Tootfinder

If currently implemented policies are continued with no increase in ambition, there is a 90% chance that the Earth will warm between 2.3°C and 4.5°C, with a best estimate of 3.5°C.
Translation: slam on the #EmergencyBrake
Keep on saying it.
‘An intergenerational crime against humanity’: what will it take for political leaders to start taking climate change seriously?

‘An intergenerational crime against humanity’: what will it take for political leaders to start taking climate change seriously?
If currently implemented policies are continued with no increase in ambition, there is a 90% chance that the Earth will warm between 2.3°C and 4.5°C, with a best estimate of 3.5°C.

This https://arxiv.org/abs/2405.19523 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Comparison of Point Process Learning and its special case Takacs-Fiksel estimation
Recently, Cronie et al. (2024) introduced the notion of cross-validation for point processes and a new statistical methodology called Point Process Learning (PPL). In PPL one splits a point process/pattern into a training and a validation set, and then predicts the latter from the former through a parametrised Papangelou conditional intensity. The model parameters are estimated by minimizing a point process prediction error; this notion was introduced as the second building block of PPL. It was…

This https://arxiv.org/abs/2405.14811 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Interacting phase diagram of twisted bilayer MoTe$_2$ in magnetic field
We study electron-electron interaction induced states of twisted bilayer MoTe$_2$ in an out-of-plane magnetic field $B\hat{\bf z}$ near one hole per moiré unit cell filling. The 3D phase diagram showing the evolution of competing phases with $B$, interaction strength and an out-of-plane electric field is presented at electron fillings that follow the Diophantine equation along Chern number $-\text{sign}\left(B\right)$ line, that is pointing away from the charge neutral filling, where we find p…

Fluid Implicit Particles on Coadjoint Orbits
Mohammad Sina Nabizadeh, Ritoban Roy-Chowdhury, Hang Yin, Ravi Ramamoorthi, Albert Chern
https://arxiv.org/abs/2406.01936

Fluid Implicit Particles on Coadjoint Orbits
We propose Coadjoint Orbit FLIP (CO-FLIP), a high order accurate, structure preserving fluid simulation method in the hybrid Eulerian-Lagrangian framework. We start with a Hamiltonian formulation of the incompressible Euler Equations, and then, using a local, explicit, and high order divergence free interpolation, construct a modified Hamiltonian system that governs our discrete Euler flow. The resulting discretization, when paired with a geometric time integration scheme, is energy and circula…

Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer ($\Pi$-MPC)
Luis Pabon, Johannes K\"ohler, John Irvin Alora, Patrick Benito Eberhard, Andrea Carron, Melanie N. Zeilinger, Marco Pavone
https://arxiv.org/abs/2404.01550

Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer ($Π$-MPC)
In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more complex models, this often complicates controller design and implementation. By leveraging the fact that many trajectories of interest are periodic, we show that perfect tracking is possible when incorporating a simple observer that estimates and comp…

This https://arxiv.org/abs/2405.19523 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Comparison of Point Process Learning and its special case Takacs-Fiksel estimation
Recently, Cronie et al. (2024) introduced the notion of cross-validation for point processes and a new statistical methodology called Point Process Learning (PPL). In PPL one splits a point process/pattern into a training and a validation set, and then predicts the latter from the former through a parametrised Papangelou conditional intensity. The model parameters are estimated by minimizing a point process prediction error; this notion was introduced as the second building block of PPL. It was…

Probing microcavity resonance spectra with intracavity emitters
Corn\'e Koks, Martin P. van Exter
https://arxiv.org/abs/2404.02563 https://

Probing microcavity resonance spectra with intracavity emitters
We measure the fluorescence spectrum of broadband emitters in an open optical microcavity with radius of curvature R = 17.7(3) um and finesse F ~= 1000. This geometry enables a combined measurement of emission spectra versus cavity length, which has several benefits over measurements at fixed wavelength or fixed cavity length alone. We demonstrate the role of the optical penetration depths on the cavity modes and provide practical working equations for its analysis. Furthermore, we show the abi…

This https://arxiv.org/abs/2402.12830 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Hidden-flavour four-quark states in the charm and bottom region
We discuss the spectrum and the internal composition of ground and excited four-quark states in the charm and bottom energy region. To this end we extend previous calculations within the framework of the relativistic four-body Faddeev-Yakubovsky equation to include quantum numbers with $J^{P C} = 0^{++} , 0^{-+} , 1^{--} , 1^{+-}$ and $1^{+ +}$ and study their internal composition in terms of heavy-light meson pairs, hadroquarkonia and diquark-antidiquark clusters. We observe similar patterns i…

This https://arxiv.org/abs/2405.19523 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Comparison of Point Process Learning and its special case Takacs-Fiksel estimation
Recently, Cronie et al. (2024) introduced the notion of cross-validation for point processes and a new statistical methodology called Point Process Learning (PPL). In PPL one splits a point process/pattern into a training and a validation set, and then predicts the latter from the former through a parametrised Papangelou conditional intensity. The model parameters are estimated by minimizing a point process prediction error; this notion was introduced as the second building block of PPL. It was…

Comparison of Point Process Learning and its special case Takacs-Fiksel estimation
Julia Jansson, Ottmar Cronie
https://arxiv.org/abs/2405.19523 https://…

Comparison of Point Process Learning and its special case Takacs-Fiksel estimation
Recently, Cronie et al. (2024) introduced the notion of cross-validation for point processes and a new statistical methodology called Point Process Learning (PPL). In PPL one splits a point process/pattern into a training and a validation set, and then predicts the latter from the former through a parametrised Papangelou conditional intensity. The model parameters are estimated by minimizing a point process prediction error; this notion was introduced as the second building block of PPL. It was…