RESEARCH CENTRE

Inria Branch at the University of
Montpellier

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

CNRS, Université de Montpellier

Project-Team

ZENITH

Scientific Data Management

IN COLLABORATION WITH: Laboratoire d’informatique, de robotique et
de microélectronique de Montpellier (LIRMM)

DOMAIN

Perception, Cognition and Interaction

THEME

Data and Knowledge Representation and
Processing

Contents

Project-Team ZENITH

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

3.1 Distributed Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Big Data and Parallel Data Management
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Data Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Machine Learning for High Dimensional Data Processing . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Data-intensive Scientific Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

6 Highlights of the year

6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Inria Emeritus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 New software, platforms, open data

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7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 Pl@ntNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.2 ThePlantGame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.3 Savime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.4 OpenAlea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7.1.5 Imitates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.6 UMX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.7 TDB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.8 UMX-PRO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7.2 Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7.2.1 The GeoLifeCLEF 2023 dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

8 New results

8.1 Distributed Data and Model Management

13
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.1.1 Scientific Workflows for Life Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8.1.2 Distributed Intelligence on the Edge-to-Cloud Continuum . . . . . . . . . . . . . . . 13
8.1.3 Model Management with Gypscie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.1.4 GeoAI for Marine Ecosystem Monitoring: a Complete Workflow to Generate Maps

from AI Model Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.1.5 A Global Infrastructure to Exploit the Potential of Digitized Collections . . . . . . . . 14
8.2 Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.2.1 Time Series Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.2.2 Spatial-time Motif Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
8.2.3 Variable-Size Segmentation for Time Series Representation . . . . . . . . . . . . . . . 15
8.2.4 kNN Matrix Profile for Knowledge Discovery from Time Series . . . . . . . . . . . . . 15
8.2.5 Explainability of large language models . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.3 Machine Learning for Biodiversity and Agroecology . . . . . . . . . . . . . . . . . . . . . . . 16
8.3.1 Deep Learning for Agroecology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.3.2 Evaluation of Species Identification and Prediction Algorithms . . . . . . . . . . . . . 16
8.3.3 New features in the Pl@ntNet platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
8.3.4 Forecasting animal migrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

9 Partnerships and cooperations

9.2 European initiatives

9.1 International research visitors

18
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
9.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
9.1.2 Visits to international teams
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
9.2.1 Horizon Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
9.2.2 H2020 projects
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
9.2.3 Other european programs/initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
9.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
9.4 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

9.3.1 Others

10 Dissemination

25
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
10.1.1 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
10.1.2 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
10.1.3 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 27
10.1.4 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
10.1.5 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
10.3.1 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
10.3.2 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

11 Scientific production

29
11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
11.2 Publications of the year

Project ZENITH

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Project-Team ZENITH

Creation of the Project-Team: 2012 January 01

Keywords

Computer sciences and digital sciences

A1.1. – Architectures

A3.1. – Data

A3.3. – Data and knowledge analysis

A3.4.4. – Optimization and learning

A5.4.3. – Content retrieval

A5.7. – Audio modeling and processing

A6.2.6. – Optimization

A9.2. – Machine learning

A9.3. – Signal analysis

Other research topics and application domains

B1.1.11. – Plant Biology

B2.6. – Biological and medical imaging

B3.3. – Geosciences

B3.5. – Agronomy

B3.6. – Ecology

B4. – Energy

B6. – IT and telecom

B6.5. – Information systems

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Florent Masseglia [Team leader, INRIA, Senior Researcher, from Apr 2023, HDR]

• Reza Akbarinia [INRIA, Researcher, HDR]

• Christophe Botella [INRIA, ISFP, from Oct 2023]

• Benjamin Bourel [CNRS, Researcher, from Oct 2023]

• Alexis Joly [INRIA, Senior Researcher, HDR]

• Antoine Liutkus [Inria, HDR]

• Diego Marcos Gonzalez [INRIA, Advanced Research Position]

• Maxime Ryckewaert [INRIA, Starting Research Position, from Sep 2023]

• Patrick Valduriez [INRIA, Emeritus, from Apr 2023, HDR]

Faculty Members

• Esther Pacitti [UNIV MONTPELLIER, Professor, HDR]

• Joseph Salmon [UNIV MONTPELLIER, Professor, HDR]

Post-Doctoral Fellows

• Raphael De Freitas Saldanha [INRIA, Post-Doctoral Fellow]

• Benjamin Deneu [INRIA]

• Pallavi Jain [LIRMM, Post-Doctoral Fellow, from Jun 2023]

• Konstantinos-Panagiotis Panousis [INRIA, Post-Doctoral Fellow, from Apr 2023]

• Lukas Picek [INRIA, Post-Doctoral Fellow, from Nov 2023]

• Rebecca Pontes Salles [INRIA, Post-Doctoral Fellow, from Dec 2023]

• Jules Vandeputte [INRIA, Post-Doctoral Fellow, from Nov 2023]

PhD Students

• Ananthu Aniraj [INRIA, from Apr 2023]

• Matteo Contini [IFREMER]

• Guillaume Coulaud [UNIV MONTPELLIER, from Oct 2023]

• Joaquim Estopinan [Inria, INRIA]

• Camille Garcin [Univ Montpellier]

• Cesar Leblanc [INRIA]

• Tanguy Lefort [UNIV MONTPELLIER]

• Ousmane Youme [University Alioune DIOP de Bambey, , from Apr 2023 until Jul 2023]

• Kawtar Zaher [INA, CIFRE, from May 2023]

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Project ZENITH

Technical Staff

• Antoine Affouard [INRIA, Engineer]

• Mathias Chouet [CIRAD, from Apr 2023]

• Baldwin Dumortier [University of Montpelliet, until Sep 2023]

• Maxime Fromholtz [INRIA, Engineer, from Feb 2023]

• Hugo Gresse [INRIA, Engineer, from Jun 2023]

• Benoit Lange [INRIA, Engineer, from Feb 2023]

• Théo Larcher [INRIA, Engineer]

• Pierre Leroy [INRIA, Engineer, from Oct 2023]

• Thomas Paillot [INRIA, from Sep 2023]

• Remi Palard [CIRAD, from Dec 2023]

• Julien Thomazo [LIRMM, from Dec 2023]

Administrative Assistant

• Cathy Desseaux [INRIA, from Sep 2023]

External Collaborators

• Hervé Goëau [CIRAD]

• François Munoz [UGA]

• Christophe Pradal [CIRAD]

• Dennis Shasha [New York University]

2 Overall objectives

Data-intensive science such as agronomy, astronomy, biology and environmental science must deal with
overwhelming amounts of experimental data, as produced through empirical observation and simulation.
Similarly, digital humanities have been faced for decades with the problem of exploiting vast amounts
of digitized cultural and historical data, such as broadcasted radio or TV content. Such data must be
processed (cleaned, transformed, analyzed) in order to draw new conclusions, prove scientific theories
and eventually produce knowledge. However, constant progress in scientific observational instruments
(e.g. satellites, sensors, large hadron collider), simulation tools (that foster in silico experimentation) or
digitization of new content by archivists create a huge data overload. For example, climate modeling data
has hundreds of exabytes.

Scientific data is very complex, in particular because of the heterogeneous methods, the uncertainty of
the captured data, the inherently multiscale nature (spatial, temporal) of many sciences and the growing
use of imaging (e.g. molecular imaging), resulting in data with hundreds of dimensions (attributes,
features, etc.). Modern science research is also highly collaborative, involving scientists from different
disciplines (e.g. biologists, soil scientists, and geologists working on an environmental project), in some
cases from different organizations in different countries. Each discipline or organization tends to produce
and manage its own data, in specific formats, with its own processes. Thus, integrating such distributed
data gets difficult as the amounts of heterogeneous data grow. Finally, a major difficulty is to interpret
scientific data. Unlike web data, e.g. web page keywords or user recommendations, which regular users
can understand, making sense out of scientific data requires high expertise in the scientific domain.

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Inria Annual Report 2023

Furthermore, interpretation errors can have highly negative consequences, e.g. deploying an oil driller
under water at a wrong position.

Despite the variety of scientific data, we can identify common features: big data; manipulated through
workflows; typically complex, e.g. multidimensional; with uncertainty in the data values, e.g., to reflect
data capture or observation; important metadata about experiments and their provenance; and mostly
append-only (with rare updates).

The three main challenges of scientific data management can be summarized by: (1) scale (big data,
big applications); (2) complexity (uncertain, high-dimensional data), (3) heterogeneity (in particular,
data semantics heterogeneity). These challenges are also those of data science, with the goal of making
sense out of data by combining data management, machine learning, statistics and other disciplines. The
overall goal of Zenith is to address these challenges, by proposing innovative solutions with significant
advantages in terms of scalability, functionality, ease of use, and performance. To produce generic results,
we strive to develop architectures, models and algorithms that can be implemented as components or
services in specific computing environments, e.g. the cloud. We design and validate our solutions by
working closely with our scientific partners in Montpellier such as CIRAD, INRAE and IRD, which provide
the scientific expertise to interpret the data. To further validate our solutions and extend the scope of our
results, we also foster industrial collaborations, even in non scientific applications, provided that they
exhibit similar challenges.

Our approach is to capitalize on the principles of distributed and parallel data management. In par-
ticular, we exploit: high-level languages as the basis for data independence and automatic optimization;
declarative languages to manipulate data and workflows; and highly distributed and parallel environ-
ments such as cluster and cloud for scalability and performance. We also exploit machine learning,
probabilities and statistics for high-dimensional data processing, data analytics and data search.

3 Research program

3.1 Distributed Data Management

Data management is concerned with the storage, organization, retrieval and manipulation of data of
all kinds, from small and simple to very large and complex. It has become a major domain of computer
science, with a large international research community and a strong industry. Continuous technology
transfer from research to industry has led to the development of powerful database systems, now at
the heart of any information system, and of advanced data management capabilities in many kinds of
software products (search engines, application servers, document systems, etc.).

To deal with the massive scale of scientific data, we exploit large-scale distributed systems, with the
objective of making distribution transparent to the users and applications. Thus, we capitalize on the
principles of large-scale distributed systems such as clusters, peer-to-peer (P2P) and cloud.

Data management in distributed systems has been traditionally achieved by distributed database
systems which enable users to transparently access and update several databases in a network using a
high-level query language (e.g. SQL). Transparency is achieved through a global schema which hides
the local databases’ heterogeneity. In its simplest form, a distributed database system supports a global
schema and implements distributed database techniques (query processing, transaction management,
consistency management, etc.). This approach has proved to be effective for applications that can benefit
from centralized control and full-fledge database capabilities, e.g. information systems. However, it
cannot scale up to more than tens of databases.

Parallel database systems extend the distributed database approach to improve performance (trans-
action throughput or query response time) by exploiting database partitioning using a multiprocessor
or cluster system. Although data integration systems and parallel database systems can scale up to
hundreds of data sources or database partitions, they still rely on a centralized global schema and strong
assumptions about the network.

In contrast, peer-to-peer (P2P) systems adopt a completely decentralized approach to data sharing.
By distributing data storage and processing across autonomous peers in the network, they can scale
without the need for powerful servers. P2P systems typically have millions of users sharing petabytes of
data over the Internet. Although very useful, these systems are quite simple (e.g. file sharing), support

Project ZENITH

5

limited functions (e.g. keyword search) and use simple techniques (e.g. resource location by flooding)
which have performance problems. A P2P solution is well-suited to support the collaborative nature of
scientific applications as it provides scalability, dynamicity, autonomy and decentralized control. Peers
can be the participants or organizations involved in collaboration and may share data and applications
while keeping full control over their (local) data sources. But for very-large scale scientific data analysis,
we believe cloud computing is the right approach as it can provide virtually infinite computing, storage
and networking resources. However, current cloud architectures are proprietary, ad-hoc, and may deprive
users of the control of their own data. Thus, we postulate that a hybrid P2P/cloud architecture is more
appropriate for scientific data management, by combining the best of both approaches. In particular, it
will enable the clean integration of the users’ own computational resources with different clouds.

3.2 Big Data and Parallel Data Management

Big data (like its relative, data science) has become a buzz word, with different meanings depending on
your perspective, e.g. 100 terabytes is big for a transaction processing system, but small for a web search
engine.

Although big data has been around for a long time, it is now more important than ever. We can see
overwhelming amounts of data generated by all kinds of devices, networks and programs, e.g. sensors,
mobile devices, connected objects (IoT), social networks, computer simulations, satellites, radiotele-
scopes, etc. Storage capacity has doubled every 3 years since 1980 with prices steadily going down,
making it affordable to keep more data around. Furthermore, massive data can produce high-value
information and knowledge, which is critical for data analysis, decision support, forecasting, business
intelligence, research, (data-intensive) science, etc.

