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1. Higher-order connection Laplacians for directed simplicial complexes

Author

Xue Gong, Desmond J Higham, Konstantinos Zygalakis, and Ginestra Bianconi

Subjects
connection Laplacian, simplicial complex, higher-order diffusion, Hodge Laplacian, magnetic Laplacian, Science, Physics, QC1-999
Abstract

Higher-order networks encode the many-body interactions existing in complex systems, such as the brain, protein complexes, and social interactions. Simplicial complexes are higher-order networks that allow a comprehensive investigation of the interplay between topology and dynamics. However, simplicial complexes have the limitation that they only capture undirected higher-order interactions while in real-world scenarios, often there is a need to introduce the direction of simplices, extending the popular notion of direction of edges. On graphs and networks the Magnetic Laplacian, a special case of connection Laplacian, is becoming a popular operator to address edge directionality. Here we tackle the challenge of handling directionality in simplicial complexes by formulating higher-order connection Laplacians taking into account the configurations induced by the simplices’ directions. Specifically, we define all the connection Laplacians of directed simplicial complexes of dimension two and we discuss the induced higher-order diffusion dynamics by considering instructive synthetic examples of simplicial complexes. The proposed higher-order diffusion processes can be adopted in real scenarios when we want to consider higher-order diffusion displaying non-trivial frustration effects due to conflicting directionalities of the incident simplices.

Published
2024
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3. Zoo guide to network embedding

Author

A Baptista, R J Sánchez-García, A Baudot, and G Bianconi

Subjects
network embedding, representation learning, network analysis, higher-order network, Science, Physics, QC1-999
Abstract

Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted great interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link prediction, node classification, and community detection. In this review, we provide a user-friendly guide to the network embedding literature and current trends in this field which will allow the reader to navigate through the complex landscape of methods and approaches emerging from the vibrant research activity on these subjects.

Published
2023
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4. Dirac signal processing of higher-order topological signals

Author

Lucille Calmon, Michael T Schaub, and Ginestra Bianconi

Subjects
higher-order networks, discrete Dirac operator, topological signals, signal processing, Science, Physics, QC1-999
Abstract

Higher-order networks can sustain topological signals which are variables associated not only to the nodes, but also to the links, to the triangles and in general to the higher dimensional simplices of simplicial complexes. These topological signals can describe a large variety of real systems including currents in the ocean, synaptic currents between neurons and biological transportation networks. In real scenarios topological signal data might be noisy and an important task is to process these signals by improving their signal to noise ratio. So far topological signals are typically processed independently of each other. For instance, node signals are processed independently of link signals, and algorithms that can enforce a consistent processing of topological signals across different dimensions are largely lacking. Here we propose Dirac signal processing, an adaptive, unsupervised signal processing algorithm that learns to jointly filter topological signals supported on nodes, links and triangles of simplicial complexes in a consistent way. The proposed Dirac signal processing algorithm is formulated in terms of the discrete Dirac operator which can be interpreted as ‘square root’ of a higher-order Hodge Laplacian. We discuss in detail the properties of the Dirac operator including its spectrum and the chirality of its eigenvectors and we adopt this operator to formulate Dirac signal processing that can filter noisy signals defined on nodes, links and triangles of simplicial complexes. We test our algorithms on noisy synthetic data and noisy data of drifters in the ocean and find that the algorithm can learn to efficiently reconstruct the true signals outperforming algorithms based exclusively on the Hodge Laplacian.

Published
2023
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5. Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics

Author

Ginestra Bianconi, Alex Arenas, Jacob Biamonte, Lincoln D Carr, Byungnam Kahng, Janos Kertesz, Jürgen Kurths, Linyuan Lü, Cristina Masoller, Adilson E Motter, Matjaž Perc, Filippo Radicchi, Ramakrishna Ramaswamy, Francisco A Rodrigues, Marta Sales-Pardo, Maxi San Miguel, Stefan Thurner, and Taha Yasseri

Subjects
complex systems, 2021 Nobel Prize in Physics, emergent phenomena, Science, Physics, QC1-999
Abstract

The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems.

Published
2023
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6. Complex quantum networks: a topical review.

Author

Nokkala, Johannes, Piilo, Jyrki, and Bianconi, Ginestra

Subjects
QUANTUM theory, ALGORITHMS (Physics), QUANTUM communication, QUANTUM cryptography, PHASE diagrams, TELECOMMUNICATION systems, BOOSTING algorithms
Abstract

These are exciting times for quantum physics as new quantum technologies are expected to soon transform computing at an unprecedented level. Simultaneously network science is flourishing proving an ideal mathematical and computational framework to capture the complexity of large interacting systems. Here we provide a comprehensive and timely review of the rising field of complex quantum networks. On one side, this subject is key to harness the potential of complex networks in order to provide design principles to boost and enhance quantum algorithms and quantum technologies. On the other side this subject can provide a new generation of quantum algorithms to infer significant complex network properties. The field features fundamental research questions as diverse as designing networks to shape Hamiltonians and their corresponding phase diagram, taming the complexity of many-body quantum systems with network theory, revealing how quantum physics and quantum algorithms can predict novel network properties and phase transitions, and studying the interplay between architecture, topology and performance in quantum communication networks. Our review covers all of these multifaceted aspects in a self-contained presentation aimed both at network-curious quantum physicists and at quantum-curious network theorists. We provide a framework that unifies the field of quantum complex networks along four main research lines: network-generalized, quantum-applied, quantum-generalized and quantum-enhanced. Finally we draw attention to the connections between these research lines, which can lead to new opportunities and new discoveries at the interface between quantum physics and network science. [ABSTRACT FROM AUTHOR]

Published
2024
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10. The mass of simple and higher-order networks.

Author

Bianconi, Ginestra

Subjects
*TOPOLOGICAL fields, *DIRAC operators, *NAMBU-Jona-Lasinio model, *BETTI numbers, *DIFFERENCE equations, *RANDOM graphs
Abstract

We propose a theoretical framework that explains how the mass of simple and higher-order networks emerges from their topology and geometry. We use the discrete topological Dirac operator to define an action for a massless self-interacting topological Dirac field inspired by the Nambu–Jona-Lasinio model. The mass of the network is strictly speaking the mass of this topological Dirac field defined on the network; it results from the chiral symmetry breaking of the model and satisfies a self-consistent gap equation. Interestingly, it is shown that the mass of a network depends on its spectral properties, topology, and geometry. Due to the breaking of the matter–antimatter symmetry observed for the harmonic modes of the discrete topological Dirac operator, two possible definitions of the network mass can be given. For both possible definitions, the mass of the network comes from a gap equation with the difference among the two definitions encoded in the value of the bare mass. Indeed, the bare mass can be determined either by the Betti number β 0 or by the Betti number β 1 of the network. We provide numerical results on the mass of different networks, including random graphs, scale-free, and real weighted collaboration networks. We also discuss the generalization of these results to higher-order networks, defining the mass of simplicial complexes. The observed dependence of the mass of the considered topological Dirac field with the topology and geometry of the network could lead to interesting physics in the scenario in which the considered Dirac field is coupled with a dynamical evolution of the underlying network structure. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics

Author

Royal Society (UK), European Commission, Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), National Science Foundation (US), Slovenian Research Agency, Generalitat de Catalunya, Air Force Office of Scientific Research (US), Bianconi, Ginestra, Arenas, Alex, Biamonte, Jacob, Carr, Lincoln D., Kahng, Byungnam, Kertesz, Janos, Kurths, Jürgen, Lü, Linyuan, Masoller, Cristina, Motter, Adilson E., Perc, Matjaž, Radicchi, Filippo, Ramaswamy, Ramakrishna, Rodrigues, Francisco A., Sales-Pardo, Marta, San Miguel, Maxi, Thurner, Stefan, Yasseri, Taha, Royal Society (UK), European Commission, Ministerio de Ciencia e Innovación (España), Agencia Estatal de Investigación (España), National Science Foundation (US), Slovenian Research Agency, Generalitat de Catalunya, Air Force Office of Scientific Research (US), Bianconi, Ginestra, Arenas, Alex, Biamonte, Jacob, Carr, Lincoln D., Kahng, Byungnam, Kertesz, Janos, Kurths, Jürgen, Lü, Linyuan, Masoller, Cristina, Motter, Adilson E., Perc, Matjaž, Radicchi, Filippo, Ramaswamy, Ramakrishna, Rodrigues, Francisco A., Sales-Pardo, Marta, San Miguel, Maxi, Thurner, Stefan, and Yasseri, Taha

Abstract

The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems.

Published
2023

13. Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics

Author

Bianconi, Ginestra, primary, Arenas, Alex, additional, Biamonte, Jacob, additional, Carr, Lincoln D, additional, Kahng, Byungnam, additional, Kertesz, Janos, additional, Kurths, Jürgen, additional, Lü, Linyuan, additional, Masoller, Cristina, additional, Motter, Adilson E, additional, Perc, Matjaž, additional, Radicchi, Filippo, additional, Ramaswamy, Ramakrishna, additional, Rodrigues, Francisco A, additional, Sales-Pardo, Marta, additional, San Miguel, Maxi, additional, Thurner, Stefan, additional, and Yasseri, Taha, additional

Published
2023
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15. Technical design report for the endcap disc DIRC *

Author

Davì, F, primary, Erni, W, additional, Krusche, B, additional, Steinacher, M, additional, Walford, N, additional, Liu, H, additional, Liu, Z, additional, Liu, B, additional, Shen, X, additional, Wang, C, additional, Zhao, J, additional, Albrecht, M, additional, Erlen, T, additional, Feldbauer, F, additional, Fink, M, additional, Freudenreich, V, additional, Fritsch, M, additional, Heinsius, F H, additional, Held, T, additional, Holtmann, T, additional, Keshk, I, additional, Koch, H, additional, Kopf, B, additional, Kuhlmann, M, additional, Kümmel, M, additional, Leiber, S, additional, Musiol, P, additional, Mustafa, A, additional, Pelizäus, M, additional, Pitka, A, additional, Reicherz, G, additional, Richter, M, additional, Schnier, C, additional, Schröder, T, additional, Sersin, S, additional, Sohl, L, additional, Sowa, C, additional, Steinke, M, additional, Triffterer, T, additional, Wiedner, U, additional, Beck, R, additional, Hammann, C, additional, Hartmann, J, additional, Ketzer, B, additional, Kube, M, additional, Rossbach, M, additional, Schmidt, C, additional, Schmitz, R, additional, Thoma, U, additional, Urban, M, additional, Bianconi, A, additional, Bragadireanu, M, additional, Pantea, D, additional, Czyzycki, W, additional, Domagala, M, additional, Filo, G, additional, Jaworowski, J, additional, Krawczyk, M, additional, Lisowski, E, additional, Lisowski, F, additional, Michałk, M, additional, Płażek, J, additional, Korcyl, K, additional, Kozela, A, additional, Kulessa, P, additional, Lebiedowicz, P, additional, Pysz, K, additional, Schäfer, W, additional, Szczurek, A, additional, Fiutowski, T, additional, Idzik, M, additional, Mindur, B, additional, Swientek, K, additional, Biernat, J, additional, Kamys, B, additional, Kistryn, S, additional, Korcyl, G, additional, Krzemien, W, additional, Magiera, A, additional, Moskal, P, additional, Przygoda, W, additional, Rudy, Z, additional, Salabura, P, additional, Smyrski, J, additional, Strzempek, P, additional, Wronska, A, additional, Augustin, I, additional, Böhm, R, additional, Lehmann, I, additional, Nicmorus Marinescu, D, additional, Schmitt, L, additional, Varentsov, V, additional, Al-Turany, M, additional, Belias, A, additional, Deppe, H, additional, Divani Veis, N, additional, Dzhygadlo, R, additional, Flemming, H, additional, Gerhardt, A, additional, Götzen, K, additional, Karabowicz, R, additional, Kurilla, U, additional, Lehmann, D, additional, Löchner, S, additional, Lühning, J, additional, Lynen, U, additional, Nakhoul, S, additional, Orth, H, additional, Peters, K, additional, Saito, T, additional, Schepers, G, additional, Schmidt, C J, additional, Schwarz, C, additional, Schwiening, J, additional, Täschner, A, additional, Traxler, M, additional, Voss, B, additional, Wieczorek, P, additional, Wilms, A, additional, Abazov, V, additional, Alexeev, G, additional, Arefiev, V A, additional, Astakhov, V, additional, Barabanov, M Yu, additional, Batyunya, B V, additional, Dodokhov, V Kh, additional, Efremov, A, additional, Fechtchenko, A, additional, Galoyan, A, additional, Golovanov, G, additional, Koshurnikov, E K, additional, Lobanov, Y Yu, additional, Lobanov, V I, additional, Malyshev, V, additional, Olshevskiy, A G, additional, Piskun, A A, additional, Samartsev, A, additional, Sapozhnikov, M G, additional, Skachkov, N B, additional, Skachkova, A N, additional, Strokovsky, E A, additional, Tokmenin, V, additional, Uzhinsky, V, additional, Verkheev, A, additional, Vodopianov, A, additional, Zhuravlev, N I, additional, Zinchenko, A, additional, Branford, D, additional, Glazier, D, additional, Watts, D, additional, Böhm, M, additional, Eyrich, W, additional, Lehmann, A, additional, Miehling, D, additional, Pfaffinger, M, additional, Stelter, S, additional, Uhlig, F, additional, Dobbs, S, additional, Seth, K, additional, Tomaradze, A, additional, Xiao, T, additional, Bettoni, D, additional, Ali, A, additional, Hamdi, A, additional, Krebs, M, additional, Nerling, F, additional, Akishina, V, additional, Gorbunov, S, additional, Kisel, I, additional, Kozlov, G, additional, Pugach, M, additional, Zyzak, M, additional, Bianchi, N, additional, Gianotti, P, additional, Guaraldo, C, additional, Lucherini, V, additional, Bracco, G, additional, Bodenschatz, S, additional, Brinkmann, K T, additional, Di Pietro, V, additional, Diehl, S, additional, Dormenev, V, additional, Düren, M, additional, Etzelmüller, E, additional, Föhl, K, additional, Galuska, M, additional, Geßler, T, additional, Gutz, E, additional, Hahn, C, additional, Hayrapetyan, A, additional, Kesselkaul, M, additional, Kühn, W, additional, Kuske, T, additional, Lange, J S, additional, Liang, Y, additional, Merle, O, additional, Metag, V, additional, Moritz, M, additional, Nanova, M, additional, Novotny, R, additional, Quagli, T, additional, Riccardi, A, additional, Rieke, J, additional, Schmidt, M, additional, Schnell, R, additional, Stenzel, H, additional, Strickert, M, additional, Thöring, U, additional, Wasem, T, additional, Wohlfahrt, B, additional, Zaunick, H G, additional, Tomasi-Gustafsson, E, additional, Ireland, D, additional, Rosner, G, additional, Seitz, B, additional, Deepak, P N, additional, Kulkarni, A, additional, Apostolou, A, additional, Babai, M, additional, Kavatsyuk, M, additional, Loehner, H, additional, Messchendorp, J, additional, Schakel, P, additional, Tiemens, M, additional, van der Weele, J C, additional, Vejdani, S, additional, Dutta, K, additional, Kalita, K, additional, Sohlbach, H, additional, Bai, M, additional, Bianchi, L, additional, Büscher, M, additional, Derichs, A, additional, Dosdall, R, additional, Erven, A, additional, Fracassi, V, additional, Gillitzer, A, additional, Goldenbaum, F, additional, Grunwald, D, additional, Jokhovets, L, additional, Kemmerling, G, additional, Kleines, H, additional, Lai, A, additional, Lehrach, A, additional, Mikirtychyants, M, additional, Orfanitski, S, additional, Prasuhn, D, additional, Prencipe, E, additional, Pütz, J, additional, Ritman, J, additional, Rosenthal, E, additional, Schadmand, S, additional, Sefzick, T, additional, Serdyuk, V, additional, Sterzenbach, G, additional, Stockmanns, T, additional, Wintz, P, additional, Wüstner, P, additional, Xu, H, additional, Zhou, Y, additional, Li, Z, additional, Ma, X, additional, Rigato, V, additional, Isaksson, L, additional, Achenbach, P, additional, Aycock, A, additional, Corell, O, additional, Denig, A, additional, Distler, M, additional, Hoek, M, additional, Lauth, W, additional, Merkel, H, additional, Müller, U, additional, Pochodzalla, J, additional, Sanchez, S, additional, Schlimme, S, additional, Sfienti, C, additional, Thiel, M, additional, Zambrana, M, additional, Ahmadi, H, additional, Ahmed, S, additional, Bleser, S, additional, Capozza, L, additional, Cardinali, M, additional, Dbeyssi, A, additional, Ehret, A, additional, Fröhlich, B, additional, Grasemann, P, additional, Haasler, S, additional, Izard, D, additional, Jorge, J, additional, Khaneft, D, additional, Klasen, R, additional, Kliemt, R, additional, Köhler, J, additional, Leithoff, H H, additional, Lin, D, additional, Maas, F, additional, Maldaner, S, additional, Michel, M, additional, Mora Espí, M C, additional, Morales Morales, C, additional, Motzko, C, additional, Noll, O, additional, Pflüger, S, additional, Rodríguez Piñeiro, D, additional, Steinen, M, additional, Walaa, E, additional, Wolff, S, additional, Zimmermann, I, additional, Fedorov, A, additional, Korzhik, M, additional, Missevitch, O, additional, Balanutsa, P, additional, Chernetsky, V, additional, Demekhin, A, additional, Dolgolenko, A, additional, Fedorets, P, additional, Gerasimov, A, additional, Goryachev, V, additional, Kirin, D Y, additional, Matveev, V A, additional, Stavinskiy, A V, additional, Balashoff, A, additional, Boukharov, A, additional, Malyshev, O, additional, Marishev, I, additional, Chandratre, V, additional, Datar, V, additional, Jha, V, additional, Kumawat, H, additional, Mohanty, A K, additional, Parmar, A, additional, Rai, A K, additional, Roy, B, additional, Sonika, G, additional, Fritzsch, C, additional, Grieser, S, additional, Hergemöller, A K, additional, Hetz, B, additional, Hüsken, N, additional, Khoukaz, A, additional, Wessels, J P, additional, Herold, C, additional, Khosonthongkee, K, additional, Kobdaj, C, additional, Limphirat, A, additional, Srisawad, P, additional, Yan, Y, additional, Blinov, A E, additional, Kononov, S, additional, Kravchenko, E A, additional, Antokhin, E, additional, Barnyakov, M, additional, Barnyakov, A Yu, additional, Beloborodov, K, additional, Blinov, V E, additional, Bobrovnikov, V S, additional, Kuyanov, I A, additional, Onuchin, A P, additional, Pivovarov, S, additional, Pyata, E, additional, Serednyakov, S, additional, Tikhonov, Y, additional, Kunne, R, additional, Marchand, D, additional, Ramstein, B, additional, van de Wiele, J, additional, Wang, Y, additional, Boca, G, additional, Burian, V, additional, Finger, M, additional, Nikolovova, A, additional, Pesek, M, additional, Peskova, M, additional, Pfeffer, M, additional, Prochazka, I, additional, Slunecka, M, additional, Gallus, P, additional, Jary, V, additional, Novy, J, additional, Tomasek, M, additional, Virius, M, additional, Vrba, V, additional, Abramov, V, additional, Belikov, N, additional, Bukreeva, S, additional, Davidenko, A, additional, Derevschikov, A, additional, Goncharenko, Y, additional, Grishin, V, additional, Kachanov, V, additional, Kormilitsin, V, additional, Levin, A, additional, Melnik, Y, additional, Minaev, N, additional, Mochalov, V, additional, Morozov, D, additional, Nogach, L, additional, Poslavskiy, S, additional, Ryazantsev, A, additional, Ryzhikov, S, additional, Semenov, P, additional, Shein, I, additional, Uzunian, A, additional, Vasiliev, A, additional, Yakutin, A, additional, Roy, U, additional, Yabsley, B, additional, Belostotski, S, additional, Gavrilov, G, additional, Izotov, A, additional, Manaenkov, S, additional, Miklukho, O, additional, Veretennikov, D, additional, Zhdanov, A, additional, Bäck, T, additional, Cederwall, B, additional, Makonyi, K, additional, Preston, M, additional, Tegner, P E, additional, Wölbing, D, additional, Godre, S, additional, Bussa, M P, additional, Marcello, S, additional, Spataro, S, additional, Iazzi, F, additional, Introzzi, R, additional, Lavagno, A, additional, Calvo, D, additional, De Remigis, P, additional, Filippi, A, additional, Mazza, G, additional, Rivetti, A, additional, Wheadon, R, additional, Martin, A, additional, Calen, H, additional, Ikegami Andersson, W, additional, Johansson, T, additional, Kupsc, A, additional, Marciniewski, P, additional, Papenbrock, M, additional, Pettersson, J, additional, Regina, J, additional, Schönning, K, additional, Wolke, M, additional, Diaz, J, additional, Pothodi Chackara, V, additional, Chlopik, A, additional, Kesik, G, additional, Melnychuk, D, additional, Slowinski, B, additional, Trzcinski, A, additional, Wojciechowski, M, additional, Wronka, S, additional, Zwieglinski, B, additional, Bühler, P, additional, Marton, J, additional, Steinschaden, D, additional, Suzuki, K, additional, Widmann, E, additional, Zimmermann, S, additional, and Zmeskal, J, additional

Published
2022
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18. Toward sub-micron pixels for short-wave infrared imaging

Author

Chang-Mo Kang, Jacob Rabinowitz, Simone Bianconi, Lining Liu, Melville Ulmer, and Hooman Mohseni

Subjects
Materials Chemistry, Electrical and Electronic Engineering, Condensed Matter Physics, Electronic, Optical and Magnetic Materials
Abstract

The sensitivity of infrared (IR) imagers can be significantly improved by reducing the size of photodetectors down to the diffraction limit. Emerging low-dimensional material enable submicron photodetectors, which can be diffraction limited and lead to significant sensitivity improvement in the critical short-wave IR band. However, reaching this limit requires pixel sizes smaller than the metal bumps needed for hybridization to silicon readout chips. Such tiny fragile pixels are susceptible to damages due to the mechanical pressure applied during flip-chip bonding, degrading the number of functional camera pixels. Herein, we systematically characterize the influence of the detector size on the imager pixel yield. We then introduce strategies for improving the yield of sub-micron pixels from less than half of total pixels to more than 3/4 of them. While we used a top-down fabrication for our detectors, the developed method is also applicable to bottom-up fabrication methods to make highly sensitive IR cameras based on emerging low-dimensional material such as catalyst-assisted nanowires.

Published
2022
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20. Probing the spectral dimension of quantum network geometries

Author

Agencia Estatal de Investigación (España), Ministerio de Economía y Competitividad (España), Nokkala, Johannes, Piilo, Jyrki, Bianconi, Ginestra, Agencia Estatal de Investigación (España), Ministerio de Economía y Competitividad (España), Nokkala, Johannes, Piilo, Jyrki, and Bianconi, Ginestra

Abstract

We consider an environment for an open quantum system described by a 'quantum network geometry with flavor' (QNGF) in which the nodes are coupled quantum oscillators. The geometrical nature of QNGF is reflected in the spectral properties of the Laplacian matrix of the network which display a finite spectral dimension, determining also the frequencies of the normal modes of QNGFs. We show that an a priori unknown spectral dimension can be indirectly estimated by coupling an auxiliary open quantum system to the network and probing the normal mode frequencies in the low frequency regime. We find that the network parameters do not affect the estimate; in this sense it is a property of the network geometry, rather than the values of, e.g., oscillator bare frequencies or the constant coupling strength. Numerical evidence suggests that the estimate is also robust both to small changes in the high frequency cutoff and noisy or missing normal mode frequencies. We propose to couple the auxiliary system to a subset of network nodes with random coupling strengths to reveal and resolve a sufficiently large subset of normal mode frequencies.

Published
2021

23. Beyond COVID-19: network science and sustainable exit strategies

Author

Bell, J, primary, Bianconi, G, additional, Butler, D, additional, Crowcroft, J, additional, Davies, P C W, additional, Hicks, C, additional, Kim, H, additional, Kiss, I Z, additional, Di Lauro, F, additional, Maple, C, additional, Paul, A, additional, Prokopenko, M, additional, Tee, P, additional, and Walker, S, additional

Published
2021
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33. Networks of superconducting nano-puddles in 1/8 doped YBa2Cu3O6.5 + y controlled by thermal manipulation

Author

Alessandro Ricci, Nicola Poccia, Gaetano Campi, Francesco Coneri, Luisa Barba, Gianmichele Arrighetti, Maurizio Polentarutti, Manfred Burghammer, Michael Sprung, Martin v Zimmermann, and Antonio Bianconi

Subjects
phase separation, inhomogeneity, cuprates, high temperature superconductivity, complex materials, Science, Physics, QC1-999
Abstract

While it is known that the nature and the arrangement of defects in complex oxides have an impact on the material functionalities, little is known about control of superconductivity by oxygen interstitial organization in cuprates. Here we report direct compelling evidence for the control of T _c by manipulation of the superconducting granular networks of nanoscale puddles, made of ordered oxygen stripes, in a single crystal of YBa _2 Cu _3 O _6.5 + _y with average formal hole doping p close to 1/8. Upon thermal treatments we were able to switch from a first network of oxygen defect striped puddles with OVIII modulation ( q _OVIII ( a *) = ( h + 3/8, k , 0) and q _OVIII ( a *) = ( h + 5/8, k , 0)) to a second network characterized by OXVI modulation ( q _OXVI (a*) = ( h + 7/16, k, 0) and q ox-VI(a*) = ( h + 9/16, k , 0)) and finally to a third network with puddles of OV periodicity ( q _OV ( a *) = (4/10, 1, 0) and q _OV ( a *) = (6/10, 1, 0)). We map the microscopic spatial evolution of the out of plane OVIII, OXVI and OV puddle nano-size distribution via scanning micro-diffraction measurements. In particular, we calculated the number of oxygen chains ( n ) and the charge density (hole concentration p ) inside each puddle, analyzing areas of 160 × 80 μ m ^2 , and recording 12 800 diffraction patterns to reconstruct each spatial map. The high spatial inhomogeneity shown by all the reconstructed spatial maps reflects the intrinsic granular structure that characterizes cuprates and iron chalcogenides, disclosing the presence of several complex networks of coexisting superconducting domains with different lattice modulations, charge densities and gaps as in the proposed multi-gap scenario called superstripes.

Published
2014
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34. Probing the spectral dimension of quantum network geometries

Author

Ginestra Bianconi, Jyrki Piilo, Johannes Nokkala, Agencia Estatal de Investigación (España), and Ministerio de Economía y Competitividad (España)

Subjects
Physics - Physics and Society, Network geometry, Computer Networks and Communications, Computer science, media_common.quotation_subject, Quantum physics, FOS: Physical sciences, Physics and Society (physics.soc-ph), Theoretical physics, Artificial Intelligence, Excellence, Hospitality, Spectral dimension, Agency (sociology), media_common, Quantum Physics, Quantum network, Simplicial complexes, business.industry, Disordered Systems and Neural Networks (cond-mat.dis-nn), State (functional analysis), Condensed Matter - Disordered Systems and Neural Networks, Computer Science Applications, Quantum Physics (quant-ph), business, Information Systems
Abstract

We consider an environment for an open quantum system described by a 'quantum network geometry with flavor' (QNGF) in which the nodes are coupled quantum oscillators. The geometrical nature of QNGF is reflected in the spectral properties of the Laplacian matrix of the network which display a finite spectral dimension, determining also the frequencies of the normal modes of QNGFs. We show that an a priori unknown spectral dimension can be indirectly estimated by coupling an auxiliary open quantum system to the network and probing the normal mode frequencies in the low frequency regime. We find that the network parameters do not affect the estimate; in this sense it is a property of the network geometry, rather than the values of, e.g., oscillator bare frequencies or the constant coupling strength. Numerical evidence suggests that the estimate is also robust both to small changes in the high frequency cutoff and noisy or missing normal mode frequencies. We propose to couple the auxiliary system to a subset of network nodes with random coupling strengths to reveal and resolve a sufficiently large subset of normal mode frequencies., JN acknowledges funding from the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711) and thanks Turku Centre for Quantum Physics for the hospitality.

