Alessandro Prest
email: prest@vision.ee.ethz.ch

Vittorio Ferrari

Cordelia Schmid
email: cordelia.schmid@inria.fr

Explicit modeling of human-object interactions in realistic videos

Keywords: Action Recognition, Human-Object Interaction, Video Analysis. !

Explicit modeling of human-object interactions in realistic videos

INTRODUCTION

Human action recognition is an open problem in computer vision. It is important for a wide range of applications, such as video indexing and surveillance. It is challenging due to the high variety of human appearances and poses within an action class. In this paper we focus on actions defined by the interaction between a person and an object, such as drinking and smoking.

Many previous approaches represent actions by distributions of low-level descriptors such as bags of spacetime interest points [START_REF] Dollar | Behavior 913 recognition via sparse spatio-temporal features[END_REF], [START_REF] Laptev | Learning 958 realistic human actions from movies[END_REF], [START_REF] Schuldt | Recognizing human actions: 985 A local SVM approach[END_REF] or describe the action as a distribution over point motion features localized in space and time on the human [START_REF] Fathi | Action recognition by learning mid-level 924 motion features[END_REF], [START_REF] Kläser | Human 955 focused action localization in video[END_REF], [START_REF] Laptev | Retrieving actions in movies[END_REF], [START_REF] Mikolajczyk | Action recognition with motion-971 appearance vocabulary forest[END_REF], [START_REF] Willems | Exemplar-993 based action recognition in video[END_REF].

In this paper, we propose a novel approach that has an explicit notion of the action object and represents an action by spatio-temporal descriptors dedicated to human-object interactions. These include the relative motion of the object with respect to the human, which is typically highly distinctive for the action. Measuring these features involves automatically localizing the human and the action object and tracking them over time as shown in fig. 1. Our method is especially designed to do this in realistic videos, such as feature films. It does not involve any component that depends on background subtraction, which makes it suitable for any camera and background motion. Moreover, the method builds on state-of-the-art object detection techniques [START_REF] Felzenszwalb | Object detection with discriminatively trained part based models[END_REF], [START_REF] Prest | Weakly supervised learning 976 of interactions between humans and objects[END_REF] Fig. 1. Human-object interactions. Top row: one drinking and two smoking instances from the Coffee & Cigarettes dataset [START_REF] Laptev | Retrieving actions in movies[END_REF]. Second row: examples from the dataset of [START_REF] Gupta | Observing human-946 object interactions: Using spatial and functional compatibility for 947 recognition[END_REF]. Human and object locations automatically produced by our method are indicated in green and cyan respectively. operating in single frames, and robustly links detections 28 over time even across many frames where the object 29 was missed, again using a state-of-the-art approach [START_REF] Sundaram | Dense point trajectories by 991 GPU-accelerated large displacement optical flow[END_REF]. fields. More recent human-centric approaches [START_REF] Laptev | Retrieving actions in movies[END_REF], [START_REF] Mikolajczyk | Action recognition with motion-971 appearance vocabulary forest[END_REF], 111 [START_REF] Kläser | Human 955 focused action localization in video[END_REF], [START_REF] Rodriguez | Action mach: a spatio-980 temporal maximum average correlation height filter for action 981 recognition[END_REF] deal with action localization in realistic video. 112 Laptev and Perez [START_REF] Laptev | Retrieving actions in movies[END_REF] aggregate local spatio-temporal 113 features over time into a spatio-temporal grid. They use 114 keyframe priming to refine the output of their method.

In [START_REF] Mikolajczyk | Action recognition with motion-971 appearance vocabulary forest[END_REF] authors also adopt a human-centric approach 116 where vocabularies of local motion and shape features 117 are combined with a voting approach. Liu et al. [START_REF] Liu | Recognizing realistic actions from 962 videos in the wild[END_REF] 118 propose a combination of static and motion low-level human-object interactions in video [START_REF] Filipovych | Recognizing primitive interactions 933 by exploring actor-object states[END_REF], [START_REF] Filipovych | Robust sequence alignment for 935 Actor-Object interaction recognition: Discovering Actor-Object 936 states[END_REF], [START_REF] Gupta | Observing human-946 object interactions: Using spatial and functional compatibility for 947 recognition[END_REF], [START_REF] Matikainen | Representing 966 pairwise spatial and temporal relations for action recognition[END_REF], 137 [START_REF] Messing | Activity recognition using the 969 velocity histories of tracked keypoints[END_REF]. Messing et al. [START_REF] Messing | Activity recognition using the 969 velocity histories of tracked keypoints[END_REF] introduce a dataset of human- Ribeiro [START_REF] Filipovych | Recognizing primitive interactions 933 by exploring actor-object states[END_REF], [START_REF] Filipovych | Robust sequence alignment for 935 Actor-Object interaction recognition: Discovering Actor-Object 936 states[END_REF] model human-object interactions based on the trajectory and appearance of spatio-temporal interest points. Their approach is demonstrated in controlled videos taken by a static camera against a static, uniform background. Importantly, the scene is seen from the actor's viewpoint. This is substantially different from the type of video we consider.

Our work is also related to methods for modeling human-object interactions in static images [START_REF] Prest | Weakly supervised learning 976 of interactions between humans and objects[END_REF], [START_REF] Yao | Grouplet: A structured image representation 1002 for recognizing human and object interactions[END_REF], [START_REF] Yao | Modeling mutual context of object and 1004 human pose in human-object interaction activities[END_REF]. However, these approaches do not take advantage of motion characteristics of actions. Furthermore, [START_REF] Yao | Grouplet: A structured image representation 1002 for recognizing human and object interactions[END_REF] operates in a constrained setup where human location is given even at test time, and [START_REF] Yao | Modeling mutual context of object and 1004 human pose in human-object interaction activities[END_REF] expects the full human body pose to be always visible.

OVERVIEW OF OUR METHOD

In this section we present an overview of our approach to action recognition, based on explicitly modeling the human-object interaction (fig. 2). We summarize the steps for training the model of an action class (sec. 3.1) and for localizing it in space and time in a novel test video (sec. 3.2). TR1. We localize and track the humans in the training video. We first apply the human detector of [START_REF] Prest | Weakly supervised learning 976 of interactions between humans and objects[END_REF] independently on each frame and then link the resulting detections over time into tracks (sec. 4). For each annotation cuboid, we select the track which best overlaps with it and cut it to the precise temporal extent. This results in our set of positive human tracks. There is exactly one such track for each cuboid.

Training

As the overall goal of our work is to learn the relative motion between humans and objects that is characteristic for the action class, we also need to track action-objects.

For each annotation cuboid, we track the object starting from the single annotated frame forward and backward in time within the temporal extent of the cuboid (sec. 4).

These form the positive object tracks (fig. 3, left). Again, there is exactly one such track for each cuboid. For each cuboid we now associate its human and object track into a positive human-object pair.

TR2. We use the object windows in all frames of all positive object tracks as positive samples for training an Fig. 2. Overview of our method. We show the steps for training (TR1 -5) and testing (TE1 -3). See text for details.

action-object detector using the recent method of [START_REF] Felzenszwalb | Object detection with discriminatively trained part based models[END_REF] The test step TE1 detects both humans and objects automatically and tracks them throughout the video. For illustration we show here only two object tracks out of many more (a positive one covering the cup and a negative one on the actor's face).

TR5. We use the descriptors from positive and nega-

TRACKING HUMANS AND OBJECTS 259

Our approach for modeling human-object interactions We take a more general approach by employing the 283 generic part-based human detector presented in [START_REF] Prest | Weakly supervised learning 976 of interactions between humans and objects[END_REF]. In addition to being small, these objects present a high 293 degree of pose and appearance variability. For this task 294 we rely on the detection approach of Felzenswalb et 295 al. [START_REF] Felzenszwalb | Object detection with discriminatively trained part based models[END_REF], which demonstrated excellent results on the 296 PASCAL VOC object detection challenge [START_REF] Everingham | The PASCAL Visual Object Classes 920 Challenge 2007[END_REF]. We use the

Tracking 301

Tracking is needed at various stages of our approach.

302

During training we need to track each action object 303 Fig. 4. The DPT-MS tracker at test time. In the first frame (left) the cup is automatically detected by an object detector. In the subsequent frames no detections are found on the cup. DPT-MS produces an object track (dashed window) by propagating the detection from the first frame according to point tracks (white segments). In the last frame (right) the cup is again detected. DPT-MS adds this detection to the track and uses it to update the confidence score, but not the location of the track.

starting from the initialization in a single annotated frame (TR1). This is a traditional tracking task [START_REF] Yang | Efficient mean-shift 1000 tracking via a new similarity measure[END_REF], [START_REF] Wu | Tracking with dynamic 998 hidden-state shape models[END_REF], [START_REF] Grabner | Semi-supervised on-line 944 boosting for robust tracking[END_REF]. Furthermore, during TR1 we need to link over time human detections obtained automatically in individual frames. This is a tracking-by-detection task [START_REF] Breitenstein | Robust tracking-906 by-detection using a detector confidence particle filter[END_REF], [START_REF] Ferrari | Progressive 931 search space reduction for human pose estimation[END_REF], [START_REF] Kläser | Human 955 focused action localization in video[END_REF].

During testing (TE1) tracking-by-detection is needed again for both humans and objects (as at this point we have an object detector from TR2).

Previous work [START_REF] Breitenstein | Robust tracking-906 by-detection using a detector confidence particle filter[END_REF], [START_REF] Ferrari | Progressive 931 search space reduction for human pose estimation[END_REF], [START_REF] Kläser | Human 955 focused action localization in video[END_REF], [START_REF] Ramanan | Tracking people 978 by learning their appearance[END_REF] has been successful in tracking people in realistic videos by linking the output of a person detector run independently on each frame (tracking-by-detection). However, tracking small objects such as cups or cigarettes in this manner is much harder because detectors tend to miss the object in many frames. As a consequence, the object motion is typically broken into many short tracks. Furthermore, tracking-by-detection does not work when we do not have a detector yet, i.e. when the object to be tracked is given only as a bounding-box in a single frame. This corresponds to the traditional tracking scenario where the target is annotated in one frame [START_REF] Yang | Efficient mean-shift 1000 tracking via a new similarity measure[END_REF], [START_REF] Wu | Tracking with dynamic 998 hidden-state shape models[END_REF], [START_REF] Grabner | Semi-supervised on-line 944 boosting for robust tracking[END_REF].

We propose here a general-purpose tracking method to robustly track multiple targets in an integrated manner that encompasses both the traditional tracking of a target annotated in one frame and the tracking-bydetection scenario. Inspired by [START_REF] Sivic | Person spotting: video 987 shot retrieval for face sets[END_REF], our algorithm takes as input any number of detection windows of the target, and propagates them forward and backward in time based on dense point-tracks. During this process, multiple windows that spatially meet in a frame are automatically merged in a single output track.

Our tracker, referred to by dense point tracks [START_REF] Sundaram | Dense point trajectories by 991 GPU-accelerated large displacement optical flow[END_REF] median shift (DPT-MS), works as follows:

1) Input. A sequence of frames {s, . . . , e} and a set of detections D i for each frame i ∈ {s, . . . , e}. At least one detection in one frame is required for the algorithm to run. If more are provided, the algorithm will try to link them over time (tracking-bydetection). Any in-between situation is supported, e.g. where some targets have a single initialization window and others have a sparse set of windows output by a detector. For producing point tracks we compute long-term point tracks using the code of [START_REF] Sundaram | Dense point trajectories by 991 GPU-accelerated large displacement optical flow[END_REF] over the entire sequence. object interactions [START_REF] Gupta | Observing human-946 object interactions: Using spatial and functional compatibility for 947 recognition[END_REF], [START_REF] Prest | Weakly supervised learning 976 of interactions between humans and objects[END_REF], [START_REF] Yao | Modeling mutual context of object and 1004 human pose in human-object interaction activities[END_REF], [START_REF] Yao | Grouplet: A structured image representation 1002 for recognizing human and object interactions[END_REF]

m(H t , O t ) = l(H t , O t ) -l(H t-1 , O t-1 ) (3

EXPERIMENTAL RESULTS

546

We present an evaluation of our method on three ex- The training annotations [START_REF] Laptev | Retrieving actions in movies[END_REF] come in the form of cuboids A which define the location in time and space of humans performing the action. For each training cuboid we complement these original annotations with a bounding-box delimiting the action-object in one frame.

Evaluating the DPT-MS tracker

In this section we evaluate our tracker presented in sec. 4. While tracking humans in the C&C dataset is quite easy [START_REF] Kläser | Human 955 focused action localization in video[END_REF], it is very challenging to track small objects such Results are presented in tab. 1 where we compare to three recent techniques [START_REF] Grabner | On-line boosting and vision[END_REF], [START_REF] Grabner | Semi-supervised on-line 944 boosting for robust tracking[END_REF], [START_REF] Kalal | P-n learning: Bootstrapping 953 binary classifiers by structural constraints[END_REF]. Interestingly, DPT-MS outperforms the more complex approaches [START_REF] Grabner | On-line boosting and vision[END_REF], [START_REF] Grabner | Semi-supervised on-line 944 boosting for robust tracking[END_REF] on this dataset, although the approach of Kalal et al. [START_REF] Kalal | P-n learning: Bootstrapping 953 binary classifiers by structural constraints[END_REF] does even better. However, it is important to note that, [ Fig. 6. Human detection performance. See text for details.

unlike [START_REF] Grabner | On-line boosting and vision[END_REF], [START_REF] Grabner | Semi-supervised on-line 944 boosting for robust tracking[END_REF], [START_REF] Kalal | P-n learning: Bootstrapping 953 binary classifiers by structural constraints[END_REF], DPT-MS is specifically designed to trained on the PASCAL 2007 VOC training set [START_REF] Everingham | The PASCAL Visual Object Classes 920 Challenge 2007[END_REF]; and

(ii) our detector [START_REF] Prest | Weakly supervised learning 976 of interactions between humans and objects[END_REF], which complements [START_REF] Felzenszwalb | Object detection with discriminatively trained part based models[END_REF] with additional detectors specialized for the face and upper-body regions (sec. 4.1). We can observe that the combination of different human part detectors [START_REF] Prest | Weakly supervised learning 976 of interactions between humans and objects[END_REF] is beneficial on this difficult Coffee & Cigarettes dataset, improving over [START_REF] Felzenszwalb | Object detection with discriminatively trained part based models[END_REF] by 7% AP.

The track methods link the detections output by the corresponding detector using the tracker presented in sec. 4.2. For this evaluation, detections are first computed on each frame in a short temporal interval around a test image, and then linked using the tracker. However, evaluation is only done on the 137 test frames, as for the single frames methods. The associated score is the one of the track, i.e., the average detection score over the track.

The tracks methods outperform substantially both their corresponding single-frame methods, confirming the contribution of our DPT-MS tracker. The "Ours

Tracks" method gets +12% AP over the single-frame detector of [START_REF] Prest | Weakly supervised learning 976 of interactions between humans and objects[END_REF]. Moreover, it also achieves 9% higher AP than the human tracker of [START_REF] Kläser | Human 955 focused action localization in video[END_REF] (AP 52%). This is remarkable, as [START_REF] Kläser | Human 955 focused action localization in video[END_REF] was trained specifically on C&C, while our detector is trained using only external data ([32, sec. 2]).

Objects. We evaluate object detection performance on frames selected from the test part of C&C. We sample either one or two frames from every positive sample depending on its temporal length. This results in 54 frames for drinking and 47 for smoking. We also evaluate on negative images (i.e. not containing the object): for each class we select a number of negative images that reflects the proportion between positive and negative frames in the test set. This results in 500 negative images for drinking and 349 for smoking. As discussed in sec. 3 769 reports the average precision (AP) and compares to [START_REF] Laptev | Retrieving actions in movies[END_REF], 770 [START_REF] Willems | Exemplar-993 based action recognition in video[END_REF], [START_REF] Kläser | Human 955 focused action localization in video[END_REF]. predicts the correct label as in [START_REF] Gupta | Observing human-946 object interactions: Using spatial and functional compatibility for 947 recognition[END_REF]. 816 2. Our reimplementation of the 3DHOG-Track classifier achieves a slightly lower performance than the one reported by [START_REF] Kläser | Human 955 focused action localization in video[END_REF] (52/22 vs 54/25). This might be because [START_REF] Kläser | Human 955 focused action localization in video[END_REF] uses a finer temporal sliding window for the test tracks (7 scales vs our 3). Fig. 9. Drinking results. Human-object pairs localized in test videos. The ordering corresponds to the ranking of the combined action classifier. We also show the rank of the individual classifiers separately (I: interaction classifier, H: 3DHOGtrack classifier). These results show that the interaction and 3DHOG-track classifiers complement each other. Samples 13 and 14 have a relatively low 3DHOG-track score, whereas the interaction classifier successfully captures the discriminative motion of the object track. In contrast, for samples 2 and 4 the object track is incorrect, resulting in a lower interaction score rank, whereas the 3DHOG-track classifier correctly scores these samples highly. It is interesting, how our method finds object tracks also on unconventional objects such as the jug in samples 25 and 32, which receive top scores by the interaction classifier. For these examples 3DHOG-track fails due to the unusual object appearance. This confirms the ability of the interaction classifier to generalize the appearance of objects and describe their relative motion wrt to the human. Failure cases of the interaction classifier are often due to other objects moving in a similar way as action objects. For example in sample 30 the actor is pouring water from a teapot, resulting in a trajectory similar to the drinking action. For the 3DHOG-track classifier, a typical failure case is when low-level features perform poorly, as is the case in scenes with difficult lighting conditions, as in sample 14, or when the object has an unusual appearance, as in 25 and 32. Other failures by 3DHOG-track (classifying a negative as positive) are due to the actor being in a pose similar to the action, but not performing it, as in sample 12. Note that the interaction classifier receives a relatively low score as there is no motion. . In many cases the interaction and 3DHOG-track classifiers agree and assign both a high score to a positive sample. Complementary scores are obtained for samples 16, 23, 30 and 49: the interaction classifier correctly penalizes these negative samples without correct object motion, whereas 3DHOG-track is unable to distinguish them and assigns high scores. Note that sample 49 is a true negative, as it represents a person holding a cigarette and not smoking. For sample 19 the object track does not cover the correct object, thus the interaction classifier gives a low score, whereas the 3DHOG-track classifier assigns a high score. (*) For the smoking action we point out that the imprecise temporal extent of the annotations sometimes leads to an incorrect evaluation: samples 7 and 8 show a person smoking, but do not meet the spatio-temporal overlap threshold with the annotations. similar motion. In the case of the combined classifier, there are only two misclassified samples, i.e, "light" is misclassified as "answer" and "pour" as "spray", see figure 11. These could probably be removed if colour information was used.

Multi-class classification on Rochester Daily Activities dataset

The Rochester Daily Activities [START_REF] Messing | Activity recognition using the 969 velocity histories of tracked keypoints[END_REF] dataset contains 10 activities of daily living recorded in a controlled environment with a static camera and background (fig. 13).