The problem of big data has three main dimensions, quoted as the three big V’s:

• Volume: refers to massive amounts of data, making it hard to store, manage, and analyze (big

analytics);

• Velocity: refers to continuous data streams being produced, making it hard to perform online

processing and analysis;

• Variety: refers to different data formats, different semantics, uncertain data, multiscale data, etc.,

making it hard to integrate and analyze.

There are also other V’s such as: validity (is the data correct and accurate?); veracity (are the results
meaningful?); volatility (how long do you need to store this data?).

Many different big data management solutions have been designed, primarily for the cloud, as cloud
and big data are synergistic. They typically trade consistency for scalability, simplicity and flexibility,
hence the new term Data-Intensive Scalable Computing (DISC). Examples of DISC systems include data
processing frameworks (MapReduce, Spark, Pregel), file systems (GFS, HDFS), NoSQL systems (BigTable,
Hbase, MongoDB), NewSQL systems (Spanner, CockroachDB, LeanXcale). In Zenith, we exploit or extend
DISC technologies to fit our needs for scientific workflow management and scalable data analysis.

3.3 Data Integration

Scientists can rely on web tools to quickly share their data and/or knowledge. Therefore, when performing
a given study, a scientist would typically need to access and integrate data from many data sources
(including public databases). Data integration can be either physical or logical. In the former, the source
data are integrated and materialized in a data warehouse or data lake. In logical integration, the integrated
data are not materialized, but accessed indirectly through a global (or mediated) schema using a data
integration system. These two approaches have different trade-offs, e.g. efficient analytics but only on
historical data for data warehousing versus real-time access to data sources for data integration systems
(e.g. web price comparators).

In both cases, to understand a data source content, metadata (data that describe the data) is crucial.
Metadata can be initially provided by the data publisher to describe the data structure (e.g. schema), data
semantics based on ontologies (that provide a formal representation of the domain knowledge) and other
useful information about data provenance (publisher, tools, methods, etc.).

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Inria Annual Report 2023

Scientific workflow systems are also useful for data integration and data analytics. They allow scientists
to describe and execute complex scientific activities, by automating data derivation processes, and
supporting various functions such as provenance management, queries, reuse, etc. Some workflow
activities may access or produce huge amounts of distributed data. This requires using distributed and
parallel execution environments. However, existing workflow management systems have limited support
for data parallelism. In Zenith, we use an algebraic approach to describe data-intensive workflows and
exploit parallelism.

3.4 Data Analytics

Data analytics refers to a set of techniques to draw conclusions through data examination. It involves
data mining, statistics, and data management, and is applied to categorical and continuous data. In the
Zenith team, we are interested in both of these data types. Categorical data designates a set of data that
can be described as “check boxes”. It can be names, products, items, towns, etc. A common illustration is
the market basket data, where each item bought by a client is recorded and the set of items is the basket.
The typical data mining problems with this kind of data are:

• Frequent itemsets and association rules. In this case, the data is usually a table with a high number
of rows and the data mining algorithm extracts correlations between column values. A typical
example of frequent itemset from a sensor network in a smart building would say that “in 20%
rooms, the door is closed, the room is empty, and lights are on.”

• Frequent sequential pattern extraction. This problem is very similar to frequent itemset discovery
but considering the order between. In the smart building example, a frequent sequence could say
that “in 40% of rooms, lights are on at time i , the room is empty at time i + j and the door is closed
at time i + j + k”.

• Clustering. The goal of clustering is to group together similar data while ensuring that dissimilar
data will not be in the same cluster. In our example of smart buildings, we could find clusters of
rooms, where offices will be in one category and copy machine rooms in another because of their
differences (hours of people presence, number of times lights are turned on/off, etc.).

Continuous data are numeric records. A temperature value or a timestamp are examples of such data.
They are involved in a widely used type of data known as time series: a series of values, ordered by time,
and giving a measure, e.g. coming from a sensor. There is a large number of problems that can apply to
this kind of data, including:

• Indexing and retrieval. The goal, here, is usually to find, given a query q and a time series dataset
D, the records of D that are most similar to q. This may involve any transformation of D by means
of an index or an alternative representation for faster execution.

• Pattern and outlier detection. The discovery of recurrent patterns or atypical sub-windows in a
time series has applications in finance, industrial manufacture or seismology, to name a few. It
calls for techniques that avoid pairwise comparisons of all the sub-windows, which would lead to
prohibitive response times.

• Clustering. The goal is the same as categorical data clustering: group similar time series and

separate dissimilar ones.

One main problem in data analytics is to deal with data streams. Existing methods have been
designed for very large data sets where complex algorithms from artificial intelligence (AI) were not
efficient because of data size. However, we now must deal with data streams, sequences of data events
arriving at high rate, where traditional data analytics techniques cannot complete in real-time, given
the infinite data size. In order to extract knowledge from data streams, the data mining community has
investigated approximation methods that could yield good result quality.

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3.5 Machine Learning for High Dimensional Data Processing

High dimensionality is inherent in applications involving images, audio and text as well as in many
scientific applications involving raster data or high-throughput data. Because of the dimensionality
curse, technologies for processing and analyzing such data cannot rely on traditional relational database
systems or data mining methods. It rather requires machine learning methods such as dimensionality
reduction, representation learning or random projection. The activity of Zenith in this domain focuses
on methods for large-scale data processing, in particular in the presence of strong uncertainty and/or
ambiguity. Actually, while small datasets are often characterized by a careful collection process, massive
amounts of data often come with outliers and spurious items, because it appears impossible to guarantee
faultless collection at massive bandwidth. Another source of noise is often the sensor itself, that may be
of low quality but of high sampling rate, or even the actual content, e.g. in cultural heritage applications
when historical content appears seriously damaged by time. To attack these difficult problems, we focus
on the following research topics:

• Uncertainty estimation. Items in massive datasets may either be uncertain, e.g. for automatically
annotated data as in image analysis, or be more or less severely corrupted by noise, e.g. in noisy
audio recordings or in the presence of faulty sensors. In both cases, the concept of uncertainty is
central for the end-user to exploit the content. In this context, we use probability theory to quantify
uncertainty and propose machine learning algorithms that may operate robustly, or at least assess
the quality of their output. This vast topic of research is guided by large-scale applications (both
data search and data denoising), and our research is oriented towards computationally effective
methods.

• Deep neural networks. A major breakthrough in machine learning performance has been the
advent of deep neural networks, which are characterized by high numbers (millions) of parameters
and scalable learning procedures. We are striving towards original architectures and methods that
are theoretically grounded and offer state-of-the-art performance for data search and processing.
The specific challenges we investigate are: very high dimensionality for static data and very long-
term dependency for temporal data, both in the case of possibly strong uncertainty or ambiguity
(e.g. hundreds of thousands of classes).

• Community service. Research in machine learning is guided by applications. In Zenith, two main
communities are targeted: botany, and digital humanities. In both cases, our observation is that
significant breakthroughs may be achieved by connecting these communities to machine learning
researchers. This may be achieved through wording application-specific problems in classical
machine learning parlance. Thus, the team is actively involved in the organization of international
evaluation campaigns that allow machine learning researchers to propose new methods while
solving important application problems.

4 Application domains

4.1 Data-intensive Scientific Applications

The application domains covered by Zenith are very wide and diverse, as they concern data-intensive
scientific applications, i.e., most scientific applications. Since the interaction with scientists is crucial to
identify and tackle data management problems, we are dealing primarily with application domains for
which Montpellier has an excellent track record, i.e., agronomy, environmental science, life science, with
scientific partners like INRAE, IRD and CIRAD. However, we are also addressing other scientific domains
(e.g. astronomy, oil extraction, music processing) through our international collaborations.

Let us briefly illustrate some representative examples of scientific applications on which we have

been working on.

• Management of astronomical catalogs. An example of data-intensive scientific applications is the
management of astronomical catalogs generated by the Dark Energy Survey (DES) project on which
we are collaborating with researchers from Brazil. In this project, huge tables with billions of tuples

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and hundreds of attributes (corresponding to dimensions, mainly double precision real numbers)
store the collected sky data. Data are appended to the catalog database as new observations are
performed and the resulting database size has hundreds of TB. Scientists around the globe can
query the database with queries that may contain a considerable number of attributes. The volume
of data that this application holds poses important challenges for data management. In particular,
efficient solutions are needed to partition and distribute the data in several servers. An efficient
partitioning scheme should try to minimize the number of fragments accessed in the execution of
a query, thus reducing the overhead associated to handle the distributed execution.

• Personal health data analysis and privacy. Today, it is possible to acquire data on many domains
related to personal data. For instance, one can collect data on daily activities, habits or health. It is
also possible to measure performance in sports. This can be done thanks to sensors, communicat-
ing devices or even connected glasses. Such data, once acquired, can lead to valuable knowledge
for these domains. For people having a specific disease, it might be important to know if they
belong to a specific category that needs particular care. For some individuals, it can be interesting
to find a category that corresponds to their performance in a specific sport and then adapt their
training with an adequate program. Meanwhile, for privacy reasons, people will be reluctant to
share their personal data and make them public. Therefore, it is important to provide them with
solutions that can extract such knowledge from everybody’s data, while guaranteeing that their
private data will not be disclosed to anyone.

• Botanical data sharing. Botanical data is highly decentralized and heterogeneous. Each actor has
its own expertise domain, hosts its own data, and describes them in a specific format. Further-
more, botanical data is complex. A single plant’s observation might include many structured and
unstructured tags, several images of different organs, some empirical measurements and a few
other contextual data (time, location, author, etc.). A noticeable consequence is that simply identi-
fying plant species is often a very difficult task; even for the botanists themselves (the so-called
taxonomic gap). Botanical data sharing should thus speed up the integration of raw observation
data, while providing users an easy and efficient access to integrated data. This requires to deal
with social-based data integration and sharing, massive data analysis and scalable content-based
information retrieval. We address this application in the context of the French initiative Pl@ntNet,
with CIRAD and IRD.

• Biological data integration and analysis. Biology and its applications, from medicine to agronomy
and ecology, are now producing massive data, which is revolutionizing the way life scientists
work. For instance, using plant phenotyping platforms such as HIRROS and PhenoArch at INRAE
Montpellier, quantitative genetic methods allow to identify genes involved in phenotypic variation
in response to environmental conditions. These methods produce large amounts of data at different
time intervals (minutes to months), at different sites and at different scales ranging from small
tissue samples to the entire plant until whole plant population. Analyzing such big data creates
new challenges for data management and data integration, but also for plant modeling. We address
this application in the context of the French initiative OpenAlea, with CIRAD and INRAE.

• Large language models for genomics. In the context of a collaboration with CNRS - INRAE, we
are developing an activity on large language models applied to genomics. In particular , our work
focuses on inverse folding, i.e., predicting a sequence of amineo acids that are able to generate
a given protein structure, with applications in the drug design industry. These models involve
training large deep models on several millions of structural data samples. We also investigated
explanatory methods for large language models.

These application examples illustrate the diversity of requirements and issues which we are addressing
with our scientific application partners. To further validate our solutions and extend the scope of our
results, we also want to foster industrial collaborations, even in non scientific applications, provided that
they exhibit similar challenges.

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5 Social and environmental responsibility

We do consider the ecological impact of our technology, especially large data management.

• We address the (major) problem of energy consumption of our ML models, by introducing energy-
based metrics to assess the energy consumption during the training on GPU of our ML models.
Furthermore, we want to improve training pipelines that reduce the need for training models from
scratch. At inference, network compression methods can reduce the memory footprint and the
computational requirements when deploying models.

• In the design of the Pl@ntnet mobile application, we adopt an eco-responsible approach, taking care
not to integrate addictive, energy-intensive or non-essential functionalities to uses that promote
the preservation of biodiversity and environment.

• To reduce our carbon footprint, we reduce to the minimum the number of long-distance trips, and
favor train as much as possible. We also foster journal publications, to avoid traveling. For instance,
in 2023, we have 12 journal publications versus 15 conference publications.

6 Highlights of the year

6.1 Awards

Daniel Rosendo’s PhD thesis "Methodologies for Reproducible Analysis of Workflows on the Edge-to-
Cloud Continuum" [47], supervised by Gabriel Antoniu, Alexandru Costan, and Patrick Valduriez (Inria,
France) obtained the Second Place at the Best PhD thesis award of the French Database Conference
(BDA), 2023.

6.2 Inria Emeritus

The workshop “Éméritat de Patrick Valduriez” on June, 5th 2023 in Montpellier gathered about 80 people,
to discuss past, current and future research in data management.

