Musical tension in harmonic intervals: behavioral and neural correlates

Musical tension is what drives our emotional experience in music listening. However, the specific role of the musical elements involved in tension-resolution perception remains largely unclear. This dissertation aims to advance the understanding of tension perception dynamics related to sensory consonance-dissonance. The first experiment aimed to design and validate a new crossmodal proprioceptive device for tension rating that overcomes some of the limitations of known tools. As a result, a psychophysical equation for the matching of physical force and psychological force was presented. The same tool was subsequently used in the second and third experiments to collect ratings of perceived tension and movement in harmonic musical intervals and standard noises. Besides, a visual analog scale (VAS) was used to allow a comparison of these two methods. The results confirmed the close relationship between sensory dissonance and perceived tension. Moreover, stimuli in the higher pitch register were perceived as more tense, confirming the primary role of pitch as a mediator of tension. The comparison between ratings obtained with the proprioceptive device and the VAS highlighted the tendency to give higher tension ratings using the VAS compared to the proprioceptive device. In the last experiment, brain electrical activity was recorded during the presentation of short tension-resolution patterns created using the most tense (perfect unison, fourth, and fifth) and the least tense harmonic intervals (augmented fourth, minor second, and inverted major seventh) to understand how consonance-dissonance can convey meaningful information on perceived tension-resolution. Results showed overall larger effects during the ‘resolution’ condition compare to the ‘tension induction’ condition, indicating that the resolution of harmonic instability towards a state of stability may be more salient than its opposite. A late positive component (LPC) was elicited, possibly reflecting deeper processing of tension-related meaning within a minimal harmonic context.

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly larger than seen during training.

Hybrid Artificial Intelligence to Extract Patterns and Rules from Argumentative and Legal Texts

This Thesis is composed of a collection of works written in the period 2019-2022, whose aim is to find methodologies of Artificial Intelligence (AI) and Machine Learning to detect and classify patterns and rules in argumentative and legal texts. We define our approach “hybrid”, since we aimed at designing hybrid combinations of symbolic and sub-symbolic AI, involving both “top-down” structured knowledge and “bottom-up” data-driven knowledge. A first group of works is dedicated to the classification of argumentative patterns. Following the Waltonian model of argument and the related theory of Argumentation Schemes, these works focused on the detection of argumentative support and opposition, showing that argumentative evidences can be classified at fine-grained levels without resorting to highly engineered features. To show this, our methods involved not only traditional approaches such as TFIDF, but also some novel methods based on Tree Kernel algorithms. After the encouraging results of this first phase, we explored the use of a some emerging methodologies promoted by actors like Google, which have deeply changed NLP since 2018-19 — i.e., Transfer Learning and language models. These new methodologies markedly improved our previous results, providing us with best-performing NLP tools. Using Transfer Learning, we also performed a Sequence Labelling task to recognize the exact span of argumentative components (i.e., claims and premises), thus connecting portions of natural language to portions of arguments (i.e., to the logical-inferential dimension). The last part of our work was finally dedicated to the employment of Transfer Learning methods for the detection of rules and deontic modalities. In this case, we explored a hybrid approach which combines structured knowledge coming from two LegalXML formats (i.e., Akoma Ntoso and LegalRuleML) with sub-symbolic knowledge coming from pre-trained (and then fine-tuned) neural architectures.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Predicting corticospinal excitability from pre-TMS EEG using convolutional neural networks

The amplitude of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) of the primary motor cortex (M1) shows a large variability from trial to trial, although MEPs are evoked by the same repeated stimulus. A multitude of factors is believed to influence MEP amplitudes, such as cortical, spinal and motor excitability state. The goal of this work is to explore to which degree the variation in MEP amplitudes can be explained by the cortical state right before the stimulation. Specifically, we analyzed a dataset acquired on eleven healthy subjects comprising, for each subject, 840 single TMS pulses applied to the left M1 during acquisition of electroencephalography (EEG) and electromyography (EMG). An interpretable convolutional neural network, named SincEEGNet, was utilized to discriminate between low- and high-corticospinal excitability trials, defined according to the MEP amplitude, using in input the pre-TMS EEG. This data-driven approach enabled considering multiple brain locations and frequency bands without any a priori selection. Post-hoc interpretation techniques were adopted to enhance interpretation by identifying the more relevant EEG features for the classification. Results show that individualized classifiers successfully discriminated between low and high M1 excitability states in all participants. Outcomes of the interpretation methods suggest the importance of the electrodes situated over the TMS stimulation site, as well as the relevance of the temporal samples of the input EEG closer to the stimulation time. This novel decoding method allows causal investigation of the cortical excitability state, which may be relevant for personalizing and increasing the efficacy of therapeutic brain-state dependent brain stimulation (for example in patients affected by Parkinson’s disease).

Tree regularized neural networks for key point analysis

This thesis contributes to the ArgMining 2021 shared task on Key Point Analysis. Key Point Analysis entails extracting and calculating the prevalence of a concise list of the most prominent talking points, from an input corpus. These talking points are usually referred to as key points. Key point analysis is divided into two subtasks: Key Point Matching, which involves assigning a matching score to each key point/argument pair, and Key Point Generation, which consists of the generation of key points. The task of Key Point Matching was approached using different models: a pretrained Sentence Transformers model and a tree-constrained Graph Neural Network were tested. The best model was the fine-tuned Sentence Transformers, which achieved a mean Average Precision score of 0.75, ranking 12 compared to other participating teams. The model was then used for the subtask of Key Point Generation using the extractive method in the selection of key point candidates and the model developed for the previous subtask to evaluate them.

Central chemoreceptors and neural mechanisms of cardiorespiratory control

The arterial partial pressure (P CO2) of carbon dioxide is virtually constant because of the close match between the metabolic production of this gas and its excretion via breathing. Blood gas homeostasis does not rely solely on changes in lung ventilation, but also to a considerable extent on circulatory adjustments that regulate the transport of CO2 from its sites of production to the lungs. The neural mechanisms that coordinate circulatory and ventilatory changes to achieve blood gas homeostasis are the subject of this review. Emphasis will be placed on the control of sympathetic outflow by central chemoreceptors. High levels of CO2 exert an excitatory effect on sympathetic outflow that is mediated by specialized chemoreceptors such as the neurons located in the retrotrapezoid region. In addition, high CO2 causes an aversive awareness in conscious animals, activating wake-promoting pathways such as the noradrenergic neurons. These neuronal groups, which may also be directly activated by brain acidification, have projections that contribute to the CO2-induced rise in breathing and sympathetic outflow. However, since the level of activity of the retrotrapezoid nucleus is regulated by converging inputs from wake-promoting systems, behavior-specific inputs from higher centers and by chemical drive, the main focus of the present manuscript is to review the contribution of central chemoreceptors to the control of autonomic and respiratory mechanisms.

Evaluation of flow patterns and elongated bubble characteristics during the flow boiling of halocarbon refrigerants in a micro-scale channel

In the present study, quasi-diabatic two-phase flow pattern visualizations and measurements of elongated bubble velocity, frequency and length were performed. The tests were run for R134a and R245fa evaporating in a stainless steel tube with diameter of 2.32 mm, mass velocities ranging from 50 to 600 kg/m(2) s and saturation temperatures of 22 degrees C, 31 degrees C and 41 degrees C. The tube was heated by applying a direct DC current to its surface. Images from a high-speed video-camera (8000 frames/s) obtained through a transparent tube just downstream the heated sections were used to identify the following flow patterns: bubbly, elongated bubbles, churn and annular flows. The visualized flow patterns were compared against the predictions provided by Barnea et al. (1983) [1], Felcar et al. (2007) [10], Revellin and Thome (2007) [3] and Ong and Thome (2009) [11]. From this comparison, it was found that the methods proposed by Felcar et al. (2007) [10] and Ong and Thome (2009) [1] predicted relatively well the present database. Additionally, elongated bubble velocities, frequencies and lengths were determined based on the analysis of high-speed videos. Results suggested that the elongated bubble velocity depends on mass velocity, vapor quality and saturation temperature. The bubble velocity increases with increasing mass velocity and vapor quality and decreases with increasing saturation temperature. Additionally, bubble velocity was correlated as linear functions of the two-phase superficial velocity. (C) 2010 Elsevier Inc. All rights reserved.

Implementation of a Control Loop Experiment in a Network-Based Control System With LonWorks Technology and IP Networks

Considering the increasing popularity of network-based control systems and the huge adoption of IP networks (such as the Internet), this paper studies the influence of network quality of service (QoS) parameters over quality of control parameters. An example of a control loop is implemented using two LonWorks networks (CEA-709.1) interconnected by an emulated IP network, in which important QoS parameters such as delay and delay jitter can be completely controlled. Mathematical definitions are provided according to the literature, and the results of the network-based control loop experiment are presented and discussed.

