dataesr/scientific-paragraphs-categorization huggingface dataset:
Ulerinea ballus Ulerinea ballus (Fig. 15  e 16) è la seconda miniatura di Regnum Animale, e rappresenta uno dei casi chiave dell'applicazione di recenti tecniche compositive algoritmiche di Mauro Lanza. La maggior parte delle tecniche sviluppate da Lanza per Systema Naturae consistono in diversi sistemi di targeting di un elemento rispetto a diversi database di dati simbolici e notazionali. Per Ulerinea ballus Lanza impiega una tecnica di individuazione automatica di gradi di prossimità spettrale tra uno spettro A e uno spettro B, generando una serie di ulteriori spettri i cui valori sono equidistanti tra A e B. Questa tecnica viene impiegata per la prima volta da Mauro Lanza in Ludus de Morte Regis, brano del 2013 per coro ed elettronica, e la illustra nella lezione tenuta per il cursus dell'IRCAM dello stesso anno 56.
Isimiosia papanabuis rappresenta un esempio di questa applicazione del processo di conversione di dati come generatore di forme musicali. Andrea Valle, infatti estrae l'impianto formale e articolativo con la tecnica dell'audioparismo da un campione audio di un secondo, tratto da una registrazione di un'improvvisazione del duo Otosimbionte, progetto musicale di Valle e Pappalardo. Nel Video 3, Andrea Valle esemplifica l'applicazione dell'audioparismo nel caso specifico del brano in analisi. si in considerazione, confrontando automaticamente questo risultato con i database analitici, i cui dati simbolici sono ottenuti nella stessa maniera. Nel Video 4, Mauro Lanza dimostra il complesso meccanismo di queste funzioni. [Video 4 https://zenodo. org/record/4903412].
Totalatelontelomichina Totalatelontelomichina (Fig. 21  e 22) è pensato come il brano conclusivo dell'intero ciclo -formalmente concluso con una breve coda. Nelle parole di Mauro Lanza, è un brano che «parla della morte», quindi la riduzione ultima allo stato di fossile. Il modello di riferimento è infatti la cantata sacra di Johann Sebastian Bach Liebster Gott, wann werd ich sterben? catalogata BWV 8, il cui testo tratta esplicitamente del tema della morte. Operativamente i compositori hanno segmentato algoritmicamente le simulazioni audio della gran parte dei brani del ciclo, considerando la battuta come unità di segmentazione, e creando un database con le analisi spettrali in forma simbolica di ogni singolo segmento. Questo database a sua volta è stato analizzato creando relazioni di prossimità tra i dati simbolici di ogni segmento.
Questo processo ha dato alla luce tre grafici tassonomici dell'intero Systema Naturae, che, pur non essendo utilizzato a livello compositivo, sono utili per rappresentare visivamente la grande quantità di dati che i compositori sono andati a trattare algoritmicamente (Fig. 23 ). A livello operativo, prendendo il brano di Bach come modello simbolico di partenza, è stato effettuato il targeting del modello sul database creato dai compositori. In Video 5 Mauro Lanza ne esemplifica l'applicazione. [Video 5 https://zenodo. org/record/4903417] Figura 21. A. Valle, M. Lanza, Fossilia, inedito 2017. Totalatelontelomichina, partitura strumentale.
Nel Video 6, Simone Pappalardo illustra la costruzione e il controllo di uno strumento elettromagnetico. [Video 6 https://zenodo. org/record/4903419] Figura 24. © S. Pappalardo, Millis(). An improvisation performance for a garden of remotely controlled mechanical instruments, inedito. Schema di costruzione di uno strumento elettromagnetico. Nel caso di Orchestra Fragile (Fig. 25 ), la componente di filtraggio -che avviene su Max/MSP -è controllata da una serie di descrittori audio che individuano quando la superficie di vetro è sul punto di rottura, riportando la risonanza del corpo ad uno stato che normalizza la sua condizione. La fragilità, come metafora, assume per l'autore diverse connotazioni concettuali: La rottura è un "momentaneo", uno stato non definitivo; la fragilità è invece un processo in divenire, auto simile, che ricopia sé stesso in forme sempre più larghe, in orchestre organizzate composte di atomi fragili.
