42 research outputs found
Unsupervised relational inference using masked reconstruction
Problem setting: Stochastic dynamical systems in which local interactions give rise
to complex emerging phenomena are ubiquitous in nature and society. This work
explores the problem of inferring the unknown interaction structure (represented as
a graph) of such a system from measurements of its constituent agents or individual
components (represented as nodes). We consider a setting where the underlying
dynamical model is unknown and where diferent measurements (i.e., snapshots) may
be independent (e.g., may stem from diferent experiments).
Method: Our method is based on the observation that the temporal stochastic evolution manifests itself in local patterns. We show that we can exploit these patterns to
infer the underlying graph by formulating a masked reconstruction task. Therefore, we
propose GINA (Graph Inference Network Architecture), a machine learning approach
to simultaneously learn the latent interaction graph and, conditioned on the interaction graph, the prediction of the (masked) state of a node based only on adjacent
vertices. Our method is based on the hypothesis that the ground truth interaction
graph—among all other potential graphs—allows us to predict the state of a node,
given the states of its neighbors, with the highest accuracy.
Results: We test this hypothesis and demonstrate GINA’s efectiveness on a wide
range of interaction graphs and dynamical processes. We fnd that our paradigm allows
to reconstruct the ground truth interaction graph in many cases and that GINA outperforms statistical and machine learning baseline on independent snapshots as well
as on time series data
Stochastic spreading on complex networks
Complex interacting systems are ubiquitous in nature and society. Computational modeling of these systems is, therefore, of great relevance for science and engineering. Complex networks are common representations of these systems (e.g., friendship networks or road networks). Dynamical processes (e.g., virus spreading, traffic jams) that evolve on these networks are shaped and constrained by the underlying connectivity. This thesis provides numerical methods to study stochastic spreading processes on complex networks. We consider the processes as inherently probabilistic and analyze their behavior through the lens of probability theory. While powerful theoretical frameworks (like the SIS-epidemic model and continuous-time Markov chains) already exist, their analysis is computationally challenging. A key contribution of the thesis is to ease the computational burden of these methods. Particularly, we provide novel methods for the efficient stochastic simulation of these processes. Based on different simulation studies, we investigate techniques for optimal vaccine distribution and critically address the usage of mathematical models during the Covid-19 pandemic. We also provide model-reduction techniques that translate complicated models into simpler ones that can be solved without resorting to simulations. Lastly, we show how to infer the underlying contact data from node-level observations.Komplexe, interagierende Systeme sind in Natur und Gesellschaft allgegenwärtig. Die computergestützte Modellierung dieser Systeme ist daher von immenser Bedeutung für Wissenschaft und Technik. Netzwerke sind eine gängige Art, diese Systeme zu repräsentieren (z. B. Freundschaftsnetzwerke, Straßennetze). Dynamische Prozesse (z. B. Epidemien, Staus), die sich auf diesen Netzwerken ausbreiten, werden durch die spezifische Konnektivität geformt. In dieser Arbeit werden numerische Methoden zur Untersuchung stochastischer Ausbreitungsprozesse in komplexen Netzwerken entwickelt. Wir betrachten die Prozesse als inhärent probabilistisch und analysieren ihr Verhalten nach wahrscheinlichkeitstheoretischen Fragestellungen. Zwar gibt es bereits theoretische Grundlagen