Sofiene Mouine
email: sofiene.mouine@inria.fr

Anne Verroust-Blondet

Advanced shape context for plant species identification using leaf image retrieval

Keywords: Image retrieval, plant species identification, context-based recognition, shape context

This paper presents a novel method for leaf species identification combining local and shape-based features. Our approach extends the shape context model in two ways. First of all, two different sets of points are distinguished when computing the shape contexts: the voting set, i.e. the points used to describe the coarse arrangement of the shape and the computing set containing the points where the shape contexts are computed. This representation is enriched by introducing local features computed in the neighborhood of the computing points. Experiments show the effectivness of our approach.

INTRODUCTION

Identifying a plant can be a tricky task even for experienced botanists, considering the huge number of species existing in the world. This task is of great importance for a number of professionals such as land managers, foresters, agronomists, etc., and can be also useful for amateur gardeners. Plant identification is generally based on the observation of the morphological characteristics of the plant (such as general character, structures of stems, roots and leaves, embryology and flowers) followed by the consultation of a guide or a known database. An important amount of information about the taxonomic identity of a plant is contained in its leaves. Moreover, leaves are present on the plants for several months in a year, whereas flowers and fruits may remain only several weeks. This is why most plant identification tools based on Content-Based Image Retrieval techniques work on leaf image databases [START_REF] Kunii | Recognizing plant species by leaf shapes: A case study of the acer family[END_REF][START_REF] Wang | Shape based leaf image retrieval[END_REF][START_REF] Mokhtarian | Matching shapes with self-intersections: application to leaf classification[END_REF][START_REF] Neto | Plant species identification using elliptic Fourier leaf shape analysis[END_REF][START_REF] Yahiaoui | Shape-based image retrieval in botanical collections[END_REF][START_REF] Du | Leaf shape based plant species recognition[END_REF][START_REF] Belhumeur | Searching the world's herbaria: A system for visual identification of plant species[END_REF][START_REF] Caballero | Plant species identification using leaf image retrieval[END_REF][START_REF] Nam | A similarity-based leaf image retrieval scheme: Joining shape and venation features[END_REF][START_REF] Park | Utilizing venation features for efficient leaf image retrieval[END_REF][START_REF] Bruno | Fractal dimension applied to plant identification[END_REF][START_REF] Backes | A complex network-based approach for boundary shape analysis[END_REF][START_REF] Casanova | IFSC/USP at ImageCLEF 2011: Plant identication task[END_REF][START_REF] Cerutti | A parametric active polygon for leaf segmentation and shape estimation[END_REF][START_REF] Goëau | Visual-based plant species identification from crowdsourced data[END_REF]. A leaf can be characterized by its color, its texture, and its shape. The color of a leaf may vary with the seasons and climatic conditions. In addition, as most plants have similar colors, this feature is not discriminant enough for the species recognition. Thus, generally, only shape and texture information are taken into account in similarity based leaf image retrieval schemes. Several techniques have been introduced to solve the problem of automatic leaf identification. Existing methods generally use a shape-based approach. This is not the case for [START_REF] Goëau | Visual-based plant species identification from crowdsourced data[END_REF] where shape and texture descriptors on oriented patches centered around Harris points are computed and a large scale matching method [START_REF] Joly | Random maximum margin hashing[END_REF] performs the leaf identification. No prior segmentation is made and Harris points are not necessarily located on the leaf margin. This approach is generic and works well on scans of leaves [START_REF] Goëau | Participation of INRIA& Pl@ntNet to ImageCLEF 2011 plant images classification task[END_REF] but may be fastidious for images with a cluttered background. Shape-based approaches mainly work on the overall shape or on the contour of the leaves. A first group of methods extracts morphological plant characters commonly used in botany. Du et al. [START_REF] Du | Leaf shape based plant species recognition[END_REF] compute eight features, Aspect Ratio, Rectangularity, Convex Area Ratio, Convex Perimeter Ratio, Sphericity, Circularity, Eccentricity and Form Factor, from the boundary of the leaves. Morphological features are also retained and used in the identification process in the parameterized segmentation representation of leaves proposed by Cerruti et al. [START_REF] Cerutti | A parametric active polygon for leaf segmentation and shape estimation[END_REF]. Eccentricity is used in the two-stage approach of Wang et al. [START_REF] Wang | Shape based leaf image retrieval[END_REF] and of Caballero and Aranda [START_REF] Caballero | Plant species identification using leaf image retrieval[END_REF] to reduce the search space.

Other shape feature extraction techniques [START_REF] Mingqiang | A survey of shape feature extraction techniques[END_REF] have been adapted or introduced to solve the plant retrieval problem. Neto et al. [START_REF] Neto | Plant species identification using elliptic Fourier leaf shape analysis[END_REF] used elliptic Fourier functions on the leaf shape. Statistical methods based on the method of Complex Networks [START_REF] Backes | A complex network-based approach for boundary shape analysis[END_REF][START_REF] Casanova | IFSC/USP at ImageCLEF 2011: Plant identication task[END_REF] on the contour of the leaf or extracting fractal dimension [START_REF] Bruno | Fractal dimension applied to plant identification[END_REF] on the contour and the venation of the leaf have been proposed. Yanikoglu et al. [START_REF] Yanikoglu | Sabanci-Okan system at ImageClef 2011: Plant identification task[END_REF] obtain good results on leaf scans with a combination of texture, shape and color descriptors among which some are based on algorithms of mathematical morphology. Different types of 2D histograms using geometric features, such as curvature, lengths, relative orientation, distances, etc., computed on the boundary points of the leaf have been presented in [START_REF] Yahiaoui | Shape-based image retrieval in botanical collections[END_REF][START_REF] Ling | Shape classification using the inner-distance[END_REF][START_REF] Caballero | Plant species identification using leaf image retrieval[END_REF]. In [START_REF] Yahiaoui | Shape-based image retrieval in botanical collections[END_REF], a 2D directional fragment histogram computes directions and relative lengths on a succession of elementary fragments on the contour. Another 2D histogram derived from the shape context [START_REF] Belongie | Shape matching and object recognition using shape contexts[END_REF] computing inner distances and angles between sample points of the leaf margin is proposed in [START_REF] Ling | Shape classification using the inner-distance[END_REF][START_REF] Belhumeur | Searching the world's herbaria: A system for visual identification of plant species[END_REF]. Curvature of the leaf contour is used in the Curvature Scale Space representation [START_REF] Mokhtarian | Robust and efficient shape indexing through curvature scale space[END_REF][START_REF] Abbasi | Reliable classification of chrysanthemum leaves through curvature scale space[END_REF] and in Caballero and Aranda 's approach [START_REF] Caballero | Plant species identification using leaf image retrieval[END_REF]. Note that the shape features computed from the leaf margin can be enriched with venation features [START_REF] Park | Utilizing venation features for efficient leaf image retrieval[END_REF][START_REF] Nam | A similarity-based leaf image retrieval scheme: Joining shape and venation features[END_REF]. Most of these shape-based ap-proaches are adapted to the leaf identification problem, but their effectiveness may depend on the quality of the contour obtained by the segmentation process. In this paper, we combine local and shape-based features to obtain an efficient and effective leaf identification method. For this purpose, we select appropriate salient points of the leaf and model local information and spatial relations by a shape context based approach [START_REF] Belongie | Shape matching and object recognition using shape contexts[END_REF]. This paper is structured as follows. A family of shape context based approaches is introduced in section 2. They work on different sets of selected points of the leaf. Local features are then introduced in our model to enrich the image description. Experimental results are presented in section 3. The last section concludes the paper and presents our future work.

ADVANCED SHAPE CONTEXT

Shape context technique [START_REF] Belongie | Shape matching and object recognition using shape contexts[END_REF] has proven its efficiency for shape retrieval, even for leaf images, with the inner shape context [START_REF] Ling | Shape classification using the inner-distance[END_REF][START_REF] Belhumeur | Searching the world's herbaria: A system for visual identification of plant species[END_REF]. To describe properly the boundary of a shape and obtain good retrieval results, a dense sampling of the contour points is necessary. Then a large number of histograms are computed and compared, making the overall technique expensive. To solve this problem, Xie et al. [START_REF] Xie | Shape matching and modeling using skeletal context[END_REF] have introduced the skeletal shape context, which uses a medial axis transform to produce an optimal sampling of the shape contour with a smaller number of points. Shape context retains only global shape information of an image region. To enrich this description Amores et al. [START_REF] Amores | Context-based object-class recognition and retrieval by generalized correlograms[END_REF] have extended the shape context approach for object classrecognition, introducing color and edge information in the histogram. They first perform region segmentation and use the region boundaries as contours of the image. Their "generalized correlograms", encoding local and spatial information, are computed on a sampled set of contour points. In our case, we have a unique class-object, the Leaf class, and our goal is to identify the species of a leaf, which can be characterized by its venation, its margin and its texture. We also retain local information in the image description (see Section 2.2) but we use here another strategy to encode the spatial information in the images. In our shape context based approach, we want to reduce the computational cost while preserving or increasing the shape matching precision. We think that introducing two different sets of points that play different roles in the shape context scheme and choosing them appropriately will help us to achieve this goal. Thus we distinguish the voting points, which is the set of points used to build the shape context histograms from the computing points on where the shape contexts are computed. In fact, the computing points correspond to characteristic or salient points of the object and, for efficiency purposes, the cardinality of this set has to be low. The voting points are other points belonging to the object and must add information on the shape when involved in the computation of shape context on the computing points. Here, as these points are used only once for each histogram, we can use a representative number of voting points. For example, for a polygonal shape, the computing points can be the extrema of boundary curvature points and the voting points the boundary points. Let us now present our approach in more details.

