Infinite Pool Research Articles

An R Package for Nonparametric Inference on Dynamic Populations with Infinitely Many Types.

Fleming-Viot diffusions are widely used stochastic models for population dynamics that extend the celebrated Wright-Fisher diffusions. They describe the temporal evolution of the relative frequencies of the allelic types in an ideally infinite panmictic population, whose individuals undergo random genetic drift and at birth can mutate to a new allelic type drawn from a possibly infinite potential pool, independently of their parent. Recently, Bayesian nonparametric inference has been considered for this model when a finite sample of individuals is drawn from the population at several discrete time points. Previous works have fully described the relevant estimators for this problem, but current software is available only for the Wright-Fisher finite-dimensional case. Here, we provide software for the general case, overcoming some nontrivial computational challenges posed by this setting. The R package FVDDPpkg efficiently approximates the filtering and smoothing distribution for Fleming-Viot diffusions, given finite samples of individuals collected at different times. A suitable Monte Carlo approximation is also introduced in order to reduce the computational cost.

World Hyper-Heuristic: A novel reinforcement learning approach for dynamic exploration and exploitation

In the real world, there are many complex problems in engineering. Every problem has a level of computational complexity, starting from simple problems and reaching NP-hard problems. Np-hard problems do not have a definite answer. Therefore, hyper-heuristic algorithms try to optimize NP-hard problems. Hyper-heuristics optimize complex search problems using a combination plate of exploration and exploitation strategies. The current algorithms need more generalizability, handling specific data types, limitations to a particular search problem, and weak performance. We propose a World hyper-heuristic (World) to address the issues using a novel reinforcement learning method. World, in two steps of rewarding and selection, dynamically switches between exploration and exploitation strategies provided in an infinite pool of meta-heuristics. We evaluated the performance of our proposed method in three phases. First, we optimized the standard functions as artificial Np-hard problems. Then, we compared real engineering examples related to discrete Np-hard problems. Finally, we implemented and analyzed problems with the continuous Np-hard problems. Our extensive comparisons with the state-of-the-art algorithms demonstrate the World’s outperformance in handling varied search problems with any data type. Among all the findings, World finds the shortest path of length 4.33e + 05, far shorter than the results of the state-of-the-art work, in benchmarked data of 10,000 real cities.

Emergent cooperative behavior in transient compartments.

We introduce a minimal model of multilevel selection on structured populations, considering the interplay between game theory and population dynamics. Through a bottleneck process, finite groups are formed with cooperators and defectors sampled from an infinite pool. After the fragmentation, these transient compartments grow until the maximal number of individuals per compartment is attained. Eventually, all compartments are merged and well mixed, and the whole process is repeated. We show that cooperators, even if interacting only through mean-field intragroup interactions that favor defectors, may perform well because of the intergroup competition and the size diversity among the compartments. These cycles of isolation and coalescence may therefore be important in maintaining diversity among different species or strategies and may help to understand the underlying mechanisms of the scaffolding processes in the transition to multicellularity.

Random item slope regression: An alternative measurement model that accounts for both similarities and differences in association with individual items.

In psychology, researchers often predict a dependent variable (DV) consisting of multiple measurements (e.g., scale items measuring a concept). To analyze the data, researchers typically aggregate (sum/average) scores across items and use this as a DV. Alternatively, they may define the DV as a common factor using structural equation modeling. However, both approaches neglect the possibility that an independent variable (IV) may have different relationships to individual items. This variance in individual item slopes arises because items are randomly sampled from an infinite pool of items reflecting the construct that the scale purports to measure. Here, we offer a mixed-effects model called random item slope regression, which accounts for both similarities and differences of individual item associations. Critically, we argue that random item slope regression poses an alternative measurement model to common factor models prevalent in psychology. Unlike these models, the proposed model supposes no latent constructs and instead assumes that individual items have direct causal relationships with the IV. Such operationalization is especially useful when researchers want to assess a broad construct with heterogeneous items. Using mathematical proof and simulation, we demonstrate that random item slopes cause inflation of Type I error when not accounted for, particularly when the sample size (number of participants) is large. In real-world data (n = 564 participants) using commonly used surveys and two reaction time tasks, we demonstrate that random item slopes are present at problematic levels. We further demonstrate that common statistical indices are not sufficient to diagnose the presence of random item slopes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Analysis of Perishable Stochastic Inventory System with Infinite Pool Capacity: An Effective Cost Optimization Model

