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arXiv 2022年

作者： Obadic, Ivica Roscher, Ribana Oliveira, Dario Augusto Borges Zhu, Xiao Xiang Data Science in Earth Observation Technical University of Munich Munich Center for Machine Learning [MCML Arcisstraße 21 Munich80333 Germany Research Center Jülich Institute of Bio- and Geosciences Plant Sciences Wilhelm-Johnen-Straße Jülich52428 Germany International AI Future Lab: Artificial Intelligence for Earth Observation TUM and with School of Applied Mathematics Getulio Vargas Foundation Rio de Janeiro Brazil Data Science in Earth Observation Technical University of Munich Arcisstraße 21 Munich80333 Germany

Automated crop-type classification using Sentinel-2 satellite time series is essential to support agriculture monitoring. Recently, deep learning models based on transformer encoders became a promising approach for crop-type classification. Using explainable machine learning to reveal the inner workings of these models is an important step towards improving stakeholders’ trust and efficient agriculture monitoring. In this paper, we introduce a novel explainability framework that aims to shed a light on the essential crop disambiguation patterns learned by a state-of-the-art transformer encoder model. More specifically, we process the attention weights of a trained transformer encoder to reveal the critical dates for crop disambiguation and use domain knowledge to uncover the phenological events that support the model performance. We also present a sensitivity analysis approach to understand better the attention capability for revealing crop-specific phenological events. We report compelling results showing that attention patterns strongly relate to key dates, and consequently, to the critical phenological events for crop-type classification. These findings might be relevant for improving stakeholder trust and optimizing agriculture monitoring processes. Additionally, our sensitivity analysis demonstrates the limitation of attention weights for identifying the important events in the crop phenology as we empirically show that the unveiled phenological events depend on the other crops in the data considered during training. © 2022, CC BY.

关键词： Time series

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An Innovative Design Of A Reusable Constellation Of CubeSats For Space Debris Removal 73

An Innovative Design Of A Reusable Constellation Of CubeSats...

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73rd International Astronautical Congress, IAC 2022

作者： Shukla, Aayush Thuluva, Sushmith Kodukula, Ananya Bharadwaj, Vyoma Jain, Alankriti Shadaksharaiah, Anushree Maligehalli Pulicallu, Greeshmanth Kadagadakai, Vishnurat Rai, Riddhi Mitra, Ruhi Prabhu, M. Nanditha Department of Artificial Intelligence & Machine Learning Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Computer Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Telecommunication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Instrumentation Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Mechanical Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Data Science Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Information Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India

Space debris is one significant setback in our attempt to explore space and enhance satellite technology. Eradicating this upshot is eminent to our future missions and a safe environment for our planet. The scale of the space junk in the low earth orbit is around few hundreds of thousands and more, sizing from 10s of centimeters in diameter and lower. Although it is inevitable for the space debris to reenter the atmosphere naturally, it is a matter of years and hence the rate is too slow. With advances in the CubeSat technology, a variety of designs are possible for CubeSat systems to function as per the mission requirements. The current paper hence nominates an idea to remove some of the inoperative satellites and it's corresponding components with the help of a constellation of CubeSats that are equipped with distributed actuators for propulsion. The CubeSats are so designed to function as a propulsive device alone with minimal subsystem requirements. The constellation is contained in a cuboidal station with CubeSats docked on all the lateral sides. Once a target body is locked and intended to be removed, the constellation releases one or more CubeSats en route to the target body. Once at site, the CubeSats attach themselves to the target and propel the body to reenter the atmosphere. This constellation station is so designed to be compatible for reusability and the maintenance of such a design is discussed in the paper. The entire approach is thoroughly analyzed investigating multiple designs for the CubeSats. An additional benefit of the proposed idea is to ensure an immediate solution for deorbiting any critically unstable debris in the future in an event of emergency. Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Space debris

