Fix recent on year change, add entries up to 23-01-08 (#201)

* Fix recent on year change, add entries up to 23-01-08

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Fix logic mistake, first month of year always 1

* Fix bib dumper, add commas to bib file, fix month mixup from inspire

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

check_inspire.py

@@ -8,6 +8,7 @@
 """
 
 import json
+import os
 from datetime import date, datetime
 
 # Import the modules to open and reading URLs and the JSON encoder
@@ -16,10 +17,10 @@
 starting_point = input("From which date do you want to start? \nFormat is YYYY-MM-DD!\n")
 starting_date = date.fromisoformat(starting_point)
 
-categories = ['hep-ph', 'hep-ex', 'hep-lat', 'hep-th', 'physics.ins-det', 'nucl-th']
+categories = ['hep-ph', 'hep-ex', 'hep-lat', 'hep-th', 'physics.ins-det', 'physics.data-an', 'nucl-th']
 print(f"Looking at arXiv categories: {categories}")
 
-keywords = ['neural network', 'machine learning']
+keywords = ['neural network', 'machine learning', 'generative models', 'diffusion models']
 print(f"scanning papers with {keywords}:")
 keyword_str = f'("{keywords}")'.replace("', '", '"%20OR%20"').replace("['", '').replace("']", "")
 keyword_str = keyword_str.replace(" ", "%20")

docs/recent.md

@@ -9,6 +9,83 @@ search:
 
 This is an automatically compiled list of papers which have been added to the living review that were made public within the previous 4 months at the time of updating. This is not an exhaustive list of released papers, and is only able to find those which have both year and month data provided in the bib reference.
 
+## January 2024
+* [From Optimal Observables to Machine Learning: an Effective-Field-Theory Analysis of $e^+e^- \to W^+W^-$ at Future Lepton Colliders](https://arxiv.org/abs/2401.02474)
+* [Physics analysis for the HL-LHC: concepts and pipelines in practice with the Analysis Grand Challenge](https://arxiv.org/abs/2401.02766)
+* [Machine Learning for Columnar High Energy Physics Analysis](https://arxiv.org/abs/2401.01802)
+* [Flow-based sampling for lattice field theories](https://arxiv.org/abs/2401.01297)
+
+## December 2023
+* [Multi-scale cross-attention transformer encoder for event classification](https://arxiv.org/abs/2401.00452)
+* [Les Houches guide to reusable ML models in LHC analyses](https://arxiv.org/abs/2312.14575)
+* [Applications of Lipschitz neural networks to the Run 3 LHCb trigger system](https://arxiv.org/abs/2312.14265)
+* [Machine Learning for Anomaly Detection in Particle Physics](https://arxiv.org/abs/2312.14190)
+* [Mitigating a discrete sign problem with extreme learning machines](https://arxiv.org/abs/2312.12636)
+* [Machine-learning-based particle identification with missing data](https://arxiv.org/abs/2401.01905)
+* [Interpretable deep learning models for the inference and classification of LHC data](https://arxiv.org/abs/2312.12330)
+* [Jet Classification Using High-Level Features from Anatomy of Top Jets](https://arxiv.org/abs/2312.11760)
+* [Anomaly detection with flow-based fast calorimeter simulators](https://arxiv.org/abs/2312.11618)
+* [Vertex Reconstruction with MaskFormers](https://arxiv.org/abs/2312.12272)
+* [Improving new physics searches with diffusion models for event observables and jet constituents](https://arxiv.org/abs/2312.10130)
+* [Testing a Neural Network for Anomaly Detection in the CMS Global Trigger Test Crate during Run 3](https://arxiv.org/abs/2312.10009)
+* [Deep Generative Models for Detector Signature Simulation: An Analytical Taxonomy](https://arxiv.org/abs/2312.09597)
+* [Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties](https://arxiv.org/abs/2312.11676)
+* [MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory](https://arxiv.org/abs/2312.08936)
+* [Integrating Particle Flavor into Deep Learning Models for Hadronization](https://arxiv.org/abs/2312.08453)
+* [Neural networks for boosted di-$\tau$ identification](https://arxiv.org/abs/2312.08276)
+* [Pre-training strategy using real particle collision data for event classification in collider physics](https://arxiv.org/abs/2312.06909)
+* [Optimizing High Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server](https://arxiv.org/abs/2312.06838)
+* [Autoencoder-Driven Clustering of Intersecting D-brane Models via Tadpole Charge](https://arxiv.org/abs/2312.07181)
+* [Improving the performance of weak supervision searches using transfer and meta-learning](https://arxiv.org/abs/2312.06152)
+* [Using deep neural networks to improve the precision of fast-sampled particle timing detectors](https://arxiv.org/abs/2312.05883)
+* [Induced Generative Adversarial Particle Transformers](https://arxiv.org/abs/2312.04757)
+* [Ranking-based neural network for ambiguity resolution in ACTS](https://arxiv.org/abs/2312.05070)
+* [Auto-tuning capabilities of the ACTS track reconstruction suite](https://arxiv.org/abs/2312.05123)
+* [High Pileup Particle Tracking with Object Condensation](https://arxiv.org/abs/2312.03823)
+* [Quark-versus-gluon tagging in CMS Open Data with CWoLa and TopicFlow](https://arxiv.org/abs/2312.03434)
+* [CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models](https://arxiv.org/abs/2312.03179)
+* [A study of topological quantities of lattice QCD by a modified DCGAN frame](https://arxiv.org/abs/2312.03023)
+* [Learning PDFs through Interpretable Latent Representations in Mellin Space](https://arxiv.org/abs/2312.02278)
+* [Scaling Laws in Jet Classification](https://arxiv.org/abs/2312.02264)
+* [Learning Feynman integrals from differential equations with neural networks](https://arxiv.org/abs/2312.02067)
+* [Fast Posterior Probability Sampling with Normalizing Flows and Its Applicability in Bayesian analysis in Particle Physics](https://arxiv.org/abs/2312.02045)
+
+## November 2023
+* [Anomaly Detection in Collider Physics via Factorized Observables](https://arxiv.org/abs/2312.00119)
+* [DeepTreeGANv2: Iterative Pooling of Point Clouds](https://arxiv.org/abs/2312.00042)
+* [Kicking it Off(-shell) with Direct Diffusion](https://arxiv.org/abs/2311.17175)
+* [Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder](https://arxiv.org/abs/2311.17162)
+* [Reconstruction of electromagnetic showers in calorimeters using Deep Learning](https://arxiv.org/abs/2311.17914)
+* [Fixed point actions from convolutional neural networks](https://arxiv.org/abs/2311.17816)
+* [Calabi-Yau Four/Five/Six-folds as $\mathbb{P}^n_\textbf{w}$ Hypersurfaces: Machine Learning, Approximation, and Generation](https://arxiv.org/abs/2311.17146)
+* [Searching for gluon quartic gauge couplings at muon colliders using the auto-encoder](https://arxiv.org/abs/2311.16627)
+* [Exploring the Synergy of Kinematics and Dynamics for Collider Physics](https://arxiv.org/abs/2311.16674)
+* [Quantum Metric Learning for New Physics Searches at the LHC](https://arxiv.org/abs/2311.16866)
+* [Optimize the event selection strategy the study the anomalous quartic gauge couplings at muon colliders using the support vector machine](https://arxiv.org/abs/2311.15280)
+* [Extraction of the microscopic properties of quasi-particles using deep neural networks](https://arxiv.org/abs/2311.15984)
+* [Neural Network Applications to Improve Drift Chamber Track Position Measurements](https://arxiv.org/abs/2311.15541)
+* [Optimal operation of cryogenic calorimeters through deep reinforcement learning](https://arxiv.org/abs/2311.15147)
+* [JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables](https://arxiv.org/abs/2311.14654)
+* [Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation](https://arxiv.org/abs/2311.14160)
+* [Non-resonant Anomaly Detection with Background Extrapolation](https://arxiv.org/abs/2311.12924)
+* [Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data: a Proof-of-Concept Study Using Monte Carlo Simulations](https://arxiv.org/abs/2311.13060)
+* [DeepTreeGAN: Fast Generation of High Dimensional Point Clouds](https://arxiv.org/abs/2311.12616)
+* [Distilling particle knowledge for fast reconstruction at high-energy physics experiments](https://arxiv.org/abs/2311.12551)
+* [PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts](https://arxiv.org/abs/2311.11011)
+* [Deep learning complete intersection Calabi-Yau manifolds](https://arxiv.org/abs/2311.11847) [[DOI](https://doi.org/10.1142/9781800613706_0005)]
+* [Study of topological quantities of lattice QCD by a modified Wasserstein generative adversarial network](https://arxiv.org/abs/2311.10108)
+* [Towards a data-driven model of hadronization using normalizing flows](https://arxiv.org/abs/2311.09296)
+* [Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network](https://arxiv.org/abs/2311.08885)
+* [Safe but Incalculable: Energy-weighting is not all you need](https://arxiv.org/abs/2311.07652)
+* [Jet Rotational Metrics](https://arxiv.org/abs/2311.06686)
+* [Learning Broken Symmetries with Resimulation and Encouraged Invariance](https://arxiv.org/abs/2311.05952)
+* [Neural Network Methods for Radiation Detectors and Imaging](https://arxiv.org/abs/2311.05726)
+* [Two Watts is All You Need: Enabling In-Detector Real-Time Machine Learning for Neutrino Telescopes Via Edge Computing](https://arxiv.org/abs/2311.04983)
+* [Generative Diffusion Models for Lattice Field Theory](https://arxiv.org/abs/2311.03578)
+* [Machine learning the breakdown of tame effective theories](https://arxiv.org/abs/2311.03437)
+* [The MadNIS Reloaded](https://arxiv.org/abs/2311.01548)
+* [Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection](https://arxiv.org/abs/2311.02038)
+
 ## October 2023
 * [Seeking Truth and Beauty in Flavor Physics with Machine Learning](https://arxiv.org/abs/2311.00087)
 * [Machine Learning Regularization for the Minimum Volume Formula of Toric Calabi-Yau 3-folds](https://arxiv.org/abs/2310.19276)
@@ -36,22 +113,3 @@ This is an automatically compiled list of papers which have been added to the li
 * [The Optimal use of Segmentation for Sampling Calorimeters](https://arxiv.org/abs/2310.04442)
 * [Neural Network Emulation of Spontaneous Fission](https://arxiv.org/abs/2310.01608)
 
-## September 2023
-* [Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming](https://arxiv.org/abs/2310.05673)
-* [Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction](https://arxiv.org/abs/2309.17351)
-* [EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion](https://arxiv.org/abs/2310.00049)
-* [Chained Quantile Morphing with Normalizing Flows](https://arxiv.org/abs/2309.15912)
-* [HyperTrack: Neural Combinatorics for High Energy Physics](https://arxiv.org/abs/2309.14113)
-* [Binary Discrimination Through Next-to-Leading Order](https://arxiv.org/abs/2309.14417)
-* [Combining Resonant and Tail-based Anomaly Detection](https://arxiv.org/abs/2309.12918)
-* [Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection](https://arxiv.org/abs/2309.13111)
-* [Refining fast simulation using machine learning](https://arxiv.org/abs/2309.12919)
-* [Suppression of Neutron Background using Deep Neural Network and Fourier Frequency Analysis at the KOTO Experiment](https://arxiv.org/abs/2309.12063)
-* [Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC](https://arxiv.org/abs/2309.12417)
-* [Boosting dark matter searches at muon colliders with Machine Learning: the mono-Higgs channel as a case study](https://arxiv.org/abs/2309.11241)
-* [Insights into neutron star equation of state by machine learning](https://arxiv.org/abs/2309.11227)
-* [BFBrain: Scalar Bounded-From-Below Conditions from Bayesian Active Learning](https://arxiv.org/abs/2309.10959)
-* [The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows](https://arxiv.org/abs/2309.09743)
-* [Applying Machine Learning Techniques to Searches for Lepton-Partner Pair-Production with Intermediate Mass Gaps at the Large Hadron Collider](https://arxiv.org/abs/2309.10197)
-* [Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter](https://arxiv.org/abs/2309.10157)
-

dump_bibtex_from_arxiv.py

@@ -44,7 +44,7 @@ def replace_collaboration_author(bib_entry):
         # Use a regular expression to find the collaboration author in the BibTeX citation
         author = re.search(r'author\s+=(.*)', bib_entry).group(1)
         # Set the collaboration name as author
-        bib_entry = bib_entry.replace(author, ' "{' + collab_name + '}"')
+        bib_entry = bib_entry.replace(author, ' "{' + collab_name + '}",')
         # Delete the collaboration field
         bib_entry = bib_entry.replace(collab_field, '')
     else:

make_md.py

@@ -264,9 +264,9 @@ def get_year_month(period_months=3):
     month_low = month_up - period_months
     dates = []
     if month_low < 1:
-        month_n = 12 + month_low
+        month_n = 13 + month_low
         dates += [(year-1,m) for m in range(month_n,13)]
-    month_low = 1 if month_low < 1 else month_low
+    month_low = 0 if month_low < 1 else month_low
     dates += [(year,m+1) for m in range(month_low,month_up)]
     return dates