Related papers: FENDL: A library for fusion research and applicati…

Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and data-efficient manner. Although a variety of deep model…

EASY-II is designed as a functional replacement for the previous European Activation System, EASY-2010. It has extended nuclear data and new software, FISPACT-II, written in object-style Fortran to provide new capabilities for predictions…

This letter presents a novel approach in the field of Active Fault Detection (AFD), by explicitly separating the task into two parts: Passive Fault Detection (PFD) and control input design. This formulation is very general, and most…

Fusion energy research increasingly depends on the ability to integrate heterogeneous, multimodal datasets from high-resolution diagnostics, control systems, and multiscale simulations. The sheer volume and complexity of these datasets…

Accurate modeling of nuclear reaction cross-sections is crucial for applications such as hadron therapy, radiation protection, and nuclear reactor design. Despite continuous advancements in nuclear physics, significant discrepancies persist…

Today reactor neutrino experiments are at the cutting edge of fundamental research in particle physics. Understanding the neutrino is far from complete, but thanks to the impressive progress in this field over the last 15 years, a few…

New evaluations for several actinoids of the third version of China Evaluated Nuclear Data Library (CENDL-3.1) have been completed during the period between 2000 and 2005. The evaluations are for all neutron induced reactions with Uranium,…

The present study aims at further development of covariant energy density functionals (CEDFs) towards more accurate description of binding energies across the nuclear chart. For the first time, infinite basis corrections to binding energies…

Existing GPU libraries often struggle to fully exploit the parallel resources and on-chip memory (SRAM) of GPUs when chaining multiple GPU functions as individual kernels. While Kernel Fusion (KF) techniques like Horizontal Fusion (HF) and…

Traditional federated learning uses the number of samples to calculate the weights of each client model and uses this fixed weight value to fusion the global model. However, in practical scenarios, each client's device and data…

We propose to develop a high-energy heavy-ion experimental database and make it accessible to the scientific community through an on-line interface. This database will be searchable and cross-indexed with relevant publications, including…

The parameters of the UNEDF2 nuclear energy density functional (EDF) model were obtained in an optimization to experimental data consisting of nuclear binding energies, proton radii, odd-even mass staggering data, fission-isomer excitation…

Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, and available in C++, command line interface, Python (under…

We rethink Federated Learning (FL) from a nested learning perspective, framing the core challenge as how to collaboratively learn optimization rules, not just static models, to tackle Non-IID client data. To address this, we propose…

One of the key challenges of collaborative machine learning, without data sharing, is multimodal data heterogeneity in real-world settings. While Federated Learning (FL) enables model training across multiple clients, existing frameworks,…

There is a growing realization that neutrinos can be used as a diagnostic tool to better understand the inner workings of a nuclear weapon. Robust estimates demonstrate that an Inverse Beta Decay (IBD) neutrino scintillation detector built…

The program package for the work with the Evaluated Nuclear Structure Data File is discussed. The program shell designed for the unification of the process of the evaluation of the nuclear data is proposed. This program shell may be used in…

Nuclear astrophysics and californium fission neutron spectrum averaged cross sections and their uncertainties for ENDF materials have been calculated. Absolute values were deduced with Maxwellian and Mannhart spectra, while uncertainties…

Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel model reduction methods, coupled with detection of abnormal modes with plasma…

Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning…