Academic literature on the topic 'Simple graph'

Essa tese introduz um conjunto de algoritmos baseados em grafos para o mapeamento eficiente de circuitos VLSI com células simples. Os algoritmos propostos se baseiam em minimizar de maneira eficiente o número de elementos lógicos usados na implementação do circuito. Posteriormente, uma quantidade significativa de esforço é aplicada na minimização do número de inversores entre esses elementos lógicos. Por fim, essa representação lógica é mapeada para circuitos compostos somente por células NAND e NOR de duas entradas, juntamente com inversores. Células XOR e XNOR de duas entradas também podem ser consideradas. Como nós também consideramos circuitos sequenciais, flips-flops também são levados em consideração. Com o grande esforço de minimização de elementos lógicos, o circuito gerado pode conter algumas células com um fanout impraticável para os nodos tecnológicos atuais. Para corrigir essas ocorrências, nós propomos um algoritmo de limitação de fanout que considera tanto a área sendo utilizada pelas células quanto a sua profundidade lógica. Os algoritmos propostos foram aplicados sobre um conjunto de circuitos de benchmark e os resultados obtidos demonstram a utilidade dos métodos. Essa tese introduz um conjunto de algoritmos baseados em grafos para o mapeamento eficiente de circuitos VLSI com células simples. Os algoritmos propostos se baseiam em minimizar de maneira eficiente o número de elementos lógicos usados na implementação do circuito. Posteriormente, uma quantidade significativa de esforço é aplicada na minimização do número de inversores entre esses elementos lógicos. Por fim, essa representação lógica é mapeada para circuitos compostos somente por células NAND e NOR de duas entradas, juntamente com inversores. Células XOR e XNOR de duas entradas também podem ser consideradas. Como nós também consideramos circuitos sequenciais, flips-flops também são levados em consideração. Com o grande esforço de minimização de elementos lógicos, o circuito gerado pode conter algumas células com um fanout impraticável para os nodos tecnológicos atuais. Para corrigir essas ocorrências, nós propomos um algoritmo de limitação de fanout que considera tanto a área sendo utilizada pelas células quanto a sua profundidade lógica. Os algoritmos propostos foram aplicados sobre um conjunto de circuitos de benchmark e os resultados obtidos demonstram a utilidade dos métodos. Adicionalmente, algumas aplicações Morethan-Moore, tais como circuitos baseados em eletrônica impressa, também podem ser beneficiadas pela abordagem proposta.<br>This thesis introduces a set of graph-based algorithms for efficiently mapping VLSI circuits using simple cells. The proposed algorithms are concerned to, first, effectively minimize the number of logic elements implementing the synthesized circuit. Then, we focus a significant effort on minimizing the number of inverters in between these logic elements. Finally, this logic representation is mapped into a circuit comprised of only two-input NANDs and NORS, along with the inverters. Two-input XORs and XNORs can also be optionally considered. As we also consider sequential circuits in this work, flip-flops are taken into account as well. Additionally, with high-effort optimization on the number of logic elements, the generated circuits may contain some cells with unfeasible fanout for current technology nodes. In order to fix these occurrences, we propose an area-oriented, level-aware algorithm for fanout limitation. The proposed algorithms were applied over a set of benchmark circuits and the obtained results have shown the usefulness of the method. We show that efficient implementations in terms of inverter count, transistor count, area, power and delay can be generated from circuits with a reduced number of both simple cells and inverters, combined with XOR/XNOR-based optimizations. The proposed buffering algorithm can handle all unfeasible fanout occurrences, while (i) optimizing the number of added inverters; and (ii) assigning cells to the inverter tree based on their level criticality. When comparing with academic and commercial approaches, we are able to simultaneously reduce the average number of inverters, transistors, area, power dissipation and delay up to 48%, 5%, 5%, 5%, and 53%, respectively. As the adoption of a limited set of simple standard cells have been showing benefits for a variety of modern VLSI circuits constraints, such as layout regularity, routability constraints, and/or ultra low power constraints, the proposed methods can be of special interest for these applications. Additionally, some More-than-Moore applications, such as printed electronics designs, can also take benefit from the proposed approach.

