From 1f24744a8b1e80f6691f30f542acb7cb8162b763 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 8 Aug 2023 15:39:02 +0200 Subject: [PATCH] chore: [pre-commit.ci] pre-commit autoupdate (#175) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * chore: [pre-commit.ci] pre-commit autoupdate updates: - [github.com/asottile/pyupgrade: v3.8.0 → v3.10.1](https://github.com/asottile/pyupgrade/compare/v3.8.0...v3.10.1) * update recent.md to contain August --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Claudius Krause --- .pre-commit-config.yaml | 2 +- docs/recent.md | 24 ------------------------ 2 files changed, 1 insertion(+), 25 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 86c8947..1e472bd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -21,7 +21,7 @@ repos: - id: trailing-whitespace - repo: https://github.com/asottile/pyupgrade - rev: v3.8.0 + rev: v3.10.1 hooks: - id: pyupgrade args: ["--py37-plus"] diff --git a/docs/recent.md b/docs/recent.md index 1012424..6ebab39 100644 --- a/docs/recent.md +++ b/docs/recent.md @@ -62,27 +62,3 @@ This is an automatically compiled list of papers which have been added to the li * [$\Sigma$ Resonances from a Neural Network-based Partial Wave Analysis on $K^-p$ Scattering](https://arxiv.org/abs/2305.01852) * [MLAnalysis: An open-source program for high energy physics analyses](https://arxiv.org/abs/2305.00964) -## April 2023 -* [Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning](https://arxiv.org/abs/2305.00493) -* [Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting](https://arxiv.org/abs/2304.14963) -* [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176) -* [Machine learning method for $^{12}$C event classification and reconstruction in the active target time-projection chamber](https://arxiv.org/abs/2304.13233) -* [A Modern Global Extraction of the Sivers Function](https://arxiv.org/abs/2304.14328) -* [Label-free timing analysis of modularized nuclear detectors with physics-constrained deep learning](https://arxiv.org/abs/2304.11930) -* [Gauge-equivariant pooling layers for preconditioners in lattice QCD](https://arxiv.org/abs/2304.10438) -* [Uncovering doubly charged scalars with dominant three-body decays using machine learning](https://arxiv.org/abs/2304.09195) -* [Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models](https://arxiv.org/abs/2304.09208) -* [Graph neural networks at the Large Hadron Collider](https://doi.org/10.1038/s42254-023-00569-0) -* [Evidence of the Schwinger mechanism from lattice QCD](https://arxiv.org/abs/2304.07800) -* [Research on the distribution formula of QCD strong coupling constant in medium and high energy scale region based on symbolic regression algorithm](https://arxiv.org/abs/2304.07682) -* [Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis](https://arxiv.org/abs/2304.07196) -* [A variational Monte Carlo algorithm for lattice gauge theories with continuous gauge groups: a study of (2+1)-dimensional compact QED with dynamical fermions at finite density](https://arxiv.org/abs/2304.05916) -* [Equivariant Graph Neural Networks for Charged Particle Tracking](https://arxiv.org/abs/2304.05293) -* [Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays](https://arxiv.org/abs/2304.03836) -* [Probing Dark QCD Sector through the Higgs Portal with Machine Learning at the LHC](https://arxiv.org/abs/2304.03237) -* [Locality-constrained autoregressive cum conditional normalizing flow for lattice field theory simulations](https://arxiv.org/abs/2304.01798) -* [Search for vector-like leptons at a Muon Collider](https://arxiv.org/abs/2304.01885) -* [Searching for anomalous quartic gauge couplings at muon colliders using principle component analysis](https://arxiv.org/abs/2304.01505) -* [Fast Point Cloud Generation with Diffusion Models in High Energy Physics](https://arxiv.org/abs/2304.01266) -* [Invariant mass reconstruction of heavy gauge bosons decaying to $\tau$ leptons using machine learning techniques](https://arxiv.org/abs/2304.01126) -