Add paper from issue

HEPML.bib

@@ -1,5 +1,20 @@
 # HEPML Papers
 
+% Aug. 23, 2023
+@article{Holmberg:2023gnn,
+    author = {Holmberg, Daniel and Golubovic, Dejan and Kirschenmann, Henning},
+    title = "{Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline}",
+    journal = {Computing and Software for Big Science},
+    year = {2023},
+    month = {8},
+    day = {23},
+    volume = {7},
+    number = {1},
+    pages = {9},
+    issn = {2510-2044},
+    doi = {10.1007/s41781-023-00103-y}
+}
+
 % Aug. 21, 2023
 @article{Dersy:2023job,
     author = "Dersy, Aur\'elien and Schwartz, Matthew D. and Zhiboedov, Alexander",

HEPML.tex

@@ -63,7 +63,7 @@
 				\\\textit{Data that have a variable with a particular order may be represented as a sequence.  Recurrent neural networks are natural tools for processing sequence data. }
 				\item \textbf{Trees}~\cite{Louppe:2017ipp,Cheng:2017rdo,Jercic:2021bfc}
 				\\\textit{Recursive neural networks are natural tools for processing data in a tree structure.}
-				\item \textbf{Graphs}~\cite{Henrion:DLPS2017,Ju:2020xty,Abdughani:2018wrw,Martinez:2018fwc,Ren:2019xhp,Moreno:2019bmu,Qasim:2019otl,Chakraborty:2019imr,DiBello:2020bas,Chakraborty:2020yfc,1797439,1801423,1808887,Iiyama:2020wap,1811770,Choma:2020cry,alonsomonsalve2020graph,guo2020boosted,Heintz:2020soy,Verma:2020gnq,Dreyer:2020brq,Qian:2021vnh,Pata:2021oez,Biscarat:2021dlj,Rossi:2021tjf,Hewes:2021heg,Thais:2021qcb,Dezoort:2021kfk,Verma:2021ceh,Hariri:2021clz,Belavin:2021bxb,Atkinson:2021nlt,Konar:2021zdg,Atkinson:2021jnj,Tsan:2021brw,Elabd:2021lgo,Pata:2022wam,Gong:2022lye,Qasim:2022rww,Ma:2022bvt,Bogatskiy:2022czk,Builtjes:2022usj,DiBello:2022iwf,Mokhtar:2022pwm,Huang:2023ssr,Forestano:2023fpj,Anisha:2023xmh,Ehrke:2023cpn,Murnane:2023kfm,Yu:2023juh,Neu:2023sfh,Wang:2023cac,McEneaney:2023vwp,Liu:2023siw,GarciaPardinas:2023pmx,Duperrin:2023elp,BelleII:2023egc}
+				\item \textbf{Graphs}~\cite{Henrion:DLPS2017,Ju:2020xty,Abdughani:2018wrw,Martinez:2018fwc,Ren:2019xhp,Moreno:2019bmu,Qasim:2019otl,Chakraborty:2019imr,DiBello:2020bas,Chakraborty:2020yfc,1797439,1801423,1808887,Iiyama:2020wap,1811770,Choma:2020cry,alonsomonsalve2020graph,guo2020boosted,Heintz:2020soy,Verma:2020gnq,Dreyer:2020brq,Qian:2021vnh,Pata:2021oez,Biscarat:2021dlj,Rossi:2021tjf,Hewes:2021heg,Thais:2021qcb,Dezoort:2021kfk,Verma:2021ceh,Hariri:2021clz,Belavin:2021bxb,Atkinson:2021nlt,Konar:2021zdg,Atkinson:2021jnj,Tsan:2021brw,Elabd:2021lgo,Pata:2022wam,Gong:2022lye,Qasim:2022rww,Ma:2022bvt,Bogatskiy:2022czk,Builtjes:2022usj,DiBello:2022iwf,Mokhtar:2022pwm,Huang:2023ssr,Forestano:2023fpj,Anisha:2023xmh,Ehrke:2023cpn,Murnane:2023kfm,Yu:2023juh,Neu:2023sfh,Wang:2023cac,McEneaney:2023vwp,Liu:2023siw,GarciaPardinas:2023pmx,Duperrin:2023elp,BelleII:2023egc,Holmberg:2023gnn}
 				\\\textit{A graph is a collection of nodes and edges.  Graph neural networks are natural tools for processing data in a tree structure.}
 				\item \textbf{Sets (point clouds)}~\cite{Komiske:2018cqr,Qu:2019gqs,Mikuni:2020wpr,Shlomi:2020ufi,Dolan:2020qkr,Fenton:2020woz,Lee:2020qil,collado2021learning,Mikuni:2021pou,Shmakov:2021qdz,Shimmin:2021pkm,ATL-PHYS-PUB-2020-014,Qu:2022mxj,Kach:2022uzq,Onyisi:2022hdh,Athanasakos:2023fhq,Kach:2023rqw}
 				\\\textit{A point cloud is a (potentially variable-size) set of points in space.  Sets are distinguished from sequences in that there is no particular order (i.e. permutation invariance).  Sets can also be viewed as graphs without edges and so graph methods that can parse variable-length inputs may also be appropriate for set learning, although there are other methods as well.}
@@ -139,7 +139,7 @@
 	\begin{itemize}
 		\item \textbf{Pileup}~\cite{Komiske:2017ubm,ATL-PHYS-PUB-2019-028,Martinez:2018fwc,Carrazza:2019efs,Maier:2021ymx,Li:2022omf,CRESST:2022qor,Kim:2023koz}
 		\\\textit{A given bunch crossing at the LHC will have many nearly simultaneous proton-proton collisions.  Only one of those is usually interesting and the rest introduce a source of noise (pileup) that must be mitigating for precise final state reconstruction.}
-		\item \textbf{Calibration}~\cite{Cheong:2019upg,ATL-PHYS-PUB-2020-001,ATL-PHYS-PUB-2018-013,Hooberman:DLPS2017,Kasieczka:2020vlh,Sirunyan:2019wwa,Baldi:2020hjm,Du:2020pmp,Kieseler:2021jxc,Pollard:2021fqv,Akchurin:2021afn,Kieseler:2020wcq,Akchurin:2021ahx,Diefenthaler:2021rdj,Polson:2021kvr,Micallef:2021src,Arratia:2021tsq,Kronheim:2021hdb,Renteria-Estrada:2021zrd,Pata:2022wam,Chadeeva:2022kay,Dorigo:2022tfi,Alves:2022gnw,Qiu:2022xvr,Akchurin:2022apq,Gambhir:2022gua,Gambhir:2022dut,Valsecchi:2022rla,Leigh:2022lpn,Darulis:2022brn,Ge:2022xrv,Guglielmi:2022ftj,Aad:2023ula,Lee:2023jew,Schwenker:2023bih,Basak:2023wzq,Grosso:2023jxp,Grosso:2023ltd,Soleymaninia:2023dds,Raine:2023fko,Khozani:2023bql,ATLAS:2023tyv,ALICETPC:2023ojd,Meyer:2023ffd}
+		\item \textbf{Calibration}~\cite{Cheong:2019upg,ATL-PHYS-PUB-2020-001,ATL-PHYS-PUB-2018-013,Hooberman:DLPS2017,Kasieczka:2020vlh,Sirunyan:2019wwa,Baldi:2020hjm,Du:2020pmp,Kieseler:2021jxc,Pollard:2021fqv,Akchurin:2021afn,Kieseler:2020wcq,Akchurin:2021ahx,Diefenthaler:2021rdj,Polson:2021kvr,Micallef:2021src,Arratia:2021tsq,Kronheim:2021hdb,Renteria-Estrada:2021zrd,Pata:2022wam,Chadeeva:2022kay,Dorigo:2022tfi,Alves:2022gnw,Qiu:2022xvr,Akchurin:2022apq,Gambhir:2022gua,Gambhir:2022dut,Valsecchi:2022rla,Leigh:2022lpn,Darulis:2022brn,Ge:2022xrv,Guglielmi:2022ftj,Aad:2023ula,Lee:2023jew,Schwenker:2023bih,Basak:2023wzq,Grosso:2023jxp,Grosso:2023ltd,Soleymaninia:2023dds,Raine:2023fko,Khozani:2023bql,ATLAS:2023tyv,ALICETPC:2023ojd,Meyer:2023ffd,Holmberg:2023gnn}
 		\\\textit{The goal of calibration is to remove the bias (and reduce variance if possible) from detector (or related) effects.}
 		\item \textbf{Recasting}~\cite{Caron:2017hku,Bertone:2016mdy,1806026,Hammad:2022wpq}
 		\\\textit{Even though an experimental analysis may provide a single model-dependent interpretation of the result, the results are likely to have important implications for a variety of other models.  Recasting is the task of taking a result and interpreting it in the context of a model that was not used for the original analysis.}

README.md

@@ -190,6 +190,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions](https://arxiv.org/abs/2304.08610)
 * [Flavour tagging with graph neural networks with the ATLAS detector](https://arxiv.org/abs/2306.04415)
 * [Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks](https://arxiv.org/abs/2306.04179)
+* [Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline](https://doi.org/{10.1007/s41781-023-00103-y})
 
 ####  Sets (point clouds)
 
@@ -857,6 +858,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [New techniques for jet calibration with the ATLAS detector](https://arxiv.org/abs/2303.17312)
 * [Correction of the baseline fluctuations in the GEM-based ALICE TPC](https://arxiv.org/abs/2304.03881)
 * [A first application of machine and deep learning for background rejection in the ALPS II TES detector](https://arxiv.org/abs/2304.08406) [[DOI](https://doi.org/10.1002/andp.202200545)]
+* [Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline](https://doi.org/{10.1007/s41781-023-00103-y})
 
 ###  Recasting
 

docs/index.md

@@ -241,6 +241,7 @@ const expandElements = shouldExpand => {
     * [GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions](https://arxiv.org/abs/2304.08610)
     * [Flavour tagging with graph neural networks with the ATLAS detector](https://arxiv.org/abs/2306.04415)
     * [Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks](https://arxiv.org/abs/2306.04179)
+    * [Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline](https://doi.org/{10.1007/s41781-023-00103-y})
 
     ####  Sets (point clouds)
 
@@ -933,6 +934,7 @@ const expandElements = shouldExpand => {
     * [New techniques for jet calibration with the ATLAS detector](https://arxiv.org/abs/2303.17312)
     * [Correction of the baseline fluctuations in the GEM-based ALICE TPC](https://arxiv.org/abs/2304.03881)
     * [A first application of machine and deep learning for background rejection in the ALPS II TES detector](https://arxiv.org/abs/2304.08406) [[DOI](https://doi.org/10.1002/andp.202200545)]
+    * [Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline](https://doi.org/{10.1007/s41781-023-00103-y})
 
 
 ??? example "Recasting"

docs/recent.md

@@ -10,6 +10,7 @@ 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.
 
 ## August 2023
+* [Jet Energy Calibration with Deep Learning as a Kubeflow Pipeline](https://doi.org/{10.1007/s41781-023-00103-y})
 * [Reconstructing $S$-matrix Phases with Machine Learning](https://arxiv.org/abs/2308.09451)
 * [Hierarchical High-Point Energy Flow Network for Jet Tagging](https://arxiv.org/abs/2308.08300)
 * [Boosting likelihood learning with event reweighting](https://arxiv.org/abs/2308.05704)