Yuxing Tang

Josiah Wang

Boyang Gao
email: boyang.gao@iit.it

Emmanuel Dellandréa

Robert Gaizauskas
email: r.gaizauskas@sheffield.ac.uk

Liming Chen
email: liming.chen@ec-lyon.frj.k.wang

Large Scale Semi-supervised Object Detection using Visual and Semantic Knowledge Transfer

Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both imagelevel and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting.

Introduction

Object detection/localization in images is one of the most widely studied problems in computer vision. Most object detectors adopt strong supervision in learning appear-ance models of object categories, that is by using training images annotated with bounding boxes encompassing the objects of interest, along with their category labels. The recent success of deep convolutional neural networks (CNN) [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] for object detection, such as DetectorNet [START_REF] Szegedy | Deep neural networks for object detection[END_REF], Over-Feat [START_REF] Sermanet | Overfeat: Integrated recognition, localization and detection using convolutional networks[END_REF], R-CNN [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF], SPP-net [START_REF] He | Spatial pyramid pooling in deep convolutional networks for visual recognition[END_REF], Fast R-CNN [11] and Faster R-CNN [START_REF] Cheng | Bing: Binarized normed gradients for objectness estimation at 300fps[END_REF], is heavily dependent on a large amount of training data manually labeled with object localizations (e.g. PASCAL VOC [8], ILSVRC (subset of Ima-geNet) [START_REF] Russakovsky | ImageNet Large Scale Visual Recognition Challenge[END_REF], and Microsoft COCO [START_REF] Lin | Microsoft COCO: common objects in context[END_REF]).

Although localized object annotations are extremely valuable, the process of manually annotating object bounding boxes is extremely laborious and unreliable, especially for large-scale databases. On the other hand, it is usually much easier to obtain annotations at image level (e.g. from user-generated tags on Flickr or Web queries). For example, ILSVRC contains image-level annotations for 1,000 categories, while object-level annotations are currently restricted to only 200 categories. One could apply imagelevel classifiers directly to detect object categories, but this will result in a poor performance as there are differences in the statistical distribution between the training data (whole images) and the test data (localized object instances). Previous work by Hoffman et al. [START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF] addresses this issue, by learning a transformation between classifiers and detectors of object categories with both image-level and object-level annotations ("strong" categories), and applying the transformation to adapt image-level classifiers to object detectors for categories with only image-level labels ("weak" categories). Part of this work involves transferring categoryspecific classifier and detector differences of visually similar "strong" categories equally to a classifier of a "weak" category to form a detector for that category. We argue that more can potentially be exploited from such similarities in an informed manner to improve detection beyond using the measures solely for nearest neighbor selection.

Our main contribution in this paper is therefore to incor- porate external knowledge about object similarities from visual and semantic domains in modeling the aforementioned category-specific differences, and subsequently transferring this knowledge for adapting an image classifier to an object detector for a "weak" category. Our proposed method is motivated by the following observations: (i) category specific difference exists between a classifier and a detector [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF][START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF]; (ii) visually and semantically similar categories may exhibit more common transferable properties than visually or semantically dissimilar categories; (iii) visual similarity and semantic relatedness are shown to be correlated, especially when measured against object instances cropped out from images (thus discarding background clutter) [6].

Intuitively, we would prefer to adapt a cat classifier to a cat detector by using the category-specific differences between the classifier and the detector of a dog rather than of a violin or a strawberry (Figure 1). The main advantage of our proposed method is that knowledge about object similarities can be obtained without requiring further object-level annotations, for example from existing image databases, text corpora and external knowledge bases.

Our work aims to answer the question: can knowledge about visual and semantic similarities of object categories (and the combination of both) help improve the performance of detectors trained in a weakly supervised setting (i.e. by converting an image classifier into an object detector for categories with only image-level annotations)? Our claim is that by exploiting knowledge about objects that are visually and semantically similar, we can better model the category-specific differences between an image classifier and an object detector and hence improve detection performance, without requiring bounding box annotations. We also hypothesize that the combination of both visual and semantic similarities can help further improve the detector performance. Experimental results on the challenging ILSVRC2013 dataset [START_REF] Russakovsky | ImageNet Large Scale Visual Recognition Challenge[END_REF] validate these claims, showing the effectiveness of our approach of transferring knowledge about object similarities from both visual and semantic domains to adapt image classifiers into object detectors in a semi-supervised manner.

Related Work

With the remarkable success of deep CNN on large-scale object recognition [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] in recent years, a substantial number of CNN-based object detection frameworks have emerged [11,[START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF][START_REF] He | Spatial pyramid pooling in deep convolutional networks for visual recognition[END_REF][START_REF] Cheng | Bing: Binarized normed gradients for objectness estimation at 300fps[END_REF][START_REF] Sermanet | Overfeat: Integrated recognition, localization and detection using convolutional networks[END_REF][START_REF] Szegedy | Deep neural networks for object detection[END_REF]. However, these object detectors are trained in a fully supervised manner, where bounding box annotations are necessary during training.

Recently, there have been several studies in CNN-based object detection in a weakly-supervised setting [3, 4, 34, 37], i.e. using training images with only image-level labels and no bounding boxes. The common practice is to jointly learn an appearance model together with the latent object location from such weak annotations. Such approaches only adopt CNN as a feature extractor, and exhaustively mine image regions extracted by region proposal approaches, such as Selective Search [START_REF] Uijlings | Selective search for object recognition[END_REF], BING [5], and EdgeBoxes [START_REF] Zitnick | Edge boxes: Locating object proposals from edges[END_REF]. Oquab et al. [START_REF] Oquab | Is object localization for free? -weakly-supervised learning with convolutional neural networks[END_REF] develop a weakly supervised CNN end-to-end learning pipeline that learns from complex cluttered scenes containing multiple objects by explicitly searching over possible object locations and scales in the image, which can predict image labels and coarse locations (but not exact bounding boxes) of objects. Hoffman et al. [START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF] propose a Large Scale Detection through Adaptation (LSDA) algorithm that learns the difference between the CNN parameters of the image classifier and object detector of a "fully labeled" category, and transfers this knowledge to CNN classifiers for categories without bounding box annotated data, turning them into detectors. For LSDA, auxiliary object-level annotations for a subset of the categories are required for training "strong" detectors. This can be considered a semi-supervised learning problem (see Section 3). We improve upon LSDA, by incorporating knowledge about visual and semantic similarities of object categories during the transfer from a classifier to a detector.

Another line of related work is to exploit knowledge transfer from various domains. Transfer learning (TL) [START_REF] Shao | Transfer learning for visual categorization: A survey[END_REF] aims to transfer knowledge across different domains or tasks. Two general categories of TL have been proposed in previous work: homogeneous TL [START_REF] Donahue | Semi-supervised domain adaptation with instance constraints[END_REF][START_REF] Oquab | Learning and transferring mid-level image representations using convolutional neural networks[END_REF][START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF] in a single domain but with different data distributions in training and testing sets, and heterogeneous TL [26,33,[START_REF] Zhu | Heterogeneous transfer learning for image classification[END_REF] across different domains or modalities. LSDA treats the transfer from classifiers to detectors as a homogeneous TL problem as the data distributions for image classification (whole image features) and object detection (image region features) are different. The adaptation from a classifier to a detector is however restricted to the visual domain. Rochan et al. [26] propose an appearance transfer method by transferring semantic knowledge (heterogeneous TL) from familiar objects to help localize novel objects in images and videos. Our work integrates knowledge transfer via both visual similarity (homogeneous TL) and semantic relatedness (heterogeneous TL) to help convert classifiers into detectors. Recently, Shu et al. [33] propose a weakly-shared Deep Transfer Network (DTN) that hierarchically learns to transfer semantic knowledge from web texts to images for image classification, building upon Stacked Auto-Encoders [2]. DTN takes auxiliary text annotations (user tags and comments) and image pairs as input, while our semantic transfer method only needs image-level labels.

Task Definition

In our semi-supervised learning case, we assume that we have a set of "fully labeled" categories and "weakly labeled" categories. For the "fully labeled" categories, a large number of training images with both image-level labels and bounding box annotations are available for learning the object detectors. For each of the "weakly labeled" categories, we have many training images containing the target object, but we do not have access to the exact locations of the objects. This is different from the semi-supervised learning proposed in previous work [START_REF] Misra | Watch and learn: Semi-supervised learning of object detectors from videos[END_REF][START_REF] Rosenberg | Semisupervised self-training of object detection models[END_REF][START_REF] Everingham | The Pascal visual object classes (VOC) challenge[END_REF], where typically a small amount of fully labeled data with a large amount of weakly labeled (or unlabeled) data are provided for each category. In our semi-supervised object detection scenario, the objective is to transfer the trained image classifiers into object detectors on the "weakly labeled" categories.

Similarity-based Knowledge Transfer

We first describe the Large Scale Detection through Adaptation (LSDA) framework [START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF], upon which our proposed approach is based (Section 4.1). We then describe our proposed knowledge transfer models with the aim of improving LSDA. Two knowledge domains are explored: (i) visual similarity (Section 4.2); (ii) semantic relatedness (Section 4.3). Finally, we combine both models to obtain our mixture transfer model, as presented in Section 4.4.

Background on LSDA

Let D be the dataset of K categories to be detected. One has access to both image-level and bounding box annotations only for a set of m (m K) "fully labeled" categories, denoted as B, but only image-level annotations for the rest of the categories, namely "weakly labeled" categories, denoted as A. Hence, a set of K image classifiers can be trained on the whole dataset D (D = A ∪ B), but only m object detectors (from B) can be learned according to the availability of bounding box annotations. The LSDA algorithm learns to convert (K -m) image classifiers (from A) into their corresponding object detectors through the following steps: Pre-training: First, an 8-layer (5 convolutional layers and 3 fully-connected (f c) layers) Alex-Net [START_REF] Krizhevsky | Imagenet classification with deep convolutional neural networks[END_REF] CNN is pre-trained on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 classification dataset [START_REF] Russakovsky | ImageNet Large Scale Visual Recognition Challenge[END_REF], which contains 1.2 million images of 1,000 categories. Fine-tuning for classification: The final weight layer (1,000 linear classifiers) of the pre-trained CNN is then replaced with K linear classifiers. This weight layer is randomly initialized and the whole CNN is then fine-tuned on the dataset D. This produces a classification network that can classify K categories (i.e., K-way softmax classifier), given an image or an image region as input. Category-invariant adaptation: Next, the classification network is fine-tuned into a detector with bounding boxes of B as input, using the R-CNN [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF] framework. As in R-CNN, a background class (f c8 BG ) is added to the output layer and fine-tuned using bounding boxes from a region proposal algorithm, e.g. Selective Search [START_REF] Uijlings | Selective search for object recognition[END_REF]. The f c8 layer parameters are category specific, with 4,097 weights (f c7 output: 4,096, plus a bias term) in each category, while the parameters of layers 1-7 are category invariant. Note that object detectors are not able to be directly trained on A, since the fine-tuning and training process requires bounding box annotations. Therefore, at this point, the category specific output layer f c8 A stays unchanged. The variation matrix of f c8 B after fine-tuning is denoted as ∆ B . Category-specific adaptation: Finally, each classifier of categories j ∈ A is adapted into a corresponding detector by learning a category-specific transformation of the model parameters. This is based on the assumption that the difference between classification and detection of a target object category has a positive correlation with those of similar (close) categories. The transformation is computed by adding a bias vector to the weights of f c8 A . This bias vector for category j is measured by the average weight change of its k nearest neighbor categories in set B, from classification to detection.

∀j ∈ A : -→ w d j = -→ w c j + 1 k k i=1 ∆ B j i ( 1
)
where ∆ B j i is the f c8 weight variation of the i th nearest neighbor category in set B for category j ∈ A.

-→ w c and -→ w d are, respectively, f c8 layer weights for the fine-tuned classification and the adapted detection network. The nearest neighbor categories are defined as those with nearest L 2norm (Euclidean distance) of f c8 weights in set B.

The fully adapted network is able to detect all K categories in test images. In contrast to R-CNN, which trains SVM classifiers on the output of the f c7 layer followed by bounding box regression on the extracted features from the pool5 layer of all region proposals, LSDA directly outputs the score of the softmax "detector", and subtracts the background score from this as the final score. This results in a small drop in performance, but enables direct adaptation from a classification network into a detection network on the "weakly labeled" categories, and significantly reduces the training time.

Hoffman et al. [START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF] demonstrated that the adapted model yielded a 50% relative mAP (mean average precision) boost for detection over the classification-only framework on the "weakly labeled" categories of the ILSVRC2013 detection dataset (from 10.31% to 16.15%). They also showed that category-specific adaptation (final LSDA step) contributes least to the performance improvement (16.15% with vs. 15.85% without this step), with the other features (adapted layers 1-7 and background class) being more important. However, we found that by properly adapting this layer, a significant boost in performance can be achieved: an mAP of 22.03% can be obtained by replacing the semi-supervised f c8 A weights with their corresponding supervised network weights and leaving the other parameters fixed. Thus, we believe that adapting this layer in an informed manner, such as making better use of knowledge about object similarities, will help improve detection.

