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.
Hybrid model combines PCA and RNN for better aerospace stock price prediction.
Optimizes neural network for aerodynamic predictions with Bayesian Optimization.
In this study, we analyze the aerospace stocks prices in order to characterize the sector behavior. The data analyzed cover the period from January 1987 to April 1999. We present a new index for the aerospace sector and we investigate the statistical characteristics of this index. Our results show that this index is we…
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 …
A data-driven approach predicts morphological development under structural instability.
In this paper, we consider a problem of failure prediction in the context of predictive maintenance applications. We present a new approach for rare failures prediction, based on a general methodology, which takes into account peculiar properties of technical systems. We illustrate the applicability of the method on th…
Deep learning detects corrosion in nuclear fuel canisters.
Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outc…
Enhanced multi-fidelity models improve digital twin accuracy and uncertainty quantification.
Self-supervised learning improves RUL prediction with limited data in fatigue damage prognosis.
Surrogate models enhance digital twin technology for dynamic systems.
Gaussian process priors are commonly used in aerospace design for performing Bayesian optimization. Nonetheless, Gaussian processes suffer two significant drawbacks: outliers are a priori assumed unlikely, and the posterior variance conditioned on observed data depends only on the locations of those data, not the assoc…
Improved aircraft structure prediction using derivative-enhanced sparse Cholesky GP method.
Paper introduces zero-space memory protection for CNNs without ECC overhead.
A digital twin for multi-scale systems uses physics-based and machine learning models.
Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize sm…
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The comm…
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
Improves industry classification for diversified companies.
Develops MIS, a probabilistic model for multi-industry classification.
Study finds environmental liability insurance reduces industrial carbon emissions.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
Analyzes Indian chemical industry post-Covid.
Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental design, that is, how to select the input settings where the compu…
New online learning algorithms improve cyberattack detection in industrial control systems.
We provide complete source code for building a fundamental industry classification based on publically available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssr…
The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication…
We give complete algorithms and source code for constructing (multilevel) statistical industry classifications, including methods for fixing the number of clusters at each level (and the number of levels). Under the hood there are clustering algorithms (e.g., k-means). However, what should we cluster? Correlations? Ret…
Quantum computing offers financial industry new optimization and risk management tools.
Study uses ML and statistical models to analyze climate impacts of industrial growth.
Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the op…
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
Tool converts industrial systems to RL environments for optimization.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
Deep learning predicts employment changes and industry health.
Nestedness has traditionally been used to detect assembly patterns in meta-communities and networks of interacting species. Attempts have also been made to uncover nested structures in international trade, typically represented as bipartite networks in which connections can be established between countries (exporters o…
Industry lacks tools to secure ML systems, study finds.
Study shows insurance industry in North Macedonia declined 10% due to COVID-19.
Company2Vec creates embeddings from company websites for fine-grained business analytics.
How do regions acquire the knowledge they need to diversify their economic activities? How does the migration of workers among firms and industries contribute to the diffusion of that knowledge? Here we measure the industry, occupation, and location-specific knowledge carried by workers from one establishment to the ne…
Novel financial time-series data representation improves industry sector classification.
Neural model learns company embeddings from data and news.
Bayesian methods improve industrial modeling under uncertainty.
Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters, such as Hollywood or Silicon Valley, have been frequently studied, systematic approaches to identify and analyze the hierarchical structure of the geo-industrial clusters at the glob…
BayPrAnoMeta tackles few-shot industrial image anomaly detection with Bayesian methods.
Advances in the field of Machine Learning and Deep Neural Networks (DNNs) has enabled rapid development of sophisticated and autonomous systems. However, the inherent complexity to rigorously assure the safe operation of such systems hinders their real-world adoption in safety-critical domains such as aerospace and med…
This research develops a dynamic risk management system for industrial companies.
We consider the problem of pricing derivatives written on some industrial loss index via utility indifference pricing. The industrial loss index is modelled by a compound Poisson process and the insurer can adjust her portfolio by choosing the risk loading, which in turn determines the demand. We compute the price of a…