1,720,960 research outputs found
Assessing Cardiac Functionality by Means of Interpretable AI and Myocardial Strain
No full textCardiac Imaging is a powerful methodology for the accurate assessment of heart functionality. Among the possible approaches, Myocardial Strain assesses the functionality of the heart by tracking the movement and deformation of myocardium during the cardiac cycle. This information, that can be acquired also by means of Cardiac Magnetic Resonance, can pave the way to the development of predictive models using machine learning. In this work, we developed a predictive model of left ventricular ejection fraction, which is a measure of the heart’s function to pump oxygen-rich blood to the body, trained using strain data. Specifically, we developed a fully interpretable model based on a rule-based Fuzzy Inference System, coupled with a novel methodology for the disambiguation of the rules. Our results show that the developed model is able to accurately estimate the ejection fraction, and can provide physicians with additional insights about the role of strain features
Our Fruitful Relationship with Sugeno Inference, from FUMOSO to pyFUME
No full textFuzzy Inference Systems (FIS) effectively model and reason
with complex and uncertain information in an interpretable, understand-
able, and transparent way. Takagi-Sugeno-Kang (TSK) is one of the most
widespread types of FIS, appreciated for its ability to output crisp values
by leveraging linear models as consequents. In this tribute to Prof. Sugeno,
we discuss our previous works based on TSK inference. In particular,
we focus on pyFUME, a Python library for automatically estimating
first-order TSK FIS from data. We introduce a relevant advancement
in pyFUME, i.e., the ability to handle categorical variables within the
consequents, which significantly enhances the model’s performance in
regression and classification tasks. This improved version completes the
foundation for pyFUME to handle mixed-type data. Our results on three
diverse datasets show the capabilities of our method, which performs
better than the previous implementation
Improving the Efficiency and the Validity of Molecular Transformers
No full textSince their advent, Transformer models have been applied across a wide range of fields, including cheminformatics. In this context, drug discovery has benefited from using Molecular Transformers by leveraging diverse string representations of molecules, such as the Simplified Molecular Input Line Entry Systems (SMILES), for a variety of tasks. In this study, we present a model focused on the optimization of a formerly developed Molecular Transformer specifically dedicated to metabolism prediction. Metabolism refers to all the biotransformations a drug undergoes once inside the human body, directly influencing its therapeutic effect and potential toxicity, and therefore represents a key topic in medicinal chemistry. Framing molecular transformation prediction as a sequence-to-sequence translation task has shown promise, but suffers from limitations such as low validity of generated molecules and high computational cost. To address this limitation, we here propose an optimized model that integrates pre-training, transfer learning, and fine-tuning techniques, already improving validity and reducing computation time. Finally, by separating the metabolism prediction task from the SMILES syntax learning, we ensure broader applicability of the proposed model across diverse datasets and a variety of SMILES-based tasks beyond metabolic transformations, expanding its potential utility
COSMICA: A GPU-Optimized Code for Solar Modulation Studies
No full textWe present COSMICA, an opensource high-performance GPU-accelerated numerical code for modeling cosmic ray solar modulation, and its application to study CR diffusion parameters. Developed within the framework of the ICSC-Italian Research Center on High-Performance Computing, Big Data and Quantum Computing (Spoke-3), COSMICA is undergoing continuous software optimization to maximize efficiency on NVIDIA architectures. COSMICA is coupled with SDEGnO, another ICSC project, designed for the efficient parameter tuning, exploring the large parameter space in solar modulation studies. As a first physical use-case study, we exploit COSMICA to investigate Forbush Decreases (FDs), which are transient cosmic ray intensity reductions caused by interplanetary disturbances. The analysis leverages the high-precision daily measurements from AMS-02, which provide cosmic ray fluxes across a wide range of rigidities. The ability to simultaneously study not only protons but also helium isotopes offers complementary insights into charge- and mass-dependent transport effects. By analyzing FD events, we assess localized variations in diffusion parameters and their impact on cosmic ray transport. The results confirm the stability of the rigidity dependence of the diffusion tensor, supporting the use of FDs as probes of localized well constrained heliospheric conditions. The computational efficiency of COSMICA paves the way for large-scale simulations, systematic FD catalogue analysis and a more in-depth understanding of the parameter that regulates the solar modulation, together with their dependencies
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Massive stochastic simulation of cosmic rays propagation in the heliosphere: The COSMICA code
No full textThe accurate modeling of galactic cosmic ray (GCR) propagation in the heliosphere requires solving the Parker Transport Equation (PTE), a multidimensional nonlinear equation that cannot be addressed analytically without strong approximations. In recent decades, stochastic differential equation (SDE)–Monte Carlo methods have emerged as a powerful numerical strategy for this problem, thanks to their numerical stability, relatively low memory requirements, and intrinsic parallelism. The increasing availability of general-purpose Graphics Processing Units (GPUs) has further revolutionized this approach by enabling massive parallelization of particle trajectories at relatively low cost. In this work, we introduce COSMICA (COde for a Speedy Montecarlo Involving Cuda Architecture), a new open-source multi-GPU code written in CUDA/C++ for the three-dimensional solution of the PTE. COSMICA has been specifically designed to optimize GPU resource usage and scalability, with strategies including memory hierarchy exploitation, register-conscious kernel design, warp-aware scheduling, and parameter reordering for multi-GPU execution. Benchmark results demonstrate that COSMICA reduces runtimes from weeks to hours for large-scale simulations. These optimizations make COSMICA a versatile tool for systematic studies of cosmic-ray modulation and parameter exploration, thereby expanding the feasibility of investigations that were previously computationally prohibitive. The present article constitutes the first part of a two-paper series, focusing on code design and computational performance; a companion paper will present its validation against benchmark models
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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