Related papers: A complete overhaul of the EELS and XAS database: …
In this work we present a theoretical study of EELS (electron-energy-loss spectroscopy) experiments on the ${\rm C}_{60}$ molecule. Our treatment of the problem is based on the simple two-fluid model originally proposed for the description…
We propose to develop a high-energy heavy-ion experimental database and make it accessible to the scientific community through an on-line interace. This database will be searchable and cross-indexed with relevant publications, including…
We describe the atomic database of the XSTAR spectral modeling code, summarizing the systematic upgrades carried out in the past twenty years to enable the modeling of K lines from chemical elements with atomic number $Z\leq 30$ and recent…
The rapid growth of solar data is driving changes in the typical workflow and algorithmic approach to solar data analysis. We present recently deployed tools to aid this evolution and layout the path for future development. The majority of…
Meta-evaluation studies of system performances in controlled offline evaluation campaigns, like TREC and CLEF, show a need for innovation in evaluating IR-systems. The field of academic search is no exception to this. This might be related…
Instrumentation developments in electron energy-loss spectroscopy (EELS) in the scanning transmission electron microscope (STEM) one decade ago paved the way for combining milli-electronvolt energy resolution in spectroscopy with…
The proliferation of digital interactions across diverse domains, such as healthcare, e-commerce, gaming, and finance, has resulted in the generation of vast volumes of event stream (ES) data. ES data comprises continuous sequences of…
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity. Traditional EEG decoding methods have achieved moderate success when…
ESPRESSO is an extremely stable high-resolution spectrograph which is currently being developed for the ESO VLT. With its groundbreaking characteristics it is aimed to be a "science machine", i.e. a fully-integrated instrument to directly…
Many modern applications require the evaluation of analytical queries on large amounts of data. Such queries entail joins and heavy aggregations that often include user-defined functions (UDFs). The most efficient way to process these…
Recent years have witnessed exponential growth in developing deep learning (DL) models for time-series electricity forecasting in power systems. However, most of the proposed models are designed based on the designers' inherent knowledge…
Distributed Search Engine Architecture (DSEA) hosts numerous independent topic-specific search engines and selects a subset of the databases to search within the architecture. The objective of this approach is to reduce the amount of space…
Though Explainable AI (XAI) has made significant advancements, its inclusion in edge and IoT systems is typically ad-hoc and inefficient. Most current methods are "coupled" in such a way that they generate explanations simultaneously with…
EASY-II is designed as a functional replacement for the previous European Activation System, EASY-2010. It has extended nuclear data and new software, FISPACT-II, written in object-style Fortran to provide new capabilities for predictions…
The Ontology Lookup Service (OLS) is an open source search engine for ontologies which is used extensively in the bioinformatics and chemistry communities to annotate biological and biomedical data with ontology terms. Recently there has…
Context: Embedded Domain-Specific Languages (EDSLs) are a common and widely used approach to DSLs in various languages, including Haskell and Scala. There are two main implementation techniques for EDSLs: shallow embeddings and deep…
To enable materials databases supporting computational and experimental research, it is critical to develop platforms that both facilitate access to the data and provide the tools used to generate/analyze it - all while considering the…
Dynamic data pruning accelerates deep learning by selectively omitting less informative samples during training. While per-sample loss is a common importance metric, obtaining it can be challenging or infeasible for complex models or loss…
To address the challenges associated with data processing at scale, we propose Dataverse, a unified open-source Extract-Transform-Load (ETL) pipeline for large language models (LLMs) with a user-friendly design at its core. Easy addition of…
easyaccess is an enhanced command line interpreter and Python package created to facilitate access to astronomical catalogs stored in SQL Databases. It provides a custom interface with custom commands and was specifically designed to access…