cssi/SciSciGPT-SciSciNet huggingface dataset:
Bayesian inference of metabolic kinetics from genome-scale multiomics data

Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, learning from these data to predict how a cell will respond to genetic changes, a key need for engineering, remains challenging. A promising technique for leveraging omics measurements in metabolic modeling involves the construction of kinetic descriptions of the enzymatic reactions that occur within a cell. Parameterizing these models from biological data can be computationally difficult, since methods must also quantify the uncertainty in model parameters resulting from the observed data. While the field of Bayesian inference offers a wide range of methods for efficiently estimating distributions in parameter uncertainty, such techniques are poorly suited to traditional kinetic models due to their complex rate laws and resulting nonlinear dynamics. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and inference methods. We demonstrate that detailed information on the posterior distribution of parameters can be obtained efficiently at a variety of problem scales, including nearly genome-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and in developing new, efficient strain designs.
Gravitational signatures of atmospheric thermal tides on Venus

Abstract Thermal tides in the atmosphere of Venus transport a considerable amount of mass, on a wide range of spatial and temporal scales. We use simulated values of the surface atmospheric pressure distribution to estimate the time-varying gravitational potential, exterior to Venus. The expected amplitudes of gravitational signatures of thermal tides are below the errors from existing Magellan mission Doppler data, and have not yet been detected. However, they are large enough that we expect them to be well resolved by any of several measurement configurations for future Venus gravity missions.
DETECTION OF X-RAYS FROM SN 1987A WITH ROSAT

Soft X-rays (0.5-2 keV) were detected with the ROSAT Position-Sensitive Proportional Counter (PSPC) from the direction of SN 1987A, which falls within an association in the Large Magellanic Cloud (LMC) that is rich in B stars. The emission is consistent with two point sources in the LMC with total luminosity of approximately 10(exp 34) ergs/s. The brighter source is identified with SN 1987A on the basis of its positional agreement. We interpret the emission as arising from the interaction of supernova ejecta with the pre-existing blue giant wind. The second source may be an unidentified Be/X-ray binary. Another possibility is that the second source is an X-ray echo from the supernova outburst. The X-ray echo interpretation reqires that the supernova emitted approximately 10(exp 47) ergs in a burst of soft X-rays.
On the energy decay rates for the 1D damped fractional Klein–Gordon equation

We consider the fractional Klein-Gordon equation in one spatial dimension, subjected to a damping coefficient, which is non-trivial and periodic, or more generally strictly positive on a periodic set. We show that the energy of the solution decays at the polynomial rate $O(t^{-\frac{s}{4-2s}})$ for $0< s<2 $ and at some exponential rate when $s\geq 2$. Our approach is based on the asymptotic theory of $C_0$ semigroups in which one can relate the decay rate of the energy in terms of the resolvent growth of the semigroup generator. The main technical result is a new observability estimate for the fractional Laplacian, which may be of independent interest.
Circulating levels of ATP is a biomarker of HIV cognitive impairment.

Abstract Background In developed countries, Human Immunodeficiency Virus type-1 (HIV-1) infection has become a chronic disease despite the positive effects of anti-retroviral therapies (ART), but still at least half of the HIV infected population shown signs of cognitive impairment. Therefore, biomarkers of HIV cognitive decline are urgently needed. Methods We analyze the opening of one of the larger channels expressed by humans, pannexin-1 (Panx-1) channels, in the uninfected and HIV infected population (n = 175). We determined channel opening and secretion of intracellular second messengers released through the channel such as PGE2 and ATP. Also, we correlated the opening of Panx-1 channels with the circulating levels of PGE2 and ATP as well as cogntive status of the individuals analyzed. Findings Here, we demonstrate that Panx-1 channels on fresh PBMCs obtained from uninfected individuals are closed and no significant amounts of PGE2 and ATP are detected in the circulation. In contrast, in all HIV-infected individuals analyzed, even the ones under effective ART, a spontaneous opening of Panx-1 channels and increased circulating levels of PGE2 and ATP were detected. Circulating levels of ATP were correlated with cognitive decline in the HIV-infected population supporting that ATP is a biomarker of cognitive disease in the HIV-infected population. Interpretation We propose that circulating levels of ATP could predict CNS compromise and lead to the breakthroughs necessary to detect and prevent brain compromise in the HIV-infected population.
Local-in-Time Solvability and Space Analyticity for the Navier–Stokes Equations with BMO-Type Initial Data

