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Mr Campfield, his daughter, and Tasmanian conman Logan
#JasonSimmons #KellyPackard #JimMcMullan
Season 6 Episode 1 "Trapped beneath the Sea: part 1"

This https://arxiv.org/abs/2107.06238 has been replaced.
link: https://scholar.google.com/scholar?q=a

GENIUS-MAWII: For Robust Mendelian Randomization with Many Weak Invalid Instruments
Mendelian randomization (MR) has become a popular approach to study causal effects by using genetic variants as instrumental variables. We propose a new MR method, GENIUS-MAWII, which simultaneously addresses the two salient phenomena that adversely affect MR analyses: many weak instruments and widespread horizontal pleiotropy. Similar to MR GENIUS (Tchetgen Tchetgen et al., 2021), we achieve identification of the treatment effect by leveraging heteroscedasticity of the exposure. We then derive…

Harnessing Orbital Hall Effect in Spin-Orbit Torque MRAM
Rahul Gupta, Chlo\'e Bouard, Fabian Kammerbauer, J. Omar Ledesma-Martin, Iryna Kononenko, Sylvain Martin, Gerhard Jakob, Marc Drouard, Mathias Kl\"aui
https://arxiv.org/abs/2404.02821

Harnessing Orbital Hall Effect in Spin-Orbit Torque MRAM
Spin-Orbit Torque (SOT) Magnetic Random-Access Memory (MRAM) devices offer improved power efficiency, nonvolatility, and performance compared to static RAM, making them ideal, for instance, for cache memory applications. Efficient magnetization switching, long data retention, and high-density integration in SOT MRAM require ferromagnets (FM) with perpendicular magnetic anisotropy (PMA) combined with large torques enhanced by Orbital Hall Effect (OHE). We have engineered PMA [Co/Ni]$_3$ FM on se…

A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system
Yu Gu, Puyang Huang, Tianhao Chen, Chenyi Fu, Aitian Chen, Shouzhong Peng, Xixiang Zhang, Xufeng Kou
https://arxiv.org/abs/2403.19374

A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system
We report a spin-orbit torque(SOT) magnetoresistive random-access memory(MRAM)-based probabilistic binary neural network(PBNN) for resource-saving and hardware noise-tolerant computing applications. With the presence of thermal fluctuation, the non-destructive SOT-driven magnetization switching characteristics lead to a random weight matrix with controllable probability distribution. In the meanwhile, the proposed CIM architecture allows for the concurrent execution of the probabilistic vector-…

Bispectrum Unbiasing for Dilation-Invariant Multi-reference Alignment
Liping Yin, Anna Little, Matthew Hirn
https://arxiv.org/abs/2402.14276 https://

Bispectrum Unbiasing for Dilation-Invariant Multi-reference Alignment
Motivated by modern data applications such as cryo-electron microscopy, the goal of classic multi-reference alignment (MRA) is to recover an unknown signal $f: \mathbb{R} \to \mathbb{R}$ from many observations that have been randomly translated and corrupted by additive noise. We consider a generalization of classic MRA where signals are also corrupted by a random scale change, i.e. dilation. We propose a novel data-driven unbiasing procedure which can recover an unbiased estimator of the bispe…

End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI
George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen
https://arxiv.org/abs/2403.10346 …

End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI
Accelerating dynamic MRI is essential for enhancing clinical applications, such as adaptive radiotherapy, and improving patient comfort. Traditional deep learning (DL) approaches for accelerated dynamic MRI reconstruction typically rely on predefined or random subsampling patterns, applied uniformly across all temporal phases. This standard practice overlooks the potential benefits of leveraging temporal correlations and lacks the adaptability required for case-specific subsampling optimization…

A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system
Yu Gu, Puyang Huang, Tianhao Chen, Chenyi Fu, Aitian Chen, Shouzhong Peng, Xixiang Zhang, Xufeng Kou
https://arxiv.org/abs/2403.19374

A noise-tolerant, resource-saving probabilistic binary neural network implemented by the SOT-MRAM compute-in-memory system
We report a spin-orbit torque(SOT) magnetoresistive random-access memory(MRAM)-based probabilistic binary neural network(PBNN) for resource-saving and hardware noise-tolerant computing applications. With the presence of thermal fluctuation, the non-destructive SOT-driven magnetization switching characteristics lead to a random weight matrix with controllable probability distribution. In the meanwhile, the proposed CIM architecture allows for the concurrent execution of the probabilistic vector-…

This https://arxiv.org/abs/2306.17260 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Incorporating Auxiliary Variables to Improve the Efficiency of Time-Varying Treatment Effect Estimation
The use of smart devices (e.g., smartphones, smartwatches) and other wearables for context sensing and delivery of digital interventions to improve health outcomes has grown significantly in behavioral and psychiatric studies. Micro-randomized trials (MRTs) are a common experimental design for obtaining data-driven evidence on mobile health (mHealth) intervention effectiveness where each individual is repeatedly randomized to receive treatments over numerous time points. Individual characterist…

Data integration methods for micro-randomized trials
Easton Huch, Inbal Nahum-Shani, Lindsey Potter, Cho Lam, David W. Wetter, Walter Dempsey
https://arxiv.org/abs/2403.13934

Data integration methods for micro-randomized trials
Existing statistical methods for the analysis of micro-randomized trials (MRTs) are designed to estimate causal excursion effects using data from a single MRT. In practice, however, researchers can often find previous MRTs that employ similar interventions. In this paper, we develop data integration methods that capitalize on this additional information, leading to statistical efficiency gains. To further increase efficiency, we demonstrate how to combine these approaches according to a general…