Abstract: The present disclosure provides a method and apparatus for distributing an electric energy quantity. The method and apparatus are used for solving a problem that electric energy cannot be reasonably distributed among multiple household appliances in the related art. The method includes that a terminal detects a first remaining electric energy quantity of a photovoltaic panel; the terminal determines, according to pre-determined identification information of at least one household appliance to be electrified corresponding to each remaining electric energy quantity range of the photovoltaic panel, first identification information corresponding to a first remaining electric energy quantity range where the first remaining electric energy quantity is located; and the terminal controls the photovoltaic panel to electrify a first household appliance of the first identification information.

Abstract: In accordance with implementations of the subject matter described herein, a solution for speech enhancement is proposed. In this solution, a target time-frequency representation at least indicating intensities of an input audio signal at different frequencies over time is obtained. The input audio signal comprises a speech component and a noise component. Frequency correlation information and time correlation information of the input audio signal is determined based on the target time-frequency representation. A target feature representation is generated based on the frequency correlation information, the time correlation information, and the target time-frequency representation. The target feature representation is for distinguishing the speech component and the noise component. An output audio signal is generated based on the target feature representation and the target time-frequency representation. The speech component is enhanced relative to the noise component in the output audio signal.

Abstract: Technologies are provided for the generating of optimal policies for bidding in auctions having unknown dynamics. In some embodiments, a computing system can configure many multi-armed bandit (MAB) models defining candidate directed contents for a sequence of pages. A particular MAB model of the many MAB models defines candidate directed contents for a particular page in the sequence of pages, where each arm in the particular MAB model corresponds to a candidate impression on the particular page. The computing system can then determine a solution to an optimization problem with respect to an objective function based on an expected long-term reward for a defined impression on the first page, a defined impression on the second page, and a defined impression on the third page. The solution results in respective directed content for presentation on the first, second, and third pages.

Abstract: Various implementations of the subject matter described herein relate to a neural network-based action detection. There is provided an action detection scheme using a neural network. The action detection scheme can design and optimize the neural network model based on respective importance of different frames such that frames that are more important or discriminative for action recognition tend to be assigned with higher weights and frames that are less important or discriminative for action recognition tend to be assigned with lower weights.

Abstract: In accordance with implementations of the subject matter described herein, a hybrid digital-analog coding scheme is proposed. In general, in accordance with implementations of the subject matter described herein, digital or analog encoding is selected at a level of chunks of a frame rather than at a level of the whole frame. Further, the encoding of a chunk is based on an expected distortion to be caused by analog transmission of the chunk over a communication channel, the distortion being estimated based on a constraint on available transmission resources over the transmission channel.

Abstract: In implementations of the subject matter described herein, an action detection scheme using a recurrent neural network (RNN) is proposed. Representation information of an incoming frame of a video and a predefined action label for the frame are obtained to train a learning network including RNN elements and a classification element. The representation information represents an observed entity in the frame. Specifically, parameters for the RNN elements are determined based on the representation information and the predefined action label. With the determined parameters, the RNN elements are caused to extract features for the frame based on the representation information and features for a preceding frame. Parameters for the classification element are determined based on the extracted features and the predefined action label. The classification element with the determined parameters generates a probability of the frame being associated with the predefined action label.

Abstract: Various implementations of the subject matter described herein relate to a neural network-based action detection. There is provided an action detection scheme using a neural network. The action detection scheme can design and optimize the neural network model based on respective importance of different frames such that frames that are more important or discriminative for action recognition tend to be assigned with higher weights and frames that are less important or discriminative for action recognition tend to be assigned with lower weights.

Abstract: High-speed hyperspectral (HSHS) video reconstruction is implemented in an imaging system. A hyperspectral snapshot is determined. A panchromatic video is determined corresponding to a frame time of the hyperspectral snapshot, the panchromatic video including a plurality of panchromatic frames. An HSHS video is generated by building a patch dictionary based at least in part on the panchromatic video and generating at least one frame of the HSHS video based at least in part on the patch dictionary, the panchromatic video, and the hyperspectral snapshot. A computing device can include a dictionary component configured to determine the patch dictionary and a reconstruction component configured to generate at least one HSHS frame based at least in part on the patch dictionary, the panchromatic video, and the hyperspectral snapshot.

Abstract: In this disclosure, a hybrid digital-analog video coding scheme is described. For a video including at least two associated sequences of frames such as a stereo video, some of the frames are digitally encoded, while the others are encoded in an analog way with reference to the digital frames. For an analog frame, a previous frame in the same sequence/view and a temporally consistent frame in another sequence/view will be encoded as digital frames. These two digital frames are used to provide side information in encoding the analog frame. At decoding side, inverse operations are performed. Such “zigzag” hybrid coding significantly improves the coding efficiency while providing robustness and good visual quality.

Abstract: In implementations of the subject matter described herein, a current captured frame of a video is compared with a respective reference frame to determine a correlation therebetween. The correlation is used to indicate a change degree of the current frame. If the correlation for the current frame is below a predetermined threshold, the current frame may not be directly determined as including a motion of an object. Instead, correlations between one or more frames before or after the current frame and their respective reference frames are took into account. If the correlations of the frames under consideration are below the predetermined threshold, it may be detected that the current frame includes a motion of an object. In this way, incorrect detection of the object motion is reduced in the cases when larger changes in frames of a video are caused by factors such as noise and error, and the accuracy of the object motion detection is improved.