Advanced shape context

Given a set of n points V and a point p of R 2 , the advanced shape context of V on p is a discrete representation of the set of n vectors defined by the pairs of points (p, q) with q ∈ V. It is represented by a coarse histogram aSC(p, V) where each pair of points (p, q), represented by a radius r and an angle θ, contributes to the bin k using the log-polar quantization introduced in [START_REF] Belongie | Shape matching and object recognition using shape contexts[END_REF] and used more recently in [START_REF] Amores | Context-based object-class recognition and retrieval by generalized correlograms[END_REF][START_REF] Ling | Shape classification using the inner-distance[END_REF][START_REF] Shechtman | Matching local self-similarities across images and videos[END_REF].

aSC(p, V) k = #{q ∈ V : q -p ∈ binp(k)}
In our implementation, the radius is quantized into 5 bins and the angle into 12 bins. In the rest of this paper, the set V is denoted the voting set of points and the set C of points p of R 2 , where the advanced shape context aSC(p, S) is computed, is called the computing set. The sets C and V are not necessarily distinct. Let us now return to our application. We suppose in this paper that a leaf image consists in a leaf picture with a nearly white uniform background. A leaf is not only characterized by its margin: its venation network may be significant. Some plant species have a high intra-variability of the leaf shape, and leaves from different species may have globally similar shapes as shown in figure 1. Thus, in this case the venation network information may In fact, salient points on the margin are extrema of curvature when leaves are lobed or toothed and are also junction points when the venation network can be extracted from the image. In our case: -The leaf margin is a closed contour calculated by using the Otsu thresholding method since the image background is homogeneous.

-The salient points are computed with the generic Harris detector, which is known to detect edge corners efficiently.

In order to test our approach on images of leaves, three scenarios are proposed by varying the computing set C and the voting set V of points in the image.

SCO Spatial relations between margin points.

Here the computing set C and the voting set V are identical. They involve the margin points, i.e. n points extracted from the margin by a uniform quantization.

C = V = {margin points}
This scenario corresponds to the shape context proposed by Belongie et al. [START_REF] Belongie | Shape matching and object recognition using shape contexts[END_REF]. Note that the venation network is not introduced here.

SC1 Spatial relations between salient points.

As in the previous case, C and V represent the same set. They both contain n salient points computed with the Harris corner detector.

C = V = {salient points}
This scenario is similar to the logo retrieval approach [START_REF] Rusiñol | Efficient logo retrieval through hashing shape context descriptors[END_REF]. The goal here is to determine whether the spatial relationships between salient points on the leaf area can characterize leaves of a given species and to evaluate the influence of the number of considered salient points on the plant identification performance.

SC2 Spatial relations between salient and margin points

Here we want to measure the spatial relationships between the salient points described in the context defined by the leaf margin. The voting set of points V is composed of all the margin points. The Harris points form the computing set C. C = V, C = {salient points} and V = {margin points} As mentioned above, the salient points may lay inside the leaf or may belong to the leaf margin. Our aim is to study the correlation between the venation network and the margin of the leaves belonging to the same species.

Local properties

The advanced shape context captures a spatial configuration of points without taking into account local properties of the image around the set C of computing points. Thus, to enrich the description, a set of local features computed on the neighborhood of each point of C is introduced. As colors are not a discriminant feature for leaves, we focus on texture and shape. To describe the texture and the shape, three local features are extracted from the grey-level of an image patch located around each computing point: -A 16 dimensional Hough histogram, hough 4 4, based on the Hough transform, is used to represent simple shapes in an image [START_REF] Ferecatu | Image retrieval with active relevance feedback using both visual and keyword-based descriptors[END_REF]: for each pixel, the gradient orientation and the projection amplitude of the pixel vector onto the tangent vector to the local edge are used to build a 2D histogram.

-A Fourier histogram, fourier 8 32 [START_REF] Ferecatu | Image retrieval with active relevance feedback using both visual and keyword-based descriptors[END_REF], based on the Fourier transform, which contains information about texture and scale: two histograms are computed in the complex plane from the Fourier transform. They represent two types of distributions of the energy: the first one is computed with a circular partition, the second uses a wedge partition. Both have an equal importance in the final signature, which gives a description of the energy in the image at several scales as well as a description of the local small scale behavior in a number of predefined directions.

-A 8 dimensional classical Edge Orientation Histogram, which is known to be suitable for non-uniform textures. These three features have given promising results when associated with Harris points on scans of leaves in [START_REF] Goëau | Participation of INRIA& Pl@ntNet to ImageCLEF 2011 plant images classification task[END_REF]. In the following, the combination of the Hough, the Fourier and the edge orientation histogram is denoted by std.

Matching Method

The matching process is the same for all the scenarios. Let I be an image and let n be the number of points of I in the computing set CI . I is represented by n feature vectors F1, F2, ..., Fn where Fi is the feature vector associated to the i th point of C. Fi is of dimension k with:

k = nbinsr × nbins θ or k = nbinsr × nbins θ × n std
where nbinsr and nbins θ are respectively the number of bins for quantified log-polar distances and the number of bins for quantified angles and n std is the size of the std feature vector presented in Section 2.2. The features matching, is done by an approximate similarity search technique based on a Locality Sensitive Hashing (LSH) method [START_REF] Paulevé | Locality sensitive hashing: A comparison of hash function types and querying mechanisms[END_REF]. We use here the Multi Probe Locality Sensitive Hashing technique proposed by Joly and Buisson [START_REF] Joly | A posteriori multi-probe locality sensitive hashing[END_REF] and the distance L2 to compute the similarity between two feature vectors. The principle of this algorithm is to project all the features in an L dimensional space and to use hash functions to reduce the search and the cost time. At query time, the features F1, F2, ..., Fn of the query image are mapped onto the hash tables and the knn nearest neighbors of each feature Fi are searched in the buckets associated to Fi. These n lists of candidate feature matches are used as input for a voting system to rank images according to the number of matched features. One can notice here that if scenario SC0 has the same histograms as the shape context methods [START_REF] Belongie | Shape matching and object recognition using shape contexts[END_REF][START_REF] Mori | Efficient shape matching using shape contexts[END_REF], the overall approach is not identical, as our matching method is different. It is the same for the SC1 scenario and the the logo retrieval approach of [START_REF] Rusiñol | Efficient logo retrieval through hashing shape context descriptors[END_REF] as, in their case, the LSH approximate search is performed on shapeme histograms.

EXPERIMENTAL RESULTS

All the approaches presented above have been tested on a set of leaf images acquired using a flat-bed scanner. This is the training subset of Pl@ntLeaves dataset that was used for the plant identification task organized within ImageCLEF 2011 [START_REF] Goëau | The CLEF 2011 plant images classification task[END_REF]. This set contains 2349 images of scans of leaves. We also compare our results with the scores of identification of ImageCLEF2011 obtained on two categories of images: scans and scan-like leaf images. Figure 2 gives a view of the different species present in the database. To evaluate the three scenarios SC0, SC1 and SC2, we use the precision P and recall R measures defined respectively by P = #relevant images #retrieved images and

R = #retrieved relevant images #relevant images

Results on the training scan dataset

Scenarios SC0, SC1, SC2 have been tested with computing sets of points of cardinality 50 and 400. Precision Recall curves are shown in Figure 3. The std curve corresponds to the retrieval using only Harris points associated with local features around them. Enriched SC2 is presented by the curve SC2+std. Examining these precision-recall curves and the MAP values of Table 3.1, we can notice that:

-All the scenarios give better results with 400 points compared to 50 points. -The information provided by the Scenario SC1 is less meaningful compared with other scenarios. This is due to the fact that, when only 50Harris points are computed, they provide -Scenario SC2 gives the best R/P curve and the MAP value compared to SC1 and SC0. However, the std method that computes local features around Harris points, is more effective than SC2. Moreover, the results are improved when local features are associated to Harris points in scenario SC2, which corresponds to the enriched SC2 (SC2+std).

-SC2 obtains good results with a small number of computing points. When SC2 is used with only 50 points, it gives similar performances to SC0 with 400 computing points.

-The enriched SC2 (SC2+std) outperforms all the other scenarios. It has the best Recall/Precision curves and the best MAP values. When 400 computing points are used, the MAP value of SC2 is 0.68. This proves that the joint use of spatial information and local information increases the identification rate on the training Scan dataset. Let us compare the retrieval results obtained using scenarios SC0 and SC2 on a Rhamnus alaternus leaf in Figure 4. With SC0, only three images are relevant among the first 15 returned images which gives a precision P = 20% for knn = 15. In fact, the overall shape of all the returned leaves is very similar. Consequently, additional information about the leaf taxonomy is needed to be robust to shape similarity between the species. This was our first motivation to build SC2 by separating two sets of points. Figure 4(b) shows the retrieval response using SC2 without including local features. The precision for the first 15 returned images is P = 80% which is much higher than the results of SC0. This can be explained by the fact that SC2 includes, with the use of salient points, informative characters within the leaf area such as venation, texture, etc. Note that the information on the contour is not lost in this schema since the voting set contains points of the leaf margin. Moreover, salient points computed with a Harris detector may be located on the boundary, in particular, for the toothed leaves contour. In Figure 5, we show two retrieval results with the same query image of Figure 4. In the case of SC0, an important 4 and a response example using SC0, red points on the right image are the points that matched with contour points of the query image. Bottom row: the query image used in Figure 4 and a response example using SC2, red points on the right image are the points that matched with Harris points of the query image number of points of the returned image matched with the contour points of the query image. However, the returned image belongs to another species. In fact, the entire configuration of the shape is very similar but the venation network is different. Thus, the spatial representation provided by SC0 may not be appropriate in this particular case. On the other hand, matched points computed with SC2 are either on the nervation, on the contour or inside the leaf. The main advantage of SC2 is that the contour is not considered as an exclusive source of information. Here, the retrieved image and the query image belong to the same species.

Comparison with ImageCLEF2011 results

Let us first introduce the context of the organized identification task ImageCLEF 2011 [START_REF] Goëau | The CLEF 2011 plant images classification task[END_REF]. The goal of the task was to find the correct tree species of each test image. The identification score is quite different from the classic measures presented above such as the MAP value and recall-precision curves. Two assumptions guided the identification score S definition: -The leaves from the same tree may be more similar than leaves from different trees.

-Photos taken by the same person will have nearly the same acquisition protocol. So, S is defined as follows in ImageCLEF 2011: Nu,p: number of pictures taken from the p th plant observed by the u th user. su,p,n: classification score (1 or 0) for the n th picture taken from the p th plant observed by the u th user.

S = 1 U U u=1 1 Pu
We focus on scans and scan-like images. Table 2 and Table 3 describe the number of images and their distribution between the different datasets.

The evaluation metric S is used to compare our approach with the others. Only the top 10 scores of ImageCLEF2011 are presented in Table 4. More details about the methods and the complete list of scores can be found in [START_REF] Goëau | The CLEF 2011 plant images classification task[END_REF]. We present here the score of SC0 and SC2 using 50 computing points. Both SC0 and SC2 outperform all the other approaches on the scan-like dataset. We obtain the best identification score using SC0 (S=0.706). SC2 is also better than the top ten scores of ImageClef with a score S=0.677. In fact, Harris points can be located on the background for the scan-like images. That is why the spatial description provided by SC2 is less accurate than SC0. We also obtain good results on the scan images. SC2 performs better than SC0 on the scans and they are respectively positioned in the third and the fourth place with respect to ImageCLEF 2011 runs. In scans and scan-like dataset, SC2+std which had the best result on the training scan dataset loses its first position. It can be explained by the fact that the combination of local and spatial relationships between Harris points is more efficient to retrieve almost identical images more than similar ones. It is worth noting that the ImageCLEF measure is normalized according to the individual plant and the user.

Figure 1 :

 1 Figure 1: Top row: Overall shape similarity between different species (Pittosporum tobira, Arbutus unedo, Rhamnus alaternus). Bottom row: Intra-variability of the species Ficus carica be useful for an identification task.In fact, salient points on the margin are extrema of curvature when leaves are lobed or toothed and are also junction points when the venation network can be extracted from the image. In our case: -The leaf margin is a closed contour calculated by using the Otsu thresholding method since the image background is homogeneous.-The salient points are computed with the generic Harris detector, which is known to detect edge corners efficiently.

Figure 2 :

 2 Figure 2: A selection of scan-like leaves (first two rows) and scan leaves of the Pl@ntNet test dataset. (Only one leaf per species is kept)

Figure 3 :

 3 Figure 3: Recall/Precision curves on the training scan dataset using 50 points (Left) and 400 points (right)

Figure 4 :

 4 Figure 4: Two retrieval responses with the same query image using SC0 for (a) and SC2 for (b). Query image Q is framed by a solid line, relevant retrieved images i.e leaves from the same species of Q are framed by a dashed line and false positive images are framed by a dotted line

Figure 5 :

 5 Figure 5: Top row: the query image used in Figure 4 and a response example using SC0, red points on the right image are the points that matched with contour points of the query image. Bottom row: the query image used in Figure 4 and a response example using SC2, red points on the right image are the points that matched with Harris points of the query image

  su,p,n U : number of users (who have at least one image in the test data). Pu: number of individual plants observed by the u th user.

Table 1 :

 1 MAP values for the different scenarios

	Approach	SC0 SC1 SC2 std SC2+std
	MAP (50points) 0.36 0.14 0.40 0.45	0,50
	MAP (400points) 0.40 0.25 0.42 0.57	0,68

a very coarse representation of the margin and the venation network.

Table 2 :

 2 Species appearing either on the test scan or on the scan-like dataset versus the train scan dataset on Pl@ntLeaves database of ImageCLEF 2011

					Number of Number of	Number of
					images	individual plants contributors
					Scan train 2349	151	17
	species Acer campestre Acer monspessulanum	TeSL TeS TrS 9 22 24 22 45	test 721 Scan-like train 717 test 180	55 51 13	13 2 1
	Acer negundo		21	17
	Acer platanoides		10	12
	Aesculus hippocastanum		4	25
	Albizia julibrissin			45
	Alnus glutinosa			8
	Arbutus unedo	1	41	34
	Betula pendula		3	76
	Carpinus Betulus			33
	Castanea sativa		45	24
	Celtis australis		24	39
	Cercis siliquastrum	11	20	49
	Corylus avellana		20	55
	Cotinus coggygria	29		64
	Crataegus azarolus			38
	Crataegus monogyna		54	23
	Diospyros kaki	7		43
	Eriobotrya japonica		21	14
	Fagus sylvatica			28
	Ficus carica	19		43
	Fraxinus angustifolia			74
	Fraxinus ornus			33
	Ginkgo biloba		15	34
	Gleditsia triacanthos			32
	Ilex aquifolium	26		40
	Juglans nigra	22		16
	Juglans regia		4	30
	Laburnum anagyroides			30
	Laurus nobilis	16	6	36
	Ligustrum vulgare			26
	Magnolia grandiflora			23
	Malus sylvestris			17
	Nerium oleander	8		88
	Olea europaea		25 125
	Paliurus spina-christi		7	65
	Phillyrea angustifolia			15
	Pistacia lentiscus		36	41
	Pistacia terebinthus			47
	Pittosporum tobira			67
	Platanus x hispanica	2		49
	Prunus mahaleb			49
	Prunus serotina			24
	Prunus spinosa			40
	Punica granatum			38
	Quercus coccifera		33	16
	Quercus ilex	29	18 123
	Quercus petraea			14
	Quercus pubescens		18	24
	Rhamnus alaternus		52	54
	Rhamnus cathartica			27
	Robinia pseudoacacia		26	32
	Salix caprea			26
	Sambucus nigra			9
	Sophora japonica			32
	Sorbus domestica			21
	Syringa vulgaris		20	51
	Tilia cordata			23
	Viburnum lantana			17
	Viburnum tinus		94	47
	Vitex agnus-castus		2	55

Table 3 :

 3 Statistics of the composition of the training and test data of the Pl@ntLeaves dataset

Table 4 :

 4 Normalized classification scores of the scan and scan-like images on the Pl@ntLeaves dataset using the evaluation metric of[START_REF] Goëau | The CLEF 2011 plant images classification task[END_REF]

	run id	Participant	Scans Scan-like
	IFSC USP run2	IFSC	0.562	0.402
	inria imedia plantnet run1	INRIA	0.658	0.464
	IFSC USP run1	IFSC	0.411	0.430
	LIRIS run3	LIRIS	0.546	0.513
	LIRIS run1	LIRIS	0.539	0.543
	Sabanci-okan-run1	SABANCI-OKAN 0.682	0.476
	LIRIS run2	LIRIS	0.530	0.508
	LIRIS run4	LIRIS	0.537	0.538
	inria imedia plantnet run2	INRIA	0.477	0.554
	IFSC USP run3	IFSC	0.356	0.187
	SC0		0.654	0.706
	SC2		0.676	0.677
	SC2+std		0.65	0.59

Acknowledgements

This research has been conducted with the support of the Agropolis Foundation through the Pl@ntNet project. I would like also to thank Vera Bakić for her help to accomplish this work.

CONCLUSION

In this paper, we have presented a new approach extending the shape context method for plant species identification on leaf images. To compute histograms, two sets of points have been introduced: the computing set and the voting set. Several scenarios based on different computing and voting sets have been proposed and tested on scans and scan-like leaf images. Scenarios SC0 and SC1 work respectively only on leaf margin points and only on salient points. Scenario SC2 uses both contour points and salient points of the leaf in the image representation. We have shown the effectiveness of the enriched SC2 on scans of leaves. Furthermore, our approach has been compared favorably to ImageCLEF2011 approaches. In this framework, we used a generic corner detector (Harris) to compute salient points on the images. Work in progress extends this approach in two ways: -We are studying specific detectors for leaf salient points based on botanical expertise -We are investing new scenarios using venation network and margin points.


Edward Lorenz
email: lorenz@gredeg.cnrs.fr

Do Labour Markets and Educational and Training Systems Matter for Innovation Outcomes? A multi-level analysis for the EU-27 1

Recent work on national systems of innovation has argued that there are systemic relations between

The paper begins in Section 2 with a consideration of the impact of labour market flexibility on innovation performance and it critically examines work from a varieties of capitalism perspective that has drawn a connection between flexibility in national labour markets and differences in the innovation style of enterprises. This leads in to a presentation of the basic hypothesis of the paper that innovative performance is supported by national systems of 'flexicurity' which combine flexibility on the labour market with the generous provision of unemployment protection including the use of active labour market policies, and broad-based systems of life-long learning. Sections 3 and 4 develop empirical indicators and present the results of econometric analysis. Section 3 uses factor analysis in order to characterise the labour market and education and training systems of the EU-27. Section 4 presents the results of the multi-level logistic analysis of innovation performance. Section 5 concludes and briefly raises the policy implications of the results.

Labour market mobility, skills and innovation performance

The contribution of labour market mobility to the innovative performance of enterprises has often been raised in the literature focusing on innovation at the regional and national levels. Labour mobility, for example has often been identified as a factor in the innovative performance of regionally clustered high-technology firms, such as the cluster of firms in Silicon Valley [START_REF] Saxenian | Beyond Boundaries: Open Labor Markets and Learning in Silicon Valley[END_REF]Angel, 1991;[START_REF] Almeida | Localisation of knowledgeand the mobility of engineers in regional networks[END_REF]; Carnoy et al. 1997, Rogers andLarsen 1984). More generally, work in economic geography has identified labour mobility as an important mechanism supporting knowledge transfer and innovation within regions [START_REF] Boschma | How does labour mobility affect the performance of plants? The importance of relatedness and geographical proximity[END_REF][START_REF] Breschi | Knowledge Spillovers and Local Innovation Systems: A Critical Survey[END_REF]Camagni, 1991;[START_REF] Gertler | Tacit knowledge and the economic geography of context, or the undefinable tacitness of being (there)[END_REF] Within a national systems framework, probably the most systematic treatment of the way labour mobility impacts on enterprise innovative performance can be found in the varieties of capitalism (VoC) literature [START_REF] Hall | Varieties of Capitalism[END_REF]. This literature draws a distinction between radical innovations associated with the introduction of new product lines or major changes in production processes, and more incremental innovations that further develop existing products and technologies. The basic argument developed in the VoC approach is that in national systems where labour is highly mobile due to the lack of restrictions on hiring and firing, and where the education and training system favours investments in general over industry or company-specific skills, enterprises will have a comparative advantage in the development of more radical innovations. The explanation for this is that the lack of restrictions on hiring and firing combined with ample supplies of mobile generally trained labour makes it easier for management to rapidly reconfigure the enterprise's knowledge base in order to introduce new product lines (Hall and Soskice, 2001, pp. 40-41).

