Pierre Glaudes

 Introduction History and geographical location of the unit

The Center for Visual Computing (CVC) unit belongs to the École Centrale Paris School. The overall interest of the CVC is Machine Learning, Computer Vision and Medical Imaging. The CVC has been recently created (2011) and has close ties to the Inria Center Saclay-Île-de-France.

Management team

The committee has thus evaluated a fairly recently formed unit. It has not been made aware of previous scientific evaluations and conclusions. Thus, the capacity of the unit to implement external recommendations could not be mesured.

This new unit is managed by one director. 2  Overall assessment of the unit

AERES nomenclature

The Center for Visual Computing is a small and recently created unit at the Ecole Centrale de Paris, with a strong connection to the Inria Center Saclay-Île-de-France thanks to the INRIA GALEN project. The GALEN forms a part of CVC and has been recently evaluated by INRIA as a future joint team project.

The objective of the unit is to develop a high standard international center for Machine Learning, Computer Vision and Medical Image Analysis. The unit targets several central problems in computer vision (Object detection, Pose estimation, Action classification, Semantic segmentation) and medical imaging (Motion estimation, Image registration and Image segmentation). It studies graphical models as a generic tool for formulating and addressing the above problems. The unit has made strong scientific contributions to all three research axes, ranging from structured output prediction and weakly-/unsupervised learning in machine learning to scale-invariant features and pose estimation in computer vision as well as deformable registration in medical image analysis. The unit also addresses a set of practical applications within medical image analysis and has a record of successful technology transer to the industry.

The international nature of this young unit (young due to its recent formation, but also due to the average age of its researchers) is remarkable, guaranteeing an undeniably global standing within various academic networks. The proximity to high-level training centers and the attractiveness of the unit enables hiring of top-quality PhD students (currently 12 PhD students). All of these elements contribute to the excellent, high-standard scientific output of the unit.

Strengths and opportunities related to the context

The CVC unit works with a strong focus on its scientific goals, addressing the topics of machine learning, computer vision and medical imaging. The unit addresses machine learning techniques, while putting them into practice in the field of medical imaging and computer vision. During the unit's development (2011)(2012)(2013), the arrival of researchers from different international universities has reinforced the theoretical aspect of the work involving machine learning. The unit's scientific output is of a very high quality and its rich range of topics (the different aspects that are dealt with) is an important asset. The CVC unit is well established in a network of international collaborations. It is remarkably well prepared for approaching in an original and creative manner such complex problems as the analysis of medical images or the problems of machine learning when, for example, the data are incomplete. The laboratory attracts PhD candidates as well as young researchers and high quality university teachers and researchers.

The CVC is involved in a large number of various contracts which contribute to more than half of its consolidated budget. It's worth noting that the unit has managed to establish a software platform named DROP (Discrete Deformable Registration) which has become an industrial product marketed by Intrasense Industrial partner in connection with the Myriam platform.

The wealth and the amount of research can sometimes make it rather difficult to understand its thematic structure. It is undeniable that input concerning many aspects of computer vision is put forward, however, it can be difficult to link together different topics. During the visit of the unit the connections between different research directions have been better clarified and the comitee has observed strong interaction between machine learning axis and the advances in the different applications of medical imaging.

While the "compact" nature of the CVC is an advantage when it comes to the high level of reactivity, it also makes the unit potentially more vulnerable. As we mentioned in the introduction, the small size of the unit can be a weakness if one thinks in terms of unity. Indeed, if one of the collaborators left, for example, the entire structure could be weakened : one researcher to a topic.

The link between the unit and the local or national academic environment remains an important question. The involvement of the unit in the national research environment (for example in GDR CNRS) should be reinforced or better precised.

Recommendations

The quality, amount, and wealth of the CVC unit's research cannot be stressed enough. As previously stated, the international foundations of the unit are an important element, which obviously contributes to the quality of the work. The fact that the CVC members are present in a great variety of networks, committees and international projects is a proof of that. We are confident that the unit will strenthen its international recognition even further in the future. The precise thematic positioning of the CVC is also a main factor of the success and recognition. The unit's small size can be seen as a strength as it allows for a large amount of reactivity and project-based work. However, the small size can also be a weakness since each research direction of the CVC critically depends on a particular researcher. We thus recommend to take special care of preserving the current unit and extending the unit with more permanent members in the near future. The number of Post-docs and visitors can also be increased in the future.

While the CVC is well-recognized internationally, there is a room for better integration of the unit with the national research environment. In the future, the unit should developp collaborations with the new Université Paris-Saclay, aiming to reinforce its role as an important player in the field of Machine Learning.

Moreover, the unit should identify appropriate medical partners and developp strong interaction with one or several hosipitals on specific medical topics. During the visit, a new link with the Pompidou hospital has been identified, this collaboration should be reinforced in the future.

 Detailed assessments Assessment of scientific quality and outputs

The unit's main objectives is to advance scientific progress in computational vision by addressing the three main themes: machine learning and optimization; computer vision; and biomedical image analysis. Mastering different theoretical approaches has enabled the unit to propose new solution and to make contributions in several areas including (i) learning with partially annotated data (weakly-supervised learning), to optimisation of higher order graphical models, for example. In addition, various ideas have been put forward concerning feature detection methods, which are an entry point of the chain. The studied questions primarily involve invariance of image descriptors to changes in scale, shape as well as intra-class variations. The coherence of the scientific project is reinforced by the fact that feature extraction is mainly approached by machine learning methods rather than from a signal processing perspective. The intersection of the first two themes (machine learning and computer vision) is therefore very strong in regard to various aspects.

The third line, focusing on medical imaging, is based on a similar type of methods. Its aim is to offer tools that pinpoint precise needs (instead of adapting non-specialized tools for biomedical uses). Some of the methods developed by the unit address medical applications using prior model or knowledge, for example the CT reconstruction with compressed sensing approach (minimization) or the problem of small training sets for brain tumor segmentation (Machine Learning). The majority of the developed mehtods use Machine Learning and Optimization.

The unit is a very small unit, but on the national scale it is a reasonably-sized targeted research unit. Due to its structure and recruiting, the CVC covers various scientific competences (optimisation, graphical models, Markov random fields, low level features, etc.) but also supplementary ones, which make it all the richer.

Overall, the unit's scientific output is of a very high standard, given both the quality of the research and the impact of its results on the scientific community. The unit has published 31 articles in peer-reviewed journals, 120 international conferences, 5 book chapters or books. These articles have been published in high quality journals of the field (IEEE Trans. on PAMI, IEEE Trans. on Image Processing, Int Journal Computer Vision, etc.). It should be noted, that top conferences of machine learning (NIPS, ICML) and computer vision (CVPR, ICCV, ECCV) are highly selective with typical acceptance rate around 25%. Publications in such conferences are considered at least as important as journal publications. The average number of articles by permanent unit member over the considered period is 6.

The CVC unit is involved in an Inria Joint Team SPLENDID created in 2012 (Self-Paced Learning for Exploiting Noisy, Diverse and Incomplete Data), which brings together CVC members and researchers from Stanford University, working together on machine learning problems. Finances issued from SPLENDID project are mainly devoted for exchanges and travels.

Assessment of the unit's academic reputation and appeal

The unit benefits from a high level of recognition, as shown by its high quality output but also by its involvement in organising top quality conferences (CVPR, ECCV, ICCV …), steering committees, and evaluation committees. Some members of the CVC participate in editorial boards of leading journals (IEEE PAMI, IJCV, IVC, Editor in Chief for CVIU). Furthermore, the unit has received several distinctions (Best paper awards at CVPR and ECCV, BMVA Sullivan Thesis Prize, IEEE fellow). Unit's national and international recognition is also visible from a large number of invited talks given by unit members at both national and international events. Academic reputation of the unit is also highlighted by the prestigeous ERC grant awarded to the CVC leader in 2011.

Since its creation, the unit has recruited several researchers following international careers. Each researcher came from a prestigious university which reinforces the unit's international standing.

The unit was able to obtain financial supports from many different sources including national calls for projects (3 ANR projects, 1 DGA project, 5 regional projects), international initiatives (FP, ERC) and from grants with industry.

Assessment of the unit's interaction with the social, economic and cultural environment CVC takes part in contractual activities with several industrial partners. Given the size of the unit, this collaboration is substantial and includes several CIFRE contracts, many patent applications and software transfers. The presentation of the unit at the meeting has given further details on the industrial collaboration and the field covered by industrial projects (in particular within medical imaging).

The unit has several patents (6), some validated, some pending. It would be interesting to obtain more details about both the thematic outline of these industrial collaborations and the results obtained or aimed for.

Within the close academic environment, CVC actively collaborates with the Supélec's Electronic Signals and Systems department and the Parietal Inria / CEA-Neurospin Center. This collaboration is reflected by thesis cosupervision, and by having obtained a Digiteo excellence chair. All these collaborations founded on thematic complementarity are reinforced by many high quality publications.

Assessment of the unit's organisation and life

Given the small size of the unit, it did not yet build a substantial structural organization. The unit works by projects in connection to particular research topics and allocated funding. There is a large degree of collaboration between researchers of the unit enabling sharing of knowledge and expertise across different research topics. The director of the unit takes a role of the unit's scientific leader, assisted by an administrative manager. As the mediumterm objective of the unit is to grow stronger and larger in numbers, particularly in the number of researchers, it will probably be necessary to organize the work of the unit by teams having specific goals and identities.

Assessment of the unit's involvement in training through research

The unit's teaching staff are involved in and are in charge of the machine learning / computer vision / biological signal processing major of the Applied Mathematics department of the École Centrale de Paris, a degree linked to the MVA (ENS-Cachan-Centrale-X-ENS-Ponts-Mines) MSc program in Applied Mathematics, Machine Learning and Computer Vision. Thanks to this involvement there is a large amount of interaction between the research unit and the top engineering students in the Parisean area, which makes it possible to recruit top-level students.

