@inproceedings{21135, abstract = {Three-dimensional (3D) microscopy data is often anisotropic with significantly lower resolution (up to 8x) along the z axis than along the xy axes. Computationally generating plausible isotropic resolution from anisotropic imaging data would benefit the visual analysis of large-scale volumes. This paper proposes niiv, a self-supervised method for isotropic reconstruction of 3D microscopy data that can quickly produce images at arbitrary output resolutions. The representation embeds a learned latent code within a neural field that describes the implicit higher-resolution isotropic image region. We use an attention-guided latent interpolation approach, which allows flexible information exchange over a local latent neighborhood. Under isotropic volume assumptions, we self-supervise this representation on low-/high-resolution lateral image pairs to reconstruct an isotropic volume from low-resolution axial images. We evaluate our method on simulated and real anisotropic electron (EM) and light microscopy (LM) data. Compared to diffusion-based baselines, niiv shows improved reconstruction quality (+1 dB PSNR) and is over three orders of magnitude faster (1,000x) to infer. Specifically, niiv reconstructs a 128^3 voxel volume in 2/10th of a second, renderable at varying (continuous) high resolutions for display. Our code is available at https://github.com/jakobtroidl/niiv-miccai.}, author = {Troidl, Jakob and Liang, Yiqing and Beyer, Johanna and Tavakoli, Mojtaba and Danzl, Johann G and Hadwiger, Markus and Pfister, Hanspeter and Tompkin, James}, booktitle = {1st International Workshop on Efficient Medical Artificial Intelligence}, isbn = {9783032139603}, issn = {1611-3349}, location = {Daejeon, South Korea}, pages = {257--267}, publisher = {Springer Nature}, title = {{niiv: Interactive Self-supervised Neural Implicit Isotropic Volume Reconstruction}}, doi = {10.1007/978-3-032-13961-0_26}, volume = {16318}, year = {2026}, } @article{21767, abstract = {The involvement of non-scientific staff in discussions about animal welfare and scientific quality is essential for biomedical research progress. In this study, we developed a survey to collect the self-perception of animal care staff (ACS) and laboratory technicians about their involvement in scientific planning and conduct. Participants were contacted to complete an anonymous online questionnaire. We obtained 850 responses, mainly from Europe: 564 from ACS and 286 from laboratory technicians. Job satisfaction was assessed as positive by ACS and laboratory technicians despite the low frequency of culture of care activities and mental health meetings. Both groups expressed their desire to be trained in research planning and conduct; however, regular training was not reported. In addition, the inability to act on animal welfare concerns owing to experimental reasons was reported by both groups. Over half of the participants felt valued and appreciated by the lead scientists or animal facility manager; however, it is not clear how they are acknowledged, as their names on the authors list or in the manuscript acknowledgments are barely included. Our results indicated that involvement of ACS and laboratory technicians in planning and conducting studies would improve their understanding of how experiments are done, and therefore communication processes, work satisfaction, animal welfare, and scientific quality. Finally, we provided recommendations to improve the engagement of ACS and laboratory technicians in discussions about animal research planning and conduct.}, author = {Gonzalez-Uarquin, Fernando and Jirkof, Paulin and Bert, Bettina and Hawkins, Penny and Angelovski, Ljupco and Baumgart, Jan and Baumgart, Nadine and Cevik, Özge S. and Franco, Nuno H. and Horata, Erdal and Kaura, Rohish and Neuhaus, Winfried and Riso, Brigida and Smith, Adrian J. and Sotiropoulos, Athanassia and Vitale, Augusto and Schober, Sophie}, issn = {1758-1117}, journal = {Laboratory Animals}, publisher = {SAGE Publications}, title = {{Building bridges: Involvement of animal care staff and laboratory technicians in experimental planning and conduct of animal studies for better job satisfaction and science}}, doi = {10.1177/00236772251400976}, year = {2026}, } @inproceedings{21949, abstract = {Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation, and edema. However, the inherently dynamic nature of the heart imposes strict limits on acquisition times, making high-resolution T1 mapping a persistent challenge. Compressed sensing (CS) approaches have reduced scan durations by undersampling k-space and reconstructing images from partial data, and recent studies show that jointly optimizing the undersampling patterns with the reconstruction network can substantially improve performance. Still, most current T1 mapping pipelines rely on static, hand-crafted masks that do not exploit the full acceleration and accuracy potential. Furthermore, most existing methods do not levarage the physical T1 decay model in optimization. In this work, we introduce T1- PILOT: an end-to-end method that explicitly incorporates the T1 signal relaxation model into the sampling–reconstruction framework to guide the learning of non-Cartesian trajectories, cross-frame alignment, and T1 decay estimation. Through extensive experiments on the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies (including learned single-mask and fixed radial or golden-angle sampling schemes), achieving higher T1 map fidelity at greater acceleration factors. In particular, we observe consistent gains in PSNR and VIF relative to existing methods, along with marked improvements in delineating finer myocardial structures. Our results highlight that optimizing sampling trajectories in tandem with the physical relaxation model leads to both enhanced quantitative accuracy and reduced acquisition times. Code for reproducing all experiments and results is available at https://github.com/tamirshor7/T1-PILOT}, author = {Shor, Tamir and Freiman, Moti and Baskin, Chaim and Bronstein, Alexander}, booktitle = {Medical Imaging with Deep Learning}, issn = {2640-3498}, keywords = {Cardiac T1 Mapping, Trajectory Optimization and Reconstruction, PhysicsInformed Deep-Learning}, location = {Taipei, Taiwan}, pages = {1969--1982}, publisher = {ML Research Press}, title = {{T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration}}, volume = {315}, year = {2026}, } @article{22607, abstract = {Farkas established that a system of linear inequalities has a solution if and only if we cannot obtain a contradiction by taking a linear combination of the inequalities. We state and formally prove several Farkas-like theorems over linearly ordered fields in Lean 4. Furthermore, we extend duality theory to the case when some coefficients are allowed to take "infinite values". Code: https://github.com/madvorak/duality/tree/v3.2.0}, author = {Dvorak, Martin and Kolmogorov, Vladimir}, issn = {3117-4604}, journal = {Annals of Formalized Mathematics}, keywords = {Farkas lemma, linear programming, extended reals, calculus of inductive constructions}, publisher = {EPI Sciences}, title = {{Duality theory in linear optimization and its extensions -- formally verified}}, doi = {10.46298/afm.14253}, volume = {2}, year = {2026}, } @misc{22242, abstract = {This deposit contains the data and analysis code accompanying the publication "Low-Noise Quantum Dots in Ultra-Shallow Ge/SiGe Heterostructures for Prototyping Hybrid Semiconducting–Superconducting Devices" (Borovkov et al.). The deposit includes the raw transport and current-noise measurements of three gate-defined quantum-dot devices as QCodes SQLite databases, the master table of the charge-noise (flank-method) analysis with the pointers linking every analyzed PSD trace to the raw data, the toy-model noise simulation datasets behind the supplementary figures, the archived analysis figures (PSD fits and lever-arm extractions), and the Python code reproducing the full analysis and all figures. The code is also maintained at https://github.com/ISTA-Nanoelectronics/noise_paper_public; instructions are provided in the README files.}, author = {Borovkov, Maksim}, publisher = {Institute of Science and Technology Austria}, title = {{Low-Noise Quantum Dots in Ultra-Shallow Ge/SiGe Heterostructures for Prototyping Hybrid Semiconducting-Superconducting Devices}}, doi = {10.15479/AT-ISTA-22242}, year = {2026}, } @article{22647, abstract = {Within the plant endomembrane system, the vesicle coat protein clathrin localizes to the plasma membrane (PM) and the trans-Golgi Network/early endosome (TGN/EE). While the role of clathrin in endocytosis at the PM is well established, its function at TGN/EE, presumably in late secretion (trafficking from the TGN/EE to the cell surface) or en route to the vacuole, is debated. Similarly debated are potential homeostatic mechanisms balancing the trafficking routes, especially