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Forced microrheology of active colloids
No full textParticle-tracking microrheology of dilute active (self-propelled) colloidal suspensions is studied by considering the external force required to maintain the steady motion of an immersed constant-velocity colloidal probe. If the probe speed is zero, the suspension microstructure is isotropic but exhibits a boundary accumulation of active bath particles at contact due to their self-propulsion. As the probe moves through the suspension, the microstructure is distorted from the non-equilibrium isotropic state, which allows us to define a microviscosity for the suspension using the Stokes drag law. For a slow probe, we show that active suspensions exhibit a swim-thinning behavior in which their microviscosity is gradually lowered from that of passive suspensions as the swim speed increases. When the probe speed is fast, the suspension activity is obscured by the rapid advection of the probe and the measured microviscosity is indistinguishable from that of passive suspensions. Generally for finite activity, the suspension exhibits a velocity-thinning behavior -- though with a zero-velocity plateau lower than passive suspensions -- as a function of the probe speed. These behaviors originate from the interplay between the suspension activity and the hard-sphere excluded volume interaction between the probe and a bath particle
Functional photoacoustic tomography of the human brain
No full textThis Conference Presentation, “Functional photoacoustic tomography of the human brain,” was recorded at SPIE Photonics West held in San Francisco, California, United States
Analysis of postreconstruction digital refocusing in Fourier ptychographic microscopy
No full textDigital refocusing is a key feature of Fourier ptychographic microscopy (FPM). It is currently performed by determining and removing the defocus aberration during the iterative phase retrieval process. We examine the feasibility of digitally refocusing an FPM image by numerically propagating the recovered complex FPM image after the phase retrieval process has been completed – in effect, disentangling the defocus correction process from the iterative phase retrieval process. If feasible, this type of postreconstruction digital refocusing can significantly reduce the FPM computational load and provide a quick and efficient way for refocusing microscopy images on the fly. We report that such an approach is infeasible for large defocus distances because the raw FPM dataset associated with a defocused sample is illconditioned for the FPM’s phase-retrieval process, and it will not output a complex-valued image that corresponds to any physically relevant image wavefront. When the defocus distance is small, the FPM can output an approximately correct image wavefront. However, this wavefront does not contain a global defocus phase term and, therefore, cannot be further focused using the digital refocusing application of a reverse global phase term. In totality, this means that postreconstruction digital refocusing does not serve a meaningful function for any defocus distance. To verify our analysis, we performed a series of experiments, and the results showed that the postreconstruction digital refocusing method is not a viable digital refocusing method
Simulating challenging correlated molecules and materials on the Sycamore quantum processor
No full textSimulating complex molecules and materials is an anticipated application of quantum devices. With strong quantum advantage demonstrated in artificial tasks, we examine how such advantage translates into modeling physical problems of correlated electronic structure. We simulate static and dynamical electronic structure on a superconducting quantum processor derived from Google's Sycamore architecture for two representative correlated electron problems: the nitrogenase iron-sulfur molecular clusters, and α-ruthenium trichloride, a proximate spin-liquid material. To do so, we simplify the electronic structure into low-energy spin models that fit on the device. With extensive error mitigation and assistance from classically simulated data, we achieve quantitatively meaningful results deploying about 1/5 of the gate resources used in artificial quantum advantage experiments on a similar architecture. This increases to over 1/2 of the gate resources when choosing a model that suits the hardware. Our work serves to convert artificial measures of quantum advantage into a physically relevant setting
More Evidence for Variable Helium Absorption from HD 189733b
No full textWe present a new Keck/NIRSPEC observation of metastable helium absorption from the upper atmosphere of HD 189733b, a hot Jupiter orbiting a nearby moderately active star. We measure an average helium transit depth of 0.420±0.013% integrated over the [-20, 20] km/s velocity range. Comparing this measurement to eight previously published transit observations with different instruments, we find that our depth is 32% (9σ) lower than the average of the three CARMENES transits, but only 16% (4.4σ) lower than the average of the five GIANO transits. We perform 1D hydrodynamical simulations of the outflow, and find that XUV variability on the order of 33%--common for this star--can change the helium absorption depth by 60%. We conclude that changes in stellar XUV flux can explain the observational variability in helium absorption. 3D models are necessary to explore other sources of variability, such as shear instability and changing stellar wind conditions
Limitations in representation of physical processes prevent successful simulation of PM_(2.5) during KORUS-AQ
