Abstract: Chromatic dispersion compensation is performed in one or more pluggable optical transceiver (POT) devices operating within an intensity-modulated direct-detection (IMDD) optical network. Compensation is performed within each POT using an electrical and/or optical chromatic dispersion module which are controlled by a set of parameters. A network computing device includes a computer processor and a host management interface for communicating with the POT. In the event of a link failure, the computer processor determines a second set of parameters to control the one or more dispersion compensation module(s) of the POT. The second set of parameters are different from a first set of parameters used to control the one or more compensation module(s) in the case of a first optical path. The computer processor causes the POT to use the second set of parameters in place of the first set of parameters.

Abstract: The present application relates to arenaviruses with rearrangements of their open reading frames (“ORF”) in their genomes. In particular, described herein is a modified arenavirus genomic segment, wherein the arenavirus genomic segment is engineered to carry a viral ORF in a position other than the wild-type position of the ORF. Also described herein are trisegmented arenavirus particles comprising one L segment and two S segments or two L segments and one S segment. The arenavirus, described herein may be suitable for vaccines and/or treatment of diseases and/or for the use in immunotherapies.

Abstract: A method of repairing a sandwich structure includes: removing a damaged portion of a core and a damaged portion of a first facesheet to form an open volume; filling the open volume with an ultraviolet-curable photomonomer; partially curing the ultraviolet-curable photomonomer to form a plurality of photopolymer waveguides by utilizing ultraviolet light; and arranging a replacement facesheet on the damaged portion of the first facesheet and over the photopolymer waveguides.

Abstract: An adjustable frame assembly for augmented reality eyewear. The frame assembly includes a face portion for supporting at least one waveguide that creates an eye box, a support rest for supporting the face portion on a user, and a coupling for adjusting the position of the face portion relative to the support rest. This enables movement of the waveguide eye box relative to the support rest to position the eye box in front of the wearer’s eyes.

Abstract: A system for the sterile filling of containers for pharmaceutical products, which includes a device for sterilizing containers and a machine for filling sterilized containers, wherein the device for sterilizing containers includes at least one autoclave, and wherein the system includes an automated container-accumulation device and means for automatically transporting the containers sterilized in the autoclave from said autoclave to the container-filling machine via said container-accumulation device.

Abstract: A technology is described for determining an intended movement from neuromuscular signals. An example method (800) includes receiving electromyography (EMG) data corresponding to single-ended channels of an electrode array (810), where EMG signals are detected by electrodes comprising the single-ended channels of the electrode array and the EMG signals are converted to the EMG data. Determining differential channel pairs for the single-ended channels of the electrode array (820) and extracting feature data from the EMG data of the differential channel pairs (830). Thereafter a feature data set is selected from the feature data of the differential channel pairs (840) and the feature data set is input to a decode model configured to correlate the feature data set to an intended movement (850). Decode output is received from the decode model indicating the intended movement (860) and the decode output is provided to a device (870).

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: In one embodiment, a modular sensor assembly configured for mounting on a vehicle includes a first set of sensors and a second set of sensors. The modular sensor assembly includes a coordinate frame baseplate including a continuous surface, and sensor mounting elements coupled to the continuous surface for mounting the first set of sensors at a first height. The coordinate frame baseplate includes a sensor platform configured for mounting the second set of sensors at a second height. The first set of sensors and the second set of sensors are coupled to the coordinate frame baseplate so as to impart a common coordinate frame for the first set of sensors mounted at the first height and the second set of sensors mounted at the second height. The modular sensor assembly includes a bridging support structure coupled to the coordinate frame baseplate and capable of being mounted on a vehicle.

Abstract: A banking device in accordance with the present disclosure includes processing circuitry that is configured to receive user authentication information from a mobile device of a user interacting with the mobile device. The user authentication information may include a token indicating that the user of the mobile device is authenticated. The processing circuitry is further configured to contact, in response to reception of the user authentication information a banking server to initiate a banking transaction; and initiate the banking transaction.

Abstract: A size M×N sparse matrix, including zero values, is multiplied with a size N vector, using a processor arrangement. A data storage linked to the processor arrangement stores the matrix in a compressed formal. Zero values are not stored. The data storage stores the vector as vector parts, each of a respective size Ki, 1<Ki<N and i=1 . . . P. A vector part comprises a vector element in common with another vector part. Each vector part is stored in a distinct memory block. Each of a plurality of the non-zero values of a matrix row is associated with a memory block storing an element of the vector having an index corresponding with a respective index of the non-zero value. The processor arrangement multiplies, in parallel, each of the plurality of the non-zero values of the matrix row by the respective vector element having a corresponding index stored in the associated memory block.