• Software was best for interfacing controllers, making logical decisions, and generating compositional structure. • People made choices (not always predictable) based on musical assessment and personal preference. For decades I had distributed these resources accordingly, in both my composed and improvised work. I was not the only one guilty of this tendency: in the heyday of MIDI, composer Ron Kuivila lamented, "we need to make computer music that sounds like electronic music" -and this was no mere semantic distinction, they did sound different  16. In 2011 I went so far as to write a paper on the musical implications of the choice of hardware or software in compositional and performance practices 17. But in the aftermath of two false starts on a new instrument I decided to confront these assumptions head on and attempt to write a program that would behave less like software as I knew it and more like unstable circuitry in the hands of a novice hacker.
• Multiple voices, with a wide range of sounds, rather than a single core technique like the sampling and multi-tap processing of the trombone. • The Arduino should serve as a "dumb" interface, converting sensor data into a serial stream to the host laptop, to reduce the need to modify its code unless new sensors are added. • Laptop software (Max/MSP) should be structured to facilitate both playing and updating: o Same software format for all voices, as far as practical. o Controllers mapped as similarly as possible for all voices.
Macintosh Software on the Macintosh is written in Max/MSP. The program is divided into a number of sub-patches that handshake with the Arduino, map sensor data to sound parameters in 20-odd voice programs, and perform other functions in support of digital sound synthesis. The patches provide visual feedback (meters, number values, color-coded indicators, etc. ), but these are intended primarily for de-bugging and rehearsal purposes. The software can be played from the trumpet with no need for visual feedback from the computer screen or interaction with the keyboard, with the occasional exception of the large-font elapsed time display (see tptdisplay below), which functions as a handy stopwatch during performance.
The tptdisplay window in the upper right displays the name of the voice currently active and the elapsed time since the DSP was turned on or the software was reset. On-screen buttons, linked to ASCII hot-keys, start and stop the timer ("return") and reset it ("x"). In a large font, this panel serves as a useful performance stopwatch, legible some distance from the computer. Above the voices patch compvoices contains Max patches for several composed works, some of which do not make use of the physical trumpet instrument. This patch is included to streamline changeover between improvising with the !trumpet and performing other software-based works, running pieces that require routing external signals through the audio interface. The various sub-patches are wrapped in poly~ objects like the main voices, to minimize CPU load.
o IR distance sensors. I experimented with a VCNL 4010 IR distance sensor, which measures short distances accurately, but quantizes the data to the nearest millimeter, which is too coarse for the 15mm of travel of the valve. (I am still on the lookout for other valve sensing options). • Accompaniment software. Although the audio output of each voice consists of a mix of three sound generators, the !trumpet is an intrinsically monophonic instrument, like the traditional trumpet. For solo improvisation I wrote a program that generated an accompaniment by analyzing my valving and breath gestures and superimposing a delayed version of this control data on secondary set of voices that went to the PA. Perhaps due to its simplicity, my accompaniment algorithm didn't seem to add much value to solo improvisation.
Sitografia (ultima consultazione 12. 03. 2021) https://andreavalle. bandcamp. com/ https://soundcloud. com/maurolanza https://vimeo. com/244839789 http://arnofabre. free. fr/en/Dropper01/Dropper01. html http://musiquealgorithmique. fr/entretien-6-andrea-valle/ https://www. soundesign. info/2009/05/20/intervista-con-andrea-valle/ https://matiere-memoire. bandcamp. com/album/mmxx-14-fully-automated-luxury-communism https://supercollider. github. io/ https://openmusic-project. github. io/ https://cycling74. com/products/max http://www. thenewnoise. it/i-regnum-di-andrea-valle-e-mauro-lanza/ https://www. musicaelettronica. it/come-costruire-un-serraturofono-a-borracce-programmabilee-interattivo-guida-teorico-pratica-1/ https://www. musicaelettronica. it/come-costruire-un-serraturofono-a-borracce-programmabilee-interattivo-guida-teorico-pratica-2/ https://www.
