Supervised Feature Compression based on Counterfactual Analysis

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, a smaller, therefore more interpretable Decision Tree can be trained, which, in addition, enhances the stability and robustness of the baseline Decision Tree. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity compared to the baseline Decision Tree.Comment: 29 pages, 12 figure

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

: The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics

Comparison between soluble ST2 and high-sensitivity troponin I in predicting short-term mortality for patients presenting to the Emergency Department with chest pain

Background: High-sensitivity cardiac troponin I (hs-cTnI) and the soluble isoform of suppression of tumorigenicity 2 (sST2) are useful prognostic biomarkers in acute coronary syndrome (ACS). The aim of this study was to test the short term prognostic value of sST2 compared with hs-cTnI in patients with chest pain. Methods: Assays for hs-cTnI and sST2 were performed in 157 patients admitted to the Emergency Department (ED) for chest pain at arrival. In-hospital and 30-day follow-up mortalities were assessed. Results: The incidence of ACS was 37%; 33 patients were diagnosed with ST elevation myocardial infarction (STEMI), and 25 were diagnosed with non-ST elevation myocardial infarction (NSTEMI). Compared with the no acute coronary syndrome (NO ACS) group, the median level of hs-cTnI was higher in ACS patients: 7.22 (5.24-14) pg/mL vs 68 (15.33-163.50) pg/mL (P35 ng/mL at ED arrival died during the 30-day follow-up. Conclusions: sST2 has a greater prognostic value for 30-day cardiac mortality after discharge in patients presenting to the ED for chest pain compared with hs-cTnI. In STEMI patients, an sST2 value > 35 ng/mL at ED arrival showed the highest predictive power for short-term mortality

Assessment of long-term prognosis at detection of early hepatocellular carcinoma remains unsolved

no abstract availabl

Viscoelastic properties of suspensions of noncolloidal hard spheres in a molten polymer

We report an experimental study on suspensions of solid particles in a viscoelastic polymer matrix. A commercial entangled poly(ε- caprolactone) was used as the suspending fluid. Noncolloidal solid spheres (diameter = 15 μm) made of polymethylmethacrylate were dispersed in the polymer via a solvent casting method. The volume fraction of the spheres was varied from 5% to 30%, thus allowing to explore both dilute and concentrated regimes. Electron scanning microscopy demonstrated homogeneous dispersion of the spheres in the matrix. We measured the rheological properties of the suspensions both in linear and nonlinear regimes with both dynamic and transient tests. The experimental results demonstrate the reinforcement effect of the particles. Both viscous and elastic moduli increase as the concentration of the particles is increased. The results show good agreement with available theories, simulations, and previous experimental data. In particular, the second order parameter of the quadratic equation that describes the dependence of the shear viscosity of the suspension upon the volume fraction of particles is in agreement with the predicted value found by Batchelor [G. K. Batchelor and J. T. Green, “The hydrodynamic interaction of two small freely-moving spheres in a linear flow field,” J. Fluid Mech. 56, 375–400 (1972); G. K. Batchelor and J. T. Green, “The determination of the bulk stress in a suspension of spherical particles to order c2,” J. Fluid Mech. 56, 401–427 (1972); and G. K. Batchelor, “The effect of Brownian motion on the bulk stress in a suspension of spherical particles,” J. Fluid Mech. 83, 97–117 (1977)]. We probe experimentally that the linear rheological behavior of suspensions of particles in viscoelastic fluids is the same as for Newtonian fluids

Towards new tools for refined management of patients with advanced hepatocellular carcinoma under systemic therapy: Some enthusiasm with a word of caution

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Improving P300 Speller performance by means of optimization and machine learning

Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user's brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as SWLDA and SVM are the most used for ERPs' classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio (SNR), it also slows down the process. In the early studies, the number of iterations was fixed (no stopping), but recently, several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.Comment: 32 pages, research articl

Expression pattern of estroprogestinic receptors in sinonasal inverted papilloma

open13openSerra A; Caltabiano R; Spinato G; Gallina S; Caruso S; Rapisarda V; Di Mauro P; Castro V; Conti A; Licciardello L; Maiolino L; Lanzafame S; Cocuzza SSerra, A; Caltabiano, R; Spinato, G; Gallina, S; Caruso, S; Rapisarda, V; Di Mauro, P; Castro, V; Conti, A; Licciardello, L; Maiolino, L; Lanzafame, S; Cocuzza,