Each activity was performed three times by five persons, is 3% higher compared to the full method of [START_REF] Messing | Activity recognition using the 969 velocity histories of tracked keypoints[END_REF], which also combines motion information with complementary contextual information.

CONCLUSION

This paper introduces an approach for learning humanobject interactions in videos. It explicitly tracks both the human and the action-object and represents the interaction as the relative position and motion of the object wrt the human. Experimental results confirm that human-object interactions, when explicitly captured by our method, are a rich source of information for action recognition and localization in video. Furthermore, we

show that the proposed interaction model captures information complementary to existing low-level descriptors.

Moreover, when combining the two, our approach improves over the state-of-the-art [START_REF] Kläser | Human 955 focused action localization in video[END_REF] on Coffee & Cigarettes as well as on the Rochester Daily Activities dataset [START_REF] Matikainen | Representing 966 pairwise spatial and temporal relations for action recognition[END_REF], [START_REF] Messing | Activity recognition using the 969 velocity histories of tracked keypoints[END_REF], and achieves the same results as [START_REF] Gupta | Observing human-946 object interactions: Using spatial and functional compatibility for 947 recognition[END_REF] on Gupta

30 Finally,

 30 our technique takes advantage of the temporal 31 continuity in video to reduce the amount of supervision 32 needed to learn an appearance model of the action object 33 as well as the interaction model. As a result, it can be 34 trained with a modest amount of annotation: for each 35 video clip of the action class we only need a spatio-36 temporal cuboid on the person and a bounding-box on 37 the action object in one frame. aggregate differences between subsequent frames of an 97 action sequence resulting in a binary motion energy im-98 age. Temporal information is included through a motion 99 history image. The method proposed in [7] operates on 100 sports footage. They compensate camera movement by 101 tracking the person and calculate optical flow in person-102 centered tracks. In [41] a method based on particle 103 filtering is used for modeling crowd flow and detect 104 anomalies. 105 All of the above mentioned human-centric approaches 106 operate either with static cameras, i.e., humans can 107 be located based on background subtraction, or with 108 simple backgrounds from which humans can be ex-109 tracted easily, as for example football or ice hockey 110

  119 features and efficient techniques for mining the most 120 discriminative ones in realistic YouTube videos. The 121 method proposed in [23] localizes actions in space and 122 time by first extracting human tracks and then detecting 123 specific actions within the tracks using a sliding window 124 classifier. Actions are described by track-aligned 3D-125 HOG features. These features are shown to be comple-126 mentary to our human-object interaction descriptors and 127 are incorporated in our final classifier. 128 The weakly-supervised approaches by Ikizler et 129 al. [20], [21] attempt to decrease the amount of super-130 vision necessary for training action classifiers. Training 131 videos for learning actions are obtained inexpensively 132 from YouTube [21]. Their approach is robust to the low-133 quality video as well as complex scenes necessary for 134 such video material. 135 Several works tackle the problem of recognizing 136

  138 object interactions recorded in controlled conditions and 139 propose a descriptor based on the velocity history of 140 tracked point features. Matikainen et al. [28] extends this 141 descriptor to include relations between pairs of tracked 142 points and quantize them into vocabularies. In contrast 143 with our work, both these approaches are based on low-144 level features to describe actions. The work most closely 145 related to ours is by Gupta et al. [19]. They model the 146 action object and the human-object motion for classifying 147 interactions between humans and objects. However, the 148 motion features used in their approach are more fragile: 149 they rely on hand trajectories to model how objects are 150 reached and grasped. In particular, the velocity profile 151 of the reaching hand and the time interval between a 152 reach and a grasp motion proved to be powerful features 153 in their experiments. Nevertheless, these fine-grained 154 features rely on motion extracted based on background 155 subtraction, which limits its applicability to static cam-156 eras and backgrounds (as opposed to uncontrolled video 157 such as feature films). Moreover, [19] requires substantial annotation effort for training, including the location of the person, of its hands, and a pixelwise segmentation of the action object in all video frames. Filipovych and

Input.

  In order to train the model for an action class, our method takes as input: (i) a long video including instances of the action class; (ii) spatio-temporal cuboids, constant in the spatial dimension. Each annotation cuboid defines the location in time and space of a human performing an instance of the action class; (iii) for each annotation cuboid, the location of the action-object is annotated in one frame within the temporal extent of the cuboid. In the following we describe each step of the training (TR) procedure, marked as TR1 -5.

  We use the detector from TR2 on the negative 216 parts of the training video (i.e. parts not overlapping 217 in time with any cuboid), and then run our tracker to 218 link the resulting detections over time, obtaining negative 219 object tracks. These are valuable 'hard negatives'. We now 220 form negative human-object pairs by associating human 221 and object tracks detected in the negative part, which 222 are close in space and time (sec. 5.2). 223 TR4. For each human-object pair we compute an inter-224 action descriptor capturing the relative location, relative 225 area and relative motion of the object wrt the human 226 (sec. 5.1). Moreover, we also compute the low-level 227 3DHOG-track descriptors [23] for each human track in 228 a pair. As a third descriptor, we use the score of the 229 object detector trained in TR2 on the object track in a 230 pair (sec. 6).

Fig. 3 .

 3 Fig. 3. Tracking at training and test time. (Left) The training step TR1 tracks the annotated bounding-boxes (dashed yellow) throughout the temporal extent of the annotation cuboid (persons in green and objects in cyan). Human tracks are automatically obtained, but only one is selected according to the best overlap with the human training annotation. (Right) The test step TE1 detects both humans and objects automatically and tracks them throughout the video. For illustration we show here only two object tracks out of many more (a positive one covering the cup and a negative one on the actor's face).

  260 depends on the availability of human and object tracks 261 in the same time period. For robustness, it is important 262 to ensure the highest possible recall for both human and 263 object tracks, as missing either of the two prevents the 264 approach from recognizing the action. 265 It is an elusive goal to design robust detectors and 266 trackers to deal with difficult, small objects such as 267 cigarettes or cups. Instead, we propose a tracking-by-268 detection approach that can be run on top of weak 269 single-frame detectors, and produces a large number of 270 candidate tracks in order to miss as few positive tracks 271 as possible, see sec. 7.1.2 for an experimental evaluation. 272 Then, in sec. 5, we introduce a highly discriminative 273 descriptor that allows to mine for relevant human-object 274 track pairs out of this pool of candidates.

  Detecting humans in the C&C dataset is par-277 ticularly hard due to their variety of appearance, pose, 278 viewpoint and lighting conditions. The previous work of 279 Klaser et al. [23] used a human detector based on HOG 280 features [4] trained on C&C to learn the specific features 281 of this dataset.

  282

284

  This detector combines four part detectors dedicated to 285 different regions of the human body (including full-body, 286 upper-body and face). It was trained from external still 287 images without using any C&C images [32, sec. 2]. Two 288 of the four components of this combined detector are 289 taken from the popular person detector of [10]. 290 Objects. Detecting small objects such as cups and 291 cigarettes is an even harder task than detecting humans.

  292

  297 windows from the positive object tracks obtained in TR2 298 as positive training data. As negative training data we 299 randomly sample windows from Caltech-101 [11].

  300

347 2 ) 3 ) 4 )

 234 Initialization. Let f be the first frame for which a 348 detection is available. For each detection D f j ∈ D f 349 create a new track T j , and add it to the overall track 350 set T . 351 Forward pass. Loop over frames i from f to e 352 a) Loop over tracks T j ∈ T 353 i) Update location. The position of T i+1 j of 354 track T j in frame i + 1 is the position 355 of T i j shifted by the median displacement 356 between frame i and i+1 of the point tracks 357 inside window T i j . 358 ii) Include a detection. If a detection D i+1 k Add new tracks. For each detection D i+1 k that 367 was not included into an existing track in step 368 3.(a).ii, we start a new track and add it to T . 369 Backward pass. Store the current tracks. Restart the 370 process from step 2, this time over the reversed 371 sequence from f to s.

372 5 )0 and 1 .

 51 Concatenate forward-backward tracks. Assemble the fi-373 nal tracks by concatenating the tracks from forward 374 pass to the (reverse) tracks from backward pass. 375 6) Confidence scores. The confidence of a track is the 376 average over the scores of the windows it contains, 377 where the windows scores are normalized between 378 Windows which are not supported by 379 any detection (see the two central images in fig. 4) 380 are given a score of 0, thus penalizing the overall 381 average.

  have shown the 466 importance of limiting the spatial range of an action-467 object wrt a human. We learn the interaction range for 468 the relative location and relative area features. After the 469 training step TR1, we have a set of positive human-470 object track pairs. For each frame in every human-object 471 pair, we compute the two interaction features, see fig. 5 472 for their distribution. For each feature, we then select 473 the range of the feature such that 90% of the mass of the 474 distribution is contained in it. Note how this threshold 475 operates at a frame level thus discarding 10% of the 476 outlying mass of the distribution and preserving relevant 477 geometric information from the remaining frames. 478 Forming pairs. The ranges learned for the spatial in-479 teraction features are used to select spatially consistent 480 pairs from the set of temporally overlapping ones. Fig. 5 481 illustrates the feature distributions and learned ranges 482 for the drinking action from Coffee & Cigarettes.

483 5 . 3 Fig. 5 .

 535 Fig. 5. Learning the interaction ranges. Histograms of relative location (left) and relative area (right) accumulated over all positive training human-object pairs. The learned ranges are shown in red. The left plot shows that the location of the cup is typically in the middle of the human window along the horizontal axis and slightly above it along the vertical axis.

  545

  as cups and cigarettes which are central to recognizing actions. These objects are often very small, occluded by the person and in difficult lighting conditions. We evaluate DPT-MS for tracking cup and cigarette objects in the training sequences for the drinking and smoking actions. We operate our tracker in a traditional scenario: we use the object location annotated in one frame of every positive training clip as initialization (sec. 3.1) and then run the tracker through the temporal extent of the action (typically < 100 frames).For evaluation only, we manually marked the object bounding-box in each frame of the training clips (throughout the paper these annotations are never used for training). We count a bounding-box output by the tracker as a correct detection if it overlaps with the ground-truth object by more than 50%. We measure recall R as number of correct detections divided by the number of frames where the object is visible. All other tracker outputs are counted as false-positives. Precision P is the percentage of correct detection. The F-measure combines these two measures as F = 2P R/(P + R).

  627 handle both traditional tracking (i.e. initialized from a 628 manual annotation in one frame) as well as tracking-by-629 detection, which simultaneously tracks a large number 630 of candidate windows and connects them over time. In 631 sec. 7.1.2 we show that this is a crucial requirement for 632 obtaining a sufficient recall in detecting and tracking the 633 object of interest in the C&C dataset. 634 Finally, DPT-MS is computationally very efficient. 635 Computing the point tracks of [39] takes 2 seconds per 636 frame and represents almost the entire runtime of DPT-637 MS. The rest of the procedure (sec. 4.2) tracks simul-638 taneously 1000 candidate windows over two frames in 639 only 10 milliseconds and it is linear in the number of 640 windows. In comparison, although [22] tracks one win-641 dow over two frames in 20 milliseconds, it would take 642 20 seconds to track 1000 windows, making it impractical 643 on a full-length movie such as the C&C dataset.

644 7 . 1 . 2 Fig. 7 .

 7127 Fig. 7. Object detection performance. See text for details.

Fig. 6

 6 Fig. 6 compares four methods. The two single frames methods run a human detector on each test image independently: (i) the popular human detector of [10],

4 . 1 ,

 41 Fig. 7 compares the performance of the object detectors on the test part of the dataset in the single frames and the

766Fig. 8

 8 Fig. 8 shows precision-recall curves for drinking and 767

784Fig. 9 7 . 2

 972 Fig. 9 and 10 show some of the top-scored human-object 785

Fig. 12 .Fig. 10 .

 1210 Fig. 12. Confusion matrices on the Gupta video dataset. (Left) performance of the interaction classifier; (Right) combined action classifier.

Fig. 11 .Fig. 13 .

 1113 Fig. 11. Human-object pairs localized on the Gupta video test set with the combined classifier. Every row shows one frame from each of the five test sequences of a class. Actions in this dataset follow precise motion patterns: each row displays samples selected to follow the temporal pattern. We also show one of the two misclassified samples, indicated with the incorrect class label overlaid.

Fig. 14 .

 14 Fig. 14. Confusion matrix on the Rochester Daily Activities dataset. Performance of the interaction classifier.

  [6] O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce. Automatic 915 annotation of human actions in video. In ICCV, 2009.

  We represent this vector by its magnitude

	440
	441	and direction.
	442	We compute an interaction feature at every frame of
	443	a human-object pair and then aggregate them into a
	444	single descriptor of fixed dimensionality as follows. For
	445	each feature we accumulate its values over the time
	446	interval in a histogram. We independently L1-normalize
	447	each histogram and then concatenate them to obtain the
	448	final interaction descriptor. The 2D relative location and
	449	relative motion cues are quantized into 16-dimensional
	450	histograms each and relative area is quantized into 4
	451	dimensions. This results in a total of 36 dimensions. We
	452	did not observe any improvement by using a higher
	453	dimensionality.
	454	5.2 Forming human-object pairs
	455	In the following we describe how to associate human
	456	and object tracks when collecting negative human-object
	457	pairs during training (step TR3) and when forming pairs
		during testing (step TE2). A simple approach would

) the difference between the relative location 438 l(H t , O t ) in frame t and l(H t-1 , O t-1 ) in frame 439 t -1. 458 be to take all temporally overlapping pairs of human 459 and object tracks. However, this would lead to a huge 460 number of pairs, which would make action detection 461 very slow. Instead, we perform a pre-selection, where 462 we associate pairs based on two interaction features from 463 sec. 5.1. 464 Learning interaction ranges. Previous works on human-465

TABLE 1 Evaluation of our DPT-MS tracker.

 1 We compare to other trackers using recall, precision and f-measure.

				Human detection performance
		1
		0.9
		0.8
		0.7
	precision	0.4 0.5 0.6
		0.3
		0.2
		0.1
		0 0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	1
							recall

Ours Tracks (61) Ours Single Frames (49) Felzenszwalb et al. Tracks (50) Felzenszwalb et al. Single Frames (42)

TABLE 2

 2 Number of tracks and recall (in parentheses) for humans H, objects O and human-object pairs (H, O) on the Coffee & Cigarettes test set.

	705	track modes. The track mode, although introducing some
	706	additional false-positives, doubles the maximum recall
	707	compared to the single frames mode, and detects more
	708	than 90% of all object instances. This fits the goal stated
	709	at the beginning of sec. 4 to produce a pool of candidate
	710	tracks which misses as few true object instances as
	711	possible. The lower performance of the track method in
	712	terms of average precision is inherent to the context we
	713	operate in: we deal with detections which are sparse
	714	in time (typically less than 30% of a positive track's
	715	frames are supported by a detection) and every frame
	716	where a detection is missing penalizes the overall score
	717	of the track. As a result, the average track score loses
		significance. The track score could certainly be made

TABLE 4 Average classification accuracy on the Gupta video dataset.

 4 The classifier based on the score of the ob-

	771
	772	ject detector (second row) performs very poorly, which
	773	confirms that a human-object interaction cannot be de-
	774	fined purely based on the appearance of the object in-

TABLE 5 Average classification accuracy on the Rochester Daily Activities dataset.

 5

718

more robust to outliers, but this was not necessary in

719

our context, which requires maximum recall. Note also 720 that this is not a problem when a reliable detector is 721 available, as is the case for human detection (fig. 6).

722

We stress that object detection and tracking in this 723 dataset is very difficult due to the highly cluttered 724 scenes, varying lighting conditions, and especially the 725 small size of the objects. In fact 76% of the objects cover 726 less than 1.5% of the image surface.

727

Human-object pairs. In order to localize an action 728 with our human-object interaction model, the human as 729 well as the object track need to be present. In order to 730 miss as few as possible human-object pairs performing 731 the action, we keep all human and object tracks, i.e., 732 we operate at the maximum recall level (right-most 733 datapoints in fig. 6 and7). For each method we present its average precision (AP) in parenthesis.

Evaluating action detection (localization in space


Hélène Collavizza

Nguyen Le Vinh

Olivier Ponsini

Michel Rueher

Antoine Rollet

Antoine Rollet The Flasher

Hélène Collavizza Le

Vinh Nguyen

The Flasher Manager Benchmarks

la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The Flasher Manager application

The Flasher Manager application [START_REF] Collavizza | Constraint-Based BMC: A Backjumping Strategy[END_REF] is a real time industrial application from a car manufacturer that was provided by Geensoft / Dassault Systems 1 . The Flasher Manager application was designed and simulated using the Simulink platform. Its specification was given by Geensoft and consists of four main properties. The Flasher Manager is concretely embedded as a C program in a car computer. This application is challenging for software verification: its complexity comes from the size of the C function generated from the Simulink module and from the number of clock cycles required for verifying each property. These benchmarks have been used to compare the performances of different bounded model-checkers and solvers [START_REF] Collavizza | Constraint-Based BMC: A Backjumping Strategy[END_REF].

The Flasher Manager Benchmarks

The following files are provided:

•1

  f1.c: C code of the Flasher Manager module (function f1) • p1.c: C code for checking property 1 • p2.c: C code for checking property 2 • p3a.c: C code for checking property 3a • p3b.c: C code for checking property 3b • p4.c: C code for checking property 4 See http://www.geensoft.com/en


Sophie Pommet
email: sophie.pommet@gredeg.cnrs.fr

The survival of venture capital backed companies : an analysis of the French case

Keywords: JEL Classification : G24 ; O32 venture capital, survival, innovation, France

We analyze the impact of venture capital on firm performance ; more precisely, we investigate whether venture capital adds value to innovative French companies in terms of increasing their survival time. To this end, we use a hand-collected data set based on a sample of 139 French companies that went public at the "Nouveau Marché" between 1996 and 2002 to compare the survival rates of venture capital backed and non-venture capital backed companies. We develop two sets of econometric models to evaluate the factors that affect the fate of French initial public offerings. First, we estimate a discrete time duration model to explain the probability of exit. Second, we apply a competing risk model to account for heterogeneity in firm exit (liquidation versus merger/acquisition). Contrary to common wisdom, the estimates show that venture capital backed companies have a lower survival rate than non-venture capital backed companies and have a higher probability of being liquidated than other firms. Our results are comparable to those obtained in previous studies on Germany and Belgium which show that receiving venture capital does not improve firm survival.

INTRODUCTION

The venture capital (VC) industry is an important source of funds for start-up firms. The industry has grown dramatically since the 1990s in both the US and Europe. According to the statistics provided by the European Venture Capital Association (EVCA1 ), the French VC industry is one of the most dynamic in Europe and is ranked second after the United Kingdom.

A key function of VC firms is to provide funding and expertise to innovative companies with high growth potential. These types of innovative companies are characterized by high failure rates [START_REF] Storey | New technology-based firms in the European union : an introduction[END_REF] and one of the roles of VC firms is to contribute to reduce high failure rates of companies (Manigart et al., 2002a). According to [START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF]Kini (2000, p.1141), «since VCs specialize in providing financing and helping venture managers develop required competencies, their presence should help to reduce the failure rate of risky projects». Although this may be true of US firms [START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF], evidence for Belgium (Manigart et al., 2002a) and Germany [START_REF] Audretsch | The effects of experience, ownership, and knowledge on IPO survival : evidence from the Neuer Markt[END_REF] does not support this statement. There is a lack of data for France, which means that we do not know whether VC improves the performance of French innovative companies and more precisely, we do not know whether the presence of VC firms helps or not to reduce failure rates among French innovative companies.

