@article{18446, abstract = {How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet ongoing question in biology. We generated morphomaps of preimplantation embryogenesis in mouse, rabbit, and monkey embryos, and these morphomaps revealed that although blastomere divisions desynchronized passively, 8-cell embryos converged toward robust three-dimensional shapes. Using topological analysis and genetic perturbations, we found that embryos progressively changed their cellular connectivity to a preferred topology, which could be predicted by a physical model in which actomyosin contractility and noise facilitate topological transitions, lowering surface energy. This mechanism favored regular embryo packing and promoted a higher number of inner cells in the 16-cell embryo. Synchronized division reduced embryo packing and generated substantially more misallocated cells and fewer inner-cell–mass cells. These findings suggest that stochasticity in division timing contributes to robust patterning.}, author = {Fabrèges, Dimitri and Corominas-Murtra, Bernat and Moghe, Prachiti and Kickuth, Alison and Ichikawa, Takafumi and Iwatani, Chizuru and Tsukiyama, Tomoyuki and Daniel, Nathalie and Gering, Julie and Stokkermans, Anniek and Wolny, Adrian and Kreshuk, Anna and Duranthon, Véronique and Uhlman, Virginie and Hannezo, Edouard B and Hiiragi, Takashi}, issn = {1095-9203}, journal = {Science}, number = {6718}, publisher = {AAAS}, title = {{Temporal variability and cell mechanics control robustness in mammalian embryogenesis}}, doi = {10.1126/science.adh1145}, volume = {386}, year = {2024}, } @inproceedings{18071, abstract = {Recent advancements on DAG-based consensus protocols allow for blockchains with improved metrics and properties, such as throughput and censorship-resistance. Variants of the Bullshark [18] consensus protocol are adopted for practical use by the Sui blockchain, for improved latency. However, the protocol is leader-based, and is strongly affected by crashed leaders that can lead to various performance issues, for example, decreased transaction throughput. In this paper, we propose HammerHead, a DAG-based consensus protocol, that is inspired by Carousel [8] and provides Leader-Utilization. Our proposal differs from Carousel, which is built for a chained consensus protocol; in HammerHead chain quality is inherited by the DAG. HammerHead needs to preserve safety and liveness, despite validators committing leader vertices asynchronously. The key idea is to update leader schedules dynamically, based on the validators' scores during the previous schedule. We implement HammerHead and show a minor improvement in performance for cases without faults. The major improvements in comparison to Bullshark appear in faulty settings. Specifically, we show a drastic, 2x-latency improvement and up to 40% increased throughput when crash faults occur (100 validators, 33 faults).}, author = {Tsimos, Giorgos and Kichidis, Anastasios and Sonnino, Alberto and Kokoris Kogias, Eleftherios}, booktitle = {Proceedings - International Conference on Distributed Computing Systems}, isbn = {9798350386059}, issn = {2575-8411}, location = {Jersey City, NJ, United States}, pages = {1377--1387}, publisher = {IEEE}, title = {{HammerHead: Leader reputation for dynamic scheduling}}, doi = {10.1109/ICDCS60910.2024.00129}, year = {2024}, } @article{17284, abstract = {Platelet homeostasis is essential for vascular integrity and immune defence1,2. Although the process of platelet formation by fragmenting megakaryocytes (MKs; thrombopoiesis) has been extensively studied, the cellular and molecular mechanisms required to constantly replenish the pool of MKs by their progenitor cells (megakaryopoiesis) remains unclear3,4. Here we use intravital imaging to track the cellular dynamics of megakaryopoiesis over days. We identify plasmacytoid dendritic cells (pDCs) as homeostatic sensors that monitor the bone marrow for apoptotic MKs and deliver IFNα to the MK niche triggering local on-demand proliferation and maturation of MK progenitors. This pDC-dependent feedback loop is crucial for MK and platelet homeostasis at steady state and under stress. pDCs are best known for their ability to function as vigilant detectors of viral infection5. We show that virus-induced activation of pDCs interferes with their function as homeostatic sensors of megakaryopoiesis. Consequently, activation of pDCs by SARS-CoV-2 leads to excessive megakaryopoiesis. Together, we identify a pDC-dependent homeostatic circuit that involves innate immune sensing and demand-adapted release of inflammatory mediators to maintain homeostasis of the megakaryocytic lineage.