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Camille Noûs

Publications and source records attributed to Camille Noûs.

At least 37 records · Page 2Linked to original sources
arXiv2022

On the top-dimensional $\ell^2$-Betti numbers

The purpose of this note is to introduce a trick which relates the (non)-vanishing of the top-dimensional $\ell^2$-Betti numbers of actions with that of sub-actions. We provide three different types of applications: we prove that the $\ell^2$-Betti numbers of Aut($F_n$) and Out($F_n$) (and of their Torelli subgroups) do not vanish in degree equal to their virtual cohomological dimension, we prove that the subgroups of the 3-manifold groups have vanishing $\ell^2$-Betti numbers in degree 3 and 2 and we prove for instance that $F_2^d \times Z$ has ergodic dimension $d + 1$.

math.GR↗
arXiv2022

Some Notes on Polyadic Concept Analysis

Despite the popularity of Formal Concept Analysis (FCA) as a mathematical framework for data analysis, some of its extensions are still considered arcane. Polyadic Concept Analysis (PCA) is one of the most promising yet understudied of these extensions. This formalism offers many interesting open questions but is hindered in its dissemination by complex notations and a lack of agreed-upon basic definitions. In this paper, we discuss in a mostly informal way the fundamental differences between FCA and PCA in the relation between contexts, conceptual structures, and rules. We identify open questions, present partial results on the maximal size of concept n-lattices and suggest new research directions.

cs.DM↗
arXiv2021

Minimizing subject-dependent calibration for BCI with Riemannian transfer learning

Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the cognitive fatigue, before even starting to use the BCI. Reducing or suppressing this subject-dependent calibration is possible by relying on advanced machine learning techniques, such as transfer learning. Building on Riemannian BCI, we present a simple and effective scheme to train a classifier on data recorded from different subjects, to reduce the calibration while preserving good performances. The main novelty of this paper is to propose a unique approach that could be applied on very different paradigms. To demonstrate the robustness of this approach, we conducted a meta-analysis on multiple datasets for three BCI paradigms: event-related potentials (P300), motor imagery and SSVEP. Relying on the MOABB open source framework to ensure the reproducibility of the experiments and the statistical analysis, the results clearly show that the proposed approach could be applied on any kind of BCI paradigm and in most of the cases to significantly improve the classifier reliability. We point out some key features to further improve transfer learning methods.

eess.SP↗
arXiv2021

Calling to CNN-LSTM for Rumor Detection: A Deep Multi-channel Model for Message Veracity Classification in Microblogs

Reputed by their low-cost, easy-access, real-time and valuable information, social media also wildly spread unverified or fake news. Rumors can notably cause severe damage on individuals and the society. Therefore, rumor detection on social media has recently attracted tremendous attention. Most rumor detection approaches focus on rumor feature analysis and social features, i.e., metadata in social media. Unfortunately, these features are data-specific and may not always be available, e.g., when the rumor has just popped up and not yet propagated. In contrast, post contents (including images or videos) play an important role and can indicate the diffusion purpose of a rumor. Furthermore, rumor classification is also closely related to opinion mining and sentiment analysis. Yet, to the best of our knowledge, exploiting images and sentiments is little investigated.Considering the available multimodal features from microblogs, notably, we propose in this paper an end-to-end model called deepMONITOR that is based on deep neural networks and allows quite accurate automated rumor verification, by utilizing all three characteristics: post textual and image contents, as well as sentiment. deepMONITOR concatenates image features with the joint text and sentiment features to produce a reliable, fused classification. We conduct extensive experiments on two large-scale, real-world datasets. The results show that deepMONITOR achieves a higher accuracy than state-of-the-art methods.

cs.CL↗
arXiv2021

Multi-frequency MRE for elasticity quantitation and optimal tissue discrimination: a two-platform liver fibrosis mimicking phantom study