There are some important weaknesses with this argument, though, that might help explain why recent attempts to empirically test the VoC hypothesis have found little support for the basic proposition regarding how national institutional arrangements favour different types of innovation. 3 First, fluid labour markets might just as easily contribute to the innovative performance of 'follower' firms that confronted by radical changes in technology seek to reconfigure their knowledge base in order to compete by imitating or introducing minor modifications to the new products initially developed by other organisations. Fluid labour markets, then, might prove an advantage for rapid catch-up in nations that are good at imitating the innovations developed in other nations.

Secondly, the VoC approach appears to be premised on the idea that the competencydestroying nature of radical innovations means that firms operating in the newly emerging industries that these innovations give rise to will be unconcerned by the loss of the industry or firm-specific skills of their existing employees (Hall and Soskice, 2001, p. 40). This premise, however, finds little support in the case-study literature. The history of the production of integrated circuits in Silicon Valley provides a case in point. Transistors which substituted for, and were competence destroying in relation to, vacuum tubes were invented in 1948 by Bell Lab scientists, including William Shockley who later founded the first semi-conductor firm in Silicon Valley. Transistors substituted for many applications of vacuum tubes and although they were competence-destroying most of the early producers of transistors were electronic firms that produced vacuum tubes, including GE, RCA, Raytheon, Sylvania, Motorola and Texas Instruments. [START_REF] Klepper | Silicon Valley -A chip off the old Detroit Bloc[END_REF].

Robert Noyce, one of the traitorous eight that founded Fairchild, invented integrated circuits containing many transistors on a substrate of semi-conductor material in 1958. Most early production of integrated circuits used a substrate of germanium. Noyce and scientists at Fairchild developed the planar process using an entire silicon substrate which eventually became the industry standard (Klepper, 2007, p. 5). Subsequent improvements in integrated circuits involved vastly increasing the number of transistors on a single chip, from as few as 10 in 1950s, to hundreds in the 1960s (medium-scale integration) and tens of thousands in the 1970s (large-scale integration). Following Fairchild's invention of the integrated circuit, the number of Silicon chip producers in Silicon Valley rapidly increased. According to the genealogy presented in [START_REF] Klepper | Silicon Valley -A chip off the old Detroit Bloc[END_REF] The lineage of Silicon Valley producers, with the majority entering the market as spin-offs from established firms in the region, points to the importance of industry-specific knowledge for success in what is generally accepted to be a radically innovative sector. The role that the clustering of firms into localised networks played in helping Silicon Valley firms to cope with the problems that labour turnover posed for preserving essential skills also speaks to importance of both industry and firm specific knowledge. This is recognised by Gordon Moore, co-founder of Intel, who in a discussion paper analysing the conditions for Silicon Valley's success clearly points to the importance of industry and firm-specific knowledge: "Aligning the goals and incentives of the firm with those of the talented individuals whose efforts build a successful firm takes on greater importance in highly technical, skill-intensive firms. The goals of the firm must be clear, and the payoffs for employees certain. The scarcity of these trained scientists and engineers makes them difficult to replace. Moreover, especially in high technology firms, employees quickly develop project-and firm-specific knowledge. When the opportunity to apply that knowledge (outside the current firm) is greati.e. in most high technology businessesthe costs of mismanaging personnel become greater." (Moore and Davis, 2001, p. 7)

Flexicurity, cognitive distance and innovative performance

An alternative understanding of the contribution of labour mobility to innovative capacity focuses on the cognitive dimensions of the innovation process and on the way diversity in knowledge supports the creation of novelty. [START_REF] Nooteboom | Interfirm alliances: analysis and design[END_REF][START_REF] Nooteboom | Learning and Innovation in organisations and economies[END_REF], in particular, has argued that an enterprise's capacity for generating novelty depends on its ability to access outside sources of complementary knowledge that display an appropriate cognitive distance in relation to existing internal knowledge. If new knowledge from outside is the same as the knowledge held by the firm's existing members, there is a risk of myopia resulting in technological inertia. On the other hand, if the cognitive distance between the firm's existing knowledge base and the external sources of new knowledge is too great, there is a risk of limited comprehensibility. As Nooteboom (2000, p. 70) observes, an optimal cognitive distance is sufficiently small to permit comprehension but sufficiently large to yield nonredundant novel knowledge.

The cognitive distance perspective is consistent with the observation and that the geographical clustering of firms operating in the same technological field may enhance their innovative performance. The concentration of firms in a region facilitates firms' access to new knowledge with appropriate cognitive distance through the inter-firm mobility of employees who have knowledge that is partially overlapping, and hence comprehensible, due to their experience of working in the same technological field, and yet diverse enough to generate novelty due to careers paths which span different firms or organisations.

From this perspective, labour market mobility within industry clusters contributes to innovative performance not so much because they make possible a thoroughgoing reconfiguration of the innovative firm's knowledge base, but because they contribute to generating appropriate levels of cognitive distance while preserving access to essential industry-specific skills. While the novel knowledge which labour mobility may yield will be an important factor in the ability of firms to generate major innovations that transform existing markets, it may also play a role in their capacity to generate relatively minor innovations which nonetheless require creative thinking and new ideas. This understanding the sources of novelty is consistent with the observation of Lundvall and [START_REF] Lam | The Learning Organisation and National Systems of Competence Building and Innovation[END_REF] that labour market mobility is a two-edged sword for the innovative firm. Highly creative firms draw their capability from the industry-specific know-how and problem solving skills that are embodied in individual experts. While codified formal professional knowledge will play a role, the industry-specific problem solving capabilities of the expert may have more to do with his or her diverse experience and the tacit knowledge generated through interaction, trialand-error and experimentation in a variety of company settings. Because these industry-specific tacit skills cannot be easily codified, the creative firm faces a problem of reproducing what has been learnt into an organizational memory and is highly vulnerable when it comes to individuals leaving the organisation.

These problems of accumulating and transferring experience-based tacit knowledge take a different form when firms are organised into localised networks and industry clusters as in Silicon Valley. Mobility across organisational borders within industrial clusters contribute to professional and social relationships which provide the 'social capital' and 'information signals' needed to ensure the efficient accumulation and transfer of tacit knowledge in an inter-firm career framework [START_REF] Saxenian | Beyond Boundaries: Open Labor Markets and Learning in Silicon Valley[END_REF].

Elsewhere, in publications co-authored with Lundvall and others (Lorenz andLundvall, 2010, 2011;Holms, et al. 2010) the argument is made that the localised networks and professional and social relationships that contribute to the efficient accumulation and transfer of tacit knowledge are more likely to develop in institutional settings with well developed systems of unemployment protection in association with active labour market policies and the provision of life-long learning opportunities. Such systems of 'flexicurity' may serve these ends for various reasons. In what follows I provide an empirical test of the links between national systems of flexicurity and enterprise innovative performance by undertaking a multilevel logistic analysis that simultaneously explores the way firm-level characteristics and the national labour market context impact on a measure of enterprise innovation performance. Section 3 begins with a characterisation of the labour market systems of the member nations of the EU-27.

Characterising national labour market systems

In order to characterise national labour market systems for the EU-27, a factor analysis is conducted on the basis of 5 aggregate indicators available on the website of Eurostat (see Table 1). Differences in the development of systems of life-long learning across EU member nations (LLL) are measured on the basis of the annual responses to the European Union Labour Force Survey. LLL is defined as the percent of the population, both active and inactive, between the ages of 24 and 65 that received education or training in the four weeks preceding the survey in 2007. LLL is broadly defined to include formal, non-formal and informal forms of learning. Formal life-long learning is defined as that provided by the degree conferring institutions of the formal educational system. Non-formal education and training refers to all forms of taught learning that occur outside the formal degree-conferring educational system. Informal learning refers to self-taught learning including the use of printed materials and on-line computer based learning. 4 This broad measure of learning serves to capture the diverse types of knowledge that may contribute to innovative performance. Factor analysis using the principal components factor method resulted in two factors with eigenvalues greater than 1. These two factors were retained for oblique factor rotation in order to improve interpretability. The first factor, which accounts for approximately 44 percent of the shared variance, is slightly over 12,000 euro per registered unemployed. 9 Amongst the southern nations, Spain stands out for its relatively high score on the flexibility scale. This reflects its high reported level of labour market flexibility that is only exceeded by the levels reported in the three Scandinavian nations. 10 With respect to the continuing vocational training scale, all the more developed EU member nations have scores that are above the EU-27 average, while the majority of the southern and new member nations have scores that are below the average. Amongst the new member nations the Czech Republic stands out for its relatively high levels of employee participation in both training courses and enterprise provision of on-the-job-training. Amongst the more developed EU nations the UK stands out as an outlier on this scale due to its very high reported level of enterprise provision of on-the-job training. 11

A multilevel analysis of innovation performance

In this section I use multi-level logistic modeling to estimate the way the characteristics of enterprises and features of the national institutional context impact on the likelihood of an enterprise innovating in the sense of demonstrating an in-house capacity for developing new products or services. In multi-level analysis data is hierarchically structured. This means that units at one level are clustered within units at the next higher level and multi-level modeling allows one to model processes at multiple levels of the population hierarchy. The main reason for doing this in the context of this paper is to provide estimates of the impact of macro or national levels context conditions on micro or enterprise-level outcomes. In particular, by using the factor scores of the 27 member nations on the two underlying factors developed in Section 2 above, multi-level analysis provides a mean for the impact of national differences in 9 For a discussion of the relatively high levels of labour market flexibility prevailing in the Baltic states despite levels of legislative employment protection that are at average levels for the EU, see Eamets and Massu (2005). 10 Although the figures for Spain may appear high in lieu of this nation's reputation for relatively inflexible labour markets, they are consistent with the figures on annual job-to-job mobility for Spain based on EU-SILK (2005) which finds levels of mobility in Spain well above the European average and ranks Spain fourth highest amongst the 21 EU nations covered. See Job Mobility in the European Union: optimizing its social and economic benefits, Danish Technical Institute, April 2008, p. 21. 11 There are no reasons to believe the reported figure of 75% of enterprises providing on-the-job training is unreliable. For a detailed description of the methodology including the quality procedures used for the UK Survey carried out under the auspices of the Department of Innovation, Universities and Skills, see [START_REF] Dent | Continuing Vocational Training Survey (CVTS3)[END_REF].

