Abstract: A machine learning model trained with a triplet loss function classifies input strings into one of multiple hierarchical categories. The machine learning model is pre-trained using masking language modeling on a corpus of unlabeled strings. The machine learning module includes an attention-based bi-directional transformer layer. Following initial training, the machine learning model is refined by additional training with a loss function that includes cross-entropy loss and triplet loss. This provides a deep learning solution to classify input strings into one or more hierarchical categories. Embeddings generated from inputs to the machine learning model capture language similarities that can be visualized in a cartesian plane where strings with similar meanings are grouped together.

Abstract: The present disclosure relates to generating a complex entity index based on a combination of atomic and deep learned attributes associated with instances of a complex entity. For example, systems described herein generate a multi-dimensional representation of entity instances based on evaluation of digital content associated with the respective entity instances. Systems described herein further generate an index representation in which similarity of entity instances are illustrated and presented via an interactive presentation that enables a user to traverse instances of an entity to observe similarities and differences between instances of an entity that have similar embeddings to one another within a multi-dimensional index space.

Abstract: A method for selecting a seed region for use in a seed-based cortical functional mapping method includes providing at least one RSN map of at least one subject, each of the at least one RSN maps comprising a plurality of functional voxels within a brain of each of the at least one subjects, each functional voxel of the plurality of functional voxels associated with a probability of membership in an RSN. A subset of the functional voxels characterizing a contiguous region is selected as the seed region, each functional voxel of the seed region having a probability of membership in the RSN above a threshold value.

Abstract: A target location for a therapeutic intervention is determined in a subject with a neurological disorder. The target location is selected within at least one resting state network (RSN) map according to a predetermined criterion for the neurological disorder. The at least one RSN map includes a plurality of functional voxels within a brain of the subject, and each functional voxel of the plurality of functional voxels is associated with a probability of membership in an RSN. Instructions are transmitted to a treatment system that cause operation to be performed on the selected target location.

Abstract: The present disclosure relates to generating a complex entity index based on a combination of atomic and deep learned attributes associated with instances of a complex entity. For example, systems described herein generate a multi-dimensional representation of entity instances based on evaluation of digital content associated with the respective entity instances. Systems described herein further generate an index representation in which similarity of entity instances are illustrated and presented via an interactive presentation that enables a user to traverse instances of an entity to observe similarities and differences between instances of an entity that have similar embeddings to one another within a multi-dimensional index space.

Abstract: A biomarker predictive of a survival outcome of a brain tumor patient is disclosed. The biomarker includes a functional connectivity matrix that includes a plurality of matrix elements. Each matrix element includes a correlation of resting-state fMRI activities of a first and second region of interest from a plurality of regions of interest within the patient's brain. Computing device and systems are disclosed to transform a resting-state fMRI dataset obtained from the patient into the biomarker and to transform the biomarker into a predicted survival outcome using a machine learning model.

Abstract: A modular support frame (1) for supporting LED panels (2), comprising several frame modules (3), wherein at least one of these frame modules (3) is configured to support an LED panel (2), and comprises at least one longitudinal rod (4, 5) of a fixed length which is at least partially double-walled, and an LED wall (26) comprising such a support frame (1).

Abstract: A modular support frame (1) for supporting LED panels (2), comprising several frame modules (3), wherein at least one of these frame modules (3) is configured to support an LED panel (2), and comprises at least one longitudinal rod (4, 5) of a fixed length which is at least partially double-walled, and an LED wall (26) comprising such a support frame (1).

Abstract: A method for determining a target location for a therapeutic intervention in a subject with a neurological disorder is disclosed that includes selecting the target location within at least one resting state network (RSN) map according to a predetermined criterion for the neurological disorder. Each of the at least one RSN maps includes a plurality of functional voxels within a brain of the subject, and each functional voxel of the plurality of functional voxels is associated with a probability of membership in an RSN. A method for monitoring an efficacy of a therapeutic intervention in a subject with a neurological disorder is also disclosed that includes comparing at least a portion of at least one pre-treatment RSN map of the subject prior to the therapeutic intervention, at least one post-treatment RSN map of the subject after the therapeutic intervention to determine changes in the at least one post-treatment RSN map.