Analysis of Perishable Stochastic Inventory System with Infinite Pool Capacity: An Effective Cost Optimization Model

In the Infinite Pool: The Cinematic Spectatorship of Sleep Has Her House (2017)

Scott Barley’s Sleep Has Her House is a recent landmark of experimental cinema, combining recent trends in the landscape and slow cinema movements to produce a painstakingly crafted meditation on nature and the viewer’s gaze upon it. Characterized by its manipulation of light and temporality, the film draws upon the various modes of viewing established by preceding experimental movements, as exemplified by the work of Warhol, Deleuze, and Brakhage, amongst others, to test the relationship between viewer and viewed. The film’s further environmental concerns, as filtered through its landscape focus, are explored through the recent writings of Plumwood, Kimmerer, and Heidegger and their concerns with animacy and human–nature relations. The result is a film that tests the relationship between man and environment through its manipulation of viewing and seeing, prompting a more animate and holistic view of the natural world.

An Analysis of Two Fluid Layers Enclosed Between Two Non-Porous Surfaces

The stability of a two-phase interface is a crucial occurrence that involves the design of many engineering applications. It correlates the spatial and droplet size-distributions of many fluid spraying applications and has a great effect on the estimations of the critical heat flux of systems that involves phase change or evaporation. However, the existing hydrodynamic models are only able to predict the stability of a plane fluid sheet, surrounded by an infinite pool of liquid. The case of a thin sheet of liquid surrounding a vapor sheet and enclosed between two walls has not been studied yet. The present paper solves this problem using a linearized stability analysis. Velocity potentials satisfying these conditions are introduced and a complete analysis is presented.

Coefficient β As Extension of KR-21 Reliability for Summed and Scaled Scores for Polytomously-scored Tests

ABSTRACT KR-21 reliability and its extension (coefficient α) gives the reliability estimate of test scores under the assumption of tau-equivalent forms. KR-21 reliability gives the reliability estimate for summed scores for dichotomous items when items are randomly sampled from an infinite pool of similar items (randomly parallel forms). The article extends KR-21 to coefficient to estimate reliability of summed scores for test with any item scoring pattern under the same assumption of KR-21. Also, the article presents coefficient to estimate this reliability for nonlinearly scaled scores for any type of items. The article shows that coefficient is slightly different from the classical index of reliability. Results show that coefficient is not equal to the classical index of reliability for summed scores and scaled scores. Moreover, results using real data on psychological instruments reveal that different score scales yield different coefficient reliability estimates that are less than coefficient Gα by a function related to form-to-form differences in averages.

Finitely convergent deterministic and stochastic iterative methods for solving convex feasibility problems

We propose finitely convergent methods for solving convex feasibility problems defined over a possibly infinite pool of constraints. Following other works in this area, we assume that the interior of the solution set is nonempty and that certain overrelaxation parameters form a divergent series. We combine our methods with a very general class of deterministic control sequences where, roughly speaking, we require that sooner or later we encounter a violated constraint if one exists. This requirement is satisfied, in particular, by the cyclic, repetitive and remotest set controls. Moreover, it is almost surely satisfied for random controls.

