Emanuela Boros
email: emanuela.boros@univ-lr.fr

Jose G Moreno
email: jose.moreno@irit.fr

Antoine Doucet
email: antoine.doucet@univ-lr.fr

Answering Matthias

Suzan Verberne

Craig Macdonald

Christin Seifert

Krisztian Balog

; Kjetil

Exploring Entities in Event Detection as Question Answering

Keywords: Event detection, Question answering, Few-shot learning

come

Introduction

Event extraction (EE) is a crucial and challenging task of information extraction (IE) that aims at identifying the instances of specified types of events in a text, generally referred to as event detection (ED), and the detection and classification of the corresponding arguments (participants). For instance, according to the ACE 2005 annotation guidelines 3 , an event is described as having the following characteristics:

the event mention is an occurrence of an event with a particular type. This is usually a sentence or a phrase that describes an event; the event trigger is the word that most clearly expresses the event mention, e.g. Attack ;

the event argument is an entity mention or temporal expression (e.g., Crime, Job-Title) that serves as a participant with a specific role in an event mention. Event arguments have an entity type, e.g. persons (PER), locations (LOC), organizations (ORG), etc.; and the argument role that is the relationship between an argument and the event in which it participates.

Following this description, from the sentence "Police have arrested four people in connection with the killings.", an event extraction system should be able to recognize the word killings as a trigger for an event of type Die, with the person (PER) entity Police as an argument with the role of an Agent and the person four people as an argument of type Person, and the word arrested as a trigger for an Arrest-Jail event type with no arguments.

In this paper, we approach the task of event detection (ED) by studying the usage of entities in a recent and under-researched paradigm for the task of event detection (ED) by casting it as a question-answering (QA) problem.

There have been several deep learning-based major techniques applied for approaching the ED task while taking advantage of entity or argument information in the literature. First, systems extensively utilized linguistic analysis, entity information, entity coreference, and other knowledge resources to capture the discrete structures for ED, focusing on the combination of these discriminative features to build statistical models [START_REF] Hong | Using cross-entity inference to improve event extraction[END_REF][START_REF] Li | Argument inference from relevant event mentions in chinese argument extraction[END_REF][START_REF] Li | Joint event extraction based on hierarchical event schemas from framenet[END_REF]. Next, neural-based approaches were based on convolutional and recurrent neural networks (CNNs and RNNs) that utilized effective feature representations from entity type embeddings [START_REF] Nguyen | Event detection and domain adaptation with convolutional neural networks[END_REF][START_REF] Chen | Event extraction via dynamic multipooling convolutional neural networks[END_REF][START_REF] Nguyen | Modeling skip-grams for event detection with convolutional neural networks[END_REF][START_REF] Nguyen | A two-stage approach for extending event detection to new types via neural networks[END_REF][START_REF] Nguyen | Joint event extraction via recurrent neural networks[END_REF][START_REF] Nguyen | Graph convolutional networks with argument-aware pooling for event detection[END_REF][START_REF] Liu | Exploiting argument information to improve event detection via supervised attention mechanisms[END_REF].

Recent approaches adopt the usage of pre-trained language models [START_REF] Vaswani | Attention is all you need[END_REF]. Since BERT [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF] broke records for several natural language processing (NLP) tasks (part-of-speech tagging, named entity recognition, etc.) and received a lot of attention, recent advances in ED imply architectures based on fine-tuning this type of models [START_REF] Zhang | Joint entity and event extraction with generative adversarial imitation learning[END_REF][START_REF] Hong | Self-regulation: Employing a generative adversarial network to improve event detection[END_REF][START_REF] Wang | Adversarial training for weakly supervised event detection[END_REF][START_REF] Boros | Event detection with entity markers[END_REF], these methods holding the state of the art for ED.

Differently from these Transformer-based methods, where event and argument detection were considered as classification tasks, a new paradigm was introduced [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF][START_REF] Liu | Event extraction as machine reading comprehension[END_REF] formulating EE as a question answering (QA)/machine reading comprehension (MRC4 ) task, where events can be extracted by responding to the 5W1H questions (who did what, when, where, why, and how). While these recent advances claim to cast the EE task as an MRC task [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF][START_REF] Liu | Event extraction as machine reading comprehension[END_REF], they mostly focus on argument extraction as QA, while for ED, the models remain formulated as a sequential classification problem that aims at detecting event triggers of specific types.

Thus, in this paper, we focus on the event detection task, and we first cast it as a QA task with the possibility of multiple answers, in the case where more than one event is present in the text. By approaching it as a QA model, not only are we able to leverage the recent advances in MRC, we also avoid the classification based-methods that can either require lots of training data and are challenged by the annotation cost or data scarcity.

Second, we take advantage of the presence of entities for extracting the events 5 , considering that informative features can be brought by additional entity markers for better distinguishing the event triggers. We agree that "Entities of the consistent type normally participate in similar events as the same role." [START_REF] Hong | Using cross-entity inference to improve event extraction[END_REF].

In addition, modeling the task as QA can improve the ED task in regard to this challenge due to the fact that the answers are only considered in relation to the context and the question, which could reduce trigger ambiguity. Furthermore, compared to classification based-methods [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF][START_REF] Liu | Event extraction as machine reading comprehension[END_REF][START_REF] Chen | Event extraction via dynamic multipooling convolutional neural networks[END_REF][START_REF] Liu | Event extraction as machine reading comprehension[END_REF][START_REF] Li | Joint event extraction via structured prediction with global features[END_REF] that generally lack this ability, we demonstrate that our proposed QA models are more effective in fewshot scenarios by showing that they are able to extract unseen event types. The work of [START_REF] Huang | Zero-shot transfer learning for event extraction[END_REF] is distinguished in the literature, where the authors prove that zeroshot learning for detecting events can be possible and efficient. They proposed to leverage existing human-constructed event schemas and manual annotations for a small set of seen types, and transfer the knowledge from the existing event types to the extraction of unseen event types. We consider this paper as our reference method for the few-shot learning setting, and we prove that modeling the ED task as QA with entity information can obtain higher performance results.

Our proposed method with entity information obtains state-of-the-art results when compared with previous models that utilize entity or argument information. Moreover, these methods could foster further research and help to study transfer learning from QA models to boost the performance of existing information extraction systems. Furthermore, compared to classification based-methods that lack this ability, we demonstrate that our proposed QA models are more effective in few-shot scenarios by showing that they are able to extract unseen event types.

Next, we continue with the related work in Section 2, and we detail the QA model with entity markers in Section 3. The experimental setup and the results are presented in Section 4. We provide a discussion of the results by analyzing the output in Section 5 and we draw conclusions in Section 6.

Related Work

Event Detection with Entity Information In the context of event detection, some works made use of gold-standard entities in different manners. Higher results can be obtained with gold-standard entity types [START_REF] Nguyen | Event detection and domain adaptation with convolutional neural networks[END_REF], by concatenating randomly initialized embeddings for the entity types. A graph neural network (GNN) based on dependency trees [START_REF] Nguyen | Graph convolutional networks with argument-aware pooling for event detection[END_REF] has also been proposed to perform event detection with a pooling method that relies on entity mentions aggregation. Arguments provided significant clues to this task in the supervised attention mechanism proposed to exploit argument information explicitly for ED proposed by [START_REF] Liu | Exploiting argument information to improve event detection via supervised attention mechanisms[END_REF]. Other methods that took advantage of argument information were jointbased approaches.

The architecture adopted by [START_REF] Liu | Jointly multiple events extraction via attention-based graph information aggregation[END_REF] was jointly extracting multiple event triggers and event arguments by introducing syntactic shortcut arcs derived from the dependency parsing trees. [START_REF] Hong | Using cross-entity inference to improve event extraction[END_REF]'s cross-entity feature-based method extracted events by using gold standard cross-entity inference in order to take advantage of the consistency of entity mentions while achieving efficient sentence-level trigger and argument (role) classification. [START_REF] Li | Joint event extraction based on hierarchical event schemas from framenet[END_REF] utilized the contextual entities in a joint framework based on a structured prediction that extracted triggers and arguments together so that the local predictions can be mutually improved.

Approaches presented by [START_REF] Nguyen | Event detection and domain adaptation with convolutional neural networks[END_REF] and [START_REF] Chen | Event extraction via dynamic multipooling convolutional neural networks[END_REF] experimented with the integration of entities in ED models based on CNNs. These models utilized effective feature representations from pre-trained word embeddings, position embeddings as well as entity type embeddings. [START_REF] Nguyen | Modeling skip-grams for event detection with convolutional neural networks[END_REF] improve the previous model proposed by [START_REF] Nguyen | Event detection and domain adaptation with convolutional neural networks[END_REF] by taking into account the possibility to have non-consecutive n-grams as basic features instead of continuous n-grams.

A different technique was explored by [START_REF] Soares | Matching the blanks: Distributional similarity for relation learning[END_REF] and it consisted in marking the entities in the relation extraction task and by studying the ability of the Transformerbased neural networks to encode relations between entity pairs. They identified a method of representation based on marking the present entities that outperform previous work in supervised relation extraction. [START_REF] Moreno | Relation classification via relation validation[END_REF] also explored the use of pre-trained neural models into the relation validation problem by explicitly using a triplet-sentence representation with marked entities, proving that the relation extraction performance could be further improved by using this additional information. Furthermore, [START_REF] Boros | Event detection with entity markers[END_REF] also proposed the use of pre-trained neural models in a BERT-based classification-based architecture for detecting events.

Event Detection as Question Answering While QA for event detection is roughly under-researched, Transformer-based models have led to striking gains in performance on MRC tasks recently, as measured on the SQuAD v1.16 [START_REF] Rajpurkar | Squad: 100,000+ questions for machine comprehension of text[END_REF] and SQuAD v2.07 [START_REF] Rajpurkar | Know what you don't know: Unanswerable questions for squad[END_REF] leaderboards.

