Abstract: An emergency ventilation system ventilates a patient and includes a chamber housing defining a breathing chamber; a piston; and a motor operably connected to the piston. The motor applies an exhalation force to move the piston in an exhalation direction applies an inhalation force to move the piston in an inhalation direction. The piston increases air in the breathing chamber as the exhalation force is applied and decreases air in the breathing chamber as the inhalation force is applied. An exhalation check valve allows airflow from the air source to the breathing chamber and not to allow airflow from the breathing chamber to the air source as the inhalation force is applied. An inhalation check valve allows airflow from the breathing chamber to the air output and not to allow airflow from the air output to the breathing chamber as the exhalation force is applied.

Abstract: Pharmaceutical composition comprising growth-hormone releasing peptide 6 (GHRP-6), tartrate buffer at pH 5.0-6.0, and trehalose as stabilizing agent, as well as the use of said composition for the manufacture of a medicament. The invention also provides a kit of parts comprising said pharmaceutical composition and a method of treatment of an individual in need thereof, which entails the administration of a therapeutically effective amount of the pharmaceutical composition comprising of GHRP-6, tartrate buffer at pH 5.0-6.0 and trehalose.

Abstract: An electrolytic medium is provided to the treatment of metallic surfaces and includes solid particles and a non-conductive fluid, the process that uses said medium and the device to carry out the process.

Abstract: Disclosed are systems, methods, and devices for generating a visualization of a deep reinforcement learning (DRL) process. State data is received, reflective of states of an environment explored by an DRL agent, each state corresponding to a time step. For each given state, saliency metrics are calculated by processing the state data, each metric measuring saliency of a feature at the time step corresponding to the given state. A graphical visualization is generated, having at least two dimensions in which: each feature of the environment is graphically represented along a first axis; and each time step is represented along a second axis; and a plurality of graphical markers representing corresponding saliency metrics, each graphical marker having a size commensurate with the magnitude of the particular saliency metric represented, and a location along the first and second axes corresponding to the feature and time step for the particular saliency metric.

Abstract: Polypeptides comprising functional mutants of an isoform of the human vascular endothelial growth factor A (VEGF-A) folded in a non-natural re-arrangement, where the second and fourth cysteine of the mutant's polypeptide chain is only forming intramolecular bridges, while the seventh and eight are only part of intermolecular bonds. The invention further comprises antigenic preparations containing at least one of these polypeptides, and the pharmaceutical compositions comprising such antigenic preparations and vaccine adjuvants. The antigenic preparations according to the invention are used in the manufacturing of a drug, for the treatment of diseases related to the increment of angiogenesis, inflammation, and immunosuppression, as well as for the restoration of the immune system.

Abstract: A computer system and method for extending parallelized asynchronous reinforcement learning for training a neural network is described in various embodiments, through coordinated operation of plurality of hardware processors or threads such that each functions as a worker agent that is configured to simultaneously interact with a target computing environment for local gradient computation based on a loss determination and to update global network parameters based at least on local gradient computation to train the neural network through modifications of weighted interconnections between interconnected computing units as gradient computation is conducted across a plurality of iterations of a target computing environment, the loss determination including at least a policy loss term (actor), a value loss term (critic), and an auxiliary control loss. Variations are described further where the neural network is adapted to include terminal state prediction and action guidance.

Abstract: The present invention is related to the use of the growth hormone releasing peptide-6 (GHRP-6) for the manufacture of a late cardioprotective and cardiac restoration medicament. Said late cardioprotective and cardiac restoration medicament comprises GHRP-6 and a pharmaceutically acceptable excipient or vehicle. It also provides a method for the treatment of a disease that involves a low cardiac output, wherein a therapeutically effective amount of a late cardioprotective and cardiac restoration medicament comprising GHRP-6 is administered to a subject in need. Said medicament allows treating in a late manner, even days later, the myocardium that suffered episodes of stunning, hibernation, ischemia and its consequences thereof.

Abstract: A computer-implemented system and method for training an auomated agent are disclosed. An example system includes: a communication interface; at least one processor; memory in communication with said at least one processor; software code stored in said memory, which when executed causes said system to: instantiate an automated agent that maintains a reinforcement learning neural network and generates, according to outputs of said reinforcement learning neural network, signals for communicating task requests; receive a plurality of states and a plurality of actions for the automated agent; initialize a learning table Q for the automated agent based on the plurality of states and the plurality of actions; compute a plurality of updated learning tables based on the initialized learning table Q using a utility function, the utility function comprising a monotonically increasing concave function; and generate an averaged learning table Q? based on the plurality of updated learning tables.

Abstract: A computer system and method for extending parallelized asynchronous reinforcement learning to include agent modeling for training a neural network is described. Coordinated operation of plurality of hardware processors or threads is utilized such that each functions as a worker process that is configured to simultaneously interact with a target computing environment for local gradient computation based on a loss determination mechanism and to update global network parameters. The loss determination mechanism includes at least a policy loss term (actor), a value loss term (critic), and a supervised cross entropy loss. Variations are described further where the neural network is adapted to include a latent space to track agent policy features.

Abstract: The approaches described herein are adapted to provide a technical, computational mechanism to aid in improving explainability of machine learning architectures or for generating more explainable machine learning architectures. Specifically, the approaches describe a proposed implementation of cascading decision tree (CDT) based representation learning data models which can be structured in various approaches to learn features of varying complexity.

Abstract: The invention relates to a position-locating arrangement for detecting the position of a mobile object that can move inside a defined area and for processing data relative to the mobile object, said arrangement comprising a mobile identification tag (11) associated with the mobile object, and a stationary receiving device (12) configured to transmit a message from the stationary receiving device (12) to a position locating server (15) in response to receipt of an identification and data message broadcast by the mobile identification tag (11), the position-locating serves: (13), remote from the mobile identification tag (11), receiving the message from the stationary receiving device (11). The arrangement of the invention is suitable for remotely determining the position of the identification tag (11) inside the defined area and processing the data relative to the mobile object carrying same.

Abstract: A medical device respirator comprising a valve assembly attached to a mask at a mask juncture; an inhale tube attached to the valve assembly and attached to an inhale filter; an exhale tube attached to the valve assembly; an inhale check valve disposed at an attachment juncture between the inhale tube and the valve assembly; an exhale check valve disposed at an attachment juncture between the valve assembly and the exhale tube; wherein inhaled air passes through the inhale filter, the inhale tube, the inhale check valve, the valve assembly, the mask juncture, the mask and into the user; and exhaled air passes through the mask, the mask juncture, the valve assembly, the exhale check valve, and the exhale tube.

Abstract: An emergency ventilation system ventilates a patient and includes a chamber housing defining a breathing chamber; a piston; and a motor operably connected to the piston. The motor applies an exhalation force to move the piston in an exhalation direction applies an inhalation force to move the piston in an inhalation direction. The piston increases air in the breathing chamber as the exhalation force is applied and decreases air in the breathing chamber as the inhalation force is applied. An exhalation check valve allows airflow from the air source to the breathing chamber and not to allow airflow from the breathing chamber to the air source as the inhalation force is applied. An inhalation check valve allows airflow from the breathing chamber to the air output and not to allow airflow from the air output to the breathing chamber as the exhalation force is applied.

Abstract: Systems are methods are provided for facilitating explainability of decision-making by reinforcement learning agents. A reinforcement learning agent is instantiated which generates, via a function approximation representation, learned outputs governing its decision-making. Data records of a plurality of past inputs for the agent are stored, each of the past inputs including values of a plurality of state variables. Data records of a plurality of past learned outputs of the agent are also stored. A group definition data structure defining groups of the state variables are received. For a given past input a given group, data generated reflective of a perturbed input by altering a value of at least one state variable is generated, and are presented to the reinforcement learning agent to obtain a perturbed learned output generated by the reinforcement learning agent; and a distance metric is generated reflective of a magnitude of difference between the perturbed learned output and the past learned output.

