arXiv research

Paper proposes a new neural network for predicting machinery RUL.

Study uses ML and statistical models to analyze climate impacts of industrial growth.

New dataset for industrial machine sounds to aid maintenance.

Deep learning diagnoses rotary machine faults without expert input.

Paper presents a data preprocessing method for PHM models.

New method detects bearing faults using multivariate statistical process control.

Graph neural networks outperform fixed molecular descriptors in property prediction.

New method detects adversarial attacks in industrial systems.

New method improves anomaly detection in acoustic signals.

Extends graph factor system to quasi-median graphs.

Survey of tools for studying hierarchical hyperbolic spaces.

The article constructs differential operators for parabolic geometries.

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and prese…

This is the second of a series of papers which are devoted to a comprehensive theory of maps between orbifolds. In this paper, we develop a basic machinery for studying homotopy classes of such maps. It contains two parts: (1) the construction of a set of algebraic invariants -- the homotopy groups, and (2) an analog o…

Paper shows mapping class groups are not extremely amenable except for specific cases.

In this paper, we compare static and dynamic (reduced form) approaches for modeling wrong-way risk in the context of CVA. Although all these approaches potentially suffer from arbitrage problems, they are popular (respectively) in industry and academia, mainly due to analytical tractability reasons. We complete the sto…

BGNNs model particle-boundary interactions efficiently.

New method connects CAT(0) spaces to hyperbolic spaces.

We use Beltrami's theorem as an excuse to present some arguments from parabolic differential geometry without any of the parabolic machinery.

New theory proves representability of PDE solutions without complex machinery.

Abstract machinery finds obstructions to uniform positive scalar curvature.

Keywords: corporate finance, Wrocław University of Economics, net profit margin lub net sales profitability

Robust X-Learner improves HTE estimation in imbalanced and heavy-tailed data.

For a real or complex semisimple Lie group $G$ and two nested parabolic subgroups $Q\subset P\subset G$, we study parabolic geometries of type $(G,Q)$. Associated to the group $P$, we introduce a class of relative natural bundles and relative tractor bundles and construct some basic invariant differential operators on …

SupRB learns rules for continuous decision problems from examples.

We prove that the Casimir operator acting on sections of a homogeneous vector bundle over a generalized flag manifold naturally extends to an invariant differential operator on arbitrary parabolic geometries. We study some properties of the resulting invariant operators and compute their action on various special types…

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…

Graph neural networks improve equipment health monitoring from multisensor data.

Improves industry classification for diversified companies.

The study finds that the export shares of machinery and food/crude materials are significantly correlated with GDP.

Extends confining subset theory to describe hyperbolic actions of solvable groups with higher rank abelianizations.

We prove the Chern-Gauss-Bonnet Theorem using sigma models whose source supermanifolds have super dimension 0|2. Along the way we develop machinery for understanding manifold invariants encoded by families of 0|n-dimensional Euclidean field theories and their quantization.

Bayesian neural networks improve RUL estimation accuracy compared to frequentist methods.

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.

This work analyzes industrial IoT data for security using machine learning.

New online learning algorithms improve cyberattack detection in industrial control systems.

A projective geometry is an equivalence class of torsion free connections sharing the same unparametrised geodesics; this is a basic structure for understanding physical systems. Metric projective geometry is concerned with the interaction of projective and pseudo-Riemannian geometry. We show that the BGG machinery of …

Instantons on various spaces can be constructed via a generalization of the Fourier transform called the ADHM-Nahm transform. An explicit use of this construction, however, involves rather tedious calculations. Here we derive a simple formula for instantons on a space with one periodic direction. It simplifies the ADHM…

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…

We prove a version of the Tits alternative for groups acting on complete, finite rank median spaces. This shows that group actions on finite rank median spaces are much more restricted than actions on general median spaces. Along the way, we extend to median spaces the Caprace-Sageev machinery and part of Hagen's theor…

We study minimal graphs in the homogeneous Riemannian 3-manifold $\widetilde{PSL_2(\mathbb{R})}$ and we give examples of invariant surfaces. We derive a gradient estimate for solutions of the minimal surface equation in this space and develop the machinery necessary to prove a Jenkins-Serrin type theorem for solutions …

We give a global version of the Bryant representation of surfaces of constant mean curvature one (cmc-1) in hyperbolic space. This allows to set the associated non-abelian period problem in the framework of flat unitary vector bundles on Riemann surfaces. We use this machinery to prove the existence of certain cmc-1 su…

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…

Systematic prolongation for Killing two-tensors in symmetric spaces.