304 research outputs found
Two problems related to the Smarandache function
The main purpose of this paper is to study the solvability of some equations involving the pseudo Smarandache function Z(n) and the Smarandache reciprocal function Sc(n), and propose some interesting conjectures
RESEARCH ON SMARANDACHE PROBLEMS IN NUMBER THEORY (VOL. II)
The research on Smarandache Problems plays a key role in the development of number theory. Therefore, many mathematicians show their interest in the Smarandache problems and they conduct much research on them. Under such circumstances, we published the book , Vol. I, in September, 2004. That book stimulated more Chinese mathematicians to pay attention to Smarandache conjectures, open and solved problems in number theory.
The First Northwest Number Theory Conference was held in Shangluo Teacher's
College, China, in March 2005. One of the sessions was dedicated to the Smarandache problems. In that session, several professors gave a talk on
Smarandache problems and many participants lectured on Smarandache problems both extensively and intensively.
This book includes 34 papers, most of which were written by participants of the
above mentioned conference. All these papers are original and have been refereed.
The themes of these papers range from the mean value or hybrid mean value of
Smarandache type functions, the mean value of some famous number theoretic
functions acting on the Smarandache sequences, to the convergence property of some infinite series involving the Smarandache type sequences
Machine-learned control-oriented flow estimation for multiactuator multi-sensor systems exemplified for the fluidic pinball
We propose the first machine-learned control-oriented flow estimation for
multiple-input multiple-output plants. Starting point is constant actuation
with open-loop actuation commands leading to a database with simultaneously
recorded actuation commands, sensor signals and flow fields. A key enabler is
an estimator input vector comprising sensor signals and actuation commands. The
mapping from the sensor signals and actuation commands to the flow fields is
realized in an analytically simple, data-centric and general nonlinear
approach. The analytically simple estimator generalizes Linear Stochastic
Estimation (LSE) for actuation commands. The data-centric approach yields flow
fields from estimator inputs by interpolating from the database -- similar to
Loiseau et al. (2018) for unforced flow. The interpolation is performed with k
Nearest Neighbors (kNN). The general global nonlinear mapping from inputs to
flow fields is obtained from a Deep Neural Network (DNN) via an iterative
training approach. The estimator comparison is performed for the fluidic
pinball plant, which is a multiple-input, multiple-output wake control
benchmark (Deng et al. 2020) featuring rich dynamics under steady controls. We
conclude that the machine learning methods clearly outperform the linear model.
The performance of kNN and DNN estimators are comparable for periodic dynamics.
Yet, DNN performs consistently better when the flow is chaotic. Moreover, a
thorough comparison regarding to the complexity, computational cost, and
prediction accuracy is presented to demonstrate the relative merits of each
estimator. The proposed method can be generalized for closed-loop flow control
plants.Comment: 34 pages, 27 figures, 4 table
Tri-Attention: Explicit Context-Aware Attention Mechanism for Natural Language Processing
In natural language processing (NLP), the context of a word or sentence plays
an essential role. Contextual information such as the semantic representation
of a passage or historical dialogue forms an essential part of a conversation
and a precise understanding of the present phrase or sentence. However, the
standard attention mechanisms typically generate weights using query and key
but ignore context, forming a Bi-Attention framework, despite their great
success in modeling sequence alignment. This Bi-Attention mechanism does not
explicitly model the interactions between the contexts, queries and keys of
target sequences, missing important contextual information and resulting in
poor attention performance. Accordingly, a novel and general triple-attention
(Tri-Attention) framework expands the standard Bi-Attention mechanism and
explicitly interacts query, key, and context by incorporating context as the
third dimension in calculating relevance scores. Four variants of Tri-Attention
are generated by expanding the two-dimensional vector-based additive,
dot-product, scaled dot-product, and bilinear operations in Bi-Attention to the
tensor operations for Tri-Attention. Extensive experiments on three NLP tasks
demonstrate that Tri-Attention outperforms about 30 state-of-the-art
non-attention, standard Bi-Attention, contextual Bi-Attention approaches and
pretrained neural language models1
Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR
In recent years, deep learning has been widely used in SAR ATR and achieved
excellent performance on the MSTAR dataset. However, due to constrained imaging
conditions, MSTAR has data biases such as background correlation, i.e.,
background clutter properties have a spurious correlation with target classes.
Deep learning can overfit clutter to reduce training errors. Therefore, the
degree of overfitting for clutter reflects the non-causality of deep learning
in SAR ATR. Existing methods only qualitatively analyze this phenomenon. In
this paper, we quantify the contributions of different regions to target
recognition based on the Shapley value. The Shapley value of clutter measures
the degree of overfitting. Moreover, we explain how data bias and model bias
contribute to non-causality. Concisely, data bias leads to comparable
signal-to-clutter ratios and clutter textures in training and test sets. And
various model structures have different degrees of overfitting for these
biases. The experimental results of various models under standard operating
conditions on the MSTAR dataset support our conclusions. Our code is available
at https://github.com/waterdisappear/Data-Bias-in-MSTAR
Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition
Vehicle recognition is a fundamental problem in SAR image interpretation.
However, robustly recognizing vehicle targets is a challenging task in SAR due
to the large intraclass variations and small interclass variations.
Additionally, the lack of large datasets further complicates the task. Inspired
by the analysis of target signature variations and deep learning
explainability, this paper proposes a novel domain alignment framework named
the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve
robustness under various operating conditions. Concisely, HDANet integrates
feature disentanglement and alignment into a unified framework with three
modules: domain data generation, multitask-assisted mask disentanglement, and
domain alignment of target features. The first module generates diverse data
for alignment, and three simple but effective data augmentation methods are
designed to simulate target signature variations. The second module
disentangles the target features from background clutter using the
multitask-assisted mask to prevent clutter from interfering with subsequent
alignment. The third module employs a contrastive loss for domain alignment to
extract robust target features from generated diverse data and disentangled
features. Lastly, the proposed method demonstrates impressive robustness across
nine operating conditions in the MSTAR dataset, and extensive qualitative and
quantitative analyses validate the effectiveness of our framework
Resveratrol attenuates ischemic brain damage in the delayed phase after stroke and induces messenger RNA and protein express for angiogenic factors
BackgroundIt has been reported recently that resveratrol preconditioning can protect the brain from ischemia–reperfusion injury. However, it was unclear whether resveratrol administration after stroke was beneficial to the delayed phases after focal cerebral ischemia injury. This study investigated the effects and possible protective mechanism of resveratrol on the delayed phase after focal cerebral ischemia injury in mice.MethodsMice were randomly assigned to five groups according to the time of administration of resveratrol. Control group mice received a corresponding volume of saline solution (0.9% NaCl) containing 20% hydroxypropyl h-cyclodextrin by gavage and were exposed to middle cerebral artery (MCA) occlusion and reperfusion injury. The treatment groups received resveratrol (50 mg/kg/d, gavage) until day 7. Ischemia group mice received their first dose 5 minutes before MCA ischemia, reperfusion group mice received their first dose 5 minutes before MCA reperfusion, first-day, group mice received their first dose 24 hours after MCA reperfusion, and third-day group mice received their first dose at 72 hours after MCA reperfusion. Brain injury was evaluated by triphenyltetrazolium chloride staining and neurologic examination 7 days after reperfusion. The microvascular cell number was examined with immunohistochemistry staining. Effect of resveratrol on matrix metalloproteinase-2 (MMP-2) and vascular endothelial growth factor (VEGF) gene expression was investigated with reverse transcriptase-polymerase chain reaction and Western blot.ResultsThe mean neurologic scores and infarct volumes of the ischemia and reperfusion groups were lower than that of the control group at 7 days after MCA reperfusion (P < .05). Immunohistochemistry staining showed significantly less reduction in the number of microvessels in the cortical area of mice of the ischemia and reperfusion groups compared with controls. The ischemic hemispheres of the ischemia and reperfusion groups showed significantly (P < .05) elevated levels of protein of MMP-2 and VEGF.ConclusionsResveratrol administration by gavage provided an important neuroprotective effect on focal cerebral ischemic injury in the delayed phase. The elevated MMP-2 and VEGF levels might be important in the neuroprotective effect of resveratrol administration by inducing angiogenesis.Clinical RelevanceStrokes can induce infarction size or neurologic disability and cause brain injury in millions of people world wide each year. However, there is no approved therapy currently, and so it is necessary to develop new treatments in the field of primary and secondary stroke to improve the prognosis. This study identified the benefits of early administration of resveratrol by gavage in the delayed phases after focal cerebral ischemic injury and further supports the possible use of resveratrol as a therapeutic agent to ameliorate ischemic infarction. Resveratrol may thus be considered as a potential candidate in the armamentarium of drugs for the early treatment in patients who sustain a stroke
Automatic Animation of Hair Blowing in Still Portrait Photos
We propose a novel approach to animate human hair in a still portrait photo.
Existing work has largely studied the animation of fluid elements such as water
and fire. However, hair animation for a real image remains underexplored, which
is a challenging problem, due to the high complexity of hair structure and
dynamics. Considering the complexity of hair structure, we innovatively treat
hair wisp extraction as an instance segmentation problem, where a hair wisp is
referred to as an instance. With advanced instance segmentation networks, our
method extracts meaningful and natural hair wisps. Furthermore, we propose a
wisp-aware animation module that animates hair wisps with pleasing motions
without noticeable artifacts. The extensive experiments show the superiority of
our method. Our method provides the most pleasing and compelling viewing
experience in the qualitative experiments and outperforms state-of-the-art
still-image animation methods by a large margin in the quantitative evaluation.
Project url: \url{https://nevergiveu.github.io/AutomaticHairBlowing/}Comment: Accepted to ICCV 202
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