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Magnetic Misalignment of Interstellar Dust Filaments
No full textWe present evidence for scale-independent misalignment of interstellar dust filaments and magnetic fields. We estimate the misalignment by comparing millimeter-wave dust-polarization measurements from Planck with filamentary structures identified in neutral-hydrogen (H i) measurements from Hi4PI. We find that the misalignment angle displays a scale independence (harmonic coherence) for features larger than the Hi4PI beamwidth (16.′2). We additionally find a spatial coherence on angular scales of O_(1°). We present several misalignment estimators formed from the auto- and cross-spectra of dust-polarization and H i-based maps, and we also introduce a map-space estimator. Applied to large regions of the high-Galactic-latitude sky, we find a global misalignment angle of ∼2°, which is robust to a variety of masking choices. By dividing the sky into small regions, we show that the misalignment angle correlates with the parity-violating TB cross-spectrum measured in the Planck dust maps. The misalignment paradigm also predicts a dust EB signal, which is of relevance in the search for cosmic birefringence but as yet undetected; the measurements of EB are noisier than those of TB, and our correlations of EB with misalignment angle are found to be weaker and less robust to masking choices. We also introduce an H i-based dust-polarization template constructed from the Hessian matrix of the H i intensity, which is found to correlate more strongly than previous templates with Planck dust B modes
Time-walk and jitter correction in SNSPDs at high count rates
No full textSuperconducting nanowire single-photon detectors (SNSPDs) are a leading detector type for time correlated single photon counting, especially in the near-infrared. When operated at high count rates, SNSPDs exhibit increased timing jitter caused by internal device properties and features of the RF amplification chain. Variations in RF pulse height and shape lead to variations in the latency of timing measurements. To compensate for this, we demonstrate a calibration method that correlates delays in detection events with the time elapsed between pulses. The increase in jitter at high rates can be largely canceled in software by applying corrections derived from the calibration process. We demonstrate our method with a single-pixel tungsten silicide SNSPD and show it decreases high count rate jitter. The technique is especially effective at removing a long tail that appears in the instrument response function at high count rates. At a count rate of 11.4 MCounts/s, we reduce the full width at 1% maximum level (FW1%M) by 45%. The method, therefore, enables certain quantum communication protocols that are rate-limited by the FW1%M metric to operate almost twice as fast
Electrically tunable conducting oxide metasurfaces for high power applications
No full textActive metasurfaces designed to operate at optical frequencies are flat optical elements that can dynamic, subwavelength-scale wavefront control of reflected or transmitted light. The practical and fundamental power-handling limits of active metasurfaces at high pulse energies and high average powers determine the potential applications for these emerging photonic components. Here, we investigate thermal performance limits of reflective gate-tunable conducting oxide metasurfaces illuminated with high power density laser beams, for both continuous wave (CW) and pulsed laser illumination. Our gate-tunable metasurfaces use indium tin oxide (ITO) as an active material, which undergoes an epsilon-near-zero (ENZ) transition under applied electrical bias. We experimentally show that under CW illumination, there is no significant change in the electrically tunable metasurface optical response for high irradiances ranging from 1.6 kW/cm² to 9.1 kW/cm² when the illuminating laser beam diameter is 7 μm. Even under an applied bias, when over 60% of the incoming light is absorbed in a 1 nm–thick charge accumulation layer within ITO, the local temperature rise in the metasurface is modest, supporting its robustness for high-power applications. Additionally, we theoretically show that in the ENZ regime, the metasurface reflectance can be increased by a factor of 10 by replacing the active ITO layer with cadmium oxide (CdO). Thus conducting oxide metasurfaces can tolerate the power densities needed in higher power applications, including free space optical communications, to light detection and ranging (LiDAR), as well as laser-based additive manufacturing
Analytical closure to the spatially-filtered Euler equations for shock-dominated flows
No full textTo ensure numerical stability in the vicinity of shocks, a variety of methods have been used, including shock-capturing schemes such as weighted essentially non-oscillatory schemes, as well as the addition of artificial diffusivities to the governing equations. Centered finite difference schemes are often avoided near discontinuities due to the tendency for significant oscillations. However, such schemes have desirable conservation properties compared to many shock-capturing schemes. The objective of this work is to derive all necessary viscous/diffusion terms from first principles and then demonstrate the performance of these analytical terms within a centered differencing framework. The physical Euler equations are spatially-filtered with a Gaussian-like filter. Sub-filter scale (SFS) terms arise in the momentum and energy equations. Analytical closure is provided for each of them by leveraging the jump conditions for a shock. No SFS terms are present in the continuity or species equations. This approach is tested for several problems involving shocks in one and two dimensions. Implemented within a centered difference code, the SFS terms perform well for a range of flow conditions without introducing excessive diffusion
Social norm bias: residual harms of fairness-aware algorithms
