text:
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series.

CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "full connectivity" of these networks make them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) CNNs take a different approach towards regularization: they take advantage of the hierarchical pattern in data and assemble patterns of increasing complexity using smaller and simpler patterns embossed in their filters. Therefore, on a scale of connectivity and complexity, CNNs are on the lower extreme.

Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.

CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns to optimize the filters (or kernels) through automated learning, whereas in traditional algorithms these filters are hand-engineered. This independence from prior knowledge and human intervention in feature extraction is a major advantage.

Definition
Convolutional neural networks are a specialized type of artificial neural networks that use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers. They are specifically designed to process pixel data and are used in image recognition and processing.

Architecture

A convolutional neural network consists of an input layer, hidden layers and an output layer. In any feed-forward neural network, any middle layers are called hidden because their inputs and outputs are masked by the activation function and final convolution. In a convolutional neural network, the hidden layers include layers that perform convolutions. Typically this includes a layer that performs a dot product of the convolution kernel with the layer's input matrix. This product is usually the Frobenius inner product, and its activation function is commonly ReLU. As the convolution kernel slides along the input matrix for the layer, the convolution operation generates a feature map, which in turn contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers.

Convolutional layers
In a CNN, the input is a tensor with shape: (number of inputs) × (input height) × (input width) × (input channels). After passing through a convolutional layer, the image becomes abstracted to a feature map, also called an activation map, with shape: (number of inputs) × (feature map height) × (feature map width) × (feature map channels).

Convolutional layers convolve the input and pass its result to the next layer. This is similar to the response of a neuron in the visual cortex to a specific stimulus. Each convolutional neuron processes data only for its receptive field. Although fully connected feedforward neural networks can be used to learn features and classify data, this architecture is generally impractical for larger inputs (e.g., high-resolution images), which would require massive numbers of neurons because each pixel is a relevant input feature. A fully connected layer for an image of size 100 × 100 has 10,000 weights for each neuron in the second layer. Convolution reduces the number of free parameters, allowing the network to be deeper. For example, using a 5 × 5 tiling region, each with the same shared weights, requires only 25 neurons. Using regularized weights over fewer parameters avoids the vanishing gradients and exploding gradients problems seen during backpropagation in earlier neural networks.

To speed processing, standard convolutional layers can be replaced by depthwise separable convolutional layers, which are based on a depthwise convolution followed by a pointwise convolution. The depthwise convolution is a spatial convolution applied independently over each channel of the input tensor, while the pointwise convolution is a standard convolution restricted to the use of   kernels.

Pooling layers
Convolutional networks may include local and/or global pooling layers along with traditional convolutional layers. Pooling layers reduce the dimensions of data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer. Local pooling combines small clusters, tiling sizes such as 2 × 2 are commonly used. Global pooling acts on all the neurons of the feature map. There are two common types of pooling in popular use: max and average. Max pooling uses the maximum value of each local cluster of neurons in the feature map, while average pooling takes the average value.

Fully connected layers

Fully connected layers connect every neuron in one layer to every neuron in another layer. It is the same as a traditional multilayer perceptron neural network (MLP). The flattened matrix goes through a fully connected layer to classify the images.

Receptive field
In neural networks, each neuron receives input from some number of locations in the previous layer. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's receptive field. Typically the area is a square (e.g. 5 by 5 neurons). Whereas, in a fully connected layer, the receptive field is the entire previous layer. Thus, in each convolutional layer, each neuron takes input from a larger area in the input than previous layers. This is due to applying the convolution over and over, which takes into account the value of a pixel, as well as its surrounding pixels. When using dilated layers, the number of pixels in the receptive field remains constant, but the field is more sparsely populated as its dimensions grow when combining the effect of several layers.

To manipulate the receptive field size as desired, there are some alternatives to the standard convolutional layer. For example, atrous or dilated convolution expands the receptive field size without increasing the number of parameters by interleaving visible and blind regions. Moreover, a single dilated convolutional layer can comprise filters with multiple dilation ratios, thus having a variable receptive field size.

Weights
Each neuron in a neural network computes an output value by applying a specific function to the input values received from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias (typically real numbers). Learning consists of iteratively adjusting these biases and weights.

The vectors of weights and biases are called filters and represent particular features of the input (e.g., a particular shape). A distinguishing feature of CNNs is that many neurons can share the same filter. This reduces the memory footprint because a single bias and a single vector of weights are used across all receptive fields that share that filter, as opposed to each receptive field having its own bias and vector weighting.

History

CNN are often compared to the way the brain achieves vision processing in living organisms.

Receptive fields in the visual cortex
Work by Hubel and Wiesel in the 1950s and 1960s showed that cat visual cortices contain neurons that individually respond to small regions of the visual field. Provided the eyes are not moving, the region of visual space within which visual stimuli affect the firing of a single neuron is known as its receptive field. Neighboring cells have similar and overlapping receptive fields.  Receptive field size and location varies systematically across the cortex to form a complete map of visual space.  The cortex in each hemisphere represents the contralateral visual field.

Their 1968 paper identified two basic visual cell types in the brain:

simple cells, whose output is maximized by straight edges having particular orientations within their receptive field
complex cells, which have larger receptive fields, whose output is insensitive to the exact position of the edges in the field.

Hubel and Wiesel also proposed a cascading model of these two types of cells for use in pattern recognition tasks.

Neocognitron, origin of the CNN architecture

The "neocognitron" was introduced by Kunihiko Fukushima in 1980.
It was inspired by the above-mentioned work of Hubel and Wiesel. The neocognitron introduced the two basic types of layers in CNNs: convolutional layers, and downsampling layers. A convolutional layer contains units whose receptive fields cover a patch of the previous layer. The weight vector (the set of adaptive parameters) of such a unit is often called a filter. Units can share filters. Downsampling layers contain units whose receptive fields cover patches of previous convolutional layers. Such a unit typically computes the average of the activations of the units in its patch. This downsampling helps to correctly classify objects in visual scenes even when the objects are shifted.

In a variant of the neocognitron called the cresceptron, instead of using Fukushima's spatial averaging, J. Weng et al. introduced a method called max-pooling where a downsampling unit computes the maximum of the activations of the units in its patch. Max-pooling is often used in modern CNNs.

Several supervised and unsupervised learning algorithms have been proposed over the decades to train the weights of a neocognitron. Today, however, the CNN architecture is usually trained through backpropagation.

The neocognitron is the first CNN which requires units located at multiple network positions to have shared weights.

Convolutional neural networks were presented at the Neural Information Processing Workshop in 1987, automatically analyzing time-varying signals by replacing learned multiplication with convolution in time, and demonstrated for speech recognition.

Time delay neural networks
The time delay neural network (TDNN) was introduced in 1987 by Alex Waibel et al. and was one of the first convolutional networks, as it achieved shift invariance. It did so by utilizing weight sharing in combination with backpropagation training. Thus, while also using a pyramidal structure as in the neocognitron, it performed a global optimization of the weights instead of a local one.

TDNNs are convolutional networks that share weights along the temporal dimension. They allow speech signals to be processed time-invariantly. In 1990 Hampshire and Waibel introduced a variant which performs a two dimensional convolution. Since these TDNNs operated on spectrograms, the resulting phoneme recognition system was invariant to both shifts in time and in frequency. This inspired translation invariance in image processing with CNNs. The tiling of neuron outputs can cover timed stages.

TDNNs now  achieve the best performance in far distance speech recognition.

Max pooling
In 1990 Yamaguchi et al. introduced the concept of max pooling, which is a fixed filtering operation that calculates and propagates the maximum value of a given region. They did so by combining TDNNs with max pooling in order to realize a speaker independent isolated word recognition system. In their system they used several TDNNs per word, one for each syllable. The results of each TDNN over the input signal were combined using max pooling and the outputs of the pooling layers were then passed on to networks performing the actual word classification.

Image recognition with CNNs trained by gradient descent
A system to recognize hand-written ZIP Code numbers involved convolutions in which the kernel coefficients had been laboriously hand designed.

Yann LeCun et al. (1989) used back-propagation to learn the convolution kernel coefficients directly from images of hand-written numbers. Learning was thus fully automatic, performed better than manual coefficient design, and was suited to a broader range of image recognition problems and image types.

Wei Zhang et al. (1988) used back-propagation to train the convolution kernels of a CNN for alphabets recognition. The model was called Shift-Invariant Artificial Neural Network (SIANN) before the name CNN was coined later in the early 1990s. Wei Zhang et al. also applied the same CNN without the last fully connected layer for medical image object segmentation (1991) and breast cancer detection in mammograms (1994).

This approach became a foundation of modern computer vision.

LeNet-5

LeNet-5, a pioneering 7-level convolutional network by LeCun et al. in 1998, that classifies digits, was applied by several banks to recognize hand-written numbers on checks () digitized in 32x32 pixel images. The ability to process higher-resolution images requires larger and more layers of convolutional neural networks, so this technique is constrained by the availability of computing resources.

Shift-invariant neural network

A shift-invariant neural network was proposed by Wei Zhang et al. for image character recognition in 1988. It is a modified Neocognitron by keeping only the convolutional interconnections between the image feature layers and the last fully connected layer.  The model was trained with back-propagation. The training algorithm were further improved in 1991 to improve its generalization ability. The model architecture was modified by removing the last fully connected layer and applied for medical image segmentation (1991) and automatic detection of breast cancer in mammograms (1994).

A different convolution-based design was proposed in 1988 for application to decomposition of one-dimensional electromyography convolved signals via de-convolution. This design was modified in 1989 to other de-convolution-based designs.

Neural abstraction pyramid

The feed-forward architecture of convolutional neural networks was extended in the neural abstraction pyramid by lateral and feedback connections. The resulting recurrent convolutional network allows for the flexible incorporation of contextual information to iteratively resolve local ambiguities. In contrast to previous models, image-like outputs at the highest resolution were generated, e.g., for semantic segmentation, image reconstruction, and object localization tasks.

GPU implementations
Although CNNs were invented in the 1980s, their breakthrough in the 2000s required fast implementations on graphics processing units (GPUs).

In 2004, it was shown by K. S. Oh and K. Jung that standard neural networks can be greatly accelerated on GPUs. Their implementation was 20 times faster than an equivalent implementation on CPU. In 2005, another paper also emphasised the value of GPGPU for machine learning.

The first GPU-implementation of a CNN was described in 2006 by K. Chellapilla et al. Their implementation was 4 times faster than an equivalent implementation on CPU. Subsequent work also used GPUs, initially for other types of neural networks (different from CNNs), especially unsupervised neural networks.

