Representing image captions as concept graphs using semantic information

Conceptual interpretation of languages has gathered peak interest in the world of artificial intelligence. The challenge in modeling various complications involved in a language is the main motivation behind our work. Our main focus in this work is to develop conceptual graphical representation for image captions. We have used discourse representation structure to gain semantic information which is further modeled into a graphical structure. The effectiveness of the model is evaluated by a caption based image retrieval system. The image retrieval is performed by computing subgraph based similarity measures. Best retrievals were given an average rating of . ± . out of 4 by a group of 25 human judges. The experiments were performed on a subset of the SBU Captioned Photo Dataset. This purpose of this work is to establish the cognitive sensibility of the approach to caption representations.

Simplicial Complexes From Graphs Toward Graph Persistence

Persistent homology is a branch of computational topology which uses geometry and topology for shape description and analysis. This dissertation is an introductory study to link persistent homology and graph theory, the connection being represented by various methods to build simplicial complexes from a graph. The methods we consider are the complex of cliques, of independent sets, of neighbours, of enclaveless sets and complexes from acyclic subgraphs, each revealing several properties of the underlying graph. Moreover, we apply the core ideas of persistence theory in the new context of graph theory, we define the persistent block number and the persistent edge-block number.

Edge-aware Graph Attention Networks: Joint Reasoning On Text and Knowledge Graphs for Biomedical Question Answering

Much of the real-world dataset, including textual data, can be represented using graph structures. The use of graphs to represent textual data has many advantages, mainly related to maintaining a more significant amount of information, such as the relationships between words and their types. In recent years, many neural network architectures have been proposed to deal with tasks on graphs. Many of them consider only node features, ignoring or not giving the proper relevance to relationships between them. However, in many node classification tasks, they play a fundamental role. This thesis aims to analyze the main GNNs, evaluate their advantages and disadvantages, propose an innovative solution considered as an extension of GAT, and apply them to a case study in the biomedical field. We propose the reference GNNs, implemented with methodologies later analyzed, and then applied to a question answering system in the biomedical field as a replacement for the pre-existing GNN. We attempt to obtain better results by using models that can accept as input both node and edge features. As shown later, our proposed models can beat the original solution and define the state-of-the-art for the task under analysis.

GENEOs, compactifications, and graphs

Our objective in this thesis is to study the pseudo-metric and topological structure of the space of group equivariant non-expansive operators (GENEOs). We introduce the notions of compactification of a perception pair, collectionwise surjectivity, and compactification of a space of GENEOs. We obtain some compactification results for perception pairs and the space of GENEOs. We show that when the data spaces are totally bounded and endow the common domains with metric structures, the perception pairs and every collectionwise surjective space of GENEOs can be embedded isometrically into the compact ones through compatible embeddings. An important part of the study of topology of the space of GENEOs is to populate it in a rich manner. We introduce the notion of a generalized permutant and show that this concept too, like that of a permutant, is useful in defining new GENEOs. We define the analogues of some of the aforementioned concepts in a graph theoretic setting, enabling us to use the power of the theory of GENEOs for the study of graphs in an efficient way. We define the notions of a graph perception pair, graph permutant, and a graph GENEO. We develop two models for the theory of graph GENEOs. The first model addresses the case of graphs having weights assigned to their vertices, while the second one addresses weighted on the edges. We prove some new results in the proposed theory of graph GENEOs and exhibit the power of our models by describing their applications to the structural study of simple graphs. We introduce the concept of a graph permutant and show that this concept can be used to define new graph GENEOs between distinct graph perception pairs, thereby enabling us to populate the space of graph GENEOs in a rich manner and shed more light on its structure.

Quantum Riemannian geometry on graphs for gauge field theory

In questo lavoro estendiamo concetti classici della geometria Riemanniana al fine di risolvere le equazioni di Maxwell sul gruppo delle permutazioni $S_3$. Cominciamo dando la strutture algebriche di base e la definizione di calcolo differenziale quantico con le principali proprietà. Generalizziamo poi concetti della geometria Riemanniana, quali la metrica e l'algebra esterna, al caso quantico. Tutto ciò viene poi applicato ai grafi dando la forma esplicita del calcolo differenziale quantico su $\mathbb{K}(V)$, della metrica e Laplaciano del secondo ordine e infine dell'algebra esterna. A questo punto, riscriviamo le equazioni di Maxwell in forma geometrica compatta usando gli operatori e concetti della geometria differenziale su varietà che abbiamo generalizzato in precedenza. In questo modo, considerando l'elettromagnetismo come teoria di gauge, possiamo risolvere le equazioni di Maxwell su gruppi finiti oltre che su varietà differenziabili. In particolare, noi le risolviamo su $S_3$.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

Thermodynamic Study Of The Micellization Of Zwitterionic Surfactants And Their Interaction With Polymers In Water By Isothermal Titration Calorimetry.

