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We aim to better characterize the conditions of the solar corona, especially with respect to the occurrence of confined and eruptive flares. In this work, we model the coronal evolution around 231 large flares observed during solar cycle 24. Using Helioseismic and Magnetic Imager vector magnetic field data around each event, we employed nonlinear force-free field extrapolations to approximate the coronal energy and helicity budgets of the solar source regions. A superposed epoch analysis and dynamical time warping applied to the time series of selected photospheric and coronal quantities were used to pin down the characteristics of the pre- and postflare time evolution, as well as to assess flare-related changes. During the 24 hours leading up to a major flare, the total magnetic energy and unsigned magnetic flux were seen to evolve closely with respect to each other, irrespective of the flare type. Prior to confined flares, the free energy evolves in a way that exhibits more of a similarity with the unsigned flux than the helicity of the current-carrying field, while the opposite trend is seen prior to eruptive flares. Furthermore, the flare type can be predicted correctly in more than 90\% of major flares when combining measures of the active regions nonpotentiality and local stability. The coronal energy and helicity budgets return to preflare levels within approximately six to 12 hours after eruptive major M-class flares, while the impact of eruptive X-flares lasts considerably longer. Finally, the postflare replenishment times of more than 12 hours after eruptive X-class flares may serve as a partial explanation for the rare observation of eruptive X-class flares within a time frame of a few hours., Comment: 22 pages, 13 figures, 3 tables; accepted for publication in A&A

Humans excel at learning abstract patterns across different sequences, filtering out irrelevant details, and transferring these generalized concepts to new sequences. In contrast, many sequence learning models lack the ability to abstract, which leads to memory inefficiency and poor transfer. We introduce a non-parametric hierarchical variable learning model (HVM) that learns chunks from sequences and abstracts contextually similar chunks as variables. HVM efficiently organizes memory while uncovering abstractions, leading to compact sequence representations. When learning on language datasets such as babyLM, HVM learns a more efficient dictionary than standard compression algorithms such as Lempel-Ziv. In a sequence recall task requiring the acquisition and transfer of variables embedded in sequences, we demonstrate HVM's sequence likelihood correlates with human recall times. In contrast, large language models (LLMs) struggle to transfer abstract variables as effectively as humans. From HVM's adjustable layer of abstraction, we demonstrate that the model realizes a precise trade-off between compression and generalization. Our work offers a cognitive model that captures the learning and transfer of abstract representations in human cognition and differentiates itself from the behavior of large language models.

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, Centaur is the first real candidate for a unified model of human cognition. We anticipate that it will have a disruptive impact on the cognitive sciences, challenging the existing paradigm for developing computational models.

Lipid membranes have complex compositions and modeling the thermodynamic properties of multi-component lipid systems remains a remote goal. In this work we attempt to describe the thermodynamics of binary lipid mixtures by mapping coarse-grained molecular dynamics systems to two-dimensional simple fluid mixtures. By computing and analysing the density fluctuations of this model lipid bilayer we determine the numerical value of the quadratic coupling term appearing in a model of regular solutions for the DPPC-DLiPC pair of lipids at three different compositions. Our methodology is general and discussed in detail., Comment: 21 pages, 15 figures

Research in various fields is currently experiencing challenges regarding the reproducibility of results. This problem is also prevalent in machine learning (ML) research. The issue arises, for example, due to unpublished data and/or source code and the sensitivity of ML training conditions. Although different solutions have been proposed to address this issue, such as using ML platforms, the level of reproducibility in ML-driven research remains unsatisfactory. Therefore, in this article, we discuss the reproducibility of ML-driven research with three main aims: (i) identifying the barriers to reproducibility when applying ML in research as well as categorize the barriers to different types of reproducibility (description, code, data, and experiment reproducibility), (ii) discussing potential drivers such as tools, practices, and interventions that support ML reproducibility, as well as distinguish between technology-driven drivers, procedural drivers, and drivers related to awareness and education, and (iii) mapping the drivers to the barriers. With this work, we hope to provide insights and to contribute to the decision-making process regarding the adoption of different solutions to support ML reproducibility., Comment: Pre-print of submission for the AI Magazine - comments to this pre-print are very welcome