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DORA: Exploring Outlier Representations in Deep Neural Networks

arXiv

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arXiv 2022年

作者： Bykov, Kirill Deb, Mayukh Grinwald, Dennis Müller, Klaus-Robert Höhne, Marina M.-C. Potsdam Germany Technical University of Berlin Berlin Germany Machine Learning Group Technical University of Berlin Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institut für Informatik Saarbrücken66123 Germany Google Research Brain Team Berlin Germany Department of Computer Science University of Potsdam Germany Department of Physics and Technology UiT Arctic University of Norway Norway

Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal representations. However, the concepts they learn remain opaque, a problem that becomes particularly acute when models unintentionally learn spurious correlations. In this work, we present DORA (data-agnOstic Representation Analysis), the first data-agnostic framework for analyzing the representational space of DNNs. Central to our framework is the proposed Extreme-Activation (EA) distance measure, which assesses similarities between representations by analyzing their activation patterns on data points that cause the highest level of activation. As spurious correlations often manifest in features of data that are anomalous to the desired task, such as watermarks or artifacts, we demonstrate that internal representations capable of detecting such artifactual concepts can be found by analyzing relationships within neural representations. We validate the EA metric quantitatively, demonstrating its effectiveness both in controlled scenarios and real-world applications. Finally, we provide practical examples from popular Computer Vision models to illustrate that representations identified as outliers using the EA metric often correspond to undesired and spurious concepts. © 2022, CC BY.

关键词： Chemical activation

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Deep CNN: A machine learning approach for driver drowsiness detection based on eye state

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Revue d'Intelligence Artificielle 2019年第6期33卷 461-466页

作者： Reddy Chirra, Venkata Rami Uyyala, Srinivasulu Reddy Kishore Kolli, Venkata Krishna Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology Tiruchirappalli620015 India Department of Computer Science and Engineering VFSTR Guntur522213 India

Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN. © 2019 Lavoisier. All rights reserved.

关键词： Face recognition

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TOWARDS DIVERSE EVALUATION OF CLASS INCREMENTAL learning: A REPRESENTATION learning PERSPECTIVE

arXiv

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arXiv 2022年

作者： Cha, Sungmin Kwak, Jihwan Shim, Dongsub Kim, Hyunwoo Lee, Moontae Lee, Honglak Moon, Taesup Computer Science Department The Courant Institute of Mathematical Sciences New York University United States Department of Electrical and Computer Engineering Seoul National University Korea Republic of Advanced Machine Learning Lab LG AI Research Korea Republic of Zhejiang Lab China Department of Information and Decision Sciences University of Illinois Chicago United States ASRI INMC IPAI AIIS Seoul National University Korea Republic of

Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data while not forgetting past learned classes. The common evaluation protocol for CIL algorithms is to measure the average test accuracy across all classes learned so far — however, we argue that solely focusing on maximizing the test accuracy may not necessarily lead to developing a CIL algorithm that also continually learns and updates the representations, which may be transferred to the downstream tasks. To that end, we experimentally analyze neural network models trained by CIL algorithms using various evaluation protocols in representation learning and propose new analysis methods. Our experiments show that most state-of-the-art algorithms prioritize high stability and do not significantly change the learned representation, and sometimes even learn a representation of lower quality than a naive baseline. However, we observe that these algorithms can still achieve high test accuracy because they enable a model to learn a classifier that closely resembles an estimated linear classifier trained for linear probing. Furthermore, the base model learned in the first task, which involves single-task learning, exhibits varying levels of representation quality across different algorithms, and this variance impacts the final performance of CIL algorithms. Therefore, we suggest that the representation-level evaluation should be considered as an additional recipe for more diverse evaluation for CIL algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： learning algorithms

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Efficient dataset generation for machine learning halide perovskite alloys

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Physical Review Materials 2025年第5期9卷 053802-053802页

作者： Henrietta Homm Jarno Laakso Patrick Rinke Department of Applied Physics Aalto University 00076 Aalto Finland Physics Department Technical University of Munich Garching Germany Atomistic Modelling Center Munich Data Science Institute Technical University of Munich Garching Germany Munich Center for Machine Learning (MCML) Munich Germany

Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional engineering of perovskite materials, but the vast complexity of the perovskite materials space poses a significant obstacle to exploration. We previously demonstrated how machine learning (ML) can accelerate property predictions for the CsPb(Cl/Br)3 perovskite alloy. However, the substantial computational demand of density functional theory (DFT) calculations required for model training prevents applications to more complex materials. Here, we introduce a data-efficient scheme to facilitate model training, validated initially on CsPb(Cl/Br)3 data and extended to the ternary alloy CsSn(Cl/Br/I)3. Our approach employs clustering to construct a compact yet diverse initial dataset of atomic structures. We then apply a two-stage active learning approach to first improve the reliability of the ML-based structure relaxations and then refine accuracy near equilibrium structures. Tests for CsPb(Cl/Br)3 demonstrate that our scheme reduces the number of required DFT calculations during the different parts of our proposed model training method by up to 20% and 50%. The fitted model for CsSn(Cl/Br/I)3 is robust and highly accurate, evidenced by the convergence of all ML-based structure relaxations in our tests and an average relaxation error of only 0.5 meV/atom.

关键词： Ternary alloys

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Lung Ultrasound for the Detection of Pulmonary Tuberculosis Using Expert- and AI-Guided Interpretation: A Prospective Cohort Study

SSRN

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SSRN 2025年

作者： Suttels, Véronique Brokowski, Trevor Wachinou, Ablo Prudence Wolleb, Julia Hada, Aboudou Rasisou Du Toit, Jacques Daniel Fiogbé, Arnauld Attannon Guendehou, Brice Alovokpinhou, Frederic Sefou, Fadyl Makpemikpa, Ginette Bessat, Cécile Roux, Alexia Garcia, Elena Brahier, Thomas Opota, Onya Doenz, Jonathan Vignoud, Julien Agodokpessi, Gildas Affolabi, Dissou Hartley, Mary-Anne Boillat-Blanco, Noémie Department of Medicine Infectious Diseases Lausanne University Hospital University of Lausanne Lausanne Switzerland Yale School of Medicine Department of Biomedical Informatics & Data Science New HavenCT06510 United States Cotonou Benin Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Emergency Department Lausanne University Hospital University of Lausanne Lausanne1011 Switzerland Institute of Microbiology University of Lausanne University Hospital Centre Lausanne Switzerland Lausanne1015 Switzerland National Reference Laboratory for Mycobacteriology Cotonou Benin Yale University United States Faculty of Health Sciences Benin Intelligent Global Health Machine Learning and Optimization Laboratory

Background: Point-of-care lung ultrasound (LUS) is a promising tool for portable sputum-free tuberculosis (TB) triage. We investigate the diagnostic performance of LUS to detect TB using expert and artificial intelligence (AI) guided interpretation. We introduce ULTR-AI (Ultrasound-led TB recognition using AI), a suite of deep learning (DL) models designed to automate TB prediction from LUS images. Methods: In this prospective cohort study in a tertiary center in urban Benin, a standardized 14-point sliding scan LUS protocol was performed for symptomatic patients by a trained operator. LUS images were reviewed by two blinded and independent readers. Same-day single sputum Xpert MTB/RIF Ultra® was the microbiological reference standard. The suite comprises three AI models, ULTR-AI, ULTR-AI[signs] and ULTR-AI[max]. ULTR-AI predicts TB directly from images using DL, ULTR-AI[signs] first generates human-recognizable pathological signs before TB risk prediction in a machine learning (ML) model. Finally, ULTR-AI[max] takes the maximal TB risk score predicted by these two models. Findings: Out of 760 patients screened, 504 were analyzed. Median age was 40 (IQR 30-52), 196 (39%) were female, 78 (15%) were people with HIV and 66 (13%) had previous TB. Overall, 192 (38%) had bacteriologically confirmed TB. Human expert interpretation of lung ultrasound achieved a sensitivity of 0·90 (95%CI: 0·89, 0·92), specificity of 0·61 (95%CI: 0·54, 0·67) [AUROC 0·84, 95% CI: 0.82-0.85]. AI-guided interpretation with ULTRA-AI[max] reached a sensitivity of 0·91 (95% CI, 0·90–0·96) and a specificity of 0·85 (95% CI, 0·74–0·88) [AUROC 0·93, 95% 0·92-0·95]. Interpretation: In this cohort, AI-guided LUS meets the WHO requirements for a sputum-free TB triage test, enabling point-of-care testing with minimal infrastructure. ULTR-AI could further help decentralize TB diagnostics in LMICs, improving timely detection. Validation in diverse populations is crucial to confirm clinical utility. © 2025,

关键词： Lung cancer

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Species-Specific Responses of Canopy Greenness to the Extreme Droughts of 2018 and 2022 for Four Abundant Tree Species in Germany

SSRN

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SSRN 2024年

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-Universität zu 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

The years 2018 and 2022 witnessed extreme drought periods in Germany, which significantly affected forests. These repeated droughts were a natural experiment that provided valuable insights into how different tree species respond to drought. Understanding trees' drought response is crucial for predicting their adaptation to climate change, thereby informing effective forest regeneration strategies. In this study, we used remotely sensed peak-season canopy greenness as a proxy for tree vitality to approximate the drought response of four dominant tree species in Germany (beech, oak, pine, and spruce). 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), atmospheric vapor pressure deficit (VPD), and climatic water balance (SPEI). Our results indicated the more isohydric species (pine, spruce) featured a greater decline in canopy greenness under these droughts than the more anisohydric species (beech, oak) despite similar soil moisture conditions. The additive models suggested that the canopy greenness of the more isohydric species and more anisohydric species were primarily driven by PAWC and VPD, respectively. Our statistical analysis indicated that oak was the only species with significantly higher canopy greenness in 2022 compared to 2018. Yet, all species are likely still susceptible to accumulated drought effects related 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 gradient in Central Europe and sheds light on the complex interactions between soil moisture, climate variables, and canopy greenness. These findings contribut

关键词： Remote sensing

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

arXiv

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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

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Adapting to noise distribution shifts in flow-based gravitational-wave inference

引用

Physical Review D 2023年第8期107卷 084046-084046页

作者： Jonas Wildberger Maximilian Dax Stephen R. Green Jonathan Gair Michael Pürrer Jakob H. Macke Alessandra Buonanno Bernhard Schölkopf Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 72076 Tübingen Germany School of Mathematical Sciences University of Nottingham University Park Nottingham NG7 2RD United Kingdom Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Am Mühlenberg 1 14476 Potsdam Germany Department of Physics East Hall University of Rhode Island Kingston Rhode Island 02881 USA URI Research Computing Tyler Hall University of Rhode Island Kingston Rhode Island 02881 USA Machine Learning in Science University of Tübingen 72076 Tübingen Germany Department of Physics University of Maryland College Park Maryland 20742 USA

Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers—producing results of comparable accuracy. These approaches (e.g., DINGO) enable amortized inference by training a normalizing flow to represent the Bayesian posterior conditional on observed data. By conditioning also on the noise power spectral density (PSD) they can even account for changing detector characteristics. However, training such networks requires knowing in advance the distribution of PSDs expected to be observed and therefore can only take place once all data to be analyzed have been gathered. Here, we develop a probabilistic model to forecast future PSDs, greatly increasing the temporal scope of trained deep learning models. Using PSDs from the second LIGO-Virgo observing run (O2)—plus just a single PSD from the beginning of the third (O3)—we show that we can train a DINGO network to perform accurate inference throughout O3 (on 37 real events). We therefore expect this approach to be a key component to enable the use of deep learning techniques for low-latency analyses of gravitational waves.

关键词： Classical black holes General relativity Gravitational wave sources Gravitational waves Artificial neural networks Astronomical black holes data analysis Gravitational wave detectors machine learning

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