惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

The GitHub Blog
The GitHub Blog
S
SegmentFault 最新的问题
MyScale Blog
MyScale Blog
有赞技术团队
有赞技术团队
V
Visual Studio Blog
T
The Blog of Author Tim Ferriss
爱范儿
爱范儿
Vercel News
Vercel News
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Y
Y Combinator Blog
Blog — PlanetScale
Blog — PlanetScale
D
DataBreaches.Net
美团技术团队
Microsoft Security Blog
Microsoft Security Blog
大猫的无限游戏
大猫的无限游戏
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
酷 壳 – CoolShell
酷 壳 – CoolShell
GbyAI
GbyAI
A
About on SuperTechFans
云风的 BLOG
云风的 BLOG
The Cloudflare Blog
宝玉的分享
宝玉的分享
V
V2EX
Microsoft Azure Blog
Microsoft Azure Blog

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Comparing the Expressive Power of the Synchronous and the...
Catuscia Palamidessi · 2013-07-08 · via cs.DC updates on arXiv.org

The Asynchronous pi-calculus, proposed by Honda and Tokoro (1991) and, independently, by Boudol (1992), is a subset of the pi-calculus (Milner, 1992) which contains no explicit operators for choice and output-prefixing. The communication mechanism of this calculus, however, is powerful enough to simulate output-prefixing, as shown by Honda and Tokoro (1991) and by Boudol (1992), and input-guarded choice, as shown by Nestmann and Pierce (2000). A natural question arises, then, whether or not it is as expressive as the full pi-calculus. We show that this is not the case. More precisely, we show that there does not exist any uniform, fully distributed translation from the pi-calculus into the asynchronous pi-calculus, up to any "reasonable" notion of equivalence. This result is based on the incapability of the asynchronous pi-calculus to break certain symmetries possibly present in the initial communication graph. By similar arguments, we prove a separation result between the pi-calculus and CCS, and between the pi-calculus and the pi-calculus with internal mobility, a subset of the pi-calculus proposed by Sangiorgi where the output actions can only transmit private names.