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Phenology, the timing of cyclical plant life events such as leaf emergence and coloration, is crucial in the bio-climatic system. Climate change drives shifts in these phenological events, impacting ecosystems and the climate itself. Accurate phenology models are essential to predict the occurrence of these phases under changing climatic conditions. Existing methods include hypothesis-driven process models and data-driven statistical approaches. Process models account for dormancy stages and various phenology drivers, while statistical models typically rely on linear or traditional machine learning techniques. Research shows that process models often outperform statistical methods when predicting under climate conditions outside historical ranges, especially with climate change scenarios. However, deep learning approaches remain underexplored in climate phenology modeling. We introduce PhenoFormer, a neural architecture better suited than traditional statistical methods at predicting phenology under shift in climate data distribution, while also bringing significant improvements or performing on par to the best performing process-based models. Our numerical experiments on a 70-year dataset of 70,000 phenological observations from 9 woody species in Switzerland show that PhenoFormer outperforms traditional machine learning methods by an average of 13% R2 and 1.1 days RMSE for spring phenology, and 11% R2 and 0.7 days RMSE for autumn phenology, while matching or exceeding the best process-based models. Our results demonstrate that deep learning has the potential to be a valuable methodological tool for accurate climate-phenology prediction, and our PhenoFormer is a first promising step in improving phenological predictions before a complete understanding of the underlying physiological mechanisms is available., Comment: journal-preprint

Reconciling agricultural production with climate-change mitigation and adaptation is one of the most formidable problems in sustainability. One proposed strategy for addressing this problem is the judicious retention of trees in agricultural systems. However, the magnitude of the current and future-potential benefit that trees contribute remains uncertain, particularly in the agricultural sector where trees can also limit production. Here we help to resolve these issues across a West African region responsible for producing $\approx$60% of the world's cocoa, a crop that contributes one of the highest per unit carbon footprints of all foods. We use machine learning to generate spatially-explicit estimates of shade-tree cover and carbon stocks across the region. We find that existing shade-tree cover is low, and not spatially aligned with climate threat. But we also find enormous unrealized potential for the sector to counterbalance a large proportion of their high carbon footprint annually, without threatening production. Our methods can be applied to other globally significant commodities that can be grown in agroforests, and align with accounting requirements of carbon markets, and emerging legislative requirements for sustainability reporting.

We evaluate the impact of pretraining Graph Transformer architectures on atom-level quantum-mechanical features for the modeling of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. We compare this pretraining strategy with two others: one based on molecular quantum properties (specifically the HOMO-LUMO gap) and one using a self-supervised atom masking technique. After fine-tuning on Therapeutic Data Commons ADMET datasets, we evaluate the performance improvement in the different models observing that models pretrained with atomic quantum mechanical properties produce in general better results. We then analyse the latent representations and observe that the supervised strategies preserve the pretraining information after finetuning and that different pretrainings produce different trends in latent expressivity across layers. Furthermore, we find that models pretrained on atomic quantum mechanical properties capture more low-frequency laplacian eigenmodes of the input graph via the attention weights and produce better representations of atomic environments within the molecule. Application of the analysis to a much larger non-public dataset for microsomal clearance illustrates generalizability of the studied indicators. In this case the performances of the models are in accordance with the representation analysis and highlight, especially for the case of masking pretraining and atom-level quantum property pretraining, how model types with similar performance on public benchmarks can have different performances on large scale pharmaceutical data.

MICADO is a first light instrument for the Extremely Large Telescope (ELT), set to start operating later this decade. It will provide diffraction limited imaging, astrometry, high contrast imaging, and long slit spectroscopy at near-infrared wavelengths. During the initial phase operations, adaptive optics (AO) correction will be provided by its own natural guide star wavefront sensor. In its final configuration, that AO system will be retained and complemented by the laser guide star multi-conjugate adaptive optics module MORFEO (formerly known as MAORY). Among many other things, MICADO will study exoplanets, distant galaxies and stars, and investigate black holes, such as Sagittarius A* at the centre of the Milky Way. After their final design phase, most components of MICADO have moved on to the manufacturing and assembly phase. Here we summarize the final design of the instrument and provide an overview about its current manufacturing status and the timeline. Some lessons learned from the final design review process will be presented in order to help future instrumentation projects to cope with the challenges arising from the substantial differences between projects for 8-10m class telescopes (e.g. ESO-VLT) and the next generation Extremely Large Telescopes (e.g. ESO-ELT). Finally, the expected performance will be discussed in the context of the current landscape of astronomical observatories and instruments. For instance, MICADO will have similar sensitivity as the James Webb Space Telescope (JWST), but with six times the spatial resolution., Comment: Proceedings of the SPIE, Volume 13096, id. 1309611 11 pp. (2024)