A recent work proposed by [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF] introduced this new paradigm for event extraction by formulating it as a QA task, which extracts the event triggers and arguments in an end-to-end manner. For detecting the event, they considered an approach based on BERT that is usually applied to sequential data. The task of ED is a classification-based method where the authors designed simple fixed templates as in what is the trigger, trigger, action, verb, without specifying the event type. For example, if they chose verb template, the input sequence would be: [CLS] verb [SEP] sentence [SEP]. Next, they use a sequential fine-tuned BERT for detecting event trigger candidates.

Another recent paper [START_REF] Liu | Event extraction as machine reading comprehension[END_REF] also approaches the event extraction task as a question answering task, similar to the [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF] method. The task remains classificationbased (instead of the span-based QA method) for trigger extraction, jointly encode [EVENT] with the sentence to compute an encoded representation, as in the approach proposed by [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF] where the special token was verb or trigger.

Event Question Answering Model with Entity

Positions, Types, and Argument Roles

We formulate the ED task as a QA task, where, for every sentence, we ask if a particular event type is present, and we expect a response with an event trigger, multiple event triggers, or none. Our model extends the BERT [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF] pre-trained model which is a stack of Transformer layers [START_REF] Vaswani | Attention is all you need[END_REF] that takes as input a sequence of subtokens, obtained by the WordPiece tokenization [START_REF] Wu | Google's neural machine translation system: Bridging the gap between human and machine translation[END_REF] and produces a sequence of context-based embeddings of these subtokens.

To feed a QA task into BERT, we pack both the question and the reference text into the input, as illustrated in Figure 1. The input embeddings are the sum of the token embeddings and the segment embeddings. The input is processed in the following manner: token embeddings (a [CLS] token is added to the input word tokens at the beginning of the question and a [SEP] token is inserted at the end of both the question and the reference text) and segment embeddings (a marker indicating the question or the reference text is added to each token). This allows the model to distinguish between the question and the text. To fine-tune BERT for a QA system, a start vector and an end vector are introduced. A linear layer is added at the top of BERT layers with two outputs for the start and end vectors of the answer. The probability of each word being the start or end word is calculated by taking a dot product between the final embedding of the word and the start or end vector, followed by a Softmax over all the words. The word with the highest probability value is considered. This method differs from the event detection approaches presented by [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF] and [START_REF] Liu | Event extraction as machine reading comprehension[END_REF] where the models are classification-based, instead of the span-based QA.

Next, for every type of event [Event Type] (Demonstrate, Die, Attack, etc.), we formulate the question by automatically generating them using the following template:

What is the [Event Type] ?

An example for a sentence containing an Attack event is illustrated in Figure 1. We also consider questions that do not have an answer in the case where an event of a specific type is not present in the sentence. When there is more than one event of the same type in a sentence, we consider that the question has multiple answers. From the n best-predicted answers, we consider all those that obtained a probability higher than a selected threshold (established on the development set). When the predicted chunks are self-contained, we consider only the first predicted event trigger. For example, if the noun chunks assault and air assault are predicted, only assault is considered.

Next, for adding entity information, we augment the input data with a series of special tokens. Thus, if we consider a sentence x = [x 0 , x 1 , . . . , x n ] with n tokens, we augment x with two reserved word pieces to mark the beginning and the end of each event entity or argument mention in the sentence.

Next, we propose three types of markers: (1) Entity Position Markers, e.g. <E> and </E> where E represents an entity of any type, (2) Entity Type Markers, e.g. <PER> and </PER> where PER represents an entity of type Person, and (3) if the event argument roles are known beforehand, Argument Role Markers, e.g. <Agent>, </Agent> where Agent is an event argument role. Thus, we modify the following sentence:

"Police have arrested four people in connection with the killings." where killings is a trigger for a Die event, and arrested is a trigger for an Arrest-Jail event, Police is one of the participants, a person (PER) with the argument role of an Agent, and four people is also a person entity (PER) with the Person argument role. The modified sentences with the three types of markers are:

(1) "<E> Police </E> have arrested <E> four people </E> in connection with the killings."

(2) "<PER> Police </PER> have arrested </PER> four people </PER> in connection with the killings."

(3) "<Agent> Police </Agent> have arrested <Person> four people </Person> in connection with the killings."

Further, an ED system should detect in the presented sentence, the trigger word killings for an event of type Die (this event has two arguments Police and four people) and arrested for an event of type Arrest-Jail (this event has no arguments). For the Argument Role Markers, if an entity has different roles in different events that are present in the same sentence, we mark the entity with all the argument roles that it has. The evaluation is conducted on the ACE 2005 corpus provided by ACE program 8 . For comparison purposes, we use the same test set with 40 news articles (672 sentences), the same development set with 30 other documents (863 sentences) and the same training set with the remaining 529 documents (14,849 sentences) as in previous studies of this dataset [START_REF] Ji | Refining event extraction through cross-document inference[END_REF][START_REF] Liao | Using document level cross-event inference to improve event extraction[END_REF]. The ACE 2005 corpus has 8 types of events, with 33 subtypes (e.g. the event type Conflict has two subtypes Attack, Demonstrate). In this paper, we refer only to the subtypes of the events, without diminishing the meaning of main event types.

Evaluation Metrics Following the same line of previous works, we consider that a trigger is correct if its event type, subtype, and offsets match those of a reference trigger. We use Precision (P), Recall (R), and F-measure (F1) to evaluate the overall performance.

Hyperparameters We used the Stanford CoreNLP toolkit9 to pre-process the data, including tokenization and sentence splitting 10 . For fine-tuning the BERTbased models, we followed the selection of hyperparameters presented by [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF]. We found that 3 × 10 -5 learning rate and a mini-batch of dimension 12 for the base models provided stable and consistent convergence across all experiments as evaluated on the development set. The maximum sequence length is set to 384 and the document stride of 128. For selecting the event triggers, we generate n = 10 candidates, and we use the same threshold for all the experiments, with a value of 0.2 that was decided on the development set.

General Evaluation

In Table 1, we present the comparison between our model and state-of-the-art approaches that utilised entity or argument information.

We compare with the MaxEnt-based model with local features in [START_REF] Li | Joint event extraction via structured prediction with global features[END_REF], the cross-entity feature-based method extracted events by using gold standard crossentity inference [START_REF] Hong | Using cross-entity inference to improve event extraction[END_REF] and the models proposed by [START_REF] Chen | Event extraction via dynamic multipooling convolutional neural networks[END_REF], [START_REF] Nguyen | Event detection and domain adaptation with convolutional neural networks[END_REF], [START_REF] Nguyen | A two-stage approach for extending event detection to new types via neural networks[END_REF], and the joint framework with bidirectional RNNs [START_REF] Nguyen | Joint event extraction via recurrent neural networks[END_REF] that experimented with the integration of entities in ED models based on a CNN-based architectures.

We also compare with the method proposed by [START_REF] Liu | Exploiting argument information to improve event detection via supervised attention mechanisms[END_REF] that also exploited entity information explicitly for ED via supervised attention mechanisms, and the graph CNN by [START_REF] Nguyen | Graph convolutional networks with argument-aware pooling for event detection[END_REF] that investigated a CNN based on dependency trees for ED with pooling method that relied on entity mentions to aggregate the convolution vectors.

We also compare with the models where the task has been approached as a QA task but still formulated as a sequential classification problem that aims at locating trigger candidates, the fine-tuned baseline BERT-base-uncased and the BERT QA Trigger [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF], and the RCEE ER (Reading Comprehension for Event Extraction, with ER that denotes that the model has golden entity refinement) [START_REF] Liu | Event extraction as machine reading comprehension[END_REF].

When compared with the previous state-of-the-art models that included entity information, except for the RCEE ER method, our models that use either the positions or the types of the entities bring a considerable improvement in the performance of trigger detection. It is clear that further marking the entities with their types can increase both precision and recall, balancing the final scores.

It is noteworthy that, while entities can be present in the entire document, arguments can only surround event triggers. Knowing the argument roles beforehand brings further improvements, we assume that an important reason for this is that, since the arguments are present only around event triggers, this could help the language model to be more aware of the existence of an event or multiple events in a sentence.

Discussion

Trigger Ambiguity Analysis

For a deeper analysis of the impact of entity information, we leverage the gradients in our proposed models to efficiently infer the relationship between the question, context, and the output response. [START_REF] Brunner | On identifiability in transformers[END_REF] studied the identifiability of attention weights and token embeddings in Transformer-based models. They show that the self-attention distributions are not directly interpretable and suggest that simple gradient explanations are stable and faithful to the model and data generating process.

Thus, as applied by [START_REF] Madsen | Visualizing memorization in rnns[END_REF], to get a better idea of how well each model memorizes and uses memory for contextual understanding, we analyze the connectivity between the desired output and the input. This is calculated as:

connectivity(t, t) = ∂y t k ∂x t 2
where t is the time index, t the output time index, and the result is the magnitude of the gradient between the logits for the desired output y t k and the input x t . The connectivity is computed with respect to both start position and end position of the answer, then it is normalized, and it is visible as saliency maps for every word in Figures 3 and 2 11 . By looking at the gradients in Figure 3, where two events of different types are present, we can observe, in the upper part of the figure, that while the model sees the word killings and arrested as impactful, it also sees the words police, connection as impactful and selects an answer in that neighborhood. Even though both trigger candidates killings and arrested have a clear impact due to their gradient values, by looking at the probability values, killings is recognized with a 99.4% probability, while arrested obtained a probability of 2.3 × 10 -7 , value that is lower than our selected threshold 0.2. In the lower part of the figure, for the question What is Arrest-Jail?, the words die, police, killings clearly influence the choice of the answer arrested.

In Figure 2, we present the same sentence with the three types of input modifications: Entity Position Markers, Entity Type Markers, and Argument Role Markers, with the What is Die? question and the correct answer killings. In the upper part of the figure, where the sentence has been augmented with the entity position markers <E> and </E>, we notice that the words that impact the most in the result are killings along with die, arrested, and police. In this case, one can also see that the end marker </E> contributed too.

In the middle part of the figure, where the sentence has been augmented with the entity position markers <PER> and </PER> for the two entities police and four people, the influence of other words as in die, arrested, and police slightly decreased. In the bottom part of the image, the gradients of these words are visibly reduced.

When the sentence is augmented with argument roles, <Agent>, </Agent>, <Person> and </Person>, the noise around the correct answer has noticeably diminished, being reduced by the additional markers. The most impactful remaining words are the word die in the question and the correct answer killings.

In order to analyze the quality of the sentence representations, we extract the [CLS] representation of each sentence for BERT-QA-base-uncased and for BERT-QA-base-uncased + Argument Role Markers. Then, we plot these representations in two spaces where the labels (colors of the dots) are the event types, as illustrated in Figure 4. On the right-hand side of the figure, where argument role markers are used, it is clear that the sentence representations clusters are more cohesive than when no entity information is considered (left-hand side), thus confirming our assumption regarding the importance of the entity informative features in a QA system.

Evaluation on Unseen Event Types

In the first scenario, we follow the same strategy as [START_REF] Du | Event extraction by answering (almost) natural questions[END_REF] where we keep 80% of event types [START_REF] Rajpurkar | Squad: 100,000+ questions for machine comprehension of text[END_REF] in the training set and 20% (6) unseen event types in the test set. More exactly, the unseen event types were chosen randomly, and they are: Marry, Trial-Hearing, Arrest-Jail, Acquit, Attack, and Declare-Bankruptcy. Table 2 presents the performance scores of our models for the unseen event types. We compare with BERT-QA-base-uncased which is our baseline that selects an event trigger in a sentence without being trained on ACE 2005 data. Since the majority of the models in Table 1 are classification-based in a sequential manner, they are not capable of handling unseen event types, and thus, we were not able to obtain performance values. From the results, without any event annotation, the BERT-QA-base-uncased obtains a low F1 value (1.38%). We observe that the performance values increase proportionally to the specificity of the markers. Thus, it is not surprising that the highest values are obtained when the argument roles are marked, also obtaining the highest precision.

In a second scenario, we consider larger amounts of unseen events, and we follow the strategy proposed by [START_REF] Huang | Zero-shot transfer learning for event extraction[END_REF], where out of the total number of event types (33), we select the top-N most popular event types as seen, while the rest remain unseen. N is set as 1, 3, 5, 10 respectively. We perform experiments in four settings (A, B, C, and D). Table 3 shows the types that were selected for training in each experiment setting. 4. Evaluation of our models on unseen event types (Hit@1 as in [START_REF] Huang | Zero-shot transfer learning for event extraction[END_REF]). The models with ♣ utilized gold standard entity mentions. The models with ♥ utilized gold standard arguments. Table 4 presents the performance scores of our models for the unseen event types. We focus on showing the effectiveness of our methods juxtaposed with the results of [START_REF] Huang | Zero-shot transfer learning for event extraction[END_REF]. We first observe, for each model, that the performance values improve as the number of seen events types. Second, one can notice that the scores also increase proportionally to the specificity of the markers.

Approaches

Conclusions and Perspectives

In this paper, we utilized a recent and under-researched paradigm for detecting events by modeling the ED as a QA task with the addition of entity and argument information. The questions were simplified to a pre-defined list with a question for every type of event present in the dataset, which allows the model to predict multiple events in a sentence. The additional informative features brought by the presence of entities and the argument roles in the same context of the events considerably increased the performance of the model, achieving state-of-the-art results. Moreover, this type of model that utilizes the entity information leveraged the ambiguity of the event triggers and demonstrate potential in detecting unseen event types.

In future work, we will focus on approaching the entity and argument detection tasks, in order to analyze the influence of the predicted event arguments and the error propagation from this task to the downstream event detection task. Furthermore, we will consider approaching both event extraction sub-tasks (ED and argument detection and classification) in a joint QA-based architecture for alleviating the aforementioned issue concerning the diffusion of detection errors.

Fig. 1 .

 1 Fig. 1. Example of input modification to fit the QA paradigm for a sentence that contains an event of type Attack. The question is separated by [SEP] token from the reference text that contains the event trigger war.

Fig. 2 .

 2 Fig. 2. An example for the Die event triggered by killings with three types of markers: Entity Position, Entity Type, and Argument Role Markers.

Fig. 3 .

 3 Fig. 3. An example of a sentence that contains two events: Die event triggered by the word killings and Arrest-Jail event triggered by arrested. The model used is BERT-QA-base-uncased.

Fig. 4 .

 4 Fig. 4. [CLS] representation of each sentence in the test set that contains at least an event for BERT-QA-base-uncased, BERT-QA-base-uncased + Entity Position Mark-ers+, BERT-QA-base-uncased + Entity Type Markers+, and BERT-QA-base-uncased + Argument Role Markers.

Table 1 .

 1 Evaluation of our models and comparison with state-of-the-art systems for event detection on the blind test data. The models with ♣ utilized gold standard entity mentions. The models with ♥ utilized gold standard arguments. Statistical significance is measured with McNemar's test. * denotes a significant improvement at p ≤ 0.01.

	4 Experiments
	Approaches	P	R	F1
	MaxEnt with local features ♣ [12]	74.5	59.1	65.9
	Cross-entity ♣ [7]	72.9	64.3	68.3
	DMCNN ♣ [4]	75.6	63.6	69.1
	Word CNN ♣ [23]	71.8	66.4	69.0
	Joint RNN ♣ [21]	66.0	73.0	69.3
	BERT-QA-base-uncased	68.4	70.5	69.5
	BERT-base [6]	67.1	73.2	70.0
	Non-Consecutive CNN ♣ [22]	-	-	71.3
	Attention-based ♣♥ [16]	78.0	66.3	71.7
	BERT QA Trigger [6]	71.1	73.7	72.3
	Graph CNN ♣ [25]	77.9	68.8	73.1
	BERT-QA-base-uncased + Entity Position Markers ♣	78.0	70.7	74.2*
	RCEE ER ♣ [15]	75.6	74.2	74.9
	BERT-QA-base-uncased + Entity Type Markers ♣	78.5	77.2	77.8*
	BERT-QA-base-uncased + Argument Role Markers ♥	83.2	80.5	81.8*

Table 2 .

 2 Evaluation of our models on unseen event types. The models with ♣ utilized gold standard entity mentions. The models with ♥ utilized gold standard arguments.

	Approaches

Table 3 .

 3 Seen types in each experiment setting as proposed by[START_REF] Huang | Zero-shot transfer learning for event extraction[END_REF]. Transport, Die, Meet, Arrest-Jail D 10 Attack, Transport, Die, Meet, Sentence, Arrest-Jail, Transfer-Money, Elect, Transfer-Ownership, End-Position

	Setting N Seen Event Types
	A	1 Attack
	B	3 Attack, Transport, Die
	C 5 Attack, Table

In one view, the recent tasks titled MRC can also be seen as the extended tasks of question answering (QA).

We note here that event extraction generally depends on previous phases as, for example, named entity recognition, entity mention coreference, and classification. Thereinto, the named entity recognition is another hard task in the ACE evaluation and not the focus of this paper. Therefore, we will temporarily skip the phase and instead directly use the entities provided by ACE, following previous work[START_REF] Hong | Using cross-entity inference to improve event extraction[END_REF][START_REF] Liao | Using document level cross-event inference to improve event extraction[END_REF][START_REF] Ji | Refining event extraction through cross-document inference[END_REF][START_REF] Chen | Event extraction via dynamic multipooling convolutional neural networks[END_REF][START_REF] Liu | Event extraction as machine reading comprehension[END_REF][START_REF] Li | Joint event extraction via structured prediction with global features[END_REF].

SQuAD v1.1 consists of reference passages from Wikipedia with answers and questions constructed by annotators after viewing the passage

[START_REF] Hong | Using cross-entity inference to improve event extraction[END_REF] SQuADv2.0 augmented the SQuAD v1.1 collection with additional questions that did not have answers in the referenced passage.

https://catalog.ldc.upenn.edu/LDC2006T06

http://stanfordnlp.github.io/CoreNLP/

The code is available at https://github.com/nlpcl-lab/ace2005-preprocessing as it consists of the same pre-processing as utilized in several other papers[START_REF] Nguyen | Event detection and domain adaptation with convolutional neural networks[END_REF][START_REF] Nguyen | Joint event extraction via recurrent neural networks[END_REF].

This work has been supported by the European Union's Horizon 2020 research and innovation program under grants 770299 (NewsEye) and 825153 (Embeddia), and by the ANNA and Termitrad projects funded by the Nouvelle-Aquitaine Region.

3 https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/ english-events-guidelines-v5.4.3.pdf


Plastic deformations, obtained by cold-working during an elaboration step, or resulting from mechanical stresses in service, modify the microstructures of pure metals and metallic alloys by multiplying dislocations. These ones can modify the reactivity of surface with in addition a possible influence of the orientation of the external surface with respect to the direction of the uniaxial deformation. This can modify their electrochemical behaviour, for example in situation of corrosion as here in sulphuric acid, sometimes in the good way as observed for pure iron, or more frequently with detrimental effect as seen for pure nickel or pure copper.

