Sérgio Nunes
email: sergio.nunes@fe.up.pt

Suzanne Little

Sumit Bhatia

Ludovico Boratto

Guillaume Cabanac

Ricardo Campos

Francisco M Couto

Stefano Faralli

Ingo Frommholz

Adam Jatowt

Alípio Jorge

Mirko Marras

Philipp Mayr

Giovanni Stilo

ECIR 2020 workshops: assessing the impact of going online

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

Introduction

ECIR 2020 1 was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2020) and move to a fully online event. In this report, we describe the experience of organising the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organisers and the workshop participants. We provide a report on the organisational aspect of these events and the consequences for participants. Covering the scientific dimension of each workshop is outside the scope of this article. A detailed account of the main conference can be found in the Spring 2020 edition of BCS Informer [BCS, 2020].

The results presented here are based on two online questionnaires involving the participation of more than 60 people: one sent to all workshop organisers and another one sent to all workshop paper authors and workshop participants. The former is presented in Section 2 to describe the workshop organisers' feedback. The latter is introduced in Section 3 to describe the participants' perspective. Our general objective is to get a sense of the feeling of both stakeholders in this first-time experience. Questions such as "What was gained/lost?"; "What was harder/easier?", "What worked?"; "What didn't work?" among many others will be dissected. We conclude this article by highlighting the main recommendations and lessons learned from this online experience.

We believe this to be of potential importance for researchers interested in organising similar events in the future.

2 Workshop Organisation

Overview

ECIR 2020 Workshops were held on April 14, 2020 (Tuesday) adopting Lisbon time (CEST+1), from 09h00 to 18h00. The first call for workshops was issued on July 22, 2019. We received a total of six proposals, one was later withdrawn, and four were accepted -three as full day events, one half-day. Workshop notifications were sent on October 1, one month after the submission deadline. On March 11, the decision to move to a fully online event was announced. We present a summary of the accepted ECIR 2020 Workshops in Table 1.

The ECIR 2020 Organisation adopted Zoom as the official platform for synchronous video conferencing. A dedicated Zoom room with password access was configured for each workshop and the details provided to the workshop organisers. A few days before the event, a draft version of ACM's best practices guide for virtual conferences [Association for Computing Machinery, 2020] was published and shared with the participants. The ECIR 2020 Organisation also created a slack workspace for use during the event and a dedicated slack channel for workshop organisers was created. This was used in particular to connect with technical support during the workshops. The specific details of how each workshop was organised are described next.

BIAS 2020 [START_REF] Boratto | International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020)[END_REF] was the 1st edition of the International Workshop on Algorithmic Bias in Search and Recommendation 2 . BIAS was organised as a full-day event, chaired by Ludovico Boratto (EURECAT, Centre Tecnològic de Catalunya), Mirko Marras (University of Cagliari), Stefano Faralli (University of Rome Unitelma Sapienza), and Giovanni Stilo (University of L'Aquila). The workshop aimed at collecting novel ideas to detect, measure, characterise, and mitigate bias in data and algorithms underlying search and recommendation applications. The goal was to provide a common ground for researchers and practitioners working in this area. The workshop had more than 70 participants. The keynote speaker -Prof. Chirag Shah from the University of Washington (USA) -highlighted how bias, especially in relation to search and recommender systems, causes material problems for users, businesses, and society at large. The scientific program included demo and paper presentations and a final discussion. The papers covered topics from search and recommendation in online dating, education, and social media, over the impact of gender bias in word embeddings, to tools that allow exploration of bias and fairness on the Web. The event concluded with a discussion session to highlight open issues, research challenges, and briefly summarise the outcomes of the workshop. The workshop proceedings volume is going to be published soon in Springer's "Communications in Computer and Information Science" series.

BIR 2020 [Cabanac et al., 2020a] was the 10th edition of the International Workshop on Bibliometric-enhanced Information Retrieval3 . BIR was organised as a full-day event and chaired by Guillaume Cabanac (Université de Toulouse), Ingo Frommholz (University of Bedfordshire), and Philipp Mayr (GESIS -Leibniz-Institute for the Social Sciences). BIR tackles issues related to academic search, at the crossroads between Information Retrieval, Natural Language Processing and Bibliometrics. The keynote speaker -George Tsatsaronis, VP Data Science at Elsevieraddressed the topic of Metrics and Trends in Assessing the Scientific Impact. Eight papers were given live (no pre-recorded videos), each one followed by a Q&A session and greetings for the 10th anniversary edition from senior scholars in our field. The proceedings are published in open access at CEUR-WS [Cabanac et al., 2020b].

SIIRH 2020 [Couto and Krallinger, 2020a] was the 1st edition of the International Workshop on Semantic Indexing and Information Retrieval for Health from Heterogeneous Content Types and Languages. SIIRH was organised as a half-day event and chaired by Francisco Couto (LASIGE, Universidade de Lisboa) and Martin Krallinger (BSC-CNS). The workshop included an open session covering novel contributions on IR technologies for heterogeneous health-related content open to multiple languages with a particular interest in the exploitation of structured controlled vocabularies and entity linking for document indexing and semantic search applications. The program committee was composed of 46 international researchers and selected three full papers and three short papers. A MESINESP/Plan TL Session was also part of the workshop for presentation and discussion of current work and new advances in the MESINESP shared task or other medical IR, QA or text categorisation evaluation campaigns, as well as the exploitation of evaluation settings and data collections generated through these kind of community evaluation efforts. The Open Session included two keynote talks, one by Dr. Cher Han Lau, entitled Corona-Tracker: A framework for managing and tracking data during crisis, and another by Lucy Lu Wang and Kyle Lo, entitled The COVID-19 Open Research Dataset. The MESINESP/Plan TL Session included one keynote talk, by George Paliouras, entitled BioASQ: The challenge and the community of biomedical semantic indexing and question answering. Recordings of all talks are publicly available in YouTube4 and the proceedings are published in open access at CEUR-WS [Couto and Krallinger, 2020b].

Text2Story 2020 [Campos et al., 2020a] was the 3rd edition of the International Workshop on Narrative Extraction from Texts 5 , chaired by Ricardo Campos (INESC TEC; Ci2 -Smart Cities Research Center, Polytechnic Institute of Tomar), Alípio Jorge (INESC TEC and FCUP -University of Porto), Adam Jatowt (Kyoto University) and Sumit Bhatia (IBM Research AI). It was organised as a full-day event and consisted of presentations of ten regular research papers, two position papers and one demonstration paper. The papers presented at the workshop6 ,7 , were made available at CEUR [Campos et al., 2020b] and cover diverse aspects of the narrative extraction problem ranging from applications of deep learning to narrative extraction, sentiment analysis, event coreference resolution to applications in different domains such as legal text, news and social media. The program also included two invited keynote talks. Sebastião Miranda, head of development at Priberam (Portugal), talked about monitoring multi-lingual, multimedia sources of information to discover relevant stories, events, and topics of interest to a specific user. Mark Finlayson, assistant professor at the School of Computing and Information Sciences of the Florida International University (USA), presented an overview of recent advances in Natural Language Processing applied to the task of narrative analysis in text.

A more detailed overview of the BIAS, BIR, and Text2Story workshops can be found in this edition of SIGIR Forum.

Feedback from Workshop Organisers

To collect feedback from workshop organisers, an online questionnaire was prepared and shared on May 2, 2020 and kept open until the end of May 5. Out of a total of 13 organisers, we received responses from 9.

The responses to the questionnaire revealed that most of the organisers had a very positive experience that generally exceeded their expectations after knowing that the event would be remote. With a view to the future, while the majority of the respondents stated that they would be willing to organise future events similarly, a significant number of them also indicated that they would do so "Only if needed". Overall, every respondent expressed their support for remote participation in the future, although some did state that this should be allowed only for specific cases.

Regarding the effort required for the organisation of the event, the respondents had very different experiences ranging from "the same effort as in-person", "more than in-person" to "less than in-person". As a preventive measure, most of the organisers requested the authors to provide pre-recorded videos of their presentations in case of some unforeseen last-minute technical glitches. Regarding the usefulness of the platforms made available to the organisers, almost all the respondents found Zoom to be a crucial tool required for organising the online event, and some of the respondents also found the Zoom Chat feature helpful for interaction with the participants. We also note that in addition to the live and synchronous communication media (Zoom Chat and Slack), traditional email also played a crucial role in planning and communication between the organisers and participants before, during, and after the event.

A more detailed analysis of the results can be found below.

Analysis of closed and ratings questions

From the respondents, all except one (8) had previous experience in organising scientific events.

To the question "What is your overall feeling about how the workshop went?", 4 answered "Very Happy" (44%) and 5 answered "Happy" (56%). No respondents reported "Unhappy" nor "Very Unhappy". To the question "How does it compare with your previous expectations (after knowing the event would be remote)?" (8 answers), 2 answered "Much better than expected" (25%) and 6 answered "Better than expected" (75%). No answers of "Worse than expected" or "Much worse than expected" were reported.

To the question "Would you organise this event again in the future in a similar online model?", 5 respondents answered "Yes" and 4 answered "Only if needed". None of the respondents answered "No" to this. To the question "For scientific events conducted in person, how do you evaluate the importance of allowing remote participations?", 4 respondents answered "Should be standard practice and allowed for anyone" and 5 answered "Should be possible only for specific cases". None of the respondents selected "Should be avoided" or "Should not be allowed". This indicates that in future, a hybrid organisation model for workshops could be used where, for example, presentations are given by paper authors in person for both the on-site and remote audience at the same time.

Coming to the questions related to organisation, for the question "How do you evaluate the overall effort associated with the organisation of the workshop online?", the majority answered "Different, but the same effort as in person" (5), with 2 respondents answering "More than in person" and 2 answering "Less than in person". For the question "Did you use pre-recorded videos or synchronous presentations?", the answers were 5 "Both, depending on conditions" and 4 "Synchronous". Figure 2.2.1 presents the results to the question "Rate the usefulness of the tools used".

Analysis of comments from open questions

"Undoubtedly, having the workshop online allowed us to reach a broader audience in terms of participants. We believe that having conferences online in the future will also enable a higher number of submissions from a number of researchers, which for different reasons, anticipate not to attend a conference."

Positive comments from the workshop organisers emphasised the higher levels of participation and the diversity of world-wide attendees due to not having to travel and the subsequent reduced costs. BIR, which had run previously, saw an increase in the number of participants from about 30 to almost 100 while other workshops also received very strong attendance -almost double the expected numbers in one case. Comments on the use of Zoom suggest that it handled the workload well and removed some of the issues with a projector and shared computers that occur sometimes in a physical setting. Zoom presentations also meant that the viewers could all clearly see the slide and post questions on the chat ahead of the Q&A session. Subsequent questions could be clearly heard and understood by participants. However not all the organisers felt that this was a positive experience with some respondents unable to identify any specific positive aspects to running an online workshop.

Comments from the workshop organisers on the negative aspects of the online workshops identified a number of specific challenges and issues. A common response highlighted the lack of interactivity and connection between participants -"limited feedback", "personal interaction was totally missing", "chatting between participants", "start[ing] a profitable discussion", "[no] quick chats with colleagues". Two respondents also said they just missed human touch and hugs and one commented that although the workshop ran well they "missed the personal contact a lot". Attempts to encourage chatting using breakout rooms were not successful, especially where attendees did not know each other previously. The natural result was the lack of atmosphere -"things were colder", "[not able to] joke around with audience", "[no] stimulating interaction", "[no] eye contact", "[no] spontaneity". The organisers also commented that as the chairs or session chairs they needed a much higher level of concentration and intensity through the event to manage the platform and bilateral discussions. Apart from a specific instance of Zoombombing [Wikipedia contributors, 2020] in one workshop, there were also some individual network issues that disrupted sessions, challenges around time zones and communicating the registration policy.

"I felt the online worked as a barrier to have people engaged when compared to a regular conference."

"All in all, the entire process is pretty much demanding and tireless. As an organiser, I didn't find it much [sic] rewarding in the sense that I didn't have time to look at the presentations."

Recommendations from the organisers

Organisers were asked to give their recommendations for best practise in running an online workshop. Key considerations in preparation include keeping the programme short and dynamic as the required level of concentration is much greater; communicating detailed instructions via email both prior to and just before the event starts and planing a test or practise meeting for presenters are just some of the listed ideas. Pre-recording talks and making them available via a channel like YouTube was also useful. A good suggestion was to involve student volunteers to help with managing communications during the event. We all learned that participants should be explicitly warned against (accidental) sharing of Zoom hyperlinks (which include the room's password) via social media as this likely led to the Zoombombing incident of one workshop. Furthermore, when using Zoom, organisers should consider activating the "waiting room" functionality to better manage the participants.

During the event, the moderator and session chair should proactively mute microphones during talks and then manage the questions either via the chat or through attendees raising hands and being asked to talk. Organisers made some suggestions to try and incentivise feedback and participation including asking people to show their face, suggesting to open the microphones to give a round of applause after talks, introducing the audience (character and number) and using Zoom Chat to contact people.

A fully online workshop can also have different opportunities post event. Organisers suggest that you can follow up by publishing the videos, slides or talks, using a chat option like Slack to have further discussions and trying to use social media and the workshop website to promote a sense of community. This is particularly difficult when participants haven't met physically. Conducting post-event surveys or other contact with presenters and participants is key.

Workshop Participation

To collect feedback from workshop participants, an online questionnaire was sent to all workshop paper authors and participants on May 6, 2020 and was kept open until the end of May 8. A total of 47 answers were received and the results are presented below.

Analysis of closed and ratings questions

Approximately 50% of all respondents were not paper authors (24 answers) and for 85% (40 answers) this was their first participation in the respective workshop. For the question "Would you have attended the workshop in Lisbon (if this pandemic had not happened)?", 19 answered "Yes" (43%), 20 answered "No" (40%), and the remaining 8 answered "Not sure".

From all respondents, a large majority reported previous participation in scientific events (87%, 41 answers). In the answer to the question "How do you compare this experience with your previous participations in scientific events?", 12 answered "Better. The remote model improved my experience" (26%), 10 answered "Similar. The remote model had no relevant impact" (21%), 18 answered "Worse. The remote model negatively impacted the event" (38%), and 7 selected "No answer. I have no relevant previous experience to compare with" (15%).

For the question "How does it compare with your previous expectations (after knowing the event would be online)?", 12 answered "Much better than expected" (26%), 31 answered "Better than expected" (66%), 4 answered "Worse than expected" (9%), and no one answered "Much worse than expected". Figure 3.1 presents the results to the question "Rate the usefulness of the following tools". We can observe that while Zoom was obviously helpful along with email communication Zoom Chat and Slack were considered as less crucial, although Zoom Chat was still seen as an important communication channel.

When asked "For scientific events conducted in person, how do you evaluate the importance of allowing remote participations?", a majority of 35 respondents answered "Should be standard practice and allowed for anyone" (75%), 11 answered "Should be possible only for specific cases" (23%), 1 answered "Should be avoided" and no one answered "Should not be allowed". This suggests that conferences and workshops could consider opening up in one way or other for remote participation. In Figure 3.1 is a comparison between the feedback from organisers and participants.

It is interesting to note the differences depending on the role assumed.

Analysis of comments from open questions

Workshop attendees who were not paper authors (26 respondents) commented positively on the improved possibility and ease of attending, reduced travel costs, the quality of the presentations, the effectiveness of the platform and the ease of asking questions as well as comments on unexpected benefits like the relaxed dress code and the option to screenshot interesting slides to follow up. The majority of negative comments referenced the lack of personal interactions and socialising, missing coffee breaks and networking with colleagues in general. The specific incident where one workshop was invaded was also a negative experience for attendees. Some found it difficult to have physical spaces without disruption due to the isolation in their home or other work commitments, and others faced internet connectivity challenges.

Responses of attendees who were authors or presenters of papers (16 responses) agreed that it was much easier and cheaper to attend and one noted the particular benefit for students to participate and the opportunity to present to a wider audience. It also meant that more than one author per paper could participate. They also liked the Q&A options, especially the chat and options to screen share. A number of respondents commented positively on the digital presentation experience and the option to pre-record their presentation that was provided. Amusingly one noted that it was easier to present without seeing the audience while another liked the option to take a break without being noticed. "I think I really missed networking with the researchers and experts from the workshop" Missing personal contact, networking and lack of interaction also came up most frequently in the negative comments from paper authors. In particular the lack of extended scientific exchange and socialisation was a prevailing concern. One commented on the difficulty in giving an engaging presentation when you cannot see the participants and the lack of eye contact. Some felt that there were too many emails, different credentials and schedule changes that made participating challenging. They also found it more difficult to focus for a full day. Many comments also noted technical difficulties including sound issues, internet access and a good suggestion was made to invest in a cheap microphone if you have to present in an online event. A comment specific to workshops was that it felt more difficult to engage with the audience when you were presenting cross-discipline work.

