56.5 (2.3)\\
				\cline{2-6}
				&\textbf{NL}&26.7 (2.8)&25.4 (1.8)&45.2 (2.8)&56.9 (2.2)\\
				\hline
			\end{tabular}
			\label{tab3}
		\end{center}
	\end{table}
	\begin{figure}[tbp]
		\centerline{\includegraphics[width = 1\columnwidth]{Plot_pre.png}}
		\caption{Results for different pre-training options, for the network with no gesture loop and for both training conditions (S and B).}
		\label{fig:plot_pre}
	\end{figure}
	\begin{comment}
	\begin{table}[tbp]
		\caption{Collected results for Study 2 - Shorter training}
		\begin{center}
			\begin{tabular}{>{\centering}p{0.05\columnwidth}
					>{\centering}p{0.05\columnwidth}
					>{\centering}p{0.15\columnwidth}
					>{\centering}p{0.15\columnwidth}
					>{\centering}p{0.15\columnwidth}
					>{\centering\arraybackslash}p{0.15\columnwidth}}
				\hline
				&&\multicolumn{4}{c}{\textbf{Pre-trainings used}}\\
				&&\multicolumn{2}{c}{\textbf{No}}&\multicolumn{2}{c}{\textbf{Both}}\\
				&&\textbf{Counting}&\textbf{Gestures}&\textbf{Counting}&\textbf{Gestures}\\
				\hline
				\multirow{2}{*}{\textbf{L}}&\textbf{S}&84.9 (8.1)&87.9 (5.9)&95.0 (2.4)&97.5 (1.3)\\
				\cline{2-6}
				&\textbf{B}&85.0 (11.7)&89.7 (5.4)&99.1 (1.4)&99.6 (0.4)\\
				\hline
				\multirow{2}{*}{\textbf{NL}}&\textbf{S}&86.8 (6.8)&88.5 (5.2)&95.9 (2.6)&97.8 (1.0)\\
				\cline{2-6}
				&\textbf{B}&87.4 (7.9)&87.4 (6.4)&99.0 (0.9)&99.5 (0.6)\\
				\hline
			\end{tabular}
			\label{tab4}
		\end{center}
	\end{table}
	\end{comment}
	The model was trained in 15 independent repetitions, where each of the training cycles (repetitions) finished with testing the network with 50 different test sets. The mean values of the results and their standard deviations (in brackets) are shown in Table~\ref{tab2} and \ref{tab3}. For statistical analysis a one-way ANOVA was used to compare each of the presented results with one another.
	\subsubsection{Counting results}
	As can be seen in the tables and in Fig.~\ref{fig:plot_pre} the best results were obtained when both pre-trainings were applied and the worst when no pre-training was used, as expected.
	Statistical analysis in all 4 conditions (S+L, S+NL, B+L, B+NL) between different pre-training options was performed:
	\begin{itemize}
		\item with no pre-training and with Stage 1A, showed that there is no significant difference.
		\item with no pre-training and with Stage 1B, showed that there is a strong significant difference (${p < 0.001}$ for all conditions)

		\item with Stage 1A pre-training and with both pre-trainings, showed that there is a strong significant difference (${p < 0.001}$ for all conditions)

		\item with Stage 1B pre-training and with both pre-trainings, that there is a significant difference for the training condition ``stay at the last one'' but no significance for ``go to base'' ($p = 0.0017$, $0.02$, $0.19$, and $0.11$ respectively)
	\end{itemize}
	\begin{comment}
	with no pre-trainings and Stage 1Awith both of show significance of the difference (p = .... respectively)

	To illustrate that even more, we conducted shorter pre-training (Stage 1A) and shorter final training in a way that their total number of sub-epochs is equal to 20000. This number of iteration corresponds to the number of iterations in Stage 2 training (so to number of iterations in training with no pre-trainings). The best results were obtained when gesture pre-training lasted 4000 iterations and final training 16000. As you can see in Table~\ref{tab4}, even for that training the results are significantly better.
	\end{comment}

	As described above, Stage 1B had a bigger positive impact on the training with respect to Stage 1A.
	\begin{figure}[tbp]
		\centerline{\includegraphics[width = 1\columnwidth]{No_both.png}}
		\caption{Typical example of Stage 2 training loss functions (counting part of it) for first 10000 sub-epochs, for trainings with different pre-training options.

		Plots obtained for training with no gesture loop and for ``Go to base'' condition.}
		\label{fig:no_both}
	\end{figure}

	However, as shown in Fig~\ref{fig:no_both}, the influence of number pre-training is easily noticeable in the first stages of the training.

	As in Study 1, there is no impact of using the gesture loop in the training. For all four pre-training options in both ``stay at the last one'' and ''go to base'' conditions the difference between training with the loop or without it was statistically not significant ($p \in [0.32, 0.85]$ for all 8 considered pairs).

	It was expected that the network will give better results when trained in the ``go to base'' condition. As mentioned before, in this case the neural network could potentially recognise the situation when the base position is achieved to stop counting. This influence is visible when Stage 1B pre-training or both pre-trainings were used. Analysis showed that for these training options (and for network with or without gesture loop) the difference (between S and B conditions) is strongly significant (${p < 0.001}$). This influence was not found when gestures' pre-training was not used (``No'' and ``Stage 1A'' pre-training options).

