Opening Speech

Thailand Phi Delta Kappa 25th AnniversaryRev. Bro. Prathip M. Komolmas, f.s.g., Ph.D.President Emeritus, Assumption Universit

Atmospheric forcing by ALADIN/MFSTEP and MFSTEP oriented tunings

International audienceALADIN/MFSTEP is a configuration of the numerical weather prediction (NWP) model ALADIN run in a dedicated real-time mode for the purposes of the MFSTEP Project. A special attention was paid to the quality of atmospheric fluxes used for the forcing of fine-scale oceanographic models. This paper describes the novelties applied in ALADIN/MFSTEP initiated by the MFSTEP demands, leading also to improvements in general weather forecasting

Classification Methods of Multiway Arrays as a Basic Tool for Food PDO Authentication

Food chain traceability, identification of adulterations, and the control of labeling compliance are topic that require the evaluation of the foodstuff in its entirety: in this respect, more and more researchers are investigating the possibility of using multidimensional or hyphenated techniques for the fingerprinting of the products. However, these techniques produce data structures that are multidimensional as well and that require proper chemometric approaches for data processing (multi-way data analysis). In this Chapter, the state-of-the-art approaches for the classification of multiway data will be discussed theoretically and compared on case studies coming form the food authenticity context, such as the traceability of extra virgin olive oils of protected denomination of origin and table wines

Approximating a Wavefunction as an Unconstrained Sum of Slater Determinants

The wavefunction for the multiparticle Schr\"odinger equation is a function of many variables and satisfies an antisymmetry condition, so it is natural to approximate it as a sum of Slater determinants. Many current methods do so, but they impose additional structural constraints on the determinants, such as orthogonality between orbitals or an excitation pattern. We present a method without any such constraints, by which we hope to obtain much more efficient expansions, and insight into the inherent structure of the wavefunction. We use an integral formulation of the problem, a Green's function iteration, and a fitting procedure based on the computational paradigm of separated representations. The core procedure is the construction and solution of a matrix-integral system derived from antisymmetric inner products involving the potential operators. We show how to construct and solve this system with computational complexity competitive with current methods.Comment: 30 page

Untargeted metabolomic profile for the detection of prostate carcinoma-preliminary results from PARAFAC2 and PLS-DA Models

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares–discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach

Indicators of dietary patterns in Danish infants at 9 months of age

Background: It is important to increase the awareness of indicators associated with adverse infant dietary patterns to be able to prevent or to improve dietary patterns early on. Objective: The aim of this study was to investigate the association between a wide range of possible family and child indicators and adherence to dietary patterns for infants aged 9 months. Design: The two dietary patterns ‘Family Food’ and ‘Health-Conscious Food’ were displayed by principal component analysis, and associations with possible indicators were analysed by multiple linear regressions in a pooled sample (n=374) of two comparable observational cohorts, SKOT I and SKOT II. These cohorts comprised infants with mainly non-obese mothers versus infants with obese mothers, respectively. Results: A lower Family Food score indicates a higher intake of liquid baby food, as this pattern shows transition from baby food towards the family's food. Infants, who were younger at diet registration and had higher body mass index (BMI) z-scores at 9 months, had lower Family Food pattern scores. A lower Family Food pattern score was also observed for infants with immigrant/descendant parents, parents who shared cooking responsibilities and fathers in the labour market compared to being a student, A lower Health-Conscious Food pattern score indicates a less healthy diet. A lower infant Health-Conscious Food pattern score was associated with a higher maternal BMI, a greater number of children in the household, a higher BMI z-score at 9 months, and a higher infant age at diet registration. Conclusions: Associations between infant dietary patterns and maternal, paternal, household, and child characteristics were identified. This may improve the possibility of identifying infants with an increased risk of developing unfavourable dietary patterns and potentially enable an early targeted preventive support

Education in the working-class home: modes of learning as revealed by nineteenth-century criminal records

The transmission of knowledge and skills within the working-class household greatly troubled social commentators and social policy experts during the first half of the nineteenth century. To prove theories which related criminality to failures in working-class up-bringing, experts and officials embarked upon an ambitious collection of data on incarcerated criminals at various penal institutions. One such institution was the County Gaol at Ipswich. The exceptionally detailed information that survives on families, literacy, education and apprenticeships of the men, women and children imprisoned there has the potential to transform our understanding of the nature of home schooling (broadly interpreted) amongst the working classes in nineteenth-century England. This article uses data sets from prison registers to chart both the incidence and ‘success’ of instruction in reading and writing within the domestic environment. In the process, it highlights the importance of schooling in working-class families, but also the potentially growing significance of the family in occupational training

Scalable Tensor Factorizations for Incomplete Data

The problem of incomplete data - i.e., data with missing or unknown values - in multi-way arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics, social network analysis, chemometrics, computer vision, communication networks, etc. We consider the problem of how to factorize data sets with missing values with the goal of capturing the underlying latent structure of the data and possibly reconstructing missing values (i.e., tensor completion). We focus on one of the most well-known tensor factorizations that captures multi-linear structure, CANDECOMP/PARAFAC (CP). In the presence of missing data, CP can be formulated as a weighted least squares problem that models only the known entries. We develop an algorithm called CP-WOPT (CP Weighted OPTimization) that uses a first-order optimization approach to solve the weighted least squares problem. Based on extensive numerical experiments, our algorithm is shown to successfully factorize tensors with noise and up to 99% missing data. A unique aspect of our approach is that it scales to sparse large-scale data, e.g., 1000 x 1000 x 1000 with five million known entries (0.5% dense). We further demonstrate the usefulness of CP-WOPT on two real-world applications: a novel EEG (electroencephalogram) application where missing data is frequently encountered due to disconnections of electrodes and the problem of modeling computer network traffic where data may be absent due to the expense of the data collection process