7 New software, platforms, open data

7.1 New software

7.1.1 Pl@ntNet

Keywords: Plant identification, Deep learning, Citizen science

Functional Description: Pl@ntNet is a participatory platform and information system dedicated to the
production of botanical data through deep learning-based plant identification. It includes 3 main
front-ends, an Android app (the most advanced and the most used one), an iOs app (being currently
re-developed) and a web version. The main feature of the application is to return the ranked list
of the most likely species providing an image or an image set of an individual plant. In addition,
Pl@ntNet’s search engine returns the images of the dataset that are the most similar to the queried
observation allowing interactive validation by the users. The back-office running on the server side
of the platform is based on Snoop visual search engine (a software developed by ZENITH) and on
NewSQL technologies for the data management. The application is distributed in more than 200
countries (30M downloads) and allows identifying about 35K plant species at present time. The
platform integrates an open access REST API (my.plantnet.org) that currently accounts for 6500
developers accounts.

Publications: hal-01629195, hal-02937618, hal-03343235, hal-01182775

Contact: Alexis Joly

Participants: Antoine Affouard, Jean-Christophe Lombardo, Pierre Bonnet, Hervé Goëau, Mathias

Chouet, Hugo Gresse, Julien Champ, Alexis Joly

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7.1.2 ThePlantGame

Keyword: Crowd-sourcing

Functional Description: ThePlantGame is a participatory game whose purpose is the production of big
taxonomic data to improve our knowledge of biodiversity. One major contribution is the active
training of the users based on innovative sub-task creation and assignment processes that are
adaptive to the increasing skills of the user. Thousands of players are registered and produce
on average about tens new validated plant observations per day. The accuracy of the produced
taxonnomic tags is very high (about 95%), which is quite impressive considering the fact that a
majority of users are beginners when they start playing.

Publication: hal-01629149

Contact: Alexis Joly

Participants: Maximilien Servajean, Alexis Joly

7.1.3 Savime

Name: Simulation And Visualization IN-Memory

Keywords: Data management., Distributed Data Management

Functional Description: SAVIME is a multi-dimensional array DBMS for scientific applications. It sup-
ports a novel data model called TARS (Typed ARray Schema), which extends the basic array data
model with typed arrays. In TARS, the support of application dependent data characteristics is
provided through the definition of TAR objects, ready to be manipulated by TAR operators. This
approach provides much flexibility for capturing internal data layouts through mapping func-
tions, which makes data ingestion independent of how simulation data has been produced, thus
minimizing ingestion time.

Publication: lirmm-01620376

Contact: Patrick Valduriez

Participants: Hermano Lustosa, Fabio Porto, Patrick Valduriez

Partner: LNCC - Laboratório Nacional de Computação Científica

7.1.4 OpenAlea

Keywords: Bioinformatics, Biology

Functional Description: OpenAlea is an open source project primarily aimed at the plant research
community. It is a distributed collaborative effort to develop Python libraries and tools that address
the needs of current and future works in Plant Architecture modeling. It includes modules to
analyze, visualize and model the functioning and growth of plant architecture. It was formally
developed in the Inria VirtualPlants team.

Release Contributions: OpenAlea 2.0 adds to OpenAlea 1.0 a high-level formalism dedicated to the
modeling of morphogenesis that makes it possible to use several modeling paradigms (Blackboard,
L-systems, Agents, Branching processes, Cellular Automata) expressed with different languages
(Python, L-Py, R, Visual Programming, ...) to analyse and simulate shapes and their development.

Publications: hal-01166298, hal-00831811

Contact: Christophe Pradal

Participants: Christian Fournier, Christophe Godin, Christophe Pradal, Frédéric Boudon, Patrick Valdur-

iez, Esther Pacitti, Yann Guédon

Partners: CIRAD, INRAE

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7.1.5 Imitates

Name: Indexing and mining Massive Time Series

Keywords: Time Series, Indexing, Nearest Neighbors

11

Functional Description: Time series indexing is at the center of many scientific works or business needs.
The number and size of the series may well explode depending on the concerned domain. These
data are still very difficult to handle and, often, a necessary step to handling them is in their indexing.
Imitates is a Spark Library that implements two algorithms developed by Zenith. Both algorithms
allow indexing massive amounts of time series (billions of series, several terabytes of data).

Publication: lirmm-01886794

Contact: Florent Masseglia

Partners: New York University, Université Paris-Descartes

7.1.6 UMX

Name: open-unmix

Keywords: Source Separation, Audio

Scientific Description: UMX implements state of the art audio/music source separation with deep
neural networks (DNNs). It is intended to serve as a reference in the domain. It has been presented
in two major scientific communications: An Overview of Lead and Accompaniment Separation in
Music (https://hal-lirmm.ccsd.cnrs.fr/lirmm-01766781) and Music Separation with DNNs (Making
it work (ISMIR 2018 Tutorial) https://sigsep.github.io/ismir2018_tutorial/index.html#/cover).

Functional Description: UMX implements audio source separation with deep learning, using the Pyt-
orch and Tensorflow frameworks. It comprises the code for both training and testing the separation
networks, in a flexible manner. Pre- and post-processing around the actual deep neural nets include
sophisticated specific multichannel filtering operations.

Publication: lirmm-01766781

Authors: Antoine Liutkus, Fabian Robert Stoter, Emmanuel Vincent

Contact: Antoine Liutkus

7.1.7 TDB

Keywords: Data assimilation, Big data, Data extraction

Scientific Description: TDB comes as a building block for audio machine learning pipelines. It is a
scraping tool that allows large scale data augmentation. Its different components allow building a
large dataset of samples composed of related audio tracks, as well as the associated metadata. Each
sample comprises a dynamic number of entries.

Functional Description: TDB is composed of two core submodules. First, a data extraction pipeline
allows scraping a provider url so as to extract large amounts of audio data. The provider is assumed
to offer audio content in a freely-accessible way through a hardcoded specific structure. The
software automatically downloads the data locally under a raw data format. To aggregate the raw
data set, a list of item ids is used. The item ids will be requested from the provider given a url in
parallel fashion. Second, a data transformation pipeline allows transforming the raw data into a
dataset that is compatible with machine learning purposes. Each produced subfolder contains a
set of audio files corresponding to a predefined set of sources, along with the associated metadata.
A working example is provided.

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Inria Annual Report 2023

Each component has several submodules, in particular, network handling and audio transcoding.
Thus, TDB can be viewed as an extract-transform-load (ETL) pipeline that enables applications
such as deep learning on large amounts of audio data, assuming that an adequate data provider url
is fed into the software.

Contact: Antoine Liutkus

Participants: Antoine Liutkus, Fabian Robert Stoter

7.1.8 UMX-PRO

Name: Unmixing Platform - PRO

Keywords: Audio signal processing, Source Separation, Deep learning

Scientific Description: UMX-PRO is written in Python using the TensorFlow 2 framework and provides
an off-the-shelf solution for music source separation (MSS). MSS consists in extracting different
instrumental sounds from a mixture signal. In the scenario considered by UMX-PRO, a mixture
signal is decomposed into a pre-defined set of so called targets, such as: (scenario 1) {"vocals",
"bass", "drums", "guitar", "other"} or (scenario 2) {"vocals", "accompaniment"}.

The following key design choices were made for UMX-PRO. The software revolves around the
training and inference of a deep neural network (DNN), building upon the TensorFlow v2 framework.
The DNN implemented in UMX-PRO is based on a BLSTM recurrent network. However, the
software has been designed to be easily extended to other kinds of network architectures to allow
for research and easy extensions. Given an appropriately formatted database (not part of UMX-
PRO), the software trains the network. The database has to be split into train and valid subsets,
each one being composed of folders called samples. All samples must contain the same set of
audio files, having the same duration: one for each desired target. For instance: {vocals.wav,
accompaniment.wav}. The software can handle any number of targets, provided they are all
present in all samples. Since the model is trained jointly, a larger number of targets increases the
GPU memory usage during training. Once the models have been trained, they can be used for
separation of new mixtures through a dedicated end-to-end separation network. Interestingly, this
end-to-end network comprises an optional refining step called expectation-maximization that
usually improves separation quality.

Functional Description: UMX-PRO implements a full audio separation deep learning pipeline in Tensor-
flow v2. It provides everything needed to train and use a deep learning model for separating music
signals, including network architecture, data pipeline, training code, inference code as well as
pre-trained weights. The software comes with full documentation, detailed comments and unit
tests.

Authors: Antoine Liutkus, Fabian Robert Stoter

Contact: Antoine Liutkus

7.2 Open data

7.2.1 The GeoLifeCLEF 2023 dataset

The difficulty to measure or predict species community composition at fine spatio-temporal resolution
and over large spatial scales severely hampers our ability to understand species assemblages and take
appropriate conservation measures. We designed a European scale dataset (GeoLifeCLEF 2023 [48])
covering around ten thousand plant species to calibrate and evaluate Species Distribution Model (SDM)
predictions of species composition in space and time at high spatial resolution (ten meters), and their
spatial transferability. For model training, we extracted and harmonized five million heterogeneous
presence-only records from selected datasets from the Global Biodiversity Information Facility and 6
thousand exhaustive presence-absence surveys both sampled during 2017-2021. We associated species
observations to diverse environmental rasters classically used in SDMs, as well as to 10 m resolution

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RGB and Near-Infra-Red satellite images and 20 years-time series of climatic variables and satellite point
values. The GeoLifeCLEF 2023 dataset is open access and the first benchmark for researchers aiming to
improve the prediction of plant species composition at a very fine spatial grain and at continental scale.

8 New results

8.1 Distributed Data and Model Management

8.1.1 Scientific Workflows for Life Science

Participants:

Reza Akbarinia, Christophe Botella, Alexis Joly, Florent Masseglia, Es-
ther Pacitti, Christophe Pradal, Patrick Valduriez.

Data driven science requires manipulating large datasets coming from various data sources through
complex workflows based on a variety of models and languages. However, current solutions are typically
ad-hoc, specialized for particular data, models and workflow systems. Our solution is an open service-
based architecture, Life Science Workflow Services (LifeSWS) [14], which provides data analysis workflow
services for life sciences. We illustrate our motivations and rationale for the architecture with real use
cases from life science. LifeSWS capitalizes on our collaboration with Brazil and NYU in developing major
systems for scientific applications.
In this context, we provide different services for plant and crop models with OpenAlea:

• Source code model transformation between programming languages. CyMLTx allows exchanging
and reusing crop model components between modeling platforms beyond programming languages
using reverse engineering for automatic code-to-code transformation [23].

• Spatio-temporal analysis and modeling of plant architecture. First, we developed a spatio-temporal
analysis workflow of strawberry architecture and 3D visualization to study the influence of en-
vironmental and genetic cues on strawberry architecture and yield [19]. Then, we developed a
functional structural model to simulate the water and solute transport inside the root architecture
to determine the respective radial and axial hydraulic conductivities in water deficit conditions [15].
Finally, we developed a SEIR (Susceptible, Exposed, Infectious, Removed) model of two damaging
wheat diseases in the context of crop mixture and agroecology [20]. The model suggests that the
barrier effect is maximized for an intermediate proportion of companion crop.

8.1.2 Distributed Intelligence on the Edge-to-Cloud Continuum

Participants: Daniel Rosendo, Patrick Valduriez.

The large scale and optimized deployment of learning-based workflows across the Edge-to-Cloud
Continuum requires extensive and reproducible experimental analysis of application execution on rep-
resentative testbeds. In this context, we propose two complementary solutions: KheOps and ProvLight.

• KheOps [39] is a collaborative environment specifically designed to enable cost-effective repro-
ducibility and replicability of Edge-to-Cloud experiments. We illustrate KheOps with a real-life
Edge-to-Cloud application. The experimental results show how KheOps helps authors to systemat-
ically perform repeatable and reproducible experiments on the Grid5000 and FIT IoT LAB testbeds.

• ProvLight [40, 47] is a tool that enables efficient provenance capture on the IoT/Edge, leveraging
simplified data models, data compression and grouping, and lightweight transmission protocols to
reduce overheads. Our validation at large scale with synthetic workloads on 64 real-life IoT/Edge
devices shows that ProvLight outperforms state-of-the-art systems in terms of in terms of speed to
capture and transmit provenance data, and resource (CPU, memory and energy) consumption.

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Inria Annual Report 2023

8.1.3 Model Management with Gypscie

Participants:

Patrick Valduriez.

The success of machine learning (ML) systems depends on data availability, volume, quality, and
efficient computing resources. A problem in this context is to reduce computational costs while main-
taining adequate accuracy of the models. To address this problem, we propose a framework to reduce
computational costs while maintaining adequate accuracy of ML models [38]. It identifies "subdomains"
within the input space and train local models that produce better predictions for samples from that
specific subdomain, instead of training a single global model on the full dataset. Our experimental
validation on two real-world datasets shows that subset modeling improves the predictive performance
compared to a single global model and allows data-efficient training.

8.1.4 GeoAI for Marine Ecosystem Monitoring: a Complete Workflow to Generate Maps from AI Model

Predictions

Participants: Matteo Contini, Alexis Joly.

Mapping and monitoring marine ecosystems imply several challenges for data collection and pro-
cessing: water depth, restricted access to locations, instrumentation costs or weather constraints for
sampling, among others. Nowadays, AI and Geographic Information System (GIS) open source software
can be combined in new kinds of workflows, to annotate and predict objects directly on georeferenced
raster data (e.g. orthomosaics). Here, we describe and share the code of a generic method to train a
deep learning model with spatial annotations and use it to directly generate model predictions as spatial
features [25]. This workflow has been tested and validated in three use cases related to marine ecosystem
monitoring at different geographic scales: (i) segmentation of corals on orthomosaics made of underwater
images to automate coral reef habitats mapping, (ii) detection and classification of fishing vessels on
remote sensing satellite imagery to estimate a proxy of fishing effort (iii) segmentation of marine species
and habitats on underwater images with a simple geolocation. Models have been successfully trained
and the models predictions are displayed with maps for the three use cases.

8.1.5 A Global Infrastructure to Exploit the Potential of Digitized Collections

Participants:

Alexis Joly.

Tens of millions of images from biological collections have been made available online in the last two
decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies,
in particular using machine learning and computer vision. While image analysis has become mainstream
in consumer applications, it is still only used on an artisanal basis in the biological collections community,
largely because the image corpora are dispersed. Yet, there is massive untapped potential for novel
applications and research if the images of collection objects could be made accessible as a single corpus.
Thus, we make the case [18] for building an infrastructure that could support image analysis of collection
objects. We show that such an infrastructure is entirely feasible as well as worth the investment.

8.2 Data Analytics

8.2.1 Time Series Prediction

Participants:

Reza Akbarinia, Florent Masseglia, Esther Pacitti.

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Time series event detection methods are evaluated mainly by standard classification metrics that
focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its
preceding or delayed effects reflected in neighboring detections. To address this problem, we proposed
SoftED metrics [54], a new set of metrics designed for soft evaluating event detection methods, which
enable the evaluation of both detection accuracy and the degree to which their detections represent
events. They improve event detection evaluation by associating events and their representative detections,
incorporating temporal tolerance in over 36% of experiments compared to the usual classification metrics.
We also continued the implementation of TSPred for time series prediction in association with data
preprocessing [44]. TSPred establishes a prediction process that seamlessly integrates nonstationary
time series transformations with state-of-the-art statistical and machine learning methods. It is made
available as an R-package, which provides functions for defining and conducting time series prediction,
including data pre(post)processing, decomposition, modeling, prediction, and accuracy assessment.

8.2.2 Spatial-time Motif Analysis

Participants:

Esther Pacitti.

Time series mining aims at discovering motifs, i.e., patterns that occur many times. However, it
is difficult to understand and characterize the meaning of the motifs obtained concerning the data
domain, and analyzing the quality of the results obtained. STMotif Explorer [26] is a spatial-time motif
analysis system that aims to interactively discover and visualize spatial-time motifs in different domains,
offering insight to users. STMotif Explorer enables users to use and implement novel spatiotemporal
motif detection techniques and then run this across various domains.
We released the GSTSM R package [28], the first tool for mining spatial time-stamped sequences in
constrained space and time. It allows users to search for spatio-temporal patterns that are not frequent in
the entire database, but are dense in restricted time-space intervals. Thus, it makes it possible to find
non-trivial patterns that would not be found using common data mining tools.

8.2.3 Variable-Size Segmentation for Time Series Representation

Participants:

Reza Akbarinia, Florent Masseglia.

SAX is one of the most popular time series representations, allowing dimensionality reduction on
the classic data mining tasks. It constructs symbolic representations by splitting the time domain into
segments of equal size where the mean values of segments represent the time series intervals. This
approximation technique is effective for time series having a uniform and balanced distribution over
the time domain. However, in the case of time series having high variation over given time intervals, this
division into segments of fixed length may lead to significant information loss.
In [17], we propose a new time series representation approach, called ASAX, that by smart selection of
variable-size segments allows to significantly reduce the information loss in the time series representation.
Particularly, we propose two new representation techniques, called ASAX_EN (based on the entropy) and
ASAX_SAE (based on SAE the Sum of Absolute Errors), that allow obtaining a variable-size segmentation of
time series with better precision in retrieval tasks thanks to the lower information loss. The experimental
results illustrate that our techniques can obtain significant performance gains in terms of precision for
similarity search compared to SAX, particularly for datasets with non-uniform distributions.

8.2.4 kNN Matrix Profile for Knowledge Discovery from Time Series

Participants:

Reza Akbarinia, Florent Masseglia.

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Inria Annual Report 2023

Matrix Profile (MP) has been proposed as a powerful technique for time series analysis, e.g., detecting
motifs or anomalies. The definition of MP in the literature is as follows: given a time series T and a
subsequence length m, the MP returns for each subsequence included in T its distance to the most similar
subsequence (1NN) in the time series. We call 1NN MP this type of MP. Although 1NN MP has been shown
useful for knowledge discovery, it has its own drawbacks. Particularly, it does not allow to detect a cluster
of discords, e.g., two subsequences that are similar to each other, but dissimilar to all other subsequences.
In [24], we propose a new type of matrix profile, called kNN MP, and illustrate its utility for knowledge
discovery from time series. Particularly, we define the kNN MP, and propose a fast algorithm to calculate
it in time series. We also propose a technique for parallel execution of the proposed algorithm by using
multiple cores of an off-the-shelf computer. The experimental evaluation illustrates the efficiency of our
solution. For example, the accuracy of anomaly detection can be improved from 37% with 1NN MP to
99% with 10NN MP, for one of the benchmarks of the Yahoo dataset.

8.2.5 Explainability of large language models

Participants:

Antoine Liutkus, Ondrej Cifka.

The increasingly widespread adoption of large language models has highlighted the need for improv-
ing their explainability. In [42], we introduce context length probing as a novel explanation technique
for causal language models, based on tracking the predictions of a model as a function of the length of
available context, and allowing to assign differential importance scores to different contexts. The tech-
nique is model-agnostic and does not rely on access to model internals beyond computing token-level
probabilities. We apply context length probing to large pre-trained language models and offer some
initial analyses and insights, including the potential for studying long-range dependencies.

8.3 Machine Learning for Biodiversity and Agroecology

8.3.1 Deep Learning for Agroecology

Participants: Hervé Goëau, Alexis Joly, Shamprikta Mehreen.

One obstacle to generalize new practices in agroecology is that they often require expert skills or
costly manual work from field workers. Digital technologies and AI in particular can play a crucial role in
removing this barrier.
In [22], we worked on automatically estimating compositions and nutritional values of seed mixes based
on Vision Transformers. For this purpose, an original open image dataset has been built containing 4,749
images of seed mixes, covering 11 seed varieties, with which 2 types of deep learning models have been
evaluated. The best-performing model, a vision transformer pre-trained with self-supervision allows an
estimation of the nutritional value of seed mixtures with a coefficient of determination R2 score of 0.91,
which demonstrates the interest of this type of approach for its possible use on a large scale.
In [30], we worked on identifying plant diseases on a large scale targeting multiple crops and diseases.
Existing methods usually address the problem with general supervised recognition tasks based on the
seen composition of the crop and the disease. However, ignoring the composition of unseen classes
during model training can lead to a reduction in model generalization. Therefore, in this work, we propose
a new approach that leverages the visual features of crop and disease from the seen composition, using
them to learn the features of unseen crop-disease composition classes.

8.3.2 Evaluation of Species Identification and Prediction Algorithms

Participants:

Alexis Joly, Herve Goeau, Benjamin Deneu, Titouan Lorieul, Cam-
ille Garcin.

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We ran a new edition of the LifeCLEF evaluation campaign [32, 31] with the involvement of hundreds
of data scientists and research teams worldwide. Overall, the study shows that the field continues to
progress year after year, and that, although the challenges that are most closely related to common tasks,
such as multi-class classification based on images, are able to profit from the most recent advances in
computer vision, certain problems are still wide open, such as the prediction of species as a function of
location (as part of the GeoLifeCLEF challenge [48]). In terms of the methods used, the results show that
convolutional neural networks (CNNs) are still a very powerful method for image and sound processing.
In 4 of the 5 challenges, the best results were obtained using CNNs. Only the PlantCLEF challenge [29]
obtained much better results (for the identification of plants from images) with the use of foundation
vision transformer models such as EVA. Complementary to vision-based models, Natural Language
Processing (NLP) models were also used successfully, in particular hybrid models such as CLIP that
efficiently learn visual concepts from natural language supervision. We believe that this principle of
combining different modalities in the training of deep learning models will be a key to future progress in
AI for biodiversity.

8.3.3 New features in the Pl@ntNet platform

Participants:

Alexis Joly, Benjamin Deneu, Jean-Christophe Lombardo, Antoine Aff-
ouard.

Pl@ntnet is a citizen observatory that relies on AI technologies to help people identify plants with
their smartphones. Over the past few years, Pl@ntNet has become one of the largest plant biodiversity
observatories in the world with several million contributors. A set of new features were developed in
2023.
First, we migrated the management of taxa towards POWO (Plant of the World Online), an online database
and resource managed by the Royal Botanic Gardens, Kew (UK). It aims to provide comprehensive and
up-to-date information on plant names, taxonomy, distribution, and other relevant botanical data.
One of the main advantages is that it allows us to manage a unique global flora that can be filtered by
biogeographical region (see here for instance). This migration required a major development effort, as it
affected all the platform’s components (database, data access layer, AI identification engine, mobile and
web front-end).
Second, we changed the AI model for species recognition to DinoV2, a self-supervised vision transformer
co-developed by Inria and Meta AI. We first tested and evaluated it on the PlantCLEF 2023 dataset
to compare its performance with other state-of-the-art models such as BEIT. We then extended its
architecture to address Pl@ntNet’s multi-task problem (view type classification, rejection and species
identification) and then integrated it into the training workflow of the platform. We benchmarked and
validated it on the platform’s test bench which allowed to confirm that it provides a performance gain on
most of our evaluation datasets. Finally, we tested it in real-world conditions on a test server for a few
weeks before putting it into production.
Third, we have developed a new feature to identify all plants in images of vegetation plots (typically
canopy quadrats). It is based on the tiling principle of the classic Pl@ntNet model and returns a list of the
species present, together with statistics on their spatial coverage. This functionality has been integrated
into the Pl@ntNet API, as well as into a beta version of the web front end currently being tested.

8.3.4 Forecasting animal migrations

Participants:

Antoine Liutkus, Ondrej Cifka.

In the context of an ongoing collaboration with ecologists from CEFE (UMR5175), the MultiNode
project (NUMEV) aims at exploiting state of the art forecasting methods for better modeling and under-
standing animal migrating behaviors.

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Inria Annual Report 2023

The movement of animals is a central component of their behavioral strategies. Statistical tools for
movement data analysis, however, have long been limited, and in particular, unable to account for
past movement information except in a very simplified way. In [49], we propose MoveFormer, a new
step-based model of movement capable of learning directly from full animal trajectories. While inspired
by the classical step-selection framework and previous work on the quantification of uncertainty in
movement predictions, MoveFormer also builds upon recent developments in deep learning, such as the
Transformer architecture, allowing it to incorporate long temporal contexts.