In 2010, Dan Ciresan et al. at IDSIA showed that even deep standard neural networks with many layers can be quickly trained on GPU by supervised learning through the old method known as backpropagation. Their network outperformed previous machine learning methods on the MNIST handwritten digits benchmark. In 2011, they extended this GPU approach to CNNs, achieving an acceleration factor of 60, with impressive results. In 2011, they used such CNNs on GPU to win an image recognition contest where they achieved superhuman performance for the first time. Between May 15, 2011 and September 30, 2012, their CNNs won no less than four image competitions. In 2012, they also significantly improved on the best performance in the literature for multiple image databases, including the MNIST database, the NORB database, the HWDB1.0 dataset (Chinese characters) and the CIFAR10 dataset (dataset of 60000 32x32 labeled RGB images).

Subsequently, a similar GPU-based CNN by Alex Krizhevsky et al. won the ImageNet Large Scale Visual Recognition Challenge 2012. A very deep CNN with over 100 layers by Microsoft won the ImageNet 2015 contest.

Intel Xeon Phi implementations
Compared to the training of CNNs using GPUs, not much attention was given to the Intel Xeon Phi coprocessor.
A notable development is a parallelization method for training convolutional neural networks on the Intel Xeon Phi, named Controlled Hogwild with Arbitrary Order of Synchronization (CHAOS).
CHAOS exploits both the thread- and SIMD-level parallelism that is available on the Intel Xeon Phi.

Distinguishing features
In the past, traditional multilayer perceptron (MLP) models were used for image recognition. However, the full connectivity between nodes caused the curse of dimensionality, and was computationally intractable with higher-resolution images. A 1000×1000-pixel image with RGB color channels has 3 million weights per fully-connected neuron, which is too high to feasibly process efficiently at scale.

For example, in CIFAR-10, images are only of size 32×32×3 (32 wide, 32 high, 3 color channels), so a single fully connected neuron in the first hidden layer of a regular neural network would have 32*32*3 = 3,072 weights. A 200×200 image, however, would lead to neurons that have 200*200*3 = 120,000 weights.

Also, such network architecture does not take into account the spatial structure of data, treating input pixels which are far apart in the same way as pixels that are close together. This ignores locality of reference in data with a grid-topology (such as images), both computationally and semantically. Thus, full connectivity of neurons is wasteful for purposes such as image recognition that are dominated by spatially local input patterns.

Convolutional neural networks are variants of multilayer perceptrons, designed to emulate the behavior of a visual cortex. These models mitigate the challenges posed by the MLP architecture by exploiting the strong spatially local correlation present in natural images. As opposed to MLPs, CNNs have the following distinguishing features:
 3D volumes of neurons. The layers of a CNN have neurons arranged in 3 dimensions: width, height and depth. Where each neuron inside a convolutional layer is connected to only a small region of the layer before it, called a receptive field. Distinct types of layers, both locally and completely connected, are stacked to form a CNN architecture.
 Local connectivity: following the concept of receptive fields, CNNs exploit spatial locality by enforcing a local connectivity pattern between neurons of adjacent layers. The architecture thus ensures that the learned "filters" produce the strongest response to a spatially local input pattern. Stacking many such layers leads to nonlinear filters that become increasingly global (i.e. responsive to a larger region of pixel space) so that the network first creates representations of small parts of the input, then from them assembles representations of larger areas.
 Shared weights: In CNNs, each filter is replicated across the entire visual field. These replicated units share the same parameterization (weight vector and bias) and form a feature map. This means that all the neurons in a given convolutional layer respond to the same feature within their specific response field. Replicating units in this way allows for the resulting activation map to be equivariant under shifts of the locations of input features in the visual field, i.e. they grant translational equivariance - given that the layer has a stride of one.
 Pooling: In a CNN's pooling layers, feature maps are divided into rectangular sub-regions, and the features in each rectangle are independently down-sampled to a single value, commonly by taking their average or maximum value. In addition to reducing the sizes of feature maps, the pooling operation grants a degree of local translational invariance to the features contained therein, allowing the CNN to be more robust to variations in their positions.

Together, these properties allow CNNs to achieve better generalization on vision problems. Weight sharing dramatically reduces the number of free parameters learned, thus lowering the memory requirements for running the network and allowing the training of larger, more powerful networks.

Building blocks

A CNN architecture is formed by a stack of distinct layers that transform the input volume into an output volume (e.g. holding the class scores) through a differentiable function. A few distinct types of layers are commonly used. These are further discussed below.

Convolutional layer
The convolutional layer is the core building block of a CNN. The layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input volume. During the forward pass, each filter is convolved across the width and height of the input volume, computing the dot product between the filter entries and the input, producing a 2-dimensional activation map of that filter. As a result, the network learns filters that activate when it detects some specific type of feature at some spatial position in the input.

Stacking the activation maps for all filters along the depth dimension forms the full output volume of the convolution layer. Every entry in the output volume can thus also be interpreted as an output of a neuron that looks at a small region in the input and shares parameters with neurons in the same activation map.

Self-supervised learning has been adapted for use in convolutional layers by using sparse patches with a high-mask ratio and a global response normalization layer.

Local connectivity

When dealing with high-dimensional inputs such as images, it is impractical to connect neurons to all neurons in the previous volume because such a network architecture does not take the spatial structure of the data into account. Convolutional networks exploit spatially local correlation by enforcing a sparse local connectivity pattern between neurons of adjacent layers: each neuron is connected to only a small region of the input volume.

The extent of this connectivity is a hyperparameter called the receptive field of the neuron. The connections are local in space (along width and height), but always extend along the entire depth of the input volume. Such an architecture ensures that the learnt filters produce the strongest response to a spatially local input pattern.

Spatial arrangement

Three hyperparameters control the size of the output volume of the convolutional layer: the depth, stride, and padding size:
 The depth of the output volume controls the number of neurons in a layer that connect to the same region of the input volume. These neurons learn to activate for different features in the input. For example, if the first convolutional layer takes the raw image as input, then different neurons along the depth dimension may activate in the presence of various oriented edges, or blobs of color.
Stride controls how depth columns around the width and height are allocated. If the stride is 1, then we move the filters one pixel at a time. This leads to heavily overlapping receptive fields between the columns, and to large output volumes. For any integer  a stride S means that the filter is translated S units at a time per output. In practice,  is rare. A greater stride means smaller overlap of receptive fields and smaller spatial dimensions of the output volume.
 Sometimes, it is convenient to pad the input with zeros (or other values, such as the average of the region) on the border of the input volume. The size of this padding is a third hyperparameter. Padding provides control of the output volume's spatial size. In particular, sometimes it is desirable to exactly preserve the spatial size of the input volume, this is commonly referred to as "same" padding.

The spatial size of the output volume is a function of the input volume size , the kernel field size  of the convolutional layer neurons, the stride , and the amount of zero padding  on the border. The number of neurons that "fit" in a given volume is then:

If this number is not an integer, then the strides are incorrect and the neurons cannot be tiled to fit across the input volume in a symmetric way. In general, setting zero padding to be  when the stride is  ensures that the input volume and output volume will have the same size spatially. However, it is not always completely necessary to use all of the neurons of the previous layer. For example, a neural network designer may decide to use just a portion of padding.

Parameter sharing
A parameter sharing scheme is used in convolutional layers to control the number of free parameters. It relies on the assumption that if a patch feature is useful to compute at some spatial position, then it should also be useful to compute at other positions. Denoting a single 2-dimensional slice of depth as a depth slice, the neurons in each depth slice are constrained to use the same weights and bias.

Since all neurons in a single depth slice share the same parameters, the forward pass in each depth slice of the convolutional layer can be computed as a convolution of the neuron's weights with the input volume. Therefore, it is common to refer to the sets of weights as a filter (or a kernel), which is convolved with the input. The result of this convolution is an activation map, and the set of activation maps for each different filter are stacked together along the depth dimension to produce the output volume. Parameter sharing contributes to the translation invariance of the CNN architecture.

Sometimes, the parameter sharing assumption may not make sense. This is especially the case when the input images to a CNN have some specific centered structure; for which we expect completely different features to be learned on different spatial locations. One practical example is when the inputs are faces that have been centered in the image: we might expect different eye-specific or hair-specific features to be learned in different parts of the image. In that case it is common to relax the parameter sharing scheme, and instead simply call the layer a "locally connected layer".

Pooling layer

Another important concept of CNNs is pooling, which is a form of non-linear down-sampling. There are several non-linear functions to implement pooling, where max pooling is the most common. It partitions the input image into a set of rectangles and, for each such sub-region, outputs the maximum.

Intuitively, the exact location of a feature is less important than its rough location relative to other features. This is the idea behind the use of pooling in convolutional neural networks. The pooling layer serves to progressively reduce the spatial size of the representation, to reduce the number of parameters, memory footprint and amount of computation in the network, and hence to also control overfitting. This is known as down-sampling. It is common to periodically insert a pooling layer between successive convolutional layers (each one typically followed by an activation function, such as a ReLU layer) in a CNN architecture. While pooling layers contribute to local translation invariance, they do not provide global translation invariance in a CNN, unless a form of global pooling is used. The pooling layer commonly operates independently on every depth, or slice, of the input and resizes it spatially. A very common form of max pooling is a layer with filters of size 2×2, applied with a stride of 2, which subsamples every depth slice in the input by 2 along both width and height, discarding 75% of the activations:
In this case, every max operation is over 4 numbers. The depth dimension remains unchanged (this is true for other forms of pooling as well).

In addition to max pooling, pooling units can use other functions, such as average pooling or ℓ2-norm pooling. Average pooling was often used historically but has recently fallen out of favor compared to max pooling, which generally performs better in practice.

Due to the effects of fast spatial reduction of the size of the representation, there is a recent trend towards using smaller filters or discarding pooling layers altogether.

"Region of Interest" pooling (also known as RoI pooling) is a variant of max pooling, in which output size is fixed and input rectangle is a parameter.

Pooling is a downsampling method and an important component of convolutional neural networks for object detection based on the Fast R-CNN architecture.

