Probabilistic Scheme Research Articles

Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game

Evolutionary game theory focuses on the fitness differences between simple discrete or probabilistic strategies to explain the evolution of particular decision-making behavior within strategic situations. Although this approach has provided substantial insights into the presence of fairness or generosity in gift-giving games, it does not fully resolve the question of which cognitive mechanisms are required to produce the choices observed in experiments. One such mechanism that humans have acquired, is the capacity to anticipate. Prior work showed that forward-looking behavior, using a recurrent neural network to model the cognitive mechanism, are essential to produce the actions of human participants in behavioral experiments. In this paper, we evaluate whether this conclusion extends also to gift-giving games, more concretely, to a game that combines the dictator game with a partner selection process. The recurrent neural network model used here for dictators, allows them to reason about a best response to past actions of the receivers (reactive model) or to decide which action will lead to a more successful outcome in the future (anticipatory model). We show for both models the decision dynamics while training, as well as the average behavior. We find that the anticipatory model is the only one capable of accounting for changes in the context of the game, a behavior also observed in experiments, expanding previous conclusions to this more sophisticated game.

Energy-Efficient Probabilistic Barrier Construction in Directional Sensor Networks

${k}$ -barrier coverage is one of the critical issues in directional sensor networks. In actual application, the sensing distance becomes small with the increase of sensing angle, and the event sensing probability is constantly changing as the distance between node and event changes. But so far, there is no high efficient distributed barrier construction scheme based on a directional probabilistic sensing model. This paper first creates a directional probabilistic sensing model with changing angle, based on which four target locations of barrier member are created in a distributed manner. In addition, the energy efficiency ratio (EER) is investigated to minimize the energy consumption. Based on these two models, a distributed probabilistic barrier construction scheme based on EER is proposed, which commences the initiator of the barrier on the west end boundary and selects suitable relay sensors from left to right until the east end boundary is reached. The target locations and corresponding orientations of barrier member are determined only by its ancestor. When there are many candidate relay sensors, the one with the largest EER is the actual relay sensor. Simulation results indicate our method could form ${k}$ -barrier coverage with smaller energy consumption than other methods.

Feller’s Scheme in Approximation by Nonlinear Possibilistic Integral Operators

ABSTRACTBy analogy with Feller’s general probabilistic scheme used in the construction of many classical convergent sequences of linear operators, in this paper, we consider a Feller-kind scheme based on the possibilistic integral, for the construction of convergent sequences of nonlinear operators. In particular, in the discrete case, all the so-called max-product Bernstein-type operators and their qualitative convergence properties are recovered. Also, discrete nonperiodic nonlinear possibilistic convergent operators of Picard type, Gauss–Weierstrass type and Poisson–Cauchy type are studied and the possibility of introduction of discrete periodic(trigonometric) nonlinear possibilistic operators of de la Vallée–Poussin type, of Fejér type and of Jackson type is mentioned as future directions of research.

Mobility-Aware Coded Probabilistic Caching Scheme for MEC-Enabled Small Cell Networks

Caching on the edge has been recognized as an effective solution to tackle the backhaul constraint of network densification. However, most related works ignored user mobility in wireless networks, which is unreasonable under the background of network densification. For a more flexible and context-aware caching decision, the concept of caching on the edge can be extended to mobile edge computing (MEC) that enables computation and storage resources at mobile edge networks. With MEC servers deployed on base stations, a huge amount of collected radio access network context data can be analyzed and utilized to render a caching scheme adaptive to user’s context-aware information. In this regard, a novel mobility-aware coded probabilistic caching scheme is proposed for MEC-enabled small cell networks (SCNs). Different from previous mobility-aware caching schemes, user mobility and distributed storage are incorporated into a conventional probabilistic caching scheme, with the aim of throughput maximization. Based on stochastic geometry theory and a modified mobility model of discrete random jumps, the explicit expression of throughput is derived. Due to the complexity of the expression, two light-weight heuristic algorithms are provided to numerically obtain the optimal solutions. Moreover, a significant trade-off among the gains of mobility diversity, content diversity, and channel selection diversity is discussed, and we further numerically analyze how such a trade-off is influenced by user mobility, content popularity, and backhaul capacity, with some fundamental insights into the application of the proposed scheme in MEC-enabled SCNs. The superiority of our proposed scheme is demonstrated by the comparisons with the classical M most popular caching scheme and the conventional probabilistic caching scheme. Numerical results show that our proposed caching scheme achieves higher throughput than those of the other two, especially when users of intense mobility request contents, of which the popularity profile is not skewed, in MEC-enabled SCNs with poor backhaul capacity, indicating that the proposed caching scheme is a promising solution for network densification.

