Analysis of Bas-Relief Generation Techniques

Simplifying the process of generating relief sculptures has been an interesting topic of research in the past decade. A relief is a type of sculpture that does not entirely extend into three-dimensional space. Instead, it has details that are carved into a flat surface, like wood or stone, such that there are slight elevations from the flat plane that define the subject of the sculpture. When viewed orthogonally straight on, a relief can look like a full sculpture or statue in the respect that a full sense of depth from the subject can be perceived. Creating such a model manually is a tedious and difficult process, akin to the challenges a painter may face when designing a convincing painting. Like with painting, certain digital tools (3D modeling programs most commonly) can make the process a little easier, but can still take a lot of time to obtain sufficient details. To further simplify the process of relief generation, a sizable amount of research has gone into developing semi-automated processes of creating reliefs based on different types of models. These methods can vary in many ways, including the type of input used, the computational time required, and the quality of the resulting model. The performance typically depends on the type of operations applied to the input model, and usually user-specified parameters to modify its appearance. In this thesis, we try to accomplish a few related topics. First, we analyze previous work in the field and briefly summarize the procedures to emphasize a variety of ways to solve the problem. We then look at specific algorithms for generating reliefs from 2D and 3D models. After explaining two of each type, a “basic” approach, and a more sophisticated one, we compare the algorithms based on their difficulty to implement, the quality of the results, and the time to process. The final section will include some more sample results of the previous algorithms, and will suggest possible ideas to enhance their results, which could be applied in continuing research on the topic

Jost Functions and Jost Solutions for Jacobi Matrices, I. A Necessary and Sufficient Condition for Szego Asymptotics

We provide necessary and sufficient conditions for a Jacobi matrix to produce orthogonal polynomials with Szeg\H{o} asymptotics off the real axis. A key idea is to prove the equivalence of Szeg\H{o} asymptotics and of Jost asymptotics for the Jost solution. We also prove $L^2$ convergence of Szeg\H{o} asymptotics on the spectrum.Comment: 49 page

Circular and Square Concrete Columns Externally Confined by CFRP Composite: Experimental Investigation and Effective Strength Models

The use of fiber reinforced polymers (FRP) jackets as an external mean to strengthen existing RC columns has emerged in recent years with very promising results [1-13], among others. Several studies on the performance of FRP wrapped columns have been conducted, using both experimental and analytical approaches. Such strengthening technique has proved t

The confinement of concrete in compression using CFRP composites – effective design equations

This paper presents the results of an experimental study on the behaviour of axially loaded short concrete columns, with different cross sections that have been externally strengthened with carbon fibre-reinforced polymer (CFRP) sheets. Six series, forming the total of 60 specimens, were subjected to axial compression. All the test specimens were loaded to failure in axial compression and investigated in both axial and transverse directions. According to the obtained test results, FRP-confined specimen failure occurs before the FRP reached the ultimate strain capacities. Thus, the failure occurs prematurely and the circumferential failure strain is lower than the ultimate strain obtained from the standard tensile testing of the FRP composite. In existing models for FRP-confined concrete, it is commonly assumed that the FRP ruptures when the hoop stress in the FRP jacket reaches its tensile strength from either flat coupon tests, which is herein referred to as the FRP material tensile strength. This phenomenon considerably affects the accuracy of the existing models for FRP-confined concrete. On the basis of the effective lateral confining pressure of the composite jacket and the effective circumferential FRP failure strain, new equations were proposed to predict the strength of FRP-confined concrete and corresponding strain for each of the cross section geometry used, circular and square. The estimations given by these equations were compared with the experimental ones and general conclusions were drawn

Planification des calendriers des rendez-vous des patients en chimiothérapie et du niveau de ressources infirmières