musicaelettronica. it/come-costruire-un-serraturofono-a-borracce-programmabilee-interattivo-guida-teorico-pratica-3/ https://www. musicaelettronica. it/campcomp-lineamenti-di-musica-audioparahttps://www. musicaelettronica. it/campcomp-lineamenti-di-musica-audiopara-2/ https://www. musicaelettronica. it/spettri-fourier-e-il-geco-1/ 80 Tra nubi, selve, giardini e tassonomie https://www. musicaelettronica. it/spettri-fourier-e-il-geco-2/ https://www. musicaelettronica. it/osservazioni-di-genetica-del-ritmo/ http://www. musicaelettronica. it/osservazioni-di-genetica-del-ritmo-2/ https://forum. ircam. fr/projects/detail/modalys/ http://support. ircam. fr/docs/Modalys/current/co/publication-web. html https://www. ircam. fr/recherche/equipes-recherche/systemes-et-signaux-sonores-audioacoustique-instruments-s3am/ https://github.
A titolo esemplificativo segnaliamo un quadrante conico in marmo da residenza privata da Delo, datato al 100 a. C. ca. : oltre ad avere una struttura simile, con una base a due sponde, ma senza modanatura frontale, presenta sul piano di posa due fori quadrati con ancora aderente una fodera di piombo funzionale al fissaggio. L'oggetto è incluso nella raccolta degli orologi solari antichi (Ancient Sundials 2016), consultabile on-line nell'ambito della piattaforma di ricerca multidisciplinare Edition Topoi (http://repository. edition-topoi. org/collection/BSDP). Per la citazione del singolo esemplare nell'articolo utilizziamo l'abbreviazione Dialface ID seguita dal suo numero identificativo (nell'esempio citato, Dialface ID 654) con collegamento ipertestuale al database nella versione digitale del testo.
64 The evidence for mystery cults, whether textual or visual, entails a system of signs, just as any language. And yet it matches a specific difficulty, because of the two features I underlined (the interdiction on divulgation and the incommunicability of experience), which deprive the modern observer of data and comparable parallels. Moreover, the investigation becomes more complex when one considers a phenomenon that F. Massa and I have proposed to designate as "mystericization", that is the extensive use of both a lexicon and an imaginaire of "mysteries" that spread in literature and in various fields of knowledge of the Roman period from the end of the first century CE onwards. 65 lutarch offers a perfect example of this trend when he presents philosophia as a μυστηριώδης θεολογία since it proceeds towards the One through successive steps, each Coping with Images of Initiations in the Mithras Cult Mythos, 15 | 2021 one marked by its own distinctive quality.
Afterwards, elution returned to 95:5:0 for 20 min, giving a total elution of 50 min. The column was kept at 18 • C and the diode array detector was set to collect data between 190-700 nm, UV peaks were monitored at 280 and 450 nm for carotenoids and 650 nm for chlorophylls. Identification and quantification of individual chlorophylls and carotenoids were achieved using commercially acquired pure standards. A lutein standard was used for quantifying zeaxanthin, antheraxanthin and β-carotene due to a lack of pure analytical standards. Commercial β-carotene standards tend to contain some α-carotene and were not used. Lutein, violaxanthin and neoxanthin, and chlorophyll a and b standards were acquired from Sigma Aldrich. The carotenoids quantified were the most common compounds in the samples, and include antheraxanthin (Ant), neoxanthin (Neo), violaxanthin (Vio), lutein (Lut), zeaxanthin (Zea), β-carotene (β-Car), VAZ (sum of Vio, Ant and Zea), Total carotenoid (sum of all carotenoids) and de-epoxidation state of xanthophyll cycle (AZ/VAZ; DEPS)  [8].
Statistical Analysis A two-way ANOVA was utilised to determine the effect of treatment (UV versus control) versus treatment days (i. e. , duration) and/or genotypes, using AOV in the package stats. As normality of variances could be assumed for all variables, all data (+UV versus -UV) were compared using pairwise t-tests at each treatment day and genotypes, respectively, using the package rstatistix. Significance was adjusted for multiple testing with the BH correction and bar plots were plotted using the package ggpubr. Principal Component Analysis (PCA), to visualise the relationship between carotenoids and photosynthetic parameters, was performed using the prcomp function in package stats  [63]. All statistical analyses were performed on R (version 4. 0. 53 in RStudio (version 1. 4. 1103)).
Data collection Clinical data, including gender, age at diagnosis, disease location, phenotype according to the Montreal classification, previous and concomitant CD-related medications, smoking status, any history of CD-related surgery, and disease activity according to the Harvey-Bradshaw index (HBI). The IBD-Disk was filled out by each patient on the day of the colonoscopy blind to its results. The IBD-Disk questionnaire includes 0À10 visual analog scales (visual analog scale; 0 = no burden; 10 = maximal burden) assessing each component of disability during the last week. Components are: joint pain, abdominal pain, body image, education and work, emotions, energy, interpersonal interactions, defecation management, sexual function, and sleep. The overall IBD-Disk score was calculated as the sum of its 10 components, ranging from 0 to 100.
Unlike traditional univariate approaches, machine learning facilitates the modeling of complex interactions between variables across heterogeneous datasets  [14]. Automatized diagnostic methods used as decision support tools have shown promising results in dementia  [15] , especially with high dimensionality methods that proven superior than classical statistical models  [16]. Therefore, an automatized machine-learning approach to multimodal markers can potentially overcome current limitations in the characterization of populations from SACs and other underrepresented regions. Here, we developed a multi-feature multimodal approach to neurodegeneration (MMAN, figure  1 ) from diverse samples of AD, bvFTD, and controls from SACs. We combined demographic (DEM) information, neuropsychological outcomes (NPSs) (cognitive screening, executive functions), structural MRI atrophy measures, and dynamic functional connectivity (FC) metrics from EEG and fMRI in an integrative approach using the eXtreme Gradient Boosting (XGBoost) machine learning classifier  [17, 18].
The XGBoost parameters were tuned by Bayesian optimization, including a data harmonization technique to remove possible site-specific biases  [19]. The differential characterization of dementia subtypes such as AD vs FTD presents several challenges (cf controls vs patients  [17] ), including overlapping atrophy patterns and variability in cognitive and neuroimaging measures among dementia subtypes  [20, 21]. To overcome these limitations, we combined cognitive screening, MRI-based morphometry, measures of EEG connectivity  [22] , and dynamic measures of resting-state fMRI connectivity  [23]. To this end, we assessed the relative weights of each feature (i. e. different markers of cognitive screening, EEG, MRI, ad fMRI) for a combined classification of AD and bvFTD using a technique from machine learning called feature importance analysis.
Most of multimodal machine learning approaches for AD characterization  [24] [25] [26] [27] [28]  focusing on MRI data come from high-income countries such as the Alzheimer's Disease Neuroimaging Initiative (ADNI)  [29]  and Open Access Series of Imaging Studies (OASIS)  [30]  databases, where both neuroimaging parameters and sample DEMs are homogeneous. Conversely, our work is developed for real-life clinical scenarios with heterogenous acquisition parameters and patients' diversity across SACs. To the best of our knowledge, this is the first multimodal approach intended for a differential characterization between two dementia subtypes. We implemented a mixed hypothesis-and datadriven approach, including a priori predictions based on the literature and machine learning analyses of relevant multimodal data.
All participants provided written informed consent following the Declaration of Helsinki. Each institutional Ethics Committee approved the protocol. In clinical settings, and specially across SAC's sites, patient's incomplete evaluations and assessment commonly occurs. Thus, we evaluated whether our MMAN model was robust against missing data in a fraction of subjects and features. As some centers may not have access to specific assessments, missing information can constitute an obstacle for this approach. We tested the same pipeline on a sub-sample (SS) without missing data and on a full-sample (FS) with missing data. The SS consisted of 54 HCs (18 from Country-1, 20 from Country-2, and 16 from Country-3), 19 patients with bvFTD (7 from Country-1, 7 from Country-2, and 5 from Country-3), and 32 patients with AD (9 from Country-1, 10 from Country-2, and 13 from Country-3).
The FS consisted of 152 HCs (51 from Country-1, 49 from Country-2, and 52 from Country-3), 54 patients with bvFTD (16 from Country-1, 20 from Country-2, and 18 from Country-3), and 76 patients with AD (25 from Country-1, 24 from Country-2, and 27 from Country-3). The FS was not completely balanced in DEM data, allowing us to test whether the classifier is robust even in the presence of these unmatched variables. To handle missing data in the FS, we used feature averaging imputation on the features that contained up to 30% missing values  [33]  (table 2). To this end, we used a single averaged value per feature to be imputed in the table fields of the subjects having missing values.