Effect of heterogeneity variation on the oil recovery performance in CO<SUB align="right">2-WAG process

The main objective of this study is to investigate the effect of the heterogeneity on the oil recovery with various WAG ratios using a probabilistic manner in a CO2-WAG process. For the probabilistic scheme, 100 cases of different permeability distributions were generated for each heterogeneity index and used as parameters for different compositional simulations. In generating the fields, the correlation lengths and directions were considered for various permeability fields within the same heterogeneity index. Oil recovery factors according to the WAG ratio were obtained from 2D areal and vertical models. As a result, the heterogeneity has a significant effect on determining the WAG ratio and performance. Especially, it was revealed that the direction of permeability has an effect on the determination of the WAG ratio. The optimum WAG ratio differs depending on the permeability direction of the reservoir. When the direction of permeability is consistent with the extension line between the injector and the producer, the proportion of water should be increased to obtain favourable oil recovery performance. [Received: December 4, 2015; Accepted: March 1, 2016]

Aggregating physiological and eye tracking signals to predict perception in the absence of ground truth

Today's driving assistance systems build on numerous sensors to provide assistance for specific tasks. In order to not patronize the driver, intensity and timing of critical responses by such systems is determined based on parameters derived from vehicle dynamics and scene recognition. However, to date, information on object perception by the driver is not considered by such systems. With advances in eye-tracking technology, a powerful tool to assess the driver's visual perception has become available, which, in many studies, has been integrated with physiological signals, i.e., galvanic skin response and EEG, for reliable prediction of object perception.We address the problem of aggregating binary signals from physiological sensors and eye tracking to predict a driver's visual perception of scene hazards. In the absence of ground truth, it is crucial to use an aggregation scheme that estimates the reliability of each signal source and thus reliably aggregates signals to predict whether an object has been perceived. To this end, we apply state-of-the-art methods for response aggregation on data obtained from simulated driving sessions with 30 subjects. Our results show that a probabilistic aggregation scheme on top of an Expectation-Maximization-based estimation of source reliabilities can predict hazard perception at a recall and precision of 96% in real-time.

Policy-based Modeling with Probabilistic Distribution and Stochastic Strategy for the Locomotion of a Cleaning Robot

Policy-based Modeling with Probabilistic Distribution and Stochastic Strategy for the Locomotion of a Cleaning Robot

Voluntary Taxation and Beyond: The Promise of Social-Contracting Voting Mechanisms

Would you volunteer to pay a carbon tax if 99% of other Americans also volunteered to pay such a tax? Instead of traditional referenda, it is possible to structure plebiscites which would only bind a subset of the population (e.g., to be subject to a carbon tax) if that subset’s individually chosen conditions for participation are met. While provision-point mechanisms with exogenously set provision points have garnered billions of dollars in private contributions, a broader class of “social contracting” mechanisms exist that allow individuals to bid on their preferred provision points. This article shows how both partial and probabilistic bidding schemes might foster voluntary subpopulation participation in a range of public good applications (including sexual assault reporting and civil disobedience), and reports results from a series of randomized surveys of Internet respondents assessing the potential support for such subgroup “social contracting.” The respondent bids would, for example, support an equilibrium in which approximately 25% of the public would voluntarily commit to pay an additional 10% tax on electricity.

Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning.

Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache. Moreover, the generalization ability of current systems is somewhat limited since they usually require elaborately collecting a dictionary of examples or carefully tuning features/components. In this paper, we present a novel representation learning framework that generates an end-to-end photo-sketch mapping through structure and texture decomposition. In the training stage, we first decompose the input face photo into different components according to their representational contents (i.e., structural and textural parts) by using a pre-trained convolutional neural network (CNN). Then, we utilize a branched fully CNN for learning structural and textural representations, respectively. In addition, we design a sorted matching mean square error metric to measure texture patterns in the loss function. In the stage of sketch rendering, our approach automatically generates structural and textural representations for the input photo and produces the final result via a probabilistic fusion scheme. Extensive experiments on several challenging benchmarks suggest that our approach outperforms example-based synthesis algorithms in terms of both perceptual and objective metrics. In addition, the proposed method also has better generalization ability across data set without additional training.

