Bien (philosophie) | EPFL Graph Search

vignette|La Bonté (1839). Plâtre (Surmoulage) au musée François Rude de Dijon. Le Bien est, avec l'Un et le Vrai, l'un des trois transcendantaux de la Scolastique ; il est surtout de nos jours la valeur normative de la morale, avec comme opposé le Mal. La détermination de ce qui est bien ou mal, à distinguer de ce qui fait du bien, de ce qui fait mal, peut se faire dans le cadre des règles de civilité, de l'honneur, de l'utilité collective, de l'intérêt public, ou au contraire particulier. Ces différents ordres peuvent être en contradiction : ce qui est bien dans un domaine, peut ne pas l'être sur un autre plan ; on parle alors soit de dilemme, soit de conflit d'intérêts. Depuis l'origine de la pensée philosophique, des accentuations différentes sur le sens prédominant de la notion de Bien ont été successivement mises en avant comme le montre le plan du Dictionnaire des concepts philosophiques. Au point de vue d'une philosophie, la signification et la pertinence même des concepts de b

Optimal Classification In Sparse Gaussian Graphic Model

Zhigang Yao

Consider a two-class classification problem where the number of features is much larger than the sample size. The features are masked by Gaussian noise with mean zero and covariance matrix Sigma, where the precision matrix Omega = Sigma(-1) is unknown but is presumably sparse. The useful features, also unknown, are sparse and each contributes weakly (i.e., rare and weak) to the classification decision. By obtaining a reasonably good estimate of Omega, we formulate the setting as a linear regression model. We propose a two-stage classification method where we first select features by the method of Innovated Thresholding (IT), and then use the retained features and Fisher's LDA for classification. In this approach, a crucial problem is how to set the threshold of IT. We approach this problem by adapting the recent innovation of Higher Criticism Thresholding (HCT). We find that when useful features are rare and weak, the limiting behavior of HCT is essentially just as good as the limiting behavior of ideal threshold, the threshold one would choose if the underlying distribution of the signals is known (if only). Somewhat surprisingly, when Omega is sufficiently sparse, its off-diagonal coordinates usually do not have a major influence over the classification decision. Compared to recent work in the case where Omega is the identity matrix [Proc. Natl. Acad. Sci. USA 105 (2008) 14790-14795; Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 367 (2009) 4449-4470], the current setting is much more general, which needs a new approach and much more sophisticated analysis. One key component of the analysis is the intimate relationship between HCT and Fisher's separation. Another key component is the tight large-deviation bounds for empirical processes for data with unconventional correlation structures, where graph theory on vertex coloring plays an important role.

Institute of Mathematical Statistics2013

Adaptive Lévy Taxis for Odor Source Localization in Realistic Environmental Conditions

Romain Jean-Paul Emery, Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar

Odor source localization with mobile robots has recently been subject to many research works, but remains a challenging task mainly due to the large number of environmental parameters that make it hard to describe gas concentration fields. We designed a new algorithm called Adaptive Lévy Taxis (ALT) to achieve odor plume tracking through a correlated random walk. In order to compare its performances with well-established solutions, we have implemented three moth-inspired algorithms on the same robotic platform. To improve the performance of the latter algorithms, we developed a rigorous way to determine one of their key parameters, the odor concentration threshold at which the robot considers to be inside or outside the plume. The methods have been systematically evaluated in a large wind tunnel under various environmental conditions. Experiments revealed that the performance of ALT is consistently good in all environmental conditions (in particular when compared to the three reference algorithms) in terms of both distance traveled to find the source and success rate.

2017

Relationship between estrogen receptor alpha location and gene induction reveals the importance of downstream sites and cofactors

Mauro Delorenzi, Felix Naef

BACKGROUND: To understand cancer-related modifications to transcriptional programs requires detailed knowledge about the activation of signal-transduction pathways and gene expression programs. To investigate the mechanisms of target gene regulation by human estrogen receptor alpha (hERalpha), we combine extensive location and expression datasets with genomic sequence analysis. In particular, we study the influence of patterns of DNA occupancy by hERalpha on expression phenotypes. RESULTS: We find that strong ChIP-chip sites co-localize with strong hERalpha consensus sites and detect nucleotide bias near hERalpha sites. The localization of ChIP-chip sites relative to annotated genes shows that weak sites are enriched near transcription start sites, while stronger sites show no positional bias. Assessing the relationship between binding configurations and expression phenotypes, we find binding sites downstream of the transcription start site (TSS) to be equally good or better predictors of hERalpha-mediated expression as upstream sites. The study of FOX and SP1 cofactor sites near hERalpha ChIP sites shows that induced genes frequently have FOX or SP1 sites. Finally we integrate these multiple datasets to define a high confidence set of primary hERalpha target genes. CONCLUSION: Our results support the model of long-range interactions of hERalpha with the promoter-bound cofactor SP1 residing at the promoter of hERalpha target genes. FOX motifs co-occur with hERalpha motifs along responsive genes. Importantly we show that the spatial arrangement of sites near the start sites and within the full transcript is important in determining response to estrogen signaling.

BioMed Central2009

Optimal Classification In Sparse Gaussian Graphic Model

Zhigang Yao

Institute of Mathematical Statistics2013