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Paddy rice methane emissions across Monsoon Asia

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Remote Sensing of Environment 2023年 284卷

作者： Ouyang, Zutao Jackson, Robert B. McNicol, Gavin Fluet-Chouinard, Etienne Runkle, Benjamin R.K. Papale, Dario Knox, Sara H. Cooley, Sarah Delwiche, Kyle B. Feron, Sarah Irvin, Jeremy Andrew Malhotra, Avni Muddasir, Muhammad Sabbatini, Simone Alberto, Ma. Carmelita R. Cescatti, Alessandro Chen, Chi-Ling Dong, Jinwei Fong, Bryant N. Guo, Haiqiang Hao, Lu Iwata, Hiroki Jia, Qingyu Ju, Weimin Kang, Minseok Li, Hong Kim, Joon Reba, Michele L. Nayak, Amaresh Kumar Roberti, Debora Regina Ryu, Youngryel Swain, Chinmaya Kumar Tsuang, Benjei Xiao, Xiangming Yuan, Wenping Zhang, Geli Zhang, Yongguang Department of Earth System Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Precourt Institute for Energy Stanford University StanfordCA United States Department of Earth and Environmental Sciences University of Illinois Chicago ChicagoIL United States Department of Biological and Agricultural Engineering University of Arkansas FayettevilleAR United States University of Tuscia Viterbo Italy IAFES division Viterbo Italy Department of Geography The University of British Columbia VancouverBC Canada Department of Geography University of Oregon EugeneOR United States Department of Environmental Science Policy And Management UC Berkeley BerkeleyCA United States Campus Fryslan University of Groningen Groningen Netherlands Department of Computer Science Stanford University StanfordCA United States Department of Geography University of Zurich Zurich Switzerland International Rice Research Institute Laguna Philippines Ispra Italy Taiwan Agricultural Research Institute Taichung Wufeng Taiwan Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China USDA ARS Delta Water Management Research Unit JonesboroAR United States Fudan University Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering and Coastal Ecosystems Research Station of the Yangtze River Estuary Shanghai China / Jiangsu Key Laboratory of Agricultural Meteorology Nanjing University of Information Science and Technology Nanjing China Department of Environmental Science Shinshu University Matsumoto Japan China Meteorological Administration Institute of Atmospheric Environment Shenyang China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu Nanjing210023 China National Center for AgroM

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes;and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit

关键词： Climate change

On coresets for regularized loss minimization

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arXiv 2019年

作者： Curtin, Ryan Im, Sungjin Moseley, Benjamin Pruhs, Kirk Samadian, Alireza Relational AI AtlantaGA30318 Computer Science and Engineering UC Merced Tepper School of Business Carnegie Mellon University Relational AI Computer Science Department University of Pittsburgh

We design and mathematically analyze sampling-based algorithms for regularized loss minimization problems that are implementable in popular computational models for large data, in which the access to the data is restricted in some way. Our main result is that if the regularizerfs effect does not become negligible as the norm of the hypothesis scales, and as the data scales, then a uniform sample of modest size is with high probability a coreset. In the case that the loss function is either logistic regression or soft-margin support vector machines, and the regularizer is one of the common recommended choices, this result implies that a uniform sample of size O(dañ) is with high probability a coreset of n points in .d. We contrast this upper bound with two lower bounds. The first lower bound shows that our analysis of uniform sampling is tight;that is, a smaller uniform sample will likely not be a core set. The second lower bound shows that in some sense uniform sampling is close to optimal, as significantly smaller core sets do not generally exist. Copyright © 2019, The Authors. All rights reserved.

关键词： Support vector machines

Unbiased estimators for random design regression

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arXiv 2019年

作者： Dereziński, Michal Warmuth, Manfred K. Hsu, Daniel Department of Electrical Engineering & Computer Science University of Michigan United States UC Santa Cruz Google Inc United States Department of Computer Science Columbia University United States

