Publications

From linear to nonlinear unfolded condat-vũ algorithm for spectro-polarimetric hight-constrast image recovery

Abstract

Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat-Vũ iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance.

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Raspberry pi single-board computers: Cost/performance rela onship over time

Abstract

This study delves into the dynamic landscape of cost versus performance ratio within the Raspberry Pi family of computers, specifically scrutinizing the Raspberry Pi B and Raspberry Pi Zero lines. Based on previous analyses, our comprehensive investigation encompasses all generations of the Raspberry Pi B and Zero lines available until January 2024. Prices are meticulously adjusted to the 2012 dollar value, aligning with the inaugural launch of the Raspberry Pi. The findings illuminate an upward in performance around 229 times over an 11-year period, coupled with a notable decline in the cost per unit of performance. The impact of the dollar’s depreciation since 2012 further accentuates these trends.

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Towards verifying security policies for infinite-state systems

Abstract

FIXME

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New algorithms for multivalued component trees

Abstract

Tree-based structures can model images—and more generally valued graphs—for processing and analysis purpose. In this framework, the component tree was natively designed for grey-level images—and more generally totally or- dered valued graphs. Ten years ago, the notion of a multivalued component tree was introduced to relax this grey-level / total order constraint. In this algorith- mic paper, we provide new tools to handle multivalued component trees. Our contributions are twofold: (1) we propose a new algorithm for the construction of the multivalued component tree; (2) we propose two strategies for building hierarchical orders on value sets, required to further build the multivalued com- ponent trees of images / graphs relying on such value sets. Codes available at: https://github.com/bnaegel/multivalued_component_tree.

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A large scale format compliance checker for TeX font metrics

Abstract

We present tfm-validate, a TeX Font Metrics format checker. The library’s core functionality is to inspect TFM files and report any discovered compliance issue. It can be run on individual files or complete directory trees. tfm-validate also provides a convenience function to (in)validate a local TeXLive installation. When run this way, the library processes every TFM file in the distribution and generates a website aggregating all the discovered non-compliance issues. One public instance of tfm-validate is now automatically triggered on a daily basis. The corresponding website is available at https://texlive.info/tfm-validate/.

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Introducing multi-layer concatenation as a scheme to combine information in water distribution cyber-physical systems

Abstract

As Water distribution infrastructures are ageing, their modernization process is leading to an increased incorporation of connected devices into these physical systems. This transition is changing the nature of water distribution control systems from physical systems to cyber-physical systems (CPS). However, this evolution is associated with an increased vulnerability to cyber-attacks. Detecting such attacks in CPS is gaining traction in the scientific community with the recent release of cyber-physical datasets that capture simultaneously the network traffic and the physical state of a water distribution testbed. This novel paradigm of conjoint availability of these two types of data from a common source infrastructure opens a new question on how to combine their information when training machine learning models for attack detection. As an alternative approach to previous models that rely on model aggregation, this paper introduces Multi-Layer Concatenation, a combination scheme to merge the information from the physical and network parts of a CPS from a data perspective, through a time-based join operation coupled with a propagation process to keep the coherence of the global system. The evaluation of its impact assesses its benefits for machine learning-based detection on three cyber-physical datasets, by measuring machine learning models’ performances on physical and network data separately, and then on data combined through the proposed scheme.

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Myhill-Nerode theorem for higher-dimensional automata

Abstract

We establish a Myhill-Nerode type theorem for higher-dimensional automata (HDAs), stating that a language is regular if and only if it has finite prefix quotient. HDAs extend standard automata with additional structure, making it possible to distinguish between interleavings and concurrency. We also introduce deterministic HDAs and show that not all HDAs are determinizable, that is, there exist regular languages that cannot be recognised by a deterministic HDA. Using our theorem, we develop an internal characterisation of deterministic languages. Lastly, we develop analogues of the Myhill-Nerode construction and of determinacy for HDAs with interfaces.

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ICDAR 2024 competition on historical map text detection, recognition, and linking

Abstract

Text on digitized historical maps contains valuable information, e.g., providing georeferenced political and cultural context. The goal of the ICDAR 2024 MapText Competition is to benchmark methods that automatically extract textual content on historical maps (e.g., place names) and connect words to form location phrases. The competition features two primary tasks—text detection and end-to-end text recognition—each with a secondary task of linking words into phrase blocks. Submissions are evaluated on two data sets: 1) David Rumsey Historical Map Collection which contains 936 map images covering 80 regions and 183 distinct publication years (from 1623 to 2012); 2) French Land Registers (created during the 19th century) which contains 145 map images of 50 French cities and towns. The competition received 44 submissions among all tasks. This report presents the motivation for the competition, the tasks, the evaluation metrics, and the submission analysis.

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Using exceptional attributed subgraph mining to explore interindividual variability in odor pleasantness processing in the piriform cortex and amygdala

Abstract

In humans, the amygdala and piriform cortex are 2 important brain structures involved in hedonic odor processing. Although the affective processing of odors in these 2 structures has been extensively studied in the past, the way in which each tested individual contributes to the observed global pattern remains little understood at this stage. The purpose of this study is to examine whether exceptional pattern extraction techniques can improve our understanding of hedonic odor processing in these brain areas while paying particular attention to individual variability. A total of 42 volunteers participated in a functional magnetic resonance imaging (fMRI) study in which they were asked to smell 6 odors and describe their hedonic valence. Classical univariate analyses (statistical parametric mapping) and data mining were performed on the fMRI data. The results from both analyses showed that unpleasant odors preferentially activate the anterior part of the left piriform cortex. Moreover, the data mining approach revealed specific patterns for pleasant and unpleasant odors in the piriform cortex but also in the amygdala. The approach also revealed the contribution of each of the 42 individuals to the observed patterns. Taken together, these results suggest that the data mining approach can be used—with standard fMRI analyses—to provide complementary information regarding spatial location and the contribution of individuals to the observed patterns.

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Relational and algebraic methods in computer science - 21st international conference, RAMiCS 2024, prague, czech republic, august 19-22, 2024, proceedings

Abstract

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