Poset associahedra as sections of graph associahedra

Poset associahedra are a family of convex polytopes recently introduced by Pavel Galashin in 2021. The associahedron An is an (n-2)-dimensional convex polytope whose facial structure encodes the ways of parenthesizing an n-letter word (among several equivalent combinatorial objects). Associahedra are deeply studied polytopes that appear naturally in many areas of mathematics: algebra, combinatorics, geometry, topology... They have many presentations and generalizations. One of their incarnations is as a compactification of the configuration space of n points on a line. Similarly, the P-associahedron of a poset P is a compactification of the configuration space of order preserving maps from P to R. Galashin presents poset associahedra as combinatorial objects and shows that they can be realized as convex polytopes. However, his proof is not constructive, in the sense that no explicit coordinates are provided. The main goal of this thesis is to provide an explicit construction of poset associahedra as sections of graph associahedra, thus solving the open problem stated in Remark 1.5 of Galashin's paper.

Graph theory and geometric deep learning

La seguente tesi propone un’introduzione al geometric deep learning. Nella prima parte vengono presentati i concetti principali di teoria dei grafi ed introdotta una dinamica di diffusione su grafo, in analogia con l’equazione del calore. A seguire, iniziando dal linear classifier verranno introdotte le architetture che hanno portato all’ideazione delle graph convolutional networks. In conclusione, si analizzano esempi di alcuni algoritmi utilizzati nel geometric deep learning e si mostra una loro implementazione sul Cora dataset, un insieme di dati con struttura a grafo.

Graph-based Vision-Language Model for Multi-Modal Retrieval of Fashion Products

Artificial Intelligence is reshaping the field of fashion industry in different ways. E-commerce retailers exploit their data through AI to enhance their search engines, make outfit suggestions and forecast the success of a specific fashion product. However, it is a challenging endeavour as the data they possess is huge, complex and multi-modal. The most common way to search for fashion products online is by matching keywords with phrases in the product's description which are often cluttered, inadequate and differ across collections and sellers. A customer may also browse an online store's taxonomy, although this is time-consuming and doesn't guarantee relevant items. With the advent of Deep Learning architectures, particularly Vision-Language models, ad-hoc solutions have been proposed to model both the product image and description to solve this problems. However, the suggested solutions do not exploit effectively the semantic or syntactic information of these modalities, and the unique qualities and relations of clothing items. In this work of thesis, a novel approach is proposed to address this issues, which aims to model and process images and text descriptions as graphs in order to exploit the relations inside and between each modality and employs specific techniques to extract syntactic and semantic information. The results obtained show promising performances on different tasks when compared to the present state-of-the-art deep learning architectures.

Graph-based User Scheduling for MIMO LEO-based Satellite Communication Systems

The study of the user scheduling problem in a Low Earth Orbit (LEO) Multi-User MIMO system is the objective of this thesis. With the application of cutting-edge digital beamforming algorithms, a LEO satellite with an antenna array and a large number of antenna elements can provide service to many user terminals (UTs) in full frequency reuse (FFR) schemes. Since the number of UTs on-ground are many more than the transmit antennas on the satellite, user scheduling is necessary. Scheduling can be accomplished by grouping users into different clusters: users within the same cluster are multiplexed and served together via Space Division Multiple Access (SDMA), i.e., digital beamforming or Multi-User MIMO techniques; the different clusters of users are then served on different time slots via Time Division Multiple Access (TDMA). The design of an optimal user grouping strategy is known to be an NP-complete problem which can be solved only through exhaustive search. In this thesis, we provide a graph-based user scheduling and feed space beamforming architecture for the downlink with the aim of reducing user inter-beam interference. The main idea is based on clustering users whose pairwise great-circle distance is as large as possible. First, we create a graph where the users represent the vertices, whereas an edge in the graph between 2 users exists if their great-circle distance is above a certain threshold. In the second step, we develop a low complex greedy user clustering technique and we iteratively search for the maximum clique in the graph, i.e., the largest fully connected subgraph in the graph. Finally, by using the 3 aforementioned power normalization techniques, a Minimum Mean Square Error (MMSE) beamforming matrix is deployed on a cluster basis. The suggested scheduling system is compared with a position-based scheduler, which generates a beam lattice on the ground and randomly selects one user per beam to form a cluster.

Application of active and transfer learning to wound image segmentation

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications on wound management for pets. The importance of a precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for the chronic wounds. The goal of the research was to propose an automated pipeline capable of segmenting natural light-reflected wound images of animals. Two datasets composed by light-reflected images were used in this work: Deepskin dataset, 1564 human wound images obtained during routine dermatological exams, with 145 manual annotated images; Petwound dataset, a set of 290 wound photos of dogs and cats with 0 annotated images. Two implementations of U-Net Convolutioal Neural Network model were proposed for the automated segmentation. Active Semi-Supervised Learning techniques were applied for human-wound images to perform segmentation from 10% of annotated images. Then the same models were trained, via Transfer Learning, adopting an Active Semi- upervised Learning to unlabelled animal-wound images. The combination of the two training strategies proved their effectiveness in generating large amounts of annotated samples (94% of Deepskin, 80% of PetWound) with the minimal human intervention. The correctness of automated segmentation were evaluated by clinical experts at each round of training thus we can assert that the results obtained in this thesis stands as a reliable solution to perform a correct wound image segmentation. The use of Transfer Learning and Active Semi-Supervied Learning allows to minimize labelling effort from clinicians, even requiring no starting manual annotation at all. Moreover the performances of the model with limited number of parameters suggest the implementation of smartphone-based application to this topic, helping the future standardization of light-reflected images as acknowledge medical images.

Anomaly Detection in Random Graph

La presenti tesi ha come obiettivo lo studio di due algoritmi per il rilevamento di anomalie all' interno di grafi random. Per entrambi gli algoritmi sono stati creati dei modelli generativi di grafi dinamici in modo da eseguire dei test sintetici. La tesi si compone in una parte iniziale teorica e di una seconda parte sperimentale. Il secondo capitolo introduce la teoria dei grafi. Il terzo capitolo presenta il problema del rilevamento di comunità. Il quarto capitolo introduce possibili definizioni del concetto di anomalie dinamiche e il problema del loro rilevamento. Il quinto capitolo propone l' introduzione di un punteggio di outlierness associato ad ogni nodo sulla base del confronto tra la sua dinamica e quella della comunità a cui appartiene. L' ultimo capitolo si incentra sul problema della ricerca di una descrizione della rete in termini di gruppi o ruoli sulla base della quale incentrare la ricerca delle anomalie dinamiche.

Knowledge-based Chord Embeddings

This thesis develops AI methods as a contribution to computational musicology, an interdisciplinary field that studies music with computers. In systematic musicology a composition is defined as the combination of harmony, melody and rhythm. According to de La Borde, harmony alone "merits the name of composition". This thesis focuses on analysing the harmony from a computational perspective. We concentrate on symbolic music representation and address the problem of formally representing chord progressions in western music compositions. Informally, chords are sets of pitches played simultaneously, and chord progressions constitute the harmony of a composition. Our approach combines ML techniques with knowledge-based techniques. We design and implement the Modal Harmony ontology (MHO), using OWL. It formalises one of the most important theories in western music: the Modal Harmony Theory. We propose and experiment with different types of embedding methods to encode chords, inspired by NLP and adapted to the music domain, using both statistical (extensional) knowledge by relying on a huge dataset of chord annotations (ChoCo), intensional knowledge by relying on MHO and a combination of the two. The methods are evaluated on two musicologically relevant tasks: chord classification and music structure segmentation. The former is verified by comparing the results of the Odd One Out algorithm to the classification obtained with MHO. Good performances (accuracy: 0.86) are achieved. We feed a RNN for the latter, using our embeddings. Results show that the best performance (F1: 0.6) is achieved with embeddings that combine both approaches. Our method outpeforms the state of the art (F1 = 0.42) for symbolic music structure segmentation. It is worth noticing that embeddings based only on MHO almost equal the best performance (F1 = 0.58). We remark that those embeddings only require the ontology as an input as opposed to other approaches that rely on large datasets.

