Toward a Collective Agenda on AI for Earth Science Data Analysis

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Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

High fidelity (HF) mathematical models describing the generation of active force in the cardiac muscle tissue typically feature a large number of state variables to capture the intrinsically complex underlying subcellular mechanisms. With the aim of drasti ...

Understanding the link between the brain's anatomy and its function through computer simulations of neural tissue models is a widely used approach in computational neuroscience. This technique enables rapid prototyping and testing of hypotheses, allowing r ...

Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as image recognition, object detection, and semantic segmentation. Even though the discriminative power of DNNs is nowadays unquestionable, serious concerns have ...

Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with disabilities (prosthetic applica ...

In this paper, we verify the large scale structure consistency relations using N-body simulations, including modes in the highly nonlinear regime. These relations (pointed out by Kehagias & Riotto and Peloso & Pietroni) follow from the symmetry of the dyna ...

Science is in revolution. The formidable scientific and technological developments of the last century have dramatically transformed the way in which we conduct scientific research. The knowledge and applications that science produces has profound conseque ...

Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated ...

In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C2GAN) for the task of keypoint-guided image generation. The proposed C2GAN is a cross-modal framework exploring a joint exploitation of the keypoint and the image data in an i ...

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

Microbial life in porous systems dominates the functioning of numerous ecosystems, ranging from stream sediments to soils. While these environments are characterized by structures that vary spatially over orders of magnitude, the traditional research focus ...

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities ...

The dramatically decreasing costs of DNA sequencing have triggered more than a million humans to have their genotypes sequenced. Moreover, these individuals increasingly make their genomic data publicly available, thereby creating privacy threats for thems ...

This paper provides a validation of a novel sampling, storage, and evaluation method named raytraverse that can quickly and accurately compute glare and visual comfort metrics including vertical illuminance, Daylight Glare Probability (DGP), and Unified Gl ...

Bosonic ultralight dark matter (ULDM) would form cored density distributions at the center of galaxies. These cores, seen in numerical simulations, admit analytic description as the lowest energy bound state solution ("soliton") of the Schroedinger-Poisson ...

Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic co ...

Understanding properly the impact of model parameters and their interactions on the predictions is required for an appropriate model assessment. For simulation of reflooding following a LOCA, this requirement is justified because the important parameters a ...

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

The human face plays an essential role in social interactions as it brings information about someone's identity, state of mind, or mood. People are, by nature, very good at catching this non-spoken information. Therefore, scientists have been interested in ...

Toward a Collective Agenda on AI for Earth Science Data Analysis

Chat with Graph Search

Beyond potentials: Integrated machine learning models for materials

Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics

Computational characteristics and hardware implications of brain tissue simulations

Understanding Deep Neural Networks using Adversarial Attacks

Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

Nonperturbative test of consistency relations and their violation

Ethics in Research

Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

Multi-layer State Evolution Under Random Convolutional Design

Microbial life in porous systems

Hierarchical graph representations in digital pathology

Simulating the Large-Scale Erosion of Genomic Privacy Over Time

Efficient Simulation for Visual Comfort Evaluations

Galactic rotation curves versus ultralight dark matter: Implications of the soliton-host halo relation

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

A Methodology for Global Sensitivity Analysis for Time-Dependent Code Output Applied to Reflood Simulation in TRACE

node2coords: Graph Representation Learning with Wasserstein Barycenters

Model-based 3D Face Reconstruction