Conclusion

In conclusion, the overall experience of running the ECIR 2020 Workshops as synchronous, fully online events was generally found to be a positive one. Approximately 25% of both organisers and participants who responded, reported it was "Much better than expected". The major advantage was felt to be the increase in both the number of attendees and the breadth, enabling those who may ordinarily have not been able to travel to participate. Especially for workshops with small budgets, online events facilitate the process of including keynote speakers. Online presentations, coupled with the requirement or option to send pre-recorded presentations have potential to alleviate the issue of no-shows which tend to be the concern of many organisers in the past. There were individual technical challenges (sound, connectivity, etc.) and one security incident but the major disadvantage was the lack of social interaction, of scientific networking and the ability to connect with people and discuss topics. Finding solutions to mitigate this decrease in interaction would greatly improve the online workshop experience.

ECIR 2020 was a fully online event, with more than 1000 registered participants. In this report, we presented the first-hand experience of both ECIR 2020 Workshop organisers and participants, collected using two online questionnaires. While this was setup to deal with a health emergency situation, we think that the options for fully, or hybrid, online events will increase in the future. Not only due to some of the aforementioned advantages but also as proactive actions to reduce academia carbon footprint associated with air travel [START_REF] Quinton | Cutting the carbon cost of academic travel[END_REF].

Figure 1 :

 1 Figure 1: Usefulness of the tools as reported by organisers.

Figure 2 :

 2 Figure 2: Usefulness of the tools as reported by participants.

Figure 3 :

 3 Figure 3: Importance of allowing remote participations as reported by organisers and participants.

Table 1 :

 1 ECIR 2020 Workshops.

	Workshop	Submissions	Accepted	Participants
	BIAS (full-day)	44	11 long; 6 short	> 70
	BIR (full-day)	9	8 full	> 70
	SIIRH (half-day)	7	3 full; 3 short	> 50
	Text2Story (full-day)	20	10 full; 2 position; 1 demo	> 70

http://bias.disim.univaq.it/ ACM SIGIR Forum

Vol. 54 No. 1 June 2020

https://sites.google.com/view/bir-ws/bir-2020

https://www.youtube.com/playlist?list=PL6RYRv3A1tLwpD4aTbSVraUZNITbkRxZd

http://text2story20.inesctec.ptACM SIGIR Forum

https://text2story20.inesctec.pt/text2story2020_slides.zip

https://www.youtube.com/channel/UC8ysv2TEpQZz_-v5T5nZSSA/featured ACM SIGIR Forum

ACM SIGIR Forum


Guillaume Cabanac
email: guillaume.cabanac@univ-tlse3.fr

Ingo Frommholz
email: ifrommholz@acm.org

Philipp Mayr
email: philipp.mayr@gesis.org

Report on the 10th Anniversary Workshop on Bibliometric-enhanced Information Retrieval (BIR 2020)

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

Searching for scientific information is a long-lived user need. In the early 1960s, Salton was already striving to enhance information retrieval by including clues inferred from bibliographic citations [START_REF] Salton | Associative document retrieval techniques using bibliographic information[END_REF]. The development of citation indexes pioneered by [START_REF] Garfield | Citation indexes for science: A new dimension in documentation through association of ideas[END_REF] proved determinant for such a research endeavour at the crossroads between the nascent fields of Bibliometrics1 and Information Retrieval (IR) -BIR. The pioneers who established these fields in Information Science -such as Salton and Garfield -were followed by scientists who specialised in one of these [START_REF] White | Visualizing a discipline: An author co-citation analysis of Information Science, 1972-1995[END_REF]], leading to the two loosely connected fields we know of today.

The purpose of the BIR workshop series founded in 2014 is to tighten up the link between IR and Bibliometrics [START_REF] Mayr | Scientometrics and information retrieval: weak-links revitalized[END_REF]. We strive to get the 'retrievalists' and 'citationists' [START_REF] White | Visualizing a discipline: An author co-citation analysis of Information Science, 1972-1995[END_REF]] active in both academia and the industry together, who are developing search engines and recommender systems such as ArnetMiner, Dimensions, Google Scholar, Microsoft Academic Search, and Semantic Scholar, just to name a few.

These bibliometric-enhanced IR systems must deal with the multifaceted nature of scientific information by searching for or recommending academic papers, patents, venues (i.e., conferences or journals), authors, experts (e.g., peer reviewers), references (to be cited to support an argument), and datasets. The underlying models harness relevance signals from keywords provided by authors, topics extracted from the full-texts, co-authorship networks, citation networks, and various classifications schemes of science.

BIR is a hot topic with growing recognition in the community in recent years: see for instance the Initiative for Open Citations [START_REF] Shotton | Funders should mandate open citations[END_REF], the Google Dataset Search [START_REF] Brickley | Google Dataset Search: Building a search engine for datasets in an open Web ecosystem[END_REF], the Indian JNU initiative for indexing the world's literature in full-text [START_REF] Pulla | The plan to mine the world's research papers[END_REF], the increasing number of retractions [START_REF] Brainard | What a massive database of retracted papers reveals about science publishing's "death penalty[END_REF], and massive studies of self-citations [START_REF] John | A standardized citation metrics author database annotated for scientific field[END_REF][START_REF] Kacem | Tracking self-citations in academic publishing[END_REF]. We believe that BIR@ECIR is a much needed scientific event for the 'retrievalists', 'citationists' and others to meet and join forces pushing the knowledge boundaries of IR applied to literature search and recommendation.

Figure 1: BIR 2020 online session -a collage as viewed from a typical conference location at home. This year BIR faced an exceptional situation due to the COVID-19 lockdown and the fact that ECIR 2020 was an online only event (Fig. 1). The experience of the at peak times 97 participants was overall positive and provides some food for thought for the planning of similar future events and how a more inclusive online experience can be combined with the traditional face-to-face delivery.

Past BIR Workshop Activities

The BIR workshop series was launched at ECIR in 2014 [START_REF] Mayr | BIR'14 Proceedings of the 1st Workshop on Bibliometric-enhanced Information Retrieval colocated with the 36th European Conference on Information Retrieval[END_REF] and it was held at ECIR each year since then. As our workshop lies at the crossroads between IR and NLP, we also ran BIR as a joint workshop called BIRNDL (Bibliometric-enhanced IR and NLP for Digital Libraries) at the JCDL [START_REF] Cabanac | BIRNDL'16: Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries co-located with the Joint Conference on Digital Libraries[END_REF] and SIGIR [Chandrasekaran and Mayr, 2019] conferences. All past workshops had a large number of participants (between ∼30 and ∼60), demonstrating the relevance of the workshop's topics.

In the following, we present an overview of the past BIR workshops and keynotes at BIR (Tab. 1-2). All pointers to the workshops and proceedings are hosted on the BIR homepage 2 . Many of the presented workshop papers appeared in extended form in one of our four BIR-related special issues: 2015 [START_REF] Mayr | Scientometrics and information retrieval: weak-links revitalized[END_REF], 2018 [START_REF] Cabanac | Bibliometric-enhanced information retrieval: Preface[END_REF], Mayr et al., 2018], and 2019 [START_REF] Atanassova | Editorial: Mining Scientific Papers: NLPenhanced Bibliometrics[END_REF].

Workshop Topics

The programme of the BIR 2020 workshop is documented at the workshop's homepage3 . The call for papers for the BIR 2020 workshop [Cabanac et al., 2020] addressed current research issues regarding 3 aspects of the search/recommendation process:

1. User needs and behaviour regarding scientific information, such as:

• Finding relevant papers/authors for a literature review.

• Measuring the degree of plagiarism in a paper.

• Identifying expert reviewers for a given submission.

• Flagging predatory conferences and journals.

• Information seeking behaviour and HCI in academic search.

2. Mining the scientific literature, such as:

• Information extraction, text mining and parsing of scholarly literature.

• Natural language processing (e.g., citation contexts).

• Discourse modelling and argument mining.

3. Academic search/recommendation systems:

• Modelling the multifaceted nature of scientific information.

• Building test collections for reproducible BIR.

• System support for literature search and recommendation.

Keynote

This year's keynote was given by George Tsatsaronis: Metrics and Trends in Assessing the Scientific Impact.

Research Papers

The following research papers were presented in 3 sessions.

£ Session: Expert Finding and Ranking Models

Greeting Notes to the 10th Anniversary BIR Edition

As BIR 2020 was our anniversary edition, we asked renowned researchers from the Scientometrics and NLP as well as Information Retrieval community beforehand to send their thoughts about the workshop series and reflect on BIR's mission. The following videos4 and our written greeting notes were submitted and included in the BIR proceedings.

