Miniature, battery-free implants promise transformative bio-electronic therapies by enabling minimally invasive implantation procedures, reducing risk, and extending device lifetime. Among all wireless power and data transfer (WPDT) modalities, magnetoelectrics (ME) has emerged as a particularly promising solution for millimeter-scale implants, boasting lower tissue attenuation and higher power transfer efficiency over conventional inductive and ultrasonic methods. However, as an acoustic resonator, ME devices face an inherent tradeoff between Q-factor and bandwidth, limiting their ability to simultaneously achieve high-speed communication and efficient wireless power transfer (WPT). To fundamentally circumvent the challenge, this paper presents dual-resonance ME WPDT that exploits the unique multimode resonances of ME transducers to realize WPT and communication at distinct frequencies. Based on this dual-resonance principle, we demonstrate reconfigurable active and passive schemes for different biomedical applications, with a proof-of-concept system including a miniature implant and an external transceiver. The active scheme achieves 60 kbps at operational distances of 6 cm with 2.5 mW implant power, while the passive backscatter offers 20 kbps continuous streaming at 4 cm with negligible power, demonstrating the first reported non-interrupted ME WPDT system and more than twice the data rate of previous ME backscatter methods. Both schemes further support on-off keying (OOK) and binary phase shift keying (BPSK) modulations, providing additional flexibility to tailor communication needs between power efficiency and robustness. The complete prototype system was validated through comprehensive in-vitro experiments and in-vivo EMG streaming in a rodent model.

This article addresses the distributed formation control issue of cooperative unmanned surface vessels (USVs) under interleaved periodic event-triggered communications. First, an adaptive event-based control protocol is designed, where the event-based neural network (NN) scheme is developed to compensate for uncertain model dynamics. Upon the designed control protocol, an interleaved periodic event-triggered mechanism (IPETM) is subsequently proposed to achieve the communication objective. Unlike the common continuous event-triggered methods and periodic event-triggered methods, in which multiple nodes are allowed to trigger their events at the same time, the proposed IPETM ensures that USVs detect their events at different times to avoid the simultaneous event triggering of different nodes. By this virtue, traffic jamming in common wireless environments can be prevented, such that potential communication delays and faults are naturally avoided. In addition, the event detecting instants of the presented IPETM are also discrete and periodic, such that it can be performed under low-computational frequencies. Through Lyapunov-based analysis, it is verified that all closed-loop signals can converge to an arbitrary small compact set with exponential convergence rates. Simulation results demonstrate the effectiveness and superiority of the proposed control scheme.

Privacy policies have disrupted the multi-billion dollar online advertising market by making real-time and precise user data untraceable, which poses significant challenges to the optimization of Return-On-Investment (ROI) constrained products in the online advertising industry. Privacy protection strategies, including event aggregation and reporting delays, hinder access to detailed and instantaneous feedback data, thus incapacitating traditional identity-revealing attribution techniques. In this paper, we introduces a novel Spending Programmed Bidding (SPB) framework to navigate these challenges. SPB is a two-stage framework that separates long horizon delivery spend planning (the macro stage) and short horizon bidding execution (the micro stage). The macro stage models the target ROI to achieve maximum utility and derives the expected spend, whereas the micro stage optimizes the bid price given the expected spend. We further extend our framework to the cross-channel scenario where the agent bids in both privacy-constrained and identity-revealing attribution channels. We find that when privacy-constrained channels are present, SPB is superior to state-of-the-art bidding methods in both offline datasets and online experiments on a large ad platform.

Conversion rate (CVR) prediction is becoming increasingly important in the multi-billion dollar online display advertising industry. It has two major challenges: firstly, the scarce user history data is very complicated and non-linear; secondly, the time delay between the clicks and the corresponding conversions can be very large, e.g., ranging from seconds to weeks. Existing models usually suffer from such scarce and delayed conversion behaviors. In this paper, we propose a novel deep learning framework to tackle the two challenges. Specifically, we extract the pre-trained embedding from impressions/clicks to assist in conversion models and propose an inner/self-attention mechanism to capture the fine-grained personalized product purchase interests from the sequential click data. Besides, to overcome the time-delay issue, we calibrate the delay model by learning dynamic hazard function with the abundant post-click data more in line with the real distribution. Empirical experiments with real-world user behavior data prove the effectiveness of the proposed method.