Miracle-dz/UniFAR-FTUs huggingface dataset:
HARPO: beam characterization of a TPC for gamma-ray polarimetry and high angular-resolution astronomy in the MeV-GeV range

A time projection chamber (TPC) can be used to measure the polarization of gamma rays with excellent angular precision and sensitivity in the MeV-GeV energy range through the conversion of photons to e+e− pairs. The Hermetic ARgon POlarimeter (HARPO) prototype was built to demonstrate this concept. It was recently tested in the polarized photon beam at the NewSUBARU facility in Japan. We present this data-taking run, which demonstrated the excellent performance of the HARPO TPC.
Nonlinear approaches to Independent Component Analysis

Recently, there has been a great interest in statistical models for learning data representations. A popular method for this task is Independent Component Analysis (ICA) which has been successfully applied for the blind separation of mixed sounds and the analysis of biomedical signals. ICA relies on strong assumptions such as the linear mixing model, the requirement that the number of sensors are equal or greater than the number of sources, and that there is no additive noise. Some of the constraints are violated in practical applications and in order to relax the assumptions new methods need to be developed which involve nonlinear unmixing or inference solutions. In this paper, we summarize some techniques that involve nonlinear ICA solutions. Two approaches are presented to tackle the nonlinear mixing case, and nonlinear ICA solutions are summarized for overcomplete representation as well as additive noise problems.
Acyldepsipeptide Analogues: A Future Generation Antibiotics for Tuberculosis Treatment

Acyldepsipeptides (ADEPs) are a new class of emerging antimicrobial peptides (AMPs), which are currently explored for treatment of pathogenic infections, including tuberculosis (TB). These cyclic hydrophobic peptides have a unique bacterial target to the conventional anti-TB drugs, and present a therapeutic window to overcome Mycobacterium Tuberculosis (M. tb) drug resistance. ADEPs exerts their antibacterial activity on M. tb strains through activation of the protein homeostatic regulatory protease, the caseinolytic protease (ClpP1P2). ClpP1P2 is normally regulated and activated by the ClpP-ATPases to degrade misfolded and toxic peptides and/or short proteins. ADEPs bind and dysregulate all the homeostatic capabilities of ClpP1P2 while inducing non-selective proteolysis. The uncontrolled proteolysis leads to M. tb cell death within the host. ADEPs analogues that have been tested possess cytotoxicity and poor pharmacokinetic and pharmacodynamic properties. However, these can be improved by drug design techniques. Moreover, the use of nanomaterial in conjunction with ADEPs would yield effective synergistic effect. This new mode of action has potential to combat and eradicate the extensive multi-drug resistance (MDR) problem that is currently faced by the public health pertaining bacterial infections, especially TB.
Application of Machine Learning to Environmental DNA Metabarcoding

Machine learning is known as an effective technique to classify big data, and coding methods for input into neural networks are critical. In this study, we have applied the machine learning to environmental deoxyribonucleic acid (DNA) metabarcoding. We propose three coding methods of the nucleic acid sequences, and one of them is two-bit coding. In this coding method, one base is represented by two bits, and four types of bases are associated with two-bit binary numbers. A three-layer perceptron is used as a neural network, and backpropagation is used as a learning rule. The accuracy for the two-bit coding is slightly higher than those for the other coding methods, and it can be decided that the two-bit coding is the best coding method mainly because of its advantage, namely, the amount of information required to indicate the nucleic acid sequences is relatively small. We conclude that the environmental DNA metabarcoding is an interesting example as a novel application of machine learning.
Semi-finished flywheel disk based on deep learning research on surface defect detection technology

The surface defects of flywheel disc semi-finished products have complex and changeable morphological characteristics and random distribution. At present, relevant enterprises can only detect them through manual visual inspection. However, the low efficiency of manual inspection and the unstable inspection quality can easily lead to false inspections and missed inspections, which cannot meet the growing demand for production capacity. In order to achieve intelligent and efficient detection of defects, this paper proposes a surface defect detection algorithm for flywheel disc semi-finished products based on improved faster region-based convolutional neural networks (Faster R-CNN). First of all, based on multi-scale feature fusion, residual feature recalibration and deformable convolution, this paper designs a feature extraction network that can better capture and characterize defect morphology. Secondly, optimize the design of Faster R-CNN algorithm, use k-means++ cluster analysis to optimize the anchor generation rules in the network, so as to adapt to the defects of large aspect ratio, the region of interest (ROI) pooling calculation method incorporating global feature information is redesigned to prevent the position deviation of candidate areas when they are mapped back to the original image. Aiming at the problem that adjacent overlapping positive samples are deleted by mistake, the soft non-maximum suppression (Soft-NMS) algorithm is used to optimize the non-maximum suppression process and increase the number of positive samples output by the region proposal network(RPN). Then, the surface defect images are collected to build a data set, aiming at the problem that the data set is small and the distribution of the number of defects in each category is unbalanced, the classical data enhancement methods are used to augment the data set and equalize the defect categories. Finally, the surface defect detection and application experiment research of flywheel disc semi-finished products is carried out. The detection accuracy of the algorithm in this paper on the surface defect test set reaches 92.7%, which is 9.6% higher than the original Faster R-CNN detection accuracy, and 18.5% higher for the detection accuracy of small minor defects, and the improvement effect is more obvious.
Architecture, Chromatin and Gene Organization of Toxoplasma gondii Subtelomeres