	\subsubsection{Gestures results}
	Similarly, as in case of counting the best performance of the network was observed when both pre-trainings were applied and the worst when no pre-training was performed.
	Statistical analysis for all 4 conditions (S+L, S+NL, B+L, B+NL) between pre-training options was performed for gestures results:
	\begin{itemize}
		\item with no pre-training and with Stage 1A, showed that there is no significant difference.
		\item with no pre-training and with Stage 1B, showed that there is a strong significant difference (${p < 0.001}$ for all conditions)

		\item with Stage 1A pre-training and with both pre-trainings, showed that there is a strong significant difference (${p < 0.001}$ for all conditions)

		\item with Stage 1B pre-training and with both pre-trainings, showed that there is a strong significant difference (${p < 0.001}$ for all conditions)

	\end{itemize}
	The influence of number recitation pre-training was not visible when comparing the training using Stage 1A pre-training with the training where no pre-training was performed. However, the number recitation pre-training had a very significant positive impact on the results when both pre-trainings were used (in comparison to the situation where only gesture pre-training was applied). This impact is

	stronger than expected for gesture results (stronger than observed in case of counting results).

	The difference of performance between networks with gesture loop and without was again not observed; it was statistically significant only in two configurations (S with Stage 1B pre-training: $p = 0.036$ and for condition B when no pre-training was performed: $p = 0.016$) in favour of the network without the loop.

	``Stay at the last one'' training gave better pointing results, with the exception of trainings where only Stage 1B pre-training was used. A one-way ANOVA analysis showed a strong significance of this difference for those conditions (${p < 0.001}$).

	This gesture trend is already visible in Stage 1B pre-training results. Those partial results are as follows:
	\begin{itemize}
		\item ``Stay at the last one'', 60.4\% (8.7)
		\item ``Go to base'', 45.3\% (4.0)
	\end{itemize}
	 It seems that in case of the training with gesture pre-training, the neural network loses some of its capability to point in favour of counting\footnote{The training loss function is calculated as a sum squared error. This definition of loss function and the fact of using gestures as real numbers cause that their weight in the final result is, in some way, lower than the counting weight}. This is not visible for other pre-training options, possibly because, in other cases there is a dedicated (by applying number pre-training) part of the network to produce numbers and so the part responsible for gestures is in some way isolated. This might be also the reason why results for training with only Stage 1B pre-training are significantly worse than those where both pre-trainings were used.

	\section{Conclusions and discussion}

	In this paper a neuro-robotics model with recurrent artificial neural network capable of producing the pointing gestures while counting the objects was proposed. The model allows us to measure the contribution of the produced pointing signal to learning to count.
	The presented studies show a high improvement of counting when the network is pre-trained to perform pointing and then use them while counting.

	Results confirms that the generation of gestures improves the model's counting abilities.

	Moreover, the results are not only better than the one obtained by the network with only visual input but comparable or even better than the results where the network uses gestures as an input.
	When we compare the results from the network with gesture input (configuration 7 in Study 1) pre-trained for recitation in ``stay at the last one'' training condition with results from NN from Study 2 (with both pre-trainings) in ``go to base'' condition we get the results 97.6\% and 99.5\% respectively. The improvement is in favour of the network with gestures produced as an output (ANOVA analysis showed statistical significance, $p = 0.0033$).

	When ``stay at the last one'' condition was used for both networks we obtained very similar results (97.6\% and 97.8\%).

	This observation stays in line with the human study presented in \cite{alibali1999function}.

	When multi-stage training was performed, the learning rate values were chosen using hyper parameter search for each stage of the training to optimize the results. Their values in pre-trainings were higher then the one found for the final training. This is also in accordance with neuroscience studies \cite{johnston2001sculpting}.

	A few issues concerning the model are discussed below. Due to the RNN implementation of discrete time the synchrony between gestures and number words produced is easily obtained. However, in the case of children, synchronising the number words with counted objects may be one of the major functions of gestures \cite{alibali1999function,graham1999role}. To address this issue, a model with continuous time would have to be considered.

	Another possible improvement of the model could be achieved by using number words more similar to names used in human language. Such a change could potentially influence the sequence of words produced by the network (e.g. if two numbers have similar names they could be swapped causing a string error).

	Finally, a more complex visual input could be used. This would possibly require significant extension of the network. In this situation, a deep neural network with spatial filters and partially unsupervised training  might be implemented, as applied for numerosity visual sensation and numerosity comparison in \cite{stoianov2012emergence}.

	The results presented in this article provide quantitative evidence in support of cognitive embodiment theory that number cognition and counting can be boosted by gestures. It seems clear that the network correlated the spatial position of the objects (and pointing procedure) with the numbers' tags, as otherwise it would not be able to take advantage of the produced gestures. The future, analysis of the internal behaviour of the model can be valuable in understanding of the contribution of pointing on learning to count.