In the framework of algebraic inversion, Magnetic Resonance Elastography (MRE) repeatability, reproducibility and robustness were evaluated on extracted shear velocities (or elastic moduli). The same excitation system was implemented at two sites equipped with clinical MR scanners of 1.5 T and 3 T. A set of four elastic, isotropic, homogeneous calibrated phantoms of distinct elasticity representing the spectrum of liver fibrosis severity was mechanically characterized. The repeatability of the measurements and the reproducibility between the two platforms were found to be excellent with mean coefficients of variations of 1.62% for the shear velocity mean values and 1.95% for the associated standard deviations. MRE velocities were robust to the amplitude and pattern variations of the displacement field with virtually no difference between outcomes from both magnets at identical excitation frequencies even when the displacement field amplitude was 6 times smaller. However, MRE outcomes were very sensitive to the number of voxels per wavelength, s, of the recorded displacement field, with relative biases reaching 62% and precision losing up to a factor 23.5. For both magnetic field strengths, MRE accuracy and precision were largely degraded outside of established conditions of validity ($6 \lesssim s \lesssim 9$) resulting in estimated shear velocity values not significantly different between phantoms of increasing elasticity. When fulfilling the spatial sampling conditions, either prospectively in the acquisition or retrospectively before the reconstruction, MRE produced quantitative measurements that allowed to unambiguously discriminate, with infinitesimal p-values, between the phantoms mimicking increasing severity of liver fibrosis.

physics.med-ph↗
arXiv2021

MONITOR: A Multimodal Fusion Framework to Assess Message Veracity in Social Networks

Users of social networks tend to post and share content with little restraint. Hence, rumors and fake news can quickly spread on a huge scale. This may pose a threat to the credibility of social media and can cause serious consequences in real life. Therefore, the task of rumor detection and verification has become extremely important. Assessing the veracity of a social media message (e.g., by fact checkers) involves analyzing the text of the message, its context and any multimedia attachment. This is a very time-consuming task that can be much helped by machine learning. In the literature, most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. In this paper, we second the hypothesis that exploiting all of the components of a social media post enhances the accuracy of veracity detection. To further the state of the art, we first propose using a set of advanced image features that are inspired from the field of image quality assessment, which effectively contributes to rumor detection. These metrics are good indicators for the detection of fake images, even for those generated by advanced techniques like generative adversarial networks (GANs). Then, we introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features (i.e., text, social context, and image features) by supervised machine learning. Such algorithms provide interpretability and explainability in the decisions taken, which we believe is particularly important in the context of rumor verification. Experimental results show that MONITOR can detect rumors with an accuracy of 96% and 89% on the MediaEval benchmark and the FakeNewsNet dataset, respectively. These results are significantly better than those of state-of-the-art machine learning baselines.

cs.SI↗
arXiv2021

Joint Management and Analysis of Textual Documents and Tabular Data within the AUDAL Data Lake

In 2010, the concept of data lake emerged as an alternative to data warehouses for big data management. Data lakes follow a schema-on-read approach to provide rich and flexible analyses. However, although trendy in both the industry and academia, the concept of data lake is still maturing, and there are still few methodological approaches to data lake design. Thus, we introduce a new approach to design a data lake and propose an extensive metadata system to activate richer features than those usually supported in data lake approaches. We implement our approach in the AUDAL data lake, where we jointly exploit both textual documents and tabular data, in contrast with structured and/or semi-structured data typically processed in data lakes from the literature. Furthermore, we also innovate by leveraging metadata to activate both data retrieval and content analysis, including Text-OLAP and SQL querying. Finally, we show the feasibility of our approach using a real-word use case on the one hand, and a benchmark on the other hand.

cs.DB↗
arXiv2021

Implicit automata in typed $λ$-calculi II: streaming transducers vs categorical semantics

We characterize regular string transductions as programs in a linear $λ$-calculus with additives. One direction of this equivalence is proved by encoding copyless streaming string transducers (SSTs), which compute regular functions, into our $λ$-calculus. For the converse, we consider a categorical framework for defining automata and transducers over words, which allows us to relate register updates in SSTs to the semantics of the linear $λ$-calculus in a suitable monoidal closed category. To illustrate the relevance of monoidal closure to automata theory, we also leverage this notion to give abstract generalizations of the arguments showing that copyless SSTs may be determinized and that the composition of two regular functions may be implemented by a copyless SST. Our main result is then generalized from strings to trees using a similar approach. In doing so, we exhibit a connection between a feature of streaming tree transducers and the multiplicative/additive distinction of linear logic. Keywords: MSO transductions, implicit complexity, Dialectica categories, Church encodings