the development of systems of flexicurity and in the provision of continuing vocational training on the likelihood of an enterprise innovating. 12 The econometric analysis of innovation performance operates at two levels, with enterprises at level-1 being clustered within nations at level-2. The variables characterising enterprises at level-1 are derived from the individual responses to the Innobarometer Survey carried out in the EU-27 and in Norway and Switzerland in October 2007 on behalf of the DG Enterprise and Industry of the European Commission. 13 The analysis here concerns only the EU-27. The Innobarometer survey provides estimates of the percentage of enterprises that have introduced new or significantly improved products or services over the period [2005][2006][2007]. The target population for the survey was enterprises employing 20 or more persons in manufacturing and services. 14 The survey used a random stratified sample with stratification according to size category and sector. Larger enterprises were oversampled in order to get enough cases for meaningful results. The target number of main interviews was 200 in each country surveyed, except Malta, Cyprus and Luxembourg where the target number of interviews was 70. This resulted in a sample size for the EU-27 of 5036 observations. 15 The measure of innovation performance is a binary variable with innovative enterprises being defined as those that have developed entirely new or significantly improved products in-house and that carryout R&D in-house. This measure of innovativeness excludes both firms that have modified or customised products that were originally developed by other organisations and firms that have simply sold on products developed by other organisations. It also excludes firms that innovate on the basis of R&D that is contracted out to other organisations or that have innovated without undertaking any expenditures on R&D. These enterprises constitute 12 Another reason for using multi-level analysis is that the failure to take into account the hierarchically structured nature of the data may lead to technical problems, with standard errors of the regression coefficients being underestimated. See Rasbash, et al. (2005, pp. 6-12) and [START_REF] Goldstein | Multilevel statistical models[END_REF]. 13 Access to the micro data from the 2007 Innobarometer Survey was kindly provided by Keith Sequeira of Unit 1 -Innovation Policy Development, DG Enterprise and Industry, European Commission, 14 The survey includes a very small percent of firms in agriculture and fishing. The person interviewed in each company was a top-level executive responsible for strategic decision-making (typically General Manager, Financial Director, or significant owner). For the survey methodology, see Innobarometer 2007 Analytical Report, DG Enterprise and Industry, European Commission, 2008, pp. 102-104. 15 Similar measures of innovativeness could have been derived from the CIS-4 survey and while the use of CIS data would in principle allow for greater precision in statistical estimates due to the larger size of the national samples, its use for multi-level analysis was precluded due to the considerable number of nations for which data cannot at present be accessed. Anonymized CIS-4 microdata can be accessed for only 15 of the EU-27, and nonanonymised data can be accessed at Eurostat's Safe centre in Luxumbourg for only 21 of the EU-27. Further, access to non-anonymised data depends on gaining authorisation of use from the nations concerned. See http://epp.eurostat.ec.europa.eu/portal/page/portal/microdata/cis 22.4 percent of the total sample. The bar chart below shows the percentage of firms that are innovative according to this definition for the 27 member nations.

Figure 2 While the criteria used clearly identify firms that have demonstrated an in-house capacity for innovation, it is important to keep in mind certain limitations of the measure. The Innobarometer survey does not include questions that would allow one to capture the importance or wider impact of innovations. In particular, the survey does not provide the basis for distinguishing between minor innovations that are marketed solely on the local market, and innovations that have wider impacts and are marketed on highly competitive international markets. This helps to account for certain of the results that may appear surprising, such as the fact that the proportions of firms classified as innovators in Latvia, Slovenia are well above the EU average or that the performance of Malta Lithuania and Portugal are somewhat higher than that of Sweden or the UK.

The independent enterprise-level variables include a five-level categorical variable for the sector of activity (SCTR), a four-level categorical variable for firm size (SIZE) and a binary variable (GRWTH) equal to 1 if the enterprise's annual income growth was greater than 10 percent between 2004 and 2006. Table 4 presents the frequencies of the level-1 variables.

The multi-level logit model

Before presenting the results of the econmetric analysis, I briefly compare the structure of the two-level random intercept logit model used here to that of the standard single-level logit model. 16 The single-level logit model takes the following form. Let y i indicate the binary reponse (0, 1) for the ith unit and let π i be the probability that y i = 1

The logit link function has the form

logit (π i ) = log (π i /1-π i ) = β 0 + β 1 x (1)
where the quantity (π i /1-π i ) is the odds that y i = 1

16 For a detailed presentation of multi-level models with dichotomous or binary responses, see Rabash (2005) and [START_REF] Goldstein | Multilevel statistical models[END_REF].

Since the enterprise-level variables are all binary 0,1 variables and the two factors measuring differences in national-level context conditions have means equal to 0, the log-odds of innovation for the reference enterprise in the 'average' nation will be the fixed parameter β 0 plus the associated random effect u 0j . In nations where the values for the context variables are above (below) average, the fixed component of the predicted log-odds of innovation for the reference enterprise will be higher (lower) than β 0 depending on whether the coefficients on the national context variables are positive or negative. For example, if the estimated coefficient on FACTOR1 measuring the importance of flexicurity is positive then, other context conditions being equal, in nations with higher levels of flexicurity the fixed component of the predicted log-odds of innovation for the reference enterprise will be greater than β 0 . Including the effects of the context variables on the dependent variable should reduce the estimated variance in the intercept term across nations ( ).

Table 4 presents the results for the random intercept model without country-level fixed effects. Focusing on the level-1 fixe effects, the results show that relative to construction and utilities enterprises in the other four sectors are more likely to innovate with the positive effect being the largest in the manufacturing and mining sector. Enterprises in the 20-49 employee range are less likely to innovate than larger enterprise and the results point to a positive impact of increases in enterprise size on the likelihood of innovating. There is a small and statistically significant positive impact of higher rates of prior income growth on the likelihood of innovating.

The random intercept estimate of .32 points to sizeable variance in the level of innovative activity across the 27 member nations after having controlled for the enterprise-level characteristics. The LR test reported at the bottom of the table tests the null hypothesis that the proportion of the total residual variance accounted for by differences between nations is 0.

This is rejected at the .000 level. EU member nations. These are estimates of how much the intercept in each nation departs from the overall average for the population of 27 nations and thus provide a measure of the importance of 'national effects' on the level of innovation activity. It is clear from the large size of the confidence intervals that the rankings are not precise and it would perhaps be best to refer to a coarser distinction between below, average and above average nations.

Figure 3

The results show that amongst the EU-15 there is an above average national-level effect on innovation in Italy, Finland, Sweden, Germany, Portugal, and France. The effect is above average in only two of the new member countries nations: Latvia and Slovenia. There is a below average national effect in Spain, Belgium and in a number of the new member nations including Bulgaria, Hungary, the Czech Republic, Slovakia, Romania and Poland. The effect is not statistically different from the average in Luxembourg, Estonia, Lithuania, Greece, Malta, Denmark, the Netherlands, Ireland, Austria, the UK and Cyprus. The ranking of countries in Figure 3 is similar but not identical with the ranking shown in the descriptive statistics in Figure 2. This can be explained by the fact that the random intercept predictions take into account differences in the size structure of firms and in industrial structure across nations. Moreover the random intercept predictions make it clear that in many cases the apparently above average national innovation performance suggested by the descriptive statistics in Figure 2 are not statistically significant.

Table 5 presents the random intercept model with country-level fixed effects in order to develop estimates of the impact of national labour market and education and training systems on the likelihood of an enterprise innovating. Including the national level fixed effects should help account for some of the variance in intercept term across nations. There are no significant changes in the coefficients on the level-1 variables. The random intercept estimate is reduced to .24 indicating that the inclusion of two level-2 context variables has accounted for about a quarter of the variance in the intercept between nations. A major result is that there is a positive and significant impact of the aggregate measure of flexicurity on the likelihood of innovating. Since the means of the underlying factors are 0 and their standard deviations are equal to1, the results show that a one standard deviation increase in a nation's score on the flexicurity scale results in a .28 expected increase in the log odds of innovating. Expressed in terms of odds, a one standard deviation increase along the flexicurity scale results in an approximately 32 percent increase in the odds of innovating.

This result provides strong support for the hypothesis developed in the paper that the combination of flexible labour markets, generous systems of unemployment including the use of active labour market policies, and well developed systems of life-long learning are associated with higher levels of national innovation performance.

The results also show a very weak negative and non-statistically significant effect of greater development of systems of continuous vocational training on the likelihood of innovating.

This result suggests that placing a strong emphasis on the development of systems of further vocational training independently of investments in other complementary institutional arrangements constitutes an insufficient basis for improving national innovative performance.

Conclusion

This paper began with some general reflections on the relation between labour market flexibility, skills and innovative performance. The argument was made that highly innovative firms need to balance the use of labour turnover in order to bring in new knowledge with policies that serve to preserve the largely tacit industry-specific problem solving capabilities that employees acquire through their practical work activity. A case was made for the way the organisation of firms into industry clusters promotes to the development of localised networks and professional and social relationships that contribute to the efficient accumulation and transfer of tacit knowledge in an inter-firm career framework. Further, it was argued that such localised networks and professional relationships are more likely to develop in national systems with well developed systems of flexicurtity.

In order to provide empirical support for this hypothesis, multi-level logistic regression analysis was used to explore simultaneously the impact of enterprise-level variables and measures of national labour market and education and training systems on the likelihood of a firm innovating.

The results support the view that firms are more likely to innovate in national institutional settings characterised by the combination of flexible labour markets, generous systems of unemployment protection including the use of active labour market policies, and well-developed systems of life-long learning.

