bluuebunny/crossref_metadata_2025_split huggingface dataset:
Collaborative Operational Security: The future of Cybersecurity for Research and Education
Authors: Crooks David , Acris James , Atherton Liam , Clark Paul , Cutrina Pau , Jordan David , McKee Shawn , Vâlsan Liviu 
Published: May 2024

No single organisation has the resources to defend its services alone against most modern malicious actors and so we must protect ourselves as a community. In the face of determined and well-resourced attackers, we must actively collaborate in this effort across HEP and more broadly across Research and Education (R&E).
Parallel efforts are necessary to respond appropriately to this requirement. We must share threat intelligence about ongoing cybersecurity incidents with our trusted partners and deploy the fine-grained security network monitoring necessary to make active use of this intelligence. We must also engage with senior management in our organizations to ensure that we work alongside any broader organisational cybersecurity development programs.
We report on progress of the Security Operations Center (SOC) Working Group, established by the WLCG but with membership encompassing the R&E sector. The goal of the Working Group is to develop reference designs for SOC deployments and empower R&E organisations to collect, leverage, and act upon targeted, contextualized, actionable threat intelligence. This report will include recent SOC deployment activities at sites with network connectivity in excess of 100Gb/s, as well as new technology designs. An important development, which is likely to form a key part of the WLCG security strategy, is the potential use of passive DNS logs to allow sites without fine-grained network monitoring to benefit from the threat intelligence available to our community.
We also report on higher-level progress in engaging with the broader community to establish common approaches to this vital area of cybersecurity.
The Rubin Observatory’s Legacy Survey of Space and Time DP0.2 processing campaign at CC-IN2P3
Authors: Le Boulc’h Quentin , Hernandez Fabio , Mainetti Gabriele 
Published: May 2024

The Vera C. Rubin Observatory, currently in construction in Chile, will start performing the Legacy Survey of Space and Time (LSST) in 2025 for 10 years. Its 8.4-meter telescope will survey the southern sky in less than 4 nights in six optical bands, and repeatedly generate about 2 000 exposures per night, corresponding to a data volume of about 20 TiB every night. Three data facilities are preparing to contribute to the production of the annual data releases: the US Data Facility will process 35% of the raw data, the UK data facility will process 25% of the raw data and the French data facility, operated by CC-IN2P3, will locally process the remaining 40% of the raw data.In the context of the Data Preview 0.2 (DP0.2), the Data Release Production pipelines have been executed on the DC-2 simulated dataset (generated by the Dark Energy Science Collaboration, DESC). This dataset includes 20 000 simulated exposures, representing 300 square degrees of Rubin images with a typical depth of 5 years.DP0.2 ran at the Interim Data Facility (based on Google cloud), and the full exercise was independently replicated at CC-IN2P3. During this exercise, 3 PiB of data and more than 200 million files were produced. In this contribution we will present a detailed description of the system that we set up to perform this processing campaign using CC-IN2P3’s computing and storage infrastructure. Several topics will be addressed: workflow generation and execution, batch job submission, memory and I/O requirements, etc. We will focus on the issues that arose during this campaign and how we addressed them and will present some perspectives after this exercise.
Multilanguage Frameworks Towards the Ideal Multilanguage Environment
Authors: Hřivnáč Julius 
Published: May 2024

High Energy Physics software has been a victim of the necessity to choose one implementation language as no really usable multi-language environment existed. Even a co-existence of two languages in the same framework (typically C++ and Python) imposes a heavy burden on the system. The role of different languages was generally limited to well encapsulated domains (like Web applications, databases, graphics), with very limited connection to the central framework.
The new development in the domain of the compilers and run-time environments has enabled ways for creating really multilanguage frameworks, with seamless, user-friendly and high-performance inter-operation of many languages, which traditionally live in disconnected domains (like C-based languages vs JVM languages or Web languages).
Various possibilities and strategies for creation of the true multi-language frameworks are discussed, emphasizing their advantages and possible road blocks.
Making Likelihood Calculations Fast: Automatic Differentiation Applied to RooFit
Authors: Singh Garima , Rembser Jonas , Moneta Lorenzo , Lange David , Vassilev Vassil 
Published: May 2024

With the growing datasets of current and next-generation HighEnergy and Nuclear Physics (HEP/NP) experiments, statistical analysis has become more computationally demanding. These increasing demands elicit improvements and modernizations in existing statistical analysis software. One way to address these issues is to improve parameter estimation performance and numeric stability using Automatic Differentiation (AD). AD’s computational efficiency and accuracy are superior to the preexisting numerical differentiation techniques, and it offers significant performance gains when calculating the derivatives of functions with a large number of inputs, making it particularly appealing for statistical models with many parameters. For such models, many HEP/NP experiments use RooFit, a toolkit for statistical modeling and fitting that is part of ROOT.
In this paper, we report on the effort to support the AD of RooFit likelihood functions. Our approach is to extend RooFit with a tool that generates overheadfree C++ code for a full likelihood function built from RooFit functional models. Gradients are then generated using Clad, a compiler-based source-codetransformation AD tool, using this C++ code. We present our results from applying AD to the entire minimization pipeline and profile likelihood calculations of several RooFit and HistFactory models at the LHC-experiment scale. We show significant reductions in calculation time and memory usage for the minimization of such likelihood functions. We also elaborate on this approach’s current limitations and explain our plans for the future.
RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation
Authors: Ivanov Marian , Ivanov Marian , Eulisse Giulio 
Published: May 2024

The ALICE experiment [1] at CERN’s LHC is specifically designed for investigating heavy ion collisions. The upgraded ALICE accommodates a tenfold increase in Pb–Pb luminosity and a two-order-of-magnitude surge in minimum bias events. To address the challenges of high detector occupancy and event pile-ups, advanced multidimensional data analysis techniques, including machine learning (ML), are indispensable. Despite ML’s popularity, the complexity of its models presents interpretation challenges, and oversimplification in analysis often leads to inaccuracies.Our objective was to develop RootInteractive, a tool for multidimensional statistical analysis. This tool simplifies data analysis across dimensions, visualizes functions with uncertainties, and validates assumptions and approximations. In RootInteractive, it is crucial to easily define the functional composition of analytical parametric and non-parametric functions, exploit symmetries, and define multidimensional "invariant" functions and corresponding alarms.RootInteractive [2] adopts a declarative programming paradigm, ensuring userfriendliness for experts, students, and educators. It facilitates interactive visualization, n-dimensional histogramming/projection, and information extraction on both Python/C++ server and Javascript client. The tool supports client/server applications in Jupyter or standalone client-side applications. Through data compression, datasets with O(107) entries and O(25) attributes can be interactively analyzed in a browser with O(0.500-1 GB) size. Representative downsampling and reweighting/pre-aggregation enable the effective analysis of one year of ALICE data for various purposes.
Anomaly Detection in Data Center IT & Physical Infrastructure
Authors: Ronchieri Elisabetta , Giommi Luca , Scarponi Luigi Benedettto , Torzi Luca , Costantini Alessandro , Duma Doina Cristina , Salomoni Davide 
Published: May 2024

Anomaly detection in data center IT and physical infrastructure is challenging due to the amount of heterogeneous data to be analyzed. Defining a solution that early identifies unexpected anomalies is particularly important to prevent data losses, breakdown of the system, and any other event considered to be critical for the activity of the data center.
In the context of the INFN CNAF data center, one of the WLCG Tier-1s, we have performed a study based on monitored cooling, electrical, and IT hardware and software metrics to identify anomalies. In the present work, we aim to explore statistical approaches and machine learning solutions in the anomaly detection field for time series numerical metrics related to IT and physical infrastructure sensors.
With the usage of statistical Z-score and percentile approaches and clustering DBSCAN technique, we have been able to group and identify anomalous events. Using the presented approach, different relevance can be attributed to the likely anomalies.
Coffea-Casa: Building composable analysis facilities for the HL-LHC
Authors: Albin Sam , Attebury Garhan , Bloom Kenneth , Bockelman Brian , Lundstedt Carl , Shadura Oksana , Thiltges John 
Published: May 2024

The large data volumes expected from the High Luminosity LHC (HL-LHC) present challenges to existing paradigms and facilities for end-user data analysis. Modern cyberinfrastructure tools provide a diverse set of services that can be composed into a system that provides physicists with powerful tools that give them straightforward access to large computing resources, with low barriers to entry. The Coffea-Casa analysis facility (AF) provides an environment for end users enabling the execution of increasingly complex analyses such as those demonstrated by the Analysis Grand Challenge (AGC) and capturing the features that physicists will need for the HL-LHC.We describe the development progress of the Coffea-Casa facility featuring its modularity while demonstrating the ability to port and customize the facility software stack to other locations. The facility also facilitates the support of batch systems while staying Kubernetes-native. We present the evolved architecture of the facility, such as the integration of advanced data delivery services (e.g. ServiceX) and making data caching services (e.g. XCache) available to end users of the facility. We also highlight the composability of modern cyberinfrastructure tools. To enable machine learning pipelines at coffee-casa analysis facilities, a set of industry ML solutions adopted for HEP columnar analysis were integrated on top of existing facility services. These services also feature transparent access for user workflows to GPUs available at a facility via inference servers while using Kubernetes as enabling technology.
Data Centre Refurbishment with the aim of Energy Saving and Achieving Carbon Net Zero
Authors: Traynor Daniel , Owen R.A. , Hays Jonathan 
Published: May 2024

Queen Mary University of London (QMUL) as part of the refurbishment of one of its’s data centres will install water to water heat pumps to use the heat produced by the computing servers to provide heat for the university via a district heating system. This will reduce the use of high carbon intensity natural gas heating boilers, replacing them with electricity which has a lower carbon intensity due to the contribution from wind, solar, hydroelectric, nuclear and biomass sources of power sources.
The QMUL GridPP cluster today provides 15PB of storage and over 20K jobs slots mainly devoted to the ATLAS experiment. The data centre that houses the QMUL GridPP cluster, was originally commissioned in 2004. By 2020 it was in significant need of refurbishment. The original design had a maximum power capacity of 200KW, no hot/cold aisle containment, down flow air conditioning units using refrigerant cooling and no raised floor or ceiling plenum.
The main requirements of the refurbishment are: To significantly improve the energy efficiency and reduce the carbon usage of the University; Improve the availability and reliability of the power and cooling; Increase the capacity of the facility to provide for future expansion; Provide a long term home for the GridPP cluster to support the computing needs of the LHC and other new large science experiments (SKA/LSST) into the next decade.
After taking into account the future requirements and likely funding allocation, floor space in the data centre and the space available to house the cooling equipment the following design was chosen: A total power capacity of 390KW with redundant feeds to each rack; 39 racks with an average of 10KW of power per rack (flexible up to 20KW); An enclosed hot aisle design with in row cooling units using water cooling; water to water heat pumps connected to the universities district heating system
An overview of the project, it’s status and expected benefits in power and carbon saving are presented.
Exploring XRootD Optimisations Using Advanced Monitoring in the UK
Authors: Currie Robert , Yuan Wenlong , Themistokleous Neofytos 
Published: May 2024

XRootD servers are commonplace to many parts of HEP data management and are a key component to data access and management strategies in both the WLCG and OSG. Deployments of XRootD instances across the UK have demonstrated the versatility and expandability of this data management software. As we become more reliant on these services, there is a requirement to collect low-level metrics to monitor service performance and behaviour. This presentation will share our recent experiences in the collection, monitoring and analysis of such metrics from servers at UK WLCG-Tier2 sites.
Building on work presented at VCHEP-2021 we have been collecting XRootD service metrics from various UK sites. In addition to this, we have developed novel technologies for recording and verifying metrics from our grid storage systems at the Edinburgh Tier2. Our custom tooling is integrated with our recently deployed monitoring platform. This work complements ongoing WLCG efforts focussing on capturing and analysing XRootD based traffic flow. Through capturing and analysing additional metrics we’re able to better remotely assess service health and provide insights to assist in debugging. The goal of this is to offer a centralised resource that monitors the performance and health of remote storage services across the UK.
Making use of these additional service metrics also allows us a greater insight into the performance of XRootD Proxy File Caches. By applying machine learning techniques to already collected metrics, we aim to determine if gains can potentially be made in optimising the behaviour of these caching systems in production at a UK WLCG-Tier2. The result of this potentially being the building and deployment of custom decision-making libraries for use with XRootDPFC services.
Measureing Carbon NetZero and the IRISCAST Project
Authors: Owen R. A. , Hays J. 
Published: May 2024

Moving towards NetZero requires robust information to enable good decision making at all levels: covering hardware procurement, workload management and operations, as well as higher level aspects encompassing grant funding processes and policy framework development. The IRISCAST project is a proofof-concept study funded as part of the UKRI DRI Net-Zero Scoping Project.
IRISCAST performed an audit of carbon costs across a multi-site heterogeneous infrastructure by collecting and analysing snapshots of actual usage across different facilities within the IRIS community (https://iris.ac.uk). Combining usage information with an analysis of the embodied carbon costs and careful mapping and consideration of the underlying assumptions resulted in an estimate of the overall carbon cost, an understanding of the key elements that contribute to the carbon cost, and the important metrics needed to measure it.
IRISCAST makes reccomendations to allow high level feedback of carbon costs to funding bodies to inform strategic decisions as well as low level feedback of carbon costs to users and user communities to drive changes in user code bases and behaviors to be more carbon efficient.
IRISCAST carbon modeling shows that estimates of carbon costs can vary by factors of 10, hence there is significant opportunity for the carbon footprint of Digital Research Infrastructures to be reduced.
Simulating Hadronization with Machine Learning
Authors: Wilkinson Michael K. 
Published: May 2024

Hadronization is an important part of physics modeling in Monte Carlo event generators, where quarks and gluons are bound into physically observable hadrons. Today’s generators rely on finely-tuned phenomenological models, such as the Lund string model; while these models have been quite successful overall, there remain phenomenological areas where they do not match data well. A machine-learning-based alternative called MLhad, intended ultimately to be data-trainable, can simulate hadronization by encoding latentspace vectors, trained to be distributed according to a user-defined distribution using the sliced-Wasserstein distance in the loss function, then decoding them. The multiplicities and cumulative kinematic distributions of pions generated with MLhad in this way match those generated using Pythia 8.
While this architecture has been successful, an alternative using normalizing flows is convenient for generating non-pion hadrons and for taking advantage of reweighting techniques to reduce computing time. Combined with new methods for reweighting the output of phenomenological models, this updated architecture should prove convenient for comparing the output of MLhad and of empirical models.
APEIRON: A Framework for High Level Programming of Dataflow Applications on Multi-FPGA Systems
Authors: Ammendola Roberto , Biagioni Andrea , Chiarini Carlotta , Ciardiello Andrea , Cretaro Paolo , Frezza Ottorino , Lo Cicero Francesca , Lonardo Alessandro , Martinelli Michele , Paolucci Pier Stanislao , Pontisso Luca , Simula Francesco , Rossi Cristian , Turisini Matteo , Vicini Piero 
Published: May 2024

High Energy Physics (HEP) Trigger and Data Acquisition systems (TDAQs) need ever increasing throughput and real-time data analytics capabilities either to improve particle identification accuracy and further suppress background events in trigger systems or to perform an efficient online data reduction for trigger-less ones.As for the requirements imposed by HEP TDAQs applications in the class of real-time dataflow processing, FPGA devices are a good fit inasmuch they can not only provide adequate compute, memory and I/O resources but also a smooth programming experience thanks to the availability of High-Level Synthesis (HLS) tools.The main motivation for the design and development of the APEIRON framework is that the currently available HLS tools do not natively support the deployment of applications over multiple FPGA devices, which severely chokes the scalability of problems that this approach could tackle. To overcome this limitation, we envisioned APEIRON as an extension of the Xilinx Vitis framework able to support a network of FPGA devices interconnected by a lowlatency direct network as the reference execution platform.Developers can define scalable applications, using a dataflow programming model inspired by Kahn Process Networks, that can be efficiently deployed on a multi-FPGAs system: the APEIRON communication IPs allow low-latency communication between processing tasks deployed on FPGAs, even if they are hosted on different computing nodes. Thanks to the use of HLS tools in the workflow, processing tasks are described in C++ as HLS kernels, while communication between tasks is expressed through a lightweight C++ API based on non-blockingsend()and blockingreceive()operations.
Porting ATLAS Fast Calorimeter Simulation to GPUs with Performance Portable Programming Models
Authors: Atif Mohammad , Dong Zhihua , Leggett Charles , Lin Meifeng , Tsulaia Vakhtang 
Published: May 2024

FastCaloSim is a parameterized simulation of the particle energy response and of the energy distribution in the ATLAS calorimeter. It is a relatively small and self-contained package with massive inherent parallelism and captures the essence of GPU offloading via important operations like data transfer, memory initialization, floating point operations, and reduction. It was identified by the High Energy Physics Center for Computational Excellence project as a good testbed for evaluating the performance and ease of portability of programming models.
In this paper, we will discuss the results of our evaluation of the porting process to Kokkos, SYCL, Alpaka, OpenMP and std::par (nvc++), and compare performance on NVIDIA, AMD and Intel GPUs, as well as multicore CPUs.
Die zukünftige Sicherstellung der hausärztlichen Versorgung aus
kommunaler Perspektive
Authors: Schillen Philip , in der Schmitten Jürgen , Bolland Johanna , Borchardt Benjamin 
Published: May 2024

Zusammenfassung
Hintergrund Die Gewährleistung eines wohnortnahen Zugangs zur
(haus-)ärztlichen Versorgung erhält in Anbetracht des fortschreitenden
Hausärztemangels auch für die Kommunen eine immer größer werdende Bedeutung. Das
Ziel der vorliegenden Studie ist es, die Perspektive der Kommunen in
Nordrhein-Westfalen (NRW) auf die gegenwärtige und zukünftige Sicherstellung der
hausärztlichen Versorgung zu eruieren und daraus Hinweise für die zukünftige
gesundheitspolitische Ausgestaltung einer wohnortnahen, bedarfsgerechten
Primärversorgung abzuleiten.
Methodik In einer Online-Umfrage im Frühjahr 2023 wurden in Kooperation
mit dem Städte- und Gemeindebund NRW, dem Landkreistag NRW sowie dem Städtetag
NRW die Bürgermeister*innen bzw. Gesundheitsdezernent*innen aller 427 Kommunen
(396 Städte und Gemeinden, 31 Kreise) in NRW zu ihrer kommunalen Perspektive auf
die (künftige) hausärztliche Versorgung befragt. Die Fragebogen-Items (n=28)
wurden in Anlehnung an zwei Erhebungen zur ärztlichen Versorgung in
Niedersachsen und in Baden-Württemberg, mit jeweils kommunalpolitischer
Zielgruppe, entwickelt und in wiederholten Pretests optimiert.
Ergebnisse An der Befragung nahmen 192 Kommunen teil (Rücklauf: 45,0%).
Die Mehrheit der Kommunen (86,6%) schätzt die Versorgung vor Ort in den nächsten
10 Jahren als (eher) nicht sichergestellt ein. Die Kommunen äußern den Wunsch
nach mehr eigenen Einflussmöglichkeiten (79,5% ja bzw. eher ja). Gleichzeitig
erachten sie ein stärkeres Engagement, insbesondere der Kassenärztlichen
Vereinigungen (85,4%), sowie eine Intensivierung landespolitischer
Verantwortungsübernahme (72,4%) für die Sicherstellung der Versorgung als
notwendig.
Schlussfolgerung Die Befragung verdeutlicht ein großes Problembewusstsein
der kommunalen Ebene für zukünftige hausärztliche Versorgungsdefizite vor Ort.
Die Ergebnisse deuten darauf hin, dass die Kommunen angesichts zunehmender
Versorgungsenpässe die Kassenärztlichen Vereinigungen sowie das Land in der
Pflicht sehen und künftig stärkeren politischen Druck ausüben werden. Die
kommunale Perspektive ist ein wesentlicher Baustein bei den Überlegungen, wie in
Zukunft die hausärztliche Versorgung sichergestellt werden kann. Die Kommunen
sollten daher von den verantwortlichen Akteuren stärker als bislang in die
Planungen und Maßnahmen zur Sicherung der hausärztlichen Versorgung einbezogen
werden.