613 research outputs found
TasselNet: Counting maize tassels in the wild via local counts regression network
Accurately counting maize tassels is important for monitoring the growth
status of maize plants. This tedious task, however, is still mainly done by
manual efforts. In the context of modern plant phenotyping, automating this
task is required to meet the need of large-scale analysis of genotype and
phenotype. In recent years, computer vision technologies have experienced a
significant breakthrough due to the emergence of large-scale datasets and
increased computational resources. Naturally image-based approaches have also
received much attention in plant-related studies. Yet a fact is that most
image-based systems for plant phenotyping are deployed under controlled
laboratory environment. When transferring the application scenario to
unconstrained in-field conditions, intrinsic and extrinsic variations in the
wild pose great challenges for accurate counting of maize tassels, which goes
beyond the ability of conventional image processing techniques. This calls for
further robust computer vision approaches to address in-field variations. This
paper studies the in-field counting problem of maize tassels. To our knowledge,
this is the first time that a plant-related counting problem is considered
using computer vision technologies under unconstrained field-based environment.Comment: 14 page
Sub-Ohmic spin-boson model with off-diagonal coupling: Ground state properties
We have carried out analytical and numerical studies of the spin-boson model
in the sub-ohmic regime with the influence of both the diagonal and
off-diagonal coupling accounted for via the Davydov D1 variational ansatz.
While a second-order phase transition is known to be exhibited by this model in
the presence of diagonal coupling only, we demonstrate the emergence of a
discontinuous first order phase transition upon incorporation of the
off-diagonal coupling. A plot of the ground state energy versus magnetization
highlights the discontinuous nature of the transition between the isotropic
(zero magnetization) state and nematic (finite magnetization) phases. We have
also calculated the entanglement entropy and a discontinuity found at a
critical coupling strength further supports the discontinuous crossover in the
spin-boson model in the presence of off-diagonal coupling. It is further
revealed via a canonical transformation approach that for the special case of
identical exponents for the spectral densities of the diagonal and the
off-diagonal coupling, there exists a continuous crossover from a single
localized phase to doubly degenerate localized phase with differing
magnetizations.Comment: 11 pages, 7 figure
Application of non-orthogonal multiple access in cooperative spectrum-sharing networks over Nakagami-m fading channels
This paper proposes a novel non-orthogonal multiple access (NOMA)-based cooperative transmission scheme for a spectrum-sharing cognitive radio network, whereby a secondary transmitter (ST) serves as a relay and helps transmit the primary and secondary messages simultaneously with employing NOMA signaling. This cooperation is particularly useful when the ST has good channel conditions to a primary receiver but lacks of the radio spectrum. To evaluate the performance of the proposed scheme, the outage probability and system throughput for the primary and secondary networks are derived in closed forms. Simulation results demonstrate the superior performance gains for both networks thanks to the use of the proposed NOMAbased cooperative transmission scheme. It is also revealed that NOMA outperforms conventional orthogonal multiple access and achieves better spectrum utilization
Design of Cooperative Non-Orthogonal Multicast Cognitive Multiple Access for 5G Systems:User Scheduling and Performance Analysis
Non-orthogonal multiple access (NOMA) is emerging as a promising, yet challenging, multiple access technology to improve spectrum utilization for the fifth generation (5G) wireless networks. In this paper, the application of NOMA to multicast cognitive radio networks (termed as MCR-NOMA) is investigated. A dynamic cooperative MCR-NOMA scheme is proposed, where the multicast secondary users serve as relays to improve the performance of both primary and secondary networks. Based on the available channel state information (CSI), three different secondary user scheduling strategies for the cooperative MCR-NOMA scheme are presented. To evaluate the system performance, we derive the closed-form expressions of the outage probability and diversity order for both networks. Furthermore, we introduce a new metric, referred to as mutual outage probability to characterize the cooperation benefit compared to non cooperative MCR-NOMA scheme. Simulation results demonstrate significant performance gains are obtained for both networks, thanks to the use of our proposed cooperative MCR-NOMA scheme. It is also demonstrated that higher spatial diversity order can be achieved by opportunistically utilizing the CSI available for the secondary user scheduling
Design of Cooperative Non-Orthogonal Multicast Cognitive Multiple Access for 5G Systems:User Scheduling and Performance Analysis
Non-orthogonal multiple access (NOMA) is emerging as a promising, yet challenging, multiple access technology to improve spectrum utilization for the fifth generation (5G) wireless networks. In this paper, the application of NOMA to multicast cognitive radio networks (termed as MCR-NOMA) is investigated. A dynamic cooperative MCR-NOMA scheme is proposed, where the multicast secondary users serve as relays to improve the performance of both primary and secondary networks. Based on the available channel state information (CSI), three different secondary user scheduling strategies for the cooperative MCR-NOMA scheme are presented. To evaluate the system performance, we derive the closed-form expressions of the outage probability and diversity order for both networks. Furthermore, we introduce a new metric, referred to as mutual outage probability to characterize the cooperation benefit compared to non cooperative MCR-NOMA scheme. Simulation results demonstrate significant performance gains are obtained for both networks, thanks to the use of our proposed cooperative MCR-NOMA scheme. It is also demonstrated that higher spatial diversity order can be achieved by opportunistically utilizing the CSI available for the secondary user scheduling
Analysis on binary responses with ordered covariates and missing data
We consider the situation of two ordered categorical variables and a binary outcome variable, where one or both of the categorical variables may have missing values. The goal is to estimate the probability of response of the outcome variable for each cell of the contingency table of categorical variables while incorporating the fact that the categorical variables are ordered. The probability of response is assumed to change monotonically as each of the categorical variables changes level. A probability model is used in which the response is binomial with parameters p ij for each cell ( i , j ) and the number of observations in each cell is multinomial. Estimation approaches that incorporate Gibbs sampling with order restrictions on p ij induced via a prior distribution, two-dimensional isotonic regression and multiple imputation to handle missing values are considered. The methods are compared in a simulation study. Using a fully Bayesian approach with a strong prior distribution to induce ordering can lead to large gains in efficiency, but can also induce bias. Utilizing isotonic regression can lead to modest gains in efficiency, while minimizing bias and guaranteeing that the order constraints are satisfied. A hybrid of isotonic regression and Gibbs sampling appears to work well across a variety of scenarios. The methods are applied to a pancreatic cancer case–control study with two biomarkers. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56130/1/2815_ftp.pd
The stability analysis of the dike slope in Bdg reservoir under the seepage of flood
AbstractThe research on the mechanics of flood infiltration induced dike collapse and its evaluating method is a complicated problem which is difficult to be solved and interested by academic field and engineering field. To solve the problem of dike slope stability under flood infiltration, the unstable seepage field of dike slope is simulated under the condition of flood discharge combining with the shear strength reduction finite element method. The paper studies the changes of unstable seepage field when flood .Combined with engineering slope example, numerical simulation is carried out and some significance conclusions are drawn through the calculation result of the example
Learning Second-Order Attentive Context for Efficient Correspondence Pruning
Correspondence pruning aims to search consistent correspondences (inliers)
from a set of putative correspondences. It is challenging because of the
disorganized spatial distribution of numerous outliers, especially when
putative correspondences are largely dominated by outliers. It's more
challenging to ensure effectiveness while maintaining efficiency. In this
paper, we propose an effective and efficient method for correspondence pruning.
Inspired by the success of attentive context in correspondence problems, we
first extend the attentive context to the first-order attentive context and
then introduce the idea of attention in attention (ANA) to model second-order
attentive context for correspondence pruning. Compared with first-order
attention that focuses on feature-consistent context, second-order attention
dedicates to attention weights itself and provides an additional source to
encode consistent context from the attention map. For efficiency, we derive two
approximate formulations for the naive implementation of second-order attention
to optimize the cubic complexity to linear complexity, such that second-order
attention can be used with negligible computational overheads. We further
implement our formulations in a second-order context layer and then incorporate
the layer in an ANA block. Extensive experiments demonstrate that our method is
effective and efficient in pruning outliers, especially in high-outlier-ratio
cases. Compared with the state-of-the-art correspondence pruning approach
LMCNet, our method runs 14 times faster while maintaining a competitive
accuracy.Comment: 9 pages, 8 figures; Accepted to AAAI 2023 (Oral
FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling
We consider the problem of task-agnostic feature upsampling in dense
prediction where an upsampling operator is required to facilitate both
region-sensitive tasks like semantic segmentation and detail-sensitive tasks
such as image matting. Existing upsampling operators often can work well in
either type of the tasks, but not both. In this work, we present FADE, a novel,
plug-and-play, and task-agnostic upsampling operator. FADE benefits from three
design choices: i) considering encoder and decoder features jointly in
upsampling kernel generation; ii) an efficient semi-shift convolutional
operator that enables granular control over how each feature point contributes
to upsampling kernels; iii) a decoder-dependent gating mechanism for enhanced
detail delineation. We first study the upsampling properties of FADE on toy
data and then evaluate it on large-scale semantic segmentation and image
matting. In particular, FADE reveals its effectiveness and task-agnostic
characteristic by consistently outperforming recent dynamic upsampling
operators in different tasks. It also generalizes well across convolutional and
transformer architectures with little computational overhead. Our work
additionally provides thoughtful insights on what makes for task-agnostic
upsampling. Code is available at: http://lnkiy.in/fade_inComment: Accepted to ECCV 2022. Code is available at http://lnkiy.in/fade_i
Learning to Upsample by Learning to Sample
We present DySample, an ultra-lightweight and effective dynamic upsampler.
While impressive performance gains have been witnessed from recent kernel-based
dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much
workload, mostly due to the time-consuming dynamic convolution and the
additional sub-network used to generate dynamic kernels. Further, the need for
high-res feature guidance of FADE and SAPA somehow limits their application
scenarios. To address these concerns, we bypass dynamic convolution and
formulate upsampling from the perspective of point sampling, which is more
resource-efficient and can be easily implemented with the standard built-in
function in PyTorch. We first showcase a naive design, and then demonstrate how
to strengthen its upsampling behavior step by step towards our new upsampler,
DySample. Compared with former kernel-based dynamic upsamplers, DySample
requires no customized CUDA package and has much fewer parameters, FLOPs, GPU
memory, and latency. Besides the light-weight characteristics, DySample
outperforms other upsamplers across five dense prediction tasks, including
semantic segmentation, object detection, instance segmentation, panoptic
segmentation, and monocular depth estimation. Code is available at
https://github.com/tiny-smart/dysample.Comment: Accepted by ICCV 202
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