Preference Mapping Of Dulce De Leche Commercialized In Brazilian Markets.

Dulce de leche samples available in the Brazilian market were submitted to sensory profiling by quantitative descriptive analysis and acceptance test, as well sensory evaluation using the just-about-right scale and purchase intent. External preference mapping and the ideal sensory characteristics of dulce de leche were determined. The results were also evaluated by principal component analysis, hierarchical cluster analysis, partial least squares regression, artificial neural networks, and logistic regression. Overall, significant product acceptance was related to intermediate scores of the sensory attributes in the descriptive test, and this trend was observed even after consumer segmentation. The results obtained by sensometric techniques showed that optimizing an ideal dulce de leche from the sensory standpoint is a multidimensional process, with necessary adjustments on the appearance, aroma, taste, and texture attributes of the product for better consumer acceptance and purchase. The optimum dulce de leche was characterized by high scores for the attributes sweet taste, caramel taste, brightness, color, and caramel aroma in accordance with the preference mapping findings. In industrial terms, this means changing the parameters used in the thermal treatment and quantitative changes in the ingredients used in formulations.

Lateral Pterygoid Muscle Volume And Migraine In Patients With Temporomandibular Disorders.

Lateral pterygoid muscle (LPM) plays an important role in jaw movement and has been implicated in Temporomandibular disorders (TMDs). Migraine has been described as a common symptom in patients with TMDs and may be related to muscle hyperactivity. This study aimed to compare LPM volume in individuals with and without migraine, using segmentation of the LPM in magnetic resonance (MR) imaging of the TMJ. Twenty patients with migraine and 20 volunteers without migraine underwent a clinical examination of the TMJ, according to the Research Diagnostic Criteria for TMDs. MR imaging was performed and the LPM was segmented using the ITK-SNAP 1.4.1 software, which calculates the volume of each segmented structure in voxels per cubic millimeter. The chi-squared test and the Fisher's exact test were used to relate the TMD variables obtained from the MR images and clinical examinations to the presence of migraine. Logistic binary regression was used to determine the importance of each factor for predicting the presence of a migraine headache. Patients with TMDs and migraine tended to have hypertrophy of the LPM (58.7%). In addition, abnormal mandibular movements (61.2%) and disc displacement (70.0%) were found to be the most common signs in patients with TMDs and migraine. In patients with TMDs and simultaneous migraine, the LPM tends to be hypertrophic. LPM segmentation on MR imaging may be an alternative method to study this muscle in such patients because the hypertrophic LPM is not always palpable.

Spinal Cord Damage In Machado-joseph Disease.

Machado-Joseph disease (SCA3) is the most frequent spinocerebellar ataxia worldwide and characterized by remarkable phenotypic heterogeneity. MRI-based studies in SCA3 focused in the cerebellum and connections, but little is known about cord damage in the disease and its clinical relevance. To evaluate the spinal cord damage in SCA3 through quantitative analysis of MRI scans. A group of 48 patients with SCA3 and 48 age and gender-matched healthy controls underwent MRI on a 3T scanner. We used T1-weighted 3D images to estimate the cervical spinal cord area (CA) and eccentricity (CE) at three C2/C3 levels based on a semi-automatic image segmentation protocol. The scale for assessment and rating of ataxia (SARA) was employed to quantify disease severity. The two groups-SCA3 and controls-were significantly different regarding CA (49.5 ± 7.3 vs 67.2 ± 6.3 mm(2), p < 0.001) and CE values (0.79 ± 0.06 vs 0.75 ± 0.05, p = 0.005). In addition, CA presented a significant correlation with SARA scores in the patient group (p = 0.010). CE was not associated with SARA scores (p = 0.857). In the multiple variable regression, we found that disease duration was the only variable associated with CA (coefficient = -0.629, p = 0.025). SCA3 is characterized by cervical cord atrophy and antero-posterior flattening. In addition, the spinal cord areas did correlate with disease severity. This suggests that quantitative analyses of the spinal cord MRI might be a useful biomarker in SCA3.