No full textHigh levels of fine particulate matter (PM_(2.5)) pollution in East Asia often exceed local air quality standards. Observations from the Korea–United States Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM_(2.5). Atmospheric models often have difficulty simulating PM_(2.5) chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM_(2.5) composition throughout the campaign and identify the mechanisms driving the pollution event. At the surface, the model underestimates sulfate by −64 % but overestimates nitrate by +36 %. The largest underestimate in sulfate occurs during the pollution event, for which models typically struggle to generate elevated sulfate concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100 % against aircraft observations. We hypothesize that this is due to a large missing sink, which we implement here as a factor of 5 increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape, resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH₃ could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO₂ hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N₂O₅ hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model's inability to simulate the buildup of PM_(2.5) during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO₂ is added to the model, which previously only considered aqueous production of sulfate from SO₂ in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO₂ simulation, implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM_(2.5). During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO₂ decreases the fraction of PM_(2.5) attributable to long-range transport from 66 % to 54 %. Locally produced sulfate increased from 1 % to 25 % of locally produced PM_(2.5), implying that local emissions controls could have a larger effect than previously thought. However, this additional uptake of SO₂ is coupled to the model nitrate prediction, which affects the aerosol liquid water abundance and chemistry driving sulfate–nitrate–ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO₂ to aerosol and simple improvements to the model nitrate simulation results in 30 % less sulfate due to 40 % less nitrate and aerosol water, and this results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and nighttime boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate + nitrate + ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM_(2.5) in South Korea to ensure continued air quality improvements
A NuSTAR and Swift view of the hard state of MAXI J1813−095
No full textWe present an analysis of the NuSTAR and Swift spectra of the black hole candidate MAXI J1813−095 in a failed-transition outburst in 2018. The NuSTAR observations show evidence of reflected emission from the inner region of the accretion disc. By modelling the reflection component in the spectra, we find a disc inner radius of Rᵢₙ < 7 r₉. This result suggests that either a slightly truncated disc or a non-truncated disc forms at a few per cent of the Eddington limit in MAXI J1813−095. Our best-fitting reflection models indicate that the geometry of the innermost accretion remains consistent during the period of NuSTAR observations. The spectral variability of MAXI J1813−095 from multi-epoch observations is dominated by the variable photon index of the Comptonization emission
Multi-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions
No full textModern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with different controllers running at multiple time scales. Yet traditional constructive methods for nonlinear controller synthesis typically "flatten" this hierarchy, focusing on a single time scale, and thereby limited the ability to make rigorous guarantees on constraint satisfaction that hold for the entire system. In this work we seek to address the stabilization of constrained nonlinear systems through a \textit{multi-rate} control architecture. This is accomplished by iteratively planning continuous reference trajectories for a nonlinear system using a linearized model and Model Predictive Control (MPC), and tracking said trajectories using the full-order nonlinear model and Control Lyapunov Functions (CLFs). Connecting these two levels of control design in a way that ensures constraint satisfaction is achieved through the use of \textit{Bézier curves}, which enable planning continuous trajectories respecting constraints by planning a sequence of discrete points. Our framework is encoded via convex optimization problems which may be efficiently solved, as demonstrated in simulation
Pre-Trained Language Models for Interactive Decision-Making
No full textLanguage model (LM) pre-training is useful in many language processing tasks. But can pre-trained LMs be further leveraged for more general machine learning problems? We propose an approach for using LMs to scaffold learning and generalization in general sequential decision-making problems. In this approach, goals and observations are represented as a sequence of embeddings, and a policy network initialized with a pre-trained LM predicts the next action. We demonstrate that this framework enables effective combinatorial generalization across different environments and supervisory modalities. We begin by assuming access to a set of expert demonstrations, and show that initializing policies with LMs and fine-tuning them via behavior cloning improves task completion rates by 43.6% in the VirtualHome environment. We then examine how our framework may be used in environments without pre-collected expert data. To do this, we integrate an active data gathering procedure into pre-trained LMs. The agent iteratively learns by interacting with the environment, relabeling the language goal of past 'failed' experiences, and updating the policy in a self-supervised loop. The active data gathering procedure also enables effective combinatorial generalization, outperforming the best baseline by 25.1%. Finally, we explain these results by investigating three possible factors underlying the effectiveness of the LM-based policy. We find that sequential input representations (vs. fixed-dimensional feature vectors) and favorable weight initialization are both important for generalization. Surprisingly, however, the format of the policy inputs encoding (e.g. as a natural language string vs. an arbitrary sequential encoding) has little influence. Together, these results suggest that language modeling induces representations that are useful for modeling not just language, but also goals and plans
Roadmap on wavefront shaping and deep imaging in complex media
No full textThe last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow us to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working in this field, which has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead