324 research outputs found
A Bayesian Collocation Integral Method for Parameter Estimation in Ordinary Differential Equations
Inferring the parameters of ordinary differential equations (ODEs) from noisy
observations is an important problem in many scientific fields. Currently, most
parameter estimation methods that bypass numerical integration tend to rely on
basis functions or Gaussian processes to approximate the ODE solution and its
derivatives. Due to the sensitivity of the ODE solution to its derivatives,
these methods can be hindered by estimation error, especially when only sparse
time-course observations are available. We present a Bayesian collocation
framework that operates on the integrated form of the ODEs and also avoids the
expensive use of numerical solvers. Our methodology has the capability to
handle general nonlinear ODE systems. We demonstrate the accuracy of the
proposed method through a simulation study, where the estimated parameters and
recovered system trajectories are compared with other recent methods. A real
data example is also provided
Determine OWA operator weights using kernel density estimation
Some subjective methods should divide input values into local
clusters before determining the ordered weighted averaging
(OWA) operator weights based on the data distribution characteristics
of input values. However, the process of clustering input values
is complex. In this paper, a novel probability density based
OWA (PDOWA) operator is put forward based on the data distribution
characteristics of input values. To capture the local cluster
structures of input values, the kernel density estimation (KDE) is
used to estimate the probability density function (PDF), which fits
to the input values. The derived PDF contains the density information
of input values, which reflects the importance of input
values. Therefore, the input values with high probability densities
(PDs) should be assigned with large weights, while the ones with
low PDs should be assigned with small weights. Afterwards, the
desirable properties of the proposed PDOWA operator are investigated.
Finally, the proposed PDOWA operator is applied to handle
the multicriteria decision making problem concerning the evaluation
of smart phones and it is compared with some existing
OWA operators. The comparative analysis shows that the proposed
PDOWA operator is simpler and more efficient than the
existing OWA operator
Confucius Queue Management: Be Fair But Not Too Fast
When many users and unique applications share a congested edge link (e.g., a
home network), everyone wants their own application to continue to perform well
despite contention over network resources. Traditionally, network engineers
have focused on fairness as the key objective to ensure that competing
applications are equitably and led by the switch, and hence have deployed fair
queueing mechanisms. However, for many network workloads today, strict fairness
is directly at odds with equitable application performance. Real-time streaming
applications, such as videoconferencing, suffer the most when network
performance is volatile (with delay spikes or sudden and dramatic drops in
throughput). Unfortunately, "fair" queueing mechanisms lead to extremely
volatile network behavior in the presence of bursty and multi-flow applications
such as Web traffic. When a sudden burst of new data arrives, fair queueing
algorithms rapidly shift resources away from incumbent flows, leading to severe
stalls in real-time applications. In this paper, we present Confucius, the
first practical queue management scheme to effectively balance fairness against
volatility, providing performance outcomes that benefit all applications
sharing the contended link. Confucius outperforms realistic queueing schemes by
protecting the real-time streaming flows from stalls in competing with more
than 95% of websites. Importantly, Confucius does not assume the collaboration
of end-hosts, nor does it require manual parameter tuning to achieve good
performance
Aggregation Weighting of Federated Learning via Generalization Bound Estimation
Federated Learning (FL) typically aggregates client model parameters using a
weighting approach determined by sample proportions. However, this naive
weighting method may lead to unfairness and degradation in model performance
due to statistical heterogeneity and the inclusion of noisy data among clients.
Theoretically, distributional robustness analysis has shown that the
generalization performance of a learning model with respect to any shifted
distribution is bounded. This motivates us to reconsider the weighting approach
in federated learning. In this paper, we replace the aforementioned weighting
method with a new strategy that considers the generalization bounds of each
local model. Specifically, we estimate the upper and lower bounds of the
second-order origin moment of the shifted distribution for the current local
model, and then use these bounds disagreements as the aggregation proportions
for weightings in each communication round. Experiments demonstrate that the
proposed weighting strategy significantly improves the performance of several
representative FL algorithms on benchmark datasets
Improving Multicast Stability in Mobile Multicast Scheme using Motion Prediction
Abstract. Stability is an important issue in multicast, especially in mobile environment where joining and leaving behaviors occur much more frequently. In this paper, we propose a scheme to improve the multicast stability by the use of motion prediction. The mobile node (MN) predicts the staying time before entering the new network, if the time is long enough, it will ask the new network to join the multicast tree as usual. Otherwise, the new network should create a tunnel to the multicast agent of MN to receive multicast packets. Considering that networks usually have different power range, the staying time is not predicted directly, and the Average Staying Time is used instead. The prediction algorithm is effective but practical which requires little calculation time and memory size. The simulation results show that the proposed scheme can improve the stability of multicast tree remarkably while bring much smaller cost
Water refilling along vessels at initial stage of willow cuttage revealed by move contrast CT
Cuttage is a widely used technique for plant propagation, whose success relies on the refilling for water transport recovery. However, requirements for refilling characterization studies, including large penetration depth, fast temporal resolution and high spatial resolution, cannot be reached simultaneously via conventional imaging techniques. So far, the dynamic process of water refilling along the vessels at the initial stage of cuttage, as well as its characteristics, remains unclear. Hereby, we developed a move contrast X-ray microtomography method which achieves 3D dynamic non-destructive imaging of water refilling at the initial stage of willow branch cuttage, without the aid of any contrast agent. Experimental results indicate three primary refilling modalities in vessels: 1) the osmosis type, mainly manifested by the osmosis of tissue through the vessel wall into the cavity; 2) the linear type, revealed as the tissue permeates to a certain extent where the liquid column in the vessels is completely formed; and 3) an osmosis-linear mixed type refilling as an intermediate state. Further analysis also exhibits a “temporal-spatial relay” mode of refilling between adjacent vessels. Since the vessel length is quite limited, the cavitation and the relay refilling mode of vessels can be an important way to achieve long-distance water transport
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design
Robust network design, which aims to guarantee network availability under
various failure scenarios while optimizing performance/cost objectives, has
received significant attention. Existing approaches often rely on model-based
mixed-integer optimization that is hard to scale or employ deep learning to
solve specific engineering problems yet with limited generalizability. In this
paper, we show that failure evaluation provides a common kernel to improve the
tractability and scalability of existing solutions. By providing a neural
network function approximation of this common kernel using graph attention
networks, we develop a unified learning-based framework, FERN, for scalable
Failure Evaluation and Robust Network design. FERN represents rich problem
inputs as a graph and captures both local and global views by attentively
performing feature extraction from the graph. It enables a broad range of
robust network design problems, including robust network validation, network
upgrade optimization, and fault-tolerant traffic engineering that are discussed
in this paper, to be recasted with respect to the common kernel and thus
computed efficiently using neural networks and over a small set of critical
failure scenarios. Extensive experiments on real-world network topologies show
that FERN can efficiently and accurately identify key failure scenarios for
both OSPF and optimal routing scheme, and generalizes well to different
topologies and input traffic patterns. It can speed up multiple robust network
design problems by more than 80x, 200x, 10x, respectively with negligible
performance gap
- …