Bayesian model transfers knowledge across different engineering fleets.
arXiv research
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The prevalent view in the economics literature is that a high level of infrastructure investment is a precursor to economic growth. China is especially held up as a model to emulate. Based on the largest dataset of its kind, this paper punctures the twin myths that, first, infrastructure creates economic value, and, se…
We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence …
Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of sys…
Hybrid framework predicts Arctic permafrost decline, risks infrastructure, and provides tools.
An increasing amount of civil engineering applications are utilising data acquired from infrastructure instrumented with sensing devices. This data has an important role in monitoring the response of these structures to excitation, and evaluating structural health. In this paper we seek to monitor pedestrian-events (su…
Novel framework identifies pump-specific deterioration rates using Bayesian hierarchical hazard modeling and causal discovery.
Study generates synthetic fNIRS data and applies machine learning for improved neuroimaging.
Survey of financial foundation models for diverse applications.
R package for machine learning in survival analysis.
Agent-based model simulates market dynamics with real-time order matching.
Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is …
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
Study evaluates machine learning methods for large-scale network reliability, revealing ANN's and PR's performance.
The Economist recently reported that infrastructure spending is the largest it is ever been as a share of world GDP. With $22 trillion in projected investments over the next ten years in emerging economies alone, the magazine calls it the "biggest investment boom in history." The efficiency of infrastructure planning a…
The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of wid…
The article first describes characteristics of major infrastructure projects. Second, it documents a much neglected topic in economics: that ex ante estimates of costs and benefits are often very different from actual ex post costs and benefits. For large infrastructure projects the consequence is cost overruns, benefi…
Cryptocurrency markets show similar returns but different volatility responses to infrastructure and regulatory shocks.
Zero Emission Vehicles (ZEV) play an important role in the decarbonization of the transportation sector. For a wider adoption of ZEVs, providing a reliable infrastructure is critical. We present a machine learning approach that uses unsupervised temporal clustering algorithm along with survey analysis to determine infr…
A novel outlier score detects new road infrastructure images.
Natural disasters can have catastrophic impacts on the functionality of infrastructure systems and cause severe physical and socio-economic losses. Given budget constraints, it is crucial to optimize decisions regarding mitigation, preparedness, response, and recovery practices for these systems. This requires accurate…
This paper analyzes various forms of concentrated liquidity in decentralized finance.
This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.
IntraLayer connects digital finance platforms efficiently.
Study optimizes inspection and monitoring of deteriorating structures using POMDPs.
This paper argues, first, that a major problem in the planning of large infrastructure projects is the high level of misinformation about costs and benefits that decision makers face in deciding whether to build, and the high risks such misinformation generates. Second, it explores the causes of misinformation and risk…
Research quantifies financial exclusion risks in UK, focusing on cash infrastructure and socio-economic factors.
Electroencephalography (EEG) is an extensively-used and well-studied technique in the field of medical diagnostics and treatment for brain disorders, including epilepsy, migraines, and tumors. The analysis and interpretation of EEGs require physicians to have specialized training, which is not common even among most do…
Fragmented exchanges arise due to speed advantages in high-activity regions.
Deep learning detects cyber-attacks in smart grid systems.
Each year, around 6 million car accidents occur in the U.S. on average. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for…
Infrastructure monitors AI/ML radiology models across multiple sites.
Automated road infrastructure mapping using connected vehicle data and deep learning.
Methodology measures financial impacts using existing credit loss infrastructure.
Develops a framework to assess infrastructure reliability under natural and malicious events.
Neural MMO simulates MMOs to study multiagent intelligence.
Unified pipeline detects multi-turn deception using geometric signals.
The study examines how modernizing settlement infrastructure affects inside money elasticity and network efficiency.
Study assesses 'big data' in materials science, highlighting challenges.
Investigates ways to train larger models with fewer resources, finding that test loss depends only on the actual number of trainable parameters.
This paper ranks Latin American countries based on AI potential.
Proposes a multi-objective variational autoencoder for smart infrastructure damage detection.
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
Novel CMG framework improves financial sentiment forecasting.
The simulator is an R package that streamlines the process of performing simulations by creating a common infrastructure that can be easily used and reused across projects. Methodological statisticians routinely write simulations to compare their methods to preexisting ones. While developing ideas, there is a temptatio…
To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platf…
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It cons…
We collect and analyze the data for working time, life expectancy, and the pair output and infrastructure of industrializing nations. During S-functional recovery from disaster the pair's time shifts yield 25 years for the infrastructure's physical lifetime. At G7 level the per capita outputs converge and the time shif…