	\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Tag Archives: Raymond Pettibon
This Week in Art 11.27-12.3: Theaster Gates Creates a Material Memorial for Tamir Rice
by Lindsey Davis | Nov 27, 2017
A look at this week's art news, including Theaster Gates preserving the materials surrounding the 2014 shooting of Tamir Rice, and events and exhibitions from Los Angeles to Amsterdam.
This Week in Art 9.25-10.1: Theaster Gates Wins Nasher Sculpture Prize
by Lindsey Davis | Sep 25, 2017
A look at this week's art news, including the third Nasher Sculpture Prize to be won by an Art21 artist, and events and exhibitions from Las Vegas to London.
This Week in Art 6.5-6.11: Tania Bruguera Launches Immigrant Empowerment Initiative in Queens
by Lindsey Davis | Jun 5, 2017
A look at this week's art news, including Tania Bruguera's latest project, CycleNews, and events and exhibitions from St. Louis to Istanbul.
This Week in Art 5.29-6.4: "Big Bling" Comes to Philadelphia
by Lindsey Davis | May 29, 2017
A look at this week's art news, including a new public art installation by Martin Puryear, and events and exhibitions everywhere from Los Angeles to Barcelona.
This Week in Art 4.24-4.30: Kerry James Marshall Makes "Time" 100 List
by Lindsey Davis | Apr 24, 2017
A look at this week's art news, including Kerry James Marshall being named one of Time's 100 Most Influential People, and events and exhibitions everywhere from Dallas to Dublin.
This Week in Art 4.3-4.9: Theaster Gates & Laylah Ali Win Awards
by Lindsey Davis | Apr 3, 2017
A look at this week's art news, including recent awards won by Theaster Gates and Laylah Ali, and events and exhibitions everywhere from New York to Napoli.
This Week in Art 3.27-4.2: 100+ Ai Weiwei Installations Coming to NYC
by Lindsey Davis | Mar 27, 2017
A look at this week's art news, including Ai Weiwei's upcoming multi-site public art project, and events and exhibitions everywhere from Dallas to Milan.
This Week in Art 2.27-3.5: Rashid Johnson's Directorial Debut
by Lindsey Davis | Feb 27, 2017
A look at this week's art news, including Rashid Johnson directing his first feature film, and events and exhibitions everywhere from Durham to Denmark.
This Week in Art 2.6-2.12: Raymond Pettibon's First Major Museum Survey
by Lindsey Davis | Feb 6, 2017
A look at this week's art news, including Raymond Pettibon's first major museum survey, and events and exhibitions everywhere from Los Angeles to London.
This Week in Art: 12.19-12.25
by Kelly Olshan | Dec 19, 2016
A look at this week's art news, including Ida Applebroog's induction to the NYFA Hall of Fame, and events and exhibitions everywhere from San Francisco to Salzburg.
A look at this week's art news, including a new designs from James Turrell, and events and exhibitions everywhere from New York to Washington State.
This Week in Art: 8.29-9.4
by Lindsey Davis | Aug 29, 2016
A look at this week's art news, including Ai Weiwei's clash with China's Yinchuan Biennale, and events and exhibitions everywhere from Santa Barbara to Basel.
This Week in Art: 7.4-7.10
by Lindsey Davis | Jul 4, 2016
A look at this week's art news, including New York Close Up's newly awarded Cine Golden Eagle, and events and exhibitions everywhere from Edinburgh to Ethiopia.
This Week in Art: 6.20-6.26
by Lindsey Davis | Jun 20, 2016
A look at this week's art news, including the latest round of Art Car commissions, and events and exhibitions everywhere from St. Petersburg to Paris.
A look at this week's art news, including a tour of projects being developed by ART21 artists and exhibitions on view everywhere from Minneapolis to Reykjavik.
A look at this week's art news, including collaborations between visual artists and musicians, commentary on conceptual art, and upcoming exhibitions in NYC and around the world.
