P6: A Declarative Language for Integrating Machine Learning in Visual Analytics
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We present latest developments in Analysis Description Language (ADL), a declarative domain-specific language describing the physics algorithm of a HEP data analysis decoupled from software frameworks. Analyses written in ADL can be integrated into any framework for various tasks. ADL is a multipurpose construct with uses ranging from analysis design to preservation, reinterpretation, queries, visualisation, combination, etc. The most advanced infrastructure to execute ADL on events is the CutLang runtime interpreter. Recent technical developments include an automated interface with different data types, generation of the abstract syntax tree, a visualization tool that that auto-converts analysis flows to graphs, incorporation of trained machine learning models and a Jupyter-based plotting tool. We also report physics implications including a large scale LHC analysis implementation and validation effort for beyond the standard model reinterpretation purposes and studies with ATLAS and CMS open data.
Isomorphic is a Ruby library for specifying isomorphisms between Ruby objects. The inventive features of the new Ruby library are as follows: Domain-specific language (DSL) for declaration of isomorphisms between Ruby objects. Functional references (viz., "lenses") for getter/setter operations. To our knowledge, this is the first library of its kind for the Ruby programming language. Moreover, to our knowledge, this is the first implementation of the "functional references" concept within the Ruby programming language.
JLab and PNNL are jointly developing an AI-ready data ecosystem that exposes the Continuous Electron Beam Acceleration Facility’s (CEBAF’s) operational configuration, lattice description, and control-system channels to agentic optimization frameworks through a standards-based semantic layer. The effort integrates the existing facility-specific CEBAF Element Database (CED) with extensions of the emerging facility-agnostic Particle Accelerator Lattice Standard (PALS) to produce a knowledge graph (KG) containing coherent, machine-interpretable views of devices, signals, and regions. With this KG, CEBAF’s setpoints, readbacks, and device hierarchies become queryable using a uniform declarative graph query language (e.g., Neo4j Cypher), providing intents and inspectable semantics suitable for agentic control. The resulting graph-backed interfaces will allow autonomous agents to retrieve authoritative machine configurations, reason over device- and signal-level relationships, and execute tuning and diagnostic workflows without bespoke CEBAF-specific logic, thereby delivering a scalable pathway from operational data to trustworthy agentic accelerator tuning frameworks.
This paper introduces novel programming strategies that leverage features of the Fortran 2023 standard of the International Standards Organization (ISO) to automatically parallelize computations on deep neural networks. The paper focuses on the interplay of object-oriented, parallel, and functional programming paradigms in the Fiats deep learning library. We demonstrate how several infrequently used language features play a role in enabling efficient, parallel execution. Specifically, the ability to explicitly declare that a procedure is pure facilitates inference in the context of the language’s loop-parallelism construct `do concurrent`. Also, explicitly prohibiting the overriding of a parent type’s type-bound procedures eliminates the need for dynamic dispatch in performance-critical code. Finally, this paper uses batch inference calculations on a neural network surrogate for atmospheric aerosol dynamics to demonstrate that LLVM Flang compiler’s automatic parallelization of `do concurrent` achieves roughly the same performance and scalability as achieved by OpenMP compiler directives. We also demonstrate that double-precision inference costs 37–72% longer runtime than default-real precision with most values in the range 57-60%.
Abstract We introduce Cropbox, a novel modelling framework that supports various aspects of crop modelling in a unique yet concise style. Building a crop model can be easily riddled with technical details looking trivial at first but later becoming major obstacles that hamper the whole development or application process. This is particularly the case when implementing models from scratch without relying on an established framework. Cropbox adopts a declarative approach providing a domain-specific language to reduce technical debt and assist modellers to focus on high-level abstraction formed by relations between variables and enclosing systems, rather than tinkering with low-level implementation details. The syntax of Cropbox framework is based on the Julia programming language and is deliberately constrained to avoid unintended side effects caused by common mistakes while its architecture remains open to extension. We highlight key capabilities of the framework through case studies featuring a leaf gas-exchange model and a whole-plant simulation model. We also illustrate potential extensions for supporting functional-structural plant modelling by demonstrating a 3D root architectural model as an example.
Videos that are shot using commodity hardware such as phones and surveillance cameras record various metadata such as time and location. We encounter suchgeospatial videoson a daily basis and such videos have been growing in volume significantly. Yet, we do not have data management systems that allow users to interact with such data effectively. In this paper, we describe Spatialyze, a new framework for end-to-end querying of geospatial videos. Spatialyze comes with a domain-specific language where users can construct geospatial video analytic workflows using a 3-step, declarative,build-filter-observeparadigm. Internally, Spatialyze leverages the declarative nature of such workflows, the temporal-spatial metadata stored with videos, and physical behavior of real-world objects to optimize the execution of workflows. Our results using real-world videos and workflows show that Spatialyze can reduce execution time by up to 5.3×, while maintaining up to 97.1% accuracy compared to unoptimized execution.
