Abstract: A computer-implemented method of simulating operation of an electric drive unit to predict one or more performance parameters of the electric drive unit is provided. The electric drive unit includes at least an electric motor. The method includes obtaining parameters defining physical properties of the electric motor, obtaining parameters defining drive currents for driving the electric motor, processing the obtained parameters using a machine learning module trained a priori to predict a spatially varying electromagnetic and/or mechanical and/or thermal profile within the electric motor during operation, and providing as output a predicted profile for the electric motor, and using the predicted profile to compute the one or more performance parameters of the electric drive unit.

Abstract: Syringe carriers for medicament delivery devices are described where a medicament delivery device sub-assembly extending along a longitudinal axis from a proximal end to a distal end is described having a tubular housing, a tubular syringe carrier inside the housing and a syringe inside the syringe carrier. The syringe includes a needle shield and a medicament container comprising a shoulder. The housing includes a flexible arm extending in the axial direction relative to the longitudinal axis, the flexible arm having an inwardly extending protrusion arranged in a gap between the shoulder and the needle shield. The syringe carrier has a proximal portion and a distal portion, and the proximal portion is arranged adjacent to the flexible arm of the housing so that movement of the flexible arm of the housing in a radial direction relative to the longitudinal axis is restricted by the proximal portion of the syringe carrier.

Abstract: This application describes syringe carriers for medicament delivery devices such as autoinjectors. In particular, an example syringe carrier is tubular and extends along a longitudinal axis from a proximal end to a distal end, the syringe carrier comprising a base section at the distal end of the syringe carrier, the base section having a fixed circumference, at least one arm extending towards the proximal end of the syringe carrier from the base section, and an expandable ring attached to the proximal end of the at least one arm, wherein the ring extends entirely around the longitudinal axis of the syringe carrier in the circumferential direction. Various other syringe carriers and associated medicament delivery devices and methods are described.

Abstract: Improved inspection techniques are described herein for checking for the presence of and identifying surface defects on a honeycomb body. The improved inspection utilizes measurement of travel of an outer surface of the honeycomb body to collect images of the outer surface. The images are combined into a composite image showing the outer surface of the honeycomb body. The composite image is analyzed to identify surface defects.

Abstract: A cassette unit sub-assembly for a medicament delivery device, the sub-assembly comprising: a body extending along a longitudinal axis from a proximal end to a distal end; a delivery member guard coaxially attached to the body and being axially movable relative to the body along the longitudinal axis; a lock member attached to the body; the lock member is movable relative to the delivery member guard between a locked position and an unlocked position; the lock member comprises a distally directed surface adjacent to a proximally directed surface of the delivery member guard in the locked position; and the distally directed surface of the lock member is spaced apart in a circumferential direction relative to the longitudinal axis from the proximally directed surface of the delivery member guard in the unlocked position.

Abstract: A method selects a federation engine to process a query. The system receives a query that specifies retrieval of data from multiple data sources at distinct physical locations. The system generates multiple federated query plans. Each federated query plan specifies execution of the query using a respective data source as the federation engine. For each federated query plan, the system forms a feature vector including two or more of: (1) a remote cost estimate for executing query operators on data sources distinct from the respective data source; (2) a local cost estimate for executing query operators on the respective data source; and (3) a data transfer cost estimate for transferring partial results from external data sources to the respective data source. The system predicts runtime for each federated query plan, selects a preferred federated query plan using the predicted runtimes, and the preferred federated query plan specifies the federation engine.

Abstract: A method builds a regression model for predicting processing times for federated queries using a variety of data sources. The method includes obtaining federated queries (e.g., from benchmarks), and generates a plurality of federated query plans for each federated query. Each federated query plan corresponds to executing a respective federated query using a respective data source as the federation engine. The method includes forming feature vectors for each federated query plan based on cost estimations for executing the respective federated query plan and cost estimations for data transfer. The method further includes training a regression model, using the feature vectors for the plurality of federated query plans, to predict runtimes for executing federated queries using the variety of data sources as a federation engine. Some implementations use the trained regression model to determine a suitable federation engine for a given federated query.

Abstract: A method samples a stream of data items. Each data item has an associated timestamp. The method assigns a priority value to each data item. Each data item is represented as a point on a two-dimensional graph whose axes are time and priority. A sliding window covers a predetermined length of time tspan and uses a backward probability decay curve to specify what priority values are included in the sliding window. This defines, for a current time tc, a current data sample consisting of data items whose timestamps t fall within the time span tc?tspan?t?tc and have priority values below the decay curve. The data sample is stored in a buffer. The process iteratively moves the sliding window forward by a time increment, creating a provisional data sample. When the size of the provisional data sample is too large or too small, the process scales the decay curve.

Abstract: Data items are aggregated in a quantile sketch according to a compactor hierarchy. A first number of data items are stored in a first buffer corresponding to a first (i.e., lowest) compaction level of the compactor hierarchy, and a top level number of data items are stored in a top buffer corresponding to a top compaction level of the compactor hierarchy. For a first subset of the first number of data items, each data item is already stored in the top buffer, and a weight of the respective data item stored in the top buffer is increased by 1. For a second subset of the first number of data items, each data item does not match any data item stored in the top buffer, and a second number of data items are selected from the second subset and stored in a second buffer of a second compaction level.

Abstract: A method of modifying a retina fundus image for a deep learning model is disclosed. In a described embodiment, the method includes converting a retina fundus image to a binary image by converting pixels of the retina fundus image to low intensity modified pixels and high intensity modified pixels of the binary image, and determining a first boundary between the low intensity modified pixels and high intensity modified pixels of the binary image. The method further includes removing outlier boundary values from the first boundary, constructing a second boundary from remaining boundary values, identifying the pixels of the retina fundus image that are within the second boundary, and constructing a modified retina fundus image containing the identified pixels for a deep learning model.

Abstract: A method estimates counts of distinct items. A system allocates a matrix B in memory and receives a data stream including many data items. Each item includes a set of query fields and a set of identifier fields. For each item, the system computes a row for the data item by hashing data values of the identifier fields. The system also computes a column for the data item by hashing a combination of data values of the query fields and the row. The system computes a bin value for the item by hashing data values of the identifier fields. The system updates the matrix B by computing B[r,c]=max(v, B[r,c]). Subsequently, the system receives a query key value. For each row in the matrix B, the system computes a column, and estimates the number of data items from the data stream matching the query key.

Abstract: Methods, non-transitory computer readable media, and computing devices that accelerate data access requests. With this technology, a hierarchy of a plurality of objects is inserted into a location database. Each of at least a subset of the plurality of objects comprises a physical storage location for data stored in a filesystem. One or more of the plurality of objects includes an object version number and a parent version number of a parent one of the plurality of objects. A determination is made when an invalidation event has occurred in the filesystem. The invalidation event is associated with one of the plurality of objects. The object version number for the one of the plurality of objects is modified to invalidate one or more of the subset of the objects, when the determining indicates that the invalidation event has occurred in the filesystem.

Abstract: This application is directed to modifying display of data visualizations. A computer system visualizes on its display a set of data in a first data space. A focus point is identified in the first data space and used as a starting point for constructing a set of geodesics, each of which is configured to connect the focus point to a respective data point in the first data space. A plurality of primary eigenvectors are determined in the first data space and they are used to create a second data space having fewer dimensions than the first data space. For each data point, the computer system determines a plurality of projection values associated with the primary eigenvectors. The set of data is re-visualized in the second data space created using the projection values of each data point.

Abstract: A method samples a stream of data items. Each data item has an associated timestamp. The method assigns a priority value to each data item. Each data item is represented as a point on a two-dimensional graph whose axes are time and priority. A sliding window covers a predetermined length of time tspan and uses a backward probability decay curve to specify what priority values are included in the sliding window. This defines, for a current time tc, a current data sample consisting of data items whose timestamps t fall within the time span tc?tspan?t?tc and have priority values below the decay curve. The data sample is stored in a buffer. The process iteratively moves the sliding window forward by a time increment, creating a provisional data sample. When the size of the provisional data sample is too large or too small, the process scales the decay curve.

Abstract: A method of modifying a retina fundus image for a deep learning model is disclosed. In a described embodiment, the method includes converting a retina fundus image to a binary image by converting pixels of the retina fundus image to low intensity modified pixels and high intensity modified pixels of the binary image, and determining a first boundary between the low intensity modified pixels and high intensity modified pixels of the binary image. The method further includes removing outlier boundary values from the first boundary, constructing a second boundary from remaining boundary values, identifying the pixels of the retina fundus image that are within the second boundary, and constructing a modified retina fundus image containing the identified pixels for a deep learning model.

Abstract: A method samples a stream of data items. Each data item has an associated timestamp. The method assigns a priority value to each data item. Each data item is represented as a point on a two-dimensional graph whose axes are time and priority. A sliding window covers a predetermined length of time tspan and uses a backward probability decay curve to specify what priority values are included in the sliding window. This defines, for a current time tc, a current data sample consisting of data items whose timestamps t fall within the time span tc?tspan?t?tc and have priority values below the decay curve. The data sample is stored in a buffer. The process iteratively moves the sliding window forward by a time increment, creating a provisional data sample. When the size of the provisional data sample is too large or too small, the process scales the decay curve.

Abstract: A process creates a data sketch for a data set with many rows. A user selects data fields from the data source. The process allocates storage for N bins, where each bin has storage space for a key value and an associated counter value (which is initialized to zero). The process sequentially accesses the rows from the data source (e.g., as a stream). For each row, the process computes a respective key value using data values for the selected data fields. When the respective key value matches a key value for a respective bin, the process increments the counter value for the respective bin. Otherwise, the process identifies a respective bin with a smallest counter value c. The process increments the counter value of the respective bin, and with probability 1/(1+c), replaces the key value of the respective bin with the respective key value.

Abstract: Methods, non-transitory computer readable media, and computing devices that accelerate data access requests. With this technology, a hierarchy of a objects is inserted into a location database. Each of at least a subset of the objects comprises a physical storage location for data stored in a filesystem. One or more of the objects includes an object version number and a parent version number of a parent one of the objects. A determination is made when an invalidation event has occurred in the filesystem. The invalidation event is associated with one of the objects. The object version number for the one of the objects is modified to invalidate one or more of the subset of the objects, when the determining indicates that the invalidation event has occurred in the filesystem.

Abstract: A process creates a data sketch for a data set with many rows. A user selects data fields from the data source. The process allocates storage for N bins, where each bin has storage space for a key value and an associated counter value (which is initialized to zero). The process sequentially accesses the rows from the data source (e.g., as a stream). For each row, the process computes a respective key value using data values for the selected data fields. When the respective key value matches a key value for a respective bin, the process increments the counter value for the respective bin. Otherwise, the process identifies a respective bin with a smallest counter value c. The process increments the counter value of the respective bin, and with probability 1/(1+c), replaces the key value of the respective bin with the respective key value.

Abstract: Broadband reflectors include a UV-reflective multilayer optical film and a VIS/IR-reflective layer. In various embodiments, the VIS/IR reflective layer may be a reflective metal layer or a multilayer optical film. Concentrated solar power systems and methods of harnessing solar energy using the broadband reflectors and optionally comprising a celestial tracking mechanism are also disclosed.