}, author = {Gärtner, Florian R and Ishikawa-Ankerhold, Hellen and Stutte, Susanne and Fu, Wenwen and Weitz, Jutta and Dueck, Anne and Nelakuditi, Bhavishya and Fumagalli, Valeria and Van Den Heuvel, Dominic and Belz, Larissa and Sobirova, Gulnoza and Zhang, Zhe and Titova, Anna and Navarro, Alejandro Martinez and Pekayvaz, Kami and Lorenz, Michael and Von Baumgarten, Louisa and Kranich, Jan and Straub, Tobias and Popper, Bastian and Zheden, Vanessa and Kaufmann, Walter and Guo, Chenglong and Piontek, Guido and Von Stillfried, Saskia and Boor, Peter and Colonna, Marco and Clauß, Sebastian and Schulz, Christian and Brocker, Thomas and Walzog, Barbara and Scheiermann, Christoph and Aird, William C. and Nerlov, Claus and Stark, Konstantin and Petzold, Tobias and Engelhardt, Stefan and Sixt, Michael K and Hauschild, Robert and Rudelius, Martina and Oostendorp, Robert A.J. and Iannacone, Matteo and Heinig, Matthias and Massberg, Steffen}, issn = {1476-4687}, journal = {Nature}, pages = {645--653}, publisher = {Springer Nature}, title = {{Plasmacytoid dendritic cells control homeostasis of megakaryopoiesis}}, doi = {10.1038/s41586-024-07671-y}, volume = {631}, year = {2024}, } @article{18920, abstract = {The globally distributed marine alga Emiliania huxleyi has cooling effect on the Earth’s climate. The population density of E. huxleyi is restricted by Nucleocytoviricota viruses, including E. huxleyi virus 201 (EhV-201). Despite the impact of E. huxleyi viruses on the climate, there is limited information about their structure and replication. Here, we show that the dsDNA genome inside the EhV-201 virion is protected by an inner membrane, capsid, and outer membrane. EhV-201 virions infect E. huxleyi by using fivefold vertices to bind to and fuse the virus’ inner membrane with the cell plasma membrane. Progeny virions assemble in the cytoplasm at the surface of endoplasmic reticulum–derived membrane segments. Genome packaging initiates synchronously with the capsid assembly and completes through an aperture in the forming capsid. The genome-filled capsids acquire an outer membrane by budding into intracellular vesicles. EhV-201 infection induces a loss of surface protective layers from E. huxleyi cells, which enables the continuous release of virions by exocytosis.}, author = {Homola, Miroslav and Büttner, Renate Carina and Füzik, Tibor and Křepelka, Pavel and Holbová, Radka and Nováček, Jiří and Chaillet, Marten L. and Žák, Jakub and Grybchuk, Danyil and Förster, Friedrich and Wilson, William H. and Schroeder, Declan C. and Plevka, Pavel}, issn = {2375-2548}, journal = {Science Advances}, number = {15}, publisher = {American Association for the Advancement of Science}, title = {{Structure and replication cycle of a virus infecting climate-modulating alga Emiliania huxleyi}}, doi = {10.1126/sciadv.adk1954}, volume = {10}, year = {2024}, } @inbook{18052, abstract = {Sodium dodecyl sulfate-digested freeze-fracture replica labeling (SDS-FRL) is an electron microscope (EM) sample preparation technique which allows for high-resolution visualization of membrane proteins with high sensitivity. However, image acquisition of specific replica profiles such as synapses in a large field of EM view needs a valid experience and a long time for manual searching. Here, we describe how to utilize deep learning for automatizing image acquisition of specific profiles of interest in replica samples. This protocol facilitates the labor-intensive collection of EM images, in particular for rare profiles. We provide instructions for using SerialEM image acquisition software in conjunction with object detection by our newly developed deep learning software DarEM, to automatically acquire tilt series of all synapses in a selected region. We then show how to perform a mostly automated analysis of gold particle labeling in the acquired images by utilizing Darea software.