Abstract: Apparatus (10), method, computer program product and computer readable medium are disclosed for deep learning. The apparatus (10) comprises at least one processor (11); at least one memory (12) including computer program code, the memory (12) and the computer program code configured to, working with the at least one processor (11), cause the apparatus (10) to use a two-dimensional activation function in a deep learning architecture, wherein the two-dimensional activation function comprises a first parameter representing an element to be activated and a second parameter representing the element's neighbors.

Abstract: The present disclosure relates to a user identification method applicable to a vehicle, the vehicle including at least two microphone arrays, the respective microphone arrays being disposed at different positions of the vehicle, respectively. The user identification method includes: receiving a voice of a user within the vehicle through the at least two microphone arrays; determining directions from the user to the microphone arrays, respectively, according to the voice; calculating an angle between any two of the directions; and identifying a type of the user based at least on the angle.

Abstract: The present disclosure relates to a user identification method applicable to a vehicle, the vehicle including at least two microphone arrays, the respective microphone arrays being disposed at different positions of the vehicle, respectively. The user identification method includes: receiving a voice of a user within the vehicle through the at least two microphone arrays; determining directions from the user to the microphone arrays, respectively, according to the voice; calculating an angle between any two of the directions; and identifying a type of the user based at least on the angle.

Abstract: An object detection method, a neural network training method, an apparatus, and an electronic device include: obtaining, through prediction, multiple fused feature graphs from images to be processed, through a deep convolution neural network for target region frame detection, obtaining multiple first feature graphs from a first subnet having at least one lower sampling layer, obtaining multiple second feature graphs from a second subnet having at least one upper sampling layer, and obtaining fused graph by fusing multiple first feature graphs and multiple second feature graphs respectively; and obtaining target region frame data according to the multiple fused feature graphs. Because the fused feature graphs better represent semantic features on high levels and detail features on low levels in images, target region frame data of big and small objects in images can be effectively extracted according to the fused feature graphs, thereby improving accuracy and robustness of object detection.

Abstract: A siphon drainage type rainfall remote measurement device with self-compensation function is provided. The bottom existence of a rain collection barrel (2) is connected via a flexible tube (14) to the bottom of a rainfall measuring tube (13) to which a siphon (5) is attached. The tube (13) and a measurement instrument (6) are installed on a holding frame (9). The instrument (6) is connected to a pressure sensor (12) and an accumulator (11) at the bottom of the tube (13). The whole device is fixed in an outer barrel (1). According to the pressure of the bottom of the tube (13), the sensor (12) measures the height of the water level in the tube (13) which is directly proportional to the rainfall. The sensor (12) signal processed by the instrument (6) is transformed into the rainfall, processed by a compensation algorithm and sent by GSM or GPRS.

Abstract: A flavor system for a non-animal derived protein consumable is provided. The flavor system includes an aqueous component; a protein binder including a mixture of at least one terpene and at least one carbonyl compound; one or more off-note blocking compounds; and a flavorant.

Abstract: A siphon drainage type rainfall remote measurement device with self-compensation function is provided. The bottom existence of a rain collection barrel (2) is connected via a flexible tube (14) to the bottom of a rainfall measuring tube (13) to which a siphon (5) is attached. The tube (13) and a measurement instrument (6) are installed on a holding frame (9). The instrument (6) is connected to a pressure sensor (12) and an accumulator (11) at the bottom of the tube (13). The whole device is fixed in an outer barrel (1). According to the pressure of the bottom of the tube (13), the sensor (12) measures the height of the water level in the tube (13) which is directly proportional to the rainfall. The sensor (12) signal processed by the instrument (6) is transformed into the rainfall, processed by a compensation algorithm and sent by GSM or GPRS.