Tootfinder

Self-healing Nodes with Adaptive Data-Sharding
Ayush Thakur, Sanskar Chauhan, Ilisha Tomar, Vaibhavi Paul, Deepak Gupta
https://arxiv.org/abs/2405.00004 ht…

Self-healing Nodes with Adaptive Data-Sharding
Data sharding, a technique for partitioning and distributing data among multiple servers or nodes, offers enhancements in the scalability, performance, and fault tolerance of extensive distributed systems. Nonetheless, this strategy introduces novel challenges, including load balancing among shards, management of node failures and data loss, and adaptation to evolving data and workload patterns. This paper proposes an innovative approach to tackle these challenges by empowering self-healing nod…

This https://arxiv.org/abs/2403.14915 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Network Learning with Directional Sign Patterns
Complex systems can be effectively modeled via graphs that encode networked interactions, where relations between entities or nodes are often quantified by signed edge weights, e.g., promotion/inhibition in gene regulatory networks, or encoding political of friendship differences in social networks. However, it is often the case that only an aggregate consequence of such edge weights that characterize relations may be directly observable, as in protein expression of in gene regulatory networks.…

Network Learning with Directional Sign Patterns
Anqi Dong, Can Chen, Tryphon T. Georgiou
https://arxiv.org/abs/2403.14915 https://arx…

Network Learning with Directional Sign Patterns
Complex systems can be effectively modeled via graphs that encode networked interactions, where relations between entities or nodes are often quantified by signed edge weights, e.g., promotion/inhibition in gene regulatory networks, or encoding political of friendship differences in social networks. However, it is often the case that only an aggregate consequence of such edge weights that characterize relations may be directly observable, as in protein expression of in gene regulatory networks.…

Meta-plasticity and memory in multi-level recurrent feed-forward networks
Gianmarco Zanardi, Paolo Bettotti, Jules Morand, Lorenzo Pavesi, Luca Tubiana
https://arxiv.org/abs/2403.13967

Meta-plasticity and memory in multi-level recurrent feed-forward networks
Network systems can exhibit memory effects in which the interactions between different pairs of nodes adapt in time, leading to the emergence of preferred connections, patterns, and sub-networks. To a first approximation, this memory can be modelled through a ``plastic'' Hebbian or homophily mechanism, in which edges get reinforced proportionally to the amount of information flowing through them. However, recent studies on glia-neuron networks have highlighted how memory can evolve due to more …