Armita Khajeh Nassiri

Nathalie Pernelle

Gianluca Quercini

Knowledge Graph Refinement based on Triplet BERT-Networks ⋆

Knowledge graph embedding techniques are widely used for knowledge graph refinement tasks such as graph completion and triple classification. These techniques aim at embedding the entities and relations of a Knowledge Graph (KG) in a low dimensional continuous feature space. Unlike KG embedding methods and inspired by works built upon pre-trained language models, this paper adopts a transformer-based triplet network. It creates textual sequences from facts and fine-tunes a triplet network of pre-trained transformer-based language models, creating an embedding space that clusters the information about an entity or relation in the KG. It adheres to an evaluation paradigm that relies on efficient spatial semantic search technique. We show that this evaluation protocol is more adapted to a few-shot setting for the relation prediction task. Our proposed GilBERT method is evaluated on triplet classification and relation prediction tasks on multiple well-known benchmark knowledge graphs such as FB13, WN11, and FB15K. We show that GilBERT achieves better or comparable results to the state-of-the-art performance on these two refinement tasks.

Introduction

Large-scale knowledge graphs (KG) have gained popularity in the industry, like those built by Amazon, Google, and Microsoft, and in academia, such as YAGO and Wikidata. Knowledge graphs can be described as a structured repository of facts presented as triples (head, relation, tail). In other words, the facts are relations holding between two entities, for instance (Barack Obama, isMarriedTo, Michel Obama). More recently, KGs have become a natural resource for many AI downstream tasks such as question answering, entity disambiguation, and rule mining [START_REF] Ji | A survey on knowledge graphs: representation, acquisition, and applications[END_REF]. Although knowledge graphs contain millions of facts, they are considerably incomplete. Three different refinement tasks pertinent to this issue have attracted substantial attention in the literature: Triple classification, Relation Prediction, and Link Prediction. Most of the approaches proposed for tackling these tasks rely on embedding entities and relations of the KG into vectorized representations.

Commonly, these embedding techniques [START_REF] Ji | A survey on knowledge graphs: representation, acquisition, and applications[END_REF] compute a defined scoring function that, given the fact, computes its plausibility. Hence, at the time of evaluation, depending on the nature of the task, the existence likelihood of unobserved facts (through substitution) should be calculated using the scoring function and then ranked. This causes additional overhead, especially for tasks such as link prediction. Others, like KG-BERT [START_REF] Yao | KG-BERT: BERT for knowledge graph completion[END_REF], can tackle the prediction task, e.g., relation prediction, as a classification task. Nevertheless, many relations in KGs are long-tail, meaning that very few facts with that relation exist in the KG. In such few-shot learning settings, the classification will be biased towards the data-rich relations, and the approach's robustness can be hampered. This paper proposes GilBERT, which employs a triplet architecture of a pre-trained language model such as BERT to cluster, in embedding space, all the facts regarding a relation or an entity. It overcomes the earlier shortcomings by fine-tuning BERT with a triplet loss that takes as input textual sequences created from facts. This way, depending on the refinement task, we force the information about an entity or a relation to be embedded close to each other, forming clusters. The evaluation protocol uses an efficient spatial semantic search technique, e.i., FAISS [START_REF] Johnson | Billion-scale similarity search with gpus[END_REF] to compute the closeness of the embedding of a test example to the point(s) in the embedding space. This reduces the overhead caused by substituting the candidates, and as shown in the experiments, it is more suited for a few-shot learning setting in the case of relation prediction. In addition, unlike translational and factorization-based models that need extra efforts to integrate any additional information such as entity descriptions, types, or paths to them [START_REF] Xie | Representation learning of knowledge graphs with entity descriptions[END_REF] [START_REF] Wang | {DOLORES}: Deep contextualized knowledge graph embeddings[END_REF], it is trivial to do so in our proposed model. We also propose strategies that capture some of the semantics of the KG to generate harder negative information for the triplet network.

Related Work

A plethora of research is devoted to finding embeddings that model entities and relations of a KG in a low dimensional continuous vector space and to the proposition of scoring functions capable of capturing different kinds of relations. A recent comprehensive study overviews these techniques [START_REF] Ji | A survey on knowledge graphs: representation, acquisition, and applications[END_REF]. We can roughly categorize these models into three different categories. First, the translational-based models such as the TransE [START_REF] Bordes | Translating embeddings for modeling multi-relational data[END_REF] and its more advanced extensions like TransH [START_REF] Wang | Knowledge graph embedding by translating on hyperplanes[END_REF] and TransR [START_REF] Lin | Modeling relation paths for representation learning of knowledge bases[END_REF], and more recently RotatE [START_REF] Sun | Rotate: Knowledge graph embedding by relational rotation in complex space[END_REF]. The translational-based models have proved to be successful from the beginning. TransE proposes one of the simplest and most prominent scoring functions that, given a fact, the head h and tail t embeddings are related by a translation vector r such that h + r ≈ t. Secondly, the tensor factorization-based models with RESCAL [START_REF] Nickel | A three-way model for collective learning on multirelational data[END_REF] being one of the very earliest works adopting this technique. Other extensions DistMult [START_REF] Yang | Embedding entities and relations for learning and inference in knowledge bases[END_REF], and ComplEx [START_REF] Trouillon | Knowledge graph completion via complex tensor factorization[END_REF] instead of the tensor product, use dot product, and Hermitian dot product, respectively. Thirdly, the neural network-Based Models like ConvE [START_REF] Dettmers | Convolutional 2d knowledge graph embeddings[END_REF] adopt CNNs with deeper scoring functions. Also, more recent transformer-based models such as KG-BERT [START_REF] Yao | KG-BERT: BERT for knowledge graph completion[END_REF], and R-MeN [START_REF] Nguyen | A relational memory-based embedding model for triple classification and search personalization[END_REF] have been proposed in the literature to solve different downstream tasks. To the best of our knowledge, KG-BERT is the only work that fine-tunes a pre-trained language model like BERT.

As pointed out in Section 1, unlike most of the previous works in the literature where the embeddings for entities and relations are learned, in our approach, we embed the training data into different clusters for entities or relations, depending on the downstream task. Following the indispensable contribution of transformers and pre-trained language models to NLP and the success of KG-BERT, it is natural to build upon BERT new methods that can be adapted to KGs. GilBERT adopts a triplet architecture that fine-tunes BERT (or any similar language model such as RoBERTa).

The proposed approach

Let G be a knowledge graph containing a collection of facts represented as triples {(h, r, t)} ⊆ E ×R×E, where E and R denote the entity and relation sets, respectively. We propose GilBERT, an approach that fine-tunes BERT such that based on the refinement task, it clusters information about an entity (e.g., for triple classification task) or a relation (e.g., for relation prediction task). For some downstream tasks, not all triple parts (h, r, t) are known at the time of evaluation; e.g., the relation is unknown for relation prediction. Hence, to create the embedding space, we only use parts of the facts that are available at the time of evaluation so that the test data can be embedded the same way as during training. For the simplicity of notation, let a Partial fact P F be a textual sequence created out of a triple (h, r, t) by concatenating the entities or entities and relations. For instance, given the triple (Barack Obama, isMarriedTo, Michel Obama), P F rt would be: "isMarriedTo Michel Obama", and P F ht would be "Barack Obama Michel Obama". In all cases, and subject to availability, it is possible to replace the entity with its description to inject more background knowledge into the model. To cluster the training data, inspired by Sentence-BERT [START_REF] Reimers | Sentence-bert: Sentence embeddings using siamese bertnetworks[END_REF], we use a triplet network architecture of BERT that maps semantically similar facts near each other and dissimilar facts distant from one another. Thus it takes as input three partial facts P F , one as the anchor, one as positive (which is similar to the anchor), and one as negative (which is dissimilar to the anchor). To obtain a fixed-sized embedding vector for the input P F , unlike KG-BERT, which uses the output of the [CLS] token produced in the last layer of BERT, a mean pooling layer is applied to BERT's output layer. According to [START_REF] Reimers | Sentence-bert: Sentence embeddings using siamese bertnetworks[END_REF] and our experiments, the output of the pooling layer results in better embedding for the given input than using the embedding of the [CLS] token. This architecture clusters all the information about an entity (or relation). It is important to note that using P F s as inputs to the model allows us to use only the parts of a fact known at the time of evaluation (e.g., the head and the tail for a relation prediction task). This avoids the evaluation time overhead of previous models caused by creating all possible substitutions and ranking their plausibility based on the learned scoring function.

Figure 1 illustrates the fine-tuning of BERT through the triplet network that is comprised of three identical networks with shared parameters. The network is fed three input textual sequences: the partial facts of anchor, positive and negative, with the aim of anchor and positive ending up in the same cluster and anchor and negative in different ones. More specifically, the euclidean distance of the embedding of anchor E a and positive E p is reduced whilst that of the anchor E a and negative E n is increased. The triplet loss function is defined as:

L = max(0, dist(E a , E p ) -dist(E a , E n ) + γ),
where γ is the safety margin ensuring that the embedding of negative is at least γ further from the anchor than the positive.

To train the model, for each gold triple (h, r, t) in G, n different input anchor, positive and negative P F s are created. It is important to create hard negative inputs so that the function L is not trivial. In literature, to learn the scoring function that yields to embeddings, the negative examples are usually the corruption of a gold triple by replacing either its h or t. More sophisticated techniques such as [START_REF] Amato | Injecting background knowledge into embedding models for predictive tasks on knowledge graphs[END_REF] utilize background knowledge such as the schema of KG to better choose negatives. We consider the more frequently observed case where a schema is not available and try to capture the semantics of the KG when generating our input data. Here-under, we provide the details of data creation depending on whether the clustering is done based on relations or entities. Clustering based on Relation. The anchor is the partial fact P F ht . Each positive sample, P F hiti is created from a fact drawn at random from the set {(h i , r, t i ) |(h i , r, t i ) ∈ G}, and the negative samples P F hj tj from the set {(h j , r ′ , t j ) |(h j , r ′ , t j ) ∈ G ∧ r ′ ∈ close r }. So for generating hard negatives, the P F is created from a triple whose relation is semantically close to the relation of the anchor and positive. Denote the set F r of all triples in G having the relation r. The set close r is chosen based on two criteria: (1) of all relations in R, the percentage of tail entities shared with F r , and (2) the percentage of head entities shared with F r . The obtained percentages have the intuition of domain and range and close r is the set of all relations whose computed sharing percentage is higher than a threshold. For instance, the close BornIn = {worksIn, diedIn, locatedIn}. Clustering Based on Entity. The clusters for entities are created using the entities appearing as head, and they can easily be extended to tail by generating reverse relations. The anchor is the partial fact P F rt or randomly P F hrt . The positive example is the partial fact created from a triple with the same head as anchor {(h, ri, t i ) |(h, r i , t i ) ∈ G}. To create negative samples that are more difficult, we adhere to the idea that entities that have the same type are semantically more related to each other. Hence we choose an entity that is likely to have the same type as the head entity h. We create P F rtj or P F hj rtj from the set {(hj, r, t j ) |(h j , r, t i ) ∈ G ∧ (h, r, t j ) / ∈ G}. This way, rather than setting apart a city from a person, which is too easy for the loss function, we disjoint two people who also share a property.

An overview of the evaluation pipeline for the relation prediction task is depicted in Figure 1. First, the partial fact of a test triple (h, r, t) is constructed in the same way as it has been done with the gold training inputs. The fine-tuned model is used to obtain the vectorized embedding E test . The similarity, through Euclidean distance, of E test to the embeddings of training inputs is used for the evaluation. Let the reference set ∆ and ∆ e be the set of embeddings of all training anchors and the set of embeddings of anchors corresponding to entity e, respectively. For the triple classification, the distance of E test is calculated with the reference set of its head entity ∆ h . And for relation prediction, the K closest points to E test in ∆ are used for choosing the most plausible relation. In both settings, to find the closest points to E test in the corresponding reference sets, we rely on FAISS [START_REF] Johnson | Billion-scale similarity search with gpus[END_REF], a library for very efficient similarity search that trades precision for speed. More details of evaluation based on each refinement task will be outlined in Section 4.

Experimental Results

Datasets. In this paper, we adopt three widely used benchmark datasets in the literature: WN11 [START_REF] Socher | Reasoning with neural tensor networks for knowledge base completion[END_REF], FB13 [START_REF] Socher | Reasoning with neural tensor networks for knowledge base completion[END_REF], and FB15K [START_REF] Bordes | Translating embeddings for modeling multi-relational data[END_REF]. The datasets are subsets of the real-world KGs, Wordnet, and Freebase, respectively. We have used the same training/validation/test splits as [START_REF] Socher | Reasoning with neural tensor networks for knowledge base completion[END_REF], and [START_REF] Bordes | Translating embeddings for modeling multi-relational data[END_REF]. The test sets of WN11 and FB13 contain both positive and negative triples, whereas the FB15K dataset only contains correct triples. The statistics of the datasets are listed in Table 1. Settings. In our experiments, we have fine-tuned the pre-trained RoBERTa-Base model for 4 epochs. We have used a batch size of 64, Adam optimizer with learning rate 2e-5, and margin γ of 5. It should be noted that we have not used any external information such as paths or entity descriptions while training the model, and doing so can potentially enhance the results. All experiments have been run on a single machine with a processor 2.6GHz, 12 cores, 64 GB of RAM, and an Nvidia TITAN V GPU. The code and datasets are available at https://github.com/armitakhn/gilbert/. Triple Classification. This task assesses whether a given triplet (h, r, t) is correct or not, i.e., binary classification on a triplet. The evaluation is done on two benchmark datasets of WN11 and FB13. For this task, we cluster based on entities appearing as head in G as explained in Section 3. The partial facts P F hrt are used randomly with a 30% chance. For the WN11 dataset, the reverse versions of relations (t, r -1 , h), are added to the KG to possibly overcome the cold start problem caused by some of the test data's heads not appearing as the head to any fact in training. We create five different positive and five negative samples for each anchor in all datasets.

For the evaluation of a triple (h, r, t), the embedding of P F hrt 's distance is calculated to the reference set ∆ h . We have exploited different decision strategies, MEAN, MAX, and MIN, to aggregate the computed distances to the reference If the aggregated distance is less than a threshold σ, then the triple is predicted positive and is predicted negative otherwise. The threshold σ is tuned on the validation data. Moreover, in our experiments, the best results were obtained by the MIN strategy and hence reported. The results of triple classification are shown in Table 2. The results for all methods have been taken from the cited papers in the Table, except for TransE, and DistMult whose results are taken from the papers [START_REF] Lin | Modeling relation paths for representation learning of knowledge bases[END_REF] and [START_REF] Yao | KG-BERT: BERT for knowledge graph completion[END_REF], respectively. Our approach, GilBERT, has the best performance on average and on the FB13 dataset and is second best after KG-BERT for the WN11 dataset.

Method WN11 FB13 Avg. TransE [START_REF] Bordes | Translating embeddings for modeling multi-relational data[END_REF] 75.9 81.5 79.2 TransH [START_REF] Wang | Knowledge graph embedding by translating on hyperplanes[END_REF] 78.8 83.3 87.7 TransR [START_REF] Lin | Modeling relation paths for representation learning of knowledge bases[END_REF] 85.9 82.5 83.9 DistMult [START_REF] Yang | Embedding entities and relations for learning and inference in knowledge bases[END_REF] 87 Relation Prediction This task is to predict the relation that holds between two given entities, i.e., (h, ?, t). For this task, the clusters are created based on relations. The network is optimized to give small distances for embeddings of partial facts that belong to the same relation and large distances for relationally unrelated ones. We create five positive and five negative samples per anchor in the experiments.

For the evaluation, the correct relation r is determined given its head and tail. For this purpose the P F ht 's embedding E test is derived. The k closest points in the embedding space (using all training points in the reference set ∆) to E test are computed. whose results are taken from [START_REF] Xie | Representation learning of knowledge graphs with hierarchical types[END_REF]. The evaluation metrics used are Hits@1 (proportion of correct relations in top 1 ranking) and Mean rank M R. Note that the reported results are after filtering. If a suggested triplet is already known to be true, i.e., if it already exists in the train, validation, or test data of the knowledge graph, it is not correct to rank it among the suggestions. The best results with GilBERT, tuned on the validation data, were obtained using the K-MODE strategy with K being set to 10. The Hits@1 results obtained by our approach are comparable to the state-of-the-art, without integrating any external information, with a very competitive Mean Rank. Moreover, as the introduction states, GilBERT is more adapted to a few-shot learning setting than KG-BERT, which tackles relation prediction as a classification task. We have investigated this by comparing the Hits@1 results of the two approaches on longtail relation, trying relations that have less than 10, 15, 20, 25, and 30 facts belonging to them in the training data and hence covering 37.8, 43.2, 48.4, 51.8, and 54.9 percent of the relations in the training data respectively. The Hits@1 results on these proportions of relations in the test data are plotted in Figure 2 for KG-BERT and GilBERT. This plot shows that GilBERT is more robust at relation prediction for relations that have been seen only a few times during training.

To elaborate more, we look at 664 relations (almost half of the relations) with less than 20 facts, comprising 8.4% of FB15K instances. Our Hits@1 result on the test data involving these properties is 64% (best results obtained with MIN strategy) compared to KG-BERT, which gives a Hits@1 of 53%.

Conclusion

This paper proposed GilBERT, a new approach that fine-tunes a triplet network of pretrained transformer-based language models by passing textual sequences created from facts. We explained how our evaluation paradigm that relies on a very efficient spatial semantic search technique has less overhead than previous approaches and is more suited for few-shot learning settings. Finally, we reported our results on two refinement tasks. In future work, we aim to try our approach for other KG refinement tasks such as link prediction, evaluating more benchmark datasets, and observing the impact of adding external information to training.

Fig. 1 :

 1 Fig. 1: (a) Overview of fine-tuning BERT using the partial facts of triples in G. (b) Overview of evaluation, clusters illustrated using UMAP on part of FB15K dataset for the relation prediction task.

Fig. 2 :

 2 Fig. 2: Comparison of KG-BERT and GilBERT hits@1 results on long-tail relations of the training data.

Table 1 :

 1 Statistics of datasets

	Dataset #R	#E	#Facts (Train/Valid/Test)
	WN11	11	38,696 112,581 2,609	10,544
	FB13	13	75,043 316,232 5,908	23,733
	FB15K 1345 14,951 483,142 50,000 59,071

Table 2 :

 2 Triples classification Accuracy results (in %) reported on the WN11, FB13 test sets.

Table 3 .

 3 The true relations of the closest points are known from the training data. We propose two different strategies for assigning a relation to our test point of interest. The strategy MIN assigns the relation of the closest point to E test . And the strategy K-MODE chooses the relation that has most frequently appeared among the k-closest points of E test . The results of the relation prediction task on the FB15K dataset are reported in All results are taken from their respective paper, except TransE and TransR

	Method	MR Hits@1
	TransE [1]	2.5	84.3
	TransR [6]	2.1	91.6
	ProjE (listwise) [10]	1.2	95.7
	TKRL [17]	1.7	92.8
	DKRL(CNN) [16]	2.5	89.0
	DKRL (CNN) + TransE [16] 2.0	90.8
	KG-BERT [19]	1.2	96.0
	GilBERT	1.3	92.0

Table 3 :

 3 Relation prediction MR and HITs@1 results on FB15K dataset.

Acknowledgements: This work has been supported by the project PSPC AIDA: 2019-PSPC-09 funded by BPI-France.


J Hirsch

M Szadkowski

S Piry

C Lacroix

L Mcleod

B Moury

V Lefebvre

A high-throughput image analysis method for assessing pepper quantitative resistance to Cucumber mosaic virus

1 Pathologie Végétale, INRAE, 84140 Montfavet, France; 2 CBGP, INRAE, CIRAD, IRD, Institut Agro, Univ. Montpellier, Montpellier, France; 3 INRAE, GAFL, F-84140 Montfavet, France. (judith.hirsch@inrae.fr) S6-P5

Cucumber mosaic virus (CMV) is an economically important virus in pepper cultures worldwide. In the absence of curative methods, growing resistant cultivars is one of the main means of controlling plant viruses. Moreover, quantitative resistance is likely to be more durable than major gene resistance for controlling CMV in pepper. To gain insight into the genetic basis of quantitative resistance to CMV, we have explored natural variation in the response of pepper to CMV in order to perform a genome-wide association study (GWAS), focusing on the initial steps of infection in the inoculated leaves. A subset of 56 INRAE accessions belonging to the 423 accessions from the pepper core-collection of the G2P-SOL project (http://www.g2p-sol.eu/) were assessed for resistance to the N strain of CMV, which induces necrotic local lesions on mechanically-inoculated leaves. The percentage of leaf surface covered by local lesions was measured as a quantitative estimate of disease severity.

A phenotyping method based on automated analysis of scanned leaf images was developed. Measurement of the leaf surface was automated using ImageJ software and was associated with accession code, plant number and leaf number using datamatrices and a dedicated R script. An analysis process using deep learning was set up to automatically detect lesions and estimate the total surface of lesions. Pepper plants infected by CMV showed a quantitative response, with pepper accessions displaying a continuum of resistance levels, from highly susceptible to highly resistant. The automated data acquisition and deep-learning pipelines make it possible to scale up the phenotyping process and analyze a much larger panel of accessions, as required in GWAS. By combining the phenotypic data reported here with the high-density genotypic dataset obtained on the G2P-SOL pepper core-collection, a GWAS will be conducted to identify Quantitative Trait Loci and candidate genes underlying early resistance to CMV.


Jean-Alexandre Long

Valentin Arnoux

Gaelle Fiard

Riccardo Autorino

Jean-Luc Descotes

Jean-Jacques Rambeaud

Bernard Boillot

Nicolas Terrier

Alexis Arvin-Berod

Alexandre Moreau-Gaudry

External validation of the RENAL nephrometry score in renal tumours treated by partial nephrectomy

Keywords: kidney cancer, partial nephrectomy, nephron-sparing surgery, renal tumour

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

External validation of the RENAL nephrometry score in renal tumours treated by partial nephrectomy

Jean-Alexandre Long, Valentin Arnoux, Gaelle Fiard, Riccardo Autorino*, Jean-Luc Descotes, Jean-Jacques Rambeaud, Bernard Boillot, Nicolas Terrier, Alexis Arvin-Berod and Alexandre Moreau-Gaudry Urology Department, Grenoble University Hospital, Grenoble, France, and *Second University of Naples, Naples, Italy What's known on the subject? and What does the study add?

• Using a standardized classification for renal tumours is a major step towards an objective comparison of the indications and expected outcomes of partial nephrectomy (PN).

• Several scores have been described, including the RENAL nephrectomy score (RNS), to evaluate the anatomical features of a renal tumour and predict the surgical challenges with particular regard to PN. Previous studies show discrepancies with regard to the effectiveness of using the RNS to predict postoperative outcomes. Although we showed that conversion to radical nephrectomy was predicted by the RNS, the occurence of complications was more difficult to predict.

Objective

• To evaluate the RENAL nephrometry score (RNS) as a predictor of the perioperative outcomes of a partial nephrectomy.

Patients and Methods

• A retrospective review of 177 consecutive patients who were candidates for an open partial nephrectomy (OPN, n = 159) or a laparoscopic partial nephrectomy (LPN, n = 18) from August 2008 to January 2011 was undertaken. • Tumour complexity was stratified into three categories: low (4-6), moderate (7-9) and high (10-12) complexity. • Complications, and surgical and renal outcomes were recorded and analysed. • Predictors of conversion to radical nephrectomy (RN) and complications were assessed using univariate and multivariate logistic regression. Multiple linear regression was used to evaluate the prediction of postoperative estimated glomerular filtration rate (eGFR) and warm ischaemia time (WIT).

Results

• The median RNS was 7 (interquartile range 6-9).

• Tumour complexity was assessed as low in 72 (40.6%), moderate in 87 (49.2%) and high in 18 patients (10.2%).

• There were no significant differences among the three groups with respect to demographic characteristics, operating time, estimated blood loss, transfusion, length of stay, complications and positive surgical margins. Conversion to RN occurred in 29 patients (16.3%). • RNS was significantly associated with an increased risk of conversion to RN (odds ratio [OR] = 3.5, P = 0.01 and OR = 6.7, P = 0.005, respectively, for moderate vs low, and high vs low complexity groups). • On multivariate analysis, RNS was the only independent predictor of WIT (P = 0.03) and conversion to RN (P = 0.008), but failed to predict postoperative eGFR (P = 0.84) and the occurrence of major complications (P = 0.91).

1

Introduction

Partial nephrectomy (PN) is considered the standard treatment for most cases of localized renal tumours. Whatever the approach (laparoscopic, open or robotic), oncological results for PN seem to be equivalent to those for radical nephrectomy (RN) [1]. Comparative studies suggest that PN should be performed in all T1, even T1b, tumours [2,3] to decrease morbidity and mortality resulting from renal insufficiency.

The feasibility of a PN depends on the anatomical characteristics of the renal mass and the experience of the surgical team. In addition to tumour size, other anatomical aspects of the tumour are usually considered. Kutikov et al.

[4] described a score, the RENAL nephrectomy score (RNS), which was based on characterization of the renal tumour, proposing this as a standardized classification to improve comparability between tumours. Other scores, the PADUA score and C-index, have been described. According to Okhunov et al.