endocytosis and late secretion. We address these questions in Arabidopsis thaliana using conditional silencing of CLATHRIN HEAVY CHAIN (CHC), conditional overexpression of the clathrin uncoating factor AUXILIN-LIKE1, and secretory mutants. CHC silencing interferes with trafficking of cargoes destined for the apoplast and the PM, supporting a function of clathrin in late secretion. The secretory cargoes become abnormally rerouted from the TGN/EE to the vacuole. Unlike CHC silencing, overexpression of AUXILIN-LIKE1 selectively inhibits clathrin-mediated endocytosis while secretion continues normally at early points of induction. Conversely, secretory mutants exhibit a reduced PM recruitment of clathrin, and variably, of the TPLATE endocytic component. Together, our data show a role of clathrin in secretion and suggest secretion as a fundamental trafficking process to which endocytosis is adjusted by a weak homeostatic mechanism.}, author = {Adamowski, Maciek and Gackowski, Adam and Matijevic, Ivana and Alotaibi, Saqer S. and Friml, Jiří}, issn = {1469-8137}, journal = {New Phytologist}, publisher = {Wiley}, title = {{The role of clathrin in post‐Golgi secretion in plant cells}}, doi = {10.1111/nph.71454}, year = {2026}, } @misc{21668, abstract = {This artifact allows to review and reproduce the experiments from the paper *A Revised Practitioner's Guide to MDP Model Checking Algorithms*. The package contains all original logfiles and derived data used to generate the plots as in the paper. Furthermore, the artifact contains the model checking tools `Storm` and `mcsta` in the version exercised in the paper, the used Docker container, as well as benchmark instances and execution scripts to reproduce the experiments. See also the artifact of the conference paper: https://zenodo.org/records/7509474}, author = {Hartmanns, Arnd and Junges, Sebastian and Quatmann, Tim and Weininger, Maximilian}, publisher = {Zenodo}, title = {{Benchmark data for the revised practitioner's guide to MDP model checking algorithms}}, doi = {10.5281/ZENODO.14500423}, year = {2025}, } @article{15128, abstract = {We prove a universal mesoscopic central limit theorem for linear eigenvalue statistics of a Wigner-type matrix inside the bulk of the spectrum with compactly supported twice continuously differentiable test functions. The main novel ingredient is an optimal local law for the two-point function $T(z,\zeta)$ and a general class of related quantities involving two resolvents at nearby spectral parameters.}, author = {Riabov, Volodymyr}, issn = {0246-0203}, journal = {Annales de l'institut Henri Poincare (B) Probability and Statistics}, number = {1}, pages = {129--154}, publisher = {Institute of Mathematical Statistics}, title = {{Mesoscopic eigenvalue statistics for Wigner-type matrices}}, doi = {10.1214/23-AIHP1438}, volume = {61}, year = {2025}, } @misc{19033, abstract = {This data set contains the simulation input files, scripts, and figures data belonging to the publication Alberto Scacchi, Carlo Rigoni, Mikko P. Haataja, Jakko V. I. Timonen, and Maria Sammalkorpi, "A Coarse-grained Model for Aqueous Two-phase Systems: Application to Ferrofluids", Journal of Colloids and Interface Science (2025). https://doi.org/10.1016/j.jcis.2025.01.256.}, author = {Scacchi, Alberto}, publisher = {Fairdata}, title = {{2025_SCACCHI_JCIS}}, doi = {10.23729/4fb80194-cdb2-4f49-94f4-f8a87b8e29c1}, year = {2025}, } @article{19416, abstract = {Recently, Biagioli and Tompkins (2023, https://doi.org/10.1029/2022ms003231) used a simple stochastic model to derive a dimensionless parameter to predict convective self aggregation (SA) development, which was based on the derivation of the maximum free convective distance ($d_{clr}$) expected in the pre-aggregated, random state. Our goal is to test and further investigate this hypothesis, namely that $d_{clr}$ can predict SA occurrence, using an ensemble of twenty-four distinct combinations of horizontal mixing, planetary boundary layer (PBL), and microphysical parameterizations. We conclude that the key impact of parameterization schemes on SA is through their control of the number of convective cores and their relative spacing, $d_{clr}$, which itself is impacted by cold-pool (CP) properties and mean updraft core size. SA is more likely when the convective core count is small, while CPs modify convective spacing via suppression in their interiors and triggering by gust-front convergence and collisions. Each parameterization scheme emphasizes a different mechanism. Subgrid-scale horizontal turbulent mixing mainly affects SA through the determination of convective core size and thus spacing. The sensitivity to the microphysics is mainly through rain evaporation and the subsequent impact on CPs, while perturbations to the ice cloud microphysics have a limited effect. Non-local PBL mixing schemes promote SA primarily by increasing convective inhibition through inversion entrainment and altering low cloud amounts, leading to fewer convective cores and larger $d_{clr}$. }, author = {Casallas Garcia, Alejandro and Tompkins, A.M. and Muller, Caroline J and Thompson, G.