RÉSUMÉ : Les problèmes de planification et de confection des horaires en chimiothérapie étaient anciennement résolus à l’aide de gabarits. La formalisation mathématique du problème est relativement nouvelle. Elle consiste à résoudre dans un premier temps le problème de confection des calendriers des rendez-vous des patients pour ensuite utiliser la solution comme donnée d’entrée afin de résoudre le problème d’affectation patients-infirmier. Le problème général sélectionne en première phase les patients en considérant la capacité de la clinique, il permet également d’estimer le besoin en ressources. La seconde phase consiste à résoudre le problème d’ordonnancement quotidien en affectant les patients aux infirmiers. Le modèle développé dans le cadre de ce projet est une extension du travail de [1] qui affecte des rendez-vous aux traitements des nouveaux patients sans perturber les décisions précédentes. La première phase est modifiée de manière à résoudre le problème de planification et à répondre à une partie des questions opérationnelles ; elle attribue non seulement la date, mais également l’heure de début des rendez-vous. Le début des traitements sont fixés à la même heure d’une fois à l’autre pour chaque patient. La première phase considère en plus de la charge horaire, la charge quotidienne ainsi que la lourdeur des patients. L’objectif de cette phase consiste à maximiser le nombre de rendez-vous planifiés en prenant en compte leur niveau de priorité des patients, la capacité des ressources humaines à absorber la charge et la capacité horaire des ressources matériels (chaises/lits). Cette phase, permet donc de planifier en amont le besoin en infirmiers et à prévoir le nombre de ressources supplémentaires nécessaire afin de couvrir la demande assignée à la liste d’attente. La liste d’attente agit tel une ressource tampon pour le problème de planification. Cette modélisation permet de mieux contrôler la quantité de travail affectée à une journée. De plus, la surestimation de la charge peut être considérée tel un avantage, étant donné qu’elle permet de pallier aux annulations de rendez-vous. En chimiothérapie, les patients doivent faire une prise de sang et/ou consulter leur oncologiste avant chaque administration de traitement, suite à ces visites, leurs rendez vous peuvent soient être annulés, reportés ou remplacés par un autre protocole médical. Le taux d’annulation varie de 4% à 20% avec une moyenne de 13%. La deuxième phase du modèle résout le problème d’ordonnancement quotidien pour la liste finale des patients et des infirmiers. Durant cette phase, la charge est distribuée équitablement avec comme but de minimiser l’écart à la charge maximale. Nous avons testé le modèle proposé à l’aide de 10 instances générées à partir des donnés collecter auprès de la clinique d’oncologie du Centre Hospitalier de l’Université de Montréal (CHUM). La clinique accueille en moyenne 70±12 patients par jour, ceci correspond à 246±24 heures de traitements. Le CHUM emploie 19 infirmiers, l’infirmière en charge assigne généralement entre 8 et 12 infirmiers à la zone d’infusion. Les autres membres du personnel sont assignés à différentes zones telle que le centre de support pour la chimiothérapie à domicile. L’infirmière en charge réaffecte les postes aux infirmiers, au courant de la journée en fonction de la charge de travail. L’optimisation permet de mieux gérer l’utilisation des ressources infirmières. Le modèle permet d’améliorer le ratio de productivité des infirmiers de 3% à 23% selon le scénario testé. En effet, au total, trois règles d’ordonnancement sont évaluées pour la deuxième phase : (1) interdiction de modifier l’heure de début des rendez-vous (3% de potentiel d’amélioration), (2) perturbation partielle de l’heure du début (8% de potentiel d’amélioration) et (3) perturbation totale (23% de potentiel d’amélioration). La résolution du modèle mathématique permet également aux patients de voir leur cas géré dans de meilleur délai, ainsi le nombre total moyen de patients en retard diminue de 9,9 à 5,5 pour l’ensemble de la période de planification.----------ABSTRACT : Planning and scheduling problems in chemotherapy were formerly solved using templates. The mathematical formalization of the problem is relatively new; it consists of first solving the planning problem and then using the solution as input to address the daily patientsnurses assignment problem. The model was proposed initially by Turkcan et al. [1] to assign a date to new patients’ treatments without changing past decisions. The general problem selects in the first phase the patients by considering the capacity of the clinic. The second phase assigns patients to nurses. The model developed as part of this project is an extension of [1] work. The first phase is modified to solve the planning problem and to answer some of the operational questions; it attributes the date and the starting time of patients’ treatments. The starting time of each treatment is fixed at the same time from one appointment to the next. The procedure considers in addition to the hourly workload, the daily workload as well as the heaviness of the patients. The objective of this phase is to maximize the number of patients by taking into account their priority level, the capacity of the medical staff to absorb the workload and the hourly capacity of the material resources chairs/beds). This phase,therefore, makes it possible to plan the need for nurses upstream and to plan the number of additional resources needed to cover the demand assigned to the waiting list. The second phase of the model solves the problem of daily patients-nurse assignment for the final list of patients. The workload is distributed with the aim to minimize the level of resources needed. Thus, the originality of this project lies in the representation of a waiting list that acts as a buffer. This modelization allows controlling the amount of work assigned to a day. Moreover, the overestimation of the workload can be considered as an advantage, since it makes it possible to compensate for cancellations. The cancellation rate varies from 4% to 20% with an average of 13%. Optimization allows patients to see their case managed in a shorter period depending on their state of health. For example, the total average of patients starting their treatments late decreases from 9,9 to 5,5. The clinic also has the potential to reduce inefficiencies due to poor planning; the model can improve the productivity ratio from 3 % to 23 % depending on the scenario tested. Indeed, in total, three scheduling rules are evaluated for the second phase: (1) prohibition the change of starting time (3% improvement), (2) partial shuffling of the starting time (8 %) and (3) total shuffling (23%). The resolution of the mathematical model thus improves the process of patients’ allocation and the clinical productivity ratio

