493 research outputs found
Non-fragile estimation for discrete-time T-S fuzzy systems with event-triggered protocol
summary:This paper investigates the non-fragile state estimation problem for a class of discrete-time T-S fuzzy systems with time-delays and multiple missing measurements under event-triggered mechanism. First of all, the plant is subject to the time-varying delays and the stochastic disturbances. Next, a random white sequence, the element of which obeys a general probabilistic distribution defined on , is utilized to formulate the occurrence of the missing measurements. Also, an event generator function is employed to regulate the transmission of data to save the precious energy. Then, a non-fragile state estimator is constructed to reflect the randomly occurring gain variations in the implementing process. By means of the Lyapunov-Krasovskii functional, the desired sufficient conditions are obtained such that the Takagi-Sugeno (T-S) fuzzy estimation error system is exponentially ultimately bounded in the mean square. And then the upper bound is minimized via the robust optimization technique and the estimator gain matrices can be calculated. Finally, a simulation example is utilized to demonstrate the effectiveness of the state estimation scheme proposed in this paper
Error Analysis of a Fully Discrete Projection Method for Magnetohydrodynamic System
In this paper, we develop and analyze a finite element projection method for magnetohydrodynamics equations in Lipschitz domain. A fully discrete scheme based on Euler semi-implicit method is proposed, in which continuous elements are used to approximate the Navier–Stokes equations and H(curl) conforming Nédélec edge elements are used to approximate the magnetic equation. One key point of the projection method is to be compatible with two different spaces for calculating velocity, which leads one to obtain the pressure by solving a Poisson equation. The results show that the proposed projection scheme meets a discrete energy stability. In addition, with the help of a proper regularity hypothesis for the exact solution, this paper provides a rigorous optimal error analysis of velocity, pressure and magnetic induction. Finally, several numerical examples are performed to demonstrate both accuracy and efficiency of our proposed scheme
Large-scale analysis of transcriptional cis-regulatory modules reveals both common features and distinct subclasses
Analysis of 280 experimentally-verified cis-regulatory modules from Drosophila reveal features both common to all and unique to distinct subclasses of modules
OpenAUC: Towards AUC-Oriented Open-Set Recognition
Traditional machine learning follows a close-set assumption that the training
and test set share the same label space. While in many practical scenarios, it
is inevitable that some test samples belong to unknown classes (open-set). To
fix this issue, Open-Set Recognition (OSR), whose goal is to make correct
predictions on both close-set samples and open-set samples, has attracted
rising attention. In this direction, the vast majority of literature focuses on
the pattern of open-set samples. However, how to evaluate model performance in
this challenging task is still unsolved. In this paper, a systematic analysis
reveals that most existing metrics are essentially inconsistent with the
aforementioned goal of OSR: (1) For metrics extended from close-set
classification, such as Open-set F-score, Youden's index, and Normalized
Accuracy, a poor open-set prediction can escape from a low performance score
with a superior close-set prediction. (2) Novelty detection AUC, which measures
the ranking performance between close-set and open-set samples, ignores the
close-set performance. To fix these issues, we propose a novel metric named
OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise
formulation that evaluates open-set performance and close-set performance in a
coupling manner. Further analysis shows that OpenAUC is free from the
aforementioned inconsistency properties. Finally, an end-to-end learning method
is proposed to minimize the OpenAUC risk, and the experimental results on
popular benchmark datasets speak to its effectiveness
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Collaborative Metric Learning (CML) has recently emerged as a popular method
in recommendation systems (RS), closing the gap between metric learning and
Collaborative Filtering. Following the convention of RS, existing methods
exploit unique user representation in their model design. This paper focuses on
a challenging scenario where a user has multiple categories of interests. Under
this setting, we argue that the unique user representation might induce
preference bias, especially when the item category distribution is imbalanced.
To address this issue, we propose a novel method called
\textit{Diversity-Promoting Collaborative Metric Learning} (DPCML), with the
hope of considering the commonly ignored minority interest of the user. The key
idea behind DPCML is to include a multiple set of representations for each user
in the system. Based on this embedding paradigm, user preference toward an item
is aggregated from different embeddings by taking the minimum item-user
distance among the user embedding set. Furthermore, we observe that the
diversity of the embeddings for the same user also plays an essential role in
the model. To this end, we propose a \textit{diversity control regularization}
term to accommodate the multi-vector representation strategy better.
Theoretically, we show that DPCML could generalize well to unseen test data by
tackling the challenge of the annoying operation that comes from the minimum
value. Experiments over a range of benchmark datasets speak to the efficacy of
DPCML
Modeling and analysis of the transmission dynamics of cystic echinococcosis: Effects of increasing the number of sheep
A transmission dynamics model with the logistic growth of cystic echinococcus in sheep was formulated and analyzed. The basic reproduction number was derived and the results showed that the global dynamical behaviors were determined by its value. The disease-free equilibrium is globally asymptotically stable when the value of the basic reproduction number is less than one; otherwise, there exists a unique endemic equilibrium and it is globally asymptotically stable. Sensitivity analysis and uncertainty analysis of the basic reproduction number were also performed to screen the important factors that influence the spread of cystic echinococcosis. Contour plots of the basic reproduction number versus these important factors are presented, too. The results showed that the higher the deworming rate of dogs, the lower the prevalence of echinococcosis in sheep and dogs. Similarly, the higher the slaughter rate of sheep, the lower the prevalence of echinococcosis in sheep and dogs. It also showed that the spread of echinococcosis has a close relationship with the maximum environmental capacity of sheep, and that they have a remarkable negative correlation. This reminds us that the risk of cystic echinococcosis may be underestimated if we ignore the increasing number of sheep in reality
Non-linear relationship between sleep duration and blood pressure in children with short stature
BackgroundEvidence regarding the relationship between sleep duration and blood pressure is controversial. Therefore, the aim of this study was to investigate the relationship between sleep duration and blood pressure in children with short stature.MethodsA total of 1,085 participants with short stature were enrolled from the Affiliated Hospital of Jining Medical University in China. The variables involved in this study included sleep duration, anthropometric indicators and biochemical parameters. Sleep duration was evaluated in a face-to-face interview.ResultsThe average age of the 1,085 selected participants was 10.2 ± 3.5 years old, and approximately 763 (70.32%) of them were male. The results of adjusted linear regression showed that sleep duration was negatively associated with systolic blood pressure z scores (SBP-Z) and diastolic blood pressure z scores (DBP-Z) after adjusting for confounders (β −0.07, 95% CI −0.13, −0.01 P = 0.038; β −0.05, 95% CI −0.10, −0.01 P = 0.035, respectively). A nonlinear relationship was detected between sleep duration and blood pressure, including SBP-Z, DBP-Z and mean arterial pressure z scores (MAP-Z). The inflection point of the nonlinear relationship between sleep duration and SBP-Z is 10 h, and the inflection point of DBP-Z and MAP-Z is 8 h.ConclusionThis study revealed a nonlinear relationship between sleep duration and blood pressure in children with short stature. The findings suggest that the optimal sleep duration in children with short stature was 8–10 h, and sleep durations either too short or too long were associated with increased blood pressure levels
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
It is usually infeasible to fit and train an entire large deep neural network
(DNN) model using a single edge device due to the limited resources. To
facilitate intelligent applications across edge devices, researchers have
proposed partitioning a large model into several sub-models, and deploying each
of them to a different edge device to collaboratively train a DNN model.
However, the communication overhead caused by the large amount of data
transmitted from one device to another during training, as well as the
sub-optimal partition point due to the inaccurate latency prediction of
computation at each edge device can significantly slow down training. In this
paper, we propose AccEPT, an acceleration scheme for accelerating the edge
collaborative pipeline-parallel training. In particular, we propose a
light-weight adaptive latency predictor to accurately estimate the computation
latency of each layer at different devices, which also adapts to unseen devices
through continuous learning. Therefore, the proposed latency predictor leads to
better model partitioning which balances the computation loads across
participating devices. Moreover, we propose a bit-level computation-efficient
data compression scheme to compress the data to be transmitted between devices
during training. Our numerical results demonstrate that our proposed
acceleration approach is able to significantly speed up edge pipeline parallel
training up to 3 times faster in the considered experimental settings
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