We try to address this gap in the empirical literature by investigating the following research question : does VC involvement improve the survival rates of French innovative companies ? To do so, we develop two sets of econometric models to evaluate the factors that affect the fates of French innovative firms. First, we estimate a discrete time duration model to explain the probability of exit. Second, we apply a competing risk model to account for heterogeneity in firm exits (failure versus merger/acquisition). Our analysis is based on hand-collected data for a sample of 139 French companies that went public at the "Nouveau Marché" between 1996 and 2002. One advantage of studying companies listed on the "Nouveau Marché" is that they belong to a small number of high-tech industries and are of fairly similar age. This makes them a relatively homogeneous group of companies, which allows us to compare the survival of VC backed and non-VC backed companies (Bottazzi and Da Rin, 2002 2 ). In addition, thanks to the strict disclosure requirements of "Nouveau Marché" we have access to detailed information on French companies for the years from the initial public offering (IPO). Also, several papers have focused on the cohort of high-tech firms that went public in the period 1996-2002, which means we can compare the impact of VC support on firms among countries to determine whether the French situation regarding VC support and firm survival is closer to the European or the US situation.

Contrary to what is commonly assumed, estimates show that the rate of survival of VC backed companies is lower than for non-VC backed companies. If we take account of mode of exit, we see that among exiting VC backed companies there is a higher probability of the exit through liquidation. Our results are comparable with those obtained from studies on Germany and Belgium that show that VC support does not improve firm survival, but contrast with those from studies of US firms.

The remainder of the paper is structured as follows. Section 1 provides an overview of the literature on the role of VC firms. Section 3 describes the data, the sample employed and the methodologies used for our analyses. Section 4 presents the findings from the empirical analyses and section 5 concludes.

THEORETICAL FRAMEWORK AND HYPOTHESES

There are positive and negative aspects to financing by VC firms. In contrast to what is generally assumed, the presence of VC does not necessarily have a positive impact on the survival probability of a company.

i. The positive impact of VC firms

There are reasons to believe that VC backed companies will show higher economic performance and higher survival rates than non-VC backed companies.

First, VC firms make decisions based on specific investment criteria, which means they should be better placed to select the most promising ventures to support [START_REF] Gompers | The Venture Capital Cycle[END_REF]. For instance, according to [START_REF] Gifford | Limited attention and the role of the venture capitalist[END_REF], US VC firms fund only around a dozen projects a year out of the thousands evaluated. In a sample of Canadian biotechnology companies, [START_REF] Baum | Picking winners or building them ? Alliance, intellectual, and human capital as selection criteria in venture financing performance of biotechnology startups[END_REF] show that VC firms are able to choose the most promising companies with the highest probabilities of success.

Second, it is common knowledge that VC firms use control mechanisms to manage business risk and reduce agency conflicts inherent in the financing of innovative firms [START_REF] Sahlman | The Structure and Governance of Venture-Capital Organization[END_REF][START_REF] Gompers | The Venture Capital Cycle[END_REF]. Among these mechanisms, [START_REF] Lerner | The syndication of venture capital investments[END_REF] and [START_REF] Gompers | Optimal Investment, Monitoring, and the Staging of Venture Capital[END_REF] identify strategies of syndication, use of specific control rights such as convertible securities3 , and the staging of capital infusions that allows projects to be abandoned at predefined stages [START_REF] Sahlman | The Structure and Governance of Venture-Capital Organization[END_REF]. It should be noted that this ability to exercise efficient monitoring is especially valuable if the funded companies belong to industrial sectors where the assets are largely intangibles (e.g. R&D investments) [START_REF] Gompers | Optimal Investment, Monitoring, and the Staging of Venture Capital[END_REF]. Also, if inside investors, that is investors with close associations with the company, are involved, this can reduce the informational asymmetries and conflicts of interest that are inherent in innovative companies [START_REF] Admati | Robust Financial Contracting and the Role of Venture Capitalists[END_REF]. Admati and Pfleiderer (1994, p.371) show that VC firms are better able to resolve agency problems than traditional investors such as banks4 because the VC firm is : «an investor who not only provides capital but also works closely with the firm, monitors it frequently, and is generally very well informed about the firm's prospects and investment opportunities».

Third, because VC firms' investment processes are so selective [START_REF] Sahlman | The Structure and Governance of Venture-Capital Organization[END_REF], the receipt of VC should convey positive information about the company which in turn will provide access to cheaper sources of financing (Manigart et al., 2002a). Thus, the better the reputation of (i.e. the longer established) the VC firm, the stronger will be the signal to other investors at the time of IPO [START_REF] Megginson | Venture Capitalist Certification in Initial Public Offerings[END_REF]. On a sample of US IPOs in the period 1983[START_REF] Evans | The relationship between firm growth, size and age : estimates for 100 manufacturing industries[END_REF][START_REF] Megginson | Venture Capitalist Certification in Initial Public Offerings[END_REF], show that venture capitalists play a crucial certification role at the time of the IPO. For instance, compared to non-VC backed companies, VC backed companies are able to raise higher amounts of funds at IPO and are introduced by more prestigious underwriters (investment banks). According to [START_REF] Megginson | Venture Capitalist Certification in Initial Public Offerings[END_REF], the presence of VC in the company's capital structure at the time of IPO is a guarantee of the quality of the IPO and therefore reduces underpricing. [START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF] show that US VC backed companies have access to more prestigious investment banks than non-VC backed companies and they find that this variable positively influences the post-IPO survival time of VC backed companies. In the case of the US [START_REF] Megginson | Venture Capitalist Certification in Initial Public Offerings[END_REF][START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF] and the UK [START_REF] Rindermann | Venture Capitalist Participation and the Performance of IPO Firms : Empirical Evidence from France, Germany, and the UK[END_REF][START_REF] Chahine | Venture Capitalists, Business Angels, and Performance of Entrepreneurial IPOs in the UK and France[END_REF], there is evidence that venture-backed firms are underwritten by higher quality investment banks than their non venture backed counterparts. However, in France [START_REF] Rindermann | Venture Capitalist Participation and the Performance of IPO Firms : Empirical Evidence from France, Germany, and the UK[END_REF][START_REF] Chahine | Venture Capitalists, Business Angels, and Performance of Entrepreneurial IPOs in the UK and France[END_REF]) and Germany [START_REF] Rindermann | Venture Capitalist Participation and the Performance of IPO Firms : Empirical Evidence from France, Germany, and the UK[END_REF], VC firms do not act as certification and there is no evidence that VC backed firms are underwritten by higher quality investment banks than non-VC backed companies.

Finally, the involvement of VC firms adds value to the innovative firm [START_REF] Sahlman | The Structure and Governance of Venture-Capital Organization[END_REF][START_REF] Gompers | The Venture Capital Cycle[END_REF]. For instance, involvement of venture capitalist firms can add value through close relationships with the managers of portfolio companies [START_REF] Sapienza | Venture capitalist governance and value added in four countries[END_REF]. VC firms can engage in face-to-face interaction with managers and have involvement on the boards of VC backed companies [START_REF] Sapienza | Impact of agency risks and task uncertainty on venture capitalist-ceo interaction[END_REF][START_REF] Fried | Strategy and the board of directors in venture capital-backed firms[END_REF], in the recruitment of top managers, and strategy development (Hellman and Puri, 2002). They can also give companies access to their professionals networks [START_REF] Sapienza | Venture capitalist governance and value added in four countries[END_REF]. These benefits seem to apply to the case of the UK and the US, but not Continental Europe. For a sample of VC firms based in the US, the UK, the Netherlands, and France, [START_REF] Sapienza | Venture capitalist governance and value added in four countries[END_REF] find that US and UK VC firms are the most involved in their investments, and add the most value to these firms. Comparatively, French VC firms are the least involved and add the least value. [START_REF] Manigart | Venture Capitalists' Appraisal of Investment Projects : An Empirical European Study[END_REF] and Manigart et al. (2002b) show also that Continental European VC firms (Belgium, Netherlands, France) are less involved in their portfolio companies and exert relatively lower levels of effort in monitoring them than their counterparts in the US and the UK.

If we assume that VC firms perform their roles well and add real value to the companies they finance or at least are able to select the most promising firms, we can propose the following hypothesis :

H1a : VC backed companies should have a higher probability of survival than non-VC backed companies.

There are various arguments for hypothesizing the reverse relationship between VC firm involvement and the survival of VC backed companies.

ii. The negative impact of VC firms First, the high rates of return reaped by venture capitalists are due to their also financing risky companies. The rates of return required by a set of VC firms (located in the US, the UK, Belgium, France and the Netherlands) vary between 36%-45% for the riskiest early-stage investments, and 26%-30% for less risky later stage developments (Manigart et al., 2002b). Companies that seek VC backing are smaller, younger, more innovative and thus more risky firms. Therefore, despite their better short-run growth opportunities, VC backed companies are on average more risky, their probabilities of survival will be lower and their failure rates higher than non-VC backed companies [START_REF] Dimov | Venture Capital Investment Strategy and Portfolio Failure Rate : A Longitudinal Study[END_REF]. According to [START_REF] Dimov | Venture Capital Investment Strategy and Portfolio Failure Rate : A Longitudinal Study[END_REF] failure rates for new ventures are around 40% in the first year, rising to 90% over 10 years.

Second, VC firms may be denied the opportunity to invest in the most promising companies. In a theoretical article, [START_REF] Amit | Entrepreneurial ability, venture investments and risk sharing[END_REF] argue that US VC funds provided very low rates of return in the 1980s (the mean internal rate of return was less than 10% at the end of 1985) because they have only the opportunity to fund the less skilled entrepreneurs and less profitable ventures. As a result of the adverse selection problem associated with asymmetric information, only the less able entrepreneurs (i.e. the less profitable ventures) will choose to involve VC firms in their capital structures. According to [START_REF] Amit | Entrepreneurial ability, venture investments and risk sharing[END_REF], this could explain the reported low average returns and high failure rates of new US ventures in the 1980s. Manigart et al. (2002a), on the other hand, argue that the high rates of return imposed by VC firms makes this source of finance very costly and that the best projects will seek funding from other, less costly sources. As a consequence, only second best projects apply for VC funding.

Finally, the development of post-investment activities takes time and the VC firm faces the strategic choice between extended involvement in the post-investment management of its portfolio companies [START_REF] Elango | How venture capital firms differ[END_REF], and optimization of outcomes. VC managers have limited amounts of time and attention to devote to their portfolio companies (Gifford, 1997), and optimizing outcomes may be at the expense of some firms [START_REF] Dimov | Venture Capital Investment Strategy and Portfolio Failure Rate : A Longitudinal Study[END_REF]. Finally, the efficient allocation of time may mean that VC managers have no involvement in the post-investment management of portfolio companies whose prospects of high or even adequate returns are not excellent, and which do not create good exit opportunities for the VC firm [START_REF] Ruhnka | The "living dead" phenomenon in venture capital investments[END_REF]. These companies may be self-sustaining in economic terms, but not viewed as sufficiently profitable to produce attractive final rates of return to warrant the continued involvement of the VC investor. Such firms are described by [START_REF] Ruhnka | The "living dead" phenomenon in venture capital investments[END_REF] as "living dead" companies. Manigart et al. (2002a) show that VC firms may decide either to try to turn-around these "living dead" portfolio companies or to liquidate them in order to devote their scarce time to star performers. In this context, [START_REF] Sapienza | When do venture capitalists add value ?[END_REF] finds that there is a positive correlation between US VC managers' involvement and the perceived performance of portfolio companies. [START_REF] Sapienza | Venture capitalist governance and value added in four countries[END_REF] provide evidence that VC managers may decide strategically to devote more time to their better performing portfolio companies in order to maximize the returns on their time (Manigart et al., 2002a).

These arguments lead to the counter-intuitive hypothesis that :

H1b : VC backed companies will have a lower probability of survival than non-VC backed companies.

DATA DESCRIPTION AND METHODOLOGY i. Selection and description of the data

The analysis uses individually collected data, based on listing prospectuses and the annual reports of French companies that went public on the "Nouveau Marché" between 1996 and 2002. The "Nouveau Marché" opened in 1996, to deal with innovative, high growth companies, and was discontinued in 20055 . Listing prospectuses contain detailed information on the financial and business situations of companies. This information is not confined to the IPO year, but includes the previous three years. We use these prospectuses and annual reports to derive quantitative information on several financial and business variables. We also collected data for one post-IPO year from companies' annual reports, and supplemented this with data from the DIANE (Van Dijk) database if annual reports were not available. Similar to the methodologies used by [START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF] and [START_REF] Coakley | Post-IPO Operating Performance, Venture Capital and the Bubble Years[END_REF], our final sample does not contain transfers from other stock markets or market tiers, foreign-incorporated companies, spin-offs, registrations at the time of a relisting after a firm was temporarily suspended, and financial companies. Our final sample contains 139 companies that went public on the "Nouveau Marché" between 1996 and 2002.

In line with [START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF], we define survivors as firms that continue to operate independently as public corporations. Thus, firms delisted from the "Nouveau Marché" due to failure or an acquisition or merger are classified as non-survivors. In this paper, we then relax the assumption of homogeneous exit by accounting for the mode of exit, namely firm liquidation, or firm acquisition/merger. According to [START_REF] Schary | The probability of exit[END_REF] there are important economic differences between the various forms of exit and, when studying firm survival, we need to consider and separate exit types. Following Manigart et al. (2002a) and [START_REF] Cockbrun | Patents and the survival of Internet-related IPOs[END_REF], we consider that at the difference of an event of liquidation, being involved in a merger or acquisition may be positive. The date of delisting and mode of exit were collected from official reports available on NYSE Euronext website and the economic newspaper Les Echos.

ii. The variables

The dependent variable in the survival models is time to failure, which is measured as the time interval (in years) from IPO date to delisting date. Survival time is the period between 1996 (the date of first listing on the "Nouveau Marché") and the year in which the company exited through liquidation or merger/acquisition. Survival time is right censored to 2009 since for continuing firms we do not have a recorded exit event.

We relate survival probability to a set of conditions for individual companies at the time of IPO. Our main independent variable is VENTURE CAPITAL, which is a dummy variable that takes the value one if the IPO firm is VC backed and zero otherwise. VC backed IPOs are defined as IPOs with venture capitalist involvement at the time of IPO. We identified VC firms from various sources : European Venture Capital Association (EVCA), Association Française des Investisseurs en Capital (AFIC), the French equivalent of EVCA and through venture capitalists website.

There is a set of conditions known to affect the survival probability of firms, including firm age, firm size, and firm growth [START_REF] Evans | The relationship between firm growth, size and age : estimates for 100 manufacturing industries[END_REF]. Following [START_REF] Audretsch | The effects of experience, ownership, and knowledge on IPO survival : evidence from the Neuer Markt[END_REF] and [START_REF] Cockbrun | Patents and the survival of Internet-related IPOs[END_REF] analysis of IPO firm survival, we define AGE as the difference in years between the founding date of the company and the date of its IPO. The size variable (SIZE) is a variable based on the value of total assets at the time of IPO. The difference in the number of the firm's employees one year before and one year after IPO is the GROWTH RATE (measured as the difference in the natural logarithm).

We include two independent variables PATENT and INTANGIBLES/ASSETS to take account of some of the firm's intangible assets. Based on the types of companies listed on the "Nouveau Marché", the potential for innovation (i.e. the stock of intangible capital) of these companies would seem important. Indeed, Baum and Silverman (2002) and [START_REF] Villalonga | Intangible resources, Tobin's q, and sustainability of performance differences[END_REF] highlight the strategic role of innovation in business survival. The variable PATENT is a dummy variable that takes the value one if the IPO firm owns one or more patent at the time of IPO and zero otherwise6 . Due to the limited availability of accounting data and especially R&D expenses7 , we calculate the ratio of intangible assets to total assets in order to take account of the nature of the assets [START_REF] Hasan | The US bankruptcy law and private equity financing : empirical evidence[END_REF] and a proportion of the intangible assets owned by the company [START_REF] Villalonga | Intangible resources, Tobin's q, and sustainability of performance differences[END_REF]. The variable INTANGIBLES/ASSETS is the capitalized amount of goodwill, patents, licenses, software, brands and advertising divided by total assets.

In order to control for differences in technology sectors and their influence on the survival rates of firms, we include aggregated industry dummy variables for six different industry sectors, based on the Euronext classification and the APE codes (French code for the sectoral classification of companies). We defined six dummy variables for the following sectors : ITS (comprising Internet, IT services, E-Commerce and software), BIOMEDICAL (comprising biomedical and pharmaceutical sectors), MEDIAS and EN-TERTAINMENT, TELECOMMUNICATIONS, TRADITIONAL (product and service), TECHNOLOGY (other high-technology and electronics products and services).

To control for time effects, we include the IPO date as a dummy variable for the years 1998, 1999 and 2000. These three dummy variables allow us to take account of the hot issue market period and its possible impact on firm survival.

iii. Survival analysis methodology

We use a survival analysis methodology to analyze the impact of the involvement of VC in firm's survival. Survival analysis originated in the bio-medical sciences and more recently has been applied to business to analyze the economic performance of newtechnology based firms [START_REF] Storey | New technology-based firms in the European union : an introduction[END_REF]. The advantage of survival analysis is that it overcomes the problem of inadvertent survivorship bias. Survival analysis allows us to take account of the most and the least promising firms, with very high or very low economic performance. In contrast to ordinary least square (OLS) or logit and probit models, survival analysis can predict whether and when an event will occur [START_REF] Jenkins | Survival Analysis[END_REF]. Unlike logit or probit models, survival analysis allows us to assess the conditional probability of failure given that the company has survived so far. Thus, survival analysis can cope with right censored data which represents situations where a failure event has not yet occurred, and with time-series data with different time horizons [START_REF] Jenkins | Survival Analysis[END_REF]. The IPO market we study is characterized by both situations [START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF]. The data are right censored since at any point in time a proportion of the companies that went public are still listed. Further, the time window is different for each firm depending on when in the sample period it went public : in our sample, firms went public between 1996 and 2002. Finally, survival analysis allows us to apply a competing risk model to account for heterogeneity in firm exit. While the literature identifies survival as indicative of positive performance and exit as representing negative performance [START_REF] Caves | Industrial Organization and New Findings on the Turnover and Mobility of Firms[END_REF], the mode of firm exit also needs to be considered. Here we consider here that at the difference of an event of liquidation, being involved in a merger or acquisition may well be positive.

