561 research outputs found
On the Derivative Imbalance and Ambiguity of Functions
In 2007, Carlet and Ding introduced two parameters, denoted by and
, quantifying respectively the balancedness of general functions
between finite Abelian groups and the (global) balancedness of their
derivatives , (providing an
indicator of the nonlinearity of the functions). These authors studied the
properties and cryptographic significance of these two measures. They provided
for S-boxes inequalities relating the nonlinearity to ,
and obtained in particular an upper bound on the nonlinearity which unifies
Sidelnikov-Chabaud-Vaudenay's bound and the covering radius bound. At the
Workshop WCC 2009 and in its postproceedings in 2011, a further study of these
parameters was made; in particular, the first parameter was applied to the
functions where is affine, providing more nonlinearity parameters.
In 2010, motivated by the study of Costas arrays, two parameters called
ambiguity and deficiency were introduced by Panario \emph{et al.} for
permutations over finite Abelian groups to measure the injectivity and
surjectivity of the derivatives respectively. These authors also studied some
fundamental properties and cryptographic significance of these two measures.
Further studies followed without that the second pair of parameters be compared
to the first one.
In the present paper, we observe that ambiguity is the same parameter as
, up to additive and multiplicative constants (i.e. up to rescaling). We
make the necessary work of comparison and unification of the results on ,
respectively on ambiguity, which have been obtained in the five papers devoted
to these parameters. We generalize some known results to any Abelian groups and
we more importantly derive many new results on these parameters
A Recursive Construction of Permutation Polynomials over with Odd Characteristic from R\'{e}dei Functions
In this paper, we construct two classes of permutation polynomials over
with odd characteristic from rational R\'{e}dei functions. A
complete characterization of their compositional inverses is also given. These
permutation polynomials can be generated recursively. As a consequence, we can
generate recursively permutation polynomials with arbitrary number of terms.
More importantly, the conditions of these polynomials being permutations are
very easy to characterize. For wide applications in practice, several classes
of permutation binomials and trinomials are given. With the help of a computer,
we find that the number of permutation polynomials of these types is very
large
Domain Adaptive Semantic Segmentation by Optimal Transport
Scene segmentation is widely used in the field of autonomous driving for
environment perception, and semantic scene segmentation (3S) has received a
great deal of attention due to the richness of the semantic information it
contains. It aims to assign labels to pixels in an image, thus enabling
automatic image labeling. Current approaches are mainly based on convolutional
neural networks (CNN), but they rely on a large number of labels. Therefore,
how to use a small size of labeled data to achieve semantic segmentation
becomes more and more important. In this paper, we propose a domain adaptation
(DA) framework based on optimal transport (OT) and attention mechanism to
address this issue. Concretely, first we generate the output space via CNN due
to its superiority of feature representation. Second, we utilize OT to achieve
a more robust alignment of source and target domains in output space, where the
OT plan defines a well attention mechanism to improve the adaptation of the
model. In particular, with OT, the number of network parameters has been
reduced and the network has been better interpretable. Third, to better
describe the multi-scale property of features, we construct a multi-scale
segmentation network to perform domain adaptation. Finally, in order to verify
the performance of our proposed method, we conduct experimental comparison with
three benchmark and four SOTA methods on three scene datasets, and the mean
intersection-over-union (mIOU) has been significant improved, and visualization
results under multiple domain adaptation scenarios also show that our proposed
method has better performance than compared semantic segmentation methods
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction
Multi-modal magnetic resonance imaging (MRI) plays a crucial role in
comprehensive disease diagnosis in clinical medicine. However, acquiring
certain modalities, such as T2-weighted images (T2WIs), is time-consuming and
prone to be with motion artifacts. It negatively impacts subsequent multi-modal
image analysis. To address this issue, we propose an end-to-end deep learning
framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to
expedite T2WIs' acquisitions. While image pre-processing is capable of
mitigating misalignment, improper parameter selection leads to adverse
pre-processing effects, requiring iterative experimentation and adjustment. To
overcome this shortage, we employ Optimal Transport (OT) to synthesize T2WIs by
aligning T1WIs and performing cross-modal synthesis, effectively mitigating
spatial misalignment effects. Furthermore, we adopt an alternating iteration
framework between the reconstruction task and the cross-modal synthesis task to
optimize the final results. Then, we prove that the reconstructed T2WIs and the
synthetic T2WIs become closer on the T2 image manifold with iterations
increasing, and further illustrate that the improved reconstruction result
enhances the synthesis process, whereas the enhanced synthesis result improves
the reconstruction process. Finally, experimental results from FastMRI and
internal datasets confirm the effectiveness of our method, demonstrating
significant improvements in image reconstruction quality even at low sampling
rates
Traditional Chinese Mind and Body Exercises for Promoting Balance Ability of Old Adults: A Systematic Review and Meta-Analysis
The purpose of this study was to provide a quantitative evaluation of the effectiveness of traditional Chinese mind and body exercises in promoting balance ability for old adults. The eligible studies were extensively searched from electronic databases (Medline, CINAHL, SportDicus, and Web of Science) until 10 May 2016. Reference lists of relevant publications were screened for future hits. The trials used randomized controlled approaches to compare the effects of traditional Chinese mind and body exercise (TCMBE) on balance ability of old adults that were included. The synthesized results of Berg Balance Scale (BBS), Timed Up and Go Test (TUG), and static balance with 95% confidence intervals were counted under a random-effects model. Ten studies were selected based on the inclusion criteria, and a total of 1,798 participants were involved in this review. The results of the meta-analysis showed that TCMBE had no significant improvement on BBS and TUG, but the BBS and TUG could be obviously improved by prolonging the intervention time. In addition, the results showed that TCMBE could significantly improve the static balance compared to control group. In conclusion, old adults who practiced TCMBE with the time not less than 150 minutes per week for more than 15 weeks could promote the balance ability
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
Efficient and high-fidelity prior sampling and inversion for complex
geological media is still a largely unsolved challenge. Here, we use a deep
neural network of the variational autoencoder type to construct a parametric
low-dimensional base model parameterization of complex binary geological media.
For inversion purposes, it has the attractive feature that random draws from an
uncorrelated standard normal distribution yield model realizations with spatial
characteristics that are in agreement with the training set. In comparison with
the most commonly used parametric representations in probabilistic inversion,
we find that our dimensionality reduction (DR) approach outperforms principle
component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform
(DCT) DR techniques for unconditional geostatistical simulation of a
channelized prior model. For the considered examples, important compression
ratios (200 - 500) are achieved. Given that the construction of our
parameterization requires a training set of several tens of thousands of prior
model realizations, our DR approach is more suited for probabilistic (or
deterministic) inversion than for unconditional (or point-conditioned)
geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D
transient hydraulic tomography data are used to demonstrate the DR-based
inversion. For the 2D case study, the performance is superior compared to
current state-of-the-art multiple-point statistics inversion by sequential
geostatistical resampling (SGR). Inversion results for the 3D application are
also encouraging
CoBigICP: Robust and Precise Point Set Registration using Correntropy Metrics and Bidirectional Correspondence
In this paper, we propose a novel probabilistic variant of iterative closest
point (ICP) dubbed as CoBigICP. The method leverages both local geometrical
information and global noise characteristics. Locally, the 3D structure of both
target and source clouds are incorporated into the objective function through
bidirectional correspondence. Globally, error metric of correntropy is
introduced as noise model to resist outliers. Importantly, the close
resemblance between normal-distributions transform (NDT) and correntropy is
revealed. To ease the minimization step, an on-manifold parameterization of the
special Euclidean group is proposed. Extensive experiments validate that
CoBigICP outperforms several well-known and state-of-the-art methods.Comment: 6 pages, 4 figures. Accepted to IROS202
Numerical simulation of separation shock characteristics of a piston type explosive bolt
A piston type explosive bolt is modeled by using a hydrocodes AUTODYN. The influence of the charge amount on the separation shock is analyzed. The results show that the separation shock of the piston type explosive bolt mainly includes two aspects: the shock caused by explosive detonation and the impact of the piston at the end of stroke. As the charge amount increases, the collision speed of piston first increases and then decreases, and the separation shock first increases and then stabilizes
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