In this work, we describe our approach to developing an intelligent and robust social robotic system for the Nadine social robot platform. We achieve this by integrating Large Language Models (LLMs) and skilfully leveraging the powerful reasoning and instruction-following capabilities of these types of models to achieve advanced human-like affective and cognitive capabilities. This approach is novel compared to the current state-of-the-art LLM-based agents which do not implement human-like long-term memory or sophisticated emotional appraisal. The naturalness of social robots, consisting of multiple modules, highly depends on the performance and capabilities of each component of the system and the seamless integration of the components. We built a social robot system that enables generating appropriate behaviours through multimodal input processing, bringing episodic memories accordingly to the recognised user, and simulating the emotional states of the robot induced by the interaction with the human partner. In particular, we introduce an LLM-agent frame for social robots, SoR-ReAct, serving as a core component for the interaction module in our system. This design has brought forth the advancement of social robots and aims to increase the quality of human-robot interaction.

We carried out very deep VLT/SPHERE imaging polarimetry of the nearby system Eps Eri based on 38.5 hours of integration time with a 600 - 900 nm broadband filter to search for polarized scattered light from a planet or from circumstellar dust using AO, coronagraphy, high precision differential polarimetry, and angular differential imaging. We have improved several data reduction and post-processing techniques and also developed new ones to further increase the sensitivity of SPHERE/ZIMPOL. The data provide unprecedented contrast limits, but no significant detection of a point source or an extended signal from circumstellar dust. For each observing epoch, we obtained a point source contrast for the polarized intensity between $2\cdot 10^{-8}$ and $4\cdot 10^{-8}$ at the expected separation of the planet Eps Eri b of 1'' near quadrature phase. The polarimetric contrast limits are about six to 50 times better than the intensity limits because polarimetric imaging is much more efficient in speckle suppression. Combining the entire 14-month data set to the search for a planet moving on a Keplerian orbit with the K-Stacker software further improves the contrast limits by a factor of about two, to about $8 \cdot 10^{-9}$ at 1''. This would allow the detection of a planet with a radius of about 2.5 Jupiter radii. The surface brightness contrast limits achieved for the polarized intensity from an extended scattering region are about 15 mag arcsec$^{-2}$ at 1'', or up to 3 mag arcsec$^{-2}$ deeper than previous limits. For Eps Eri, these limits exclude the presence of a narrow dust ring and they constrain the dust properties. This study shows that the polarimetric contrast limits for reflecting planets with SPHERE/ZIMPOL can be improved to a level $<10^{-8}$ simply by collecting more data over many nights and using the K-Stacker software., Comment: 26 pages, 20 figures, 5 tables. Accepted for publication in A&A

The ultrafast relaxation dynamics of tetracene following UV excitation to a bright singlet state S6 has been studied with time-resolved photoelectron spectroscopy. With the help of high-level ab-initio multireference perturbation theory calculations, we assign photoelectron signals to intermediate dark electronic states S3, S4 and S5 as well as a to a low-lying electronic state S2. The energetic structure of these dark states has not been determined experimentally previously. The time-dependent photoelectron yields assigned to the states S6, S5 and S4 have been analyzed and reveal the depopulation of S6 within 50 fs, while S5 and S4 are populated with delays of about 50 and 80 fs. The dynamics of the lower-lying states S3 and S2 seem to agree with a delayed population coinciding with the depopulation of the higher-lying states S4-S6, but could not be elucidated in full detail due to the low signal levels of the corresponding two-photon ionization probe processes., Comment: The following article has been accepted by The Journal of Chemical Physics. After it is published, it will be found at https://doi.org/10.1063/5.0214006

In order to improve our understanding on the pre-requisites of eruptive solar flares, we study and compare different measures that characterize the eruptive potential of solar active regions - the critical height for torus instability as a local measure and the helicity ratio as a global measure - with the structural properties of the underlying magnetic field, namely the altitude of the center of the current-carrying magnetic structure. Using time series of 3D optimization-based nonlinear force-free magnetic field models for 10 different active regions (ARs) around the time of large solar flares, we determine the altitudes of the current-weighted centers of the non-potential model structures. Based on the potential magnetic field, we inspect the decay index, $n$, in multiple vertical planes oriented along of or perpendicular to the flare-relevant polarity inversion line, and estimate the critical height ($h_{\mathrm{crit}}$) for torus instability (TI) using different thresholds of $n$. The critical heights are interpreted with respect to the altitudes of the current-weighted centers of the associated non-potential structures, as well as the eruptive character of the associated flares, and the eruptive potential of the host AR, as characterized by the helicity ratio. Our most important findings are that (i) $h_{\mathrm{crit}}$ is more segregated in terms of flare type than the helicity ratio, and that (ii) coronal field configurations with a higher eruptive potential (in terms of the helicity ratio) also appear to be more prone to TI. Furthermore, we find no pronounced differences in the altitudes of the non-potential structures prior to confined and eruptive flares., Comment: Accepted for publication in A & A journal, 16 pages, and 8 figures

Ecological rationality refers to the notion that humans are rational agents adapted to their environment. However, testing this theory remains challenging due to two reasons: the difficulty in defining what tasks are ecologically valid and building rational models for these tasks. In this work, we demonstrate that large language models can generate cognitive tasks, specifically category learning tasks, that match the statistics of real-world tasks, thereby addressing the first challenge. We tackle the second challenge by deriving rational agents adapted to these tasks using the framework of meta-learning, leading to a class of models called ecologically rational meta-learned inference (ERMI). ERMI quantitatively explains human data better than seven other cognitive models in two different experiments. It additionally matches human behavior on a qualitative level: (1) it finds the same tasks difficult that humans find difficult, (2) it becomes more reliant on an exemplar-based strategy for assigning categories with learning, and (3) it generalizes to unseen stimuli in a human-like way. Furthermore, we show that ERMI's ecologically valid priors allow it to achieve state-of-the-art performance on the OpenML-CC18 classification benchmark., Comment: 27 pages (9 pages of main text, 4 pages of references, and 14 pages of appendix), 13 figures, and 7 Tables

Context. Relative field line helicity (RFLH) is a recently developed quantity which can approximate the density of relative magnetic helicity. Aims. This paper aims to determine whether RFLH can be used as an indicator of solar eruptivity. Methods. Starting from magnetographic observations from the Helioseismic and Magnetic Imager instrument onboard the Solar Dynamic Observatory of a sample of seven solar active regions (ARs), which comprises over 2000 individual snapshots, we reconstruct the AR's coronal magnetic field with a widely-used non-linear force-free method. This enables us to compute RFLH using two independent gauge conditions for the vector potentials. We focus our study around the times of strong flares in the ARs, above the M class, and in regions around the polarity inversion lines (PILs) of the magnetic field, and of RFLH. Results. We find that the temporal profiles of the relative helicity that is contained in the magnetic PIL follow those of the relative helicity that is computed by the accurate volume method for the whole AR. Additionally, the PIL relative helicity can be used to define a parameter similar to the well-known parameter R (Schrijver 2007), whose high values are related with increased flaring probability. This helicity-based R-parameter correlates well with the original one, showing in some cases even higher values, and additionally, it experiences more pronounced decreases during flares. This means that there exists at least one parameter deduced from RFLH, that has important value as a solar eruptivity indicator., Comment: accepted by Astronomy & Astrophysics