• Andrea Scharnhorst (DANS, The Netherlands): Building bridges Companion video https://youtu.be/kNPVZZ7Mq0M

• Dietmar Wolfram (University of Wisconsin-Milwaukee, USA) Video https://youtu.be/BKNDYq09_-M

• Suzan Verberne (Leiden University, The Netherlands) Video https://youtu.be/eM1Kwevrdkc

• Mike Thelwall (University of Wolverhampton, UK): Why we need another ten years of Bibliometric-enhanced Information Retrieval Companion video https://youtu.be/Ld1s6mEpA2Q

Table 1 :

 1 Overview of the BIR workshop series

	Year Conference	Venue	Papers	Proceedings
		City	Country
	2014 ECIR	Amsterdam	NL	6	Vol-1143
	2015 ECIR	Vienna	AT	6	Vol-1344
	2016 ECIR	Padua	IT	8	Vol-1567
	2016 JCDL	Newark	US	10 + 10 a	Vol-1610
	2017 ECIR	Aberdeen	UK	12	Vol-1823
	2017 SIGIR	Tokyo	JP	11	Vol-1888
	2018 ECIR	Grenoble	FR	9	Vol-2080
	2019 ECIR	Cologne	DE	14	Vol-2345
	2019 SIGIR	Paris	FR	16 + 10 b	Vol-2414
	2020 ECIR	Lisbon c	PT	8	Vol-2591
	a with CL-SciSumm 2016 Shared Task;
	b with CL-SciSumm 2019 Shared Task;

c held online due to the COVID-19 pandemic 2 https://sites.google.com/view/bir-ws

Bibliometrics refers to the statistical analysis of the academic literature[START_REF] Pritchard | Statistical bibliography or bibliometrics? [Documentation notes[END_REF] and plays a key role in scientometrics: the quantitative analysis of science and innovation[Leydesdorff and Milojević,

2015].

Vol. 54 No. 1 June 2020

https://sites.google.com/view/bir-ws/bir-2020 ACM SIGIR Forum

Please see the full playlist at https://bit.ly/BIR-greetings ACM SIGIR Forum

https://github.com/PhilippMayr/Bibliometric-enhanced-IR_Bibliography/

https://sites.google.com/view/scientometrics-si2019-bir

https://ornlcda.github.io/SDProc/

https://bit.ly/Frontiers2020 ACM SIGIR Forum

a SCIM: Scientometrics; NLP: Natural Language Processing; IR: Information Retrieval • Iana Atanassova (Université Bourgogne Franche-Comté, France) and Marc Bertin (University of Lyon, France): BIR: A time and a place to envision concepts and tools around Bibliometrics Companion video https://youtu.be/hLgTB0av_b0

• Henry Small (SciTech Strategies, USA): Some questions for information science arising from the history and philosophy of science Companion video https://youtu.be/xOpFB0r0WPg

Further Readings and Next Steps

Since 2016 we maintain the "Bibliometric-enhanced-IR Bibliography" 5 that collects scientific papers which appear in collaboration with the BIR/BIRNDL events. Currently the BIR organizers edit a Special issue on "Bibliometrics and Information Retrieval" 6 in the journal Scientometrics (Springer). All accepted and published papers are listed on the SI page accordingly.

Research on scholarly document processing has for many years been scattered across multiple venues like ACL, SIGIR, JCDL, CIKM, EMNLP, LREC, NAACL, KDD, and others. Our next strategic step is the First Workshop on Scholarly Document Processing (SDP) 7 will be held in November 2020 in conjunction with the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). This workshop and initiative will be organized by a diverse group of researchers (organizers from BIR, BIRNDL, Workshop on Mining Scientific Publications/WOSP, and Big Scholar) which have expertise in NLP, ML, Text Summarization/Mining, Computational Linguistics, Discourse Processing, IR, and others. The SDP workshop will be also a all-virtual event.

The BIR community is invited to submit research papers to a new Research Topic "Mining Scientific Papers Volume II: Knowledge Discovery and Data Exploitation" in the journal Frontiers in Research Metrics and Analytics 8 (Deadline for submissions is September 1, 2020).


François-Xavier Carton

Matthieu Chabanas

Bodil K R Munkvold

Ingerid Reinertsen

Jack H Noble

Automatic segmentation of brain tumor in intraoperative ultrasound images using 3D U-Net

Keywords: Brain tumor, intraoperative ultrasound, segmentation, deep learning

Because of the deformation of the brain during neurosurgery, intraoperative imaging can be used to visualize the actual location of the brain structures. These images are used for image-guided navigation as well as determining whether the resection is complete and localizing the remaining tumor tissue. Intraoperative ultrasound (iUS) is a convenient modality with short acquisition times. However, iUS images are difficult to interpret because of the noise and artifacts. In particular, tumor tissue is difficult to distinguish from healthy tissue and it is very difficult to delimit tumors in iUS images. In this paper, we propose an automatic method to segment low grade brain tumors in iUS images using a 2-D and 3-D U-Net. We trained the networks on three folds with twelve training cases and five test cases each. The obtained results are promising, with a median Dice score of 0.72. The volume differences between the estimated and ground truth segmentations were similar to the intra-rater volume differences. While these results are preliminary, they suggest that deep learning methods can be successfully applied to tumor segmentation in intraoperative images.

INTRODUCTION

Intraoperative ultrasound (iUS) is frequently used during tumor resection in neurosurgery to visualize the tumor and surrounding anatomical structures. Ultrasound images can guide the surgeon to ensure that the resection is as complete as possible while healthy tissue and functional eloquent areas are preserved. However, these images may contain noise and artifacts, and the field of view is limited. The signal for healthy and tumor tissue can be poorly contrasted in iUS images, so accurately delineating the tumor is difficult. Additionally, the structures imaged in iUS and preoperative MR (pMR) do not fully match, due to deformations induced by the brain shift phenomenon [START_REF] Gerard | Brain shift in neuronavigation of brain tumors: A review[END_REF] and the resection itself. All these elements make the interpretation of iUS images of the brain particularly challenging. Computer aided intervention techniques have been widely used for non-rigid registration of pMR and iUS images, as reported in the review papers [START_REF] Gerard | Brain shift in neuronavigation of brain tumors: A review[END_REF][START_REF] Bayer | Intraoperative imaging modalities and compensation for brain shift in tumor resection surgery[END_REF] and in recent works. [START_REF] Riva | 3d intra-operative ultrasound and mr image guidance: pursuing an ultrasound-based management of brainshift to enhance neuronavigation[END_REF][START_REF] Iversen | Automatic intraoperative correction of brain shift for accurate neuronavigation[END_REF][START_REF] Machado | Non-rigid registration of 3d ultrasound for neurosurgery using automatic feature detection and matching[END_REF][START_REF] Nitsch | Automatic and efficient mri-us segmentations for improving intraoperative image fusion in image-guided neurosurgery[END_REF] A MICCAI Challenge was also organised specifically on this topic. [START_REF] Xiao | Evaluation of mri to ultrasound registration methods for brain shift correction: The curious2018 challenge[END_REF] An alternative to registration can be to analyze the iUS images directly to assist the surgeon in guidance and decision making. For instance, identifying anatomical structures such as the sulci or ventricles in pre-resection iUS can help surgeons to localise the tumor and build a mental map of the surrounding structures in the operating room. Also, delineating the tumor contours in post-resection iUS can be used as a quality control to ensure the tumor resection is complete. An advantage of analyzing the iUS images in that context is that it may not be required to track the US probe, which would clearly simplify the surgical setup. In this study, we have first focused on the segmentation of the brain tumor in pre-resection iUS images.

Automatic segmentation of brain tumors in MR images has been extensively studied. For instance, the BRATS challenge is organised each year on this topic. [START_REF] Menze | The multimodal brain tumor image segmentation benchmark (brats)[END_REF] However, very few works have addressed this problem in iUS images. [START_REF] Ritschel | Brain tumor classification on intraoperative contrast-enhanced ultrasound[END_REF][START_REF] Ilunga-Mbuyamba | Patient-specific model-based segmentation of brain tumors in 3d intraoperative ultrasound images[END_REF] In particular, these works focused on high grade tumors only. To the best of our knowledge, no method has been applied to low grade brain tumors in iUS, which are even more challenging to segment than high grade tumors due to a lower contrast. Related automatic methods have focused on segmenting more salient structures like the falx cerebri, sulci; [START_REF] Nitsch | Automatic and efficient mri-us segmentations for improving intraoperative image fusion in image-guided neurosurgery[END_REF][START_REF] Canalini | Segmentation-based registration of ultrasound volumes for glioma resection in image-guided neurosurgery[END_REF] or resection cavities. In this work, we evaluate machine learning methods to automatically segment brain tumors in iUS images acquired just after dura opening and before resection had started. We present 2-D and 3-D U-Net-based tumor segmentation models.

