Distribution For Modeling Data Research Articles

Üstellenmiş UEHL Dağılımı: Özellikler ve Uygulamalar

In this paper, we propose a distribution for modeling data defined on a unit interval using an exponentiated transformation. The new distribution is based on the unit exponential half-logistic distribution, a member of proportional hazard models. Several measures of the statistical characterization of the distribution are discussed. The statistical inference of the parameters of the proposed distribution is studied by the maximum likelihood method. To explore the properties of the maximum likelihood estimates of the parameters, simulation studies are carried out under various scenarios. Furthermore, a real dataset is analyzed to demonstrate the performance of the distribution.

FedQMIX: Communication-efficient federated learning via multi-agent reinforcement learning

Since the data samples on client devices are usually non-independent and non-identically distributed (non-IID), this will challenge the convergence of federated learning (FL) and reduce communication efficiency. This paper proposes FedQMIX, a node selection algorithm based on multi-agent reinforcement learning(MARL), to address these challenges. Firstly, we observe a connection between model weights and data distribution, and a clustering algorithm can group clients with similar data distribution into the same cluster. Secondly, we propose a QMIX-based mechanism that learns to select devices from clustering results in each communication round to maximize the reward, penalizing the use of more communication rounds and thereby improving the communication efficiency of FL. Finally, experiments show that FedQMIX can reduce the number of communication rounds by 11% and 30% on the MNIST and CIFAR-10 datasets, respectively, compared to the baseline algorithm (Favor).

The Exponential T-X Gompertz Model for Modeling Real Lifetime Data: Properties and Estimation

In the real world, many applications require enhanced variants of well-known distributions. The new distributions are generally more adaptable for simulating real-world data with high skewness and kurtosis. Choosing the best statistical distribution for modeling data is very important and demanding. In this paper, we provide a new fl exible model for modeling lifetime data that is achieved by adding a component to baseline distributions. The new model has three parameters, known as the exponential T-X Gompertz distribution. Its probability density function could be skewed and unimodal. Reliability, hazard rate, quantile, and the moment generating function are just a few of the distributional properties that can be inferred from the suggested model. To estimate the unknown parameters, maximum likelihood estimation is utilized. In addition, Monte Carlo simulation experiments are performed to evaluate the performance of the maximum likelihood estimators. Finally, two real-world data sets are shown to evaluate the proposed model’s potential with that of various existing models.

PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks

Federated learning is gaining popularity as a distributed machine learning method that can be used to deploy AI-dependent Internet of Things applications while protecting client data privacy and security. Due to the differences of clients, a single global model may not perform well on all clients, so the personalized federated learning method, which trains a personalized model for each client that better suits its individual needs, becomes a research hotspot. Most personalized federated learning research, however, focuses on data heterogeneity while ignoring the need for model architecture heterogeneity. Most existing federated learning methods uniformly set the model architecture of all clients participating in federated learning, which is inconvenient for each client’s individual model and local data distribution requirements, and also increases the risk of client model leakage. This article proposes a federated learning method based on co-training and generative adversarial networks (GANs) that allows each client to design its own model to participate in federated learning training independently without sharing any model architecture or parameter information with other clients or a center. In our experiments, the proposed method outperforms the existing methods in mean test accuracy by 42% when the client’s model architecture and data distribution vary significantly.

A New Weighted-Lindley Distribution: Properties, Classical and Bayesian Estimation with an Application

The choice of the most suitable statistical distribution for modeling data is very important. Generally, the new distributions are more flexible to model real data that present a high degree of skewness and kurtosis. In this paper, we define a new one-parameter lifetime distribution, so-called weighted-Lindley distribution. Some of its basic properties are investigated. Some classical and Bayesian methods of estimation have been used for estimating its parameter. The behavior of these estimators were investigated by a graphical simulation study. A real data set is analyzed to investigate the flexibility of the new weighted-Lindley distribution.

Cubic Transmuted Rayleigh Distribution: Theory and Application

In this paper, the Rayleigh distribution is generalized using the cubic transmuted (CT) family studied by Rahman et al. (in EJPAM, 12(3), 1106-1121, 2019) to propose a cubic transmuted Rayleigh distribution. An overall description is presented here for the distributional properties, parameter estimation, inference procedure, reliability behavior, and distribution of different order statistics. A real-life data set is used to demonstrate the applicability of the proposed distribution for modeling data.

Stochastic graph recurrent neural network

Representation learning over dynamic graphs has attracted much attention because of its wide applications. Recently, sequential probabilistic generative models have achieved impressive results because they can model data distributions. However, modeling the distribution of dynamic graphs is still extremely challenging. Existing methods usually ignore the mutual interference of stochastic states and deterministic states. Besides, the assumption that latent variables follow Gaussian distributions is usually inappropriate. To address these problems, we propose stochastic graph recurrent neural network (SGRNN), a sequential generative model for the representation learning over dynamic graphs. It separates stochastic states and deterministic states in the iterative process. To improve the flexibility of latent variables, we set the prior distribution and posterior distribution as semi-implicit distributions and propose DSI-SGRNN. In addition, to alleviate the KL-vanishing problem in SGRNN, a simple and interpretable structure is proposed based on the lower bound of KL-divergence. The proposed structure introduces a few extra parameters and can be implemented with a few lines of code modification. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed model.

Triple-Level Model Inferred Collaborative Network Architecture for Video Deraining.

Video deraining is an important issue for outdoor vision systems and has been investigated extensively. However, designing optimal architectures by the aggregating model formation and data distribution is a challenging task for video deraining. In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances. In particular, to mitigate the problem that existing methods cannot cover various rain streaks distribution, we first design a hyper-parameter optimization model about task variable and hyper-parameter. Based on the proposed optimization model, we design a collaborative structure for video deraining. This structure includes Dominant Network Architecture (DNA) and Companionate Network Architecture (CNA) that is cooperated by introducing an Attention-based Averaging Scheme (AAS). To better explore inter-frame information from videos, we introduce a macroscopic structure searching scheme that searches from Optical Flow Module (OFM) and Temporal Grouping Module (TGM) to help restore latent frame. In addition, we apply the differentiable neural architecture searching from a compact candidate set of task-specific operations to discover desirable rain streaks removal architectures automatically. Extensive experiments on various datasets demonstrate that our model shows significant improvements in fidelity and temporal consistency over the state-of-the-art works. Source code is available at https://github.com/vis-opt-group/TMICS.

Entropy-based redundancy analysis and information screening

The ongoing data explosion introduced unprecedented challenges to the information security of communication networks. As images are one of the most commonly used information transmission carriers; therefore, their data redundancy analysis and screening are of great significance. However, most of the current research focus on the algorithm improvement of commonly used image datasets. Thus, we should consider an important question: Is there data redundancy in the open datasets? Considering the factors of model structures and data distribution to ensure the generalization, we conducted extensive experiments to compare the average accuracy based on few random data to the baseline accuracy based on all data. The results show serious data redundancy in the open datasets from different domains. For instance, with the aid of deep model, only 20% data can achieve more than 90% of the baseline accuracy. Further, we proposed a novel entropy-based information screening method, which outperforms the random sampling under many experimental conditions. In particular, considering 20% of data, for the shallow model, the improvement is approximately 10%, and for the deep model, the ratio to the baseline accuracy increases to greater than 95%. Moreover, this work can also serve as a new way of learning from a few valuable samples, compressing the size of existing datasets and guiding the construction of high-quality datasets in the future.

Fast-Convergent Federated Learning With Adaptive Weighting

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally. The non-independent-and-identically-distributed (non-IID) data samples across participating nodes slow model training and impose additional communication rounds for FL to converge. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fed</monospace> erated <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ad</monospace> a <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</monospace> tive Weighting ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdp</monospace> ) algorithm that aims to accelerate model convergence under the presence of nodes with non-IID dataset. We observe the implicit connection between the node contribution to the global model aggregation and data distribution on the local node through theoretical and empirical analysis. We then propose to assign different weights for updating the global model based on node contribution adaptively through each training round. The contribution of participating nodes is first measured by the angle between the local gradient vector and the global gradient vector, and then, weight is quantified by a designed non-linear mapping function subsequently. The simple yet effective strategy can reinforce positive (suppress negative) node contribution dynamically, resulting in communication round reduction drastically. Its superiority over the commonly adopted Federated Averaging ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</monospace> ) is verified both theoretically and experimentally. With extensive experiments performed in Pytorch and PySyft, we show that FL training with <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAdp</monospace> can reduce the number of communication rounds by up to 54.1% on MNIST dataset and up to 45.4% on FashionMNIST dataset, as compared to <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedAvg</monospace> algorithm.

Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network.

Continuous sign language recognition is a weakly supervised task dealing with the identification of continuous sign gestures from video sequences, without any prior knowledge about the temporal boundaries between consecutive signs. Most of the existing methods focus mainly on the extraction of spatio-temporal visual features without exploiting text or contextual information to further improve the recognition accuracy. Moreover, the ability of deep generative models to effectively model data distribution has not been investigated yet in the field of sign language recognition. To this end, a novel approach for context-aware continuous sign language recognition using a generative adversarial network architecture, named as Sign Language Recognition Generative Adversarial Network (SLRGAN), is introduced. The proposed network architecture consists of a generator that recognizes sign language glosses by extracting spatial and temporal features from video sequences, as well as a discriminator that evaluates the quality of the generator’s predictions by modeling text information at the sentence and gloss levels. The paper also investigates the importance of contextual information on sign language conversations for both Deaf-to-Deaf and Deaf-to-hearing communication. Contextual information, in the form of hidden states extracted from the previous sentence, is fed into the bidirectional long short-term memory module of the generator to improve the recognition accuracy of the network. At the final stage, sign language translation is performed by a transformer network, which converts sign language glosses to natural language text. Our proposed method achieved word error rates of 23.4%, 2.1% and 2.26% on the RWTH-Phoenix-Weather-2014 and the Chinese Sign Language (CSL) and Greek Sign Language (GSL) Signer Independent (SI) datasets, respectively.

Properties of the Transmuted Burr XII Distribution, Regression and its Applications

In this paper, we define and study a four-parameter model called the transmuted Burr XII distribution. We obtain some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, order statistics, probability weighted moments and entropies. We formulate and develop a log-linear model using the new distribution so-called the log-transmuted Burr XII distribution for modeling data with a unimodal failure rate function, as an alternative to the log-McDonald Burr XII, log-beta Burr XII, log-Kumaraswamy Burr XII, log-Burr XII and logistic regression models. The flexibility of the proposed models is illustrated by means of three applications to real data sets.

The four-parameter Fréchet distribution: Properties and applications

In this article, we propose a new four-parameter Fréchet distribution called the odd Lomax Fréchet distribution. The new model can be expressed as a linear mixture of Fréchet densities. We provide some of its mathematical properties. The estimation of the model parameters is performed by the maximum likelihood method. We illustrate the good performance of the maximum likelihood estimates via a detailed numerical simulation study. The importance and usefulness of the proposed distribution for modeling data are illustrated using two real data applications.

A new extended generalized Gompertz distribution with statistical properties and simulations

Statistical distributions are very useful in describing and predicting real world phenomena. In many applied areas there is a clear need for the extended forms of the well-known distributions. Generally, the new distributions are more flexible to model real data that present a high degree of skewness and kurtosis. The choice of the best-suited statistical distribution for modeling data is very important.In this article, we proposed an extended generalized Gompertz (EGGo) family of EGGo. Certain statistical properties of EGGo family including distribution shapes, hazard function, skewness, limit behavior, moments and order statistics are discussed. The flexibility of this family is assessed by its application to real data sets and comparison with other competing distributions. The maximum likelihood equations for estimating the parameters based on real data are given. The performances of the estimators such as maximum likelihood estimators, least squares estimators, weighted least squares estimators, Cramer-von-Mises estimators, Anderson-Darling estimators and right tailed Anderson-Darling estimators are discussed. The likelihood ratio test is derived to illustrate that the EGGo distribution is better than other nested models in fitting data set or not. We use R software for simulation in order to perform applications and test the validity of this model.

Neighbourhood Structure Preserving Cross-Modal Embedding for Video Hyperlinking

Video hyperlinking is a task aiming to enhance the accessibility of large archives, by establishing links between fragments of videos. The links model the aboutness between fragments for efficient traversal of video content. This paper addresses the problem of link construction from the perspective of cross-modal embedding. To this end, a generalized multi-modal auto-encoder is proposed. The encoder learns two embeddings from visual and speech modalities, respectively, whereas each of the embeddings performs self-modal and cross-modal translation of modalities. Furthermore, to preserve the neighbourhood structure of fragments, which is important for video hyperlinking, the auto-encoder is devised to model data distribution of fragments in a dataset. Experiments are conducted on Blip10000 dataset using the anchor fragments provided by TRECVid Video Hyperlinking (LNK) task over the years of 2016 and 2017. This paper shares the empirical insights on a number of issues in cross-modal learning, including the preservation of neighbourhood structure in embedding, model fine-tuning and issue of missing modality, for video hyperlinking.

The New Odd Log-Logistic Generalised Half-Normal Distribution: Mathematical Properties and Simulations

The new distributions are very useful in describing real data sets, because these distributions are more flexible to model real data that present a high degree of skewness and kurtosis. The choice of the best-suited statistical distribution for modeling data is very important.In this paper, A new class of distributions called the {\it New odd log-logistic generalized half-normal} (NOLL-GHN) family with four parameters is introduced and studied. This model contains sub-models such as half-normal (HN), generalized half-normal (GHN )and odd log-logistic generalized half-normal (OLL-GHN) distributions.some statistical properties such as moments and moment generating function have been calculated.The Biases and MSE's of estimator methods such as maximum likelihood estimators , least squares estimators, weighted least squares estimators,Cramer-von-Mises estimators, Anderson-Darling estimators and right tailed Anderson-Darling estimators are calculated.The fitness capability of this model has been investigated by fitting this model and others based on real data sets. The maximum likelihood estimators are assessed with simulated real data from proposed model. We present the simulation in order to test validity of maximum likelihood estimators.

CM-GANs

It is known that the inconsistent distributions and representations of different modalities, such as image and text, cause the heterogeneity gap, which makes it very challenging to correlate heterogeneous data and measure their similarities. Recently, generative adversarial networks (GANs) have been proposed and have shown their strong ability to model data distribution and learn discriminative representation. It has also been shown that adversarial learning can be fully exploited to learn discriminative common representations for bridging the heterogeneity gap. Inspired by this, we aim to effectively correlate large-scale heterogeneous data of different modalities with the power of GANs to model cross-modal joint distribution. In this article, we propose Cross-modal Generative Adversarial Networks (CM-GANs) with the following contributions. First, a cross-modal GAN architecture is proposed to model joint distribution over the data of different modalities. The inter-modality and intra-modality correlation can be explored simultaneously in generative and discriminative models. Both compete with each other to promote cross-modal correlation learning. Second, the cross-modal convolutional autoencoders with weight-sharing constraint are proposed to form the generative model. They not only exploit the cross-modal correlation for learning the common representations but also preserve reconstruction information for capturing the semantic consistency within each modality. Third, a cross-modal adversarial training mechanism is proposed, which uses two kinds of discriminative models to simultaneously conduct intra-modality and inter-modality discrimination. They can mutually boost to make the generated common representations more discriminative by the adversarial training process. In summary, our proposed CM-GAN approach can use GANs to perform cross-modal common representation learning by which the heterogeneous data can be effectively correlated. Extensive experiments are conducted to verify the performance of CM-GANs on cross-modal retrieval compared with 13 state-of-the-art methods on 4 cross-modal datasets.

Bias prevention of maximum likelihood estimates for skew-normal-Cauchy distribution

This work focuses on the so-called skew-normal-Cauchy distribution, which is a convenient alternative to the skew-normal distribution for modeling data in presence of asymmetries. A stochastic representation and further nice properties of the skew-normal-Cauchy distribution are considered. Despite these desirable properties, the skew-normal-Cauchy model presents similar peculiarities as the skew-normal model in estimating the skewness parameter. Particularly, in finite samples, the maximum likelihood estimator of the shape parameter can be infinite with positive probability. In order to address this problem a modified score function is used. Also a quasi-likelihood approach is considered for obtaining confidence intervals. BMI data of elite athletes are used to illustrate this issue.

Lessons learned from 25 years of PMIP model-data distribution

Lessons learned from 25 years of PMIP model-data distribution

Task-Aware Compressed Sensing With Generative Adversarial Networks

In recent years, neural network approaches have been widely adopted for machine learning tasks, with applications in computer vision. More recently, unsupervised generative models based on neural networks have been successfully applied to model data distributions via low-dimensional latent spaces. In this paper, we use Generative Adversarial Networks (GANs) to impose structure in compressed sensing problems, replacing the usual sparsity constraint. We propose to train the GANs in a task-aware fashion, specifically for reconstruction tasks. We also show that it is possible to train our model without using any (or much) non-compressed data. Finally, we show that the latent space of the GAN carries discriminative information and can further be regularized to generate input features for general inference tasks. We demonstrate the effectiveness of our method on a variety of reconstruction and classification problems.