[5] all three scores were predictors of warm ischaemia time (WIT) and percent change in postoperative creatinine levels, and Hew et al. [6] showed that the RNS and PADUA were both able to predict the risk of postoperative complications. We chose to evaluate the RNS, as it was the first score used in the literature. The aim of the present study was to determine whether RNS correlates with the surgical outcomes of a PN and postoperative renal function.

Patients and Methods

Study Population

We identified, retrospectively, 177 consecutive patients scheduled for PN from August 2008 to January 2011. All patients scheduled for a PN were enrolled. Our institutional review board-approved, prospectively maintained, kidney cancer database was used to identify the study population. Patients were preoperatively staged using CT or MRI. Open PN (OPN) or laparoscopic PN (LPN) was performed by four surgeons, using an extraperitoneal and a transperitoneal approach, respectively.

According to our institutional policy, we treated each patient as conservatively as possible, regardless of tumour size and location. The approach was chosen independently by each surgeon according to his experience. None of the solitary kidneys (n = 19) were treated laparoscopically.

Tumour Assessment

All tumours were classified according to the RNS by the same observer (J.A.L.), who was blinded to the patient's outcome. CT

Results

Demographic and clinical data are shown in Table 1. The median patient age was 55 (48-66) years. Most patients were male (n = 122; 68.9%) and the median tumour size was 31 (20-45) mm. RN was performed in 29 patients (16.4%) including 26 intra-operative conversions to RN for uncontrolled bleeding (n = 2) and for oncological reasons (n = 24). Three RNs had to be performed postoperatively because of uncontrolled bleeding (n = 1), a renal artery thrombosis (n = 1) and a persistent urinary leak after failure of ureteric stent placement (n = 1). All RNs performed were referred to as conversions to RN. On univariate analysis (Table 2), patients undergoing RN had larger tumours (median size 4.0 vs 3.0 cm; P = 0.006) and a higher RNS (8 vs 7; P = 0.006). A laparoscopic approach was not correlated with a higher risk of conversion to RN (P = 0.5); conversions to RN were performed in 13.8% of LPN cases and in 15.8% of OPN cases. The stratification of RNS into three complexity groups showed a correlation with an increasing risk of conversion to RN (odds ratio [OR] = 3.5, P = 0.01 when comparing moderate vs low complexity groups and OR = 6.7, P = 0.005 for high vs low complexity groups).

Major complications were assessed after excluding patients undergoing conversion to RN for oncological reasons. Patients developing major complications had a significantly higher BMI (24.9 vs 27.6 kg/m 2 , P < 0.001) and a significantly higher rate of malignant tumours (95.2% vs 75.8%, P = 0.047). No statistical difference was found in RNS or complexity group among patients developing major complications or not (Table 3). On multivariate analysis, after adjusting for BMI and histology, RNS as a continuous variable failed to predict the occurrence of major complications (OR = 0.98, 95% CI 0.75-1.29, P = 0.906). Complexity group also failed to predict an increased risk of major complications (OR = 0.77, 95% CI 0.27-2.2, P = 0.622 for moderate vs low complexity groups and OR = 1.86, 95% CI 0.41-8.57, P = 0.424 for high vs low complexity groups).

On multivariate linear analysis adjusting for ASA score (b= -0.648, 95% CI -3.85-2.55, P = 0.69) and laparoscopic approach (b=0.67, 95% CI: -6.68-10.4, P = 0.67), RNS was the only predictor of WIT (b=5.89, 95% CI 2.045-9.75, P = 0.03). Postoperative eGFR was not predicted by RNS. The only independent predictors of postoperative eGFR were preoperative eGFR and ASA score (Table 4). Complexity group was an independent predictor of conversion to RN, especially for the low vs moderate complexity group (Table 5).

Discussion

The anatomical characterization of a renal tumour before surgical treatment has long lacked standardization. Since the study by Patard et al.

[1], tumour size defined by its diameter has no longer been a limiting factor: elective PN can be safely and effectively performed for tumours up to 7 cm.

The first anatomical characterization to evaluate the predicted difficulty of a PN was published by Kutikov and Uzzo [7]. The objective of this integrated anatomical system was to predict the oncological feasibility of a PN using a classification based upon criteria that every surgeon considered separately before surgery. They intended to standardize descriptions of renal masses and allow meaningful comparisons.

In the present retrospective review of 177 patients scheduled for PN, the RNS was correlated with renal mass complexity as measured by the rate of conversion to RN and WIT. Based on this information, surgeons can optimize patient care by choosing the most appropriate approach and by eventually referring the patient to a high-volume centre.

We reported higher rates of conversion to RN (16.4%) than those reported in other previously published series. Galvin et al.

[10] reported a rate of 6% in a recent study. This difference may be explained by the high rate of intra-operative conversions to RN for oncological reasons (n = 82, 7%) in the present study, resulting from a systematic attempt to be the as conservative as possible, independent of tumour size and location. In the present series, the RNS was not a predictor of major complications. We chose to exclude from the analysis of complications these cases of intra-operative conversion to RN to avoid minimizing our complication rate after RN. We described an overall complication rate comparable with previously published series. Porpiglia et al. [11] reported an overall complication rate of 21.4% in their review. We chose to focus only on major complications because of their clinical significance, in that they prolong hospital stay and induce long-term morbidity or potential mortality. In the present series, the variables that correlated with an increased risk of complications were BMI and tumour malignancy. After adjusting for these two variables, RNS still failed to predict major complications. This might be explained by the insufficient statistical power of the present study, given the low frequency of complications and our choice to only take into account the less frequent major complications. However, the rate (12.4%) of major complications (Clavien 3 and 4) in the present series remains high compared with previous studies. Retrospectively, it is difficult to know what could explain our high rate. On multivariate analysis, the laparoscopic approach was not found to be associated with major complications; however, although it did not reach significance, we noted that major complications occurred in 19% of LPNs. We believe that in the present series, laparoscopy could have increased our major complication rate.

We found that RNS was an independent predictor of WIT but failed to show a correlation with postoperative eGFR in multivariate linear analysis. Preoperative renal function in our series was low (77.4 mL/min/1.73 m 2 ). This could be related to comorbidities of the patients and the high number of solitary kidneys. To adjust for this confounding factor, preoperative eGFR was included in the multivariate model predicting postoperative eGFR.

Only few studies report a link between anatomical features of the tumour and postoperative renal function, Okhunov et al. [5] reported that RNS was associated with percent change in creatinine level. The assessment of RNS requires the use of coronal reconstructions to define the polar lines limiting the sinus, according to Kutikov and Uzzo [7], but the images, particular those from MRI, are not always suitable. Moreover, designation of anterior or posterior location is not always feasible because of the lack of a clear demarcation of the lateral peritoneal reflection. In the present study, on univariate analysis, anterior or posterior locations were not associated with a higher rate of conversion to RN or the occurrence of major complications. Measuring the distance between the tumour and the urinary collecting system also appears complex and hardly reproducible.