Mapping the Contemporary Studies on Alliances and Strategic Networks

The competition among the companies depends on the velocity and efficience they can create and commercialize knowledge in a timely and cost-efficient manner. In this context, collaboration emerges as a reaction to the environmental changes. Although strategic alliances and networks have been exploited in the strategic literature for decades, the complexity and continuous usage of these cooperation structures, in a world of growing competition, justify the continuous interest in both themes. This article presents a scanning of the contemporary academic production in strategic alliances and networks, covering the period from January 1997 to august 2007, based on the top five journals accordingly to the journal of Citation Report 2006 in the business and management categories simultaneously. The results point to a retraction in publications about strategic alliances and a significant growth in the area of strategic. networks. The joint view of strategic alliances and networks, cited by some authors a the evolutionary path of study, still did not appear salient. The most cited topics found in the alliance literature are the governance structure, cooperation, knowledge transfer, culture, control, trust, alliance formation,,previous experience, resources, competition and partner selection. The theme network focuses mainly on structure, knowledge transfer and social network, while the joint vision is highly concentrated in: the subjects of alliance formation and the governance choice.

Governing coordination: Behavioural principles and neural correlates

The coordination of movement is governed by a coalition of constraints. The expression of these constraints ranges from the concrete—the restricted range of motion offered by the mechanical configuration of our muscles and joints; to the abstract—the difficulty that we experience in combining simple movements into complex rhythms. We seek to illustrate that the various constraints on coordination are complementary and inclusive, and the means by which their expression and interaction are mediated systematically by the integrative action of the central nervous system (CNS). Beyond identifying the general principles at the behavioural level that govern the mutual interplay of constraints, we attempt to demonstrate that these principles have as their foundation specific functional properties of the cortical motor systems. We propose that regions of the brain upstream of the motor cortex may play a significant role in mediating interactions between the functional representations of muscles engaged in sensorimotor coordination tasks. We also argue that activity in these ldquosupramotorrdquo regions may mediate the stabilising role of augmented sensory feedback.

Effect of density on spatial distribution, pit formation and pit diameter of Myrmeleon acer Walker, (Neuroptera : Myrmeleontidae): patterns and processes

The effect of increasing population density on the formation of pits, their size and spatial distribution, and on levels of mortality was examined in the antlion Myrmeleon acer Walker. Antlions were kept at densities ranging from 0.4 to 12.8 individuals per 100 cm(2). The distribution of pits was regular or uniform across all densities, but antlions constructed proportionally fewer and smaller pits as density increased. Mortality through cannibalism was very low and only occurred at densities greater than five individuals per 100 cm(2). Antlions in artificially crowded situations frequently relocated their pits and when more space became available, individuals became more dispersed with time. Redistribution of this species results from active avoidance of other antlions and sand throwing associated with pit construction and maintenance, rather than any attempt to optimise prey capture per se.

Bleeding patterns and menstrual-related symptoms with the continuous use of a contraceptive combination of ethinylestradiol and drospirenone: a randomized study

Background: The objective of this study was to compare bleeding patterns of women using a contraceptive combination of 30 mcg ethinylestradiol (EE) and 3 mg drospirenone (DRSP) continuously or cyclically. Menstrual-related symptoms were also evaluated. Study Design: This open, prospective, randomized study evaluated 78 women using the EE/DRSP combination continuously for 168 days or for six 28-day cycles, each followed by a 7-day hormone-free interval. A diary with pre-established scales was used to assess the frequency and intensity of bleeding and menstrual-related symptoms. Results: Amenorrhea increased with continuous use; 62.2% of women with continuous use were amenorrheic at the end of treatment (95% CI: 46.6-77.8%). Dysmenorrhea, headache, acne, nausea, edema and increased appetite improved significantly in the continuous-use group, and mastalgia and edema in the cyclic-use group (p<.05). Six subjects in the continuous-use group (15.4%) and three in the cyclic-use group (7.7%) discontinued due to adverse events. Conclusions: Continuous use was associated with amenorrhea and fewer menstrual-related symptoms compared to cyclic use. (C) 2010 Elsevier Inc. All rights reserved.

Altered loading patterns and femoral bone mineral density in children with unilateral Legg-Calve-Perthes disease

Maximization of bone accrual during the growing years is thought to be an important factor in minimizing fracture risk in old age. Mechanical loading through physical activity has been recommended as a modality for the conservation of bone mineral in adults; however, few studies have evaluated the impact of different loading regimes in growing children. The purpose of this study was to compare bone mineral density (BMD) in weight-bearing and non-weight-bearing limbs in 17 children with unilateral Legg Calve Perthes Disease (LCPD). Children with this condition have an altered weight-bearing pattern whereby there is increased mechanical loading on the noninvolved normal hip and reduced loading on the involved painful hip. Thus, these children provide a unique opportunity to study the impact of differential mechanical loading on BMD during the growing years while controlling for genetic disposition. BMD at four regions of the proximal femur (trochanter, intertrochanter, femoral neck, total of the regions) was measured using dual energy x-ray absorptiometry (DXA), and the values were compared between the involved and noninvolved sides of the children with LCPD. The BMD of both sides also were compared with normative values based on both chronological and skeletal age data. A significantly higher BMD was found on the noninvolved side over the involved side for all regions (P