While analytics of sleep electroencephalography (EEG) holds certain advantages over other methods in clinical applications, high variability across subjects poses a significant challenge when it comes to deploying machine learning models for classification tasks in the real world. In such instances, machine learning models that exhibit exceptional performance on a specific dataset may not necessarily demonstrate similar proficiency when applied to a distinct dataset for the same task. The scarcity of high-quality biomedical data further compounds this challenge, making it difficult to evaluate the model's generality comprehensively. In this paper, we introduce Transfer Euclidean Alignment - a transfer learning technique to tackle the problem of the dearth of human biomedical data for training deep learning models. We tested the robustness of this transfer learning technique on various rule-based classical machine learning models as well as the EEGNet-based deep learning model by evaluating on different datasets, including human and mouse data in a binary classification task of detecting individuals with versus without traumatic brain injury (TBI). By demonstrating notable improvements with an average increase of 14.42% for intraspecies datasets and 5.53% for interspecies datasets, our findings underscore the importance of the use of transfer learning to improve the performance of machine learning and deep learning models when using diverse datasets for training.

However, most of our understanding is restricted to settings where each Given only a few observed entries from a lowrank row and each column have more observations than the rank matrix X, matrix completion is the problem of the underlying matrix. It is natural that past work operated of imputing the missing entries, and it formalizes under this assumption because full matrix completion a wide range of real-world settings that involve is impossible without it: for a rank-r matrix X with estimating missing data. However, when shape m d, one can show that estimating the matrix is there are too few observed entries to complete impossible with o(r(m + d)) observations. Nonetheless, the matrix, what other aspects of the underlying many important applications do not satisfy this assumption: matrix can be reliably recovered? We study one for example, in low-coverage genotype imputation (Li such problem setting, that of "one-sided" matrix et al., 2009), we might sequence d = 2,000 people for completion, where our goal is to recover the 10,000 genetic variants each, out of the m = 10,000,000 right singular vectors of X, even in the regime genetic variants in humans. Represented as a matrix, we where recovering the left singular vectors is impossible, have a 10,000,000 2,000 matrix with 2,000 10,000 = which arises when there are more rows 20,000,000 total observations, or about two observations than columns and very few observations. We propose per row on average, which is certainly much less than the a natural algorithm that involves imputing rank of the matrix.

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from MRI gradient-echo phase signal and typically requires several processing steps. These steps involve phase unwrapping, brain volume extraction, background phase removal and solving an ill-posed inverse problem. The resulting susceptibility map is known to suffer from inaccuracy near the edges of the brain tissues, in part due to imperfect brain extraction, edge erosion of the brain tissue and the lack of phase measurement outside the brain. This inaccuracy has thus hindered the application of QSM for measuring the susceptibility of tissues near the brain edges, e.g., quantifying cortical layers and generating superficial venography. To address these challenges, we propose a learning-based QSM reconstruction method that directly estimates the magnetic susceptibility from total phase images without the need for brain extraction and background phase removal, referred to as autoQSM. The neural network has a modified U-net structure and is trained using QSM maps computed by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82 years were employed for patch-wise network training. The network was validated on data dissimilar to the training data, e.g. in vivo mouse brain data and brains with lesions, which suggests that the network has generalized and learned the underlying mathematical relationship between magnetic field perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic susceptibility of anatomical structures near the edges of the brain including the veins covering the cortical surface, spinal cord and nerve tracts near the mouse brain boundaries. The advantages of high-quality maps, no need for brain volume extraction and high reconstruction speed demonstrate its potential for future applications.