The CVC is taking part in the Massive Open Online Course Initiatives (MOOC), on the topic of Machine Learning. This is yet another source of recognition for the unit, but it is alse a means to become known to an audience of international students. The CVC is also taking part in the organisation of a Summer School: Modalities, Methodologies & Clinical Research (BIOMED).

Despite its recent creation, the unit has a very rich record of thesis defenses, with 3 HDRs and 18 PhDs over the period of study (some of the PhDs are co-supervised). Every thesis conducted during the relevant period resulted in at least one publication in an international journal. PhD graduates from CVC all find jobs either in academia or industry (11 are working in the industry, 5 in higher education and research, and 2 are studying at a post-doctoral level). The average length of the phD is 3.7 years.

The CVC unit conducts a training policy for its PhD scholars in various ways. It organizes bi-monthly seminars that include talks by external speakers and work presentation by the members of the unit. Student of the unit participate in thematic schools and various conferences. In addition, students are encouraged to spend a trimester at international research laboratories. PhD students also undertake training programs offered by the doctoral school of the Ecole Centrale de Paris and INRIA.

Assessment of the strategy and the five-year plan

For the next five years, the unit has planned to continue research along its three current research directions (Machine Learning and optimisation, Computer vision and Biomedical Image Analysis). For each of these directions scientific challenges have been identified. It's clear that the development of new Machine Learning tools will play a major role for computer vision and medical image analysis. The proposed research on learning from large-scale and partially annotated data as well as efficient inference techniques for higher-order graphical models is timely in the general scientific context, but is also particularly well-suited to the research direction of the unit.

Another ground-breaking scientific direction, which has already been brought forward on several occasions, is that of addressing big data including large-scale repositories with 3D and multi-modal data. Analysis of large-scale data will require revision of certain approaches but also addressing the problem of computational complexity.

Concerning medical imaging, the focus is mainly on in-vivo imaging, which presents new challenges such as accurate real time image analysis. In the medical field, as well as when integrating 3D information, adapting models for a longitudinal study (longitudinal organ modelling) has been put forward, in order to analyse a pathology's evolution.

The future plan of the CVC is scientifically coherent and relies on the unit's well-proven skills, which make its realization possible. The topics approached are original, however, direct practical applications of the first two directions (Machine Learning and Computer Vision) have not been detailed.

In regard to the unit itself, the unit has clearly shown its wish to grow by increasing the number of permanent researchers, which is of course a prerequisite for the structure's continuity. A unit-building objective has been mentioned following the model of Inria, that is to say project units of 3-4 people working on the three mentioned topics. This ensures a high scale of reactivity but one must make sure that the new units continue working together in a coherent fashion.

During the reorganisation of Parisian laboratories and universities, the CVC plans to consider long-term collaborations with other research groups. A search for new partners will continue previously initiated discussion and will aim to further improve scientific output of the unit as well as to preserve the units' identity and visibility. In particular the unit plans interactions with the new Université Paris-Saclay, aiming to reinforce their role as an important player in the field of Machine Learning. It also appears important to developp a very strong interaction with hospital partners. The CVC receives strong support from École Centrale Paris in regard to its future growth and scientific development.

ST6

  Sciences et technologies de l'information et de la communication spent at least 12 months in the unit 2 Number of Research Supervisor Qualifications (HDR) taken Qualified research supervisors (with an HDR) or similar positions 1 3

to the Decree of 3 november 2006 1 , -Mr. Didier HOUSSIN, president -Mr

 Supervising bodies' general comments

Despite the AERES' requests, it had not received any comments by the time this evaluation was published.


Evaluation report

This report is the result of the evaluation by the experts committee, the composition of which is specified below.

The assessments contained herein are the expression of an independent and collegial deliberation of the committee.

Unit name:

Biology of Cancer and Infection No new team was hired during the period considered.

All teams are located in the same building but on two separate floors. A project for a new building gathering all the teams of iRTSV was presented. This would represent a major opportunity for the research entity as it would increase their contacts with other research teams.

Management team

After restructuring of the BCI Unit, there are 3 teams, headed by Ms. Ina ATTRÉE, Ms. Sabine BAILLY and Ms. Isabelle VILGRAIN. The Unit is headed by Mr. Jean-Jacques FEIGE (director) and Ms. Ina ATTRÉE (co-director). 2  Overall assessment of the unit

HCERES

Global assessment of the unit

Excellent scientific quality and output, based on number and quality of scientific publications in leading scientific journals and of patents.

Sound scientific strategy of the teams and the unit overall with focus on the role of endothelium in infection and cancer, enabling intense scientific interactions and collaborations between the research entity members.

Excellent collaborations among research entity scientists and with clinicians are in place. The translational orientation represents a major strength of the unit and has led to the creation of a start-up company, as well as involvement in several clinical trials that are based on discoveries made by research entity members.

Excellent international and national scientific reputation of the director and the team leaders with many invited congress presentations and organisational responsibilities.

Outstanding interactions with the social, economic and cultural environment.

Strengths and opportunities in relation to the context

Excellent scientific originality, quality and output of the director, team leaders and scientists.

Excellent translational impact with 4 clinical trials and one start-up company distinguishes the unit.

Excellent technology platforms are available and/or maintained by the unit.

Outstanding interactions with the social, economic and cultural environment.

Strong involvement in a number of national infrastructures.

Highly motivated staff and excellent training environment for students at the Master and PhD levels.

Weaknesses and threats related to the context

For all teams, and thus for the unit as a whole, the current funding scheme represents a potential weakness since the team leaders spend a considerable amount of time in preparing relatively small, short-term grant applications. This, however, is balanced by the fact that common facilities, supported by the CEA campus, are freely accessible to the teams, which explains that external funding can be kept relatively low without being a major threat.

International scientific involvement, e.g. in EU projects/initiatives/training and high-level competitive funding is not well developed.

There are rather few post-doctoral fellows and some difficulties with the recruitment of excellent junior scientists.

No clear description of the strategy to determine the future areas of investigation of the unit and to continue to ensure the renewal of the team leaders is provided. In addition, no external scientific advisory board is scheduled to promote the reflection.

Recommendations

The unit members are encouraged to continue their excellent research strategies and to continue to publish in the leading journals in their areas.

It is encouraged to further foster the interactions between team 1 and teams 2 and 3.

There is a large number of creative individual projects pursued within each team, despite a rather low number of staff. To further increase international competitiveness, a prioritization of the projects and increased collaborations and technology sharing might be helpful.

More comprehensive and/or internationally competitive funding for research and training networks, including ATIP-Avenir, Equipe FRM and ERC programs, should be sought, given the scientific excellence of the team leaders.

More international visibility at conferences is encouraged, as well as writing of general reviews, also to facilitate recruitment of young scientists and of post-doctoral fellows with international fellowships.

Based on the rather elevated average age of the senior scientific staff, team leaders and PIs are strongly encouraged to brainstorm together to promote the unit and define strategies for its development. A strong institutional support, e.g. by the university, might facilitate the recruitment of outstanding younger scientists or established teams who are competitive for the ATIP-Avenir, ERC or @RAction ANR fundings.

  BOURHIS, Université de Lille 1 (representative of the CNU) Ms Florence NIEDERGANG (representative of the CoNRS) Scientific delegate representing the HCERES: Mr Jean ROSENBAUM Representatives of the unit's supervising institutions and bodies: Mrs Christelle BRETON (representative of the Doctoral School "Chimie et Sciences du Vivant" EDCSV -ED n°218) Ms Alix DE LA COSTE, CEA Mr Franck FIESCHI, Université Grenoble 1 Ms Chantal LASSERRE, INSERM Mr Frank LAFONT, CNRS 1  Introduction History and geographical location of the unit The Biology of Cancer and Infection (BCI) laboratory was created in January 2011 by unifying 3 INSERM teams (headed by Mr. Jean-Jacques FEIGE, Mr. Philippe HUBER and Mr. Jacques BAUDIER) and one CNRS unit (headed by Mr. François BOULAY). The BCI was integrated into the Research Institute in Technology and Life Science (iRTSV) which is located on the campus of CEA-Grenoble. Based on the last AERES evaluation, the BCI was organized in 5 teams, headed by Ms. Ina ATTRÉE, Ms. Sabine BAILLY, Mr. Claude COCHET, Jean-Jacques FEIGE and Ms. Danielle GULINO. Restructuring of the BCI Unit was now performed into 3 teams, headed by Ms. Ina ATTRÉE (Bacterial Pathogenesis and Cellular Responses), Ms. Sabine BAILLY (BMP family in Angiogenesis and Lymphangiogenesis) and Ms. Isabelle VILGRAIN (Cell Invasion in Angiogenesis and Cancer).

  Adequate and transparent distribution of funds between unit and teams. Highly motivated staff and well suited organization for staff representation in decision-making and for scientific training of students and post-doctoral fellows. Excellent track record in training through research, based on continuous work of previous PhD students in science. Excellent education and motivation of PhD students and post-doctoral researchers. Excellent training opportunities in a large variety of methods and technical applications. PhD students and post-docs are well trained and have received a number of scientific awards.

