Abstract: The present disclosure relates generally to pharmaceutical compositions comprising cannabidiol (CBD), ?-9-tetrahydrocannabinol (THC) and linalool. In some embodiments, the pharmaceutical compositions are useful in the treatment of an inflammatory condition, e.g., useful for the treatment of pain.

Abstract: Techniques are disclosed relating to managing virtual data sources (VDSs), including creating and using VDSs. A virtual data source manager (VDSM) that is executing on a computer system may receive a VDS creation request to create a particular VDS. The VDS creation request may include information that is usable to identify a data origin of the particular VDS and an ingestion routine. The VDSM may use the ingestion routine to create the particular VDS that includes a bitmap index that is representative of data stored at the origin. In response to a data request corresponding to the origin, the particular VDS may be used to initiate a query to the particular VDS for data without accessing the origin. The VDSM may further maintain various software plugins that are executable to perform corresponding functions in relation to the VDSM and the VDSs managed by the VDSM.

Abstract: Techniques are disclosed relating to performing a join operation with bitmap indexes of virtual data sources (VDSs). A distributed compute cluster having a set of compute nodes may maintain a plurality of VDSs based on a set of corresponding origins. A given VDS of the plurality of VDSs may include a bitmap index representative of a portion of data stored at the set of origins. The distributed compute cluster may receive a request for data stored across at least first and second bitmap indexes of the plurality of VDSs. The distributed compute cluster may perform a join operation to generate a join result having data from the first and second bitmap indexes. The distributed compute cluster may return a response that includes the join result.

Abstract: The disclosure relates to a tank for storing volatile gas under pressure and a structure comprising the tank. The tank has a wall formed of a filament wound carbon fibre reinforced polymer (CFRP). The CFRP may have a graphene nanomaterial filler dispersed in the polymer adhesive matrix. The structure includes a frame for bearing static and dynamic forces from internal and external loads, the frame including the tank, the tank being an active load bearing structural element configured as a stressed member in the frame such that, in the structure in use, the tank bears static and dynamic forces from internal and external loads. One or more of: the filament winding pattern of the carbon fibre, the wall thickness, the wall shape, or the material properties of the polymer matrix including the dispersed graphene; is configured such that the tank has mechanical properties required by the design of the structure.

Abstract: A cable gland sealing member comprising: a sleeve (19) arranged to be slidably received within a passage (9) defined in a body (5) of a cable gland, the sleeve (19) arranged to receive a settable sealing material for forming a seal to a cable passing through the sleeve, wherein the sleeve (19) is defined by an annular side wall extending along an axial length; wherein an outer surface (71) of the sleeve (19) includes a tapered portion (95) such that the external diameter of the sleeve (19) widens in the tapered portion (95); and wherein the outer surface (71) of the sleeve (19) is arranged to form a seal with the cable gland body (5). A cable gland is also disclosed using such a sealing member.

Abstract: Systems and methods for automated path-based recommendation for risk mitigation are provided. An entity assessment server, responsive to a request for a recommendation for modifying a current risk assessment score of an entity to a target nsk assessment score, accesses an input attribute vector for the entity and clusters of entities defined by historical attribute vectors. The entity assessment server assigns the input attribute vector to a particular cluster and determines a requirement on movement from a first point to a second point in a multi-dimensional space based on the statistics computed from the particular cluster. The first point corresponds to the current risk assessment score and the second point corresponds to the target risk assessment score. The entity assessment server computes an attribute-change vector so that a path defined by the attribute-change vector complies with the requirement and generates the recommendation from the attribute-change vector.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. Input predictor variables can be analyzed to identify common factors of these predictor variables. The neural network can be trained to enforce a monotonic relationship between each common factor of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under this monotonic constraint. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: Certain aspects involve optimizing neural networks or other models for assessing risks and generating explanatory data regarding predictor variables used in the model. In one example, a system identifies predictor variables. The system generates a neural network for determining a relationship between each predictor variable and a risk indicator. The system performs a factor analysis on the predictor variables to determine common factors. The system iteratively adjusts the neural network so that (i) a monotonic relationship exists between each common factor and the risk indicator and (ii) a respective variance inflation factor for each common factor is sufficiently low. Each variance inflation factor indicates multicollinearity among the common factors. The adjusted neural network can be used to generate explanatory indicating relationships between (i) changes in the risk indicator and (ii) changes in at least some common factors.