Recently, Ficarra et al.

[14] published a series describing the PADUA classification. Compared with the RNS, their score has the advantage of defining the sinus borders as the first slides that show the presence of adipose tissue, allowing the use of only axial images. We believe the main difference between these two scores in favour of PADUA classification is the absence of determination of the distance between the urinary collecting system and the tumour: PADUA classification only considers the involvement of the sinus or the pelvicalyceal system. The C-index, described by Simmons et al. [START_REF] Simmons | Kidney tumor location measurement using the C index method[END_REF], is another method of quantifying the proximity of kidney tumours to the renal central sinus.

Recently, Okhunov et al.

[5] compared the three scores described above in 101 patients. All scores were predictors of WIT and percent change in creatinine level; however, they failed to predict the occurrence of complications.

Using a standardized classification for renal tumours is a major step towards an objective comparison of the indications and expected outcomes of PN. We believe that the results of emerging laparoscopic and robotic techniques should be evaluated in light of these tumour classifications.

The present study has several limitations. Because of its retrospective nature, complications may have been omitted. Our prospectively maintained database reduces this risk, however, and complications not recorded are more likely to be minor. The evaluation of postoperative eGFR is limited by a relatively short follow-up because most patients were followed in their referring hospitals after surgery. Choosing to include all patients independently of the approach may also have influenced the results. Few studies report that functional and early oncological outcomes were similar in laparoscopic and open approaches; in fact, LPN was associated with additional postoperative morbidity compared with OPN [START_REF] Gill | Comparison of 1,800 laparoscopic and open partial nephrectomies for single renal tumors[END_REF]. Retrospectively in the present study, it is difficult to know whether the patients who finally underwent a laparoscopic RN could have benefited from an open conversion rather than a conversion to laparoscopic RN. Among patients who lost their kidney, most of them had hilar involvement or an insufficient residual kidney volume. Since OPN is still considered a standard of care, we recommend to convert to open these patients rather than perform a LPN. Most patients underwent OPN because LPN still represents a challenging procedure requiring a long learning curve but, on multivariate analysis, no significant difference in surgical outcome was found between laparoscopic and open approaches.

Another limitation is the difference in surgeon's experience, which was not evaluated in the present study. In addition, we deliberately chose not to disclose intermediate-and long-term oncological outcomes, as our goal was not to correlate RNS with oncological outcomes.

In the present series, RNS was able to predict the surgical difficulty of a PN by predicting the risk of conversion to RN and longer WIT; however, the score failed to predict the occurrence of major complications and postoperative renal function.

Conflict of Interest

None declared.

Table 1

 1 Demographic data, tumour characteristics, intra-operative and postoperative outcomes in 177 patients.

	Variable	Value
	Median (IQR) age, years Male, n (%) Median (IQR) BMI, kg/m 2 Right side, n (%) ASA score >3, n (%) Malignant tumour, n (%) OPN, n (%) Location pole, n (%) Lower Mid Upper Median (IQR) tumour size, mm Conversion to RN, n (%) Median (IQR) RNS Complexity group, n (%) Low Moderate High RNS location, n (%) anterior posterior n/a Positive margins, n (%) Median (IQR) operating time, min Blood transfusions, n (%) Median (range) hospital stay, days Median (IQR) WIT, min Overall complications, n (%) Minor Major Surgical complications, n (%) Medical complications, n (%) Median (IQR) preoperative eGFR, mL/min/1.73 m 2 Median (IQR) postoperative eGFR, mL/min/1.73 m 2	55 (48-66) 122 (68.9) 25.3 (22.7-27.6) 90 (50.8) 46 (28) 141 (79.6) 159 (89.8) 72 (40.7) 58 (32.8) 47 (26.6) 31 (20-45) 29 (16.4) 7 (6-9) 72 (40.6) 87 (49.2) 18 (10.2) 80 (45) 62 (35) 35 (19.7) 3 (1.6) 135 (120-180) 31 (17) 9 (8-11) 18 (11-25) 40 (22.6) 18 (10.1) 22 (12.4) 22 (12.4) 18 (10.2) 77.4 (63.3-90.9) 55.5 (49.6-69.3)

Table 2

 2 Conversion to RN.

	Patient characteristic,	Conversion to	PN success,	P
	N = 177	RN, n = 29	n = 148
	Median (SE) age Male, n (%) Median (IQR) BMI, kg/m 2 Right side, n (%) Laparoscopy, n (%) ASA score, n (%) 1 2 3 Malignant tumour, n (%) Size, mm Location, n (%) Lower pole Mid Upper pole RNS RNS location, n (%) anterior posterior n/a Complexity group, n (%) Low Moderate High Median (SE) postoperative eGFR	57.9 (24.6) 20 (69) 26.7 (24.1-27.6) 17 (58.6) 4 (13.8) 6 (20.7) 10 (34.5) 7 (24.1) 26 (89.6) 40 (30-55) 14 (48.3) 7 (24.1) 8 (27.6) 8 (7-9) 10 (34.5) 12 (41.4) 7 (24.1) 5 (17.2) 18 (62) 6 (20.7) 49.22 (14.7)	55.0 (25.77) 102 (69) 25.2 (2.7-27.4) 73 (49.3) 14 (9.5) 46 (31.8) 56 (37.8) 39 (26.3) 115 (77.7) 30 (20-40) 58 (39.2) 51 (34.5) 39 (26.3) 7 (6-8) 70 (47.3) 50 (33.8) 28 (18.9) 67 (45.3) 69 (46.6) 12 (8.1) 61.48 (21.35)	0.25 0.83 0.11 0.47 0.5 0.82 0.22 0.006* 0.52 0.006* 0.44 0.008* 0.02*

*Statistically significant.

Table 3

 3 Univariate analysis of occurrence of major complications (excluding patients who underwent RN for oncological reasons).

