CutLang v2: Advances in a Runtime-Interpreted Analysis Description Language for HEP Data

We will present the latest developments in CutLang, the runtime interpreter of a recently-developed analysis description language (ADL) for collider data analysis. ADL is a domain-specific, declarative language that describes the contents of an analysis in a standard and unambiguous way, independent of any computing framework. In ADL, analyses are written in human-readable plain text files, separating object, variable and event selection definitions in blocks, with a syntax that includes mathematical and logical operations, comparison and optimisation operators, reducers, four-vector algebra and commonly used functions. Adopting ADLs would bring numerous benefits to the LHC experimental and phenomenological communities, ranging from analysis preservation beyond the lifetimes of experiments or analysis software to facilitating the abstraction, design, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents. Since their initial release, ADL and CutLang have been used for implementing and running numerous LHC analyses. In this process, the original syntax from CutLang v1 has been modified for better ADL compatibility, and the interpreter has been adapted to work with that syntax, resulting in the current release v2. Furthermore, CutLang has been enhanced to handle object combinatorics, to include tables and weights, to save events at any analysis stage, to benefit from multi-core/multi-CPU hardware among other improvements. In this contribution, these and other enhancements are discussed in details. In addition, real life examples from LHC analyses are presented together with a user manual.

Download Full-text

Recent advances in ADL, CutLang and adl2tnm

EPJ Web of Conferences ◽

10.1051/epjconf/202125103062 ◽

2021 ◽

Vol 251 ◽

pp. 03062

Author(s):

Harrison B. Prosper ◽

Sezen Sekmen ◽

Gokhan Unel ◽

Arpon Paul

Keyword(s):

Data Analysis ◽

Future Prospects ◽

Description Language ◽

Event Selection ◽

Plain Text ◽

Logical Operations ◽

Vector Algebra ◽

Object Variable ◽

Long Term Preservation

This paper presents an overview and features of an Analysis Description Language (ADL) designed for HEP data analysis. ADL is a domainspecific, declarative language that describes the physics content of an analysis in a standard and unambiguous way, independent of any computing frameworks. It also describes infrastructures that render ADL executable, namely CutLang, a direct runtime interpreter (originally also a language), and adl2tnm, a transpiler converting ADL into C++ code. In ADL, analyses are described in humanreadable plain text files, clearly separating object, variable and event selection definitions in blocks, with a syntax that includes mathematical and logical operations, comparison and optimisation operators, reducers, four-vector algebra and commonly used functions. Recent studies demonstrate that adapting the ADL approach has numerous benefits for the experimental and phenomenological HEP communities. These include facilitating the abstraction, design, optimization, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents and long term preservation of the analyses beyond the lifetimes of experiments. Here we also discuss some of the current ADL applications in physics studies and future prospects based on static analysis and differentiable programming.

Download Full-text

LumNet

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3463500 ◽

2021 ◽

Vol 5 (2) ◽

pp. 1-20

Author(s):

Prince U.C. Songwa ◽

Aaqib Saeed ◽

Sachin Bhardwaj ◽

Thijs W. Kruisselbrink ◽

Tanir Ozcelebi

Keyword(s):

Control Systems ◽

Real Life ◽

Visual Performance ◽

Lighting Control ◽

Domain Specific ◽

Real Life Setting ◽

Added Benefit ◽

Measurement Devices ◽

Indoor Spaces ◽

Learned Features

High-quality lighting positively influences visual performance in humans. The experienced visual performance can be measured using desktop luminance and hence several lighting control systems have been developed for its quantification. However, the measurement devices that are used to monitor the desktop luminance in existing lighting control systems are obtrusive to the users. As an alternative, ceiling-based luminance projection sensors are being used recently as these are unobtrusive and can capture the direct task area of a user. The positioning of these devices on the ceiling requires to estimate the desktop luminance in the user's vertical visual field, solely using ceiling-based measurements, to better predict the experienced visual performance of the user. For this purpose, we present LUMNET, an approach for estimating desktop luminance with deep models through utilizing supervised and self-supervised learning. Our model learns visual representations from ceiling-based images, which are collected in indoor spaces within the physical vicinity of the user to predict average desktop luminance as experienced in a real-life setting. We also propose a self-supervised contrastive method for pre-training LUMNET with unlabeled data and we demonstrate that the learned features are transferable onto a small labeled dataset which minimizes the requirement of costly data annotations. Likewise, we perform experiments on domain-specific datasets and show that our approach significantly improves over the baseline results from prior methods in estimating luminance, particularly in the low-data regime. LUMNET is an important step towards learning-based technique for luminance estimation and can be used for adaptive lighting control directly on-device thanks to its minimal computational footprint with an added benefit of preserving user's privacy.

