Abstract: In one aspect the invention provides a fumigant gas capture apparatus. This apparatus includes at least one channel adapted for connection to a volume containing a fumigant gas, the channel defining an inlet which receives gas and an outlet which allows gas to exit the channel. The apparatus also includes at least one drive structure arranged to drive fumigant gas through the channel, at least one spray nozzle adapted to deliver a treatment liquid into the channel, and at least one packing element positioned within the channel to allow a spray nozzle or nozzle to spray treatment liquid on to the packing element.

Abstract: The present invention relates generally to the field of industrial biotechnology and particularly to an improved hydrolysis method for increasing sugar production from a high solids concentration of lignocellulosic biomass, especially one derived from Municipal Solid Waste (MSW) by enzymatic hydrolysis of a lignocellulosic biomass to obtain a slurry, wherein the hydrolysis comprises aliquot additions of enzyme and lignocellulosic biomass; and removal of sugars from the slurry and washing of the residual lignocellulosic biomass.

Abstract: A knowledge interface is provided that interacts with a user to identify a solution to a customer problem or issue with respect to a particular product or service. The knowledge interface includes data processing functionality configured to dynamically generate a number of components that are presented in at least one display window for display to the user. The components include first data identifying a set of predetermined symptoms linked to the problem or issue and related interface elements for classification of the set of predetermined symptoms, second data identifying a set of predetermined root causes linked to the set of predetermined symptoms and related interface elements for classification of the set of predetermined root causes, and third data identifying a set of solutions linked to the set of predetermined root causes. The third data identifies a best solution based upon the predetermined root causes and their associated class designations.

Abstract: The current document is directed to a safe-operation-constrained reinforcement-learning-based application manager that can be deployed in various different computational environments, without extensive manual modification and interface development, to manage the computational environments with respect to one or more reward-specified goals. Control actions undertaken by the safe-operation-constrained reinforcement-learning-based application manager are constrained, by stored action filters, to constrain state/action-space exploration by the safe-operation-constrained reinforcement-learning-based application manager to safe actions and thus prevent deleterious impact to the managed computational environment.

Abstract: The current document is directed to an administrator-monitored reinforcement-learning-based application manager that can be deployed in various different computational environments to manage the computational environments with respect to one or more reward-specified goals. Certain control actions undertaken by the administrator-monitored reinforcement-learning-based application manager are first proposed, to one or more administrators or other users, who can accept or reject the proposed control actions prior to their execution. The reinforcement-learning-based application manager can therefore continue to explore the state/action space, but the exploration can be parametrically constrained as well as by human-administrator oversight and intervention.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that that are trained using adversarial training. During adversarial training, potentially disadvantageous next actions are selected for issuance by an automated reinforcement-learning-based application manager at a lower frequency than selection of next actions, according to a policy that is learned to provide optimal or near-optimal control over a computing environment that includes one or more applications controlled by the automated reinforcement-learning-based application manager.

Abstract: The current document is directed to methods and systems for simulation-based training of automated reinforcement-learning-based application managers. Simulators are generated from data collected from controlled computing environments controlled and may employ any of a variety of different machine-learning models to learn state-transition and reward models. The current disclosed methods and systems provide facilities for visualizing aspects of the models learned by a simulator and for initializing simulator models using domain information. In addition, the currently disclosed simulators employ weighted differences computed from simulator-generated and training-data state transitions for feedback to the machine-learning models to address various biases and deficiencies of commonly employed difference metrics in the context of training automated reinforcement-learning-based application managers.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that use local agents. Local agents provide finer-granularity monitoring of an application or application subcomponents and provide continued application management in the event of interruption of network traffic between an automated reinforcement-learning-based application manager and the application or application subcomponents managed by the automated reinforcement-learning-based application manager.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that obtain increased computational efficiency by reusing learned models and by using human-management experience to truncate state and observation vectors. Learned models of managed environments that receive component-associated inputs can be partially or completely reused for similar environments. Human managers and administrators generally use only a subset of the available metrics in managing an application, and that subset can be used as an initial subset of metrics for learning an optimal or near-optimal control policy by an automated reinforcement-learning-based application manager.

Abstract: A color correction mask programmatically generated in software on an HMD device is applied to a gaze region on a display field of view (FOV) on a head-mounted display (HMD) device to optimize system resources while rendering a display. Eye monitoring sensors are utilized to track movement of a user's eyes while the user operates the HMD device to determine a gaze position of the user's eyes on the display FOV. Using the determined gaze position, a dynamic foveal gaze region is sized around the gaze position so that the foveal portion of the display FOV is color-corrected, that is, color non-uniformities are reduced or eliminated. In other implementations, a gaze-based weighting mode is implemented in which measurements of the user's full eye or eye orbit are utilized to color correct a larger surface area of the display FOV relative to the foveal color correction mode.