It is proved that there exists a local-in-time solution $$u\in C([0,T),bmo({\mathbb {R}}^d)^d)$$ of the Navier–Stokes equations such that every u(t) has an analytic extension on a complex domain whose size only depends on t (and increases with t) and the external force f, assuming only that the initial velocity $$u_0$$ is a local BMO function. Our method for proving this is a combination and refinement of the work by Grujic and Kukavica (J Funct Anal 152(2):447–466, 1998. https://doi.org/10.1006/jfan.1997.3167), Guberovic (Discrete Contin Dyn Syst 27(1):231–236, 2010. https://doi.org/10.3934/dcds.2010.27.231), and Kozono et al. (Kyushu J Math 57(2):303–324, 2003. https://doi.org/10.2206/kyushujm.57.303). One challenging step is the estimation of the heat and Stokes semigroups from BMO-type spaces to $$L^\infty $$; a result that is itself of independent interest. We also apply the idea to the analyticity of vorticity with the assistance of Calderon–Zygmund theory.
A model of grain boundary complexion transitions and grain growth in Yttria-doped alumina

Abstract In this work, we present a physically-parameterized microstructure evolution model for the Yttria-doped alumina system. Yttria-doped alumina is a well-known ceramic system which undergoes first-order phase-like transitions at grain boundaries, which can radically alter interface properties. The change in interfacial properties in turn can radically change microstructure outcomes during processing, including the induction of abnormal grain growth modes. In this work, we develop a simulation that evolves alumina microstructure as a function of yttria concentration and temperature. In the window studied, we achieve strong agreement with reviewed experimental results in identifying the windows for large grains, small grains, abnormal grain growth, and complexion transition kinetics (as measured by JMAK analysis). We then apply the model to study and demonstrate how the possible inclusion of second-phase particles or uneven solute distribution profiles will impact microstructure evolution. It is found that particles do not significantly affect abnormal grain growth in the window studied (but do lead to reduced grain size through pinning effects). It is found that even modest amounts of solute inhomogeneity will result in substantial changes in microstructure outcomes, frequently leading to clusters of abnormal grains. This model largely corroborates the expectations and hypotheses made from recent experimental studies in oxide-doped alumina systems. Further, it is found that there exists a peak transition fraction for the system at which abnormal grain size tends to be maximized.
Investigation of the Effect of Perforated Sheath on Thermal-Flow Characteristics Over a Gas Turbine Reverse-Flow Combustor—Part 1: Experiment

Reverse-flow combustors have been used in heavy, land-based gas turbines for many decades. A sheath is typically installed over the external walls of the combustor and transition piece to provide enhanced cooling through hundreds of small jet impingement cooling, followed by a strong forced convention channel flow. However, this cooling is at the expense of large pressure loss. With the modern advancement in metallurgy and thermal-barrier coating technologies, it may become possible to remove this sheath to recover the pressure loss without causing thermal damage to the combustor chamber and the transition piece walls. However, without the sheath, the flow inside the dump diffuser may exert nonuniformly reduced cooling on the combustion chamber and transition piece walls. The objective of this paper is to investigate the difference in flow pattern, pressure drop, and heat transfer distribution in the dump diffuser and over the outer surface of the combustor with and without a sheath. Both experimental and computational studies are performed and presented in Part 1 and Part 2, respectively. The experiments are conducted under low pressure and temperature laboratory conditions to provide a database to validate the computation model, which is then used to simulate the thermal-flow field surrounding the combustor and transition piece under elevated gas turbine operating conditions.The experimental results show that the pressure loss between the dump diffuser inlet and exit is 1.15% of the total inlet pressure for the non-sheathed case and 1.9% for the sheathed case. This gives a 0.75 percentage point (or 40%) reduction in pressure losses. When the sheath is removed in the laboratory, the maximum increase of surface temperature is about 35%, with an average increase of 13%–22% based on the temperature scale of 23 K, which is the temperature difference of bulk inlet and outlet temperature.Copyright © 2013 by ASME
Spectral Exterior Calculus