}, author = {Kleindienst, David and Costanzo, Tommaso and Shigemoto, Ryuichi}, booktitle = {New Aspects in Analyzing the Synaptic Organization of the Brain}, editor = {Lübke, Joachim H.R. and Rollenhagen, Astrid}, isbn = {9781071640180}, issn = {1940-6045}, pages = {123--137}, publisher = {Springer Nature}, title = {{Automated Imaging and Analysis of Synapses in Freeze-Fracture Replica Samples with Deep Learning}}, doi = {10.1007/978-1-0716-4019-7_8}, year = {2024}, } @misc{14842, abstract = {Eva Benkova received a PhD in Biophysics at the Institute of Biophysics of the Czech Academy of Sciences in 1998. After working as a postdoc at the Max Planck Institute in Cologne and the Center for Plant Molecular Biology (ZMBP) in Tübingen, she became a group leader at the Plant Systems Biology Department of the Vlaams Instituut voor Biotechnologie (VIB) in Gent. In 2012, she transitioned to an Assistant Professor position at the Institute of Science and Technology Austria (ISTA) where she was later promoted to Professor. Since 2021, she has served as the Dean of the ISTA Graduate School. As a plant developmental biologist, she focuses on unraveling the molecular mechanisms and principles that underlie hormonal interactions in plants. In her current work, she explores the intricate connections between hormones and regulatory pathways that mediate the perception of environmental stimuli, including abiotic stress and nitrate availability.}, author = {Benková, Eva}, booktitle = {Current Biology}, issn = {1879-0445}, number = {1}, pages = {R3--R5}, publisher = {Elsevier}, title = {{Eva Benkova}}, doi = {10.1016/j.cub.2023.11.039}, volume = {34}, year = {2024}, } @misc{23025, abstract = {The globally distributed marine alga Emiliania huxleyi produces reflective calcite disks (coccoliths) that increase the albedo of ocean water and thus reduce the heat absorption in the ocean, which cools the Earth’s climate. The population density of E. huxleyi is restricted by Nucleocytoviricota viruses, including E. huxleyi virus 201 (EhV-201). Despite the impact of E. huxleyi viruses on the climate, there is limited information about their structure and replication. Here we show that the dsDNA genome inside the EhV-201 virion is protected by an inner membrane, capsid, and outer membrane decorated with numerous transmembrane proteins. The virions are prone to deformation, and parts of their capsids deviate from the icosahedral arrangement. EhV-201 virions infect E. huxleyi by using their fivefold vertex to bind to a host cell and fuse the virus’s inner membrane with the plasma membrane. Whereas the replication of EhV-201 probably occurs in the nucleus, virions assemble in the cytoplasm at the surface of endoplasmic reticulum-derived membrane segments. Genome packaging initiates synchronously with the capsid assembly and completes through an aperture in the forming capsid. Upon the completion of genome packaging, the capsids change conformation, which enables them to acquire an outer membrane by budding into intracellular vesicles. EhV-201 infection induces a loss of surface protective layers from E. huxleyi cells, which allows the continuous release of virions by exocytosis. Our results provide insight into how EhVs bypass the surface protective layers of E. huxleyi and exploit the organelles of an infected cell for progeny assembly.}, author = {Homola, Miroslav and Büttner, Carina and Füzik, Tibor and Krepelka, Pavel and Holbová, Radka and Chaillet, Marten and Nováček, Jiří and Förster, Friedrich and Wilson, William and Schroeder, Declan and Plevka, Pavel}, publisher = {Dryad}, title = {{Data: Structure and replication cycle of a virus infecting climate-modulating alga Emiliania huxleyi}}, doi = {10.5061/dryad.p5hqbzkw0}, year = {2024}, } @article{15146, abstract = {The extracellular matrix (ECM) serves as a scaffold for cells and plays an essential role in regulating numerous cellular processes, including cell migration and proliferation. Due to limitations in specimen preparation for conventional room-temperature electron microscopy, we lack structural knowledge on how ECM components are secreted, remodeled, and interact with surrounding cells. We have developed a 3D-ECM platform compatible with sample thinning by cryo-focused ion beam milling, the lift-out extraction procedure, and cryo-electron tomography. Our workflow implements cell-derived matrices (CDMs) grown on EM grids, resulting in a versatile tool closely mimicking ECM environments. This allows us to visualize ECM for the first time in its hydrated, native context. Our data reveal an intricate network of extracellular fibers, their positioning relative to matrix-secreting cells, and previously unresolved structural entities. Our workflow and results add to the structural atlas of the ECM, providing novel insights into its secretion and assembly.}, author = {Zens, Bettina and Fäßler, Florian and Hansen, Jesse and Hauschild, Robert and Datler, Julia and Hodirnau, Victor-Valentin and Zheden, Vanessa and Alanko, Jonna H and Sixt, Michael K and Schur, Florian KM}, issn = {1540-8140}, journal = {Journal of Cell Biology}, number = {6}, publisher = {Rockefeller University Press}, title = {{Lift-out cryo-FIBSEM and cryo-ET reveal the ultrastructural landscape of extracellular matrix}}, doi = {10.1083/jcb.202309125}, volume = {223}, year = {2024}, } @article{17463, abstract = {Allosteric modulation of protein function, wherein the binding of an effector to a protein triggers conformational changes at distant functional sites, plays a central part in the control of metabolism and cell signalling1,2,3. There has been considerable interest in designing allosteric systems, both to gain insight into the mechanisms underlying such ‘action at a distance’ modulation and to create synthetic proteins whose functions can be regulated by effectors4,5,6,7. However, emulating the subtle conformational changes distributed across many residues, characteristic of natural allosteric proteins, is a significant challenge8,9. Here, inspired by the classic Monod–Wyman–Changeux model of cooperativity10, we investigate the de novo design of allostery through rigid-body coupling of peptide-switchable hinge modules11 to protein interfaces12 that direct the formation of alternative oligomeric states. We find that this approach can be used to generate a wide variety of allosterically switchable systems, including cyclic rings that incorporate or eject subunits in response to peptide binding and dihedral cages that undergo effector-induced disassembly. Size-exclusion chromatography, mass photometry13 and electron microscopy reveal that these designed allosteric protein assemblies closely resemble the design models in both the presence and absence of peptide effectors and can have ligand-binding cooperativity comparable to classic natural systems such as haemoglobin14. Our results indicate that allostery can arise from global coupling of the energetics of protein substructures without optimized side-chain–side-chain allosteric communication pathways and provide a roadmap for generating allosterically triggerable delivery systems, protein nanomachines and cellular feedback control circuitry.}, author = {Pillai, Arvind and Idris, Abbas and Philomin, Annika and Weidle, Connor and Skotheim, Rebecca and Leung, Philip J.Y. and Broerman, Adam and Demakis, Cullen and Borst, Andrew J. and Praetorius, Florian M and Baker, David}, issn = {1476-4687}, journal = {Nature}, pages = {911–920 }, publisher = {Springer Nature}, title = {{De novo design of allosterically switchable protein assemblies}}, doi = {10.1038/s41586-024-07813-2}, volume = {632}, year = {2024}, } @article{18934, abstract = {The assembly of biomolecular condensate in eukaryotic cells and the accumulation of amyloid deposits in neurons are processes involving the nucleation and growth (NAG) of new protein phases. To therapeutically target protein phase separation, drug candidates are tested in in vitro assays that monitor the increase in the mass or size of the new phase. Limited mechanistic insight is, however, provided if empirical or untestable kinetic models are fitted to these progress curves. Here we present the web server NAGPKin that quantifies NAG rates using mass-based or size-based progress curves as the input data. A report is generated containing the fitted NAG parameters and elucidating the phase separation mechanisms at play. The NAG parameters can be used to predict particle size distributions of, for example, protein droplets formed by liquid-liquid phase separation (LLPS) or amyloid fibrils formed by protein aggregation. Because minimal intervention is required from the user, NAGPKin is a good platform for standardized reporting of LLPS and protein self-assembly data. NAGPKin is useful for drug discovery as well as for fundamental studies on protein phase separation. NAGPKin is freely available (no login required) at https://nagpkin.i3s.up.pt .}, author = {Sárkány, Zsuzsa and Figueiredo, Francisco and Macedo-Ribeiro, Sandra and Martins, Pedro M.}, issn = {1939-4586}, journal = {Molecular Biology of the Cell}, number = {3}, publisher = {American Society for Cell Biology}, title = {{NAGPKin: Nucleation-and-growth parameters from the kinetics of protein phase separation}}, doi = {10.1091/mbc.e23-07-0289}, volume = {35}, year = {2024}, } @article{18757, abstract = {Segmentation is a critical data processing step in many applications of cryo-electron tomography. Downstream analyses, such as subtomogram averaging, are often based on segmentation results, and are thus critically dependent on the availability of open-source software for accurate as well as high-throughput tomogram segmentation. There is a need for more user-friendly, flexible, and comprehensive segmentation software that offers an insightful overview of all steps involved in preparing automated segmentations. Here, we present Ais: a dedicated tomogram segmentation package that is geared towards both high performance and accessibility, available on GitHub. In this report, we demonstrate two common processing steps that can be greatly accelerated with Ais: particle picking for subtomogram averaging, and generating many-feature segmentations of cellular architecture based on in situ tomography data. Featuring comprehensive annotation, segmentation, and rendering functionality, as well as an open repository for trained models at aiscryoet.org, we hope that Ais will help accelerate research and dissemination of data involving cryoET.}, author = {Last, Mart G.F. and Abendstein, Leoni and Voortman, Lenard M. and Sharp, Thomas H.}, issn = {2050-084X}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{Streamlining segmentation of cryo-electron tomography datasets with Ais}}, doi = {10.7554/eLife.98552}, volume = {13}, year = {2024}, } @article{18445, abstract = {Acquisition of specialized cellular features is controlled by the ordered expression of transcription factors (TFs) along differentiation trajectories. Here, we find a member of the Onecut TF family, ONECUT3, expressed in postmitotic neurons that leave their Ascl1+/Onecut1/2+ proliferative domain in the vertebrate hypothalamus to instruct neuronal differentiation. We combined single-cell RNA-seq and gain-of-function experiments for gene network reconstruction to show that ONECUT3 affects the polarization and morphogenesis of both hypothalamic GABA-derived dopamine and thyrotropin-releasing hormone (TRH)+ glutamate neurons through neuron navigator-2 (NAV2). In vivo, siRNA-mediated knockdown of ONECUT3 in neonatal mice reduced NAV2 mRNA, as well as neurite complexity in Onecut3-containing neurons, while genetic deletion of Onecut3/ceh-48 in C. elegans impaired neurocircuit wiring, and sensory discrimination-based behaviors. Thus, ONECUT3, conserved across neuronal subtypes and many species, underpins the polarization and morphological plasticity of phenotypically distinct neurons that descend from a common pool of Ascl1+ progenitors in the hypothalamus.}, author = {Zupančič, Maja and Keimpema, Erik and Tretiakov, Evgenii O. and Eder, Stephanie J. and Lev, Itamar and Englmaier, Lukas and Bhandari, Pradeep and Fietz, Simone A. and Härtig, Wolfgang and Renaux, Estelle and Villunger, Andreas and Hökfelt, Tomas and Zimmer, Manuel and Clotman, Frédéric and Harkany, Tibor}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Concerted transcriptional regulation of the morphogenesis of hypothalamic neurons by ONECUT3}}, doi = {10.1038/s41467-024-52762-z}, volume = {15}, year = {2024}, } @inproceedings{14213, abstract = {We introduce a method to segment the visual field into independently moving regions, trained with no ground truth or supervision. It consists of an adversarial conditional encoder-decoder architecture based on Slot Attention, modified to use the image as context to decode optical flow without attempting to reconstruct the image itself. In the resulting multi-modal representation, one modality (flow) feeds the encoder to produce separate latent codes (slots), whereas the other modality (image) conditions the decoder to generate the first (flow) from the slots. This design frees the representation from having to encode complex nuisance variability in the image due to, for instance, illumination and reflectance properties of the scene. Since customary autoencoding based on minimizing the reconstruction error does not preclude the entire flow from being encoded into a single slot, we modify the loss to an adversarial criterion based on Contextual Information Separation. The resulting min-max optimization fosters the separation of objects and their assignment to different attention slots, leading to Divided Attention, or DivA. DivA outperforms recent unsupervised multi-object motion segmentation methods while tripling run-time speed up to 104FPS and reducing the performance gap from supervised methods to 12% or less. DivA can handle different numbers of objects and different image sizes at training and test time, is invariant to permutation of object labels, and does not require explicit regularization.}, author = {Lao, Dong and Hu, Zhengyang and Locatello, Francesco and Yang, Yanchao and Soatto, Stefano}, booktitle = {1st Conference on Parsimony and Learning}, location = {Hong Kong, China}, title = {{Divided attention: Unsupervised multi-object discovery with contextually separated slots}}, year = {2024}, } @inproceedings{18114, abstract = {This paper presents Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations, revealing the underlying dynamics of data and enhancing interpretability and efficiency in data modeling. Central to our approach is a novel Relaxed Linear Programming Solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs. This integrates well with neural networks and surpasses the limitations of traditional ODE solvers enabling scalable GPU parallel processing. Overall, Mechanistic Neural Networks demonstrate their versatility for scientific machine learning applications, adeptly managing tasks from equation discovery to dynamic systems modeling. We prove their comprehensive capabilities in analyzing and interpreting complex scientific data across various applications, showing significant performance against specialized state-of-the-art methods. Source code is available at https://github.com/alpz/mech-nn.}, author = {Pervez, Adeel A and Locatello, Francesco and Gavves, Efstratios}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {40484--40501}, publisher = {ML Research Press}, title = {{Mechanistic neural networks for scientific machine learning}}, volume = {235}, year = {2024}, } @inproceedings{18964, abstract = {Object-centric learning (OCL) extracts the representation of objects with slots, offering an exceptional blend of flexibility and interpretability for abstracting low-level perceptual features. A widely adopted method within OCL is slot attention, which utilizes attention mechanisms to iteratively refine slot representations. However, a major draw-back of most object-centric models, including slot attention, is their reliance on predefining the number of slots. This not only necessitates prior knowledge of the dataset but also overlooks the inherent variability in the number of objects present in each instance. To overcome this fundamental limitation, we present a novel complexity-aware object auto-encoder framework. Within this framework, we introduce an adaptive slot attention (AdaSlot) mecha-nism that dynamically determines the optimal number of slots based on the content of the data. This is achieved by proposing a discrete slot sampling module that is responsible for selecting an appropriate number of slots from a candidate list. Furthermore, we introduce a masked slot decoder that suppresses unselected slots during the decoding process. Our framework, tested extensively on object discovery tasks with various datasets, shows performance matching or exceeding top fixed-slot models. Moreover, our analysis substantiates that our method exhibits the capability to dynamically adapt the slot number according to each instance's complexity, offering the potential for further exploration in slot attention research. Project will be available at https://kfan21.github.io/AdaSlot/}, author = {Fan, Ke and Bai, Zechen and Xiao, Tianjun and He, Tong and Horn, Max and Fu, Yanwei and Locatello, Francesco and Zhang, Zheng}, booktitle = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, location = {Seattle, WA, United States}, publisher = {IEEE}, title = {{Adaptive slot attention: Object discovery with dynamic slot number}}, doi = {10.1109/cvpr52733.2024.02176}, year = {2024}, } @inproceedings{18956, abstract = {Group Activity Recognition (GAR) aims to detect the activity performed by multiple actors in a scene. Prior works model the spatio-temporal features based on the RGB, optical flow or keypoint