What's wrong with classes? The theory of Knowledge

This paper wants to investigate the deepest meaning of the word class that is often used in machine learning and classification as a well-defined concept. This adventure will lead the reader to the fundamentals of Mathematics like set theory from Zermelo-Fraenkel. This will be our start, like is all Mathematics, to understand how well defined is the class concept. A broader theory will be outlined with the courageous attempt to give an homogenous framework to deal with machine learning problems

Scale Object Selection (SOS) through a hierarchical segmentation by a multi-spectral per-pixel classification

International audienceIn high resolution multispectral optical data, the spatial detail of the images are generally smaller than the dimensions of objects, and often the spectral signature of pixels is not directly representative of classes we are interested in. Thus, taking into account the relations between groups of pixels becomes increasingly important, making object­oriented approaches preferable. In this work several scales of detail within an image are considered through a hierarchical segmentation approach, while the spectral information content of each pixel is accounted for by a per­pixel classification. The selection of the most suitable spatial scale for each class is obtained by merging the hierarchical segmentation and the per­pixel classification through the Scale Object Selection (SOS) algorithm. The SOS algorithm starts processing data from the highest level of the hierarchical segmentation, which has the least amount of spatial detail, down to the last segmentation map. At each segmentation level, objects are assigned to a specific class whenever the percentage of pixels belonging to the latter, according to a pixel­based procedure, exceeds a predefined threshold, thereby automatically selecting the most appropriate spatial scale for the classification of each object. We apply our method to multispectral, panchromatic and pan­sharpened QuickBird images

Student Sliced Inverse Regression

International audienceSliced Inverse Regression (SIR) has been extensively used to reduce the dimension of the predictor space before performing regression. SIR is originally a model free method but it has been shown to actually correspond to the maximum likelihood of an inverse regression model with Gaussian errors. This intrinsic Gaussianity of standard SIR may explain its high sensitivity to outliers as observed in a number of studies. To improve robustness, the inverse regression formulation of SIR is therefore extended to non-Gaussian errors with heavy-tailed distributions. Considering Student distributed errors it is shown that the inverse regression remains tractable via an Expectation- Maximization (EM) algorithm. The algorithm is outlined and tested in the presence of outliers, both in simulated and real data, showing improved results in comparison to a number of other existing approaches

Collaborative Sliced Inverse Regression

International audienceSliced Inverse Regression (SIR) is an effective method for dimensionality reduction in high-dimensional regression problems. However, the method has requirements on the distribution of the predictors that are hard to check since they depend on unobserved variables. It has been shown that, if the distribution of the predictors is elliptical, then these requirements are satisfied.In case of mixture models, the ellipticity is violated and in addition there is no assurance of a single underlying regression model among the different components. Our approach clusterizes the predictors space to force the condition to hold on each cluster and includes a merging technique to look for different underlying models in the data. A study on simulated data as well as two real applications are provided. It appears that SIR, unsurprisingly, is not capable of dealing with a mixture of Gaussians involving different underlying models whereas our approach is able to correctly investigate the mixture

The role of diallyl thiosulfinate associated with nuciferine and diosgenin in the treatment of premature ejaculation: a pilot study

Objective: To assess the efficacy and safety of an association of diallyl thiosulfinate with nuciferine and diosgenin in the treatment of a group of patients suffering from premature ejaculation (PE), primary or secondary to erectile dysfunction (ED). Materials and methods: From July 2015 to October 2016, 143 patients (mean age 25.3; range 18-39) affected by PE completed the study and were finally analyzed in this phase I study. All patients, after clinical assessment and laboratory evaluation were asked to take an association of diallyl thiosulfinate with nuciferine and diosgenin as oral tablet, once a day, on alternate days, for three months. At the baseline and after three months of treatment, each patient was asked to complete the following questionnaires: International Index of Erectile Function (IIEF-5), Premature Ejaculation Diagnostic Tool (PEDT), Male Sexual Health Questionnaire (MSHQ). Results: A statistical significant improvement in terms of erectile function, comparing the IIEF-5 value at baseline and follow-up visit was found (respectively IIEF-5: 8.7 vs 14.01; p < 0.001). Moreover, at follow-up visit, 97/143 men (67.8%) referred a subjective improvement of the erection quality and a better control of the ejaculation (PROs). The IELT improved too between the baseline evaluation and the follow-up visit (p < 0.001). Conclusion: In conclusion, our study, even if supported by preliminary results, showed how Diallyl Thiosulfinate, Nuciferine and Diosgenin is able to improve the control of ejaculation in patients suffering from PE, primary or secondary to ED without any significant adverse effects

Three vs. Four Cycles of Neoadjuvant Chemotherapy for Localized Muscle Invasive Bladder Cancer Undergoing Radical Cystectomy: A Retrospective Multi-Institutional Analysis

Three or four cycles of cisplatin-based chemotherapy is the standard neoadjuvant treatment prior to cystectomy in patients with muscle-invasive bladder cancer. Although NCCN guidelines recommend 4 cycles of cisplatin-gemcitabine, three cycles are also commonly administered in clinical practice. In this multicenter retrospective study, we assessed a large and homogenous cohort of patients with urothelial bladder cancer (UBC) treated with three or four cycles of neoadjuvant cisplatin-gemcitabine followed by radical cystectomy, in order to explore whether three vs. four cycles were associated with different outcomes

Neutrophil percentage-to-albumin ratio predicts mortality in bladder cancer patients treated with neoadjuvant chemotherapy followed by radical cystectomy

To investigate the prognostic role of neutrophil percentage-to-albumin ratio (NPAR) in muscle-invasive bladder cancer (MIBC) patients treated with neoadjuvant chemotherapy (NAC) and radical cystectomy (RC)

What's wrong with classes? The theory of Knowledge

This paper wants to investigate the deepest meaning of the word class that is often used in machine learning and classification as a well-defined concept. This adventure will lead the reader to the fundamentals of Mathematics like set theory from Zermelo-Fraenkel. This will be our start, like is all Mathematics, to understand how well defined is the class concept. A broader theory will be outlined with the courageous attempt to give an homogenous framework to deal with machine learning problems

Réduction de dimension via Sliced Inverse Regression : Idées et nouvelles propositions

This thesis proposes three extensions of Sliced Inverse Regression namely: Collaborative SIR, Student SIR and Knockoff SIR.One of the weak points of SIR is the impossibility to check if the Linearity Design Condition (LDC) holds. It is known that if X follows an elliptic distribution thecondition holds true, in case of a mixture of elliptic distributions there are no guaranties that the condition is satisfied globally, but locally holds. Starting from this consideration an extension is proposed. Given the predictor variable X, Collaborative SIR performs initially a clustering. In each cluster, SIR is applied independently. The result from each component collaborates to give the final solution.Our second contribution, Student SIR, comes from the need to robustify SIR. Since SIR is based on the estimation of the covariance, and contains a PCA step, it is indeed sensitive to noise. To extend SIR, an approach based on a inverse formulation of SIR proposed by R.D. Cook has been used.Finally Knockoff SIR is an extension of SIR to perform variable selection and give sparse solution that has its foundations in a recently published paper by R. F. Barber and E. J. Candès that focuses on the false discovery rate in the regression framework. The underlying idea of this paper is to construct copies of the original variables that have some properties. It is shown that SIR is robust to this copies and a strategy is proposed to use this result for variable selection and to generate sparse solutions.Cette thèse propose trois extensions de la Régression linéaire par tranches (Sliced Inverse Regression, SIR), notamment Collaborative SIR, Student SIR et Knockoff SIR.Une des faiblesses de la méthode SIR est l’impossibilité de vérifier si la Linearity Design Condition (LDC) est respectée. Il est établi que, si x suit une distribution elliptique, la condition est vraie ; dans le cas d’une composition de distributions elliptiques il n y a aucune garantie que la condition soit vérifiée globalement, pourtant, elle est respectée localement.On va donc proposer une extension sur la base de cette considération. Étant donné une variable explicative x, Collaborative SIR réalise d’abord un clustering. Pour chaque cluster, la méthode SIR est appliquée de manière indépendante.Le résultat de chaque composant contribue à créer la solution finale.Le deuxième papier, Student SIR, dérive de la nécessité de robustifier la méthode SIR.Vu que cette dernière repose sur l’estimation de la covariance et contient une étape APC, alors elle est sensible au bruit.Afin d’étendre la méthode SIR on a utilisé une stratégie fondée sur une formulation inverse du SIR, proposée par R.D. Cook.Finalement, Knockoff SIR est une extension de la méthode SIR pour la sélection des variables et la recherche d’une solution sparse, ayant son fondement dans le papier publié par R.F. Barber et E.J. Candès qui met l’accent sur le false discovery rate dans le cadre de la régression. L’idée sous-jacente à notre papier est de créer des copies de variables d’origine ayant certaines proprietés.On va montrer que la méthode SIR est robuste par rapport aux copies et on va proposer une stratégie pour utiliser les résultats dans la sélection des variables et pour générer des solutions spars