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The present work is a novel, systematic study of the effect of density functional theory input parameters on the vacancy formation energy (VFE), migration barrier for diffusion, and electronic structure for each element in the CoCrNi medium-entropy alloy (MEA). In particular, the novelties include: (1) calculating the aforementioned properties of Co, Cr, or Ni, in the CoCrNi MEA using magnetic and non-magnetic states, and two versions of the generalized gradient approximation: Perdew, Burke, and Ernzerhof (PBE) and the PBE version for solids (PBEsol), and (2) a detailed comparison of 0 K activation energy to experimental creep activation energies. First-principles calculations at 0 K are performed using the Vienna ab-initio simulation package. Special quasirandom structures (SQS) and Widom-type substitution are employed. For each element, Co, Cr, or Ni, non-magnetic calculations result in a higher VFE and larger range of calculated values for the configurations studied. The averaged migration barrier is the highest for Co in the CoCrNi for three of four sets of calculation parameters in the configurations studied. Finally, the results indicate that the average 0 K activation energy for diffusion makes up 70–80% of the experimental creep activation energy, depending on the exchange-correlation functional employed. 

Abstract We review two magnetic tunnel junction (MTJ) approaches for compact, low-power, CMOS-integrated true random number generation (TRNG). The first employs passive-read, easy-plane superparamagnetic MTJs (sMTJs) that generate thermal-fluctuation-driven bitstreams at 0.5–1 Gb s⁻¹per device. The second uses MTJs with magnetically stable free layers, operated with stochastic write pulses to achieve switching probabilities of about 0.5 (i.e. write error rates of $\simeq 0.5$), achieving $\gtrsim 0.1$ Gb s⁻¹per device; we refer to these as stochastic-write MTJs (SW-MTJs). Randomness from both approaches has been validated using the NIST SP 800-22r1a test suites. sMTJ approach uses a read-only cell with low power and can be compatible with most advanced CMOS nodes, while SW-MTJs leverage standard CMOS MTJ process flows, enabling co-integration with embedded spin-transfer torque magnetic random access memory. Both approaches can achieve deep sub-0.01 µm²MTJ footprints and offer orders-of-magnitude better energy efficiency than CPU/GPU-based generators, enabling placement near logic for high-throughput random bitstreams for probabilistic computing, statistical modeling, and cryptography. In terms of performance, sMTJs generally suit applications requiring very high data-rate random bits near logic processors, such as probabilistic computing or large-scale statistical modeling. Whereas SW-MTJs are attractive option for edge-oriented microcontrollers, providing entropy sources for computing or cryptographic enhancement. We highlight the strengths, limitations, and integration challenges of each approach, emphasizing the need to reduce device-to-device variability in sMTJs—particularly by mitigating magnetostriction-induced in-plane anisotropy—and to improve temporal stability in SW-MTJs for robust, large-scale deployment. 

Personalising decision-making assistance to different users and tasks can improve human-AI team performance, such as by appropriately impacting reliance on AI assistance. However, people are different in many ways, with many hidden qualities, and adapting AI assistance to these hidden qualities is difficult. In this work, we consider a hidden quality previously identified as important: overreliance on AI assistance. We would like to (i) quickly determine the value of this hidden quality, and (ii) personalise AI assistance based on this value. In our first study, we introduce a few probe questions (where we know the true answer) to determine if a user is an overrelier or not, finding that correctly-chosen probe questions work well. In our second study, we improve human-AI team performance, personalising AI assistance based on users’ overreliance quality. Exploratory analysis indicates that people learn different strategies of using AI assistance depending on what AI assistance they saw previously, indicating that we may need to take this into account when designing adaptive AI assistance. We hope that future work will continue exploring how to infer and personalise to other important hidden qualities.