by Nettrice Gaskins | Sep 22, 2014
Do Ho Suh installs a fishing net in Austin, Raymond Pettibon leaves his mark on New York City streetwear, An-My Lê exhibits a magnum opus in the UK, and more in this roundup of ART21-featured artists.
The Walker Curates the News: 09.08.14
by Walker Art Center | Sep 8, 2014
The latest in contemporary art news as told by the Walker Art Center.
by Nettrice Gaskins | May 12, 2014
Kara Walker's first public art project opens in Brooklyn, Bruce Nauman receives the Kiesler Prize, Raymond Pettibon has a new book out, and more in this week's roundup.
by Nettrice Gaskins | Jan 27, 2014
Yinka Shonibare MBE weaves together themes of history and education, James Turrell unveils never-before-seen works, Jeff Koons talks about his iconic balloon dogs, and more.
John Baldessari plays serious games, Cao Fei presents a zombie movie set, Laurie Simmons explores identity and desire on the Internet, and more.
Art21 New York Close Up
by Nettrice Gaskins | Jun 17, 2013
Rashid Johnson, James Turrell, Mike Kelley, Cindy Sherman, and more.
Raymond Pettibon quotes rapper Notorious B.I.G., Ai Weiwei pitches 1,000 tents, Paul McCarthy has multiple exhibitions in New York City, and more.
by Nettrice Gaskins | May 6, 2013
Cao Fei celebrates abundance, Julie Mehretu has two concurrent solo shows, Raymond Pettibon and Judy Pfaff are honored, and much more.
by Nettrice Gaskins | Mar 4, 2013
Kara Walker exhibits in Chicago, Ann Hamilton shows new textile-inspired prints, Fred Wilson receives a Mayor's Award—all this and more in our Weekly Roundup.
by Nettrice Gaskins | Oct 8, 2012
This week's roundup has news on An-My Lê, Raymond Pettibon, Kiki Smith and other artists featured in Art21 programs and documentaries.
by Nettrice Gaskins | Jul 16, 2012
This week's roundup has the latest news on Kara Walker, Cindy Sherman, Robert Adams and many other artists featured in Art21 programs.
by Nettrice Gaskins | Mar 19, 2012
This week's roundup features news on Cai Guo-Qiang, Glenn Ligon, Kiki Smith and many more Art21 featured artists.
by Nettrice Gaskins | Feb 20, 2012
This week's roundup has news on Martin Puryear, Walton Ford, Tim Hawkinson and other artists featured in Art21's programs.
by Nettrice Gaskins | Feb 6, 2012
In this week's roundup we remember Mike Kelley, and get the latest news on Bruce Nauman, Eleanor Antin, Judy Pfaff and other Art21 artists.
by Nettrice Gaskins | Nov 21, 2011
In this week's roundup Allora & Calzadilla explore causes of discontent, Paul McCarthy channels aggression, the Red Hot Chili Peppers pay homage to Raymond Pettibon, Laurie Anderson is sampled, and …
In this week's roundup Kiki Smith's graphic works are on display in Atlanta, Ann Hamilton's work focuses on mouths and hands, Raymond Pettibon opens a cultural grab bag of artwork, …
by Nettrice Gaskins | Nov 7, 2011
In this week's roundup Hiroshi Sugimoto explores a Buddhist stupa, Florian Maier-Aichen lectures in NYC, Pratt honors Laurie Anderson and William Wegman, Matthew Ritchie debuts in Los Angeles, and more. …
by Nettrice Gaskins | Oct 10, 2011
In this week's roundup Beryl Korot prints and weaves video, Jenny Holzer is honored, artists explore being American, several others celebrate creating art in Los Angeles, and more. Beryl Korot's exhibition, Video — Text/Weave/Line, …
In this week's roundup, Allora & Calzadilla are firsts in Venice, Barbara Kruger wraps a room, Chris Rock praises Michael Ray Charles, and much more. Allora & Calzadilla are installing …
Art in the Streets: Untamed Expression and the Clash of Cultures
by Nettrice Gaskins | Apr 29, 2011
While in California, I made a pit stop to see Art in the Streets at MOCA in Los Angeles. Art in the Streets is the first major historical exhibition of …