In the next subsections, we will introduce our knowledge transfer methods using two different kinds of similarity measurements to select the nearest categories and weight them accordingly to better adapt the f c8 layer, which can efficiently convert an image classifier into an object detector for a "weakly labeled" category.

Knowledge transfer via visual similarity

Intuitively, the object detector of an object category may be more similar to those of visually similar categories than of visually distinct categories. For example, a cat detector may approximate a dog detector better than a strawberry detector, since cat and dog are both mammals sharing common attributes in terms of shape (both have four legs, two ears, two eyes, one tail) and texture (both have fur). Therefore, given a "fully labeled" dataset B and a "weakly labeled" dataset A, our objective is to model the visual similarity between each category j ∈ A and all the other categories in B, and to transfer this knowledge for transforming classifiers into detectors for A. Visual similarity measure: Visual similarity measurements are often obtained by computing the distance between feature distributions such as the f c6 or f c7 layer of a CNN, or in the case of LSDA the f c8 layer parameters. In our work, we instead forward propagate an image through the whole fine-tuned classification network (created by the second step in Section 4.1) to obtain a K-dimensional classification score vector. This score vector encodes the probabilities of an image being each of the K object categories. Consequently, for all the positive images of an object category j ∈ A, we can directly accumulate the scores of each dimension, on a balanced validation dataset. We assume that the normalized accumulated scores (range [0,1]) imply the similarities between category j and other categories: the larger the score, the more it visually resembles category j. This assumption is supported by the analysis of deep CNNs [START_REF] Agrawal | Analyzing the performance of multilayer neural networks for object recognition[END_REF][START_REF] Jia | Caffe: Convolutional architecture for fast feature embedding[END_REF][START_REF] Zeiler | Visualizing and understanding convolutional networks[END_REF]: CNNs are apt to confuse visually similar categories, on which they might have higher prediction scores. The visual similarity (denoted s v ) between a "weakly labeled" category j ∈ A and a "fully labeled" category i ∈ B is defined as:

s v (j, i) ∝ 1 N N n=1 CNN(I n ) i (2)
where I n is a positive image from category j of the validation set of A, N is the number of positive images for this category, and CNN(I n ) i is the i th CNN output of the softmax layer on I n , namely, the probability of I n being category i ∈ B as predicted by the fine-tuned classification network. s v (j, i) ∈ [0, 1] is the degree of similarity after normalization on all the categories in B.

Note that we adopt the f c8 outputs since most of the computation is integrated into the end-to-end Alex-Net framework except for the accumulation of classification scores in the end, saving the extra effort otherwise required for distance computation if f c6 or f c7 were to be used (two methods produce similar range of results). Weighted nearest neighbor scheme: Using Eq. ( 1), we can transfer the model parameters based on a category's k nearest neighbor categories selected by Eq. (2). This allows us to directly compare our visual similarity measure to that of LSDA which uses the Euclidean distance between the f c8 parameters. An alternative to Eq. ( 1) is to consider a weighted nearest neighbor scheme, where weights can be assigned to different categories based on how visually similar they are to the target object category. This is intuitive, as different categories will have varied degrees of similarity to a particular class, and some categories may have only a few (or many) visually similar classes. Thus, we modify Eq. ( 1) and define the transformation via visual similarity based on the proposed weighted nearest neighbor scheme as:

∀j ∈ A : -→ w d j v = -→ w c j + m i=1 s v (j, i)∆ B j i (3)
It is worth noting that Eq. ( 1) is a special case of Eq. ( 3), where m = k and s v (j, i) = 1/k.

Knowledge transfer via semantic relatedness

Following prior work [6, 26, 27], we observe that visual similarity is correlated with semantic relatedness. According to [6], this relationship is particularly strong when measurements are focused on the category instances themselves, ignoring image backgrounds. This observation is quite intriguing for object detection, where the main focus is on the target objects themselves. Hence, we draw on this fact and propose transferring knowledge from the natural language domain to help improve semi-supervised object detection. Semantic similarity measure: Semantic similarity is a well-explored area within the Natural Language Processing community. Recent advances in word embeddings trained on large-scale text corpora [19,24] have helped progress research in this area, as it has been observed that semantically related word vectors tend to be close in the embedding space, and that the embeddings capture various linguistic regularities [START_REF] Mikolov | Linguistic regularities in continuous space word representations[END_REF]. Thus, we encode each of the K categories as a word vector, more specifically a 300dimensional word2vec embedding [19]. As each category is a WordNet [9] synset, we represent each category as the sum of the word vectors for each term in its synset, normalized to unit vector by its L 2 -norm. Out-of-vocabulary words are addressed by attempting to match case variants of the words (lowercase, Capitalized), e.g. "aeroplane" is not in the vocabulary, but "Aeroplane" is. Failing that, we represent multiword phrases by the sum of the word vectors of each in-vocabulary word of the phrase, normalized to unit vector ("baby"+"bed" for baby bed). In several cases, we also augment synset terms with any category label defined in ILSVRC2013 that is not among the synset terms defined in WordNet (e.g. "bookshelf" for the WordNet synset bookcase, and "tv" and "monitor" for display).

Word embeddings often conflate multiple senses of a word into a single vector, leading to an issue with poly-semous words. We observed this with many categories, for example seal (animal) is close to nail and tie (which, to further complicate matters, is actually meant to refer to its clothing sense); or the stationery ruler being related to lion. Since ILSVRC2013 categories are actually WordNet synsets, it makes perfect sense to exploit WordNet to help disambiguate the word senses. Thus, we integrate corpusbased representations with semantic knowledge from Word-Net, by using AutoExtend [START_REF] Rothe | Autoextend: Extending word embeddings to embeddings for synsets and lexemes[END_REF] to encode the categories as synset embeddings in the original word2vec embedding space. AutoExtend exploits the interrelations between synsets, words and lexemes to learn an auto-encoder based on these constraints, as well as constraints on WordNet relations such as hypernyms (encouraging poodle and dog to have similar embeddings). We observed that AutoExtend has indeed helped form better semantic relations between the desired categories: seal is now clustered with other animal categories like whale and turtle, and the nearest neighbors for ruler are now rubber eraser, power drill and pencil box. In our detection experiments (Section 5), we found that while the 'naive' word embeddings performed better than the baselines, the synset embeddings yielded even better results. Thus, we only report the results for the latter.

We represent each category j ∈ A and i ∈ B with their synset embeddings, and compute the L 2 -norm of each pair d s (j, i) as their semantic distance. The semantic similarity s s (j, i) is inversely proportional to d s (j, i). We can then transfer the semantic knowledge to the appearance model using Eq. (3) or its special case Eq. (1) as before.