Évolution des génomes par mutations locales et globales : une approche d’alignement

Durant leur évolution, les génomes accumulent des mutations pouvant affecter d’un nucléotide à plusieurs gènes. Les modifications au niveau du nombre et de l’organisation des gènes dans les génomes sont dues à des mutations globales, telles que les duplications, les pertes et les réarrangements. En comparant les ordres de gènes des génomes, il est possible d’inférer les événements évolutifs les plus fréquents, le contenu en gènes des espèces ancestrales ainsi que les histoires évolutives ayant menées aux ordres observés. Dans cette thèse, nous nous intéressons au développement de nouvelles méthodes algorithmiques, par approche d’alignement, afin d’analyser ces différents aspects de l’évolution des génomes. Nous nous intéressons à la comparaison de deux ou d’un ensemble de génomes reliés par une phylogénie, en tenant compte des mutations globales. Pour commencer, nous étudions la complexité théorique de plusieurs variantes du problème de l’alignement de deux ordres de gènes par duplications et pertes, ainsi que de l’approximabilité de ces problèmes. Nous rappelons ensuite les algorithmes exacts, en temps exponentiel, existants, et développons des heuristiques efficaces. Nous proposons, dans un premier temps, DLAlign, une heuristique quadratique pour le problème d’alignement de deux ordres de gènes par duplications et pertes. Ensuite, nous présentons, OrthoAlign, une extension de DLAlign, qui considère, en plus des duplications et pertes, les réarrangements et les substitutions. Nous abordons également le problème de l’alignement phylogénétique de génomes. Pour commencer, l’heuristique OrthoAlign est adaptée afin de permettre l’inférence de génomes ancestraux au noeuds internes d’un arbre phylogénétique. Nous proposons enfin, MultiOrthoAlign, une heuristique plus robuste, basée sur la médiane, pour l’inférence de génomes ancestraux et d’histoires évolutives d’un ensemble de génomes représentés aux feuilles d’un arbre phylogénétique.During the evolution process, genomes accumulate mutations that may affect the genome at different levels, ranging from one base to the overall gene content. Global mutations affecting gene content and organization are mainly duplications, losses and rearrangements. By comparing gene orders, it is possible to infer the most frequent events, the gene content in the ancestral genomes and the evolutionary histories of the observed gene orders. In this thesis, we are interested in developing new algorithmic methods based on an alignment approach for comparing two or a set of genomes represented as gene orders and related through a phylogenetic tree, based on global mutations. We study the theoretical complexity and the approximability of different variants of the two gene orders alignment problem by duplications and losses. Then, we present the existing exact exponential time algorithms, and develop efficient heuristics for these problems. First, we developed DLAlign, a quadratic time heuristic for the two gene orders alignment problem by duplications and losses. Then, we developed OrthoAlign, a generalization of DLAlign, accounting for most genome-wide evolutionary events such as duplications, losses, rearrangements and substitutions. We also study the phylogenetic alignment problem. First, we adapt our heuristic OrthoAlign in order to infer ancestral genomes at the internal nodes of a given phylogenetic tree. Finally, we developed MultiOrthoAlign, a more robust heuristic, based on the median problem, for the inference of ancestral genomes and evolutionary histories of extent genomes labeling leaves of a phylogenetic tree

Detecting malicious nodes using game theory and reinforcement learning in software-defined networks