While the relevance of the analysis for EU-policy can only be alluded to here, the results provide support for key elements of the EU 2020 strategy. In particular, they point to important synergies between the objective of improving the innovative performance of European enterprises and central aspects of the European Employment Strategy, including expanding and improving investments in human capital through the provision of efficient lifelong learning open to all, and promoting greater flexibility with security though the use of active labour market policies, policies to promote labour mobility and adequate systems of social security. Moreover, while an analysis based on cross-sectional data cannot address dynamic problems of institutional change , the comparison of EU member nations clearly suggests that flexicurity is not an all or nothing proposition and that nations rather can be characterised in terms of the degree to which they have put in place flexicurtiy regimes. This in turn provides support for an underlying premise of the European Employment Strategy that institutional change can be brought about progressively and that even incremental advances in the direction of desired policy goals can be to good effect.

  , many of these were spin-offs from existing firms and of the 23 spin-offs generated by 1969 eight came out of Fairchild. Further entries, many of them spin-offs of existing enterprises, occurred during the early 1970s, and by 1975 the dominance of Silicon Valley in the production of integrated circuit was established, with Silicon Valley firms accounting for 38 percent of the market.

Firstly

  , in terms of incentives, the security such systems provide through income maintenance and support programmes for moving the unemployed into employment can encourage individuals to commit themselves to what would otherwise be perceived as unacceptably risky forms of employment and career paths. Second, well-developed and diversified systems of life-long-learning support not only formal training for the updating of scientific and the technical skills, but also informal forms of learning, including on-the-job training, that contribute to the acquisition of the tacit and experience-based knowledge that are crucial to the innovative activities of firms.

  Thus formal forms of lifelong learning can contribute to the updating of the formal scientific and technical knowledge required to keep abreast rapid changes in technology. Non-formal and informal learning typically contribute to the acquisition of more applied or experiencebased knowledge, including knowledge that may have little apparent relation to work-related activities. Further, by including in the measure of life-long learning the further education and training received by inactive persons, it is possible to takes into account that the knowledge gained during periods of inactivity may prove of value to the innovation-related activities of persons who have recently entered the labour market. In order to capture the acquisition of more firm-specific and work-related-skills, two measures are used. The first, CVTC, which is based on the 2005 Continuing Vocational Training Survey, provides a measure of the acquisition of codified or formal knowledge. It is defined as the percentage of employees in all enterprises receiving continuing vocational training courses, either on or off the premises of the enterprise. The second, OJT, also is based on the results of the 2005 Continuing Vocational Training Survey, provides a measure of the acquisition of more tacit and experience-based knowledge. It is defined as the percentage of all enterprises that provide their employees with on-the-job training. 5

  positively correlated with the measures of labour market flexibility, life-long-learning and expenditures on active and passive labour market policies per registered unemployed. This component can be interpreted as an indicator of flexicurity. It indicates that EU member nations can be scored on a scale measuring the extent to which they have developed institutional set-up combing what are often described as the three core components of flexicurity systems: generous unemployment protection including expenditures on active labour market policies, high levels of labour market flexibility, and well developed systems of lifelong learning. 8 Such arrangements arguably create an environment that is supportive of strategies of continuous knowledge exploration due to the way they combine porous organisational boundaries that permit the continuous insertion of new knowledge and ideas from outside the firm, and social protection that serves to preserve industry-specific skills. The second factor, which accounts for about 40 percent of the shared variance, is positively correlated with the two measures of continuous vocational training: the provision of formal training courses, either by the employer or by a third party, and the provision of on-the-job training. This factor, simply referred to as continuing vocational training, shows that EU member nations can be scored along a scale measuring the importance of their investments in further vocational training.

Figure 1

 1 Figure 1 below shows graphically the position of the 27 member nations with respect to the two new latent variables or factors. The horizontal axes shows the scores or values of the nations on the flexicurity scale, with nations situated farther to the right scoring higher on this scale. The vertical axis shows the values of the nations on the continuous vocational training scale, with those situated higher on the axis scoring higher on this scale.

Figure 1

 1 Figure 1

  ES SK PL RO BE LU AT EE CY UK EU GR SE IE PT LT MT NL DK DE SI LV IT FR FI Percentage of Innovative Firms by Nation: EU-27

Figure 3 ,

 3 Figure 3, on the basis of the random intercept regression results, presents a bar chart showing the modal estimates of the random intercepts with 95 percent confidence intervals for the 27

Table 1 Country-level variables for Factor Analysis of Labour Market Systems

 1 Differences in the fluidity of national labour markets fluid (FLEX) are captured with an indicator of short-term labour market flexibility based on the quarterly returns of the European Union Labour Force Survey. FLEX is defined as the share of persons aged 15 and over whose job started within the last three months and the figures presented in Table2 arefor the 2 nd quarter of 2007. FLEX is a general measure of labour market flexibility that includes both the job-to-job mobility of persons who have changed their employer over the reference period and the mobility of inactive persons into active employment.6 FLEX thus provides a means of capturing the way innovative firms may benefit not only from knowledge flows linked resulting from job-to-job mobility, but also from new knowledge acquired through hiring persons who were recently inactive.

		FLEX	LLL	CVTC	OJT	LMP
	AT	4.6	12.8	33	32	25537
	BE	3.7	7.2	40	41	26532
	BG	5.2	1.3	15	17	473
	CY	5.4	8.4	30	19	6152
	CZ	2.5	5.7	59	42	1102
	DE	4.2	7.8	30	48	12710
	DK	8	29.2	35	30	59193
	EE	4.6	7	24	31	433
	ES	7.3	10.4	33	26	10666
	FI	9.1	23.4	39	35	19774
	FR	6.3	7.5	46	29	15302
	GR	2.6	2.1	14	6	2559
	HU	3.1	3.6	16	18	1562
	IE	5.1	7.6	49	43	24779
	IT	3.7	6.2	29	11	10708
	LT	4.7	5.3	15	18	810
	LU	2.3	7	49	44	34370
	LV	6.5	7.1	15	9	971
	MT	3.3	6	32	31	2151
	NL	5.1*	16.6	34	31	39826
	PL	4.8	5.1	21	17	1244
	PT	3.5	4.4	28	22	6130
	RO	3.1	1.3	17	19	506
	SE	8.9	18.6	46	34	19544
	SI	4.4	14.8	50	28	2850
	SK	2.6	3.9	38	32	604
	UK	4.2	20	33	75	2710
	Source: Eurostat's electronic database.
	* Imputed value. See ft nt. 6 below.

5 Figures on the percentage of all enterprises providing on-the-job training are used rather than the percentage of employees involved in such training due to the fact that values for the latter series are missing for both the UK and Ireland. To qualify as continuous vocational training, the EU CVT survey manual sets three criteria: training must be planned in advance, it must serve the goal of learning and it must be partly financed by the enterprise; See 'The 3rd Continuing Vocational Training Survey (CVTS3): European Union Manual. (http://circa.europa.eu/Public/irc/dsis/edtcs/library?l=/public/continuing_vocational/master_finalpdf/_EN_1.0_& a=d)

In order to capture differences in the level of protection that unemployed persons benefit from, national expenditures on active and passive labour market policies (LMP) are taken from Eurostat's Labour Market Policy database. Active and passive expenditures are defined as those targeted at one of the following: the unemployed, the employed at risk of becoming unemployed and inactive persons who would like to enter the labour market but are disadvantaged in some way. Active measures include expenditures on training, job rotation and job sharing, employment incentives, direct job creation and start-up incentives. Passive measures include expenditures on out of work income maintenance and early retirement. In order to control for differences in the unemployment rate on national expenditures, the figures reported in Table

2

are total active and passive expenditures per registered unemployed person.

Table 2 Rotated factor loadings and unique variances

 2 Table 2 below reports the resulting factor loadings or correlations between the two factors and the five original variables as well as the uniqueness for each variable. 7

	variable	Factor 1	Factor 2	Uniqueness
	FLEX	.934	-.0235	.167
	LLL	.829	.291	.125
	OJT	-.048	.900	.207
	CVTC	.064	.851	.248
	LMP	.652	.325	.379

Table 3 Frequencies for the employee-level variables

 3

	Annual income
	growth 2004-2006
	Increase > 10 %	Total
	1,787	5,036
	Size
	20-49 employees	50-249	250-499	> 499	Total
	1,860	2,049	519	608	5,036
	Sector
	Manufacturing and	Construction	Retail and	Business and	Other	Total
	Mining	and utilities	other services	financial
	2,025	566	1535	services 811	99	5,036

Table 4 Multi-level logistic regressions Model 1 Enterprise-level fixed effects

 4

	Intercept	-2.45***
	Manufacturing	1.27***
	Construction, utilities	Reference
	Retail and other services	.33**
	Business and financial services	.83***
	Other	.55**
	20 -49 employees	Reference
	50 -249 employees	.41***
	250 -499 employees	.94***
	>499 employees	1.13***
	Increase in income > 10%	.30***
	between 2004 and 2006
	Random effects
	Intercept	.32 (.10)
	LR vs. logistic regression	Chibar2(01) = 191.00
		Prob>=chibar2 = 0.000
	*** significant at .01 level; ** .05 level; * .10 level

Table 5 Multi-level logistic regression with contextual effects Model 2 Enterprise-level fixed effects

 5

	Intercept	-2.45***
	Manufacturing	1.28***
	Construction, utilities	Reference
	Retail and other services	.33**
	Business and financial services	.83***
	Other	.56**
	20 -49 employees	Reference
	50 -249 employees	.41***
	250 -499 employees	.94***
	>499 employees	1.13***
	Increase in annual income > 10	.30***
	percent
	Country-level fixed effects
	Factor1 (flexicurity)	.28***
	Factor2 (continuing vocational	-.03
	training)
	Random effects
	Intercept	.24 (.08)
	LR test v. logistic regression	Chibar2(01) = 141.62
		Prob>=0.000

*** significant at .01 level; ** .05 level; * .10 level

For empirical tests based on the use patent citation data, see[START_REF] Taylor | Empirical evidence against Varieties of Capitalism's theory of technological innovation[END_REF] and[START_REF] Akkermans | Do 'liberal market economies' really innovate more radically than 'coordinated market economies'? Hall and Soskice reconsidered[END_REF].

