Projected effects of declining anthropogenic aerosols on the southern annular mode by Leon D Rotstayn

“…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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A critical approach to Machine Learning forecast capabilities: creating a predictive biography in the age of the Internet of Behaviour (IoB) by Diego Díaz, Clara Boj

“…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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Quantiprot - a Python package for quantitative analysis of protein sequences by Bogumił M. Konopka, Marta Marciniak, Witold Dyrka

“…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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Cyclic Federated Learning Method Based on Distribution Information Sharing and Knowledge Distillation for Medical Data by Liang Yu, Jianjun Huang

“…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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Fitting a deep generative hadronization model by Jay Chan, Xiangyang Ju, Adam Kania, Benjamin Nachman, Vishnu Sangli, Andrzej Siódmok

“…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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Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images by Wenxiang Chen, Yingna Li, Zhengang Zhao

“…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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A Machine Learning Pipeline for Forecasting Time Series in the Banking Sector by Olga Gorodetskaya, Yana Gobareva, Mikhail Koroteev

“…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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Non-intrusive modeling for integrated energy system based on two-stage GAN by Qiuye Sun, Chengze Ren, Jingwei Hu, Rui Wang

“…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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Computer Aided Assessment of impact of Plastic Waste During COVID-19 Pandemidc in Urban Areas of Developing Countries by David F. Rojas, Nicolás Galvan, David E. Romo, Viatcheslav V. Kafarov

“…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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Machine Learning Generation of Dynamic Protein Conformational Ensembles by Li-E Zheng, Shrishti Barethiya, Erik Nordquist, Jianhan Chen

“…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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Field Geometry and the Spatial and Temporal Generalization of Crop Classification Algorithms—A Randomized Approach to Compare Pixel Based and Convolution Based Methods by Mario Gilcher, Thomas Udelhoven

“…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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Aftershocks following the 2011 Tohoku-Oki earthquake driven by both stress transfer and afterslip by Taku Ueda, Aitaro Kato

“…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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Machine Learning Models to Predict Protein–Protein Interaction Inhibitors by Bárbara I. Díaz-Eufracio, José L. Medina-Franco

“…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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Semisupervised hyperspectral image classification based on generative adversarial networks and spectral angle distance by Ying Zhan, Yufeng Wang, Xianchuan Yu

“…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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Demográfia és növekedés – Ronald Lee és Andrew Mason növekedési modelljei és az általuk felvázolt jövőkép by Éva Berde, Izabella Kuncz

“…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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Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry by Leda Tortora

“…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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Comparative assessment of computational models for the effective tensile strength of nano-reinforced composites by Mateo Duarte-García, Iván David Patiño-Arcila, César Augusto Isaza-Merino

“…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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GammaGAN: Gamma-Scaled Class Embeddings for Conditional Video Generation by Minjae Kang, Yong Seok Heo

“…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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Image Synthesis for Solar Flare Prediction by Elad Amar, Ohad Ben-Shahar

“…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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Prediction model of elastic constants of BCC high-entropy alloys based on first-principles calculations and machine learning techniques by G. Hayashi, K. Suzuki, T. Terai, H. Fujii, M. Ogura, K. Sato

“…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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