redpajama_set_name: RedPajamaCommonCrawl


At this initial stage of growth lighting should be chosen specifically for propagation purposes. Intense lighting and heat will overwhelm your plants and stunt their growth.
In this section you will find a range of fluorescent lighting all designed to provide appropriate lighting for propagation and minimal heat output, but still provide the necessary amount of lumens for a plant to survive.
Compact Fluorescent Lamps (or CFLs) provide a cheap and effective way of nurturing young plants. Available as 125W bulbs, they can be used with a reflector or simply with a lampholder. One CFL is usually enough for a large propagator or an area up to 1m2.
Depending of the size of your grow room you may require additional reflectors, bulbs and holders, all of these are found in this section.


text:
Women Talk Tech Episode 6: Building a Community
Meet the woman who built the largest online engineering community on Instagram
Available on iTunes.
On this week's Women Talk Tech podcast we talk to Mina Dezz, Founder of the largest online community on Instagram "Iron Ring Girls" created to bring women in engineering together. Mina was a graduate of Civil Engineering in 2014 from the University of Toronto and is currently working in residential construction building custom homes. Mina impressively built a community of over 20,000 followers and created the guide "Building a Powerful Mind while Studying Engineering".
This episode addresses the common challenges women experience in Engineering such as loneliness, independence, and the drastic change in environment. Mina speaks about her experience throughout university and how proper preparation can help manage this change a lot better.
Marie and Mina discuss:
The challenges girls face transitioning from high school to university
How to prepare for the engineering journey
The importance of communication and people skills in engineering
Dealing with uncertainty after graduation
Mina provides many helpful tips and strategies for dealing with a profession that is mostly men, many of which the technology industry should be adopting today. Her advice "always ask" is one of the most important lessons all young women should learn as they work their way up no matter what the career choice.
Follow Instagram's Women Talk Tech and Iron Ring Girls for daily inspiration.
About Mina Dezz
Mina has been working in the field of engineering since she graduated in 2014. She graduated from Civil Engineering with a minor in Engineering Business from the University of Toronto. She started working in urban transportation design but switched to construction after 2 years. Mina currently works in residential construction, building custom homes.
Mina is also the creator of the largest online community of women in engineering on Instagram "IronRingGirls". She has written her entire experience in studying engineering in her guide "Building a Powerful Mind while Studying Engineering".
Connect with Mina and join her community on Instagram: @ironringgirls


