Global Probability Research Articles

Localized Small Cell Caching: A Machine Learning Approach Based on Rating Data

Caching the most popular contents at the wireless network edge such as small-cell base stations (SBSs) is a smart way of reducing duplicated content transmissions and offloading the mobile data traffic in the network backhaul. Currently, most small-cell caching strategies are conceived, designed, and optimized based on the global content request probability (GCRP), with very limited consideration of the individual content request probability (ICRP) reflecting personal preferences. To enable more efficient wireless caching, in this paper, we propose a novel localized deterministic caching framework, drawing upon the recent advances in recommendation systems based on machine learning techniques. By introducing the concept of the rating matrix, we first propose a new Bayesian learning method to predict personal preferences and estimate the ICRP. This crucial information is then incorporated into our caching strategy for maximizing the system throughput, or equivalently, minimizing the download latency, where a deterministic caching algorithm based on reinforcement learning is proposed to optimize the content placement. To this end, we extend the framework to enable device-to-device (D2D) connections to further reduce the download delay, and also design a feedback mechanism to improve the accuracy in the ICRP estimation. Our simulation results verified that with the estimated ICRP and the proposed caching strategy, the proposed framework can significantly outperform the existing methods in terms of hit rate and system throughput.

Dense semantic embedding network for image captioning

Recently, attributes that contain high-level semantic information of image are always used as a complementary knowledge to improve image captioning performance. However, the use of attributes in prior works cannot excavate the latent visual concepts effectively. At each time step, the semantic information which is sensitive to the predicted word could be different. In this paper, we propose a Dense Semantic Embedding Network (DSEN) for this task. The distinct operation of this network is to densely embed the attributes with the multi-modal of image and text at each step of word generation. The discriminative semantic information hidden in these attributes is formatted in form of global likelihood probabilities. As a result, this dense embedding can modulate the feature distributions of the image, text modals and the hidden states to explicit semantic representation. Furthermore, to improve the discrimination of attributes, a Threshold ReLU (TReLU) is proposed. In addition, a bidirectional LSTM structure is incorporated into the DSEN to capture both the previous and future contexts. Extensive experiments on the COCO and Flickr30K datasets achieve superior results when compared with the state-of-the-art models for the tasks of both image captioning and image-text cross modal retrieval. Most remarkably, our method obtains outstanding performance on the retrieval task, compared with the state-of-the-art models.

Medicinal Sulphur Polypore Mushroom Laetiporus sulphureus (Agaricomycetes) Plus Tiny Amounts of Essential Oils Decrease the Activity of Crohn Disease.

A mixture of Laetiporus sulphureus (LS) and essential oils of Origanum vulgare, Cinnamomum verum, and Syzygium aromaticum (LOCS) was recommended for 24 patients with Crohn disease (CD). Patients were followed up for at least 24 months (median 32 months; range, 24-54 months). Nineteen patients were compliant with the treatment, although 3 stopped taking it, relapsed, began the LOCS regimen again, and recovered; 5 patients were not compliant at all. Compliant patients experienced flares of CD, with a CD Activity Index > 220, for 5.6% of the surveillance period (median 34 months). Noncompliant patients experienced flares of CD, with a CD Activity Index > 220, for 75.0% of the surveillance period (median 26 months; P < 10-6). Each case was then analyzed with a Bayesian approach that considered the probability of CD spontaneously disappearing within 2 weeks after intake of LOCS, the reappearance of CD within 2 weeks after stopping LOCS, and the disappearance of CD again after rechallenge. For the 19 compliant patients, the global posterior probability (probability that all the events observed were due to coincidence only) was equal to 10-6. Thus it can be concluded that LOCS decreases the CD Activity Index. Further investigations are necessary to unravel the mechanism of action of LOCS and to confirm its positive effect over periods as long as 5 to 10 years.

Cross-Tier Dual-Connectivity Designs of Three-Tier Hetnets With Decoupled Uplink/Downlink and Global Coverage Performance Evaluation

In the conventionally coupled uplink (UL) and downlink (DL) association (CUDA) scheme, the UL performance is limited greatly by the DL parameters such as the density and power of base stations (BSs). To overcome this issue, this paper focuses on a novel three-tier heterogeneous network (HetNet) as well as global performance evaluation, where the cross-tier dual-connectivity (DC) and decoupled UL and DL association (DUDA) are integrated. On one hand, with the cross-tier DC, in DL or UL, typical user equipment is associated simultaneously with the primary and secondary BSs that are located at different tiers. On the other hand, with DUDA the primary and secondary access points of UL and DL are not necessarily the same. The proposed cross-tier DC scheme meets the need of the next generation networks where control channel comes from Macro BS and data channels from small cell BSs. For such HetNets, the work first presents a comprehensive investigation of the cross-tier DC design of all possible DUDA modes and the corresponding association conditions. Second, by simplifying the association conditions the work gives the comprehensive derivations in terms of the association probabilities and the statistical descriptions of the primary and secondary UL access distances. Third, by using the method from stochastic geometry this paper derives the general form of UL coverage probabilities by creating a common model of DUDA designs so that the global UL average coverage probability (ACP) of the whole network is achieved. The presented numerical and simulated analysis first validates the derivations. Then, the analysis focuses on the global UL ACP comparison of the whole HetNet between the DUDA and CUDA designs. The comparison of global ACP shows that the ACP of DUDA design is higher than the one of the CUDA design. Although the powers and densities of BSs impose the outstanding impact on the ACP of the CUDA designs, they have a very limited effect on one of the DUDA designs due to the utilization of decoupling association.

Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System

Accurate and up-to-date information on the spatial and geographical characteristics of agricultural areas is an indispensable value for the various activities related to agriculture and research. Most agricultural studies and policies are carried out at the field level, for which precise boundaries are required. Today, high-resolution remote sensing images provide useful spatial information for plot delineation; however, manual processing is time-consuming and prone to human error. The objective of this paper is to explore the potential of deep learning (DL) approach, in particular a convolutional neural network (CNN) model, for the automatic outlining of agricultural plot boundaries from orthophotos over large areas with a heterogeneous landscape. Since DL approaches require a large amount of labeled data to learn, we have exploited the open data from the Land Parcel Identification System (LPIS) from the Chartered Community of Navarre, Spain. The boundaries of the agricultural plots obtained from our methodology were compared with those obtained using a state-of-the-art methodology known as gPb-UCM (global probability of boundary followed by ultrametric contour map) through an error measurement called the boundary displacement error index (BDE). In BDE terms, the results obtained by our method outperform those obtained from the gPb-UCM method. In this regard, CNN models trained with LPIS data are a useful and powerful tool that would reduce intensive manual labor in outlining agricultural plots.

Unscented Kalman Filter Based on Spectrum Sensing in a Cognitive Radio Network Using an Adaptive Fuzzy System

In this paper, we proposed the unscented Kalman filter (UKF) based on cooperative spectrum sensing (CSS) scheme in a cognitive radio network (CRN) using an adaptive fuzzy system—in this proposed scheme, firstly, the UKF to apply the nonlinear system which is used to minimize the mean square estimation error; secondly, an adaptive fuzzy logic rule based on an inference engine to estimate the local decisions to detect a licensed primary user (PU) that is applied at the fusion center (FC). After that, the FC makes a global decision by using a defuzzification procedure based on a proposed algorithm. Simulation results show that the proposed scheme achieved better detection gain than the conventional schemes like an equal gain combining (EGC) based soft fusion rule and a Kalman filter (KL) based soft fusion rule under any conditions. Moreover, the proposed scheme achieved the lowest global probability of error compared to both the conventional EGC and KF schemes.

Coevolution of Non-Stationary Stochastic Strategies with Discount Factors

Evolutionary games have been extensively used to model and understand various complex real world situations, especially when investigating how cooperation evolve among self-interested players involved in strategic interactions. Many situations exhibit iterated interactions where players use past experiences (game history) to decide on their next actions. Cooperation evolves when a player trades the short-term benefits of being selfish with the long-term benefits of being cooperative, and this is only feasible if future interactions have sufficiently large discount factor (i.e., direct reciprocity settings). How do players decide on the discount factor is an interesting research question, it may depend on the player's personal experience or on public knowledge. In this study, we present novel models and investigate the cooperation levels in a coevolutionary environment of stochastic strategies. We extend the stochastic strategies' representation to allow players to evolve probabilities to terminate future interactions, and we show that this significantly decrease the levels of cooperation. We then show that the previous conclusion still holds when using global statistics as global (population-level) termination probabilities. We illustrate that enhancing the probability to cooperate in the game's initial round increases the levels of cooperation when termination probabilities are used by players.

