Abstract: In some aspects, a computing system may use machine learning to determine whether a control is faulty or generate recommendations to make a modification to a control. may identify a portion of problematic computer code that implements the faulty control through the use of machine learning. A computing system may use machine learning to generate embeddings that map incident data and control data (e.g., computer-readable code of a control) to the same vector space. Further, a computing system may use a weighting mechanism that may be used to weight each sample used to train a machine learning model, which may allow a model to train more efficiently.

Abstract: A method for executing actions based on event data using machine learning is disclosed. The method comprises: receiving occasion data associated with a user; analyzing, using a trained machine learning model, the occasion data to identify an occasion associated with a first classification, wherein the trained machine learning model has been trained based on (i) training occasion data that includes information regarding one or more occasions associated with the training occasion data and (ii) training classification data that includes a prior classification for each of the occasions, to learn relationships between the training occasion data and the training classification data, such that the trained machine learning model is configured to use the learned relationships to identify an occasion associated with a first classification in response to input of the occasion data; determining an action based on the occasion associated with the first classification; and automatically executing the action.

Abstract: In some aspects, a computing system may use machine learning to determine whether a control is faulty or generate recommendations to make a modification to a control. may identify a portion of problematic computer code that implements the faulty control through the use of machine learning. A computing system may use machine learning to generate embeddings that map incident data and control data (e.g., computer-readable code of a control) to the same vector space. Further, a computing system may use a weighting mechanism that may be used to weight each sample used to train a machine learning model, which may allow a model to train more efficiently.

Abstract: A method for machine-learning based action generation, and more specifically, using machine-learning to dynamically adjust financial account payments and fees. The method may comprise: receiving user data; determining whether a trigger condition has been met; upon determining that a trigger condition has been met, generating, using a trained machine-learning model, one or more actions based on the user data associated with the user, wherein the trained machine-learning model has been trained based on (i) training user data and (ii) training action data, to learn relationships between the training user data and the training actions data, such that the trained machine-learning model is configured to use the learned relationships to generate one or more actions in response to input of the user data associated with the user; selecting a first action of the one or more actions; and automatically executing the first action.

Abstract: In some aspects, a computing system may use machine learning to determine whether a control is faulty or generate recommendations to make a modification to a control. may identify a portion of problematic computer code that implements the faulty control through the use of machine learning. A computing system may use machine learning to generate embeddings that map incident data and control data (e.g., computer-readable code of a control) to the same vector space. Further, a computing system may use a weighting mechanism that may be used to weight each sample used to train a machine learning model, which may allow a model to train more efficiently.

Abstract: Systems and methods for external account authentication are disclosed herein. They include receiving a call to pair the external account with a secure account, extracting external data from the external account, the external data corresponding to external account content, providing user activity data from the secure account as an input to an authentication machine learning model, providing the external data as an input to the authentication machine learning model, the authentication machine learning model configured to output a certainty level that the external account is associated with a user of the secure account based on the external data and the activity data, receiving the certainty level from the authentication machine learning model, determining that the certainty level meets a certainty threshold, and pairing the external account with the secure account based on determining that the certainty level meets the certainty threshold.

Abstract: Provided herein are methods of treating multiple myeloma, comprising administering to a subject with multiple myeloma an inhibitor of Nuclear Receptor Binding SET Domain Protein 2 (NSD2). Also provided are methods of use wherein the multiple myeloma has previously been determined to have a 4:14 chromosome translocation (t(4;14)).

Abstract: Systems for improvising sensorimotor function of a patient, wherein the various systems can include at least one force and/or pressure sensor associated with at least one lower limb or prosthesis of the patient, at least one motion and/or angle sensor associated with at least one lower limb or prosthesis of the patient, a processor configured to receive force and/or pressure signals and motion and/or angle signals and generate an estimated center of pressure and a center of gravity, and generate balance stimulation signals based on the estimated center of pressure and the center of gravity, and at least one sensory stimulation unit disposed on at least one lower limb or prosthesis of the patient, wherein the at least one sensory stimulation unit comprises at least two stimulators that are actuable to provide stimulation to the patient based on the balance stimulation signals.

Abstract: Technologies and implementations for a defibrillator electrode having communicative capabilities are generally disclosed.

Abstract: This application relates to a water treatment process. The process comprises contacting contaminated water with a catalyst, introducing hydrogen and an oxygen-containing gas into the contaminated water, and reacting hydrogen and oxygen in the presence of the catalyst and the contaminated water.

Abstract: This application relates to a water treatment process. The process comprises contacting contaminated water with a catalyst, introducing hydrogen and an oxygen-containing gas into the contaminated water, and reacting hydrogen and oxygen in the presence of the catalyst and the contaminated water.

Abstract: Compositions and methods for preventing and treating Neisseria gonorrhoeae infection with AR-12 alone or in combination with antibiotics are provided.

Abstract: Compositions and methods for preventing and treating Neisseria gonorrhoeae infection with AR-12 alone or in combination with antibiotics are provided.

Abstract: A tool for use in wellbore drilling that is suitable for use with a drill string for drilling a wellbore having an inner wall. The tool comprises a cylindrical abrasion surface for contacting the inner-wall and generating particles of solid material, and a smearing device coupled with the drill string for applying radial and circumferential force to smear the generated particles onto the inner wall of the wellbore.

Abstract: The invention relates to a tool for use in wellbore drilling, suitable for use with a drill string for drilling a wellbore having an inner wall, the tool comprising means for generating particles of solid material from the formed inner wall of a drilled wellbore, and means for directing the generated particles to the inner wall of the wellbore and a method of forming a layer of solid particles on the inner wall of a wellbore drilled by such a drill string.

Abstract: A heart fitness evaluation system is set up in booth form in which a participating person may have the fitness condition of his heart evaluated based on a step test protocol. Personalized data is transmitted to the system in the form of selected answers to questions proposed, and the person is classified according to such data provided for the purposes of data comparison. The person engages in supervised exercise over a timed period to accelerate heart functions. After the activity, the person's heart is monitored to determine the recovery rate of the heart over real time. Comparison of the recovery rate is made against stored physiological data relating to heart rate recovery of classified persons, which is translated in to the fitness level of the person's heart and displaying such results.