cs.LO↗
arXiv2021

ArchaeoDAL: A Data Lake for Archaeological Data Management and Analytics

With new emerging technologies, such as satellites and drones, archaeologists collect data over large areas. However, it becomes difficult to process such data in time. Archaeological data also have many different formats (images, texts, sensor data) and can be structured, semi-structured and unstructured. Such variety makes data difficult to collect, store, manage, search and analyze effectively. A few approaches have been proposed, but none of them covers the full data lifecycle nor provides an efficient data management system. Hence, we propose the use of a data lake to provide centralized data stores to host heterogeneous data, as well as tools for data quality checking, cleaning, transformation, and analysis. In this paper, we propose a generic, flexible and complete data lake architecture. Our metadata management system exploits goldMEDAL, which is the most complete metadata model currently available. Finally, we detail the concrete implementation of this architecture dedicated to an archaeological project.

cs.DB↗
arXiv2021

Cooling with a subsonic flow of quantum fluid

Miniature heaters are immersed in flows of quantum fluid and the efficiency of heat transfer is monitored versus velocity, superfluid fraction and time. The fluid is $^4$He helium with a superfluid fraction varied from 71% down to 0% and an imposed velocity up to 3 m/s, while the characteristic sizes of heaters range from 1.3 $μ$m up to few hundreds of microns. At low heat fluxes, no velocity dependence is observed. In contrast, some velocity dependence emerges at larger heat flux, as reported previously, and three non-trivial properties of heat transfer are identified. First, at the largest superfluid fraction (71%), a new heat transfer regime appears at non-null velocities and it is typically 10% less conductive than at zero velocity. Second, the velocity dependence of the mean heat transfer is compatible with the square-root dependence observed in classical fluids. Surprisingly, the prefactor to this dependence is maximum for an intermediate superfluid fraction or temperature (around 2 K). Third, the heat transfer time series exhibit highly conductive short-lived events. These \textit{cooling glitches} have a velocity-dependent characteristic time, which manifest itself as a broad and energetic peak in the spectrum of heat transfer time series, in the kHz range. After showing that the velocity dependence can be attributed to the breaking of superfluidity within a thin shell surrounding heaters, an analytical model of forced heat transfer in a quantum flow is developed to account for the properties reported above. We argue that large scale flow patterns must form around the heater, having a size proportional to the heat flux (here two decades larger than the heater diameter) and resulting in a turbulent wake. The observed spectral peaking of heat transfer is quantitatively consistent with the formation of a Von Kármán vortex street in the wake of a bluff body.

physics.flu-dyn↗
arXiv2021

Coining goldMEDAL: A New Contribution to Data Lake Generic Metadata Modeling

The rise of big data has revolutionized data exploitation practices and led to the emergence of new concepts. Among them, data lakes have emerged as large heterogeneous data repositories that can be analyzed by various methods. An efficient data lake requires a metadata system that addresses the many problems arising when dealing with big data. In consequence, the study of data lake metadata models is currently an active research topic and many proposals have been made in this regard. However, existing metadata models are either tailored for a specific use case or insufficiently generic to manage different types of data lakes, including our previous model MEDAL. In this paper, we generalize MEDAL's concepts in a new metadata model called goldMEDAL. Moreover, we compare goldMEDAL with the most recent state-of-the-art metadata models aiming at genericity and show that we can reproduce these metadata models with goldMEDAL's concepts. As a proof of concept, we also illustrate that goldMEDAL allows the design of various data lakes by presenting three different use cases.

cs.DB↗
arXiv2021

RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020

This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies on Riemannian Geometry, with a twist. Instead of using the classical covariance matrices, we also rely on measures of functional connectivity. Our approach ranked 1st on the task 1 of the competition.