The influence of the global/local probability effect on the neural processing of cues and targets. A functional systems approach.

Global and local probability effects were explored in a visuo-auditory version of the central cue Posner's paradigm through the analysis of the neural Event-Related Potentials (ERPs) generated in the interaction between validity sequence effects (local probability) and block validity effects (global probability). Four behavioral measures (response times, correct/incorrect anticipations, and incorrect responses), three pre-target ERPs (visual P1/N1 and Contingent Negative Variation (CNV)), and six post-target ERPs (auditory N1, P2, Processing Negativity (PN), P3a, P3b and Late Slow Positivity (LSP)) were considered. Four types of trial-sequences (Valid-Valid, Invalid-Valid, Invalid-Invalid, Valid-Invalid) and three types of trial-blocks, with different validity/invalidity proportions (50%, 68%, and 86% of valid trials), were employed. Present data replicate previous reports on the validity sequence effects on ERPs (local probability): (i) higher CNV on trials preceded by valid trials; (ii) higher PN on valid trials preceded by invalid trials compared to valid trials preceded by valid trials; and (iii) higher P3a/P3b and LSP on invalid trials preceded by valid trials compared to invalid trials preceded by invalid trials. In summary, local probability showed more effects modulating the brain responses than global probability and/or their interaction. Among a number of other hypotheses, the functional systems theory would account for the ability of the previous trial to modify processing in the current trial.

An Otsu multi-thresholds segmentation algorithm based on improved ACO

For the traditional multi-thresholds segmentation algorithms, usually it would take too much time in finding the optimal solution. As one of the widely used swarm-intelligence optimization algorithms, ant colony optimization (ACO) algorithm has been introduced to optimize the thresholding search process. The traditional ACO is improved in this paper to get a faster convergence speed and applied in Otsu multi-thresholds segmentation algorithms. When the ant colony is initialized, each member of the ant colony is distributed evenly in the solution space, so that it could search the entire solution space as fast as possible. In the search process, the random step length of ants moving is generated by the Levy flight pattern, but the global transition probability of the traditional ACO is used to control the search range of the ant colony. The experimental results show that the proposed algorithm could obtain the optimal thresholds faster and more effectively than the traditional Otsu algorithm and the Otsu based on traditional ACO.

Convergence to Equilibrium in Fokker–Planck Equations

The present paper is devoted to the investigation of long-time behaviors of global probability solutions of Fokker–Planck equations with rough coefficients. In particular, we prove the convergence of probability solutions under a Lyapunov condition in terms of the Markov semigroup associated to the stationary one. A generalization of earlier results on the existence and uniqueness of global probability solutions is also given.

Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling

The use of local information for the classification and modelling of spatial variables has increased with the application of statistical and machine learning algorithms, such as support vector machines (SVMs). This study presents a new local SVM (LSVM) model that was developed to model the probability of urban development and simulate urban growth in a subregion in the southwestern suburb of the Tehran metropolitan area, Iran, for the periods of 1992–1996 and 1996–2002. Based on the focal training sample, the model was calibrated using the cross-validation method, and the optimal bandwidth was determined. The results were compared with those of a nonlinear global SVM (GSVM) model that was calibrated based on the ten-fold cross-validation method. This study then evaluated an integrated SVM model (LGSVM) obtained based on a weighted combination of the local and global urban development probabilities. A comparison of the probability maps showed a higher accuracy for the LGSVM than for either the LSVM or GSVM model. To assess the performance of the LSVM, GSVM and LGSVM models in the simulation of urban growth, probability maps were employed as the transition rules for urban cellular automata. The results show that a trade-off between local and global SVM models can enhance the performance of urban growth modelling.