Non-linear force-free extrapolations are a common approach to estimate the 3D topology of coronal magnetic fields based on photospheric vector magnetograms. The force-free assumption is a valid approximation at coronal heights, but for the dense plasma conditions in the lower atmosphere, this assumption is not satisfied. In this study, we utilize multi-height magnetic field measurements in combination with physics-informed neural networks to advance solar magnetic field extrapolations. We include a flexible height-mapping, which allows us to account for the different formation heights of the observed magnetic field measurements. The comparison to analytical and simulated magnetic fields demonstrates that including chromospheric magnetic field measurements leads to a significant improvement of our magnetic field extrapolations. We also apply our method to chromospheric line-of-sight magnetograms, from the Vector Spectromagnetograph (VSM) on the Synoptic Optical Long-term Investigations of the Sun (SOLIS) observatory, in combination with photospheric vector magnetograms, from the Helioseismic Magnetic Imager (HMI) onboard the Solar Dynamic Observatory (SDO). The comparison to observations in extreme ultraviolet wavelengths shows that the additional chromospheric information leads to a better agreement with the observed coronal structures. In addition, our method intrinsically provides an estimate of the corrugation of the observed magnetograms. With this new approach, we make efficient use of multi-height magnetic field measurements and advance the realism of coronal magnetic field simulations.

aTrain is an open-source and offline tool for transcribing audio data in multiple languages with CPU and NVIDIA GPU support. It is specifically designed for researchers using qualitative data generated from various forms of speech interactions with research participants. aTrain requires no programming skills, runs on most computers, does not require an internet connection, and was verified not to upload data to any server. aTrain combines OpenAI's Whisper model with speaker recognition to provide output that integrates with the popular qualitative data analysis software tools MAXQDA and ATLAS.ti. It has an easy-to-use graphical interface and is provided as a Windows-App through the Microsoft Store allowing for simple installation by researchers. The source code is freely available on GitHub. Having developed aTrain with a focus on speed on local computers, we show that the transcription time on current mobile CPUs is around 2 to 3 times the duration of the audio file using the highest-accuracy transcription models. If an entry-level graphics card is available, the transcription speed increases to 20% of the audio duration., Comment: Install via Microsoft store: apps.microsoft.com/store/detail/atrain/9N15Q44SZNS2. Github: github.com/BANDAS-Center/aTrain

Hypothesis: Measuring rotational and translational Brownian motion of single spherical particles reveals dissipations due to the interaction between the particle and the environment. Experiments: In this article, we show experiments where the in-plane translational and two rotational drag coefficients of a single spherical Brownian particle can be measured. These particle drags are functions of the particle size and the particle-wall distance, and of the viscous dissipations at play. We measure drag coefficients for Janus particles close to a solid wall and close to a lipid bilayer membrane. Findings: For a particle close to wall, we show that according to hydrodynamic models, particlewall distance and particle size can be determined. For a particle partially wrapped by lipid membranes, in absence of strong binding interactions, translational and rotational drags are significantly larger than the ones of non-wrapped particles. Beside the effect of the membrane viscosity, we show that dissipations in the deformed membrane cap region strongly contribute to the drag coefficients.

Magnetic free energy powers solar flares and coronal mass ejections (CMEs), and the buildup of magnetic helicity might play a role in the development of unstable structures that subsequently erupt. To better understand the roles of energy and helicity in large flares and eruptions, we have characterized the evolution of magnetic energy and helicity associated with 21 X-class flares from 2010 to 2017. Our sample includes both confined and eruptive events, with 6 and 15 in each category, respectively. Using HMI vector magnetic field observations from several hours before to several hours after each event, we employ (a) the Differential Affine Velocity Estimator for Vector Magnetograms (DAVE4VM) to determine the photospheric fluxes of energy and helicity, and (b) non-linear force-free field (NLFFF) extrapolations to estimate the coronal content of energy and helicity in source-region fields. Using Superposed Epoch analysis (SPE), we find, on average: (1) decreases in both magnetic energy and helicity, in both photospheric fluxes and coronal content, that persist for a few hours after eruptions, but no clear changes, notably in relative helicity, for confined events; (2) significant increases in the twist of photospheric fields in eruptive events, with twist uncertainties too large in confined events to constrain twist changes (and lower overall twist in confined events); and (3) on longer time scales (event time +12 hours), replenishment of free magnetic energy and helicity content to near pre-event levels for eruptive events. For eruptive events, magnetic helicity and free energy in coronal models clearly decrease after flares, with the amounts of decrease proportional to each region's pre-flare content., Comment: 25 pages, 15 figures, 1 table, accepted for publication in the Astrophysical Journal