eess.SP↗
arXiv2021

Towards modelling Ghostly DLAs

We use simple models of the spatial structure of the quasar broad line region (BLR) to investigate the properties of so-called ghostly damped Lyman-α (DLA) systems detected in SDSS data. These absorbers are characterized by the presence of strong metal lines but no Hi Lyman-α trough is seen in the quasar spectrum indicating that, although the region emitting the quasar continuum is covered by an absorbing cloud, the BLR is only partially covered. One of the models has a spherical geometry, another one is the combination of two wind flows whereas the third model is a Keplerian disk. The models can reproduce the typical shape of the quasar Lyman-α emission and different ghostly configurations. We show that the DLA Hi column density can be recovered precisely independently of the BLR model used. The size of the absorbing cloud and its distance to the centre of the AGN are correlated. However it may be possible to disentangle the two using an independent estimate of the radius from the determination of the particle density. Comparison of the model outputs with SDSS data shows that the wind and disk models are more versatile than the spherical one and can be more easily adapted to the observations. For all the systems we derive logN(Hi)(cm^{-2})>20.5. With higher quality data it may be possible to distinguish between the models.

astro-ph.GA↗
arXiv2020

Compact reduction in Lipschitz free spaces

We prove a general principle satisfied by weakly precompact sets of Lipschitz-free spaces. By this principle, certain infinite dimensional phenomena in Lipschitz-free spaces over general metric spaces may be reduced to the same phenomena in free spaces over their compact subsets. As easy consequences we derive several new and some known results. The main new results are: $\mathcal F(X)$ is weakly sequentially complete for every superreflexive Banach space $X$, and $\mathcal F(M)$ has the Schur property and the approximation property for every scattered complete metric space $M$.

math.FA↗
arXiv2020

An FFT-solver used for virtual Dynamic Mechanical Analysis experiments: Application on a glassy/amorphous system and on a particulate composite

FFT-based solvers are increasingly used by many researcher groups interested in modelling the mechanical behavior associated to a heterogeneous microstructure. A development is reported here that concerns the viscoelastic behavior of composite structures generally studied experimentally through Dynamic Mechanical Analysis (DMA). A parallelized computation code developed under complex-valued quantities provides virtual DMA experiments directly in the frequency domain on a heterogenous system described by a voxel grid of mechanical properties. The achieved precision and computation times are very good. An effort has been made to show the application of such virtual DMA tool starting from two examples found in the literature: the modelling of glassy/amorphous systems at a small scale and the modelling of experimental data obtained in temperature sweeping mode by DMA on a particulate composite made of glass beads and a polystyrene matrix, at a larger scale. Both examples show how virtual DMA can contribute to question, analyze, understand relaxation phenomena either on the theoretical or experimental point of view.

physics.class-ph↗
arXiv2020

Data Lakes for Digital Humanities

Traditional data in Digital Humanities projects bear various formats (structured, semi-structured, textual) and need substantial transformations (encoding and tagging, stemming, lemmatization, etc.) to be managed and analyzed. To fully master this process, we propose the use of data lakes as a solution to data siloing and big data variety problems. We describe data lake projects we currently run in close collaboration with researchers in humanities and social sciences and discuss the lessons learned running these projects.

cs.DB↗
arXiv2020

The ping pong ball water cannon

The course "Phy Ex" was created by Yves Couder in the Paris VII university to teach experimental physics through projects. In this article, we present this teaching method through a particular project that took place in the autumn semester 2019: the ping-pong ball water cannon. In this experiment, a glass containing water and a floating table tennis ball is dropped from some height to the ground. Following the impact, the ball is ejected vertically upwards at speeds that can be several times the impact speed. We report the student team's initial dimensional and order-of-magnitude analysis, and describe the successive experimental setups that showed (1) that free flight is essential for the phenomenon to occur, (2) that the order of magnitude of the ball ejection momentum is correctly predicted by a momentum balance based on integrating the pressure impulse during impact and (3) that making the ball surface more wettable, or stirring the liquid, drastically increases the momentum transfer. The proposed explanation, confirmed by direct high-speed video observations, is that the immersion depth of the ball increases during free fall due to capillary forces or vortex depression-in the absence of buoyancy-and that the enormous excess pressure on the bottom of the ball during impact drives the ball up towards its buoyancy equilibrium. The transfered momentum is sufficient to expel the ball at high velocity, very similar to the formation of liquid jets in collapsing cavities in liquids.

physics.ed-ph↗