Abstract Mafia, or Werewolf, is a strategic game where two teams compete to eliminate each other’s players through deception and hidden roles. The game dynamics and role interactions share notable similarities with adversarial behaviors in network security, making it a valuable framework for modeling cyber threats, particularly botnet detection. In this paper, we introduce a novel game-theoretic approach to botnet detection, leveraging the strategic deception dynamics of the Mafia game to model adversarial behavior in cybersecurity. We present a mathematical model for Mafia games, formulating winning strategies for different roles using linear relations and reinforcement learning techniques. Furthermore, we establish a direct mapping between Mafia game roles and network security components, illustrating how botnet attack patterns align with hidden-role game mechanics. Our proposed detection strategies are applied to real-world network attack scenarios, demonstrating their effectiveness in mitigating botnet threats. We evaluate the model using applicable security metrics and compare the results with existing detection methodologies to validate the approach. Our findings indicate that the suggested strategies improve detection accuracy by 12% over conventional methods. Additionally, we conduct network emulations using Mininet, simulating Mirai botnet infections. The results show that the true positive and true negative detection rates for a network modeled by the Mafia game framework reach 71% and 91%, respectively. These insights provide a foundation for integrating deception-based modeling into modern intrusion detection systems, enhancing network resilience against adaptive cyber threats.Abstract Mafia, or Werewolf, is a strategic game where two teams compete to eliminate each other’s players through deception and hidden roles. The game dynamics and role interactions share notable similarities with adversarial behaviors in network security, making it a valuable framework for modeling cyber threats, particularly botnet detection. In this paper, we introduce a novel game-theoretic approach to botnet detection, leveraging the strategic deception dynamics of the Mafia game to model adversarial behavior in cybersecurity. We present a mathematical model for Mafia games, formulating winning strategies for different roles using linear relations and reinforcement learning techniques. Furthermore, we establish a direct mapping between Mafia game roles and network security components, illustrating how botnet attack patterns align with hidden-role game mechanics. Our proposed detection strategies are applied to real-world network attack scenarios, demonstrating their effectiveness in mitigating botnet threats. We evaluate the model using applicable security metrics and compare the results with existing detection methodologies to validate the approach. Our findings indicate that the suggested strategies improve detection accuracy by 12% over conventional methods. Additionally, we conduct network emulations using Mininet, simulating Mirai botnet infections. The results show that the true positive and true negative detection rates for a network modeled by the Mafia game framework reach 71% and 91%, respectively. These insights provide a foundation for integrating deception-based modeling into modern intrusion detection systems, enhancing network resilience against adaptive cyber threats

A Mathematical Model for Analyzing Honeynets and Their Cyber Deception Techniques

Abstract As a way of obtaining useful information about the adversaries behavior with a low rate of false detection, honeypots have made significant advancements in the field of cybersecurity. They are also powerful in wasting the adversaries resources and attracting their attention from other critical assets in the network. A deceptive network with multiple honeypots is called a honeynet. The honeypots in a honeynet aim to cooperate in order to increase their deception power. Professional adversaries utilize strong detection mechanisms to discover the existence of the honeypots in a network. When an adversary finds that a deception mechanism is deployed, it may change their behavior and cause malicious effects on the network. Therefore, a honeynet has to be deceptive enough in order not to be identified. This paper aims to review the techniques that are designed for the honeynets to make them improve their deception performance. The recent related surveys do not focus on the honeynet-specific techniques, and also have no comparison analysis. The main presented techniques in this paper are fully investigated through comparative analysis and simulation scenarios. Some suggestions on the research gap are also provided. The results of this paper can be used by the honeynet developers and researchers to improve their work.Abstract As a way of obtaining useful information about the adversaries behavior with a low rate of false detection, honeypots have made significant advancements in the field of cybersecurity. They are also powerful in wasting the adversaries resources and attracting their attention from other critical assets in the network. A deceptive network with multiple honeypots is called a honeynet. The honeypots in a honeynet aim to cooperate in order to increase their deception power. Professional adversaries utilize strong detection mechanisms to discover the existence of the honeypots in a network. When an adversary finds that a deception mechanism is deployed, it may change their behavior and cause malicious effects on the network. Therefore, a honeynet has to be deceptive enough in order not to be identified. This paper aims to review the techniques that are designed for the honeynets to make them improve their deception performance. The recent related surveys do not focus on the honeynet-specific techniques, and also have no comparison analysis. The main presented techniques in this paper are fully investigated through comparative analysis and simulation scenarios. Some suggestions on the research gap are also provided. The results of this paper can be used by the honeynet developers and researchers to improve their work