Tootfinder

It annoys me that a US non-profit group called the "Southeast Asia Resource Action Center" is actually only focused on immigrant communities from 3 #Mekong countries.
The focus is fine and good - its just the name that annoys me.

Opinion Dynamics for Utility Maximizing Agents: Exploring the Impact of Resource Penalty
Prashil Wankhede, Nirabhra Mandal, Sonia Mart\'inez, Pavankumar Tallapragada
https://arxiv.org/abs/2404.04912

Opinion Dynamics for Utility Maximizing Agents: Exploring the Impact of Resource Penalty
We propose a continuous-time nonlinear model of opinion dynamics with utility-maximizing agents connected via a social influence network. A distinguishing feature of the proposed model is the inclusion of an opinion-dependent resource-penalty term in the utilities, which limits the agents from holding opinions of large magnitude. The proposed utility functions also account for how the relative resources within the social group affect both an agent's stubbornness and social influence. Each agent…

BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling
Yunyi Zhao, Wei Zhang, Erhai Hu, Qingyu Yan, Cheng Xiang, King Jet Tseng, Dusit Niyato
https://arxiv.org/abs/2404.05802

BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling
Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries. However, it is challenging, largely because sorting batteries is costly and hardly automated to group batteries based on battery types. In this paper, we introduce a machine learning-based approach for battery-type classification and address the daunting problem of data scarcity for the application. We propose BatSort which applies transfer learning to utilize the existing knowledge …

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
Raphael Koster, Miruna P\^islar, Andrea Tacchetti, Jan Balaguer, Leqi Liu, Romuald Elie, Oliver P. Hauser, Karl Tuyls, Matt Botvinick, Christopher Summerfield
https://arxiv.org/abs/2404.15059<…

Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciprocation that sustain the commons? Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design an allocation mechanism that endogenously promotes sustainable contributions from human participants to a common pool reso…

Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?
Harry Mayne, Guy Parsons, Adam Mahdi
https://arxiv.org/abs/2403.02945

Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?
The use of unsupervised learning to identify patient subgroups has emerged as a potentially promising direction to improve the efficiency of Intensive Care Units (ICUs). By identifying subgroups of patients with similar levels of medical resource need, ICUs could be restructured into a collection of smaller subunits, each catering to a specific group. However, it is unclear whether common patient subgroups exist across different ICUs, which would determine whether ICU restructuring could be ope…

This https://arxiv.org/abs/2002.11484 has been replaced.
link: https://scholar.google.com/scholar?q=a

Entanglement as a resource for naturalness
A novel approach to understanding the hierarchy problem is presented making use of topological aspects of the renormalisation group and the ER-EPR interpretation of entanglement. A common discussion of the renormalisation group, the black hole horizon and the expected entanglement between outgoing Hawking radiation and the interior, the cosmic censorship mechanism and the cosmological constant problem is envisaged.

This https://arxiv.org/abs/2402.08551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Nonstabilizerness of permutationally invariant systems
Typical measures of nonstabilizerness of a system of $N$ qubits require computing $4^N$ expectation values, one for each Pauli string in the Pauli group, over a state of dimension $2^N$. In this Letter, we show that, if the system is invariant under permutations, this resource overhead can be exponentially decreased to $O(N^3)$ expectation values on a state with a dimension $O(N)$, allowing for a polynomial-time evaluation of the nonstabilizerness up to hundreds of qubits.