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Scale-Dependent Dynamic Alignment (SDDA) in Els\"asser field fluctuations is theorized to suppress nonlinearities and modulate the energy spectrum. Limited empirical evidence exists for SDDA within the solar wind turbulence's inertial range. We analyzed data from the WIND mission to assess the effects of compressibility, intermittency, and imbalance on SDDA. SDDA consistently appears at energy-containing scales, with a trend toward misalignment at inertial scales. Compressible fluctuations show no increased alignment; however, their impact on SDDA's overall behavior is minimal. The alignment angles inversely correlate with field gradient intensity, likely due to "anomalous" or "counterpropagating" wave packet interactions. This suggests that SDDA originates from mutual shearing of Els\"asser fields during imbalanced ($\delta \boldsymbol{z}^{\pm} \gg \delta \boldsymbol{z}^{\mp}$) interactions. Rigorous thresholding on field gradient intensity reveals SDDA signatures across much of the inertial range. The scaling of Els\"asser increments' alignment angle, $\Theta^{z}$, steepens with increasing global Alfv\'enic imbalance, while the angle between magnetic and velocity field increments, $\Theta^{ub}$, becomes shallower. $\Theta^{ub}$ only correlates with global Els\"asser imbalance, steepening as the imbalance increases. Furthermore, increasing alignment in $\Theta^{ub}$ persists deep into the inertial range of balanced intervals but collapses at large scales for imbalanced ones. Simplified theoretical analysis and modeling of high-frequency, low-amplitude noise in the velocity field indicate significant impacts on alignment angle measurements even at very low frequencies, with effects growing as global imbalance increases.

In recent years, water needs increased, driven by climate change and world population growth. In this context, we study groundwater flow in the vadose zone of Beauce aquifer (O-ZNS site, France). This lacustrine limestone vadose zone is characterized by multi-scale heterogeneities. They are defined by strongly various pore structures. This leads to uncertainties for reservoir properties prediction using geophysical methods which impacts reservoir models for flow simulations.In this study, we combined microstructure description and petrophysical analysis in order to model and predict reservoir properties based on different limestones facies and to infer the influence of weathering/fracturing on both acoustics and electrical properties.A total of 16 samples from these facies were cored and characterized by their porosity, permeability, acoustic velocities, complex electrical properties and microstructure analysis.Based on our multi-method approach, we demonstrated the influence of rock structure on reservoir properties prediction and modelling. Petrophysical and microstructure characterization have highlighted two main facies (microporous and homogenous facies and macroporous and heterogenous facies) which can be used to improve reservoir and flow models. However, further development is needed in order to quantify macropores and their link with weathering and to assess permeability models using electrical properties.

Circularly polarized, nearly parallel propagating waves are prevalent in the solar wind at ion-kinetic scales. At these scales, the spectrum of turbulent fluctuations in the solar wind steepens, often called the transition-range, before flattening at sub-ion scales. Circularly polarized waves have been proposed as a mechanism to couple electromagnetic fluctuations to ion gyromotion, enabling ion-scale dissipation that results in observed ion-scale steepening. Here, we study Parker Solar Probe observations of an extended stream of fast solar wind ranging from 15-55 solar radii. We demonstrate that, throughout the stream, transition-range steepening at ion-scales is associated with the presence of significant left handed ion-kinetic scale waves, which are thought to be ion-cyclotron waves. We implement quasilinear theory to compute the rate at which ions are heated via cyclotron resonance with the observed circularly polarized waves given the empirically measured proton velocity distribution functions. We apply the Von Karman decay law to estimate the turbulent decay of the large-scale fluctuations, which is equal to the turbulent energy cascade rate. We find that the ion-cyclotron heating rates are correlated with, and amount to a significant fraction of, the turbulent energy cascade rate, implying that cyclotron heating is an important dissipation mechanism in the solar wind.

Effective management of cooling tower systems requires thorough disinfection. While traditional chemical water treatment methods are currently the most prominent strategy, they are costly and yield limited results when relied upon as the sole approach. Cross-flow microsand filtration systems offer a promising alternative with the added benefit of potentially increasing evaporative cooling efficiency, thus saving energy. A comprehensive data-driven analysis over two cooling seasons evaluated the energetic performance of a system equipped with and without an operating filter. For similar environmental conditions, the coefficient of performance was on average 18% higher and was higher 63% of the time when the filter was operating, indicating superior heat transfer efficiency and significant energy savings. It was also 41% higher during periods of high cooling demand. Consequently, the filter and the system work more efficiently at high wet-bulb temperature and thermal load. Machine learning modeling suggested that operating the filter year-round could save between 5% and 13% of the energy bill, primarily during the cooling season. Continuous filter operation is essential as it mitigates biofouling, underscoring its long-term significance, even during periods of lower thermal loads. Integrating filtration systems into cooling tower management therefore fosters sustainable practices by decreasing energy consumption and biofouling.

The diversity and complementarity of sensors available for Earth Observations (EO) calls for developing bespoke self-supervised multimodal learning approaches. However, current multimodal EO datasets and models typically focus on a single data type, either mono-date images or time series, which limits their impact. To address this issue, we introduce OmniSat, a novel architecture able to merge diverse EO modalities into expressive features without labels by exploiting their alignment. To demonstrate the advantages of our approach, we create two new multimodal datasets by augmenting existing ones with new modalities. As demonstrated for three downstream tasks -- forestry, land cover classification, and crop mapping -- OmniSat can learn rich representations without supervision, leading to state-of-the-art performances in semi- and fully supervised settings. Furthermore, our multimodal pretraining scheme improves performance even when only one modality is available for inference. The code and dataset are available at https://github.com/gastruc/OmniSat.

We analyze in-situ observations of imbalanced solar wind turbulence to evaluate MHD turbulence models grounded in "Critical Balance" (CB) and "Scale-Dependent Dynamic Alignment" (SDDA). At energy injection scales, both outgoing and ingoing modes exhibit a weak cascade; a simultaneous tightening of SDDA is noted. Outgoing modes persist in a weak cascade across the inertial range, while ingoing modes shift to a strong cascade at $\lambda \approx 3 \times 10^{4} d_i$, with associated spectral scalings deviating from expected behavior due to "anomalous coherence" effects. The inertial range comprises two distinct sub-inertial segments. Beyond $\lambda \gtrsim 100 d_i$, eddies adopt a field-aligned tube topology, with SDDA signatures mainly evident in high amplitude fluctuations. The scaling exponents $\zeta_{n}$ of the $n$-th order conditional structure functions, orthogonal to both the local mean field and fluctuation direction, align with the analytical models of Chandran et al. 2015 and Mallet et al. 2017, indicating "multifractal" statistics and strong intermittency; however, scaling in parallel and displacement components is more concave than predicted, possibly influenced by expansion effects. Below $\lambda \approx 100 d_i$, eddies become increasingly anisotropic, evolving into thin current sheet-like structures. Concurrently, $\zeta_{n}$ scales linearly with order, marking a shift towards "monofractal" statistics. At $\lambda \approx 8 d_i$, the increase in aspect ratio halts, and the eddies become quasi-isotropic. This change may signal tearing instability, leading to reconnection, or result from energy redirection into the ion-cyclotron wave spectrum, aligning with the "helicity barrier". Our analysis utilizes 5-point structure functions, proving more effective than the traditional 2-point method in capturing steep scaling behaviors at smaller scales.

Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design. However, building datasets of RNA 3D structures and making appropriate modeling choices remains time-consuming and lacks standardization. In this chapter, we describe the use of rnaglib, to train supervised and unsupervised machine learning-based function prediction models on datasets of RNA 3D structures.

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

We present a study by Scanning Tunneling Microscopy, supported by ab initio calculations, of the interaction between graphene and monolayer (semiconducting) PtSe$_2$ as a function of the twist angle ${\theta}$ between the two layers. We analyze the PtSe$_2$ contribution to the hybrid interface states that develop within the bandgap of the semiconductor to probe the interaction. The experimental data indicate that the interlayer coupling increases markedly with the value of ${\theta}$, which is confirmed by ab initio calculations. The moir\'e patterns observed within the gap are consistent with a momentum conservation rule between hybridized states, and the strength of the hybridization can be qualitatively described by a perturbative model., Comment: Article: 12 pages, 3 figures; Supporting Informations: 19 pages, 12 figures

