FCA modelling for CPS interoperability optimization in Industry 4.0

International audienceCyber-Physical Systems (CPS) lead to the 4-th Industrial Revolution (Industry 4.0) that will have benefits of high flexibility of production, easy and so more accessible participation of all involved parties of business processes. The Industry 4.0 production paradigm is characterized by autonomous behaviour and intercommunicating properties of its production elements across all levels of manufacturing processes so one of the key concept in this domain will be the semantic interoperability of systems. This goal can benefit of formal methods well known various scientific domains like artificial intelligence, machine learning and algebra. So the current investigation is on the promising approach named Formal Concept Analysis (FCA) to structure the knowledge and to optimize the CPS interoperability

Super-Schur Polynomials for Affine Super Yangian Y(gl^1∣1)\mathsf{Y}(\widehat{\mathfrak{gl}}_{1|1})

We explicitly construct cut-and-join operators and their eigenfunctions -- the Super-Schur functions -- for the case of the affine super-Yangian $\mathsf{Y}(\widehat{\mathfrak{gl}}_{1|1})$. This is the simplest non-trivial (semi-Fock) representation, where eigenfunctions are labeled by the superanalogue of 2d Young diagrams, and depend on the supertime variables $(p_k,\theta_k)$. The action of other generators on diagrams is described by the analogue of the Pieri rule. As well we present generalizations of the hook formula for the measure on super-Young diagrams and of the Cauchy formula. Also a discussion of string theory origins for these relations is provided.Comment: 27 pages, 3 figure

Optical properties of refractory metal based thin films

There is a growing interest in refractory metal thin films for a range of emerging nanophotonic applications including high temperature plasmonic structures and infrared superconducting single photon detectors. We present a detailed comparison of optical properties for key representative materials in this class (NbN, NbTiN, TiN and MoSi) with texture varying from crystalline to amorphous. NbN, NbTiN and MoSi have been grown in an ultra-high vacuum sputter deposition system. Two different techniques (sputtering and atomic layer deposition) have been employed to deposit TiN. We have carried out variable angle ellipsometric measurements of optical properties from ultraviolet to mid infrared wavelengths. We compare with high resolution transmission electron microscopy analysis of microstructure. Sputter deposited TiN and MoSi have shown the highest optical absorption in the infrared wavelengths relative to NbN, NbTiN or ALD deposited TiN. We have also modelled the performance of a semi-infinite metal air interface as a plasmonic structure with the above mentioned refractory metal based thin films as the plasmonic components. This study has implications in the design of next generation superconducting nanowire single photon detector or plasmonic nanostructure based devices

Generative Flow Networks as Entropy-Regularized RL

The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between RL and GFlowNets to a general case. We demonstrate how the task of learning a generative flow network can be efficiently redefined as an entropy-regularized RL problem with a specific reward and regularizer structure. Furthermore, we illustrate the practical efficiency of this reformulation by applying standard soft RL algorithms to GFlowNet training across several probabilistic modeling tasks. Contrary to previously reported results, we show that entropic RL approaches can be competitive against established GFlowNet training methods. This perspective opens a direct path for integrating reinforcement learning principles into the realm of generative flow networks