Electron-attachment rates for carbon-rich molecules in protoplanetary atmospheres: the role of chemical differences

The formation of anionic species in the interstellar medium from interaction of linear molecules containing carbon, nitrogen and hydrogen as atomic components (polyynes) with free electrons in the environment is modelled via a quantum treatment of the collision dynamics. The ensuing integral cross sections are employed to obtain the corresponding attachment rates over a broad range of temperatures for the electrons. The calculations unequivocally show that a parametrization form often employed for such rates yields a broad range of values that turn out to be specific for each molecular species considered, thus excluding using a unique set for the whole class of polyynes.Comment: accepted to be published on MNRA

The power of the Monstrous: An introduction to the special issue

Alterity and Otherness have often been the privileged field of contemplation within Western philosophy. Since the Presocratic philosophers, Being has been defined in relation to – and more often opposed to – non-Being, just as Goodness has been considered in relation to Evil, Beauty in relation to the Ugly, Society in relation to Nature, and the examples could be multiplied ad libitum. Every identity is shaped in opposition to an excluded other, an outside, or some thing. Identity and alterity are thus constructed as two inseparable sides of a single, coherent philosophical discourse, or rather a field of various discourses that comprise a philosophy, associated with - although not limited to - the early centuries of what we call modernity

Ancient Legislators in Modern Thought: Guest Editors’ Preface

Non present

SOUR: an Outliers Detection Algorithm in Learning to Rank (Abstract)

Outlier data points are known to affect negatively the learning process of regression or classification models, yet their impact in the learning-to-rank scenario has not been thoroughly investigated so far. In this talk we present our effort to solve this research problem. The full version of this work will appear at ICTIR 2022 [1]. We designed SOUR, a learning-to-rank method that detects and removes outliers before building an effective ranking model. We limit our analysis to gradient boosting decision trees, but our algorithm can be easily adapted to handle different learning strategy, such as artificial Neural Network. SOUR searches for outlier instances that are consistently incorrectly ranked in several consecutive iterations of the learning process. We performed an extensive evaluation analysis on three publicly available datasets and we empirically demonstrated that i) removing a limited number of outlier data instances before re-training a new model, provides statistically significant improvements in term of effectiveness ii) SOUR outperforms state-of-the-art de-noising and outlier detection methods such as [2]. Finally, we investigated how the removal of the outliers affects the ensemble structure and we found that the ensemble leaves were purer when trained without the presence of the outliers

Filtering out Outliers in Learning to Rank

Outlier data points are known to affect negatively the learning process of regression or classification models, yet their impact in the learning-to-rank scenario has not been thoroughly investigated so far. In this work we propose SOUR, a learning-to-rank method that detects and removes outliers before building an effective ranking model. We limit our analysis to gradient boosting decision trees, where SOUR searches for outlier instances that are incorrectly ranked in several iterations of the learning process. Extensive experiments show that removing a limited number of outlier data instances before re-training a new model provides statistically significant improvements, and that SOUR outperforms state-of-the-art de-noising and outlier detection methods

LambdaRank Gradients are Incoherent

In Information Retrieval (IR), the Learning-to-Rank (LTR) task requires building a ranking model that optimises a specific IR metric. One of the most effective approaches to do so is the well-known LambdaRank algorithm. LambdaRank uses gradient descent optimisation, and at its core, it defines approximate gradients, the so-called lambdas, for a non-differentiable IR metric. Intuitively, each lambda describes how much a document's score should be pushed up/down to reduce the ranking error. In this work, we show that lambdas may be incoherent w.r.t. the metric being optimised: e.g., a document with high relevance in the ground truth may receive a smaller gradient push than a document with lower relevance. This behaviour goes far beyond the expected degree of approximation. We analyse such behaviour of LambdaRank gradients and we introduce some strategies to reduce their incoherencies. We demonstrate through extensive experiments, conducted using publicly available datasets, that the proposed approach reduces the frequency of the incoherencies in LambdaRank and derivatives, and leads to models that achieve statistically significant improvements in the NDCG metric, without compromising the training efficiency

Freedom, equality and conflict: Rousseau on Machiavelli

Rousseau’s praise for Machiavelli in the Social Contract goes along with his condemnation of partial association and political conflicts. Yet Machiavelli builds his theory precisely around the idea of the constructive role of conflicts, seeing the irreducible multiplicity of the many as the source of a positive conflictuality. Is the ontological primacy of Rousseau’s singularity in the general will compatible with the political primacy of Machiavelli’s conflictual multiplicity? By exploring Rousseau’s strategy in his use of Machiavelli, I will argue that the key to interpreting the ambiguities of Rousseau’s reading lies in the evaluation of the differences in the relationship between multiplicity and singularity in both authors. While the people produces an immanent and conflictualistic ground for power in Machiavelli, in Rousseau it is subjected to a transcendent process of ontological submission to the general will

Nature and laws: teleology, teratology and transcendence in Montesquieu

Non. present