Channel Max Pooling
A CMP operation layer conducts the MP operation along the channel side among the corresponding positions of the consecutive feature maps for the purpose of redundant information elimination. The CMP makes the significant features gather together within fewer channels, which is important for fine-grained image classification that needs more discriminating features. Meanwhile, another advantage of the CMP operation is to make the channel number of feature maps smaller before it connects to the first fully connected (FC) layer. Similar to the MP operation, we denote the input feature maps and output feature maps of a CMP layer as F ∈ R(C×M×N) and C ∈ R(c×M×N), respectively, where C and c are the channel numbers of the input and output feature maps, M and N are the widths and the height of the feature maps, respectively. Note that the CMP operation only changes the channel number of the feature maps. The width and the height of the feature maps are not changed, which is different from the MP operation.

ReLU layer
ReLU is the abbreviation of rectified linear unit, which applies the non-saturating activation function . It effectively removes negative values from an activation map by setting them to zero. It introduces nonlinearities to the decision function and in the overall network without affecting the receptive fields of the convolution layers.

Other functions can also be used to increase nonlinearity, for example the saturating hyperbolic tangent , , and the sigmoid function . ReLU is often preferred to other functions because it trains the neural network several times faster without a significant penalty to generalization accuracy.

Fully connected layer
After several convolutional and max pooling layers, the final classification is done via fully connected layers. Neurons in a fully connected layer have connections to all activations in the previous layer, as seen in regular (non-convolutional) artificial neural networks. Their activations can thus be computed as an affine transformation, with matrix multiplication followed by a bias offset (vector addition of a learned or fixed bias term).

Loss layer

The "loss layer", or "loss function", specifies how training penalizes the deviation between the predicted output of the network, and the true data labels (during supervised learning). Various loss functions can be used, depending on the specific task.

The Softmax loss function is used for predicting a single class of K mutually exclusive classes. Sigmoid cross-entropy loss is used for predicting K independent probability values in . Euclidean loss is used for regressing to real-valued labels .

Hyperparameters

Hyperparameters are various settings that are used to control the learning process. CNNs use more hyperparameters than a standard multilayer perceptron (MLP).

Kernel size
The kernel is the number of pixels processed together. It is typically expressed as the kernel's dimensions, e.g., 2x2, or 3x3.

Padding
Padding is the addition of (typically) 0-valued pixels on the borders of an image. This is done so that the border pixels are not undervalued (lost) from the output because they would ordinarily participate in only a single receptive field instance. The padding applied is typically one less than the corresponding kernel dimension. For example, a convolutional layer using 3x3 kernels would receive a 2-pixel pad, that is 1 pixel on each side of the image.

Stride
The stride is the number of pixels that the analysis window moves on each iteration. A stride of 2 means that each kernel is offset by 2 pixels from its predecessor.

Number of filters
Since feature map size decreases with depth, layers near the input layer tend to have fewer filters while higher layers can have more. To equalize computation at each layer, the product of feature values va with pixel position is kept roughly constant across layers. Preserving more information about the input would require keeping the total number of activations (number of feature maps times number of pixel positions) non-decreasing from one layer to the next.

The number of feature maps directly controls the capacity and depends on the number of available examples and task complexity.

Filter size
Common filter sizes found in the literature vary greatly, and are usually chosen based on the data set.

The challenge is to find the right level of granularity so as to create abstractions at the proper scale, given a particular data set, and without overfitting.

Pooling type and size

Max pooling is typically used, often with a 2x2 dimension. This implies that the input is drastically downsampled, reducing processing cost.

Large input volumes may warrant 4×4 pooling in the lower layers. Greater pooling reduces the dimension of the signal, and may result in unacceptable information loss. Often, non-overlapping pooling windows perform best.

Dilation
Dilation involves ignoring pixels within a kernel. This reduces processing/memory potentially without significant signal loss. A dilation of 2 on a 3x3 kernel expands the kernel to 5x5, while still processing 9 (evenly spaced) pixels. Accordingly, dilation of 4 expands the kernel to 9x9.

Translation equivariance and aliasing
It is commonly assumed that CNNs are invariant to shifts of the input. Convolution or pooling layers within a CNN that do not have a stride greater than one are indeed equivariant to translations of the input. However, layers with a stride greater than one ignore the Nyquist-Shannon sampling theorem and might lead to aliasing of the input signal While, in principle, CNNs are capable of implementing anti-aliasing filters, it has been observed that this does not happen in practice  and yield models that are not equivariant to translations.
Furthermore, if a CNN makes use of fully connected layers, translation equivariance does not imply translation invariance, as the fully connected layers are not invariant to shifts of the input. One solution for complete translation invariance is avoiding any down-sampling throughout the network and applying global average pooling at the last layer. Additionally, several other partial solutions have been proposed, such as anti-aliasing before downsampling operations, spatial transformer networks, data augmentation, subsampling combined with pooling, and capsule neural networks.

Evaluation
The accuracy of the final model is based on a sub-part of the dataset set apart at the start, often called a test-set. Other times methods such as k-fold cross-validation are applied. Other strategies include using conformal prediction.

Regularization methods

Regularization is a process of introducing additional information to solve an ill-posed problem or to prevent overfitting. CNNs use various types of regularization.

Empirical

Dropout
Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net (ignored) with probability  or kept with probability , so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed. Only the reduced network is trained on the data in that stage. The removed nodes are then reinserted into the network with their original weights.

In the training stages,  is usually 0.5; for input nodes, it is typically much higher because information is directly lost when input nodes are ignored.

At testing time after training has finished, we would ideally like to find a sample average of all possible  dropped-out networks; unfortunately this is unfeasible for large values of . However, we can find an approximation by using the full network with each node's output weighted by a factor of , so the expected value of the output of any node is the same as in the training stages. This is the biggest contribution of the dropout method: although it effectively generates  neural nets, and as such allows for model combination, at test time only a single network needs to be tested.

By avoiding training all nodes on all training data, dropout decreases overfitting. The method also significantly improves training speed. This makes the model combination practical, even for deep neural networks. The technique seems to reduce node interactions, leading them to learn more robust features that better generalize to new data.

DropConnect

DropConnect is the generalization of dropout in which each connection, rather than each output unit, can be dropped with probability . Each unit thus receives input from a random subset of units in the previous layer.

DropConnect is similar to dropout as it introduces dynamic sparsity within the model, but differs in that the sparsity is on the weights, rather than the output vectors of a layer. In other words, the fully connected layer with DropConnect becomes a sparsely connected layer in which the connections are chosen at random during the training stage.

Stochastic pooling
A major drawback to Dropout is that it does not have the same benefits for convolutional layers, where the neurons are not fully connected.

In stochastic pooling, the conventional deterministic pooling operations are replaced with a stochastic procedure, where the activation within each pooling region is picked randomly according to a multinomial distribution, given by the activities within the pooling region. This approach is free of hyperparameters and can be combined with other regularization approaches, such as dropout and data augmentation.

An alternate view of stochastic pooling is that it is equivalent to standard max pooling but with many copies of an input image, each having small local deformations. This is similar to explicit elastic deformations of the input images, which delivers excellent performance on the MNIST data set. Using stochastic pooling in a multilayer model gives an exponential number of deformations since the selections in higher layers are independent of those below.

Artificial data

Because the degree of model overfitting is determined by both its power and the amount of training it receives, providing a convolutional network with more training examples can reduce overfitting. Because these networks are usually trained with all available data, one approach is to either generate new data from scratch (if possible) or perturb existing data to create new ones. For example, input images can be cropped, rotated, or rescaled to create new examples with the same labels as the original training set.

Explicit

Early stopping

One of the simplest methods to prevent overfitting of a network is to simply stop the training before overfitting has had a chance to occur. It comes with the disadvantage that the learning process is halted.

Number of parameters
Another simple way to prevent overfitting is to limit the number of parameters, typically by limiting the number of hidden units in each layer or limiting network depth. For convolutional networks, the filter size also affects the number of parameters. Limiting the number of parameters restricts the predictive power of the network directly, reducing the complexity of the function that it can perform on the data, and thus limits the amount of overfitting. This is equivalent to a "zero norm".

Weight decay
A simple form of added regularizer is weight decay, which simply adds an additional error, proportional to the sum of weights (L1 norm) or squared magnitude (L2 norm) of the weight vector, to the error at each node. The level of acceptable model complexity can be reduced by increasing the proportionality constant('alpha' hyperparameter), thus increasing the penalty for large weight vectors.

L2 regularization is the most common form of regularization. It can be implemented by penalizing the squared magnitude of all parameters directly in the objective. The L2 regularization has the intuitive interpretation of heavily penalizing peaky weight vectors and preferring diffuse weight vectors. Due to multiplicative interactions between weights and inputs this has the useful property of encouraging the network to use all of its inputs a little rather than some of its inputs a lot.

L1 regularization is also common. It makes the weight vectors sparse during optimization. In other words, neurons with L1 regularization end up using only a sparse subset of their most important inputs and become nearly invariant to the noisy inputs. L1 with L2 regularization can be combined; this is called elastic net regularization.

Max norm constraints
Another form of regularization is to enforce an absolute upper bound on the magnitude of the weight vector for every neuron and use projected gradient descent to enforce the constraint. In practice, this corresponds to performing the parameter update as normal, and then enforcing the constraint by clamping the weight vector  of every neuron to satisfy . Typical values of  are order of 3–4. Some papers report improvements when using this form of regularization.

Hierarchical coordinate frames
Pooling loses the precise spatial relationships between high-level parts (such as nose and mouth in a face image). These relationships are needed for identity recognition. Overlapping the pools so that each feature occurs in multiple pools, helps retain the information. Translation alone cannot extrapolate the understanding of geometric relationships to a radically new viewpoint, such as a different orientation or scale. On the other hand, people are very good at extrapolating; after seeing a new shape once they can recognize it from a different viewpoint.

An earlier common way to deal with this problem is to train the network on transformed data in different orientations, scales, lighting, etc. so that the network can cope with these variations. This is computationally intensive for large data-sets. The alternative is to use a hierarchy of coordinate frames and use a group of neurons to represent a conjunction of the shape of the feature and its pose relative to the retina. The pose relative to the retina is the relationship between the coordinate frame of the retina and the intrinsic features' coordinate frame.

Thus, one way to represent something is to embed the coordinate frame within it. This allows large features to be recognized by using the consistency of the poses of their parts (e.g. nose and mouth poses make a consistent prediction of the pose of the whole face). This approach ensures that the higher-level entity (e.g. face) is present when the lower-level (e.g. nose and mouth) agree on its prediction of the pose. The vectors of neuronal activity that represent pose ("pose vectors") allow spatial transformations modeled as linear operations that make it easier for the network to learn the hierarchy of visual entities and generalize across viewpoints. This is similar to the way the human visual system imposes coordinate frames in order to represent shapes.

