On the reconstruction of diagonal elements of density matrix of quantum optical states by on/off detectors

We discuss a scheme for reconstructing experimentally the diagonal elements of the density matrix of quantum optical states. Applications to PDC heralded photons, multi-thermal and attenuated coherent states are illustrated and discussed in some details.Comment: 10 pages, presented at Palermo "TQMFA2005" Conference. To appear on "Open Systems & Information Dynamics" (2006

Leveraging full-text article exploration for citation analysis

Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases

Predicting student academic performance by means of associative classification

The Learning Analytics community has recently paid particular attention to early predict learners’ performance. An established approach entails training classification models from past learner-related data in order to predict the exam success rate of a student well before the end of the course. Early predictions allow teachers to put in place targeted actions, e.g., supporting at-risk students to avoid exam failures or course dropouts. Although several machine learning and data mining solutions have been proposed to learn accurate predictors from past data, the interpretability and explainability of the best performing models is often limited. Therefore, in most cases, the reasons behind classifiers’ decisions remain unclear. This paper proposes an Explainable Learning Analytics solution to analyze learner-generated data acquired by our technical university, which relies on a blended learning model. It adopts classification techniques to early predict the success rate of about 5000 students who were enrolled in the first year courses of our university. It proposes to apply associative classifiers at different time points and to explore the characteristics of the models that led to assign pass or fail success rates. Thanks to their inherent interpretability, associative models can be manually explored by domain experts with the twofold aim at validating classifier outcomes through local rule-based explanations and identifying at-risk/successful student profiles by interpreting the global rule-based model. The results of an in-depth empirical evaluation demonstrate that associative models (i) perform as good as the best performing classification models, and (ii) give relevant insights into the per-student success rate assignments

Adulteration of essential oils: A multitask issue for quality control. three case studies: Lavandula angustifolia mill., Citrus limon (l.) Osbeck and Melaleuca alternifolia (Maiden & Betche) Cheel

The quality control of essential oils (EO) principally aims at revealing the presence of adulterations and at quantifying compounds that are limited by law by evaluating EO chemical compositions, usually in terms of the normalised relative abundance of selected markers, for comparison to reference values reported in pharmacopoeias and/or international norms. Common adulterations of EO consist of the addition of cheaper EO or synthetic materials. This adulteration can be detected by calculating the percent normalised areas of selected markers or the enantiomeric composition of chiral components. The dilution of the EO with vegetable oils is another type of adulteration. This adulteration is quite devious, as it modifies neither the qualitative composition of the resulting EO nor the marker’s normalised percentage abundance, which is no longer diagnostic, and an absolute quantitative analysis is required. This study aims at verifying the application of the two above approaches (i.e., normalised relative abundance and absolute quantitation) to detect EO adulterations, with examples involving selected commercial EO (lavender, bergamot and tea tree) adulterated with synthetic components, EO of different origin and lower economical values and heavy vegetable oils. The results show that absolute quantitation is necessary to highlight adulteration with heavy vegetable oils, providing that a reference quantitative profile is available

Minimization of defects generation in laser welding process of steel alloy for automotive application

Laser welding (LW) thanks to its flexibility, limited energy consumption and simple realization has a prominent role in several industrial sectors. LW process requires careful parameters' tuning to avoid generating internal defects in the microstructure or a poor weld depth, which reduce the joining mechanical strength and result in waste. This work exploits a supervised machine learning algorithm to optimize the process parameters to minimize the generated defects, while catering for design specifications and tolerances to predict defect generation probability. The work outputs a predictive quality control model to reduce non-destructive controls in the LW of aluminum for automotive applications