Subtelomeres (ST) are chromosome regions that separate telomeres from euchromatin and play relevant roles in various biological processes of the cell. While their functions are conserved, ST structure and genetic compositions are unique to each species. This study aims to identify and characterize the subtelomeric regions of the 13 Toxoplasma gondii chromosomes of the Me49 strain. Here, STs were defined at chromosome ends based on poor gene density. The length of STs ranges from 8.1 to 232.4 kbp, with a gene density of 0.049 genes/kbp, lower than the Me49 genome (0.15 kbp). Chromatin organization showed that H3K9me3, H2A.X, and H3.3 are highly enriched near telomeres and the 5′ end of silenced genes, decaying in intensity towards euchromatin. H3K4me3 and H2A.Z/H2B.Z are shown to be enriched in the 5′ end of the ST genes. Satellite DNA was detected in almost all STs, mainly the sat350 family and a novel satellite named sat240. Beyond the STs, only short dispersed fragments of sat240 and sat350 were found. Within STs, there were 12 functional annotated genes, 59 with unknown functions (Hypothetical proteins), 15 from multigene FamB, and 13 from multigene family FamC. Some genes presented low interstrain synteny associated with the presence of satellite DNA. Orthologues of FamB and FamC were also detected in Neospora caninum and Hammondia hammondi. A re-analysis of previous transcriptomic data indicated that ST gene expression is strongly linked to the adaptation to different situations such as extracellular passage (evolve and resequencing study) and changes in metabolism (lack of acetyl-CoA cofactor). In conclusion, the ST region of the T. gondii chromosomes was defined, the STs genes were determined, and it was possible to associate them with high interstrain plasticity and a role in the adaptability of T. gondii to environmental changes.
Origins of HIV, HCoV-HKU1, SFTSV, and MERS-CoV and Beyond

Tales of tracing the origins of human immunodeficiency virus (HIV), human coronavirus HKU1 (HCoV-HKU1), severe fever with thrombocytopenia syndrome virus (SFTSV), and Middle East respiratory syndrome coronavirus (MERS-CoV) can enlighten the scientific search for the origins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we detail key research studies on the origins of these four viruses.
Coordination Protocol and Admission Control for Distributed Services in System-of-Systems With Real-Time Requirements

System-of-Systems (SoS) offer unprecedented potential for new types of emerging services, which significantly exceed the capabilities of the constituting systems. SoS in safety-critical domains (e.g., medical applications, smart grid, disaster recovery, defense) are prominent examples, but they have stringent real-time and reliability requirements. Therefore, a suitable temporal and spatial allocation of resources is required both within each constituent system and in the wide area networks between them. This paper introduces an algorithm for admission control and resources’ allocation, which considers these requirements and the autonomy of the constituent systems. To simulate a realistic admission control and resources’ allocation process of a typical SoS network, a simulated case study with eight constituent systems, six services, and twenty-five processes/requests is developed. The suggested admission control and resources’ allocation process’s performance is measured in terms of gain in the execution time and blockage probability. A sensitivity analysis is carried out to evaluate the influence of the number of constituent systems and the number of services sought by the received processes/requests on the efficacy of the proposed process. The results show that the proposed admission control and resources’ allocation process have very low blockage probability, high gain in the execution time, and high resources’ utilization.
Factors affecting the percept of Yanny v. Laurel (or mixed): Insights from a large-scale study on Swiss German listeners

In May 2018, Yanny v. Laurel went viral: when listening to the same audio clip, some people claimed to hear only Yanny , others insisted it must be Laurel , and some had a mixed percept. Phoneticians have identified the acoustic features which caused this perceptual ambiguity, but we still know little about the factors affecting individuals’ perception of the illusion. We conducted a controlled study with 974 Swiss German listeners, balanced for age, gender, and regional origin. Overall, nearly two thirds heard Yanny , one quarter Laurel , and about 12% had a mixed percept. We found age, gender, and electronic device to play a significant role: younger, female, and laptop-using participants demonstrated higher proportions of Yanny responses. These findings contribute to the growing body of research on polyperceivable words.
Forecasting Oil Prices: A Comparative Analysis between Neural Network and Regression Models