No full textMany modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive attribute like gender or race. However, these algorithms seldom account for within-group heterogeneity and biases that may disproportionately affect some members of a group. In this work, we characterize Social Norm Bias (SNoB), a subtle but consequential type of algorithmic discrimination that may be exhibited by machine learning models, even when these systems achieve group fairness objectives. We study this issue through the lens of gender bias in occupation classification. We quantify SNoB by measuring how an algorithm’s predictions are associated with conformity to inferred gender norms. When predicting if an individual belongs to a male-dominated occupation, this framework reveals that “fair” classifiers still favor biographies written in ways that align with inferred masculine norms. We compare SNoB across algorithmic fairness techniques and show that it is frequently a residual bias, and post-processing approaches do not mitigate this type of bias at all
Wnt/BMP Mediated Metabolic Reprogramming Preserves Multipotency of Neural Crest-Like Stem Cells
No full textNeural crest-like stem cells resembling embryonic neural crest cells (NCs) can be derived from adult human tissues such as the epidermis. However, these cells lose their multipotency rapidly in culture limiting their expansion for clinical use. Here, we show that the multipotency of keratinocyte-derived NCs (KC-NCs) can be preserved by activating the Wnt and BMP signaling axis, promoting expression of key NC-specifier genes and ultimately enhancing their differentiation potential. We also show that transcriptional changes leading to multipotency are linked to metabolic reprogramming of KC-NCs to a highly glycolytic state. Specifically, KC-NCs treated with CHIR and BMP2 rely almost exclusively on glycolysis for their energy needs, as seen by increased lactate production, glucose uptake, and glycolytic enzyme activities. This was accompanied by mitochondrial depolarization and decreased mitochondrial ATP production. Interestingly, the glycolytic end-product lactate stabilized β-catenin and further augmented NC-gene expression. Taken together, our study shows that activation of the Wnt/BMP signaling coordinates the metabolic demands of neural crest-like stem cells governing decisions regarding multipotency and differentiation, with possible implications for regenerative medicine
Forecasting subcritical cylinder wakes with Fourier Neural Operators
No full textWe apply Fourier neural operators (FNOs), a state-of-the-art operator learning technique, to forecast the temporal evolution of experimentally measured velocity fields. FNOs are a recently developed machine learning method capable of approximating solution operators to systems of partial differential equations through data alone. The learned FNO solution operator can be evaluated in milliseconds, potentially enabling faster-than-real-time modeling for predictive flow control in physical systems. Here we use FNOs to predict how physical fluid flows evolve in time, training with particle image velocimetry measurements depicting cylinder wakes in the subcritical vortex shedding regime. We train separate FNOs at Reynolds numbers ranging from Re = 240 to Re = 3060 and study how increasingly turbulent flow phenomena impact prediction accuracy. We focus here on a short prediction horizon of ten non-dimensionalized time-steps, as would be relevant for problems of predictive flow control. We find that FNOs are capable of accurately predicting the evolution of experimental velocity fields throughout the range of Reynolds numbers tested (L2 norm error < 0.1) despite being provided with limited and imperfect flow observations. Given these results, we conclude that this method holds significant potential for real-time predictive flow control of physical systems
Safety-Critical Control with Bounded Inputs via Reduced Order Models
No full textGuaranteeing safe behavior on complex autonomous systems -- from cars to walking robots -- is challenging due to the inherently high dimensional nature of these systems and the corresponding complex models that may be difficult to determine in practice. With this as motivation, this paper presents a safety-critical control framework that leverages reduced order models to ensure safety on the full order dynamics -- even when these models are subject to disturbances and bounded inputs (e.g., actuation limits). To handle input constraints, the backup set method is reformulated in the context of reduced order models, and conditions for the provably safe behavior of the full order system are derived. Then, the input-to-state safe backup set method is introduced to provide robustness against discrepancies between the reduced order model and the actual system. Finally, the proposed framework is demonstrated in high-fidelity simulation, where a quadrupedal robot is safely navigated around an obstacle with legged locomotion by the help of the unicycle model
Multiviral Quartet Nanocages Elicit Broad Anti-Coronavirus Responses for Proactive Vaccinology
No full textDefending against future pandemics may require vaccine platforms that protect across a range of related pathogens. The presentation of multiple receptor-binding domains (RBDs) from evolutionarily-related viruses on a nanoparticle scaffold elicits a strong antibody response to conserved regions. Here we produce quartets of tandemly-linked RBDs from SARS-like betacoronaviruses coupled to the mi3 nanocage through a SpyTag/SpyCatcher spontaneous reaction. These Quartet Nanocages induce a high level of neutralizing antibodies against several different coronaviruses, including against viruses not represented on the vaccine. In animals primed with SARS-CoV-2 Spike, boost immunizations with Quartet Nanocages increased the strength and breadth of an otherwise narrow immune response. Quartet Nanocages are a strategy with potential to confer heterotypic protection against emergent zoonotic coronavirus pathogens and facilitate proactive pandemic protection
spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content
No full textDroplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack of strategies to enrich for high-quality material or specific cell types at the moment of cell encapsulation and the absence of implementable multi-step enzymatic processes that increase capture. Here we alleviate both bottlenecks using fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei, fixed cells or target cell types and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half. We harness these unique properties to deliver a high-quality molecular atlas of mouse brain development, despite starting with highly damaged input material, and provide an atlas of nascent RNA transcription during mouse organogenesis. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost