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

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