Tootfinder

Randomization inference for stepped-wedge designs with noncompliance with application to a palliative care pragmatic trial
Jeffrey Zhang, Zhe Chen, Katherine R. Courtright, Scott D. Halpern, Michael O. Harhay, Dylan S. Small, Fan Li
https://arxiv.org/abs/2509.14598

Randomization inference for stepped-wedge designs with noncompliance with application to a palliative care pragmatic trial
While palliative care is increasingly commonly delivered to hospitalized patients with serious illnesses, few studies have estimated its causal effects. Courtright et al. (2016) adopted a cluster-randomized stepped-wedge design to assess the effect of palliative care on a patient-centered outcome. The randomized intervention was a nudge to administer palliative care but did not guarantee receipt of palliative care, resulting in noncompliance (compliance rate ~30%). A subsequent analysis using m…

PBL: Kans op halen klimaatdoel 2030 heel erg klein; stevig, structureel extra beleid nodig.
Het is heel erg onwaarschijnlijk dat Nederland het wettelijke klimaatdoel van 55 procent emissiereductie in 2030 haalt. Ook voor het halen van bindende Europese doelen voor hernieuwbare energie en energieverbruik ligt Nederland niet op koers. Meer inzet op energiebesparing kan daarbij helpen. Dit blijkt uit de Klimaat- en Energieverkenning (KEV) 2025.

Kans op halen klimaatdoel 2030 heel erg klein; stevig, structureel extra beleid nodig
Het is heel erg onwaarschijnlijk dat Nederland het wettelijke klimaatdoel van 55 procent emissiereductie in 2030 haalt.

Implementation of the Martini-Ericson-Chanfray-Marteau RPA-based (anti)neutrino cross-section model in the GENIE neutrino event generator
Lavinia Russo (Sorbonne Universit\'e, CNRS/IN2P3, Laboratoire de Physique Nucl\'eaire et de Hautes Energies), Marco Martini (IPSA-DRII, 63 boulevard de Brandebourg, Ivry-sur-Seine, France, Sorbonne Universit\'e, CNRS/IN2P3, Laboratoire de Physique Nucl\'eaire et de Hautes Energies), Stephen Dolan (CERN, European Organization for Nucle…

Implementation of the Martini-Ericson-Chanfray-Marteau RPA-based (anti)neutrino cross-section model in the GENIE neutrino event generator
We discuss the first implementation of the Martini-Ericson-Chanfray-Marteau RPA-based (anti)neutrino cross-section model for quasielastic (1p1h) and multinucleon (2p2h and 3p3h) excitations in the widely used GENIE neutrino event generator. Validation steps are presented, in particular, through direct comparisons of GENIE cross section output with original calculations performed by the authors of the model. Predictions for $^{12}$C, $^{16}$O and $^{40}$Ar are compared with some available T2K an…

A Bayesian approach to time-domain Photonic Doppler Velocimetry
J. R. Allison (First Light Fusion Ltd), R. Bordas (First Light Fusion Ltd), J. Read (First Light Fusion Ltd), G. Burdiak (First Light Fusion Ltd), V. Beltr\'an (First Light Fusion Ltd), N. Joiner (First Light Fusion Ltd), H. Doyle (First Light Fusion Ltd), N. Hawker (First Light Fusion Ltd), J. Skidmore (First Light Fusion Ltd), T. Ao (Sandia National Laboratories), A. Porwitzky (Sandia National Laboratories), D. Dolan…

A Bayesian approach to time-domain Photonic Doppler Velocimetry
Photonic Doppler Velocimetry (PDV) is an established technique for measuring the velocities of fast-moving surfaces in high-energy-density experiments. In the standard approach to PDV analysis, a short-time Fourier transform (STFT) is used to generate a spectrogram from which the velocity history of the target is inferred. The user chooses the form, duration and separation of the window function. Here we present a Bayesian approach to infer the velocity directly from the PDV oscilloscope trace,…

Foxconn reports Q2 net operating profit up 27% YoY to ~$1.9B, beating estimates, on $59.73B revenue, on the strength of its growing AI server business (Dylan Butts/CNBC)
https://www.cnbc.com/2025/08/14/ai-demand-

AI demand boosts iPhone maker Foxconn's second-quarter profit by 27%, beating forecasts
Foxconn reported a second-quarter operating profit beat on strength of its growing artificial intelligence server business.

OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft
Zihao Wang, Muyao Li, Kaichen He, Xiangyu Wang, Zhancun Mu, Anji Liu, Yitao Liang
https://arxiv.org/abs/2509.13347

OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft
The choice of action spaces is a critical yet unresolved challenge in developing capable, end-to-end trainable agents. This paper first presents a large-scale, systematic comparison of prominent abstracted action spaces and tokenizers for Vision-Language-Action (VLA) or hierarchical agent models in the open-ended Minecraft. Our analysis reveals that no single action space is universally optimal; instead, the most effective abstraction is highly task-dependent, creating a dilemma for building ge…

Magnetic fields in the intracluster medium with TNG-Cluster: properties, morphology, and tangential anisotropy
Katrin Lehle, Mateusz Ruszkowski, Dylan Nelson, Marine Prunier, Annalisa Pillepich
https://arxiv.org/abs/2507.12517

Magnetic fields in the intracluster medium with TNG-Cluster: properties, morphology, and tangential anisotropy
We characterize the magnetic field properties of 352 massive galaxy clusters from the TNG-Cluster magnetohydrodynamical cosmological simulation with a focus on central magnetic field morphology in cool-core (CC) vs non-cool-core (NCC) clusters. We present the central values and radial profiles of magnetic field strength and plasma parameter as a function of mass, cooling status and redshift. Compared to low-redshift observations, TNG-Cluster produces reasonable magnetic field amplitudes in the …

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models
Ann-Kathrin Dombrowski, Dillon Bowen, Adam Gleave, Chris Cundy
https://arxiv.org/abs/2507.11544

The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models
Open-weight large language models (LLMs) unlock huge benefits in innovation, personalization, privacy, and democratization. However, their core advantage - modifiability - opens the door to systemic risks: bad actors can trivially subvert current safeguards, turning beneficial models into tools for harm. This leads to a 'safety gap': the difference in dangerous capabilities between a model with intact safeguards and one that has been stripped of those safeguards. We open-source a toolkit to est…

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
Hang Lv, Sheng Liang, Hao Wang, Hongchao Gu, Yaxiong Wu, Wei Guo, Defu Lian, Yong Liu, Enhong Chen
https://arxiv.org/abs/2507.04756

CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
Personalized text generation has become crucial for adapting language models to diverse and evolving users' personal context across cultural, temporal, and contextual dimensions. While existing methods often rely on centralized fine-tuning or static preference alignment, they struggle to achieve real-time adaptation under resource constraints inherent to personal devices. This limitation creates a dilemma: large cloud-based models lack access to localized user-specific information, while small …

Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning
Gustavo C. Mangold, Heitor C. M. Fernandes, Mendeli H. Vainstein
https://arxiv.org/abs/2507.02211

Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning
Recent studies in the spatial prisoner's dilemma games with reinforcement learning have shown that static agents can learn to cooperate through a diverse sort of mechanisms, including noise injection, different types of learning algorithms and neighbours' payoff knowledge.In this work, using an independent multi-agent Q-learning algorithm, we study the effects of dilution and mobility in the spatial version of the prisoner's dilemma. Within this setting, different possible actions for the algor…