An abundance of research has been performed to understand the physics of latent thermal energy storage with phase change material. Some analytical and numerical findings have been validated by experiments, but there are few free and open-source models available to the general public for use in systems simulation and analysis. The Modelica programming language is a good avenue to make such models available, because it is object-oriented, equation-based, declarative, and acausal. These characteristics have enabling the creation of component model libraries that can be used to build larger system simulations for design analysis. The authors have previously developed a numerical framework to model phase change thermal storage and have validated model predictions with experiments. The objectives of this paper are to describe the transfer of the numerical framework to an implementation in a Modelica component model and to validate the Modelica model with data from the experiment and the original numerical framework.
eCC++ is a new compiler construction framework for embedding domain-specific programming languages within C++. That is, the host language is C++, the guest language is the DSL to be embedded in C++, and eCC++ is the tool that enables the embedding. The eCC++ framework is composed of three main components: a front-end, an API for program verification based on a functional and declarative intermediate representation (IR), and a Multi-Level Intermediate Representation (MLIR) code generator. The eCC++ front-end consists of a library of C++ classes and operators that can be used to define the guest language. Guest sources are compiled with any standard C++ compiler, and when run, the resulting executable generates an eCC++ IR representation of the program, which can be verified within the eCC++ framework. Finally, eCC++ allows for high-level and domain-specific optimizations before generating MLIR. In summary, eCC++ aims to act as a generic front-end that enables embedding guest languages into C++, and provides necessary compiler technology for program verification, targeting the existing capabilities in the MLIR infrastructure. The paper evaluates the eCC++ expressiveness and usability describing the process of embedding GraphIt, a high-performance graph language in C++.
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The Mu2e experiment will search for coherent, neutrinoless conversion of muons to elections in an aluminum stopping target. A simulation program is used to experiment and design the production target of the Mu2e experiment. The module uses programing languages and libraries to work. The program called the Offline uses C++ as the programing language in the module. To construct the simulation, the Geant4 library constructs shapes and uses them in the simulation program, ROOT. The offline program simplifies the creation of the tube by organizing them into parameter objects and nesting them with other variables required to construct the tube-like center point, material, or if it is allowed to conflict with other solids. The parameter varies for every solid tube. The geometry file uploads the parameters by declaring and organizing the parameters in C++ using the configuration tools. The author describes the development and the future of this project.
The Mu2e experiment will search for coherent, neutrinoless conversion of muons to elections in an aluminum stopping target. A simulation program is used to experiment and design the production target of the Mu2e experiment. The module uses programing languages and libraries to work. The program called the Offline uses C++ as the programing language in the module. To construct the simulation, the Geant4 library constructs shapes and uses them in the simulation program, ROOT. The offline program simplifies the creation of the tube by organizing them into parameter objects and nesting them with other variables required to construct the tube-like center point, material, or if it is allowed to conflict with other solids. The parameter varies for every solid tube. The geometry file uploads the parameters by declaring and organizing the parameters in C++ using the configuration tools. The author describes the development and the future of this project.
A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.
PySolate is a Python‐based toolset that implements the continuous wavelet transform and nonlinear thresholding operations to denoise or designal seismic data, following Langston and Mousavi (2019). This filtering approach can remove microseismic noise to isolate intermediate‐period seismic signals that are key to enabling full‐waveform modeling and analysis of smaller‐magnitude regional events. This approach is best for the application to signals with frequency or time separation of signal and noise, in contrast to Fourier analysis, which is effective when signal and noise are separated in frequency. We demonstrate the Python toolset using the six announced Democratic People’s Republic of Korea declared nuclear tests, showing the effectiveness of isolating the seismic signal compared to standard bandpass filtering. In conclusion, we also demonstrate the ease of using the toolset with any Python processing tools.
The increasing availability of large digital corpora of cross-linguistic data is revolutionizing many branches of linguistics. Overall, it has triggered a shift of attention from detailed questions about individual features to more global patterns amenable to rigorous, but statistical, analyses. This engenders an approach based on successive approximations where models with simplified assumptions result in frameworks that can then be systematically refined, always keeping explicit the methodological commitments and the assumed prior knowledge. Therefore, they can resolve disputes between competing frameworks quantitatively by separating the support provided by the data from the underlying assumptions. These methods, though, often appear as a ‘black box’ to traditional practitioners. In fact, the switch to a statistical view complicates comparison of the results from these newer methods with traditional understanding, sometimes leading to misinterpretation and overly broad claims. We describe here this evolving methodological shift, attributed to the advent of big, but often incomplete and poorly curated, data, emphasizing the underlying similarity of the newer quantitative to the traditional comparative methods and discussing when and to what extent the former have advantages over the latter. In this review, we cover briefly both randomization tests for detecting patterns in a largely model-independent fashion and phylolinguistic methods for a more model-based analysis of these patterns. We foresee a fruitful division of labor between the ability to computationally process large volumes of data and the trained linguistic insight declaring worthy prior commitments and interesting hypotheses in need of comparison.
The Chemical Weapons Convention (CWC) is an international disarmament and non-proliferation treaty that mandates a comprehensive ban on chemical weapons. This treaty entered-into-force in 1997 and has a nearly universal membership of world states. The CWC provides definitions for what constitutes a chemical weapon and describes chemicals for which verification measures and regulatory oversight are required. These chemicals, which must be declared to the Organisation for the Prohibition of Chemical Weapons (OPCW) for verification purposes, appear in three Schedules within the CWC. The schedules list specific chemical substances and families of chemicals defined by molecular structure descriptions that allow a range of specified variable groups. The text of an international treaty provides a basis for laws and regulatory requirements at international and national levels that must be enacted to comply with treaty obligations. These laws and regulations must account for the “chemistry” that is described in the treaty text, which has been negotiated and agreed upon by world governments. In this paper, we look at some of the chemicals described within the CWC and discuss how atoms and molecules, nomenclature and Chemical Abstracts Service (CAS) numbers, isomer enumeration, ring strain and stereochemistry influence the obligations of States to an international treaty. The material draws upon the authors’ experiences from an interactive “Science for Diplomats” initiative that has provided a forum for engaging with chemical disarmament decision makers to aid in their understanding of fundamental concepts in chemistry that have impact on treaty implementation. We hope this article will spark interest from chemistry students and educators in the provision of science advice for policymakers and provide policymakers with an appreciation of the complexity and importance of understanding the language of chemistry in support of a world free of chemical weapons.