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Understanding how galaxies quench their star formation is crucial for studies of galaxy evolution. Quenching is related to the cold gas decrease. In the first paper we showed that the dust removal timescale in early-type galaxies (ETGs) is about 2.5 Gyr. Here we present carbon monoxide (CO) and 21 cm hydrogen (H I) line observations of these galaxies and measure the timescale of removal of the cold interstellar medium (ISM). We find that all the cold ISM components (dust, molecular and atomic gas) decline at similar rates. This allows us to rule out a wide range of potential ISM removal mechanisms (including starburst-driven outflows, astration, a decline in the number of asymptotic giant branch stars), and artificial effects like stellar mass-age correlation, environmental influence, mergers, and selection bias, leaving ionization by evolved low-mass stars and ionization/outflows by supernovae Type Ia or active galactic nuclei as viable mechanisms. We also provide evidence for an internal origin of the detected ISM. Moreover, we find that the quenching of star formation in these galaxies cannot be explained by a reduction in gas amount alone, because the star formation rates (SFRs) decrease faster (on a timescale of about 1.8 Gyr) than the amount of cold gas. Furthermore, the star formation efficiency of the ETGs (SFE = SFR/MH2) is lower than that of star-forming galaxies, whereas their gas mass fractions (fH2 = MH2/M*) are normal. This may be explained by the stabilization of gas against fragmentation, for example due to morphological quenching, turbulence, or magnetic fields., Comment: Astrophysical Journal, published; 29 pages, 13 figures, 6 tables; V2: matched the published version and typos in the SFR and outflow equations fixed - no change in the conclusions

Decentralized autonomous organizations (DAOs) are a new, rapidly-growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the DAO ecosystem where existing gaps might be tackled through a new data set or by applying tools and ideas from existing research fields such as political science, computer science, economics, law, and organizational science. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the wider research community to join the global effort to invent the next generation of organizations., Comment: includes major coordination problems

Axial vectors, such as current or magnetization, are commonly used order parameters in time-reversal symmetry breaking systems. These vectors also break isotropy in three dimensional systems, lowering the spatial symmetry. We demonstrate that it is possible to construct a fully isotropic and inversion-symmetric three-dimensional medium where time-reversal symmetry is systematically broken. We devise a cubic crystal with scalar time-reversal symmetry breaking, implemented by hopping through chiral magnetic clusters along the crystal bonds. The presence of only the spatial symmetries of the crystal -- finite rotation and inversion symmetry -- is sufficient to protect a topological phase. The realization of this phase in amorphous systems with average continuous rotation symmetry yields a statistical topological insulator phase. We demonstrate the topological nature of our model by constructing a bulk integer topological invariant, which guarantees gapless surface spectrum on any surface with several overlapping Dirac nodes, analogous to crystalline mirror Chern insulators. We also show the expected transport properties of a three-dimensional statistical topological insulator, which remains critical on the surface for odd values of the invariant., Comment: 18 pages, 4 figures

We report on the effect of magnetic impurities on the microscopic superconducting (SC) properties of the kagome-lattice superconductor La(Ru$_{1-x}$Fe$_{x}$)$_{3}$Si$_{2}$ using muon spin relaxation/rotation. A strong suppression of the superconducting critical temperature $T_{\rm c}$, the SC volume fraction, and the superfluid density was observed. We further find a correlation between the superfluid density and $T_{\rm c}$ which is considered a hallmark feature of unconventional superconductivity. Most remarkably, measurements of the temperature-dependent magnetic penetration depth ${\lambda}$ reveal a change in the low-temperature behavior from exponential saturation to a linear increase, which indicates that Fe doping introduces nodes in the superconducting gap structure at concentrations as low as $x=$~0.015. Our results point to a rare example of unconventional superconductivity in the correlated kagome lattice and accessible tunability of the superconducting gap structure, offering new insights into the microscopic mechanisms involved in superconducting order., Comment: 8 page article with 4 figures, 7 page Supplementary Information with 4 figures

