Related papers: Extensive Comparison between INRIM and a Secondary…
Uncertainty in the calibration of gravitational-wave (GW) detector data leads to systematic errors which must be accounted for in setting limits on the strength of GW signals. When cross-correlation measurements are made using data from a…
A light injection system using LEDs and optical fibres was designed for the calibration and monitoring of the photomultiplier array of the SNO+ experiment at SNOLAB. Large volume, non-segmented, low-background detectors for rare event…
Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observation that modern neural network classifiers are poorly…
To evaluate different technologies for calorimetry at the International Linear Collider, the CALICE collaboration has constructed a highly granular analog hadron calorimeter with small scintillator cells, individually read out by silicon…
This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving, (b) data-efficient, and (c) high expressive power. We show…
Calibrating a multiclass predictor, that outputs a distribution over labels, is particularly challenging due to the exponential number of possible prediction values. In this work, we propose a new definition of calibration error that…
Quantum instruments describe outcome probability as well as state change induced by measurement of a quantum system. Incompatibility of two instruments, i. e. the impossibility to realize them simultaneously on a given quantum system,…
Measurement incompatibility has proved to be an important resource for quantum information processing. In this work, we present an operational approach that leverages classical operations on the inputs (pre-processing) and outputs…
The CALICE collaboration investigates diferent technology options for highly granular calorimeters for detectors at a future electron-positron collider. One of the devices constructed and tested by this collaboration is a 1m3 prototype of a…
Large language models (LLMs) are increasingly used in social science as scalable measurement tools for converting unstructured text into variables that can enter standard empirical designs. Measurement validity demands more than high…
Calibration is a key step in the signal processing pipeline of any radio astronomical instrument. The required sky, ionospheric and instrumental models for this step can suffer from various kinds of incompleteness. In this paper we analyze…
The proposed International Linear Collider (ILC) is well-suited for discovering physics beyond the Standard Model and for precisely unravelling the structure of the underlying physics. The physics return of the ILC can be maximized by the…
Dual-field interferometric observations with VLTI/GRAVITY sometimes require the use of a "binary calibrator", a binary star whose individual components remain unresolved by the interferometer, with a separation between 400 and 2000 mas for…
Atomic force microscope (AFM) users often calibrate the spring constants of cantilevers using functionality built into individual instruments. This is performed without reference to a global standard, which hinders robust comparison of…
The tool-use ability of Large Language Models (LLMs) has a profound impact on a wide range of industrial applications. However, LLMs' self-control and calibration capability in appropriately using tools remains understudied. The problem is…
Discovery of electrocatalytic materials for high-performance energy conversion and storage applications relies on the adequate characterization of their intrinsic activity, which is currently hindered by the dearth of a protocol for…
Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern language models (LMs). We study in-context learning (ICL), a…
The ILD detector is proposed for an electron-positron collider with collision centre-of-mass energies from 90~\GeV~to about 1~\TeV. It has been developed over the last 10 years by an international team of scientists with the goal to design…
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain. However, these works have primarily focused on classification accuracy,…
Recent human computer-interaction (HCI) studies using electromyography (EMG) and inertial measurement units (IMUs) for upper-limb gesture recognition have claimed that inertial measurements alone result in higher classification accuracy…