Recommender systems are widely used in websites and applications to help users find relevant content based on their interests. Graph neural networks achieved state- of-the- art results in the field of recommender systems, working on data represented in the form of a graph. However, most graph- based solutions hold challenges regarding computational complexity or the ability to generalize to new users. Therefore, we propose a novel graph- based recommender system, by modifying Simple Graph Convolution, an approach for efficient graph node classification, and add the capability of generalizing to new users. We build our proposed recommender system for recommending the articles of Peltarion Knowledge Center. By incorporating two data sources, implicit user feedback based on pageview data as well as the content of articles, we propose a hybrid recommender solution. Throughout our experiments, we compare our proposed solution with a matrix factorization approach as well as a popularity- based and a random baseline, analyse the hyperparameters of our model, and examine the capability of our solution to give recommendations to new users who were not part of the training data set. Our model results in slightly lower, but similar Mean Average Precision and Mean Reciprocal Rank scores to the matrix factorization approach, and outperforms the popularity- based and random baselines. The main advantages of our model are computational efficiency and its ability to give relevant recommendations to new users without the need for retraining the model, which are key features for real- world use cases.<br>Rekommendationssystem används ofta på webbplatser och applikationer för att hjälpa användare att hitta relevant innehåll baserad på deras intressen. Med utvecklingen av grafneurala nätverk nådde toppmoderna resultat inom rekommendationssystem och representerade data i form av en graf. De flesta grafbaserade lösningar har dock svårt med beräkningskomplexitet eller att generalisera till nya användare. Därför föreslår vi ett nytt grafbaserat rekommendatorsystem genom att modifiera Simple Graph Convolution. De här tillvägagångssätt är en effektiv grafnodsklassificering och lägga till möjligheten att generalisera till nya användare. Vi bygger vårt föreslagna rekommendatorsystem för att rekommendera artiklarna från Peltarion Knowledge Center. Genom att integrera två datakällor, implicit användaråterkoppling baserad på sidvisningsdata samt innehållet i artiklar, föreslår vi en hybridrekommendatörslösning. Under våra experiment jämför vi vår föreslagna lösning med en matrisfaktoriseringsmetod samt en popularitetsbaserad och en slumpmässig baslinje, analyserar hyperparametrarna i vår modell och undersöker förmågan hos vår lösning att ge rekommendationer till nya användare som inte deltog av träningsdatamängden. Vår modell resulterar i något mindre men liknande Mean Average Precision och Mean Reciprocal Rank poäng till matrisfaktoriseringsmetoden och överträffar de popularitetsbaserade och slumpmässiga baslinjerna. De viktigaste fördelarna med vår modell är beräkningseffektivitet och dess förmåga att ge relevanta rekommendationer till nya användare utan behov av omskolning av modellen, vilket är nyckelfunktioner för verkliga användningsfall.

We explore the problem of transforming plane triangulations using simultaneous diagonal flips. Wagner showed that any n-vertex plane triangulation can be transformed to any other plane triangulation on equal number of vertices using a finite sequence of diagonal flips. Later on it has been established that O(n) individual flips suffice to complete this transformation. Bose et al. showed that the transformation can also be done in 4 × ( 2 / log 54/53 + 2 / log 6/5 ) logn + 2 ≈ 327.1 log n simultaneous flips. This bound is asymptotically tight. We present two algorithms to improve the leading coefficient of this bound for transforming any plane triangulation into any other. The first of the two algorithms lowers this bound down to 4 × ( 2 / log 12/11 + 2 / log 9/7 ) logn + 2 ≈ 85.8 log n. By processing and preprocessing the interior and exterior of the triangulation’s Hamiltonian cycle parallelly in an interlaced fashion, we make further improvement of the algorithm from ≈ 327.1 log n down to 12 / log 6/5 logn + 2 ≈ 45.6 log n.