text:
SUNDAY LIFESTYLE
HEALTH AND FAMILY
LIFESTYLE BUSINESS
FOOD AND LEISURE
Vitamilk: Making school children healthier, one bottle at a time
(Philstar.com) - December 13, 2019 - 1:29pm
MANILA, Philippines — Through the "Kalusugan Para sa Kinabukasan" School Caravan, 100 schools across Luzon have joined the cause of championing better health among students as a means towards achieving their dreams and aspirations in life.
Behind this project are the Department of Health and Department of Education, supported by Vitamilk, rallying over 100,000 students, teachers, and parents in raising awareness on the importance of making healthier food choices and its impact in securing a successful future.
The participants engaged in informative activities, games, as well as health seminars led by nutritionists of DOH and Food and Nutrition Research Institute (FNRI). They also touched on proper nutrition as well as the benefits of soy milk as a healthy milk alternative to incorporate in their diet.
After encountering thousands of students, teachers and parents from different walks of life, here are four important takeaways that we've learned:
1. Keeping healthy options available leads to a healthy lifestyle
School canteens play a vital role in the development of students as it is a place for buying food and drink during break time. Having healthy options such as vegetables, bread and milk can help students become more active in class.
2. Food choice affects performance
Students don't necessarily want to eat junk food all the time, especially if they are made fully aware of how it can be bad for their health and their future. Eating the right foods and drinking healthy beverages will give students a better chance at acing their classes!
3. Nutrition is an investment
As cliché as it may sound, health will always be wealth. Just like education, which gives you an advantage in life, so does proper nutrition.
4. Healthy lifestyle doesn't have to be expensive
Yes, nutrition is an investment, but it doesn't have to be a pricey one! One great example is a 200ml bottle of Vitamilk, packed with proteins and minerals everyone needs for a healthy muscular system and better cardiovascular health, all at the affordable price of P15!
Part of Vitamilk's long-term commitment with the Department of Education and the Department of Health is to promote a healthy lifestyle in school communities.
While the caravan has ended, this is only the beginning for Vitamilk. The company sees the caravan as a step toward an ultimate goal: to improve the lives of students nationwide by providing healthy and delicious products.
DEPARTMENT OF HEALTH SCHOOL CARAVAN VITAMILK
Coronavirus: new disease spreading in Asia revives SARS fears
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CHINAMISCHIEF REEFSANGLEY AIRPORT


Business sold without major shareholders knowledge ?
Odd this one .... The mother in law has been a majore shareholder and director of a ltd Company for several years despite her age of 88 , this is also odd as she has no understanding of the business and this has seemed to be set up by the son . The son has recently passed away so leaving the mum in sole control . We as a family has spoken to the sons wife advising that she will be resigning as director as the family feels this has been done as coercion and shouldnt take responsibilty for a company that offers all the risk but no benefits . We got the mum in law to resign as director using the correct form downloaded from companies house .


text:
Collision Avoidance, Bombs, Turrets news
Post news RSS	Collision Avoidance, Bombs, Turrets
New weapons and better movement controls coming in dev6.
Posted by Zarkonnen on Feb 24th, 2015
The new version of Airships is about to drop, and as I said before, it mostly concentrates on visual improvements. There are, however, some gameplay changes too, including some new weaponry.
Something that came out of recent playtesting was that some players really hated the ships' lack of collision avoidance. If you told an airship to fly from point A to B, it would do so, happily crashing into everything in its path. This was a bit strange and could be pretty frustrating. So now, airships automatically cancel their move orders if they're about to collide with something otherwise. This isn't foolproof: it's possible they can't brake in time. But it definitely reduces the number of unintentional collisions.
What about intentional collisions, though, also known as ramming? For that there's a new command next to "Move" explicitly called "Ram". It works exactly like the move command used to: the ship moves to its target spot with no concern for collisions. Note that the best way to ram an opponent still is to first get on the same level as it, and then choose a move target significantly past it, so that your ship impacts in a straight line and at full speed.
An added bonus: more dramatic crew shouts.
Bombs and Turrets
This is something I'd planned to put in for ages, and it kept on getting pushed back. So in the end I went and added these new weapons, and it turned out to be a lot easier than I thought.
Bomb Bay
60 blast damage
5 second reload
500 accuracy
10° fire arc pointing down
20 cost
These do a lot of damage, but your target needs to be directly below you. They also don't move very fast, and aren't super-accurate, but they work great for smashing apart large defensive buildings for little cost.
Dorsal / Ventral Turret
70 piercing damage
190° fire arc pointing up
120 cost
Turreted weapons, finally! Compared to a normal cannon these are much bulkier and more expensive, but they're flexible and pack quite a punch. And of course, the dorsal one is great for shooting down bombers, while the ventral one is a nice addition to a heavy bomber...
Just for comparison, here's the same stats for a normal cannon:
90° fire arc pointing forward
So that's what's coming towards you very soon indeed.
drjd24 Feb 24 2015
The bombs might just be the most over-powered weapon in the game as of now... Hopefully they won't :)
Zarkonnen Feb 25 2015
The stats look quite scary, but in my playtesting so far, the fact that they're not very accurate and have a really awkward firing arc seems to offset this. But it's possible I'm wrong and they'll get totally nerfed in a patch. :D
Airships: Conquer the Skies Real Time Strategy


