Probabilistic Correlation Research Articles

Development of a Probabilistic Correlation between the Gamma Ray Index and Shale Volume Factor to Improve Resource Estimation for the Niger Delta Basin, Nigeria

The shale volume factor is among the critical petrophysical parameters for reservoir characterization and formation evaluation. Inaccurate estimates of the shale volume factor can lead to poor reserves or resource estimates and wrong business decisions. While the current industry standard is to estimate the shale volume factor from the gamma ray logs using the concept of the gamma ray index, a relationship between the shale volume factor and the gamma-ray index needs to be established for any region/basin under consideration. For most applications in the Niger Delta Basin, a linear relationship is often assumed. However, there is no proven relationship between the shale volume factor and the gamma-ray index for the formations in the Niger Delta Basin. This paper proposes a new shale volume factor prediction correlation for the Niger Delta Basin in Nigeria. The correlation development is based on establishing a relationship between the shale volume factor obtained from cores and the gamma ray index obtained from petrophysical logs for over thirty wells drilled across the Niger Delta Basin. The results show that the relationship between the shale volume factor and the gamma-ray index is not linear as often assumed but a power law model. The new probabilistic correlation predicts lower shale volume factors than the linear model for all ranges of the gamma-ray index. This recent correlation will significantly impact how the hydrocarbon resources and reserves are quantified in the Niger Delta Basin.

Learning Semantic Alignment Using Global Features and Multi-Scale Confidence

Semantic alignment aims to establish pixel correspondences between images based on semantic consistency. It can serve as a fundamental component for various downstream computer vision tasks, such as style transfer and exemplar-based colorization, etc. Many existing methods use local features and their cosine similarities to infer semantic alignment. However, they struggle with significant intra-class variation of objects, such as appearance, size, etc. In other words, contents with the same semantics tend to be significantly different in vision. To address this issue, we propose a novel deep neural network of which the core lies in global feature enhancement and adaptive multi-scale inference. Specifically, two modules are proposed: an enhancement transformer for enhancing semantic features with global awareness; a probabilistic correlation module for adaptively fusing multi-scale information based on the learned confidence scores. We use the unified network architecture to achieve two types of semantic alignment, namely, cross-object semantic alignment and cross-domain semantic alignment. Experimental results demonstrate that our method achieves competitive performance on five standard cross-object semantic alignment benchmarks, and outperforms the state of the arts in cross-domain semantic alignment.

Seismic damage assessment for flexural double skin composite walls based on the modified Park-Ang model

The damage indices are widely used to evaluate the performance level and damage state of structural elements in buildings subjected to seismic actions. Despite the widespread promotion of double-skin composite walls (DSCWs) in global construction practices, the research related to the seismic damage assessment of DSCW members is still limited. In order to establish a seismic damage assessment procedure for DSCW members, an experimental database that includes 37 test results of DSCW specimens subjected to cyclic loads was built in this paper. Based on an existing modified Park-Ang model, an empirical expression of the combination coefficient is introduced. The damage state levels of DSCWs are determined as follows: Basically intact (DS0), Slight damage (DS1), Mild damage (DS2), Moderate damage (DS3), Relatively severe damage (DS4), Severe damage (DS5), and Critical collapse (DS6). The typical damage characteristics and repair methods are introduced and discussed. Subsequently, a comprehensive statistical analysis is conducted to study the probabilistic correlation between these damage states and the corresponding index specific to DSCW members. Fragility functions for each damage state associated with the damage index have been developed. The threshold values of the damage index corresponding to the damage states are also provided. This paper intends to provide a theoretical foundation for performance-based seismic design and post-earthquake damage assessment of DSCW structures.

Multiparty Dual Learning.

The performance of machine learning algorithms heavily relies on the availability of a large amount of training data. However, in reality, data usually reside in distributed parties such as different institutions and may not be directly gathered and integrated due to various data policy constraints. As a result, some parties may suffer from insufficient data available for training machine learning models. In this article, we propose a multiparty dual learning (MPDL) framework to alleviate the problem of limited data with poor quality in an isolated party. Since the knowledge-sharing processes for multiple parties always emerge in dual forms, we show that dual learning is naturally suitable to handle the challenge of missing data, and explicitly exploits the probabilistic correlation and structural relationship between dual tasks to regularize the training process. We introduce a feature-oriented differential privacy with mathematical proof, in order to avoid possible privacy leakage of raw features in the dual inference process. The approach requires minimal modifications to the existing multiparty learning structure, and each party can build flexible and powerful models separately, whose accuracy is no less than nondistributed self-learning approaches. The MPDL framework achieves significant improvement compared with state-of-the-art multiparty learning methods, as we demonstrated through simulations on real-world datasets.

Stochastic simulation of velocity pulses of near-fault ground motions based on multivariate copula modeling

Due to the featured high-amplitude, long-period pulses in velocity or displacement time–history, the near-fault ground motion may have a stronger destructive effect on engineering structures. Therefore, studying velocity pulses modeling of near-fault ground motions has been an important topic in the earthquake engineering community. However, the variability and probabilistic correlation of pulse model parameters have not received enough attention. To this end, an elegant method for stochastic simulation of velocity pulses of near-fault ground motions is proposed based on the classical Gabor wavelet modeling and probabilistic correlation analysis of associated model parameters by multivariate copula modeling. Especially, an efficient scheme of iteratively determining the optimal copulas pertinent to edges of a complex vine structure is developed, and two parameters relevant to the near-fault ground motion records, i.e., the rupture distance and the strongest pulse orientation, are introduced which provides a physical basis for addressing the dependence structure among variables. For illustrative purposes, the ground accelerograms recorded by near-fault instruments in the 1999 Chi-Chi earthquake are used. It is shown that the multivariate copula modeling enables the efficient representation of probabilistic correlation between model parameters which is consistent with their physical mechanism. The time histories and spectral characteristics of velocity pulses of near-fault ground motions and their stochastic fluctuations with respect to the rupture distance are well simulated. While the empirical linear regression model does not meet the requirements of random vibration analysis and thus cannot guide the safety design of seismic structures.

Design of deep learning model for radio resource allocation in 5G for massive iot device

With recent advancements in Internet of Things (IoT) communications, the demand for fastest communication is an utmost concern of the IoT technology. The advent of 5G telecommunication networks enables to bridge the demand on satisfying the Quality-of-Service (QoS) concerns in IoT communication. The massive number of devices in IoT communication is henceforth do not lie under limited resource allocation by embedding the 5G telecommunication network. The existing deep learning approaches lack in the allocation of resource in the energy constrained environment. In this paper, we address the above limitation of allocation the resource to prominent IoT devices using Deep Belief Network. It is efficient in the utilization of hidden layers and the probabilistic correlation among the instances are constructed effectively. In this Deep Belief Network, the adoption of rules will imply the resource allocation with the devices of top priority and it reduces based on the priority. DBN learns the context of the 5G and connected IoT devices whereas the resources are provisioned effectively through the learning. The proposed strategy beats the current state-of-the-art techniques, whereas the DBN achieves 74 % of successful job completion that is effective compared to other existing strategies. The simulation is performed to test the efficacy of Deep Learning on allocation 5G resources to IoT model. The results show that the proposed model achieves higher level of allocating the resources than other methods.

CEO overconfidence: Towards a new measure

Bayesian network theory is used to construct a novel probability-based measure for CEO overconfidence. This measure is estimated by studying the probabilistic correlation between CEO overconfidence and several CEO- and firm-specific determinants of overconfidence, that have been documented in the literature. Using S&P 500 firms over the period 2007–2017, we show that the established Bayesian network model has a high fitting and prediction accuracy of CEO overconfidence. This novel measure of CEO overconfidence can be used to conduct empirical studies in corporate and behavioral finance. It also provides a tool to improve decision-making in firms and corporate governance.

Unsupervised deep learning method for bridge condition assessment based on intra-and inter-class probabilistic correlations of quasi-static responses

Data-driven methods for structural condition assessment have been extensively investigated using deep learning (DL). However, studies on quasi-static response data-based structural health diagnoses are relatively insufficient. The difficulty is that quasi-static response data contain coupled effects of structural parameters and external loads. Considering that the correlation between quasi-static responses subjected to identical external loads is only a function of structural parameters and independent from the external loads, the correlation can therefore be employed as an indicator of the structural condition. This study proposes a condition assessment approach for cable-stayed bridges based on correlation modeling between the deflection of girders and tension in cables. The correlation is modeled by an unsupervised DL network comprising two variational autoencoders (AE) and two generative adversarial networks (GANs). The input and output are marginal probability density functions (PDFs). The DL network is trained as the reconstruction and translation processes to model the intra-class and inter-class correlations. Assumptions of shared latent space and cycle consistency are taken to ensure mutual modeling capacity. The Wasserstein distance between the predicted and ground-truth PDFs of tension in cables is used as an indicator of the structural condition. Using probabilistic correlation of quasi-static responses only requires the PDF of external loads to be identical and does not need the external loads to be precisely identical at any moment, thus relieving time-synchronization restrictions for different sensors. The results show that the predicted PDFs agree well with the ground-truth values under normal conditions. Furthermore, the Wasserstein distance is sensitive to damage and shows noticeable variations when the damage of the stay cable occurs.

Causation in memory: necessity, reliability and probability

In this paper, I argue that causal theories of memory are typically committed to two independent, non-mutually entailing theses. The first thesis pertains to the necessity of appropriate causation in memory, specifying a condition token memories need to satisfy. The second pertains to the explanation of memory reliability in causal terms, and it concerns memory as a type of mental state. Post-causal theories of memory can reject only the first (weak post-causalism) or both (strong post-causalism) theses. Upon this backdrop, I examine Werning’s (2020) causalist argument from probabilistic correlation. I argue that it doesn’t establish the necessity of appropriate causation, and thus it can only target strong post-causalist theories. I end up by presenting some general considerations, suggesting that memories may not always be causally linked to past experiences.

Criteria for naturalness in conceptual spaces

Conceptual spaces are a frequently applied framework for representing concepts. One of its central aims is to find criteria for what makes a concept natural. A prominent demand is that natural concepts cover convex regions in conceptual spaces. The first aim of this paper is to analyse the convexity thesis and the arguments that have been advanced in its favour or against it. Based on this, I argue that most supporting arguments focus on single-domain concepts (e.g., colours, smells, shapes). Unfortunately, these concepts are not the primary examples of natural concepts. Building on this observation, the second aim of the paper is to develop criteria for natural multi-domain concepts. The representation of such concepts has two main aspects: features that are associated with the concept and the probabilistic correlation pattern which the concept captures. Conceptual spaces, together with probabilistic considerations, provide a helpful framework to approach these aspects. With respect to feature representation, the existence of characteristic features (i.e., that apples have a specific taste) is essential. Moreover, natural concepts capture peaks of a probabilistic distribution over complex spaces. They carve up nature at its joints, that is, at areas with no or low probabilistic density. This last aspect is shown to be closely related to the convexity demand.

JSPNet: Learning joint semantic & instance segmentation of point clouds via feature self-similarity and cross-task probability

In this paper, we propose a novel method named JSPNet, to segment 3D point cloud in semantic and instance simultaneously. First, we analyze the problem in addressing joint semantic and instance segmentation, including the common ground of cooperation of two tasks, conflict of two tasks, quadruplet relation between semantic and instance distributions, and ignorance of existing works. Then we introduce our method to reinforce mutual cooperation and alleviate the essential conflict. Our method has a shared encoder and two decoders to address two tasks. Specifically, to maintain discriminative features and characterize inconspicuous content, a similarity-based feature fusion module is designed to locate the inconspicuous area in the feature of current branch and then select related features from the other branch to compensate for the unclear content. Furthermore, given the salient semantic feature and the salient instance feature, a cross-task probability-based feature fusion module is developed to establish the probabilistic correlation between semantic and instance features. This module could transform features from one branch and further fuse them with the other branch by multiplying probabilistic matrix. Experimental results on a large-scale 3D indoor point cloud dataset S3DIS and a part-segmentation dataset ShapeNet have demonstrated the superiority of our method over existing state-of-the-arts in both semantic and instance segmentation. The proposed method outperforms PointNet with 12% and 26% improvements and outperforms ASIS with 2.7% and 4.3% improvements in terms of mIoU and mPre. Code of this work has been made available at https://github.com/Chenfeng1271/JSPNet.

Probabilistic correlation of renewable energies within energy hubs for cooperative games in integrated energy markets

Utilizing renewable energy sources like wind turbines and solar panels is increasing annually due to environmental and economic opportunities. Wind speed and solar irradiation depend on the time of day and the location of the site. The correlation of renewable energies with load and consequently with price in an energy market yields a more accurate estimation of energy optimization and consumption patterns. This problem will be more complicated when energy hubs choose to play in the integrated energy markets based on their coalitions with other energy hubs. An energy hub may join another energy hub to cooperate in an energy market or play self-autonomous to maximize its profit. This paper proposes a spatiotemporal correlation of wind speed, solar irradiation, and price responsive loads considering cooperative and non-cooperative modes of energy hubs based on a game-theoretic approach in an integrated energy market. This paper applies mathematical programming with equilibrium constraints to solve the highly competitive behavior of the energy hubs in this market considering loss. Two comparative and comprehensive case studies demonstrate the strategic actions of the energy hubs in these complicated conditions.

A theoretical relationship between probabilistic relative operating characteristic skill and deterministic correlation skill in dynamical seasonal climate prediction

Understanding the relationship between probabilistic and deterministic prediction skills is of important significance for the study of seasonal forecasting and verification. Based on the Brier skill score methodology, we have previously found a theoretical relationship between the probabilistic resolution skill and the deterministic correlation (i.e., anomaly correlation; AC) skill and a lack of necessary or consistent relationship between the probabilistic reliability skill and the deterministic skill in dynamical seasonal prediction. Here, we further theoretically investigate the relationship between the probabilistic relative operating characteristic (ROC) skill and the deterministic skill. The ROC measures the discrimination attribute of probabilistic forecast quality, another important attribute besides the resolution and reliability. With some simplified assumptions, we first derive theoretical expressions for the hit and false-alarm rates that are basic ingredients for the ROC curve, then demonstrate a sole dependence of the ROC curve on the AC, and finally analytically derive a relationship between the related ROC score and the AC. Such a theoretically derived ROC-AC relationship is further examined using dynamical models’ ensemble seasonal hindcasts, which is well verified. The finding here along with our previous findings implies that the discrimination and resolution attributes of probabilistic seasonal forecast skill are intrinsically equivalent to the corresponding deterministic skill, while the reliability appears to be the fundamental attribute of the probabilistic skill that differs from the deterministic skill, which constitutes an understanding of the fundamental similarities and difference between the two types of seasonal forecasting skills and predictability and can offer important implications for the study of seasonal forecasting and verification.

Information retrieval from multi-domain specific research proposal using hierarchical-based neural network clustering algorithm

Research project selection is an essential task for government and private agencies. When a huge number of research proposals are received, it is common to group them along with their similarities in research discipline areas. In this paper, a new framework is proposed to assign the proposal to reviewer and identify the proposal theme automatically. First, a list of multi word terms are extracted automatically from the domain specific corpus of text documents into a hierarchy based on their semantic similarity. Second, the proximity of two vectors can be calculated using measures such as probabilistic correlation. The conditional probability is used to model the probability that a term appear together in a record. Third, a hierarchical-based neural network clustering algorithm is used to get an information retrieval from multi-domain specific research proposal.

INFORMATION RETRIEVAL FROM MULTI DOMAIN SPECIFIC RESEARCH PROPOSAL USING HIERARCHICAL BASED NEURAL NETWORK CLUSTERING ALGORITHM

Research project selection is an essential task for government and private agencies. When a huge number of research proposals are received, it is common to group them along with their similarities in research discipline areas. In this paper, a new framework is proposed to assign the proposal to reviewer and identify the proposal theme automatically. First, a list of multi word terms are extracted automatically from the domain specific corpus of text documents into a hierarchy based on their semantic similarity. Second, the proximity of two vectors can be calculated using measures such as probabilistic correlation. The conditional probability is used to model the probability that a term appear together in a record. Third, a hierarchical-based neural network clustering algorithm is used to get an information retrieval from multi-domain specific research proposal.

Joint Learning of Question Answering and Question Generation

Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in the literature. In this paper, we present two training algorithms for learning better QA and QG models through leveraging one another. The first algorithm extends Generative Adversarial Network (GAN), which selectively incorporates artificially generated instances as additional QA training data. The second algorithm is an extension of dual learning, which incorporates the probabilistic correlation of QA and QG as additional regularization in training objectives. To test the scalability of our algorithms, we conduct experiments on both document based and table based question answering tasks. Results show that both algorithms improve a QA model in terms of accuracy and QG model in terms of BLEU score. Moreover, we find that the performance of a QG model could be easily improved by a QA model via policy gradient, however, directly applying GAN that regards all the generated questions as negative instances could not improve the accuracy of the QA model. Our algorithm that selectively assigns labels to generated questions would bring a performance boost.

