Related papers: A cookbook for the XMMSAS
Clinical dietary assessment can generate detailed but high-dimensional nutrient and food-group information that is difficult to translate quickly into counselling priorities. This paper proposes an explainable unsupervised-to-supervised…
The exploration and study of exoplanets remain at the frontier of astronomical research, challenging scientists to continuously innovate and refine methodologies to navigate the vast, complex data these celestial bodies produce. This…
This paper summarizes the needs of the dark matter and neutrino communities as it relates to computation. The scope includes data acquisition, triggers, data management and processing, data preservation, simulation, machine learning, data…
In this paper, we introduce a new, local formulation of the ensemble Kalman Filter approach for atmospheric data assimilation. Our scheme is based on the hypothesis that, when the Earth's surface is divided up into local regions of moderate…
This manual describes a set of utilities developed for Lattice QCD computations. They are collectively called QCDUtils. They comprise a set of Python programs each of them with a specific function: download gauge ensembles from the public…
This paper discusses the possible ways to analyse the data, adopting a matrix notation, so often used in Bioassays. The paper also reviews the Multistage Models (MM). The MM class of models is applied for extrapolation, to the region of…
The XMM-Newton observatory has the largest collecting area flown so far for an X-ray imaging system, resulting in a very high sensitivity over a broad spectral range. In order to exploit fully these performances, a very accurate calibration…
This document concerns data readiness in the context of machine learning and Natural Language Processing. It describes how an organization may proceed to identify, make available, validate, and prepare data to facilitate automated analysis…
The purpose of this work is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides python utilities to…
This article proposes a Mix Neural Network (MNN) based on CNN-FCNN for predicting magnetic loss of different materials. In traditional magnetic core loss models, empirical equations usually need to be regressed under the same external…
Bringing a high-dimensional dataset into science-ready shape is a formidable challenge that often necessitates data compression. Compression has accordingly become a key consideration for contemporary cosmology, affecting public data…
Changing dietary habits and keeping food diary encourages fewer calorie consumption, and thus weight loss. Studies have shown that people who keep food diary are more successful in losing weight and keeping it off. However, no study has…
We present an open-source computer program written in Python language for quantum measurement and related issues. In our program, quantum states and operators, including quantum gates, can be developed into a quantum-object function…
Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition systems rely on static datasets characterized by a…
Current needs of nuclear science and technology include complete, well-documented, and easily verifiable nuclear data. The complete data records require supporting nuclear bibliography, presently stored in dedicated libraries, in addition,…
The in-orbit imaging performance of the three X-ray telescopes on board of the X-ray astronomy observatory XMM-Newton is presented and compared with the performance measured on ground at the MPE PANTER test facility. The comparison shows an…
We announce the public release of PynPoint, a Python package that we have developed for analysing exoplanet data taken with the angular differential imaging observing technique. In particular, PynPoint is designed to model the point spread…
This paper introduces matrix product state (MPS) decomposition as a computational tool for extracting features of multidimensional data represented by higher-order tensors. Regardless of tensor order, MPS extracts its relevant features to…
We present a set of algorithms implementing multidimensional scaling (MDS) for large data sets. MDS is a family of dimensionality reduction techniques using a $n \times n$ distance matrix as input, where $n$ is the number of individuals,…
Based on the community input at the National Academy of Sciences (NAS) Madison and Austin workshops in July and December 2017, respectively, this whitepaper was prepared and submitted to the NAS in the category of US fusion theory and…