Assessment of assumptions underlying models of prokaryotic pangenome evolution

BackgroundThe genomes of bacteria and archaea evolve by extensive loss and gain of genes which, for any group of related prokaryotic genomes, result in the formation of a pangenome with the universal, asymmetrical U-shaped distribution of gene commonality. However, the evolutionary factors that define the specific shape of this distribution are not thoroughly understood.ResultsWe investigate the fit of simple models of genome evolution to the empirically observed gene commonality distributions and genome intersections for 33 groups of closely related bacterial genomes. A model with an infinite external gene pool available for gene acquisition and constant genome size (IGP-CGS model), and two gene turnover rates, one for slow- and the other one for fast-evolving genes, allows two approaches to estimate the parameters for gene content dynamics. One is by fitting the model prediction to the distribution of the number of genes shared by precisely k genomes (gene commonality distribution) and another by analyzing the distribution of the number of genes common for k genome sets (k-cores). Both approaches produce a comparable overall quality of fit, although the former significantly overestimates the number of the universally conserved genes, while the latter overestimates the number of singletons. We further explore the effect of dropping each of the assumptions of the IGP-CGS model on the fit to the gene commonality distributions and show that models with either a finite gene pool or unequal rates of gene loss and gain (greater gene loss rate) eliminate the overestimate of the number of singletons or the core genome size.ConclusionsWe examine the assumptions that are usually adopted for modeling the evolution of the U-shaped gene commonality distributions in prokaryote genomes, namely, those of infinitely many genes and constant genome size. The combined analysis of genome intersections and gene commonality suggests that at least one of these assumptions is invalid. The violation of both these assumptions reflects the limited ability of prokaryotes to gain new genes. This limitation seems to stem, at least partly, from the horizontal gene transfer barrier, i.e., the cost of accommodation of foreign genes by prokaryotes. Further development of models taking into account the complexity of microbial evolution is necessary for an improved understanding of the evolution of prokaryotes.

Bio-inspired design: the impact of collaboration between engineers and biologists on analogical transfer and ideation

To develop innovative technical solutions, designers seek inspiration from nature and its almost infinite pool of biological solutions. However, understanding biological solutions and transferring appropriate analogies to develop technical solutions pose a considerable challenge. A strategy to facilitate interdisciplinary understanding is collaboration between engineers and biologists. So far, the impact of this type of collaboration on analogical transfer and ideation has not been studied in an experimental setting. To close this research gap, this work examines design experiments with engineer–biologist pairs, half of whom were provided with a support for analogical transfer (BioId Support). Engineer–biologist pairs were compared to single disciplinary pairs with regards to the transfer of selected analogy categories. The impact of the transferred analogy categories on the quality of solution ideas was analysed—quality was regarded as an indicator for the solution ideas’ innovative potential. The results show a positive influence of the supported collaboration between engineers and biologists on the transferred analogy categories. These analogy categories positively influence the quality of solution ideas. This work indicates benefits of collaboration between engineers and biologists for bio-inspired design. In addition, it provides a starting point for improving bio-inspired design methods.

Numerical Simulation of Vapor–Bubble Collapse—Heat Transfer and Nonlinear Dynamics Issues

Abstract In this work, we compute the dynamics of a spherical vapor-bubble in an infinite pool of subcooled water during bubble collapse using our semi-analytical method. The main contribution of this work is to bring out the dynamics of nonmonotonic bubble collapse describing heat transfer characteristics and nonlinear dynamics. The dynamics shows the variation of radius with time for collapsing vapor bubble at different subcooling ΔTsub of 1.40 K to 35 K. The present approach accurately determines the bubble radius decreasing with time and has been compared with our experimental results, the experiment from literature, the other theories, and correlations. As it is noted that the literature lacks steady-state analysis of oscillating bubble collapse, we also report the steady-state analysis and the bifurcation analysis of bubble collapse at a pressure of 1.0 atm to check the stability of bubble collapse. The effect of ΔTsub and initial bubble radius R0 on dynamics of bubble collapse has been analyzed. The collapse of big bubbles involves with the bubble oscillations because of a large contribution of liquid inertia and the collapse of very small bubbles essentially occurs in heat transfer regime.