	Patient characteristic,	No major complication	Major complication	P
	N = 153	(Clavien 0-2), n = 132	(Clavien 3-4), n = 21
	Median (IQR) age Male, n (%) Median (IQR) BMI, kg/m 2 Right side, n (%) Laparoscopy, n (%) ASA score, n (%) 1 2 3 Malignant tumour, n (%) Median (IQR) tumour size, mm Location, n (%) Lower pole Mid Upper pole Median (IQR) RNS RNS location, n (%) anterior posterior n/a Complexity group, n (%) Low Moderate High	56 (47-63.2) 90 (68.7%) 24.9 (22.3-27.2) 68 (51.5) 11 (8.3) 52 (39.4) 45 (34.1) 35 (26.5) 100 (75.8) 30 (20-40) 52 (39.4) 45 (34.1) 35 (26.5) 7 (6-8) 60 (45.4) 46 (34.8) 46 (34.8) 59 (44.7) 63 (47.7) 10 (7.6)	55 (52-67) 17 (80.9%) 27.6 (26.5-28.3) 9 (42.8) 4 (19) 9 (42.9) 6 (28.6) 6 (28.6) 20 (95.2) 33.50 (23.7-42) 9 (42.8) 6 (28.6) 6 (28.6) 7 (5-9) 12 (57.1) 5 (23.8) 4 (19.1) 10 (47.6) 8 (38.1) 3 (14.3)	0.40 0.31 <0.001* 0.49 0.13 0.46 0.047* 0.67 0.88 0.86 0.55 0.51

*Statistically significant.

Table 4

 4 Multivariate linear analysis for prediction of postoperative eGFR.

		Coefficient	95% CI	P
	RNS ASA score Approach (LPN vs OPN) WIT Preoperative eGFR	0.098 -0.254 -0.198 0.012 0.75	-2.73; 8.67 -17.07; -2.12 -30.08; 0.98 -0.29; 0.34 0.45; 0.77	0.84 0.013* 0.064 0.34 <0.001*
	*Statistically significant.

Table 5

 5 Multivariate logistic regression analysis for prediction of conversion to RN.

	Variable	OR	95% CI	P
	RNS groups Intermediate vs low High vs low Laparoscopy vs open BMI ASA score Malignant vs benign tumour	0.78 0.473 0.32 1.18 1.38 0.44	0.15-0.41 0.14-1.61 0.06-1.56 0.96-1.24 0.47-4.06 0.31-0.44	0.008* 0.03* 0.23 0.16 0.15 0.62 0.44
	*Statistically significant.

, Rais-Bahrami S, George AK et al.

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Toufik Sarni

Audrey Queudet

Patrick Valduriez
email: patrick.valduriez@inria.fr

Real-time scheduling of transactions in multicore systems

Transactional memory has attracted much interest for multicore systems as it eases programming and avoids the problems of lock-based methods. However, introducing real-time scheduling of transactions in multicore systems is an open problem. Existing solutions for real-time scheduling consider either tasks in multiprocessor systems or transactions in database systems. In this paper, we show that these solutions are not suitable for multicore systems. And we discuss the main challenges to introduce real-time scheduling within transactional memory in multicore systems.

Introduction

With the advent of multicore systems, the transactional memory concept has attracted much interest from both academy [START_REF] Herlihy | Transactional memory: Architectural support for lock-free data structures[END_REF][START_REF] Shavit | Software transactional memory[END_REF] and industry [START_REF] Tremblay | A third-generation 65nm 16-core 32-thread plus 32-scout-thread cmt sparc r processor[END_REF] as it eases programming and avoids the problems of lock-based methods. By supporting the ACI (Atomicity, Consistency and Isolation) properties of transactions, transactional memory relieves the programmer from dealing with locks to access resources. More important, it avoids the severe problems of lock-based methods such as deadlock situations and priority inversions. Furthermore, in the case of multicore systems, lock-based synchronization can reduce the data bandwidth by blocking several processes that try to access critical sections, thus reducing processors utilization. While lock-based methods systematically block all accesses to shared resources, transactional memory allows several transactions to access resources in parallel. A transaction is either aborted when a conflict is detected, or committed in case of successful completion. However, real-time scheduling of transactions, which is needed for many realtime applications, is an open problem in multicore systems. Existing solutions for real-time scheduling of either tasks in multiprocessor systems or transactions in database systems are not suitable for multicore systems. Real-time scheduling of tasks in multiprocessor systems does not consider important features of multicore systems, such that the presence of on-chip shared caches. Real-time scheduling of transactions in database systems has been around since the 80s but assuming either centralized or distributed systems. Thus, the solutions are not suitable for multicore systems as well.

1

In this paper, we briefly review these main solutions and discuss the main challenges to introduce real-time scheduling within transactional memory in multicore systems. The rest of the paper is organized as follows. Section 2 reviews real-time task scheduling in multiprocessor systems. Section 3 reviews real-time transaction scheduling in database systems. Finally, Section 4 discusses the main challenges for real-time transactional memory.

Multiprocessor real-time task scheduling

Context. We consider the scheduling of a task system on multiprocessors. For each task, a set of jobs is associated. At any time, each processor executes at most one job. The task has a period and an execution requirement. When a job is released, it executes during the execution requirement of the task, and once the period is elapsed, an other job of the task, is released.

Classification. In multiprocessor systems, two alternative paradigms for scheduling collections of tasks have been considered: partitioned and global scheduling. For the partitioned approach, the tasks are statically assigned to processors and then executed on a single processor. Each processor has its own scheduling queue of tasks which is independent of other processors and the migration of jobs or tasks to other processors is not allowed. Under the global scheduling approach, migrations are only allowed at job boundaries. A single queue and only one policy is applied to tasks.

Real-time scheduling algorithms. The classification for uniprocessor systems is usually based on the priority (static or dynamic) assigned to tasks. For preemptive uniprocessor systems, Earliest Deadline First (EDF) is optimal [START_REF] Liu | Scheduling algorithms for multiprogramming in a hard-real-time environment[END_REF]. Unfortunately, EDF is not optimal on multiprocessors either under the partitioned or the global approaches [START_REF] Calandrino | On the scalability of realtime scheduling algorithms on multicore platforms: A case study[END_REF], called respectively P-EDF and G-EDF. Another class of scheduling algorithms, which differs from the previous ones, is the Pfair algorithm [START_REF] Baruah | Proportionate progress: A notion of fairness in resource allocation[END_REF]. It is based on the idea of proportionate fairness and ensures that each task is executed with uniform rate. Tasks are broken into quantum-length subtasks and time is subdivided into a sequence of subintervals of equal lengths called windows. A subtask must executes within the associated window and migration is allowed for each subtask. PD2 [START_REF] Leung | Handbook of scheduling : algorithms, models, and performance analysis[END_REF] is an optimal scheduling variant of Pfair.

