∪∪ - - (piede passo terra cielo). Discorso sull’andare della poesia.

Con questo intervento desideravo condurre brevemente, per balzi e illuminazioni fugaci, dentro unpaesaggio dove poesia, voce, camminare, trasferimento trovano una collusione. Pur con connessionidi tipo interdisciplinare, a tratti forse troppo ardite, e passaggi assolutamente personali e intimi sperodi aver dimostrato come la poesia sia di fatto un fare del corpo, nel corpo radicato, che ci porta versoterritori inesplorati

GA for feature selection of EEG heterogeneous data

The electroencephalographic (EEG) signals provide highly informative data on brain activities and functions. However, their heterogeneity and high dimensionality may represent an obstacle for their interpretation. The introduction of a priori knowledge seems the best option to mitigate high dimensionality problems, but could lose some information and patterns present in the data, while data heterogeneity remains an open issue that often makes generalization difficult. In this study, we propose a genetic algorithm (GA) for feature selection that can be used with a supervised or unsupervised approach. Our proposal considers three different fitness functions without relying on expert knowledge. Starting from two publicly available datasets on cognitive workload and motor movement/imagery, the EEG signals are processed, normalized and their features computed in the time, frequency and time-frequency domains. The feature vector selection is performed by applying our GA proposal and compared with two benchmarking techniques. The results show that different combinations of our proposal achieve better results in respect to the benchmark in terms of overall performance and feature reduction. Moreover, the proposed GA, based on a novel fitness function here presented, outperforms the benchmark when the two different datasets considered are merged together, showing the effectiveness of our proposal on heterogeneous data.Comment: submitted to Expert Systems with Application

Inner speech recognition through electroencephalographic signals

This work focuses on inner speech recognition starting from EEG signals. Inner speech recognition is defined as the internalized process in which the person thinks in pure meanings, generally associated with an auditory imagery of own inner "voice". The decoding of the EEG into text should be understood as the classification of a limited number of words (commands) or the presence of phonemes (units of sound that make up words). Speech-related BCIs provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals, improving the quality of life of people who have lost the capability to speak, by restoring communication with their environment. Two public inner speech datasets are analysed. Using this data, some classification models are studied and implemented starting from basic methods such as Support Vector Machines, to ensemble methods such as the eXtreme Gradient Boosting classifier up to the use of neural networks such as Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM). With the LSTM and BiLSTM models, generally not used in the literature of inner speech recognition, results in line with or superior to those present in the stateof-the-art are obtained.Comment: Submitted to the Italian Workshop on Artificial Intelligence for Human Machine Interaction (AIxHMI 2022), December 02, 2022, Udine, Ital

Transcriptome dynamics in the asexual cycle of the chordate Botryllus schlosseri

Background: We performed an analysis of the transcriptome during the blastogenesis of the chordate Botryllus schlosseri, focusing in particular on genes involved in cell death by apoptosis. The tunicate B. schlosseri is an ascidian forming colonies characterized by the coexistence of three blastogenetic generations: filter-feeding adults, buds on adults, and budlets on buds. Cyclically, adult tissues undergo apoptosis and are progressively resorbed and replaced by their buds originated by asexual reproduction. This is a feature of colonial tunicates, the only known chordates that can reproduce asexually. Results: Thanks to a newly developed web-based platform (http://botryllus.cribi.unipd.it), we compared the transcriptomes of the mid-cycle, the pre-take-over, and the take-over phases of the colonial blastogenetic cycle. The platform is equipped with programs for comparative analysis and allows to select the statistical stringency. We enriched the genome annotation with 11,337 new genes; 581 transcripts were resolved as complete open reading frames, translated in silico into amino acid sequences and then aligned onto the non-redundant sequence database. Significant differentially expressed genes were classified within the gene ontology categories. Among them, we recognized genes involved in apoptosis activation, de-activation, and regulation. Conclusions: With the current work, we contributed to the improvement of the first released B. schlosseri genome assembly and offer an overview of the transcriptome changes during the blastogenetic cycle, showing up- and down-regulated genes. These results are important for the comprehension of the events underlying colony growth and regression, cell proliferation, colony homeostasis, and competition among different generations

Life history and ecological genetics of the colonial ascidian Botryllus schlosseri

The colonial ascidian Botryllus schlosseri is a cosmopolitan, marine filter feeder, introduced as a laboratory research organism in the 1950s. Currently, it is widely used in many laboratories to investigate a variety of biological questions. Recently, it has become a species of concern, as it is an invasive species in many coastal environments. Here, we review studies on the geographical distribution of the species, sexual and asexual reproduction in the field, tolerance to temperature, salinity and anthropogenic activity, polychromatism, enzymatic polymorphism, and the genetic basis of pigmentation. Studying the relationship between genetic polymorphism and the adaptation of B. schlosseri to environmental stress is a challenge of future research and will improve our understanding of its evolutionary success and invasive potential

The evolution of AI approaches for motor imagery EEG-based BCIs

The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer Interfaces (BCIs) allow the direct communication between humans and machines by exploiting the neural pathways connected to motor imagination. Therefore, these systems open the possibility of developing applications that could span from the medical field to the entertainment industry. In this context, Artificial Intelligence (AI) approaches become of fundamental importance especially when wanting to provide a correct and coherent feedback to BCI users. Moreover, publicly available datasets in the field of MI EEG-based BCIs have been widely exploited to test new techniques from the AI domain. In this work, AI approaches applied to datasets collected in different years and with different devices but with coherent experimental paradigms are investigated with the aim of providing a concise yet sufficiently comprehensive survey on the evolution and influence of AI techniques on MI EEG-based BCI data.Comment: Submitted to Italian Workshop on Artificial Intelligence for Human Machine Interaction (AIxHMI 2022), December 02, 2022, Udine, Ital

A no-reference metric for demosaicing artifacts that fits psycho-visual experiments

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Benchmark dataset of memes with text transcriptions for automatic detection of multi-modal misogynistic content

In this paper we present a benchmark dataset generated as part of a project for automatic identification of misogyny within online content, which focuses in particular on memes. The benchmark here described is composed of 800 memes collected from the most popular social media platforms, such as Facebook, Twitter, Instagram and Reddit, and consulting websites dedicated to collection and creation of memes. To gather misogynistic memes, specific keywords that refer to misogynistic content have been considered as search criterion, considering different manifestations of hatred against women, such as body shaming, stereotyping, objectification and violence. In parallel, memes with no misogynist content have been manually downloaded from the same web sources. Among all the collected memes, three domain experts have selected a dataset of 800 memes equally balanced between misogynistic and non-misogynistic ones. This dataset has been validated through a crowdsourcing platform, involving 60 subjects for the labelling process, in order to collect three evaluations for each instance. Two further binary labels have been collected from both the experts and the crowdsourcing platform, for memes evaluated as misogynistic, concerning aggressiveness and irony. Finally for each meme, the text has been manually transcribed. The dataset provided is thus composed of the 800 memes, the labels given by the experts and those obtained by the crowdsourcing validation, and the transcribed texts. This data can be used to approach the problem of automatic detection of misogynistic content on the Web relying on both textual and visual cues, facing phenomenons that are growing every day such as cybersexism and technology-facilitated violence

Predicting complexity perception of real world images

The aim of this work is to predict the complexity perception of real world images.We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images

A multi-artifact EEG denoising by frequency-based deep learning

Electroencephalographic (EEG) signals are fundamental to neuroscience research and clinical applications such as brain-computer interfaces and neurological disorder diagnosis. These signals are typically a combination of neurological activity and noise, originating from various sources, including physiological artifacts like ocular and muscular movements. Under this setting, we tackle the challenge of distinguishing neurological activity from noise-related sources. We develop a novel EEG denoising model that operates in the frequency domain, leveraging prior knowledge about noise spectral features to adaptively compute optimal convolutional filters for noise separation. The model is trained to learn an empirical relationship connecting the spectral characteristics of noise and noisy signal to a non-linear transformation which allows signal denoising. Performance evaluation on the EEGdenoiseNet dataset shows that the proposed model achieves optimal results according to both temporal and spectral metrics. The model is found to remove physiological artifacts from input EEG data, thus achieving effective EEG denoising. Indeed, the model performance either matches or outperforms that achieved by benchmark models, proving to effectively remove both muscle and ocular artifacts without the need to perform any training on the particular type of artifact.Comment: Accepted at the Italian Workshop on Artificial Intelligence for Human-Machine Interaction (AIxHMI 2023), November 06, 2023, Rome, Ital