相关论文: DECal, a Python tool for the efficiency calculatio…

We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and unified framework based on PyTorch that allows users to…

We show that, during inference with Convolutional Neural Networks (CNNs), more than 2x to $8x ineffectual work can be exposed if instead of targeting those weights and activations that are zero, we target different combinations of value…

PyEOC is an open-source Python code for determining electro-optic (EO) coefficients of thin-film materials from the static and dynamic reflectivity measurements. It uses the experimental results, the transfer-matrix method and implements a…

An open source two-dimensional (2D) thermal finite element (FE) model of the Directed Energy Deposition (DED) process is developed using the Python-based FEniCS framework. The model incrementally deposits material ahead of the laser focus…

The DArk Matter Particle Explorer (DAMPE) is a space experiment designed to search for dark matter indirectly by measuring the spectra of photons, electrons, and positrons up to 10 TeV. The BGO electromagnetic calorimeter (ECAL) is its main…

Computationally expensive temperature and power grid analyses are required during the design cycle to guide IC design. This paper employs encoder-decoder based generative (EDGe) networks to map these analyses to fast and accurate…

Two-dimensional solid state gaseous detector for thermal and cold neutrons is created. The detector has active area of 128 x 128 $mm^2$, $^{10}B$ neutron converter, and gas chamber with thin windows. The resistive charge-division readout is…

The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory. IDTxl provides functionality to…

The CALDER project aims at developing cryogenic light detectors with high sensitivity to UV and visible light, to be used for particle tagging in massive bolometers. Indeed the sensitivity of CUORE can be increased by a factor of 3, thanks…

A series of far-detector programs have been proposed for operation at various interaction points of the Large Hadron Collider during the upcoming runs. Investigating the potential and complementarity of these experiments for new-physics…

Excellent jet energy measurement is important at the International Linear Collider (ILC) because most of interesting physics processes decay into multi-jet final states. We employ a particle flow method to reconstruct particles, hence…

Cryogenic solid state detectors are widely used in dark matter and neutrino experiments, and require a sensible raw data analysis. For this purpose, we present Cait, an open source Python package with all essential methods for the analysis…

A new design of a detector plane of sub-millimetre thickness for an electromagnetic sampling calorimeter is presented. It is intended to be used in the luminometers LumiCal and BeamCal in future linear $e^+e^-$ collider experiments. The…

There has been an increasing interest in the monitoring of nuclear fuel for power reactors by detecting the anti-neutrinos produced during operation. Small liquid scintillator detectors have already demonstrated sensitivity to operational…

The next generation of bolometric experiments searching for rave events, in particular for the neutrino-less double beta decay, needs fast, high-sensitivity and easy-to-scale cryogenic light detectors. The CALDER project (2014-2020)…

This paper presents the design and optimization of a LYSO crystal electromagnetic calorimeter (ECAL) for the DarkSHINE experiment, which aims to search for dark photons as potential mediators of dark forces. The ECAL design was evaluated…

Thermal detectors are a powerful instrument for the search of rare particle physics events. Inorganic crystals are classically used as thermal detectors held in supporting frames made of copper. In this work a novel approach to the…

We present MXtalTools, a flexible Python package for the data-driven modelling of molecular crystals, facilitating machine learning studies of the molecular solid state. MXtalTools comprises several classes of utilities: (1) synthesis,…

Multitask learning (MTL) has become prominent for its ability to predict multiple tasks jointly, achieving better per-task performance with fewer parameters than single-task learning. Recently, decoder-focused architectures have…

A prototype Scintillator-Tungsten electromagnetic calorimeter (ScECAL) for the ILC detector was tested in 2008 at the Fermilab test beam. Data were collected with electron, pion and muon beams in the energy range 1 to 32GeV combined with…