Abstract: A compressor includes a compressor housing and a compression subassembly installed in the compressor housing. The compressor housing includes a shell defining a first chamber and an end cap defining a second chamber. The compression subassembly includes a support plate positioned in the compressor housing between the shell and the end cap, a non-orbiting scroll attached to the support plate and extending from the support plate into the first chamber, a main bearing housing positioned in the first chamber and attached to the non-orbiting scroll, and an orbiting scroll positioned between the non-orbiting scroll and the main bearing housing. The support plate separates the first chamber and the second chamber and supports the compression subassembly in the compressor housing.

Abstract: An antirotation valve system for controlling flow through a valve opening defined in a compressor surface. The antirotation valve system includes a reed and a backer each having one mounting opening and one antirotation feature, and a washer.

Abstract: A compressor includes a compressor housing and a compression subassembly installed in the compressor housing. The compressor housing includes a shell defining a first chamber and an end cap defining a second chamber. The compression subassembly includes a support plate positioned in the compressor housing between the shell and the end cap, a non-orbiting scroll attached to the support plate and extending from the support plate into the first chamber, a main bearing housing positioned in the first chamber and attached to the non-orbiting scroll, and an orbiting scroll positioned between the non-orbiting scroll and the main bearing housing. The support plate separates the first chamber and the second chamber and supports the compression subassembly in the compressor housing.

Abstract: A noise-reducing preamp system includes: a power supply circuit configured to provide electrical power to the noise-reducing preamp system and including a 0 volt reference; a signal amplification circuit including: a preamplifier circuit having an external reference input terminal and configured to receive an analog input signal, and a variable gain controller circuit configured to control the gain of the preamplifier circuit, wherein the signal amplification circuit is configured to amplify the analog input signal to generate an analog amplified signal; and a DC offset and biasing circuit coupled to the preamplifier circuit and the 0 volt reference of the power supply circuit, the DC offset and biasing circuit configured to: receive the 0 volt reference as a feedback signal, process the analog amplified signal to remove a DC offset and generate an analog output signal, and provide the analog output signal to the external reference input terminal of the preamplifier circuit of the signal amplification circuit

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: 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.