As our semantic representations are in the form of vectors, we explore an alternative similarity measure as used in [26]. We assume that each vector of a "weakly labeled" category j ∈ A (denoted as v j ) can be approximately represented by a linear combination of all the m word vectors in B: v j ≈ Γ j V , where V = [v 1 ; v 2 ; . . . ; v i ; . . . ; v m ], and Γ j = [γ 1 j , γ 2 j , . . . , γ i j , . . . , γ m j ] is a set of coefficients of the linear combination. We are motivated to find the solution Γ j which contains as few non-zero components as possible, since we tend to reconstruct category j with fewer categories from B (sparse representation). This optimal solution Γ j can be formulated as the following optimization:

Γ j = arg min Γj >0 ( v j -Γ j V 2 + λ Γ j 0 ) (4)
Note that Γ j > 0 is a positive constraint on the coefficients, since negative components of sparse solutions for semantic transferring are meaningless: we only care about the most similar categories and not dissimilar categories. We solve Eq. (4) by using the positive constraint matching pursuit (PCMP) algorithm [START_REF] Gao | Music sparse decomposition onto a midi dictionary of musical words and its application to music mood classification[END_REF]. Therefore, the final transformation via semantic transferring is formulated as:

∀j ∈ A : -→ w d j s = -→ w c j + m i=1 s s (j, i)∆ B j i ( 5
)
where s s (j, i) = γ i j in the sparse representation case.

Mixture transfer model

We have proposed two different knowledge transfer models. Each of them can be integrated into the LSDA framework independently. In addition, since we consider the visual similarity at the whole image level and the semantic relatedness at object level, they can be combined simultaneously to provide complementary information. We use a simple but very effective combination of the two knowledge transfer models as our final mixture transfer model. Our mixture model is a linear combination of the visual similarity and the semantic similarity:

s = intersect[αs v + (1 -α)s s ] (6)
where intersect[•] is a function that takes the intersection of cooccurring categories between visual and sparse semantic related categories. α ∈ [0, 1] is a parameter used to control the relative influence of the two similarity measurements. α is set to 1 when only considering visual similarity transfer, and 0 for the semantic similarity transfer. We will analyze this parameter in Section 5.3.

Experiments

Dataset overview

We investigate the proposed knowledge transfer models for large scale semi-supervised object detection on the ILSVRC2013 detection dataset covering 200 object categories. The training set is not exhaustively annotated because of its sheer size. There are also fewer annotated objects per training image than the validation and testing image (on average 1.53 objects for training vs. 2.5 objects for validation set). We follow all the experiment settings as in [START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF], and simulate having access to image-level annotations for all 200 categories and bounding box annotations only for the first 100 categories (alphabetical order). We separate the dataset into classification and detection sets. For the classification data, we use 200,000 images in total from all 200 categories of the training subset (around 1,000 images per category) and their image-level labels. The validation set is roughly split in half: val1 and val2 as in [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF]. For the detection training set, we take the images with their bounding boxes from only the first 100 categories (B) in val1 (around 5,000 images in total). Since the validation dataset is relatively small, we then augment val1 with 1,000 bounding box annotated images per class from the training set (following the same protocol of [START_REF] Girshick | Rich feature hierarchies for accurate object detection and semantic segmentation[END_REF][START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF]). Finally, we evaluate our knowledge transfer framework on the val2 dataset (9,917 images in total).

Implementation details

In all the experiments, we consider LSDA [START_REF] Hoffman | LSDA: Large scale detection through adaptation[END_REF] as our baseline model and follow their main settings. For the semantic representation, we use word2vec CBoW embeddings pre-trained on part of the Google News dataset comprising about 100 billion words [19]. We train AutoExtend [START_REF] Rothe | Autoextend: Extending word embeddings to embeddings for synsets and lexemes[END_REF] using WordNet 3.0 to obtain synset embeddings, and using equal weights for the synset, lexeme and Word-Net relation constraints (α = β = 0.33). As all categories are nouns, we use only hypernyms as the WordNet relation constraint. For the sparse representation of a target word vector in Eq. (4), we limit the maximum number of nonzero components to 20, since a target category has strong correlation with a small number of source categories.

Quantitative evaluation on the "weakly labeled" categories

Setting LSDA as the baseline, we compare the detection performance of our proposed knowledge transfer methods against LSDA. The results are summarized in Table 1. As we are concerned with the detection of the "weakly labeled" categories, we focus mainly on the second column of the table (mean average precision (mAP) on A). Rows 1-5 in Table 1 are the baseline results for LSDA. The first row shows the detection results by applying a classification network (i.e., weakly supervised learning, and without adaptation) trained with only classification data, achieving only an mAP of 10.31% on the "weakly labeled" 100 categories. The last row shows the results of an oracle detection network which assumes that bounding boxes for all 200 categories are available (i.e., supervised learning). This is treated as the upper bound (26.25%) of the fully supervised framework. We observed that the best result obtained by LSDA is to adapt both category independent and category specific layers, and transforming with the weighted f c8 layer weight change of its 100 nearest neighbor categories (weighted-100 with 16.33% in Table 1). Our "weighted" scheme works steadily better than its "average" counterpart.

For our visual knowledge transfer model, we show steady improvement over the baseline LSDA methods when considering the average weight change of both 5 and 10 visually similar categories, with 1.45% and 1.47% increase in mAP, respectively. This proves that our proposed visual similarity measure is superior to that of LSDA, showing that category-specific adaptation can indeed be improved based on knowledge about the visual similarities between categories. Further improvement is achieved by modeling individual weights of all 100 source categories according to their degree of visual similarities to the target category (weighted-100 with 19.02% in the table). This verifies our supposition that the transformation from a classifier to a detector of a certain category is more related to visually similar categories, and is proportional to their degrees of sim- 2. Some example visualizations of (a) visual similarity (first row in the figure), (b) semantic similarity (middle row) and (c) mixture similarity (last row) between a target "weakly labeled" category and its source categories from which to transfer knowledge. For each target category, the top-10 weighted nearest neighbor categories are shown. The magnitude of each column bar shows the relative weight (degree of similarity sv, ss, s in Eq. ( 6), where α is set to 0.6). ilarity. For example, motorcycle is most similar to bicycle. Thus the weight change from a bicycle classifier to detector has the largest influence on the transformation of motorcycle. The influence of less visually relevant categories, such as cart and chain saw, is much smaller. For visually dissimilar categories (apple, fig, hotdog, etc.), the influence is extremely insignificant. We show some examples of visual similarities between a target category and its source categories in the first row of Figure 2. For each target category, the top-10 weighted nearest neighbor categories with their similarity degrees are visualized.