Residual noise photons in a readout resonator become a major source of dephasing for a superconducting qubit when the resonator is optimized for a fast, high-fidelity dispersive readout. Here, we propose and demonstrate a nonlinear Purcell filter that suppresses such an undesirable dephasing process without sacrificing the readout performance. When a readout pulse is applied, the filter automatically reduces the effective linewidth of the readout resonator, increasing the sensitivity of the qubit to the input field. The noise tolerance of the device we have fabricated is shown to be enhanced by a factor of 3 relative to a device with a linear filter. The measurement rate is enhanced by another factor of 3 by utilizing the bifurcation of the nonlinear filter. A readout fidelity of 99.4% and a quantum nondemolition fidelity of 99.2% are achieved using a 40-ns readout pulse. The nonlinear Purcell filter will be an effective tool for realizing a fast, high-fidelity readout without compromising the coherence time of the qubit., Comment: 13 pages, 9 figures

In this article, we consider the problem of stabilizing stochastic processes, which are constrained to a bounded Euclidean domain or a compact smooth manifold, to a given target probability density. Most existing works on modeling and control of robotic swarms that use PDE models assume that the robots' dynamics are holonomic, and hence, the associated stochastic processes have generators that are elliptic. We relax this assumption on the ellipticity of the generator of the stochastic processes, and consider the more practical case of the stabilization problem for a swarm of agents whose dynamics are given by a controllable driftless control-affine system. We construct state-feedback control laws that exponentially stabilize a swarm of nonholonomic agents to a target probability density that is sufficiently regular. State-feedback laws can stabilize a swarm only to target probability densities that are positive everywhere. To stabilize the swarm to probability densities that possibly have disconnected supports, we introduce a semilinear PDE model of a collection of interacting agents governed by a hybrid switching diffusion process. The interaction between the agents is modeled using a (mean-field) feedback law that is a function of the local density of the swarm, with the switching parameters as the control inputs. We show that the semilinear PDE system is globally asymptotically stable about the given target probability density. The stabilization strategies are verified without inter-agent interactions is verified numerically for agents that evolve according to the Brockett integrator and a nonholonomic system on the special orthogonal group of 3-dimensional rotations $SO(3)$. The stabilization strategy with inter-agent interactions is verified numerically for agents that evolve according to the Brockett integrator and a holonomic system on the sphere $S^2$., Comment: Extended preprint. Detailed proofs regarding properties of the generators. An additional numerical example of a density control problem in SO(3)

Robust feature matching forms the backbone for most Visual Simultaneous Localization and Mapping (vSLAM), visual odometry, 3D reconstruction, and Structure from Motion (SfM) algorithms. However, recovering feature matches from texture-poor scenes is a major challenge and still remains an open area of research. In this paper, we present a Stereo Visual Odometry (StereoVO) technique based on point and line features which uses a novel feature-matching mechanism based on an Attention Graph Neural Network that is designed to perform well even under adverse weather conditions such as fog, haze, rain, and snow, and dynamic lighting conditions such as nighttime illumination and glare scenarios. We perform experiments on multiple real and synthetic datasets to validate the ability of our method to perform StereoVO under low visibility weather and lighting conditions through robust point and line matches. The results demonstrate that our method achieves more line feature matches than state-of-the-art line matching algorithms, which when complemented with point feature matches perform consistently well in adverse weather and dynamic lighting conditions.

The non-Hermitian skin effect is a phenomenon in which an extensive number of states accumulates at the boundaries of a system. It has been associated to nontrivial topology, with nonzero bulk invariants predicting its appearance and its position in real space. Here, we demonstrate that the non-Hermitian skin effect has weaker bulk-edge correspondence than topological insulators: when translation symmetry is broken by a single non-Hermitian impurity, skin modes are depleted at the boundary and accumulate at the impurity site, without changing any bulk invariant. Similarly, a single non-Hermitian impurity may deplete the states from a region of Hermitian bulk.

In July 2022, the Center for Security and Emerging Technology (CSET) at Georgetown University and the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center convened a workshop of experts to examine the relationship between vulnerabilities in artificial intelligence systems and more traditional types of software vulnerabilities. Topics discussed included the extent to which AI vulnerabilities can be handled under standard cybersecurity processes, the barriers currently preventing the accurate sharing of information about AI vulnerabilities, legal issues associated with adversarial attacks on AI systems, and potential areas where government support could improve AI vulnerability management and mitigation. This report is meant to accomplish two things. First, it provides a high-level discussion of AI vulnerabilities, including the ways in which they are disanalogous to other types of vulnerabilities, and the current state of affairs regarding information sharing and legal oversight of AI vulnerabilities. Second, it attempts to articulate broad recommendations as endorsed by the majority of participants at the workshop.