The corrosion behaviour of not alloyed steels also depends on both the plastic strain amount and the orientation. This potentially leads to possible accelerated corrosion in some zones of a piece with varying sections along its axis and plastically deformed in traction. This may for example occur by galvanic coupling between a not plastically deformed zone (great sections leading to elastic deformation only) and more sollicited other zones (smaller sections really plastically deformed), as well as between differently oriented areas. The plastic deformation by compression can lead to an improvement of the resistance of iron against corrosion or the parallel orientation. Its polarization resistance Rp increases and its corrosion current Icorr decreases (as for stainless steel 304 in NaCl solution [1]) for the Fe"para" electrode. The order between not-deformed iron and deformed iron tends to be inversed if one considers the perpendicular orientation (Fe"perp").

These differences remain when the inhibitor allylthiourea is added to the solution.

The situation appears inversed in the case of nickel since the values of Rp and of Icorr tend to show a better behaviour in corrosion for the not deformed nickel than for the deformed nickel, as formerly reported in another study of polycrystalline Ni in a sulphuric solution [2]. About copper, which is the noblest metal of this study, it can be corroded essentially in presence of dissolved oxygen in such solution (higher potentials), and its corrosion in active state is logically greatly lower than for Ni and for Fe. The polarization resistance are the greatest and the corrosion currents the lowest. In its case too, the plastic deformation by compression induces a decrease in corrosion resistance.

Additional information is given in the present study since one also noted an effect of the orientation of the surface with regards to the deformation direction: faster corrosion for the perpendicular orientation than for the parallel one. Earlier works showed that such a dependence on the strained state was not so clear concerning non ferrous metallic alloys [7,8].

In the steels too, several differences of corrosion behaviour in the acid solution of the study were noted between the not deformed zone and the more or less deformed zones. The observations are quite clear (increase in corrosion rate when plastic deformation increases) and consistent with previous results concerning different types of steels [3][4][5][6].

1

  Faculty of Sciences and Technologies, 2 I.U.T. Nancy-Brabois, Department of Mechanical Engineering, 3 Institut Jean Lamour (UMR CNRS 7198), Department CP2S, team 206, University Henri Poincaré, B.P. 70239, 54506 Vandoeuvre-lès-Nancy, France patrice.berthod@ijl.nancy-universite.fr Changes of corrosion behaviour in a sulphuric acid solution for different metals and alloys after plastic deformation Changes of corrosion behaviour in a sulphuric acid solution for different metals and alloys after plastic deformation Sullivan De Sousa 1 , Jean-Pierre Philippe 2 , Patrice Berthod 3 Faculté des Sciences et Technologies Eurocorr 2011 -Stockholm (Sweden) -4-8 September 2011 J6When mechanical stresses are applied to metallic alloys these ones can be deformed elastically, and in some cases plastically, this depending on the level of the applied stresses. It is generally considered that an alloy becomes less corrosion-resistant when it is previously plastically deformed. Numerous works about the effects of a mechanical deformation on the corrosion behaviour of alloys were done since at least forty years, initially concerning cold-worked stainless steels tested in corrosion in sulphuric or chlorides-containing solutions. It appeared that the chemical composition and the fabrication conditions of the considered alloys are often too complex to deduce well established general rules in reality and several effects of the plastic deformation on the corrosion behaviour can be found, sometimes in opposite directions.The aim of this work is to study the influences of uniaxial plastic deformations on the corrosion behaviour (characterized according to the Stern-Geary and Tafel methods) in a simple acid solution (H 2 SO 4 2N), first for pure cast metals (Cu, Ni, Fe) deformed or not in compression, and second for two carbon steels, one ferritic and one ferrito-pearlitic, deformed or not in traction.surf ace perp end icular to the co mpression axis surf ace parallel to the comp ressio n axisCu Ni Fe Puresurf ace p arallel to the tract io n axis surf ace p erpendicular to the traction axis /cm²) Ecorr / HNE (mV) Ferritic steel -Ecorr and Icorr (Tafel)


PhD student; Joe Aoun
email: joe.aoun@imag.fr

Bruno Bzeznik

Researcher; Vincent Breton

Engineer; Mehdi Leabad

PhD student; Julien Dimastromatteo

PhD Yiannis Georgiou

Olivier Richard

Engineer; Pierre Neyron

Laurent Desbat

Joe Mr

Ph.D Aoun

Peter Sloot

Mr Dear

Sloot

Affiliation

Monte Carlo validation of a µ-SPECT imaging system on the lightweight grid CiGri

Keywords: Lightweight GRID, GATE, CIMENT GRID, Monte Carlo Simulation, Small Animal SPECT Lightweight GRID, GATE, CIMENT GRID, Monte Carlo Simulation, Small Animal SPECT tomography, nuclear imaging, dynamic CT, sampling

Monte Carlo validation of a mu-SPECT imaging system on the lightweight grid CiGri

Main text 1. Introduction

Preclinical small animal researches have become a major focus in nuclear medicine [START_REF] Del Guerra | State-of-the-art of PET SPECT and CT for small animal imaging[END_REF]. New therapeutic and diagnostic studies are first investigated and validated on mice or rats before their application to patients [START_REF] Constantinesco | Quel avenir pour l'imagerie TEMP du petit animal ? = What does the future hold for small animal SPECT imaging?[END_REF]. In consequence, we can find many dedicated small field of view scanners for SPECT (Single Photon Emission Computed Tomography) [START_REF] Smith | Design and modelling of the Hybrid Portable Gamma Camera system[END_REF][START_REF] Berthot | Monte Carlo simulation of gamma cameras using GEANT[END_REF][START_REF] Garibaldi | Optimization of compact gamma cameras for breast imaging[END_REF][START_REF] Lazaro | Validation of the GATE Monte Carlo simulation platform for modelling a CsI(Tl) scintillation camera dedicated to small-animal imaging[END_REF][START_REF] Beekman | Design and simulation of a high-resolution stationary SPECT system for small animals[END_REF] and PET (Positron Emission Tomography) [START_REF] Merheb | Full modelling of the MOSAIC animal PET system based on the GATE Monte Carlo simulation code[END_REF][START_REF] Vandervoort | Monte Carlo modelling of singles-mode transmission data for small animal PET scanners[END_REF] which have been designed in the last decade for these purposes. Before simulated data can be trusted, the simulation model must be validated by comparing it with the corresponding experimental results. Scanner models should incorporate all the physical effects (attenuation, scatter…) in order to improve the quantification and the quality of SPECT images [START_REF] King | Attenuation scatter and spatial resolution compensation in SPECT[END_REF]. When imaging with 125 I (27.5 keV) a concentration of radioactivity placed in the centre of a rat-sized cylinder of water, the scatterto-primary ratio can reach up to 30% and the photon attenuation can reduce the activity quantitation by almost 50% (25% with 99m Tc -140 keV) [START_REF] Hwang | Assessment of the sources of error affecting the quantitative accuracy of SPECT imaging in small animals[END_REF]. Such degradations are very difficult to compensate for by an experimental or analytical approach [START_REF] Buvat | Monte-Carlo methods in PET and SPECT[END_REF]. Few researchers have tried to assess an analytical and experimental estimation of photon distributions in SPECT projections [START_REF] Riauka | Experimental and numerical investigation of the 3D SPECT photon detection kernel for non-uniform attenuating media[END_REF][START_REF] Wells | Analytical calculation of photon distributions in SPECT projections[END_REF]. Monte Carlo Simulations (MCS) have greatly contributed to solve these problems thanks to their accuracy and the possibility of modelling all the physical processes in an independent way [START_REF] Lazaro | Fully 3D Monte Carlo reconstruction in SPECT: a feasibility study[END_REF][START_REF] Bitar | Fully 3D Monte Carlo image reconstruction in SPECT using functional regions[END_REF]. Since the 1960s, Monte Carlo methods have become an essential tool in the field of nuclear medicine [START_REF] Morel | La simulation Monte Carlo en médecine nucléaire = Monte Carlo simulation in nuclear medicine[END_REF]. Unfortunately, the use of MCS has limitations in computing time. Different strategies have been suggested to reduce the computing time such as the acceleration techniques [START_REF] Buvat | Monte Carlo simulations in emission tomography and GATE: An overview[END_REF]. Another solution to speed up MCS is to combine Monte Carlo and non-Monte Carlo modelling [START_REF] Buvat | Monte Carlo simulations in emission tomography and GATE: An overview[END_REF]. A third option that has recently been explored is the deployment of computing grids, also known as the parallelization of the MCS [START_REF] Breton | DataGrid Prototype of a Biomedical Grid[END_REF]. This consists in sub-dividing a long simulation into short ones. Each MCS needs a Random Number Stream (RNS) to produce the physical interactions in question. The distribution of MCS on multiple computing resources requires the generated streams to be independent [START_REF] Mascagni | Parameterizing parallel multiplicative lagged-Fibonacci generators[END_REF].