METHODS

Data

The RESECT public database 13 contains acquisitions from 23 patients with low grade gliomas. For each patient, two pMR volumes (T1w and T2-FLAIR scans) and three iUS 3-D volumes were acquired before, during, and after resection. We used as the ground truth (GT) the manual segmentations by Munkvold et al., [START_REF] Munkvold | Tumor volume assessment in low-grade gliomas: A comparison of preoperative magnetic resonance imaging to coregistered intraoperative 3-dimensional ultrasound recordings[END_REF] which were available for 17 volumes before resection. Figure 1 shows examples of ground truth segmentations for three cases.

Segmentation Network

We implemented three network architectures based on the U-Net architecture presented by Ronneberger et al., [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF] as illustrated figure 2. The models were implemented using the keras library (https://keras.io).

We first tested a 2-D version which was applied to iUS images to segment the resection cavity in brain tumor surgeries in another study. [START_REF] Carton | Automatic segmentation of brain tumor resections in intraoperative ultrasound images[END_REF] We trained two networks 2D-1 and 2D-9 with one and nine adjacent slices as input, respectively. Input size was 256x256, so we used a sliding window (with a stride of 64) for the training and testing phases. We then trained a 3-D network (3D), in which the input and output are 3-D patches (128x128x128) and the convolution layers are 3-D convolutions. We used a 3-D sliding window with a stride of 32 voxels.

Because the 3-D network has more context in 3-D in the input, it is expected that this model will outperform the 2-D models. However, a smaller patch size was chosen due to GPU memory constraints. This increased computation time, because a larger number of patches were evaluated. Thus, comparing 2-D and 3-D networks is interesting.

The loss function to train the models is the Dice loss function (equation 1) as it is commonly used with segmentation neural networks on unbalanced datasets. [START_REF] Milletari | V-net: Fully convolutional neural networks for volumetric medical image segmentation[END_REF] loss(y true , y pred ) = 1 -Dice(y true , y pred ) = 1 -2 y true y pred + [START_REF] Ilunga-Mbuyamba | Patient-specific model-based segmentation of brain tumors in 3d intraoperative ultrasound images[END_REF] 14 0.77 (0.09) Methods R. [START_REF] Ritschel | Brain tumor classification on intraoperative contrast-enhanced ultrasound[END_REF] and I. [START_REF] Ilunga-Mbuyamba | Patient-specific model-based segmentation of brain tumors in 3d intraoperative ultrasound images[END_REF] were evaluated on high grade tumors. The numbers in brackets are standard deviations.

y true + y pred + (1) 2-D U-Net 2-D U-Net 3-D U-Net 2D-1 2D-9 3D

Data Processing

During training, we augmented the data to prevent overfitting. At the beginning of each epoch, the volumes were transformed with random transformations: affine transformation, scaling, and grid deformation. Each of these three transformation were applied with a probability of 0.5.

The volumes were first normalized by subtracting the mean and dividing by the standard deviation. Then, patches were extracted with a 2-D or 3-D sliding window and the network is evaluated on each patch.

In the test phase, an estimated volume was reconstructed for each case by averaging the estimated patches. The volumes were then thresholded to obtain a binary mask (with a threshold value of 0.5) and only the largest connected component was kept in the final result.

We evaluated all three networks on three folds with five test cases each (randomly selected).

RESULTS AND DISCUSSION

Network Comparison

Dice scores for 2D-1, 2D-9, and 3-D are provided in table 1 and figure 3 shows boxplots of these Dice scores. Overall, the 3-D network performed better than the 2-D networks and 2D-9 performed better than 2D-1. A Wilcoxon signed rank test shows statistical significance between 2D-1 and 3D (p-value of 0.011) and between 2D-9 and 3D (p-value of 0.004). This shows that context is particularly important to segment the tumor.

Among the presented methods, 3D achieved the best results both qualitatively and when comparing Dice scores for most cases. However, the average computation time for one case was 5 minutes on a NVIDIA R GeForce GTX TITAN X, due to a high number of patches per cases. In comparison, the 2-D networks were much faster, with an average of 15 seconds per case. As such, a suitable model can be chosen depending on time and accuracy constraints. The computation time for the 3-D network could be reduced by increasing the stride of the sliding window. This would reduce the number of patches to be processed, at the cost of reducing the overlap between patches and potentially lowering accuracy.

Results for the 3-D Network

The estimated segmentations were close to the ground truth segmentations, excluding two outlier cases (1 and 3). Excluding these two cases, the Dice scores ranged from 0.55 to 0.93, with a mean of 0.75. Quantitative results are presented in table 2 and examples of segmented slices in figure 6. A notable case is case 18, in which a sulcus splits the tumor in two components. This case was correctly segmented around the sulcus and the estimated segmentation matched the ground truth in most of the tumor (except a small undersegmented area on the left).

The two outlier cases were the smallest tumors in the dataset. The tumor volume was 1.1 mL in case 3 and 6.1 mL in case 1, while the mean tumor volume was 28.6 mL. Figure 4 shows the distribution of tumor volumes. Additionally, the intensity surrounding the tumor in these cases is brighter than other cases because the tumors lie next to the tentorium. As such, the contrast between tumor tissue and the surroundings is poorer. These unusual tumor characteristics and size can explain why the networks failed to segment these cases. It is expected that training the network on more cases would improve the robustness of the model to the variations between q q q q this study, low grade tumors existing methods, high grade tumors 2D-1 2D-9 3D R. [START_REF] Ritschel | Brain tumor classification on intraoperative contrast-enhanced ultrasound[END_REF] and a boxplot on 14 cases for I.. 10 q q q 0 50 100 volume (mL) cases. Segmenting other anatomical structures (e.g. the sulci) along with tumor tissue could also improve the differentiation between structures.

The intra-rater tumor volume difference for the ground truth was reported in. [START_REF] Munkvold | Tumor volume assessment in low-grade gliomas: A comparison of preoperative magnetic resonance imaging to coregistered intraoperative 3-dimensional ultrasound recordings[END_REF] This same volume difference was then computed between the ground truth and the segmentations estimated with the 3D network. The two outliers were excluded because comparing non-overlaping volumes is meaningless. Results on the remaining 13 test cases are reported in table 2 and figure 5. The absolute percentage difference ranged from 6% to 27% for the intra-rater variability, whereas the volume difference between ground truth and estimations ranged from 5% to 59%. The intra-rater difference for case 1 was the highest (57%). For many cases, especially in fold 1, the differences are of the same magnitude.

Comparison with Existing High Grade Tumor Segmentation Methods

Ritschel et al. [START_REF] Ritschel | Brain tumor classification on intraoperative contrast-enhanced ultrasound[END_REF] and Ilunga-Mbuyamba et al. [START_REF] Ilunga-Mbuyamba | Patient-specific model-based segmentation of brain tumors in 3d intraoperative ultrasound images[END_REF] proposed iUS tumor segmentation methods that were evaluated on 13 and 14 cases, respectively (see table 1 and figure 3). The method proposed by Ritschel et al. obtained a mean Dice score of 0.73. Ilunga et al. reported a mean Dice score of 0.65 without registration and 0.77 with a rigid registration with a pMR. Both of these methods were used to segment high grade tumors. In contrast, in this study we have segmented low grade tumors, which are significantly less contrasted in iUS image and are thus more difficult to segment. Despite this higher difficulty of segmenting low grade tumors, the Dice scores with our 3-D model (excluding the two outliers cases) are comparable to the existing methods.

CONCLUSIONS

We evaluated 2-D and 3-D neural networks to segment brain tumors in iUS volumes. We obtained promising results even segmenting tumor tissue in such images is very difficult. Tumor segmentations could provide surgeons additional information when analyzing iUS images.

In future work, we will investigate how to add information from the pMR to the input to improve the results. Even though the pMR and iUS do not fully match due to brain shift, neural networks could potentially leverage pMR information to more accurately segment the tumor in the iUS. It would also be interesting to apply the method we presented to ultrasound images acquired after resection started.

The results we presented in this work are preliminary. In particular, the dataset size is very small especially for deep learning models. Increasing the dataset size could not only enable better training but also allow a more thorough analysis of the results and a more reliable validation of the models. Nonetheless, our results suggest that deep learning could be used for ultrasound image analysis, and in particular for delineating the tumor in such images.

