Zobrazeno 1 - 10
of 4 644
pro vyhledávání: '"P Maréchal"'
Autor:
Monnet, Nathan, Maréchal, Loïc
We introduce a novel approach to text classification by combining doc2vec embeddings with advanced clustering techniques to improve the analysis of specialized, high-dimensional textual data. We integrate unsupervised methods such as Louvain, K-means
Externí odkaz:
http://arxiv.org/abs/2410.11573
Autor:
Maréchal, Loïc, Monnet, Nathan
We use a methodology based on a machine learning algorithm to quantify firms' cyber risks based on their disclosures and a dedicated cyber corpus. The model can identify paragraphs related to determined cyber-threat types and accordingly attribute se
Externí odkaz:
http://arxiv.org/abs/2409.08728
Autor:
Borges, Beatriz, Foroutan, Negar, Bayazit, Deniz, Sotnikova, Anna, Montariol, Syrielle, Nazaretzky, Tanya, Banaei, Mohammadreza, Sakhaeirad, Alireza, Servant, Philippe, Neshaei, Seyed Parsa, Frej, Jibril, Romanou, Angelika, Weiss, Gail, Mamooler, Sepideh, Chen, Zeming, Fan, Simin, Gao, Silin, Ismayilzada, Mete, Paul, Debjit, Schöpfer, Alexandre, Janchevski, Andrej, Tiede, Anja, Linden, Clarence, Troiani, Emanuele, Salvi, Francesco, Behrens, Freya, Orsi, Giacomo, Piccioli, Giovanni, Sevel, Hadrien, Coulon, Louis, Pineros-Rodriguez, Manuela, Bonnassies, Marin, Hellich, Pierre, van Gerwen, Puck, Gambhir, Sankalp, Pirelli, Solal, Blanchard, Thomas, Callens, Timothée, Aoun, Toni Abi, Alonso, Yannick Calvino, Cho, Yuri, Chiappa, Alberto, Sclocchi, Antonio, Bruno, Étienne, Hofhammer, Florian, Pescia, Gabriel, Rizk, Geovani, Dadi, Leello, Stoffl, Lucas, Ribeiro, Manoel Horta, Bovel, Matthieu, Pan, Yueyang, Radenovic, Aleksandra, Alahi, Alexandre, Mathis, Alexander, Bitbol, Anne-Florence, Faltings, Boi, Hébert, Cécile, Tuia, Devis, Maréchal, François, Candea, George, Carleo, Giuseppe, Chappelier, Jean-Cédric, Flammarion, Nicolas, Fürbringer, Jean-Marie, Pellet, Jean-Philippe, Aberer, Karl, Zdeborová, Lenka, Salathé, Marcel, Jaggi, Martin, Rajman, Martin, Payer, Mathias, Wyart, Matthieu, Gastpar, Michael, Ceriotti, Michele, Svensson, Ola, Lévêque, Olivier, Ienne, Paolo, Guerraoui, Rachid, West, Robert, Kashyap, Sanidhya, Piazza, Valerio, Simanis, Viesturs, Kuncak, Viktor, Cevher, Volkan, Schwaller, Philippe, Friedli, Sacha, Jermann, Patrick, Käser, Tanja, Bosselut, Antoine
Publikováno v:
PNAS (2024) Vol. 121 | No. 49
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes.
Externí odkaz:
http://arxiv.org/abs/2408.11841
Mechanical degradation in electrode materials during successive electrochemical cycling is critical for battery lifetime and aging properties. A common strategy to mitigate electrode mechanical degradation is to suppress the volume variation induced
Externí odkaz:
http://arxiv.org/abs/2406.04939
The Paris agreement is the first-ever universally accepted and legally binding agreement on global climate change. It is a bridge between today's and climate-neutrality policies and strategies before the end of the century. Critical to this endeavor
Externí odkaz:
http://arxiv.org/abs/2402.12973
Autor:
Celeny, Daniel, Maréchal, Loïc
We extract firms' cyber risk with a machine learning algorithm measuring the proximity between their disclosures and a dedicated cyber corpus. Our approach outperforms dictionary methods, uses full disclosure and not devoted-only sections, and genera
Externí odkaz:
http://arxiv.org/abs/2402.04775
Along with the increasing frequency and severity of cyber incidents, understanding their economic implications is paramount. In this context, listed firms' reactions to cyber incidents are compelling to study since they (i) are a good proxy to estima
Externí odkaz:
http://arxiv.org/abs/2402.04773
Early-stage firms play a significant role in driving innovation and creating new products and services, especially for cybersecurity. Therefore, evaluating their performance is crucial for investors and policymakers. This work presents a financial ev
Externí odkaz:
http://arxiv.org/abs/2402.04765
We have developed a poly(dimethylsiloxane) (PDMS) microfluidic chip to study the directional drying of a colloidal dispersion confined in a channel. Our measurements on a dispersion of silica nanoparticles once again revealed the phenomenology common
Externí odkaz:
http://arxiv.org/abs/2401.05139
Publikováno v:
Modelling Simul. Mater. Sci. Eng. 32, 055005 (2024)
The Swift-Hohenberg (SH) and Phase-Field Crystal (PFC) models are minimal yet powerful approaches for studying phenomena such as pattern formation, collective order, and defects via smooth order parameters. They are based on a free-energy functional
Externí odkaz:
http://arxiv.org/abs/2312.08154