Personnel BDAR Ability Assessment Model Based on Bayesian Stochastic Assessment Method

Methods based on deep learning have great utility in the current field of sentiment classification. To better optimize the setting of hyper-parameters in deep learning, a hybrid learning particle swarm optimization with fuzzy logic (HLPSO-FL) is proposed in this paper. Hybrid learning strategies are divided into mainstream learning strategies and random learning strategies. The mainstream learning strategy is to define the mainstream particles in the cluster and build a scale-free network through the mainstream particles. The random learning strategy makes full use of historical information and speeds up the convergence of the algorithm. Furthermore, fuzzy logic is used to control algorithm parameters to balance algorithm exploration and exploration performance. HLPSO-FL has completed comparison experiments on benchmark functions and real sentiment classification problems respectively. The experimental results show that HLPSO-FL can effectively complete the hyperparameter optimization of sentiment classification problem in deep learning and has strong convergence.

Multi-agent path finding (MAPF), in multi-agent systems, is a challenging and meaningful problem, in which all agents are required to effectively reach their goals concurrently with not colliding with each other and avoiding the obstacles. Effective extraction from the agent’s observation, effective utilization of historical information, and efficient communication with neighbor agents are the challenges to completing the cooperative task. To tackle these issues, in this paper, we propose a well-designed model, which utilizes the local states of nearby agents and obstacles and outputs an optimal action for each agent to execute. Our approach has three major components: 1) observation encoder which uses CNN to extract local partial observation and GRU to make full use of historical information, 2) communication block which uses attention mechanism to combine the agent’s partial observation with its neighbors, and 3) decision block with the purpose to output the final action policy. Based on the three major components, all agents formulate their own decentralized policies to apply. Finally, we use success rate and extra time rate to measure our approach and other well-known algorithms. The results show that our method outperforms the baselines, demonstrating the efficiency and effectiveness of our approach, especially in the case of large scale in the world.KeywordsMulti-agent path finding (MAPF)Reinforcement learningDecentralized planningDeep learning

Recently, many researches are focusing on millimeter wave (mmWave) due to its large bandwidth resources. In the next 5G generation, wireless multi-gigabit (WiGig) is developed based on IEEE 802.11ad/ay standards at unlicensed mmWave bands for providing extremely high throughput. However, mmWave has high propagation loss due to high frequency transmission properties. Beamforming (BF) technique is adopted to solve this problem, and therefore, we have to perform beam training before data transmission. In the standard, the conventional method for beam training is exhaustive beam search (EBS) which takes too much time so that the data transmission time will decrease. On the other hand, blocked environments may severely degrade WiGig beam training performance, and most of existing algorithms do not consider this issue. Recently, machine and deep learning have been widely used in the wireless communication field. We propose a learning-based beam training (LBT) to simultaneously learn about wireless environments and beam training candidates. We select simplified neural network (NN) model to achieve lower computation overhead. To further refine learning information, we propose two enhanced algorithm, expanded LBT (LBT-E) and history-aided expanded LBT(LBT-HE). LBT-E aims to tackle unexpected slight deviation and LBT-HE make use of historical information to improve beam matching accuracy with acceptable latency. In our simulation, our proposed learning-based schemes achieve much higher throughput compared to EBS and algorithms in existing literatures.

Bayesian system reliability assessment method with maximum entropy as prior distribution is developed in the present analysis. The system reliability assessment model is established, and the method and process of system reliability assessment are put forward. The method is based on the theory of information entropy. The probability density function and distribution function of system reliability parameters are given by using maximum entropy method. Prior distribution of Bayesian analysis method is the distribution function. Likelihood function is given by a small amount of field test data. According to Bayesian methods, posterior distribution is determined by using prior distribution and likelihood function. The point estimation and interval estimation of system reliability parameters are calculated in order to evaluate the system reliability. The method solves better the problem of system reliability assessment for small sample cases, and is an optimum method for reliability assessment of large and complex systems.

Grey correlation evaluation is a good method that deals combat effectiveness with poor information, but it is disturbed by subjective factors. Rough set theory is introduced to determine index weight and deduce subjective factors influence of the evaluation. An actual application indicates that this model is valid and feasible.

Remote sensing has become important in pattern classification from the view point of global environmental problems according to the progress of space technology. But the classification performance with remote sensing data depends on a training data set for supervised classification. However, it is difficult to select it from remote sensing images, since remote sensing data includes various kinds of categories and similar information, which depend on sensors. Therefore, one must take care of its selection. The authors investigated a training data set by independent component analysis (ICA) and proposed a pattern classification system for remote sensing data based on neural network theory. From independent component analysis, training data for each pattern are converted to independent data set regardless of observation sensors. Using the BP algorithm, a layered neural network is trained such that the training pattern can be classified within a level. The experiments on LANDSAT TM data show that this approach produces excellent classification results compared with conventional statistical approaches, which are Bayesian and distance method etc.