Optimizing Data Placement for Cost Effective and High Available Multi-Cloud Storage

With the advent of big data age, data volume has been changed from trillionbyte to petabyte with incredible speed. Owing to the fact that cloud storage offers the vision of a virtually infinite pool of storage resources, data can be stored and accessed with high scalability and availability. But a single cloud-based data storage has risks like vendor lock-in, privacy leakage, and unavailability. Multi-cloud storage can mitigate these risks with geographically located cloud storage providers. In this storage scheme, one important challenge is how to place a user's data cost-effectively with high availability. In this paper, an architecture for multi-cloud storage is presented. Next, a multi-objective optimization problem is defined to minimize total cost and maximize data availability simultaneously, which can be solved by an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions called the Pareto-optimal set. Then, a method is proposed which is based on the entropy method to determine the most suitable solution for users who cannot choose one from the Pareto-optimal set directly. Finally, the performance of the proposed algorithm is validated by extensive experiments based on real-world multiple cloud storage scenarios.

Connaissance et reconnaissance chez Hobbes et Rousseau: la transparence est l’obstacle. Par Stéphane Vinolo

Stéphane Vinolo’s book is an ingenious examination of social contract theory, illustrated principally by the political thought of Hobbes and Rousseau but supplemented by more recent discussions of game theory on the one hand, and the thought of René Girard, Jacques Lacan, and Jacques Derrida on the other. The aim of the book is to show how the concept of individual autonomy can be reconciled with the idea of a durable and stable political society when, at first sight, the firmly individualistic orientation of the first seems to rule out the durability and stability of the second. The problem, of course, is an old one and, in different guises, was central to the concerns of the many generations of German philosophers, from Kant to Heidegger, whose thought was so salient to Girard, Lacan, and Derrida. It was, in short, the problem which, in the seventeenth and eighteenth centuries, was associated with the concept of sociability. For Vinolo, the problem was formulated initially by Hobbes with his claim that humans have no natural ability to prioritize their desires. This means that there is no natural human capacity to identify a highest good, whether something divine or something as apparently fundamental as the desire for self-preservation. There are, instead, only sequences of desires with nothing apart from chronology to distinguish them. Nor can individuals impose their desires for any durable periods of time because, as both Hobbes and Rousseau emphasized, humans are sufficiently equal and organized to be able to neutralize any individual drive for domination. And, being roughly equal, no individual could prevent another from giving priority to the short term over the long term, as Rousseau showed with the example of a stag hunt. If all these considerations require no special human insight and are instead common knowledge, then, as Vinolo argues, this potentially infinite pool of knowledge of knowledge simply makes violence rational. This, as should be apparent, is the point of the pun in the title of his book. Instead, as with Jean Starobinski, of thinking about politics in terms of the obstacles to transparency, applying game theory to Hobbes and Rousseau seems to show that transparency itself is the obstacle and, according to Vinolo, the same type of reflective loop applies to the passions. The way out is, therefore, misrecognition rather than recognition, or something more like a Rawlsian veil of ignorance, supplied here, however, by the idea of a sovereign state. It is not clear, however, at least in terms of Vinolo’s final description of the properties of a contractually based sovereign state, what gives the combination of sovereignty and the state the ability to block the spiral of competitive recognition. On his terms, it works because it is external, like a humanly created artificial god. Notwithstanding the rigour and clarity of Vinolo’s argument, students of modern politics might be surprised.

Quasi-Soft Variable Structure Control of Discrete-Time Systems With Input Saturation

In the effort to improve the convergence rate of linear controllers, at the same time avoiding abrupt input transitions and implementation difficulties of time-optimal ones, soft variable structure control (VSC) may be considered. The classical formulation of soft VSC in continuous time domain assumes smooth, yet instantaneous switching in an infinite pool of controllers, which is not possible in the now commonplace digital control schemes. In discrete-time systems, the control structure may be changed only at the sampling instants, which results at best in quasi-soft behavior. This brief presents a design of quasi-soft VSC system, which by directly covering the discretization effects, permits maintaining the favorable properties of fast convergence and smooth inputs in discrete time domain, thus bringing the realization of soft VSC closer to the industrial reality. The properties of the presented control system are formally proved and verified experimentally.

