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Drone flight data reveal energy and greenhouse gas emissions savings for small package delivery

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arXiv 2021年

作者： Rodrigues, Thiago A. Patrikar, Jay Oliveira, Natalia L. Scott Matthews, H. Scherer, Sebastian Samaras, Constantine Department of Civil and Environmental Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh15213 United States Department of Statistics and Data Science Carnegie Mellon University 5000 Forbes Avenue Pittsburgh15213 United States Machine Learning Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh15213 United States

The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will affect the energy productivity of package delivery and require new methods to understand the associated energy consumption and greenhouse gas (GHG) emissions. Here we combine empirical testing of 187 quadcopter flights with first principles analysis to develop a usable energy model for drone package delivery. We develop a machine learning algorithm to assess energy use across three different flight regimes: takeoff, cruise, and landing. Our model shows that, in the US, a small electric quadcopter drone with a payload of 1 kg would consume approximately 0.05 MJ/km and result in 41 g of CO2e per package. The energy per package delivered by drones (0.19 MJ/package) can be up to 96% lower than conventional transportation modes. Our open model and generalizable coefficients can assist stakeholders in understanding and improving the energy use of drone package delivery. © 2021, CC BY-NC-SA.

关键词： Energy utilization

learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

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arXiv 2021年

作者： Andéol, Léo Kawakami, Yusei Wada, Yuichiro Kanamori, Takafumi Müller, Klaus-Robert Montavon, Grégoire Machine Learning group Technische Universität Berlin Berlin10587 Germany Tokyo Institute of Technology Tokyo Japan Berlin Institute for the Foundations of Learning and Data – BIFOLD Berlin10587 Germany Fujitsu Laboratories Ltd. Japan RIKEN AIP Japan Max Planck Institute for Informatics Stuhlsatzenhausweg 4 Saarbrücken66123 Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Google Deepmind Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany

Domain shifts in the training data are common in practical applications of machine learning;they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by learning a domain-invariant representation. However, common ML losses do not give strong guarantees on how consistently the ML model performs for different domains, in particular, whether the model performs well on a domain at the expense of its performance on another domain. In this paper, we build new theoretical foundations for this problem, by contributing a set of mathematical relations between classical losses for supervised ML and the Wasserstein distance in joint space (i.e. representation and output space). We show that classification or regression losses, when combined with a GAN-type discriminator between domains, form an upper-bound to the true Wasserstein distance between domains. This implies a more invariant representation and also more stable prediction performance across domains. Theoretical results are corroborated empirically on several image datasets. Our proposed approach systematically produces the highest minimum classification accuracy across domains, and the most invariant representation. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

Combined compression and encryption of linear wireless sensor network data using autoencoders

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Scientific reports 2025年第1期15卷 17771页

作者： N Shylashree Sachin Kumar Hong Min Department of Electronics and Communication RV College of Engineering Affiliated to VTU Belagavi Bengaluru 560059 India. Big Data and Machine Learning Lab South Ural State University Chelyabinsk Russia. Akian College of Science and Engineering American University of Armenia Yerevan 0019 Armenia. School of Computing Gachon University Seongnam Republic of Korea. hmin@gachon.ac.kr.

In a linear wireless sensor network (LWSN), sensor nodes are deployed in a linear fashion to monitor and gather data along a linear path or route. Generally, the base station collects the data from the contiguously placed nodes in the same order. When the sensors are deployed closely and with a gradual variation of sensor data along the route, a high degree of correlation exists among the sensed data. The sensed data sequence can be compressed with very low loss in such a situation. In this paper, a joint compression and encryption method for LWSN data is presented. The method is based on the dimension reduction property of an autoencoder at the bottleneck section. The Encoder part of the trained Autoencoder, housed at the Base Station (BS), reduces the number of data samples at the encoded output. Hence, the data gets compressed at the output of the Encoder. The output of the Encoder is encrypted using an asymmetric encryption that provides immunity to the Chosen Plaintext Attack. Thus, both data compression and encryption are achieved together at the BS. Therefore, the procedure at the BS is denoted as joint compression and encryption. The encrypted data is sent to the Cloud Server for secured storage and further distribution to the End User, where it is decrypted and subsequently decompressed by the Decoder part of the trained Autoencoder. The decompressed data sequence is very nearly equal to the original data sequence. The proposed lossy compression has a mean square reconstruction error of less than 0.5 for compression ratios in the range of 5 to 10. The compression time taken is short even though the Autoencoder training process, which occurs once in a while, takes a relatively long time.