Abstract: Certain aspects involve an automated recommendation for risk mitigation. An entity assessment server, responsive to a request for a recommendation for achieving a target status of a risk indicator, accesses a set of input attributes for the entity and obtains a quantity of available resource useable for modifying at least resource-dependent attribute values of the entity. The entity assessment server generates a resource allocation plan for the available resource according to a first risk assessment model and updates the set of input attribute values based on the resource allocation plan. The entity assessment server further determines an updated value of the risk indicator for the entity based on the updated set of input attribute values according to a second risk assessment model and generates the recommendation to include the resource allocation plan of the available resource if the updated value of the risk indicator achieves the target status.

Abstract: A video item is provided for playback at a media player executing at a user device. The media player is logically divided into multiple sections. An indication of a touch gesture made by a user with respect to a touchscreen of the user device is received. Whether the touch gesture with respect to the touchscreen of the user device qualifies as a first predetermined touch gesture is determined. The first predetermined touch gesture is within a portion of a user interface (UI) that is includes in a first section of the media player. Responsive to determining the touch gesture with respect to the touchscreen of the user device qualifies as the first predetermined gesture, the playback of the video item is moved from a first point in time to a second point in time by a predetermined amount of time.

Abstract: Systems and methods of quantum sensing include depositing a sample volume onto an ensemble of quantum defects, hyperpolarizing spins in the sample volume, performing a sensing sequence, and reading out information regarding electronic spin states of the quantum defects in the ensemble of quantum defects, which sense the hyperpolarized spins in the sample volume.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: Certain aspects involve models for generating code executed on data-processing platforms. One example involves receiving an electronic data-processing model, which generates an analytical output from input attributes weighted with respective modeling coefficients. A target data-processing platform is identified that requires bin ranges for the modeling coefficients and reason codes for the input attributes. Modeling code is generated that implements the electronic data-processing model with the bin ranges and the reason codes. The processor outputs executable code that implements the electronic data-processing model.

Abstract: Systems and methods for automated path-based recommendation for risk mitigation are provided. An entity assessment server, responsive to a request for a recommendation for modifying a current risk assessment score of an entity to a target nsk assessment score, accesses an input attribute veclor for the entity and clusters of entities defined by historical attribute vectors. The entity assessment server assigns the input attribute vector to a particular cluster and determines a requirement on movement from a first point to a second point in a multi-dimensional space based on tire statistics computed from tltc particular cluster The first point corresponds to tire current risk assessment score and the second point corresponds to the target nsk assessment score. The entity assessment server computes an attribute-changc vecior so that a path defined by the attribute-change vector complies with the requirement and generates tlic recommendation from the attribute-changc vector.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: Systems, devices, and methods for dispensing a of fluidic sample to each of a plurality of targets are disclosed. An example apparatus for dispensing a fluidic sample includes an inlet port to input a sample material, and a first sample nozzle and a second sample nozzle fluidically coupled to the inlet port to expel the sample material, wherein a tubular junction fluidically couples the inlet port to a first tubular fluid path that terminates in the first sample nozzle and to a second tubular fluid path that terminates in the second sample nozzle, wherein a cross-sectional area of the tubular junction is less than an average cross-sectional area of the first tubular fluid path and the second tubular fluid path, and wherein the cross-sectional area of the tubular junction is selected to draw the sample material from the inlet port into the tubular junction via capillary action.

Abstract: A blower assembly having a blower housing, an impeller fan within the blower housing, the impeller fan being adapted for rotation about an axis and having a plurality of impeller blades and having an axial length, a motor having a stator and a rotor, the motor having an axial length, the rotor being configured to rotate relative to the stator for rotation about the axis, the rotor and the impeller fan being coupled so that the impeller fan rotates with the rotor about the axis, wherein a ratio of the axial length of the motor to the axial length of the impeller fan is less than 0.3, and a motor support bracket operatively securing the stator to one of the first and second side walls of the blower housing.