Deep Character-Level Click-Through Rate Prediction for Sponsored Search

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This is inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specially, the proposed architectures only consider the textual content appearing in a query-advertisement pair as input, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.Comment: SIGIR2017, 10 page

Analysis of optical near-field energy transfer by stochastic model unifying architectural dependencies

We theoretically and experimentally demonstrate energy transfer mediated by optical near-field interactions in a multi-layer InAs quantum dot (QD) structure composed of a single layer of larger dots and N layers of smaller ones. We construct a stochastic model in which optical near-field interactions that follow a Yukawa potential, QD size fluctuations, and temperature-dependent energy level broadening are unified, enabling us to examine device-architecture-dependent energy transfer efficiencies. The model results are consistent with the experiments. This study provides an insight into optical energy transfer involving inherent disorders in materials and paves the way to systematic design principles of nanophotonic devices that will allow optimized performance and the realization of designated functions

Bioans: bio-inspired ambient intelligence protocol for wireless sensor networks

This paper describes the BioANS (Bio-inspired Autonomic Networked Services) protocol that uses a novel utility-based service selection mechanism to drive autonomicity in sensor networks. Due to the increase in complexity of sensor network applications, self-configuration abilities, in terms of service discovery and automatic negotiation, have become core requirements. Further, as such systems are highly dynamic due to mobility and/or unreliability; runtime self-optimisation and self-healing is required. However the mechanism to implement this must be lightweight due to the sensor nodes being low in resources, and scalable as some applications can require thousands of nodes. BioANS incorporates some characteristics of natural emergent systems and these contribute to its overall stability whilst it remains simple and efficient. We show that not only does the BioANS protocol implement autonomicity in allowing a dynamic network of sensors to continue to function under demanding circumstances, but that the overheads incurred are reasonable. Moreover, state-flapping between requester and provider, message loss and randomness are not only tolerated but utilised to advantage in the new protocol

Improved self-gain in deep submicrometer strained silicon-germanium pMOSFETs with HfSiOx/TiSiN gate stacks

The self-gain of surface channel compressively strained SiGe pMOSFETs with HfSiOx/TiSiN gate stacks is investigated for a range of gate lengths down to 55 nm. There is 125% and 700% enhancement in the self-gain of SiGe pMOSFETs compared with the Si control at 100 nm and 55 nm lithographic gate lengths, respectively. This improvement in the self-gain of the SiGe devices is due to 80% hole mobility enhancement compared with the Si control and improved electrostatic integrity in the SiGe devices due to less boron diffusion into the channel. At 55 nm gate length, the SiGe pMOSFETs show 50% less drain induced barrier lowering compared with the Si control devices. Electrical measurements show that the SiGe devices have larger effective channel lengths. It is shown that the enhancement in the self-gain of the SiGe devices compared with the Si control increases as the gate length is reduced thereby making SiGe pMOSFETs with HfSiOx/TiSiN gate stacks an excellent candidate for analog/mixed-signal applications