A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Cryptocurrency markets show similar returns but different volatility responses to infrastructure and regulatory shocks.
Cryptocurrency markets treat infrastructure failures and regulatory shocks differently, but the effect is not statistically significant.
CyPhERS provides real-time event info for CPSs, avoiding downtime.
Many studies in economics deal with the non-reliability cost to assess insurance fees or investment analyses, but none takes into consideration the mechanical aspect of reliability analysis. Other studies in mechanics give some tools and methods to carry out reliability analyses and fragility study. This study develope…
Study uses satellite data to predict tailings dam collapse risk.
Wastewater infrastructure systems deteriorate over time due to a combination of physical and chemical factors. Failure of this significant infrastructure could affect important social, environmental, and economic impacts. Furthermore, recognizing the optimized timeline for inspection of sewer pipelines are challenging …
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce…
Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Traditionally, maintenance of points is based on a fixed time inter…
The study reveals fundamental limits of fraud detection in card payment networks.
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…
Credit networks represent a way of modeling trust between entities in a network. Nodes in the network print their own currency and trust each other for a certain amount of each other's currency. This allows the network to serve as a decentralized payment infrastructure---arbitrary payments can be routed through the net…
Bayesian network framework assesses urban risks across multiple domains.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
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…
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.
Survey of AI-based outlier detection methods for various domains.
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 …
Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a large-scale system, the various hardware, software, and tooling logs are often maintained separately, making it difficult to review the logs…
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…
Hybrid framework predicts Arctic permafrost decline, risks infrastructure, and provides tools.
Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance …
This study analyzes dynamic connectedness in global supply chain infrastructure portfolios, identifying key risk factors and extreme events.
IntraLayer connects digital finance platforms efficiently.
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.
Novel method improves load estimation in power grids using anomaly and change point detection.
Deep learning detects cyber-attacks in smart grid systems.
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.
Study assesses 'big data' in materials science, highlighting challenges.
NCPF model improves traffic data imputation with neural and tensor methods.
A reinforcement learning algorithm improves graph construction for robustness.
Distributed securities exchanges may become de facto fragmented if they span geographical regions with asymmetric computer infrastructure. First, we build an economic model of a decentralized exchange with two miner clusters, standing in for compact areas of economic activity (e.g., cities). "Local" miners in the area …
This paper ranks Latin American countries based on AI potential.
Economics tool predicts failure times in reliability systems.
Proposes a multi-objective variational autoencoder for smart infrastructure damage detection.
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
Unified model predicts multi-mode failure with multi-sensor data.
PAGER detects failures in deep regression models using a new framework.
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…
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…
Risk Advisor predicts and mitigates ML deployment failures.
CalNF models rare failures with limited data, improving safety in autonomous systems.
Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the key challenges in PHM is to accurately predict impending failures in the equipment. In recent years, solutions for failure prediction have evo…