A spectral approach to building the exterior calculus in manifold learning problems is developed. The spectral approach is shown to converge to the true exterior calculus in the limit of large data. Simultaneously, the spectral approach decouples the memory requirements from the amount of data points and ambient space dimension. To achieve this, the exterior calculus is reformulated entirely in terms of the eigenvalues and eigenfunctions of the Laplacian operator on functions. The exterior derivatives of these eigenfunctions (and their wedge products) are shown to form a frame (a type of spanning set) for appropriate $L^2$ spaces of $k$-forms, as well as higher-order Sobolev spaces. Formulas are derived to express the Laplace-de Rham operators on forms in terms of the eigenfunctions and eigenvalues of the Laplacian on functions. By representing the Laplace-de Rham operators in this frame, spectral convergence results are obtained via Galerkin approximation techniques. Numerical examples demonstrate accurate recovery of eigenvalues and eigenforms of the Laplace-de Rham operator on 1-forms. The correct Betti numbers are obtained from the kernel of this operator approximated from data sampled on several orientable and non-orientable manifolds, and the eigenforms are visualized via their corresponding vector fields. These vector fields form a natural orthonormal basis for the space of square-integrable vector fields, and are ordered by a Dirichlet energy functional which measures oscillatory behavior. The spectral framework also shows promising results on a non-smooth example (the Lorenz 63 attractor), suggesting that a spectral formulation of exterior calculus may be feasible in spaces with no differentiable structure.
Evidence for a Growing Population of Eastern Migratory Monarch Butterflies Is Currently Insufficient

The eastern migratory population of monarch butterflies has experienced a multi-decadal decline, but a recent increase in abundance (to 6.05 ha in winter 2018) has led some observers to question whether the population has reversed its long-standing decline and embarked on a trajectory of increasing abundance. We examined this possibility through changepoint analyses, assessing whether a change in trajectory existed. We found evidence of a change in trajectory in 2014, but insufficient statistical support for a significantly increasing population since that time (β = 0.285, 95% CI = -0.127, 0.697). If the population estimate for winter 2019 is ≥4.0 ha, we will then be able to credibly assert the population has been increasing since 2014. However, given estimated levels of time series variability, presumed habitat capacity and no recent change in status or trend, there was a 13.5% probability of observing a population estimate as large or larger than was reported for winter 2018. Our analyses highlight the incredible difficulty in drawing robust conclusions from annual changes in abundance over a short time series, especially for an insect that commonly exhibits considerable year-to-year variation. Thus, we urge caution when drawing conclusions regarding species status and trends for any species for which limited data are available.
Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications

We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain high-degree nodes), based on which we study fundamental trade-offs between the number of measurements, the complexity of the graph class, and the probability of error. We first derive a necessary condition on the number of measurements. Then, by considering a three-stage recovery scheme, we give a sufficient condition for recovery. Furthermore, assuming the measurements are Gaussian IID, we prove upper and lower bounds on the (worst-case) sample complexity for both noisy and noiseless recovery. In the special cases of the uniform distribution on trees with $n$ nodes and the Erdős-Renyi $(n,p)$ class, the fundamental trade-offs are tight up to multiplicative factors with noiseless measurements. In addition, for practical applications, we design and implement a polynomial-time (in $n$ ) algorithm based on the three-stage recovery scheme. Experiments show that the heuristic algorithm outperforms basis pursuit on star graphs. We apply the heuristic algorithm to learn admittance matrices in electric grids. Simulations for several canonical graph classes and IEEE power system test cases demonstrate the effectiveness and robustness of the proposed algorithm for parameter reconstruction.
Surgeon Experience with Parental Leave Policies Varies Based on Practice Setting

The increase in female surgeons has resulted in scrutiny of widely variable parental leave policies. We hypothesized that academic and private practice surgeons have different experiences based on difference in workplace expectations. A 25-question survey was disseminated via social media and through the Association of Women Surgeons social media platforms from June 1 to September 15, 2017. An analysis of attending surgeons working in the USA in an academic or private practice setting was performed. Of 1115 total respondents, 477 were attending surgeons practicing in the USA. Practice distribution was 34% private and 47% academic. There was no difference in marital status, work status, or the number who report having been pregnant between the groups. Compared to academic surgeons, private practice surgeons were statistically less likely to have paid leave (p < 0.001) and were more likely to continue to pay benefits while on leave (p < 0.001). Private practitioners were more likely to return to work sooner than desired due to financial (p = 0.022) and supervisor (p = 0.004) pressures and were more likely to leave a job (p = 0.01). Academic surgeons were more likely to experience a delay in job advancement (p = 0.031). On multivariate analysis, more than two pregnancies were associated with an increased risk of perception of a bias and discrimination against pregnancy in the workplace. Parental leave policies and attitudes vary between academic and private practice, creating unique challenges for female surgeons and different issues for family planning depending on employment model.
Decision analysis for sustainable development: The case of renewable energy planning under uncertainty

Abstract Multi-Criteria Decision Analysis (MCDA) methods are increasingly used to aid decision-making for sustainable development. However, although uncertainty is present in all decision environments, dealing with incomplete and vague information in decision analysis remains a challenge. Our objective is to simplify the incorporation of uncertainty in the scoring of alternatives in MCDA processes. We present a modified ELimination and Choice Translating REality (ELECTRE) III model, in which the uncertainty in the performance scores is expressed as lower/upper bounds and then added to the model’s discrimination thresholds. Unlike other uncertainty approaches developed in the literature (such as those based on fuzzy set theory), our approach does not require additional knowledge apart from understanding the ELECTRE III model. To test its validity and suitability, we apply it for the evaluation of renewable energy resources for Turkey – hydro, wind, geothermal, solar, and biomass – under five main criteria: technological, technical, economic, environmental, and socio-politic. Our results indicate that wind energy is the best alternative for Turkey, followed by solar energy, which is in line with country’s Vision 2023 energy targets.
Peer R&D disclosure and corporate innovation: Evidence from American depositary receipt firms

Abstract In this paper, we examine the effect of peer research and development (RD disclosures of higher quality appear to promote innovation by ameliorating financing frictions. Overall, this study provides evidence of R&D disclosure as an industry-wide determinant of innovation, thereby contributing to literature on the real effects of peer disclosures.
Multiway k-Cut in Static and Dynamic Graphs: A Maximum Entropy Principle Approach

This work presents a maximum entropy principle based algorithm for solving minimum multiway k-cut problem defined over static and dynamic digraphs. A multiway k-cut problem requires partitioning the set of nodes in a graph into k subsets, such that each subset contains one prespecified node, and the corresponding total cut weight is minimized. These problems arise in many applications and are computationally complex (NP-hard). In the static setting this article presents an approach that uses a relaxed multiway k-cut cost function; we show that the resulting algorithm converges to a local minimum. This iterative algorithm is designed to avoid poor local minima with its run-time complexity as ~ O(kIN3), where N is the number of vertices and I is the number of iterations. In the dynamic setting, the edge-weight matrix has an associated dynamics with some of the edges in the graph capable of being influenced by an external input. The objective is to design the dynamics of the controllable edges so that multiway k-cut value remains small (or decreases) as the graph evolves under the dynamics. Also it is required to determine the time-varying partition that defines the minimum multiway k-cut value. Our approach is to choose a relaxation of multiway k-cut value, derived using maximum entropy principle, and treat it as a control Lyapunov function to design control laws that affect the weight dynamics. Simulations on practical examples of interactive foreground-background segmentation, minimum multiway k-cut optimization for non-planar graphs and dynamically evolving graphs that demonstrate the efficacy of the algorithm, are presented.
A Tutorial on Concentration Bounds for System Identification

We provide a brief tutorial on the use of concentration inequalities as they apply to system identification of state-space parameters of linear time invariant systems, with a focus on the fully observed setting. We draw upon tools from the theories of large-deviations and self-normalized martingales, and provide both data-dependent and independent bounds on the learning rate.
Loss of IKK Subunits Limits NF-κB Signaling in Reovirus-Infected Cells.

