相关论文: UFO 2.0 -- The Universal Feynman Output format

We present a new model format for automatized matrix-element generators, the so- called Universal FeynRules Output (UFO). The format is universal in the sense that it features compatibility with more than one single generator and is…

The program FeynRules is a Mathematica package developed to facilitate the implementation of new physics theories into high-energy physics tools. Starting from a minimal set of information such as the model gauge symmetries, its particle…

We present new features of the FeynRules and MadGraph5/aMC@NLO programs for the automatic computation of decay widths that consistently include channels of arbitrary final-state multiplicity. The implementations are generic enough so that…

Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation of these digital objects. This paper reflects on the…

FeynRules is a Mathematica-based package which addresses the implementation of particle physics models, which are given in the form of a list of fields, parameters and a Lagrangian, into high-energy physics tools. It calculates the…

We release FeynRules and UFO model files for the $\nu$SMEFT -- the effective field theory of the Standard Model extended with right-handed neutrinos, $N_R$. These model files include dimension-five and dimension-six Higgs-$N_R$ operators.…

We present a new version 3.2 of the LanHEP software package. New features include UFO output, color sextet particles and new substutution techniques which allow to define new routines.

FeynMaster is a multi-tasking software for particle physics studies. By making use of already existing programs (FeynRules, QGRAF, FeynCalc), FeynMaster automatically generates Feynman rules, generates and draws Feynman diagrams, generates…

UFO is a new implementation of FORMAN, a declarative monitoring language, in which rules are compiled into execution monitors that run on a virtual machine supported by the Alamo monitor architecture.

The Userfault Object (UFO) framework explores avenues of cooperating with the operating system to use memory in non-traditional ways. We implement a framework that employs the Linux kernel's userfault mechanism to fill the contents of…

Leveraging external knowledge to enhance the reasoning ability is crucial for commonsense question answering. However, the existing knowledge bases heavily rely on manual annotation which unavoidably causes deficiency in coverage of…

FeAmGen.jl is a Julia package designed to generate Feynman diagrams and their corresponding amplitudes for various processes in particle physics. Utilizing the models in the Universal Feynman Output (UFO) format and Qgraf for diagram…

We present a new version of FeynGrav. The present version supports Feynman rules for matter with non-vanishing mass and $SU(N)$ Yang-Mills model. We revisit the gauge fixing procedure for gravity and derive interaction rules valid for an…

We present a subtraction scheme for ultraviolet (UV) divergent, infrared (IR) safe scalar Feynman integrals in dimensional regularization with any number of scales. This is done by the introduction of $u$-variables, which are a suitable…

Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags. However, real-world annotations are often…

We present the new version 2.0 of the Feynman integral reduction program Kira and describe the new features. The primary new feature is the reconstruction of the final coefficients in integration-by-parts reductions by means of finite field…

We introduce UFO, a modular aerial robotic platform for transforming a rigid object into a multirotor robot. To achieve this, we develop flight modules, in the form of a control module and propelling modules, that can be affixed to an…

Generalist models have achieved remarkable success in both language and vision-language tasks, showcasing the potential of unified modeling. However, effectively integrating fine-grained perception tasks like detection and segmentation into…

This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collection of multiple AI functions. UFO aims to benefit each single…

Probabilistic forecasting of irregularly sampled time series is crucial in domains such as healthcare and finance, yet it remains a formidable challenge. Existing Neural Controlled Differential Equation (Neural CDE) approaches, while…