Applications

Image recognition
CNNs are often used in image recognition systems. In 2012 an error rate of 0.23% on the MNIST database was reported. Another paper on using CNN for image classification reported that the learning process was "surprisingly fast"; in the same paper, the best published results as of 2011 were achieved in the MNIST database and the NORB database. Subsequently, a similar CNN called
AlexNet won the ImageNet Large Scale Visual Recognition Challenge 2012.

When applied to facial recognition, CNNs achieved a large decrease in error rate. Another paper reported a 97.6% recognition rate on "5,600 still images of more than 10 subjects". CNNs were used to assess video quality in an objective way after manual training; the resulting system had a very low root mean square error.

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object classification and detection, with millions of images and hundreds of object classes. In the ILSVRC 2014, a large-scale visual recognition challenge, almost every highly ranked team used CNN as their basic framework. The winner GoogLeNet (the foundation of DeepDream) increased the mean average precision of object detection to 0.439329, and reduced classification error to 0.06656, the best result to date. Its network applied more than 30 layers. That performance of convolutional neural networks on the ImageNet tests was close to that of humans. The best algorithms still struggle with objects that are small or thin, such as a small ant on a stem of a flower or a person holding a quill in their hand. They also have trouble with images that have been distorted with filters, an increasingly common phenomenon with modern digital cameras. By contrast, those kinds of images rarely trouble humans. Humans, however, tend to have trouble with other issues. For example, they are not good at classifying objects into fine-grained categories such as the particular breed of dog or species of bird, whereas convolutional neural networks handle this.

In 2015 a many-layered CNN demonstrated the ability to spot faces from a wide range of angles, including upside down, even when partially occluded, with competitive performance. The network was trained on a database of 200,000 images that included faces at various angles and orientations and a further 20 million images without faces. They used batches of 128 images over 50,000 iterations.

Video analysis
Compared to image data domains, there is relatively little work on applying CNNs to video classification. Video is more complex than images since it has another (temporal) dimension. However, some extensions of CNNs into the video domain have been explored. One approach is to treat space and time as equivalent dimensions of the input and perform convolutions in both time and space. Another way is to fuse the features of two convolutional neural networks, one for the spatial and one for the temporal stream. Long short-term memory (LSTM) recurrent units are typically incorporated after the CNN to account for inter-frame or inter-clip dependencies. Unsupervised learning schemes for training spatio-temporal features have been introduced, based on Convolutional Gated Restricted Boltzmann Machines and Independent Subspace Analysis. It's Application can be seen in Text-to-Video model.

Natural language processing
CNNs have also been explored for natural language processing. CNN models are effective for various NLP problems and achieved excellent results in semantic parsing, search query retrieval, sentence modeling, classification, prediction and other traditional NLP tasks.
Compared to traditional language processing methods such as recurrent neural networks, CNNs can represent different contextual realities of language that do not rely on a series-sequence assumption, while RNNs are better suitable when classical time series modeling is required.

Anomaly Detection
A CNN with 1-D convolutions was used on time series in the frequency domain (spectral residual) by an unsupervised model to detect anomalies in the time domain.

Drug discovery
CNNs have been used in drug discovery. Predicting the interaction between molecules and biological proteins can identify potential treatments. In 2015, Atomwise introduced AtomNet, the first deep learning neural network for structure-based drug design. The system trains directly on 3-dimensional representations of chemical interactions. Similar to how image recognition networks learn to compose smaller, spatially proximate features into larger, complex structures, AtomNet discovers chemical features, such as aromaticity, sp3 carbons, and hydrogen bonding. Subsequently, AtomNet was used to predict novel candidate biomolecules for multiple disease targets, most notably treatments for the Ebola virus and multiple sclerosis.

Checkers game
CNNs have been used in the game of checkers. From 1999 to 2001, Fogel and Chellapilla published papers showing how a convolutional neural network could learn to play checker using co-evolution. The learning process did not use prior human professional games, but rather focused on a minimal set of information contained in the checkerboard: the location and type of pieces, and the difference in number of pieces between the two sides. Ultimately, the program (Blondie24) was tested on 165 games against players and ranked in the highest 0.4%. It also earned a win against the program Chinook at its "expert" level of play.

Go
CNNs have been used in computer Go. In December 2014, Clark and Storkey published a paper showing that a CNN trained by supervised learning from a database of human professional games could outperform GNU Go and win some games against Monte Carlo tree search Fuego 1.1 in a fraction of the time it took Fuego to play. Later it was announced that a large 12-layer convolutional neural network had correctly predicted the professional move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GNU Go in 97% of games, and matched the performance of the Monte Carlo tree search program Fuego simulating ten thousand playouts (about a million positions) per move.

A couple of CNNs for choosing moves to try ("policy network") and evaluating positions ("value network") driving MCTS were used by AlphaGo, the first to beat the best human player at the time.

Time series forecasting
Recurrent neural networks are generally considered the best neural network architectures for time series forecasting (and sequence modeling in general), but recent studies show that convolutional networks can perform comparably or even better. Dilated convolutions might enable one-dimensional convolutional neural networks to effectively learn time series dependences. Convolutions can be implemented more efficiently than RNN-based solutions, and they do not suffer from vanishing (or exploding) gradients. Convolutional networks can provide an improved forecasting performance when there are multiple similar time series to learn from. CNNs can also be applied to further tasks in time series analysis (e.g., time series classification or quantile forecasting).

Cultural Heritage and 3D-datasets
As archaeological findings like clay tablets with cuneiform writing are increasingly acquired using 3D scanners first benchmark datasets are becoming available like HeiCuBeDa providing almost 2.000 normalized 2D- and 3D-datasets prepared with the GigaMesh Software Framework. So curvature-based measures are used in conjunction with Geometric Neural Networks (GNNs) e.g. for period classification of those clay tablets being among the oldest documents of human history.

Fine-tuning
For many applications, the training data is less available. Convolutional neural networks usually require a large amount of training data in order to avoid overfitting. A common technique is to train the network on a larger data set from a related domain. Once the network parameters have converged an additional training step is performed using the in-domain data to fine-tune the network weights, this is known as transfer learning. Furthermore, this technique allows convolutional network architectures to successfully be applied to problems with tiny training sets.

Human interpretable explanations
End-to-end training and prediction are common practice in computer vision. However, human interpretable explanations are required for critical systems such as a self-driving cars. With recent advances in visual salience, spatial attention, and temporal attention, the most critical spatial regions/temporal instants could be visualized to justify the CNN predictions.

Related architectures

Deep Q-networks
A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning agents, DQNs that utilize CNNs can learn directly from high-dimensional sensory inputs via reinforcement learning.

Preliminary results were presented in 2014, with an accompanying paper in February 2015. The research described an application to Atari 2600 gaming. Other deep reinforcement learning models preceded it.

Deep belief networks

Convolutional deep belief networks (CDBN) have structure very similar to convolutional neural networks and are trained similarly to deep belief networks. Therefore, they exploit the 2D structure of images, like CNNs do, and make use of pre-training like deep belief networks. They provide a generic structure that can be used in many image and signal processing tasks. Benchmark results on standard image datasets like CIFAR have been obtained using CDBNs.

Notable libraries
Caffe: A library for convolutional neural networks. Created by the Berkeley Vision and Learning Center (BVLC). It supports both CPU and GPU. Developed in C++, and has Python and MATLAB wrappers.
Deeplearning4j: Deep learning in Java and Scala on multi-GPU-enabled Spark. A general-purpose deep learning library for the JVM production stack running on a C++ scientific computing engine. Allows the creation of custom layers. Integrates with Hadoop and Kafka.
Dlib: A toolkit for making real world machine learning and data analysis applications in C++.
Microsoft Cognitive Toolkit: A deep learning toolkit written by Microsoft with several unique features enhancing scalability over multiple nodes. It supports full-fledged interfaces for training in C++ and Python and with additional support for model inference in C# and Java.
TensorFlow: Apache 2.0-licensed Theano-like library with support for CPU, GPU, Google's proprietary tensor processing unit (TPU), and mobile devices.
Theano: The reference deep-learning library for Python with an API largely compatible with the popular NumPy library. Allows user to write symbolic mathematical expressions, then automatically generates their derivatives, saving the user from having to code gradients or backpropagation. These symbolic expressions are automatically compiled to CUDA code for a fast, on-the-GPU implementation.
Torch: A scientific computing framework with wide support for machine learning algorithms, written in C and Lua.

See also
 Attention (machine learning)
 Convolution
 Deep learning
 Natural-language processing
 Neocognitron
 Scale-invariant feature transform
 Time delay neural network
 Vision processing unit

Notes

References

External links
 CS231n: Convolutional Neural Networks for Visual Recognition — Andrej Karpathy's Stanford computer science course on CNNs in computer vision
 An Intuitive Explanation of Convolutional Neural Networks — A beginner level introduction to what Convolutional Neural Networks are and how they work
 Convolutional Neural Networks for Image Classification  — Literature Survey

Neural network architectures
Computer vision
Computational neuroscience


text:
Egill Skallagrímsson was een 10e-eeuwse skald. Hij was een krijger en beroemd hofdichter in de Vikingtijd. Hij was de grote antiheld van de IJslandse literatuur. Egils saga verhaalt over hem.

Hij schreef, net als anderen in die tijd, in het runenschrift. Het Latijnse alfabet werd pas in de 13e eeuw geïntroduceerd.

Wetenswaardigheden
De brouwerij Ölgerðin Egill Skallagrímsson in Reykjavik is naar hem vernoemd.

Externe links
 Egil Skallagrimsson and the Viking Ideal door Christina von Nolcken, op de website van de Universiteit van Chicago
Egil's Bones - op de website van de Universiteit van Californië
Engelse vertaling van de saga

IJslands dichter
Skald
Persoon in de 10e eeuw

redpajama_set_name: RedPajamaWikipedia


text:
Die Freibadclique steht für:

 Die Freibadclique (Roman), Roman von Oliver Storz (2008)
 Die Freibadclique (Film), deutsch-tschechischer Film von Friedemann Fromm (2017)


text:
La septième saison, aussi connue comme Jour 7, de la série télévisée 24 devait commencer le mardi .

La saison débute à 8 heures du matin et s'achève à la même heure le lendemain. Cette saison se déroulant quatre ans après la saison 6, l'action se déroulerait donc en  bien que l'épisode ne soit pas daté.