For the definition of lifelong learning, see the Eurostat Quality Report: http://circa.europa.eu/irc/dsis/employment/info/data/eu_lfs/LFS_MAIN/Adhoc_modules/2003/ExplanatoryNotes /Final_Report_Ahm2003_EN.pdf

The figures are taken from,Romans, F. Data in Focus, 'Population and Social Conditions', 21/2007, Eurostat. Quarterly data for the Netherlands is reported as unreliable by Eurostat either due to the small sample size or due to the large non-response rate. The reported figure of 1.7 percent for the Netherlands, the lowest for the EU-27, does appear to be unrealistically low when compared with figures for annual job-to-job mobility based on the European Union Statistics on Income and Living Conditions (EU-SILK) survey which covered 21 EU nations in 2005. EU-SILK results showed annual job-to-job mobility in the Netherland in 2005 as being only slightly below the EU-21 average of 8.8 percent. Because of the unreliability of the data for the Netherlands, I have treated it as missing and have imputed the value using STATA's impute command. The imputed value of 5.1 percent for the Netherlands is slightly above the unweighted average for the EU-27 of 4.8 percent. For an overview of the EU-SILK based results, see Job Mobility in the European Union: optimizing its social and economic benefits, Danish Technical Institute, April 2008.

The two factors are weakly correlated with a correlation coefficient equal to 0.214.

See, for example, Bredgaard, et al. (2005) and[START_REF] Masden | Flexicurity through Labour market policies and institutions in Denmark[END_REF].

my gratitude to Keith Sequeira of Unit 1 -Innovation Policy Development,

The use of a two-level model is signaled by presence of subscripts i and j with the subscripts j varying across the level 2 units and the subscripts i varying from individual to individual within the level 2 units. Unlike the single-level logit model, in the two-level random intercept model the intercept term consists of two terms: a fixed component β 0 and a random effect u 0j due to the fact that the level 2 units are treated as a random sample from a population of units.

(2)

The random effect u 0j measures the departure of the jth unit's intercept from the average or summary intercept across all level 2 units predicted by the fixed parameter, β 0 . It is assumed that u 0j follows a normal distribution with mean zero and variance = Level-2 context variables x 2j can be included in order to estimate the fixed effects of differences in context conditions on the dependent variable. Such direct effects modify the intercept and reduce the variabilty in the intercept across level-2 units (u 0j ).

logit (π

(3)

The objective here is to predict the likelihood of an enterprise being innovative in terms of factors operating at two levels, the enterprise level and the national level. The structure of the full model that is estimated, including the national-level fixed effects, is given in equation ( 4).

The subscripts j vary across the sample of 27 EU nations and the subscrits i vary from enterprise to enterprise within nations.

logit (π

with


est destinée au dépôt

  Mechanisms of human smooth muscle cell proliferation and transplant vasculopathy induced by HLA class I antibodies: in vitro and in vivo studies. Magali Trayssac, Anne Nègre-Salvayre, Mogens Thomsen To cite this version: Magali Trayssac, Anne Nègre-Salvayre, Mogens Thomsen. Mechanisms of human smooth muscle cell proliferation and transplant vasculopathy induced by HLA class I antibodies: in vitro and in vivo studies.: Antibody-induced transplant vasculopathy. Human Immunology, 2012, 73 (12), pp.1253-60. 10.1016/j.humimm.2012.06.012. inserm-00726886


R Borderie

P Bougault

N Edelbruck

S Le Neindre

S Barlini

G Carboni

E Casini

Rosato

M F Rivet
email: rivet@ipno.in2p3.fr

E Bonnet

B Borderie

R Bougault

P Edelbruck

N Le Neindre

S Carboni

G Casini

E Rosato

G Tortone

Status and performances of the FAZIA project

Introduction

With the advent of exotic beams delivered by present and future accelerator facilities, it is a challenge for physicists to design detection arrays able to completely identify (atomic number and mass, energy) the nuclear reaction products, taking thus full advantage of these new beams. Indeed one of the present motivations for studying nuclear collisions is to improve the knowledge of the nuclear equation of state, and particularly of the density dependence of its symmetry energy term, E sym [1,2,[START_REF] Rivet | Lecture given at the Joliot-Curie school[END_REF]. In this aim, at moderate and low energies, dissipative collisions induced by exotic projectiles should provide new information on the neck-instabilities, and on the achievement of isospin equilibration, both directly depending on E sym . In the same energy domain a study of the de-excitation properties of isotopic chains of compound nuclei, with complete isotopic identification of the emitted nuclei and of the final residue [START_REF] Marini | Proc. II Int. Workshop on Compound Nucleus Reactions[END_REF], will allow to test the N/Z dependence of the level density parameter; this will give unique information on the temperature dependence of E sym . Pushing CN towards the drip lines will permit to explore decay modes that cannot be predicted by the Weisskopf theory (clustering, multifragmentation) and thus to test the validity domain of the basic assumptions of that theory. With an increased knowledge of the de-excitation of hot nuclei, when going around the Fermi energy, thermodynamical properties of multifragmenting nuclear systems (excitation energy, caloric curves) should be more accurately determined [START_REF] Viola | Dynamics and Thermodynamics with nuclear degrees of freedom[END_REF]6], providing more precise values of limiting temperatures. Measurements of fragment isotopic distributions should also permit to constrain E sym .

At present time, the most powerful 4π arrays suffer from several drawbacks. INDRA [7], is composed of Ionisation Chamber-Silicon-CsI(Tl) telescopes and uses ∆E-E and light shape analysis to identify nuclei. It well identifies the atomic number of the charged products up to Z∼60, with a threshold of ∼ 1 A MeV, but its isotopic identification is limited to Z=6. CHIMERA [START_REF] Pagano | Reverse collaboration)[END_REF] is an ensemble of Silicon-CsI(Tl) telescopes which identifies nuclei through time of flight, ∆E-E and light shape analysis techniques. It has very low thresholds for mass identification, but much higher ones for Z-identification. CHIMERA gives isotopic identification, Z and A, up to Z=13 [9]. Even specific correlators such as LASSA [10], based on ∆E-E identification, have an isotopic separation only up to Z=9. The reader is sent to ref. [START_REF] De Souza | Dynamics and Thermodynamics with nuclear degrees of freedom[END_REF] for a more exhaustive detector review. The unsurpassed nucleus identifiers are still spectrometers, which provide mass charge, energy and velocity of nuclei with a high resolution. They cover however a very small solid angle and cannot fully analyse many-body events, unless they are coupled to other detection arrays.

In this context a group of french and italian physicists studying nuclear dynamics and thermodynamics started, at the dawn of the century, a vigourous R&D aiming at obtaining isotopic identification up to mass 50 over a large solid angle. The main idea was to examine the detailed shape of the current signal created in a silicon detector by the passage of a charged particle, by sampling it with a fast ADC. This group later became the FAZIA Collaboration, which nowadays comprises about 60 physicists from France, Italie, Poland, Romania and Spain.

The FAZIA telescope

FAZIA is intended to work for studying reactions from the Coulomb barrier up to 100 A MeV. It was thought as a 4π array comprising a large number (several thousands) of three-stage telescopes, two silicon detectors (Si1: 300 µm and Si2: 500 µm) followed by a CsI(Tl) scintillator readout by a photodiode. Gas detectors are avoided because of their mechanical size. The desired low identification thresholds are provided by pulse shape analysis (PSA) of the signals of the first silicon, with the help of time of flight measurement for very slow particles [12,13]. In order to improve the performances of present arrays, a lot of work was performed on the response of CsI(Tl) scintillators and above all on that of silicon detectors.

Silicon detectors: the lessons of R&D

When one wants to use the shape of the signal created by a charged particle traversing a silicon detector of finite area, it is mandatory that the detector response be independent of the impact position. This implies that the field should be homogeneous in the bulk of the detector. An answer to this requirement is obtained by using n-type implanted silicon doped with phosphorus through neutron transmutation; this process provides silicon wafers with resistivity around 3000 Ω.cm and resistivity fluctuations smaller than 4% FWHM (against 30% for standard silicon wafers). Another constraint is that particles must not cross the detector following a crystal axis or plane: as known for 50 years, detectors must be cut off-axis to avoid channeling effects which induce large fluctuations in the response to monoenergetic particles [14,15]. Note that this constraint limits the angular aperture of a telescope; the FAZIA telescopes have a square area 2×2 cm 2 . They will be mounted between 1 and 1.2 m from the target. Finally the bias voltage on the detector is kept constant by permanently monitoring the reverse current and correcting the applied voltage.

We chose a reverse mounting so that particles enter through the low-field side of the detector, in order to maximize the shape differences between elements and isotopes [13]. However although in this last reference overbiasing detectors was recommended, we found that a better identification was obtained when using the detector depletion voltage, or even slightly less. A compromise has still to be found between shape discrimination and good timing for time of flight.

The CsI(Tl) scintillators

The scintillator length was fixed to 10 cm, to make FAZIA operational up to 100 A MeV by stopping all light charged particles. Having such a length, the crystals are tapered to keep the same angular aperture at both ends. In order to favor scintillation efficiency for heavy ions, we chose a Tl concentration of ∼2000 ppm [16]. The light response and resolution, as well as its homogeneity along the detector length were improved by wrapping the crystals with a highly reflective material, 3M Vikuity T M ESR [17]. The CsI(Tl) are coupled to 2×2 cm 2 photodiodes.

Front-end electronics

The Front End Electronics board of FAZIA, designed at IPN Orsay, houses all the necessary modules for complete processing of the three detector signals of two telescopes. The prototype version is represented in figure 1. The detectors are connected to the preamplifiers, located on the board, by kapton strips. The preamplifiers are derived from the PACI [18], without the current output; the current signal, I, is now obtained in the FEE board by analogically deriving the charge signal, Q. The gain on the charge output is 2 mV/MeV.

Each signal is sent to a fast ADC (see table 1), apart from the Q signal of Si1 which is processed twice, with a high gain for light particles and fragments, and with a low gain for heavier ions, in order to improve PSA and Time of Flight in both cases (see sect. 3.1). Thus a FEE board comprises 6×2 14 bit ADC's. The high voltages for the two silicon detectors are built up locally in the FEE card and are individually controlled (section 2.1); the card performs the voltage compensation due to reverse current. One FPGA is in charge of a preprocessing of all signals for each telescope. It stores and reads out the Q and I waveforms. It gives a zero-level trigger which signals that the telescope was hit. It then provides a precise value of the energy deposited in Si1, Si2 and CsI(Tl)+PD by means of trapezoidal filters. The FEE card also holds one pulser for linearity calibration purpose and clocks to synchronize the different signals. Adapted from [19].

