On characters of Chevalley groups vanishing at the non-semisimple elements

Let G be a finite simple group of Lie type. In this paper we study characters of G that vanish at the non-semisimple elements and whose degree is equal to the order of a maximal unipotent subgroup of G. Such characters can be viewed as a natural generalization of the Steinberg character. For groups G of small rank we also determine the characters of this degree vanishing only at the non-identity unipotent elements.Comment: Dedicated to Lino Di Martino on the occasion of his 65th birthda

CCD and photon-counting photometric observations of asteroids carried out at Padova and Catania observatories

We present the results of observational campaigns of asteroids performed at Asiago Station of Padova Astronomical Observatory and at M.G. Fracastoro Station of Catania Astrophysical Observatory, as part of the large research programme on Solar System minor bodies undertaken since 1979 at the Physics and Astronomy Department of Catania University. Photometric observations of six Main-Belt asteroids (27 Euterpe, 173 Ino, 182 Elsa, 539 Pamina, 849 Ara, and 984 Gretia), one Hungaria (1727 Mette), and two Near-Earth Objects (3199 Nefertiti and 2004 UE) are reported. The first determination of the synodic rotational period of 2004 UE was obtained. For 182 Elsa and 1727 Mette the derived synodic period of 80.23+/-0.08 h and 2.981+/-0.001 h, respectively, represents a significant improvement on the previously published values. For 182 Elsa the first determination of the H-G magnitude relation is also presented.Comment: 19 pages, 11 figures, accepted for publication in Planetary and Space Scienc

Prospective teachers' interpretative knowledge: giving sense to subtraction algorithms

The process of interpretation and assessment of students’ mathematical productions represents a crucial aspect of teachers’ practices. In such processes, teachers rely on the so-called interpretative knowledge, which includes particular aspects of their mathematical and pedagogical knowledge, their view of mathematics, and their values. In this paper, we analyze and discuss prospective primary teachers’ interpretative knowledge gained through their assessment of different subtraction algorithms

Feynman graphs and the large dimensional limit of multipartite entanglement

We are interested in the properties of multipartite entanglement of a system composed by $n$ $d$-level parties (qudits). Focussing our attention on pure states we want to tackle the problem of the maximization of the entanglement for such systems. In particular we effort the problem trying to minimize the purity of the system. It has been shown that not for all systems this function can reach its lower bound, however it can be proved that for all values of $n$ a $d$ can always be found such that the lower bound can be reached. In this paper we examine the high-temperature expansion of the distribution function of the bipartite purity over all balanced bipartition considering its optimization problem as a problem of statistical mechanics. In particular we prove that the series characterizing the expansion converges and we analyze the behavior of each term of the series as $d\to \infty$.Comment: 29 pages, 11 figure

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Towards Optimal Energy-Water Supply System Operation for Agricultural and Metropolitan Ecosystems

The energy-water demands of metropolitan regions and agricultural ecosystems are ever-increasing. To tackle this challenge efficiently and sustainably, the interdependence of these interconnected resources has to be considered. In this work, we present a holistic decision-making framework which takes into account simultaneously a water and energy supply system with the capability of satisfying metropolitan and agricultural resource demands. The framework features: (i) a generic large-scale planning and scheduling optimization model to minimize the annualized cost of the design and operation of the energy-water supply system, (ii) a mixed-integer linear optimization formulation, which relies on the development of surrogate models based on feedforward artificial neural networks and first-order Taylor expansions, and (iii) constraints for land and water utilization enabling multi-objective optimization. The framework provides the operational profiles of all energy-water system elements over a given time horizon, which uncover potential synergies between the essential food, energy, and water resource supply systems.Comment: Part of the Foundations of Computer-Aided Process Operations and Chemical Process Control (FOCAPO/CPC) 2023 Proceeding

Continuous monitoring of hydrogen and carbon dioxide at Mt Etna

This study assessed the use of an H2 fuel cell as an H2-selective sensor for volcano monitoring. The resolution, repeatability, and cross-sensitivity of the sensor were investigated and evaluated under known laboratory conditions. A tailor-made device was developed and used for continuously monitoring H2 and CO2 at Mt Etna throughout 2009 and 2010. The temporal variations of both parameters were strongly correlated with the evolution of the volcanic activity during the monitoring period. In particular, the CO2 flux exhibited long-term variations, while H2 exhibited pulses immediately before the explosive activity that occurred at Mt Etna during 2010

The Logical Intelligence Enhancement Program (LIEP) for the improvement of cognitive abilities. Premilinary findings

The Logical Intelligence Enhancement Program (LIEP) is a program specifically addressed to students aging from 6 to 12. It consists of a series of exercises of different types (verbal inferences, understanding of graphs and tables, series of digits, etc.) and increasing difficulty, properly devised to activate and train the abilities of logical reasoning. Hopefully, such an enhancement should result in an improvement of academic achievements, especially in low proficiency learner students. Here we report on a study carried out on a large cohort of fifth-grade students. The results demonstrate the effectiveness of LIEP in improving students’ cognitive abilities and abstract reasoning

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)—a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies