165 research outputs found
Association between transforming growth factor β1 polymorphisms and atrial fibrillation in essential hypertensive subjects
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets
We investigate a data quality-aware dynamic client selection problem for
multiple federated learning (FL) services in a wireless network, where each
client offers dynamic datasets for the simultaneous training of multiple FL
services, and each FL service demander has to pay for the clients under
constrained monetary budgets. The problem is formalized as a non-cooperative
Markov game over the training rounds. A multi-agent hybrid deep reinforcement
learning-based algorithm is proposed to optimize the joint client selection and
payment actions, while avoiding action conflicts. Simulation results indicate
that our proposed algorithm can significantly improve training performance.Comment: 6 pages,8 figure
Improved Feature Distillation via Projector Ensemble
In knowledge distillation, previous feature distillation methods mainly focus
on the design of loss functions and the selection of the distilled layers,
while the effect of the feature projector between the student and the teacher
remains under-explored. In this paper, we first discuss a plausible mechanism
of the projector with empirical evidence and then propose a new feature
distillation method based on a projector ensemble for further performance
improvement. We observe that the student network benefits from a projector even
if the feature dimensions of the student and the teacher are the same. Training
a student backbone without a projector can be considered as a multi-task
learning process, namely achieving discriminative feature extraction for
classification and feature matching between the student and the teacher for
distillation at the same time. We hypothesize and empirically verify that
without a projector, the student network tends to overfit the teacher's feature
distributions despite having different architecture and weights initialization.
This leads to degradation on the quality of the student's deep features that
are eventually used in classification. Adding a projector, on the other hand,
disentangles the two learning tasks and helps the student network to focus
better on the main feature extraction task while still being able to utilize
teacher features as a guidance through the projector. Motivated by the positive
effect of the projector in feature distillation, we propose an ensemble of
projectors to further improve the quality of student features. Experimental
results on different datasets with a series of teacher-student pairs illustrate
the effectiveness of the proposed method
Understanding the Effects of Projectors in Knowledge Distillation
Conventionally, during the knowledge distillation process (e.g. feature
distillation), an additional projector is often required to perform feature
transformation due to the dimension mismatch between the teacher and the
student networks. Interestingly, we discovered that even if the student and the
teacher have the same feature dimensions, adding a projector still helps to
improve the distillation performance. In addition, projectors even improve
logit distillation if we add them to the architecture too. Inspired by these
surprising findings and the general lack of understanding of the projectors in
the knowledge distillation process from existing literature, this paper
investigates the implicit role that projectors play but so far have been
overlooked. Our empirical study shows that the student with a projector (1)
obtains a better trade-off between the training accuracy and the testing
accuracy compared to the student without a projector when it has the same
feature dimensions as the teacher, (2) better preserves its similarity to the
teacher beyond shallow and numeric resemblance, from the view of Centered
Kernel Alignment (CKA), and (3) avoids being over-confident as the teacher does
at the testing phase. Motivated by the positive effects of projectors, we
propose a projector ensemble-based feature distillation method to further
improve distillation performance. Despite the simplicity of the proposed
strategy, empirical results from the evaluation of classification tasks on
benchmark datasets demonstrate the superior classification performance of our
method on a broad range of teacher-student pairs and verify from the aspects of
CKA and model calibration that the student's features are of improved quality
with the projector ensemble design.Comment: arXiv admin note: text overlap with arXiv:2210.1527
National Farmers Union Property and Casualty Company v. Wesern Casualty and Surety Company : Brief of Respondent
Appeal from a Judgment of the District Court of Salt Lake County Honorable Dean E. Conder, Judg
SDN-controlled and Orchestrated OPSquare DCN Enabling Automatic Network Slicing with Differentiated QoS Provisioning
In this work, we propose and experimentally assess the automatic and flexible
NSs configurations of optical OPSquare DCN controlled and orchestrated by an
extended SDN control plane for multi-tenant applications with differentiated
QoS provisioning. Optical Flow Control (OFC) protocol has been developed to
prevent packet losses at switch sides caused by packet contentions.Based on the
collected resource topology of data plane, the optical network slices can be
dynamically provisioned and automatically reconfigured by the SDN control
plane. Meanwhile, experimental results validate that the priority assignment of
application flows supplies dynamic QoS performance to various slices running
applications with specific requirements in terms of packet loss and
transmission latency. In addition, the capability of exposing traffic
statistics information of data plane to SDN control plane enables the
implementation of load balancing algorithms further improving the network
performance with high QoS. No packet loss and less than 4.8 us server-to-server
latency can be guaranteed for the sliced network with highest priority at a
load of 0.5
Nitrogen addition strengthens the stabilizing effect of biodiversity on productivity by increasing plant trait diversity and species asynchrony in the artificial grassland communities
BACKGROUND AND AIMS: Nitrogen (N) enrichment usually weakens the stabilizing effect of biodiversity on productivity. However, previous studies focused on plant species richness and thus largely ignored the potential contributions of plant functional traits to stability, even though evidence is increasing that functional traits are stronger predictors than species richness of ecosystem functions. METHODS: We conducted a common garden experiment manipulating plant species richness and N addition levels to quantify effects of N addition on relations between species richness and functional trait identity and diversity underpinning the 'fast-slow' economics spectrum and community stability. RESULTS: Nitrogen addition had a minor effect on community stability but increased the positive effects of species richness on community stability. Increasing community stability was found in the species-rich communities dominated by fast species due to substantially increasing temporal mean productivity relative to its standard deviation. Furthermore, enhancement in 'fast-slow' functional diversity in species-rich communities dominated by fast species under N addition increased species asynchrony, resulting in a robust biodiversity-stability relationship under N addition the artificial grassland communities. CONCLUSION: The findings demonstrate mechanistic links between plant species richness, 'fast-slow' functional traits, and community stability under N addition, suggesting that dynamics of biodiversity-stability relations under global changes are the results of species-specific responses of 'fast-slow' traits on the plant economics spectrum
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