De nombreux systèmes complexes du monde réel peuvent être représentés par des graphes, où les nœuds représentent des entités et les liens des relations entre les paires de nœuds. La prédiction de liens (LP) est l'un des problèmes les plus intéressants et les plus anciens dans le domaine de l'exploration de graphes ; elle prédit la probabilité d'un lien entre deux nœuds non connectés. Cette thèse étudie le problème LP dans les graphes simples et les graphes de connaissances (KGs). La première partie de cette thèse se concentre sur le problème LP dans les graphes simples. Dans la première étude, des approches basées sur la similarité et sur l'encastrement sont évaluées et comparées sur des graphes simples de différents domaines. L'étude a également identifié la difficulté de fixer le seuil du score de similarité pour calculer la métrique de précision des approches basées sur la similarité et a proposé une nouvelle méthode pour calculer la métrique. Les résultats ont montré la supériorité attendue des approches basées sur l'intégration. Cependant, chaque approche basée sur la similarité s'est avérée compétitive sur des graphes aux propriétés spécifiques. Nous avons pu vérifier expérimentalement que les approches basées sur la similarité sont explicables mais manquent de généralisation, tandis que les approches basées sur l'encastrement sont générales mais non explicables. La deuxième étude tente de surmonter la limitation de l'inexplicabilité des approches basées sur l'encastrement en découvrant des connexions intéressantes entre elles et les approches basées sur la similarité. La troisième étude démontre comment les approches basées sur la similarité peuvent être assemblées pour concevoir une approche LP supervisée explicable. Il est intéressant de noter que l'étude montre des performances LP élevées pour l'approche supervisée sur différents graphes, ce qui est très satisfaisant. La deuxième partie de la thèse se concentre sur les LP dans les KGs. Un KG est représenté comme une collection de triplets RDF, (head,relation,tail) où les entités head et tail sont reliées par une relation spécifique. Le problème de LP dans un KG est formulé comme la prédiction de la tête ou de la queue manquante dans un triplet. La LP basée sur l'incorporation de KG est devenue très populaire ces dernières années, et la génération de triplets négatifs est une tâche importante dans les méthodes d'incorporation. La quatrième étude traite d'une nouvelle méthode appelée SNS pour générer des triplets négatifs de haute qualité. Nos résultats montrent une meilleure performance LP lorsque SNS est utilisé que lorsque d'autres méthodes d'échantillonnage négatif sont utilisées. La deuxième étude traite d'une nouvelle méthode d'extraction de règles neuro-symboliques et d'une stratégie d'abduction pour expliquer les LP par une approche basée sur l'intégration en utilisant les règles apprises. La troisième étude applique notre LP explicable pour développer une nouvelle approche de repositionnement des médicaments pour COVID-19. L'approche apprend un ensemble d'enchâssements d'entités et de relations dans un KG centré sur COVID-19 pour obtenir un meilleur enchâssement des éléments du graphe. Pour la première fois à notre connaissance, des méthodes de criblage virtuel sont ensuite utilisées pour évaluer les prédictions obtenues à l'aide des embeddings. L'évaluation moléculaire et les chemins explicatifs apportent de la fiabilité aux résultats de prédiction et sont de nouvelles méthodes complémentaires et réutilisables pour mieux évaluer les molécules proposées pour le repositionnement. La dernière étude propose une architecture distribuée pour l'apprentissage des KG embeddings dans des environnements distribués et parallèles. Les résultats révèlent que l'apprentissage dans l'environnement distribué proposé, par rapport à un apprentissage centralisé, réduit considérablement le temps de calcul des méthodes d'incorporation KG sans affecter les performances des LP<br>Many real-world complex systems can be well-represented with graphs, where nodes represent objects or entities and links/relations represent interactions between pairs of nodes. Link prediction (LP) is one of the most interesting and long-standing problems in the field of graph mining; it predicts the probability of a link between two unconnected nodes based on available information in the current graph. This thesis studies the LP problem in graphs. It consists of two parts: LP in simple graphs and LP knowledge graphs (KGs). In the first part, the LP problem is defined as predicting the probability of a link between a pair of nodes in a simple graph. In the first study, a few similarity-based and embedding-based LP approaches are evaluated and compared on simple graphs from various domains. he study also criticizes the traditional way of computing the precision metric of similarity-based approaches as the computation faces the difficulty of tuning the threshold for deciding the link existence based on the similarity score. We proposed a new way of computing the precision metric. The results showed the expected superiority of embedding-based approaches. Still, each of the similarity-based approaches is competitive on graphs with specific properties. We could check experimentally that similarity-based approaches are fully explainable but lack generalization due to their heuristic nature, whereas embedding-based approaches are general but not explainable. The second study tries to alleviate the unexplainability limitation of embedding-based approaches by uncovering interesting connections between them and similarity-based approaches to get an idea of what is learned in embedding-based approaches. The third study demonstrates how the similarity-based approaches can be ensembled to design an explainable supervised LP approach. Interestingly, the study shows high LP performance for the supervised approach across various graphs, which is competitive with embedding-based approaches.The second part of the thesis focuses on LP in KGs. A KG is represented as a collection of RDF triples, (head,relation,tail) where the head and the tail are two entities which are connected by a specific relation. The LP problem in a KG is formulated as predicting missing head or tail entities in a triple. LP approaches based on the embeddings of entities and relations of a KG have become very popular in recent years, and generating negative triples is an important task in KG embedding methods. The first study in this part discusses a new method called SNS to generate high-quality negative triples during the training of embedding methods for learning embeddings of KGs. The results we produced show better LP performance when SNS is injected into an embedding approach than when injecting state-of-the-art negative triple sampling methods. The second study in the second part discusses a new neuro-symbolic method of mining rules and an abduction strategy to explain LP by an embedding-based approach utilizing the learned rules. The third study applies the explainable LP to a COVID-19 KG to develop a new drug repurposing approach for COVID-19. The approach learns ”ensemble embeddings” of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. Molecular evaluation and explanatory paths bring reliability to prediction results and constitute new complementary and reusable methods for assessing KG-based drug repurposing. The last study proposes a distributed architecture for learning KG embeddings in distributed and parallel settings. The results of the study that the computational time of embedding methods improves remarkably without affecting LP performance when they are trained in the proposed distributed settings than the traditional centralized settings