Unit workforce Number as at 30/06/2014 Number as at 01/01/2016 N1: Permanent professors

	nomenclature
	SVE1_LS4 Physiologie, physiopathologie, biologie systémique médicale
	SVE1_LS6 Immunologie, microbiologie, virologie, parasitologie
	SVE1_LS3 Biologie cellulaire, biologie du développement animal
	Unit workforce
	and similar positions	5	5
	N2: Permanent researchers from Institutions and similar positions	17	13
	N3: Other permanent staff (without research duties)	17 (15,8)	18 (16,8)
	N4: Other professors (Emeritus Professor, on-contract Professor, etc.)	1	2
	N5: Other researchers from Institutions (Emeritus Research Director, Postdoctoral students, visitors, etc.)	6	2
	N6: Other contractual staff (without research duties)	2
	TOTAL N1 to N6	48 (46,8)	40 (38,8)

Unit workforce Number as at 30/06/2014 Number as at 01/01/2016

	Doctoral students	6
	Theses defended	11
	Postdoctoral students having spent at least 12 months in the unit	10
	Number of Research Supervisor Qualifications (HDR) taken	3
	Qualified research supervisors (with an HDR) or similar positions	17	14

The president of HCERES "countersigns the evaluation reports set up by the experts committees and signed by their chairman." (Article 8, paragraph 5)

The evaluation reports "are signed by the chairman of the expert committee". (Article 11, paragraph 2)


Uwe Mr

Schlattner

Pascal Mr

Ferre

Andrew Mr

Halestrap

Jean-François Mr

Nice Tanti

Girard

Ms Christelle Breton

Eric Mr

Defrancq

Ms Anne Rochat

This report is the result of the evaluation by the experts committee, the composition of which is specified below.

The assessments contained herein are the expression of an independent and collegial deliberation of the committee.

 Introduction History and geographical location of the unit

The laboratory of fundamental and applied bioenergetics (LBFA) is located at the Joseph Fourier University campus in Grenoble where it occupies the Biology B building. Created in 2002 under the supervision of both University Joseph Fourier and Inserm, it was recreated in 2011 as the UMR_S 1055 with a new director and two teams. The main topic of the unit is the study of mitochondrial bioenergetics in physiological and pathological situations and LBFA has gained an international recognition in this field. The present proposal marks a turning point since LBFA should move in a new research building with up-to-date facilities and also since a number of the researchers/engineers/technicians initially associated with the unit have left or have unfortunately deceased. Some new staff will join the unit for the next contract but researchers will belong essentially to university with an absence of full-time researchers from Inserm, CNRS or INRA.

Management team

The unit is presently directed by Mr Uwe SCHLATTNER who is also leading the project for the next contract. 2  Overall assessment of the unit

HCERES nomenclature

SVE1_LS4

Unit workforce

Global assessment of the unit

The unit (36 permanent staff) is composed of two teams and works on an important although not widely studied topic, cell bioenergetics. The studies range from molecular to more integrated physiological and physiopathological approaches with an emphasis on system biology. The unit has a real expertise and reputation in the field and has produced interesting findings concerning for instance the role of specific mitochondrial kinases. The production in the last five years (since 2009) is important (150 papers) and competitive although there are no topranking publications originating directly from the unit and a lack of common publications between the 2 teams. The unit has obtained a steady flow of funding and has attracted numerous students. Its academic reputation is very good although it could be improved at the international level. The unit Leader has achieved an excellent internal organization, with established common resources and a dynamic scientific life. Concerning the future, the movement to new facilities and the belonging to a federative structure of system biology present great opportunities. The projects are clear and credible for one team but less defined and integrated for the other with a range of rather different projects. One major challenge for the next contract is the necessity to recruit full-time scientists since the work force has been reduced even though new university staff and a researcher from the "Commissariat à l'énergie atomique" are part of the project. In this context, one might ask whether a more unified project should be set up which would allow the unit to concentrate on the most promising topics.

Strengths and opportunities in relation to the context

The unit has a long-standing know-how and reputation in the field of bioenergetics. A key aspect is the multidisciplinary approach ranging from chemistry, biophysics, structural studies, cellular and molecular biology and human physiology. The scientific production is very good (around 150 papers since 2009) with some important findings and many collaborative studies as well as the production of innovative tools. There is a steady flow of funding from national, european and industrial sources. There are close collaborations with some industrial partners leading to clinical trials in the field of diabetes, reflecting the possibilities for translational work with clinicians.

The unit is very attractive for PhD students with about 19 theses for the contract period.

The director had a very active policy for the development of a system biology approach, resulting in a federative structure with other laboratories (BEeSY).

The director has set up an efficient intra-unit organisation with technical facilities and a quality approach which should lead to an "ISO 9001" recognition.

In the context of the "Plan Campus", a new building is presently under construction (with the present director being the scientist in charge) next to the present building and the unit will move to this building in 2016. It will offer a range of extended facilities such as a large animal house, culture rooms, technical facilities for "omics" approaches and so on. A mass spec-proteomics group is joining the unit which should extend the analytic capacity of the unit.

Weaknesses and threats related to the context

Although the production is very good, the number of top-ranking publications is modest and no very major breakthrough is reported.

Except for the director, the number of internationally recognized members is rather low as seen from congress invitations and reviews.

The unit has suffered unfortunately due to the loss of key members and this has not been fully compensated. The major weakness here is probably the absence of full-time researchers who could give an impetus to the ongoing research. Some of the projects are ill-defined and address a wide range of fields, a fact that in the present staff context is questionable. In addition, there seem to be very few collaborations between the two teams leading to common publications, although this should be easily feasible.

Finally although the relocation to a new building is certainly an opportunity, it will require large amounts of funding which could be difficult to raise in the present economical context.

Unit workforce Number as at 30/06/2014 Number as at 01/01/2016 N1: Permanent

	professors and similar positions	15	15
	N2: Permanent researchers from Institutions and similar positions	1	1
	N3: Other permanent staff (without research duties)	11	13
	N4: Other professors (Emeritus Professor, on-contract Professor, etc.)	4	2
	N5: Other researchers (Emeritus Research Director, Postdoctoral students, visitors, etc.)	4	1
	N6: Other contractual staff (without research duties)	1	1
	TOTAL N1 to N6	36	33
	Unit workforce	Number as at 30/06/2014	Number as at 01/01/2016
	Doctoral students	10
	Theses defended	19

Postdoctoral students having spent at least 12 months in the unit 3

Number of Research Supervisor Qualifications (HDR) taken

Qualified research supervisors (with an HDR) or similar positions 13

Recommendations

A key to the longer term success of the unit is the recruitment of new highly-talented and well-motivated researchers, allowing the enhancement of the international impact of the work, but without introducing new topics, poorly related to the unit core subjects. Although recruitment is a difficult task, this must be the main priority of the unit, together with attracting good quality post-doctoral fellows. The same kind of recommendation was already given in the last evaluation.

The experts committee also recommends that the number of topics is decreased in order to enhance the work force on each, focusing on what represents the core activity of the unit, and thus allowing more visible outputs to be achieved. The existence of two different teams with very few interactions might also be questionable and an integration into a single well-defined project could be an interesting alternative.

the different projects presented, team 1 should focus on project 1 (Energy signalling via oligomeric kinase complexes) and the first 2 topics (energy homeostasis, iron homeostasis) of project 2 (System bioenergetics studied in cellular model systems). The experts committee further recommends the merger of project 1 of team 2 (Mitochondria and cell death) into a single team project in team 1 which could then be very competitive with a more defined focus. Among the other projects on nutrition of team 2, the one on citrulline is probably the most likely to yield interesting and translatable results. The experts committee is not convinced that other nutritional / toxicology projects could be internationally competitive without very significant investments.


András Antos
email: antos@sztaki.hu

Rémi Munos
email: remi.munos@inria.fr

Csaba Szepesvári
email: szepesva@cs.ualberta.ca

Fitted Q-iteration in continuous action-space MDPs

We consider continuous state, continuous action batch reinforcement learning where the goal is to learn a good policy from a sufficiently rich trajectory generated by some policy. We study a variant of fitted Q-iteration, where the greedy action selection is replaced by searching for a policy in a restricted set of candidate policies by maximizing the average action values. We provide a rigorous analysis of this algorithm, proving what we believe is the first finite-time bound for value-function based algorithms for continuous state and action problems.

Preliminaries

We will build on the results from [START_REF] Antos | Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path[END_REF][START_REF] Antos | Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path[END_REF][START_REF] Antos | Value-iteration based fitted policy iteration: learning with a single trajectory[END_REF] and for this reason we use the same notation as these papers. The unattributed results cited in this section can be found in the book [START_REF] Bertsekas | Stochastic Optimal Control (The Discrete Time Case)[END_REF].

A discounted MDP is defined by a quintuple (X , A, P, S, γ), where X is the (possible infinite) state space, A is the set of actions, P : X × A → M (X ) is the transition probability kernel with P (•|x, a) defining the next-state distribution upon taking action a from state x, S(•|x, a) gives the corresponding distribution of immediate rewards, and γ ∈ (0, 1) is the discount factor. Here X is a measurable space and M (X ) denotes the set of all probability measures over X . The Lebesguemeasure shall be denoted by λ. We start with the following mild assumption on the MDP: Assumption A1 (MDP Regularity) X is a compact subset of the d X -dimensional Euclidean space, A is a compact subset of [-A ∞ , A ∞ ] d A . The random immediate rewards are bounded by Rmax and that the expected immediate reward function, r(x, a) = rS(dr|x, a), is uniformly bounded by R max : r ∞ ≤ R max .

A policy determines the next action given the past observations. Here we shall deal with stationary (Markovian) policies which choose an action in a stochastic way based on the last observation only.