We study large-amplitude, very oblique Alfv\'en waves at low $\beta$, with small gradient length scales, comparable to the ion inertial scale $d_i$. Such waves have large density fluctuations, and slight dispersion from finite-frequency and finite ion sound radius effects. We derive a weakly nonlinear evolution equation governing the behaviour of the waves in one dimension, and categorize the different solitons appearing in different regimes: the regular solitons involve full rotations of the transverse magnetic field, similar to modified Korteweg-de Vries (mKdV) solitons (our nonlinear equation reduces to the mKdV equation in the long-wavelength limit). However, for sufficiently small soliton widths, some become singular, small-amplitude solitons with density discontinuities, and are thus expected to become strongly dissipative in a real plasma. These solutions may be useful in explaining some aspects of the sharp, ion-scale magnetic field rotations (switchbacks) observed in the near-Sun solar wind by Parker Solar Probe., Comment: 16 pages, 5 figures, accepted by Physics of Plasmas

While there has been significant progress in evaluating and comparing different representations for learning on protein data, the role of surface-based learning approaches remains not well-understood. In particular, there is a lack of direct and fair benchmark comparison between the best available surface-based learning methods against alternative representations such as graphs. Moreover, the few existing surface-based approaches either use surface information in isolation or, at best, perform global pooling between surface and graph-based architectures. In this work, we fill this gap by first adapting a state-of-the-art surface encoder for protein learning tasks. We then perform a direct and fair comparison of the resulting method against alternative approaches within the Atom3D benchmark, highlighting the limitations of pure surface-based learning. Finally, we propose an integrated approach, which allows learned feature sharing between graphs and surface representations on the level of nodes and vertices $\textit{across all layers}$. We demonstrate that the resulting architecture achieves state-of-the-art results on all tasks in the Atom3D benchmark, while adhering to the strict benchmark protocol, as well as more broadly on binding site identification and binding pocket classification. Furthermore, we use coarsened surfaces and optimize our approach for efficiency, making our tool competitive in training and inference time with existing techniques. Our code and data can be found online: $\texttt{github.com/Vincentx15/atomsurf}$, Comment: 10 pages

In magnetized, stratified astrophysical environments such as the Sun's corona and solar wind, Alfv\'enic fluctuations ''reflect'' from background gradients, enabling nonlinear interactions and thus dissipation of their energy into heat. This process, termed ''reflection-driven turbulence,'' is thought to play a crucial role in coronal heating and solar-wind acceleration, explaining a range of observational correlations and constraints. Building on previous works focused on the inner heliosphere, here we study the basic physics of reflection-driven turbulence using reduced magnetohydrodynamics in an expanding box -- the simplest model that can capture the local turbulent plasma dynamics in the super-Alfv\'enic solar wind. Although idealized, our high-resolution simulations and simple theory reveal a rich phenomenology that is consistent with a diverse range of observations. Outwards-propagating fluctuations, which initially have high imbalance, decay nonlinearly to heat the plasma, becoming more balanced and magnetically dominated. Despite the high imbalance, the turbulence is strong because Els\"asser collisions are suppressed by reflection, leading to ''anomalous coherence'' between the two Els\"asser fields. This coherence, together with linear effects, causes the turbulence to anomalously grow the ''anastrophy'' (squared magnetic potential) as it decays, forcing the energy to rush to larger scales and forming a ''$1/f$-range'' energy spectrum as it does so. At late times, the expansion overcomes the nonlinear and Alfv\'enic physics, forming isolated, magnetically dominated ''Alfv\'en vortex'' structures that minimize their nonlinear dissipation. These results can plausibly explain the observed radial and wind-speed dependence of turbulence imbalance, residual energy, plasma heating, and fluctuation spectra, as well as making testable predictions for future observations.

HelioSwarm (HS) is a NASA Medium-Class Explorer mission of the Heliophysics Division designed to explore the dynamic three-dimensional mechanisms controlling the physics of plasma turbulence, a ubiquitous process occurring in the heliosphere and in plasmas throughout the universe. This will be accomplished by making simultaneous measurements at nine spacecraft with separations spanning magnetohydrodynamic and sub-ion spatial scales in a variety of near-Earth plasmas. In this paper, we describe the scientific background for the HS investigation, the mission goals and objectives, the observatory reference trajectory and instrumentation implementation before the start of Phase B. Through multipoint, multiscale measurements, HS promises to reveal how energy is transferred across scales and boundaries in plasmas throughout the universe., Comment: Submitted to Space Science Reviews, 60 pages, 15 figures, 2 videos