EEG markers Participants completed a 10 min long high density EEG acquisition, on a 128-channel system with preamplified sensors and a DC coupling amplifier, at a sampling rate of 1024. Across centers, data were recorded via Biosemi Active-two 128-channel systems with pre-amplified sensors and a DC coupling amplifier, at a sampling rate of 1024 Hz. Analog filters were set at 0. 03 and 100 Hz. A digital bandpass filter between 0. 5 and 45 Hz was applied offline to remove unwanted frequency components. The reference was set to link mastoids for recordings and re-referenced offline to the average of all electrodes. Eye movements or blink artifacts were corrected with independent component analysis  [36]  and with a visual inspection protocol  [37] [38] [39]. Bad channels were replaced via statistically weighted spherical interpolation (based on all sensors)  [40].
The data was divided in 1000 ms segments from the beginning until the end of the recording. All EEG signal processing steps were implemented on MATLAB software (vR2016a) through the EEGLAB (v14. 1. 2)  [41]   toolbox. During the 10 min long resting state protocol, participants were instructed not to think about anything in particular while keeping awake, still, and with eyes closed. We measured linear interactions between oscillatory signals using phase-locking value (PLV)  [22]  and non-linear information sharing via the weighted symbolic mutual information (wSMI) metric  [42]. Connectivity was averaged across segments to create the adjacency matrix. To reduce the number of features while preserving topographic specificity, we defined 16 regions of interest (ROIs) of eight electrodes for each lobe and hemisphere.
The maps were created using spatial intensity gradients across tissue classes; therefore, they were not simply reliant on absolute signal intensity. Since the ensuing maps were not restricted to the voxel resolution of the original data, they can detect submillimeter differences between groups. FreeSurfer's morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and field strengths. Full details on the implemented methods can be found elsewhere  [44]. Finally, the volume, area, and thickness from each segmentation based on the Desikan-Killiany parcellation of cortical and subcortical areas  [45]  were quantified. The plain-text output of the FreeSurfer's pipeline was post-processed on Python (version 3. 7. 4, Python Software Foundation) and transformed into a better structure for statistical analysis.
To avoid potential biases due to differences among the participants' head size  [46] , volume measures of each area were normalized as a percentage of the estimated total intracranial volume (provided also in FreeSurfer's results). For the resting-state protocol, participants were asked not to think about anything in particular, to keep their eyes closed, and to avoid moving or falling asleep. In each center, we obtained threedimensional volumetric and 10 min long restingstate MRI sequences from all participants. First, to ensure that magnetization achieved a steady state, we discarded the first five volumes of each subject's resting-state recording. Then, images were preprocessed in MATLAB using an open-access toolbox: the Data Processing Assistant for Resting-State fMRI (DPARSF V2. 3)  [47] , which generates an automatic pipeline for fMRI analysis by calling the Statistical Parametric Mapping software (SPM12)  [48]  and the Resting-State fMRI Data Analysis Toolkit (REST V.
Machine-learning methods To limit biases and obtain more representative results, we employed a k-fold validation approach (k = 10) using 80% of the sample for training and validation, and 20% out-of-folds sample as an independent testset. This testing dataset was never used for hyperparameter tuning, data reduction or feature engineering to evaluate the generalizability of our results. First, we performed a site normalization process for each feature of both HCs and patients via z-scores based on the mean and standard deviation of the corresponding center's HCs. This process was applied within each fold to avoid information leakage (figure 1(C)). Afterwards, we performed feature stabilization by forward sequential feature selection  [54]  to obtain the best subset of features for each subject-group classification pair (figure 1(D)).
e. a function representing the difference between the estimated and true values) pointing in the negative gradient direction  [58]. When compared to other GBM algorithms, XGBoost provides regularized boosting, helping to reduce overfitting and thus providing more generalizable results  [57, 59]. For a fast and accurate machine learning model hyper-parameter tuning on big datasets comprising of several features, we employed Bayesian optimization  [60, 61]. The XGBoost has several hyper-parameters, such as the learning rate, the minimum loss reduction required to make a further partition of a leaf node, the maximum depth of a tree, the maximum number of leaves, and the regularization weights. In order to choose the best parameters for the classification in this high dimensional hyper-parameter space, we used Bayesian optimization  [60, 61]  (figure  1(F) ).