The value of a policy π when it is started from a state x is defined as the total expected discounted reward that is encountered while the policy is executed:

V π (x) = E π [ ∞ t=0 γ t R t |X 0 = x].
Here R t ∼ S(•|X t , A t ) is the reward received at time step t, the state, X t , evolves according to X t+1 ∼ P (•|X t , A t ), where A t is sampled from the distribution determined by π. We use Q π : X × A → R to denote the action-value function of policy π: Q

π (x, a) = E π [ ∞ t=0 γ t R t |X 0 = x, A 0 = a].
The goal is to find a policy that attains the best possible values, V * (x) = sup π V π (x), at all states x ∈ X . Here V * is called the optimal value function and a policy π * that satisfies V π * (x) = V * (x) for all x ∈ X is called optimal. The optimal action-value function

Q * (x, a) is Q * (x, a) = sup π Q π (x, a).
We say that a (deterministic stationary) policy π is greedy w.r.t. an action-value function Q ∈ B(X × A), and we write π = π(•; Q), if, for all x ∈ X , π(x) ∈ argmax a∈A Q(x, a). Under mild technical assumptions, such a greedy policy always exists. Any greedy policy w.r.t. Q * is optimal. For π : X → A we define its evaluation operator, T

π : B(X × A) → B(X × A), by (T π Q)(x, a) = r(x, a) + γ X Q(y, π(y)) P (dy|x, a). It is known that Q π = T π Q π . Further, if we let the Bellman operator, T : B(X × A) → B(X × A), defined by (T Q)(x, a) = r(x, a) + γ X sup b∈A Q(y, b) P (dy|x, a) then Q * = T Q * . It is known that V π and Q π are bounded by R max /(1 -γ), just like Q * and V * . For π : X → A, the operator E π : B(X × A) → B(X ) is defined by (E π Q)(x) = Q(x, π(x)), while E : B(X × A) → B(X ) is defined by (EQ)(x) = sup a∈A Q(x, a).
Throughout the paper F ⊂ {f : X × A → R} will denote a subset of real-valued functions over the state-action space X × A and Π ⊂ A X will be a set of policies. For ν ∈ M (X ) and f : X → R measurable, we let (for p ≥ 1)

f p p,ν = X |f (x)| p ν(dx). We simply write f ν for f 2,ν . Further, we extend • ν to F by f 2 ν = A X |f | 2 (x, a) dν(x) dλ A (a)
, where λ A is the uniform distribution over A. We shall use the shorthand notation νf to denote the integral f (x)ν(dx). We denote the space of bounded measurable functions with domain X by B(X ). Further, the space of measurable functions bounded by 0 < K < ∞ shall be denoted by B(X ; K). We let • ∞ denote the supremum norm.

Fitted Q-iteration with approximate policy maximization

We assume that we are given a finite trajectory, {(X t , A t , R t )} 1≤t≤N , generated by some stochastic stationary policy π b , called the behavior policy:

A t ∼ π b (•|X t ), X t+1 ∼ P (•|X t , A t ), R t ∼ S(•|X t , A t ), where π b (•|x) is a density with π 0 def = inf (x,a)∈X ×A π b (a|x) > 0.
The generic recipe for fitted Q-iteration (FQI) [START_REF] Ernst | Tree-based batch mode reinforcement learning[END_REF] is

Q k+1 = Regress(D k (Q k )), (1)
where Regress is an appropriate regression procedure and D k (Q k ) is a dataset defining a regression problem in the form of a list of data-point pairs:

D k (Q k ) = (X t , A t ), R t + γ max b∈A Q k (X t+1 , b) 1≤t≤N . 1
Fitted Q-iteration can be viewed as approximate value iteration applied to action-value functions. To see this note that value iteration would assign the value

(T Q k )(x, a) = r(x, a) + γ max b∈A Q k (y, b) P (dy|x, a) to Q k+1 (x, a) [6]
. Now, remember that the regression function for the jointly distributed random variables (Z, Y ) is defined by the conditional expectation of Y given

Z: m(Z) = E [Y |Z]. Since for any fixed function Q, E [R t + γ max b∈A Q(X t+1 , b)|X t , A t ] = (T Q)(X t , A t )
, the regression function corresponding to the data D k (Q) is indeed T Q and hence if FQI solved the regression problem defined by Q k exactly, it would simulate value iteration exactly.

However, this argument itself does not directly lead to a rigorous analysis of FQI: Since Q k is obtained based on the data, it is itself a random function. Hence, after the first iteration, the "target" function in FQI becomes random. Furthermore, this function depends on the same data that is used to define the regression problem. Will FQI still work despite these issues? To illustrate the potential difficulties consider a dataset where X 1 , . . . , X N is a sequence of independent random variables, which are all distributed uniformly at random in [0, 1]. Further, let M be a random integer greater than N which is independent of the dataset (X t ) N t=1 . Let U be another random variable, uniformly distributed in [0, 1]. Now define the regression problem by Y t = f M,U (X t ), where f M,U (x) = sgn(sin(2 M 2π(x + U ))). Then it is not hard to see that no matter how big N is, no procedure can estimate the regression function f M,U with a small error (in expectation, or with high probability), even if the procedure could exploit the knowledge of the specific form of f M,U . On the other hand, if we restricted M to a finite range then the estimation problem could be solved successfully. The example shows that if the complexity of the random functions defining the regression problem is uncontrolled then successful estimation might be impossible.

Amongst the many regression methods in this paper we have chosen to work with least-squares methods. In this case Equation (1) takes the form

Q k+1 = argmin Q∈F N t=1 1 π b (A t |X t ) Q(X t , A t ) -R t + γ max b∈A Q k (X t+1 , b) 2 . ( 2
)
We call this method the least-squares fitted Q-iteration (LSFQI) method. Here we introduced the weighting 1/π b (A t |X t ) since we do not want to give more weight to those actions that are preferred by the behavior policy.

Besides this weighting, the only parameter of the method is the function set F. This function set should be chosen carefully, to keep a balance between the representation power and the number of samples. As a specific example for F consider neural networks with some fixed architecture. In this case the function set is generated by assigning weights in all possible ways to the neural net.

Then the above minimization becomes the problem of tuning the weights. Another example is to use linearly parameterized function approximation methods with appropriately selected basis functions.

In this case the weight tuning problem would be less demanding. Yet another possibility is to let F be an appropriate restriction of a Reproducing Kernel Hilbert Space (e.g., in a ball). In this case the training procedure becomes similar to LS-SVM training [START_REF] Cristianini | An introduction to support vector machines (and other kernel-based learning methods)[END_REF].

As indicated above, the analysis of this algorithm is complicated by the fact that the new dataset is defined in terms of the previous iterate, which is already a function of the dataset. Another complication is that the samples in a trajectory are in general correlated and that the bias introduced by the imperfections of the approximation architecture may yield to an explosion of the error of the procedure, as documented in a number of cases in, e.g., [START_REF] Boyan | Generalization in reinforcement learning: Safely approximating the value function[END_REF].

Nevertheless, at least for finite action sets, the tools developed in [START_REF] Antos | Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path[END_REF][START_REF] Antos | Value-iteration based fitted policy iteration: learning with a single trajectory[END_REF][START_REF] Antos | Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path[END_REF] look suitable to show that under appropriate conditions these problems can be overcome if the function set is chosen in a judicious way. However, the results of these works would become essentially useless in the case of an infinite number of actions since these previous bounds grow to infinity with the number of actions. Actually, we believe that this is not an artifact of the proof techniques of these works, as suggested by the counterexample that involved random targets. The following result elaborates this point further: Proposition 2.1. Let F ⊂ B(X × A). Then even if the pseudo-dimension of F is finite, the fatshattering function of

F ∨ max = V Q : V Q (•) = max a∈A Q(•, a), Q ∈ F can be infinite over (0, 1/2). 2
Without going into further details, let us just note that the finiteness of the fat-shattering function is a sufficient and necessary condition for learnability and the finiteness of the fat-shattering function is implied by the finiteness of the pseudo-dimension [START_REF] Bartlett | Fat-shattering and the learnability of real-valued functions[END_REF].The above proposition thus shows that without imposing further special conditions on F, the learning problem may become infeasible.

One possibility is of course to discretize the action space, e.g., by using a uniform grid. However, if the action space has a really high dimensionality, this approach becomes unfeasible (even enumerating 2 d A points could be impossible when d A is large). Therefore we prefer alternate solutions.

Another possibility is to make the functions in F, e.g., uniformly Lipschitz in their state coordinates.

Then the same property will hold for functions in F ∨ max and hence by a classical result we can bound the capacity of this set (cf. pp. 353-357 of [START_REF] Kolmogorov | ǫ-entropy and ǫ-capacity of sets in functional space[END_REF]). One potential problem with this approach is that this way it might be difficult to get a fine control of the capacity of the resulting set.

In the approach explored here we modify the fitted Q-iteration algorithm by introducing a policy set Π and a search over this set for an approximately greedy policy in a sense that will be made precise in a minute. Our algorithm thus has four parameters: F, Π, K, Q 0 . Here F is as before, Π is a user-chosen set of policies (mappings from X to A), K is the number of iterations and Q 0 is an initial value function (a typical choice is Q 0 ≡ 0). The algorithm computes a sequence of iterates (Q k , πk ), k = 0, . . . , K, defined by the following equations:

π0 = argmax π∈Π N t=1 Q 0 (X t , π(X t )), Q k+1 = argmin Q∈F N t=1 1 π b (A t |X t ) Q(X t , A t ) -R t + γQ k (X t+1 , πk (X t+1 )) 2 , (3)
πk+1 = argmax π∈Π N t=1 Q k+1 (X t , π(X t )). (4)
Thus, ( 3) is similar to (2), while (4) defines the policy search problem. The policy search will generally be solved by a gradient procedure or some other appropriate method. The cost of this step will be primarily determined by how well-behaving the iterates Q k+1 are in their action arguments. For example, if they were quadratic and if π was linear then the problem would be a quadratic optimization problem. However, except for special cases 3 the action value functions will be more complicated, in which case this step can be expensive. Still, this cost could be similar to that of searching for the maximizing actions for each t = 1, . . . , N if the approximately maximizing actions are similar across similar states.

This algorithm, which we could also call a fitted actor-critic algorithm, will be shown to overcome the above mentioned complexity control problem provided that the complexity of Π is controlled appropriately. Indeed, in this case the set of possible regression problems is determined by the set

F ∨ Π = { V : V (•) = Q(•, π(•)), Q ∈ F, π ∈ Π } ,
and the proof will rely on controlling the complexity of F ∨ Π by selecting F and Π appropriately.

