Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A downside to diffusion is that it performs slowly in comparison to the naive k-NN search, which causes a non-trivial online computational cost on large datasets. To overcome this weakness, we propose a novel diffusion technique in this paper. In our work, instead of applying diffusion to the query, we precompute the diffusion results of each element in the database, making the online search a simple linear combination on top of the k-NN search process. Our proposed method becomes 10∼ times faster in terms of online search speed. Moreover, we propose to use late truncation instead of early truncation in previous works to achieve better retrieval performance.

Advances in artificial intelligence and in particular machine learning and neural networks have given rise to a new generation of virtual assistants and chatbots. Within this work, we present NADiA - Neurally Animated Dialog Agent - that leverages both the user's verbal input as well as their facial expressions to respond in a meaningful way. NADiA combines a neural language model that generates appropriate responses to user prompts, a convolutional neural network for facial expression analysis, and virtual human technology that is deployed on a mobile phone. Here, we evaluate NADiA's anthropomorphic characteristics and its ability to understand the human interlocutor using both subjective as well as objective measures. We find that NADiA significantly outperforms state of the art chatbot technology and produces comparable behavior to human generated reference outputs.