The micellization of a homologous series of zwitterionic surfactants, a group of sulfobetaines, was studied using isothermal titration calorimetry (ITC) in the temperature range from 15 to 65 °C. The increase in both temperature and the alkyl chain length leads to more negative values of ΔGmic(0) , favoring the micellization. The entropic term (ΔSmic(0)) is predominant at lower temperatures, and above ca. 55-65 °C, the enthalpic term (ΔHmic(0)) becomes prevalent, figuring a jointly driven process as the temperature increases. The interaction of these sulfobetaines with different polymers was also studied by ITC. Among the polymers studied, only two induced the formation of micellar aggregates at lower surfactant concentration: poly(acrylic acid), PAA, probably due to the formation of hydrogen bonds between the carboxylic group of the polymer and the sulfonate group of the surfactant, and poly(sodium 4-styrenesulfonate), PSS, probably due to the incorporation of the hydrophobic styrene group into the micelles. The prevalence of the hydrophobic and not the electrostatic contributions to the interaction between sulfobetaine and PSS was confirmed by an increased interaction enthalpy in the presence of electrolytes (NaCl) and by the observation of a significant temperature dependence, the latter consistent with the proposed removal of hydrophobic groups from water.

Estudo da capacidade de complexação e sua relação com algumas variáveis ambientais em cinco represas do Rio Tietê/Brasil

The copper and cadmium complexation properties in natural sediment suspensions of reservoirs of the Tietê River were studied using the solid membrane copper and cadmium ion-selective electrodes. The complexation and the average conditional stability constants were determined under equilibrium conditions at pH=6.00 ± 0.05 in a medium of 1.0 mol L-1 sodium nitrate, using the Scatchard method. The copper and cadmium electrodes presented Nernstian behavior from 1x10-6 to 1x10-3 mol L-1 of total metal concentration. Scatchard graphs suggest two classes of binding sites for both metals. A multivariate study was done to correlate the reservoirs and the variables: complexation properties, size, total organic carbon, volatile acid sulfide, E II and pH.

Metodo de reconstrução tridimensional para avaliação postural

Universidade Estadual de Campinas. Faculdade de Educação Física

Treinamento fisico militar e aptidão fisica relacionada a saude : estudo a partir de conscritos do Tiro-de-Guerra 02-40 Sorocaba, S.P

Universidade Estadual de Campinas. Faculdade de Educação Física

Teste de banco : adequação da altura do ergometro a estatura, para individuos a partir de 09 anos de idade, de ambosos sexos, praticantes e não praticantes de atividade fisica

Universidade Estadual de Campinas. Faculdade de Educação Física

Perspectivas de atuação profissional : um estudo de caso das olimpiadas especiais

Universidade Estadual de Campinas . Faculdade de Educação Física

Teste de banco com carga continua para analise do volume de oxigenio (VO2) predito e analisado por tempo de esforço em pessoas treinadas (TRD), ativas (ATV) e destreinadas (DTR) a partir dos 13 anos : proposta de validação

Universidade Estadual de Campinas . Faculdade de Educação Física

Desenvolvimento de uma metodologia para reconstrução da superficie do pe humano por videogrametria

Universidade Estadual de Campinas . Faculdade de Educação Física

Uma perspectiva computacional sobre catálise enzimática

Enzymes are extremely efficient catalysts. Here, part of the mechanisms proposed to explain this catalytic power will be compared to quantitative experimental results and computer simulations. Influence of the enzymatic environment over species along the reaction coordinate will be analysed. Concepts of transition state stabilisation and reactant destabilisation will be confronted. Divided site model and near-attack conformation hypotheses will also be discussed. Molecular interactions such as covalent catalysis, general acid-base catalysis, electrostatics, entropic effects, steric hindrance, quantum and dynamical effects will also be analysed as sources of catalysis. Reaction mechanisms, in particular that catalysed by protein tyrosine phosphatases, illustrate the concepts.