Sa diffusion a été retardée d'un an pour ne commencer que le dimanche . En effet, à la suite de la grève des scénaristes qui dura plus de trois mois à Hollywood, de nombreuses séries ont été perturbées (24 fait partie des séries les plus touchées avec Lost, Desperate Housewives et Grey's Anatomy).

Le , la Fox a diffusé un téléfilm de 90 minutes intitulé 24 : Redemption pour combler l'attente de cette . Ce téléfilm fait le lien entre la fin des événements du sixième jour et le début du septième.

Synopsis
Alors qu'il doit répondre devant le Sénat des crimes commis par la Cellule anti-terroriste récemment dissoute, Jack Bauer voit son audition interrompue par le FBI, qui le recrute pour faire face à une nouvelle menace. En effet, son ancien ami et collègue Tony Almeida, qu'il croyait mort depuis près de 6 ans, a pris la tête d'un groupe terroriste qui commet des vols de haute technologie à Washington avant de s'en prendre aux infrastructures critiques du pays en commençant par le contrôle aérien; et Jack est le seul en mesure de l'arrêter.

Distribution

 Kiefer Sutherland (VF : Patrick Béthune) : Jack Bauer (24/24)
 Carlos Bernard (VF : Bertrand Liebert) : Tony Almeida (20/24)
 Annie Wersching (VF : Laurence Dourlens) : Agent Spécial Renee Walker du FBI (24/24)
 Cherry Jones : Présidente Allison Taylor (23/24)
 Janeane Garofalo (VF : Valérie Nosrée) : Analyste Janis Gold du FBI (21/24)
 Jeffrey Nordling : Agent Spécial en Charge Larry Moss du FBI (19/24)
 Bob Gunton : Ethan Kanin, Chef de cabinet de la Maison-Blanche (18/24)
 Mary Lynn Rajskub : Chloe O'Brian, ex-Analyste de la Cellule Anti-Terroriste (13/24)
 James Morrison (VF : Hervé Jolly) : Bill Buchanan, ex-directeur de la Cellule Anti-Terroriste (10/24)
 Colm Feore : Premier Homme Henry Taylor (12/24)
 Rhys Coiro : Analyste Sean Hillinger du FBI
 Frank John Hughes (VF : Thierry Wermuth) : Tim Woods, Secrétaire à la Sécurité Intérieure
 Mark Derwin : Joe Stevens, Secrétaire d'État
 Sprague Grayden : Olivia Taylor, fille de la Présidente Alisson Taylor et de Henri Taylor
 Jon Voight (VF : Bernard Tiphaine) : Jonas Hodges, PDG de la société militaire privée Starkwood
 Hakeem Kae-Kazim : Colonel Iké Dubaku, commandant en second de "Armée de la Liberté de la Nation" et bras droit du Général Juma
 Rory Cochrane : Greg Seaton, bras droit de Hodges
 Warren Kole : Agent Brian Gedge du Secret Service
 Elisha Cuthbert : Kim Bauer
 Peter Wingfield : David Emerson, patron de Tony Almeida
 Will Patton : Alan Wilson, chef de la cabale à l'attaque biologique et tête de la conspiration lors de la saison 5
 Kurtwood Smith : Sénateur Blaine Mayer, président de la commission d'enquête sur la Cellule Anti-Terroriste
 Isaac de Bankolé : Ule Motobo, Premier Ministre du Sangala
 Cameron Daddo : Vice-Président Mitchell Hayworth
 Ryan Cutrona : Amiral John Smith, Chef des Opérations navales
 Carlo Rota : Morris O'Brian, ex-agent de la Cellule Anti-Terroriste
 Carly Pope : Samantha Roth, petite amie de Roger Taylor, décédé
 Mark Kiely : Agent Edward Vossler du Secret Service
 Tony Todd (VF : Benoît Allemane): Général Benjamin Juma, commandant de "Armée de la Liberté de la Nation"
 John Billingsley : Michael Latham, responsable de la sécurité informatique des infrastructures nationales critiques
 Ever Carradine : Analyste Erika du FBI
 Nick Chinlund : Masters, un mercenaire d'Almeida
 Rocky McMurray : Général Vincent, Commandant du Corps des Marines
 Tommy Flanagan : Gabriel Schecter, contrebandier et trafiquant d'armes
  : Commandant de bord Sato, pilote du vol Sunrise 153
 Acteur non nommé : Alama Motobo, épouse du Premier Ministre Motobo
 Christina Chang : Dr Sunny Macer (5/24)

Scénario
La saison 7 se déroule quatre ans après les événements du Jour 6 à Washington DC et commence à 8 heures du matin, heure de la côte Est. La précédente saison à avoir commencé à 8 heures est la saison 2.

Révélée à la TV Squad, l'une des  du début de la saison est la mort de Roger, le fils de la présidente Allison Taylor. Sa mort s'apparenterait à un suicide mais son père Henry découvrira que c'est un meurtre et fera tout pour le prouver.

La journée débute sur Jack Bauer, jugé pour ses actions criminelles des journées précédentes par une commission sénatoriale dirigée par le sénateur Mayer, notamment concernant le meurtre d'un terroriste du nom d'Ibrahim Haddad dans un bus. Le procès de Jack est une partie du procès des acteurs de la cellule anti-terroriste, qui a été démantelée quelques mois plus tôt, et dont les membres ont à répondre de leurs actes, dont Jack Bauer. L'agent du FBI Renee Walker vient interrompre le procès de Jack Bauer pour demander son aide dans l'attaque terroriste que subit les États-Unis. Ce complot est dirigé par le colonel Ike Dubaku, aidé de Tony Almeida, et consiste à prendre le contrôle des infrastructures informatisées, comme le contrôle aérien, et les infrastructures chimiques pour provoquer des catastrophes.

Bill Buchanan apparaît à partir de l'épisode 3 en se faisant contacter par Jack par l'intermédiaire de Tony. Mais il meurt dans l'épisode 13 en se sacrifiant pour sauver la présidente.

Dans la deuxième partie de la saison, les États-Unis subissent une attaque biologique orchestrée par la société militaire Starkwood dirigé par Jonas Hodges.

Tony Almeida fait son retour. Annoncé mort après s'être fait planter une seringue de produit toxique par Christopher Henderson dans l'épisode de la saison 5 [07.00 PM- 08.00PM], il fait son retour en tant que terroriste, qui est peut-être infiltré. On découvrira plus tard dans la saison les réelles motivations de Tony.

Distribution
 Les jours de Peter McNicol (Tom Lennox) sur 24 sont finis (ou presque). L'acteur a conclu un marché pour retourner dans Numb3rs sur CBS. Il apparaît néanmoins dans le prequel de la saison 7, tout comme Powers Boothe (le vice-président Noah Daniels dans la saison 6).

 Howard Gordon a raconté, dans une interview, que le personnage de Gregory Itzin (Charles Logan) n'est pas mort et qu'il reviendra dans la prochaine saison.

 Carlo Rota joue un petit rôle dans la saison. À la fin de la saison précédente, son personnage Morris apprend que son ex-femme Chloe attend un enfant, qui est maintenant né et a environ 3 ans.

 L'acteur Jon Voight joue l'un des principaux « méchants » de la saison 7, Jonas Hodges (qui joue aussi dans le préquel de la saison 7), il apparaît dans la seconde partie de saison. Voight n'avait plus joué dans une série depuis les années 60 et Gunsmoke.

 Rory Cochrane, qui a joué dans la mini-série The Company et incarnait Tim Spadle dans Les Experts : Miami, vient de s'ajouter au casting très étoffé de la saison 7 de 24 Heures chrono. Il joue le bras droit de Jonas Hodges.

 Plusieurs personnages des saisons précédentes apparaissent en cours de saison : Mary Lynn Rajskub (Chloe O'Brian), Carlo Rota (Morris O'Brian) ou encore Elisha Cuthbert (Kim Bauer) dans la dernière partie.

Tournage
La scène finale de l'épisode 7x02 a été tournée à San Pedro en Californie, là où des feux de forêt ont éclaté en fin .

La production du « Jour 7 » devait débuter fin juin mais fut reportée au  pour permette aux scénaristes de réécrire leur histoire. Puis la production fut reportée une dernière fois au , afin que les scénaristes puissent boucler le plus d'épisodes possible.

Grève des scénaristes
À cause d'importantes grèves des scénaristes de toutes les séries américaines dont 24, la Fox a eu une pénurie de fictions inédites en mars/. Concernant 24, les scénaristes se sont arrêtés à l'écriture de l'épisode 9 quand la grève a démarré (en ).
La septième saison de 24 a donc été exceptionnellement précédée d'une longue attente. C'est pourquoi, un "prequel" de 90 minutes a été diffusé le  pour faire le lien entre la saison six et la septième.

Épisodes

Jour 7 : 8:00 - 9:00

Jour 7 : 9:00 - 10:00

Jour 7 : 10:00 - 11:00

Jour 7 : 11:00 - 12:00

Jour 7 : 12:00 - 13:00

Jour 7 : 13:00 - 14:00

Jour 7 : 14:00 - 15:00

Jour 7 : 15:00 - 16:00

Jour 7 : 16:00 - 17:00

Jour 7 : 17:00 - 18:00

Jour 7 : 18:00 - 19:00

Jour 7 : 19:00 - 20:00

Jour 7 : 20:00 - 21:00

Jour 7 : 21:00 - 22:00

Jour 7 : 22:00 - 23:00

Jour 7 : 23:00 - 00:00

Jour 7 : 00:00 - 01:00

Jour 7 : 01:00 - 02:00

Jour 7 : 02:00 - 03:00

Jour 7 : 03:00 - 04:00

Jour 7 : 04:00 - 05:00

Jour 7 : 05:00 - 06:00

Jour 7 : 06:00 - 07:00

Jour 7 : 07:00 - 08:00

Références

7


text:
Улица Хо́спиталю ( — Госпитальная) — улица в Риге, в Видземском предместье, в микрорайоне Браса. Проходит от перекрёстка улиц Миера и Менесс до пересечения с улицами Упес и Инвалиду.

Длина улицы — . Почти на всём протяжении замощена булыжником; небольшой участок
заасфальтирован. От начала улицы до перекрёстка с улицей Зирню движение одностороннее (в направлении улицы Зирню). Общественный транспорт по улице не курсирует.

История
Улица начала формироваться в середине XVIII века как дорога, ведущая к военному госпиталю, открытому в 1754 году. В списках улиц города Риги улица Хоспиталю впервые упоминается 1810 году. Её наименование всегда происходило от названия госпиталя, но в разные периоды времени немного отличалось по форме: Hospitāļa, Hospitāļu, Spitāļu, а в 1930-е годы даже Slimnīcas iela (Больничная) или Kara Slimnīcas iela. Принципиальных переименований улицы не было.

В середине XIX века улица пересекала территорию госпиталя, ведя до нынешней улицы Лактас и далее, в современный район Саркандаугава, где носила название Кара Хоспиталя ( — улица Военного Госпиталя). В 1885 году из улицы Кара Хоспиталя были образованы нынешние улицы Патверсмес и Озолу, а территория госпиталя — преобразована, в результате чего улица Хоспиталю получила современные границы.

Примечательные здания
 В конце улицы расположено здание военного госпиталя (построено в 1835 году), окружённое старинным парком. Этот комплекс является памятником культуры государственного значения.
 Многоквартирные жилые дома № 4 (построен в 1914 г.), № 5 (1910 г.), № 22 (1911 г.), № 28 (1911 г.), № 30 (1914 г.), № 34 (1912 г.), № 35/37 (1913 г.) и № 36 (1912 г.) являются охраняемыми памятниками архитектуры местного значения.

Прилегающие улицы
Улица Хоспиталю пересекается со следующими улицами:

 Улица Миера
 Улица Менесс
 Улица Зирню
 Улица Иередню
 Улица Казарму
 Улица Карейвью
 Улица Иерочу
 Улица Упес
 Улица Инвалиду

Примечания

Улицы Брасы


text:
es un futbolista japonés que se desempeña como defensa.

Trayectoria

Clubes

Estadística de carrera

J. League

Referencias

Personas de la prefectura de Nara
Futbolistas de Japón
Futbolistas del Sanfrecce Hiroshima
Futbolistas del Vissel Kobe
Futbolistas del Hokkaido Consadole Sapporo
Futbolistas del Tokushima Vortis
Futbolistas del Yokohama FC
Futbolistas del Giravanz Kitakyushu


text:
Franz Konrad (* 1. März 1906 in Liesing, Niederösterreich; † 6. März 1952 in Warschau) war ein österreichischer SS-Hauptsturmführer und als Verwaltungsführer zuständig für die "Werteerfassung" im Warschauer Ghetto, der als Kriegsverbrecher hingerichtet wurde. Konrad war für den Raub jüdischen Vermögens im Warschauer Ghetto zuständig (vgl. Raubgold).

Leben
Franz Konrad, Sohn eines Bergmannes, arbeitete nach dem Abschluss seiner Schulzeit als Verkäufer in der Lebensmittelbranche und anschließend bis 1931 als Filialleiter eines Konsumvereins. Danach war Konrad arbeitslos, trat am 1. April 1932 der österreichischen NSDAP (Mitgliedsnummer 1.085.499) und am 1. September 1932 der SS (SS-Nummer 46.204) bei. Konrad, Mitglied im Lebensborn e. V., war seit 1931 verheiratet und wurde in der Folge Vater dreier Kinder. Nach dem NSDAP-Verbot in Österreich am 19. Juni 1933 leitete Konrad den SS-Sturm 5/II/38. Wegen Beteiligung am Juliputsch 1934 wurde Konrad verhaftet und im Anhaltelager Wöllersdorf interniert. Nach seiner Flucht aus Österreich im Juli 1935 gelangte Konrad ins Deutsche Reich und erhielt im SS-Lager Weißenfeld, nun auch als Mitglied der Waffen-SS, eine militärische Ausbildung. Danach absolvierte er eine Verwaltungsausbildung und kehrte nach dem "Anschluss Österreichs" im April 1938 nach Österreich, als hauptamtlicher SS-Führer und Verwaltungsführer des SS-Abschnitts XXXV, Sturmbann III/94, zurück.

Im November 1939, nach Ausbruch des Zweiten Weltkrieges, wurde Konrad einberufen als hauptamtlicher Verwaltungsführer der allgemeinen SS und Oberscharführer der Waffen-SS nach Warschau. Nach einer Weiterbildung an der Verwaltungsschule bei der Leibstandarte SS Adolf Hitler war Konrad für den Bau und die Einrichtung von Truppenunterkünften und die Beschlagnahmung von Gütern für die Truppe verantwortlich. Bis Mitte Juni 1942 fungierte Konrad als Verpflegungsoffizier der Truppe und wurde danach Leiter für die "Werteerfassung" im Warschauer Ghetto zum Raub jüdischen Vermögens. Diese Dienststelle unterstand dem SS- und Polizeiführer in Warschau Ferdinand von Sammern-Frankenegg und war für die Requisition, Sortierung und Weiterleitung der jüdischen Vermögenswerte, Geldmittel, Einrichtungsgegenstände und Maschinen zuständig. Zudem wurde er ab Ende April 1942 beim Inspekteur des Reit- und Fahrwesens in Warschau eingesetzt.

Ab dem 1. April 1943 wurde Konrad zum SS- und Polizeiführer Odilo Globocnik nach Lublin abkommandiert und bei der Ostindustrie GmbH eingesetzt. Von dort wurde er dem SS- und Polizeiführer in Warschau Ferdinand von Sammern-Frankenegg zur Verfügung gestellt und fungierte zunächst weiterhin als Leiter für die Werteerfassung jüdischen Vermögens im Warschauer Ghetto. Während des Aufstandes im Warschauer Ghetto nahm Konrad an dessen Niederschlagung teil und führte am 19. April 1943 eine Gruppe Juden zum Umschlagplatz, wo diese von jüdischen Widerstandskämpfern befreit wurden. Konrad konnte jedoch flüchten. Im Verlauf des Jahres 1943 war Konrad dann mit der Überprüfung des Kriegsgefangenenlagers Włodawa, das in ein Arbeitslager umgewandelt werden sollte, beschäftigt. Zudem war er mit der Werteerfassung im Ghetto Białystok, dessen Verlagerung sowie mit dem Ausbau der Maschinen im Restghetto von Warschau, beauftragt. Ende 1943 beendete Konrad seine Tätigkeit für die Ostindustrie GmbH und fungierte ab dem 3. Januar 1944 als Verwaltungsleiter des SS-Remonteamt Fischhorn-Bruck-Fusch.

Nach Kriegsende wurde Konrad verhaftet und im "Prozess gegen Stroop und Konrad" wegen der Teilnahme an der Liquidierung des Warschauer Ghettos zum Tod durch den Strang verurteilt. Am 6. März 1952 wurde Konrad in Warschau hingerichtet.

NSDAP-interne Auszeichnungen

 Ehrenwinkel der Alten Kämpfer
 SS-Zivilabzeichen
 Totenkopfring der SS
 Julleuchter

Militärauszeichnungen im Zweiten Weltkrieg
 Kriegsverdienstkreuz (1939) II. Klasse mit Schwertern, 1940
 Eisernes Kreuz (1939) II. Klasse, 1942
 Medaille Winterschlacht im Osten 1941/42
 Kriegsverdienstkreuz (1939) I. Klasse mit Schwertern, 1944

Literatur
 Joachim Jahns: Der Warschauer Ghettokönig. Dingsda, Leipzig 2009, ISBN 978-3-928498-99-9.
 Ernst Klee: Das Personenlexikon zum Dritten Reich: Wer war was vor und nach 1945. Fischer TB, Frankfurt 2007, ISBN 978-3-596-16048-8.
 Joseph Wulf: Das Dritte Reich und seine Vollstrecker. Die Liquidation von 500.000 Juden im Ghetto Warschau. Arani, Berlin 1961.

Einzelnachweise

Täter des Holocaust
Warschauer Ghetto
Person (deutsche Besetzung Polens 1939–1945)
SS-Mitglied
Angehöriger der Waffen-SS
Teilnehmer am Juliputsch
NSDAP-Mitglied
Hingerichtete Person (NS-Kriegsverbrechen)
Hingerichtete Person (Volksrepublik Polen)
Österreicher
Geboren 1906
Gestorben 1952
Mann


text:
Spačva är en skog i Kroatien.   Den ligger i länet Srijem, i den östra delen av landet,  km öster om huvudstaden Zagreb.

Trakten ingår i den hemiboreala klimatzonen. Årsmedeltemperaturen i trakten är  °C. Den varmaste månaden är juli, då medeltemperaturen är  °C, och den kallaste är december, med  °C. Genomsnittlig årsnederbörd är  millimeter. Den regnigaste månaden är maj, med i genomsnitt  mm nederbörd, och den torraste är augusti, med  mm nederbörd.

Källor

Skogar i Srijem


text:
Argentina Sorgo Films presenta: Obras en vivo es el segundo álbum en vivo de Luis Alberto Spinetta solista, grabado a fines del año 2001 en el estadio Obras Sanitarias para presentar el álbum Silver Sorgo (2001). El audio incluido en este CD, es tan solo una pequeña porción de lo que fue el recital original en Obras, de casi 3 horas de duración (entre clásicos y los temas nuevos de Silver Sorgo). La señal de televisión Canal (á) transmitió el concierto, pero de igual manera, solo televisó los temas que figuran en el disco, además de incluir Tonta luz, El enemigo, Ana no duerme (en una versión más completa de 7 minutos) y Good night (original de The Beatles), hecha a dúo con la corista Graciela Cosceri. El recital estaba planeado para el 21 de diciembre de dicho año. Pero por el caos desatado en la economía argentina, que terminó con el gobierno de Fernando de la Rúa, obligó a que el concierto sea cambiado de fecha. Asimismo, incluyó un homenaje a George Harrison, a tan solo un mes de su muerte.

Lista de temas
Todos los temas pertenecen a Spinetta excepto Don't Bother Me de Harrison

 No te busques ya en el umbral
 Ekathe 1
 Don't bother me
 El mar es de llanto
 Mi sueño de hoy
 ¿No ves que ya no somos chiquitos?
 Al ver, verás
 Sagrado tesoro
 Perdido en ti
 La verdad de las grullas
 Ana no duerme
Bonus:
 Tonta luz (remix)
 El enemigo (videoclip)
 Tonta luz (videoclip)

Músicos
Daniel Wirzt: Batería
Javier Malosetti: Bajo
Martín García Reinoso: Guitarra
Claudio Cardone: Piano y teclados
Mono Fontana: Teclados en 6 y 7
Rafael Arcaute: Teclados
Graciela Cósceri: Coros
Pricky Pricolo: Percusión
Dante Spinetta: Guitarra en 11
Spinetta: Guitarra y voz
Grupo Invitado: Geo Ramma

Video-Clips
El CD original contaba con dos bonus de animación flash, que eran los videoclips de El enemigo y Tonta luz. También, como promoción de este disco, está el videoclip en vivo de Ana no duerme.

