450 research outputs found
A Critical Quantity for Noise Attenuation in Feedback Systems
Feedback modules, which appear ubiquitously in biological regulations, are often subject to disturbances from the input, leading to fluctuations in the output. Thus, the question becomes how a feedback system can produce a faithful response with a noisy input. We employed multiple time scale analysis, Fluctuation Dissipation Theorem, linear stability, and numerical simulations to investigate a module with one positive feedback loop driven by an external stimulus, and we obtained a critical quantity in noise attenuation, termed as “signed activation time”. We then studied the signed activation time for a system of two positive feedback loops, a system of one positive feedback loop and one negative feedback loop, and six other existing biological models consisting of multiple components along with positive and negative feedback loops. An inverse relationship is found between the noise amplification rate and the signed activation time, defined as the difference between the deactivation and activation time scales of the noise-free system, normalized by the frequency of noises presented in the input. Thus, the combination of fast activation and slow deactivation provides the best noise attenuation, and it can be attained in a single positive feedback loop system. An additional positive feedback loop often leads to a marked decrease in activation time, decrease or slight increase of deactivation time and allows larger kinetic rate variations for slow deactivation and fast activation. On the other hand, a negative feedback loop may increase the activation and deactivation times. The negative relationship between the noise amplification rate and the signed activation time also holds for the six other biological models with multiple components and feedback loops. This principle may be applicable to other feedback systems
Making Python Code Idiomatic by Automatic Refactoring Non-Idiomatic Python Code with Pythonic Idioms
Compared to other programming languages (e.g., Java), Python has more idioms
to make Python code concise and efficient. Although pythonic idioms are well
accepted in the Python community, Python programmers are often faced with many
challenges in using them, for example, being unaware of certain pythonic idioms
or do not know how to use them properly. Based on an analysis of 7,638 Python
repositories on GitHub, we find that non-idiomatic Python code that can be
implemented with pythonic idioms occurs frequently and widely. Unfortunately,
there is no tool for automatically refactoring such non-idiomatic code into
idiomatic code. In this paper, we design and implement an automatic refactoring
tool to make Python code idiomatic. We identify nine pythonic idioms by
systematically contrasting the abstract syntax grammar of Python and Java. Then
we define the syntactic patterns for detecting non-idiomatic code for each
pythonic idiom. Finally, we devise atomic AST-rewriting operations and
refactoring steps to refactor non-idiomatic code into idiomatic code. We test
and review over 4,115 refactorings applied to 1,065 Python projects from
GitHub, and submit 90 pull requests for the 90 randomly sampled refactorings to
84 projects. These evaluations confirm the high-accuracy, practicality and
usefulness of our refactoring tool on real-world Python code. Our refactoring
tool can be accessed at 47.242.131.128:5000.Comment: 12 pages, accepted to ESEC/FSE'202
Effects of fence enclosure on vegetation community characteristics and productivity of a degraded temperate meadow steppe in Northern China
Species composition and biomass are two important indicators in assessing the effects of restoration measures of degraded grasslands. In this paper, we present a field study on the temporal changes in plant community characteristics, species diversity and biomass production in a degraded temperate meadow steppe in response to an enclosure measure in Hulunbuir in Northern China. Our results showed that the plant community responded positively to the fence enclosure in terms of vegetation coverage, height, above- and belowground biomass. A year-to-year increase in aboveground biomass was observed, and this increase plateaued at the ninth year of the enclosure. Our results also showed that the existing dominant and foundation species gained predominance against other species. The sum of the biomass of these two species was more than doubled after the ninth year of the enclosure. However, belowground biomass only briefly increased until the fifth year of the enclosure and then decreased until the end of the experimental period. Plant diversity, evenness, and richness indices showed similar trends to that of belowground biomass. Overall, we found that the degraded temperate meadow steppe responded significantly positively to the enclosure treatment, but an optimal condition was only reached after approximately 5-7 years of continuous protection, providing a solid use case for grassland conservation and management at regional scales
AKConv: Convolutional Kernel with Arbitrary Sampled Shapes and Arbitrary Number of Parameters
Neural networks based on convolutional operations have achieved remarkable
results in the field of deep learning, but there are two inherent flaws in
standard convolutional operations. On the one hand, the convolution operation
be confined to a local window and cannot capture information from other
locations, and its sampled shapes is fixed. On the other hand, the size of the
convolutional kernel is fixed to k k, which is a fixed square shape,
and the number of parameters tends to grow squarely with size. It is obvious
that the shape and size of targets are various in different datasets and at
different locations. Convolutional kernels with fixed sample shapes and squares
do not adapt well to changing targets. In response to the above questions, the
Alterable Kernel Convolution (AKConv) is explored in this work, which gives the
convolution kernel an arbitrary number of parameters and arbitrary sampled
shapes to provide richer options for the trade-off between network overhead and
performance. In AKConv, we define initial positions for convolutional kernels
of arbitrary size by means of a new coordinate generation algorithm. To adapt
to changes for targets, we introduce offsets to adjust the shape of the samples
at each position. Moreover, we explore the effect of the neural network by
using the AKConv with the same size and different initial sampled shapes.
AKConv completes the process of efficient feature extraction by irregular
convolutional operations and brings more exploration options for convolutional
sampling shapes. Object detection experiments on representative datasets
COCO2017, VOC 7+12 and VisDrone-DET2021 fully demonstrate the advantages of
AKConv. AKConv can be used as a plug-and-play convolutional operation to
replace convolutional operations to improve network performance. The code for
the relevant tasks can be found at https://github.com/CV-ZhangXin/AKConv.Comment: 10 pages, 5 figure
Photoacoustic tomography through a whole adult human skull with a photon recycler
Photoacoustic tomography (PAT) of the human brain is challenging due to the fact that the skull strongly absorbs and scatters light, and attenuates and distorts ultrasound as well. For the first time, we demonstrated the feasibility of PAT through a whole adult human skull. A photon recycler (PR) was built to increase light transmittance through the skull. Both a graphite target and a canine brain were imaged through the skull. Use of the PR was found to improve the photoacoustic signal-to-noise ratio by a factor of 2.4. In addition, subtraction of photoacoustic signals that arise from light absorption within the skull significantly improved the contrast of the target. Our results indicate that PAT can potentially be applied to in vivo human brain imaging
16-user OFDM-CDMA optical access network
We demonstrate a 16Ă—2.5 Gb/s (40 Gb/s aggregate) OFDM-CDMA PON for next-generation access applications. Four-channel error-free transmission over 25 km SMF shows 6 dB coding gain, with 0.1 dB dispersion and 0.9 dB crosstalk penalties
Formation mechanism of abnormally large grains in a polycrystalline nickel-based superalloy during heat treatment processing
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