The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a user visit. To place an ad automatically and optimally, it is critical for advertisers to devise a learning algorithm to cleverly bid an ad impression in real-time. Most previous works consider the bid decision as a static optimization problem of either treating the value of each impression independently or setting a bid price to each segment of ad volume. However, the bidding for a given ad campaign would repeatedly happen during its life span before the budget runs out. As such, each bid is strategically correlated by the constrained budget and the overall effectiveness of the campaign (e.g., the rewards from generated clicks), which is only observed after the campaign has completed. Thus, it is of great interest to devise an optimal bidding strategy sequentially so that the campaign budget can be dynamically allocated across all the available impressions on the basis of both the immediate and future rewards. In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set. By modeling the state transition via auction competition, we build a Markov Decision Process framework for learning the optimal bidding policy to optimize the advertising performance in the dynamic real-time bidding environment. Furthermore, the scalability problem from the large real-world auction volume and campaign budget is well handled by state value approximation using neural networks.Comment: WSDM 201

Core/shell nanostructures are fascinating for many advanced applications including strong permanent magnets, magnetic recording, and biotechnology. They are generally achieved via chemical approaches, but these techniques limit them to nanoparticles. Here, we describe a three-dimensional (3D) self-assembly of core/shell-like nanocomposite magnets, with hard-magnetic Nd2Fe14B core of ∼45 nm and soft-magnetic α-Fe shell of ∼13 nm, through a physical route. The resulting Nd2Fe14B/α-Fe core/shell-like nanostructure allows both large remanent magnetization and high coercivity, leading to a record-high energy product of 25 MGOe which reaches the theoretical limit for isotropic Nd2Fe14B/α-Fe nanocomposite magnets. Our approach is based on a sequential growth of the core and shell nanocrystals in an alloy melt. These results make an important step toward fabricating core/shell-like nanostructure in 3D materials.

In general, there is a trade-off between magnetization and coercivity in nanocomposite magnets. Here, we demonstrate a simultaneous enhancement of both the magnetization and coercivity in bulk α-Fe/Nd2Fe14B nanocomposite magnets prepared via a severe plastic deformation (SPD) compared with thermally annealed magnets. The enhanced magnetization results from a high fraction (>30%) of α-Fe phase induced by SPD, while the increase in coercivity from 4.6 to 7.2 kOe is attributed to an enhancement in domain wall pinning strength. This study shows an increase in energy product is possible in the nanocomposite magnets for a large inclusion of soft-magnetic phase.

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Understanding the deformation-induced nanocrystallization in amorphous alloys at room temperature is proved to be a great challenge. In the present study, the formation of vacancy-type defects richly surrounded with Fe atoms in amorphous Nd9Fe85B6 subjected to room-temperature high-pressure torsion deformation is directly evidenced by positron lifetime measurements combined with a coincident Doppler broadening measurement of the positron-electron annihilation photons. This demonstrates a direct experimental evidence for shear inducing the enrichment of Fe atoms in the deformed amorphous alloy. These results presented here are of importance for understanding the deformation-induced nanocrystallization and for the technique development of nanocrystalline materials.