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Inverse decision theory (IDT) aims to learn a performance metric for classification by eliciting expert classifications on examples. However, elicitation in practical settings may require many classifications of potentially ambiguous examples. To improve the efficiency of elicitation, we propose the cooperative inverse decision theory (CIDT) framework as a formalization of the performance metric elicitation problem. In cooperative inverse decision theory, the expert and a machine play a game where both are rewarded according to the expert’s performance metric, but the machine does not initially know what this function is. We show that optimal policies in this framework produce active learning that leads to an exponential improvement in sample complexity over previous work. One of our key findings is that a broad class of sub-optimal experts can be represented as having uncertain preferences. We use this finding to show such experts naturally fit into our proposed framework extending inverse decision theory to efficiently deal with decision data that is sub-optimal due to noise, conflicting experts, or systematic error 

Abstract Terahertz (THz) spin dynamics and vanishing stray field make antiferromagnetic (AFM) materials the most promising candidate for the next-generation magnetic memory technology with revolutionary storage density and writing speed. However, owing to the extremely large exchange energy barriers, energy-efficient manipulation has been a fundamental challenge in AFM systems. Here, we report an electrical writing of antiferromagnetic orders through a record-low current density on the order of 10 6 A cm −2 facilitated by the unique AFM-ferromagnetic (FM) phase transition in FeRh. By introducing a transient FM state via current-induced Joule heating, the spin-orbit torque can switch the AFM order parameter by 90° with a reduced writing current density similar to ordinary FM materials. This mechanism is further verified by measuring the temperature and magnetic bias field dependences, where the X-ray magnetic linear dichroism (XMLD) results confirm the AFM switching besides the electrical transport measurement. Our findings demonstrate the exciting possibility of writing operations in AFM-based devices with a lower current density, opening a new pathway towards pure AFM memory applications. 

Abstract In Fall 2020, universities saw extensive transmission of SARS-CoV-2 among their populations, threatening health of the university and surrounding communities, and viability of in-person instruction. Here we report a case study at the University of Illinois at Urbana-Champaign, where a multimodal “SHIELD: Target, Test, and Tell” program, with other non-pharmaceutical interventions, was employed to keep classrooms and laboratories open. The program included epidemiological modeling and surveillance, fast/frequent testing using a novel low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD, and digital tools for communication and compliance. In Fall 2020, we performed >1,000,000 covidSHIELD tests, positivity rates remained low, we had zero COVID-19-related hospitalizations or deaths amongst our university community, and mortality in the surrounding Champaign County was reduced more than 4-fold relative to expected. This case study shows that fast/frequent testing and other interventions mitigated transmission of SARS-CoV-2 at a large public university.