We construct families of translationally invariant, nearest-neighbour Hamiltonians on a 2D square lattice of d-level quantum systems (d constant), for which determining whether the system is gapped or gapless is an undecidable problem. This is true even with the promise that each Hamiltonian is either gapped or gapless in the strongest sense: it is promised to either have continuous spectrum above the ground state in the thermodynamic limit, or its spectral gap is lower-bounded by a constant. Moreover, this constant can be taken equal to the operator norm of the local operator that generates the Hamiltonian (the local interaction strength). The result still holds true if one restricts to arbitrarily small quantum perturbations of classical Hamiltonians. The proof combines a robustness analysis of Robinson’s aperiodic tiling, together with tools from quantum information theory: the quantum phase estimation algorithm and the history state technique mapping Quantum Turing Machines to Hamiltonians.

The police murder of George Floyd in Minneapolis in May 2020 (just twenty-two city blocks from where I write) sparked global demonstrations and renewed long-standing struggles for change. At the southern border, government agents separated migrant children from their families and confined detainees in cages. Politicization of the pandemic made Asian Americans targets of violent racist outbursts. Indigenous women continued to suffer disproportionate harm with little attention to their plight. Finally, the right-wing insurrection at the U.S. Capitol in Washington, DC, in January 2021 marked the moment as especially volatile.

We implemented universal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing of patients undergoing surgical procedures as a means to conserve personal protective equipment (PPE). The rate of asymptomatic coronavirus disease 2019 (COVID-19) was <0.5%, which suggests that early local public health interventions were successful. Although our protocol was resource intensive, it prevented exposures to healthcare team members.

The past decade has witnessed the advent of nanophotonics, where light–matter interaction is shaped, almost at will, with human-made designed nanostructures. However, the design process for these nanostructures has remained complex, often relying on the intuition and expertise of the designer, ultimately limiting the reach and penetration of this groundbreaking approach. Recently, there has been an increasing number of studies in applying machine learning techniques for the design of nanostructures. Most of these studies engage deep learning techniques, which entail training a deep neural network (DNN) to approximate the highly nonlinear function of the underlying physical process of the interaction between light and the nanostructures. At the end of the training, the DNN allows for on-demand design of nanostructures (i.e., the model can infer nanostructure geometries for desired light spectra). In this article, we review previous studies for designing nanostructures, including recent advances where a DNN is trained to generate a two-dimensional image of the designed nanostructure, which is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry. This allows for better generalization, with higher applicability for real-world design problems.

Timely access to care services is crucial to support people with dementia and their family carers to live well. Carers of people with dementia (N = 390), recruited from eight countries, completed semi-structured interviews about their experiences of either accessing or not using formal care services over a 12-month period in the Access to Timely Formal Care (Actifcare) study. Participant responses were summarised using content analysis, categorised into clusters and frequencies were calculated. Less than half of the participants (42.3%) reported service use. Of those using services, 72.8 per cent reported timely access and of those not using services 67.2 per cent were satisfied with this situation. However, substantial minorities either reported access at the wrong time (27.2%), or feeling dissatisfied or mixed feelings about not accessing services (32.8%). Reasons for not using services included use not necessary yet, the carer provided support or refusal. Reasons given for using services included changes in the condition of the person with dementia, the service's ability to meet individual needs, not coping or the opportunity to access services arose. Facilitators and barriers to service use included whether participants experienced supportive professionals, the speed of the process, whether the general practitioner was helpful, participant's own proactive attitude and the quality of information received. To achieve timely support, simplified pathways to use of formal care services are needed.

We present the second data release (DR2) of the SkyMapper Southern Survey, a hemispheric survey carried out with the SkyMapper Telescope at Siding Spring Observatory in Australia, using six optical filters: u, v, g, r, i, z. DR2 is the first release to go beyond the $\sim\!18$ mag (10 $\sigma$ ) limit of the Shallow Survey released in the first data release (DR1), and includes portions of the sky at full survey depth that reach $>\!21$ mag in g and r filters. The DR2 photometry has a precision as measured by internal reproducibility of 1% in u and v, and 0.7% in griz. More than 21 000 $\deg^2$ have data in some filters (at either Shallow or Main Survey depth) and over 7 000 $\deg^2$ have deep Main Survey coverage in all six filters. Finally, about 18 000 $\deg^2$ have Main Survey data in i and z filters, albeit not yet at full depth. The release contains over 120 000 images, as well as catalogues with over 500 million unique astrophysical objects and nearly 5 billion individual detections. It also contains cross-matches with a range of external catalogues such as Gaia DR2, Pan-STARRS1 DR1, GALEX GUVcat, 2MASS, and AllWISE, as well as spectroscopic surveys such as 2MRS, GALAH, 6dFGS, and 2dFLenS.