Controlled unknown quantum operations on hybrid systems**Project supported by the National Natural Science Foundation of China (Grant Nos. 61303039 and 61201253), Chunying Fellowship, and Fundamental Research Funds for the Central Universities, China (Grant No. 2682014CX095).

Any unknown unitary operations conditioned on a control system can be deterministically performed if ancillary subspaces are available for the target systems [Zhou X Q, et al. 2011 Nat. Commun. 2 413]. In this paper, we show that previous optical schemes may be extended to general hybrid systems if unknown operations are provided by optical instruments. Moreover, a probabilistic scheme is proposed when the unknown operation may be performed on the subspaces of ancillary high-dimensional systems. Furthermore, the unknown operations conditioned on the multi-control system may be reduced to the case with a control system using additional linear circuit complexity. The new schemes may be more flexible for different systems or hybrid systems.

Probabilistic analysis of containment structural performance in severe accidents

To study the capacity of reactor containment under pressurization, it is important to know the way that this structure reacts under internal pressure loading. This is usually performed through structural fragility analysis for assuring that the containment structure has high capability of withstanding the loads associated with severe accident phenomena and its consequent pressurization, therefore the potential for significant radioactive release is small. In this paper, the methodology of structural fragility analysis for pressurization is thoroughly reviewed, and its practical application is demonstrated on a real case Pressurized Water Reactor containment. The reactor containment is analyzed using finite element method, and the ultimate pressure is computed. Containment performance is further analyzed using the structural fragility method. The structural fragility serves as the interface between the structural analysis model and the risk model of the systems. Therefore, a probabilistic scheme is used to represent the structural fragility. Final outcome of this study is the overpressure fragility curve which depicts the probability of failure of the containment at a given value of pressure. This curve is a key element in conducting probabilistic safety assessment (level 2 PSA) of nuclear power plants.

Stochastic upscaling of heterogeneous materials

AbstractWe propose a probabilistic strategy to upscale the material spatial variability from fine to coarse scale. To implement this idea in a numerical framework, we consider the coarse‐scale as stochastic, i. e. its material properties are considered uncertain and modeled as random variables/fields. Numerical examples are shown to demonstrate the applicability of proposed approach. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Secret sharing schemes for compartmented access structures

In this paper, we devise ideal and probabilistic secret sharing schemes for two kinds of compartmented access structures. The first one is a compartmented access structures with hierarchical compartments. The second one is the compartmented access structures with strictly lower bounds. We propose ideal and probabilistic schemes for these two compartmented access structures by using the idea of bivariate interpolation.

Existence of equilibrium for infinite system of interacting diffusions

ABSTRACTWe develop and implement new probabilistic strategy for proving basic results about long-time behavior for interacting diffusion processes on unbounded lattice. The concept of the solution used is rather weak as we construct the process as a solution to suitable infinite-dimensional martingale problem. However, the techniques allow us to consider cases where the generator of the particle is degenerate elliptic operator. As a model example, we present the situation where the operator arises from Heisenberg group. In the last section, we provide further examples that can be handled using our methods.

MODELING AND SIMULATION OF THRESHOLD ANALYSIS FOR PVFS IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) suffer serious damage from false positive and negative attacks due to their hardware restrictions. The sensor network causes both unnecessary energy consumption and information loss through false reports and normal reports, which include false message authentication codes (MACs). A probabilistic voting-based filtering scheme (PVFS) effectively detects the two types of attacks through a pre-defined threshold, which is the number of detected false MACs in a report. Since the threshold significantly influences the ability to detect attacks, the sensor network should be simulated to ensure proper function. In this paper, we describe the development and simulation of a PVFS-based WSN using a discrete event system specification. The experimental results showed that PVFS with a threshold of 2 reduced energy usage by about 16% and improved the detected false reports as compared with a PVFS with a threshold of 3.

Non-linear eigenspace visual object tracking

This paper presents a visual object tracking algorithm using an eigenspace representation. Previous approaches to eigenspace methods for object tracking use vectorized image regions as observations. Here, feature vectors associated to pixels of the target template are considered to be individual observations of the target object. The collection of observations is learned using non-linear subspace projection to arrive at an eigenspace representation. This representation allows tracking pixel-wise but the pixels are tied together using subspace representation which provides a robust and compact representation of the object. Localization and segmentation are carried out by deriving a similarity function in the eigenspace representation. A gradient descent and mean-shift based techniques are derived to maximize the similarity function with respect to the transformation parameters. The de-noising and clustering capabilities of the eigenspace representation lead to a localization procedure that is robust to noise and partial occlusion. For fast moving objects and to recover from total occlusion, a probabilistic search strategy, based on particle filter, is also developed. A unique feature of our approach is that it permits segmentation in addition to localization when multiple templates of the target are given. The performance of the algorithm is tested on real world tracking examples.