In order to compare the survival profiles of VC backed and non-VC backed IPO firms, we develop two sets of econometric models : we estimate a discrete time duration model and compute a competing risk model to account for heterogeneity in firm exit8 . We focus on the survival time of the firm, which is a duration variable9 . Let T be the number of years that our companies have survived up to 2009. The cumulative distribution function of the duration time T is denoted as F and is defined as :

F (t) = P r(T ≤ t) with t ≥ 0.
This function gives the probability that the duration T is less than or equal to t. In our case, it is the probability that a company is delisted before t years after its IPO. The value of t ranges between 0 and 13 years.

The survival probability is given by :

P r(T > t) = 1 -F (t) ≡ S(t)
It describes the proportion of IPO firms that survive in each successive time interval. It provides the likelihood that a randomly selected IPO company will survive longer than t which is a specific time period. Then, the hazard function h(t) is the conditional failure rate defined as the probability of exit during a very small time interval assuming the firm has survived to the beginning of that interval. The hazard function is defined in terms of the probability density function and the cumulative distribution function. The hazard function is given by :

h(t) = f (t) S(t) = lim ∆t→0 P r(t<T ≤t+∆t|T >t) ∆t
where ∆t is a very small time interval. This conditional probability is the probability that exit occurs in the time interval [t, t + ∆t], based on no exit before the beginning of time t. Following [START_REF] Fontana | Product Innovation and Survival in a High Tech Industry[END_REF], in this study, we estimate a discrete time duration model for data grouped into intervals following the approach proposed in Prentice and Gloeckler (1978). The hazard rate function for firm i at time t > 0 with t = 1, ..., T , is assumed to take the proportional hazard form : θ it = θ 0 (t). exp(X ′ it β) where θ 0 (t) is the baseline hazard function and X it is the vector of the model parameters [START_REF] Sueyoshi | A Class of Binary Response Models for Grouped Duration Data[END_REF]. The discrete time formulation of the hazard of exit for company i in time interval t is given by a complementary log-log function such as :

log(-log(1 -h t (X it ))] = X ′ it β + θ(t) h t (X it ) = 1 -exp{-exp(X ′ it β + θ(t))} (1)
where θ(t) is the baseline hazard function relating the hazard rate h t (X it ) at the th interval with the spell duration [START_REF] Fontana | Product Innovation and Survival in a High Tech Industry[END_REF]. The model parameters are estimated using the maximum likelihood method. The model we estimate is a complementary log-log model with a baseline hazard function defined by the logarithm of time. This complementary log-log specification has the advantage of being the discrete-time counterpart of an underlying continuous-time proportional hazards model (Cox model with a Weibull hazard function) [START_REF] Jenkins | Easy Ways To Estimate Discrete Time Duration Models[END_REF].

Then, a complementary log-log model allows us to relax the assumption of homogeneous exit by accounting for the mode of exit, namely firm liquidation, or firm acquisition/merger. In order to take account of the mode of exit, we compute a competing risk model (CMR). A CMR is an extension of a standard duration model with the possibility of exit to one of several destination state [START_REF] Jenkins | Survival Analysis[END_REF]. In practical terms, we estimate a complementary log-log model similarly to (1) but allowing the full set of parameters to vary according to the different destinations :

h t (X ijt ) = 1 -exp{-exp(X ′ it β j + θ j (t))} (2)
where j = 1 or 2 respectively, depending on the mode of exit (liquidation versus merger/acquisition) where the two destinations are treated as independent 10 (Fontana and Nesta, 2009). This model (2) can be estimated by a multinomial logistic CRM in which forms of exit can be treated as independent [START_REF] Jenkins | Survival Analysis[END_REF]. In order to test whether each exit can be treated as independent of all other alternatives we perform a Hausman test for independence of the irrelevant alternatives test (IIA). Additionaly, we perform a Wald test to test if the different categorical exits can or cannot be combined.

EMPIRICAL RESULTS

i. Summary statistics and graphical analysis

Summary descriptive statistics for these explanatory variables are reported in table 1.

Tab. 1: descriptive statistics. Our sample contains 87 VC backed and 52 non-VC backed companies that went public on the "Nouveau Marché" in the period 1996-2002. The average age and growth rate of VC and non-VC backed firms is fairly equivalent. VC backed firms on average are larger than non-VC backed firms. The most important difference between the two types of firms is for the dummy variable PATENT. Firms that are financed by one or more venture capitalists are on average more innovative than others.

In order to test for differences in survival because of the presence of VC firms, we compare the survival functions of the group of VC backed firms with the group of non-VC backed firms. If VC involvement positively influences the survival profile of IPO firms, we would expect the survival function curve of VC backed firms to be above that of non-VC backed firms. This graphical analysis indicates that VC involvement negatively influences the survival profile of IPO issuers11 . If we compare the survival rates of the group of non-VC with the group of VC backed firms (see Table 5 in the appendix), we obtain that in general, survival rates decrease by about 3% in the 5-year period and by about 22% in the 10-year period. For non-VC backed firms, the percentage of firms that exited during our period of observation was 20.68, compared to 22.54 for VC backed firms. In almost every year, the estimated survival rate for VC backed firms was lower than for non-VC backed firms. Over time, these differences between groups increase as the survival rates of the two groups follow divergent paths12 . These first results tend to support the hypothesis H1b and indicate a negative influence of the involvement of VC firms on firm survival.

Proportion surviving

We corroborate these first results through multivariate analysis.

ii. Determinants of firm survival

In all the regressions, we consider 139 companies, 61 of which eventually exit the "Nouveau Marché". All firm level variables take the values at the time of firm's IPO. All duration models include a full vector of the industry dummy variables. Expanding the dataset by time intervals yields a total of 1155 observations13 .

A positive coefficient increases the value of the hazard function and indicates that the particular covariate increases the likelihood of the firm being delisted and thus reduces its trading period. A negative coefficient decreases the likelihood of the firm being delisted and increases its probability of survival.

We estimate five models using a discrete time duration model with a Weibull hazard function (see Table 2). In the first model we look at the impact of time duration on the hazard rate of exit. We then sequentially add AGE, SIZE and GROWTH RATE (model 2), VENTURE CAPITAL (model 3), PATENT (model 4) and INTANGIBLES/ASSETS (model 5).

The coefficient for AGE indicates that firm age always has a statistically significant and positive effect on survival, confirming the findings in the literature. In particular, age increases the expected survival time of a firm by 6% on average. The effect of growth rate is significantly positive in all models, increasing the expected survival time of a firm by 44% on average. The effect of size is only significantly positive in model 5 and with a marginal effect.

The variable PATENT shows no statistically significant effect but the coefficient of INTANGIBLES/ASSETS has a significant positive sign, suggesting that possessing a higher stock of intangible capital increases the probability of being delisted. However, it should be noted that this result may be due to the fact that a high stock of intangible capital may be an attractive asset for potential acquirers of the firm [START_REF] Chaudhuri | Capturing the Real Value in High-tech Acquisitions[END_REF]. So, we have to consider the different modes of exit in order to mitigate our results and to have a better understanding of the role of this variable on firm exit.

Industry sectors never exert a statistically significant influence on survival. Dummy variables for the years 1998 and 2000 have a negative and statistically significant influence on firm survival.

The direct effect of the presence of venture capitalists on firm survival is expressed by the coefficient of the dummy variable VENTURE CAPITAL. This variable is always statistically significant and has a negative effect on survival. Being financed by one or more VC firm decreases expected survival time by 86% compared to non-VC backed firms. Clearly VC involvement negatively influences the survival profile of IPO firms. Our findings validate the hypothesis H1b but do not support hypothesis H1a of a positive influence of VC firms on survival. Our results are in line with those in [START_REF] Audretsch | The effects of experience, ownership, and knowledge on IPO survival : evidence from the Neuer Markt[END_REF] and Manigart et al. (2002a) for Germany and Belgium but not with those obtained by [START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF] and [START_REF] Cockbrun | Patents and the survival of Internet-related IPOs[END_REF] for the US. Thus, our study reinforces the difference between US and European VC industries in terms of performance.

However, these results lead to the following question : is this negative effect of the presence of VC on firm survival the result of an exit through a merger/acquisition or through liquidation ? Being involved in a merger or acquisition may well be positive in contrast to liquidation. In order to have a better understanding of the role of VC firms in the financing and the performance of innovative companies, we relax the assumption of homogeneous exit by accounting for the mode of exit, that is, firm liquidation or firm acquisition/merger. We want to distinguish between these modes of exit in order to discover what determines firm liquidation and firm merger/acquisition.

iii. Determinants of mode of exit

Of the 139 companies that went public on the "Nouveau Marché" between 1996 and 2002, 78 (56.12%) still operating in 2009 ; 22 (15.83%) had exited through liquidation (i.e., they failed) and 39 (28.05%) had been acquired by or merged with a third party.

We start our second empirical investigation by accounting for these different types of exit : by either liquidation or merger/acquisition. Table 3 reports the results for the multinomial logistic competing risk model.

Column (1) reports the results for the comparisons between the alternatives of exiting by merger/acquisition and surviving. Results of the comparison between exit by liquidation, and survival are reported in column (2). Column (3) compares the two alternatives Tab. 3: Competing risk model : the determinants of the exit forms of exiting by merger/acquisition and exiting by liquidation. Before discussing these results, we need to test for the existence of significant differences in exit types. The Hausman test of independence of irrelevant alternatives (IIA) implies that the outcomes "merger/acquisition and "survival (column 1) and the outcomes "liquidation and "survival (column 2) are independent of the third alternative. Next, the results for the Wald test are in line with the Hausman test. As a consequence, there are significant differences in the factors that drive the various outcomes in general, and between liquidation and acquisition outcomes in particular.

