Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter

Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations are a core constituent of modern cosmology, while the required computations are both expensive and time-consuming. At the same time, the comparatively fast simulation of dark matter requires fewer resources, which has led to the emergence of machine learning algorithms for baryon inpainting as an active area of research; here, recreating the scatter found in hydrodynamic simulations is an ongoing challenge. This paper presents the first application of physics-informed neural networks to baryon inpainting by combining advances in neural network architectures with physical constraints, injecting theory on baryon conversion efficiency into the model loss function. We also introduce a punitive prediction comparison based on the Kullback-Leibler divergence, which enforces scatter reproduction. By simultaneously extracting the complete set of baryonic properties for the Simba suite of cosmological simulations, our results demonstrate improved accuracy of baryonic predictions based on dark matter halo properties, successful recovery of the fundamental metallicity relation, and retrieve scatter that traces the target simulation's distribution

Orchestration of IT/Cloud and Networks: From Inter-DC Interconnection to SDN/NFV 5G Services

The so-called 5G networks promise to be the foundations for the deployment of advanced services, conceived around the joint allocation and use of heterogeneous resources,including network, computing and storage. Resources are placed on remote locations constrained by the different service requirements, resulting in cloud infrastructures (as pool of resources) that need to be interconnected. The automation of the provisioning of such services relies on a generalized orchestra tion, defined as to the coherent coordination of heterogeneous systems, applied to common cases such as involving heterogeneous network domains in terms of control or data plane technologies, or cloud and network resources. Although cloud-computing platforms do take into account the need to interconnect remote virtual machine instances, mostly rely on managing L2 overlays over L3 (IP). The integration with transport networks is still not fully achieved, including leveraging the advances in software defined networks and transmission. We start with an overview of network orchestration, considering different models; we extend them to take into account cloud manage ment while mentioning relevant existing initiatives and conclude with the NFV architecture

Adaptation and coordinated evolution of plant hydraulic traits

Altres ajuts: ICREA Academia awardHydraulic properties control plant responses to climate and are likely to be under strong selective pressure, but their macro-evolutionary history remains poorly characterised. To fill this gap, we compiled a global dataset of hydraulic traits describing xylem conductivity (Ks), xylem resistance to embolism (P50), sapwood allocation relative to leaf area (Hv) and drought exposure (ψmin), and matched it with global seed plant phylogenies. Individually, these traits present medium to high levels of phylogenetic signal, partly related to environmental selective pressures shaping lineage evolution. Most of these traits evolved independently of each other, being co-selected by the same environmental pressures. However, the evolutionary correlations between P50 and ψmin and between Ks and Hv show signs of deeper evolutionary integration because of functional, developmental or genetic constraints, conforming to evolutionary modules. We do not detect evolutionary integration between conductivity and resistance to embolism, rejecting a hardwired trade-off for this pair of traits

Experimental SDN Control Solutions for Automatic Operations and Management of 5G Services in a Fixed Mobile Converged Packet-Optical Network

5G networks will impose network operators to accommodate services demanding heterogeneous and stringent requirements in terms of increased bandwidth, reduced latency, higher availability, etc. as well as enabling emerging capabilities such as slicing. Operators will be then forced to make notable investments in their infrastructure but the revenue is not envisaged to be proportional. Thereby, operators are seeking for more cost-effective solutions to keep their competitiveness. An appealing solution is to integrate all (broadband) services including both fixed and mobile in a convergent way. This is referred to as Fixed Mobile Convergence (FMC). FMC allows seamlessly serving any kind of access service over the same network infrastructure (access, aggregation and core) and relying on common set of control and operation functions. To this end, FMC leverages the benefits provided by Software Defined Networking (SDN) and Network Function Virtualization (NFV). First, we discuss some of the explored FMC solutions and technologies, from both structural and functional perspectives Next, focusing on a Multi-Layer (Packet and Optical) Aggregation Network, we report two implemented and experimentally validated SDN/NFV orchestration architectures providing feasibleThis work has been partially funded by the Spanish Ministry MINECO projects DESTELLO (TEC2015-69256-R) and 5G-REFINE (TEC2017-88373-R), and the EU H2020 5G TRANSFORMER project (grant no. 761536)

Integrating Industry 4.0 in Higher Education Using Challenge-Based Learning: An Intervention in Operations Management

Industry 4.0 is predicted to significantly transform the jobs and skill profiles of workers. Implications for higher education may involve dramatic changes in the demand for knowledge and skills. In response to this, a Challenge-Based Learning (CBL) intervention was designed with the aim of developing working skills for the future of work on undergraduate students by embedding the Industry 4.0 theme in the Operations Management curricula. The CBL intervention was implemented in two different academic terms at a UK university, and views from 302 undergraduate business students were captured using document analysis. The benefits are reported in terms of knowledge acquisition and the application and development of key desirable working abilities for the future. The results suggest that CBL increases students’ understanding of Industry 4.0 issues in real-life settings. It also provides an environment for soft-skills training for skills, including collaboration, communication, planning a problem-solving. This study provides a blueprint for the implementation of CBL in the Operations Management curricula. The study validates existing findings obtained from the application of CBL in other disciplines. Whilst the proposed CBL intervention might be easily replicated in business schools in the UK, the findings on students’ experiences might not be directly generalized to other contexts or disciplines