CARACTERÍSTICAS DE QUALIDADE DO CONTRAFILÉ (m. L. dorsi) DE TOUROS JOVENS DA RAÇA NELORE

Quality traits of boneless rib cut (L. dorsi muscle) from Nelore young bulls. To study the meat quality traits of Nelore breed young bulls, and the effect of age (690-780 days) on them, 113 animals were slaughtered after 109 days of intensive feeding with 20% concentrate and 80% roughage. All the carcasses were graded at the slaughter floor by the Federal Inspection and chilled for 24 hours (Tinitial=5°C, Tfinal=2°C). Fifty one half carcasses (right side), type B - B R A S I L `s grading system - from animals of 23 to 26 months were boned and separated into commercial cuts. Two steaks (2.5cm thick) were removed from each boneless rib cut (m. L. dorsi), vacuum packaged and aged for 7 days (0-2°C). The pH varied from 5.44 to 5.83 and only two samples had pH ³ 5.70. The L* (brightness) average value was 34.85. The water and fat content were 75.65% and 1.71%, respectively. The average WB shear force was 6.70kg, and it was not affected by age (690-734 days), but presented a trend (t test, p=0.22) for increasing values between 735 and 780 days. Animal age did not affect other quality traits (t test, p>0.20). It was concluded that the rib cut from Nelore young bulls may not have a good acceptability in exigent markets, and that carcasses graded B, presumed to be the best grade, do not necessarily present the best meat quality characteristics.

Ibopamina 2% vs. sobrecarga hídrica como teste provocativo para glaucoma

PURPOSE: To compare the 2% ibopamine provocative test with the water drinking test as a provocative test for glaucoma. METHODS: Primary open-angle glaucoma patients and normal individuals were selected from CEROF-Universidade Federal de Goiânia UFG, and underwent the 2% ibopamine provocative test and the water drinking test in a randomized fashion, at least 1 week apart. Intraocular pressure (IOP) before and after both tests, Bland-Altman graph, sensitivity and specificity (as mesured by ROC curves) were obtained for both methods. RESULTS: Forty-seven eyes from 25 patients were included (27 eyes from 15 glaucoma patients and 20 eyes from 10 normal individuals), with a mean age of 54.2 ± 12.7 years. The mean MD of glaucoma patients was -2.8 ± 2.11 dB. There was no statistically difference in the baseline IOP (p=0.8) comparing glaucoma patients, but positive after the provocative tests (p=0.03), and in the IOP variation (4.4 ± 1.3 mmHg for ibopamine and 3.2 ± 2.2 mmHg for water drinking test, p=0.01). There was no difference in all studied parameters for normal individuals. The Bland-Altman graph showed high dispersion comparing both methods. The areas under the ROC curve were 0.987 for the ibopamine provocative test, and 0.807 for the water-drinking test. CONCLUSION: In this selected subgroup of glaucoma patients with early visual field defect, the ibopamine provocative test has shown better sensitivity/specificity than the water drinking test.

Representação da área de responsabilidade de jogadores de futebol através do Diagrama de Voronoi

Universidade Estadual de Campinas . Faculdade de Educação Física

Educação física escolar e gênero : diferentes maneiras de participar das aulas

Universidade Estadual de Campinas. Faculdade de Educação Física

As roupas esportivas em Revista na cidade de Belo Horizonte (1929-1950) : moldes, recortes e costuras

Universidade Estadual de Campinas. Faculdade de Educação Física