: After the war between Russia and Ukraine and its implications on various economies, energy security be-came a trending subject at the international level in 2022. Crude oil is an essential resource that plays a strategic role and its fluctuation has a major impact not only on the firms’ profitability but also the stability of several countries. This research examines the possibility of forecasting oil prices where artificial neural network methods in addition to the multiple linear regressions were used in order to attempt to come out with a decent model that can help in forecasting oil prices. A comparison between these two models took place in order to choose the best one. The uniqueness of this research relied on the fact that 26 different variables were used all together, some of them were used for the first time in order to build the forecasting model. The period for constructing and testing both models extended from August 2006 to the beginning of 2019. The neural network model that was built showed to be more promising than the model that used multiple linear regression.
Bottom-up discovery of structure and variation in response tokens ('backchannels') across diverse languages

Response tokens (also known as backchannels, continuers, or feedback) are a frequent feature of human interaction, where they serve to display understanding and streamline turn-taking. We propose a bottom-up method to study responsive behaviour across 16 languages (8 language families). We use sequential context and recurrence of turns formats to identify candidate response tokens in a language-agnostic way across diverse conversational corpora. We then use UMAP clustering directly on speech signals to represent structure and variation. We find that (i) written orthographic annotations underrepresent the at-tested variation, (ii) distinctions between formats can be gradient rather than discrete, (iii) most languages appear to make available a broad distinction between a minimal nasal format ‘mm’ and a fuller ‘yeah’-like format. Charting this aspect of human interaction contributes to our understanding of interactional infrastructure across languages and can inform the design of speech technologies.
Data Augmentation for End-to-end Silent Speech Recognition for Laryngectomees

Silent speech recognition (SSR) predicts textual information from silent articulation, which is an algorithm design in silent speech interfaces (SSIs). SSIs have the potential of recov-ering the speech ability of individuals who lost their voice but can still articulate (e.g., laryngectomees). Due to the lo-gistic difficulties in articulatory data collection, current SSR studies suffer limited amount of dataset. Data augmentation aims to increase the training data amount by introducing variations into the existing dataset, but has rarely been investigated in SSR for laryngectomees. In this study, we investigated the effectiveness of multiple data augmentation approaches for SSR including consecutive and intermittent time masking, articulatory dimension masking, sinusoidal noise injection and randomly scaling. Different experimental setups including speaker-dependent, speaker-independent, and speaker-adaptive were used. The SSR models were end-to-end speech recognition models trained with connectionist temporal classification (CTC). Electromagnetic articulography (EMA) datasets collected from multiple healthy speakers and laryngectomees were used. The experimental results have demonstrated that the data augmentation approaches explored performed differently, but generally improved SSR performance. Especially, the consecutive time masking has brought significant improvement on SSR for both healthy speakers and laryngectomees.
Λ-symmetry and background independence of noncommutative gauge theory on Bbb Rn

Background independence of noncommutative Yang-Mills theory on n is discussed. The quantity θθ−θ is found to be background dependent at subleading order, and it becomes background independent only when the ordinary gauge field strength F is constant. It is shown that, at small values of B, the noncommutative Dirac-Born-Infeld action possesses Λ-symmetry at least to subleading order in θ if F damps fast enough at infinity.
Neutron star high‐mass binaries as the origin of SGR/AXP

A close high-mass binary system consisting of a neutron star (NS) and a massive OB supergiant companion is expected to lead to a Thorne-Żytkow object (TZO) structure, which consists of a NS core and a stellar envelope. We use the scenario machine program to calculate the formation tracks of TZOs in close high-mass NS binaries and their subsequent evolution. We propose and demonstrate that the explosion and instant contraction of a TZO structure leave its stellar remnant as a soft gamma-ray repeater and an anomalous X-ray pulsar respectively. (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
NTF of Spectral and Spatial Features for Tracking and Separation of Moving Sound Sources in Spherical Harmonic Domain

This paper presents a novel Non-negative Tensor Factorization (NTF) based approach to tracking and separation of moving sound sources, formulated in the Spherical Harmonic Domain (SHD). In particular, at first, we redefine an already existing Ambisonic NTF by introducing time-dependence into the Spatial Covariance Matrix (SCM) model. Next, we further extend the time-dependent SCM by incorporating a newly proposed NTF model of the spatial features, thereby introducing spatial components. To exploit the relationship between the positions of sound sources in adjacent time frames, resulting from the naturally occurring continuity of the movement itself, we impose local smoothness on time-dependent components of the spatial features. To this end, we propose a suitable posterior probability with Gibbs prior, and finally we derive the corresponding up-date rules. The experimental evaluation is based on first-order Ambisonic recordings of speech utterances and musical instruments in several scenarios with moving sources.
Automatic Qualitative Analysis of Ordinary Differential Equations Using Piecewise Linear Approximations