Download Full-text

The FAST-HEP toolset: Using YAML to make tables out of trees

EPJ Web of Conferences ◽

10.1051/epjconf/202024506016 ◽

2020 ◽

Vol 245 ◽

pp. 06016

Author(s):

Benjamin Edward Krikler ◽

Olivier Davignon ◽

Lukasz Kreczko ◽

Jacob Linacre

Keyword(s):

Data Processing ◽

Analysis Software ◽

Description Language ◽

Industry Standard ◽

Level Data ◽

European Group ◽

Proof Of Principle

The Faster Analysis Software Taskforce (FAST) is a small, European group of HEP researchers that have been investigating and developing modern software approaches to improve HEP analyses. We present here an overview of the key product of this effort: a set of packages that allows a complete implementation of an analysis using almost exclusively YAML files. Serving as an analysis description language (ADL), this toolset builds on top of the evolving technologies from the Scikit-HEP and IRIS-HEP projects as well as industry-standard libraries such as Pandas and Matplotlib. Data processing starts with event-level data (the trees) and can proceed by adding variables, selecting events, performing complex user-defined operations and binning data, as defined in the YAML description. The resulting outputs (the tables) are stored as Pandas dataframes which can be programmatically manipulated and converted to plots or inputs for fitting frameworks. No longer just a proof-of-principle, these tools are now being used in CMS analyses, the LUX-ZEPLIN experiment, and by students on several other experiments. In this talk we will showcase these tools through examples, highlighting how they address the different experiments’ needs, and compare them to other similar approaches.

Download Full-text

Improvement of Software Engineering by Modeling Knowledge-Intensive Business Processes

Software Applications ◽

10.4018/978-1-60566-060-8.ch148 ◽

2009 ◽

pp. 2528-2546

Author(s):

Jane Fröming ◽

Norbert Gronau ◽

Simone Schmid

Keyword(s):

Software Engineering ◽

Business Processes ◽

Real Life ◽

Knowledge Modeling ◽

Description Language ◽

Knowledge Intensive

The Knowledge Modeling and Description Language (KMDL®) allows analysts to identify process patterns, which leads to improvements in knowledge-intensive processes. After modeling the business processes, knowledge and process potentials in daily business processes can be unleashed. The following contribution presents a specification of KMDL® for software engineering (KMDL®-SE). A real-life example is used to explain KMDL®-SE.

Download Full-text

Py3plex toolkit for visualization and analysis of multilayer networks

Applied Network Science ◽

10.1007/s41109-019-0203-7 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 4

Author(s):

Blaž Škrlj ◽

Jan Kralj ◽

Nada Lavrač

Keyword(s):

Complex Networks ◽

Temporal Change ◽

Real Life ◽

Analysis Software ◽

Multilayer Networks ◽

Speed Up ◽

Node Embeddings ◽

Synthetic Networks

Abstract Complex networks are used as means for representing multimodal, real-life systems. With increasing amounts of data that lead to large multilayer networks consisting of different node and edge types, that can also be subject to temporal change, there is an increasing need for versatile visualization and analysis software. This work presents a lightweight Python library, Py3plex, which focuses on the visualization and analysis of multilayer networks. The library implements a set of simple graphical primitives supporting intra- as well as inter-layer visualization. It also supports many common operations on multilayer networks, such as aggregation, slicing, indexing, traversal, and more. The paper also focuses on how node embeddings can be used to speed up contemporary (multilayer) layout computation. The library’s functionality is showcased on both real and synthetic networks.

Download Full-text

Control, Design and Error Variables in Decomposition of Design Optimization Problems

Volume 2: 28th Design Automation Conference ◽

10.1115/detc2002/dac-34085 ◽

2002 ◽

Author(s):

Jose Martinez Escanaverino ◽

Jose A. Llamos Soriz ◽

Alejandra Garcia Toll ◽

Tania Ortiz Cardenas

Keyword(s):

Design Optimization ◽

Design Automation ◽

Optimization Problems ◽

Real Life ◽

Control Design ◽

Maximum Matching ◽

Specific Knowledge ◽

Graph Theoretic ◽

Domain Specific ◽

Domain Specific Knowledge

A method for the rational choice of control, design and error variables in optimization problems is devised, based on the reduction of the maximum matching of the problem graph to the Dulmage-Mendelsohn canonical form. The method allows the designer to find with minimum effort appropriate sets of control, design and error variables that lead to an ultimate decomposition in design optimization problems of any dimension. In design automation, this procedure is useful as a rationale to plan manual interventions, where designers guide the process according to domain-specific knowledge. The proposed technique is rigorous and intuitive, thanks to the application of sound graph-theoretic concepts. A real-life example of mechanical engineering design shows the applicability of the method.

Download Full-text

EVALUATING SPEAKING COURSE AS REFLECTED TO THE STUDENTS' PROBLEM-BASED LEARNING

10.31227/osf.io/kxnqv ◽

2018 ◽

Author(s):

Didik Rinan Sumekto

Keyword(s):

Learning Styles ◽

Learning Strategies ◽

Language Production ◽

Real Life ◽

Problem Based Learning ◽

Problem Solving Skills ◽

Individual Level ◽

Domain Specific ◽

Teacher Student ◽

Good Learning

This paper is aimed at revealing the evaluation on speakings' (1) performance in classroom; and (2) adopting the students' problem-based learning. Communication in the classroom is definitely embedded in meaning-focused activity. This activity requires a teacher in classroom to tailor his or her instruction carefully to the needs of learners and teach them how to listen to others, how to talk to others, and how to negotiate meaning in a shared context. Learners will learn how to communicate verbally and non-verbally as their language store and language skills development. In designing activities, a teacher should consider all the skills conjointly as he or she interacts with learners in natural behavior, for in real life as in classroom. The following activities appear to be particular relevant to eliciting spoken-language production. For instance, a teacher provides learners with opportunities to learn from auditory and visual experiences, which enable them to develop flexibility in their learning styles and also to demonstrate the optimal use of different learning strategies and behaviors for different tasks. To support the speaking ability, media of teaching speaking can be adopted from the following aids, such as aural, visual, material-aided, and culture awareness. The expected outcomes of a problem-based learning (PBL) activity are (1) acquiring knowledge and skills that can be transferred to solve similar problems on an individual level and (2) constructing a shared knowledge and promoting mutual understanding on the group level. The instruments used in a PBL activity are tools--whiteboards, computers, as well as domain-specific tools like experimental instruments, places-discussing rooms, library, and laboratory, and documents-learning materials and learning records. The community of a PBL activity is broad and consists of the learners who are involved in or have influence on the activity in some forms. In PBL activities, learners may have different expertise and different learning interests. PBL promotes learners' confidence in their problem solving skills and strives to make them self-directed learners, even such confidence does not come immediately, it can be fostered by good instruction. A teacher who provides a good learning circumstance in the classroom with the positive teacher-student and student-student interaction, gives learners a sense of ownership over their learning, develops relevant and meaningful problems and learning methods, and empowers learners with valuable skills that will enhance learner's motivation to learn and ability to achieve.

Download Full-text

Finding Forces

Mechanical Engineering ◽

10.1115/1.2009-oct-3 ◽

2009 ◽

Vol 131 (10) ◽

pp. 33-36

Author(s):

Jean Thilmany

Keyword(s):

Development Process ◽

Process Analysis ◽

Physical Phenomenon ◽

Real Life ◽

Simulation Software ◽

Integrated Systems ◽

Analysis Software ◽

Loosely Coupled ◽

Analysis Process ◽

Life Behavior

This article explains how today's analysis software are helping engineers to quickly solve for more than one physical phenomenon at a time. Today's simulation software mirror real-life behavior. Engineers can now run multiple analyses within the same application or within loosely coupled applications. Other systems allow users to solve for more than one force at the same time. The capability to solve in tandem or to work with integrated systems greatly speeds the analysis process. Analysis advances also allow engineering firms to call upon the analysis software to design complex and never-before-seen products, to test for safety quicker than before, and to cut costs by perfecting designs earlier in the development process.

Download Full-text

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv

GigaScience ◽

10.1093/gigascience/giz095 ◽

2019 ◽

Vol 8 (11) ◽

Cited By ~ 7

Author(s):

Farah Zaib Khan ◽

Stian Soiland-Reyes ◽

Richard O Sinnott ◽

Andrew Lonie ◽

Carole Goble ◽

...

Keyword(s):

Best Practice ◽

Real Life ◽

Practical Implementation ◽

Specific Information ◽

Practical Application ◽

Data Intensive ◽

Domain Specific ◽

Practice Recommendations ◽

Software Platforms ◽

Research Objects

AbstractBackgroundThe automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Computationally driven data-intensive experiments using workflows enable automation, scaling, adaptation, and provenance support. However, there are still several challenges associated with the effective sharing, publication, and reproducibility of such workflows due to the incomplete capture of provenance and lack of interoperability between different technical (software) platforms.ResultsBased on best-practice recommendations identified from the literature on workflow design, sharing, and publishing, we define a hierarchical provenance framework to achieve uniformity in provenance and support comprehensive and fully re-executable workflows equipped with domain-specific information. To realize this framework, we present CWLProv, a standard-based format to represent any workflow-based computational analysis to produce workflow output artefacts that satisfy the various levels of provenance. We use open source community-driven standards, interoperable workflow definitions in Common Workflow Language (CWL), structured provenance representation using the W3C PROV model, and resource aggregation and sharing as workflow-centric research objects generated along with the final outputs of a given workflow enactment. We demonstrate the utility of this approach through a practical implementation of CWLProv and evaluation using real-life genomic workflows developed by independent groups.ConclusionsThe underlying principles of the standards utilized by CWLProv enable semantically rich and executable research objects that capture computational workflows with retrospective provenance such that any platform supporting CWL will be able to understand the analysis, reuse the methods for partial reruns, or reproduce the analysis to validate the published findings.

Download Full-text