Our semantic knowledge transfer model also showed marked improvement over the LSDA baseline (Table 1, Rows 9-12), and is comparable to the results of the visual transfer model. This suggests that the cross-domain knowledge transfer from semantic relatedness to visual similarity is very effective. The best performance for the semantic transfer model (19.04%) is obtained by sparsely reconstructing the target category with the source categories using the synset embeddings. The results of using synset embeddings (18.32%, using weighted-100, the same below) are superior to using 'naive' word2vec embeddings The state-of-the-art result for semi-supervised detection on this dataset is achieved by our mixture transfer model which combines visual similarity and semantic relatedness. A boost in performance of 3.88% on original split (3.82%±0.12%, based on 6 different splits of the dataset) is achieved over the best result reported by LSDA on the "weakly labeled" categories. We show examples of transferred categories with their corresponding weights for several target categories in the bottom row of Figure 2. The parameter α in Eq. ( 6) for the mixture model weights is set to 0.6 for final detection, where α ∈ {0, 0.2, 0.4, 0.5, 0.6, 0.8, 1} is chosen via cross-validation on the val1 detection set (Figure 5). This suggests that the transferring of visual similarities is slightly more important than semantic relatedness, although both are indeed complementary. Figures 3 and4 show some examples of correct and incorrect detections respectively. Although our proposed mixture transfer model achieves the state-of-theart in detecting the "weakly labeled" categories, it is still occasionally confused by visually similar categories.

Conclusion

In this paper, we investigated how knowledge about object similarities from both visual and semantic domains can be transferred to adapt an image classifier to an object detector in a semi-supervised setting. We found clear evidence that both visual and semantic similarities play an essential role in improving the adaptation process, and that the combination of the two modalities yielded state-of-the-art performance, suggesting that knowledge inherent in visual and semantic domains is complementary. Future work includes extracting more knowledge from different domains, using better representations, and investigating the possibility of using category-invariant properties, e.g. the difference between feature distributions of whole images and target objects, to help knowledge transfer. We believe that the combination of knowledge from different domains is key to improving semi-supervised object detection.

Figure 1 .

 1 Figure1. An illustration of our similarity-based knowledge transfer model. The question we investigate is whether knowledge about object similarities -visual and semantic -can be exploited to improve detectors trained in a semi-supervised manner. More specifically, to adapt the image-level classifier (up-left) of a "weakly labeled" category (no bounding boxes) into a detector (up-right), we transfer information about the classifier and detector differences of "strong" categories (with image-level and bounding box annotations, bottom of the figure) by favoring categories that are more similar to the target category (e.g. transfer information from dog and tiger rather than basketball or bookshelf to produce a cat detector).

Figure 3 .

 3 Figure 3. Examples of correct detections (true positives) of our mixture knowledge transfer model on ILSVRC2013 images. For each image, only detections for the "weakly labeled" target category (text below image) are listed.

Figure 4 .

 4 Figure 4. Examples of incorrect detections (confusion with other objects) of our mixture knowledge transfer model on ILSVRC2013 images. The detected object label is shown in the top-left of its bounding box.

( 17 .

 17 83%) and WordNet based measures such as path-based similarity (17.08%) and Lin similarity [17] (17.31%). Several examples visualizing the related categories of the 10 largest semantic reconstruction coefficients are shown in the middle row of Figure 2. We observe that semantic relatedness indeed correlates with visual similarity.

Figure 5 .

 5 Figure 5. Sensitivity of parameter α vs. mAP.

Table 1 .

 1 Detection mean average precision (mAP) on ILSVRC2013 val2. The first row shows the basic performance of directly using all classification parameters for detection, without adaptation or knowledge transfer (i.e., weakly supervised learning). The last row shows results of an oracle detection network which assumes that bounding boxes for all 200 categories are available (i.e., supervised learning). The second row shows the baseline LSDA results using only feature adaptation. Rows 3-5 show the performance of LSDA for adapting both the feature layers (layer 1-7) and the class-specific layer (layer 8), by considering different numbers of neighbor categories. Rows 6-8, 9-12 and row 13 show the results of our visual transfer, semantic transfer and mixture transfer model, respectively.

	Method			Number of Nearest Neighbors	mAP on B: "Fully labeled" 100 Categories	mAP on A: "Weakly labeled" 100 Categories	mAP on D: All 200 Categories
	Classification Network		-		12.63	10.31	11.90
	LSDA (only class invariant adaptation)	-		27.81	15.85	21.83
				avg/weighted -5	28.12 / -	15.97 / 16.12	22.05 / 22.12
	LSDA (class invariant & specific adapt)	avg/weighted -10	27.95 / -	16.15 / 16.28	22.05 / 22.12
				avg/weighted -100	27.91 / -	15.96 / 16.33	21.94 / 22.12
				avg/weighted -5	27.99 / -	17.42 / 17.59	22.71 / 22.79
	Ours (visual transfer)		avg/weighted -10	27.89 / -	17.62 / 18.41	22.76 / 23.15
				avg/weighted -100	28.30 / -	17.38 / 19.02	22.84 / 23.66
				avg/weighted -5	28.01 / -	17.32 / 17.53	22.67 / 22.77
	Ours (semantic transfer)		avg/weighted -10	28.00 / -	16.67 / 17.50	22.31 / 22.75
				avg/weighted -100	28.14 / -	17.04 / 18.32	23.23 / 23.28
				Sparse rep. -≤20	28.18	19.04	23.66
	Ours (mixture transfer model)		-		28.04	20.03 ↑3.88	24.04
	Oracle: Full Detection Network		-		29.72	26.25	28.00
	(a)	lion	motorcycle	rugby ball	saxophone	wine bottle
	visual
	similarity
	(b)	lion	motorcycle	rugby ball	saxophone	wine bottle
	semantic
	similarity
		lion	motorcycle	rugby ball	saxophone	wine bottle
	(c)
	mixed
	combination

* This work was supported by the EU CHIST-ERA D2K Visual Sense (Visen) project (ANR-12-CHRI-0002-04 and EPSRC EP/K019082/1).


