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Testing station for fast screening of through silicon via-enabled application-specific integrated circuits for hard x-ray imaging detectors
No full textApplication-specific integrated circuits (ASICs) are used in space-borne instruments for signal processing and detector readout. The electrical interface of these ASICs to frontend printed circuit boards is commonly accomplished with wire bonds. Through silicon via (TSV) technology has been proposed as an alternative interconnect technique that will reduce assembly complexity of ASIC packaging by replacing wire bonding with flip-chip bonding. TSV technology is advantageous in large detector arrays where TSVs enable close detector tiling on all sides. Wafer-level probe card testing of TSV ASICs is frustrated by solder balls introduced onto the ASIC surface for flip-chip bonding that hamper alignment. Therefore, we developed the ASIC test stand (ATS) to enable rapid screening and characterization of individual ASIC die. We successfully demonstrated ATS operation on ASICs originally developed for CdZnTe detectors on the Nuclear Spectroscopic and Telescope Array (NuSTAR) mission that were later modified with TSVs in a via-last process. We tested both backside blind-TSVs and frontside through-TSVs, with results from internal test pulser measurements that demonstrate performance equal to or exceeding the probe card wafer-level testing data. The ATS can easily be expanded or duplicated to parallelize ASIC screening for large area imaging detectors of future space programs
Toward a Data-Driven Template Model for Quadrupedal Locomotion
No full textThis work investigates a data-driven template model for trajectory planning of dynamic quadrupedal robots. Many state-of-the-art approaches involve using a reduced-order model, primarily due to computational tractability. The spirit of the trajectory planning approach in this work draws on recent advancements in the area of behavioral systems theory. Here, we aim to capitalize on the knowledge of well-known template models to construct a data-driven model, enabling us to obtain an information rich reduced-order model. In particular, this work considers input-output states similar to that of the single rigid body model and proceeds to develop a data-driven representation of the system, which is then used in a predictive control framework to plan a trajectory for quadrupeds. The optimal trajectory is passed to a low-level and nonlinear model-based controller to be tracked. Preliminary experimental results are provided to establish the efficacy of this hierarchical control approach for trotting and walking gaits of a high-dimensional quadrupedal robot on unknown terrains and in the presence of disturbances
Speckle Interferometric Observations With the Gemini 8-m Telescopes: Signal-to-Noise Calculations and Observational Results
No full textUsing 3 years of observations with the Zorro and ’Alopeke speckle interferometric instruments at Gemini South and North, respectively, we present an analysis of the sensitivity of the data taken in two narrow-band optical filters centered at 562 and 832 nm (widths of 54 and 40 nm, respectively). In this paper we focus on model calculations of the predicted signal-to-noise values achievable and the results of over 2500 actual observations. We find that S/N values of several 100 are easily achieved, but that the sky background during full moon is a very limiting factor in the observations, especially those performed in the short-wavelength (blue) optical spectral range and for targets fainter than R ∼14. A comparison of our Gemini speckle observations over six observing semesters reveals that red band-pass observations provide more robust results in general, likely due to better atmospheric performance at these wavelengths. Using the identical instruments on Gemini North and South, we find that similar results are obtained, yielding typical contrast limits of 5-9 magnitudes from the diffraction limit out to 1.2″ for a range of target brightness (optical magnitudes from ∼ 3 to >16). Using our S/N model along with the observational results, an estimation of the contrast limits achievable for a given observation can be predicted based on the target brightness, sky illumination and seeing conditions, and the total integration time
A table of n-component handlebody links of genus n+1 up to six crossings
No full textA handlebody link is a union of handlebodies of positive genus embedded in 3-space, which generalises the notion of links in classical knot theory. In this paper, we consider handlebody links with a genus two handlebody and n-1 solid tori, n > 1. Our main result is the classification of such handlebody links with six crossings or less, up to ambient isotopy
The Geophysical Environment of (486958) Arrokoth—A Small Kuiper Belt Object Explored by New Horizons
No full textNASA's New Horizons mission performed the first flyby of a small Kuiper Belt Object (KBO), (486958) Arrokoth on 1 January 2019. The fast flyby revealed a fascinating, flattened, contact binary replete with a variety of unexpected geologic terrains. However, the irregular shape and constraints imposed by the fast flyby makes it a challenge to understand these features. Here we use the latest New Horizons shape models of Arrokoth to investigate its geophysical environment, including its surface slopes, gravity field, and moments of inertia—which are critical context for understanding Arrokoth's formation, evolution, and peculiar geology. We find that Arrokoth's surface features have a complicated relationship to its geophysical environment. For example, bright material tends to be concentrated in geopotential lows (like the neck), consistent with mass wasting—however, this trend is not consistently observed across Arrokoth. Mass wasting may naturally explain some aspects of Arrokoth's geology, but the actual dynamics of material transport may be complicated owing to Arrokoth's unique shape, spin-rate, and inferred density. While New Horizons's fast and distant flyby precluded directly measuring Arrokoth's mass, we used techniques previously pioneered for comets and asteroids to infer its density. We find that Arrokoth has a low bulk density of ρ = 235 kg/m3 (1σ range: 155–600 kg/m3). This density is low compared to previously explored small bodies, but is comparable to comets, select binary KBOs, and the ring-moons of Saturn. This low density may be a critical data-point for understanding the formation of planetesimals at the dawn of the Solar System
Alpha desynchronization during Stroop test unmasks cognitively healthy individuals with abnormal CSF Amyloid/Tau
No full textSynaptic dysfunctions precede cognitive decline in Alzheimer's disease by decades, affect executive functions, and can be detected by quantitative electroencephalography (qEEG). We used quantitative electroencephalography combined with Stroop testing to identify changes of inhibitory controls in cognitively healthy individuals with an abnormal versus normal ratio of cerebrospinal fluid (CSF) amyloid/total-tau. We studied two groups of participants (60–94 years) with either normal (CH-NAT or controls, n = 20) or abnormal (CH-PAT, n = 21) CSF amyloid/tau ratio. We compared: alpha event-related desynchronization (ERD), alpha spectral entropy (SE), and their relationships with estimated cognitive reserve. CH-PATs had more negative occipital alpha ERD, and higher frontal and occipital alpha SE during low load congruent trials, indicating hyperactivity. CH-PATs demonstrated fewer frontal SE changes with higher load, incongruent Stroop testing. Correlations of alpha ERD with estimated cognitive reserve were significant in CH-PATs but not in CH-NATs. These results suggested compensatory hyperactivity in CH-PATs compared to CH-NATs. We did not find differences in alpha ERD comparisons with individual CSF amyloid(A), p-tau(T), total-tau(N) biomarkers
Particle-Assisted Laser-Induced Inertial Cavitation for High Strain-Rate Soft Material Characterization
No full textBackground: While there are few reliable techniques for characterizing highly compliant and viscoelastic materials under large deformations, laser-induced Inertial Microcavitaton Rheometry (IMR) was recently developed to fill this void and to characterize soft materials at high to ultra-high strain rates (O(10³)∼O(10⁸)⁻¹). Yet, one of the current limitations in IMR has been the dependence of the cavitation nucleation physics on the intrinsic material properties often generating extreme deformation levels and thus complicating material characterization procedures.
Objective: The objective of this study was to develop an experimental approach for modulating laser-induced cavitation (LIC) bubble amplitudes and their resulting maximum material deformations. Lowering the material stretch ratios during inertial cavitation will provide an experimental platform of broad applicability to a large class of polymeric materials and environmental conditions.
Methods: Experimental methods include using three types of micron-sized nucleation seed particles and varying laser energies in polyacrylamide hydrogels of known concentration. Using a Quadratic law Kelvin-Voigt material model, we implemented ensemble-based data assimilation (DA) techniques to robustly quantify the nonlinear constitutive material parameters, up through the first, second, and third bubble collapse cycles. Fitted values were then used to simulate bubble dynamics to compute critical bubble collapse Mach numbers, and to assess time-varying uncertainties of the full cavitation dynamics with respect to the current state-of-the art theoretical model featured in the IMR model.
Results: While varying laser energy modulated bubble amplitude, seed particles successfully expanded (more than doubled) the finite deformation regime (i.e., maximum material stretch, λ_(max) ≈ 4 - 9). Comparing experimental data to IMR simulations, we found that fitting beyond the first bubble collapse, as well as increasing laser energy, increased the bubble radius fit error, and larger λ_(max) values exhibited increasingly violent bubble behavior (marked by increasing collapse Mach numbers greater than 0.08). Additionally, time-varying analysis showed the greatest model uncertainty during initial bubble collapse, where bubbles nucleated at lower laser energies and resulting λ_(max) had less uncertainty at collapse compared to higher laser energy and λ_(max) cases.
Conclusions: This study indicates IMR’s current theoretical framework might be lacking important additional cavitation and/or material physics. However, expanding the finite deformation regime of soft materials to attain lower stretch regimes enables broader applicability to a larger class of soft polymeric materials and will enable future, systematic development and incorporation of more complex physics and constitutive models including damage and failure mechanisms into the theoretical framework of IMR
Identification of a distinct ductal subpopulation with self-renewal and differentiation potential from the adult murine pancreas
No full textPancreatic ducts function to deliver digestive enzymes into the intestines. Upon injury, ducts can become proliferative and contribute to tissue regeneration; however, the identity of the ductal cells that contribute to these processes is unknown. We combined fluorescence-activated cell sorting, a methylcellulose-containing 3-dimensional culture, droplet RNA-sequencing, and a clonal lineage tracing tool to identify and isolate a distinct subpopulation of pancreatic ductal cells that exhibit progenitor cell properties. These ductal cells are unique in that they form tightly-bound clusters (termed FSCmid-high), with an average of 8 cells per cluster. FSCmid-high clusters comprise only about 0.1% of the total pancreas, are tri-potent for duct, acinar and endocrine lineages, and self-renew robustly in vitro. Transcriptomic analysis of FSCmid-high clusters reveals enrichment for genes involved in cell-cell interactions, organ development, and cancer pathways. FSCmid-high clusters express embryonic pancreatic progenitor markers Sox9, Pdx1, and Nkx6-1 at both transcription and protein levels. FSCmid-high clusters are resistant to enzymatic dissociation and survive severe in vivo acinar injury, which induces formation of ductal rosettes that become proliferative within 14 days. Thus, FSCmid-high clusters represent a small subset of ductal cells with progenitor cell properties. These rare progenitor-like duct cell clusters have implications in pancreas regeneration and tumor initiation/progression
Generative Adversarial Neural Operators
No full textWe propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces. The natural sciences and engineering are known to have many types of data that are sampled from infinite-dimensional function spaces, where classical finite-dimensional deep generative adversarial networks (GANs) may not be directly applicable. GANO generalizes the GAN framework and allows for the sampling of functions by learning push-forward operator maps in infinite-dimensional spaces. GANO consists of two main components, a generator neural operator and a discriminator neural functional. The inputs to the generator are samples of functions from a user-specified probability measure, e.g., Gaussian random field (GRF), and the generator outputs are synthetic data functions. The input to the discriminator is either a real or synthetic data function. In this work, we instantiate GANO using the Wasserstein criterion and show how the Wasserstein loss can be computed in infinite-dimensional spaces. We empirically study GANOs in controlled cases where both input and output functions are samples from GRFs and compare its performance to the finite-dimensional counterpart GAN. We empirically study the efficacy of GANO on real-world function data of volcanic activities and show its superior performance over GAN. Furthermore, we find that for the function-based data considered, GANOs are more stable to train than GANs and require less hyperparameter optimization
Optimal Competitive-Ratio Control
No full textInspired by competitive policy designs approaches in online learning, new control paradigms such as competitive-ratio and regret-optimal control have been recently proposed as alternatives to the classical H₂ and H_∞ approaches. These competitive metrics compare the control cost of the designed controller against the cost of a clairvoyant controller, which has access to past, present, and future disturbances in terms of ratio and difference, respectively. While prior work provided the optimal solution for the regret-optimal control problem, in competitive-ratio control, the solution is only provided for the sub-optimal problem. In this work, we derive the optimal solution to the competitive-ratio control problem. We show that the optimal competitive ratio formula can be computed as the maximal eigenvalue of a simple matrix, and provide a state-space controller that achieves the optimal competitive ratio. We conduct an extensive numerical study to verify this analytical solution, and demonstrate that the optimal competitive-ratio controller outperforms other controllers on several large scale practical systems. The key techniques that underpin our explicit solution is a reduction of the control problem to a Nehari problem, along with a novel factorization of the clairvoyant controller's cost. We reveal an interesting relation between the explicit solutions that now exist for both competitive control paradigms by formulating a regret-optimal control framework with weight functions that can also be utilized for practical purposes