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1. Characterization of MPC-based Private Inference for Transformer-based Models

   https://doi.org/10.1109/ISPASS55109.2022.00025  

   Wang, Yongqin; Suh, G. Edward; Xiong, Wenjie; Lefaudeux, Benjamin; Knott, Brian; Annavaram, Murali; Lee, Hsien-Hsin S. (May 2022, 2022 ISPASS)

   Provides a detailed characterization of MPC based NLP model inference. 

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2. Review of MXene-based nanocomposites for photocatalysis

   https://doi.org/10.1016/j.chemosphere.2020.129478  

   Im, Jong Kwon; Sohn, Erica Jungmin; Kim, Sewoon; Jang, Min; Son, Ahjeong; Zoh, Kyung-Duk; Yoon, Yeomin (May 2021, Chemosphere)

   null (Ed.)
3. Reward-based training of recurrent neural networks for cognitive and value-based tasks

   https://doi.org/10.7554/eLife.21492  

   Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing (January 2017, eLife)

   A major goal in neuroscience is to understand the relationship between an animal’s behavior and how this is encoded in the brain. Therefore, a typical experiment involves training an animal to perform a task and recording the activity of its neurons – brain cells – while the animal carries out the task. To complement these experimental results, researchers “train” artificial neural networks – simplified mathematical models of the brain that consist of simple neuron-like units – to simulate the same tasks on a computer. Unlike real brains, artificial neural networks provide complete access to the “neural circuits” responsible for a behavior, offering a way to study and manipulate the behavior in the circuit. One open issue about this approach has been the way in which the artificial networks are trained. In a process known as reinforcement learning, animals learn from rewards (such as juice) that they receive when they choose actions that lead to the successful completion of a task. By contrast, the artificial networks are explicitly told the correct action. In addition to differing from how animals learn, this limits the types of behavior that can be studied using artificial neural networks. Recent advances in the field of machine learning that combine reinforcement learning with artificial neural networks have now allowed Song et al. to train artificial networks to perform tasks in a way that mimics the way that animals learn. The networks consisted of two parts: a “decision network” that uses sensory information to select actions that lead to the greatest reward, and a “value network” that predicts how rewarding an action will be. Song et al. found that the resulting artificial “brain activity” closely resembled the activity found in the brains of animals, confirming that this method of training artificial neural networks may be a useful tool for neuroscientists who study the relationship between brains and behavior. The training method explored by Song et al. represents only one step forward in developing artificial neural networks that resemble the real brain. In particular, neural networks modify connections between units in a vastly different way to the methods used by biological brains to alter the connections between neurons. Future work will be needed to bridge this gap. 

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4. Comparing genome‐based estimates of relatedness for use in pedigree‐based conservation management

   https://doi.org/10.1111/1755-0998.13630  

   Hauser, Samantha S.; Galla, Stephanie J.; Putnam, Andrea S.; Steeves, Tammy E.; Latch, Emily K. (May 2022, Molecular Ecology Resources)

   Abstract Researchers have long debated which estimator of relatedness best captures the degree of relationship between two individuals. In the genomics era, this debate continues, with relatedness estimates being sensitive to the methods used to generate markers, marker quality, and levels of diversity in sampled individuals. Here, we compare six commonly used genome‐based relatedness estimators (kinship genetic distance [KGD], Wang maximum likelihood [TrioML], Queller and Goodnight [R_xy], Kinship INference for Genome‐wide association studies [KING‐robust), and pairwise relatedness [R_AB], allele‐sharing coancestry [AS]) across five species bred in captivity–including three birds and two mammals–with varying degrees of reliable pedigree data, using reduced‐representation and whole genome resequencing data. Genome‐based relatedness estimates varied widely across estimators, sequencing methods, and species, yet the most consistent results for known first order relationships were found usingR_xy,R_AB, and AS. However, AS was found to be less consistently correlated with known pedigree relatedness than eitherR_xyorR_AB. Our combined results indicate there is not a single genome‐based estimator that is ideal across different species and data types. To determine the most appropriate genome‐based relatedness estimator for each new data set, we recommend assessing the relative: (1) correlation of candidate estimators with known relationships in the pedigree and (2) precision of candidate estimators with known first‐order relationships. These recommendations are broadly applicable to conservation breeding programmes, particularly where genome‐based estimates of relatedness can complement and complete poorly pedigreed populations. Given a growing interest in the application of wild pedigrees, our results are also applicable to in situ wildlife management. 

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5. Review of computer-based assessment for learning in elementary and secondary education: Computer-based assessment for learning

   https://doi.org/10.1111/jcal.12172  

   Shute, V.J.; Rahimi, S. (February 2017, Journal of Computer Assisted Learning)