Scheduling with shared resources. The protocols managing resources in real-time systems are usually used in a hard real-time context, such as M-PCP and FMLP1 [START_REF] Brandenburg | Real-time synchronization on multiprocessors: To block or not to block, to suspend or spin?[END_REF] under EDF. For Pfair scheduling, a lock-free algorithm has been proposed [START_REF] Leung | Handbook of scheduling : algorithms, models, and performance analysis[END_REF] to ensure that some task is always making progress. Indeed, classical lock-based algorithms cannot satisfy this property.

What about multicore? To our knowledge, only one recent paper [START_REF] Calandrino | On the scalability of realtime scheduling algorithms on multicore platforms: A case study[END_REF] deals with the scalability of the scheduling algorithms presented above, on multicore platforms 2 . One main conclusion of the authors is that on multicore platforms with on-chip shared caches, both the small size of the caches and the memory bandwith have negative impact on the algorithms, allowing migrations (i.e G-EDF, PD 2 ). Furthermore, for the global approach, the scheduling overheads greatly depend on the way of implementing the run queues. On the other hand, without resource sharing, P-EDF performs well for this study. This means that pure global algorithms will not scale, and thus real-time global policies need to be revisited for many-core architectures. More particularly, the scheduler should be able to control more precisely the sharing of processor's internal resources (i.e. cache levels) by real-time tasks.

Real-time transactions scheduling in databases

Like real-time tasks, real-time transactions are classified according to the criticity of their deadlines: hard, soft or firm. The hard3 class is rarely considered. Most studies assume the scheduling of transactions either in soft4 or firm 5 classes.

Real-time concurrency control. The scheduler of transactions in database systems, has a concurrency control protocol, which resolves conflicts between transactions when they occur, in order to maintain database consistency. In real-time database systems, not only database consistency should be satisfied, the transactions must meet their deadlines too [START_REF] Abbott | Scheduling real-time transactions: a performance evaluation[END_REF]. Real-time concurrency control can be either pessimistic or optimistic. The pessimistic protocols systematically restrict all accesses to shared resources. For optimistic protocols, the detection and resolution of conflicts can happen after their occurrence. Many real-time concurrency protocols have been proposed (see [START_REF] Shanker | Distributed real time database systems: background and literature review[END_REF] for survey) either pessimistic or optimistic, for centralized or distributed systems. Intuitively, it seems that optimistic protocols have better performance. However, this is not be easy to verify since it depends on several parameters when doing comparison studies [START_REF] Huang | Concurrency control in real-time database system : Optimistic scheme vs. two-phase locking[END_REF].

Pessimistic algorithms. Pessimistic algorithms use locking. The 2-PL-HP (High Priority) protocol [START_REF] Abbott | Scheduling real-time transactions: a performance evaluation[END_REF] resolves the conflicts between transactions by considering their priorities. The priority of the transaction is based on its deadline. This protocol ensures that a high-priority transaction will not be blocked by a lower-priority transaction. Thus, it prevents deadlock situations.

Optimistic concurrency controls For firm real-time transactions, the m kfirm [START_REF] Haubert | m k -firm real-time distributed transactions[END_REF] protocol considers m mandatory transactions and km optional transactions. Mandatory (firm) transactions are aborted if they miss their deadlines. Optional transactions are executed only if there is no remainder mandatory transaction. The protocol tries to satisfy a maximum number of optional transactions to meet their deadlines. The Cost Conscious Approach (CCA) [START_REF] Hong | Real-time transaction scheduling: a cost conscious approach[END_REF] improves 2PL-HP. The CCA protocol uses at run-time, the dynamic parameters of the transaction to reevaluate its priority. Some parameters are the rollbacktimes and the estimated remaining execution time. Speculative Concurrency Control (SCC) is an hybrid protocol [START_REF] Bestavros | Speculative concurrency control[END_REF] which combines the advantages of both pessimistic and optimistic protocols. When the conflict is detected in a trans-action, a shadow transaction is created to "save" the conflict zone. Thus, the transaction can meet its deadline since only a proportion of itself (from the saved zone) is restarted. This protocol needs more resources than others since it duplicates both resources and the transactions when the conflicts occur.

What about transactional memory? A recent paper [START_REF] Yoo | Adaptive transaction scheduling for transactional memory systems[END_REF] deals with the scheduling of transactions for transactional memory. However, although it addresses the time aspect with an adaptive approach, the real-time scheduling of transactions is not considered and remain an open problem. Although real-time concurrency control schemes provide a general framework for real-time transactions, actual implementations of these schemes can entail significant overhead, thus the interest of considering transactional memory where transactions are memory-resident data.

Discussion

Scheduling both tasks and transactions in multicore systems requires new policies that consider the multicore architecture features. Real-time scheduling of tasks for shared-memory multiprocessor systems is a good starting point but needs to carefully consider the presence of on-chip shared caches. Real-time scheduling of transactions has been considered for uniprocessor or multiprocessor architectures, without shared memory (distributed systems) but needs to be tested on multicore platforms to evaluate its performance. Indeed, it is often claimed that the SCC protocol underperforms the other ones since it duplicates the resources and the transactions. But in a multicore system, with multi-threading technology, is this still true? In distributed systems, the m k -firm protocol outperforms SCC [START_REF] Haubert | m k -firm real-time distributed transactions[END_REF]. But does this comparison still hold for a multicore system at a single site? If we consider the CCA protocol for real-time scheduling of transactions, what are the impacts of knowing the rollback-times and the remaining execution times of the transactions in multicore systems? Addressing these main questions could help us formalizing the introduction of real-time scheduling of transactions within transactional memory. Furthermore, with the advent of transactional multicore, the real-time scheduler of transactions could be integrated in hardware. It is then important to study the interactions between the schedulers of both tasks and transactions. Experiments similar to those presented in [START_REF] Calandrino | On the scalability of realtime scheduling algorithms on multicore platforms: A case study[END_REF][START_REF] Brandenburg | Real-time synchronization on multiprocessors: To block or not to block, to suspend or spin?[END_REF] should be conducted in order to determine which real-time policy among (G-P)-EDF and PD 2 , is more efficient in conjunction with the real-time scheduling of transactions. Thus, defining the utilization rate of the transactions on a given core can be helpful. For example, this rate can help the real-time task scheduler to promote the task with the smallest priority because having a transaction close to its deadline.

FMLP is a locking-based synchronization mechanism adapted both for P-EDF and G-EDF

The study was conducted using the SUN-Niagara platform with

logical CPUs.

System cannot tolerate the missing of deadline, which can have catastrophic impacts.

The system could accept the transaction even if it misses its deadline.

Missing the deadline causes to abort the transaction.