431 research outputs found
Study of architecture and protocols for reliable multicasting in packet switching networks
Group multicast protocols have been challenged to provide scalable solutions that meet the following requirements: (i) reliable delivery from different sources to all destinations within a multicast group; (ii) congestion control among multiple asynchronous sources. Although it is mainly a transport layer task, reliable group multicasting depends on routing architectures as well.
This dissertation covers issues of both network and transport layers. Two routing architectures, tree and ring, are surveyed with a comparative study of their routing costs and impact to upper layer performances. Correspondingly, two generic transport protocol models are established for performance study. The tree-based protocol is rate-based and uses negative acknowledgment mechanisms for reliability control, while the ring-based protocol uses window-based flow control and positive acknowledgment schemes. The major performance measures observed in the study are network cost, multicast delay, throughput and efficiency. The results suggest that the tree architecture costs less at network layer than the ring, and helps to minimize latency under light network load. Meanwhile, heavy load reliable group multicasting can benefit from ring architecture, which facilitates window-based flow and congestion control.
Based on the comparative study, a new two-hierarchy hybrid architecture, Rings Interconnected with Tree Architecture (RITA), is presented. Here, a multicast group is partitioned into multiple clusters with the ring as the intra-cluster architecture, and the tree as backbone architecture that implements inter-cluster multicasting. To compromise between performance measures such as delay and through put, reliability and congestion controls are accomplished at the transport layer with a hybrid use of rate and window-based protocols, which are based on either negative or positive feedback mechanisms respectively. Performances are compared with simulations against tree- and ring-based approaches. Results are encouraging because RITA achieves similar throughput performance as the ring-based protocol, but with significantly lowered delay.
Finally, the multicast tree packing problem is discussed. In a network accommodating multiple concurrent multicast sessions, routing for an individual session can be optimized to minimize the competition with other sessions, rather than to minimize cost or delay. Packing lower bound and a heuristic are investigated. Simulation show that congestion can be reduced effectively with limited cost increase of routings
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Co-saliency detection aims to discover the common and salient foregrounds
from a group of relevant images. For this task, we present a novel adaptive
graph convolutional network with attention graph clustering (GCAGC). Three
major contributions have been made, and are experimentally shown to have
substantial practical merits. First, we propose a graph convolutional network
design to extract information cues to characterize the intra- and interimage
correspondence. Second, we develop an attention graph clustering algorithm to
discriminate the common objects from all the salient foreground objects in an
unsupervised fashion. Third, we present a unified framework with
encoder-decoder structure to jointly train and optimize the graph convolutional
network, attention graph cluster, and co-saliency detection decoder in an
end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency
detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method
obtains significant improvements over the state-of-the-arts on most of them.Comment: CVPR202
Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models
Recently Large Language Models (LLMs) have demonstrated their amazing text
understanding and generation capabilities. However, even stronger LLMs may
still learn incorrect knowledge from the training corpus, as well as some
knowledge that is outdated over time. Direct secondary fine-tuning with data
containing new knowledge may be ineffective in updating knowledge due to the
conflict between old and new knowledge. In this paper, we propose a new
paradigm for fine-tuning called F-Learning (Forgetting before Learning), which
is based on parametric arithmetic to achieve forgetting of old knowledge and
learning of new knowledge. Experimental results on two publicly available
datasets demonstrate that our proposed F-Learning can obviously improve the
knowledge updating performance of both full fine-tuning and LoRA fine-tuning.
Moreover, we have also discovered that forgetting old knowledge by subtracting
the parameters of LoRA can achieve a similar effect to subtracting the
parameters of full fine-tuning, and sometimes even surpass it significantly.Comment: 8 pages, 2 figures, 2 table
Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction
The surging demand for high-definition video streaming services and large
neural network models (e.g., Generative Pre-trained Transformer, GPT) implies a
tremendous explosion of Internet traffic. To mitigate the traffic pressure,
architectures with in-network storage have been proposed to cache popular
contents at devices in closer proximity to users. Correspondingly, in order to
maximize caching utilization, it becomes essential to devise an effective
popularity prediction method. In that regard, predicting popularity with
dynamic graph neural network (DGNN) models achieve remarkable performance.
However, DGNN models still suffer from tackling sparse datasets where most
users are inactive. Therefore, we propose a reformative temporal graph network,
named semantics-enhanced temporal graph network (STGN), which attaches extra
semantic information into the user-content bipartite graph and could better
leverage implicit relationships behind the superficial topology structure. On
top of that, we customize its temporal and structural learning modules to
further boost the prediction performance. Specifically, in order to efficiently
aggregate the diversified semantics that a content might possess, we design a
user-specific attention (UsAttn) mechanism for temporal learning module. Unlike
the attention mechanism that only analyzes the influence of genres on content,
UsAttn also considers the attraction of semantic information to a specific
user. Meanwhile, as for the structural learning, we introduce the concept of
positional encoding into our attention-based graph learning and adopt a
semantic positional encoding (SPE) function to facilitate the analysis of
content-oriented user-association analysis. Finally, extensive simulations
verify the superiority of our STGN models and demonstrate the effectiveness in
content caching
Energy efficient coordinated precoding design for a multicell system with RF energy harvesting
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning
The age of information metric fails to correctly describe the intrinsic
semantics of a status update. In an intelligent reflecting surface-aided
cooperative relay communication system, we propose the age of semantics (AoS)
for measuring semantics freshness of the status updates. Specifically, we focus
on the status updating from a source node (SN) to the destination, which is
formulated as a Markov decision process (MDP). The objective of the SN is to
maximize the expected satisfaction of AoS and energy consumption under the
maximum transmit power constraint. To seek the optimal control policy, we first
derive an online deep actor-critic (DAC) learning scheme under the on-policy
temporal difference learning framework. However, implementing the online DAC in
practice poses the key challenge in infinitely repeated interactions between
the SN and the system, which can be dangerous particularly during the
exploration. We then put forward a novel offline DAC scheme, which estimates
the optimal control policy from a previously collected dataset without any
further interactions with the system. Numerical experiments verify the
theoretical results and show that our offline DAC scheme significantly
outperforms the online DAC scheme and the most representative baselines in
terms of mean utility, demonstrating strong robustness to dataset quality.Comment: This work has been submitted to the IEEE for possible publicatio
- …