Abstract: Provided herein are, inter alia, antibody binding domains for cluster of differentiation 3 T cell receptor (CD3), antibody binding domains for epidermal growth factor receptor (EGFR), cleavable linker sequences, and protease-activatable bispecific fusion proteins such as protease-activatable T cell engagers, as well as uses and methods of treatment.

Abstract: Provided herein are, inter alia, antigen-binding molecules with binding specificity to cluster of differentiation 3 T cell receptor (CD3), antigen-binding molecules with binding specificity to prostate-specific membrane antigen (PSMA), cleavable linker sequences, and protease-activatable bispecific fusion proteins such as protease-activatable T cell engagers, as well as uses and methods of treatment.

Abstract: Provided herein are, inter alia, antibody binding domains for cluster of differentiation 3 T cell receptor (CD3), antibody binding domains for epidermal growth factor receptor (EGFR), cleavable linker sequences, and protease-activatable bispecific fusion proteins such as protease-activatable T cell engagers, as well as uses and methods of treatment.

Abstract: Provided herein are, inter alia, antigen-binding molecules with binding specificity to cluster of differentiation 3 T cell receptor (CD3), antigen-binding molecules with binding specificity to prostate-specific membrane antigen (PSMA), cleavable linker sequences, and protease-activatable bispecific fusion proteins such as protease-activatable T cell engagers, as well as uses and methods of treatment.

Abstract: The disclosed concepts relate to pretraining of machine learning models. One example method involves performing separate optimization of a first machine learning model and a second machine learning model. The first machine learning model can be optimized based at least on first predictions and the second machine learning model can be optimized based at least on second predictions. The first predictions can represent predictions of masked values in first sequences of values values, and the second predictions can represent whether or not the first values were replaced with different values predicted by the first machine learning model.

Abstract: An autonomous vehicle includes a chassis for housing an aircraft ground contacting structure (GCS) and one or more GCS coupling and lifting mechanisms, GCS securing mechanisms, drivetrain, batteries and a sensor stack for performing autonomous navigation. Further, the vehicle incorporates multiple sensors, such as high-resolution machine vision cameras, GPS modules, Lidars, ultrasonic range sensors, and radars. The sensors deliver spatial perception capabilities to the vehicle and feed relevant data to onboard computing units to achieve location-based navigation, precision alignment with aircraft, obstacle detection and collision avoidance capabilities. A series of batteries deliver power to all components, including but not limited to, motors, electromechanical units, onboard computers, sensors and any other component requiring electrical input. External ports allow the vehicle to recharge batteries after each utilization cycle without replacement.

Abstract: An approach for determining similar text documents. The approach can calculate a first set of vectors for a first cluster of text documents and a first comparison vector for a text document of interest. The approach can select a subset of text documents from the cluster of text documents based on comparing the vectors from the first set of vectors to the first comparison vector and picking a predetermined number of closest comparison text documents. The approach can calculate a second set of vectors for the subset of documents and second comparison vector for the document of interest. The approach can generate similarity ratings for the subset of documents based on pairwise comparisons of the second comparison vector and the second set of vectors. The approach can generate a ranked list of the second cluster of text documents based on the similarity ratings.

Abstract: An approach for determining similar text documents. The approach can calculate a first set of vectors for a first cluster of text documents and a first comparison vector for a text document of interest. The approach can select a subset of text documents from the cluster of text documents based on comparing the vectors from the first set of vectors to the first comparison vector and picking a predetermined number of closest comparison text documents. The approach can calculate a second set of vectors for the subset of documents and second comparison vector for the document of interest. The approach can generate similarity ratings for the subset of documents based on pairwise comparisons of the second comparison vector and the second set of vectors. The approach can generate a ranked list of the second cluster of text documents based on the similarity ratings.