Published
2020
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35. Experimental analysis of colour constancy and colour augmentation for painting classification by artistic genre: preliminary results

Author

Francesco Bianconi

Subjects
Painting, media_common.quotation_subject, Art, Visual arts, media_common
Abstract

Automatic painting classification by author, artistic genre and/or other attributes has generated considerable research interest in recent years. Being one of the visual features that mark the difference between artists and artistic genres, colour plays a fundamental role in this process. Colour is the result of the interaction among the intrinsic properties of the material, the illumination conditions and the response of the imaging device. Consequently, the same painting/artwork will look significantly different when imaged under varied conditions, which can be a potential source of bias for automated recognition procedures. One can compensate for such variations either via colour calibration or colour pre-processing. In this work we investigate the latter, and, in particular, evaluate the effectiveness of colour constancy and colour augmentation when coupled with hand-crafted and deep learning features for painting classification by artistic genre. In our experiments neither approach showed a clear advantage compared with no pre-processing at all. Colour constancy brought some improvement in certain cases, whereas colour augmentation virtually provided no benefit despite its adding a significant computational overload to the procedure.

Published
2020
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36. The higher-order spectrum of simplicial complexes: a renormalization group approach

Author

Ginestra Bianconi and Marcus Reitz

Subjects
Statistics and Probability, Physics - Physics and Society, Pure mathematics, FOS: Physical sciences, General Physics and Astronomy, General Relativity and Quantum Cosmology (gr-qc), Physics and Society (physics.soc-ph), Fixed point, Network topology, 01 natural sciences, Spectrum (topology), General Relativity and Quantum Cosmology, 010305 fluids & plasmas, High Energy Physics - Lattice, 0103 physical sciences, Order (group theory), High Energy Physics, 010306 general physics, Condensed Matter - Statistical Mechanics, Mathematical Physics, Mathematics, Statistical Mechanics (cond-mat.stat-mech), Computer Science::Information Retrieval, High Energy Physics - Lattice (hep-lat), Statistical and Nonlinear Physics, Disordered Systems and Neural Networks (cond-mat.dis-nn), Condensed Matter - Disordered Systems and Neural Networks, Renormalization group, Diffusion process, Flow (mathematics), Modeling and Simulation, Quantum gravity
Abstract

Network topology is a flourishing interdisciplinary subject that is relevant for different disciplines including quantum gravity and brain research. The discrete topological objects that are investigated in network topology are simplicial complexes. Simplicial complexes generalize networks by not only taking pairwise interactions into account, but also taking into account many-body interactions between more than two nodes. Higher-order Laplacians are topological operators that describe higher-order diffusion on simplicial complexes and constitute the natural mathematical objects that capture the interplay between network topology and dynamics. We show that higher-order up and down Laplacians can have a finite spectral dimension, characterizing the long time behaviour of the diffusion process on simplicial complexes that depends on their order $m$. We provide a renormalization group theory for the calculation of the higher-order spectral dimension of two deterministic models of simplicial complexes: the Apollonian and the pseudo-fractal simplicial complexes. We show that the RG flow is affected by the fixed point at zero mass, which determines the higher-order spectral dimension $d_S$ of the up-Laplacians of order $m$ with $m\geq 0$., Comment: (39 pages,6 figures)

Published
2020
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37. Welcome to JPhys Complexity

Author

Ginestra Bianconi

Subjects
Artificial Intelligence, Computer Networks and Communications, Computer science, Field (Bourdieu), Natural (music), Engineering ethics, Open access journal, Computer Science Applications, Information Systems
Abstract

JPhys Complexity is a new open access journal reporting high-quality quantitative research in complexity. Complexity pervades natural and man-made systems including the brain, the climate, society and the economy; and requires a highly interdisciplinary approach at the frontiers between physics, mathematics, computer science, biology, economics and the social sciences. JPhys Complexity will showcase exciting new research in this cutting-edge field and provide a platform for research favouring the exchange of knowledge between different disciplines.

Published
2020
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38. Evaluation of visual descriptors for painting categorisation

Author

Francesco Bianconi and Raquel Bello-Cerezo

Subjects
Painting, Computer science, business.industry, Visual descriptors, 0202 electrical engineering, electronic engineering, information engineering, 020206 networking & telecommunications, 020201 artificial intelligence & image processing, 02 engineering and technology, Artificial intelligence, computer.software_genre, business, computer, Natural language processing
Published
2018

43. LoCuSS: The Splashback Radius of Massive Galaxy Clusters and Its Dependence on Cluster Merger History.

Author

Bianconi, Matteo, Buscicchio, Riccardo, Smith, Graham P., McGee, Sean L., Haines, Chris P., Finoguenov, Alexis, and Babul, Arif

Subjects
*GALAXY clusters, *DATA modeling, *LUMINOSITY
Abstract

We present the direct detection of the splashback feature using the sample of massive galaxy clusters from the Local Cluster Substructure Survey (LoCuSS). This feature is clearly detected (above 5σ) in the stacked luminosity density profile obtained using the K-band magnitudes of spectroscopically confirmed cluster members. We obtained the best-fit model by means of Bayesian inference, which ranked models including the splashback feature as more descriptive of the data with respect to models that do not allow for this transition. In addition, we have assessed the impact of the cluster dynamical state on the occurrence of the splashback feature. We exploited the extensive multiwavelength LoCuSS data set to test a wide range of proxies for the cluster formation history, finding the most significant dependence of the splashback feature location and scale according to the presence or absence of X-ray emitting galaxy groups in the cluster infall regions. In particular, we report for the first time that clusters that do not show massive infalling groups present the splashback feature at a smaller clustercentric radius rsp/r200,m = 1.158 ± 0.071 than clusters that are actively accreting groups rsp/r200,m = 1.291 ± 0.062. The difference between these two subsamples is significant at 4.2σ, suggesting a correlation between the properties of the cluster potential and its accretion rate and merger history. Similarly, clusters that are classified as old and dynamically inactive present stronger signatures of the splashback feature, with respect to younger, more active clusters. We are directly observing how fundamental dynamical properties of clusters reverberate across vastly different physical scales. [ABSTRACT FROM AUTHOR]

Published
2021
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45. Room temperature superconductivity dome at a Fano resonance in superlattices of wires.

Author

Mazziotti, Maria Vittoria, Jarlborg, Thomas, Bianconi, Antonio, and Valletta, Antonio

Abstract

Recently room temperature superconductivity with degrees Celsius has been discovered in a pressurized complex ternary hydride, CSHx, which is a carbon- and hydrogen-doped H3S alloy. The nanoscale structure of H3S is a particular realization of the 1993 patent claim of superlattice of quantum wires for room temperature superconductors and the maximum TC occurs at the top of a superconducting dome. Here we focus on the electronic structure of materials showing nanoscale heterostructures at the atomic limit made of a superlattice of quantum wires like hole-doped cuprate perovskites, and organics focusing on A15 intermetallics and pressurized hydrides. We provide a perspective of the theory of room temperature multigap superconductivity in heterogeneous materials tuned at a shape resonance or Fano resonance in the superconducting gaps near a Lifshitz transition focusing on H3S where the maximum TC occurs where the multiband metal is tuned by pressure near a Lifshitz transition. Here the superconductivity dome of TC vs. pressure is driven by both electron-phonon coupling and contact exchange interaction. We show that the TC amplification up to room temperature is driven by the Fano resonance between a superconducting gap in the anti-adiabatic regime and other gaps in the adiabatic regime. In these cases the TC amplification via contact exchange interaction is the missing term in conventional multiband BCS and anisotropic Migdal-Eliashberg theories including only Cooper pairing. [ABSTRACT FROM AUTHOR]

Published
2021
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46. A Hybrid Piezoelectric and Inductive Rotational Energy Harvester

Author

Jessica Pealo, Reza Rashidi, Jacob Bianconi, and Jesse Hallett

Subjects
Materials science, Magnet, Acoustics, Rotational speed, Inductor, DC motor, Energy harvesting, Piezoelectricity, Computer Science::Other, Voltage, Rotational energy
Abstract

This paper presents the development of a rotational energy harvester that generates a power based on a combination of piezoelectric and electromagnetic methods. The device utilizes two piezoelectric beams and two inductors and potentially multiple elements on a stationary plate to maximize the harvested energy. The piezoelectric beams generate voltages when deflected with a rotating magnet. In addition, the inductors generate voltages when a magnet passes over them. In order to maximize the harvested energy and frequency, sixteen magnets were embedded in the rotating plate to excite the elements with a higher frequency. A 3D printer was used to make the enclosure, and stationary and rotating components of the device. An assembly of Arduino microcontroller, DC motor, IR sensor and LCD screen was used to test the device. The harvester utilized a closed loop feedback system to monitor rotational speed. The device was tested at various rotational speeds from 130 rpm to 510 rpm. Voltage output versus time graphs were analyzed for each speed. It was determined that raising the number of moving magnets and rotational speed increase not only the frequency but also the voltage amplitude in both piezoelectric beams and inductors. The developed energy harvesting device can be used in any existing rotational system such as vehicle tires, bicycles, rotating machines and exercise equipment.

Published
2019
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