We have adopted a new tool to invert magnetotelluric data for the 1D model based on deep Q-networks (DQN), which works as a stochastic optimization method. By transforming the inversion problem into a Markov decision process, the tool learns by trial and error to find the optimal path for updating the model to fit the observed data. The DQN method converges to the target through different paths (e.g., Bayesian or other stochastic methods) and can partially provide the probability distribution of the inversion results, which can be used for uncertainty estimation. The DQN search space gradually decreases as the learning experience progresses, accelerating the single inversion and approximating the optimal result. To check the effectiveness of the DQN inversion, the five- and eight-layer models were separately designed to test the robustness of the DQN for the initial model and the noise level. A further comparison with Occam’s method and the Bayesian method indicated that our DQN obtained more robust inversion results for data contaminated by different noise levels. The inversion results with the survey data from Zhagaitunuoergong area, Inner Mongolia, China, well reveals the shape of the interface basement.

Along with the country's comprehensive strength, the people's wealth is also more and more substantial, in every aspect of life has been significantly improved, people also pay more attention to environmental protection. The harm of environmental pollution should not be underestimated. Once the environmental pollution accident occurs, it must be handled promptly, or a series of consequences are very serious. Therefore, it is necessary to study the Bayesian method of water quality emergency monitoring in the disposal of environmental pollution accidents. The purpose of this article was to solve the slow progress of water quality monitoring in the disposal of environmental pollution accidents. Through the Bayesian method of environmental pollution accident disposal in water quality emergency monitoring, the target node variables, intermediate node variables, and evidence node variables are used for analysis. Construct a Bayesian network topology. This article uses the fuzzy Bayesian network risk assessment model to provide a reference for the emergency monitoring of water quality disposal in regional environmental pollution accidents and conduct risk assessment to facilitate the next work arrangement. The results show that the Bayesian method of water quality emergency monitoring in environmental pollution accident disposal can provide effective feedback. Compared with traditional methods, the risk assessment of water quality monitoring for environmental pollution accidents is 40% faster and 10% more accurate in calculating the probability of accidents caused by basic factors. The application of the Bayesian water quality emergency monitoring method in environmental pollution accident disposal is conducive to improving the efficiency of environmental pollution disposal, in-depth analysis of the existing problems in the current environmental emergency management work, and suggestions for improving the emergency mechanism of sudden environmental pollution accident. The purpose was to provide reference for the government to properly handle sudden environmental pollution accidents.

The Sixth International Symposium on Neural Networks (ISNN 2009)

A phylogeny is an evolutionary tree tracing the shared history, including common ancestors, of a set of extant species or “taxa”. Phylogenies are increasingly reconstructed on the basis of molecular data (DNA and protein sequences) using statistical techniques such as likelihood and Bayesian methods. Algorithmically, these techniques suffer from the discrete nature of tree topology space. Since the number of tree topologies increases exponentially as a function of the number of taxa, and each topology requires a separate likelihood calculation, it is important to restrict the search space and to design efficient heuristics. Distance methods for phylogeny reconstruction serve this purpose by inferring trees in a fraction of the time required for the more statistically rigorous methods. Distance methods also provide fairly accurate starting trees to be further refined by more sophisticated methods. Moreover, the input to a distance method is the matrix of pairwise evolutionary distances among taxa, which are estimated by maximum likelihood, so that distance methods also have sound statistical justifications.

Bayesian statistical methods have recently been combined with conventional methods for fisheries stock assessment (e.g. catch-age analysis) to provide a conceptually elegant approach for providing fishery management advice under uncertainty. Uncertainties in the advice provided can be conveyed using posterior probability distributions (or “posteriors”) for the potential outcomes of each policy option. Posteriors can be estimated using data (e.g. catch-age data and relative abundance indices) for the fish population of interest and prior probability distributions for population model parameters (e.g. stock-recruit function parameters) based on data from similar fish populations. Despite growing interest, Bayesian methods remain accessible to relatively few. To increase the accessibility of these methods, the conceptual basis for Bayesian statistical estimation is reviewed and set in the context of fisheries stock assessment. The use of Bayesian methods is illustrated by fitting a logistic model to relative abundance indices for Namibian hake (Merlucius capensis and M. paradoxus) and presenting a decision analysis of alternative harvest policy options. Some alternative approaches are outlined for constructing prior and posterior probability distributions and some recent applications in fisheries. Some of the problems that can be encountered while implementing Bayesian methods are also discussed.

Traditional neural network training is usually based on the maximum likelihood to obtain the appropriate network parameters including weights and biases given the training data. However, if the available data are finite and noisy, the maximum likelihood-based network training can cause the neural network after being trained to overfit the noisy data. This problem has been overcome by using the Bayesian inference applied to the neural network training in various applications. The Bayesian inference can allow values of regularization parameters to be found using only the training data. In addition, the Bayesian approach also allows different models (e.g., neural networks with different numbers of hidden units to be compared using only the training data). Neural networks trained with Bayesian inference can be also known as Bayesian neural networks (BNNs). This chapter focuses on BNNs for classification problems with considerations of model complexity of BNNs conveniently handled by a method known as the evidence framework.

Various works from the literature aimed at accelerating Bayesian inference in inverse problems. Stochastic spectral methods have been recently proposed as surrogate approximations of the forward uncertainty propagation model over the support of the prior distribution. These representations are efficient because they allow affordable simulation of a large number of samples from the posterior distribution. Unfortunately, they do not perform well when the forward model exhibits strong nonlinear behavior with respect to its input.In this work, we first relate the fast (exponential) L2-convergence of the forward approximation to the fast (exponential) convergence (in terms of Kullback-Leibler divergence) of the approximate posterior. In particular, we prove that in case the prior distribution isuniform, the posterior is at least twice as fast as the convergence rate of the forwardmodel in those norms. The Bayesian inference strategy is developed in the framework of a stochastic spectral projectionmethod. The predicted convergence rates are then demonstrated for simple nonlinear inverse problems of varying smoothness.We then propose an efficient numerical approach for the Bayesian solution of inverse problems presenting strongly nonlinear or discontinuous systemresponses. This comes with the improvement of the forward model that isadaptivelyapproximated by an iterative generalized Polynomial Chaos-based representation. The numerical approximations and predicted convergence rates of the former approach are compared to the new iterative numericalmethod for nonlinear time-dependent test cases of varying dimension and complexity, which are relevant regarding our hydrodynamics motivations and therefore regarding hyperbolic conservation laws and the apparition of discontinuities in finite time.

We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard ratings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d612827e85"> <b>Objectives:</b> The utility and feasibility of Neural network (NN) and Bayesian in PK-PD analysis were investigated. Recently, Hierarchical Deep Compartment Modeling (HDCM) workflow was shown to be able to quantify uncertainty of NN model predicted parameters and was successfully tested on synthetic data from 10 subjects [ <a class="xref-link" href="#r1">1</a>]. While conceptually appealing, this approach incurs prohibitive run time for typical clinical datasets due to the complexity of the high-dimensional Bayesian posterior distribution. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d612827e93">To enable the full covariate modeling in PK-PD analyses with practical run time, this research utilized flow-based generative deep learning [ <a class="xref-link" href="#r2">2</a>] in addition to NN and Bayesian inference. A novel approach was developed, which trains normalizing flows (NFs) [ <a class="xref-link" href="#r2">2</a>] to generate both samples and their associated probabilities from the posterior distribution. This approach allows breaking down Bayesian inference from a large dataset into manageable subtasks that can be completed sequentially while accumulating all information from the whole dataset. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d612827e101"> <b>Methods:</b> The workflow was implemented in Julia to perform PopPK/PD analysis on simulated dataset for a hypothetical small molecule drug with pharmacodynamic activity following stimulatory response function. The data included 302 subjects from phase 1 and phase 2 studies, with formulation, subject type, weight, gender, age as PK covariates and 3 PD covariates (PDcov1-3). <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d612827e106">The covariate-PK/PD parameter relationships were modeled by NN. Loose priors and standard normal distributions were set for fixed, random effect and NN parameters. The initial analysis step processed one sub-dataset (first subtask). The No-U-Turn Sampler (NUTS) [ <a class="xref-link" href="#r3">3</a>] was used to draw samples from the intermediate posterior distribution. A NF [ <a class="xref-link" href="#r2">2</a>] was trained to establish a probability distribution from these samples. The NF-induced distribution became the prior distribution input for the second analysis step, with the likelihood evaluated only for the next sub-dataset. This iterative procedure was repeated until the whole dataset was analyzed. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d612827e114"> <b>Results:</b> A six-step RealNVP[ <a class="xref-link" href="#r4">4</a>] architecture using NN conditioners of 2 hidden layers, width 100, was adequate for carrying posterior information forward across 30 sub-datasets. NUTS produced well-mixing chains (R^ &lt; 1.01). Full covariate analysis testing was completed within 3 days on a 64-core compute node. Posterior predictive checks confirmed that predicted concentrations/PD responses based on the posterior distributions captured observed drug concentrations/responses. Forest plots generated from the NN-predicted covariate effects correctly identified covariate-parameter relationships consistent with NONMEM-based Stepwise Covariate Modeling approach [ <a class="xref-link" href="#r5">5</a>]. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d612827e125"> <b>Conclusions:</b> Bayesian updates enabled by normalizing flows was shown to be a promising direction for improving scalability of NN-based PopPK/PD analyses. The newly developed method adds flexibility to analysis planning, allowing the posterior distribution to be provisionally generated from existing data while waiting for additional clinical studies, from which new information can be readily incorporated.