Academic literature on the topic 'Attractiveness, Stochastic order'

Online learning to rank (OLTR) interactively learns to choose lists of items from a large collection based on certain click models that describe users' click behaviors. Most recent works for this problem focus on the stochastic environment where the item attractiveness is assumed to be invariant during the learning process. In many real-world scenarios, however, the environment could be dynamic or even arbitrarily changing. This work studies the OLTR problem in both stochastic and adversarial environments under the position-based model (PBM). We propose a method based on the follow-the-regularized-leader (FTRL) framework with Tsallis entropy and develop a new self-bounding constraint especially designed for PBM. We prove the proposed algorithm simultaneously achieves O(log T) regret in the stochastic environment and O(m√nT) regret in the adversarial environment, where T is the number of rounds, n is the number of items and m is the number of positions. We also provide a lower bound of order Ω(m√nT) for adversarial PBM, which matches our upper bound and improves over the state-of-the-art lower bound. The experiments show that our algorithm could simultaneously learn in both stochastic and adversarial environments and is competitive compared to existing methods that are designed for a single environment.

We employ a mathematical model (a phase oscillator model) to describe the deterministic and stochastic features of frog choruses in which male frogs attempt to avoid call overlaps. The mathematical model with a general interaction term is identified using a Bayesian approach, and it qualitatively reproduces the stationary and dynamical features of the empirical data. In addition, we quantify the magnitude of attention paid among the male frogs from the identified model, and then analyse the relationship between attention and behavioural parameters using a statistical approach. Our analysis demonstrates a negative correlation between attention and inter-frog distance, and also suggests a behavioural strategy in which male frogs selectively attend to a less attractive male frog (i.e. a male producing calls at longer intervals) in order to more effectively advertise their superior relative attractiveness to females.

Countering counterfeit products protects the health of consumers, improves the quality of life and the competitiveness of the national economy. The turnover of counterfeit products entails a decrease in customs and tax payments, the attractiveness of investments in production and sectors of the economy, interferes with the observance of quality standards, and the construction of an innovative technological economy. Digital techniques expand the possibilities of understanding the trends of the phenomenon under consideration, which is applicable when constructing planning documents. Mathematical modeling methods make it possible to construct appropriate forecasts. To build models, it is proposed to use the provisions of the theory of probability with an emphasis on ensuring the reliability of the results. The indicators of digital models built on the basis of statistical data will be stochastic in nature. This makes it possible to generate digital forecasts that are “calculated” and verified. The use of quantitative models developed by the exact sciences requires correct application when considering issues of social and legal phenomena in order to comply with methodological soundness. For this, the theoretical distribution laws developed by the theory of probability are used. Suggestions for the practical use of the proposed methods for solving counterfeit counterfeiting issues are presented. The main area of application of the proposed digital modeling approaches is the construction of normative forecasts for planning. The main parameters of such plans are quantified. This approach can be applied when drawing up interstate plans and development strategies.

The article presents a description and some illustrative results of the application of two optimization models for a two-component nuclear energy system consisting of thermal and fast reactors in a closed nuclear fuel cycle. These models correspond to two possible options of developing Russian nuclear energy system, which are discussed in the expert community: (1) thermal and fast reactors utilizing uranium and mixed oxide fuel, (2) thermal reactors utilizing uranium oxide fuel and fast reactors utilizing mixed nitride uranium-plutonium fuel. The optimization models elaborated using the IAEA MESSAGE energy planning tool make it possible not only to optimize the nuclear energy system structure according to the economic criterion, taking into account resource and infrastructural constraints, but also to be used as a basis for developing multi-objective, stochastic and robust optimization models of a two-component nuclear energy system. These models were elaborated in full compliance with the recommendations of the IAEA’s PESS and INPRO sections, regarding the specification of nuclear energy systems in MESSAGE. The study is based on publications of experts from NRC “Kurchatov Institute”, JSC “SSC RF-IPPE”, ITCP “Proryv”, JSC “NIKIET”. The presented results demonstrate the characteristic structural features of a two-component nuclear energy system for conservative assumptions in order to illustrate the capabilities of the developed optimization models. Consideration is also given to the economic feasibility of a technologically diversified nuclear energy structure providing the possibility of forming on its base a robust system in the future. It has been demonstrated that given the current uncertainties in the costs of nuclear fuel cycle services and reactor technologies, it is impossible at the moment to make a reasonable conclusion regarding the greatest attractiveness of a particular option in terms of the economic performance.

