Abstract: A method of audio content identification includes using a two-stage classifier. The first stage includes previously-existing classifiers and the second stage includes a new classifier. The outputs of the first and second stages calculated over different time periods are combined to generate a steering signal. The final classification results from a combination of the steering signal and the outputs of the first and second stages. In this manner, a new classifier may be added without disrupting existing classifiers.

Abstract: The present invention belongs to animal experiments field, disclosed a single factor modeling-constraint method and device for mice chronic fatigue syndrome, the modeling-constraint method is specifically using mineral water bottle to restraint mice; the mineral water bottle is divided into upper, middle and lower parts; the body of mineral water bottle is uniformly drilled for mice to breathe; the inner of the mineral water bottle is blocked by a cardboard to reduce the move space of mice; cover the mice through the divided bottle; one end of the opening is tightly attach to the wall, the other end is tightly attach to the mice feeding box, and keeping 30 minutes to restraint. The present invention utilizes mineral water bottle to make three simple restraint tube, not only saves the costs of experiment, but also turns waste into treasure, which is a simple operation, and increases the possibility of the experiment.

Abstract: The present invention generally relates to microscopy, and to systems and methods for imaging or determining nucleic acids or other desired targets, for instance, within cells. In certain aspects, a sample is contained within an expandable material, which is expanded and imaged in some fashion. Expansion of the material improves the effective resolution of the subsequent image. This may be combined, for example, with other super-resolution techniques, such as STORM, and/or with techniques such as MERFISH for determining nucleic acids such as mRNA within the sample, for example, by binding nucleic acid probes to the sample. Other aspects are generally directed to compositions or devices for use in such methods, kits for use in such methods, or the like.

Abstract: The present invention belongs to animal experiments field, disclosed a single factor modeling-constraint method and device for mice chronic fatigue syndrome, the modeling-constraint method is specifically using mineral water bottle to restraint mice; the mineral water bottle is divided into upper, middle and lower parts; the body of mineral water bottle is uniformly drilled for mice to breathe; the inner of the mineral water bottle is blocked by a cardboard to reduce the move space of mice; cover the mice through the divided bottle; one end of the opening is tightly attach to the wall, the other end is tightly attach to the mice feeding box, and keeping 30 minutes to restraint. The present invention utilizes mineral water bottle to make three simple restraint tube, not only saves the costs of experiment, but also turns waste into treasure, which is a simple operation, and increases the possibility of the experiment.

Abstract: A method for steering binauralization of audio is provided. The method comprises steps of: receiving (410) an audio input signal, calculating (430) a confidence value indicating a likelihood that a current audio frame of the audio input signal comprises binauralized audio; determining (450) a state signal based on the confidence value; determining (460) a steering signal, based on the first confidence value, the state signal and an energy value of the audio frame; and generating (470) an audio output signal with steered binauralization by processing the audio input signal according to the steering signal.

Abstract: A method of audio content identification includes using a two-stage classifier. The first stage includes previously-existing classifiers and the second stage includes a new classifier. The outputs of the first and second stages calculated over different time periods are combined to generate a steering signal. The final classification results from a combination of the steering signal and the outputs of the first and second stages. In this manner, a new classifier may be added without disrupting existing classifiers.

Abstract: The present disclosure provides a call processing method, apparatus, electronic device and storage medium and relates to the field of cloud computing. The method may comprise: obtaining a calling subscriber's status information in real time while an intelligent dialogue robot is used to make a call with the calling subscriber; when it is determined that a call form of the intelligent dialogue robot needs to be adjusted, correspondingly adjusting the call form of the intelligent dialogue robot according to current status information of the calling subscriber. The solution of the present disclosure may be employed to improve the call performance of the intelligent dialogue robot.

Abstract: A method for steering binauralization of audio is provided. The method comprises steps of: receiving (410) an audio input signal, calculating (430) a confidence value indicating a likelihood that a current audio frame of the audio input signal comprises binauralized audio; determining (450) a state signal based on the confidence value; determining (460) a steering signal, based on the first confidence value, the state signal and an energy value of the audio frame; and generating (470) an audio output signal with steered binauralization by processing the audio input signal according to the steering signal.

Abstract: A method for image dehazing based on adaptively improved linear global atmospheric light of a dark channel. A haze image in haze weather is first obtained, a variation angle of atmospheric light of the image is obtain through calculating a slope of a connection line between a center and a center of gravity of a binary image of the image, a linear atmospheric light map that varies regularly along a variation direction of the atmospheric light is obtains, a dehazed image is solved through an atmospheric scattering model, and then a processed haze image taken in the haze weather is output.

Abstract: Embodiments of training signal processing models for component replacement in signal processing systems are disclosed. A device for training a third signal processing model via a machine learning method include a first calculating unit, a second calculating unit and a training unit. The first calculating unit calculates a first output from each first sample in a first sample set based on a first signal processing model. The second calculating unit calculates a second output from the first sample based on a second signal processing model. The training unit trains a third signal processing model by minimizing a first cost including a first error between the first output and a third output, so that the combination of the second signal processing model and the third signal processing model can simulate the behaviors of the first signal processing model on the first sample set. The third output is an output of the third signal processing model in response to an input including the second output.

Abstract: The present disclosure provides a call processing method, apparatus, electronic device and storage medium and relates to the field of cloud computing. The method may comprise: obtaining a calling subscriber's status information in real time while an intelligent dialogue robot is used to make a call with the calling subscriber; when it is determined that a call form of the intelligent dialogue robot needs to be adjusted, correspondingly adjusting the call form of the intelligent dialogue robot according to current status information of the calling subscriber. The solution of the present disclosure may be employed to improve the call performance of the intelligent dialogue robot.

Abstract: A method for image dehazing based on adaptively improved linear global atmospheric light of a dark channel. A haze image in haze weather is first obtained, a variation angle of atmospheric light of the image is obtain through calculating a slope of a connection line between a center and a center of gravity of a binary image of the image, a linear atmospheric light map that varies regularly along a variation direction of the atmospheric light is obtains, a dehazed image is solved through an atmospheric scattering model, and then a processed haze image taken in the haze weather is output.

Abstract: The present invention generally relates to microscopy, and to systems and methods for imaging or determining nucleic acids or other desired targets, for instance, within cells. In certain aspects, a sample is contained within an expandable material, which is expanded and imaged in some fashion. Expansion of the material improves the effective resolution of the subsequent image. This may be combined, for example, with other super-resolution techniques, such as STORM, and/or with techniques such as MERFISH for determining nucleic acids such as mRNA within the sample, for example, by binding nucleic acid probes to the sample. Other aspects are generally directed to compositions or devices for use in such methods, kits for use in such methods, or the like.

Abstract: Embodiments of training signal processing models for component replacement in signal processing systems are disclosed. A device for training a third signal processing model via a machine learning method include a first calculating unit, a second calculating unit and a training unit. The first calculating unit calculates a first output from each first sample in a first sample set based on a first signal processing model. The second calculating unit calculates a second output from the first sample based on a second signal processing model. The training unit trains a third signal processing model by minimizing a first cost including a first error between the first output and a third output, so that the combination of the second signal processing model and the third signal processing model can simulate the behaviors of the first signal processing model on the first sample set. The third output is an output of the third signal processing model in response to an input including the second output.