und Paradigmen (wie das SIS-Epidemiemodell und zeitkontinuierliche Markov-Ketten), aber ihre Analyse ist rechnerisch aufwändig. Ein wesentlicher Beitrag dieser Arbeit besteht darin, die Rechenlast dieser Methoden zu verringern. Wir erforschen Methoden zur effizienten Simulation dieser Prozesse. Anhand von Simulationsstudien untersuchen wir außerdem Techniken für optimale Impfstoffverteilung und setzen uns kritisch mit der Verwendung mathematischer Modelle bei der Covid-19-Pandemie auseinander. Des Weiteren führen wir Modellreduktionen ein, mit denen komplizierte Modelle in einfachere umgewandelt werden können. Abschließend zeigen wir, wie man von Beobachtungen einzelner Knoten auf die zugrunde liegende Netzwerkstruktur schließt
Collected Papers (on Neutrosophic Theory and Applications), Volume VI
This sixth volume of Collected Papers includes 74 papers comprising 974 pages on (theoretic and applied) neutrosophics, written between 2015-2021 by the author alone or in collaboration with the following 121 co-authors from 19 countries: Mohamed Abdel-Basset, Abdel Nasser H. Zaied, Abduallah Gamal, Amir Abdullah, Firoz Ahmad, Nadeem Ahmad, Ahmad Yusuf Adhami, Ahmed Aboelfetouh, Ahmed Mostafa Khalil, Shariful Alam, W. Alharbi, Ali Hassan, Mumtaz Ali, Amira S. Ashour, Asmaa Atef, Assia Bakali, Ayoub Bahnasse, A. A. Azzam, Willem K.M. Brauers, Bui Cong Cuong, Fausto Cavallaro, Ahmet Çevik, Robby I. Chandra, Kalaivani Chandran, Victor Chang, Chang Su Kim, Jyotir Moy Chatterjee, Victor Christianto, Chunxin Bo, Mihaela Colhon, Shyamal Dalapati, Arindam Dey, Dunqian Cao, Fahad Alsharari, Faruk Karaaslan, Aleksandra Fedajev, Daniela Gîfu, Hina Gulzar, Haitham A. El-Ghareeb, Masooma Raza Hashmi, Hewayda El-Ghawalby, Hoang Viet Long, Le Hoang Son, F. Nirmala Irudayam, Branislav Ivanov, S. Jafari, Jeong Gon Lee, Milena Jevtić, Sudan Jha, Junhui Kim, Ilanthenral Kandasamy, W.B. Vasantha Kandasamy, Darjan Karabašević, Songül Karabatak, Abdullah Kargın, M. Karthika, Ieva Meidute-Kavaliauskiene, Madad Khan, Majid Khan, Manju Khari, Kifayat Ullah, K. Kishore, Kul Hur, Santanu Kumar Patro, Prem Kumar Singh, Raghvendra Kumar, Tapan Kumar Roy, Malayalan Lathamaheswari, Luu Quoc Dat, T. Madhumathi, Tahir Mahmood, Mladjan Maksimovic, Gunasekaran Manogaran, Nivetha Martin, M. Kasi Mayan, Mai Mohamed, Mohamed Talea, Muhammad Akram, Muhammad Gulistan, Raja Muhammad Hashim, Muhammad Riaz, Muhammad Saeed, Rana Muhammad Zulqarnain, Nada A. Nabeeh, Deivanayagampillai Nagarajan, Xenia Negrea, Nguyen Xuan Thao, Jagan M. Obbineni, Angelo de Oliveira, M. Parimala, Gabrijela Popovic, Ishaani Priyadarshini, Yaser Saber, Mehmet Șahin, Said Broumi, A. A. Salama, M. Saleh, Ganeshsree Selvachandran, Dönüș Șengür, Shio Gai Quek, Songtao Shao, Dragiša Stanujkić, Surapati Pramanik, Swathi Sundari Sundaramoorthy, Mirela Teodorescu, Selçuk Topal, Muhammed Turhan, Alptekin Ulutaș, Luige Vlădăreanu, Victor Vlădăreanu, Ştefan Vlăduţescu, Dan Valeriu Voinea, Volkan Duran, Navneet Yadav, Yanhui Guo, Naveed Yaqoob, Yongquan Zhou, Young Bae Jun, Xiaohong Zhang, Xiao Long Xin, Edmundas Kazimieras Zavadskas
Numerical and Evolutionary Optimization 2020
This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications
Evolutionary Computation
This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field
Physical Adsorption of Linear Hydrocarbon Quadrupoles on Graphite and MgO (100): Effects of the Compatibility of Surface and Molecular Symmetries
The process of physical adsorption finds a practical role in wide-ranging fields from catalysis, to lubrication, and even optoelectronics. Furthermore, it provides a mechanism to probe the fundamental understanding of intermolecular forces and how symmetries can play a role in the behavior of a system. Linear quadrupoles preferentially adopt square-T configurations when confined in two dimensions. This would lead the system to adopt a four-fold symmetry in the molecular lattice.
