In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing. In the design and deployment of such a large-scale and complicated system, we faced several challenges including model selection, performance evaluation, serving and other practical issues. At the core of Smart Compose is a large-scale neural language model. We leveraged state-of-the-art machine learning techniques for language model training which enabled high-quality suggestion prediction, and constructed novel serving infrastructure for high-throughput and real-time inference. Experimental results show the effectiveness of * Equal contribution.our proposed system design and deployment approach. This system is currently being served in Gmail.

In this work we present the details of the implementation of Fotofiti(FF), a website that provides automatic semantic annotation of digital photographs, event management and social network integration. We describe our technique for real-time online semantic annotation using global features from both content and context. Classification experiments using various learning techniques were performed on a realworld data-set. Additionally, a scalable landmark recognition system which utilizes local features is discussed.

In this work, we present Fotofiti(FF), a web-based personal photo organizer with automatic image annotation, event management and social network integration. We describe our technique for real-time online semantic annotation of user photos. Additionally, a landmark recognition system which utilizes local features is discussed.

Statistical static timing analysis (SSTA) has been a popular research topic in recent years. A fundamental issue with applying SSTA in practice today is the lack of reliable and efficient statistical timing models (STM). Among many types of parameters required to be carefully modeled in an STM, spatial delay correlations are recognized as having significant impact on SSTA results. In this work, we assume that exact modeling of spatial delay correlations is quite difficult, and propose an experimental methodology to resolve this issue. The modeling accuracy requirement is relaxed by allowing SSTA to impose upper bounds and lower bounds on the delay correlations. These bounds can then be refined through learning the actual delay correlations from path delay testing on silicon. We utilize SSTA as the platform for learning and propose a Bayesian approach for learning spatial delay correlations. The effectiveness of the proposed methodology is illustrated through experiments on benchmark circuits.

Fotowiki (FW) is a wiki-based map service that integrates visual and textual information with map. FW divides a geographical area into sub-areas.An individual responsible for providing information about a sub-area enters collected data into a wiki page. FW uploads distributed wiki-pages, and overlays the information on the map. This demonstration shows FW's architecture and functionalities.