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Airtable alternatives available from Agaric: https://agaric.coop/blog/airtable-alternatives-available-agaric

Looking for an Airtable exit before it is priced out from under you or becomes unusable?
https://agaric.coop/blog/airtable-alternatives-available-agaric

Color Therapy. Noisy evolution.
Two snapshots of my piece C-SCAPE, 2022, a multi-organism 1.5D cellular automata simulation. Patterns of symbiosis and annihilation.
Made with #CSCAPE

Edge-Based QoS-Aware Adaptive Task Placement: A Closed-Loop Control in Multi-Robot Systems
Thien Tran, Jonathan Kua, Thuong Hoang, Minh Tran, Honghao Lyu, Jiong Jin
https://arxiv.org/abs/2606.00552 https://arxiv.org/pdf/2606.00552 https://arxiv.org/html/2606.00552
arXiv:2606.00552v1 Announce Type: new
Abstract: Multi-robot systems (MRS) increasingly offload compute-intensive perception tasks to edge nodes to meet strict time-sensitive Quality-of-Service (QoS) constraints. However, static task orchestration on a shared edge node can severely degrade QoS due to network latency, jitter, and edge-resource contention. We present a pilot edge-centric MRS testbed using Raspberry Pi nodes to evaluate a camera-to-manipulator pipeline under three modes: local execution, static offloading, and a QoS-aware Adaptive Task Placement (ATP) controller. ATP scores candidate placements using a multi-metric cost (normalized latency, CPU utilization, and switching overhead) over two-second control windows. The closed-loop visual servoing testbed is instrumented with sub-millisecond clock synchronization, network emulation, and detailed monitoring of multiple metrics across nodes to capture realistic jitter. Experimental results under compute-stress and network-fault scenarios show that static edge offloading reduces on-board CPU load but amplifies tail latency and deadline misses. In contrast, the QoS-aware ATP controller, by switching task placement based on measured latency and utilization thresholds, consistently lowers deadline violations and tail latency. Overall, the results position ATP as a practical edge-side control primitive for MRS and concrete design guidelines for Cloud-Edge Robotics deployments within the broader cloud-fog automation, while motivating QoS-aware multi-objective workload orchestration for industrial cyber-physical systems.
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Don't miss today's Metacurity for a concise run-down of the most critical infosec developments you should know, including
--Instructure forms deal with ShinyHunters who promise to destroy stolen Canvas data,
--US intel agencies want to evaluate AI model while Commerce seems to back away,
--OpenAI launches cybersecurity model Daybreak,
--Euro countries sell spyware to rights violators,
--Binance claims AI system saved $10b in scam losses,
--Shai-Hulu…

Instructure forms deal with ShinyHunters who promise to destroy stolen Canvas data
US intel agencies want to evaluate AI model while Commerce seems to back away, OpenAI launches cybersecurity model Daybreak, Euro countries sell spyware to rights violators, Binance claims AI system saved $10b in scam losses, Shai-Hulud supply-chain campaign compromised npm and PyPi packages, more

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- SparseX: Efficient Segment-Level KV Cache Sharing for Interleaved LLM Serving
Quqing Zhang, Kai Chen, Ning Liao, Zehao Lin, Bo Tang, Feiyu Xiong, Zhiyu Li, Xiaoxing Wang
https://arxiv.org/abs/2606.01751 https://mastoxiv.page/@arXiv_csPF_bot/116679373304506229
- Emergence-as-Code as a Foundation for Self-Governing Reliable Systems
Anatoly A. Krasnovsky
https://arxiv.org/abs/2602.05458
- Multi-GPU Hybrid Particle-in-Cell Monte Carlo Simulations for Exascale Computing Systems
Jeremy J. Williams, et al.
https://arxiv.org/abs/2603.24508 https://mastoxiv.page/@arXiv_physicsplasmph_bot/116294552595944129
- Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
Vincent-Daniel Yun, Junhyuk Jo, Sai Praneeth Karimireddy, Sunwoo Lee
https://arxiv.org/abs/2605.15491 https://mastoxiv.page/@arXiv_csLG_bot/116594618179410746
- Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under A...
Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi, Omid Kashefi
https://arxiv.org/abs/2605.20341 https://mastoxiv.page/@arXiv_csLG_bot/116611611001064687
- Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning
Trinh Pham, Viet Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen
https://arxiv.org/abs/2605.23595 https://mastoxiv.page/@arXiv_csLG_bot/116634269889993647
toXiv_bot_toot

Replaced article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/1]:
- Optimal Proximity Gap for Folded Reed--Solomon Codes via Subspace Designs
Fernando Granha Jeronimo, Lenny Liu, Pranav Rajpal
https://arxiv.org/abs/2601.10047 https://mastoxiv.page/@arXiv_csIT_bot/115903956641689136
- Breaking Symmetry in D2D Coded Caching: Optimal Communication with Low Subpacketization
Xiang Zhang, Giuseppe Caire, Mingyue Ji
https://arxiv.org/abs/2602.12220 https://mastoxiv.page/@arXiv_csIT_bot/116062725600909840
- A New Approach to Code Smoothing Bounds
Tsuyoshi Miezaki, Yusaku Nishimura, Katsuyuki Takashima
https://arxiv.org/abs/2603.18077 https://mastoxiv.page/@arXiv_csIT_bot/116260380391748925
- Fluid Antenna Systems Enabling 6G HRLLC With Port Switching Delay
Xusheng Zhu, Kai-Kit Wong, Hao Xu, Chenguang Rao, Hyundong Shin
https://arxiv.org/abs/2605.06275 https://mastoxiv.page/@arXiv_csIT_bot/116537839161835575
- Accurate Estimation of Mutual Information in High Dimensional Data
Eslam Abdelaleem, K. Michael Martini, Ilya Nemenman
https://arxiv.org/abs/2506.00330 https://mastoxiv.page/@arXiv_physicsdataan_bot/114618315134354611
- SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
Ayush Zenith, Arnold Zumbrun, Neel Raut, Jing Lin
https://arxiv.org/abs/2510.06596 https://mastoxiv.page/@arXiv_csCV_bot/115343628773188610
- SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Use...
Weihang Cao, Mustafa Doger, Sennur Ulukus
https://arxiv.org/abs/2512.13666 https://mastoxiv.page/@arXiv_csCR_bot/115729118798706696
- A theory of learning data statistics in diffusion models, from easy to hard
Lorenzo Bardone, Claudia Merger, Sebastian Goldt
https://arxiv.org/abs/2603.12901 https://mastoxiv.page/@arXiv_statML_bot/116237842977106276
- On the independence number of de Bruijn graphs
Pietro Majer, Matteo Novaga
https://arxiv.org/abs/2604.14671 https://mastoxiv.page/@arXiv_mathCO_bot/116419097221023806
- Information bottleneck for learning the phase space of dynamics from high-dimensional experimenta...
K. Michael Martini, Eslam Abdelaleem, Paarth Gulati, Ilya Nemenman
https://arxiv.org/abs/2604.24662 https://mastoxiv.page/@arXiv_physicsdataan_bot/116481236697063671
- Improved Amenability Bounds for Local Coordination Games
Ron Peretz, Dean Kraizberg
https://arxiv.org/abs/2606.01963 https://mastoxiv.page/@arXiv_csGT_bot/116679490095124433
toXiv_bot_toot

Crosslisted article(s) found for nlin.CD. https://arxiv.org/list/nlin.CD/new
[1/1]:
- Differentiable Cardiac Electrophysiology Simulations for Dynamical State and Parameter Estimation
Adarsh Pashikanti, Shrey Chowdhary, Alex Ho, Weizhen Li, Emilia Entcheva, Jan Christoph
https://arxiv.org/abs/2607.15492 https://mastoxiv.page/@arXiv_physicsmedph_bot/116951198419845163
- Scaling regimes of the Kuramoto-Sivashinsky equation from the functional renormalization group
Liubov Gosteva, Nicol\'as Wschebor, L\'eonie Canet
https://arxiv.org/abs/2607.15784 https://mastoxiv.page/@arXiv_condmatstatmech_bot/116951186789607648
- Diffusion-induced instabilities promote cooperation in eco-evolutionary networks
Sourav Roy, Md Sayeed Anwar, Timoteo Carletti, Matjaz Perc, Dibakar Ghosh
https://arxiv.org/abs/2607.15989 https://mastoxiv.page/@arXiv_physicssocph_bot/116951225127032889
- Dynamic models with $p$ parameters are identified by $2p 1$ random features
Michael Wieck-Sosa, Cosma Rohilla Shalizi
https://arxiv.org/abs/2607.16035 https://mastoxiv.page/@arXiv_statME_bot/116951248720959599
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