324 research outputs found
Fast gaschromatographic/mass spectrometric determination of diuretics and masking agents in human urine. Development and validation of a productive screening protocol for antidoping analysis
Modeling Quality and Machine Learning Pipelines through Extended Feature Models
The recently increased complexity of Machine Learning (ML) methods, led to
the necessity to lighten both the research and industry development processes.
ML pipelines have become an essential tool for experts of many domains, data
scientists and researchers, allowing them to easily put together several ML
models to cover the full analytic process starting from raw datasets. Over the
years, several solutions have been proposed to automate the building of ML
pipelines, most of them focused on semantic aspects and characteristics of the
input dataset. However, an approach taking into account the new quality
concerns needed by ML systems (like fairness, interpretability, privacy, etc.)
is still missing. In this paper, we first identify, from the literature, key
quality attributes of ML systems. Further, we propose a new engineering
approach for quality ML pipeline by properly extending the Feature Models
meta-model. The presented approach allows to model ML pipelines, their quality
requirements (on the whole pipeline and on single phases), and quality
characteristics of algorithms used to implement each pipeline phase. Finally,
we demonstrate the expressiveness of our model considering the classification
problem
Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development
The problem of predicting the training time of machine learning (ML) models
has become extremely relevant in the scientific community. Being able to
predict a priori the training time of an ML model would enable the automatic
selection of the best model both in terms of energy efficiency and in terms of
performance in the context of, for instance, MLOps architectures. In this
paper, we present the work we are conducting towards this direction. In
particular, we present an extensive empirical study of the Full Parameter Time
Complexity (FPTC) approach by Zheng et al., which is, to the best of our
knowledge, the only approach formalizing the training time of ML models as a
function of both dataset's and model's parameters. We study the formulations
proposed for the Logistic Regression and Random Forest classifiers, and we
highlight the main strengths and weaknesses of the approach. Finally, we
observe how, from the conducted study, the prediction of training time is
strictly related to the context (i.e., the involved dataset) and how the FPTC
approach is not generalizable
Data-Driven Analysis of Gender Fairness in the Software Engineering Academic Landscape
Gender bias in education gained considerable relevance in the literature over
the years. However, while the problem of gender bias in education has been
widely addressed from a student perspective, it is still not fully analysed
from an academic point of view. In this work, we study the problem of gender
bias in academic promotions (i.e., from Researcher to Associated Professor and
from Associated to Full Professor) in the informatics (INF) and software
engineering (SE) Italian communities. In particular, we first conduct a
literature review to assess how the problem of gender bias in academia has been
addressed so far. Next, we describe a process to collect and preprocess the INF
and SE data needed to analyse gender bias in Italian academic promotions.
Subsequently, we apply a formal bias metric to these data to assess the amount
of bias and look at its variation over time. From the conducted analysis, we
observe how the SE community presents a higher bias in promotions to Associate
Professors and a smaller bias in promotions to Full Professors compared to the
overall INF community
Non-conventional extractions and antioxidant properties evaluation of microalga Spirulina maxima
Design, Synthesis and Pharmacological Evaluation of Anti-Pyroptotic Compounds Acting on NLRP3 Inflammasome Signaling Pathway: a New Start for Anti-Inflammatory Drugs?
Synthesis and preliminary evaluation of model compounds targeting the NLRP3 inflammasome pathways
Physical and Functional Interaction of CARMA1 and CARMA3 with Iκ Kinase γ-NFκB Essential Modulator
CARMA proteins are scaffold molecules that contain a caspase recruitment domain and a membrane-associated guanylate kinase-like domain. CARMA1 plays a critical role in mediating activation of the NFkappaB transcription factor following antigen receptor stimulation of both B and T lymphocytes. However, the biochemical mechanism by which CARMA1 regulates activation of NFkappaB remains to be determined. Here we have shown that CARMA1 and CARMA3 physically associate with Ikappa kinase gamma/NFkappaB essential modulator (IkappaKgamma-NEMO) in lymphoid and non-lymphoid cells. CARMA1 participates to an inducible large molecular complex that contains IkappaKgamma/NEMO, Bcl10, and IkappaKalpha/beta kinases. Expression of the NEMO-binding region of CARMA3 exerts a dominant negative effect on Bcl10-mediated activation of NFkappaB. Thus, our results provide direct evidence for physical and functional interaction between CARMA and the IkappaK complex and offer a biochemical framework to understand the molecular activities controlled by CARMA-1, -2, and -3 and Bcl10
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