Álbumes de Luis Alberto Spinetta
Álbumes de Argentina de 2002
Álbumes en vivo de 2002
Álbumes grabados en Estadio Obras Sanitarias
Álbumes en español
Álbumes de rock en español


text:
U jezgri možemo uočiti jednu ili više jezgrica koje prepoznajemo po razlici u lomu svijetlosti. Jezgrica je mjesto u jezgri u kojemu se sintetizira rRNK koja se kasnije veže s ribosomskim proteinima u ribosomske podkomponente i zatim prolaze kroz pore jezgrine membrane i odlaze u citoplazmu gdje se spajaju u 80S ribosome. 80S je sedimentacijski koeficijent izražen u jedinicama Svedberg(S), on je karakterističan za eukariote, dok bakterije imaju 70S koeficijent sedimentacije.

Jezgrica (nucleolus) najvidljivija je struktura jezgre u stanici koja se ne dijeli, najčešće se 1-7 jezgrica nalazi u jezgri (nucleusu).
Jezgrica ima dvije vrlo važne uloge koje su usko povezane sa sintezom proteina: 1. U jezgrici transkripcijom nastaje rRNA koji zajedno s ribosomskim proteinima čine ribosom na kojem se odvija sinteza proteina u stanici. 2. Jezgrica je mjesto sklapanja ribosomskih podjedinica.

U jezgrici se nalaze dugačke DNA molekule koje izviru iz kromosoma, a na kojim se nalaze geni za sintezu rRNA. Takva nakupina gena unutar jezgrice vrlo je važna te se naziva nukleolarni organizator. Takvi geni se velikom brzinom procesom transkripcije uz prisutnost enzima rRNA polimeraze prepisuju do rRNA (rRNK). Tako novonastale rRNA molekule u jezgrici se sklapaju s ribosomskim proteinima čineći ribosomske podjedinice, koje se sklapaju u ribosome na kojima se procesima transkripcije i translacije odvija sinteza vrlo važnih makromolekula koje imaju brojne uloge u našem organizmu - sinteza proteina.

Vidi
mala jezgričina RNK

jezgrica


text:
Lexical choice is the subtask of Natural language generation that involves choosing the content words (nouns, verbs, adjectives, and adverbs) in a generated text.  Function words (determiners, for example) are usually chosen during realisation.

Examples
The simplest type of lexical choice involves mapping a domain concept (perhaps represented in an ontology) to a word.  For example, the concept Finger might be mapped to the word finger.

A more complex situation is when a domain concept is expressed using different words in different situations.  For example, the domain concept Value-Change can be expressed in many ways
 The temperature rose: the verb rose is used for a Value-Change in temperature which increases the value
 The temperature fell: the verb fell is used for a Value-Change in temperature which decreases the value
 The rain got heavier: the phrase got heavier is used for a Value-Change in precipitation amount when the precipitation is rain.

Sometimes words can communicate additional contextual information, for example
 The temperature plummeted: the verb plummeted is used for a Value-Change in temperature which decreases the value, when the change is rapid and large

Contextual information is especially significant for vague terms such as tall.  For example, a 2m tall man is tall, but a 2m tall horse is small.

Linguistic perspective
Lexical choice modules must be informed by linguistic knowledge of how the system's input data maps onto words.  This is a question of semantics, but it is also influenced by syntactic factors (such as collocation effects) and pragmatic factors (such as context).

Hence NLG systems need linguistic models
of how meaning is mapped to words in the target domain (genre) of the NLG system.  Genre tends to be very important; for example the verb veer has a very specific meaning in weather forecasts (wind direction is changing in a clockwise direction) which it does not have in general English, and a weather-forecast generator must be aware of this genre-specific meaning.

In some cases there are major differences in how different people use the same word; for example, some people use by evening to mean 6PM and others use it to mean midnight.  Psycholinguists have shown that when people speak to each other, they agree on a common interpretation via lexical alignment; this is not something which NLG systems can yet do.

Ultimately, lexical choice must deal with the fundamental issue of how language relates to the non-linguistic world.  For example, a system which chose colour terms such as red to describe objects in a digital image would need to know which RGB pixel values could generally be described as red; how this was influenced by visual (lighting, other objects in the scene) and linguistic (other objects being discussed) context; what pragmatic connotations were associated with red (for example, when an apple is called red, it is assumed to be ripe as well as have the colour red); and so forth.

Algorithms and models
A number of algorithms and models have been developed for lexical choice in the research community, for example Edmonds developed a model for choosing between near-synonyms (words with similar core meanings but different connotations).  However such algorithms and models have not been widely used in applied NLG systems; such systems have instead often used quite simple computational models, and invested development effort in linguistic analysis instead of algorithm development.

References

Computational linguistics
Natural language processing


text:
Steinkjer – stacja kolejowa w Steinkjer, w regionie Nord-Trøndelag w Norwegii, jest oddalona od Trondheim o 121,5 km. Znajduje się na wysokości 3,6 m n.p.m.

Ruch pasażerski

Należy do linii Nordlandsbanen i jest końcową stacją kolei aglomeracyjnej w Trondheim, obsługuje lokalny ruch do Trondheim S i dalekobieżny do Bodø. W ciągu dnia odchodzi z niej ok. 30 pociągów.

Obsługa pasażerów
Poczekalnia, kasa biletowa, telefon publiczny, schowki bagażowe, pokój obsługi niemowląt, parking na 30 samochodów, parking rowerowy, kiosk, przystanek autobusowy, postój taksówek.

Przypisy

Stacje i przystanki kolejowe w regionie Nord-Trøndelag
Steinkjer


text:
Kirkelig Forening for den Indre Mission i Danmark (ofta enbart kallad Indre Mission) är en lågkyrklig, evangelisk-luthersk missionsorganisation inom den danska folkkyrkan.

Samlande gestalt var kyrkoherde Vilhelm Beck, från föreningens stiftelse den 13 september 1861 till dennes död 1901.

Indre Mission uppstod som en pietistisk väckelserörelse som, i motsättning till upplysningstidens rationalistiska kristendomssyn, hävdade Guds ords och de lutherska bekännelseskrifternas auktoritet och poängterade vikten av ett radikalt personligt fromhetsliv.

Indre Missions huvudsakliga verksamhet föregår i missionshus runt om i Danmark. Tidigare drev man även en rad missionshotell, som bland annat kännetecknades av att de inte sålde alkohol. Idag finns det bara två sådana missionshotell kvar, ett i Köpenhamn och ett i Svendborg. Däremot drivs fortfarande ett flertal sjömanshem av, den Indre Mission närstående, Indenlandsk Sømandsmission.

Referenser

Externa länkar
 Indre Mission (engelska)
 Indre Mission Cross

Danska folkkyrkan
Religiösa organisationer bildade 1861
Kristna missionsorganisationer
Lågkyrklighet


text:
The rock drawings in Valcamonica (Camonica Valley) are located in the Province of Brescia, Italy,  and constitute the largest collections of prehistoric petroglyphs in the world.   The collection was recognized by UNESCO in 1979 and was Italy's first recognized World Heritage Site.  UNESCO has formally recognized more than 140,000 figures and symbols, but new discoveries have increased the number of catalogued incisions to between 200,000 and 300,000.  The petroglyphs are spread on all surfaces of the valley, but concentrated in the areas of Darfo Boario Terme, Capo di Ponte, Nadro, Cimbergo and Paspardo.

Characteristics
The vast majority of the petroglyphs are made on subglacially streamlined bedrock and glacial erratic surfaces revealed as the last glacier to form the valley retreated approximately 15,000 years ago.

Many of the incisions were made over a time period of eight thousand years preceding the Iron Age (1st millennium BC), while petroglyphs of the last period are attributed to the people of Camuni, mentioned by Latin sources.  The petroglyph tradition does not end abruptly.  Engravings have been identified (although in very small number; not comparable with the great prehistoric activity) from the Roman period, medieval period and are possibly even contemporary, up to the 19th century. Most of the cuts have been made using the "martellina" technique and lesser numbers obtained through graffiti.

The figures are sometimes simply superimposed without apparent order.  Others instead appear to have a logical relationship between them; for example, a picture of a religious rite or a hunting scene or fight.  This approach explains the scheme of images, each of which is an ideogram that is not the real object, but its "idea".  Their function pertains to celebratory rituals: commemorative, initiatory and propitiatory; first in the field of religion, then later even secular, which were held on special occasions, either single or recurrent.  Among the most-famous symbols found in Valcamonica is the so-called "Rosa camuna" (Camunian rose), which was adopted as the official symbol of the region of Lombardy.

The historiated cycle: themes and periodization
In the 1960s, the archaeologist Emmanuel Anati, among the first to systematically study the area, drew up a chronology of rock carvings.  It compared the style and types of the symbols to identify possible correlations with the traditional historical periodization, from Prehistory to the Middle Ages.

According to Anati's research results, Val Camonica is divided into Proto-Camunian, Camunian I, II, III, IV and Post-Camunian (Roman, Medieval ages and recent) periods.

Epipaleolithic
The earliest rock carvings date back to epipaleolithic (or Mesolithic, Proto-Camunian, 8th-6th millennium BC), several millennia after the retreat of the glacier that covered the Val Camonica (Würm glaciation).  Those carvings were the work of passing nomadic hunters, following the migrations of their prey.  The figures represented in fact depict large animals such as deer and elk, which are the typical prey of that period. The drawings also describe animals wounded with spears. This kind of rock art can be typical for hunters-gatherers and associated with blades and microlites industry. Similar representations are present in the town park stone carvings of Luine (comune of Darfo Boario Terme).

Neolithic
During the Neolithic period (the 5th to 1st centuries of the 4th millennium BC approximately-5500-3300 BC), agricultural practices spread in Val Camonica, correlated with the formation of the first sedentary settlements.  In the field of rock art, human figures and sets of geometric elements, such as rectangles, circles, and dots, constitute the main elements of the compositions and complete the symbolic meaning of the anthropomorphical petroglyphs. Similar carvings are present in the Regional Reserve of Rock Engravings of Ceto, Cimbergo, and Paspardo. The pertaining to the Neolithic of the schematic anthropomorphic figures, so called "oranti" ("praying figures"), is questioned, as some scholars refer them to the Bronze Age. According to this interpretation, the  only set of figures pertaining to the Neolithic, or to a Neolithic-first Copper Age phase (4th mill. BC), is constituted by the geometric patterns, which are interpreted as topographic representation of a cultivated and ploughed land.