Abstract: In accordance with implementations of the subject matter described herein, a hybrid digital-analog coding scheme is proposed. In general, in accordance with implementations of the subject matter described herein, digital or analog encoding is selected at a level of chunks of a frame rather than at a level of the whole frame. Further, the encoding of a chunk is based on an expected distortion to be caused by analog transmission of the chunk over a communication channel, the distortion being estimated based on a constraint on available transmission resources over the transmission channel.

Abstract: In implementations of the subject matter described herein, an action detection scheme using a recurrent neural network (RNN) is proposed. Representation information of an incoming frame of a video and a predefined action label for the frame are obtained to train a learning network including RNN elements and a classification element. The representation information represents an observed entity in the frame. Specifically, parameters for the RNN elements are determined based on the representation information and the predefined action label. With the determined parameters, the RNN elements are caused to extract features for the frame based on the representation information and features for a preceding frame. Parameters for the classification element are determined based on the extracted features and the predefined action label. The classification element with the determined parameters generates a probability of the frame being associated with the predefined action label.

Abstract: In this disclosure, a hybrid digital-analog video coding scheme is described. For a video including at least two associated sequences of frames such as a stereo video, some of the frames are digitally encoded, while the others are encoded in an analog way with reference to the digital frames. For an analog frame, a previous frame in the same sequence/view and a temporally consistent frame in another sequence/view will be encoded as digital frames. These two digital frames are used to provide side information in encoding the analog frame. At decoding side, inverse operations are performed. Such “zigzag” hybrid coding significantly improves the coding efficiency while providing robustness and good visual quality.

Abstract: In implementations of the subject matter described herein, an action detection scheme using a recurrent neural network (RNN) is proposed. Joint locations for a skeleton representation of an observed entity in a frame of a video and a predefined action label for the frame are obtained to train a learning network including RNN elements and a classification element. Specifically, first weights for mapping the joint locations to a first feature for the frame generated by a first RNN element in a learning network and second weights for mapping the joint locations to a second feature for the frame generated by a second RNN element in the learning network are determined based on the joint locations and the predefined action label. The first and second weights are determined by increasing a first correlation between the first feature and a first subset of the joint locations and a second correlation between the second feature and the first subset of the joint locations.

Abstract: High-speed hyperspectral (HSHS) video reconstruction is implemented in an imaging system. A hyperspectral snapshot is determined. A panchromatic video is determined corresponding to a frame time of the hyperspectral snapshot, the panchromatic video including a plurality of panchromatic frames. An HSHS video is generated by building a patch dictionary based at least in part on the panchromatic video and generating at least one frame of the HSHS video based at least in part on the patch dictionary, the panchromatic video, and the hyperspectral snapshot. A computing device can include a dictionary component configured to determine the patch dictionary and a reconstruction component configured to generate at least one HSHS frame based at least in part on the patch dictionary, the panchromatic video, and the hyperspectral snapshot.

Abstract: In implementations of the subject matter described herein, an action detection scheme using a recurrent neural network (RNN) is proposed. Joint locations for a skeleton representation of an observed entity in a frame of a video and a predefined action label for the frame are obtained to train a learning network including RNN elements and a classification element. Specifically, first weights for mapping the joint locations to a first feature for the frame generated by a first RNN element in a learning network and second weights for mapping the joint locations to a second feature for the frame generated by a second RNN element in the learning network are determined based on the joint locations and the predefined action label. The first and second weights are determined by increasing a first correlation between the first feature and a first subset of the joint locations and a second correlation between the second feature and the first subset of the joint locations.

Abstract: In implementations of the subject matter described herein, a current captured frame of a video is compared with a respective reference frame to determine a correlation therebetween. The correlation is used to indicate a change degree of the current frame. If the correlation for the current frame is below a predetermined threshold, the current frame may not be directly determined as including a motion of an object. Instead, correlations between one or more frames before or after the current frame and their respective reference frames are took into account. If the correlations of the frames under consideration are below the predetermined threshold, it may be detected that the current frame includes a motion of an object. In this way, incorrect detection of the object motion is reduced in the cases when larger changes in frames of a video are caused by factors such as noise and error, and the accuracy of the object motion detection is improved.

Abstract: An apparatus comprising one or more processors configured to process a first image at a first resolution to generate a first solved image at the first resolution, determine a difference between the first solved image and a function of the first image, and generate a second solved image at a second resolution higher than the first resolution based at least in part on the difference between the first solved image and the function of the first image.

Abstract: Some examples include receiving a microblog entry from a social stream domain. Further, some implementations include determining, based on a topic space associated with the social stream domain and a media domain, a topic that is associated with the microblog entry. Some implementations include determining, based on the topic space, one or more media items that are associated with the topic.

Abstract: An apparatus comprising one or more processors configured to process a first image at a first resolution to generate a first solved image at the first resolution, determine a difference between the first solved image and a function of the first image, and generate a second solved image at a second resolution higher than the first resolution based at least in part on the difference between the first solved image and the function of the first image.