A Ventura
email: andrea.ventura@le.infn.it

S Armstrong

A Assamagan

J T M Baines

C P Bee

M Bellomo

M Biglietti

J A Bogaerts

V Boisvert

M Bosman

⋆ S Armstrong

K A Assamagan

G Carlino

B Caron

P Casado

G Cataldi

D Cavalli

M Cervetto

G Comune

P Conde Muino

F Conventi

A De Santo

J M De Seixas

M Diaz Gomez

A Di Mattia

A Dos Anjos

M Dosil

N Ellis

D Emeliyanov

B Epp

S Falciano

A Farilla

S George

V Ghete

S González

M Grothe

S Kabana

A Khomich

G Kilvington

N Konstantinidis

A Kootz

A Lowe

L Luminari

T Maeno

J Masik

C Meessen

A G Mello

G Merino

R Moore

P Morettini

A Negri

N Nikitin

A Nisati

C Padilla

N Panikashvili

F Parodi

V Perez Reale

J L Pinfold

P Pinto

M Primavera

Z Qian

S Resconi

S Rosati

C Sanchez

C Santamarina

D A Scannicchio

C Schiavi

E Segura

S Sivoklokov

R Soluk

E Stefanidis

S Sushkov

M Sutton

S Tapprogge Aa

E Thomas

F Touchard

B Venda Pinto

V Vercesi

P Werner

S Wheeler

F J Wickens

W Wiedenmann

M Wielers Dd

G Zobernig

Implementation and Performance of the Event Filter Muon Selection for the ATLAS experiment at LHC A. Ventura on behalf of the ATLAS TDAQ group ⋆

published or not. The documents may come

Implementation and performance of the event filter muon selection for the ATLAS experiment at LHC

the requirements and in the interfaces between the two HLT stages, they have a coherent approach to event selection. Therefore, the design of a common core software framework has been implemented in order to allow the HLT architecture to be flexible to changes (background conditions, luminosity, description of the detector, etc.).

Algorithms working in the Event Filter are designed to work not only in a general purpose or exclusive mode, but they have been implemented in such a way to process given trigger hypotheses produced at a previous stage in the HLT dataflow. This is done by acting in separate steps, so that decisions to go further in the process are taken at every new step. An overview of the HLT processing steps is given and the working principles of the EF offline algorithms for muon reconstruction and identification (MOORE and MuId) are discussed in deeper detail. The reconstruction performances of these algorithms in terms of efficiency, momentum resolution, rejection power and execution times on several samples of simulated single muon events are presented, also taking into account the high background environment that is expected for ATLAS. Index Terms-ATLAS, HLT, Muons, Event Filter.

I. INTRODUCTION

A TLAS is a general-purpose high-energy physics experi- ment to investigate proton-proton collisions at a centerof-mass energy of 14 T eV , currently under construction at the Large Hadron Collider (LHC) facility of the European Organization for Nuclear Research (CERN) in Geneva, Switzerland. The ATLAS detector [START_REF] Zobernig | ATLAS Detector and Physics Performance Technical Design Report[END_REF] has been designed to study the wide number of physics processes at the LHC, including searches for unobserved phenomena like the Higgs boson and new particles predicted by super-symmetric models.

In the LHC program an initial luminosity of 2 × 10 33 cm -2 s -1 will be delivered to ATLAS and then a full design luminosity of 10 34 cm -2 s -1 will be reached, with an average of 23 collisions per bunch crossing. Owing to the high number of final state particles at a proton-proton collider, ATLAS has required highly granular and large scale detector systems, involving a total number of electronic channels of the order of 10 8 .

The extremely high bunch crossing rate at LHC (40 M Hz) and the very high radiation environment in which all the detectors and their electronics have to work, demand unprecedented performances for the ATLAS Trigger and Data Acquisition (TDAQ) systems.

The main challenge of the ATLAS Trigger is to exploit the full physics potential of the experiment in spite of a limited storage capability. In particular, the task of the TDAQ system is to reduce the huge volume of data produced by the ATLAS detector itself (corresponding to an average event size of about 1.6 MByte) down to a manageable amount.

In the LVL1 stage [START_REF]ATLAS Level-1 Trigger: Technical Design Report[END_REF], implemented in a custom hardware, the trigger will reduce the initial event rate to 75 kHz. At this level, coarse-granularity information from the calorimeter and muon spectrometer systems based on high p T signals has to be very quickly treated in order to achieve selection/rejection of events with a latency time not exceeding 2 µs.

Two software-based triggers follow: the Level-2 (LVL2) and the Event Filter (EF), that comprise the so-called ATLAS High Level Trigger (HLT) system [START_REF]ATLAS High-Level Triggers, DAQ and DCS Technical Proposal, CERN/LHCC 2000-17[END_REF]. Their task is to bring the input event rate given by the LVL1 to the data acquisition rate of ∼ 100 Hz. They are implemented on commodity processor nodes running a commercially available operating system. A diagram illustrating the three levels of the ATLAS trigger is shown in Fig. 1.

II. THE ATLAS HIGH LEVEL TRIGGER

Similarly to LVL1, the event selection in HLT is based on inclusive high-p T signals, with the aim not to introduce biases and to be open to possible signatures of new physics. Both stages of HLT use the same trigger selection framework and differ essentially in the amount of data they have to access for each event. Not strictly defined bounds between LVL2 and EF allow the HLT to have the best possible adaptability for working in different running conditions.

The Level-2 [START_REF] Mattia | Online Muon Reconstruction in the ATLAS Level-2 Trigger System[END_REF] works on a farm of processor nodes running software algorithms which have been specifically developed to take a decision on each event with an average latency of 10 ms. Geometrical information provided by the first level trigger can guide the access to the event data in terms of Regions of Interest (RoIs), i.e. parts of the detector where the interesting physics signals have been already found at the previous stage of the trigger chain. A quicker access to data can be achieved by circumscribing the reconstruction only to the RoIs: even if this seeding strategy is quite complex to achieve, the effective networking and computing power are drastically brought down to few per-cent of what would be needed for the full event reconstruction. From the incoming 75 kHz, the LVL2 can reduce the event rate to ∼ 1 kHz.

After an event passes the second level trigger, it is sent to the Event Filter, that refines the selection according to the LVL2 classification and performs a complete reconstruction of the full event with more detailed alignment and calibration data, based on the use of sophisticated offline algorithms. The rate is finally reduced to a rate of ∼ 100 Hz with a ∼ 1 s latency time. At the end of the selection, events are written to mass storage.

Besides operating in a general purpose mode, all algorithms in the Event Filter must be able to work in seeded mode, guided by hypotheses elaborated in the earlier trigger levels. Moreover, algorithms have to be organized with suitable modularity, in order to work properly as Event Filter in the HLT environment both for the final standard data acquisition and for test beam data.

III. MUON RECONSTRUCTION AND IDENTIFICATION

Reconstructing and identifying muons with high accuracy represents an essential task to take full advantage from the physics potential at LHC: events with muons in the final state can provide evidence of new physics or relevant signature for b-physics. A muon moving through the ATLAS detector leaves hits in the Inner Detector [START_REF]ATLAS Inner Detector Technical Design Report[END_REF] and in the Muon Spectrometer [START_REF]ATLAS Muon Spectrometer Technical Design Report[END_REF], as well as in the electromagnetic and hadronic calorimeters. Momenta are measured via magnetic deflection of muon tracks in a system of three large superconducting air-core toroid magnets, instrumented with trigger and high precision tracking chambers. The magnet field is mostly orthogonal to the muon trajectories, and the degradation of resolution due to multiple scattering is reduced to minimum. The Muon Spectrometer has the stand-alone capability to measure muon momenta with a resolution ∆p T /p T < 10% up to 1 T eV /c.

The best possible measurement of the muon momentum can be obtained by combining information from the Muon Spectrometer and the Inner Detector. The tails in the p T resolution distribution of the Muon Spectrometer are reduced and the charge determination for high energy muons is improved (thanks to the longer lever arm). The discrimination of muons from secondaries can be furthermore improved and muons from kaons or pions can be rejected by asking for tracks originated in the primary vertex. A better discrimination of muons in jets is possible, thus improving the efficiency of inner tracker pattern recognition, which is lower than for isolated muons. Track fragments in the inner chambers of the Muon Spectrometer can be associated with track segments in the Inner Detector: a higher efficiency can be therefore obtained in reconstructing low-energy muons not reaching middle/outer Muon Spectrometer chambers.

The offline packages "Muon Object Oriented REconstruction" (MOORE) and "MuonIdentification" (MuId) have been developed in the ATHENA [START_REF]Athena Developer Guide[END_REF] framework for the purposes of muon reconstruction and identification in the ATLAS Muon Spectrometer. The former performs track reconstruction in the Muon Spectrometer while the latter extrapolates the track to the vertex and combines Muon Spectrometer tracks with Inner Detector track segment. Their working principles are discussed in this paper, and their implementation in the ATLAS High Level Trigger framework at the Event Filter stage (TrigMOORE) is then presented. [START_REF] Shank | Track Reconstruction in the ATLAS Muon Spectrometer with MOORE[END_REF] is an offline package for track reconstruction in the full η range (barrel+endcaps) of the Muon Spectrometer. The description given here is limited to the barrel, since at present the trigger chain considers only this region. In the ATLAS offline environment, MOORE starts from collections of digits or clusters in the Muon Spectrometer volume to build fitted reconstructed tracks with parameters measured at the entrance of the Muon Spectrometer.

IV. MOORE MOORE

All the reconstruction proceeds in successive stages, each one performed by an algorithm module that creates partially or finally reconstructed objects by using objects produced by the previous algorithms. After being built, objects are temporarily stored and kept available for other modules. A rigorous separation is made between data and algorithms: algorithms have to know how data objects are structured before accessing to or creating them, but objects must be independent of algorithms. The stepped sequence used in the reconstruction allows to define which algorithm will produce an object at run-time.

MOORE begins its overall reconstruction process by looking for activity regions in two different projections of the Muon Spectrometer: firstly in the φ trigger hits from RPCs and subsequently in the r-z view considering the precision hits of MDTs.

Drift distances inside MDTs are computed starting from drift times by applying time-to-distance relations, taking into account the time of flight, the second coordinate, the Lorentz effect and the propagation along the wire. To reconstruct a track segment, the best tangential line to the drift circles is taken among the possible four. All MDT segments of the outer and middle stations are then combined by the pattern recognition procedure. After that, MDT hits in the segments are considered together with the φ information coming from RPCs, and track candidates are consequently obtained.

If successfully fitted, outer track candidates are kept for further processing and used to associate inner station MDT hits. When a collection of RPC hits and MDT hits provides a successful fit from at least two layers, a track is definitively built. The track fit procedure is based on the iPatRec [START_REF] Clifft | IPATREC: Inner Detector Pattern-Recognition and Track Fitting[END_REF] package, developed for the Inner Detector. Final refinements allocate scattering centers along each track, so allowing the track fit to take into account effects due to energy loss and Coulomb scattering. Hits with residuals above a given threshold are discarded: this can occur either because of faults in assigning digits to tracks during pattern recognition, or because of a poor local spatial resolution in case of badly measured drift distances. Once the fit procedure completes, the resulting track is accepted and its parameters are expressed at the first measured point inside the Muon Spectrometer.

V. MUID

To be properly used for physics studies, a track has to be extrapolated to its production point. This crucial task is achieved by means of another offline package, MuId [START_REF] Th | A Muon Identification and Combined Reconstruction Procedure for the ATLAS Detector at the LHC at CERN[END_REF], which has been designed to efficiently identify muon tracks by combining tracks in the Muon Spectrometer with the corresponding track found by iPatRec in the Inner Detector.

At a first step, MOORE tracks are extrapolated back to the vertex region, so that the kinematic parameters can be compared to those reconstructed in the Inner Detector. MuId accesses the previously reconstructed track and propagates it back through the magnetic field to obtain the track parameters and their associated covariance matrix corresponding to the point of closest approach to the beam intersection. Effects due to multiple scattering in the calorimeters are taken into account by a parametrization with a set of scattering planes. Moreover, muon energy loss is estimated both from the calorimeter measurements and from a function parametrized with muon's η and p T . Up to this stage, MOORE and MuId can be executed in sequence as a standalone package for the Muon reconstruction (MuId StandAlone mode).

Subsequently, Inner Detector and Muon Spectrometer tracks are matched together, and a five d.o.f. χ 2 is built with the parameter differences and summed covariances. A combined fit is performed at the vertex for all combinations above a given χ 2 probability. Hits found in the two subdetectors with separate standalone algorithms are then used to combine tracks (MuId Combined mode). In case of a satisfactory combined fit, all matches to the Inner Detector are finally kept as identified muons.

VI. RECONSTRUCTION PERFORMANCES

The reconstruction performances of the packages MOORE and MuId have been evaluated using single muons samples with fixed transverse momentum, in the range from 3 GeV /c to 1 T eV /c, produced for the Data Challenge 1 (DC1).

In Fig. 2 the efficiencies of the offline muon reconstruction algorithms are shown at different transverse momenta: beside MOORE and MuId (both StandAlone and Combined versions), the reconstruction performance in the Inner Detector with iPatRec is reported. Global resolution on 1/p T is presented in Fig. 3 as a function of p T . Transverse momentum is better measured by the Inner Detector at low values and by the Muon Spectrometer at high values.