During this period, domesticated animals are encountered such as dogs, goats and bulls.

Till the end of the Camunian II period, objects of worshipping became wider. Particularly, the dog, the first-domesticated animal started to be worshipped.

Copper Age
During the Copper Age (or Chalcolithic period, the 3rd millennium BC approximately-3300-2500 BC), new symbols appeared, documenting the emergence of the wheel, the wagon and the first forms of metallurgy. Alpine rock art began to develop in this period. Rocks were stained with celestial symbols, animals, weapons, depictions of plowing, chains of human-beings and other signs. Although the anthropomorphic figures were primitive at first, later these images were even more intense and more complicated by adding weapons, astral disks and rectangular that symbolize the world in addition to simple figures.  These monuments, preserved mainly in the Archaeological Park of National Massi Cemmo and in that of Asinino-Anvòia (Ossimo), indicate a ritual function linked to the veneration of ancestors.

Bronze Age
During the Bronze Age (2nd millennium BC, approximately), engravings on rock outcrops took on the issue of weapons, reflecting the greater emphasis given them by the warriors in the Camunian society of the time.  Continuing emphasis was given to geometric shapes (circles and variants), in continuity with engravings from earlier eras.

Iron Age
The engravings of the Iron Age (1st millennium BC) are attributed to the people of Camunni and constitute about 70-80% of all census figures.  These works manifest their ideals of heroic masculinity and superiority.  Dominant themes include representations of duels and human figures, even large ones, flaunting their weapons, their muscles and their genitals.  There are also figures of cabins, labyrinths, footprints, hunting scenes and other symbols. Topographic patterns are also present, mainly in the central part of the Iron Age (6th-4th century BC), like in the famous Bedolina Map, firstly studied by Miguel Beltrán Llorís and more recently by Cristina Turconi for the Milan University, one of the best known engraved surfaces of the Camonica Valley.

Roman Age
During the Roman domination of Val Camonica (~100 BC - ~500 AD) petroglyph activity suffered a sharp contraction, entering a phase of latency.

Medieval Age
The Middle Ages in Val Camonica marked a resumption of the rock engravery, starting from the first phase of the Medieval Ages.  Many of the inscriptions during this period are Christian symbols, such as crosses and keys, that were joined and overlapped with those considered pagan symbols, in the background, in an attempt to sacralize these places.

Discovery and evaluation
The first documented report of the engraved stones dates back to 1909, when Walther Laeng pointed out to the National Committee for the Protection of Monuments two boulders decorated around Cemmo (Capo di Ponte). Only in the 1920s, however, did the rocks pique the interest of scholars, including Giuseppe Bonafini, geologist Senofonte Squinabol, and, since 1929, Torinese anthropologist Giovanni Marro and Florentine archaeologist Paolo Graziosi.  Soon numerous engravings were also discovered on the surrounding rocks, and research was conducted not only by Marro, but also by Raffaele Battaglia for the Superintendent to the Antiquities of Padua.

In the 1930s, the reputation of the cuts was more commonly known in Italy and abroad, so that between 1935 and 1937 an extensive campaign of studies was conducted by Germans Franz Altheim and Erika Trautmann. Altheim started reading Nazi ideologies into the engravings, which were soon imitated in a fascist work by Marro, identifying them as evidence of a supposed ancestral Aryan race.

The mapping and cataloging resumed after the Second World War, led by Laeng and conducted by scholars of the infant Museum of Natural Sciences of Brescia, consisting of both national and international experts. In 1955, with the institution of the Parco nazionale delle incisioni rupestri di Naquane  by the Archaeological Superintendent of Lombardy, work began to preserve the rocks and their inscriptions.  The explorations of Emmanuel Anati began in 1956 and discovered new petroglyphs.  The systematic nature of these studies enabled him to publish, in 1960, the first volume of general summary about "La civilization du Val Camonica".  In 1964, Anati founded the Centro Camuno di Studi Preistorici (Camunian Center of Prehistoric Studies).  The first "Valcamonica Symposium", was held in 1968; first in a long series of conferences, convening  in Valcamonica many scholars of art and prehistoric life.

After its inclusion by UNESCO as World Heritage Site number 94, continuing research has further broadened the heritage rocks recorded.

Parks of rock art

See also
Rock art Natural reserve of Ceto, Cimbergo and Paspardo
Camunni
Camunic language
Val Camonica
Bedolina Map

Notes

Bibliography
  Emanuele Süss, Rock Carvings in the Valcamonica, 1954.
 Emmanuel Anati, Camonica Valley: A Depiction of Village in the Alps From Neolithic Times to the Birth of Christ as Revealed by Thousands of Newly found Rock Carvings, 1961.
 Emmanuel Anati, Evolution and style in Camunian rock art: An inquiry into the formation of European civilization, 1976.
 Emmanuel Anati, I Camuni, 1982.
 Emmanuel Anati, Valcamonica rock art: A new history for Europe, 1994.
 The intellectual expressions of prehistoric man, art, and religion, Acts of the Valcamonica Symposium, 1979.

External links

Petroglyphs
World Heritage Sites in Italy
Buildings and structures in Lombardy
Rock art in Europe
Parks in Lombardy
Tourist attractions in Lombardy


text:
Nation:

Ronald William "Ron" Clarke, född 21 februari 1937 i Melbourne i Victoria, död 17 juni 2015 i Gold Coast i Queensland, var en australisk friidrottare, borgmästare och skribent. Mest känd är han nog som medel- och långdistanslöpare, då han under 1960-talet satte inte mindre än totalt sjutton världsrekord.

Han studerade vid Melbourne High School. Hans bror Jack Clarke och far Tom spelade australisk fotboll i Victorian Football League (högsta divisionen i Australien) med Essendon.

1956, när Clarke fortfarande var en lovande 19-åring, blev han utvald att tända den olympiska elden på olympiastadion vid öppningsceremonin under sommar-OS i Melbourne.

Under 1960-talet vann Clarke 12 australiska mästerskap på distanser från 1 500 meter till 6 engelska mil.

I OS erhöll han som bäst en bronsmedalj på 10 000 meter vid spelen i Tokyo 1964. Till OS 1968 i Mexico City anlände han till spelen som storfavorit, men kollapsade och avled nästan som en följd av höjdsjuka under den stenhårda tiotusenmetersfinalen. Han fick en permanent skada på hjärtat vid detta tillfälle, mycket på grund av att han inte var lika förberedd för den höga höjden som vissa andra medtävlare.

Under Samväldesspelen 1962 vann han en silvermedalj på 10 000 meter och i samma tävling fyra år senare samma valör på både 3 och 6 engelska mil.

1965 var utan tvekan Clarkes största år som idrottsman. Under en 44 dagar lång turné i Europa tävlade han 18 gånger och satte 12 världsrekord, inklusive den sällan löpta distansen 20 000 meter. Han sänkte sitt eget rekord på 10 000 meter med 39 sekunder, och blev därmed den förste att löpa milen under 28 minuter. Resultatet var den smått legendariska tiden 27:39,4.

Även på 5.000 meter gjorde han en stor pressning av världsrekordet med tiden 13.16,6 på Stockholms Stadion den 5 juli 1966. Clarkes rekord på 5.000 och 10.000 meter stod sig tills Lasse Viren slog dem 1972. Clarke gjorde sammanlagt sex världsrekordlopp på dessa distanser, under åren 1963-1966.

Clarke var en stayer, det vill säga en löpare med förmåga att tåla hård fart under lång tid, men hade en relativt dålig spurt. För att vinna över ett fält med goda spurtare behövde Clark hård fart från början av loppet, för att därefter långsamt öka denna i en långspurt. Hans snabbhet förbättrades under säsongerna 1967 och 1968, då han också satte nya världsrekord på distansen 2 engelska mil, som bäst till 8:19,6.

2004 valdes han till borgmästare i Gold Coast, Queensland genom att besegra Gary Baildon. Clarke avgick 2012.

Källor

Externa länkar
https://web.archive.org/web/20050724181357/https://www.goldcoastcity.com.au/t_std.asp?PID=346
Profile on Australian Athletics
http://www.sporting-heroes.net/athletics-heroes/displayhero.asp?HeroID=153
http://www.athletics.com.au/fanzone/hall_of_fame/ron_clarke

Australiska medeldistanslöpare
Australiska långdistanslöpare
Världsrekordhållare på 10 000 meter löpning
Australiska olympiska bronsmedaljörer
Olympiska bronsmedaljörer 1964
Tävlande vid olympiska sommarspelen 1964 från Australien
Tävlande i friidrott vid olympiska sommarspelen 1964
Tävlande vid olympiska sommarspelen 1968 från Australien
Tävlande i friidrott vid olympiska sommarspelen 1968
Tändare av den olympiska elden
Borgmästare i Australien
Idrottare från Victoria, Australien
Födda 1937
Avlidna 2015
Män


text:
Trefor Proud é um maquiador estadunidense. Venceu o Oscar de melhor maquiagem e penteados na edição de 2000 por Topsy-Turvy, ao lado de Christine Blundell.

Ligações externas

Maquiadores dos Estados Unidos
Maquilhadores premiados com o Óscar


text:
Lluís Galera Isern (el Masnou, Maresme, 12 de juliol de 1912 - 1999) fou un historiador i arqueòleg autodidacta català, fundador i primer director del Museu Municipal de Nàutica del Masnou.

Fill de Ramon Galera i Planas, empresari de Barcelona, i de Dolors Isern i Isern, de cal Titit, i membre d'una familia nombrosa, ja que eren nou germans. D'ofici representant comercial, es dedicà a l'arqueologia i la història. Va dirigir les excavacions arqueòlogiques que es van dur a terme al Masnou, Teià i Alella durant el Franquisme. A partir de l'any 1958 va codirigir les excavacions a la vila de Cal Ros de les Cabres amb Josep de Calassanç Serra i Ràfols. L'any 1966 va descobrir el jaciment del Veral de Vallmora, a Teià, que actualment forma part del Parc Arqueològic Cella Vinaria de Teià. També va participar a les excavacions per la construcció de l'autopista Barcelona-Mataró.

Va escriure diversos articles de divulgació històrica i sobre les seves troballes a publicacions com els programes de festa major del Masnou o a la revista Puig Castellar. També va escriure el llibre Notes històriques de la parroquia de Sant Feliu d'Alella (1959), en coautoria amb Salvador Artés i Llovet.

Referències

Masnovins
Arqueòlegs catalans