We analyze the complete chain of effects caused by a solar eruptive event in order to better understand the dynamic evolution of magnetic-field related quantities in interplanetary space, in particular that of magnetic flux and helicity. We study a series of connected events (a confined C4.5 flare, a flare-less filament eruption and a double-peak M-class flare) that originated in NOAA active region (AR) 12891 on 2021 November 1 and November 2. We deduce the magnetic structure of AR 12891 using stereoscopy and nonlinear force-free (NLFF) magnetic field modeling, allowing us to identify a coronal flux rope and to estimate its axial flux and helicity. Additionally, we compute reconnection fluxes based on flare ribbon and coronal dimming signatures from remote sensing imagery. Comparison to corresponding quantities of the associated magnetic cloud (MC), deduced from in-situ measurements from Solar Orbiter and near-Earth spacecraft, allows us to draw conclusions on the evolution of the associated interplanetary coronal mass ejection (ICME). The latter are aided through the application of geometric fitting techniques (graduated cylindrical shell modeling; GCS) and interplanetary propagation models (drag based ensemble modeling; DBEM) to the ICME. NLFF modeling suggests the host AR's magnetic structure in the form of a left-handed (negative-helicity) sheared arcade/flux rope reaching to altitudes of 8-10 Mm above photospheric levels, in close agreement with the corresponding stereoscopic estimate. Revealed from GCS and DBEM modeling, the ejected flux rope propagated in a self-similar expanding manner through interplanetary space. Comparison of magnetic fluxes and helicities processed by magnetic reconnection in the solar source region and the respective budgets of the MC indicate a considerable contribution from the eruptive process, though the pre-eruptive budgets appear of relevance too., Comment: 14 pages, 9 Figures, 3 Tables, 1 Movie; accepted for publication in A&A

Nonlinear force-free (NLFF) modeling is regularly used in order to indirectly infer the 3D geometry of the coronal magnetic field, not accessible on a regular basis by means of direct measurements otherwise. We study the effect of binning in time series NLFF modeling of individual active regions (ARs) in order to quantify the effect of a different underlying spatial sampling on the quality of modeling as well as on the derived physical parameters. We apply an optimization method to sequences of SDO/HMI vector magnetogram data at three different plate scales for three solar ARs to obtain nine NLFF model time series. From the NLFF models, we deduce active-region magnetic fluxes, electric currents, magnetic energies and relative helicities, and analyze those with respect to the underlying spatial sampling. We calculate various metrics to quantify the quality of the derived NLFF models and apply a Helmholtz decomposition to characterize solenoidal errors. At a given spatial sampling, the quality of NLFF modeling is different for different ARs, as well as varies along of the individual model time series. For a given AR, modeling at a given spatial sampling is not necessarily of superior quality compared to that performed with a different plate scale. Generally, the NLFF model quality tends to be higher for larger pixel sizes with the solenoidal quality being the ultimate cause for systematic variations in model-deduced physical quantities. Optimization-based modeling based on binned SDO/HMI vector data delivers magnetic energies and helicity estimates different by $\lesssim$30\%, given that concise checks ensure the physical plausibility and high solenoidal quality of the tested model. Thus, spatial-sampling-induced differences are relatively small compared to that arising from other sources of uncertainty., Comment: Accepted for publication in Astronomy and Astrophysics, 11 pages, 8 figures