}, issn = {1942-2466}, journal = {Journal of Advances in Modeling Earth Systems}, number = {3}, publisher = {Wiley}, title = {{Sensitivity of self-aggregation and the key role of the free convection distance}}, doi = {10.1029/2024MS004791}, volume = {17}, year = {2025}, } @article{19443, abstract = {In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training datasets. The protracted generation time of physics-based models, often employed to produce these datasets, limits their broad applicability. We introduce FakET, a method based on neural style transfer, capable of simulating the forward operator of any cryo transmission electron microscope. It can be used to adapt a synthetic training dataset according to reference data producing high-quality simulated micrographs or tilt-series. To assess the quality of our generated data, we used it to train a state-of-the-art localization and classification architecture and compared its performance with a counterpart trained on benchmark data. Remarkably, our technique matches the performance, boosts data generation speed 750x, uses 33x less memory, and scales well to typical transmission electron microscope detector sizes. It leverages GPU acceleration and parallel processing. The source code is available at https://github.com/paloha/faket/.}, author = {Harar, Pavol and Herrmann, Lukas and Grohs, Philipp and Haselbach, David}, issn = {1878-4186}, journal = {Structure}, number = {4}, pages = {820--827.e4}, publisher = {Elsevier}, title = {{FakET: Simulating cryo-electron tomograms with neural style transfer}}, doi = {10.1016/j.str.2025.01.020}, volume = {33}, year = {2025}, } @misc{19771, abstract = {This artifact allows to review and reproduce the Isabelle proofs and practical experiments from the paper *Fixed Point Certificates for Reachability and Expected Rewards in MDPs*. The contents are two-fold: First, the artifact contains a formally verified certificate checker for the certificates presented in the paper. The formal Isabelle/HOL proofs of the background theory can be inspected, checked by Isabelle and the code extraction can be retraced. Second, the artifact contains a modified version of the model checking tool `Storm` with support for certificate generation. Together with the provided scripts and benchmark files, this allows to reproduce the experiments from the paper. An appropriate subset of the experiments is given to allow a review in a timely manner. In addition, original logfiles from our experiments are provided, allowing a detailed inspection. The package includes convenient installation scripts for [the TACAS 2023 VM](https://doi.org/10.5281/zenodo.7113223) (based on Ubuntu 22.04). A native installation on Linux or macOS systems (including the newer ARM-based machines) is also possible.}, author = {Chatterjee, Krishnendu and Quatmann, Tim and Schäffeler, Maximilian and Weininger, Maximilian and Winkler, Tobias and Zilken, Daniel}, publisher = {Zenodo}, title = {{Artifact: Fixed point certificates for reachability and expected rewards in MDPs}}, doi = {10.5281/ZENODO.14626585}, year = {2025}, } @article{19796, abstract = {Motivation: Boolean networks are popular dynamical models of cellular processes in systems biology. Their attractors model phenotypes that arise from the interplay of key regulatory subcircuits. A succession diagram (SD) describes this interplay in a discrete analog of Waddington’s epigenetic attractor landscape that allows for fast identification of attractors and attractor control strategies. Efficient computational tools for studying SDs are essential for the understanding of Boolean attractor landscapes and connecting them to their biological functions. Results: We present a new approach to SD construction for asynchronously updated Boolean networks, implemented in