14,698 research outputs found
High-resolution transport-of-intensity quantitative phase microscopy with annular illumination
For quantitative phase imaging (QPI) based on transport-of-intensity equation
(TIE), partially coherent illumination provides speckle-free imaging,
compatibility with brightfield microscopy, and transverse resolution beyond
coherent diffraction limit. Unfortunately, in a conventional microscope with
circular illumination aperture, partial coherence tends to diminish the phase
contrast, exacerbating the inherent noise-to-resolution tradeoff in TIE
imaging, resulting in strong low-frequency artifacts and compromised imaging
resolution. Here, we demonstrate how these issues can be effectively addressed
by replacing the conventional circular illumination aperture with an annular
one. The matched annular illumination not only strongly boosts the phase
contrast for low spatial frequencies, but significantly improves the practical
imaging resolution to near the incoherent diffraction limit. By incorporating
high-numerical aperture (NA) illumination as well as high-NA objective, it is
shown, for the first time, that TIE phase imaging can achieve a transverse
resolution up to 208 nm, corresponding to an effective NA of 2.66. Time-lapse
imaging of in vitro Hela cells revealing cellular morphology and subcellular
dynamics during cells mitosis and apoptosis is exemplified. Given its
capability for high-resolution QPI as well as the compatibility with widely
available brightfield microscopy hardware, the proposed approach is expected to
be adopted by the wider biology and medicine community.Comment: This manuscript was originally submitted on 20 Feb. 201
Exploring Downvoting in Blockchain-based Online Social Media Platforms
In recent years, Blockchain-based Online Social Media (BOSM) platforms have
evolved fast due to the advancement of blockchain technology. BOSM can
effectively overcome the problems of traditional social media platforms, such
as a single point of trust and insufficient incentives for users, by combining
a decentralized governance structure and a cryptocurrency-based incentive
model, thereby attracting a large number of users and making it a crucial
component of Web3. BOSM allows users to downvote low-quality content and aims
to decrease the visibility of low-quality content by sorting and filtering it
through downvoting. However, this feature may be maliciously exploited by some
users to undermine the fairness of the incentive, reduce the quality of highly
visible content, and further reduce users' enthusiasm for content creation and
the attractiveness of the platform. In this paper, we study and analyze the
downvoting behavior using four years of data collected from Steemit, the
largest BOSM platform. We discovered that a significant number of bot accounts
were actively downvoting content. In addition, we discovered that roughly 9% of
the downvoting activity might be retaliatory. We did not detect any significant
instances of downvoting on content for a specific topic. We believe that the
findings in this paper will facilitate the future development of user behavior
analysis and incentive pattern design in BOSM and Web3
New Results about the Boomerang Uniformity of Permutation Polynomials
In EUROCRYPT 2018, Cid et al. \cite{BCT2018} introduced a new concept on the
cryptographic property of S-boxes: Boomerang Connectivity Table (BCT for short)
for evaluating the subtleties of boomerang-style attacks. Very recently, BCT
and the boomerang uniformity, the maximum value in BCT, were further studied by
Boura and Canteaut \cite{BC2018}. Aiming at providing new insights, we show
some new results about BCT and the boomerang uniformity of permutations in
terms of theory and experiment in this paper. Firstly, we present an equivalent
technique to compute BCT and the boomerang uniformity, which seems to be much
simpler than the original definition from \cite{BCT2018}. Secondly, thanks to
Carlet's idea \cite{Carlet2018}, we give a characterization of functions
from to itself with boomerang uniformity by
means of the Walsh transform. Thirdly, by our method, we consider boomerang
uniformities of some specific permutations, mainly the ones with low
differential uniformity. Finally, we obtain another class of -uniform BCT
permutation polynomials over , which is the first binomial.Comment: 25 page
Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models
Data augmentation has become a standard component of vision pre-trained
models to capture the invariance between augmented views. In practice,
augmentation techniques that mask regions of a sample with zero/mean values or
patches from other samples are commonly employed in pre-trained models with
self-/semi-/fully-supervised contrastive losses. However, the underlying
mechanism behind the effectiveness of these augmentation techniques remains
poorly explored. To investigate the problems, we conduct an empirical study to
quantify how data augmentation affects performance. Concretely, we apply 4
types of data augmentations termed with Random Erasing, CutOut, CutMix and
MixUp to a series of self-/semi-/fully- supervised pre-trained models. We
report their performance on vision tasks such as image classification, object
detection, instance segmentation, and semantic segmentation. We then explicitly
evaluate the invariance and diversity of the feature embedding. We observe
that: 1) Masking regions of the images decreases the invariance of the learned
feature embedding while providing a more considerable diversity. 2) Manual
annotations do not change the invariance or diversity of the learned feature
embedding. 3) The MixUp approach improves the diversity significantly, with
only a marginal decrease in terms of the invariance
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