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Excitons are the neutral quasiparticles that form when Coulomb interactions create bound states between electrons and holes. Due to their bosonic nature, excitons are expected to condense and exhibit superfluidity at sufficiently low temperatures. In interacting Chern insulators, excitons may inherit the nontrivial topology and quantum geometry from the underlying electron wavefunctions. We theoretically investigate the excitonic bound states and superfluidity in flat-band insulators pumped with light. We find that the exciton wavefunctions exhibit vortex structures in momentum space, with the total vorticity being equal to the difference of Chern numbers between the conduction and valence bands. Moreover, both the exciton binding energy and the exciton superfluid density are proportional to the Brillouin-zone average of the quantum metric and the Coulomb potential energy per unit cell. Spontaneous emission of circularly polarized light from radiative decay is a detectable signature of the exciton vorticity. We propose that the vorticity can also be experimentally measured via the nonlinear anomalous Hall effect, whereas the exciton superfluidity can be detected by voltage-drop quantization through a combination of quantum geometry and Aharonov–Casher effect. Topological excitons and their superfluid phase could be realized in flat bands of twisted Van der Waals heterostructures. 

The cores of dense stars are a powerful laboratory for studying feebly coupled particles such as axions. Some of the strongest constraints on axionlike particles and their couplings to ordinary matter derive from considerations of stellar axion emission. In this work we study the radiation of axionlike particles from degenerate neutron star matter via a lepton-flavor-violating coupling that leads to muon-electron conversion when an axion is emitted. We calculate the axion emission rate per unit volume (emissivity) and by comparing with the rate of neutrino emission, we infer upper limits on the lepton-flavor-violating coupling that are at the level of $\left|g_{ae\mu }\right|\lesssim {10}^{-6}$. For the hotter environment of a supernova, such as SN 1987A, the axion emission rate is enhanced and the limit is stronger, at the level of $\left|g_{ae\mu }\right|\lesssim {10}^{-11}$, competitive with laboratory limits. Interestingly, our derivation of the axion emissivity reveals that axion emission via the lepton-flavor-violating coupling is suppressed relative to the familiar lepton-flavor-preserving channels by the square of the plasma temperature to muon mass ratio, which is responsible for the relatively weaker limits. Published by the American Physical Society2024 

There is limited research about how groups solve collective action problems in uncertain environments, especially if groups are confronted with unknown unknowns. We aim to develop a more comprehensive view of the characteristics that allow both groups and individuals to navigate such issues more effectively. In this article, we present the results of a new online experiment where individuals make decisions of whether to contribute to the group or pursue self-interest in an environment with high uncertainty, including unknown unknowns. The behavioral game, Port of Mars is framed as a first-generation habitat on Mars where participants have to make decisions on how much to invest in the shared infrastructure to maintain system health and how much to invest in personal goals. Participants can chat during the game, and take surveys before and after the game in order to measure personality attributes and observations from the game. Initial results suggest that a higher average social value orientation and more communication are the key factors that explain why some groups are more successful than others in surviving Port of Mars. Neither other attributes of players nor the group’s communication content explain the observed differences between groups. 

Abstract Live imaging of the brain extracellular matrix (ECM) provides vital insights into changes that occur in neurological disorders. Current techniques such as second or third‐harmonic generation offer limited contrast for live imaging of the brain ECM. Here, a new method, pan‐ECM via chemical labeling of extracellular proteins, is introduced for live brain ECM imaging. pan‐ECM labels all major ECM components in live tissue including the interstitial matrix, basement membrane, and perineuronal nets. pan‐ECM enables in vivo observation of the ECM heterogeneity between the glioma core and margin, as well as the assessment of ECM deterioration under stroke condition, without ECM shrinkage from tissue fixation. These findings indicate that the pan‐ECM approach is a novel way to image the entire brain ECM in live brain tissue with optical resolution. pan‐ECM has the potential to advance the understanding of ECM in brain function and neurological diseases. 

Image registration is an essential task in medical image analysis. We propose two novel unsupervised diffeomorphic image registration networks, which use deep Residual Networks (ResNets) as numerical approximations of the underlying continuous diffeomorphic setting governed by ordinary differential equations (ODEs), viewed as a Eulerian discretization scheme. While considering the ODE-based parameterizations of diffeomorphisms, we consider both stationary and non-stationary (time varying) velocity fields as the driving velocities to solve the ODEs, which give rise to our two proposed architectures for diffeomorphic registration. We also employ Lipschitz-continuity on the Residual Networks in both architectures to define the admissible Hilbert space of velocity fields as a Reproducing Kernel Hilbert Spaces (RKHS) and regularize the smoothness of the velocity fields. We apply both registration networks to align and segment the OASIS brain MRI dataset. Experimental results demonstrate that our models are computational efficient and achieve comparable registration results with a smoother deformation field. 

Diffeomorphic registration faces challenges for high dimensional images, especially in terms of memory limits. Existing approaches either downsample/crop original images or approximate underlying transformations to reduce the model size. To mitigate this, we propose a Dividing and Down-sampling mixed Registration network (DDR-Net), a general architecture that preserves most of the image information at multiple scales while reducing memory cost. DDR-Net leverages the global context via downsampling the input and utilizes local details by dividing the input images to subvolumes. Such design fuses global and local information and obtains both coarse- and fine-level alignments in the final deformation fields. We apply DDR-Net to the OASIS dataset. The proposed simple yet effective architecture is a general method and could be extended to other registration architectures for better performance with limited computing resources.