Abstract: Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. Machine learning modeling is used to reconstruct the data for the untested cylinders. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges. In a test mode, some portion of the cylinders is chosen for testing. Each wedge in the chosen cylinders is tested and labeled defective or non-defective. The test data for each defective wedge is run through a machine learning defect management logic, and inferences are made for the defective/non-defective status of the untested wedges.

Abstract: Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. Machine learning modeling is used to reconstruct the data for the untested cylinders. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges. In a test mode, some portion of the cylinders is chosen for testing. Each wedge in the chosen cylinders is tested and labeled defective or non-defective. The test data for each defective wedge is run through a machine learning defect management logic, and inferences are made for the defective/non-defective status of the untested wedges.

Abstract: Methods are provided for managing defects in Hard Disk Drive (HDD) storage devices. In particular, only a portion of the cylinders of an HDD is tested. A bag of machine learning models is used to reconstruct the data for the untested cylinders. A defect file for the HDD is generated, a classifier model may be applied to the defect file, and one or more neural network models may be applied. If the defects are unsuitable for use by the models, then a scan of the entire HDD is run instead. An HDD comprises a rotating disk and a read/write head actuated above the disk surface. The disk may be formatted into concentric data tracks, with each track being divided into sectors. The tracks may be organized into zones (groups of tracks called cylinders), and the axially parallel sectors in each cylinder may be organized into wedges.

Abstract: A self-supporting surgical retractor is provided for retracting soft tissue and anatomy at a spinal surgical site. The retractor includes a tubular base defining an upper rim and a lower rim and a working channel there between the upper and lower rims, in which the base and lower rim are sized and configured to be seated on adjacent vertebral bodies spanning an intervertebral space. At least two elongated legs are provided, each projecting from the upper rim and each including a fixation feature at a free end thereof. Each of the legs has a length sufficient for the fixation feature to be outside the body of a patient when the lower rim is seated on adjacent vertebral bodies of the patient. The retractor is formed of a radio-transparent or radio-lucent material.

Abstract: A method for generating descriptions of classification results, comprising classifying original inputs in a data set using a classifier. For one or more of the plurality of original inputs, apply an adjustment to one or more features of the original input to produce a respective adjusted input, and classify the respective adjusted input using the classifier. For one or more of the adjusted inputs, record a corresponding original input into a training set, and record information associated with the adjusted input into the training set. The method further comprises training a model based on the training set, such that the model is configured to receive an original model input and identify a hypothetical adjustment that would produce an adjusted model input that would be assigned an adjusted model classification with a different class than a class assigned to the original model input.

Abstract: A self-supporting surgical retractor is provided for retracting soft tissue and anatomy at a spinal surgical site. The retractor includes a tubular base defining an upper rim and a lower rim and a working channel there between the upper and lower rims, in which the base and lower rim are sized and configured to be seated on adjacent vertebral bodies spanning an intervertebral space. At least two elongated legs are provided, each projecting from the upper rim and each including a fixation feature at a free end thereof. Each of the legs has a length sufficient for the fixation feature to be outside the body of a patient when the lower rim is seated on adjacent vertebral bodies of the patient. The retractor is formed of a radio-transparent or radio-lucent material.

Abstract: Methods are provided for deploying machine learning operations within existing storage devices for streamlining various calibration processes. Machine learning operations are specifically designed to generate inference data as a substitute for various measurements taken during calibration. These operations may be verified through additional sample measurements and rolled back when the results of the machine learning operations are outside of a range of approved values. Storage devices designed to utilize machine learning methods within calibration processes can include a non-volatile memory for storing data, executable instructions, and a processor to conduct a variety of steps. The steps can include executing an application stored in the non-volatile memory and receiving a request for measurement data from the application.

Abstract: Methods are provided for tactically deploying machine learning operations within existing storage devices without the need for additional capital investment. Machine learning operations are specifically designed to locate and evaluate multiple types of data to complete an operation, including synthesizing missing data. These operations may be processed within a SoC of a storage device as embedded software. Storage devices designed to utilize machine learning methods within existing configurations can include a non-volatile memory for storing data, executable instructions, and a processor to conduct a variety of steps. The steps can include executing a plurality of applications stored in the non-volatile memory, and receiving a request for data, including measurements, from at least one of the applications. The steps can further determine if the requested data is suitable for substitution by an inference and subsequently select at least one machine learning model for generating a suitable inference.

Abstract: Methods are provided for tactically deploying machine learning operations within existing storage devices without additional capital investment. Machine learning operations can be processed within a SoC of a storage device as embedded software. Storage device designed to utilize machine learning methods within existing configurations can include a non-volatile memory for storing data and executable instructions and a processor to conduct a variety of steps. The steps can include executing a plurality of applications stored in the non-volatile memory, and receiving a request for data, including measurements, from at least one of the plurality of applications. The steps can further determine if the requested data is suitable for substitution by an inference and subsequently select at least one machine learning model for generating a suitable inference.

Abstract: The present invention relates to a platform that may be utilised in the field of biosensors. According to the present invention there is a method of manufacturing a platform for use in bio-sensing applications comprising the steps of: a) providing a substrate having electrodes thereon; b) performing an overprinting step by overprinting the electrodes with a precursor solution; c) performing a drying step to dry the precursor solution to form a print layer on the electrodes; d) performing a further overprinting step by overprinting the print layer with a precursor solution to increase the print layer thickness; e) performing a transformation step to at least partially transform the print layer from a first substance to a second substance different to the first substance.