A Semi-analytical Method for Computing the Dynamics of Bubble Growth: The Effect of Superheat and Operating Pressure

In this work, we propose a semi-analytical method for computing the dynamics of a spherical-bubble growth in an infinite pool of superheated water. The dynamics shows the temporal variation of bubble radius in superheat of 1.4–17.89 K. The method accurately computes the bubble growth controlled by surface tension, particularly in very low superheats, in which the existing theories are not adequate. The present method consistent with the existing theories computes the bubble growth controlled by heat transfer in high superheats. The semi-analytical method with a modification of heat transfer at the interface is capable of predicting the bubble growth in pool boiling experiments and numerical simulations. The effect of increasing operating pressure on the bubble growth decreases the growth constant following a power law. The variations in energy components due to pressure difference, inertia, surface tension, and thermal diffusion reveal some interesting insights of the bubble-growth mechanism.

Functional difference equations in the problem on the forced oscillations of a fluid in an infinite pool with conical bottom

Functional difference equations in the problem on the forced oscillations of a fluid in an infinite pool with conical bottom

Data Storage Management in Cloud Environments

Storage as a Service (StaaS) is a vital component of cloud computing by offering the vision of a virtually infinite pool of storage resources. It supports a variety of cloud-based data store classes in terms of availability, scalability, ACID (Atomicity, Consistency, Isolation, Durability) properties, data models, and price options. Application providers deploy these storage classes across different cloud-based data stores not only to tackle the challenges arising from reliance on a single cloud-based data store but also to obtain higher availability, lower response time, and more cost efficiency. Hence, in this article, we first discuss the key advantages and challenges of data-intensive applications deployed within and across cloud-based data stores. Then, we provide a comprehensive taxonomy that covers key aspects of cloud-based data store: data model, data dispersion, data consistency, data transaction service, and data management cost. Finally, we map various cloud-based data stores projects to our proposed taxonomy to validate the taxonomy and identify areas for future research.

A unified treatment of some perturbed fixed point iterative methods with an infinite pool of operators

In this paper, we introduce a subclass of strictly quasi-nonexpansive operators which consists of well-known operators as paracontracting operators (e.g., strictly nonexpansive operators, metric projections, Newton and gradient operators), subgradient projections, a useful part of cutter operators, strictly relaxed cutter operators and locally strongly Féjer operators. The members of this subclass, which can be discontinuous, may be employed by fixed point iteration methods; in particular, iterative methods used in convex feasibility problems. The closedness of this subclass, with respect to composition and convex combination of operators, makes it useful and remarkable. Another advantage with members of this subclass is the possibility to adapt them to handle convex constraints. We give convergence result, under mild conditions, for a perturbation resilient iterative method which is based on an infinite pool of operators in this subclass. The perturbation resilient iterative methods are relevant and important for their possible use in the framework of the recently developed superiorization methodology for constrained minimization problems. To assess the convergence result, the class of operators and the assumed conditions, we illustrate some extensions of existence research works and some new results.

A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments

SummaryLarge‐scale scientific problems are often modeled as workflows. The ever‐growing data and compute requirements of these applications has led to extensive research on how to efficiently schedule and deploy them in distributed environments. The emergence of the latest distributed systems paradigm, cloud computing, brings with it tremendous opportunities to run scientific workflows at low costs without the need of owning any infrastructure. It provides a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay‐per‐use basis. However, along with these benefits come numerous challenges that need to be addressed to generate efficient schedules. This work identifies these challenges and studies existing algorithms from the perspective of the scheduling models they adopt as well as the resource and application model they consider. A detailed taxonomy that focuses on features particular to clouds is presented, and the surveyed algorithms are classified according to it. In this way, we aim to provide a comprehensive understanding of existing literature and aid researchers by providing an insight into future directions and open issues.