Viruses commonly antagonize innate immune pathways that are primarily driven by nuclear factor kappa B (NF-κB), interferon regulatory factor (IRF), and the signal transducer and activator of transcription proteins (STAT) family of transcription factors. Such a strategy allows viruses to evade immune surveillance and maximize their replication. Using an unbiased transcriptome sequencing (RNA-seq)-based approach to measure gene expression induced by transfected viral genomic RNA (vgRNA) and reovirus infection, we discovered that mammalian reovirus inhibits host cell innate immune signaling. We found that, while vgRNA and reovirus infection both induce a similar IRF-dependent gene expression program, gene expression driven by the NF-κB family of transcription factors is lower in infected cells. Potent agonists of NF-κB such as tumor necrosis factor alpha (TNF-α) and vgRNA failed to induce NF-κB-dependent gene expression in infected cells. We demonstrate that NF-κB signaling is blocked due to loss of critical members of the inhibitor of kappa B kinase (IKK) complex, N F-κB e ssential mo difier (NEMO), and IKKβ. The loss of the IKK complex components prevents nuclear translocation and phosphorylation of NF-κB, thereby preventing gene expression. Our study demonstrates that reovirus infection selectively blocks NF-κB, likely to counteract its antiviral effects and promote efficient viral replication.IMPORTANCE Host cells mount a response to curb virus replication in infected cells and prevent spread of virus to neighboring, as yet uninfected, cells. The NF-κB family of proteins is important for the cell to mediate this response. In this study, we show that in cells infected with mammalian reovirus, NF-κB is inactive. Further, we demonstrate that NF-κB is rendered inactive because virus infection results in reduced levels of upstream intermediaries (called IKKs) that are needed for NF-κB function. Based on previous evidence that active NF-κB limits reovirus infection, we conclude that inactivating NF-κB is a viral strategy to produce a cellular environment that is favorable for virus replication.
Experience with access functions in an experimental compiler

This paper describes an access function subsystem embedded in portions of an experimental microcode compiler which was built and used during 1973-6 using the IBM PL/I optimizing compiler under VM/370 and CMS. The use of the access function subsystem in this context was itself an experiment, performed by a group for all of whom PL/I was a new language and VM/370 a new operating system. The implementation of the subsystem was done strictly within the confines of the PL/I language. The basic objectives were ease of use, provision of a focal point for global storage management, extensive run-time validity checking with appropriate diagnostics, and data protection. Beyond satisfying these objectives, the subsystem proved more valuable than anticipated due to positive contributions made to debugging code in the VM/370 interactive development environment.
Genetic algorithm-guided deep learning of grain boundary diagrams: Addressing the challenge of five degrees of freedom

Abstract Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. Here, potentially transformative research is represented by constructing GB property diagrams as functions of temperature and bulk composition, also called “complexion diagrams,” as a general materials science tool on par with phase diagrams. However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs). It is essentially a “mission impossible” to construct property diagrams for GBs as a function of five DOFs by either experiments or modeling. Herein, we combine isobaric semi-grand canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN) models to tackle this grand challenge. The DNN prediction is ∼108 faster than atomistic simulations, thereby enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Notably, excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter are more complex and much less understood, but they are ubiquitous and often limit the performance properties of real polycrystals as the weak links. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm.
ePixM: a CMOS monolithic sensor for LCLS II

THE SLAC Linac Coherent Light Source (LCLS) upgrade, known as LCLS-II, will provide extremely bright X-ray laser light at higher rate, moving from the actual 120 pulses per second (120 Hz) to 1 million pulses per second (1 MHz). The facility will operate in a soft X-ray range (250 eV - 2 keV), performing experiments in a wide range of fields that are now impossible.
Speed and Diffusion of Kinesin-2 Are Competing Limiting Factors in Flagellar Length-Control Model

Flagellar length control in Chlamydomonas is a tractable model system for studying the general question of organelle size regulation. We have previously proposed that the diffusive return of the kinesin motor that powers intraflagellar transport can play a key role in length regulation. Here, we explore how the motor speed and diffusion coefficient for the return of kinesin-2 affect flagellar growth kinetics. We find that the system can exist in two distinct regimes, one dominated by motor speed and one by diffusion coefficient. Depending on length, a flagellum can switch between these regimes. Our results indicate that mutations can affect the length in distinct ways. We discuss our theory's implication for flagellar growth influenced by beating and provide possible explanations for the experimental observation that a beating flagellum is usually longer than its immotile mutant. These results demonstrate how our simple model can suggest explanations for mutant phenotypes.
The Returns on Human Capital:Good News on Wall Street is Bad News on Main Street

We use a standard single-agent model to conduct a simple consumption growth accounting exercise. Consumption growth is driven by news about current and expected future returns on the market portfolio. We impute the residual of consumption growth innovations that cannot be attributed to either news about financial asset returns or future labor income growth to news about expected future returns on human wealth, and we back out the implied human wealth and market return process. Innovations in current and future human wealth returns are negatively correlated with innovations in current and future financial asset returns, regardless of the elasticity of intertemporal substitution.
A SAR-Assisted Two-Stage Pipeline ADC