9 Partnerships and cooperations

9.1 International research visitors

9.1.1 Visits of international scientists

International visits to the team

Dennis Shasha

Status: researcher

Institution of origin: New York University

Country: USA

Dates: 17 april - 11 june

Context of the visit: AI3P project (MUSE iSite)

Mobility program/type of mobility: research stay, lectures

Fabio Porto

Status: researcher

Institution of origin: Laboratório Nacional de Computação Científica (LNCC)

Country: Brazil

Dates: 28 may - 6 june, 21 oct - 30 oct

Context of the visit: HPDaSc associated team

Mobility program/type of mobility: research stay, lectures

9.1.2 Visits to international teams

Esther Pacitti

Visited institution: LNCC, Universidade Federal do Rio de Janeiro (UFRJ), Universidade Federal Flumin-

ense (UFF), Centro Federal de Educação Tecnológica (CEFET)

Country: Brazil

Dates: 21 march - 9 may, 9 july - 20 august

Context of the visit: HPDaSc associated team

Mobility program/type of mobility: research stay

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Project ZENITH

Patrick Valduriez

Visited institution: LNCC, UFRJ, UFF, CEFET, USP

Country: Brazil

Dates: 21 march - 9 may, 9 july - 20 august

Context of the visit: HPDaSc associated team

Mobility program/type of mobility: research stay

Visited institution: University of Waterloo

Country: Canada

Dates: 1-5 sept

Context of the visit: VLDB conference

Mobility program/type of mobility: lecture

Christophe Pradal

Visited institution: Wageningen University

Country: The Nederlands

Dates: 28 - 30 august

Context of the visit: PhD jury, lecture

Mobility program/type of mobility: research stay

Visited institution: University of Florida

Country: USA

Dates: 26 nov - 3 december

Context of the visit: International consortium building

Mobility program/type of mobility: research stay

9.2 European initiatives

9.2.1 Horizon Europe

B3 B3 project on cordis.europa.eu

Title: Biodiversity Building Blocks for policy

Duration: From March 1, 2023 to August 31, 2026

Partners:

• INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA),

France

• UNIVERSITATEA OVIDIUS DIN CONSTANTA (OVIDIUS UNIVERSITY OF CONSTANTA), Ro-

mania

• MARTIN-LUTHER-UNIVERSITAT HALLE-WITTENBERG (MLU), Germany

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Inria Annual Report 2023

• Global Biodiversity Information Facility (GBIFS), Denmark

• EIGEN VERMOGEN VAN HET INSTITUUT VOOR NATUUR- EN BOSONDERZOEK (EV INBO),

Belgium

• LA TROBE UNIVERSITY (LTU), Australia

• JUSTUS-LIEBIG-UNIVERSITAET GIESSEN (JLU), Germany

• UNIVERSIDADE DE AVEIRO (UAveiro), Portugal

• SOUTH AFRICAN NATIONAL BIODIVERSITY INSTITUTE (SANBI), South Africa

• AGENTSCHAP PLANTENTUIN MEISE (AGENCE JARDIN BOTANIQUE DE MEISE), Belgium

• ALMA MATER STUDIORUM - UNIVERSITA DI BOLOGNA (UNIBO), Italy

• PENSOFT PUBLISHERS (PENSOFT), Bulgaria

• STELLENBOSCH UNIVERSITY (SU UNIVERSITY OF STELLENBOSCH), South Africa

Inria contact: Alexis Joly

Coordinator: AGENTSCHAP PLANTENTUIN MEISE

Summary: The world is changing rapidly; climate change, land use change, pollution and natural
resource exploitation are creating a global crisis for biodiversity whose magnitude and dynamics
are hard to quantify. Decision makers at all levels need up-to-date information from which to
evaluate policy options. For this reason rapid, reliable, repeatable monitoring of biodiversity data
is needed at all scales from local to global. Only by leveraging large volumes of data, advanced
modeling techniques and powerful computing tools can we hope to synthesize these data within
timescales that are relevant to policy.

Data on biodiversity come from a diverse range of sources, citizen scientists, museums, herbaria
and researchers are all major contributors, but increasingly new technologies are being deployed,
such as automatic sensors, camera traps, eDNA and satellite tracking. Integrating these data is
a major challenge, but is necessary if we are to create dependable information on biodiversity
change. B3 will use the concept of data cubes to simplify and standardize access to biodiversity
data using the Essential Biodiversity Variables framework. These cubes will be used, in conjunction
with other environmental data and scenarios, as the basis for models and indicators of past, current
and future biodiversity.

The overarching goal of the project is to provide easy access to tools in a cloud computing envir-
onment, in real-time and on-demand, with state of the art prediction models of biodiversity, that
will output models and indicators of biodiversity status and change. The project envisages a future
where primary biodiversity data are seamlessly integrated into monitoring and forecasting such
that policy and management can proactively respond to problems while at the same time reduce
the costs of monitoring and management, and the negative impacts of biodiversity change.

GUARDEN GUARDEN project on cordis.europa.eu

Title: safeGUARDing biodivErsity aNd critical ecosystem services across sectors and scales

Duration: From November 1, 2022 to October 31, 2025

Partners:

• INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA),

France

• PARC NATIONAL DE PORT-CROS (CONSERVATOIRE BOTANIQUE NATIONAL MEDITERRA-

NEEN DE PORQUEROLLES), France

• STICHTING NATURALIS BIODIVERSITY CENTER (NATURALIS), Netherlands

• MINISTRY OF AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT OF CYPRUS,

Cyprus

Project ZENITH

21

• PLYMOUTH MARINE LABORATORY LIMITED (PML), United Kingdom

• UNIVERSITY OF ANTANANARIVO, Madagascar

• CHAROKOPEIO PANEPISTIMIO (HAROKOPIO UNIVERSITY OF ATHENS (HUA)), Greece

• AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS (CSIC), Spain

• DRAXIS ENVIRONMENTAL SA (DRAXIS), Greece

• EBOS TECHNOLOGIES LIMITED (eBOS), Cyprus

• CENTRE DE COOPERATION INTERNATIONALE EN RECHERCHE AGRONOMIQUE POUR

LEDEVELOPPEMENT - C.I.R.A.D. EPIC (CIRAD), France

• AGENTSCHAP PLANTENTUIN MEISE (AGENCE JARDIN BOTANIQUE DE MEISE), Belgium

• ENVECO ANONYMI ETAIRIA PROSTASIAS KAI DIAHIRISIS PERIVALLONTOS A.E. (ENVECO

S.A. ENVIRONMENTAL PROTECTION AND MANAGEMENT), Greece

• AREA METROPOLITANA DE BARCELONA (AMB), Spain

• FREDERICK UNIVERSITY FU (FREDERICK UNIVERSITY FU), Cyprus

• EREVNITIKO PANEPISTIMIAKO INSTITOUTO SYSTIMATON EPIKOINONION KAI YPOLO-
GISTON (RESEARCH UNIVERSITY INSTITUTE OF COMMUNICATION AND COMPUTER
SYSTEMS), Greece

Inria contact: Alexis Joly

Coordinator: CIRAD

Summary: GUARDEN’s main mission is to safeguard biodiversity and its contributions to people by
bringing them at the forefront of policy and decision-making. This will be achieved through the
development of user-oriented Decision Support Applications (DSAs), and leveraging on Multi-
Stakeholder Partnerships (MSPs). They will take into account policy and management objectives
and priorities across sectors and scales, build consensus to tackle data gaps, analytical uncertainties
or conflicting objectives, and assess options to implement adaptive transformative change. To do
so, GUARDEN will make use of a suite of methods and tools using deep learning, earth observation,
and hybrid modeling to augment the amount of standardized and geo-localized biodiversity data,
build-up a new generation of predictive models of biodiversity and ecosystem status indicators
under multiple pressures (human and climate), and propose a set of complementary ecological in-
dicators likely to be incorporated into local management and policy. The GUARDEN approach will
be applied at sectoral case studies involving end users and stakeholders through Multi-Stakeholder
Partnerships, and addressing critical cross-sectoral challenges (at the nexus of biodiversity and
deployment of energy/transport infrastructure, agriculture, and coastal urban development). Thus,
the GUARDEN DSAs shall help stakeholders engaged in the challenge to improve their holistic
understanding of ecosystem functioning, biodiversity loss and its drivers and explore the potential
ecological and societal impacts of alternative decisions. Upon the acquisition of this new know-
ledge and evidence, the DSAs will help end-users not only navigate but also (re-)shape the policy
landscape to make informed all-encompassing decisions through cross-sectoral integration.

MAMBO MAMBO project on cordis.europa.eu

Title: Modern Approaches to the Monitoring of BiOdiversity

Duration: From September 1, 2022 to August 31, 2026

Partners:

• INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA),

France

• AARHUS UNIVERSITET (AU), Denmark

• STICHTING NATURALIS BIODIVERSITY CENTER (NATURALIS), Netherlands

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Inria Annual Report 2023

• THE UNIVERSITY OF READING, United Kingdom

• HELMHOLTZ-ZENTRUM FUR UMWELTFORSCHUNG GMBH - UFZ, Germany

• ECOSTACK INNOVATIONS LIMITED, Malta

• UK CENTRE FOR ECOLOGY & HYDROLOGY, United Kingdom

• CENTRE DE COOPERATION INTERNATIONALE EN RECHERCHE AGRONOMIQUE POUR

LEDEVELOPPEMENT - C.I.R.A.D. EPIC (CIRAD), France

• PENSOFT PUBLISHERS (PENSOFT), Bulgaria

• UNIVERSITEIT VAN AMSTERDAM (UvA), Netherlands

Inria contact: Alexis Joly

Coordinator: AARHUS UNIVERSITET

Summary: EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives,
demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. How-
ever, efforts to monitor wildlife and other species groups are spatially and temporally fragmented,
taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will
develop, test and implement enabling tools for monitoring conservation status and ecological
requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together
the technical expertise of computer science, remote sensing, social science expertise on human-
technology interactions, environmental economy, and citizen science, with the biological expertise
on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement
and knowledge exchange (WP1) and the integration of new technology with existing research
infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring
species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and
habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify
user and policy needs for biodiversity monitoring and investigate the requirements for setting up
a virtual lab to automate workflow deployment and efficient computing of the vast data streams
(from on the ground sensors, and remote sensing) required to improve monitoring activities across
Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration
sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of
different tools, integrate data streams and upscale results (WP6). This will feed into the co-design
of future, improved and more cost-effective monitoring schemes for species and habitats using
novel technologies (WP7), and thus lead to a better management of protected sites and species.

9.2.2 H2020 projects

COS4CLOUD COS4CLOUD project on cordis.europa.eu

Title: Co-designed Citizen Observatories Services for the EOS-Cloud

Duration: From November 1, 2019 to February 28, 2023

Partners:

• INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA),

France

• TREBOLA ORGANIZACION ECOLOGICA (TREBOLA ORGANIZACION ECOLOGICA), Colom-

bia

• ETHNIKO KAI KAPODISTRIAKO PANEPISTIMIO ATHINON (UOA), Greece

• BINEO CONSULTING S.L., Spain

• SCHMIDT NORBERT CARL (DDQ), Netherlands

• CONSERVATION EDUCATION AND RESEARCH TRUST (EARTHWATCH), United Kingdom

• AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS (CSIC), Spain

Project ZENITH

23

• SCIENCE FOR CHANGE, SL (SCIENCE FOR CHANGE), Spain

• SVERIGES LANTBRUKSUNIVERSITET (SWEDISH UNIVERSITY OF AGRICULTURAL SCI-

ENCES), Sweden

• 52 NORTH SPATIAL INFORMATION RESEARCH GMBH (52°North GmbH), Germany

• DYNAIKON LTD, United Kingdom

• VEREIN DER EUROPAEISCHEN BURGERWISSENSCHAFTEN - ECSA E.V. (EUROPEAN CIT-

IZEN SCIENCE ASSOCIATION), Germany

• CENTRO DE INVESTIGACION ECOLOGICA Y APLICACIONES FORESTALES (CREAF-CERCA),

Spain

• SECURE DIMENSIONS GMBH (SECURE DIMENSIONS), Germany

• THE OPEN UNIVERSITY (OU), United Kingdom

Inria contact: Alexis Joly

Coordinator: CREAF-CERCA

Summary: COS4CLOUD (Co-designed citizen observatories for the EOS-Cloud) aims to design, pro-
totyped and implemented services that address the Open Science challenges shared by Citizen
observatories of biodiversity, based on the experience of platforms like: Artportalen, Natusfera,
iSpot, as well as other environmental quality monitoring platforms like: FreshWater Watch, KdU-
INO, OdourCollect, iSpex and CanAir.io. The innovative services will be designed, prototyped
and implemented for improving the data and information quality using deep machine learning,
automatic video recognition, advanced mobile app interfaces, and other cutting-edge technologies,
based on data models and data protocols validated by traditional science. The new services will
provide mechanisms to ensure the visibility and recognition of data contributors and the tools
to improve networking between various stakeholders. Novel innovative digital services will be
developed through the integration of CS products, generated by different providers, following open
standards to ensure their interoperability, and offered in agile, fit-for-purpose and sustainable site
available through EOSC hub, including a discovery service, to both traditional and citizen scientists.
The design of new services will be user oriented, engaging a wide range of stakeholders in society,
government, industry, academia, agencies, and research to co-design service requirements. As a
result, COS4CLOUD will integrate citizen science in the European Open Science Cloud, bringing
Citizen Science (CS) projects as a service for the scientific community and society at large.

9.2.3 Other european programs/initiatives

Creation of a Collaborative Doctoral Partnership between the EU Joint Research Centered of Ispra and
the university of Montpellier - first PhD student to be recruited in 2024

9.3 National initiatives
Pl@ntAgroEco (PEPR Agroécologie & Numérique), (2023-2027), 1.6 Meuro.

Participants:

Antoine Affouard, Christophe Botella, Hervé Goëau, Hugo Gresse,
Alexis Joly, Thomas Paillot.

Agroecology necessarily involves crop diversification, but also the early detection of diseases, deficien-
cies and stresses (hydric, etc.), as well as better management of biodiversity. The main stumbling block
is that this paradigm shift in agricultural practices requires expert skills in botany, plant pathology and
ecology that are not generally available to those working in the field, such as farmers or agri-food techni-
cians. Digital technologies, and artificial intelligence in particular, can play a crucial role in removing this
barrier to access to knowledge.

The aim of the Pl@ntAgroEco project will be to design, experiment with and develop new high-impact
agro-ecology services within the Pl@ntNet platform. This includes : AI and plant science research ; agile

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Inria Annual Report 2023

development of new components within the platform; organizing participatory science programs and
animating the Pl@ntNet user community.

