1,668 research outputs found
A study of energy correction for the electron beam data in the BGO ECAL of the DAMPE
The DArk Matter Particle Explorer (DAMPE) is an orbital experiment aiming at
searching for dark matter indirectly by measuring the spectra of photons,
electrons and positrons originating from deep space. The BGO electromagnetic
calorimeter is one of the key sub-detectors of the DAMPE, which is designed for
high energy measurement with a large dynamic range from 5 GeV to 10 TeV. In
this paper, some methods for energy correction are discussed and tried, in
order to reconstruct the primary energy of the incident electrons. Different
methods are chosen for the appropriate energy ranges. The results of Geant4
simulation and beam test data (at CERN) are presented
The impact of the Internet on household consumption expenditure: an empirical study based on China Family Panel Studies data
This article empirically analyzes the impact of Internet usage on
household consumption expenditure based on the China Family
Panel Studies (CFPS) data for three periods 2014, 2016, and 2018.
The results show that Internet use significantly increases household
consumption. This boost persists after adding a series of
control variables, accounting for differences in time and region, or
changing the measurement of the main explanatory variables.
After introducing instrumental variables to overcome potential
endogeneity problems and further including the Internet use of
the financial decision maker’s spouse for a series of robustness
tests, the findings remain robust. The positive boost is even more
significant. Finally, heterogeneity analysis is conducted for different
consumption types, urban and rural areas, gender of financial
decision-makers, and use of other Internet tools
Detecting Textual Adversarial Examples through Randomized Substitution and Vote
A line of work has shown that natural text processing models are vulnerable
to adversarial examples. Correspondingly, various defense methods are proposed
to mitigate the threat of textual adversarial examples, eg, adversarial
training, input transformations, detection, etc. In this work, we treat the
optimization process for synonym substitution based textual adversarial attacks
as a specific sequence of word replacement, in which each word mutually
influences other words. We identify that we could destroy such mutual
interaction and eliminate the adversarial perturbation by randomly substituting
a word with its synonyms. Based on this observation, we propose a novel textual
adversarial example detection method, termed Randomized Substitution and Vote
(RS&V), which votes the prediction label by accumulating the logits of k
samples generated by randomly substituting the words in the input text with
synonyms. The proposed RS&V is generally applicable to any existing neural
networks without modification on the architecture or extra training, and it is
orthogonal to prior work on making the classification network itself more
robust. Empirical evaluations on three benchmark datasets demonstrate that our
RS&V could detect the textual adversarial examples more successfully than the
existing detection methods while maintaining the high classification accuracy
on benign samples.Comment: Accepted by UAI 2022, code is avaliable at
https://github.com/JHL-HUST/RS
Learning Robust Medical Image Segmentation from Multi-source Annotations
Collecting annotations from multiple independent sources could mitigate the
impact of potential noises and biases from a single source, which is a common
practice in medical image segmentation. Learning segmentation networks from
multi-source annotations remains a challenge due to the uncertainties brought
by the variance of annotations and the quality of images. In this paper, we
propose an Uncertainty-guided Multi-source Annotation Network (UMA-Net), which
guides the training process by uncertainty estimation at both the pixel and the
image levels. First, we developed the annotation uncertainty estimation module
(AUEM) to learn the pixel-wise uncertainty of each annotation, which then
guided the network to learn from reliable pixels by weighted segmentation loss.
Second, a quality assessment module (QAM) was proposed to assess the
image-level quality of the input samples based on the former assessed
annotation uncertainties. Importantly, we introduced an auxiliary predictor to
learn from the low-quality samples instead of discarding them, which ensured
the preservation of their representation knowledge in the backbone without
directly accumulating errors within the primary predictor. Extensive
experiments demonstrated the effectiveness and feasibility of our proposed
UMA-Net on various datasets, including 2D chest X-ray segmentation, fundus
image segmentation, and 3D breast DCE-MRI segmentation
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