Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Generative Adversarial Networks and Image Synthesis
Abstract
Deep generative models---neural networks that learn data's distribution and sample from it---matured from variational autoencoders' latent geometry to diffusion models' state-of-the-art images. This article presents a narrative review of that arc's canonical line: Kingma and Welling's 2013 auto-encoding variational Bayes, Goodfellow and colleagues' 2014 generative adversarial networks, Mirza and Osindero's 2014 conditional GANs, Rezende and Mohamed's 2015 normalizing flows, Sohl-Dickstein and colleagues' 2015 nonequilibrium thermodynamics, Arjovsky and colleagues' 2017 Wasserstein GANs, Karras and colleagues' 2019 style-based generator, Ho and colleagues' 2020 denoising diffusion, Ramesh and colleagues' 2021 text-to-image generation, Dhariwal and Nichol's 2021 diffusion-beats-GANs result, Nichol and Dhariwal's 2021 improved diffusion, and Rombach and colleagues' 2022 latent diffusion. The synthesis is organized around three themes: latent foundations, in which autoencoders and flows made sampling principled; adversarial training, in which games between generator and discriminator produced realism; and diffusion's rise, in which denoising trajectories conquered synthesis. It is concluded that generative modeling's decade ran from likelihood's compromise to sampling's triumph---and that latent diffusion is the field's new foundation.
A rational design for next-generation thermo-responsive nanocarriers is proposed, in which polymer chemistry, nanoparticle structure, experimental characterization, and mechanistic modelling are integrated from the earliest stages of material development.
M. Schifone, Giuseppe Nunziata, Filippo Rossi· Advances in Colloid and Inte...· 2 citations
This paper describes the formulation of a numerical model for simulating environmentally driven one-dimensional (1D) ground movements of expansive clay. The formulation is based on a finite-element model that simulates the redistribution of matric suction through a diffusion-type equation, explicitly accounting for volume changes due to wetting and drying of the clay. We synthesize and modify highly nonlinear constitutive relationships for (1) hysteretic soil water retention; (2) reversible soil shrinkage and expansion of clay; and (3) hydraulic conductivity, explicitly incorporating desiccation cracks through a multidomain framework and assuming a critical surface crack depth. These models are well-calibrated to published laboratory tests on a reference expansive clay, Denver bentonite. We demonstrate capabilities of the proposed formulation to simulate the response of a homogeneous expansive clay to periods of drying and wetting, considering the initial matric suction, saturated hydraulic conductivity of the intact clay, and critical crack depth as three primary sources of uncertainty. We compare ensemble model simulations with measured ground movements from an instrumented expansive clay test site in Texas over a 3-year period using detailed records of potential evapotranspiration and precipitation. By assigning weights to the ensemble simulations based on their performance, we constrain the ranges of the three key uncertain parameters. The results showed very reasonable first-order agreement with the measured data and highlight the potential of the proposed formulation. We anticipate that more reliable predictions can be achieved through direct measurements of actual in situ evaporation rates and local soil properties.
Mahdi Seyyedan, Jiali Ma, Ivo Rosa Montenegro et al.· Journal of Geotechnical and...· 1 citation
This paper examines whether differences in the speed with which traded assets respond to a common market shock can predict subsequent relative returns. The framework combines a lagged rolling factor model with Absorption Gap (AG), which measures an asset’s response error, and Shock Coherence (SC), which characterizes the contemporaneous market state. The public specification is evaluated using executable next-open timing, explicit transaction costs, dependence-aware inference, randomized-signal benchmarks, chronological diagnostics, and machine-learning extensions. The study uses 24 ETFs from 4 January 2010 through 28 August 2026, with eight factor proxies excluded from the 16-asset traded cross-section. The corrected public baseline produces a combined Rank IC of -0.00592, an approximately flat zero-cost gross result, and materially negative performance after transaction costs. A within-date randomized-signal benchmark yields an empirical two-sided p-value of 0.299, while standalone Absorption Gap, coherence-conditioned tests, chronological subsamples, and machine-learning models provide no robust evidence of economically viable public alpha. The contribution is therefore methodological as much as empirical: the paper connects an economic hypothesis about heterogeneous information absorption to an executable trading test, documents why the disclosed implementation fails, separates diagnostic and exploratory analysis from confirmatory evidence, and establishes a reproducible public baseline while keeping the proprietary alpha layer outside the evidence package.
Khaybullina Alina· Zenodo (CERN European Organi...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.