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Avro Lancaster B. Mk. VII, NX611, 'Just Jane' LE-C/H DX-C/F Lincolnshire Aviation Heritage Centre, East Kirby England, 2014.
Avro Lancaster B. Mk. X, KB726 VR-A, 419 (Moose) Sqn, RCAF Canadian Warplane Heritage Museum, 2014.
Avro Lancaster B. Mk. I, PA474 KC-A 'BBMF DV385 Thumper III' England 2014.
KW172121 12 Squadron Avro Lancaster PH-H - Panavia Tornado GR.4 1915 -2014 Scheme.
Avro Lancaster B.III LM321 PH-H, 12 Sqn, Wickenby, England 1942.
Panavia Tornado GR.4A ZA395 'Commemorative scheme' 1915-2014, 12 Sqn Lossiemouth Scotland 2014.
Avro Lancaster B Mk.I, LM130, JO-N "Nick The Nazi Neutralizer" 463 Squadron, RAF Waddington, Lincolnshire, 1945.
Avro Lancaster B Mk. X KB772 VR-R 'Ropey' 419 Squadron RCAF, Middleton St George, April 1945.
Erratum - Title should read 1/72 scale on decal sheet.
In February 1939, the Squadron moved to Honington received Wellingtons, and it was with these that it was involved in anti-shipping sorties in the early stages of World War II. These were replaced in turn by the famous Lancaster bomber in September 1942 and the unit became part of Bomber Command's strategic offensive against German targets and was now based at Waddington. Following a move to nearby Bardney, No. 9 Sqn specialised in dropping large bombs and the 12,000 (5.440kg) 'Tallboy' in particular.
On the night of 4th 5th July 1944, Bomber command sent a force of 231 Lancaster's and 15 Mosquito's to attack the V1 supply depot at Saint Leu D'Esserent. With the target area having been identified previously, the air crews mapped their route in order to miss major towns and known flak installations which they might encounter over France. What had not been foreseen was the amount of Luftwaffe fighters which had been assembled to defend the supply depot. This force would have a serious outcome with no less than 13 RAF Lancaster's being shot down over occupied territory.The bombing raid over Saint Leu D'Esserent was planned at night with the 'bombs away' set for 01H45 am. This particular night was a clear sky with bright moonlight. The attacking force assembled over England between 23h30 and started it journey over the English Channel and the French coast.
Avro lancaster Bomber 'Lonesome Lola' LL845 WS"L"
This aircraft replaced Lancaster Mk111 W5010 WS"L" which failed to return from an operation to Leipzig on the 19/20th February 1944.
LL845 undertook its first operation with IX Sqn on the 15/16th March 1944 when P/O Clark and crew (including F/O Matthews) took it to Stuttgart, this was P/O Clarke's 10th operation.
The artwork on the photograph shows 8 bombs which indicates that it had carried out 8 operations, the records show that the 8th operation was carried out on the 10/11th April 1944 when Ft/Sgt Horne and crew took it to Tours.
The 9th operation was carried out on the 11/12th April when P/O Clark and crew (including F/O Matthews) took it to Aachen . Assuming the art work (bombs) were added/painted after each operation the photograph must have been taken on the 11th April 1944 prior to P/O Clark undertaking the operation to Aachen. LL845 operated with IX Sqn until the end of the war.
On the 15th September 1944 S/L Pooley and crew flew it to Yagodnik in Russia for the first Tallboy" operation against the German Battleship Tirpitz" by IX and 617 Sqn's. W4964 WS"J" another IX Squadron Lancaster was the only aircraft to obtain a direct hit on the Tirpitz" during this operation, this caused the Germans to take it further South to Tromso hopefully for repair, however this became within range of Lancaster's flying from a forward base in Scotland if the mid upper gunner was taken out and extra long range petrol tanks were inserted into the fuselage.
Two more Tallboy" operations were undertaken by IX and 617 on the 29th October and finally on the 12th November 1944 when it was capsized. LL845 took part on the 29th October operation but was iced up and could not take off on the 12th November.
LL845 carried out its last wartime operation on the 25th April 1945 when F/L McIntosh and crew took it to Berchtesgaden .
427 equipped originally with Wellington IIIs, the squadron had re-equipped with Wellington Xs by the end of March 1943, and continued to operate with the Mark X until 28 - 29th April. Early in May 1943, when the squadron moved to Leeming, its home for the next three years, it began to re-equip with Halifax Vs, which type it flew on operations for eight months before changing to Halifax IIIs. On 2nd/3rd March 1945 came No. 427's last sorties on Halifax IIIs and about a week later, on 11th March, its Lancaster Is and IIIs received their operational baptism.
Matthew Ferguson practiced his craft from 1941 until 1945, painted on at least four different types of aircraft, developed station and squadron badges, pioneered placing artwork on bomber jackets worn by aircrew, and even painted artwork on bombs. An Albertan, he was clearly the most significant Canadian nose artist of the Second World War and likely in the history of the art form.
After emigrating from England as a small child, Matthew Ferguson was raised in Calgary, Alberta where he enlisted with the Calgary Highlanders when war broke out. Six months later he transferred to the Royal Canadian Air Force and became an airframe mechanic.
Matthews Ferguson painted this artwork on a 4000 pound "cookie" bomb to celebrate the 2000th operation by No. 424 "Tiger" Squadron. A photograph was taken and then the bomb was loaded into Lancaster RF-128 (QB-V) and delivered on 21 March 1945. The aircraft was known as "Victorious Virgin" and was flown by F/L J. F. Thomas and crew. The art shows a winged tiger breaking out of an Easter egg and this particular bomb was a special present for Hitler from the squadron. It was dropped on the oil refinery at Hemminstedt, near Heide, Germany. The nose art image on the Lancaster was based on the November 1944 "Varga" pin-up that appeared in Esquire magazine.
Ferguson's "Victorious Virgin" flew the squadron's 2000th four-engined sortie on 21 March, 1945. The 4000 lb. "cookie" bomb was painted by Ferguson and labelled, "An Easter Egg for Hitler." This is the only known example of 'nose art' on a bomb.
On the night of 4th 5th July 1944, Bomber command sent a force of 231 Lancasters and 15 Mosquitoes to attack the V1 supply depot at Saint Leu D'Esserent. With the target area having been identified previously, the air crews mapped their route in order to miss major towns and known flak installations which they might encounter over France. What had not been foreseen was the amount of Luftwaffe fighters which had been assembled to defend the supply depot. This force would have a serious outcome with no less than 13 RAF Lancasters being shot down over occupied territory.
9 Squadron sent LL845, W4964, JA957, JA690, LL785, ME833, EE136, LM453, LL884, LL914, DV161, PB146, ME809, DV396.
9 squadron lost Lancaster 'Fanlight Fanny' LL785 WS-F which crashed in a pasture next to the small hamlet of Le Tillet killing all onboard.
A total of 430 Avro Lancaster Mk. X's were built in Canada by Victory Aircraft Limited at Malton, Ontario.
The first order placed was for a total of 300 aircraft. Their serial numbers were from KB700 through KB999. These Lancasters were delivered between September 1943 and March 1945. The Lancasters with serial numbers KB700 through KB774 were fitted with Merlin 38 engines. All subsequent aircraft were fitted with Merlin 224's.
None of the 430 aircraft were lost while being tested at Malton. Almost all of the Lancaster Mk. X's were ferried to England and none were lost in transit.