In linear regression we wish to estimate the optimum linear least squares predictor for a distribution over d-dimensional input points and real-valued responses, based on a small sample. Under standard random design analysis, where the sample is drawn i.i.d. from the input distribution, the least squares solution for that sample can be viewed as the natural estimator of the optimum. Unfortunately, this estimator almost always incurs an undesirable bias coming from the randomness of the input points, which is a significant bottleneck in model averaging. In this paper we show that it is possible to draw a non-i.i.d. sample of input points such that, regardless of the response model, the least squares solution is an unbiased estimator of the optimum. Moreover, this sample can be produced efficiently by augmenting a previously drawn i.i.d. sample with an additional set of d points, drawn jointly according to a certain determinantal point process constructed from the input distribution rescaled by the squared volume spanned by the points. Motivated by this, we develop a theoretical framework for studying volume-rescaled sampling, and in the process prove a number of new matrix expectation identities. We use them to show that for any input distribution and ∊ > 0 there is a random design consisting of O(d log d + d/∊) points from which an unbiased estimator can be constructed whose expected square loss over the entire distribution is bounded by 1 + ∊ times the loss of the optimum. We provide efficient algorithms for constructing such unbiased estimators in a number of practical settings. In one such setting, we let the input distribution be uniform over a large dataset of n d points. Here, we obtain the first unbiased least squares estimator that can be constructed in time nearly-linear in the data size, resulting in strong guarantees for model averaging. We achieve these computational gains by introducing a new algorithmic technique, called distortion-free intermediate sa

关键词： Sampling

A GPT-4 Reticular Chemist for Guiding MOF Discovery**

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Angewandte Chemie 2023年第46期135卷

作者： Zhiling Zheng Zichao Rong Dr. Nakul Rampal Prof. Dr. Christian Borgs Prof. Dr. Jennifer T. Chayes Prof. Dr. Omar M. Yaghi Department of Chemistry Kavli Energy Nanoscience Institute and Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley Berkeley CA-94720 United States Department of Electrical Engineering and Computer Sciences and Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley Berkeley CA-94720 United States Department of Electrical Engineering and Computer Sciences Department of Statistics Department of Mathematics School of Information and Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley Berkeley CA-94720 United States KACST—UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh 11442 Saudi Arabia

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities.

关键词： Artificial Intelligence Crystals Large Language Model Metal–Organic Frameworks Synthesis

AdaptHD: Adaptive Efficient Training for Brain-Inspired Hyperdimensional Computing

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AdaptHD: Adaptive Efficient Training for Brain-Inspired Hype...

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Mohsen Imani Justin Morris Samuel Bosch Helen Shu Giovanni De Micheli Tajana Rosing Computer Science and Engineering UC San Diego La Jolla CA USA Integrated Systems Laboratory EPFL Lausanne Switzerland

Brain-inspired Hyperdimensional (HD) computing is a promising solution for energy-efficient classification. HD emulates cognition tasks by exploiting long-size vectors instead of working with numeric values used in contemporary processors. However, the existing HD computing algorithms have lack of controllability on the training iterations which often results in slow training or divergence. In this work, we propose AdaptHD, an adaptive learning approach based on HD computing to address the HD training issues. AdaptHD introduces the definition of learning rate in HD computing and proposes two approaches for adaptive training: iteration-dependent and data-dependent. In the iteration-dependent approach, AdaptHD uses a large learning rate to speedup the training procedure in the first iterations, and then adaptively reduces the learning rate depending on the slope of the error rate. In the data-dependent approach, AdaptHD changes the learning rate for each data point depending on how far off the data was misclassified. Our evaluations on a wide range of classification applications show that AdaptHD achieves 6.9× speedup and 6.3× energy efficiency improvement during training as compared to the state-of-the-art HD computing algorithm.

关键词： Training Adaptation models Error analysis Data models Training data Encoding Computational modeling

Self-synchronization of multiple vehicles using ambient impulsive noise 4

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Self-synchronization of multiple vehicles using ambient impu...

4th Underwater Communications and Networking Conference, ucomms 2018

作者： Naughton, Perry Salam, Tahiya Tueller, Peter Roux, Philippe Schurgers, Curt Kastner, Ryan Jaffe, Jules Roberts, Paul Department of Electrical and Computer Engineering UC San Diego United States Department of Computer Science and Engineering UC San Diego United States ISTerre Université Grenoble Alpes CNRS Grenoble France Scripps Institution of Oceanography UC San Diego United States

ISBN: (纸本)9781538664421

Clock synchronization is important when considering a swarm of underwater vehicles. Unfortunately, accurate clocks are expensive, and acoustic communication is power intensive, preventing large numbers of vehicles from being deployed for long periods of time. We propose a technique where vehicles achieve relative synchronization by leveraging sounds already present in the underwater environment. This provides a synchronization method that does not rely on expensive clocks, communication, or additional infrastructure. We demonstrate that our technique accurately tracks clock offsets between vehicles during an at sea deployment. © 2018 IEEE.

关键词： Vehicles

Associative convolutional layers

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arXiv 2019年

作者： Omidvar, Hamed Akhlaghi, Vahideh Franceschetti, Massimo Gupta, Rajesh K. University of California San Diego United States Department of Electrical and Computer Engineering UC San Diego San DiegoCA92093 United States Department of Computer Science and Engineering UC San Diego San DiegoCA92093 United States

Motivated by the necessity for parameter efficiency in distributed machine learning and AI-enabled edge devices, we provide a general and easy to implement method for significantly reducing the number of parameters of Convolutional Neural Networks (CNNs), during both the training and inference phases. We introduce a simple auxiliary neural network which can generate the convolutional filters of any CNN architecture from a low dimensional latent space. This auxiliary neural network, which we call "Convolutional Slice Generator" (CSG), is unique to the network and provides the association between its convolutional layers. During the training of the CNN, instead of training the filters of the convolutional layers, only the parameters of the CSG and their corresponding "code vectors" are trained. This results in a significant reduction of the number of parameters due to the fact that the CNN can be fully represented using only the parameters of the CSG, the code vectors, the fully connected layers, and the architecture of the CNN. To show the capability of our method, we apply it to ResNet and DenseNet architectures, using the CIFAR-10 and ImageNet-1000 datasets without any hyper-parameter tuning. Experiments show that our approach, even when applied to already compressed and efficient CNNs such as DenseNet-BC, significantly reduces the number of network parameters. In two models based on DenseNet-BC with ≈ 2× reduction in one of them we had a slight improvement in accuracy and in another one, with ≈ 2× reduction the change in accuracy is negligible. In case of ResNet-56, ≈ 2.5× reduction leads to an accuracy loss within 1%. When applying this approach to ResNet-18 on ImageNet-1000 dataset, we achieved a top-1 error that is 1.7% better than the original network while having 1.5× reduction in the number of parameters. In case of ResNet-50, our approach reduces the number of parameters to less than the number of parameters of ResNet-18, namely by ≈ 1.7×, while the top-1 e

关键词： Convolution

Optimal path and illumination design for multiframe motion deblurring

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Optimal path and illumination design for multiframe motion d...

Imaging Systems and Applications, ISA 2018

作者： Dean, Sarah Phillips, Zachary Waller, Laura Recht, Ben Department of Electrical Engineering and Computer Sciences UC Berkeley Berkeley United States Graduate Group in Applied Science and Technology UC Berkeley Berkeley United States

We propose an extension of coded illumination design for motion deblurring to multiframe imaging, where a large field-of-view sample is recovered from many motion-blurred measurements captured while scanning the sampl... 详细信息

ISBN: (纸本)9781943580446

关键词： Three dimensional displays

Error-triggered three-factor learning dynamics for crossbar arrays

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arXiv 2019年

作者： Payvand, Melika Fouda, Mohammed Kurdahi, Fadi Eltawil, Ahmed Neftci, Emre O. Institute of neuroinformatics University ETH of Zurich Zurich Switzerland Department of Electrical Engineering and Computer Science UC Irvine IrvineCA92697-2625 United States Department of Cognitive Sciences Department of Computer Science UC Irvine IrvineCA92697-2625 United States

Recent breakthroughs suggest that local, approximate gradient descent learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn in situ as accurately as conventional processors is still missing. Here, we propose a subthreshold circuit architecture designed through insights obtained from machine learning and computational neuroscience that could achieve such accuracy. Using a surrogate gradient learning framework, we derive local, error-triggered learning dynamics compatible with crossbar arrays and the temporal dynamics of SNNs. The derivation reveals that circuits used for inference and training dynamics can be shared, which simplifies the circuit and suppresses the effects of fabrication mismatch. We present SPICE simulations on XFAB 180nm process, as well as large-scale simulations of the spiking neural networks on event-based benchmarks, including a gesture recognition task. Our results show that the number of updates can be reduced hundred-fold compared to the standard rule while achieving performances that are on par with the state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Network architecture