Le sujet principal de la thèse sont les systèmes de particules en interaction, qui sont des classes de processus spatio-temporels. Ces systèmes décrivent l'évolution de particules en interaction les unes avec les autres sur un espace discret fini ou infini. Dans la partie I, nous examinons l'ordre stochastique dans un système de particules avec multiples naissances, morts et sauts sur l'espace d-dimensionnel à coordonnées entières. Nous donnons des applications pour des modèles biologiques de diffusion d'épidémies et de systèmes de dynamiques de métapopulations. Dans la partie II, nous analysons la marche aléatoire coalescente dans une classe de graphes aléatoires finis qui modèlent les réseaux sociaux, les graphes "small word"
The main subject of the thesis is concerned with interacting particle systems, which are classes of spatio-temporal stochastic processes describing the evolution of particles in interaction with each other on a finite or infinite discrete space. In part I we investigate the stochastic order in a particle system with multiple births, deaths and jumps on the d-dimensional lattice. We give applications on biological models of spread of epidemics and metapopulations dynamics systems. In part II we analyse the coalescing random walk in a class of finite random graphs modeling social networks, the small world graphs

The main subject of the thesis is concerned with interacting particle systems, which are classes of spatio-temporal stochastic processes describing the evolution of particles in interaction with each other. The particles move on a finite or infinite discrete space and on each element of this space the state of the configuration is integer valued. Configurations of particles evolve in continuous time according to a Markov process. Here the space is either the infinite deterministic d-dimensional lattice or a random graph given by the finite d-dimensional torus with random matchings. In Part I we investigate the stochastic order in a particle system with multiple births, deaths and jumps on the d-dimensional lattice: stochastic order is a key tool to understand the ergodic properties of a system. We give applications on biological models of spread of epidemics and metapopulation dynamics systems. In Part II we analyse the coalescing random walk in a class of finite random graphs modeling social networks, the small world graphs. We derive the law of the meeting time of two random walks on small world graphs and we use this result to understand the role of random connections in meeting time of random walks and to investigate the behavior of coalescing random walks.

Abstract Field development planning activities involve decisions that entail multi-billion-dollar investments. Making right design choices is crucial to the techno-economic success of the project. In this paper we demonstrate how state-of-the-art numerical model-based optimization techniques can support asset teams in this complex decision-making process. Optimization of real-life field development cases can be computationally very demanding due to the cost of running large amounts of large-scale reservoir simulations, the large number of variables to be optimized and the necessity of accounting for uncertainties, among other reasons. In this work we employ TNO’s EVEReST optimization technology leveraging the StoSAG stochastic gradient-based method to achieve optimized solutions in computationally efficient manner. Besides its computational attractiveness, the StoSAG method also renders the optimization framework flexible to be customized to any specific optimization problem, e.g., optimization of any type of field development decisions, coupling to any industry-standard reservoir simulators and handling any type of objective and constraint functions. The optimization framework has been used to optimize the expansion of the development plan of a large field in the Middle East, in particular to support decisions concerning the drilling of (up to) 38 new wells and the upgrading of surface facilities to accommodate incremental production. The challenge is posed by the field being comprised of multiple carbonate reservoirs for which multiple types of decisions need to be optimized, often simultaneously i.e. well target locations, trajectory design features, number and type of wells and their distribution across the different reservoirs as well as the drilling sequence. An economic objective function was used with the operational constraints per reservoir accounted for within the framework. Optimization found non-trivial optimal solutions resulting in significant improvements in the economic objective. The optimal strategy revealed improved distribution of wells (and types) among the reservoirs, more suitable drilling order and superior well locations and trajectories compared to the initial strategy. Optimization found well locations and trajectories taking into account complex local well interaction in already densely populated reservoirs with wells. The optimized solutions were benchmarked against the development plan designed by the asset team, and the comparison of strategies confirmed the added value of numerical optimization as a tool to expedite the search of improved development strategies. The nature of the employed optimization method allowed optimized solutions to be achieved with a reduced number of reservoir simulations, which was crucial for the success of this study due to the time-consuming reservoir simulations involved. This showcases the computational advantage of the optimization method and highlights EVEReST as an enabler technology for optimization under uncertainty, where an ensemble of large-scale models is to be considered and the number of required reservoir simulations tends to be even larger.