At low transverse momenta the main source of trigger rate in the LVL1 muon system comes from in-flight decays of pions and kaons. The goal of the HLT muon trigger is to reject such muons while having high selection efficiency on prompt muons. It is therefore crucial to combine reconstructed tracks information from the Inner Detector and the Muon Spectrometer. To investigate the rejection of the Muon Event Filter a sample of simulated inclusive muons from b b → µX events and muons from K or π in-flight decays has been simulated and studied. In Fig. 4 the corresponding reconstruction efficiencies are represented as functions of the transverse momentum of the prompt muons and of the parent mesons.

VII. TRIGMOORE AS EVENT FILTER ALGORITHM

In order to avoid explicit dependencies on the Trigger in the Offline environment, the MOORE software has been isolated for the Event Filter in the TrigMOORE package. This C++/Object-Oriented package can be run in two different main strategies:

• wrapped strategy -In this mode algorithms access to the full event, and are executed exactly as those in the offline version.

• seeded strategy -In this mode the reconstruction is performed with a seeded search of the regions of relevant activity in the detector. In this strategy, differently from what happens in the wrapped one, the algorithms access only those chambers belonging to the geometrical areas of the detector identified by the HLT Region Selector [START_REF]HLT PESA Core Software meetings[END_REF], according to hypotheses found in the previous trigger levels (either LVL1 or LVL2). The seeded strategy has been integrated and tested within the full muon slice (LVL1 simulation, LVL2 and Event Filter). The two strategies have been applied on samples of single muons with different p T .

In Fig. 5 the p T resolution has been plotted as a function of transverse momentum in the case of a seeded strategy driven by muon reconstructed RoIs coming from the LVL1 [START_REF] Adams | Moore as Event Filter in the ATLAS High Level Trigger[END_REF].

A. Studies with cavern background

The Muon Spectrometer is very sensitive to the low energy physics background that will be present in the ATLAS experimental hall. A realistic study of reconstruction performances has to consider minimum bias (at the design luminosity ∼ 23 inelastic interactions will be produced at every beam crossing) and cavern background. This background is fundamentally due to particles produced in the interaction of primary hadrons from proton-proton collisions with the materials of the detector and of the collider. These particles (mainly neutrons) interact with matter and produce secondaries, behaving like a gas of timeuncorrelated neutral and charged particles diffusing through the apparatus and throughout the cavern.

For a conservative analysis of such background, besides the simulated samples containing just muons, the reconstruction with TrigMOORE has been tested on single muon events with background superimposition. Besides the "nominal" background intensity (as predicted by FLUKA [START_REF] Fassò | FLUKA: Status and Prospects for Hadronic Applications[END_REF] and GCALOR [START_REF] Zeitnitz | The GEANT-CALOR Interface and Benchmark Calculations for Zeus Calorimeters[END_REF], see also [START_REF] Baranov | Estimation of Radiation Background, Impact on Detectors, Activation and Shielding Optimization in ATLAS[END_REF]), scenarios obtained by boosting by a factor 2, 5, and 10 the nominal background levels have been considered.

In Fig. 6 the efficiency of TrigMOORE seeded by LVL1 is shown as a function of the cut on the number of σ's of p rec Tp gen T , in case of single muons with p T = 100 GeV /c, both in case of no-background and in case of background occurring with factors ×1 and ×5.

B. Timing performances

Specific timing tests have been performed with an optimizedcode version of TrigMOORE on an Intel XEON(TM) 2.4 GHz processor, with 1 GB RAM. To estimate the time needed by TrigMOORE to process a single event, only the algorithmic contribution should be considered (and not the dead time in accessing the event). Average execution times per event are shown in Tab. I for the seeded and the wrapped versions of TrigMOORE at different p T values and also with background added (×1 and ×2 factors). The reconstruction procedure involves track extrapolation to the vertex (MuId). Also the time for accessing data is included. To compute these values a 95% fraction of events has been retained, rejecting the events with the longest processing times. These results (discussed in [START_REF] Shank | Track Reconstruction in the ATLAS Muon Spectrometer with MOORE[END_REF], [START_REF] Adams | Moore as Event Filter in the ATLAS High Level Trigger[END_REF] in more detail) are to be compared with the 1 s latency time requested for an algorithm working as Event Filter.

VIII. CONCLUSIONS

An overall ∼ 10 6 reduction factor is requested to the ATLAS trigger system in order to bring the huge initial event rate at LHC down to reasonable rates for interesting physics events to be acquired and subsequently studied. This requires that offline algorithms optimized for physics analysis have to be inserted within the High Level Trigger environment. In order to accomplish this, a specialized version of the offline package MOORE has been implemented to work in the HLT system, taking into account the need of facing particular data access requirements and reduced latency times.

The reconstruction performances of the packages MOORE and MuId have been discussed, in terms of momentum resolution, efficiency, rejection power and execution times. Different single muons samples of fixed transverse momentum have been used, also investigating the effects induced by cavern background. The results described in this work demonstrate that MOORE and MuId are capable of functioning as Event Filter within the ATLAS trigger system.

Fig. 1 A

 1 Fig. 1 A SCHEMATIC REPRESENTATION OF THE TRIGGER AND DATA ACQUISITION SYSTEM IN ATLAS.

5 Fig. 2 EFFICIENCY 3 MOMENTUM

 523 Fig. 2 EFFICIENCY OF SINGLE MUON RECONSTRUCTION AS A FUNCTION OF p T . THE DIFFERENT MARKS CORRESPOND TO THE RECONSTRUCTION ALGORITHMS DESCRIBED IN THE TEXT.

4 RECONSTRUCTIONFig. 5 TRANSVERSE

 45 Fig. 4 RECONSTRUCTION EFFICIENCY FOR µ PROMPT AND FOR MUONS COMING FROM PIONS/KAONS AS A FUNCTION OF THE p T OF THE INITIAL PARTICLE.

Fig. 6 RECONSTRUCTION

 6 Fig. 6 RECONSTRUCTION EFFICIENCIES WITH TRIGMOORE SEEDED BY LVL1 ON 100 GeV /c p T SINGLE MUONS WITHOUT AND WITH BACKGROUND ADDITION. ×1 CORRESPONDS TO THE NOMINAL BACKGROUND INTENSITY.

TABLE I TIMING

 I TESTS WITH SEEDED AND WRAPPED TRIGMOORE.

	Muon	Time (ms)	Time (ms)
	sample	seeded mode	wrapped mode
	(GeV /c)	average (rms)	average (rms)
	8	73 (30)	68 (30)
	20	59 (15)	58 (21)
	50	61 (21)	58 (25)
	100	61 (19)	64 (26)
	300	75 (23)	64 (32)
	100 ×1	763 (37)	2680 (450)
	100 ×2	1218 (50)	5900 (1100)