Long Time Behavior and Global Dynamics of Simplified Von Karman Plate Without Rotational Inertia Driven by White Noise

Without the assumption that the coefficient of weak damping is large enough, the existence of the global random attractors for simplified Von Karman plate without rotational inertia driven by either additive white noise or multiplicative white noise are proved. Instead of the classical splitting method, the techniques to verify the asymptotic compactness rely on stabilization estimation of the system. Furthermore, a clear relationship between in-plane components of the external force that act on the edge of the plate and the expectation of radius of the global random attractors can be obtained from the theoretical results. Based on the relationship between global random attractor and random probability invariant measure, the global dynamics of the plates are analyzed numerically. With increasing the in-plane components of the external force that act on the edge of the plate, global D-bifurcation, secondary global D-bifurcation and complex local dynamical behavior occur in motion of the system. Moreover, increasing the intensity of white noise leads to the dynamical behavior becoming simple. The results on global dynamics reveal that random snap-through which seems to be a complex dynamics intuitively is essentially a simple dynamical behavior.

Sensing Time Optimisation and Sensor Selection in Multi‐channel Multi‐antenna Wireless Cognitive Sensor Networks

Energy efficiency of cooperative spectrum sensing is a critical issue in wireless cognitive sensor networks (CSNs). However, sensor nodes consume high energy to support cognitive radio (CR) functionalities such as channel sensing. In this study, the energy-efficient cooperative spectrum sensing in multi-channel multi-antenna CSNs is investigated. First, a multi-channel CSN is considered to obtain sensing sensors and sensing time for each band in a CR system. The criteria is to minimise energy consumption of cooperative spectrum sensing under the global probabilities of detection and false alarm constraints. The authors employ a convex optimisation method to obtain optimal sensing time and sensing nodes for each band. Next, their analysis is extended into multi-antenna sensors, which is a promising solution instead of using more sensors. In multi-antenna cooperative spectrum sensing, diversity helps sensors behave virtually the same as multi-sensors systems. Then, the problem of sensor selection and also setting the sensing time for each band is solved based on the convex optimisation framework. Simulation results highlight that in their proposed algorithms a significant energy is saved compared with the baseline methods with equal sensing time in all bands.

Chaos in continuously monitored quantum systems: An optimal-path approach

We predict that continuously monitored quantum dynamics can be chaotic. The optimal paths between past and future boundary conditions can diverge exponentially in time when there is time-dependent evolution and continuous weak monitoring. Optimal paths are defined by extremizing the global probability density to move between two boundary conditions. We investigate the onset of chaos in pure-state qubit systems with optimal paths generated by a periodic Hamiltonian. Specifically, chaotic quantum dynamics are demonstrated in a scheme where two non-commuting observables of a qubit are continuously monitored, and one measurement strength is periodically modulated. The optimal quantum paths in this example bear similarities to the trajectories of the kicked rotor, or standard map, which is a paradigmatic example of classical chaos. We emphasize connections with the concept of resonance between integrable optimal paths and weak periodic perturbations, as well as our previous work on "multipaths", and connect the optimal path chaos to instabilities in the underlying quantum trajectories.

Dynamic Threshold Selection Approach in Voting Rule for Detection of Primary User Emulation Attack

DOI: 10.5152/iujeee.2018.1819 Cognitive radio (CR) technology presents a mechanism for efficient spectrum usage. Spectrum sensing is an essential CR function which includes an intelligent signal processing algorithm to identify the vacant frequency bands. Cooperative spectrum sensing (CSS) has been widely adopted to improve the performance of CR networks. Unfortunately, CR networks are vulnerable to security threats. In this study, we propose an optimal threshold selection approach to address one of the most important attacks called primary user emulation attack (PUEA). In PUEA, a malicious attacker mimics some important primary signal features and deceives CR sensors to prevent them from accessing the available channels. In this study, we assume a malicious PUEA which is relatively located near the potential user (PU) transmitter and senses the spectrum and accurately detects the vacant frequency bands to transmit its fake signal. We estimate attack strength and then apply the K-out-of-N rule to obtain an optimum and dynamic threshold K, minimizing the global error probability. Here, the attack strength is defined as the ratio of the average transmission power of the PUEA to the average power of the PU. The achieved simulation results indicate that the performance of the suggested method is satisfactory in detecting the malicious PUEA compared with conventional methods.

