Related papers: Spartan: Sparse Robust Addressable Networks

Dynamic behaviors are becoming prevalent in tensor applications, like machine learning, where many widely used models contain data-dependent tensor shapes and control flow. However, the limited expressiveness of prior programming…

Peer-to-Peer (P2P) networks provide a significant solution for file sharing among peers connected to Internet. It is fast and completely decentralised system with robustness. But due to absence of a server documents on a P2P network are not…

Much of our commerce and traveling depend on the efficient operation of large scale networks. Some of those, such as electric power grids, transportation systems, communication networks, and others, must maintain their efficiency even after…

As ByteDance's business expands, the substantial infrastructure expenses associated with centralized Content Delivery Network (CDN) networks have rendered content distribution costs prohibitively high. In response, we embarked on exploring…

Spiking Neural Networks (SNNs) have attracted great attention for their energy-efficient operations and biologically inspired structures, offering potential advantages over Artificial Neural Networks (ANNs) in terms of energy efficiency and…

Decentralized unpermissioned peer-to-peer networks are inherently vulnerable to spam when they allow arbitrary participants to submit content to a common public index or registry; preventing this is difficult due to the absence of a central…

Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms. This paper studies how to identify all authentic connections…

We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process, and naturally generalizes existing probabilistic models with…

This paper proposes a scalable and resilient real-time multi-party communication architecture for the delivery of mixed media streams, for which content centric networking, with its intelligent network layer, is chosen for implementation to…

Future systems based on nano-scale devices will provide great potentials for scaling up in system complexity, yet they will be highly susceptible to operational faults. While spare units can be generally used to enhance reliability, they…

Most of the existing P2P content distribution schemes implement a random or rarest piece first dissemination procedure to avoid duplicate transmission of the same pieces of data and rare pieces of data occurring in the network. This problem…

Modern computing requires efficient and dependable data transport. Current solutions like Bluetooth, SMS (Short Message Service), and Email have their restrictions on efficiency, file size, compatibility, and cost. In order to facilitate…

Online social networking has quickly become one of the most common activities of Internet users. As social networks evolve, they encourage users to share more information, requiring the users, in turn, to place more trust into social…

Distributed Machine Learning refers to the practice of training a model on multiple computers or devices that can be called nodes. Additionally, serverless computing is a new paradigm for cloud computing that uses functions as a…

Most permissionless blockchain networks run on peer-to-peer (P2P) networks, which offer flexibility and decentralization at the expense of performance (e.g., network latency). Historically, this tradeoff has not been a bottleneck for most…

Virtual topologies in peer-to-peer networks can reduce the traffic consumed by altering the logical connectivity of peers without altering the underlying network. However, such sparsely connected virtual topologies do not focus on the needs…

Simulators are the most dominant and eminent tool for analyzing and investigating different type of networks. The simulations can be executed with less cost as compared to large scale experiment as less computational resources are required…

We investigate the robustness of sparse artificial neural networks trained with adaptive topology. We focus on a simple yet effective architecture consisting of three sparse layers with 99% sparsity followed by a dense layer, applied to…

Grid superscheduling requires support for efficient and scalable discovery of resources. Resource discovery activities involve searching for the appropriate resource types that match the user's job requirements. To accomplish this goal, a…

We consider the problem of constructing distributed overlay networks, where nodes in a reconfigurable system can create or sever connections with nodes whose identifiers they know. Initially, each node knows only its own and its neighbors'…