(1) (2) (3)
From the parameter estimates in column (1), we can see that GROWTH RATE and SIZE have a negative and significant coefficient, suggesting that larger firms and firms with higher growth rates have higher probabilities of surviving than of exiting by merger/acquisition. These results for firm size and firm growth are in line with those in [START_REF] Botman | What Determines the Survival of Internet IPOs ?[END_REF] and [START_REF] Fama | New lists : Fundamentals and survival rates[END_REF] for the US market. The coefficient of INTANGIBLES/ASSETS indicates that firms with high stocks of intangible assets (in total assets) have a higher probability of exiting by a merger/acquisition than of surviving. Thus, those companies that were merged or acquired during mainly the years 1998 and 2000, are comparatively smaller than those firms that survived, they have lower growth rates but a higher ratio of intangibles (in total assets). Firms with large stocks of intangibles may represent a threat but also an opportunity for larger firms to acquire valuable intangible capital and distinctive skills [START_REF] Fontana | Product Innovation and Survival in a High Tech Industry[END_REF]. Intangibles are difficult to acquire or develop [START_REF] Villalonga | Intangible resources, Tobin's q, and sustainability of performance differences[END_REF] which makes patents, licenses, brands and other intangible assets attractive in highly competitive sectors such as ITS Chaudhuri and Tabrizi 1999).

From the parameter estimates in column (2), we see that AGE has a negative and significant coefficient indicating that younger firms have a higher probability of exiting by liquidation than of surviving. This result is in line with that obtained in [START_REF] Esteve-Pérez | A competing risks analysis of firms'exit[END_REF] in that younger companies have a higher probability of exiting by liquidation than of surviving. Our result is similar to the result in [START_REF] Cockbrun | Patents and the survival of Internet-related IPOs[END_REF] that older firms at the time of IPO have a lower probability of being liquidated whereas age has no effect on the probability of exiting by merger/acquisition in comparison to surviving.

The positive significance of VENTURE CAPITAL in column (2) is interesting. The positive parameter estimate suggests that firms that are financed by VC firms have a higher probability of exiting by liquidation than of surviving. VC backed companies have a 2.73 times higher probability of being liquidated than of surviving. This result is comparable to that obtained by Manigart et al. (2002a) for Belgium which shows that VC backed companies have a higher probability of not surviving due to negative events (bankruptcy and closure). However, this result contrasts with that obtained by [START_REF] Cockbrun | Patents and the survival of Internet-related IPOs[END_REF] for the US that VC backed firms have a higher probability of exiting by merger and acquisition than of exiting by liquidation when there is an exit event. In the case of France, it seems that the negative aspects linked to financing by a VC firm overcome the positive aspects : not only do VC backed companies have lower survival rates but, they also have a higher probability of being liquidated than of staying on the market. This result leads to the following questions : is this result anecdotal ? Is VC financing really decisive in explaining the probability of exiting by liquidation ?

To answer these questions, we compute the marginal effects of all the significant variables. Table 4 displays the predicted probability of each mode of exit14 . We observe that for the median firm at the average IPO year, the overall liquidation rate is 0.3%, the probability of merger/acquisition is 2.07% and the survival probability is 97.63%15 .

At first sight, the overall probability of exit, either by liquidation or by merger/acquisition, seems low : the median firm has nine in ten chances of continuing to operate independently as a public listed company. Comparing this result with the overall exit rate for the whole period 1996-2002 suggests the presence of large effects at the margin for each independent variable. Following the methodology in [START_REF] Fontana | Product Innovation and Survival in a High Tech Industry[END_REF], we compute the marginal effects of all the significant variables. Again following [START_REF] Fontana | Product Innovation and Survival in a High Tech Industry[END_REF], we compute the discrete change in the predicted probability by imputing a variation of two quintiles around the median value of each continuous variable (i.e. from the 30th to the 70th) percentile holding all other variables constant. For the dichotomous variable VEN-TURE CAPITAL, we compute the discrete change from being non-VC backed to being VC backed. All changes are reported as absolute and relative changes in probability 16 .

We observe first that all our significant variables have a large effect at the margins, and second, that the marginal effect of VENTURE CAPITAL confirms that the presence of VC firms is a determinant of the mode of exit. Receiving finance from one or more VC firms multiplies the hazard rate of exiting by liquidation by a factor of 2.6 : for a VC backed company, then, the probability of exit is three times as high as for a non-VC backed company17 . Thus, the influence of VC involvement on firm survival is not anecdotal ; VC investors play a decisive role in determining a firm's survival and exit probabilities.

In our view, possessing a high stock of intangible assets (over total of assets) could also be an explanation for why a firm exits as the result of a merger/acquisition rather than liquidation, compared to staying in the market, while venture capital involvement is a more dominant reason for why a firm exits as the result of a liquidation rather than merger/acquisition compared to staying in the market.

Our analysis has the following empirical implications and contributes to the literature on VC and the effect on firm survival. Our analysis of the performance of French VC investors and their impact on the survival time of French innovative companies shows that although the French VC industry is one of the most dynamic in Europe, this dynamism does not imply good performance. If we compare the performance of the French VC industry to those in other European countries and the US, we see that the performance of the French VC industry is closer to performance in Europe than in the US. The European Tab. 4: Marginal effects of firm-level variables on the mode of exit The predicted probability is computed using the median values of the continuous variables (time, age, growth rate, size, intangibles/assets) and the mean values of the dichotomous variables (venture capital, IPO year, sector dummy variables).

All marginal effects are computed as discrete change, holding all other independent variables constant at their mean or median values.

VC industry seems to be quite homogeneous in terms of performance.

Our findings have implications for the French VC industry. It is believed that VC firms fulfill a valuable role in the economy because they are financial institutions that provide funding and expertise specifically to innovative companies. However our results do not indicate that VC firms make a positive contribution in terms of survival of funded firms. We need to encourage greater professionalism among French VC firms in order to improve their selection, monitoring and value adding performance.

CONCLUSION

This paper analyses the survival of a unique sample of 87 VC backed and 52 non-VC backed companies that went public on the "Nouveau Marché" in the period 1996-2002. We looked at firms' hazard rates by considering exit as a homogeneous event. In line with studies on Germany and Belgium [START_REF] Audretsch | The effects of experience, ownership, and knowledge on IPO survival : evidence from the Neuer Markt[END_REF]Manigart et al., 2002a) but in contrast to findings for the US [START_REF] Jain | Does the presence of venture capitalists improve the survival profile of IPO firms ?[END_REF]Cokbrun and Wagner, 2007), we find that receiving VC does not improve the survival profiles of IPO firms. Receiving VC funding is negatively related to the duration of the period of listing on the "Nouveau Marché". We extended our analysis to the case of heterogeneous exit (i.e. by liquidation versus merger/acquisition). Contrary to the [START_REF] Cockbrun | Patents and the survival of Internet-related IPOs[END_REF] findings, our results show that VC involvement improves the probability of IPO firms being liquidated rather than continuing to operate independently as public listed companies.

Our study has some limitations. First, we focus on the most successful companies that went public ; our data do not cover all the companies financed by VC firms in the period 1996-2002. Second, the bubble period of 1998-2000 figures is included in our period of analysis perhaps has led to some overestimation of the poor survival of VC backed firms. However, our period of analysis is comparable to that in [START_REF] Audretsch | The effects of experience, ownership, and knowledge on IPO survival : evidence from the Neuer Markt[END_REF] for Germany and Cokbrun and Wagner (2007) for the US, therefore, this cannot be the main reason for VC backed firms showing a lower probability of survival than non-VC backed firms.

Fig. 1 :

 1 Fig. 1: Survival function of VC and non-VC backed firms.

EVCA : http ://www.evca.eu/

Like Bottazzi and Da Rin (2002) andCoackley et al. (2007), we do not match VC backed IPOs to non-VC backed IPOs based on a set of characteristics.

See Schmidt (2003).

More precisely, we are comparing VC firms with French or Anglo-Saxon banks which pay less attention to monitoring companies than do German or Japan banks.

There were no IPO between 2002 and[START_REF] Jenkins | Survival Analysis[END_REF]

This variable should be interpreted with caution because some of the firms in our sample were not able to protect their innovations by patents at the time of IPO. In contrast to the US system, the EU and especially the French legal system does not allow firms (easily) to obtain patents on software.

[START_REF] Bottazzi | Venture Capital in Europe and the Financing of Innovative Companies[END_REF] collected data on R&D expenditure for a set of five European new markets. They explain that their sample of firms was reduced by almost half due to the lack of data on R&D expenditure.

We use the methodology in[START_REF] Fontana | Product Innovation and Survival in a High Tech Industry[END_REF].

These explanations are based on[START_REF] Jenkins | Survival Analysis[END_REF].

For more details on CRM, see[START_REF] Jenkins | Survival Analysis[END_REF].

This graphical analysis is confirmed by the Wilcoxon test. The Wilcoxon test indicates a significant difference in survival functions among the two categories of firms (Pr>Chi2= 0.0816).

See life-table estimates of survival rates presented in appendix Table5.

These results are robust to alternative specifications of the hazard rate function (Table 6 in appendix) and to the inclusion of firms' unobserved heterogeneity (Table 7 in appendix).

According to[START_REF] Fontana | Product Innovation and Survival in a High Tech Industry[END_REF] methodology, we compute the predicted probability using the median values of the continuous variables (time, age, growth rate, size, intangibles/assets) and the mean values of the dichotomous variables (venture capital, IPO year, sector dummy variables). All the marginal effects are computed as discrete change, holding all the other independent variables constant at their mean or median values.

Note that the sum of the predicted probabilities equals unity.

Note that we can compute the marginal effects of all the significant variables for the first discrete time duration model using a Weibull hazard function.

If we compute the marginal effects for the complementary log-log model, we find that being financed by one or more venture capital firms multiplies the hazard rate of leaving the market by a factor of 1.8.