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1981
Projected effects of declining anthropogenic aerosols on the southern annular mode
Published 2013-01-01“…This study considers the response of the southern annular mode (SAM) in austral summer to declining aerosols in simulations forced by Representative Concentration Pathway 4.5 (RCP4.5) using CSIRO-Mk3.6, a CMIP5-generation model. A ten-member ensemble forced by RCP4.5 for the period 2006–2100 is compared with another experiment, which is identical except that emissions of anthropogenic aerosols are held fixed at their 2005 values. …”
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1982
A critical approach to Machine Learning forecast capabilities: creating a predictive biography in the age of the Internet of Behaviour (IoB)
Published 2023-01-01“…Machine Biography, for its part, investigates how current artificial intelligence techniques can predict and induce future human behaviour, for which we have used various forecast and generative models trained with data from our own digital activity, in order to generate another set of books with our foreseeable activity for the year 2050. …”
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1983
Quantiprot - a Python package for quantitative analysis of protein sequences
Published 2017-07-01“…Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.…”
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1984
Cyclic Federated Learning Method Based on Distribution Information Sharing and Knowledge Distillation for Medical Data
Published 2022-12-01“…The first stage is an offline preparation process in which all clients train a generator model on local datasets and pass the generator to neighbouring clients to generate virtual shared data. …”
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1985
Fitting a deep generative hadronization model
Published 2023-09-01“…As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. Deep generative models are a natural replacement for classical techniques, since they are more flexible and may be able to improve the overall precision. …”
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1986
Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images
Published 2022-02-01“…In view of the above situation, we propose a vibration damper detection-image generation model called DamperGAN based on multi-granularity Conditional Generative Adversarial Nets. …”
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1987
A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector
Published 2021-12-01“…The methodology described in the article was tested on three cases and showed the ability to generate models that are superior in accuracy to similar predictive models described in the literature by at least three percentage points. …”
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1988
Non-intrusive modeling for integrated energy system based on two-stage GAN
Published 2022-06-01“…In the TS-GAN, the first-stage GAN is used to generate the operating data of each equipment identified by non-invasive monitoring, and the second-stage GAN distinguishes the accumulated data generated by first-stage GAN and further modifies the generator models of the first-stage GAN. Finally, the effectiveness and accuracy of the proposed method are verified by the simulation of an energy region.…”
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1989
Computer Aided Assessment of impact of Plastic Waste During COVID-19 Pandemidc in Urban Areas of Developing Countries
Published 2022-09-01“…The proposed simulation consists of two models: a municipal waste generation model and a sanitary emergency (COVID-19) dynamics prediction model. …”
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1990
Machine Learning Generation of Dynamic Protein Conformational Ensembles
Published 2023-05-01“…In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.…”
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1991
Field Geometry and the Spatial and Temporal Generalization of Crop Classification Algorithms—A Randomized Approach to Compare Pixel Based and Convolution Based Methods
Published 2021-02-01“…The study argues that it is always important to check both, in order to generate models that are useful beyond the scope of the training data. …”
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1992
Aftershocks following the 2011 Tohoku-Oki earthquake driven by both stress transfer and afterslip
Published 2023-06-01“…Our results suggest the importance of combining these two end-member aftershock generation models to explain aftershock activity and thus provide new insights into the relationship between afterslip and spatiotemporal aftershock distribution. …”
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1993
Machine Learning Models to Predict Protein–Protein Interaction Inhibitors
Published 2022-11-01“…In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). …”
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1994
Semisupervised hyperspectral image classification based on generative adversarial networks and spectral angle distance
Published 2023-12-01“…Since the differences between spectra can be quickly calculated using the spectral angle distance, the convergence speed of the GAN can be improved, and the samples generated by the generator model in the GAN are closer to the real spectrum. …”
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1995
Demográfia és növekedés – Ronald Lee és Andrew Mason növekedési modelljei és az általuk felvázolt jövőkép
Published 2019-03-01“…The first model is an overlapping generations model, and the another two are neoclassical growth models which incorporated both human and physical capital. …”
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1996
Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry
Published 2024-03-01“…Subsequently, it presents a thorough exploration of the potential of generative AI to transform established practices and introduce novel applications through multimodal generative models, data generation and data augmentation. …”
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1997
Comparative assessment of computational models for the effective tensile strength of nano-reinforced composites
Published 2022-11-01“…The results obtained demonstrated a minimum relative error of 44.7%, 10.1%, and 10.6% for First-Generation, Second-Generation, and Third-Generation models, respectively. Moreover, linear and non-linear behaviors were found in the evaluated models, being coherent with the number and kind of parameters required for the assessment. …”
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1998
GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation
Published 2023-09-01“…Compared with the prior conditional video generation model, ImaGINator, our model yielded relative improvements of 1.61%, 1.66%, and 0.36% in terms of PSNR, SSIM, and LPIPS, respectively. …”
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1999
Image Synthesis for Solar Flare Prediction
Published 2024-01-01“…Our method consists of (1) synthetic oversampling of line-of-sight magnetograms using Gaussian mixture model representation, followed by (2) a global optimization technique to ensure consistency of both physical features and flare precursors, and (3) the mapping of the generated representations to realistic magnetogram images using deep generative models. We show that these synthetically generated data indeed improve the capacity of solar flare prediction models and that, when tested on such a state-of-the-art model, it significantly enhances its forecasting performance, achieving an F1-score as high as 0.43 ± 0.08 and a true skill statistic of 0.64 ± 0.10 for X-class flares in the 24 hr operational solar flare data split.…”
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2000
Prediction model of elastic constants of BCC high-entropy alloys based on first-principles calculations and machine learning techniques
Published 2022-12-01“…By optimizing the selection of descriptors based on the genetic algorithm (GA), prediction errors of 10.2 GPa, 4.5 GPa, 2.4 GPa and 7.7 GPa are achieved for bulk modulus $$B$$, shear moduli $$c{^{\prime}}$$, $${c_{44}}$$ and Young’s modulus $$E$$, respectively. By using the generated model we propose some HEAs with low $$E$$. It is well known that the magnitude of $$E$$ is closely related to the shape of the calculated density of states (DOS). …”
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