Abstract:
Techniques and apparatus for generating dense natural language descriptions for video content are described. In one embodiment, for example, an apparatus may include at least one memory and logic, at least a portion of the logic comprised in hardware coupled to the at least one memory, the logic to receive a source video comprising a plurality of frames, determine a plurality of regions for each of the plurality of frames, generate at least one region-sequence connecting the determined plurality of regions, apply a language model to the at least one region-sequence to generate description information comprising a description of at least a portion of content of the source video. Other embodiments are described and claimed.
Abstract:
Press-button circuit and a driving method thereof are provided. A press-button circuit includes a first sampling port(G1); a second sampling port(G2); a common terminal(GND); a first control device(K1), coupled to the first sampling port (G1) and the second sampling port(G2); at least one first press-button sub-circuit(A1,A2,…,Am), coupled to the first sampling port(G1) and the common terminal(GND); and at least one second press-button sub-circuit(C1,C2,…,Cn), coupled to the second sampling port(G2) and the common terminal(GND).
Abstract:
Techniques are provided for training and operation of a topic-guided image captioning system. A methodology implementing the techniques includes generating image feature vectors, for an image to be captioned, based on application of a convolutional neural network (CNN) to the image. The method further includes generating the caption based on application of a recurrent neural network (RNN) to the image feature vectors. The RNN is configured as a long short-term memory (LSTM) RNN. The method further includes training the LSTM RNN with training images and associated training captions. The training is based on a combination of: feature vectors of the training image; ature vectors of the associated training caption; and a multimodal compact bilinear (MCB) pooling of the training caption feature vectors and an estimated topic of the training image. The estimated topic is generated by an application of the CNN to the training image.
Abstract:
A vehicle control wearable apparatus (101) is provided. The vehicle control wearable apparatus (101) includes a detector (201) configured to detect an intoxicating substance intake level of a potential driver of a vehicle; a vehicle operation state controller (202) coupled to the detector (201) and configured to receive the intoxicating substance intake level from the detector (201); and a first data transceiver (203) coupled to the vehicle operation state controller (202). The vehicle operation state controller (202) is configured to conduct a comparison between the intoxicating substance intake level and a first threshold level, and control the first data transceiver (203) to send a blocking signal based on a first result of the comparison at a first time point. The blocking signal controlling the vehicle in a blocked state thereby prevents the potential driver from driving the vehicle.
Abstract:
A novel process for preparation of Rebaudioside D (RD), and other related naturally occurring sweeteners is provided. RD is a natural sweetening agent which can decrease the bitter aftertaste of steviol glycosides. The said process is suitable for commercial manufacturing by using readily available natural products and nontoxic reagents.
Abstract:
Described herein are systems and methods for providing deeply stacked automated program synthesis. In one embodiment, an apparatus to perform automated program synthesis includes a memory to store instructions for automated program synthesis and a compute cluster coupled to the memory. The compute cluster supports the instructions for performing the automated program synthesis including partitioning sketched data into partitions, training diverse sets of individual program synthesis units each having different capabilities with partitioned sketched data and for each partition applying respective transformations, and generating sketched baseline data for each individual program synthesis unit.
Abstract:
Described herein are advanced artificial intelligence agents for modeling physical interactions. An apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.
Abstract:
Methods and systems for advanced and augmented training of deep neural networks (DNNs) using synthetic data and innovative generative networks. A method includes training a DNN using synthetic data, training a plurality of DNNs using context data, associating features of the DNNs trained using context data with features of the DNN trained with synthetic data, and generating an augmented DNN using the associated features.
Abstract:
The present invention relates to a novel process for preparation of Rebaudioside D (RD), and other related naturally occurring sweeteners. RD is a natural sweetening agent which can decrease the bitter aftertaste of steviol glycosides. The invention relates to an efficient process that is suitable for commercial manufacturing by using readily available natural products and nontoxic reagents.