Showing 1–10 of 10 results for author: Greve, M

Multiparametric Quantum Sensing of Liquids Using NV Centres and Tethered Magnetic Nanoparticles

Authors: Johannes Fiedler, Martin Møller Greve, Justas Zalieckas

Abstract: We propose a new concept for non-invasive, multiparametric liquid analysis based on nitrogen-vacancy (NV) centre magnetometry, relaxometry and surface-tethered magnetic nanoparticles. Magnetic nanoparticles are anchored to a diamond surface via DNA strands, forming nanoscale mechanical oscillators whose thermally driven motion is strongly influenced by the surrounding liquid environment. The resul… ▽ More We propose a new concept for non-invasive, multiparametric liquid analysis based on nitrogen-vacancy (NV) centre magnetometry, relaxometry and surface-tethered magnetic nanoparticles. Magnetic nanoparticles are anchored to a diamond surface via DNA strands, forming nanoscale mechanical oscillators whose thermally driven motion is strongly influenced by the surrounding liquid environment. The resulting time-dependent magnetic fields couple to near-surface NV centres and are detected via optically detected magnetic resonance or changes in spin coherence time. By spatially patterning the diamond surface with regions functionalised by DNA tethers of different lengths, sequences, or chemical modifications, a single liquid is mapped onto a high-dimensional quantum response vector rather than a single scalar observable. Changes in viscosity, molecular adsorption, or chemical interactions modify the dynamics of magnetic nanoparticles in a region-specific manner, enabling differential sensing across the surface. We outline the physical transduction mechanism, discuss relevant scaling relations, and assess experimental feasibility using established wide-field NV magnetometry and relaxometry methods. The proposed platform combines quantum sensing with surface heterogeneity, offering a versatile route toward parallel, label-free liquid characterisation. △ Less

Submitted 2 June, 2026; originally announced June 2026.

From gas to stars along the spiral wave: CO, HCN, and star formation variations across the spiral arms in NGC 4321 and M51

Authors: Minou Greve, Lukas Neumann, Mallory Thorp, Dario Colombo, Frank Bigiel, Miguel Querejeta, Sharon E. Meidt, Ashley T. Barnes, Zein Bazzi, Ralf S. Klessen, Adam K. Leroy, Hsi-An Pan, Jérôme Pety, Marina Ruiz-García, Eva Schinnerer, Rowan Smith, Sophia Stuber, Jiayi Sun, Antonio Usero, Thomas G. Williams

Abstract: Molecular clouds form stars from the interstellar medium via gravitational collapse, following a sequence from low-density gas to high-density cores and eventually the formation of stars. In classical density wave theory, gas clouds orbiting the galaxy experience gas compression and triggered star formation, while encountering the gravitational well of spiral arms. We aim to trace these different… ▽ More Molecular clouds form stars from the interstellar medium via gravitational collapse, following a sequence from low-density gas to high-density cores and eventually the formation of stars. In classical density wave theory, gas clouds orbiting the galaxy experience gas compression and triggered star formation, while encountering the gravitational well of spiral arms. We aim to trace these different phases of the molecular cloud life cycle via tracers of molecular gas (CO), dense molecular gas (HCN), and star formation (H$α$, 24 $μ$m) within the spiral arms of two grand-design spiral galaxies: NGC 4321 and M51 (NGC 5194). In the spiral arms of these galaxies, we investigate the relation between molecular gas, dense gas, and star formation (CO-HCN-SFR) at matched physical resolutions of 270 pc and 125 pc in NGC 4321 and M51, respectively. We employed spiral arm masks for these galaxies and investigate trends of HCN/CO and SFR/HCN (SFR/CO), which serve as proxies for the dense gas fraction and dense (molecular) gas star formation efficiency, perpendicular to the spiral arm spines. We find that HCN/CO, SFR/CO, and SFR/HCN increase from the upstream towards the downstream side of both spiral arms of NGC 4321, while their trends are less prominent in M51. Our results indicate that large-scale galactic dynamics (e.g. density waves) can induce a sequence of gas density and star formation-to-gas density variations perpendicular to the spiral arms. This sequence contributes to the increased scatter seen among spectroscopic ratios such as HCN/CO and SFR/HCN at sub-kiloparsec scales. △ Less

Submitted 6 May, 2026; originally announced May 2026.