A popular way of exploiting computing power for scientific research on a single site is the utilisation of networks of computers as a single unified computing resource, called cluster computing. On those systems, the resource allocation to users and computation tasks is handled through batch queuing software such as PBSPro [START_REF] Nitzberg | PBS Pro: Grid computing and scheduling attributes[END_REF], Torque, LSF, SLURM [START_REF] Yoo | SLURM: Simple Linux Utility for Resource Management[END_REF], OAR [START_REF] Capit | A batch scheduler with high level components[END_REF] and SGE [START_REF] Gentzsch | Sun Grid Engine: towards creating a compute power grid[END_REF]. Larger scale scientific research, linking geographically distributed computational resources, is achieved by technologies called computational grids or grid computing. The project with the greatest visibility on grid computing is Globus [START_REF] Foster | Globus: A metacomputing infrastructure toolkit[END_REF]. This mainstream approach provides a software infrastructure that enables applications to handle distributed heterogeneous computing resources as a single virtual machine. However, the installation, configuration, customization and maintenance of a system like Globus, is a rather complicated task and requires a highly skilled support team, which only a few laboratories are willing to afford. The opportunity of performing large computations at low-cost has motivated scientists to come up with another technological solution. This approach works through the exploitation of idle cycles of desktop PCs, known as voluntary computing or desktop grid. Other alternative technologies, based on the idea of harvesting unused resources of multiple distinct administrative domains that want to share their resources, are provided by the projects OurGrid [START_REF] Cirne | Labs of the world, unite!!![END_REF] and Condor [START_REF] Butt | A self-organizing flock of condors[END_REF]. Under these circumstances, this paper introduces two grid tools, CiGri (CIMENT Grid) [START_REF] Georgiou | Evaluations of the lightweight grid CiGri upon the grid5000 platform[END_REF][START_REF] Georgiou | Simple, fault tolerant, lightweight grid computing approach for bag-of-tasks applications[END_REF] and ComputeMode [START_REF] Dupros | IGGI, a computing framework for large scale parametric simulations: Application to uncertainty analysis with toughreact[END_REF], used on the computing resources of the University of Grenoble, to report the validation of the Biospace small field of view gamma camera using the GATE Monte Carlo Simulations toolkit.

The paper is organized as follows: Chapter 2 presents the materials and methods used in this study: we introduce CiGri, ComputeMode, the GATE MCS toolkit, the gamma camera we studied and the methods we used to deploy the simulations on the grid and to validate the simulated results compared to experimental measurements. Chapter 3 offers an analysis of the simulation results as well as an overall view of the grid performances. Last and not least, chapter 4 presents some conclusions and perspectives.

Materials and Methods

CiGri grid

A lightweight grid

CiGri (CIMENT Grid, https://ciment.imag.fr/CiGri) is a simple, lightweight, scalable and fault tolerant grid system which exploits the unused resources of a set of computing clusters. CiGri middleware is a free open source software under GPL licence developed at the University of Grenoble (France) and used upon the production clusters of the CIMENT project [START_REF] Georgiou | Evaluations of the lightweight grid CiGri upon the grid5000 platform[END_REF][START_REF] Georgiou | Simple, fault tolerant, lightweight grid computing approach for bag-of-tasks applications[END_REF] since 2002. Working discreetly on the interconnected clusters (without influencing their normal functionality), it makes use of a transparent technique to harness the idle cluster resources for executing large scale scientific computations. The platform supports only a specific type of applications, called bag-of-tasks, which represent a set of independent sequential tasks. Hence, by tracking this specific kind of applications in a computing centre like CIMENT ( counting half a dozen of clusters ) and by running those applications at this transparent grid level, we "smooth" the overall load of the clusters (cf. Figure 1). Bag-of-tasks applications are removed from the local peak of an overloaded cluster and spread on other clusters that have idle resources.

The concept of a lightweight grid concerns infrastructures that simplify the general problem of the grids. This simplification generally goes through a certain homogeneity of services and administration procedures by adopting the same services and configurations on all interconnected clusters. Our main interest on CIGRI system is to provide a platform that focuses on the research and development of specific problems that come along with the execution of tasks (scheduling, fault tolerance...) on distributed grid environments. Our approach does not deal with critical problems inherent to computational grids like security, authentication mechanisms and resource location.

Best-effort computing with OAR

One of the simplifications assumed by the lightweight grid concept is that CiGri relies on the OAR batch scheduler [START_REF] Capit | A batch scheduler with high level components[END_REF]. A resources management software (or batch scheduler) handles the resource allocation to users and tasks on a computing cluster. OAR and CiGri are developed by the same research team. It provides an interesting feature used to not influence the normal functionality of the interconnected clusters on a CiGri platform. This is the concept of "best-effort" tasks: these are jobs which have a zero execution priority and are submitted only if there is a free resource. Moreover, if during the execution of those jobs the resource is requested by a local cluster user, the "best-effort" job is killed by the local batch scheduler and CiGri grid server is notified (cf. Figure 2).

Fig.2

This CiGri/OAR collaboration does not mean that OAR is mandatory for the local users' tasks submissions. It means that OAR must be installed on all the clusters of a CiGri platform to provide the "best-effort" interface for CIGRI remote jobs; but in the same time, it may be coupled to another batch scheduler for the local cluster jobs. This coupling option has already been tested with PBSPro, Torque and SGE.

CiGri Fault Treatment

Considering this best-effort feature and the aim of scalability (large bag-of-tasks applications means > 100k tasks), it is straightforward that CiGri has to provide support for error management. In CiGri fault treatment mechanism, the initial concern is to be able to locate, log and categorize the different errors that are possible to occur (ex. Cluster momentarily not responding). To ensure the platforms' reliability, it provides automatic actions to be taken for every different category.

Some types of events can be automatically and transparently treated. This happens for example, in the case of the interference failures where the grid best-effort task is killed by the local cluster batch scheduler, so that the resource can be occupied, by a higher priority local cluster job.

On such a grid infrastructure, the walltime of the tasks is a very important parameter as the life expectancy depends on the load and the turnaround time of the cluster jobs. Thus, the tasks have to be either relatively short or we have to use specific Checkpointing mechanisms. Such mechanisms are already implemented and are currently under evaluation [START_REF] Georgiou | Evaluations of the lightweight grid CiGri upon the grid5000 platform[END_REF]. Concerning the application discussed in this paper, we were able to generate rather small jobs (less than one hour) to have a good probability of ending without "being killed" (see the resubmission percentage in 3.5).

Furthermore, jobs length must not be too short because of the overhead time generated by the submission. Currently, CiGri submits jobs sequentially. In case we have hundreds of free resources, it may take several minutes to fill them all with best-effort jobs. Thus, with very short jobs (of the order of minutes), we may spend more time submitting than computing. This is a known issue, and OAR is currently evolving to offer an "array-job" functionality that will help a lot minimizing the submission overhead of CiGri.

CiGri architecture

The CIGRI software is composed of a server which communicates with all the batch schedulers of the clusters. It acts like a normal user, submitting minimum priority jobs on the best-effort queue of the batch schedulers. There is no specific CIGRI software installed on the clusters, except OAR. Nevertheless, it just takes advantage of the classic system tools (ssh, bash, sudo, cat, ls, scp, tar...) and the NFS file system of the cluster. An optional LDAP client may be used for the authentication, but it is not mandatory. Users may have different login accounts on the different clusters. Then CiGri makes the binding between the grid login and the clusters' logins. Fig. 3 The system is designed with a modular architecture based upon two high level components: an SQL database and a scripting language (in fact, two scripting languages: Perl and Ruby). The database is used to holding all the internal data and works as the only communication medium between the modules (cf. Figure 3). It is used to store information such as: the state of clusters' nodes, the state of submitted jobs, the various errors that can be obtained and logged, logging of all activities, information about the users, etc. The choice of a database as the central component ensures the management of big amounts of information and guarantees the reliability of the platform.

The central part of the CIGRI software is made of a collection of independent modules. Each of them is in charge of a specific task such as jobs scheduling, jobs monitoring, error logging, jobs submitting, statistics updating, and results collecting. The whole system is managed by a central module which is in charge of calling the other modules to perform either regular tasks (such as monitoring) or on-demand tasks (such as submission).

Another important functionality is the collection of results from the clusters, along with the cleaning of temporary files. A specially designed module auxiliary to the system is responsible for periodically collecting the result files, archiving them on the grid server and at the same time erasing them from the cluster. Hence, the user can collect the results of its computation on one centralised place and at the same time, the cluster storage resources are not overbooked by grid application data. Moreover, a private web portal exists to monitor the jobs on the grid.

Jobs submission

A grid user logs on a central host, through ssh and from there, jobs are prepared, submitted and collected. The job is described into a simple JDL (Job Description Language) file. Parameters are simply listed into a second file. One list of parameters per line corresponds to a single job. Each first parameter on a line is by convention the name of the output file or directory that will be collected once the job is ended. The most difficult part of the work, for a user, is to have the application installed and ready for running on every cluster of the grid he plans to use. This is generally achieved with the help of the local administrators (here again, we stick to the concept of a "lightweight" grid, where the administrators are easily reachable). Once the application is installed everywhere, the user generally needs to "feed" it with the input data. But, all the data have to be available for every job upon all clusters, even if only a subset will be used as the jobs are spread all over the grid. This is because we don't know in advance which task is going to be executed on which cluster and because there's no easy way to transfer only the needed files at the beginning of the job without creating a bottleneck. Studies are currently made for facilitating and optimizing the input data supply. One of the most probable solutions is to deploy a distributed file system that can easily work over tunnels, like GlusterFS [START_REF] Marinov | GlusterFS, Openfest 2007 Fifth Annual Conference[END_REF] for example. A distributed file system could also be an answer to the problem of Checkpoint files availability.

Nevertheless, during this study, the input data were small enough (a few megabytes) to be entirely copied upon every cluster before each experiment. That was achieved by using a simple loop from the central CiGri host.