Energy consumption minimization and throughput improvement in cognitive radio networks by joint optimization of detection threshold, sensing time and user selection

Cooperative spectrum sensing schemes proposed to solve the hidden terminal problem and mitigate multipath fading and shadowing effects, which enhance the sensing performance and throughput in cognitive radio (CR) neworks. However, increasing the number of cooperative SUs leads to more communication overhead, which will increase the energy consumption of the CR network. In this paper, a new scheme is proposed to solve the joint optimization problem of the sensing time, the detection threshold and the selection of the sensing and data transmitting secondary users (SUs) for improvement of the throughput and minimization of the energy consumption of the CR network under the constrains on the global probability of detection and global probability of false alarm. For these purposes, we find the optimal values of detection threshold and sensing time such that the detection constraints are satisfied. The convex optimization methods are used to determine the sensing and data transmitting SUs. The simulation results show that there exists the optimal detection threshold and sensing time for selected sensing and transmitting SUs that can improve the average throughput and minimize the energy consumption of the CR network in comparison to other schemes.

Capability of the Stochastic Seismic Inversion in Detecting the Thin Beds: a Case Study at One of the Persian Gulf Oilfields

The aim of seismic inversion is mapping all of the subsurface structures from seismic data. Due to the band-limited nature of the seismic data, it is difficult to find a unique solution for seismic inversion. Deterministic methods of seismic inversion are based on try and error techniques and provide a smooth map of elastic properties, while stochastic methods produce high-resolution maps of elastic properties with the same probability. The current paper studies a stochastic method of seismic inversion which was applied to one of the Persian Gulf oilfields. Joint posterior distribution of elastic properties was calculated using Bayesian principle; then a sequential Gaussian simulation technique was performed to decompose the global probability function of elastic properties into some local probability functions at each trace location. The sampling of the local probability functions was performed, and two hundred realizations of the elastic properties were generated. The results of the stochastic inversion were found to be capable of modeling heterogeneities of the reservoir. The generated realizations provided the possibility to uncertainties assessment by calculating the variance of the elastic properties. It was found out that the uncertainty increased in locations far away from the well. Moreover, stochastic inversion, unlike deterministic one, was found to be capable of detecting thin beds (3.5 to 5.7 m) embedded within the reservoir.

Performance Improvement of Cooperative Spectrum Sensing in the Presence of Primary User Emulation Attack

Cognitive radio (CR) is an enabling technology to effectively address the spectrum scarcity, and it will considerably improve the spectrum utilization of future wireless communication systems. Cooperative spectrum sensing (CSS) has been developed to take advantage of spectrum diversity of CR users. Security is an important issue, but not well addressed in CR networks. This study considers a security problem arising from primary user emulation attack (PUEA) in CR networks. In PUEA, a malicious attacker transmits an emulated primary signal and defrauds the CR users to prevent them from accessing spectrum holes. We assume a smart and probabilistic attacker, in the sense that it is able to exactly detect the spectrum holes and it transmits its signals in both vacant and occupied primary bands. Two important PUEA parameters are estimated and then used to obtain the optimal voting rule for minimizing the global error probability. These attack parameters include the probabilities of the PUEA signals in both occupied and unoccupied frequency bands. We formulate and investigate the optimal voting rule for a CR network operating in the presence of a malicious attacker. The obtained results indicate performance improvement provided by the proposed method compared with the conventional methods.

A pathwise approach to the extinction of branching processes with countably many types

We consider the extinction events of Galton–Watson processes with countably infinitely many types. In particular, we construct truncated and augmented Galton–Watson processes with finite but increasing sets of types. A pathwise approach is then used to show that, under some sufficient conditions, the corresponding sequence of extinction probability vectors converges to the global extinction probability vector of the Galton–Watson process with countably infinitely many types. Besides giving rise to a family of new iterative methods for computing the global extinction probability vector, our approach paves the way to new global extinction criteria for branching processes with countably infinitely many types.

Open and closed-loop stochastic dynamics of a class of nonlinear chemical processes with multiplicative noise

The open and closed-loop global state probability density function behavior of a class of single-state nonlinear chemical processes subjected to additive and multiplicative white noises is characterized with analytic formulae through Fokker Planck (FP) theory, in terms of: (i) stationary state probability density function (PDF), (ii) PDF evolution along deterministic, diffusion, and escape time scales, (iii) conditions for PDF metastability along escape time scale, and (iv) dependency of PDF motion on deterministic dynamics. Comparing with noise additivity, multiplicativeness can yield similar or substantially different open-loop PDF evolution behavior. The application of control to an open-loop (possibly fragile and metastable) multimodal PDF yields a closed-loop robust monomodal PDF with mode regulation capability. The developments and findings are illustrated with numerical simulations of FP’s PDE equation.