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Efficient quantum amplitude encoding of polynomial functions
Get PDFPublisher Copyright: © 2024 Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften. All rights reserved.Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency of this process leads to a major bottleneck for the application of these algorithms. Here, we present and compare two efficient methods for the amplitude encoding of real polynomial functions on n qubits. This case holds special relevance, as any continuous function on a closed interval can be uniformly approximated with arbitrary precision by a polynomial function. The first approach relies on the matrix product state representation (MPS). We study and benchmark the approximations of the target state when the bond dimension is assumed to be small. The second algorithm combines two subroutines. Initially we encode the linear function into the quantum registers either via its MPS or with a shallow sequence of multi-controlled gates that loads the linear function’s Hadamard-Walsh series, and we explore how truncating the Hadamard-Walsh series of the linear function affects the final fidelity. Applying the inverse discrete Hadamard-Walsh transform converts the state encoding the series coefficients into an amplitude encoding of the linear function. Thus, we use this construction as a building block to achieve an exact block encoding of the amplitudes corresponding to the linear function on k0 qubits and apply the quantum singular value transformation that implements a polynomial transformation to the block encoding of the amplitudes. This unitary together with the Amplitude Amplification algorithm will enable us to prepare the quantum state that encodes the polynomial function on k0 qubits. Finally we pad n − k0 qubits to generate an approximated encoding of the polynomial on n qubits, analyzing the error depending on k0. In this regard, our methodology proposes a method to improve the state-of-the-art complexity by introducing controllable errors.Peer reviewe
Erratum: General existence and determination of conjugate fields in dynamically ordered magnetic systems [Phys. Rev. E 104, 044125 (2021)
No full textThis corrects the article DOI: 10.1103/PhysRevE.104.044125
The different roles of innovation intermediaries to generate value
No full textPublisher Copyright: © Crown 2023.The development of innovation management practices toward openness, societalgrand challenges and emerging technologies have changed the roles and supportingactivities of innovation intermediaries. Innovation intermediaries are considered to beorganizations that generate value to other institutions or societies within an innovationsystem. Despite the growth of innovation intermediary research in recent years, thereis still a lack of clarity about the different roles that intermediaries can play and the wayin which they generate value to the other institutions, industry and/or society. Thispaper reviews current research to identify contemporary roles of innovationintermediaries and explore the mechanisms they use to generate value. Through theuse of bibliographic coupling the paper presents a robust analysis of the intellectualstreams and key concepts underpinning innovation intermediary research. The papermakes a contribution to the ongoing debate by proposing a framework that explains thedifferent roles of innovation intermediaries (knowledge broker, knowledge transferenabler, orchestrator, and value generator) and the functions embedded within theroles at different levels of analysis, i.e., firm, industry, and national. The paperconcludes by discussing the theoretical and practical implications of the framework anddetails key areas for future research.Peer reviewe
Source apportionment of ultrafine particles in urban Europe
No full textPublisher Copyright: © 2024 The Author(s)There is a body of evidence that ultrafine particles (UFP, those with diameters ≤ 100 nm) might have significant impacts on health. Accordingly, identifying sources of UFP is essential to develop abatement policies. This study focuses on urban Europe, and aims at identifying sources and quantifying their contributions to particle number size distribution (PNSD) using receptor modelling (Positive Matrix Factorization, PMF), and evaluating long-term trends of these source contributions using the non-parametric Theil-Sen's method. Datasets evaluated include 14 urban background (UB), 5 traffic (TR), 4 suburban background (SUB), and 1 regional background (RB) sites, covering 18 European and 1 USA cities, over the period, when available, from 2009 to 2019. Ten factors were identified (4 road traffic factors, photonucleation, urban background, domestic heating, 2 regional factors and long-distance transport), with road traffic being the primary contributor at all UB and TR sites (56–95 %), and photonucleation being also significant in many cities. The trends analyses showed a notable decrease in traffic-related UFP ambient concentrations, with statistically