604 research outputs found
Inter-tier Interference Suppression in Heterogeneous Cloud Radio Access Networks
Incorporating cloud computing into heterogeneous networks, the heterogeneous
cloud radio access network (H-CRAN) has been proposed as a promising paradigm
to enhance both spectral and energy efficiencies. Developing interference
suppression strategies is critical for suppressing the inter-tier interference
between remote radio heads (RRHs) and a macro base station (MBS) in H-CRANs. In
this paper, inter-tier interference suppression techniques are considered in
the contexts of collaborative processing and cooperative radio resource
allocation (CRRA). In particular, interference collaboration (IC) and
beamforming (BF) are proposed to suppress the inter-tier interference, and
their corresponding performance is evaluated. Closed-form expressions for the
overall outage probabilities, system capacities, and average bit error rates
under these two schemes are derived. Furthermore, IC and BF based CRRA
optimization models are presented to maximize the RRH-accessed users' sum rates
via power allocation, which is solved with convex optimization. Simulation
results demonstrate that the derived expressions for these performance metrics
for IC and BF are accurate; and the relative performance between IC and BF
schemes depends on system parameters, such as the number of antennas at the
MBS, the number of RRHs, and the target signal-to-interference-plus-noise ratio
threshold. Furthermore, it is seen that the sum rates of IC and BF schemes
increase almost linearly with the transmit power threshold under the proposed
CRRA optimization solution
A Penalized Multi-trait Mixed Model for Association Mapping in Pedigree-based GWAS
In genome-wide association studies (GWAS), penalization is an important
approach for identifying genetic markers associated with trait while mixed
model is successful in accounting for a complicated dependence structure among
samples. Therefore, penalized linear mixed model is a tool that combines the
advantages of penalization approach and linear mixed model. In this study, a
GWAS with multiple highly correlated traits is analyzed. For GWAS with multiple
quantitative traits that are highly correlated, the analysis using traits
marginally inevitably lose some essential information among multiple traits. We
propose a penalized-MTMM, a penalized multivariate linear mixed model that
allows both the within-trait and between-trait variance components
simultaneously for multiple traits. The proposed penalized-MTMM estimates
variance components using an AI-REML method and conducts variable selection and
point estimation simultaneously using group MCP and sparse group MCP. Best
linear unbiased predictor (BLUP) is used to find predictive values and the
Pearson's correlations between predictive values and their corresponding
observations are used to evaluate prediction performance. Both prediction and
selection performance of the proposed approach and its comparison with the
uni-trait penalized-LMM are evaluated through simulation studies. We apply the
proposed approach to a GWAS data from Genetic Analysis Workshop (GAW) 18
Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
Recently, fine-tuning pre-trained code models such as CodeBERT on downstream
tasks has achieved great success in many software testing and analysis tasks.
While effective and prevalent, fine-tuning the pre-trained parameters incurs a
large computational cost. In this paper, we conduct an extensive experimental
study to explore what happens to layer-wise pre-trained representations and
their encoded code knowledge during fine-tuning. We then propose efficient
alternatives to fine-tune the large pre-trained code model based on the above
findings. Our experimental study shows that (1) lexical, syntactic and
structural properties of source code are encoded in the lower, intermediate,
and higher layers, respectively, while the semantic property spans across the
entire model. (2) The process of fine-tuning preserves most of the code
properties. Specifically, the basic code properties captured by lower and
intermediate layers are still preserved during fine-tuning. Furthermore, we
find that only the representations of the top two layers change most during
fine-tuning for various downstream tasks. (3) Based on the above findings, we
propose Telly to efficiently fine-tune pre-trained code models via layer
freezing. The extensive experimental results on five various downstream tasks
demonstrate that training parameters and the corresponding time cost are
greatly reduced, while performances are similar or better. Replication package
including source code, datasets, and online Appendix is available at:
\url{https://github.com/DeepSoftwareAnalytics/Telly}.Comment: Accepted by ISSTA 2023 (The 32nd ACM SIGSOFT International Symposium
on Software Testing and Analysis
SHA-SCP: A UI Element Spatial Hierarchy Aware Smartphone User Click Behavior Prediction Method
Predicting user click behavior and making relevant recommendations based on
the user's historical click behavior are critical to simplifying operations and
improving user experience. Modeling UI elements is essential to user click
behavior prediction, while the complexity and variety of the UI make it
difficult to adequately capture the information of different scales. In
addition, the lack of relevant datasets also presents difficulties for such
studies. In response to these challenges, we construct a fine-grained
smartphone usage behavior dataset containing 3,664,325 clicks of 100 users and
propose a UI element spatial hierarchy aware smartphone user click behavior
prediction method (SHA-SCP). SHA-SCP builds element groups by clustering the
elements according to their spatial positions and uses attention mechanisms to
perceive the UI at the element level and the element group level to fully
capture the information of different scales. Experiments are conducted on the
fine-grained smartphone usage behavior dataset, and the results show that our
method outperforms the best baseline by an average of 10.52%, 11.34%, and
10.42% in Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, respectively
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