Meredith College

We evaluate the impact of independent, central review of epilepsy syndromes and seizure classification in randomized controlled treatment trials of Lennox-Gastaut syndrome (LGS) and Dravet syndrome (DS). Central review of two recent trials determined that 5.6% of enrolled DS participants and 3.4% of enrolled LGS participants pending randomization did not meet criteria for the epilepsy syndrome under study. Furthermore, 23% of DS participants and 32% of LGS participants had at least one seizure reclassified; 63.8% of DS sites and 78% of LGS sites had at least one participant with a seizure reclassification. Independent, central review of participants in pediatric ASM trials improves consistency across study sites, as well as overall accuracy of syndromic diagnosis and seizure classification, and may reduce placebo response rate.

Transgender individuals continue to be the victims of rampant discrimination in the United States across professions and communities. Discrimination against transgender individuals by police officers may result in avoiding interactions with law enforcement. With a surge of non-biased policing training initiatives aimed at identifying and eliminating biases among police officers, and laws prohibiting discrimination by law enforcement officers, this study explores transgender individuals’ perceptions and experiences with law enforcement. Using a logistic regression, we find that transgender respondents do not feel comfortable going to the police, with many instances of abuse by police. Based on these findings, we make recommendations on how police can support the transgender community which is currently under attack.

Microbiology Society journals contain high-quality research papers and topical review articles. We are a not-for-profit publisher and we support and invest in the microbiology community, to the benefit of everyone. This supports our principal goal to develop, expand and strengthen the networks available to our members so that they can generate new knowledge about microbes and ensure that it is shared with other communities.

This chapter examines operations associated with matrix and tensor multiplications, which are expressed as X ∘ Y or simply XY mathematically. It looks at several additional common linear algebra routines, such as finding the matrix inverse and eigenvalue decomposition. Performing multiplication between vector, matrix, and higher dimensional tensors, finding matrix inverse, and computing eigenvalue decomposition are common tasks when implementing machine learning models that are typically described using mathematical terms. The np.matmul operations perform general matrix multiplications that are applied over multidimensional batches. The tensordot operation is a generalization of all of the matrix product operations. It performs dot product along certain axes. Different integer values of the axes argument seem to dictate different tensor multiplication behaviors. Einstein Summation Convention is a generalization of various forms of vector, matrix, and tensor summations and multiplications The chapter provides a list of commonly used linear algebra routines implemented by NumPy, Tensorflow, and PyTorch.

This chapter presents the evolution of language modeling using the transformer architecture to illustrate how machine learning models, specifically, large language models like LLaMA (Large Language Model Meta AI), came to be. The sinusoidal position information is hard-coded. However, it is also possible to learn to represent the position information. An approach to learning to represent positional information is to treat positions as a categorical feature. Then, through back-propagation, the network learns to efficiently encode positional information. Normalization can help stabilize neural network training by reducing the likelihood of exploding or vanishing gradients and improving model generalization. In the original transformer paper, the authors used position encodings added on top of the input embedding vectors of each word token to allow the attention model to disambiguate words from different positions along the sequence.