Il s'agit d'une contribution dans le domaine de la géométrie métrique du plan projectif complexe CP2 et de la variété de Grassmann réelle des plans dans R6. On s'intéresse à l'étude de tous les p-uplets, p ≥ 3, de droites équiangulaires dans C3 et des p-uplets de plans équi-isoclins dans R6. Sachant que 9 est le nombre maximum de droites équiangulaires que l'on peut construire dans C3, on décrit une méthode qui permet de construire tous les p-uplets de droites équiangulaires pour tout pϵ[3,9]. En particulier, on construit dans C3 cinq classes de congruence de quadruplets de droites équiangulaires dont une dépend d'un paramètre réel ɣ que l'on étend à une famille infinie de sextuplets de droites équiangulaires dépendant du même paramètre réel ɣ. En outre, on donne les angles pour lesquels nos sextuplets s'étendent au-delà et jusqu'aux 9-uplets. On sait qu'il existe un p-uplet, p≥3, de plans équi-isoclins engendrant Rr, r≥4, de paramètre c, 0<br>This work contributes to the field of metric geometry of the complex projective plane CP2 and the real Grassmannian manifold of the planes in R6. More specifically, we study all p-tuples, p ≥ 3, of equiangular lines in C3 or equidistant points in CP2, and p-tuples of equi-isoclinic planes in R6. Knowing that 9 is the maximum number of equiangular lines that can be constructed in C3, we develop a method to obtain all p-tuples of equiangular lines for all p ϵ [3,9]. In particular, we construct in C3 five congruence classes of quadruples of equiangular lines, one of which depends on a real parameter ɣ, which we extend to an infinite family of sextuples of equiangular lines depending on the same real parameter ɣ. In addition, we give the angles for which our sextuples extend beyond and up to 9-tuples. We know that there exists a p-tuple, p ≥ 3, of equi-isoclinic planes generating Rr, r ≥ 4, with parameter c, 0&lt; c &lt;1, if and only if there exists a square symmetric matrix, called Seidel matrix, of p × p square blocks of order 2, whose diagonal blocks are all zero and the others are orthogonal matrices in O(2) and whose smallest eigenvalue is equal to - 1/c and has multiplicity 2p-r. In this thesis, we investigate the case r=6 and we also show that we can explicitly determine the spectrum of all Seidel matrices of order 2p, p ≥ 3 whose off-diagonal blocks are in {R0, S0} where R0 and S0 are respectively the zero-angle rotation and the zero-angle symmetry. We thus show an unexpected link between some p-tuples of equi-isoclinic planes in Rr and simple graphs of order p

GraphQL is a database handling API created by Facebook, that provides an effective al-ternative to REST-style architectures. GraphQL provides the ability for a client to spec-ify exactly what data it wishes to receive. A problem with GraphQL is that the freedomof creating customized requests allows data to be included several times in the response,growing the response’s size exponentially. The thesis contributes to the field of GraphQLanalysis by studying the prevalence of simple cycles in GraphQL schemas. We have im-plemented a locally-run tool and webtool using Tarjan’s and Johnson’s algorithms, thatparses the schemas, creates a directed graph and enumerates all simple cycles in the graph.A collection of schemas was analysed with the tool to collect empirical data. It was foundthat 39.73 % of the total 2094 schemas contained at least one simple cycle, with the averagenumber of cycles per schema being 4. The runtime was found to be on average 11 mil-liseconds, most of which consisted of the time for parsing the schemas. It was found that44 out of the considered schemas could not be enumerated due to containing a staggeringamount of simple cycles. It can be concluded that it is possible to test schemas for cyclicityand enumerate all simple cycles in a given schema efficiently.

We study character generating functions (character generators) of simple Lie algebras. The expression due to Patera and Sharp, derived from the Weyl character formula, is ¯rst re- viewed. A new general formula is then found. It makes clear the distinct roles of \outside" and \inside" elements of the integrity basis, and helps determine their quadratic incompati- bilities. We review, analyze and extend the results obtained by Gaskell using the Demazure character formulas. We ¯nd that the fundamental generalized-poset graphs underlying the character generators can be deduced from such calculations. These graphs, introduced by Baclawski and Towber, can be simpli¯ed for the purposes of constructing the character generator. The generating functions can be written easily using the simpli¯ed versions, and associated Demazure expressions. The rank-two algebras are treated in detail, but we believe our results are indicative of those for general simple Lie algebras.<br>vii, 92 leaves ; 29 cm.