Abstract: The present invention is related to the use of the growth hormone releasing peptide-6 (GHRP-6) for the manufacture of a late cardioprotective and cardiac restoration medicament. Said late cardioprotective and cardiac restoration medicament comprises GHRP-6 and a pharmaceutically acceptable excipient or vehicle. It also provides a method for the treatment of a disease that involves a low cardiac output, wherein a therapeutically effective amount of a late cardioprotective and cardiac restoration medicament comprising GHRP-6 is administered to a subject in need. Said medicament allows treating in a late manner, even days later, the myocardium that suffered episodes of stunning, hibernation, ischemia and its consequences thereof.

Abstract: The invention discloses a pharmaceutical combination comprising epidermal growth factor (EGF) and the hexapeptide secretagogue GHRP6, useful for the restoration of brain damage, by stimulating mechanisms of differentiation and neuronal specialization in undifferentiated cells residing in the central nervous system or in neuronal cells. The invention also encompasses the use of the above mentioned biomolecules in the manufacture of a pharmaceutical combination for the restoration of brain damage. In addition, the invention provides a method of restoring brain damage in which a therapeutically effective amount of a pharmaceutical combination comprising EGF and GHRP6 is administered to a patient in need thereof. The efficacy of the pharmaceutical combination has a broad therapeutic window: it is independent of whether the pharmacological intervention occurs in the first or further hours after the brain damage, which broadens the therapeutic window.

Abstract: Disclosed are systems, methods, and devices for training a learning agent. A learning agent that maintains a reinforcement learning neural network is instantiated. State data reflective of a state of an environment explored by the learning agent is received. An uncertainty metric calculated upon processing the state data, the uncertainty metric measuring epistemic uncertainty of the learning agent. Upon determining that the uncertainty metric exceeds a pre-defined threshold: a request signal requesting an action suggestion from a demonstrator is sent; a suggestion signal reflective of the action suggestion is received; and an action signal to implement the action suggestion is sent.

Abstract: Disclosed are systems, methods, and devices for generating a visualization of a deep reinforcement learning (DRL) process. State data is received, reflective of states of an environment explored by an DRL agent, each state corresponding to a time step. For each given state, saliency metrics are calculated by processing the state data, each metric measuring saliency of a feature at the time step corresponding to the given state. A graphical visualization is generated, having at least two dimensions in which: each feature of the environment is graphically represented along a first axis; and each time step is represented along a second axis; and a plurality of graphical markers representing corresponding saliency metrics, each graphical marker having a size commensurate with the magnitude of the particular saliency metric represented, and a location along the first and second axes corresponding to the feature and time step for the particular saliency metric.

Abstract: A fixed-wing aerial vehicle includes: a fuselage with an on-board power supply; at least one fixed-wing or aerodynamic surface having at least one heater configured to be energized by the power supply, and having a superhydrophobic coating applied to at least a portion of the at least one fixed-wing or the aerodynamic surface that may be subject to icing; at least one capacitive sensor configured to detect ice formation on the at least one fixed-wing or aerodynamic surface; and a controller configured to selectively activate the at least one heater in response to detected ice formation.

Abstract: A computer system and method for extending parallelized asynchronous reinforcement learning for training a neural network is described in various embodiments, through coordinated operation of plurality of hardware processors or threads such that each functions as a worker agent that is configured to simultaneously interact with a target computing environment for local gradient computation based on a loss determination and to update global network parameters based at least on local gradient computation to train the neural network through modifications of weighted interconnections between interconnected computing units as gradient computation is conducted across a plurality of iterations of a target computing environment, the loss determination including at least a policy loss term (actor), a value loss term (critic), and an auxiliary control loss. Variations are described further where the neural network is adapted to include terminal state prediction and action guidance.