This paper explores automating the qualitative analysis of physical systems. It describes a program, called PLR, that takes parameterized ordinary differential equations as input and produces a qualitative description of the solutions for all initial values. PLR approximates intractable nonlinear systems with piecewise linear ones, analyzes the approximations, and draws conclusions about the original systems. It chooses approximations that are accurate enough to reproduce the essential properties of their nonlinear prototypes, yet simple enough to be analyzed completely and efficiently. It derives additional properties, such as boundedness or periodicity, by theoretical methods. I demonstrate PLR on several common nonlinear systems and on published examples from mechanical engineering.
Exploring Few-Shot Fine-Tuning Strategies for Models of Visually Grounded Speech

In this paper, we study models of visually-grounded speech (VGS) in a few-shot setting. Beginning with a model that was pre-trained to associate natural images with speech waveforms describing the images, we probe the model’s ability to learn to recognize novel words and their visual referents from a limited number of additional examples. We define new splits for the SpokenCOCO dataset to facilitate few-shot word and object acquisition, explore various few-shot fine-tuning strategies in an effort to mitigate the catastrophic forgetting phenomenon, and identify several techniques that work well in this respect
Periprocedural Myocardial Infarction following Elective Percutaneous Coronary Interventions

The prognostic relevance of periprocedural myocardial infarction (PMI) in patients with chronic coronary syndrome undergoing percutaneous coronary intervention (PCI) is still matter of debate, particularly regarding the type (cardiac troponin or creatin kinase-MB) and different thresholds of biomarkers elevation, as the importance of associated ancillary criteria of ischemia or concomitant angiographic complications. There are still uncertainties regarding the value of PMI as event which is prognostically equivalent to spontaneous myocardial infarction or if it simply represents a marker of baseline risk, atherosclerotic burden and procedural complexity. In the present review, we will present the mechanisms and predictors of PMI occurring during PCI and potential treatment strategies to reduce its occurrence. We will also overview all commonly adopted definitions of PMI, which carry different prevalence and prognostic implications in daily practice and clinical trials. Finally, we will discuss the impact of different PMI definitions on the interpretation of trials results, emphasizing the importance of adequate endpoints selection in the planning and interpretation of clinical trials.
Textile-Based Wearable Sensor for Skin Hydration Monitoring

This research describes a wearable skin hydration sensor based on cotton textile to determine the state of hydration within the skin via impedance analysis. The sensor structure comprises a textile substrate, thermoplastic over-layer, conductive patterns, and encapsulant, designed for stable and reliable monitoring of the skin’s impedance change in relation to hydration level. The porcine skin with different hydration levels was prepared as a model system of the skin, and the textile-based sensor carefully investigated the porcine skin samples’ impedance characteristics. The impedance study reveals that (1) the total impedance of skin decreases as its hydration level increases, and (2) the impedance of the stratum corneum and epidermis layers are more dominantly affected by the hydration level of the skin than the dermis layer. Even after repetitive bending cycles, the impedance data of skin measured by the sensor exhibit a reliable dependence on the skin hydration level, which validates the flexibility and durability of the sensor. Finally, it is shown that the textile-based skin hydration sensor can detect various body parts’ different hydration levels of human skin while maintaining a stable conformal contact with the skin. The resulting data are well-matched with the readings from a commercial skin hydration sensor.
Multimodal Persuasive Dialogue Corpus using Teleoperated Android

We collected a corpus of persuasive dialogues containing multimodal information for building a persuasive dialogue system that encourages users to change their behaviors using multi-modal information. The corpus is constructed with an android robot that was remotely controlled by the WoZ method during user interactions with the system. We transcribed the collected speech and annotated dialogue-act labels. We also extracted the facial features of the dialogue participants. Pre-and post-questionnaires identiﬁed the subjects’ personality, their awareness of the target domain of persuasion, the changes in their awareness before/after the persuasion, and whether they agreed to the persuasion during the dialogues. In addition, we conducted a follow-up survey with each subject to investigate whether the persuasion actually affected their behavioral change. Moreover, we built linear classiﬁers that predict per-suasion success to investigate effective features.
Cartof Fierbinte - La Patata bollente! I confini tra competenze sociali e sanitarie e tra clinica e riabilitazione

Teo Vignoli, Miriam Savarese, Michela Tazzini, Stefano Aringhieri, Paola Avveduti, Giorgia Bondi, Monica Bosi, Simona Brunetti, Luigi Caputo, Gaetano Cerreto, Luigi De Donno, Sabrina Ferroni, Daniela Forlano, Giuseppa Ghetti, Cinzia Ghidoni, Daniele Innocenti, Giovanna Maria Latino, Mirta Malpassi, Andrea Martini, Elisa Martino, Antonella Monti, Angela Piovani, Federica Righi, Barbara Rossi, Marina Caterina Staccioli, Edoardo Cozzolino