Comments: Accepted publication in A&A, 12 pages, 5 appendix, 15 figures

The lifetime of 100,000 molecular clouds in the nearby Universe

Authors: Z. Bazzi, M. I. N. Kobayashi, D. Colombo, F. Bigiel, A. K. Leroy, S. E. Meidt, R. S. Klessen, E. Rosolowsky, R. Chown, D. A. Dale, S. Dlamini, M. Greve, S. K. Stuber, M. Boquien, T. G. Williams, H. -A. Pan, M. Querejeta, L. Ramambason, A. Romanelli, T. Saito, L. E. C. Romano, M. J. Jiménez-Donaire, H. Kim, D. Pathak, H. Koziol , et al. (3 additional authors not shown)

Abstract: Multiple mechanisms are proposed for the formation of giant molecular clouds (GMCs), from gravitational free-fall caused by self-gravity to stellar feedback-driven gas compression. Both the galactic environment and galaxy conditions could play an additional role in enhancing the formation via their gas surface density and star formation activity. In this paper, we make use of a catalog of 108,466… ▽ More Multiple mechanisms are proposed for the formation of giant molecular clouds (GMCs), from gravitational free-fall caused by self-gravity to stellar feedback-driven gas compression. Both the galactic environment and galaxy conditions could play an additional role in enhancing the formation via their gas surface density and star formation activity. In this paper, we make use of a catalog of 108,466 GMCs identified by F770W PHANGS--JWST imaging across 66 galaxies at a homogenized resolution of 30~pc. We measure the mass spectra in various galactic regions, whose power-law slopes vary from $-1.2$ to $-2.0$. We then estimate the formation time of each cloud using a model where GMCs form from multiple feedback compression, and find that clouds with masses $\leq 10^{5}\,M_{\odot}$ form, on average, in 20~Myr, with more massive clouds ($\sim 10^{6}$--$10^{7}\,M_{\odot}$) taking up to 100~Myr. We also find that cloud formation proceeds most rapidly in the central regions of galaxies, with formation timescales that are typically shorter by $\sim 5$--$10$~Myr compared to galactic disks. This effect is most pronounced in central molecular zones with enhanced star formation, highlighting the role of intense massive star formation, high molecular gas surface densities, and strong supersonic compressions in accelerating cloud formation. However, star formation is generally inefficient as the cloud lifetime is $\sim 1\,\%$ of the molecular depletion time. The formation time of clouds is $\sim 0.1$~dex longer than the free-fall time. This hints that magnetic fields, stellar feedback, or other mechanisms may prolong their formation instead of immediate free-fall collapse. This indicates a longevity of massive GMCs. The GMC ages also show only limited variation with galactocentric radius in both spiral and disk galaxies, suggesting that cloud formation proceeds similarly in these galaxy types. △ Less

Submitted 27 April, 2026; originally announced April 2026.

Comments: Accepted for Publication in A&A

Directed Nano-antennas for Laser Fusion

Authors: FUSENOW, NAPLIFE Collaborations, :, Zsuzsanna Márton, Imene Benabdelghani, Márk Aladi, Judit Budai, Aldo Bonasera, Attila Bonyár, Mária Csete, Tibor Gilinger, Martin Greve, Jan-Petter Hansen, Gergely Hegedűs, Ádám Inger, Miklos Kedves, Károly Osvay, István Papp, Péter Rácz, András Szenes, Ágnes Szokol, Dávid Vass, Parvin Varmazyar, Miklós Veres, Konstantin Zsukovszki , et al. (3 additional authors not shown)

Abstract: Why do we use nano-antennas for fusion? In three sentences: The present laser induced fusion plans use extreme mechanical shock compression to get one hotspot and then ignition. Still fusion burning spreads slower than expansion, and mechanical instabilities may also develop. With nano-antennas in radiation dominated systems, simultaneous ignition can be achieved in the whole target volume and the… ▽ More Why do we use nano-antennas for fusion? In three sentences: The present laser induced fusion plans use extreme mechanical shock compression to get one hotspot and then ignition. Still fusion burning spreads slower than expansion, and mechanical instabilities may also develop. With nano-antennas in radiation dominated systems, simultaneous ignition can be achieved in the whole target volume and there is no time left for mechanical instabilities. Ignition is achieved with protons accelerated in the direction of the nanoantennas that are orthogonal to the direction of laser irradiation. Present laser fusion methods are based on extreme and slow mechanical compression with an ablator surface on the fuel target pellet to increase compression and eliminate penetration of laser electromagnetic energy into the target. This arises from a mistaken assumption, [1] that the detonation normal 4-vector should have vanishing time-like component, and this assumption eliminates the possibility to rapid or even simultaneous, radiation dominated detonations, (which are well known in the burning (or hadronization) of Quark Gluon Plasma). △ Less

Submitted 28 May, 2026; v1 submitted 8 January, 2026; originally announced January 2026.