significant decreasing trends for the total traffic-related factors of −5.40 and −2.15 % yr−1 for the TR and UB sites, respectively. This abatement is most probably due to the implementation of European emissions standards, particularly after the introduction of diesel particle filters (DPFs) in 2011. However, DPFs do not retain nucleated particles generated during the dilution of diesel exhaust semi-volatile organic compounds (SVOCs). Trends in photonucleation were more diverse, influenced by a reduction in the condensation sink potential facilitating new particle formation (NPF) or by a decrease in the emissions of UFP precursors. The decrease of primary PM emissions and precursors of UFP also contributed to the reduction of urban and regional background sources.Peer reviewe
Fracture characterization of structural steel S690Q by using mini-CT specimens
No full textPublisher Copyright: © Published under licence by IOP Publishing Ltd.Mini-CT specimens are an interesting alternative when characterising the fracture behaviour of structural materials and there are issues with regard to, for example, the amount of available material, the irradiation level (in nuclear materials) or material inhomogeneities. Furthermore, in ferritic-pearlitic steels, the characterisation of the fracture behaviour within the ductile-to-brittle transition zone (DBTZ) is of particular interest, given that the material may behave very different, in terms of fracture toughness, when operating at different but relatively close temperatures within this zone. In many occasions, the definition of the DBTZ behaviour is performed through the Master Curve (MC) methodology and, thus, by testing standardised fracture specimens (e.g., CT, SENB) and determining the material Reference Temperature (T0). The use of mini-CT specimens to define T0 has been validated in a wide range of steels used in nuclear industry, but its application to structural steels has been scarce. Thus, this work gathers the fracture characterisation results (T0) obtained in structural steel S690Q, comparing them to those obtained by using conventional standardized SENB specimens. It is shown that, for this particular structural steel, the use of miniaturized specimens provides a T0 value (-89.3°C) which is comparable to the value obtained from conventional larger specimens (-110°C).Peer reviewe
Corrosion resistance of electroplated coatings based on chromium trivalent-baths
No full textPublisher Copyright: © 2024 Elsevier B.V.Cr-C coatings were obtained from two different Cr(III)-baths based on sulphate and chloride salts, respectively. A tailored characterization was carried out by scanning electron microscopy, several spectroscopies and Mott-Schottky approaches (including Point Defect Model) to find out the impact in the corrosion performance of the morphology, composition, and semiconducting properties of the native oxide layers. The polarisation curves using 0.01 M NaCl have shown better corrosion resistance capabilities for the chloride-based Cr(III) coatings which is in agreement with the lower crystal size and the less defective native oxide layer (using a borate buffer electrolyte).Peer reviewe
Circularity potential of building products: Material Flow Analysis of façade building components
No full textPublisher Copyright: © Copyright by Fundacja PLEA 2024 Conference, Wrocław 2024.The construction industry, accounting for a significant portion of energy consumption and greenhouse gas emissions, is pivotal in achieving Europe's ambitious climate neutrality goal by 2050. Circular Economy (CE) principles and the renovation of existing buildings are identified as promising strategies to reduce raw material and energy consumption. However, the lack of knowledge and guidelines for effective CE design and construction in the built environment, along with heterogeneous metrics and standards, pose challenges. The research outlines an investigation study aimed at developing Key Performance Indicators (KPIs) for evaluating building products based on CE principles, focusing on façade renovation. The study emphasizes the need for a holistic approach, considering both material input and output flows, and introduces qualitative and quantitative KPIs addressing aspects such as recyclability, modularity, and local materials. The research proposes established frameworks like Life Cycle Assessment (LCA), Material Flow Analysis (MFA), and the Level(s) framework, but recognizes their limitations in assessing circularity comprehensively. The methodology involves a comprehensive analysis of material streams and circularity potential for nine components crucial to achieving a net-zero façade renovation. Results from the material flow analysis demonstrate the environmental impact of selected building products, such as insulation panels and photovoltaic panels. The research underscores the importance of informed design choices, leveraging adaptable KPIs, and visualizing resource flows to enhance decision-making for sustainable construction practices aligned with CE principles.Peer reviewe
