Tanat Tonguthaisri, CISSP®’s Post

Memory tests are surrogate measures of encoding and retrieval capabilities, but Embic's advanced analytics quantify these latent processes. Now they are available to the global research community through ADNI: https://lnkd.in/dm3vTxqw

If it's a professional goal to present your work on establishment statistics in 2024, consider ICES VII. Taking place in Glasgow, Scotland, it's a unique opportunity to reach a global audience interested in the world of survey methodology. The deadline for contributed abstracts and software demo proposals is January 15. Get the details at https://lnkd.in/eydamFBr. #establishmentstatistics #ICES2024 #censusdata #surveymethodology

Memory tests are surrogate measures of encoding and retrieval capabilities, but Embic's advanced analytics quantify these latent processes. Now they are available to the global research community through ADNI: https://lnkd.in/gUFBSvrB

📃Scientific paper: Isolated Insular Stroke: An Interesting Report of Two Cases Continued on ES/IODE ➡️ https://etcse.fr/o2ml ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

📃Scientific paper: Isolated Insular Stroke: An Interesting Report of Two Cases Continued on ES/IODE ➡️ https://etcse.fr/o2ml ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

I am happy to share our open access #PDS paper, " A two-step framework for validating causal effect estimates". The paper introduces a statistical framework for comparing #RWE to unbiased causal estimates. The framework focuses on addressing two main sources of bias from observational data. The framework makes additional contributions to Causal Roadmap, the RCT-DUPLICATE Initiative, and target trial emulation. An R package #RCTrep is developed to implement the framework. Check out here: https://lnkd.in/ex3NeyNY Many thanks to my co-authors and #IKNL.

We are pleased to announce that our paper, titled "Optimizing the Parameters of Pipelined Multi-party Computation for Privacy-preserving Machine Learning Applications," has been accepted for presentation at the "IEEE International Conference on Communication 2024 (ICC24)". In this research, Richard H., Dr. Oscar G. Bautista, and Dr. Kemal Akkaya tackle the performance challenges of Privacy-Preserving Machine Learning (PPML) by introducing pipelined Multi-party Computation (MPC). Employing a Multi-Objective Optimization (MOO) model, we focus on achieving an optimal configuration to minimize execution time and associated costs. Our evaluation showcases a substantial reduction in execution time compared to traditional matrix multiplications, providing valuable insights into the impact of latency and bandwidth considerations on PPML optimization.

#mdpisymmetry Article "The Optimal Deployment Strategy of Mega-Constellation Based on Markov Decision Process" is available at https://brnw.ch/21wLJvJ Authors: Xuefeng Wang, Shijie Zhang and Hongzhu Zhang Harbin Institute of Technology #Markovdecisionprocess

📃Scientific paper: Embedding phylogenetic trees in networks of low treewidth Abstract: International audience; Given a rooted, binary phylogenetic network and a rooted, binary phylogenetic tree, can the tree be embedded into the network? This problem, called \textsc\{Tree Containment\}, arises when validating networks constructed by phylogenetic inference methods.We present the first algorithm for \(rooted\) \textsc\{Tree Containment\} using the treewidth $t$ of the input network $N$ as parameter, showing that the problem can be solved in $2^\{O\(t^2\)\}\cdot\|N\|$ time and space. Continued on ES/IODE ➡️ https://etcse.fr/KpAu ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

In an exciting new paper, Jeffry Setiadi and others adapted our force field fitting infrastructure to make use of host-guest molecular binding data. They used this capability to tune the parameters of an implicit solvation model, and found that this new model improved prediction of protein-ligand binding affinities. Using an implicit solvent model can speed up binding simulations by as much as 10x, so the improved accuracy for this particular case could be very valuable in virtual compound screening for drug development. The trade-off, worse accuracy for predicting hydration free energies, suggests that there is still a lot of work to be done in this area, but the capability they developed to make use of this additional type of dataset will enable new explorations in force field science. https://lnkd.in/eynn_nAw