All information from the FPGA is transmitted to the acquisition system through a block card (section 4), the connector between both cards is visible on the low rhs of the photo.

Performances

Particle identification

We found that improving the qualities of the silicon detectors of the telescope also greatly enlarged the isotopic identification when using the ∆E-E method for Si1 vs Si2 responses.

Figure 2 shows that masses are separated for Z up to 23 with this technique. We have found isotopic identification on the same mass range when using the ∆E-E method with (Si1+Si2) vs CsI(Tl) [19].

Identifying nuclei stopped in one silicon implies using two parameters provided by this single detector. One of these is the measured energy of a particle; for the second we chose either the rise time of the charge signal, τ Q , or the maximum amplitude of the current signals (I max or E/I max ). The Particle IDentification (PID) data presented in figure 3 were obtained with a detector for which the latter method was more performant, and Z identification is observed up to Z=54 [19]; in that case we performed PSA on the second silicon, in which most of the produced nuclei Results for DS Strip Silicon detector were stopped. Indeed figures 2 and 3 were obtained with the same telescope detecting nuclei produced in 35 A MeV 129 Xe+ 124 Sn reactions. The 306µm second silicon1 was exceptionnally good, having a resistivity ρ = 3000 Ω.cm with a FWHM of the ρ distribution across the detector of 0.6% [20]; it was biased 3% above the depletion voltage. Figure 3 also shows that no mass identification is visible, even for the lightest elements. This is in disagreement with some of our results, where isotopes were seen for Z up to Z=7-8, see figure 4 and ref [15]. This discrepancy was attributed to the gain of the preamplifier, which was 3 times larger for the data of figure 4 than for those displayed in figure 3. This led us to adopt two gains for the coding of the energy signal of the first silicon detector of each FAZIA telecope.

Foreseen possible improvements

In view of possible developments aiming at an increased granularity, we have tested the PSA of the response of Double Sided Strip Silicon Detectors (DSSSD). The map displayed in figure 5 was obtained with a 500 µm nTD silicon, with 16 strips on each side. Particles entered through the low-field (ohmic) side. Same results were obtained when taking signals from the P-side or the ohmic-side. The figure shows that elements are identified with resolution and range similar to those of the other detectors. Another solution which is thought of, because if would save quite an amount of money for the 4π array, is to replace the FAZIA modules at backward angles (past 90 o in the laboratory) by Single Chip Telescopes, SCT. This arrangement suppresses one electronic channel, because the silicon detector preceding the CsI(Tl) can be used both as a detector and as a photodiode for the scintillator light readout. SCT's were tested during the FAZIA R&D and, using suitable algorithms, it was shown that performances comparable to those of a FAZIA telescope were obtained for H and He isotope identification, whereas they are not as good for light fragments, particularly at low energy [21]. For this reason SCT should be positioned at angles where only few and light fragments are emitted. Energy thresholds for Zidentification. Black lines and black triangles refer to the energy necessary to punch through a 300 µm silicon for ∆E-E identification. Left part: silicon with resistivity FWHM 0.6%, using two PSA methods, low-field entrance (from [19]). Right part: silicon with resistivity FWHM 4%; full circles low-field entrance, open squares high field entrance. (from [22])

Energy identification thresholds

We can observe in the inset of figure 3, showing part of a E vs τ Q map, that at low energy nuclei can not be identified with a PSA method: all ridge lines converge into a single locus. We quantified the energy threshold for Z-identification, using a factor of merit, F oM = ( P ID 2 -P ID 1 )/(F W HM 1 + F W HM 2 ) where P ID i are the average PID values for nuclei Z 1 and Z 2 =Z 1 +1, and the F W HM i are the full widths at half maximum of the PID peaks. Two elements are considered as well separated when F oM >0. 7.

In figure 6 the thresholds so determined are plotted in several cases, and compared to those imposed by the punching-through of a 300 µm silicon when using a ∆E-E method. A general remark is that PSA does significantly lower the Z-identification thresholds. The left part of the figure corresponds to results obtained for the highly homogeneous silicon of figure 3, when particles enter the detector through the low field side. The two PSA methods, E vs τ Q and E vs I max , give close thresholds, from 2 A MeV for Z=6 to 6 A MeV for Z=30 and 11 A MeV for Z=50. The right part of the figure displays data obtained for a less homogeneous detector (4% ρ-FWHM). In that case we compared the thresholds obtained, in the same conditions, when particles enter through the low or high field side [22]. We firstly note that much lower thresholds are measured when the detector is reverse mounted, confirming that this is the best choice when using PSA techniques. Second, if we compare the thresholds obtained for rear entrance in the two detectors (left and right pictures), we note the influence of the resistivity homogeneity: they are about 1.5-2.5 times lower for the ρ-homogeneous detector.

The problem of radiation damages

As soon as silicon detectors started to be used, it was observed that the crystal structure, and thus the detector response, was degraded by the detected particles, particularly when very heavy ions stop in a detector. This raises problems when using silicon detectors with heavy beams, at angles where they detect the high rate elastic scattering events. Any degradation of the response of the detector becomes unacceptable when one makes use of pulse shape analysis. A specific study of the radiation damages was undertaken during our R&D, and we could reach quantitave conclusions. We irradiated a 300µm silicon detector with 35 A MeV Xe ions. Collimators and degraders allowed to define a zone in which the ions punch through the detector and a second one where the ions were stopped [23].

Up to 6×10 8 ions/cm2 ions hit the detector. Minor effects on the measured values of E and τ Q were observed when the Xe ions crossed the silicon. Conversely dramatic differences were found on both parameters when the ions stopped in the detector. At maximum fluence the energy has decreased by 20% whereas τ Q dropped by a factor 2.5.

These observations make delicate the use of very heavy ions for FAZIA experiments, particularly at low energy where the grazing angle is large. It would be interesting to test in the future if some annealing process could allow to recover acceptable functioning of the damaged silicon detectors.

FAZIA demonstrator

The FAZIA project is presently in the phase of building a demonstrator. This demonstrator will be an array of 192 telescopes arranged in 12 blocks of 16. The final (i.e. for the future 4π array) electronics and mechanical solutions are adopted. We feel useful to minimize the detectorpreamplifier-digitizer distances, therefore the FEE is located inside the vacuum chamber 2 . The electronic arrangement is represented in figure 8. The 8 FEE boards necessary to treat the 16 telescopes are piled up and all connected to a single block card. The block card connects the FEE to the acquisition system by means of an optical fiber insuring the air-vacuum feed-through. Through this fiber it receives a 48 V voltage out of which it builds the photodiode high voltages and different low voltages which are transmitted to the FEE boards to generate the low voltages for preamplifiers and the high voltages for silicon detectors. It also houses one flash ADC to receive the accelerator RF signal which will be used for time of flight measurement. Due to the compacity of the electronics, an efficient cooling system is required; all FEE and the block cards lean on a copper plate in which circulates a water-antifreeze liquid.

A completely equipped block will be tested at LNS (Catania) at the end of 2012. The demonstrator is expected to be ready in 2014. It will first be coupled to existing arrays such as INDRA (GANIL-SPIRAL2), CHIMERA (LNS-FRIBS), GARFIELD (LNL-SPES) in order to get new and improved physics results. The modularity of the blocks allows to mount them in different mechanical configurations, depending on the experiment. For instance dissipative collisions will be studied by arranging the blocks in a coplanar belt at intermediate angles. Other planes are to study fusion reactions between light nuclei. In that aim the 12 FAZIA blocks will replace the forward rings of INDRA, as shown in figure 9. See [START_REF] Casini | Letter of Intent at Spiral2 week[END_REF] for more details on some proposed letters of intent.

Figure 1 .

 1 Figure 1. Pattern and photo of the prototype of the FEE board of FAZIA. The crossed small FPGA is removed in the final board.

Figure 2 .Figure 3 .

 23 Figure 2. Distribution of particle identification function from a ∆E-E map between the Si1 and Si2 signals. From [19].

Figure 4 .

 4 Figure 4. PSA: E vs E/I max for particles produced in a 35 A MeV 129 Xe+ 58 Ni. The full energy range of the ordinate axis is 700 MeV.

Figure 5 .

 5 Figure 5. PSA: E vs E/I max for particles produced in a 35 A MeV 129 Xe+ 58 Ni and detected in a DSSSD.

  Figure 6.Energy thresholds for Zidentification. Black lines and black triangles refer to the energy necessary to punch through a 300 µm silicon for ∆E-E identification. Left part: silicon with resistivity FWHM 0.6%, using two PSA methods, low-field entrance (from[19]). Right part: silicon with resistivity FWHM 4%; full circles low-field entrance, open squares high field entrance. (from[22])

Figure 7 .

 7 Figure 7. Evolution of τ Q and of the measured energy with the number of detected Xe ions. In the "implantation zone", the ions are stopped in the detector. (from [23])

Figure 8 .

 8 Figure 8. Mechanical arrangement of a FAZIA block.

Figure 9 .

 9 Figure 9. Mechanical coupling of the 12 blocks of the FAZIA demonstrator (right) with rings 6-17 of INDRA inside the INDRA vacuum chamber. (courtesy of Y. Merrer, LPC Caen).

Table 1 .

 1 Characteristics of the fast ADC's for processing the different signals of the silicon detectors and of the photodiodes. E range indicates the full scale energy range.

			Q signal		I signal
	Detector	E range sampling rate resolution sampling rate resolution
	Si1	250 MeV 4 GeV	250 Ms/s 100 Ms/s	14 bits 14 bits	250 Ms/s	14 bits
	Si2	4 GeV	100 Ms/s	14 bits	250 Ms/s	14 bits
	CsI(Tl) + PD	4 GeV	100 Ms/s	14 bits

this telescope comprised two 300 µm silicon, it was thus not a "standard" FAZIA telescope

for all test results presented in the previous section, only the preamplifiers were in vacuum, implying several meters of differential cable between PA and FEE