Institut de Convergence Agriculture numérique #DigitAg, (2017-2023), 275 Keuro.

Participants:

Alexis Joly, Florent Masseglia, Esther Pacitti, Christophe Pradal,
Patrick Valduriez.

#DigitAg brings together in a partnership of seventeen actors (public research and teaching organiza-
tions, transfer actors and companies) with the objective of accelerating and supporting the development
of agriculture companies in France and in southern countries based on new tools, services and uses.
Based in Montpellier with an office in Toulouse and Rennes and led by Irstea, #DigitAg’s ambition is to
become a world reference for digital agriculture. In this project, Zenith is involved in the analysis of big
data from agronomy, in particular, plant phenotyping and biodiversity data sharing.

ANR PerfAnalytics (2021-2024), 100 Keuro.

Participants:

Reza Akbarinia, Florent Masseglia.

The objective of the PerfAnalytics project is to analyze sport videos in order to quantify the sport
performance indicators and provide feedback to coaches and athletes, particularly to French sport
federations in the perspective of the Paris 2024 Olympic games. A key aspect of the project is to couple
the existing technical results on human pose estimation from video with scientific methodologies from
biomechanics for advanced gesture objectivation. The motion analysis from video represents a great
potential for any monitoring of physical activity. In that sense, it is expected that exploitation of results
will be able to address not only sport, but also the medical field for orthopedics and rehabilitation.

PPR Antibiorésistance: structuring tool "PROMISE" (2021-2024), 240 Keuro.

Participants:

Reza Akbarinia, Florent Masseglia.

The objective of the PROMISE (PROfessional coMmunIty network on antimicrobial reSistancE) project
is to build a large data warehouse for managing and anlyzing antimicrobial resitance (AMR) data. It
gathers 21 existing professional networks and 42 academic partners from three sectors, human, animal,
and environment. The project is based on the following transdisciplinary and cross-sectoral pillars: i)
fostering synergies to improve the one-health surveillance of antibiotic consumption and AMR, ii) data
sharing for improving the knowledge of professionals, iii) improving clinical research by analyzing the
shared data.

PNR "Beerisk" (2022-2025). 200K Keuro.

Participants:

Reza Akbarinia, Florent Masseglia.

The objective of this project is to analyze honeybee daily mortality rates, represented as time series,

in order to detect anomalies and study the lethal effects of bees exposure to pesticides.

Project ZENITH

25

Plan national Ecoantibio "INTERSECTION" (2024-2028), 175 Keuros

Participants:

Reza Akbarinia, Florent Masseglia.

The objective of the INTERSECTION project is to produce intersectoral and territorial indicators for
monitoring resistance and use of antibiotics in France, and to facilitate the use and analysis of these
indicators, in a One health approach.

CASDAR CARPESO (2020-2022), 87 Keuro.

Participants:

Julien Champ, Hervé Goëau, Alexis Joly.

In order to facilitate the agro-ecological transition of livestock systems, the main objective of the
project is to enable the practical use of meslin (grains and forages) by demonstrating their interests
and remove sticking points on the nutritional value of the meslin. Therefore, it develops AI-based tools
allowing to automatically assess the nutritional value of meslin from images. The consortium includes 10
chambers of agriculture, 1 Technical Institute (IDELE) and 2 research organizations (Inria, CIRAD).

9.3.1 Others
Pl@ntNet consortium membership (2019-20XX), 80 Keuro / year

Participants:

Alexis Joly, Jean-Christophe Lombardo, Hervé Goëau, Hugo Gresse,
Mathias Chouet, Antoine Affouard, David Margery.

This contract between four research organisms (Inria, INRAE, IRD and CIRAD) aims at sustaining the
Pl@ntNet platform in the long term. It has been signed in November 2019 in the context of the InriaSOFT
national program of Inria. Each partner subscribes a subscription of 20K euros per year to cover engineer-
ing costs for maintenance and technological developments. In return, each partner has one vote in the
steering committee and the technical committee. He can also use the platform in his own projects and
benefit from a certain number of service days within the platform. The consortium is not fixed and is
intended to be extended to other members in the coming years.

9.4 Regional initiatives

Regional project "DACLIM" (2023-2026), 70 Keuros

Participants:

Reza Akbarinia, Florent Masseglia.

The objective of this project is to develop scalable techniques based on massive data distribution to
enable the efficient detection of anomalies in large climate databases. The detection of anomalies in
climate data can provide climatologists with insights into the behavior of various climatological variables,
understanding of extreme events such as heatwaves and cold snaps, as well as the prediction of these
types of events.

10 Dissemination

Participants: Reza Akbarinia, Alexis Joly, Antoine Liutkus, Florent Masseglia, Es-

ther Pacitti, Christophe Pradal, Patrick Valduriez.

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Inria Annual Report 2023

10.1 Promoting scientific activities

General chair, scientific chair

• R. Akbarinia: Organization chair of the French Database Conference (BDA), October 23-26, 2023,

Montpellier

• P. Valduriez: Inria-Brasil Workshop, April 10-14, 2023, Sao Paulo and Rio de Janeiro, Brazil

• P. Valduriez: Inria-Brasil Workshop on Digital Sciences and Energy, PUC-Rio, Rio de Janeiro, Brazil

• A. Joly: LifeCLEF 2023 Workshop chair, part of Conference and Labs of the Evaluation Forum (CLEF)

2023, Thessaloniki, Greece

Member of the conference program committees

• R. Akbarinia: ECML-PKDD 2023, EDBT 2023, IEEE BigData 2023, AIMLSystems 2023.

• E. Pacitti: BDA 2023.

• C. Pradal: FSPM 2023.

• F. Masseglia: ICDM, DSAA, DS, PAKDD, AIKE, SimBIG, SAC

• A. Joly: CLEF 2023

10.1.1 Journal

Editor, Associate editor

• R. Akbarinia: associate editor of IEEE Transactions on Knowledge and Data Engineering (TKDE)

journal.

Member of the editorial boards

• R. Akbarinia: Transactions on Large Scale Data and Knowledge Centered Systems (TLDKS).

• P. Valduriez: Distributed and Parallel Databases.

Reviewer - reviewing activities

• R. Akbarinia: IEEE Transactions on Fuzzy Systems, Journal of Supercomputing.

• A. Joly: Nature Communications, Nature reports, PLOS One, Ecology (Wiley), Journal of Supercom-

puting

• F. Masseglia: JMLR, DMKD

10.1.2 Invited talks

• P. Valduriez

– "Data Science and Innovation", COPPE/UFRJ, Rio de Janeiro, May 5, 2023 and CEFET, Rio de

Janeiro, 3 May 2023.

– "Life Science Workflow Services (LifeSWS): motivations and architecture", The Data Systems

Seminar Series, University of Waterloo, September 5, 2023.

– "Big Data Technologies", Inria Paris, November 23, 2023.

• A. Joly

– "Cooperative learning for biodiversity monitoring", STATLEARN 2023, Montpellier, April 6,

2023.

Project ZENITH

27

– "Pl@ntNet under the hood", ML-MTP, March 23, 2023.

– "Pl@ntNet Citizen observatory & research platform", journées de l’école doctorale de Saint-

Malo, Février 6-8, 2023.

• C. Pradal

– "Towards digital twins for plants : Connecting 3d plant models and phenotyping", Seminar

on Digital Twins for agri-environmental applications, Wageningen, August 30, 2023

– "Data-intensive scientific workflows for model-assisted high-throughput phenotyping", FSPM2023,

Berlin, March 27-31, 2023

10.1.3 Leadership within the scientific community

• A. Joly: contributed to an OECD book on "Artificial Intelligence in science", aimed at a broad
readership, including policy makers, the public, and stakeholders in all areas of science. We co-
authored the chapter entitled "Advancing the productivity of science with citizen science and
artificial intelligence" [43].

• E. Pacitti: Member of the Steering Committee of the BDA conference.

10.1.4 Scientific expertise

• R. Akbarinia: member of the evaluation comitee (section 27) of University of Montpellier.

• R. Akbarinia: member of the selection committee for Associate Professor (Maître de Conférence),

University Toulouse 3 Paul Sabatier

• C. Pradal: member of the INRAE evaluation comitee CSS (Scientific Specialist Commission) in Plant

Integrated Biology

• A. Joly: GENCI expert committee (AI thematic)

• A. Joly: expert for LABEX CEMEB (call for postdoctoral research projects 2023)

• P. Valduriez: consultant on big data for the Software Heritage project

10.1.5 Research administration

• R. Akbarinia: Scientific referent for research data at Inria branch of University of Montpellier;

Member of Inria national commission for research data.

• F. Masseglia: deputy scientific director of Inria for the domain "Perception, Cognition And Interac-

tion".

• E. Pacitti: manager of Polytech’ Montpellier’s International Relationships for the computer science

department (100 students).

• P. Valduriez: scientific manager for the Latin America zone at Inria’s Direction of Foreign Relation-

ships (DRI) and scientific director of the Inria-Brasil strategic partnership.

• C. Pradal: Team leader with C. Granier of the PhenoMEn team of the AGAP Institute.

• A. Joly: co-manager of a Collaborative Doctoral Partnership between the EU Joint Research Centered

of Ispra and the university of Montpellier

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Inria Annual Report 2023

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

Most permanent members of Zenith teach at the Licence and Master degree levels at UM2.

Esther Pacitti:

• IG3: Database design, physical organization, 54h, level, L3, 50 students.

• IG4: Distributed Databases and NoSQL, 80h , level M1, 50 students.

• Large Scale Information Management (Iot, Recommendation Systems, Graph Databases), 27h, level

M2, 20 students.

• Supervision of industrial projects

• Supervision of master internships.

• Supervision of computer science discovery projects.

Patrick Valduriez:

• Professional: Big Data Architectures, 24h, level M2, Capgemini Institut.

Reza Akbarinia:

• Big Data Management, 6h, level L3, University of Montpellier 3.

Alexis Joly:

• lecturer at the IDESSAI summer school 2023 .

• lecturer for an Inria academy master class at LVMH.

10.2.2 Supervision

PhD & HDR:

• Defended PhD: Daniel Rosendo, Enabling HPC-Big Data Convergence for Intelligent Extreme-Scale

Analytics, Univ. Rennes. Advisors: Gabriel Antoniu, Alexandru Costan, Patrick Valduriez.

• Defended PhD: Camille Garcin, Multi-class classification with high label ambiguity and a long-tailed

distribution. Advisors: Joseph Salmon, Maximilien Servajean, Alexis Joly.

• Defended PhD: Joaquim Estopinan, Species Conservation Status Prediction. Advisors: Alexis Joly,

François Munoz, Maximilien Servajean, Pierre Bonnet.

• PhD in progress: Cesar Leblanc, Predicting biodiversity future trajectories through deep learning.

Advisors: Alexis Joly, Maximilien Servajean, Pierre Bonnet.

• PhD in progress: Tanguy Lefort, Ambiguity of classification labels and expert feedback. Advisors:

Joseph Salmon, Benjamin Charlier, Alexis Joly.

• PhD in progress: Kawtar Zaher, Novel class retrieval through interactive learning. Advisors: Olivier

Buisson, Alexis Joly.

• PhD in progress: Matteo Contini, Multi-scale monitoring of coastal marine biodiversity. Advisors:

Sylvain Bonhommeau, Alexis Joly.

• PhD in progress: Guillaume Coulaud, Anomaly Detection in Big Climate Data. Advisors: Reza

Akbarinia, Audrey Brouillet, Florent Masseglia.

Project ZENITH

10.2.3 Juries

Members of the team participated to the following PhD or HDR committees:

• R. Akbarinia (reviewer): Qtong Wang, Paris Cité University.

• P. Valduriez (reviewer): Saalik Hatia, Sorbonne Université.

• A. Joly (jury president): Samy BENSLIMANE, Université de Montpellier.

A. Joly was a member of the jury of the INRAE participatory research award 2023

29

10.3 Popularization

10.3.1 Articles and contents

• A. Joly:

– Author of the article "Tous botanistes !" in the magazine Le numérique est-il un progrès

durable?, Pour la science, 2023

– Author of two book chapters in Le lien à la nature à l’ère du numérique, ISTE publisher, Émilie

Kohlmann, 2023

10.3.2 Interventions

• A. Joly:

– Main guest of a 1 hour TV program of Sorcier TV - youtube

– Main guest of the Radio program “Carnets de campagne” (France Inter) - podcast

11 Scientific production

11.1 Major publications

[1] C. Botella, A. Joly, P. Bonnet, F. Munoz and P. Monestiez. ‘Jointly estimating spatial sampling effort
and habitat suitability for multiple species from opportunistic presence-only data’. In: Methods in
Ecology and Evolution 12.5 (1st Feb. 2021), pp. 933–945. DOI: 10.1111/2041-210X.13565. URL:
https://hal.umontpellier.fr/hal-03150701.

[2] B. Deneu, M. Servajean, P. Bonnet, C. Botella, F. Munoz and A. Joly. ‘Convolutional neural networks
improve species distribution modelling by capturing the spatial structure of the environment’. In:
PLoS Computational Biology 17.4 (19th Apr. 2021), e1008856. DOI: 10.1371/journal.pcbi.1008
856. URL: https://hal.inrae.fr/hal-03220977.

[3] M. Fontaine, R. Badeau and A. Liutkus. ‘Separation of Alpha-Stable Random Vectors’. In: Signal
Processing (Jan. 2020), p. 107465. DOI: 10.1016/j.sigpro.2020.107465. URL: https://hal.in
ria.fr/hal-02433213.

[4] C. Garcin, M. Servajean, A. Joly and J. Salmon. ‘Stochastic smoothing of the top-K calibrated hinge
loss for deep imbalanced classification’. In: ICML 2022 - 39th International Conference on Machine
Learning. Vol. 162. Baltimore, United States: PMLR, 2022, pp. 7208–7222. URL: https://hal.inri
a.fr/hal-03828747.

[5] G. Heidsieck, D. de Oliveira, E. Pacitti, C. Pradal, F. Tardieu and P. Valduriez. ‘Cache-aware scheduling
of scientific workflows in a multisite cloud’. In: Future Generation Computer Systems 122 (2021),
pp. 172–186. DOI: 10.1016/j.future.2021.03.012. URL: https://hal.archives-ouvertes
.fr/hal-03189130.

[6]

J. Liu, N. Moreno Lemus, E. Pacitti, F. Porto and P. Valduriez. ‘Parallel Computation of PDFs on Big
Spatial Data Using Spark’. In: Distributed and Parallel Databases 38 (2020), pp. 63–100. DOI: 10.10
07/s10619-019-07260-3. URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-02045144.

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[7] A. Liutkus, O. Cífka, S.-L. Wu, U. ¸Sim¸sekli, Y.-H. Yang and G. Richard. ‘Relative Positional Encoding
for Transformers with Linear Complexity’. In: ICML 2021 - 38th International Conference on
Machine Learning. Proceedings of the 38th International Conference on Machine Learning. Virtual
Only, United States, 18th July 2021. URL: https://hal.telecom-paris.fr/hal-03256451.

[8] A. Liutkus, U. ¸S. Im¸sekli, S. Majewski, A. Durmus and F.-R. Stöter. ‘Sliced-Wasserstein Flows: Non-
parametric Generative Modeling via Optimal Transport and Diffusions’. In: 36th International
Conference on Machine Learning (ICML). Long Beach, United States, June 2019. URL: https://hal
.inria.fr/hal-02191302.

[9] T. Mondal, R. Akbarinia and F. Masseglia. ‘kNN matrix profile for knowledge discovery from time
series’. In: Data Mining and Knowledge Discovery 37.3 (May 2023), pp. 1055–1089. DOI: 10.1007/s
10618-022-00883-8. URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-04225369.

[10] D. Oliveira, J. Liu and E. Pacitti. Data-Intensive Workflow Management: For Clouds and Data-
Intensive and Scalable Computing Environments. Vol. 14. Synthesis Lectures on Data Management
4. Morgan&Claypool Publishers, May 2019, pp. 1–179. DOI: 10.2200/S00915ED1V01Y201904DTM0
60. URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-02128444.

[11] T. Özsu and P. Valduriez. Principles of Distributed Database Systems - Fourth Edition. Télécharger la
3ieme et 4ieme édition : lien dans “ voir aussi ”. Springer, 2020, pp. 1–674. URL: https://hal-lir
mm.ccsd.cnrs.fr/lirmm-02265930.

[12] D.-E. Yagoubi, R. Akbarinia, F. Masseglia and T. Palpanas. ‘Massively Distributed Time Series
Indexing and Querying’. In: IEEE Transactions on Knowledge and Data Engineering 32.1 (2020),
pp. 108–120. DOI: 10.1109/TKDE.2018.2880215. URL: https://hal-lirmm.ccsd.cnrs.fr/l
irmm-02197618.

[13] C. Zhang, R. Akbarinia and F. Toumani. ‘Efficient Incremental Computation of Aggregations over
Sliding Windows’. In: 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
(SIGKDD 2021). Singapore, Singapore, 2021, pp. 2136–2144. DOI: 10.1145/3447548.3467360.
URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-03359490.

11.2 Publications of the year

International journals

[14] R. Akbarinia, C. Botella, A. Joly, F. Masseglia, M. Mattoso, E. Ogasawara, D. de Oliveira, E. Pacitti, F.
Porto, C. Pradal, D. Shasha and P. Valduriez. ‘Life Science Workflow Services (LifeSWS): motivations
and architecture’. In: Transactions on Large-Scale Data- and Knowledge-Centered Systems. Lecture
Notes in Computer Science 14280 (2023), pp. 1–24. DOI: 10.1007/978-3-662-68100-8_1. URL:
https://hal-lirmm.ccsd.cnrs.fr/lirmm-04173545.

[15] F. Bauget, V. Protto, C. Pradal, Y. Boursiac and C. Maurel. ‘A root functional-structural model
allows to assess effects of water deficit on water and solute transport parameters’. In: Journal of
Experimental Botany 74.5 (13th Mar. 2023), pp. 1594–1608. DOI: 10.1093/jxb/erac471. URL:
https://inria.hal.science/hal-03915413.

[16] M. van Der Velde, H. Goëau, P. Bonnet, R. d’Andrimont, M. Yordanov, A. Affouard, M. Claverie,
B. Czúcz, N. Elvekjær, L. Martinez-Sanchez, X. Rotllan-Puig, A. Sima, A. Verhegghen and A. Joly.
‘Pl@ntNet Crops: merging citizen science observations and structured survey data to improve
crop recognition for agri-food-environment applications’. In: Environmental Research Letters 18.2
(24th Jan. 2023), p. 025005. DOI: 10.1088/1748-9326/acadf3. URL: https://hal.science/ha
l-03965684.

[17] L. Djebour, R. Akbarinia and F. Masseglia. ‘Variable-Size Segmentation for Time Series Representa-
tion’. In: Transactions on Large-Scale Data- and Knowledge-Centered Systems 53 (2023), pp. 34–65.
DOI: 10.1007/978-3-662-66863-4_2. URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-038
82927.

Project ZENITH

31

[18] Q. Groom, M. Dillen, W. Addink, A. Ariño, C. Bölling, P. Bonnet, L. Cecchi, E. Ellwood, R. Figueira,
P.-Y. Gagnier, O. Grace, A. Güntsch, H. Hardy, P. Huybrechts, R. Hyam, A. Joly, V. K. Kommineni,
I. Larridon, L. Livermore, R. J. Lopes, S. Meeus, J. Miller, K. Milleville, R. Panda, M. Pignal, J. Poelen,
B. Ristevski, T. Robertson, C. Rufino, J. Santos, M. Schermer, B. Scott, K. Seltmann, H. Teixeira,
M. Trekels and J. Gaikwad. ‘Envisaging a global infrastructure to exploit the potential of digitised
collections’. In: BDJ Open 11 (30th Nov. 2023). DOI: 10.22541/au.166678848.82362633/v2. URL:
https://inria.hal.science/hal-03871553.

[19] M. Labadie, K. Guy, M.-N. Demené, Y. Caraglio, G. Heidsieck, A. Gaston, C. Rothan, Y. Guédon,
C. Pradal and B. Denoyes. ‘Spatio-temporal analysis of strawberry architecture: insights into the
control of branching and inflorescence complexity’. In: Journal of Experimental Botany 74.12
(27th June 2023), pp. 3595–3612. DOI: 10.1093/jxb/erad097. URL: https://hal.inrae.fr/ha
l-04084880.

[20]

[21]

S. Levionnois, C. Pradal, C. Fournier, J. Sanner and C. Robert. ‘Modeling the Impact of Proportion,
Sowing Date, and Architectural Traits of a Companion Crop on Foliar Fungal Pathogens of Wheat
in Crop Mixtures’. In: Phytopathology 113.10 (8th Nov. 2023), pp. 1876–1889. DOI: 10.1094/PHYTO-
06-22-0197-R. URL: https://inria.hal.science/hal-04302997.

J. Liu, D. Dong, X. Wang, A. Qin, X. Li, P. Valduriez, D. Dou and D. Yu. ‘Large-scale Knowledge
Distillation with Elastic Heterogeneous Computing Resources’. In: Concurrency and Computation:
Practice and Experience 35.26 (2023), e7272. DOI: 10.1002/cpe.7272. URL: https://hal-lirmm
.ccsd.cnrs.fr/lirmm-03740277.

[22] S. Mehreen, H. Goëau, P. Bonnet, S. Chau, J. Champ and A. Joly. ‘Estimating Compositions and
Nutritional Values of Seed Mixes Based on Vision Transformers’. In: Plant Phenomics 5 (10th Nov.
2023). DOI: 10.34133/plantphenomics.0112. URL: https://inria.hal.science/hal-0432
2179.

[23] C. A. Midingoyi, C. Pradal, A. Enders, D. Fumagalli, P. Lecharpentier, H. Raynal, M. Donatelli,
D. Fanchini, I. Athanasiadis, C. Porter, G. Hoogenboom, F. Oliveira, D. Holzworth and P. Martre.
‘Crop modeling frameworks interoperability through bidirectional source code transformation’. In:
Environmental Modelling and Software 168 (Oct. 2023), p. 105790. DOI: 10.1016/j.envsoft.202
3.105790. URL: https://hal.inrae.fr/hal-04256535.

[24] T. Mondal, R. Akbarinia and F. Masseglia. ‘kNN matrix profile for knowledge discovery from time
series’. In: Data Mining and Knowledge Discovery 37.3 (May 2023), pp. 1055–1089. DOI: 10.1007/s
10618-022-00883-8. URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-04225369.

[25]

J. Talpaert Daudon, M. Contini, I. Urbina-Barreto, B. Elliott, F. Guilhaumon, S. Bonhommeau, A.
Joly and J. Barde. ‘GeoAI for Marine Ecosystem Monitoring: a Complete Workflow to Generate
Maps from AI Model Predictions’. In: ISPRS International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Sciences XLVIII-4/W7-2023 (22nd June 2023), pp. 223–230. DOI:
10.5194/isprs-archives-XLVIII-4-W7-2023-223-2023. URL: https://hal.science/hal
-04204101.

International peer-reviewed conferences

[26] H. Borges, A. Castro, C. Souza, J. Rodrigues, F. A. Machado Porto, R. Coutinho, E. Pacitti and E.
Ogasawara. ‘STMotif Explorer: A Tool for Spatiotemporal Motif Analysis’. In: SBBD 2023 – Simpósio
Brasileiro de Banco de Dados. Belo Honrizonte, Brazil, 25th Sept. 2023. URL: https://hal-lirmm
.ccsd.cnrs.fr/lirmm-04283828.

[27] C. Botella, B. Deneu, D. Marcos, M. Servajean, T. Larcher, C. Leblanc, J. Estopinan, P. Bonnet and A.
Joly. ‘Overview of GeoLifeCLEF 2023: Species Composition Prediction with High Spatial Resolution
at Continental Scale Using Remote Sensing’. In: CLEF 2023 - Working Notes of the Conference
and Labs of the Evaluation Forum. Vol. 3497. CEUR Workshop Proceedings. Thessalokini, Greece,
18th Dec. 2023, pp. 1954–1971. URL: https://hal.science/hal-04322255.

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[28] A. Castro, H. Borges, C. Souza, J. Rodrigues, F. A. Machado Porto, E. Pacitti, R. Coutinho and E.
Ogasawara. ‘GSTSM Package: Finding Frequent Sequences in Constrained Space and Time’. In:
BDA 2023 – 39e Conférence sur la Gestion de Données – Principes, Technologies et Applications.
Montpellier, France, 23rd Oct. 2023, pp. 1–4. URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-0
4283772.

[29] H. Goëau, P. Bonnet and A. Joly. ‘Overview of PlantCLEF 2023: Image-based Plant Identification
at Global Scale’. In: CEUR Workshop Proceedings. CLEF-WN 2023 - 24th Working Notes of the
Conference and Labs of the Evaluation Forum. Vol. 3497. Working Notes of the Conference and
Labs of the Evaluation Forum (CLEF 2023). Thessalonique, Greece, 18th Sept. 2023, pp. 1972–1981.
URL: https://hal.science/hal-04345310.

