Tootfinder

The implications are interesting enough when we apply this to systems like capitalism or national governments, but there are other very interesting implications when applied to systems like race or gender.
Like, as a cis man the only way I can be free to express and explore my own masculinity is if the masculinity I participate in is one which allows anyone the freedom to leave. Then I have an obligation to recognize the validity of nom-masculine trans identity as a necessary component of my own. If I fail to do this, then I trap myself in masculinity and allow the system to control me rather than me to be a free participant in the system.
But if it's OK to escape but not enter, that's it's own restriction that constrains the freedom to leave. It creates a barrier that keeps people in by the fear that they cannot return. So in order for me to be free in my cis masculine identity, I must accept non-masculine trans identities as they are and accept detransitioning as also valid.
But I also need to accept trans-masc identities because restricting entry to my masculinity means non-consensually constraining other identities. If every group imposes an exclusion against others coming in, that, by default, makes it impossible to leave every other group. This is just a description of how national borders work to trap people within systems, even if a nation itself allows people to "freely" leave.
So then, a free masculinity is one which recognizes all configurations of trans identities as valid and welcomes, if not celebrates, people who transition as affirmations of the freedom of their own identity (even for those who never feel a reason to exercise that same freedom).
The most irritating type of white person may look at this and say, "oh, so then why can't I be <not white>?" Except that the critique of transratial identities has never been "that's not allowed" and has always been "this person didn't do the work." If that person did the work, they would understand that the question doesn't make sense based on how race is constructed. That person might understand that race, especially whiteness, is more fluid than they at first understood. They might realize that whiteness is often chosen at the exclusion of other racialized identities. They would, perhaps, realize that to actually align with any racialized identity, they would first have to understand the boot of whiteness on their neck, have to recognize the need to destroy this oppressive identity for their own future liberation. The best, perhaps only, way to do this would be to use the privilege afforded by that identity to destroy it, and in doing so would either destroy their own privilege or destroy the system of privilege. The must either become themselves completely ratialized or destroy the system of race itself such being "transracial" wouldn't really make sense anymore.
But that most annoying of white person would, of course, not do any such work. Nevertheless, one hopes that they may recognize the paradox that they are trapped by their white identity, forced forever by it to do the work of maintaining it. And such is true for all privileged identities, where privilege is only maintained through restrictions where these restrictions ultimately become walls that imprison both the privileged and the marginalized in a mutually reinforcing hell that can only be escaped by destroying the system of privilege itself.

Utilizing Model-Free Reinforcement Learning for Optimizing Secure Multi-Party Computation Protocols
Javad Sayyadi, Mahdi Nangir, Mahmood Mohassel Feghhi, Hamid Sayyadi
https://arxiv.org/abs/2510.07814 …

Utilizing Model-Free Reinforcement Learning for Optimizing Secure Multi-Party Computation Protocols
In this manuscript, we explore the application of model-free reinforcement learning in optimizing secure multiparty computation (SMPC) protocols. SMPC is a crucial tool for performing computations on private data without the need to disclose it, holding significant importance in various domains, including information security and privacy. However, the efficiency of current protocols is often suboptimal due to computational and communicational complexities. Our proposed approach leverages model-…

"Wertheimer argues that the famous Section 230 of the Communications Decency Act--which protects companies from liability for content posted by users to their websites--needs to be reinterpreted to exclude “platforms that actively promote content using reinforcement learning-based recommendation algorithms.”"
(Original title: Platform Temperance)

Platform Temperance
Notes on a new wave of the Techlash

Long-Term Mapping of the Douro River Plume with Multi-Agent Reinforcement Learning
Nicol\`o Dal Fabbro, Milad Mesbahi, Renato Mendes, Jo\~ao Borges de Sousa, George J. Pappas
https://arxiv.org/abs/2510.03534

Long-Term Mapping of the Douro River Plume with Multi-Agent Reinforcement Learning
We study the problem of long-term (multiple days) mapping of a river plume using multiple autonomous underwater vehicles (AUVs), focusing on the Douro river representative use-case. We propose an energy - and communication - efficient multi-agent reinforcement learning approach in which a central coordinator intermittently communicates with the AUVs, collecting measurements and issuing commands. Our approach integrates spatiotemporal Gaussian process regression (GPR) with a multi-head Q-network…

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Yousef Emami, Seyedsina Nabavirazavi, Jingjing Zheng, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida
https://arxiv.org/abs/2510.05698

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Recently, Unmanned Aerial Vehicles (UAVs) are increasingly being investigated to collect sensory data in post-disaster monitoring scenarios, such as tsunamis, where early actions are critical to limit coastal damage. A major challenge is to design the data collection schedules and flight velocities, as unfavorable schedules and velocities can lead to transmission errors and buffer overflows of the ground sensors, ultimately resulting in significant packet loss. Meanwhile, online Deep Reinforcem…

On the Enumeration of all Unique Paths of Recombining Trinomial Trees
Ethan Torres, Ramavarapu Sreenivas, Richard Sowers
https://arxiv.org/abs/2510.02727 https://

On the Enumeration of all Unique Paths of Recombining Trinomial Trees
Recombining trinomial trees are a workhorse for modeling discrete-event systems in option pricing, logistics, and feedback control. Because each node stores a state-dependent quantity, a depth-$D$ tree naively yields $\mathcal{O}(3^{D})$ trajectories, making exhaustive enumeration infeasible. Under time-homogeneous dynamics, however, the graph exhibits two exploitable symmetries: (i) translational invariance of nodes and (ii) a canonical bijection between admissible paths and ordered tuples enc…

Optimal Batched Scheduling of Stochastic Processing Networks Using Atomic Action Decomposition
Jim Dai, Manxi Wu, Zhanhao Zhang
https://arxiv.org/abs/2510.06033 https://

Optimal Batched Scheduling of Stochastic Processing Networks Using Atomic Action Decomposition
Stochastic processing networks (SPNs) have broad applications in healthcare, transportation, and communication networks. The control of SPN is to dynamically assign servers in batches under uncertainty to optimize long-run performance. This problem is challenging as the policy dimension grows exponentially with the number of servers, making standard reinforcement learning and policy optimization methods intractable at scale. We propose an atomic action decomposition framework that addresses thi…

This is particularly bad in #Italy, where #WhatsApp is the default means of communication. In other countries I've lived in at least they consider alternatives. In Italy people don't even ask you if you have an account before adding you in school parent groups or contacting you for work over WhatsApp.

Why people don’t demand data privacy – even as governments and corporations collect more personal information
How people talk about data privacy can overcome – or reinforce – their skepticism that things can change.

Network Topology and Information Efficiency of Multi-Agent Systems: Study based on MARL
Xinren Zhang, Sixi Cheng, Zixin Zhong, Jiadong Yu
https://arxiv.org/abs/2510.07888 https:…

Network Topology and Information Efficiency of Multi-Agent Systems: Study based on MARL
Multi-agent systems (MAS) solve complex problems through coordinated autonomous entities with individual decision-making capabilities. While Multi-Agent Reinforcement Learning (MARL) enables these agents to learn intelligent strategies, it faces challenges of non-stationarity and partial observability. Communications among agents offer a solution, but questions remain about its optimal structure and evaluation. This paper explores two underexamined aspects: communication topology and informatio…

Intelligent Optimization of Wireless Access Point Deployment for Communication-Based Train Control Systems Using Deep Reinforcement Learning
Kunyu Wu, Qiushi Zhao, Zihan Feng, Yunxi Mu, Hao Qin, Xinyu Zhang, Xingqi Zhang
https://arxiv.org/abs/2509.24819

Intelligent Optimization of Wireless Access Point Deployment for Communication-Based Train Control Systems Using Deep Reinforcement Learning
Urban railway systems increasingly rely on communication based train control (CBTC) systems, where optimal deployment of access points (APs) in tunnels is critical for robust wireless coverage. Traditional methods, such as empirical model-based optimization algorithms, are hindered by excessive measurement requirements and suboptimal solutions, while machine learning (ML) approaches often struggle with complex tunnel environments. This paper proposes a deep reinforcement learning (DRL) driven f…