A fundamental result in the theory of Brownian motion is the Einstein-Sutherland relation between mobility and diffusion constant. Any classical linear response transport coefficient obeys a similar Einstein-Helfand relation. We show in this work how to derive the interleaflet friction coefficient of lipid bilayer by means of an adequate generalisation of the Einstein relation. Special attention must be paid in practical cases to the constraints on the system center of mass position that must be enforced when coupling the system to thermostat., Comment: Original Article

The excimer formation dynamics of pyrene-labeled molecules in lipid bilayers depends on molecular motion over distances of the order of 1-2 nm. From the concentration dependence of the excimer photoemission curve, it is possible to derive a value for the lipid self-diffusion coefficient. This technique has been intensively used in the past twenty years, leading to rather large numerical values for self-diffusion compared with other approaches based on fluorescent probes tracking. In most cases, the interpretation of the experimental data rely on models for diffusion limited 2d reaction rates, or comparison with 2d lattice random walks. Our approach uses realistic molecular dynamics trajectories to reinterpret these experiments. Based on a well established coarse-grained model for lipid MD simulations (Martini), we show how to relate simulation results to experimental data on excimer formation. Our procedure is quite general and is applicable to all diffusion-limited kinetic processes. Key to our approach is the determination of the acceleration factor of lipid coarse-grained numerical models compared to reality. We find a significant reduction of the diffusion coefficient values, in particular when interleaflet association is taken into account. Our work does not point to deviation from a diffusion-limited mechanism but indicates that the excimer formation across bilayer leaflets could be hindered., Comment: Original manuscript

Minimal surface problems arise naturally in many soft matter systems whose free energies are dominated by surface or interface energies. Of particular interest are the shapes, stability and mechanical stresses of minimal surfaces spanning specific geometric boundaries. The "catenoid" is the best-known example where an analytical solution is known which describes the form and stability of a minimal surface held between two parallel, concentric circular frames. Here we extend this problem to non-axisymmetric, parallel frame shapes of different orientations, by developing a perturbation approach around the known catenoid solution. We show that the predictions of the perturbation theory are in good agreement with experiments on soap films and finite element simulations (Surface Evolver). Combining theory, experiment and simulation, we analyse in depth how the shapes, stability and mechanical properties of the minimal surfaces depend on the type and orientation of elliptical and three-leaf clover shaped frames. In the limit of perfectly aligned non-axisymmetric frames, our predictions show excellent agreement with a recent theory established by Alimov et al (M. M. Alimov, A. V. Bazilevsky and K. G. Kornev, Physics of Fluids, 2021, 33, 052104). Moreover, we put in evidence the intriguing capacity of minimal surfaces between non-axisymmetric frames to transmit a mechanical torque despite being completely liquid. These forces could be interesting to exploit for mechanical self-assembly of soft matter systems or as highly sensitive force captors., Comment: 28 pages, 10 figures main paper, 8 figures in Appendix

Majority of stroke survivors are left with poorly functioning paretic hands. Current rehabilitation devices have failed to motivate the patients enough to continue rehabilitation exercises. The objective of this project, MIDAS (Multi-sensorial Immersive Dynamic Autonomous System) is a proof of concept by using an immersive system to improve motivation of stroke patients for hand rehabilitation. MIDAS is intended for stroke patients who suffer from light to mild stroke. MIDAS is lightweight and portable. It consists of a hand exoskeleton subsystem, a Virtual Reality (VR) subsystem, and an olfactory subsystem. Altogether, MIDAS engages four out of five senses during rehabilitation. To evaluate the efficacy of MIDAS a pilot study consisting of three sessions is carried out on five stroke affected patients. Subsystems of MIDAS are added progressively in each session. The game environment, sonic effects, and scent released is carefully chosen to enhance the immersive experience. 60% of the scores of user experience are above 40 (out of 56). 96% Self Rehabilitation Motivation Scale (SRMS) rating shows that the participants are motivated to use MIDAS and 87% rating shows that MIDAS is exciting for rehabilitation. Participants experienced elevated motivation to continue stroke rehabilitation using MIDAS and no undesired side effects were reported.