The dissipation of magnetized turbulence is fundamental to understanding energy transfer and heating in astrophysical systems. Collisionless interactions, such as resonant wave-particle process, are known to play a role in shaping turbulent astrophysical environments. Here, we present evidence for the mediation of turbulent dissipation in the solar wind by ion-cyclotron waves. Our results show that ion-cyclotron waves interact strongly with magnetized turbulence, indicating that they serve as a major pathway for the dissipation of large-scale electromagnetic fluctuations. We further show that the presence of cyclotron waves significantly weakens observed signatures of intermittency in sub-ion-kinetic turbulence, which are known to be another pathway for dissipation. These observations results suggest that in the absence of cyclotron resonant waves, non-Gaussian, coherent structures are able to form at sub-ion-kinetic scales, and are likely responsible for turbulent heating. We further find that the cross helicity, i.e. the level of Alfv\'enicity of the fluctuations, correlates strongly with the presence of ion-scale waves, demonstrating that dissipation of collisionless plasma turbulence is not a universal process, but that the pathways to heating and dissipation at small scales are controlled by the properties of the large-scale turbulent fluctuations. We argue that these observations support the existence of a helicity barrier, in which highly Alfv\'enic, imbalanced, turbulence is prevented from cascading to sub-ion scales thus resulting in significant ion-cyclotron resonant heating. Our results may serve as a significant step in constraining the nature of turbulent heating in a wide variety of astrophysical systems.

Magnetic fluctuations in the solar wind are often observed to maintain constant magnitude of the magnetic field in a manner consistent with spherically-polarized large-amplitude Alfv\'en waves. We investigate the effect of spherical polarization on the magnetic spectral index through a statistical survey of magnetic fluctuations observed by Parker Solar Probe between 20$R_\odot$ and 200$R_\odot$. We find that deviations from spherical polarization, i.e., changes in $|\mathbf{B}|$ (compressive fluctuations) and one-dimensional discontinuities, have a dramatic effect on the scaling behavior of the turbulent fluctuations. We show that shallow $k^{-3/2}$ spectra are only observed for constant magnetic field strength, three-dimensional structures, which we identify as large amplitude Alfv\'en waves. The presence of compressive fluctuations coincides with a steepening of the spectrum up to $k^{-5/3}$. Steeper power law scalings approaching $k^{-2}$ are observed when the fluctuations are dominated by discontinuities. Near-sun fluctuations are found to be the most spherically polarized, suggesting that this spherical state is fundamental to the generation of the solar wind. With increasing distance from the Sun, fluctuations are found to become less three dimensional and more compressive, which may indicate the breakdown of the Alfv\'enic equilibrium state., Comment: 10 pages, 5 figures. Submitted to The Astrophysical Journal

Terahertz (THz) Spintronic emitters based on ferromagnetic/metal junctions have become an important technology for the THz range, offering powerful and ultra-large spectral bandwidths. These developments have driven recent investigations of two-dimensional (2D) materials for new THz spintronic concepts. 2D materials, such as transition metal dichalcogenides (TMDs), are ideal platforms for SCC as they possess strong spin-orbit coupling (SOC) and reduced crystal symmetries. Moreover, SCC and the resulting THz emission can be tuned with the number of layers, electric field or strain. Here, epitaxially grown 1T-PtSe$_2$ and sputtered Ferromagnet (FM) heterostructures are presented as a novel THz emitter where the 1T crystal symmetry and strong SOC favor SCC. High quality of as-grown PtSe$_2$ layers is demonstrated and further FM deposition leaves the PtSe$_2$ unaffected, as evidenced with extensive characterization. Through this atomic growth control, the unique thickness dependent electronic structure of PtSe$_2$ allows the control of the THz emission by SCC. Indeed, we demonstrate the transition from the inverse Rashba-Edelstein effect in one monolayer to the inverse spin Hall effect in multilayers. This band structure flexibility makes PtSe$_2$ an ideal candidate as a THz spintronic 2D material and to explore the underlying mechanisms and engineering of the SCC for THz emission., Comment: 26 pages, 6 figures

The article presents several methods for the arithmetic of finite abelian groups. We introduce a tool - already used by Delsarte in [1] as I found out later - analogous to Dirichlet's convolution to obtain combinatorial results on these groups. Using this convolution and some group actions, we deduce an interesting fact : the number of generating subsets of a finite abelian group is always a multiple of the order of the group. Eventually, we prove a theorem about the generation of the group of permutations of an abelian group G using only transpositions and translations from the group G., Comment: in French language

The growing reliance of society on social media for authentic information has done nothing but increase over the past years. This has only raised the potential consequences of the spread of misinformation. One of the growing methods in popularity is to deceive users using a deepfake. A deepfake is an invention that has come with the latest technological advancements, which enables nefarious online users to replace their face with a computer generated, synthetic face of numerous powerful members of society. Deepfake images and videos now provide the means to mimic important political and cultural figures to spread massive amounts of false information. Models that can detect these deepfakes to prevent the spread of misinformation are now of tremendous necessity. In this paper, we propose a new deepfake detection schema utilizing two deep learning algorithms: long short term memory and multilayer perceptron. We evaluate our model using a publicly available dataset named 140k Real and Fake Faces to detect images altered by a deepfake with accuracies achieved as high as 74.7%, Comment: 5 Pages

Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (RS) semantic segmentation is costly and time-consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by adapting a model trained on a source domain, where labels are available, to a target domain, without annotations. UDA, while gaining importance in computer vision, is still under-investigated in RS. Thus, we propose a new lightweight model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module (GeoMT), which utilizes geographical coordinates to align the source and target domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic segmentation loss to the frequency of classes. This approach is the first to use geographical metadata for UDA in semantic segmentation. It reaches state-of-the-art performances (47,22% mIoU), reducing at the same time the number of parameters (33M), on a subset of the FLAIR dataset, a recently proposed dataset properly shaped for RS UDA, used for the first time ever for research scopes here.

We study the nonlinear evolution of very oblique small-scale Alfv\'en waves with $k_\perp d_i\gtrsim 1$. At these scales, the waves become significantly compressive, unlike in MHD, due to the Hall term in the equations. We demonstrate that when frequencies are small compared to the ion gyrofrequency and amplitudes small compared to unity, no new nonlinear interaction appears due to the Hall term alone at the lowest non-trivial order, even when $k_\perp d_i \sim 1$. However, at the second non-trivial order, we discover that the Hall physics leads to a slow but resonant nonlinear interaction between co-propagating Alfv\'en waves, an inherently 3D effect. Including the effects of finite temperature, finite frequency, and electron inertia, the two-fluid Alfv\'en wave also becomes dispersive once one or more of $k_\perp \rho_s$, $k_\perp d_e$, or $k_\parallel d_i$ becomes significant: for oblique waves at low $\beta$ as studied here, this can be at a much smaller scale than $d_i$. We show that the timescale for one-dimensional steepening of two-fluid Alfven waves is only significant at these smaller dispersive scales, and also derive an expression for the amplitude of driven harmonics of a primary wave. Importantly, both new effects are absent in gyrokinetics and other commonly used reduced two-fluid models. Our calculations have relevance for the interpretation of laboratory Alfv\'en wave experiments, as well as shedding light on the physics of turbulence in the solar corona and inner solar wind, where the dominant nonlinear interaction between counter-propagating waves is suppressed, allowing these new effects to become important., Comment: 17 pages; submitted to Physics of Plasmas

Retrieval of rain from Passive Microwave radiometers data has been a challenge ever since the launch of the first Defense Meteorological Satellite Program in the late 70s. Enormous progress has been made since the launch of the Tropical Rainfall Measuring Mission (TRMM) in 1997 but until recently the data were processed pixel-by-pixel or taking a few neighboring pixels into account. Deep learning has obtained remarkable improvement in the computer vision field, and offers a whole new way to tackle the rain retrieval problem. The Global Precipitation Measurement (GPM) Core satellite carries similarly to TRMM, a passive microwave radiometer and a radar that share part of their swath. The brightness temperatures measured in the 37 and 89 GHz channels are used like the RGB components of a regular image while rain rate from Dual Frequency radar provides the surface rain. A U-net is then trained on these data to develop a retrieval algorithm: Deep-learning RAIN (DRAIN). With only four brightness temperatures as an input and no other a priori information, DRAIN is offering similar or slightly better performances than GPROF, the GPM official algorithm, in most situations. These performances are assumed to be due to the fact that DRAIN works on an image basis instead of the classical pixel-by-pixel basis.