12

 12

Figure 1 .

 1 Figure 1. Example of tumor iUS images and their ground truth segmentation from Munkvold et al.14

Figure 2 .

 2 Figure 2. Schematic of the three U-Net based models.

Figure 3 .

 3 Figure 3. Dice scores for our three models on low grade tumor cases (left) and for existing methods on high grade tumor cases (right). The left box contains boxplots of Dice scores on 15 test cases. The right box shows the mean Dice score over 13 cases for R.[START_REF] Ritschel | Brain tumor classification on intraoperative contrast-enhanced ultrasound[END_REF] and a boxplot on 14 cases for I..[START_REF] Ilunga-Mbuyamba | Patient-specific model-based segmentation of brain tumors in 3d intraoperative ultrasound images[END_REF]

Figure 4 .

 4 Figure 4. Tumor volumes (17 volumes).

Figure 5 .

 5 Figure 5. Absolute tumor volume difference between ground truth and estimated segmentations (top) and between two manual segmentations (bottom).

Fold

Figure 6 .

 6 Figure 6. Tumor segmentation results with the 3D network (green: ground truth; blue: estimated segmentation).

Table 1 .

 1 Comparison of brain tumor iUS segmentation methods.

	Method Cases	Dice	Median Dice	Precision Sensitivity Specificity
	2D-1	15	0.47 (0.28)	0.54	0.68 (0.31) 0.44 (0.28) 0.99 (0.01)
	2D-9	15	0.61 (0.21)	0.65	0.62 (0.25) 0.70 (0.22) 0.98 (0.02)
	3D	15	0.65 (0.28)	0.74	0.65 (0.30) 0.68 (0.29) 0.99 (0.01)
	R. 9	13	0.73		0.71 (0.13) 0.76 (0.15) 0.94 (0.05)
	I.

Table 2 .

 2 Quantitative results for the 3D network (iUS images only).

	Fold Case Dice score GT volume Intra-rater difference Prediction difference
	1	1	0.00	6.1 mL	3.5 mL (57%)	N/A
	1	2	0.76	6.2 mL	1.7 mL (27%)	1.4 mL ( 22%)
	1	12	0.81	12.9 mL	2.7 mL (20%)	3.7 mL ( 28%)
	1	16	0.74	6.5 mL	1.6 mL (24%)	1.8 mL ( 27%)
	1	19	0.93	23.2 mL	-1.6 mL (-6%)	-1.5 mL ( -6%)
	2	6	0.66	12.5 mL	3.2 mL (25%)	-4.0 mL (-31%)
	2	8	0.70	33.9 mL	0.9 mL (2%)	3.7 mL ( 10%)
	2	15	0.77	13.3 mL	1.5 mL (11%)	-3.6 mL (-27%)
	2	16	0.66	6.5 mL	1.6 mL (24%)	-3.4 mL (-51%)
	2	18	0.72	23.4 mL	6.5 mL (27%)	1.3 mL ( 5%)
	3	3	0.00	1.1 mL	0.0 mL (0%)	N/A
	3	5	0.55	21.2 mL	1.9 mL (9%)	-9.4 mL (-44%)
	3	7	0.77	21.6 mL	5.9 mL (27%)	-4.9 mL (-22%)
	3	21	0.83	97.1 mL	12.2 mL (12%)	-26.1 mL (-26%)
	3	23	0.88	19.6 mL	1.1 mL (5%)	-0.6 mL ( -3%)

ACKNOWLEDGMENT This work was partly supported by the French National Research Agency (ANR) through the framework ANR-11-LABX-0004 and ANR-15-IDEX-02. This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, Tennessee, USA.


Michel Fliess
email: michel.fliess@polytechnique.edu

Cédric Join
email: cedric.join@cran.uhp-nancy.fr

Wilfrid Perruquetti
email: wilfrid.perruquetti@ec-lille.fr.

Real-time estimation for switched linear systems

Keywords: Linear systems, switching systems, hybrid systems, state estimation, switching signal estimation, numerical differentiation

We extend previous works on real-time estimation, via algebraic techniques, to the recovering of the switching signal and of the state for switching linear systems. We characterize also singular inputs for which the switched systems become undistinguishable. Several convincing numerical experiments are illustrating our techniques which are easily implementable.

I. INTRODUCTION

Many systems encountered in practice exhibit switchings between several subsystems, both as a result of controller design, such as in switching supervisory control, and inherently by nature, such as when a physical plant has the capability of undergoing several operational modes.

Switched systems may be viewed as higher-level abstractions of hybrid systems, obtained by neglecting the details of the discrete behavior. Informally, a switched system is composed of a family of dynamical subsystems (linear or nonlinear), and a rule, called the switching law, that orchestrates the switching between them. In recent years, there has been increasing interest in the control problems of switched systems due to their significance from both a theoretical and practical point of view and also because of their inherently interdisciplinary nature. So several important results for switched systems have been achieved, including various results on stability [START_REF] Agrachev | Lie-algebraic stability criteria for switched systems[END_REF], [START_REF] Arapostathis | Stability and controllability of planar conwise systems[END_REF], [START_REF] Boscain | Stability of planar switched systems: The linear single input case[END_REF], [START_REF] Branicky | Stability of switched and hybrid systems[END_REF], [START_REF] Branicky | Multiple Lyapunov functions and other analysis tools for switched and hybrid systems[END_REF], [START_REF] Hespanha | Stability of switched systems with average dwell-time[END_REF], [START_REF] Liberzon | Basic problems in stability and design of switched systems[END_REF], [START_REF] Vu | Common Lyapunov functions for families of commuting nonlinear systems[END_REF], [START_REF] Mancilla-Aguilar | A converse Lyapunov theorem for nonlinear switched systems[END_REF], [START_REF] Pettersson | Stability and robustness for hybrid systems[END_REF], [START_REF] Skafidas | Stability results for switched controller systems[END_REF] (with many applications see, for example, [START_REF] Buisson | On the stabilisation of switching electrical power converters[END_REF] for application to electrical power converters), stabilization [START_REF] Bourdais | Stabilisation de systèmes non linéaires à commutation à l'aide de fonctions de Lyapunov contrôlées[END_REF], [START_REF] Moulay | Stabilization of nonlinear switched systems using control Lyapunov functions[END_REF], [START_REF] De Persis | Switched nonlinear systems with state dependent dwell time[END_REF], [START_REF] De Persis | Supervisory control with statedependent dwell-time logic and constraints[END_REF], [START_REF] Pettersson | An alysis and design of hybrid systems[END_REF], [START_REF] Wicks | Construction of piecewise Lyapunov functions for stabilizing switched systems[END_REF], [START_REF] Wicks | Solution of coupled Lyapunov equations for the stabilization of multimodal linear systems[END_REF], [START_REF] Wicks | Switched controller synthesis for the quadratic stabilization of a pair of unstable linear systems[END_REF], [START_REF] Zhai | Quadratic stabilizability of switched linear systems with polytopic uncertainties[END_REF], [START_REF] Wang | Linear matrix inequality approach to quadratic stabilisation of switched systems[END_REF], tracking [START_REF] Bourdais | Towards a model-free output tracking of switched nonlinear systems[END_REF], controllability results [START_REF] Sun | Controllability and reachability criteria for switched linear control[END_REF], [START_REF] Xie | Controllability of switched linear systems[END_REF], and inputto-state properties, . . . . See, e.g., [START_REF] Branicky | Topology of hybrid systems[END_REF], [START_REF] Brockett | Essays on Control: Perspectives in the Theory and its Applications, chap. Hybrid models for motion control systems[END_REF], [START_REF] Liberzon | Basic problems in stability and design of switched systems[END_REF], [START_REF] Liberzon | Switching in Systems and Control[END_REF], [START_REF] Sun | Switched Linear Systems[END_REF] for a survey of this type of results.

Observability and state estimation is a key problem for such systems, where discrete and continuous parts are mixed. Consider, for instance, a finite set of ordinary differential equations (ODE) 1

ẋ = f i (t, x, u), where f i : R×R n ×R m → R n , i ∈ I {1, .
. . , M }. The vector-valued output is a function of the continuous state. The observation problem is to determine the continuous state and the active ODE driving the current continuous state evolution. A generic setting for the observability of switched linear systems in continuous setting has been given in [START_REF] Babaali | Observability of switched linear systems in continuous time[END_REF]. In [START_REF] Alessandri | Switching observers for continuous-time and discrete-time systems[END_REF], [START_REF] Vidal | Observability and identifiability of jump-linear systems[END_REF], [START_REF] Vidal | Observability of linear hybrid systems[END_REF] the observability of switched linear systems in the case of deterministic switching signal was carried out. Unobserved switching case was analyzed in [START_REF] Santis | On observability and detectability of continuous-time linear switching systems[END_REF].