QoS-Aware Resource Allocation for Device-to-Device Communications With Channel Uncertainty

In device-to-device (D2D) communications, channel state information (CSI) is exploited to manage the interference between D2D users and regular cellular users (CUs) and improve system performance. However, obtaining the accurate CSI is usually difficult and causes high overhead, particularly when the links are not connected to the base station (BS), such as the links between regular CUs and D2D receivers (CU-D links). In this paper, we investigate the signaling overhead and performance tradeoff in D2D communications with channel uncertainty. To limit interference to regular CUs, we only allow the resource of a CU to be reused by, at most, one D2D pair. We also assume that only partial CSI of the CU-D links is available at the BS and develop two different strategies to deal with the channel uncertainty, namely, probabilistic and partial feedback schemes. We first derive a probability-based resource-allocation scheme by utilizing channel statistical characteristics to maximize the overall throughput of the CUs and admissible D2D pairs while guaranteeing their quality of service (QoS) in terms of signal-to-interference-plus-noise ratio (SINR) and outage probability, respectively. Then, we propose an efficient feedback scheme to reduce the overhead of CSI feedback while providing near-optimal performance. In addition, we propose a combined scheme to take advantages of both probabilistic and partial feedback schemes. It is shown by simulation that there exists an optimal threshold of the outage probability for probabilistic scheme while the partial feedback scheme is robust to the channel models. Furthermore, the combined scheme outperforms the probabilistic and the partial feedback schemes in terms of overall throughput.

Effective Cultural and Economic Indicators on Business Communications Growth (With an Emphasis on Iran)

Today, society became an international trade lifeline. The study of this science has become a necessity to understand the cultural differences in intercultural communication. Although it increasingly crossed the borders and business barriers had been denied, but cultural boundaries do not sidestep easily and unlike legal, political, or economic, business environment tangible aspects, culture is largely invisible. Hence, an aspect of international trade is often overlooked. For economic growth and development inside and outside the borders, attentions should be paid to the cultural aspects of our society and other societies.The purpose of this study was to evaluate the effect of cultural dimensions on growth of economic indicators. This study is a practical in terms nature and purpose, and is descriptive, and library study in terms of data collection. In order to data collection, questionnaire and financial information of financial institutions were used (information of economic indicators of Asian Development Bank and the Central Bank of the Islamic Republic of Iran). Validity and reliability of the questionnaire were evaluated through Cronbach's alpha and exploratory and confirmatory factor analyses were done. The population in this study was the country's capital market participants. Sampling was done through probabilistic sampling strategy and simple random sampling method was used and 92 persons were estimated through the Morgan table. Structural equation modeling was used, to test the hypotheses. The results of this study showed that, there is a positive relationship between cultural dimensions and growth of economic indicators.Keywords: cultural dimensions, communication, globalization, economic indicators

Probabilistic 16-QAM Shaping in WDM Systems

This work proposes a probabilistic shaping scheme for optical WDM systems, where nonlinear interference noise depends on the input optical signal power distribution. With $16$ -QAM, a white Gaussian channel analysis shows that the shaped constellation is able to achieve a reach improvement of up to $7\%$ , while split-step Fourier method simulations suggest that even higher gains are possible in practice. An example system is developed for a transmission distance around $3280$ km. A constellation mapping and a low-density parity-check code are developed for this regime to show a reach improvement of $7.1\%$ . These shaping schemes can also be extended to $64$ -QAM, where a reach improvement of over $10\%$ is expected.

Resonance fluorescence spectrum of aΛ-type quantum emitter close to a metallic nanoparticle

We theoretically study the resonance fluorescence spectrum of a three-level quantum emitter coupled to a spherical metallic nanoparticle. We consider the case that the quantum emitter is driven by a single laser field along one of the optical transitions. We show that the development of the spectrum depends on the relative orientation of the dipole moments of the optical transitions in relation to the metal nanoparticle. In addition, we demonstrate that the location and width of the peaks in the spectrum are strongly modified by the exciton-plasmon coupling and the laser detuning, allowing to achieve controlled strongly subnatural spectral line. A strong antibunching of the fluorescent photons along the undriven transition is also obtained. Our results may be used for creating a tunable source of photons which could be used for a probabilistic entanglement scheme in the field of quantum information processing.