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As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions. 

Superconducting qubits are a promising platform for building a larger-scale quantum processor capable of solving otherwise intractable problems. In order for the processor to reach practical viability, the gate errors need to be further suppressed and remain stable for extended periods of time. With recent advances in qubit control, both single- and two-qubit gate fidelities are now in many cases limited by the coherence times of the qubits. Here we experimentally employ closed-loop feedback to stabilize the frequency fluctuations of a superconducting transmon qubit, thereby increasing its coherence time by 26% and reducing the single-qubit error rate from (8.5 ± 2.1) × 10⁻⁴to (5.9 ± 0.7) × 10⁻⁴. Importantly, the resulting high-fidelity operation remains effective even away from the qubit flux-noise insensitive point, significantly increasing the frequency bandwidth over which the qubit can be operated with high fidelity. This approach is helpful in large qubit grids, where frequency crowding and parasitic interactions between the qubits limit their performance.

 

Abstract System noise identification is crucial to the engineering of robust quantum systems. Although existing quantum noise spectroscopy (QNS) protocols measure an aggregate amount of noise affecting a quantum system, they generally cannot distinguish between the underlying processes that contribute to it. Here, we propose and experimentally validate a spin-locking-based QNS protocol that exploits the multi-level energy structure of a superconducting qubit to achieve two notable advances. First, our protocol extends the spectral range of weakly anharmonic qubit spectrometers beyond the present limitations set by their lack of strong anharmonicity. Second, the additional information gained from probing the higher-excited levels enables us to identify and distinguish contributions from different underlying noise mechanisms. 

Particle transport and localization phenomena in condensed-matter systems can be modeled using a tight-binding lattice Hamiltonian. The ideal experimental emulation of such a model utilizes simultaneous, high-fidelity control and readout of each lattice site in a highly coherent quantum system. Here, we experimentally study quantum transport in one-dimensional and two-dimensional tight-binding lattices, emulated by a fully controllable 3 × 3 array of superconducting qubits. We probe the propagation of entanglement throughout the lattice and extract the degree of localization in the Anderson and Wannier-Stark regimes in the presence of site-tunable disorder strengths and gradients. Our results are in quantitative agreement with numerical simulations and match theoretical predictions based on the tight-binding model. The demonstrated level of experimental control and accuracy in extracting the system observables of interest will enable the exploration of larger, interacting lattices where numerical simulations become intractable.

 

Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new, hidden features that allow users to integrate novel computational methods. 

Host–parasite interactions may often be subject to opposing evolutionary forces, which likely influence the evolutionary trajectories of both partners. Natural selection and genetic drift are two major evolutionary forces that act in host and parasite populations. Further, population size is a significant determinant of the relative strengths of these forces. In small populations, drift may undermine the persistence of beneficial alleles, potentially impeding host adaptation to parasites. Here, we investigate two questions: (a) can selection pressure for increased resistance in small, susceptible host populations overcome the effects of drift and (b) can resistance be maintained in small host populations? To answer these questions, we experimentally evolved the hostCaenorhabditis elegansagainst its bacterial parasite,Serratia marcescens, for 13 host generations. We found that strong selection favouring increased host resistance was insufficient to counteract drift in small populations, resulting in persistently high host mortality. Additionally, in small populations of resistant hosts, we found that selection for the maintenance of resistance is not always sufficient to curb the loss of resistance. We compared these results with selection in large host populations. We found that initially resistant, large host populations were able to maintain high levels of resistance. Likewise, initially susceptible, large host populations were able to gain resistance to the parasite. These results show that strong selection pressure for survival is not always sufficient to counteract drift. In consideration ofC.elegans natural population dynamics, we suggest that drift may often impede selection in nature.