关键词： Autoencoder with supervised learning Chosen plaintext attack Integer matrix keys Joint compression and encryption Linear wireless sensor network Secured cloud storage

Land instability Compounds the risk of sea level rise in Alexandria, Egypt

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International Journal of Applied Earth Observation and Geoinformation 2025年 141卷

作者： Rejoice Thomas Sara Zouriq Shahryar Fazli Amr Fawzy Nikolay Grisel Todorov Surendra Maharjan Wenzhao Li Erik Linstead Daniele Struppa Hesham El-Askary Earth Systems Science and Data Solutions Lab Chapman University Orange CA 92866 USA Schmid College of Science and Technology Chapman University Orange CA 92866 USA Ministry of Water Resources and Irrigation Cairo Egypt Machine Learning and Assistive Technology Lab (MLAT) Chapman University Orange CA 92866 USA Fowler School of Engineering Chapman University Orange CA 92866 USA Department of Environmental Sciences Faculty of Science Alexandria University Moharem Bek Alexandria 21522 Egypt

The coastal region of Alexandria Governorate in Egypt holds significant strategic importance for trade while being susceptible to extreme weather events. It confronts a dual challenge of the rising sea levels and, as found in this study, land instability. While much attention has been rightly directed towards sea level rise (SLR), the stability of the land warrants equal consideration. Here, a comprehensive analysis of land stability is conducted in Alexandria by measuring Line of Sight (LOS) displacements and assessing their topographical, hydrological, and coastal impacts. Persistent Scatterer Interferometry technique is used to measure the LOS displacements in association with land cover classification and elevation data. Moreover, in the absence of a recent, openly accessible Digital Elevation Model (DEM) with satisfactory resolution, we developed a novel approach to update DEM using the Small Baseline Subset method. This approach enabled us to generate a projected DEM for the year 2040, facilitating the creation of a stream network based on this “future DEM”. Our observations revealed displacements ranging from −14.0 to 6.7 mm/year, predominantly occurring in low-lying and built-up areas, suggesting anthropogenic activities as a likely cause. The current and future stream networks show stream shifting and widening. Stream shifting indicates dynamic changes in topography and hydrological conditions, while stream widening suggests alterations in the hydrological network due to increased urban runoff or changes in the natural watershed. Furthermore, we found that sea surface temperature and SLR are projected to increase until the end of the century in the Mediterranean Sea near Alexandria. This escalation raises the likelihood of intensified tropical-like hurricane events in the region, commonly referred to as “medicanes”. The importance of this research is in offering a framework for comprehensive land stability assessments, along with demonstrating the scalabili

关键词： Persistent Scatterer Interferometry Small Baseline Subset Digital Elevation Model Medicanes Sea Surface Temperature Sea Level Rise

Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones

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arXiv 2020年

作者： Hu, Addison J. Kuusela, Mikael Lee, Ann B. Giglio, Donata Wood, Kimberly M. Department of Statistics & Data Science Carnegie Mellon University United States Department of Atmospheric and Oceanic Sciences University of Colorado Boulder United States Department of Hydrology and Atmospheric Sciences University of Arizona United States Machine Learning Department Carnegie Mellon University United States

Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and for an understanding of the role that TCs play in the global climate system. However, that characterization is complicated by the high-noise ocean environment, correlations inherent in spatio-temporal data, relative scarcity of in situ observations, and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into an ANOVA decomposition model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multi-step procedure, which (1) characterizes the upper ocean seasonal shift during the TC season;(2) models the variability in the temperature observations;(3) fits a thin-plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track's right side. Copyright © 2020, The Authors. All rights reserved.

关键词： Tropical cyclone

Weisfeiler and Leman go machine learning: The Story so far

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arXiv 2021年

作者： Morris, Christopher Lipman, Yaron Maron, Haggai Rieck, Bastian Kriege, Nils M. Grohe, Martin Fey, Matthias Borgwardt, Karsten Department of Computer Science RWTH Aachen University Aachen Germany Meta AI Research Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel NVIDIA Research Tel Aviv Israel AIDOS Lab Institute of AI for Health Helmholtz Zentrum München and Technical University of Munich Munich Germany Faculty of Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria Kumo.AI Mountain ViewCA United States Machine Learning & Computational Biology Lab Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland

In recent years, algorithms and neural architectures based on the Weisfeiler–Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm’s use in a machine-learning setting, focusing on the supervised regime. We discuss the theoretical background, show how to use it for supervised graph and node representation learning, discuss recent extensions, and outline the algorithm’s connection to (permutation-)equivariant neural architectures. Moreover, we give an overview of current applications and future directions to stimulate further research. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

A case for humans-in-the-loop: Decisions in the presence of erroneous algorithmic scores

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arXiv 2020年

作者： De-Arteaga, Maria Fogliato, Riccardo Chouldechova, Alexandra Heinz College Machine Learning Department Carnegie Mellon University PittsburghPA United States Department of Statistics and Data Science Carnegie Mellon University PittsburghPA United States Heinz College Carnegie Mellon University PittsburghPA United States

The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologies, it is key to study how experts alter their decisions when using such tools. In this paper, we study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We focus on the question: Are humans capable of identifying cases in which the machine is wrong, and of overriding those recommendations? We first show that humans do alter their behavior when the tool is deployed. Then, we show that humans are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk, even when overriding the recommendation requires supervisory approval. These results highlight the risks of full automation and the importance of designing decision pipelines that provide humans with autonomy. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision support systems

Reducing Training data Needs with Minimal Multilevel machine learning (M3L)

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arXiv 2023年

作者： Heinen, Stefan Khan, Danish von Rudorff, Guido Falk Karandashev, Konstantin Arrieta, Daniel Jose Arismendi Price, Alastair J.A. Nandi, Surajit Bhowmik, Arghya Hermansson, Kersti Anatole von Lilienfeld, O. Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry University of Toronto St. George Campus TorontoON Canada University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Department of Chemistry-Ångström Laboratory Uppsala University Box 538 UppsalaSE-75121 Sweden Acceleration Consortium University of Toronto 80 St George St TorontoONM5S 3H6 Canada Departments of Chemistry University of Toronto St. George Campus TorontoON Canada Department of Energy Conversion and Storage DTU Anker Engelunds Vej Kgs. LyngbyDK-2800 Denmark Department of Materials Science and Engineering University of Toronto St. George campus TorontoON Canada Department of Physics University of Toronto St. George campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data Berlin Germany

For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon footprint, training data efficiency is key. We introduce minimal multilevel machine learning (M3L) which optimizes training data set sizes using a loss function at multiple levels of reference data in order to minimize a combination of prediction error with overall training data acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies and electron affinities of thousands of organic molecules at various levels of theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, and PNOCCSD(T)F12, and treating tens with basis sets TZ, cc-pVTZ, and AVTZF12. Our M3L benchmarks for reaching chemical accuracy in distinct chemical compound sub-spaces indicate substantial computational cost reductions by factors of ∼ 1.01, 1.1, 3.8, 13.8 and 25.8 when compared to heuristic sub-optimal multilevel machine learning (M2L) for the data sets QM7b, QM9LCCSD(T), EGP, QM9CCSD(T)AE , and QM9CCSD(T)EA , respectively. Furthermore, we use M2L to investigate the performance for 76 density functionals when used within multilevel learning and building on the following levels drawn from the hierarchy of Jacobs Ladder: LDA, GGA, mGGA, and hybrid functionals. Within M2L and the molecules considered, mGGAs do not provide any noticeable advantage over GGAs. Among the functionals considered and in combination with LDA, the three on average top performing GGA and Hybrid levels for atomization energies on QM9 using M3L correspond respectively to PW91, KT2, B97D, and τ-HCTH, B3LYP∗(VWN5), TPSSH. © 2023, CC0.

关键词： Carbon footprint

A unified approach to robust mean estimation

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arXiv 2019年

作者： Prasad, Adarsh Balakrishnan, Sivaraman Ravikumar, Pradeep Machine Learning Department Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States

In this paper, we develop connections between two seemingly disparate, but central, models in robust statistics: Huber?'s ϵ-contamination model and the heavy-tailed noise model. We provide conditions under which this connection provides near-statisticallyoptimal estimators. Building on this connection, we provide a simple variant of recent computationally-efficient algorithms for mean estimation in Huber?fs model, which given our connection entails that the same efficient sample-pruning based estimators is simultaneously robust to heavy-tailed noise and Huber contamination. Furthermore, we complement our efficient algorithms with statistically-optimal albeit computationally intractable estimators, which are simultaneously optimally robust in both models. We study the empirical performance of our proposed estimators on synthetic datasets, and find that our methods convincingly outperform a variety of practical baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： machine learning