the biologist’s Boolean attractor landscape mapper, biobalm. We compare biobalm to similar tools and find a substantial performance increase in SD construction, attractor identification, and attractor control. We perform the most comprehensive comparative analysis to date of the SD structure in experimentally-validated Boolean models of cell processes and random ensembles. We find that random models (including critical Kauffman networks) have relatively small SDs, indicating simple decision structures. In contrast, nonrandom models from the literature are enriched in extremely large SDs, indicating an abundance of decision points and suggesting the presence of complex Waddington landscapes in nature. Availability and implementation: The tool biobalm is available online at https://github.com/jcrozum/biobalm. Further data, scripts for testing, analysis, and figure generation are available online at https://github.com/jcrozum/biobalm-analysis and in the reproducibility artefact at https://doi.org/10.5281/zenodo.13854760.}, author = {Trinh, Van Giang and Park, Kyu Hyong and Pastva, Samuel and Rozum, Jordan C.}, issn = {1367-4811}, journal = {Bioinformatics}, number = {5}, publisher = {Oxford University Press}, title = {{Mapping the attractor landscape of Boolean networks with biobalm}}, doi = {10.1093/bioinformatics/btaf280}, volume = {41}, year = {2025}, } @inproceedings{20032, abstract = {We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) (Pervez et al., 2024), we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. This significant improvement enables efficient modeling of long-term dynamics without sacrificing accuracy or interpretability. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources. Consequently, S-MNN can drop-in replace the original MNN in applications, providing a practical and efficient tool for integrating mechanistic bottlenecks into neural network models of complex dynamical systems. Source code is available at https://github.com/IST-DASLab/ScalableMNN.}, author = {Chen, Jiale and Yao, Dingling and Pervez, Adeel A and Alistarh, Dan-Adrian and Locatello, Francesco}, booktitle = {13th International Conference on Learning Representations}, isbn = {9798331320850}, location = {Singapore, Singapore}, pages = {63716--63737}, publisher = {ICLR}, title = {{Scalable mechanistic neural networks}}, year = {2025}, } @article{20546, abstract = {Rocky debris covers around 7.3 % of the global glacier area, influencing ice melt rates and the surface mass balance of glaciers, making the dynamics and hydrology of debris-covered glaciers distinct from those of clean-ice glaciers. Accurate representation of debris in models is challenging, as measurements of the physical properties and thickness of the supraglacial debris layer are scarce. Here, we compile a database of measured and reported bulk physical properties and layer thicknesses of supraglacial debris that we call the supraglacial Debris Database (DebDaB) and that is open to community submissions. The majority of the database (90 %) is compiled from 172 sources in the literature, and the remaining 10 % was previously unpublished. DebDaB contains 8741 data entries for supraglacial debris layer thickness, of which 1770 entries also include sub-debris ablation rates, 179 thermal conductivity of debris, 160 aerodynamic surface roughness length, 79 debris albedo, 59 debris emissivity, and 37 debris porosity. The data are distributed over 84 glaciers in 13 regions in the Global Terrestrial Network for Glaciers. We show regional differences in the distribution of debris thickness measurements in DebDaB and fit simplified Østrem curves to 19 glaciers with sufficient debris thickness and ablation data. The data in DebDaB can be used for energy balance, melt, and surface mass balance studies by incorporating site-specific debris properties or for evaluation of remote sensing estimates of debris thickness and surface roughness. They can also help future field campaigns on debris-covered glaciers by identifying observation gaps. DebDaB's uneven spatial coverage points to sampling biases in community efforts to observe debris-covered glaciers, with some regions (e.g. central Europe and South Asia) well-sampled but others having gaps with prevalent debris (e.g. the Andes and Alaska). Debris thickness measurements are mostly concentrated at lower elevations, leaving higher-elevation debris-covered areas undersampled and suggesting that our knowledge of debris properties might not be representative of all elevations. The aims of DebDaB, as an openly available dataset, are to evolve over time, to be updated, and to add to community submissions as new data on supraglacial properties become available. The data described in this paper can be accessed from Zenodo at https://doi.org/10.5281/zenodo.14224835 (Groeneveld et al., 2025).}, author = {Fontrodona-Bach, Adrià and Groeneveld, Lars and Miles, Evan and McCarthy, Michael and Shaw, Thomas and Melo Velasco, Juan Vicente and Pellicciotti, Francesca}, issn = {1866-3516}, journal = {Earth System Science Data}, number = {8}, pages = {4213--4234}, publisher = {Copernicus Publications}, title = {{DebDaB: A database of supraglacial debris thickness and physical properties}}, doi = {10.5194/essd-17-4213-2025}, volume = {17}, year = {2025}, } @article{17240, abstract = {We prove an upper bound on the energy density of the dilute spin-\(\frac {1}{2}\) Fermi gas capturing the leading correction to the kinetic energy\(8\pi a\rho _\uparrow\rho _\downarrow\) with an error of size smaller than\(a\rho^{2}(a^ 3\rho)^{1/3-\varepsilon}\) for any\(\varepsilon> 0\), where a denotes the scattering length of the interaction. The result is valid for a large class of interactions including interactions with a hard core. A central ingredient in the proof is a rigorous version of a fermionic cluster expansion adapted from the formal expansion of Gaudin et al. (Nucl Phys A 176(2):237–260, 1971. https://doi.org/10.1016/0375-9474(71)90267-3).}, author = {Lauritsen, Asbjørn Bækgaard}, issn = {1424-0637}, journal = {Annales Henri Poincare}, pages = {203--243}, publisher = {Springer Nature}, title = {{Almost optimal upper bound for the ground state energy of a dilute Fermi gas via cluster expansion}}, doi = {10.1007/s00023-024-01450-1}, volume = {26}, year = {2025}, } @article{20850, abstract = {We provide an estimate for the number of nontrivial integer points on the Pellian surface t^2 - du^2 = 1 in a bounded region. We give a lower bound on the size of fundamental solutions for almost all d in a certain class, based on a recent conjecture of Browning and Wilsch about integer points on log K3 surfaces. We also obtain an upper bound on the average of class number in this class, assuming the same conjecture.}, author = {Diao, Yijie}, issn = {2118-8572}, journal = {Journal de theorie des nombres de Bordeaux}, number = {3}, pages = {973--988}, publisher = {Université de Bordeaux}, title = {{Class numbers and integer points on some Pellian surfaces}}, doi = {10.5802/jtnb.1348}, volume = {37}, year = {2025}, } @inproceedings{21068, abstract = {Causal reasoning and discovery, two fundamental tasks of causal analysis, often face challenges in applications due to the complexity, noisiness, and highdimensionality of real-world data. Despite recent progress in identifying latent causal structures using causal representation learning (CRL), what makes learned representations useful for causal downstream tasks and how to evaluate them are still not well understood. In this paper, we reinterpret CRL using a measurement model framework, where the learned representations are viewed as proxy measurements of the latent causal variables. Our approach clarifies the conditions under which learned representations support downstream causal reasoning and provides a principled basis for quantitatively assessing the quality of representations using a new Test-based Measurement EXclusivity (T-MEX) score. We validate T-MEX across diverse causal inference scenarios, including numerical simulations and real-world ecological video analysis, demonstrating that the proposed framework and corresponding score effectively assess the identification of learned representations and their usefulness for causal downstream tasks. Reproducible code can be found at https://github.com/shimenghuang/a-measurement-perspective-of-crl.