3 The main theoretical result

Outline of the analysis

In order to gain some insight into the behavior of the algorithm, we provide a brief summary of its error analysis. The main result will be presented subsequently. For f ,Q ∈ F and a policy π, we define the t th TD-error as follows:

d t (f ; Q, π) = R t + γQ(X t+1 , π(X t+1 )) -f (X t , A t ).
Further, we define the empirical loss function by

LN (f ; Q, π) = 1 N N t=1 d 2 t (f ; Q, π) λ(A)π b (A t |X t )
,

where the normalization with λ(A) is introduced for mathematical convenience. Then (3) can be written compactly as

Q k+1 = argmin f ∈F LN (f ; Q k , πk ).
The algorithm can then be motivated by the observation that for any f ,Q, and π, LN (f ; Q, π) is an unbiased estimate of

L(f ; Q, π) def = f -T π Q 2 ν + L * (Q, π), (5)
where the first term is the error we are interested in and the second term captures the variance of the random samples:

L * (Q, π) = A E [Var [R 1 + γQ(X 2 , π(X 2 ))|X 1 , A 1 = a]] dλ A (a).
3 Linear quadratic regulation is such a nice case. It is interesting to note that in this special case the obvious choices for F and Π yield zero error in the limit, as can be proven based on the main result of this paper.

This result is stated formally by

E LN (f ; Q, π) = L(f ; Q, π).
Since the variance term in [START_REF] Ernst | Tree-based batch mode reinforcement learning[END_REF]

is independent of f , argmin f ∈F L(f ; Q, π) = argmin f ∈F f -T π Q 2 ν . Thus, if πk were greedy w.r.t. Q k then argmin f ∈F L(f ; Q k , πk ) = argmin f ∈F f -T Q k 2 ν .
Hence we can still think of the procedure as approximate value iteration over the space of action-value functions, projecting T Q k using empirical risk minimization on the space F w.r.t. • ν distances in an approximate manner. Since πk is only approximately greedy, we will have to deal with both the error coming from the approximate projection and the error coming from the choice of πk . To make this clear, we write the iteration in the form

Q k+1 = T πk Q k + ε ′ k = T Q k + ε ′ k + (T πk Q k -T Q k ) = T Q k + ε k ,
where ε ′ k is the error committed while computing

T πk Q k , ε ′′ k def = T πk Q k -T Q k is
the error committed because the greedy policy is computed approximately and ε k = ε ′ k + ε ′′ k is the total error of step k. Hence, in order to show that the procedure is well behaved, one needs to show that both errors are controlled and that when the errors are propagated through these equations, the resulting error stays controlled, too. Since we are ultimately interested in the performance of the policy obtained, we will also need to show that small action-value approximation errors yield small performance losses. For these we need a number of assumptions that concern either the training data, the MDP, or the function sets used for learning.

Assumptions

Assumptions on the training data

We shall assume that the data is rich, is in a steady state, and is fast-mixing, where, informally, mixing means that future depends weakly on the past.

Assumption A2 (Sample Path Properties) Assume that {(X t , A t , R t )} t=1,...,N is the sample path of π b , a stochastic stationary policy. Further, assume that {X t } is strictly stationary (X t ∼ ν ∈ M (X )) and exponentially β-mixing with the actual rate given by the parameters (β, b, κ). 4 We further assume that the sampling policy π b satisfies π 0 = inf (x,a)∈X ×A π b (a|x) > 0.

The β-mixing property will be used to establish tail inequalities for certain empirical processes. 5Note that the mixing coefficients do not need to be known. In the case when no mixing condition is satisfied, learning might be impossible. To see this just consider the case when X 1 = X 2 = . . . = X N . Thus, in this case the learner has many copies of the same random variable and successful generalization is thus impossible. We believe that the assumption that the process is in a steady state is not essential for our result, as when the process reaches its steady state quickly then (at the price of a more involved proof) the result would still hold.

Assumptions on the MDP

In order to prevent the uncontrolled growth of the errors as they are propagated through the updates, we shall need some assumptions on the MDP. A convenient assumption is the following one [START_REF] Munos | Finite time bounds for sampling based fitted value iteration[END_REF]: Assumption A3 (Uniformly stochastic transitions) For all x ∈ X and a ∈ A, assume that P (•|x, a) is absolutely continuous w.r.t. ν and the Radon-Nikodym derivative of P w.r.t. ν is bounded

uniformly with bound C ν : C ν def = sup x∈X ,a∈A dP (•|x,a) dν ∞ < +∞.
Note that by the definition of measure differentiation, Assumption A3 means that P (•|x, a) ≤ C ν ν(•). This assumption essentially requires the transitions to be noisy. We will also prove (weaker) results under the following, weaker assumption:

Assumption A4 (Discounted-average concentrability of future-state distributions) Given ρ, ν, m ≥ 1 and an arbitrary sequence of stationary policies {π m } m≥1 , assume that the futurestate distribution ρP π1 P π2 . . . P πm is absolutely continuous w.r.t. ν. Assume that c(m)

def = sup π1,...,πm d(ρP π 1 P π 2 ...P πm ) dν ∞ satisfies C ρ,ν def = (1 -γ) 2 m≥1 mγ m-1 c(m) < +∞.
We shall call C ρ,ν the discounted-average concentrability coefficient of the future-state distributions.

The number c(m) measures how much ρ can get amplified in m steps as compared to the reference distribution ν. Hence, in general we expect c(m) to grow with m. In fact, the condition that C ρ,µ is finite is a growth rate condition on c(m). Thanks to discounting, C ρ,µ is finite for a reasonably large class of systems (see the discussion in [START_REF] Munos | Finite time bounds for sampling based fitted value iteration[END_REF]).

A related assumption is needed in the error analysis of the approximate greedy step of the algorithm:

Assumption A5 (The random policy "makes no peak-states") Consider the distribution µ = (ν × λ A )P which is the distribution of a state that results from sampling an initial state according to ν and then executing an action which is selected uniformly at random. 6 Then Γ ν = dµ/dν ∞ < +∞.

Note that under Assumption A3 we have Γ ν ≤ C ν . This (very mild) assumption means that after one step, starting from ν and executing this random policy, the probability of the next state being in a set is upper bounded by Γ ν -times the probability of the starting state being in the same set.

Besides, we assume that A has the following regularity property: Let Py(a, h, ρ)

def = (a ′ , v) ∈ R d A +1 : a -a ′ 1 ≤ ρ, 0 ≤ v/h ≤ 1 -a -a ′
1 /ρ denote the pyramid with hight h and base given by the ℓ 1 -ball B(a, ρ)

def = a ′ ∈ R d A : a -a ′ 1 ≤ ρ centered at a.
Assumption A6 (Regularity of the action space) We assume that there exists α > 0, such that for all a ∈ A, for all ρ > 0, λ(Py(a, 1, ρ) ∩ (A × R)) λ(Py(a, 1, ρ)) ≥ min α, λ(A) λ(B(a, ρ))

.

For example, if A is an ℓ 1 -ball itself, then this assumption will be satisfied with α = 2 -d A .

Without assuming any smoothness of the MDP, learning in infinite MDPs looks hard (see, e.g., [START_REF] Ng | PEGASUS: A policy search method for large MDPs and POMDPs[END_REF][START_REF] Bartlett | Sample complexity of policy search with known dynamics[END_REF]). Here we employ the following extra condition:

Assumption A7 (Lipschitzness of the MDP in the actions) Assume that the transition probabilities and rewards are Lipschitz w.r.t. their action variable, i.e., there exists L P , L r > 0 such that for all (x, a, a ′ ) ∈ X × A × A and measurable set B of X , |P (B|x, a) -

P (B|x, a ′ )| ≤ L P a -a ′ 1 , |r(x, a) -r(x, a ′ )| ≤ L r a -a ′ 1 .
Note that previously Lipschitzness w.r.t. the state variables was used, e.g., in [START_REF] Munos | Finite time bounds for sampling based fitted value iteration[END_REF] to construct consistent planning algorithms.

Assumptions on the function sets used by the algorithm

These assumptions are less demanding since they are under the control of the user of the algorithm. However, the choice of these function sets will greatly influence the performance of the algorithm, as we shall see it from the bounds. The first assumption concerns the class F: Assumption A8 (Lipschitzness of candidate action-value functions) Assume F ⊂ B(X × A) and that any elements of F is uniformly Lipschitz in its action-argument in the sense that |Q(x, a) -Q(x, a ′ )| ≤ L A a -a ′ 1 holds for any x ∈ X , a,a ′ ∈ A, and Q ∈ F.

We shall also need to control the capacity of our function sets. We assume that the reader is familiar with the concept of VC-dimension. 7 Here we use the pseudo-dimension of function sets that builds upon the concept of VC-dimension: Definition 3.1 (Pseudo-dimension). The pseudo-dimension V F + of F is defined as the VCdimension of the subgraphs of functions in F (hence it is also called the VC-subgraph dimension of F).

Since A is multidimensional, we define V Π + to be the sum of the pseudo-dimensions of the coordinate projection spaces, Π k of Π:

V Π + = d A k=1 V Π + k , Π k = { π k : X → R : π = (π 1 , . . . , π k , . . . , π d A ) ∈ Π } .
Now we are ready to state our assumptions on our function sets:

Assumption A9 (Capacity of the function and policy sets)

Assume that F ⊂ B(X × A; Q max ) for Q max > 0 and V F + < +∞. Also, A ⊂ [-A ∞ , A ∞ ] d A and V Π + < +∞.
Besides their capacity, one shall also control the approximation power of the function sets involved. Let us first consider the policy set Π. Introduce

e * (F, Π) = sup Q∈F inf π∈Π ν(EQ -E π Q).
Note that inf π∈Π ν(EQ -E π Q) measures the quality of approximating νEQ by νE π Q. Hence, e * (F, Π) measures the worst-case approximation error of νEQ as Q is changed within F. This can be made small by choosing Π large.

