44 Results Found

A diffusion-taking value in probability-measures on a graph with vertex set V, ${\sum }_{i\in V}{x}_i{\delta }_i$ is studied. The masses on each vertex satisfy the stochastic differential equation of the form $d{x}_i={\sum }_{j\in N\left(i\right)}\sqrt{x_i{x}_j}d{B}_{ij}$ on the simplex, where $\{$...

Time-varying autoregressive (TVAR) models are widely used for modeling of non-stationary signals. Unfortunately, online joint adaptation of both states and parameters in these models remains a challenge. In this paper, we represent the TVAR model by...

Graphical log-linear models are effective for representing complex structures that emerge from high-dimensional data. It is challenging to fit an appropriate model in the high-dimensional setting and many existing methods rely on a convenient class o...

Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support becaus...

Community detection in graphs can be viewed as the estimation of a partition map that remains stable under admissible perturbations of graph topology and node attributes. While modern graph neural networks (GNNs) achieve strong empirical accuracy, th...

The Bayesian network is a directed, acyclic graphical model that can offer a structured description for probabilistic dependencies among random variables. As powerful tools for classification tasks, Bayesian classifiers often require computing joint...

We consider multivariate-centered Gaussian models for the random vector $(Z^1,\dots ,Z^p)$, whose conditional structure is described by a homogeneous graph and which is invariant under the action of a permutation subgroup. The following paper is concer...

Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often...

It is a relatively well-known fact that in problems of Bayesian model selection, improper priors should, in general, be avoided. In this paper we will derive and discuss a collection of four proper uniform priors which lie on an ascending scale of in...

Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for r...

Extractive summarization aims to produce a concise version of a document by extracting information-rich sentences from the original texts. The graph-based model is an effective and efficient approach to rank sentences since it is simple and easy to u...

Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach,...

This paper presents an advanced computational approach to assess the risk of damage to masonry buildings subjected to negative kinematic impacts of underground mining exploitation. The research goals were achieved using selected tools from the area o...

Bayesian statistical inference under unknown or hard to asses likelihood functions is a very challenging task. Currently, approximate Bayesian computation (ABC) techniques have emerged as a widely used set of likelihood-free methods. A vast number of...

The identification of homogeneous groups of actors in a local AHP-multiactor context based on their preferences is an open problem, particularly when the number of decision-makers is high. To solve this problem in the case of using stochastic AHP, th...

Bayesian wavelet regression offers a mathematically grounded framework for modeling neuro-retinal signals that exhibit substantial noise, high dimensionality, and complex variations across temporal and spatial scales. Such characteristics are inheren...

Due to the particularity of loess engineering geology, loess railway tunnel accidents occur frequently. Based on the theoretical basis of risk management, this study evaluated the service performance of loess railway tunnels. Based on the improved TO...

Forecasting antimicrobial resistance (AMR) is critical for public health, yet most models neglect the interconnected nature of agricultural systems. Focusing on ciprofloxacin resistance in Campylobacter jejuni—a leading foodborne pathogen in po...

Graph signal sampling has been widely studied in recent years, but the accurate signal models required by most of the existing sampling methods are usually unavailable prior to any observations made in a practical environment. In this paper, a sequen...

A common approach to improve medical image classification is to add more features to the classifiers; however, this increases the time required for preprocessing raw data and training the classifiers, and the increase in features is not always benefi...

This study systematically compares three unsupervised segmentation algorithms (Simple Linear Iterative Clustering (SLIC), Felzenszwalb’s graph-based method, and the Watershed algorithm) in combination with two classification approaches: Random...

As cleaner production gains prominence in wooden product manufacturing, green evaluation of process schemes during early design is crucial. However, dust concentration, a key environmental indicator in wood product manufacturing, is often evaluated i...

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework tha...

Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack pa...

Based on the four sets of faults data measured in the practical LCC-HVDC transmission project of China Southern Power Grid Tianshengqiao (Guangxi Province, China)–Guangzhou (Guangdong Province, China) HVDC transmission project, a fault diagnosi...

Accurately and efficiently predicting bottomhole pressure (BHP) is of great importance for safe drilling in complex formations. Many researchers have conducted extensive investigations into intelligent BHP prediction techniques. However, the current...

Short-term prediction of wave height is paramount in oceanic operation-related activities. Statistical models have advantages in short-term wave prediction as complex physical process is substantially simplified. However, previous statistical models...

Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challe...

A Bayesian network (BN) is an uncertainty processing model that simulates human cognitive thinking on the basis of probability theory and graph theory. Its network topology is a directed acyclic graph (DAG) that can be manually constructed through ex...

Rapid cooperative detection of stationary targets by multiple unmanned aerial vehicles (UAVs) is important in time-critical missions such as search and rescue. However, the online coordination of probabilistic inference, distributed communication, an...

With the rapid development of Industrial Internet of Things technology, the industrial control system (ICS) faces more and more security threats, which may lead to serious risks and extensive damage. Naturally, it is particularly important to constru...

Mixed-signal simulation is indispensable for verifying modern integrated circuits that tightly couple analog and digital subsystems, yet the field lacks a unified framework for systematically comparing its diverse methodologies. This paper addresses...

Modeling the number of individuals in different states is a principal tool in the event of an epidemic. The natural transition of individuals between possible states often includes deliberate interference such as isolation or vaccination. Thus, the m...

SWANP-AI (Smart Water Network Partitioning with Artificial Intelligence) is a web application with AI natively embedded in its core engines for automated Water Network Partitioning (WNP) of water distribution networks. It is presented as the web-base...

Influenza A(H1N1)pdm09 remains one of the predominant seasonal influenza viruses worldwide and continues to evolve under the combined effects of host immunity, vaccination, and changing epidemiological conditions. However, the long-term impact of the...

Estimation of distribution algorithms (EDAs) are optimization methods that search for explicit probabilistic models which are used to sample promising candidate solutions. The optimization process consists of a sequence of incremental updates to an i...

Industrial artificial intelligence (AI) fault-diagnostic systems can identify plausible causes under uncertainty, but their outputs alone do not determine how diagnostic support should be delivered during maintenance. This paper presents WrenchBuddy,...

In order to study the genetic structure and population geographic distribution pattern of coastal mussel populations in the coast of China, mitochondrial DNA (COI and Cytb genes) were used to analyze the genetic diversity, genetic structure, and popu...

Short-term load forecasting plays a significant role in the operation of power systems. Recently, deep learning has been generally employed in short-term load forecasting, primarily in the extraction of the characteristics of digital information in a...

Higher-education assessment combines heterogeneous indicators whose interactions are difficult to interpret using a single deterministic score. This study develops an interpretable artificial intelligence (AI) workflow that integrates Pearson correla...

Approximate computing has been a good paradigm of energy-efficient accelerator design. Accurate and fast error estimation is critical for appropriate approximate techniques selection so that power saving (or performance improvement) can be maximized...

The use of Artificial Intelligence (AI) algorithms has enhanced WiFi fingerprinting-based indoor localization. However, most existing approaches are limited to 2D coordinate estimation, which leads to significant performance declines in multi-floor e...

Photodynamic therapy (PDT) is a minimally invasive therapeutic approach based on the light-induced activation of a photosensitizer in the presence of molecular oxygen, resulting in the generation of reactive oxygen species, including singlet oxygen....

Ongoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions. This Stats invited featu...