Abstract: A self-supporting surgical retractor is provided for retracting soft tissue and anatomy at a spinal surgical site. The retractor includes a tubular base defining an upper rim and a lower rim and a working channel there between the upper and lower rims, in which the base and lower rim are sized and configured to be seated on adjacent vertebral bodies spanning an intervertebral space. At least two elongated legs are provided, each projecting from the upper rim and each including a fixation feature at a free end thereof. Each of the legs has a length sufficient for the fixation feature to be outside the body of a patient when the lower rim is seated on adjacent vertebral bodies of the patient. The retractor is formed of a radio-transparent or radio-lucent material.

Abstract: Hydrogel compositions and methods of using hydrogel compositions are disclosed. Advantageously, the hydrogel compositions offer the ability to promote cellular expansion and/or cellular differentiation of various neuronal cells. The hydrogel compositions can further be used in toxicity screening assays for neurotoxicants.

Abstract: A bend stiffener (50) includes an elongate stiffener body (52) having polymer material and two ends. A passage (58) extends through the stiffener body from end to end for receiving and embracing a flexible member (60). The stiffener body includes a coupling (66) for mounting the stiffener body in cantilever fashion. The stiffener body is sufficiently flexible to curve somewhat along with the flexible member under a bending load but is sufficiently stiff to resist excessive curvature which could damage the flexible member, and sufficiently resilient to recover its original shape afterwards. The stiffener body includes at least two stiffener body parts (64a, 64b) defining the passage. Each stiffener body part includes a respective interface member (68, 70) having material stiffer than the polymer material of the stiffener body. A securing arrangement (80, 83) secures the interface members of different stiffener body parts together around the elongate flexible member.

Abstract: The invention relates to a buoyancy module (100, 200, 300, 400, 500, 700) which comprises multiple buoyancy elements (104, 204, 304, 404, 504, 604, 704) coupled to one another. Each of the buoyancy elements has a recess (110, 210) and the buoyancy elements are laterally juxtaposed with their recesses aligned with one another to encircle the elongate member in use. Multiple buoyancy elements are stacked one upon another along a direction which is axial with respect to the elongate member in use, to form a buoyancy module of a predetermined axial depth. The recesses of the buoyancy modules together form an axially extending passage through which the elongate member passes.

Abstract: A central management system polls a lighting node in a mesh lighting network, sending a command message for the node to measure a link quality indication (LQI) of the received command message. The node returns a status message, including in its payload the measured LQI. The management system computes from the node's status messages, a statistical characterization of the link quality of the node and compares it with a threshold quality value. If the comparison indicates sufficient degradation in link quality, the management system automatically transmits a disconnect command to the node. In response, the node disconnects and enters a discovery mode to find a better quality link to rejoin the network. In example embodiments, the lighting node may also report its illumination and operational status in the payload of the status message, along with the LQI, enabling the management system to determine whether illumination by the node is failing.

Abstract: A central management system polls a lighting node in a mesh lighting network, sending a command message for the node to measure a link quality indication (LQI) of the received command message. The node returns a status message, including in its payload the measured LQI. The management system computes from the node's status messages, a statistical characterization of the link quality of the node and compares it with a threshold quality value. If the comparison indicates sufficient degradation in link quality, the management system automatically transmits a disconnect command to the node. In response, the node disconnects and enters a discovery mode to find a better quality link to rejoin the network. In example embodiments, the lighting node may also report its illumination and operational status in the payload of the status message, along with the LQI, enabling the management system to determine whether illumination by the node is failing.

Abstract: Examples relate to a method and implementations of general illumination light emitters, an image display device and an image diffuser in a luminaire. The image display device is configured to output an image having a reduced, or first, pixel image fill factor and, as a result, might appear pixelated. To mitigate the pixelation, the image diffuser has a predetermined image diffusion angle and is a predetermined distance from the image display device. The image diffuser outputs an image having a second image pixel fill factor that is greater than the first image pixel fill factor. The appearance of the outputted image appears to be formed from fuzzy pixels. Characteristics related to the image, device, diffuser, and their arrangement may be optimized to provide the fuzzy pixels. A luminaire may output an image formed of the fuzzy pixels and general illumination lighting to an area.

Abstract: The examples relate to various representations of data utilized for control of a software configurable lighting device. The software configurable lighting device includes an image display device and a general illumination device. Each data representation includes a pre-defined data structure such that lighting control information and image data may be interlaced within the respective representation.

Abstract: Example embodiments of the invention efficiently organize, control and prioritize a distributed lighting network. A mesh lighting network is organized from the bottom up into groups of lighting devices with control signal repeaters fanning out control signals to each group. Each lighting device is capable of forwarding control signals to additional lighting devices. During build-out of the mesh network, as new lighting devices are installed, the control network automatically selects a device whose repeater function will provide the most reliable and efficient distribution of control signals to the new group of devices being installed.

Abstract: The examples relate to various implementations of a software configurable lighting device, having an enhance display device that is able to generate light sufficient to provide general illumination of a space in which the lighting device is installed and provide an image display. The general illumination is provided by additional light sources and/or improved display components of the enhanced display device.