Exploiting desktop PCs for nightly computing: ComputeMode

ComputeMode is another useful tool that was used through CiGri to provide additional computing resources during this study. ComputeMode is a free software that creates a computing cluster by rebooting idle workstations in an intranet to a diskless operating system at periodic scheduled times (mostly during the night). Initially developed in 2004, it is now co-maintained by the developers of CiGri since it is a valuable tool used in conjunction with this grid. The ComputeMode service is provided by a single server connected to the same LAN as the PCs that are targeted to be used for computation when not used for another matter. ComputeMode core components include a special PXE boot manager, an OAR server/front-end, a NFS server providing diskless operating systems and home directories, and a powerful web interface for the management of the resources and schedules. Using that interface, administrators can create schedules (calendar) for automatically booting PCs on specific systems at specific times. In fact this schedule capability is not only useful for managing volatile computing systems but can also serve to handle the start-up process of machine for a third party cloning system, for nightly automatic re-installation for instance. That is how the tool is actually used in one of the department of Grenoble University, hence proving being helpful both to the local administrators of the department for the management of the pool of workstations, and to the CIMENT project by providing extra computing resources during nights and weekends.

Like CiGri and OAR, ComputeMode is built on top of a SQL database that holds the information with regard to the workstations it manages: MAC addresses, available operating systems and schedules. The administration interface is written in PHP and interacts with the system components through the database. A complex configuration of DHCP server and a modified TFTP server allow workstations to be automatically registered in the database and to smartly load the judicious operating system at boot time, based on the schedules and possible actions from a user that might be in front of the machine unexpectedly. A very light HTTP agent is also provided for both Windows and Linux, to run as a service of the normal operating system of the workstation while not in the computation mode, polling the server to know when it is time to reboot in order to switch operating system. On the other hand, while in the computation mode, the machine poll the server as well, in order to switch back at the end of a computation scheduled, but will also automatically switch to the normal operating system at reboot if a user shows up and need the machine, without requesting more interaction than power cycling the machine using the keyboard or the power switch.

From the grid resource management point of view, new workstations are automatically recorded into the batch scheduler and a simple click only is necessary in the administration interface to configure a group of workstations to be in the computation mode at some schedule. It is important to notice that when a workstation is in the computation mode, the local disk is not activated (diskless operating system, remote file system), which ensures the owner or local user of the workstation that the local operating system and data won't be modified and can't be compromised. The computing operating system is a diskless GNU Linux system which is hence easily configurable from the ComputeMode server in a centralized manner, allowing the grid administrators to install applications and libraries very efficiently, since no deployment phase is needed.

From the job management point of view, resources of a ComputeMode cluster are volatile: they may show up or disappear at some time of the day and nobody can really know in advance if a job will be completed in time before the resource actually disappear. Moreover, in such a cluster, it is quite common to use PCs from classrooms or offices which places are not as reliable as machine rooms, hence where random failures can happen like bad network connectivity, power failures or unexpected reboots. This makes the use of CiGri even more helpful since it is indeed designed to manage such "best-effort" systems. Furthermore, one can notice that often, only one or two of the ComputeMode resources are available during the day and can be used for the preparation or the testing of a job. All other resources appear in CiGri as unavailable, just like any resource of the grid which is used by a local user or in a failure state. Then if a grid campaign is running, as soon as a PC handled by ComputeMode switch to the computation mode, for instance at the end of the day, CiGri sends jobs to it just like to any other idle resource of any other classical cluster. CiGri will as well get back into the bag-of-tasks any job that was not finished when related ComputeMode resources become unavailable, for instance in the morning when workstations must operate their normal system for serving users. Those uncompleted jobs are then resubmitted to other clusters of the grid that are available during the day. For CiGri, ComputeMode provides indeed a temporary but auto-managed pool of numerous resources for the grid, by just optimizing the utilization of existing resources of the University.

Monte Carlo simulation toolkit

GATE (Geant4 Application for Tomographic Emission) [START_REF] Jan | GATE: a simulation toolkit for PET and SPECT[END_REF] is a Monte Carlo simulation toolkit based on the general-purpose simulation package Geant4 [START_REF] Agostinelli | GEANT4: A simulation toolkit[END_REF]. It was developed in order to model treatment and diagnosis examinations such as radiotherapy, SPECT and PET in the field of nuclear medicine (and recently CT-scan exams). GATE is an open source code which uses approximately 200 C++ classes from Geant4. User does not have to program in C++ thanks to an extended version of Geant4 scripting version. Thus, the user can easily build a simulation (macro) by using a script language. Many researchers have been using GATE for its flexibility and its accurate modelling of different detector designs [START_REF] Staelens | Monte Carlo simulations of a scintillation camera using GATE: validation and application modelling[END_REF] and very complex geometries [START_REF] Sakellios | GATE simulations for small animal SPECT/PET using voxelized phantoms and rotating-head detectors[END_REF]. The major drawback of GATE is the computation time especially when simulating realistic configurations such as voxelized emission and attenuation maps. Several solutions were proposed to accelerate some of the GATE simulations: (i) setting thresholds for the production of secondary electrons, x-rays and delta-rays, (ii) limiting the emission angle to a certain range, (iii) replacing the disintegration scheme by a source of monoenergetic gammas, (iv) parametrizing replicas for the collimator hole arrays (SPECT), (v) compressing voxelized phantom [START_REF] Taschereau | Compressed voxels for high-resolution phantom simulations in GATE[END_REF], (vi) techniques such as variance reduction, bootstrapping and jackknifing, (vii) splitting the simulation on a cluster [START_REF] Beenhouwer | Optimizing the Scalability of Parallelized GATE Simulations[END_REF], and recently on the grid [START_REF] Maigne | Parallelization of Monte Carlo simulations and submission to a grid environment[END_REF].

Nowadays, GATE is largely used on grids. Indeed, Monte Carlo Simulations can be packed in independent bags of tasks. They are thus easily and efficiently computed on distributed computers. To distribute a long sub-divided simulation on multiple processors, one should also sub-divide the associated long Random Number Stream (RNS) into short ones. However, these short RNS have to be independent because any intra or inter-sequence correlation could lead to inaccurate results [START_REF] Reuillon | Rigorous Distribution of Stochastic Simulations Using the DistMe Toolkit[END_REF]. The parallelization of the RNS was accomplished by using the sequence splitting method [START_REF] Maigne | Parallelization of Monte Carlo simulations and submission to a grid environment[END_REF] (also known as the random spacing method). The pseudo random numbers generator (PRNG) James Random (period 2 144 ), implemented by default in GATE, was replaced by the Mersenne Twister PRNG due to his huge period (2 19937 ) [START_REF] Reuillon | Rigorous Distribution of Stochastic Simulations Using the DistMe Toolkit[END_REF].

Tab.1

The work described in this paper was done by using GATE 3.1.1 built upon Geant4.8.1. Table 1 shows the number of clusters/CPUs on which GATE was installed. The GATE macro contains a unique random number status and an output filename. These two parameters are renamed according to their appropriate value during the distribution process. Output files were analysed with the ROOT object oriented data analysis framework (http://root.cern.ch/).

The γ Imager: system configuration

The Biospace γ Imager (http://www.biospacelab.com/index.html) is a high resolution planar scintigraphic camera which associates a 4 mm thick NaI(Tl) crystal and a PSPMT Hamamatsu R3292 which leads to a circular 100 mm diameter field of view. While most high resolution cameras use a pixelated crystal, the scintillation crystal of the γ Imager has a continuous 120 mm diameter with a 100 mm circular diameter active area. An aluminium protection layer, 0.8 mm thick, is placed in front of the crystal. The 5 Inch diameter PSPMT is equipped with a bialkali photocathode, 11 multiplication dynodes, 1 reflective dynode and a multiwire anode with 28 (X) + 28 (Y) wires. The readout of the 56 anode signals enables to calculate the spatial position (X, Y) and the energy for each event. A removable low-energy high-resolution parallel hole collimator with 35 mm thickness is used. The flat-to-flat distance of the hexagonal holes is 1.3 mm and the septum thickness in all directions is 0.2 mm. The whole detection head is surrounded with a 15 mm thick lead shielding. The γ Imager consists of two detector heads, only one head was used in this study.

Simulation of the Biospace gamma camera

Fig.4

The geometry of the γ Imager was accurately described in GATE. Figure 4 (a) shows the model of the detector head. The dimensions and the material of each part of the real camera were modelled as realistically as possible. The detector head simulation was thus composed of the lead collimator, a 1.2 mm air gap space, the aluminium protection, the NaI(Tl) crystal and the lead shielding of the whole head. As the plate scintillation crystal is 4 mm thick, about 38% of the 140 keV incident photons pass through the crystal without interacting. Thus, a significant number of photons will not be detected if we do not take into account the backcompartment. A 10 µm thick gold layer is placed behind the crystal, followed by a 0.39 mm Epoxy layer which separates it from the 1 mm light guide made of quartz. Another 0.15 thick Epoxy layer is positioned behind the light guide followed by the PSPMT, modelled as a 2 mm borosilicate glass entrance window and a 110 mm nickel back-part. The detector backcompartment was ended by a 15 mm rear lead shielding. Figure 4 (b) illustrates the relative scattering contribution of each layer in the model (source at 2 cm from the collimator), by counting the scattered events in each particular part divided by the total number of scatter events. During the acquisitions, a solution containing 99m Tc, a photon emitter at 140 keV, was put in a glass capillary of 1.4 mm inner diameter and 1.8 mm outer diameter and 6 mm length. The capillary was held by a Pyrex Ruler (150 mm x 40 mm x 3 mm). The Ruler and the capillary were also simulated.

Validation of the small animal camera

The validation of the small animal SPECT camera is based on the comparison of four parameters measured experimentally with the corresponding simulation data; Energy Spectra, Spatial Resolution (FWHM), Sensitivity and Image of a capillary phantom. The three first parameters represent three basic features of a gamma camera, while the image of an inhomogeneous phantom allows a visual comparison between experimental and simulated data.