Abstract: The current document is directed to an automated reinforcement-learning-based application manager that uses action tags and metric tags. In various implementations, actions and metrics are associated with tags. Different types of tags can contain different types of information that can be used to greatly improve the computational efficiency by which the reinforcement-learning-based application manager explores the action-state space in order to determine and maintain an optimal or near-optimal management policy by providing a vehicle for domain knowledge to influence control-policy decision making.

Abstract: The current document is directed to transfer of training received by a first automated reinforcement-learning-based application manager while controlling a first application is transferred to a second automated reinforcement-learning-based application manager which controls a second application different from the first application. Transferable training provides a basis for automated generation of applications from application components. Transferable training is obtained from composition of applications from application components and composition of reinforcement-learning-based-control-and-learning constructs from reinforcement-learning-based-control-and-learning constructs of application components.

Abstract: The current document is directed to automated reinforcement-learning-based application managers that learn and improve the reward function that steers reinforcement-learning-based systems towards optimal or near-optimal policies. Initially, when the automated reinforcement-learning-based application manager is first installed and launched, the automated reinforcement-learning-based application manager may rely on human-application-manager action inputs and resulting state/action trajectories to accumulate sufficient information to generate an initial reward function. During subsequent operation, when it is determined that the automated reinforcement-learning-based application manager is no longer following a policy consistent with the type of management desired by human application managers, the automated reinforcement-learning-based application manager may use accumulated trajectories to improve the reward function.

Abstract: A suspended scaffolding connector includes a central spine comprising an anchor connector at a top end thereof, a bottom lug at a bottom end thereof, and a top lug situated between the anchor connector and bottom lug. The connector further includes at least one top truss connector attached to the top lug and at least one bottom truss connector attached to the bottom lug, where the top and bottom truss connectors are configured and spaced for structural connection to top and bottom connectors of a scaffolding truss. The connector also includes top and bottom truss ledger connectors attached to the spine and situated between the top and bottom lugs and adapted for connection to a scaffolding ledger, and the anchor connector is adapted for connection to a suspension anchor and to support the scaffolding truss through the top and bottom truss connectors.

Abstract: An inertial measurement system comprising: a first, roll gyro with an axis oriented substantially parallel to the spin axis of the projectile; a second gyro and a third gyro with axes arranged with respect to the roll gyro; a controller, arranged to: compute a current projectile attitude from the outputs of the first, second and third gyros; operate a Kalman filter that receives a plurality of measurement inputs including at least roll angle, pitch angle and yaw angle and that outputs at least a roll angle error; initialise the Kalman filter with a roll angle error uncertainty representative of a substantially unknown roll angle; generate at least one pseudo-measurement from stored expected flight data; provide said pseudo-measurement(s) to the corresponding measurement input of the Kalman filter; and apply the roll angle error from the Kalman filter as a correction to the roll angle.

Abstract: An inertial measurement system (200) for a longitudinal projectile, comprising a first, roll gyro to be oriented substantially parallel to the longitudinal axis of the projectile; a second gyro and a third gyro with axes arranged with respect to the roll gyro such that they define a three dimensional coordinate system.

Abstract: A protein kinase that includes a donor molecule and an acceptor molecule, methods of making the protein kinase, and methods of using the protein kinase are described. Measurement of the conformation of the kinase can be obtained using intramolecular FRET. The protein kinase can be used to, for example, identify conformational changes involved in kinase regulation, that is, as an allostery sensor; to identify kinase-binding molecules including, for example, kinase inhibitors; to identify cancer therapeutics; or for high-throughput screening.

Abstract: A method of compensating for signal error is described, comprising: receiving a first signal from a first sensor, said first signal indicative of a movement characteristic; applying an error compensation to said first signal to produce an output signal; applying a variable gain factor to said error compensation; receiving a second signal from a second sensor indicative of said movement characteristic; wherein said error compensation is calculated using the difference between said output signal and said second signal, and said variable gain factor is calculated using said first signal.

Abstract: An example device may include processing circuitry and a management controller. The processing circuitry may include a communications interface that includes a first register and a second register. The first register may include a freshness bit and a number of first data bits. The second register may include a number of second data bits that correspond, respectively, to the first data bits. The processing circuitry may write variously to the first data bits in response to detected events, set the freshness bit in response to the management controller reading the first data bits, and reset the freshness bit if any of the first data bits are written to. The management controller may read the first data bits, perform predetermined processing based thereon, write to the second data bits based on the predetermined processing, and request a register transfer.

Abstract: An eye-tracking system is provided. The system includes an at least partially transparent visible light waveguide having a visible light display region configured to emit visible light to impinge upon an eye of a user. A light source is configured to emit at least infrared (IR) light that travels along an IR light path to impinge on the eye. A microelectromechanical system (MEMS) scanning mirror positioned in the IR light path is configured to direct the IR light along the IR light path. A relay positioned in the IR light path downstream of the MEMS scanning mirror includes at least one mirror configured to reflect the IR light along the IR light path. At least one sensor is configured to receive the IR light after being reflected by the eye.