In OS X 10.5's QuickTime Player, it was easy to control how quickly you could fast-forward or rewind a playing movie. There were, in fact, two ways to do so. Select Window -> Show A/V Controls... I am trying to get a movie uploaded from a mobile device to play in a browser. It works fine in Chrome and FF and IE9(compatibility mode) but I cannot get it to play in IE9 not in compatibility mode.
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The 3GPP component is available as a separate download. 3GPP support is provided as an add-on to the primary QuickTime package. Macintosh users can also use Software Update in OS X to update to QuickTime 6.3 and to get the 3GPP component.
Whether you want to control your computer's iTunes library or need to take over other hardware—like an Amazon Fire TV or Roku set-top box – these 15 apps have you covered. No more digging through your couch cushions looking for the remote. With an iPhone remote control, just reach into your pocket to control your media experience.
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Maino releases yet another banger with his new track "Watch Me Do It." The track features Coke Boys' French Montana and Hustle Gang's T.I. Maino has been working on his new EP, King of Brooklyn which will be out everywhere February 4th.
Listen to Watch Me Do It and weigh in below.


text:
Home » Concentric Analgesics launches CA-008 clinical development program in post-surgical pain
Concentric Analgesics launches CA-008 clinical development program in post-surgical pain
Concentric Analgesics, a clinical-stage biopharmaceutical pain company, has initiated a phase Ib clinical trial with CA-008, a proprietary, water-soluble prodrug that converts into capsaicin, a potent TRPV-1 agonist. Results from this clinical trial are expected in the first quarter of 2018.
The phase Ib study is a randomized, double-blind clinical trial, targeting approximately 32 patients undergoing bunionectomy. The goal of the single, ascending-dose trial is to examine the safety, tolerability and pharmacokinetics of CA-008.
"This study marks an important milestone for Concentric, as we look forward to generating clinical data with CA-008 in surgical patients," said John Donovan, M.D., CEO of Concentric Analgesics. "As an anesthesiologist, I can attest that there is a clear clinical mandate to reduce the reliance on opioids after surgery, especially in the 24 to 96 hour time window when the intensivity of care shifts from provider to patient. CA-008's therapeutic profile as a long-acting, site-specific non-opioid analgesic makes it uniquely positioned to address this critical unmet medical need. Our mission of displacing opioids in multi-modal post-surgical pain management has never been more important."
"CA-008 has the potential to dramatically improve how we manage post-surgical pain, particularly in the initial 24 to 96 hours, thereby reducing the need for opioids," said Michael F. Dillingham, M.D., Adjunct Clinical Professor, Department of Orthopaedic Surgery at Stanford University, and Program Director, SOAR (Sports Orthopedic and Rehabilitation) Medical Associates in Palo Alto, CA. "The drug's potential to deliver a long-lasting, pain-selective effect following a simple, single administration is very attractive and could help establish CA-008 as an important new standard of care."
The national spotlight on the opioid epidemic continues to grow. A 2016 US News & World Report revealed that one in 10 patients report becoming addicted or dependent on opioids following surgery. With more than 100 million surgeries being performed in the US in 2017, 30 million of which require post-surgical pain management with non-over the counter (OTC) pain medications, the mandate to develop safe and effective non-opioid alternatives is clear.
Recent figures from the Centers for Disease Control and Prevention suggest that 145 Americans now die every day from opioid overdoses, while a 2013 study in Pharmacotherapy documents the widespread toll of opioid-related adverse events on patients, the health care system and society at large, including:
4x higher risk of inpatient mortality
55% longer length of stay (hospitalization)
47% higher cost of care
36% increased risk of 30-day readmission
In addition to the company's post-surgical program, Concentric expects to complete a series of pre-clinical research studies by the end of 2017 for CA-011, its program in osteoarthritis. Osteoarthritis is the most common joint disease affecting over 30 million Americans, more than 14 million of whom suffer from osteoarthritis of the knee.
Global News Clinical Intelligence


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An assistant professor at the University of Rhode Island is offering a course next semester that confronts the increasingly prominent issue of Western misconceptions regarding Islam.
Katrin Jomaa has taught 'Secularism and Islamism in the Modern World', PSC 482, twice since she came to URI in 2013. Jomaa, who is Lebanese, Muslim and wears a hijab, approaches the conflicting history and social identities of Islam and Western religion through multiple lenses.
"Compartmentalized understanding [of religion] will not do the job." Jomaa said that her dual appointment as faculty in the political science and philosophy departments gives her the ability to offer students a more holistic understanding of religion.
Though difference is considered, Jomaa does not think the inconsistencies between the religions is what prompts the misunderstanding Westerners often have of Islam. "There is a misunderstanding for each towards the other," she said.
While domestic shootings in the US are rarely pegged as acts of terror by mainstream media, she said the media has shown to be quick to jump at any chance to call a crime involving Muslims terrorism. However, though crimes like the 9/11 attacks on New York City and the recent Paris attack were committed in the name of Islam, terrorists like ISIS actually violate Islamic ideals.
Students interested in enrolling in the course for the 2015 spring semester can contact Jomaa for a permission number at kjomaa@uri.edu. Â The class will run every Tuesday and Thursday from 11 a.m. to 12:15 p.m.


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У статті наведено подробиці втрат 51-го парашутно-десантного полку Збройних сил РФ.

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documentary | True Endeavors Presents: music, tours, tickets, mp3 downloads, concert pictures, videos, Madison WI.
Fans of Baltimore's wizard of electro revelry already know what incredible live shows he creates, but they rarely get as close of a look into the method behind his madness as in the debut installment of Q2 Music's quickie documentary series featuring artists in their studios. Take a peek inside Dan Deacon's chamber of tunes and 'toons here.
He is set to open the rescheduled Animal Collective show at the Orpheum Theater on October 16. Tickets go on sale today at 10am.


This probably isn't new enough because as much as I liked the 67 win Mavs, the Warriors upset wasn't included in the video.
How is g-state defeats dallas in there? I'd say thats top 5 without hesitation.
That Nuggets win back in 94, was it?, was trully miraculous. But the recent Warriors-Mavs series should get a spot in there as well. That was pretty amazing as well.
The vid came out during the 2007 playoffs which means that that series couldn't have been included in the vid.
Good video. Golden State taking the Mavs out in 2007 would surely make all current lists.
Let the Maverick bashing start now!!!!!
As before mentioned the Warriors beating Dallas would definitely be in this if it were more recent. Pretty good list overall.
Where is Warriors vs MAvs that was great! I do like those 10 but Warriors vs Mavs should be in the Top 10!
as i reacall in the 1994 nugget season, there star player was out for most of the season, thas y they upset. thas y the #1 SHOULD BE warriors beating mavs in 6.
GREATEST UPSET PLAYOFF MOMENT: 8. WARRIORS BEAT 1. MAVS in 6!
uhhh 8th seeded Knicks go and destroy everybody and go into the finals? And only miss the title because an ewing injuriy?
I don't think they beat San Antonio. I think the series goes 7 though.
10 and 9 don't really make sense, the greatest upsets should include teams that actually won the series. 6 wasn't much of an upset either.