}, author = {Yao, Dingling and Huang, Shimeng and Cadei, Riccardo and Zhang, Kun and Locatello, Francesco}, booktitle = {39th Annual Conference on Neural Information Processing Systems}, issn = {1049-5258}, location = {San Diego, CA, United States}, publisher = {Neural Information Processing Systems Foundation}, title = {{The third pillar of causal analysis? A measurement perspective on causal representations}}, volume = {38}, year = {2025}, } @article{20704, abstract = {Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables, like atom types or spin states, remains underexplored. In this work, we introduce a discrete flow matching model, tailored for systems with discrete phase-space coordinates (e.g., the Ising model or a multicomponent system on a lattice). This approach enables a single model to sample free energy surfaces over a wide temperature range with minimal training overhead, and the model generation is scalable to larger lattice sizes than those in the training set. We demonstrate our approach on the 2D Ising model, showing efficient and reliable free energy sampling. These results highlight the potential of flow matching for low-cost, scalable free energy sampling in discrete systems and suggest promising extensions to alchemical degrees of freedom in crystalline materials. The codebase developed for this work is openly available at https://github.com/tuoping/alchemicalFES.}, author = {Tuo, Ping and Zeng, Zezhu and Chen, Jiale and Cheng, Bingqing}, issn = {1549-9626}, journal = {Journal of Chemical Theory and Computation}, number = {22}, pages = {11427--11435}, publisher = {American Chemical Society}, title = {{Scalable multitemperature free energy sampling of classical Ising spin states}}, doi = {10.1021/acs.jctc.5c01248}, volume = {21}, year = {2025}, } @article{22568, abstract = {The recent surge in reservoir construction has increased global surface water storage, with Mainland Southeast Asia (MSEA) being a significant hotspot. Such infrastructural evolution demands updates in water management strategies and hydrological models. However, information on actual reservoir storage is hard to acquire, especially for transboundary river basins. To date, no high-resolution spatiotemporal dataset on absolute storage time series is available for reservoirs in MSEA. To address this gap, we present (1) a comprehensive openaccess database of absolute storage time series (sub-monthly) for 186 reservoirs (larger than 0.1 km3) in MSEA spanning the period 1985–2023 and (2) an analysis of the reservoir storage dynamics. This dataset is derived from remote sensing observations, integrating satellite-based water surface area extraction from high-resolution (30 m) images and area–elevation–storage (A–E–S) relationships to estimate reservoir level and storage dynamics. The MSEA database includes static (area–elevation–storage curves, water frequency, and reservoir extent) and dynamic (area, water level, and absolute storage time series) components for each reservoir. The 186 reservoirs collectively store around 175 km3 of water, with a minimum of 140 km3 and a maximum of 210 km3. They cover an average area of 8700 km2, ranging from a minimum of 6500 km2 to a maximum of 10 000 km2. We show that the combined average reservoir storage increased from 70 to 160 km3 (+130 %) from 2008 to 2017, primarily contributed by reservoirs in the Irrawaddy, Red, Upper Mekong, and Lower Mekong basins. Our in situ validation provides a good match between estimated storage and in situ observations, with 50 % of the validation sites (10 out of 20) showing an R2 > 0.7 and an average nRMSE < 14 %. The indirect validation (based on altimetry-converted storage) shows even better results, with an R2 > 0.7 and an average nRMSE < 12 % for 70 % (14 out of 20) of the reservoirs. Furthermore, the analysis of the 2019–2020 drought event in the MSEA region reveals that nearly 30 %–40 % of the region experienced more than 5 months of drought, with the most significant impact on reservoirs in Cambodia and Thailand. As a result, storage departures ranged by up to −40 % in some reservoirs, highlighting significant impacts on water availability. Overall, this analysis demonstrates the potential of the inferred storage time series for assessing real-life water-related problems in Mainland Southeast Asia, with the possibility of applying the method to estimate reservoir storage time series in other parts of the world. The reservoir storage database in Mainland Southeast Asia (MSEA-Res database) and the associated Python code are publicly available on Zenodo at https://doi.org/10.5281/zenodo.14844580 (Mahto et al., 2025).}, author = {Mahto, Shanti Shwarup and Fatichi, Simone and Galelli, Stefano}, issn = {1866-3516}, journal = {Earth System Science Data}, number = {6}, pages = {2693--2712}, publisher = {Copernicus Publications}, title = {{A 1985–2023 time series dataset of absolute reservoir storage in Mainland Southeast Asia (MSEA-Res)}}, doi = {10.5194/essd-17-2693-2025}, volume = {17}, year = {2025}, }