It is clear that if one knows which current ODE is active (the current index "i") then one can say something about the current continuous state. Thus a key problem is the real time computation of the so-called switching signal defined by σ(t, x) : R × R n → I, (t, x) → σ(t, x), where σ(t, x) ∈ I {1, . . . , M } corresponds to the index associated with the current active ODE. In the rest of the paper we will only consider linear ordinary differential equations (LODE). Taking a pair of such LODE, one can distinguish the two LODE if for any non trivial input the two systems produce two different outputs (roughly speaking). On this basis, one can encounter the following facts:

1) some pairs of LODE may be indistinguishable: any non trivial input will produce for the two systems the same output. 2) for a given pair of LODE, some peculiar inputs will produce the same outputs: the two systems are indistinguishable. For example, for the two following LODE ẏ1 = u 1 , ÿ2 + ẏ2 = 2u 2 , it is clear that if the input of these systems is u 1 = u 2 = exp(t) then the two systems have a common trajectory namely y(t) = exp(t).

It is thus important to characterize such singular inputs and then to give efficient tools for observations.Except for these pathological cases, the state (continuous and discrete or the switching signal) reconstruction of an hybrid system is a three stage process as follows:

1) using the measured output y (and eventually the input u) one needs to reconstruct y, ẏ, . . . , y (ky) up to some finite order k y which is not necessarily equal to the state dimension (k y may be smaller) and eventually u, u, . . . , u (km) , 2) reconstruct the switching signal or the discrete state: a set of signal are constructed using the obtained reconstructed signals y, ẏ, . . . , y (ky) ; u, u, . . . , u (km) in order to distinguish the active subsystem. 3) reconstruct the continuous state: using for example step 1 or eventually a refined technic using the knowledge of the actual active subsystem.

II. DINSTINGUISHABILITY

A. Problem formulation: an Input-output behavior Consider switched monovariable 2 linear systems of the form:

ẋ = A σ(t) x + B σ(t) u, y = C σ(t) x + D σ(t) u, x ∈ R n , u ∈ R, y ∈ R, (1)
where σ(t) is the switching signal taking value within the index set I = {1, ..., M }. In the rest we address the problem of the reconstruction of the switching signal σ(t) in"realtime", and of the state variables if is possible. From the problem formulation it is clear that for each i ∈ I we only need an input/output representation of our linear subsystems.

In the rest we adopt a matrix transfer representation of the input/output behavior:

a i d dt y i = b i d dt u, i ∈ I. (2)

B. Distinguishability

Consider two monovariable linear systems with transfer

functions bi ai , i = 1, 2, a i , b i ∈ R[s], b i = 0, (a i , b i ) = 1.
It is clear that they do not exhibit the same input-output behavior if the two transfer functions are different, i.e., a 1 b 2 -a 2 b 1 = 0. There might exist nevertheless particular inputs for which the two outputs coincide.

Assume that two input-output systems satisfy

a i ( d dt )y i = b i ( d dt )u, i = 1, 2.
It is clear that we cannot distinguish their input-output behaviors if, and only if, u and y = y 1 = y 2 satisfy the matrix differential equation

a 1 -b 1 a 2 -b 2 y u = 0 0 . (3)
Classic algebraic manipulations show that Eq. ( 3) is equivalent to

A d dt u = 0 B d dt y = 0. (4)
where

A, B ∈ R[ d dt ]
, AB = 0. The two systems are said to be strongly distinguishable if, and only if, A and B are constant. This is equivalent saying that the two systems have the same input-output behavior only for u = y 1 = y 2 = 0. If not the two systems are said to be weakly distinguishable. The next result summarizes the above computations.

Theorem 1 In (4) A and B are given by

A = gcd(b 1 p 1 1 , b 2 p 2 1 ), B = (a 2 b ′ 1 -a 1 b ′ 2 ) , (5)
where b = gcd(b 1 , b 2 ), b 1 = bb ′ 1 , b 2 = bb ′ 2 , a = gcd(a 1 , a 2 ), a 1 = aa ′ 1 , a 2 = aa ′ 2 , p 1 = lcm(a ′ 1 , a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ), and p 1 = p 1 1 a ′ 1 = p 1 2 (a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ), p 2 = lcm(a ′ 2 , a ′ 2 b ′ 1 - a ′ 1 b ′ 2 )
, and

p 2 = p 2 1 a ′ 2 = p 2 2 (a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ). Strong distin- guishability is equivalent to (a 2 b ′ 1 -a 1 b ′ 2 ) ∈ R \ {0} and gcd(b 1 p 1 1 , b 2 p 2 1 ) ∈ R \ {0}.
2 Extension to the multivariable case will be discussed in a coming paper.

Proof: Omitted for sake of brevity (Just use simple algebraic manipulations on polynomials).

Remark 2

The two systems are said to be strongly distinguishable if, and only if, the only solution to (4)-( 5) is the trivial one that is y = u = 0. This is equivalent saying that the two systems have the same input-output behavior only for u = y 1 = y 2 = 0. If not the two systems are said to be weakly distinguishable.

C. Example: first order systems

Let us consider [START_REF] Agrachev | Lie-algebraic stability criteria for switched systems[END_REF] with n = 1 and M = 2 : for a given i ∈ I the corresponding system has a transfert function of the form

F i (s) = k i 1 + τ i s , k i = b i a i , τ i = 1 a i , (6)
the two transfer functions are different iff

k 1 (1 + τ 2 s) -k 2 (1 + τ 1 s) = 0. (7)
Nevertheless for the two systems some input/output behavior cannot be distinguish iff

(1 + τ 1 s) -k 1 (1 + τ 2 s) -k 2 y u = 0 0 . (8) a
) k 1 τ 2 = k 2 τ 1 and k 1 = k 2 , thus (8)
has a unique singular behavior for which the two systems can not be distinguishu = 0, y = 0, which means that, when at rest, the two systems can not be distinguished: they are strongly distinguishable. This result comes directly from Theorem 1: since

a 1 = 1 + τ 1 s, b 1 = k 1 , a 2 = 1 + τ 2 s, b 2 = k 2 we have (a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ) = k 1 -k 2 , it comes out that p 1 = lcm(a ′ 1 , a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ) = (k 1 -k 2 ) (1 + τ 1 s) , p 2 = lcm(a ′ 2 , a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ) = (k 1 -k 2 ) (1 + τ 2 s
) and thus Eq (4) and ( 5) reads as

gcd(b 1 p 1 1 , b 2 p 2 1 )u = 0 ⇔ (k 1 -k 2 ) u = 0, (a 2 b ′ 1 -a 1 b ′ 2 ) y = 0 ⇔ (k 1 -k 2 ) y = 0, (9)
leading to the following singular i/o: y = 0, u = 0. b) k 1 τ 2 -k 2 τ 1 = 0, thus (8) has a unique singular behavior for which the two systems can not be distinguish

0 = (k 1 τ 2 -k 2 τ 1 ) ẏ + (k 1 -k 2 )y, (10)
u = τ 1 ẏ + y k 1 = τ 2 ẏ + y k 2 , (11)
solutions of (10) are of the form y = c exp(αt), α = -(k1-k2) (k1τ2-k2τ1) which include the case c (constant) when k 1 = k 2 ; moreover [START_REF] Brockett | Essays on Control: Perspectives in the Theory and its Applications, chap. Hybrid models for motion control systems[END_REF] gives u = c τ1α+1 k1 exp(αt) leading to u = d exp(αt) with d = c (τ2-τ1) (k1τ2-k2τ1) . Thus (8) has a unique singular behavior for which the two systems can not be distinguish y = c, u = c k1 when k 1 = k 2 or y = c exp(αt), u = d exp(αt), when k 1 = k 2 . This can be obtained directly suing Theorem 1, for example:

from which one gets b ′ 1 = 2, b ′ 2 = 1, a 1 = a ′ 1 , a 2 = a ′
2 , and finally :

(a ′ 2 b ′ 1 -a ′ 1 b ′ 2 )) = 3s + 1. (12)
Thus p 1 = lcm(s + 1, 3s + 1) = (s + 1) (3s + 1) and p 2 = lcm(2s + 1, 3s + 1) = (2s + 1) (3s + 1) leads to 4) and ( 5) reads as

p 1 = p 1 1 a ′ 1 = p 1 2 (a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ), p 1 1 = (3s + 1) , p 1 2 = (s + 1) p 2 = p 2 1 a ′ 2 = p 2 2 (a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ), p 2 1 = (3s + 1) , p 2 2 = (2s + 1) Eq (
gcd(b 1 p 1 1 , b 2 p 2 1 )u = 0 ⇔ (3s + 1) u = 0, (a 2 b ′ 1 -a 1 b ′ 2 ) y = 0 ⇔ (3s + 1) y = 0, (13)
leading to the following singular i/o: y = y 0 exp(-t/3), u = u 0 exp(-t/3), note that y0 3 = u 0 .