Learning Soil Physics from Partial Knowledge and Data: Partitioning Capillary and Adsorbed Soil Water

Authors: Sarem Norouzi, Per Moldrup, Ben Moseley, David Robinson, Dani Or, Tobias L. Hohenbrink, Budiman Minasny, Morteza Sadeghi, Emmanuel Arthur, Markus Tuller, Mogens H. Greve, Lis W. de Jonge

Abstract: Soil physics models have long relied on simplifying assumptions to represent complex processes, yet such assumptions can strongly bias model predictions. Here, we propose a paradigm-shifting differentiable hybrid modeling (DHM) framework that instead of simplifying the unknown, learns it from data. As a proof of concept, we apply the hybrid approach to the challenge of partitioning the soil water… ▽ More Soil physics models have long relied on simplifying assumptions to represent complex processes, yet such assumptions can strongly bias model predictions. Here, we propose a paradigm-shifting differentiable hybrid modeling (DHM) framework that instead of simplifying the unknown, learns it from data. As a proof of concept, we apply the hybrid approach to the challenge of partitioning the soil water retention curve (SWRC) into capillary and adsorbed water components, a problem where traditional assumptions have led to divergent results. The hybrid framework derives this partitioning directly from data while remaining guided by a few parsimonious and universally accepted physical constraints. Using basic soil physical properties as inputs, the hybrid model couples an analytical formula for the dry end of the SWRC with data-driven physics-informed neural networks that learn the wet end, the transition between the two ends, and key soil-specific parameters. The model was trained on a SWRC dataset from 482 undisturbed soil samples from Central Europe, spanning a broad range of soil texture classes and organic carbon contents. The hybrid model successfully learned both the overall shape and the capillary and adsorbed components of the SWRC. Notably, the model revealed physically meaningful pore-scale features without relying on explicit geometrical assumptions about soil pore shape or its distribution. Moreover, the model revealed a distinctly nonlinear transition between capillary and adsorbed domains, challenging the linear assumptions invoked in previous studies. The methodology introduced here provides a blueprint for learning other soil processes where high-quality datasets are available but mechanistic understanding is incomplete. △ Less

Submitted 29 October, 2025; originally announced October 2025.

Time-Resolved Data-Driven Surrogates of Hall-effect Thrusters

Authors: Adrian S Wong, Christine M Greve, Daniel Q Eckhardt

Abstract: The treatment of Hall-effect thrusters as nonlinear, dynamical systems has emerged as a new perspective to understand and analyze data acquired from the thrusters. The acquisition of high-speed data that can resolve the characteristic high-frequency oscillations of these thruster enables additional levels of classification in these thrusters. Notably, these signals may serve as unique indicators f… ▽ More The treatment of Hall-effect thrusters as nonlinear, dynamical systems has emerged as a new perspective to understand and analyze data acquired from the thrusters. The acquisition of high-speed data that can resolve the characteristic high-frequency oscillations of these thruster enables additional levels of classification in these thrusters. Notably, these signals may serve as unique indicators for the full state of the system that can aid digital representations of thrusters and predictions of thruster dynamics. In this work, a Reservoir Computing framework is explored to build surrogate models from experimental time-series measurements of a Hall-effect thruster. Such a framework has shown immense promise for predicting the behavior of low-dimensional yet chaotic dynamical systems. In particular, the surrogates created by the Reservoir Computing framework are capable of both predicting the observed behavior of the thruster and estimating the values of certain measurements from others, known as inference. △ Less

Submitted 12 August, 2024; originally announced August 2024.

Iterative Learning Control of Fast, Nonlinear, Oscillatory Dynamics (Preprint)

Authors: John W. Brooks, Christine M. Greve

Abstract: The sudden onset of deleterious and oscillatory dynamics (often called instabilities) is a known challenge in many fluid, plasma, and aerospace systems. These dynamics are difficult to address because they are nonlinear, chaotic, and are often too fast for active control schemes. In this work, we develop an alternative active controls system using an iterative, trajectory-optimization and paramete… ▽ More The sudden onset of deleterious and oscillatory dynamics (often called instabilities) is a known challenge in many fluid, plasma, and aerospace systems. These dynamics are difficult to address because they are nonlinear, chaotic, and are often too fast for active control schemes. In this work, we develop an alternative active controls system using an iterative, trajectory-optimization and parameter-tuning approach based on Iterative Learning Control (ILC), Time-Lagged Phase Portraits (TLPP) and Gaussian Process Regression (GPR). The novelty of this approach is that it can control a system's dynamics despite the controller being much slower than the dynamics. We demonstrate this controller on the Lorenz system of equations where it iteratively adjusts (tunes) the system's input parameters to successfully reproduce a desired oscillatory trajectory or state. Additionally, we investigate the system's dynamical sensitivity to its control parameters, identify continuous and bounded regions of desired dynamical trajectories, and demonstrate that the controller is robust to missing information and uncontrollable parameters as long as certain requirements are met. The controller presented in this work provides a framework for low-speed control for a variety of fast, nonlinear systems that may aid in instability suppression and mitigation. △ Less

Submitted 30 May, 2024; originally announced May 2024.

