Tootfinder

Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks
Yiwei Zou, Ting Li, Zong-fu Luo
https://arxiv.org/abs/2403.04977 …

Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks
Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis, providing essential reference for discerning the significance of nodes within complex networks. These measures find wide applications in critical tasks, such as community detection and network dismantling. However, their practical implementation on extensive networks remains computationally demanding due to their high time complexity. To mitigate these computational challenges, numerous approximat…

I noticed a few recent cameras (Fujifilm, Panasonic) are including network connectivity, which is good. Unfortunately this capability seems to be tied to Adobe’s proprietary Frame.io service, which feels really wrong.
Unfortunately camera makers are generally bad at software.
I’m not really sure what better option, as in not locked in to one vendor, to propose.
#photography

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

NotNets: Accelerating Microservices by Bypassing the Network
Peter Alvaro, Matthew Adiletta, Adrian Cockroft, Frank Hady, Ramesh Illikkal, Esteban Ramos, James Tsai, Robert Soul\'e
https://arxiv.org/abs/2404.06581

NotNets: Accelerating Microservices by Bypassing the Network
Remote procedure calls are the workhorse of distributed systems. However, as software engineering trends, such as micro-services and serverless computing, push applications towards ever finer-grained decompositions, the overhead of RPC-based communication is becoming too great to bear. In this paper, we argue that point solutions that attempt to optimize one aspect of RPC logic are unlikely to mitigate these ballooning communication costs. Rather, we need a dramatic reappraisal of how we provid…

Bayesian Survival Analysis by Approximate Inference of Neural Networks
Christian Marius Lillelund, Martin Magris, Christian Fischer Pedersen
https://arxiv.org/abs/2404.06421

Bayesian Survival Analysis by Approximate Inference of Neural Networks
Predicting future events always comes with uncertainty, but traditional non-Bayesian methods cannot distinguish certain from uncertain predictions or explain the confidence in their predictions. In survival analysis, Bayesian methods applied to state-of-the-art solutions in the healthcare and biomedical field are still novel, and their implications have not been fully evaluated. In this paper, we study the benefits of modeling uncertainty in deep neural networks for survival analysis with a foc…

Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs
Arijit Nag, Animesh Mukherjee, Niloy Ganguly, Soumen Chakrabarti
https://arxiv.org/abs/2403.05434

Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs
Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages(HRLs). A few of them have been trained in low-resource languages (LRLs) and give decent performance. Owing to the prohibitive costs of training LLMs, they are usually used as a network service, with the client charged by the count of input and output tokens. The number of tokens strongly depends on the script and language, as well as the LLM's sub-word vocabulary. We show t…

Microsoft announces Zero Trust DNS (ZTDNS). My quick, not-so-well-informed take is that it looks RPZ-like, but seemingly specific to Windows environments with all the bells, whistles, and functions that implies.
#DNS

NotNets: Accelerating Microservices by Bypassing the Network
Peter Alvaro, Matthew Adiletta, Adrian Cockroft, Frank Hady, Ramesh Illikkal, Esteban Ramos, James Tsai, Robert Soul\'e
https://arxiv.org/abs/2404.06581

NotNets: Accelerating Microservices by Bypassing the Network
Remote procedure calls are the workhorse of distributed systems. However, as software engineering trends, such as micro-services and serverless computing, push applications towards ever finer-grained decompositions, the overhead of RPC-based communication is becoming too great to bear. In this paper, we argue that point solutions that attempt to optimize one aspect of RPC logic are unlikely to mitigate these ballooning communication costs. Rather, we need a dramatic reappraisal of how we provid…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs
Arijit Nag, Animesh Mukherjee, Niloy Ganguly, Soumen Chakrabarti
https://arxiv.org/abs/2403.05434

Cost-Performance Optimization for Processing Low-Resource Language Tasks Using Commercial LLMs
Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages(HRLs). A few of them have been trained in low-resource languages (LRLs) and give decent performance. Owing to the prohibitive costs of training LLMs, they are usually used as a network service, with the client charged by the count of input and output tokens. The number of tokens strongly depends on the script and language, as well as the LLM's sub-word vocabulary. We show t…