data types. On the contrary, our hypothesis is that by only using the RGB data without temporality, the performance can be maintained with a negligible loss in accuracy. To that end, we propose a novel GAR technique for volleyball videos, DECOMPL, which consists of two complementary branches. In the visual branch, it extracts the features using attention pooling. In the coordinate branch, it considers the configuration of the players and extracts the spatial information from the box coordinates. Moreover, we analyzed the Volleyball dataset that the recent literature is mostly based on, and systematically reannotated it to emphasize the group concept. Experimental results demonstrated the effectiveness of the proposed model DECOMPL, which delivered the best/second best GAR performance with the reannotations/original annotations among the comparable state-of-the-art methods. Code and new annotations are available at GitHub: https://github.com/berkerdemirel/decompl}, author = {Demirel, Berker and Ozkan, Huseyin}, booktitle = {2024 IEEE International Conference on Image Processing}, issn = {2381-8549}, location = {Abu Dhabi, United Arab Emirates}, pages = {977--983}, publisher = {IEEE}, title = {{Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image}}, doi = {10.1109/icip51287.2024.10647499}, year = {2024}, } @inproceedings{14946, abstract = {We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partially observed setting in which each view constitutes a nonlinear mixture of a subset of underlying latent variables, which can be causally related. We prove that the information shared across all subsets of any number of views can be learned up to a smooth bijection using contrastive learning and a single encoder per view. We also provide graphical criteria indicating which latent variables can be identified through a simple set of rules, which we refer to as identifiability algebra. Our general framework and theoretical results unify and extend several previous work on multi-view nonlinear ICA, disentanglement, and causal representation learning. We experimentally validate our claims on numerical, image, and multi-modal data sets. Further, we demonstrate that the performance of prior methods is recovered in different special cases of our setup. Overall, we find that access to multiple partial views offers unique opportunities for identifiable representation learning, enabling the discovery of latent structures from purely observational data.}, author = {Yao, Dingling and Xu, Danru and Lachapelle, Sébastien and Magliacane, Sara and Taslakian, Perouz and Martius, Georg and Kügelgen, Julius von and Locatello, Francesco}, booktitle = {12th International Conference on Learning Representations}, location = {Vienna, Austria}, publisher = {Curran Associates}, title = {{Multi-view causal representation learning with partial observability}}, year = {2024}, } @article{17302, abstract = {The mountain cryosphere and groundwater play pivotal roles in shaping the hydrological cycle, yet their connectivity remains incompletely understood. Current knowledge on meltwater recharge and consequent groundwater discharge processes is better developed for snow–groundwater connectivity than for glacier–groundwater connectivity. Estimates of meltwater recharge vary considerably, which is probably a function of not only inherent catchment characteristics but also of the different spatio-temporal scales involved and the uncertainties in the methods used. This hinders a comprehensive understanding of the mountain water cycle. As glaciers retreat, permafrost thaws and snowpack diminishes, the relative importance of mountain groundwater is expected to increase. However, shifting and declining recharge from the cryosphere may decrease absolute groundwater amounts and fluxes with as-yet unknown effects on catchment-scale hydrological processes. We therefore stress the need to better quantify mountain cryosphere–groundwater connectivity to predict climate change impacts on mountain water supply and to support sustainable water resource management of downstream socio-ecological systems.}, author = {van Tiel, Marit and Aubry-Wake, Caroline and Somers, Lauren and Andermann, Christoff and Avanzi, Francesco and Baraer, Michel and Chiogna, Gabriele and Daigre, Clémence and Das, Soumik and Drenkhan, Fabian and Farinotti, Daniel and Fyffe, Catriona Louise and de Graaf, Inge and Hanus, Sarah and Immerzeel, Walter and Koch, Franziska and McKenzie, Jeffrey M. and Müller, Tom and Popp, Andrea L. and Saidaliyeva, Zarina and Schaefli, Bettina and Schilling, Oliver S. and Teagai, Kapiolani and Thornton, James M. and Yapiyev, Vadim}, issn = {2731-6084}, journal = {Nature Water}, pages = {624--637}, publisher = {Springer Nature}, title = {{Cryosphere–groundwater connectivity is a missing link in the mountain water cycle}}, doi = {10.1038/s44221-024-00277-8}, volume = {2}, year = {2024}, } @inproceedings{18971, abstract = {Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases. Addressing this challenge typically involves methods relying on prior knowledge and group annotation to remove spurious correlations, which may not be readily available in many applications. In this paper, we establish a novel connection between unsupervised object-centric learning and mitigation of spurious correlations. Instead of directly inferring subgroups with varying correlations with labels, our approach focuses on discovering concepts: discrete ideas that are shared across input samples. Leveraging existing object-centric representation learning, we introduce CoBalT: a concept balancing technique that effectively mitigates spurious correlations without requiring human labeling of subgroups. Evaluation across the benchmark datasets for sub-population shifts demonstrate superior or competitive performance compared state-of-the-art baselines, without the need for group annotation. Code is available at https://github.com/rarefin/CoBalT}, author = {Arefin, Rifat and Zhang, Yan and Baratin, Aristide and Locatello, Francesco and Rish, Irina and Liu, Dianbo and Kawaguchi, Kenji}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {1672--1688}, publisher = {ML Research Press}, title = {{Unsupervised concept discovery mitigates spurious correlations}}, volume = {235}, year = {2024}, } @article{14938, abstract = {High elevation headwater catchments are complex hydrological systems that seasonally buffer water and release it in the form of snow and ice melt, modulating downstream runoff regimes and water availability. In High Mountain Asia (HMA), where a wide range of climates from semi-arid to monsoonal exist, the importance of the cryospheric contributions to the water budget varies with the amount and seasonal distribution of precipitation. Losses due to evapotranspiration and sublimation are to date largely unquantified components of the water budget in such catchments, although they can be comparable in magnitude to glacier melt contributions to streamflow. 
Here, we simulate the hydrology of three high elevation headwater catchments in distinct climates in HMA over 10 years using an ecohydrological model geared towards high-mountain areas including snow and glaciers, forced with reanalysis data. 
Our results show that evapotranspiration and sublimation together are most important at the semi-arid site, Kyzylsu, on the northernmost slopes of the Pamir mountain range. Here, the evaporative loss amounts to 28% of the water throughput, which we define as the total water added to, or removed from the water balance within a year. In comparison, evaporative losses are 19% at the Central Himalayan site Langtang and 13% at the wettest site, 24K, on the Southeastern Tibetan Plateau. At the three sites, respectively, sublimation removes 15%, 13% and 6% of snowfall, while evapotranspiration removes the equivalent of 76%, 28% and 19% of rainfall. In absolute terms, and across a comparable elevation range, the highest ET flux is 413 mm yr-1 at 24K, while the highest sublimation flux is 91 mm yr-1 at Kyzylsu. During warm and dry years, glacier melt was found to only partially compensate for the annual supply deficit.}, author = {Fugger, Stefan and Shaw, Thomas and Jouberton, Achille and Miles, Evan and Buri, Pascal and McCarthy, Michael and Fyffe, Catriona Louise and Fatichi, Simone and Kneib, Marin and Molnar, Peter and Pellicciotti, Francesca}, issn = {1748-9326}, journal = {Environmental Research Letters}, keywords = {Public Health, Environmental and Occupational Health, General Environmental Science, Renewable Energy, Sustainability and the Environment}, publisher = {IOP Publishing}, title = {{Hydrological regimes and evaporative flux partitioning at the climatic ends of High Mountain Asia}}, doi = {10.1088/1748-9326/ad25a0}, volume = {19}, year = {2024}, }