Another related quantity is the one-step Bellman-error of F w.r.t. Π. This is defined as follows: For a fixed policy π, the one-step Bellman-error of F w.r.t. T π is defined as

E 1 (F; π) = sup Q∈F inf Q ′ ∈F Q ′ -T π Q ν .
Taking again a pessimistic approach, the one-step Bellman-error of F is defined as

E 1 (F, Π) = sup π∈Π E 1 (F; π).
Typically by increasing F, E 1 (F, Π) can be made smaller (this is discussed at some length in [START_REF] Antos | Value-iteration based fitted policy iteration: learning with a single trajectory[END_REF]). However, it also holds for both Π and F that making them bigger will increase their capacity (pseudo-dimensions) which leads to an increase of the estimation errors. Hence, F and Π must be selected to balance the approximation and estimation errors, just like in supervised learning.

The main result

Theorem 3.2. Under Assumptions A1, A2, and A5-A9, for all δ > 0 we have with probability at least 1 -δ: given Assumption A3 (respectively A4),

V * -V π K ∞ (resp. V * -V π K 1,ρ ), is bounded by C        E 1 (F, Π) + e * (F, Π) + (log N + log(K/δ)) κ+1 4κ N 1/4   1 d A +1 + γ K     
, where C depends on d A , V F + , (V Π + k ) d A k=1 , γ, κ, b, β, C ν (resp. C ρ,ν ), Γ ν , L A , L P ,L r , α, λ(A), π 0 , Q max , R max , Rmax , and A ∞ . In particular, C scales with V κ+1 4κ(d A +1) , where V = 2V F + + V Π + plays the role of the "combined effective" dimension of F and Π.

Discussion

We have presented what we believe is the first finite-time bounds for continuous-state and actionspace RL that uses value functions. Further, this is the first analysis of fitted Q-iteration, an algorithm that has proved to be useful in a number of cases, even when used with non-averagers for which no previous theoretical analysis existed (e.g., [START_REF] Riedmiller | Neural fitted Q iteration -first experiences with a data efficient neural reinforcement learning method[END_REF][START_REF] Kalyanakrishnan | Batch reinforcement learning in a complex domain[END_REF]). In fact, our main motivation was to show that there is a systematic way of making these algorithms work and to point at possible problem sources the same time. We discussed why it can be difficult to make these algorithms work in practice. We suggested that either the set of action-value candidates has to be carefully controlled (e.g., assuming uniform Lipschitzness w.r.t. the state variables), or a policy search step is needed, just like in actorcritic algorithms. The bound in this paper is similar in many respects to a previous bound of a Bellman-residual minimization algorithm [START_REF] Antos | Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path[END_REF]. It looks that the techniques developed here can be used to obtain results for that algorithm when it is applied to continuous action spaces. Finally, although we have not explored them here, consistency results for FQI can be obtained from our results using standard methods, like the methods of sieves. We believe that the methods developed here will eventually lead to algorithms where the function approximation methods are chosen based on the data (similar to adaptive regression methods) so as to optimize performance, which in our opinion is one of the biggest open questions in RL. Currently we are exploring the possibility of this.

Since the designer controls Q k , we may assume that it is continuous, hence the maximum exists.

The proof of this and the other results are given in the appendix, available in the extended version of this paper, downloadable from http://hal.inria.fr/inria-00185311/en/.

For the definition of β-mixing, see e.g.[START_REF] Antos | Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path[END_REF].

We say "empirical process" and "empirical measure", but note that in this work these are based on dependent (mixing) samples.

Remember that λA denotes the uniform distribution over the action set A.

Readers not familiar with VC-dimension are suggested to consult a book, such as the one by Anthony and Bartlett[START_REF] Anthony | Neural Network Learning: Theoretical Foundations[END_REF].

Acknowledgments

András Antos would like to acknowledge support for this project from the Hungarian Academy of Sciences (Bolyai Fellowship). Csaba Szepesvári greatly acknowledges the support received from the Alberta Ingenuity Fund, NSERC, the Computer and Automation Research Institute of the Hungarian Academy of Sciences.


Evaluation report

This report is the result of the evaluation by the experts committee, the composition of which is specified below.

The assessments contained herein are the expression of an independent and collegial deliberation of the committee.

Unit name:

Institute for Neurodegenerative Disorders (mainly). Its establishment was the result of considerable efforts and represented a major step forward in the structuring of the Neuroscience research conducted at this site as part of the "Neurocampus" project. This "Neurocampus" features three research institutes, each of them being under the direction of leading scientists in their field of research, all spatially organized as a campus for facilitating the mutualisation of the large equipment or facilities. The strategic implantation in close vicinity from the University hospital of Bordeaux indicates the objective of the Neurocampus: to establish a synergy between basic and applied research.

The campus is spatially organized around the Neurocentre Magendie (NM) with two additional Institutes, the "Institut Interdisciplinaire en Neurosciences" (IINS) and the "Institut des Maladies Neurodégénératives" (IMN), and a Neuroscience school. The NM, led by Mr Pier-Vicenzo PIAZZA, Neurocampus coordinator, investigates the pathophysiology of conditions such as pain, memory impairment, addiction, stress-related disorders, etc., in the context of preclinical development in rodent models of these conditions. Upstream the NM, the IINS, led by Mr Daniel CHOQUET, investigates the functioning of the elementary element of the nervous system, the synapse using state-of-the-art imaging technologies allowing single molecule imaging. Downstream these two institutes, the IMN includes both preclinical (rodent and primate) and clinical researchers with a clear goal of developing new therapeutic approaches of neurodegenerative disorders by facilitating the translational research from bench to bedside. IINS and IMN will be housed in 2016 in a brand new building established in front of the current NM IMN buildings.

Management team

A governance system was established, headed by the Director of the IMN and the Executive Committee. Two Adjunct Directors were nominated: Mr François TISON, Professor of Neurology, heads the clinical branch of the IMN. Ms Céline VÉGA-ROÏATTI, Neuroscience Assistant Professor, is in charge of the administration affairs. Together with Mr Christian GROSS, Neuroscience Assistant Professor, she is also in charge of the planning of the new building, interacting with the architects and contractors. Administrative and maintenance personnel were grouped within a single team. The centralized management of human and financial resources was created. Serious infrastructure problems (fire prevention, health and safety, the reorganization and reassignment of common and laboratory areas) were solved with the help of institutional partners. A special effort was made to organize the scientific life and follow-up of students. The scattered geographical localization of the various teams did not prevent scientific and technical collaborations as well as transversal projects among teams. These projects contributed to preparing the IMN for the evolution of its scientific project. 2  Overall assessment of the unit

HCERES

Global assessment of the unit

The main strength of the unit is undoubtedly its overall excellent scientific level and outputs. Several teams are at the forefront of the European research and this scientific excellence is testified by an impressive record of publications in high-ranking journals with the best paper of the year ANA, the Neuroimage best paper award and 5 scientists being in the top 1%.

The IMN is also remarkable for its ability to conduct translational research with several original preclinical models that directly pave the way for innovative medicine. Relations with clinical departments and expert and investigation centre are excellent. Technical innovation is also remarkable related with mature industry exploitation.

Another critical strength is the ability of IMN to attract many researchers and post-doctoral students from abroad (13 different countries). This results in very good international collaborations that increase the impact of the lab. The very good and stimulating Master and Doctoral training is an added value.

The capacity to obtain financial support (over 3 million euros external resources during last 3 years) with PIA1, ANR grants, as well as European grants (Sympath, Newwalk, JPND) already secured, give confidence upon the capacity of IMN to conduct the research programs as planned.

Finally, but importantly, the research unit enjoys the full support of the University of Bordeaux, the CNRS as well as the CEA and INRIA. In fact, the IMN will benefit from a new building (Bordeaux Neurocampus) mid 2016.

Strengths and opportunities in relation to the context

A clear strength of the IMN over the past years has been the ability of most of the teams to conduct a high level of competitive research:

• Several teams have international impact, in particular in the area of Parkinson's disease translational research, memory processes and Alzheimer disease, functional imaging, as indicated by high impact publications;

• Several teams were successful to conduct innovative and risky projects with long-term vision;

• Ability to attract a relatively large number of good PhD and post-doctoral researchers.

Weaknesses and threats related to the context

The day-to-day scientific programs of some teams are perturbed by the new building construction, the reconditioning of existing buildings and works in the facilities. The Bordeaux imaging platform is not fully equipped yet (installation of the MRI scanners is expected to be effective mid-2015). However, these factors are expected to have minimal long-term impact. The position of the adjunct Director in charge of administrative affairs has to be secured.

Recommendations

The IMN has the potential to be recognized as a world leader in the neurodegenerative diseases field. Particular attention should be paid to increase the proportion of papers in high-impact journals. A stronger involvement in fundamental research should help to reach this realistic goal. Recruitment strategy should take in consideration the strong need in technicians and non-PhD engineers to take care of platform activities and also contribute to research activities. The Unit has clearly prioritized their objectives for the next 5 year contract, taking account the resources that have already been secured. Therefore, the only recommendation is to remain focused on major objectives.