AI for Explaining Decisions in Multi-Agent Environments

Authors: Sarit Kraus, Amos Azaria, Jelena Fiosina, Maike Greve, Noam Hazon, Lutz Kolbe, Tim-Benjamin Lembcke, Jörg P. Müller, Sören Schleibaum, Mark Vollrath

Abstract: Explanation is necessary for humans to understand and accept decisions made by an AI system when the system's goal is known. It is even more important when the AI system makes decisions in multi-agent environments where the human does not know the systems' goals since they may depend on other agents' preferences. In such situations, explanations should aim to increase user satisfaction, taking int… ▽ More Explanation is necessary for humans to understand and accept decisions made by an AI system when the system's goal is known. It is even more important when the AI system makes decisions in multi-agent environments where the human does not know the systems' goals since they may depend on other agents' preferences. In such situations, explanations should aim to increase user satisfaction, taking into account the system's decision, the user's and the other agents' preferences, the environment settings and properties such as fairness, envy and privacy. Generating explanations that will increase user satisfaction is very challenging; to this end, we propose a new research direction: xMASE. We then review the state of the art and discuss research directions towards efficient methodologies and algorithms for generating explanations that will increase users' satisfaction from AI system's decisions in multi-agent environments. △ Less

Submitted 12 October, 2019; v1 submitted 10 October, 2019; originally announced October 2019.

Comments: This paper has been submitted to the Blue Sky Track of the AAAI 2020 conference. At the time of submission, it is under review. The tentative notification date will be November 10, 2019. Current version: Name of first author had been added in metadata

ACM Class: I.2

Eliminating Variables in Boolean Equation Systems

Authors: Bjørn Møller Greve, Håvard Raddum, Gunnar Fløystad, Øyvind Ytrehus

Abstract: Systems of Boolean equations of low degree arise in a natural way when analyzing block ciphers. The cipher's round functions relate the secret key to auxiliary variables that are introduced by each successive round. In algebraic cryptanalysis, the attacker attempts to solve the resulting equation system in order to extract the secret key. In this paper we study algorithms for eliminating the auxil… ▽ More Systems of Boolean equations of low degree arise in a natural way when analyzing block ciphers. The cipher's round functions relate the secret key to auxiliary variables that are introduced by each successive round. In algebraic cryptanalysis, the attacker attempts to solve the resulting equation system in order to extract the secret key. In this paper we study algorithms for eliminating the auxiliary variables from these systems of Boolean equations. It is known that elimination of variables in general increases the degree of the equations involved. In order to contain computational complexity and storage complexity, we present two new algorithms for performing elimination while bounding the degree at $3$, which is the lowest possible for elimination. Further we show that the new algorithms are related to the well known \emph{XL} algorithm. We apply the algorithms to a downscaled version of the LowMC cipher and to a toy cipher based on the Prince cipher, and report on experimental results pertaining to these examples. △ Less

Submitted 23 October, 2017; originally announced October 2017.

Comments: 21 pages, 3 figures, Journal paper

Letterplace and co-letterplace ideals of posets

Authors: Gunnar Fløystad, Bjørn Møller Greve, Jürgen Herzog

Abstract: To a natural number $n$, a finite partially ordered set $P$ and a poset ideal ${\mathcal J}$ in the poset $Hom(P,[n])$ of isotonian maps from $P$ to the chain on $n$ elements, we associate two monomial ideals, the letterplace ideal $L(n,P;{\mathcal J})$ and the co-letterplace ideal $L(P,n;{\mathcal J})$. These ideals give a unified understanding of a number of ideals studied in monomial ideal theo… ▽ More To a natural number $n$, a finite partially ordered set $P$ and a poset ideal ${\mathcal J}$ in the poset $Hom(P,[n])$ of isotonian maps from $P$ to the chain on $n$ elements, we associate two monomial ideals, the letterplace ideal $L(n,P;{\mathcal J})$ and the co-letterplace ideal $L(P,n;{\mathcal J})$. These ideals give a unified understanding of a number of ideals studied in monomial ideal theory in recent years. By cutting down these ideals by regular sequences of variable differences we obtain: multichain ideals and generalized Hibi type ideals, initial ideals of determinantal ideals, strongly stable ideals, $d$-partite $d$-uniform ideals, Ferrers ideals, edge ideals of cointerval $d$-hypergraphs, and uniform face ideals. △ Less

Submitted 24 July, 2016; v1 submitted 19 January, 2015; originally announced January 2015.

Comments: 24 pages, updated introduction and references, and some minor improvements

MSC Class: Primary: 13F20; 05E40; Secondary: 06A11