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Handbook of career development : international perspectives
This book is focused on work, occupation and career development: themes that are fundamental to a wide range of human activities and relevant across all cultures. Yet theorizing and model building about this most ubiquitous of human activities from international perspectives have not been vigorous. An examination of the literature pertaining to career development, counseling and guidance that has developed over the last fifty years reveals theorizing and model building have been largely dominated by Western epistemologies, some of the largest workforces in the world are in the developing world. Career guidance is rapidly emerging as a strongly felt need in these contexts. If more relevant models are to be developed, frameworks from other cultures and economies must be recognized as providing constructs that would offer a deeper understanding of career development. This does not mean that existing ideas are to be discarded. Instead, an integrative approach that blends universal principles with particular needs could offer a framework for theorizing, research and practice that has wider relevance. The central objective of this handbook is to draw the wisdom and experiences of different cultures together to consider both universal and specific principles for career guidance and counseling that are socially and economically relevant to contemporary challenges and issues. This book is focused on extending existing concepts to broader contexts as well as introducing new concepts relevant to the discipline of career guidance and counseling.
Book
Evolution of Regional, Age and Gender Demographics in the ATLAS Collaboration
The ATLAS Collaboration consists of around 6000 members from over 100 different countries. Regional, age and gender demographics of the collaboration are presented, including the time evolution over the lifetime of the experiment. In particular, the relative fraction of women is discussed, including their share of contributions, recognition and positions of responsibility, including showing how these depend on other demographic measures.
Journal Article
Leveraging Language Models to Navigate Conference Abstracts: An Open-Source Approach
Large Language Models (LLMs) have emerged as a transformative tool in society and are steadily working their way into scientific workflows. Despite their known tendency to hallucinate, rendering them perhaps unsuitable for direct scientific pipelines, LLMs excel in text-related tasks, offering a unique solution to manage the overwhelming volume of information presented at large conferences such as ACAT, ICHEP, and CHEP. These proceedings present an innovative opensource application that harnesses the capabilities of an LLM to rank conference abstracts based on a user’s specified interests. By providing a list of interests to the LLM, it can sift through a multitude of abstracts, identifying those most relevant to the user, effectively helping to tailor the conference experience. The LLM, in this context, serves an assistant role, aiding conference attendees in navigating the deluge of information typical of large conferences. These proceedings will detail the workings of this application, provide prompts to optimize its use, and discuss potential future directions for this type of application.
Journal Article
Differentiable Programming: Neural Networks and Selection Cuts Working Together
Differentiable Programming could open even more doors in HEP analysis and computing to Artificial Intelligence/Machine Learning. Current common uses of AI/ML in HEP are deep learning networks – providing us with sophisticated ways of separating signal from background, classifying physics, etc. This is only one part of a full analysis – normally skims are made to reduce dataset sizes by applying selection cuts, further selection cuts are applied, perhaps new quantities calculated, and all of that is fed to a deep learning network. Only the deep learning network stage is optimized using the AI/ML gradient decent technique. Differentiable programming offers us a way to optimize the full chain, including selection cuts that occur during skimming. This contribution investigates applying selection cuts in front of a simple neural network using differentiable programming techniques to optimize the complete chain on toy data. There are several well-known problems that must be solved – e.g., selection cuts are not differentiable, and the interaction of a selection cut and a network during training is not well understood. This investigation was motived by trying to automate reduced dataset skims and sizes during analysis – HL-LHC analyses have potentially multi-TB dataset sizes and an automated way of reducing those dataset sizes and understanding the trade-offs would help the analyser make a judgement between time, resource usages, and physics accuracy. This contribution explores the various techniques to apply a selection cut that are compatible with differentiable programming and how to work around issues when it is bolted onto a neural network. Code is available.
Journal Article
Developing a Declarative Analysis Language: LINQToROOT
The HEP community is preparing for the LHC’s Run 3 and 4. One of the big challenges for physics analysis will be developing tools to efficiently express an analysis and able to efficiently process the x10 more data expected. Recently, interest has focused on declarative analysis languages: a way of specifying a physicists’ intent and leaving everything else to the underlying system. The underlying system takes care of finding the data - powering the event processing loop – and even exactly how to most efficiently apply a desired jet selection. If this works, this would allow an analyser to test their algorithm on a small amount of data on their GPU-less laptop and then run it on a large amount of data on a server with multiple large GPU’s without having to alter their code. The LINQToROOT project, started almost seven years ago, fits this model. It has been used and tested in three ATLAS published analyses. LINQToROOT is based on the Language Integrated Query system built into the cross-platform C# language. It enables writing strongly-typed queries on a ROOT’s TTree’s data and transcribes the data to a C++ algorithm that can run in ROOT. Recent work on this system has had two goals: improving analysis efficiency and better understanding the requirements of a declarative analysis language. For example, a good analysis language should be able to abstract away the backend – recent work has increased the possible back ends from formerly the single Windows ROOT backend to one that runs on Linux, the Windows Linux-subsystem, and an experimental one that allows for PROOF like parallel processing – all done with almost no change to the analysis code itself. Any analysis language must also be rich enough to support an experiment’s data model. To test this, some experiments with the full ATLAS xAOD data model have been performed. All of this has been done while attempting to keep the project close to its original goals: quick turnaround for real ATLAS physics analysis. This work will be discussed in some detail along with thoughts and lessons that have helped shape our thinking about an Analysis Language and perhaps our approach to future physics analysis employing declarative analysis.
Journal Article
Evaluating query languages and systems for high-energy physics data
In the domain of high-energy physics (HEP), general-purpose query languages have found little adoption in analysis. This is surprising regarding SQL-based systems, as HEP data analysis matches SQL’s processing model well: the data is fully structured and makes use of predominantly standard operators. To better understand the situation, we select six general-purpose query engines, from both the SQL and NoSQL domain, and analyze their performance, scalability, and usability in HEP analysis, employing standard HEP tools as baseline. We also identify a set of core language features needed to support HEP data analysis. Our results reveal an interesting and complex picture: several query languages provide a rich and natural query development experience, while others fall short. In terms of performance, our results reveal that many of the database systems are one or two orders of magnitude slower than the standard HEP analysis tools, while others manage to scale and perform well. These conclusions suggest that while the existing data processing systems are viable candidates for HEP analysis, there is still work to be done to improve their performance and ability to succinctly express HEP queries.
Journal Article
FuncADL: Functional Analysis Description Language
The traditional approach in HEP analysis software is to loop over every event and every object via the ROOT framework. This method follows an imperative paradigm, in which the code is tied to the storage format and steps of execution. A more desirable strategy would be to implement a declarative language, such that the storage medium and execution are not included in the abstraction model. This will become increasingly important to managing the large dataset collected by the LHC and the HL-LHC. A new analysis description language (ADL) inspired by functional programming, FuncADL, was developed using Python as a host language. The expressiveness of this language was tested by implementing example analysis tasks designed to benchmark the functionality of ADLs. Many simple selections are expressible in a declarative way with FuncADL, which can be used as an interface to retrieve filtered data. Some limitations were identified, but the design of the language allows for future extensions to add missing features. FuncADL is part of a suite of analysis software tools being developed by the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP). These tools will be available to develop highly scalable physics analyses for the LHC.
Journal Article
Building a Columnar Analysis Demonstrator for ATLAS PHYSLITE Open Data using the Python Ecosystem
The ATLAS experiment is in the process of developing a columnar analysis demonstrator, which takes advantage of the Python ecosystem of data science tools. This project is inspired by the analysis demonstrator from IRIS-HEP. The demonstrator employs PHYSLITE OpenData from the ATLAS collaboration, the new Run 3 compact ATLAS analysis data format. The tight integration of ROOT features within PHYSLITE presents unique challenges when integrating with the Python analysis ecosystem. The demonstrator is constructed from ATLAS PHYSLITE OpenData, ensuring the accessibility and reproducibility of the analysis. The analysis pipeline of the demonstrator incorporates a comprehensive suite of tools and libraries. These include uproot for data reading, awkward-array for data manipulation, Dask for parallel computing, and hist for histogram processing. For the purpose of statistical analysis, the pipeline integrates cabinetry and pyhf, providing a robust toolkit for analysis. A significant component of this project is the custom application of corrections, scale factors, and systematic errors using ATLAS software. The infrastructure and methodology for these applications will be discussed in detail during the presentation, underscoring the adaptability of the Python ecosystem for high energy physics analysis.
Journal Article
Operating the 200 Gbps IRIS-HEP Demonstrator for ATLAS
The ATLAS experiment is currently developing columnar analysis frameworks which leverage the Python data science ecosystem. We describe the construction and operation of the infrastructure necessary to support demonstrations of these frameworks, with a focus on those from IRIS-HEP. One such demonstrator aims to process the compact ATLAS data format PHYSLITE at rates exceeding 200 Gbps. Various access configurations and setups on different sites are explored, including direct access to a dCache storage system via Xrootd, the use of ServiceX, and the use of multiple XCache servers equipped with NVMe storage devices. Integral to this study was the analysis of network traffic and bottlenecks, worker node scheduling and disk configurations, and the performance of an S3 object store. The system’s overall performance was measured as the number of processing cores scaled to over 2,000 and the volume of data accessed in an interactive session approached 200 TB. The presentation will delve into the operational details and findings related to the physical infrastructure that underpins these demonstrators.
Journal Article
The 200 Gbps Challenge: Imagining HL-LHC analysis facilities
The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a “Grand Challenge” format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a “200 Gbps Challenge”, aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes — including software, services, and facilities — built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today’s facilities, setting the stage for the next exercise at twice the scale.
Journal Article