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6/18/25 ☀️ AM: -- Happy Wednesday morning ... Vance said Trump wants ... (Punchbowl News)
https://punchbowl.news/archive/61825-am/
http://www.memeorandum.com/250618/p22#a250618p22

Wir dachten der Abend vor der Wohndose am Steinbacher See hätte sich gerächt, aber es sind wohl Mitbringsel vom #PinkBowl #ErimaGFL #AmericanFootball

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
Debasish Jana, Pinakpani Pal, Pawan Kumar
https://arxiv.org/abs/2506.12616

Real-Time Agile Software Management for Edge and Fog Computing Based Smart City Infrastructure
The evolution of smart cities demands scalable, secure, and energy-efficient architectures for real-time data processing. With the number of IoT devices expected to exceed 40 billion by 2030, traditional cloud-based systems are increasingly constrained by bandwidth, latency, and energy limitations. This paper leverages the ROOF (Real-time Onsite Operations Facilitation) framework with decentralized computing at intermediary fog and peripheral edge network layers to reduce latency by processing …

Serving Large Language Models on Huawei CloudMatrix384
Pengfei Zuo, Huimin Lin, Junbo Deng, Nan Zou, Xingkun Yang, Yingyu Diao, Weifeng Gao, Ke Xu, Zhangyu Chen, Shirui Lu, Zhao Qiu, Peiyang Li, Xianyu Chang, Zhengzhong Yu, Fangzheng Miao, Jia Zheng, Ying Li, Yuan Feng, Bei Wang, Zaijian Zong, Mosong Zhou, Wenli Zhou, Houjiang Chen, Xingyu Liao, Yipeng Li, Wenxiao Zhang, Ping Zhu, Yinggang Wang, Chuanjie Xiao, Depeng Liang, Dong Cao, Juncheng Liu, Yongqiang Yang, Xiaolong Bai, Yi Li, H…

Serving Large Language Models on Huawei CloudMatrix384
The rapid evolution of large language models (LLMs), driven by growing parameter scales, adoption of mixture-of-experts (MoE) architectures, and expanding context lengths, imposes unprecedented demands on AI infrastructure. Traditional AI clusters face limitations in compute intensity, memory bandwidth, inter-chip communication, and latency, compounded by variable workloads and strict service-level objectives. Addressing these issues requires fundamentally redesigned hardware-software integrati…

SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Min Li, Chen Chen, Zhenghao Li, Yin Liu, Shanshan Shan, Peng Wu, Pengfei Rong, Feng Liu, G. Bruce Pike, Alan H. Wilman, Hongfu Sun, Yang Gao
https://arxiv.org/abs/2506.13293

SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Quantitative susceptibility mapping (QSM) provides a valuable tool for quantifying susceptibility distributions in human brains; however, two types of opposing susceptibility sources (i.e., paramagnetic and diamagnetic), may coexist in a single voxel, and cancel each other out in net QSM images. Susceptibility source separation techniques enable the extraction of sub-voxel information from QSM maps. This study proposes a novel SUSEP-Net for susceptibility source separation by training a dual-br…

Kolmogorov-Arnold Network for Gene Regulatory Network Inference
Tsz Pan Tong, Aoran Wang, George Panagopoulos, Jun Pang
https://arxiv.org/abs/2506.13740 ht…

Kolmogorov-Arnold Network for Gene Regulatory Network Inference
Gene regulation is central to understanding cellular processes and development, potentially leading to the discovery of new treatments for diseases and personalized medicine. Inferring gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data presents significant challenges due to its high dimensionality and complexity. Existing tree-based models, such as GENIE3 and GRNBOOST2, demonstrated scalability and explainability in GRN inference, but they cannot distinguish regula…

GenControl: Generative AI-Driven Autonomous Design of Control Algorithms
Chenggang Cui, Jiaming Liu, Peifeng Hui, Pengfeng Lin, Chuanlin Zhang, Frede Blaabjerg
https://arxiv.org/abs/2506.12554

GenControl: Generative AI-Driven Autonomous Design of Control Algorithms
Designing controllers for complex industrial electronic systems is challenging due to nonlinearities and parameter uncertainties, and traditional methods are often slow and costly. To address this, we propose a novel autonomous design framework driven by Large Language Models (LLMs). Our approach employs a bi-level optimization strategy: an LLM intelligently explores and iteratively improves the control algorithm's structure, while a Particle Swarm Optimization (PSO) algorithm efficiently refin…

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
Pink Fairies:
🎵 Do It (single edit)
#PinkFairies
https://pinkfairies.bandcamp.com/track/do-it-1970-demo
https://open.spotify.com/track/4c0UFMRWdoYTtEZLBYXlzy

6/9/2025_3 ☀️ Punchbowl News AM (Punchbowl News)
https://punchbowl.news/archive/692025_3-punchbowl-news-am/
http://www.memeorandum.com/250609/p10#a250609p10

Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
Tianze Wang, Yifei Liu, Chen Chen, Pengfei Zuo, Jiawei Zhang, Qizhen Weng, Yin Chen, Zhenhua Han, Jieru Zhao, Quan Chen, Minyi Guo
https://arxiv.org/abs/2506.12370

Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
Modern AI clusters, which host diverse workloads like data pre-processing, training and inference, often store the large-volume data in cloud storage and employ caching frameworks to facilitate remote data access. To avoid code-intrusion complexity and minimize cache space wastage, it is desirable to maintain a unified cache shared by all the workloads. However, existing cache management strategies, designed for specific workloads, struggle to handle the heterogeneous AI workloads in a cluster …