Experimental set-up

In this study, 16 planar acquisitions were made altogether. The radioactive background was first measured during 30 minutes without any activity and was subtracted from all the other measurements after normalizing it to the same acquisition duration. Then, 14 acquisitions were performed using a liquid source of 99m Tc (140 keV) which was put in a thin capillary (1.4 mm inner diameter and 6 mm long). Total activity was 70 µCi (2.59 MBq). The capillary was first placed in the air at 2, 7, 10 and 16.5 cm from the detector's surface. Then, the capillary at 16.5 cm was positioned above a cylindrical beaker (10.5 cm inner diameter and 15.6 cm long) which was put on the collimator. The beaker was used three times: empty, filled with 400 ml (4.62 cm height) and with 1000 ml (11.55 cm height) of water. All the seven configurations were performed in a first set of measurements with the source at the centre of the camera Field Of View (FOV), and a second set was performed with the source 2 cm off-centred. The sixteenth configuration was performed so as to evaluate the image of the capillary phantom. The phantom consisted of four parallel capillaries (1.4 mm inner diameter and 31.5 mm long), with a capillary-to-capillary distance of 5 mm. The capillaries were filled with 99m Tc solutions of different activities (from the least (right) to the most (left) radioactive) (cf. Figures 8 (a) and (b)): 81 µCi, 129 µCi, 220 µCi and 611 µCi. The phantom was 5 cm far from the scintillation camera. Events were recorded in an energy window between 40 and 186 keV for a duration of 5 minutes for each acquisition. The size of the projections was 256 x 256 pixels (pixel size ~ 0.39 mm).

GATE simulations

The fifteen configurations mentioned above were accurately reproduced using GATE. Monoenergetic gamma rays (140 keV) were emitted in 4 π steradians. The physical processes involving photon interactions (photoelectric effect, Compton scattering and Rayleigh scattering) were modelled using the low-energy electromagnetic package of GEANT4, while gamma-conversion was disabled. The energy resolution Full Width Half Maximum (FWHMe) of the camera was modelled by the convolution of the output data using a Gaussian distribution with user defined mean and FWHM as stated by the manufacturer (11.5% FWHMe at 140 keV). The intrinsic spatial resolution FWHMi was also modelled in the same way (2.2 mm FWHMi at 140 keV).

Parallelization of the simulations

Many models of the camera have been tested in order to obtain the most realistic detector. A big simulation of one billion events was generated for each configuration. The simulation was split into 1000 small simulations, 1 million emitted events each, and was then distributed on CiGri. The 1000 small output files were collected from the grid, merged into one file (on a local CPU) and finally analysed using ROOT. Simulating 1 million events takes 10 minutes on a local CPU (Pentium IV, 3.2 GHz, 1 Go RAM) and requires ~ 30 million random numbers. Thus, a global sequence of more than 30 billion PRN was used for each simulation (which is small for the used PRNG Mersenne Twister).

Comparison parameters

Experimental and simulated data were compared basing them on the following quantities:

• Energy Spectra.

The energy spectra of events recorded in the whole FOV (40-186 keV) was sketched for five configurations: (i) in air: source placed at 2 and 10 cm from the collimator, (ii) in water: source placed at 16.5 cm from the collimator above the empty beaker which then is filled with 4.62 and 11.55 cm of water.

• Spatial Resolution.

The spatial resolution was estimated by measuring the FWHM of profiles perpendicular to the axis of the line source. The FWHM was calculated for the 14 configurations by drawing a 6 pixel thick profile (~ 2.345 mm) through the source in a plane perpendicular to the cylinder axis (X-axis) in the Y-axis direction. Events were detected in the photopeak window (126-154 keV) to reduce the noise effects.

• Sensitivity.

The system sensitivity, defined as the number of detected events divided by the number of emitted events, was evaluated for the 7 configurations where the source is placed in the centre of the FOV. Data were acquired in 3 energy windows: (i) photopeak window 126-154 keV, (ii) Compton window 92-125 keV and (iii) total FOV window 40-186 keV.

• Image of a capillary phantom.

The image of the capillary phantom was acquired in the whole FOV in the energy window 40-186 keV. A 6 pixels thick profile was also drawn through the 4 line sources seen on the image (cf. Figures 8 (a

Results and discussions

Energy Spectra

Figure 5 shows the experimental and simulated energy spectra of the 99m Tc point source placed in the air at 2 and 10 cm from the collimator: contributions of the simulated photons scattered within different components of the detector head and within the capillary and the ruler are also plotted. The experimental and simulated energy spectra were normalized to the same number of counts detected at 140 keV.

As illustrated in figures 5 (a) and (b), including a back-compartment model was essential as well as the simulation of the capillary and the ruler to obtain a good agreement between measured and simulated spectra between 80 and 120 keV.

Fig.5

Good agreements between experimental and simulated energy spectra can be noticed especially in the photopeak and Compton windows. Differences observed in figures 5 (a) and (b) between 60 and 100 keV could come from the imperfect estimated shape and material of the ruler and from the unawareness of a layer which was not modelled behind the crystal. Considering the PSPMT as a continuous layer of Nickel could also lead to such differences [START_REF] Aoun | Validation of the Small Animal Biospace Gamma Imager Model Using GATE Monte Carlo Simulations on the Grid[END_REF].

Energy spectra obtained for the 99m Tc source at 16.5 cm from the collimator, with 0, 4.62 (400 ml) and 11.55 cm (1000 ml) water thickness are shown in figure 6. The scattered photons contributions were also drawn.

The scattered photons within the phantom have increased, as expected, proportionally to the water thickness. Good agreements between experimental and simulated energy spectra could be observed for the two configurations with the empty beaker which then is filled with 4.62 cm thick of water.

The same differences with the source in air between 60 and 100 keV were maintained in figures 6 (a) and (b), which confirms that the back-compartment was not accurately modelled. But it is not the case in figure 6 (c) because backscattered photons (scattered within the ruler) have less influence on the energy spectra. It was reduced by the presence of an 11.55 cm thickness of water.

The disagreement in figure 6 (b) (4.62 cm of water) between 75 and 85 keV and between 110 and 120 keV is lower than in figure 6 (c) (11.55 cm of water) and doesn't figure at all when the beaker is empty (cf. Figure 6 (a)). Thus, these differences are proportional to the water thickness and could be the result of the impurity of the real water used in the measurements.

Spatial resolution

Experimental FWHM for a centred and off-centred source were well reproduced in all configurations by the simulations where discrepancies are below 2% and 5% respectively, except the source at 2 cm. The 5% differences might be due to the imperfect modelling of the PSPMT non-uniform response in GATE [START_REF] Lazaro | Validation of the GATE Monte Carlo simulation platform for modelling a CsI(Tl) scintillation camera dedicated to small-animal imaging[END_REF]. The approximate modelling of the ruler was also noticed as the disagreements of the FWHM for the source centred and off-centred at 2 cm is clearly higher than the other configurations.

Tab.2

Several SPECT and PET scanner models have been validated in the literature using MCS. Some of these works have performed a comparison between experimental and simulated spatial resolutions and have illustrated the following differences: 8.3% in µ-SPECT [START_REF] Garibaldi | Optimization of compact gamma cameras for breast imaging[END_REF], 6% in µ-PET [START_REF] Merheb | Full modelling of the MOSAIC animal PET system based on the GATE Monte Carlo simulation code[END_REF], 4.5% [START_REF] Staelens | Monte Carlo simulations of a scintillation camera using GATE: validation and application modelling[END_REF] and 2% [START_REF] Assié | Validation of the Monte Carlo simulator GATE for indium-111 imaging[END_REF] in SPECT and 11-18% in PET [START_REF] Lamare | Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE[END_REF]. It should be known that the results below cannot be compared to those in our study because each validation was performed differently (source, collimator, crystal…), even in the case of the µ-SPECT system. Nevertheless, it could be useful to assert that a 2% or 5% difference is well acceptable.

Sensitivity

The Biospace small animal camera sensitivity values obtained in Air in three energy windows (total FOV 40-186 keV, photopeak window 126-154 keV and Compton window 92-125 keV) with GATE compared to the measured values are plotted in figure 7 (a) for the 4 source-collimator distances 2, 7, 10 and 16.5 cm. Relative differences between experimental and calculated values were respectively: (i) 40-186 keV: 6.8%, 4.6%, 4.1% and 5.7%; (ii) 126-154 keV: 7.9%, 6%, 5.9% and 7.2%; (iii) 92-125 keV: 8.1%, 4%, 1.4% and 5.9%.

Results of the system sensitivity with the phantom (empty, 400 ml and 1000 ml) derived from the experiment measurements and the simulations are drawn in figure 7 (b) for the source placed at 16.5 cm from the collimator where events were detected in the same three energy windows. Relative differences between experimental and simulated values for the different water thickness (0, 4.62 and 11.55 cm) were: (i) 40-186 keV: 1.5%, 3.8% and 3.1%; (ii) 126-154 keV: 3.2%, 4.4% and 1.9%; (iii) 92-125 keV: 0.3%, 0.5% and 5.6%.

Fig.7

The simulation was able to reproduce the system sensitivity in the three energy windows for all the configurations within an 8% error. This difference could result from the simple modelling of the inhomogeneous materials and the complex shapes of the different detector components which only the constructor accurately knows. The 8% differences between experimental and simulated system sensitivities are relatively a good result if we look at other illustrated disagreements: 2% in µ-SPECT [START_REF] Lazaro | Validation of the GATE Monte Carlo simulation platform for modelling a CsI(Tl) scintillation camera dedicated to small-animal imaging[END_REF], 3% in µ-PET [START_REF] Merheb | Full modelling of the MOSAIC animal PET system based on the GATE Monte Carlo simulation code[END_REF], 6.1% [START_REF] Staelens | Monte Carlo simulations of a scintillation camera using GATE: validation and application modelling[END_REF] and 9% [START_REF] Assié | Validation of the Monte Carlo simulator GATE for indium-111 imaging[END_REF] in SPECT and 10% in PET [START_REF] Lamare | Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE[END_REF].