  , University of Lausanne, Switzerland Ms Lydia KERKERIAN, CNRS, Institut de biologie du développement, Marseille Mr Ayikoé Guy MENSAH-NYAGAN, Faculty of Medicine, Strasbourg (representative of the CNU) Ms Iris OREN, University of Edinburgh, United Kingdom Ms Sheela VYAS, Institut de Biologie Paris-Seine, Paris (representative of the CoNRS) Scientific delegate representing the HCERES: Mr Jean-Marie ZAJAC Representatives of the unit's supervising institutions and bodies: Ms Alix dE LA COSTE, CEA Mr Pierre DOS SANTOS, University of Bordeaux Ms Nathalie LERESCHE, CNRS Mr Roger MARTHAN (representative of the Doctoral School n°154) Ms Monique THONNAT, INRIA 1  Introduction History and geographical location of the unit The Institute of Neurodegenerative Diseases (IMN) was created on January 1st, 2011. It resulted from the merging of several teams originating mainly from two former CNRS units, while the other researchers involved in these two units formed the INCIA (Aquitaine Institute for Cognitive and Integrative Neuroscience). It led to the pooling of CNRS, INSERM and University personnel and resources dedicated to studying Movement Disorders and Dementias

Unit workforce Number as at 30/06/2014 Number as at 01/01/2016

	Unit workforce
	N1: Permanent professors and similar positions	20	22
	N2: Permanent researchers from Institutions and similar positions	19	27
	N3: Other permanent staff (without research duties)	20	25
	N4: Other professors (Emeritus Professor, on-contract Professor, etc.)	3	3
	N5: Other researchers (Emeritus Research Director, Postdoctoral students, visitors, etc.)	9	1
	N6: Other contractual staff (without research duties)	11	11
	TOTAL N1 to N6	82	89
	Unit workforce	Number as at 30/06/2014	Number as at 01/01/2016
	Doctoral students	49
	Theses defended	24
	Postdoctoral students having spent at least 12 months in the unit	5
	Number of Research Supervisor Qualifications (HDR) taken	2
	Qualified research supervisors (with an HDR) or similar positions	30
	nomenclature
	SVE1_LS5
	SVE1_LS4
	SVE1_LS7


Evaluation report

This report is the result of the evaluation by the experts committee, the composition of which is specified below.

The assessments contained herein are the expression of an independent and collegial deliberation of the committee.

Unit name:

Institute of Cellular Biochemistry and Genetics The proposed Research entity is located in Bordeaux. It is an Unité Mixte de Recherche with two supporting institutions, the CNRS and the University of Bordeaux. It will be the continuation of the previous UMR 5095. Its contour will remain unchanged except for the adjunction of two additional groups, which are currently being selected. The unit is composed of 12 teams, the main scientific interests of which lie, broadly speaking, in cellular metabolism and cell division.

Management team

Mr Bertrand DAIGNAN-FORNIER is the director of the unit. He is in charge, together with the Scientific Council, composed of the group leaders, of the scientific policy of the Institute.

HCERES nomenclature

Principal: SVE1_LS3 Secondary: SVE1_LS2 2  Overall assessment of the unit

SVE1_LS1

Unit workforce

Unit workforce

Global assessment of the unit

The unit comprises twelve teams which, for a large majority, delivered excellent to outstanding outputs during the past term. Its attractiveness is attested by the recruitment of young scientists and its scientific momentum is reflected by the overall scientific production of the unit and its success in grant applications, which are both remarkable. While the number of published articles remained stable, the average quality, as assessed by the impact factor of the journals, has improved (the number of paper published in journals with IF<4 is below 30 % and those published in journals with IF> 8 is about ~15 %). The next term is expected to see the arrival of two additional group leaders who are currently being selected, confirming the strategy of the unit to sustain its development by external growth. The foreseen team leader's interests are on the intracellular trafficking in yeast and on hydrogen sulfide as a gasotransmitter. From a scientific standpoint this will further widen the breadth of the unit's members' interest, which already encompasses a broad spectrum of subjects.

Strengths and opportunities in relation to the context

The overall excellent outputs of the individual teams highlight the quality of the scientific and technical environment that the unit can provide. It is well inserted within the local scientific network and, in particular, benefits from the local initiatives such as Bordeaux Cancer Initiative or Bordeaux Synthetic Biology Initiative. The unit is developing new imaging approaches, including super-resolution microscopy in addition to the molecular genetics and structural approaches for which IBGC is well recognized. The unit has a strong emphasis on cell biology with particular focus on mitochondrial biology (from structure to mitophagy) and on cell division (from chromosome sorting and transmission to growth and quiescence). The projects conducted in the unit resort to several biological models, prominent amongst which are yeasts. The past term has seen the introduction and development of multicellular organisms such as C. elegans and D. melanogaster as new models. This allows taking advantage of the respective merits of these different models and could create novel synergies between the various teams.

Weaknesses and threats related to the context

The broad spectrum of the subjects combined to the full scientific and financial independence of the group leaders make the Institute look more alike an assembly of autonomous teams than a unit strictly speaking. Emblematic of this, is the fact that two teams are physically located outside the building with no short-term plans or even willingness to integrate them geographically. The current recruitment of two additional groups essentially aims at further backing up the competitiveness of the unit. Although this is undoubtedly a commendable objective, it stands short as a scientific policy. As regards to this latter issue, the strongest links put forward by the unit are more methodological than scientific, strictly speaking. In the long-term this may not be strong enough to counteract centrifugal forces. Besides, there are genuinely overlapping scientific interests that are strikingly overlooked. This likely is because they have sprung during the course of the long and renowned history of the unit and have not yet soaked in deeply enough to tear down the picket fences that arise between groups that naturally focus on their own projects. In any case, these overlaps could be fruitfully exploited and would make the unit more than the sum of its, otherwise, excellent individual teams.

Recommendations

The past term has seen significant changes in the contours and scientific interests of the unit. Overall these have been fruitfully conducted as attested by the attractiveness of the unit and the quality of the scientific productions of most of the teams that compose the unit that has remarkably improved. These efforts should be continued and sustained. The unit is now entering another period during which the potential added values of being a unit would deserve being further exploited.

Additional benefit could be taken from the various scientific synergies that exist between groups and they could be promoted without denting the scientific autonomy of the various teams. In general, the scientific policy of the Unit would gain in momentum if being more strongly focused on its scientific content rather than on its performance as defined by its competitiveness. As an example, a historical major strength of the unit has been research on mitochondrial biogenesis. One of the internationally recognized leader of this field, Dr DI RAGO, is going to retire over the next few years, and no clear plans to consolidate this research axis seem to be implemented. As another example, the glaring ERC grant undoubtedly rains down upon the entire unit but it will remain a glitter if it stays as little integrated into the overall unit as it presently is.

Ideally, the unit members should build upon their own scientific strengths to find the resources to rejuvenate the Unit's modus operandi. One may, for example, consider regrouping several teams sharing common interests or tools within departments, keeping in mind that the objective is to lay bridges and not to build additional counterproductive compartments.

Alternatively and not exclusively, an external Scientific Advisory Board may provide the required initial spark to start the process, keeping in mind that, ultimately, the changes will be conducted by the unit members.

From a practical standpoint, premises allowing collegial lunch or coffee breaks would constitute a simple and valuable investment to foster exchanges between team members and provide informal opportunities for sparkling debates.

  , Institut de Biologie Physico-Chimique, Paris Experts: Mr Antonio BARRIENTOS, Miller School of Medicine, Miami, USA Mr Frédéric BOUILLAUD, Institut Cochin, Paris Mr Bernard OFFMAN, Faculté des Sciences et Techniques, Nantes (representative of the CNU) Mr Tiago OUTEIRO, Center for Nanoscale Microscopy and Molecular Physiology of the Brain, Goettingen, Germany Ms Anne PAOLETTI, Institut Curie, Paris Mr Jesus REQUENA, University of Santiago de Compostela, Spain Mr Bruno ROBERT, Institut de biologie et de technologies de Saclay (CEA), Gif sur Yvette Ms Marie-Hélène VERLHAC, Collège de France, Paris (representative of the CoNRS) Ms Katja WASSMANN, Laboratoire de Biologie du Développement, Paris Scientific delegate representing the HCERES: Mr Pierre COUBLE Representatives of the unit's supervising institutions and bodies: Mr Pierre DOS SANTOS, University of Bordeaux Mr Laurent KODJABACHIAN, CNRS Mr Roger MARTHAN (representative of Doctoral School "Sciences de la Vie et de la Santé" -SVS -n°154) 1  Introduction History and geographical location of the unit

Unit workforce Number as at 30/06/2014 Number as at 01/01/2016

	Doctoral students	8
	Theses defended	19
	Postdoctoral students having spent at least 12 months in the unit	7
	Number of Research Supervisor Qualifications (HDR) taken	5
	Qualified research supervisors (with an HDR) or similar positions	33	33
		Number as at	Number as at
		30/06/2014	01/01/2016
	N1: Permanent professors and similar positions	15	15
	N2: Permanent researchers from Institutions and similar positions	22 (21.8)	22
	N3: Other permanent staff (without research duties)	23 (21.8)	21 (20.5)
	N4: Other professors (Emeritus Professor, on-contract Professor, etc.)	1	1
	N5: Other researchers (Emeritus Research Director, Postdoctoral students, visitors, etc.)	7	1
	N6: Other contractual staff (without research duties)	9
	TOTAL N1 to N6	77 (75.6)	60 (59.5)

signed by their chairman." (Article 8, paragraph 5) 2 The evaluation reports "are signed by the chairman of the expert committee". (Article 11, paragraph 2)


Evaluation report

This report is the result of the evaluation by the experts committee, the composition of which is specified below.

The assessments contained herein are the expression of an independent and collegial deliberation of the committee.

Unit name:

Actions for onCogenesis understanding and Target Identification in ONcology The "Action for onCogenesis understanding and Target Identification in Oncology (ACTION)" research unit results from a single team research group under the direction of Mr Josy REIFFERS, also director of the Bergonié Institute. It is located in the Bergonié Institute in Bordeaux and includes several clinicians and pathologists from the institute, together with scientists working in different Inserm units, as expected for a unit aiming at merging clinical and more fundamental research. Most of the research activity is at Bergonié Institute, but one team currently is located at the CHU de Bordeaux (Pessac and Inserm 1035) and will join the unit upon its creation. A project leader in the Institute Européen de Chimie et de Biologie (IECB), an incubator for research teams, will join team 3 when enough space will be available.

Management team

Action is headed by Mr Pierre-Louis SOUBEYRAN. It is managed by a unit board, including the director and all researchers, which meets monthly. A general assembly gathering all the unit researchers is held every year and a lab council, including representative of all personal, half elected and half nominated, meets 3 or 4 times a year. 2  Overall assessment of the unit

HCERES nomenclature

Global assessment of the unit

The ACTION research unit (Actions for onCogenesis understanding and Target Identification in ONcology) is a conglomeration of three research teams carrying out important bench-to-bedside research which tackles oncogenic processes in the sarcoma, myeloid leukaemia biology, breast cancer therapy and prediction of response to cancer treatment. It was composed of 1 team located at the Bergonié Institute in Bordeaux when created in 2007. The project of ACTION for the next 5 years includes the creation of 3 teams. In combination the 3 teams have strong scientific background, important complementary skills enabling basic, translational and clinical investigations and the potential for delivering research that can bring about change in the clinical management of oncology. Three research axes interest the teams that currently constitute the ACTION:

team 1: Oncogenesis and therapeutic targeting of the sarcoma cell (ONCOSARC); team 2: Genetic Diversity and Resistance to Therapy: Mammary and Leukemic Oncogenesis (MLO); team 3: Validation and Identification of New Targets in cancer and AGEing (VINTAGE).

The teams are also integrated into a "Les Sites de Recherche Intégrée sur le Cancer" SIRIC program. This is an excellent unit proposal that integrates research groups already included in the previous research unit located at the Bergonié Institute and other research teams currently in other locations. All three teams that participate to the present proposal have excelled in cancer research, through the development and application to cancer of high-throughput molecular studies and other functional and clinical studies.

Teams within the unit have developed in the past excellent research in sarcoma clinical prediction, myeloid leukaemia biology, breast cancer therapy and cancer treatment in elderly patients, and have a very high international reputation. Within these niches, the research teams play a leading role in the international scenario.

The unit has also been able to attract some top investigators in basic aspects of cancer research such as senescence, autophagy and others.

Strengths and opportunities in relation to the context

The main asset of the unit is the interaction between a cancer clinical centre and multiple basic research Inserm groups working in Bordeaux. This has already led to the recognition of a SIRIC by INCa (Institut National du Cancer). Consolidation of the integration between all the different agents involved in cancer research in Bordeaux may facilitate the consolidation and the further development of other ambitious projects within the research unit.

The unit includes multiple experienced researchers in different aspects of cancer research, with publications in the top journals of the field such as Nature Medicine, Molecular Cell and Lancet Oncology.

The unit is developing projects in connection with International Cancer Genome Consortium (ICGC) and European Organisation for Research and Treatment of Cancer (EORTC). Involvement in ICGC projects should likely facilitate access to high-throughput sequencing and bioinformatics technology, and eventual publications in top journals.

Existence in Bordeaux of additional basic and clinical cancer research groups, whose integration in the unit should facilitate the consecution of the unit objectives.

Weaknesses and threats related to the context

Cancer research is a highly dynamic field, where the pursuit of excellence is always required. The unit is encouraged to follow generating solid experimental and clinical data that can be published in the top cancer journals Team leaders have been able to secure a reasonable funding obtained in competitive European agencies. The unit should ambition to successfully apply for ERC and other top European projects in the Horizon 2020 program.

Recommendations

Excellence. The unit is doing a great job. Cancer research is an exciting and highly dynamic subject, where the pursuit of excellence is a requisite for the successful clinical translation.

Focus. The research team should focus more on precise niches of excellence, trying to improve the connections between the different sub-projects. Further maturation of the unit should allow the identification of biologically and clinically meaningful long-term programs that could become the focus of the team's activity.

Consolidation of common platforms and resources in animal facilities, bioinformatics and functional studies together with attraction of competitive researchers could permit the development and consolidation of additional research groups.

  PIRIS, Hospital Universitario Marques de Valdecilla, Spain Experts: Mr Olivier BERNARD, Génétique des tumeurs, Institut Gustave Roussy (representative of the CSS Inserm) Mr Olivier COQUERET, Inserm, Université d'Angers (representative of the CNU) Mr Francisco X REAL, Centro Nacional de Investigaciones Oncológicas, Spain Scientific delegate representing the HCERES: Ms Maryam MEHRPOUR Representatives of the unit's supervising institutions and bodies: Mr Pierre DOS SANTOS, Bordeaux University Ms Karine GIONNET, Inserm Ms Isabelle HENRY, Inserm Mr Yannick LUNG, Bordeaux University Mr Roger MARTHAN (director of the ED SVS n°154 "Science de la vie et de la santé") 1  Introduction History and geographical location of the unit

Unit workforce Number as at 30/06/2014 Number as at 01/01/2016

	Doctoral students	5
	Theses defended	13
	Postdoctoral students having spent at least 12 months in the unit	5
	Number of Research Supervisor Qualifications (HDR) taken
	Qualified research supervisors (with an HDR) or similar positions	14	13
	Unit workforce	Number as at 30/06/2014	Number as at 01/01/2016
	N1: Permanent professors and similar positions	10	10
	N2: Permanent researchers from Institutions and similar positions	4	5
	N3: Other permanent staff (without research duties)	16	19
	N4: Other professors (Emeritus Professor, on-contract Professor, etc.)	10	14
	N5: Other researchers (Emeritus Research Director, Postdoctoral students, visitors, etc.)	1	3
	N6: Other contractual staff
	(without research duties)
	TOTAL N1 to N6	41	51

Principal : SVE1_LS1 Biologie moléculaire et structurale, biochimie Secondaire : SVE1_LS4 Physiologie, physiopathologie, biologie systémique médicale SVE1_LS3 Biologie cellulaire, Biologie du développement animal SVE1_LS2 Génétique, génomique, bioinformatique SVE1_LS7 Epidémiologie, santé publique, recherche clinique, technologies biomédicales Unit workforce


president Didier Houssin

Pierre Mr

Philip

Lino Mr

Nobili

Ms Christelle

Charley Monaca

Ms Marie-Pia D'ortho

Jean-Marie Mr

Zajac

Mr Pierre

Jean-Louis Mr

Vercher

 Introduction History and geographical location of the unit

The SANPSY unit, created in 2011, is part of the Bordeaux Neuroscience Community, the latter being an excellence of the University of Bordeaux. It is located in a 1000 square meter wing at the 13 th floor of the Bordeaux teaching Hospital (CHU Pellegrin), and it is at short walking distance from the Clinic for addiction treatment (Charles Perren Hospital). The research centre has been completed in June 2014 with the complete installation of Equipex equipment (a multifaceted immersed environment to conduct clinical research) and the opening of the new Sleep Clinic (10 beds) at the same floor of the hospital nearby the SANPSY unit.

Management team

The management of SANPSY is excellent. The unit is directed by a very dynamic team leader who also plays a critical role in the definition of scientific activities, organization of the research unit and in fund raising process. 2  Overall assessment of the unit

HCERES nomenclature

SVE1_LS4

SVE1_LS7

Unit workforce

Unit workforce

Global assessment of the unit

The activity of the SANPSY unit is original both on the side of sleep and of addiction research. In particular, the main strength of this unit is the capability to conduct studies with different, but integrated approaches, going from epidemiological field studies to sophisticated in-lab experiments by exploiting multidisciplinary expertise in neurology, psychiatry, virtual reality, neuropsychology and electrophysiology to investigate sleep and psychiatry disorders. Moreover, the possibility to use different virtual environments offers a unique opportunity to conduct novel experiments not only aimed at evaluating the effects of sleep deprivation and fatigue on attention, cognitive functions and risk of accidents, but also allowing to conduct original studies on neuro-psychiatric diseases such as Attention Deficit Hyperactivity Disorder (ADHD). Another very promising research activity of the unit is the one based on affective computing. Finally the big prospective cohort of addicted patients studied using advanced mobile technologies, allows the recordings of outpatient data in the everyday lives, thus providing a powerful "research tool" for the unit.

Undoubtedly the SANPSY unit constitutes a distinguished and excellent research entity both in the neuroscience community of Bordeaux and at the international level.

Strengths and opportunities in relation to the context

The presence of a very well equipped platform, with a multifaceted immersed environment, together with the research and technical expertise of the group, including also computer research engineers, represents an exceptional situation that allows conducting high-level experimental and clinical studies. The interdisciplinary approach of the research activity of the unit is a particular strength. Another important strength is given by the opportunity to follow a big cohort of addicted patients using technologies allowing the assessment of outpatient data in the everyday lives.

Weaknesses and threats related to the context

The main weaknesses of the unit relies on the small number of full time researchers, the very low number of PhD achieved in the team over the past four years as well as the very low number of students in the lab. In particular, the project of team 3 is supported by only one researcher; although she has an excellent background, she needs collaborators.

6

Recommendations

It is crucial to increase the number of full-time researchers employed. Moreover, it is recommended to recruit more post-doctoral students and attract more students. We strongly encourage to further increase the collaboration and integration of teams 1 and 2 and, in the other hand, we believe that team 1 and 3 should be merged (see detailed comments of team 3). A higher number of formal lab meetings should be planned.

From a research point of view, the implementation of electrophysiological skills (EEG, HdEEG) during experiments using virtual reality (especially within the driving simulator) could further increase the level of scientific activities.

Number as at 30/06/2014 Number as at 01/01/2016 N1: Permanent professors

	and similar positions	2	2
	N2: Permanent researchers from Institutions and similar positions	2	2
	N3: Other permanent staff (without research duties)	7	7
	N5: Other researchers (Emeritus Research Director, Postdoctoral students, visitors, etc.)	6	7
	N6: Other contractual staff (without research duties)	10	9
	TOTAL N1 to

N4:

Other professors (Emeritus Professor, on-contract Professor, etc.)

N6 27 Unit workforce Number as at 30/06/2014 Number as at 01/01/2016

	Doctoral students
	Theses defended	2
	Postdoctoral students having spent at least 12 months in the unit	2
	Number of Research Supervisor Qualifications (HDR) taken
	Qualified research supervisors (with an HDR) or similar positions	5	5

signed by their chairman.

Evaluation report

This report is the result of the evaluation by the experts committee, the composition of which is specified below.

The assessments contained herein are the expression of an independent and collegial deliberation of the committee.

Unit name: