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Occam's razor for AI: Coarse-graining Hammett Inspired Product Ansatz in Chemical Space

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arXiv 2023年

作者： Bragato, Marco Von Rudorff, Guido Falk Von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 1416 WienAT1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

data-hungry machine learning methods have become a new standard to efficiently navigate chemical compound space for molecular and materials design and discovery. Due to the severe scarcity and cost of high-quality experimental or synthetic simulated training data, however, data-acquisition costs can be considerable. Relying on reasonably accurate approximate baseline labels with low computational complexity represents one of the most effective strategies to curb data-needs, e.g. through Δ-, transfer-, or multi-fidelity learning. We introduce baseline labels in the form of a generic coarse-graining Hammett Inspired Product (HIP) Ansatz which generalizes the empirical Hammett equation towards arbitrary systems and properties. Numerical evidence for the applicability of HIP includes solvation free energies of molecules, formation energies of quaternary elpasolite crystals, carbon adsorption energies on heterogeneous catalytic surfaces, HOMO-LUMO gaps of metallorganic complexes, and activation energies for SN2 reactions. After calibration on the same training sets, HIP yields an effective baseline for improved Δ-quantum machine learning models with superior data-efficiency when compared to previously introduced specialised domain-specific models. © 2023, CC0.

关键词： data acquisition

Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling

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arXiv 2024年

作者： Hsu, Alan Ho, Matthew Lin, Joyce Markey, Carleen Ntampaka, Michelle Trac, Hy Póczos, Barnabás Department of Astronomy Harvard University CambridgeMA02138 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States UMR 7095 98 bis bd Arago ParisF-75014 France Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Department of Physics University of Wisconsin-Madison MadisonWI53726 United States McWilliams Center for Cosmology and Astrophysics Carnegie Mellon University PittsburghPA15213 United States Data Science Mission Office Space Telescope Science Institute BaltimoreMD21218 United States Department of Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional inputs, and generates realizations of corresponding gas and dark matter maps by sampling from a learned data posterior. We train and validate the performance of our model by using mock data from a hydrodynamical cosmological simulation. The model accurately reconstructs both the mean and spread of the radial density profiles in the spatial domain to within 5%, indicating that the model is able to distinguish between clusters of different sizes. In the spectral domain, the model achieves close-to-unity values for the bias and cross-correlation coefficients, indicating that the model can accurately probe cluster structures on both large and small scales. Our experiments demonstrate the ability of score models to learn a strong, nonlinear, and unbiased mapping between input observables and fundamental density distributions of galaxy clusters. These diffusion models can be further fine-tuned and generalized to not only take in additional observables as inputs, but also real observations and predict unknown density distributions of galaxy clusters. © 2024, CC BY.

关键词： Galaxies

Species-specific responses of canopy greenness to the extreme droughts of 2018 and 2022 for four abundant tree species in Germany

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science of the Total Environment 2025年 958卷 177938页

作者： Wang, Yixuan Rammig, Anja Blickensdörfer, Lukas Wang, Yuanyuan Zhu, Xiao Xiang Buras, Allan Professorship for Land Surface-Atmosphere Interactions Technical University of Munich Hans-Carl-v.-Carlowitz-Platz 2 Freising85354 Germany Thünen Institute of Farm Economics Bundesallee 63 Braunschweig38116 Germany Thünen Institute of Forest Ecosystems Alfred-Moeller-Straße 1 Eberswalde16225 Germany Earth Observation Lab Geography Department Humboldt University of Berlin Unter den Linden 6 Berlin10099 Germany Chair of Data Science in Earth Observation Technical University of Munich Arcisstraße 21 Munich80333 Germany Munich Center for Machine Learning Arcisstraße 21 Munich80333 Germany

Germany experienced extreme drought periods in 2018 and 2022, which significantly affected forests. These drought periods were natural experiments, providing valuable insights into how different tree species respond to drought. The quantification of species-specific drought responses may help to identify the most climate-change-resilient tree species, thereby informing effective forest regeneration strategies. In this study, we used remotely sensed peak-season canopy greenness as a proxy for tree vitality to estimate the drought response of four widely abundant tree species in Germany (oak, beech, spruce, and pine). We focused on two questions: (1) How were the four tree species affected by these two droughts? (2) Which environmental parameters primarily determined canopy greenness? To address these questions, we combined a recently published tree species classification map with remotely sensed canopy greenness and environmental variables related to plant available water capacity (PAWC) and atmospheric vapor pressure deficit (VPD). Our results indicate that the more isohydric species featured a greater decline in canopy greenness under these droughts compared to the more anisohydric species despite similar soil moisture conditions. Based on spatial lag models, we found that the influence of PAWC on canopy greenness increases with increasing isohydricity while the influence of VPD decreases. Our statistical analysis indicates that oak was the only species with significantly higher canopy greenness in 2022 compared to 2018. Yet, all species are likely to be susceptible to accumulated drought effects, such as insufficient recovery time and increased vulnerability to biotic pathogens, in the coming years. Our study provides critical insights into the diverse responses of different tree species to changing environments over a large environmental gradient in Central Europe and sheds light on the complex interactions between soil moisture, climate variables, and canopy gre

关键词： Forest ecology

Revealing excited states of rotational Bose–Einstein condensates

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arXiv 2023年

作者： Yin, Jianyuan Huang, Zhen Cai, Yongyong Du, Qiang Zhang, Lei School of Mathematical Sciences Laboratory of Mathematics and Applied Mathematics Peking University Beijing100871 China Department of Mathematics National University of Singapore Singapore119076 Singapore Department of Mathematics University of California BerkeleyCA94720 United States School of Mathematical Sciences Beijing Normal University Beijing100875 China Department of Applied Physics and Applied Mathematics Data Science Institute Columbia University New YorkNY10027 United States Beijing International Center for Mathematical Research Center for Quantitative Biology Center for Machine Learning Research Peking University Beijing100871 China

Rotational Bose–Einstein condensates can exhibit quantized vortices as topological excitations. In this study, the ground and excited states of the rotational Bose–Einstein condensates are systematically studied by calculating the stationary points of the Gross–Pitaevskii energy functional. Various excited states and their connections at different rotational frequencies are revealed in solution landscapes constructed with the constrained high-index saddle dynamics method. Four excitation mechanisms are identified: vortex addition, rearrangement, merging, and splitting. We demonstrate changes in the ground state with increasing rotational frequencies and decipher the evolution of the stability of ground states. Copyright © 2023, The Authors. All rights reserved.

关键词： Excited states

Position: Bayesian Deep learning is Needed in the Age of Large-Scale AI

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers

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arXiv 2021年

作者： Zhu, Tianyu Hiller, Markus Ehsanpour, Mahsa Ma, Rongkai Drummond, Tom Reid, Ian Rezatofighi, Hamid The Department of Electrical and Computer Systems Engineering Monash University Australia The School of Computing and Information Systems The University of Melbourne Australia The Australian Institute for Machine Learning The University of Adelaide Australia The Department of Data Science and AI Monash University Australia The Australian Centre for Robotic Vision Australia

Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field. Most existing approaches are not able to properly handle multi-object tracking challenges such as occlusion, in part because they ignore long-term temporal information. To address these shortcomings, we present MO3TR: a truly end-to-end Transformer-based online multi-object tracking (MOT) framework that learns to handle occlusions, track initiation and termination without the need for an explicit data association module or any heuristics. MO3TR encodes object interactions into long-term temporal embeddings using a combination of spatial and temporal Transformers, and recursively uses the information jointly with the input data to estimate the states of all tracked objects over time. The spatial attention mechanism enables our framework to learn implicit representations between all the objects and the objects to the measurements, while the temporal attention mechanism focuses on specific parts of past information, allowing our approach to resolve occlusions over multiple frames. Our experiments demonstrate the potential of this new approach, achieving results on par with or better than the current state-of-the-art on multiple MOT metrics for several popular multi-object tracking benchmarks. © 2021, CC BY.

关键词： Tracking (position)

Deep learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather

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arXiv 2025年

作者： Bommer, Philine L. Kretschmer, Marlene Spuler, Fiona R. Bykov, Kirill Höhne, Marina M.-C. Understandable Machine Intelligence Lab TU Berlin Berlin Germany Department of Data Science ATB Potsdam Germany Leipzig Institute for Meteorology Leipzig University Leipzig Germany Department of Meteorology University of Reading Reading United Kingdom The Alan Turing Institute London United Kingdom Institute of Computer Science University of Potsdam Potsdam Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin Germany

Predictions on subseasonal-to-seasonal (S2S) timescales—ranging from two weeks to two months—are crucial for early warning systems but remain challenging owing to chaos in the climate system. Teleconnections, such as the stratospheric polar vortex (SPV) and Madden-Julian Oscillation (MJO), offer windows of enhanced predictability, however, their complex interactions remain underutilized in operational forecasting. Here, we developed and evaluated deep learning architectures to predict North Atlantic-European (NAE) weather regimes, systematically assessing the role of remote drivers in improving S2S forecast skill of deep learning models. We implemented (1) a Long Short-term Memory (LSTM) network predicting the NAE regimes of the next six weeks based on previous regimes, (2) an Index-LSTM incorporating SPV and MJO indices, and (3) a ViT-LSTM using a Vision Transformer to directly encode stratospheric wind and tropical outgoing longwave radiation fields. These models are compared with operational hindcasts as well as other AI models. Our results show that leveraging teleconnection information enhances skill at longer lead times. Notably, the ViT-LSTM outperforms ECMWF’s subseasonal hindcasts beyond week 4 by improving Scandinavian Blocking (SB) and Atlantic Ridge (AR) predictions. Analysis of high-confidence predictions reveals that NAO−, SB, and AR opportunity forecasts can be associated with SPV variability and MJO phase patterns aligning with established pathways, also indicating new patterns. Overall, our work demonstrates that encoding physically meaningful climate fields can enhance S2S prediction skill, advancing AI-driven subseasonal forecast. Moreover, the experiments highlight the potential of deep learning methods as investigative tools, providing new insights into atmospheric dynamics and predictability. © 2025, CC BY.

关键词： Deep learning

Estimate the efficiency of multiprocessor's cash memory work algorithms

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arXiv 2021年

作者： Hamada, Mohamed A. Abdallah, Abdelrahman International Information Technology University Almaty Almaty050000 Kazakhstan Department of Machine Learning & Data Science Satbayev University Almaty Almaty050013 Kazakhstan National Open Research Laboratory for Information and Space Technologies Satbayev University Almaty Almaty050013 Kazakhstan

Many computer systems for calculating the proper organization of memory are among the most critical issues. Using a tier cache memory (along with branching prediction) is an effective means of increasing modern multi-core processors' performance. Designing high-performance processors is a complex task and requires preliminary verification and analysis of the model level, usually used in analytical and simulation modeling. The refinement of extreme programming is an unfortunate challenge. Few experts disagree with the synthesis of access points. This article demonstrates that Internet QoS and 16-bit architectures are always incompatible, but it's the same situation for write-back caches. The solution to this problem can be implemented by analyzing simulation models of different complexity in combination with the analytical evaluation of individual algorithms. This work is devoted to designing a multi-parameter simulation model of a multi-process for evaluating the performance of cache memory algorithms and the optimality of the structure. Optimization of the structures and algorithms of the cache memory allows you to accelerate the interaction of the memory process and improve the performance of the entire system. © 2021, CC BY.

关键词： Cache memory

Automated segmentation and volume measurement of intracranial carotid artery calcification on non-contrast CT

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arXiv 2021年

作者： Bortsova, Gerda Bos, Daniel Dubost, Florian Vernooij, Meike W. Ikram, M. Kamran van Tulder, Gijs de Bruijne, Marleen Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Epidemiology Erasmus MC Rotterdam Netherlands Department of Biomedical Data Science Stanford University United States Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Faculty of Science Radboud University Netherlands Machine Learning Section Department of Computer Science University of Copenhagen Copenhagen Denmark

Purpose To develop and evaluate a fully-automated deep-learning-based method for assessment of intracranial carotid artery calcification (ICAC). Methods This was a secondary analysis of prospectively collected data from the Rotterdam study (2003-2006) to develop and validate a deep-learning-based method for automated ICAC delineation and volume measurement. Two observers manually delineated ICAC in non-contrast CT scans of 2,319 participants (mean age 69 ± 7 years;1154 women [53.2%]) and a deep learning model was trained to segment ICAC and quantify its volume. Model performance was assessed by comparing manual and automated segmentations and volume measurements to those produced by an independent observer (available in 47 scans), comparing the segmentation accuracy in a blinded qualitative visual comparison by an expert observer, and comparing the association with first stroke incidence from the scan date until 2016. All method performance metrics were computed using 10-fold cross-validation. Results The automated delineation of ICAC reached sensitivity of 83.8% and positive predictive value (PPV) of 88%. The intraclass correlation between automatic and manual ICAC volume measures was 0.98 (95% CI: 0.97, 0.98;computed in the entire dataset). Measured between the assessments of independent observers, sensitivity was 73.9%, PPV was 89.5%, and intraclass correlation was 0.91 (95% CI: 0.84, 0.95;computed in the 47-scan subset). In the blinded visual comparisons of 294 regions, automated delineations were judged as more accurate than manual delineations in 131 regions, less accurate in 94 regions, and equally accurate in the rest of the regions (131 of 225;58.2%;P =.01). The association of ICAC volume with incident stroke was similarly strong for both automated (hazard ratio, 1.38 [95% CI: 1.12, 1.75] and manually measured volumes (hazard ratio, 1.48 [95% CI: 1.20, 1.87]). Conclusions The developed model was capable of automated segmentation and volume quantification of

关键词： Biomineralization