Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: A solar assembly includes a single-slope crossbeam, a plurality of clip angle brackets, and a plurality of photovoltaic (PV) modules. Each PV module is supported by at least two of the plurality of clip angle brackets, and a height of the plurality of angle brackets differ from each other in order to allow the PV modules to be shingled.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Systems and methods for automated custom training of a scoring model are disclosed herein. The method include: receiving a plurality of responses received from a plurality of students in response to providing of a prompt; identifying an evaluation model relevant to the provided prompt, which evaluation model can be a machine learning model trained to output a score relevant to at least portions of a response; generating a training indicator that provides a graphical depiction of the degree to which the identified evaluation model is trained; determining a training status of the model; receiving at least one evaluation input when the model is identified as insufficiently trained; updating training of the evaluation model based on the at least one received evaluation input; and controlling the training indicator to reflect the degree to which the evaluation model is trained subsequent to the updating of the training of the evaluation model.

Abstract: Reducing slider bounce within a hard disk drive. A force is received at a first material while the first material is in contact with a disk of a hard disk drive; the first material comprising a portion that is flexible in a first direction and is substantially non-flexible in a second direction. The first direction is a direction that is normal to the disk and the second direction is a direction that is parallel to a surface of the disk. The force is substantially absorbed by the portion that is flexible to reduce the force associated with interaction between the first material and the disk, thereby reducing slider bounce within the hard disk drive.

Abstract: A control object including a knob element is displayed in a GUI. A first user input can be detected that indicates selection of the knob element. In response to the first user input, the knob element can be visually augmented. The visual augmentation can include displaying text labels of range limit values and a slider element with the knob element. The slider element can include a fill bar to indicate the current value of the knob element. A second input can be a linear motion on or near the slider control. In response to the second user input, both the fill bar of the slider element and a fill portion of the knob element can be visually augmented to indicate the change in the value. A cursor image of a pointing device providing the second input can be hidden at least during the receiving of the second input.

Abstract: A control object including a knob element is displayed in a GUI. A first user input can be detected that indicates selection of the knob element. In response to the first user input, the knob element can be visually augmented. The visual augmentation can include displaying text labels of range limit values and a slider element with the knob element. The slider element can include a fill bar to indicate the current value of the knob element. A second input can be a linear motion on or near the slider control. In response to the second user input, both the fill bar of the slider element and a fill portion of the knob element can be visually augmented to indicate the change in the value. A cursor image of a pointing device providing the second input can be hidden at least during the receiving of the second input.

Abstract: Reducing slider bounce within a hard disk drive. A force is received at a first material while the first material is in contact with a disk of a hard disk drive; the first material comprising a portion that is flexible in a first direction and is substantially non-flexible in a second direction. The first direction is a direction that is normal to the disk and the second direction is a direction that is parallel to a surface of the disk.

Abstract: A method models and calibrates the fly height of a slider above the disk for disk drives. The calibration scheme uses the Wallace spacing loss equation and laser doppler velocimetry to predict fly height and detect the actual fly height of the slider. The slider is vibrated at selected resonances, such as by capacitive coupling to the disk, and the fly height is gradually reduced. In one version, contact between the slider and disk may be detected using an arm electronics sensor. The amplitude of mean-to-peak, vibration detection is used as an indication of the actual fly height and to calibrate the modeled fly height.

Abstract: A method models and calibrates the fly height of a slider above the disk for disk drives. The calibration scheme uses the Wallace spacing loss equation and laser doppler velocimetry to predict fly height and detect the actual fly height of the slider. The slider is vibrated at selected resonances, such as by capacitive coupling to the disk, and the fly height is gradually reduced. In one version, contact between the slider and disk may be detected using an arm electronics sensor. The amplitude of mean-to-peak, vibration detection is used as an indication of the actual fly height and to calibrate the modeled fly height.

Abstract: An apparatus, system, and method are disclosed for fly height detection. The apparatus includes an actuator configured to induce vibrations in a glide head that is configured to detect physical asperities on a disk surface. The apparatus may also include a second end of the suspension arm coupled with the glide head and a sensor module that is configured to communicate with a control module in response to the glide head coming in contact with at least one physical asperity. The system includes the apparatus, a disk having a surface comprising physical asperities, and a head gimbal assembly configured to calibrate a fly height margin for the disk. The method includes inducing vibrations in a glide head, detecting physical asperities on a disk surface, and communicating with a control module in response to the glide head coming in contact with at least one physical asperity.

Abstract: A method models and calibrates the fly height of a slider above the disk for disk drives. The calibration scheme uses the Wallace spacing loss equation and laser doppler velocimetry to predict fly height and detect the actual fly height of the slider. The slider is vibrated at selected resonances, such as by capacitive coupling to the disk, and the fly height is gradually reduced. In one version, contact between the slider and disk may be detected using an arm electronics sensor. The amplitude of mean-to-peak, vibration detection is used as an indication of the actual fly height and to calibrate the modeled fly height.

Abstract: Techniques for mixing multiple input channel signals into multiple output channel signals are provided. A graphical user interface (GUI), which includes multiple indicators, is displayed. The input channel signals are mixed to produce multiple output channel signals. The mixing is performed based on the distance between the indicators' positions in the GUI. According to one embodiment of the invention, the mixing is also performed based on the angle formed between the indicators. Thus, the extent to which an input channel signal is carried by an output channel signal is, in one embodiment of the invention, a function of both the distance between the indicators and an angle formed by the indicators in the GUI.