Instant fuzzy search using probabilistic-correlation based ranking

Background : Instant search recommends completions of the query ‘on the fly’, and instantly displays the results with every keystroke. It is desirable that these query results be robust against typographical errors that appear not only in the query but also in the documents. Additionally, instant search requires instant response time and ranking of the results to focus on the most important answers. Method: In this study, simple and efficient methods for instant fuzzy single keyword and multi-keyword search that are resilient to typographical errors and that employ no more than inverted and forward indices are studied. While computing search results incrementally using the cached results, the answers are ranked based on their relevance to the query using probabilistic correlation-based ranking. Findings: Experiments are conducted on data sets DBLP and Medline and the execution time for obtaining answers to instant fuzzy single keyword search is recorded for different prefix lengths. Similarly, the execution time for obtaining answers to instant fuzzy multi-keyword search is recorded for sub-queries of two keywords and three keywords for various prefix lengths on the same data set. Furthermore, in order to measure the usefulness of the proposed correlation-based ranking, precision is calculated for the search results. Experimental evaluation demonstrates the efficacy of the instant fuzzy search algorithms and the probabilistic correlation-based ranking. Applications: The proposed instant fuzzy keyword search for single and multiple keywords not only improves the efficiency but also the quality of the search results. Keywords: Keyword Search, Multi-keyword Search, Fuzzy Search, Probabilistic Correlation

Probabilistic multiple correlation based term weighting scheme for measuring similarity of unstructured text records

Probabilistic multiple correlation based term weighting scheme for measuring similarity of unstructured text records

Physical synthesis method for global reliability analysis of engineering structures

Global reliability analysis is quite essential for safety design and serviceability maintenance of modern engineering structures. Various reliability analysis methods have been proposed though, the issues of combination explosion as well as probabilistic correlation of system failure paths remain challenging. Practical applications of these methods are thus hindered, especially when complex, implicit, and nonlinear performance functions are involved. There isn’t an efficient way out of this dilemma until the emergence of the probability density evolution method (PDEM) combined with the equivalent extreme value event principle in recent years. The governing partial differential equations regarding the probability density evolution of a stochastic dynamical system are decoupled to a one-dimensional generalized probability density evolution equation (GPDEE) by employing the random event description. In conjunction with the PDEM, this paper explores a physical synthesis approach towards the global reliability analysis of actual engineering structures. Evaluation of multiple failure potential is incorporated into the mathematical resolving of the GPDEE for structural response at a representative point. An accurate and efficient solving procedure for the global reliability of probabilistic dissipative systems is presented where no need of seeking the failure paths and their associated probabilities. Numerical applications are examined to illustrate the use of the physical synthesis method as a practical analysis tool.

Micro‐computed tomography imaging and probabilistic modelling of rock fracture by freeze–thaw

AbstractA major problem in studies of rock fracture by frost is the paucity of direct observations in space and time of the initiation and growth of microcracks and their transition to macrocracks. Such observations are essential to understand the location, timing and controls of rock fracture by freeze–thaw. The aim of the present work is to image and elucidate the early stages of rock fracture by applying imaging and statistical methods to a frost‐weathering experiment using intact specimens of a limestone (chalk) and sandstone. First, micro‐computed tomography (μ‐CT) is used to visualize rock fracture in three dimensions over the course of 20 freeze–thaw cycles and to estimate transverse strain using a pixel‐based approach. Second, probabilistic correlation functions are applied to quantify the progressive expansion of the fracture phase and associated damage to rock specimens. The method of μ‐CT is demonstrated for visualizing the growth and coalescence of microcracks and their transition to macrocracks. Fracture proceeded faster and to a greater extent in chalk relative to sandstone, and the macrocracks in chalk were mostly concentric and vertical. Both fracture development and positive transverse strain (dilation) accelerated after cycle 15, suggesting that a threshold has been exceeded, after which macrocracks were evident. Of three probabilistic correlation functions applied to the μ‐CT results, the modified lineal‐path function – which measures the continuous connectivity of the fracture phase in a specific direction – reveals that damage was more extensive in the chalk than the sandstone. It also allows a novel approach to define and quantify three zones of microcracking during freeze–thaw cycling of anisotropic rock: (1) the zone of inherent flaws; (2) the zone of active microcracking; and (3) the zone of weak influence during microcracking. The broader significance of this work is that it provides a new approach to investigate mechanistically how frost action damages rock. © 2019 John Wiley & Sons, Ltd.