Image of a capillary phantom

Fig.8

Figure 8 shows a visual comparison between (a) experimental and (b) simulated images of the capillary phantom as well as the (c) horizontally drawn profiles (6 pixels thick ~ 2.345 mm) through these images. The comparison of the two images and profiles shows a good agreement. However, the simulation was unable to perfectly reproduce the local distortion which could account for the 5% differences between experimental and simulated FWHM for an off-centred source (cf. Table 2). [START_REF] Lazaro | Validation of the GATE Monte Carlo simulation platform for modelling a CsI(Tl) scintillation camera dedicated to small-animal imaging[END_REF] have modelled the non-uniform PSPMT response (CsI(Tl) array small gamma camera) using a corrective map obtained experimentally. This simple approach has partially corrected the local distortion from the GATE images. It has also reduced the errors between experimental and simulated FWHM and system sensitivity values.

Calculation time

Table 3 contains the computing time of a long simulation (1000 jobs) on a local CPU (Pentium IV, 3.2 GHz, 1 Go RAM) and on CiGri: during the day, during nights and weekends and an estimated average of the CPUs availability. CiGri has reduced the duration of a simulation compared to 1 CPU computation by a factor of 42, 67 and 56 respectively. Resubmitted jobs rate on CiGri, defined as the number of resubmitted jobs divided by the number of executed jobs, is also represented at the right of table 3. 16.9% of the jobs were killed and then were resubmitted by CiGri during the day, which is as expected higher than the 10.2% resubmission rate during the nights and weekends (more users during the day). The estimated average of the resubmitted jobs is 13.4%.

Each long simulation corresponds to one configuration. The material composition and the thickness of many components of the detector weren't exactly known (PSPMT, Gold layer…). In consequence, about 200 simulations were carried out in order to optimise the model of the Biospace camera. All these simulations would have taken 4 years of computation time on a local CPU, but on CiGri it took only 25 days (cf. Table 3). In the ideal case, where no job is killed, the 200 simulations would have taken about 22 days on CiGri. Thus, the 200 simulations would not have been feasible on a local CPU. Reducing the number of simulations would have altered the quality of the results. So the deployment of the Grid was crucial in this study to get accurate results.

Tab.3

Conclusions and perspectives

Nuclear medical imaging needs a better physical model to improve the quantification and the quality of the reconstructed images. Our study has brought into light that the Monte Carlo Simulations toolkit GATE was able to accurately model the Biospace small field of view γ Imager. The camera model can thus be used for future researches such as developing new attenuation and diffusion correction algorithms. This paper has also proved the interest of grids in order to obtain accurate MCS results in SPECT within reasonable elapsed time. The CIMENT Grid, powered by CiGri, was an essential tool to develop and assess our MCS of the γ Imager on a grid.

In this paper, we have presented the lightweight grid CiGri. We have experimented CiGri on a real life medical imaging application. Compared to other grid systems like Condor, Ourgrid or Globus, the lightweight grid CiGri is very simple to install, to administrate and to use. CiGri is well suited for parametric applications involving a large bag of independent tasks. It was not designed to support parallel tasks with strong dependences. This simplification allowed us however to build a grid very easy to grasp. Beginners can earn their spurs on a local grid before trying to use an international grid. Users needing a reasonable computation time for bag-of-tasks applications are able to quickly obtain results. Moreover, the use of campus clusters can be optimized as their idle CPU cycles can be dedicated to parametric computations thanks to CiGri instead of being wasted. Additionally, CiGri offers a way to efficiently exploit volatile resources like desktop workstations managed by a tool like ComputeMode.

Planned future works concerning CiGri, include the integration of the new "array jobs" feature, supported upon OAR, which will minimize the submission time overhead of a large number of jobs; a distributed file system usage to facilitate and optimize the input data transfers; and the Checkpointing integration using BLCR (Berkeley Lab Checkpoint/Restart) [START_REF] Hargrove | Berkeley Lab Checkpoint/Restart (BLCR) for linux clusters[END_REF] for better global efficiency of the grid and particularly a minimization of the resubmission percentage [START_REF] Georgiou | Simple, fault tolerant, lightweight grid computing approach for bag-of-tasks applications[END_REF].

Furthermore, we are also working upon the use of virtualization technologies to facilitate the applications deployment. It will allow us to abstract the diversity of operating systems. Virtualization can also be used as another way to perform Checkpointing.

Concerning the medical imaging application, our future study will be the development of an iterative reconstruction algorithm in which long GATE simulations will be carried out at each iteration. For such studies, a higher scale of computation power is needed. The EGEE (Enabling Grid for E-sciencE) [START_REF] Jacq | Grid enabled virtual screening against malaria[END_REF] Grid with a next multi-core processor generation should be able to meet our requests.

Fig. 1

 1 Fig.1

  ) and (b)).

Fig. 6

 6 Fig.6

FiguresFigure 4 .

 4 Figures (in color on the web and in black and White for printing)

Figure 5 .

 5 Figure 5. Experimental and simulated energy spectra for a 99m Tc source positioned in air at (a) 2 cm and (b) 10 cm from the camera.

Figure 6 .

 6 Figure 6. Experimental and simulated energy spectra for a 99m Tc source positioned at 16.5 cm from the collimator above a beaker filled with (a) 0 cm, (b) 4.62 cm and (c) 11.55 cm of water.

Figure 7 .

 7 Figure 7. Comparison between experimental and simulated system sensitivities in three energy windows for the centred source placed at: (a) 2, 7, 10 and 16.5 cm in air; (b) 16.5 cm with three water thickness 0, 4.62 and 11.55 cm.

Figure 8 .

 8 Figure 8. Comparison between (a) experimental and (b) simulated images of a four capillaries phantom filled with different 99m Tc concentrations and (c) horizontal profiles through the middle of these images.

Figure

  Figure(s) 1 Click here to download high resolution image

Table 1 .

 1 Number of clusters and CPUs available to use GATE on CiGri.

Table 2 .

 2 Comparison between experimental and simulated spatial resolutions for the centred and off-centred sources placed at: 2, 7, 10 and 16.5 cm in air; 16.5 cm with three water thickness 0, 4.62 and 11.55 cm.

Table 3 .

 3 Comparison between computing time of 1 and 200 simulations on a local CPU (Pentium IV, 3.2 GHz, 1 Go RAM) and on the CiGri Grid with the percentage of the resubmitted jobs. Example of load balancing of a grid composed of 2 clusters. CiGri modules and their operation.

	Tables
			Total	GATE availability	GATE availability
					(max)		(average)
		Clusters CPUs	Clusters CPUs	Clusters CPUs
	Day	11	886	7	430	7	125
	Night and Weekend	11	886	7	555	7	215

Acknowledgement

The authors are very grateful to Professor Daniel Fagret of the INSERM U877 Laboratory for providing us with the γ Imager. We would also like to thank Serge Maitrejean of Biospace LAB for his technical support. CiGri has been supported by the Region Rhône-Alpes (grant CPER CIRA).

Vitae

Joe Aoun received the MSc degree in particle physics from the Blaise Pascal University, Clermont-Ferrand, France, in 2005. He is currently a PhD candidate in the Laboratory of Techniques of medical engineering and complexity -Informatics, mathematics and application of Grenoble at the Joseph Fourier University (TIMC-IMAG, Grenoble, France) and in the Laboratory of Particle Physics (LPC) at the Blaise Pascal University. He is involved in Single Photon Emission Computed Tomography and CT-scan (SPECT/CT) images registration with the Data Consistency Conditions and Monte Carlo simulations for SPECT imaging on Grids.

Bruno Bzeznik currently works as a Research Engineer (Grenoble University) on the CIMENT project (http://ciment.ujf-grenoble.fr). He is the gridmaster of the local grid and acts as a UNIX and network expert on the HPC platforms of the University. During another half of its working time, he collaborates in the Laboratory of Informatics of Grenoble (LIG) in the MESCAL team, as a contributor to the administration of the national IT grid Grid'5000 and co-developer of the grid tools OAR, OARGRID and CiGri. This latter activity helps Bruno Bzeznik in being a gateway between HPC-research and HPC-production in the local area.

Vincent Breton received his Engineer degree from Ecole Centrale de Paris in 1985 and his

PhD in Nuclear Physics from the University of Paris XI-Orsay in 1990. Since 1990, he has been a Researcher at the French National Centre for Scientific Research (CNRS). In 2001, he founded a research group (http://clrpcsv.in2p3.fr) on the application to biomedical sciences of the IT technologies and tools used in high energy physics. Co-founder of the GATE collaboration (http://opengate.in2p3.fr) gathering more than 20 research laboratories around the world, co-founder of the Healthgrid (http://www.healthgrid.org) and WISDOM (http://wisdom.healthgrid.org) initiatives, he has been leading the French national GLOP (ACI GRID 2002) and regional Auvergrid grid projects as well as coordinating work packages in several European projects within FP5 (DataGrid) and FP6 (EGEE, BioInfoGrid, Embrace, SHARE, EGEE-II) Framework Programmes.

Mehdi Leabad received the B.S. degree in Physics from Grenoble Polytechnics National Institute (INPG), Grenoble, France, in 2004. After working as a technical engineer in multimodal preclinical imagery at the Laboratory of Electronics and Information Technologies (LETI), he is currently product engineer of Biospace Lab, an international preclinical imagery company. He manages principally the developments of SPECT/CT and autoradiography imagers.

Julien