• When k 1 = 1, τ 1 = 1, k 2 = 2, τ 2 = 1: k 1 τ 2 -k 2 τ 1 = -1 = 0, one has a 1 = s + 1, b 1 = 1, a 2 = s + 1, b 2 = 2 from which one gets b ′ 1 = 1, b ′ 2 = 2, a 1 = a ′ 1 = a 2 = a ′
2 , and finally

(a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ) = -(s + 1) . ( 14
)
Thus p 1 = lcm(s+1, s+1) = (s + 1) and p 2 = lcm(s+ 1, s + 1) = (s + 1) leads to 4) and ( 5) reads as

p 1 = p 1 1 a ′ 1 = p 1 2 (a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ), p 1 1 = 1, p 1 2 = -1, p 2 = p 2 1 a ′ 2 = p 2 2 (a ′ 2 b ′ 1 -a ′ 1 b ′ 2 ), p 2 1 = 1, p 2 2 = -1, Eq (
gcd(b 1 p 1 1 , b 2 p 2 1 )u = 0 ⇔ u = 0, (a 2 b ′ 1 -a 1 b ′ 2 ) y = 0 ⇔ (s + 1) y = 0, (15)
leading to the following singular i/o: u = 0, y = y 0 exp(-t).

III. SWITCHING SIGNAL ESTIMATION

Assume from now on that all the subsystems models are known and that any pair is strongly distinguishable. Let us consider a switching system defined by a finite collection of input/output behaviors driven by LODE satisfying the above given assumptions. As soon as the system is not at rest, for the given control, the measured output can be used to determine which subsystem is active. From now we want to obtain effective real-time algorithm to determine the current "i". If one is able to construct in real time the following quantities

r i (t) = a i d dt y i -b i d dt u,
it is clear that the current "i" is such that r i (t) = 0 on a sub-set of R with non zero measure. The problem is thus reduced to the real-time computation of time derivative of the output and input despite the noise. The numerical differentiation technics introduced below are of non asymptotic nature, and the desired estimation can be obtained instantaneously (there is a singularity at time t = 0). But in practice they are numerically implemented with discrete measured data, thus from a practical point of view, it will be necessary that the sampling time should be small enough with respect to the duration time between two successive switchings 3 .

A. Numerical differentiation

Consider a signal4 y(t) = ∞ i=0 y (i) (0) t i i! which is assumed to be analytic around t = 0 and its truncated Taylor expansion y N (t) = N i=0 y (i) (0) t i i! at order N . The usual rules of symbolic calculus in Schwartz's distribution theory [START_REF] Schwartz | Théorie des distributions[END_REF] yield y (N +1) N (t) = y(0)δ (N ) + . . . + y (N ) (0)δ, where δ is the Dirac measure at zero. Multiply both sides by (-t) i and apply the rules tδ = 0, tδ (i) = -iδ (i-1) , i ≥ 1. We obtain a triangular system of linear equations from which the derivatives y (i) (0) can be obtained (1

≤ i ≤ N ) (-t) i y (N +1) N (t) = N ! (N -i)! δ (N -i) y(0) + . . . + δy (N -i) (0) (16
) It means that the coefficients y(0), . . . , y (N ) (0) are linearly identifiable [START_REF] Fliess | An algebraic framework for linear identification[END_REF], [START_REF] Fliess | Closed-loop parametric identification for continuous-time linear systems via new algebraic techniques[END_REF]. The time derivatives of y N (t), the Dirac measures and its derivatives are removed by integrating with respect to time both sides of Eq. ( 16) at least ν times (ν > N ):

t 0 tν-1 0 • • • t1 0 (-τ ) i y (N +1) N dt ν-1 • • • dt 1 dτ = N ! (N -i)! t ν-N -i-1 (ν -N -i -1)! y(0)+. . .+ t ν-1 (ν -1)! y (N -i) (0)
It is clear that the numerical estimation rely on lim N →+∞ [y (i) N (0)] estim (t) = y (i) (0). Remark 3 These iterated integrals are low pass filters which attenuate the noises, which are viewed as highly fluctuating phenomena (see [START_REF] Fliess | Analyse non standard du bruit[END_REF] for more details).

Remark 4

The above formulae may easily be extended to sliding time windows in order to obtain real time estimates (see [START_REF] Mboup | A revised look at numerical differentiation with an application to nonlinear feedback control[END_REF] for further details).

B. Algorithm

Off line: 1) determine the maximum number of output derivative necessary for estimation (take the highest derivative of the output in the collection of LODE describing the switching system), 2) test distinguishability using conditions given in [START_REF] Boscain | Stability of planar switched systems: The linear single input case[END_REF] which will provide the "bad" input, let us note that the second relation of ( 5) can be used to check if the input is a "bad" one just by checking if it satisfies the differential relation. On line: 1) using Alien technics (see section III-A) compute y, ẏ, . . . , y (kymax) ; u, u, . . . , u (kumax) ,

Fig. 1 :Fig. 2 :Fig. 3 :

 123 Fig. 1: Free noise results: sinusoidal input

• When k 1 = 2, τ 1 = 1, k 2 = 1, τ 2 = 2: k 1 τ 2 -k 2 τ 1 =

In practice at least 100 times smaller (Thus Zeno phenomenon is assume not to happen).

This approach started in[START_REF] Fliess | Compression différentielle de transitoires bruités[END_REF]. See[START_REF] Fliess | Non-linear estimation is easy[END_REF] for references and[START_REF] Nöthen | Beiträge zur rekonstruktion nicht direkt gemessener größen bei der silizium-einkristallzüchtung nach dem czochralski-verfahren[END_REF] for interesting discussions and comparisons.

2) check if r i (t) is zero for some time interval then the corresponding active subsystem is the "i-th", 3) deduce the continuous state estimate using 1.

C. Example

Let us consider the following switching system [START_REF] Alessandri | Switching observers for continuous-time and discrete-time systems[END_REF] where

u + u, For the first order systems, in that follows, x 2 is enforced to zero. Moreover, the output continuity is ensured between two systems whereas initial condition of derivative output is randomly chosen in [-0.5, +0.5]. Residuals associated to previous systems are

where [•] e is the estimation of • and to [y] e corresponds the y denoised signal. The following table gives the singular input/ouput for which distinuishability is lost.

Without noise, output derivatives are estimated according to the well known Euler's method. A convincing simulation when y 0 = u 0 = 1 shows that systems 1 and 2 are indistinguishable (i.e. r 1 = r 2 = 0). Figure 1, system distinguishability is obtained easily and ensures a very good state estimation (see figure 1-(d)).

In noisy case (additive output noise N (0, 0.01)), Euler's method is not available. We propose to apply recent results on derivative estimation (see [START_REF] Mboup | A revised look at numerical differentiation with an application to nonlinear feedback control[END_REF]) in order to evaluate residuals. They are approximatively null when the associated system is active and becomes non zero in other case. However, to take the decision, that is to say to know what is the active system, is not easy (see figure 2-(c)). Here, the mean of each residual is calculated along a sliding window. Thus at the smallest mean of residual is associated the active system. According to this logic, states are estimated (figure 2-(d)). Figure 3, rather than to estimate output derivative in real time, a small constant and known delay is allowed for estimations (see [START_REF] Mboup | A revised look at numerical differentiation with an application to nonlinear feedback control[END_REF] for more details). In this case, as shown figure 3-(a), in exactly the same simulation context than previously, decision according to residuals is easier.

Clearly, figure 3-(b), state estimation is improved.

IV. CONCLUSION

System distinguishability has been investigated. Easy to check necessary and sufficient conditions were obtained, which provide "bad input" that should be avoided in order to be able to distinguish the subsystems of the whole linear switching system. The proposed technics can be implemented in real time as soon as the sampling period is small enough with respect to the duration time between two successive switchings. They provide estimation of the index corresponding to the current active subsystem, and the state variable of this subsystem, via methods which are quite robust with respect to corrupting noises. Our approach will be extended to other switching systems (nonlinear, and taking into account the discrete part) as for other kind of systems (linear partial differential equations) in some future publications.