[30] A. Y. Hao Chai, S. Han Lee, F. S. Tay, Y. Lung Then, H. Goëau, P. Bonnet and A. Joly. ‘Pairwise Feature
Learning for Unseen Plant Disease Recognition’. In: 2023 IEEE International Conference on Image
Processing (ICIP). ICIP 2023 - 30th IEEE International Conference on Image Processing. Kuala
Lumpur, Malaysia: IEEE, 8th Oct. 2023, pp. 306–310. DOI: 10.1109/ICIP49359.2023.10222401.
URL: https://hal.inrae.fr/hal-04214623.

[31] A. Joly, C. Botella, L. Picek, S. Kahl, H. Goëau, B. Deneu, D. Marcos, J. Estopinan, C. Leblanc and T.
Larcher. ‘Overview of LifeCLEF 2023: evaluation of AI models for the identification and prediction
of birds, plants, snakes and fungi’. In: Lecture Notes in Computer Science. CLEF 2023 - 14th Interna-
tional Conference of the CLEF Association. Vol. LNCS-14163. Experimental IR Meets Multilinguality,
Multimodality, and Interaction 14th International Conference of the CLEF Association, CLEF
2023, Thessaloniki, Greece, September 18–21, 2023, Proceedings. Thessalokini, Greece: Springer
Nature Switzerland, 11th Sept. 2023, pp. 416–439. DOI: 10.1007/978-3-031-42448-9_27. URL:
https://hal.science/hal-04322219.

[32] A. Joly, S. Kahl, L. Picek, C. Botella, D. Marcos, M. Šulc, M. Hrúz, S. S. Moussi, M. Servajean, E. Cole,
A. Durso, H. Glotin, R. Planqué, W.-P. Vellinga, H. Klinck, T. Denton, I. Eggel, P. Bonnet and H. Müller.
‘LifeCLEF 2023 teaser: Species Identification and Prediction Challenges’. In: LNCS. Lecture Notes in
Computer Science. ECIR 2023 - 45th European Conference on Information Retrieval. Vol. 13982.
Advances in Information Retrieval : 45th European Conference on Information Retrieval, ECIR
2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part III. Dublin, Ireland: Springer, 16th Mar.
2023, pp. 568–576. DOI: 10.1007/978-3-031-28241-6_65. URL: https://inria.hal.science
/hal-04204378.

[33]

S. Kahl, T. Denton, H. Klinck, H. Reers, F. Cherutich, H. Glotin, H. Goëau, W.-P. Vellinga, R. Planqué
and A. Joly. ‘Overview of BirdCLEF 2023: Automated Bird Species Identification in Eastern Africa’.
In: CEUR Workshop Proceedings. CLEF 2023 - Working Notes of the Conference and Labs of the
Evaluation Forum. Vol. 3497. Working Notes of the Conference and Labs of the Evaluation Forum
(CLEF 2023). Thessalonique, Greece, 18th Sept. 2023, pp. 1934–1942. URL: https://hal.inrae.f
r/hal-04345437.

[34] R. van der Klis, S. Alaniz, M. Mancini, C. F. Dantas, D. Ienco, Z. Akata and D. Marcos. ‘PDiscoNet:
Semantically consistent part discovery for fine-grained recognition’. In: ICCV 2023 - International
Conference on Computer Vision. 2023 IEEE/CVF International Conference on Computer Vision
(ICCV). Paris, France, Oct. 2023. URL: https://hal.inrae.fr/hal-04183747.

[35] L. Perthame, C. Pradal, A. Jullien, F. Rees, C. Richard-Molard and G. Arman. ‘Identification of shoot
architectural traits to promote winter oilseed rape vigour during the vegetative growth: a simulation
approach’. In: IRC 2023 - 16th International Rapeseed Congress. Sydney, Australia, 24th Sept. 2023.
URL: https://hal.inrae.fr/hal-04263001.

[36] L. Perthame, F. Rees, X. Cornilleau, C. Richard-Molard, C. Pradal and A. Jullien. ‘SIMBAL: A structural-
functional plant model to simulate C and N dynamics andshoot-root architecture of winter oilseed
rape associated with legumes’. In: FSPM2023 - 10th International Conference on Functional-
Structural Plant Model. Book of Abstracts of the 10th International Conference on Functional-
Structural Plant Models. Berlin, Germany, 2023. URL: https://hal.inrae.fr/hal-04262992.

Project ZENITH

33

[37] F. Rees, M. Gauthier, R. Barillot, C. Richard-Molard, A. Jullien, C. Chenu, C. Pradal and B. An-
drieu. ‘Quantitative importance of various rhizodeposition processes: lessons from a mechanistic
functional-structural root model’. In: FSPM 2023 - 10th International Conference on Functional-
Structural Plant Models. Berlin, Germany, 2023. URL: https://hal.inrae.fr/hal-04098521.

[38] V. Ribeiro, E. Pena, R. Saldanha, R. Akbarinia, P. Valduriez, F. Arif, J. Stoyanovich and F. Porto. ‘Subset
Modelling: A Domain Partitioning Strategy for Data-efficient Machine-Learning’. In: SBBD 2023
Companion Proceedings series. SBBD 2023 - Simpósio Brasileiro de Banco de Dados (Proceedings of
the 38th Brazilian Symposium on Databases). Belo Horizonte, Brazil, 25th Sept. 2023, pp. 318–323.
DOI: 10.5753/sbbd.2023.232829. URL: https://hal-lirmm.ccsd.cnrs.fr/lirmm-042641
25.

[39] D. Rosendo, K. Keahey, A. Costan, M. Simonin, P. Valduriez and G. Antoniu. ‘KheOps: Cost-effective
Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments’. In: ACM REP ’23:
Proceedings of the 2023 ACM Conference on Reproducibility and Replicability. REP 2023 - ACM
Conference on Reproducibility and Replicability. Santa Cruz, CA, United States: ACM, 28th June
2023, pp. 62–73. DOI: 10.1145/3589806.3600032. URL: https://hal.science/hal-04157720.

[40] D. Rosendo, M. Mattoso, A. Costan, R. Souza, D. Pina, P. Valduriez and G. Antoniu. ‘ProvLight:
Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum’. In: IEEE Cluster 2023 -
IEEE International Conference on Cluster Computing. Santa Fe, New Mexico, United States: IEEE,
2023, pp. 1–13. URL: https://hal.science/hal-04161546.

Conferences without proceedings

[41] A. Aniraj, C. F. Dantas, D. Ienco and D. Marcos. ‘Masking Strategies for Background Bias Removal in
Computer Vision Models’. In: Workshop "Out of Distribution Generalization in Computer Vision"
in conjunction with ICCV 2023. Paris, France, 2nd Oct. 2023. URL: https://hal.science/hal-0
4184449.

[42] O. Cífka and A. Liutkus. ‘Black-box language model explanation by context length probing’. In:
ACL Anthology. ACL 2023 - 61st Annual Meeting of the Association for Computational Linguistics.
Vol. Volume 2: Short Papers. Annual Meeting of the Association for Computational Linguistics
(2023). Toronto, Canada, 2023, pp. 1067–1079. DOI: 10.18653/v1/2023.acl- short.92. URL:
https://hal.umontpellier.fr/hal-03917930.

Scientific book chapters

[43] L. Ceccaroni, J. L. Oliver, E. Roger, J. Bibby, P. Flemons, K. Michael and A. Joly. ‘Advancing the
productivity of science with citizen science and artificial intelligence’. In: Artificial Intelligence in
Science: Challenges, Opportunities and the Future of Research. 23rd June 2023. DOI: 10.1787/6956
3b12-en. URL: https://inria.hal.science/hal-04204315.

[44] R. Salles, E. Pacitti, E. Bezerra, C. Marques, C. Pacheco, C. Oliveira, F. A. Machado Porto and
E. Ogasawara. ‘TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction
Models’. In: Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV : Special Issue
on Data Management - Principles, Technologies, and Applications. Vol. LNCS14160. Lecture Notes
in Computer Science. 22nd Sept. 2023, pp. 41–55. DOI: 10.1007/978-3-662-68014-8_2. URL:
https://hal-lirmm.ccsd.cnrs.fr/lirmm-04283842.

Doctoral dissertations and habilitation theses

[45]

J. Estopinan. ‘A predictive approach to determining the joint conservation status of species’.
Université de Montpellier (UM), FRA, 28th Nov. 2023. URL: https://theses.hal.science/tel-
04366847.

[46] C. Garcin. ‘Loss functions for set-valued classification’. Université de Montpellier, 29th Sept. 2023.

URL: https://inria.hal.science/tel-04379493.

[47] D. Rosendo. ‘Methodologies for Reproducible Analysis of Workflows on the Edge-to-Cloud Con-

tinuum’. INSA RENNES, 1st June 2023. URL: https://hal.science/tel-04167278.

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Reports & preprints

[48] C. Botella, B. Deneu, D. Marcos, M. Servajean, J. Estopinan, T. Larcher, C. Leblanc, P. Bonnet and
A. Joly. The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial
resolution across Europe. 4th Aug. 2023. URL: https://hal.science/hal-04152362.

[49] O. Cífka, S. Chamaillé-Jammes and A. Liutkus. MoveFormer: a Transformer-based model for step-
selection animal movement modelling. 6th Mar. 2023. DOI: 10.1101/2023.03.05.531080. URL:
https://hal.umontpellier.fr/hal-04023957.

[50] C. Garcin, M. Servajean, J. Salmon and A. Joly. A two-head loss function for deep Average-K classific-
ation. 2023. DOI: 10.48550/arXiv.2303.18118. URL: https://inria.hal.science/hal-042
04318.

[51] T. Lefort, B. Charlier, A. Joly and J. Salmon. Peerannot: classification for crowdsourced image datasets

with Python. 2023. URL: https://hal.science/hal-04202889.

[52] Q. Leroy, O. Buisson and A. Joly. How does the degree of novelty impacts semi-supervised representa-
tion learning for novel class retrieval? 17th Oct. 2023. URL: https://inria.hal.science/hal-0
3871584.

[53] G. Morand, A. Joly, T. Rouyer, T. Lorieul and J. Barde. Predicting species distributions in the open
oceans with convolutional neural networks. 12th Aug. 2023. DOI: 10.1101/2023.08.11.551418.
URL: https://inria.hal.science/hal-04204332.

[54] R. Salles, J. Lima, R. Coutinho, E. Pacitti, F. Masseglia, R. Akbarinia, C. Chen, J. M. Garibaldi, F. A.
Machado Porto and E. S. Ogasawara. SoftED: Metrics for Soft Evaluation of Time Series Event
Detection. 2023. DOI: 10.48550/arXiv.2304.00439. URL: https://hal-lirmm.ccsd.cnrs.fr
/lirmm-04280618.

Other scientific publications

[55] P. Leroy, B. Fruchard, T. Goyallon, G. Duprat, C. Avezou, P. Dewilde and L. Reveret. ‘Design of a
Software Suite to Support Indexing, Annotating, and Analyzing Climbing Videos’. In: ECSS 2023 -
28th Congress of the European College of Sport Science. Paris, France, 4th July 2023. URL: https:
//hal.science/hal-04330587.
<scratchpad>
- Main topic: Research Centre at the University of Montpellier
- Key points:
  - Collaboration between Inria, CNRS, Université de Montpellier, and other partners
  - Focus on data-intensive scientific applications, including agronomy, astronomy, biology, and environmental science
  - Emphasis on scalability, functionality, ease of use, and performance
  - Development of innovative solutions for distributed and parallel data management
  - Application of machine learning, probabilities, and statistics for high-dimensional data processing
- Structure: The document is organized into sections on Research Program, Application Domains, Social and Environmental Responsibility, Highlights of the Year, New Software and Platforms, Open Data, and Scientific Production
- Potential artifacts to ignore: Web-related elements, such as links, HTML tags, and unnecessary formatting
</scratchpad>

<final_summary>
The Research Centre at the University of Montpellier is a collaboration between Inria, CNRS, and Université de Montpellier, focusing on data-intensive scientific applications. The centre aims to develop innovative solutions for distributed and parallel data management, applying machine learning, probabilities, and statistics for high-dimensional data processing. Key application domains include agronomy, astronomy, biology, and environmental science, with a focus on scalability, functionality, ease of use, and performance. The centre also explores social and environmental responsibility, including energy consumption and data sharing. Through its research program, the centre aims to advance the state-of-the-art in data-intensive scientific applications, leveraging the power of distributed and parallel computing to drive scientific discovery.
The Research Centre at the University of Montpellier is a collaboration between Inria, CNRS, and Université de Montpellier, focusing on data-intensive scientific applications. The centre aims to develop innovative solutions for distributed and parallel data management, applying machine learning, probabilities, and statistics for high-dimensional data processing. Key application domains include agronomy, astronomy, biology, and environmental science, with a focus on scalability, functionality, ease of use, and performance. The centre also explores social and environmental responsibility, including energy consumption and data sharing. Through its research program, the centre aims to advance the state-of-the-art in data-intensive scientific applications, leveraging the power of distributed and parallel computing to drive scientific discovery.