Letteratura scientifica selezionata sul tema "Corrélations parasites"
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Articoli di riviste sul tema "Corrélations parasites":
Ibrahim, A. A., S. T. Mbap, T. Ibrahim e Y. P. Mancha. "Variation in helminth susceptibility of indigenous chickens in Kano and Jigawa States of Nigeria". Nigerian Journal of Animal Production 48, n. 5 (10 novembre 2021): 51–68. http://dx.doi.org/10.51791/njap.v48i5.3187.
Pozio, E., P. Rossi e M. Amati. "Épidémiologie de la trichinellose en Italie : Corrélation entre le cycle sauvage et l’homme". Annales de Parasitologie Humaine et Comparée 62, n. 5 (1987): 456–61. http://dx.doi.org/10.1051/parasite/1987625456.
Ngotta Biyon, Jacques Bruno, Arriane Barbara Iyodi, Yves Donald Wafo Tchoue, JosephMarie Ondoua e Victor Désiré Taffouo. "Parasitisme des Loranthaceae sur Theobroma cacao L. (Malvaceae) dans l’arrondissement de Tombel (Sud-Ouest Cameroun)". International Journal of Biological and Chemical Sciences 16, n. 3 (28 agosto 2022): 1113–22. http://dx.doi.org/10.4314/ijbcs.v16i3.17.
MANDONNET, N., G. AUMONT, J. FLEURY, L. GRUNER, J. BOUIX, J. VU TIEN KHANG e H. VARO. "Résistance aux strongles gastro-intestinaux des caprins. Influence de différents environnements tropicaux sur l’expression du potentiel génétique de résistance". INRAE Productions Animales 10, n. 1 (7 febbraio 1997): 91–98. http://dx.doi.org/10.20870/productions-animales.1997.10.1.3980.
Benhamou, N., e K. Picard. "La résistance induite : une nouvelle stratégie de défense des plantes contre les agents pathogènes". Article de synthèse 80, n. 3 (12 aprile 2005): 137–68. http://dx.doi.org/10.7202/706189ar.
Tesi sul tema "Corrélations parasites":
Ouro, Bodi Dissadama. "Etude des effets parasites du transistor à haute mobilité électronique : corrélation avec les aspects technologiques et la fiabilité". Bordeaux 1, 1992. http://www.theses.fr/1992BOR10643.
Bose, Tulika. "Transfer learning for abusive language detection". Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0019.
The proliferation of social media, despite its multitude of benefits, has led to the increased spread of abusive language. Such language, being typically hurtful, toxic, or prejudiced against individuals or groups, requires timely detection and moderation by online platforms. Deep learning models for detecting abusive language have displayed great levels of in-corpus performance but underperform substantially outside the training distribution. Moreover, they require a considerable amount of expensive labeled data for training.This strongly encourages the effective transfer of knowledge from the existing annotated abusive language resources that may have different distributions to low-resource corpora. This thesis studies the problem of transfer learning for abusive language detection and explores various solutions to improve knowledge transfer in cross-corpus scenarios.First, we analyze the cross-corpus generalizability of abusive language detection models without accessing the target during training. We investigate if combining topic model representations with contextual representations can improve generalizability. The association of unseen target comments with abusive language topics in the training corpus is shown to provide complementary information for a better cross-corpus transfer.Secondly, we explore Unsupervised Domain Adaptation (UDA), a type of transductive transfer learning, with access to the unlabeled target corpus. Some popular UDA approaches from sentiment classification are analyzed for cross-corpus abusive language detection. We further adapt a BERT model variant to the unlabeled target using the Masked Language Model (MLM) objective. While the latter improves the cross-corpus performance, the other UDA methods perform sub-optimally. Our analysis reveals their limitations and emphasizes the need for effective adaptation methods suited to this task.As our third contribution, we propose two DA approaches using feature attributions, which are post-hoc model explanations. Particularly, the problem of spurious corpus-specific correlations is studied that restrict the generalizability of classifiers for detecting hate speech, a sub-category of abusive language. While the previous approaches rely on a manually curated list of terms, we automatically extract and penalize the terms causing spurious correlations. Our dynamic approaches improve the cross-corpus performanceover previous works both independently and in combination with pre-defined dictionaries.Finally, we consider transferring knowledge from a resource-rich source to a low-resource target with fewer labeled instances, across different online platforms. A novel training strategy is proposed, which allows flexible modeling of the relative proximity of neighbors retrieved from the resource-rich corpus to learn the amount of transfer. We incorporate neighborhood information with Optimal Transport that permits exploitingthe embedding space geometry. By aligning the joint embedding and label distributions of neighbors, substantial improvements are obtained in low-resource hate speech corpora
Capitoli di libri sul tema "Corrélations parasites":
"5. Infection chronique : corrélations avec certaines pathologies cérébrales". In Un parasite à la conquête du cerveau, 103–24. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-2048-1-007.
"5. Infection chronique : corrélations avec certaines pathologies cérébrales". In Un parasite à la conquête du cerveau, 103–24. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-2048-1.c007.