Two archetypal surfaces often studied in physisorption research are MgO (100), which has a four-fold symmetry of alternating charges, and the basal plane of graphite, which has a six-fold symmetry to its non-polar, weakly corrugated surface. These differing surface symmetries provide two test cases for comparison. In the case of MgO (100), the molecule-molecule and molecule-surface interaction are synergistic, both driving the film towards the same symmetry; whereas for graphite, the six-fold surface symmetry is incompatible with the preferred four-fold interaction symmetry of the molecules. This presents the opportunity for structurally frustrated systems to arise.
Acetylene and allene are both simple, linear, rigid hydrocarbons with large quadrupole moments of similar strength. The most distinct variations between these two molecules are size and axial rotational symmetry. These molecules, just like the surface, provide two simple, but contrasting symmetry effects. The simple point group of truly linear molecules of acetylene allow for them to lie completely flat against a surface. The 90-degree dihedral angle between the hydrogen pairs on opposing sides of allene molecules prevent them from easily being able to lie perfectly flat against the surface, creating another opportunity for broken symmetry in the molecule-surface interactions – this instance in the vertical direction rather than the two dimensional adsorption plane.
This investigation aims to study the behavioral properties of acetylene and allene films through thermodynamic, structural, and phase behavior analyses when adsorbed on both graphite and MgO. To this end, a combination of volumetric adsorption isotherms, elastic neutron diffraction, and computational modeling have been employed
Particle Swarm Optimization
Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field
Efficient, Dependable Storage of Human Genome Sequencing Data
A compreensão do genoma humano impacta várias áreas da vida. Os dados oriundos do genoma humano são enormes pois existem milhões de amostras a espera de serem sequenciadas e cada genoma humano sequenciado pode ocupar centenas de gigabytes de espaço de armazenamento. Os genomas humanos são críticos porque são extremamente valiosos para a investigação e porque podem fornecer informações delicadas sobre o estado de saúde dos indivíduos, identificar os seus dadores ou até mesmo revelar informações sobre os parentes destes. O tamanho e a criticidade destes genomas, para além da quantidade de dados produzidos por instituições médicas e de ciências da vida, exigem que os sistemas informáticos sejam escaláveis, ao mesmo tempo que sejam seguros, confiáveis, auditáveis e com custos acessíveis. As infraestruturas de armazenamento existentes são tão caras que não nos permitem ignorar a eficiência de custos no armazenamento de genomas humanos, assim como em geral estas não possuem o conhecimento e os mecanismos adequados para proteger a privacidade dos dadores de amostras biológicas. Esta tese propõe um sistema de armazenamento de genomas humanos eficiente, seguro e auditável para instituições médicas e de ciências da vida. Ele aprimora os ecossistemas de armazenamento tradicionais com técnicas de privacidade, redução do tamanho dos dados e auditabilidade a fim de permitir o uso eficiente e confiável de infraestruturas públicas de computação em nuvem para armazenar genomas humanos. As contribuições desta tese incluem (1) um estudo sobre a sensibilidade à privacidade dos genomas humanos; (2) um método para detetar sistematicamente as porções dos genomas que são sensíveis à privacidade; (3) algoritmos de redução do tamanho de dados, especializados para dados de genomas sequenciados; (4) um esquema de auditoria independente para armazenamento disperso e seguro de dados; e (5) um fluxo de armazenamento completo que obtém garantias razoáveis de proteção, segurança e confiabilidade a custos modestos (por exemplo, menos de 1/Genome/Year) by integrating the proposed mechanisms with appropriate storage configurations