1,721,176 research outputs found
Large-Scale Photonic Ising Machine by Spatial Light Modulation
No full textQuantum and classical physics can be used for mathematical computations that are hard to tackle by conventional electronics. Very recently, optical Ising machines have been demonstrated for computing the minima of spin Hamiltonians, paving the way to new ultrafast hardware for machine learning. However, the proposed systems are either tricky to scale or involve a limited number of spins. We design and experimentally demonstrate a large-scale optical Ising machine based on a simple setup with a spatial light modulator. By encoding the spin variables in a binary phase modulation of the field, we show that light propagation can be tailored to minimize an Ising Hamiltonian with spin couplings set by input amplitude modulation and a feedback scheme. We realize configurations with thousands of spins that settle in the ground state in a low-temperature ferromagneticlike phase with all-to-all and tunable pairwise interactions. Our results open the route to classical and quantum photonic Ising machines that exploit light spatial degrees of freedom for parallel processing of a vast number of spins with programmable couplings
Supply chain resilience research trends: A literature overview
No full textSupply Chain Resilience has been broadly studied during the last decades, especially within the academic community. Therefore, the present research article aims to provide a broad view of the scientific literature about Resilience within Supply Chain research. First, a trend analysis of these topics research and publications is presented. Then, a more detailed analysis is shown, in order to produce bibliometric maps and tables summarizing the main scientific trends under these topics: the latter analysis will provide useful insight of the most used keywords on the subject and the connections among them
A framework proposal for research into silver labour from a resilient perspective
Full text link[EN] Currently, enterprises face changes in the labour structure, determined by demographic factors. Population ageing in Europe is a relevant issue that has to be carefully analysed to be prepared and adapt European aspects and conditions to today¿s generation. In light of this, the present paper proposes a framework as a first attempt to support enterprises in their management of the workforce age increasing average from a resilient perspective. The framework was built based on three constituent capacities of enterprise resilience: preparedness, adaptive, recovery capacities. For each one, research macro-areas and enterprise features were identified. Further research lines will focus on validating this framework and on extending its development to include specific actions.This research was supported by the Programme to support the
academic career of the faculty of the Universitat Politècnica
de València 2019/2020 as part of Project Enterprise and
Supply Chain Resilience Enhancement granted to Dr.
Raquel Sanchis, who wishes to thank Università Politecnica
delle Marche, particularly the Department of Industrial
Engineering and Mathematical Science, for its support,
during her stay, to conduct the present research. This research
was also supported partly by the Spanish Ministry of Science,
Innovation and Universities Project entitled Optimisation of
zero-defects production technologies enabling supply chains
4.0 (CADS4.0) (RTI2018-101344-B-I00).Sanchis, R.; Mula, J.; Marcucci, G.; Bevilacqua, M. (2021). A Framework Proposal for Research into Silver Labour from a Resilient Perspective. IFAC-PapersOnLine. 54(1):930-935. https://doi.org/10.1016/j.ifacol.2021.08.189S93093554
Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons
No full textWe study artificial neural networks with nonlinear waves as a computing reservoir. We discuss universality and the conditions to learn a dataset in terms of output channels and nonlinearity. A feed-forward three-layered model, with an encoding input layer, a wave layer, and a decoding readout, behaves as a conventional neural network in approximating mathematical functions, real-world datasets, and universal Boolean gates. The rank of the transmission matrix has a fundamental role in assessing the learning abilities of the wave. For a given set of training points, a threshold nonlinearity for universal interpolation exists. When considering the nonlinear Schrödinger equation, the use of highly nonlinear regimes implies that solitons, rogue, and shock waves do have a leading role in training and computing. Our results may enable the realization of novel machine learning devices by using diverse physical systems, as nonlinear optics, hydrodynamics, polaritonics, and Bose-Einstein condensates. The application of these concepts to photonics opens the way to a large class of accelerators and new computational paradigms. In complex wave systems, as multimodal fibers, integrated optical circuits, random, topological devices, and metasurfaces, nonlinear waves can be employed to perform computation and solve complex combinatorial optimization
Photonic extreme learning machine by free-space optical propagation
Full text linkPhotonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energyefficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here, we present a neuromorphic photonic scheme, i.e., the photonic extreme learning machine, which can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field acting as a feature mapping space. We experimentally demonstrate learning from data on various classification and regression tasks, achieving accuracies comparable with digital kernel machines and deep photonic networks. Our findings point out an optical machine learning device that is easy to train, energetically efficient, scalable, and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data
Knowledge Registration Module Design for Enterprise Resilience Enhancement
Full text link[EN] The present situation characterized by the coronavirus pandemic has made businesses to be aware about the importance of being resilient to face undesirable impacts like the one caused by this pandemic. One of the constituent capacities of enterprise resilience is the recovery ability to bounce back and restore the operations after disruptions¿ occurrence. This paper is focused on the recovery perspective of enterprise resilience and its enhancement through knowledge registration. This research proposes the design of the Knowledge Registration Module addressed to the register of valuable information at different knowledge level with the main aim to reuse this piece of information to facilitate the recovery process when the same or an unexpected similar disruptive event occurs. Future research lines will be based on applying the knowledge approach to real cases to study the influence of knowledge management in the enhancement of enterprise resilience.This research was supported by the Programme to support the academic career of the faculty of the Universitat Politecnica de Valencia 2019/2020 as part of Project 'Enterprise and Supply Chain Resilience Enhancement' granted to Dr. Raquel Sanchis, who wishes to thank Universita Politecnica delle Marche, particularly the Department of Industrial Engineering and Mathematical Science, for its support, during her stay, to conduct the present research.Sanchis, R.; Marcucci, G.; Alarcón Valero, F.; Poler, R. (2021). Knowledge Registration Module Design for Enterprise Resilience Enhancement. IFAC-PapersOnLine. 54(1):1029-1034. https://doi.org/10.1016/j.ifacol.2021.08.122S1029103454
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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