477 research outputs found
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
Merging mobile edge computing (MEC) functionality with the dense deployment
of base stations (BSs) provides enormous benefits such as a real proximity, low
latency access to computing resources. However, the envisioned integration
creates many new challenges, among which mobility management (MM) is a critical
one. Simply applying existing radio access oriented MM schemes leads to poor
performance mainly due to the co-provisioning of radio access and computing
services of the MEC-enabled BSs. In this paper, we develop a novel user-centric
energy-aware mobility management (EMM) scheme, in order to optimize the delay
due to both radio access and computation, under the long-term energy
consumption constraint of the user. Based on Lyapunov optimization and
multi-armed bandit theories, EMM works in an online fashion without future
system state information, and effectively handles the imperfect system state
information. Theoretical analysis explicitly takes radio handover and
computation migration cost into consideration and proves a bounded deviation on
both the delay performance and energy consumption compared to the oracle
solution with exact and complete future system information. The proposed
algorithm also effectively handles the scenario in which candidate BSs randomly
switch on/off during the offloading process of a task. Simulations show that
the proposed algorithms can achieve close-to-optimal delay performance while
satisfying the user energy consumption constraint.Comment: 14 pages, 6 figures, an extended version of the paper submitted to
IEEE JSA
An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion
Medical images play an important role in clinical applications. Multimodal
medical images could provide rich information about patients for physicians to
diagnose. The image fusion technique is able to synthesize complementary
information from multimodal images into a single image. This technique will
prevent radiologists switch back and forth between different images and save
lots of time in the diagnostic process. In this paper, we introduce a novel
Dilated Residual Attention Network for the medical image fusion task. Our
network is capable to extract multi-scale deep semantic features. Furthermore,
we propose a novel fixed fusion strategy termed Softmax-based weighted strategy
based on the Softmax weights and matrix nuclear norm. Extensive experiments
show our proposed network and fusion strategy exceed the state-of-the-art
performance compared with reference image fusion methods on four commonly used
fusion metrics.Comment: 8 pages, 8 figures, 3 table
Twitter Stance Detection with Textual, Sentiment, and Target-specific Models
Today more and more users express their opinions and stances on social media platforms such as Twitter. In this paper, I proposed different approaches to automatically detect the stance of a single tweet. I investigated whether including additional sentiment polarity information and the target information would be beneficial for the stance detection task. Moreover, I also researched whether target-specific features could be generalized to other datasets with different targets for the stance detection task.Master of Science in Information Scienc
Building Program Vector Representations for Deep Learning
Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep architectures cannot be trained effectively with pure back
propagation. In this pioneering paper, we propose the "coding criterion" to
build program vector representations, which are the premise of deep learning
for program analysis. Our representation learning approach directly makes deep
learning a reality in this new field. We evaluate the learned vector
representations both qualitatively and quantitatively. We conclude, based on
the experiments, the coding criterion is successful in building program
representations. To evaluate whether deep learning is beneficial for program
analysis, we feed the representations to deep neural networks, and achieve
higher accuracy in the program classification task than "shallow" methods, such
as logistic regression and the support vector machine. This result confirms the
feasibility of deep learning to analyze programs. It also gives primary
evidence of its success in this new field. We believe deep learning will become
an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1
Fine-grained Conversational Decoding via Isotropic and Proximal Search
General-purpose text decoding approaches are usually adopted for dialogue
response generation. Although the quality of the generated responses can be
improved with dialogue-specific encoding methods, conversational decoding
methods are still under-explored. Inspired by \citet{wu2023learning} that a
good dialogue feature space should follow the rules of locality and isotropy,
we present a fine-grained conversational decoding method, termed
\textit{isotropic and proximal search (IPS)}. Our method is designed to
generate the semantic-concentrated response, while still maintaining
informativeness and discrimination against the context. Experiments show that
our approach outperforms existing decoding strategies in the dialogue field
across both automatic and human evaluation metrics. More in-depth analyses
further confirm the effectiveness of our approach.Comment: Accepted to EMNLP 2023 Main Conferenc
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