The Centerline-Cross Entropy Loss for Vessel-Like Structure Segmentation: Better Topology Consistency Without Sacrificing Accuracy
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Achieving accurate vessel segmentation in medical images is crucial for various clinical applications, but current methods often struggle to balance topological consistency (preserving vessel network structure) with segmentation accuracy (overlap with ground-truth). Although various strategies have been proposed to address this challenge, they typically necessitate significant modifications to network architecture, more annotations, or entail prohibitive computational costs, providing only partial topological improvements. The clDice loss was recently proposed as an elegant and efficient alternative to preserve topology in tubular structure segmentation. However, segmentation accuracy is penalized and it lacks robustness to noisy annotations, mirroring the limitations of the conventional Dice loss. This work introduces the centerline-Cross Entropy (clCE) loss function, a novel approach which capitalizes on the robustness of Cross-Entropy loss and the topological focus of centerline-Dice loss, promoting optimal vessel overlap while maintaining faithful network structure. Extensive evaluations on diverse publicly available datasets (2D/3D, retinal/coronary) demonstrate clCE’s effectiveness. Compared to existing losses, clCE achieves superior overlap with ground truth while simultaneously improving vascular connectivity. This paves the way for more accurate and clinically relevant vessel segmentation, particularly in complex 3D scenarios. We share an implementation of the clCE loss function in github.com/cesaracebes/centerline_CE.Peer reviewe
A data-driven computational methodology towards a pre-hospital Acute Ischaemic Stroke screening tool using haemodynamics waveforms
No full textPublisher Copyright: © 2023 The Author(s)Background and Objective: Acute Ischaemic Stroke (AIS), a significant global health concern, results from occlusions in cerebral arteries, causing irreversible brain damage. Different type of treatments exist depending on the size and location of the occlusion. Challenges persist in achieving faster diagnosis and treatment, which needs to happen in the first hours after the onset of symptoms to maximize the chances of patient recovery. The current diagnostic pipeline, i.e. “drip and ship”, involves diagnostic via advanced imaging tools, only available in large clinical facilities, which poses important delays. This study investigates the feasibility of developing a machine learning model to diagnose and locate occluding blood clots from velocity waveforms, which can be easily be obtained with portable devices such as Doppler Ultrasound. The goal is to explore this approach as a cost-effective and time-efficient alternative to advanced imaging techniques typically available only in large hospitals. Methods: Simulated haemodynamic data is used to conduct blood flow simulations representing healthy and different AIS scenarios using a population-based database. A Machine Learning classification model is trained to solve the inverse problem, this is, detect and locate a potentially occluding thrombus from measured waveforms. The classification process involves two steps. First, the region where the thrombus is located is classified into nine groups, including healthy, left or right large vessel occlusion, left or right anterior cerebral artery, and left or right posterior cerebral artery. In a second step, the bifurcation generation of the thrombus location is classified as small, medium, or large vessel occlusion. Results: The proposed methodology is evaluated for data without noise, achieving a true prediction rate exceeding 95% for both classification steps mentioned above. The inclusion of up to 20% noise reduces the true prediction rate to 80% for region detection and 70% for bifurcation generation detection. Conclusions: This study demonstrates the potential effectiveness and efficiency of using haemodynamic data and machine learning to detect and locate occluding thrombi in AIS patients. Although the geometric and topological data used in this study are idealized, the results suggest that this approach could be applicable in real-world situations with appropriate adjustments. Source code is available in https://github.com/ahmetsenemse/Acute-Ischaemic-Stroke-screening-tool-.This work has been partially funded by a María Zambrano research fellowship at Universitat Politècnica de Catalunya, granted by Ministerio de Universidades to MA and by a project funded by Ministerio de Ciencia e Innovación (Grant PID2022-136668OA-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”).Peer reviewe
Probabilistic feature selection for improved asset lifetime estimation in renewables. Application to transformers in photovoltaic power plants
No full textPublisher Copyright: © 2024 Elsevier LtdThe increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection.Peer reviewe