Change-points in functional time series can be detected using the CUSUM-statistic, which is a non-linear functional of the partial sum process. Various methods have been proposed to obtain critical values for this statistic. In this paper we use the functional autoregressive sieve bootstrap to imitate the behavior of the partial sum process and we show that this procedure asymptotically correct estimates critical values under the null hypothesis. We also establish the consistency of the corresponding bootstrap based test under local alternatives. The finite sample performance of the procedure is studied via simulations under the null -hypothesis and under the alternative.

Scanning tunneling microscopy is the method of choice for characterizing charge density waves by imaging the variation in atomic-scale contrast of the surface. Due to the measurement principle of scanning tunneling microscopy, the electronic and lattice degrees of freedom are convoluted, making it difficult to disentangle a structural displacement from spatial variations in the electronic structure. In this work, we quantify the influence of the displacement of the surface-terminating Se atoms on the 3 x 3 charge density wave contrast in scanning probe microscopy images of 2H-NbSe2. In scanning tunneling microscopy images, we observe the 3 x 3 charge density wave superstructure and atomic lattice at all probed tip-sample distances. In contrast, non-contact atomic force microscopy images show both periodicities only at small tip-sample distances while, unexpectedly, a 3 x 3 superstructure is present at larger tip-sample distances. Using density functional theory calculations, we qualitatively reproduce the experimental findings and reveal that the 3 x 3 superstructure at different tip-sample distances in non-contact atomic force microscopy images is a result from different underlying interactions. In addition, we show that the displacement of the surface-terminating Se atoms has a negligible influence to the contrast in scanning tunneling microscopy images. Our work presents a method on how to discriminate the influence of the surface corrugation from the variation of the charge density to the charge density wave contrast in scanning probe microscopy images, which can foster the understanding of the charge density wave mechanism in low-dimensional materials.

Utilizing the electron orbital degree of freedom in heterostructures is attracting increasing attention due to the possibility of achieving much larger conversion rates between charge and orbital angular momentum flow compared to the intrinsic electron spin. Here, we consider orbital angular momentum currents in a tight-binding multiorbital lattice model and derive their continuity equation. From it, we observe that the current is not conserved and apply similar considerations to the recently discovered altermagnets. We find nonzero orbital torque terms, elucidate their physical mechanism, and show numerically that they contribute to dampening the orbital angular momentum current flowing in multiorbital heterostructures. Moreover, we compute the orbital RKKY interaction and find it exhibits similar characteristics as the orbital angular momentum current mediating it, thus serving as a direct experimental probe of such currents., Comment: 7 pages, 4 figures

Message passing neural networks (MPNNs) operate on graphs by exchanging information between neigbouring nodes. MPNNs have been successfully applied to various node-, edge-, and graph-level tasks in areas like molecular science, computer vision, natural language processing, and combinatorial optimization. However, most MPNNs require training on large amounts of labeled data, which can be costly and time-consuming. In this work, we explore the use of various untrained message passing layers in graph neural networks, i.e. variants of popular message passing architecture where we remove all trainable parameters that are used to transform node features in the message passing step. Focusing on link prediction, we find that untrained message passing layers can lead to competitive and even superior performance compared to fully trained MPNNs, especially in the presence of high-dimensional features. We provide a theoretical analysis of untrained message passing by relating the inner products of features implicitly produced by untrained message passing layers to path-based topological node similarity measures. As such, untrained message passing architectures can be viewed as a highly efficient and interpretable approach to link prediction.

Altermagnets is a class of antiferromagnetic materials which has electron bands with lifted spin degeneracy in momentum space but vanishing net magnetization and no stray magnetic fields. Because of these properties, altermagnets have attracted much attention for potential use in spintronics. We here show that despite the absence of bulk magnetization, the itinerant electrons in altermagnets can generate a magnetization close to edges and vacuum interfaces. We find that surface-induced magnetization can also occur for conventional antiferromagnets with spin-degenerate bands, where the magnetization from the itinerant electrons originates from a subtle yet nonvanishing redistribution of the probability density on the unit-cell level. An intuitive explanation of this effect in a phenomenological model is provided. In the altermagnetic case, the induced magnetization has a different spatial dependence than in the antiferromagnetic case due to the anisotropy of the spin-polarized Fermi surfaces, causing the edge-induced Friedel oscillations of the spin-up and -down electron densities to have different periods. We employ both a low-energy effective continuum model and lattice tight-binding calculations. Our results have implications for the usage of altermagnets and antiferromagnets in nanoscale spintronic applications., Comment: 16 pages, 12 figures. Published version

The Microwave SQUID Multiplexer ({\mu}MUX) is the device of choice for the readout of a large number of Low-Temperature Detectors in a wide variety of experiments within the fields of astronomy and particle physics. While it offers large multiplexing factors, the system noise performance is highly dependent on the cold and warm-readout electronic systems used to read it out, as well as the demodulation domain and parameters chosen. In order to understand the impact of the readout systems in the overall detection system noise performance, first we extended the available {\mu}MUX simulation frameworks including additive and multiplicative noise sources in the probing tones (i.e. phase and amplitude noise), along with the capability of demodulating the scientific data, either in resonator's phase or scattering amplitude. Then, considering the additive noise as a dominant noise source, the optimum readout parameters to achieve minimum system noise were found for both open-loop and flux-ramp demodulation schemes in the aforementioned domains. Later, we evaluated the system noise sensitivity to multiplicative noise sources under the optimum readout parameters. Finally, as a case study, we evaluated the optimal demodulation domain and expected system noise level for a typical Software-Defined Radio (SDR) readout system. This work leads to an improved system performance prediction and noise engineering based on the available readout electronics and selected demodulation domain., Comment: This article has been submitted to the Journal of Applied Physics. This is the revised version (V1)

Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanity's biggest challenges, climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they focus on specific, local regions at high resolution, or they offer global coverage at low resolution. There is a need for a machine learning-ready, globally representative, high-resolution benchmark. Our findings indicate significant variability in biomass estimates across different vegetation types, emphasizing the necessity for a dataset that accurately captures global diversity. To address these gaps, we introduce a comprehensive new dataset that is globally distributed, covers a range of vegetation types, and spans several years. This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery. Additionally, it includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map. We also produce a dense, high-resolution (10m) map of AGB predictions for the entire area covered by the dataset. Rigorously tested, our dataset is accompanied by several benchmark models and is publicly available. It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation. The GitHub repository github.com/ghjuliasialelli/AGBD serves as a one-stop shop for all code and data.

Access to detailed war impact assessments is crucial for humanitarian organizations to effectively assist populations most affected by armed conflicts. However, maintaining a comprehensive understanding of the situation on the ground is challenging, especially in conflicts that cover vast territories and extend over long periods. This study presents a scalable and transferable method for estimating war-induced damage to buildings. We first train a machine learning model to output pixel-wise probability of destruction from Synthetic Aperture Radar (SAR) satellite image time series, leveraging existing, manual damage assessments as ground truth and cloud-based geospatial analysis tools for large-scale inference. We further post-process these assessments using open building footprints to obtain a final damage estimate per building. We introduce an accessible, open-source tool that allows users to adjust the confidence interval based on their specific requirements and use cases. Our approach enables humanitarian organizations and other actors to rapidly screen large geographic regions for war impacts. We provide two publicly accessible dashboards: a Ukraine Damage Explorer to dynamically view our pre-computed estimates, and a Rapid Damage Mapping Tool to easily run our method and produce custom maps.

Ensuring high-quality data is paramount for maximizing the performance of machine learning models and business intelligence systems. However, challenges in data quality, including noise in data capture, missing records, limited data production, and confounding variables, significantly constrain the potential performance of these systems. In this study, we propose an architecture-agnostic algorithm, Gradient Guided Hypotheses (GGH), designed to address these challenges. GGH analyses gradients from hypotheses as a proxy of distinct and possibly contradictory patterns in the data. This framework entails an additional step in machine learning training, where gradients can be included or excluded from backpropagation. In this manner, missing and noisy data are addressed through a unified solution that perceives both challenges as facets of the same overarching issue: the propagation of erroneous information. Experimental validation of GGH is conducted using real-world open-source datasets, where records with missing rates of up to 98.5% are simulated. Comparative analysis with state-of-the-art imputation methods demonstrates a substantial improvement in model performance achieved by GGH. Specifically in very high scarcity regimes, GGH was found to be the only viable solution. Additionally, GGH's noise detection capabilities are showcased by introducing simulated noise into the datasets and observing enhanced model performance after filtering out the noisy data. This study presents GGH as a promising solution for improving data quality and model performance in various applications.

We propose a framework for the analysis of transmission channels in a large class of dynamic models. To this end, we formulate our approach both using graph theory and potential outcomes, which we show to be equivalent. Our method, labelled Transmission Channel Analysis (TCA), allows for the decomposition of total effects captured by impulse response functions into the effects flowing along transmission channels, thereby providing a quantitative assessment of the strength of various transmission channels. We establish that this requires no additional identification assumptions beyond the identification of the structural shock whose effects the researcher wants to decompose. Additionally, we prove that impulse response functions are sufficient statistics for the computation of transmission effects. We demonstrate the empirical relevance of TCA for policy evaluation by decomposing the effects of policy shocks arising from a variety of popular macroeconomic models.

We propose altermagnet-superconductor junctions as a way to achieve a thermoelectric response in metals free of external or stray magnetic fields. We combine qualitative analysis in a simplified model with a more rigorous approach based on the inverse proximity effect in the functional-integral formulation. We show that coupling an altermagnet to a superconductor in a bilayer induces a momentum-dependent spin-splitting in the superconductor. When tunneling occurs between this bilayer and a different altermagnet, a spin-dependent particle-hole symmetry breakdown arises in the transport, which leads to a thermoelectric response. Our results show that the altermagnet-superconductor junctions may achieve comparable thermoelectric performance to ferromagnet-superconductor junctions, featuring a nonmonotonic dependence of the figure of merit on the strength of the altermagnetic splitting. We also point out an often overlooked fact regarding the inverse proximity effect in superconductors, namely that even in a normal metal-superconductor junction there is a minigap in the superconductor, which gives rise to a four-peak structure in the DOS reminiscent of spin-split superconductors. Our results show that altermagnetic metals, unlike conventional antiferromagnets, can be used for efficient cryogenic thermoelectricity., Comment: 14 pages, 11 multi-panel figures

We propose a method for obtaining parsimonious decompositions of networks into higher order interactions which can take the form of arbitrary motifs.The method is based on a class of analytically solvable generative models, where vertices are connected via explicit copies of motifs, which in combination with non-parametric priors allow us to infer higher order interactions from dyadic graph data without any prior knowledge on the types or frequencies of such interactions. Crucially, we also consider 'degree--corrected' models that correctly reflect the degree distribution of the network and consequently prove to be a better fit for many real world--networks compared to non-degree corrected models. We test the presented approach on simulated data for which we recover the set of underlying higher order interactions to a high degree of accuracy. For empirical networks the method identifies concise sets of atomic subgraphs from within thousands of candidates that cover a large fraction of edges and include higher order interactions of known structural and functional significance. The method not only produces an explicit higher order representation of the network but also a fit of the network to analytically tractable models opening new avenues for the systematic study of higher order network structures., Comment: 18 pages, 3 figures, Supporting Information (SI.pdf)

We present a learning-based approach to reconstruct buildings as 3D polygonal meshes from airborne LiDAR point clouds. What makes 3D building reconstruction from airborne LiDAR hard is the large diversity of building designs and especially roof shapes, the low and varying point density across the scene, and the often incomplete coverage of building facades due to occlusions by vegetation or to the viewing angle of the sensor. To cope with the diversity of shapes and inhomogeneous and incomplete object coverage, we introduce a generative model that directly predicts 3D polygonal meshes from input point clouds. Our autoregressive model, called Point2Building, iteratively builds up the mesh by generating sequences of vertices and faces. This approach enables our model to adapt flexibly to diverse geometries and building structures. Unlike many existing methods that rely heavily on pre-processing steps like exhaustive plane detection, our model learns directly from the point cloud data, thereby reducing error propagation and increasing the fidelity of the reconstruction. We experimentally validate our method on a collection of airborne LiDAR data of Zurich, Berlin and Tallinn. Our method shows good generalization to diverse urban styles.

Identifying superconducting materials with spin-polarized Cooper pairs is an important objective both for exploration of new fundamental physics and for cryogenic applications in spintronics and quantum sensing. We here compute the critical temperature $T_c$ of the superconducting transition in a bilayer comprised of a superconductor with an intrinsic spin-triplet order parameter and a ferromagnet. We determine how $T_c$ varies both with the thickness of the ferromagnet and its magnetization direction. We show that both the orbital and spin part of the triplet superconducting order parameter leave clear signatures in $T_c$ which do not appear in a bilayer of a conventional s-wave superconductor and a ferromagnet. In particular, the dependence of $T_c$ on these variables changes depending on whether or not the superconducting order parameter features Andreev bound-states and also changes qualitatively when the magnetization is rotated in the plane of the ferromagnetic film. Measurements of $T_c$ in such bilayers are therefore useful to identify the pairing symmetry of intrinsic triplet superconductors.

Objective. The availability of magnetic nanoparticles with medical approval for human intervention is fundamental to the clinical translation of magnetic particle imaging (MPI). In this work, we thoroughly evaluate and compare the magnetic properties of an magnetic resonance imaging (MRI) approved tracer to validate its performance for MPI in future human trials. Approach. We analyze whether the recently approved MRI tracer Resotran is suitable for MPI. In addition, we compare Resotran with the previously approved and extensively studied tracer Resovist, with Ferrotran, which is currently in a clinical phase III study, and with the tailored MPI tracer Perimag. Main results. Initial magnetic particle spectroscopy measurements indicate that Resotran exhibits performance characteristics akin to Resovist, but below Perimag. We provide data on four different tracers using dynamic light scattering, transmission electron microscopy, vibrating sample magnetometry measurements, magnetic particle spectroscopy to derive hysteresis, point spread functions, and a serial dilution, as well as system matrix based MPI measurements on a preclinical scanner (Bruker 25/20 FF), including reconstructed images. Significance. Numerous approved magnetic nanoparticles used as tracers in MRI lack the necessary magnetic properties essential for robust signal generation in MPI. The process of obtaining medical approval for dedicated MPI tracers optimized for signal performance is an arduous and costly endeavor, often only justifiable for companies with a well-defined clinical business case. Resotran is an approved tracer that has become available in Europe for MRI. In this work, we study the eligibility of Resotran for MPI in an effort to pave the way for human MPI trials.

Chemical reactivity models are developed to predict chemical reaction outcomes in the form of classification (success/failure) or regression (product yield) tasks. The vast majority of the reported models are trained solely on chemical information such as reactants, products, reagents, and solvents, but not on the details of a synthetic protocol. Herein incorporation of procedural text with the aim to augment the Graphormer reactivity model and improve its accuracy is presented. Two major approaches are used: training an adapter Graphormer model that is provided with a GPT-2-derived latent representation of the text procedure (ReacLLaMA-Adapter) and labeling an unlabeled part of a dataset with the LLaMA 2 model followed by training the Graphormer on an extended dataset (Zero-Shot Labeling ReacLLaMA). Both methodologies enhance the discernment of unpromising reactions, thereby providing more accurate models with improved specificity.

The so-called Transport of Intensity Equation (TIE) phase retrieval technique is widely applied in light, x-ray and electron optics to reconstruct, e.g., refractive indices, electric and magnetic fields in solids. Here, we present a largely improved TIE reconstruction algorithm, which properly considers intensity variations as well as unknown boundary conditions in a finite difference implementation of the Transport of Intensity partial differential equation. That largely removes reconstruction artifacts encountered in state-of-the-art Poisson solvers of the TIE, and hence significantly increases the applicability of the technique.

Computer-Aided Design (CAD) model reconstruction from point clouds is an important problem at the intersection of computer vision, graphics, and machine learning; it saves the designer significant time when iterating on in-the-wild objects. Recent advancements in this direction achieve relatively reliable semantic segmentation but still struggle to produce an adequate topology of the CAD model. In this work, we analyze the current state of the art for that ill-posed task and identify shortcomings of existing methods. We propose a hybrid analytic-neural reconstruction scheme that bridges the gap between segmented point clouds and structured CAD models and can be readily combined with different segmentation backbones. Moreover, to power the surface fitting stage, we propose a novel implicit neural representation of freeform surfaces, driving up the performance of our overall CAD reconstruction scheme. We extensively evaluate our method on the popular ABC benchmark of CAD models and set a new state-of-the-art for that dataset. Project page: https://www.obukhov.ai/point2cad}{https://www.obukhov.ai/point2cad.