This paper formulates the maximum efficiency of multiple-transmitter inductive power transfer system in terms of system kQ-product. We show that the cross-coupling among transmitters does not affect the maximum efficiency. More importantly, the square of system kQ-product is equal to the sum of squares of kQ-products of individual transmitter-receiver links. This result provides an intuitive insight into the characteristics of system efficiency.

We propose a sinusoidal phase modulation method to achieve both the frequency stabilization of an external-cavity laser diode (ECLD) to an 127I2 saturated absorption transition near 633 nm and displacement measurement using a Mach–Zehnder interferometer. First, the frequency of the ECLD is stabilized to the b21 hyperfine component of the P(33) 6-3 transition of 127I2 by combining sinusoidal phase modulation by an electro-optic modulator and frequency modulation spectroscopy by chopping the pump beam using an acousto-optic modulator. Even though a small modulation index of m  =  3.768 rad is utilized, a relative frequency stability of 10−11 order is obtained over a sampling time of 400 s. Secondly, the frequency-stabilized ECLD is applied as a light source to a Mach–Zehnder interferometer. From the two consecutive modulation harmonics (second and third orders) involved in the interferometer signal, the displacement of the moving mirror is determined for four optical path differences (L0  =  100, 200, 500, and 1000 mm). The measured modulation indexes for the four optical path differences coincide with the designated value (3.768 rad) within 0.5%. Compared with the sinusoidal frequency modulation Michelson interferometer (Vu et al 2016 Meas. Sci. Technol. 27 105201) which was demonstrated by some of the same authors of this paper, the phase modulation Mach–Zhender interferometer could fix the modulation index to a constant value for the four optical path differences. In this report, we discuss the measurement principle, experimental system, and results.

Data quality is a key concern for artificial intelligence (AI) efforts that rely on crowdsourced data collection. In the domain of medicine in particular, labeled data must meet high quality standards, or the resulting AI may perpetuate biases or lead to patient harm. What are the challenges involved in expert medical labeling? How do AI practitioners address such challenges? In this study, we interviewed members of teams developing AI for medical imaging in four subdomains (ophthalmology, radiology, pathology, and dermatology) about their quality-related practices. We describe one instance of low-quality labeling being caught by automated monitoring. The more proactive strategy, however, is to partner with experts in a collaborative, iterative process prior to the start of high-volume data collection. Best practices including 1) co-designing labeling tasks and instructional guidelines with experts, 2) piloting and revising the tasks and guidelines, and 3) onboarding workers enable teams to identify and address issues before they proliferate.

This paper presents closed-form expressions of optimal loads that achieve the maximum efficiency for inductive power transfer (IPT) system with multiple receivers. We model the system as a passive N-port network and jointly optimize both reactive and resistive components of the loads. The derived expression allows one to predict RF-to-RF efficiency of IPT system with any number of receivers, under any coupling condition and through any type of media.

Online social networking platforms regularly support hundreds of millions of users, who in aggregate generate substantially more data than can be stored on any single physical server. As such, user data are distributed, or sharded, across many machines. A key requirement in this setting is rapid retrieval not only of a given user's information, but also of all data associated with his or her social contacts, suggesting that one should consider the topology of the social network in selecting a sharding policy. In this paper we formalize the problem of efficiently sharding large social network databases, and evaluate several sharding strategies, both analytically and empirically. We find that random sharding-the de facto standard-results in provably poor performance even when nodes are replicated to many shards.By contrast, we demonstrate that one can substantially reduce querying costs by identifying and assigning tightly knit communities to shards. In particular, we introduce a scalable sharding algorithm that outperforms both random and location-based sharding schemes.