Successive approximation register (SAR) ADC architectures are popular for achieving high energy efficiency but they suffer from resolution and speed limitations. On the other hand pipeline ADC architectures can achieve high resolution and speed but have lower energy-efficiency and are more complex. We propose a two-stage pipeline ADC architecture with a large first-stage resolution, enabled with the help of a SAR-based sub-ADC. The prototype 12b 50 MS/s ADC achieves an ENOB of 10.4b at Nyquist, and a figure-of-merit of 52 fJ/conversion-step. The ADC achieves low-power, high-resolution and high-speed operation without calibration. The ADC is fabricated in 65 nm and 90 nm CMOS and occupies a core area of only 0.16 mm 2 .
Updating Human Capital Decisions: Evidence from SAT Score Shocks and College Applications

We estimate whether students update their collegepplication portfolios in response to large, unanticipated information shocks generated by the release of SAT scores -- a primary component of admissions decisions. Exploiting new population data on the timing of college selection and a policy that induces students to choose colleges prior to taking exam, we find that the release of scores causes students to update their portfolios in terms of selectivity, tuition, and sector. However, the magnitude of updating is too modest to significantly reduce unexplained variation across students, suggesting that non-academic factors may be the dominant determinants of college choice.
The Impact of Disability Benefits on Labor Supply: Evidence from the VA's Disability Compensation Program

United States. Social Security Administration (grant #10-P-98363-1-05 to the National Bureau of Economic Research as part of the SSA Retirement Research Consortium)
Multivariate Forecast Evaluation and Rationality Testing

Abstract In this paper, we propose a new family of multivariate loss functions to test the rationality of vector forecasts without assuming independence across variables. When only one variable is of interest, the loss function reduces to the flexible asymmetric family proposed by Elliott, Komunjer, and Timmerman (2008). Following their methodology, we derive~a GMM test for multivariate forecast rationality that allows the forecaster's loss to be nonseparable across variables and takes into account forecast estimation uncertainty. We use our test to study the joint rationality of macroeconomic forecasts in the growth rate of nominal output, CPI inflation rate, and short-term interest rate.
Sweeping by sessile drop coalescence

During coalescence of liquid drops contacting a solid, the liquid sweeps wetted and solid-projected areas. The extent of sweeping dictates the performance of devices such as self-cleaning surfaces, anti-frost coatings, water harvesters, and dropwise condensers. For these applications, weakly- and non-wetting solid substrates are preferred as they enhance drop dynamical behavior. Accordingly, our coalescence studies here are restricted to drops with contact angle 90° ≤ θ0 ≤ 180°. Binary sessile drop coalescence is the focus, with volume of fluid simulations employed as the primary tool. The simulations, which incorporate a Kistler dynamic contact angle model, are first validated against three different experimental substrate systems and then used to study the influence of solid wettability on sweeping by modifying θ0. With increasing θ0 up to 150°, wetted and projected swept areas both increase as drop center of mass heightens. For θ0 ≥ 150°, coalescence-induced drop jumping occurs owing to the decreasing wettability of the substrate and a focusing of liquid momentum due to the symmetry-breaking solid. In this regime, projected swept area continues to increase with θ0 while wetted swept area reaches a maximum and then decreases. The sweeping results are interpreted using the mechanical energy balance from hydrodynamic theory and also compared to free drop coalescence.
System Reliability Analysis With Second-Order Saddlepoint Approximation

AbstractThe second-order saddlepoint approximation (SOSPA) has been used for component reliability analysis for higher accuracy than the traditional second-order reliability method (SORM). This wor...
Applying the swept rule for solving explicit partial differential equations on heterogeneous computing systems

Applications that exploit the architectural details of high-performance computing (HPC) systems have become increasingly invaluable in academia and industry over the past two decades. The most important hardware development of the last decade in HPC has been the general purpose graphics processing unit (GPGPU), a class of massively parallel devices that now contributes the majority of computational power in the top 500 supercomputers. As these systems grow, small costs such as latency—due to the fixed cost of memory accesses and communication—accumulate in a large simulation and become a significant barrier to performance. The swept time-space decomposition rule is a communication-avoiding technique for time-stepping stencil update formulas that attempts to reduce latency costs. This work extends the swept rule by targeting heterogeneous, CPU/GPU architectures representing current and future HPC systems. We compare our approach to a naive decomposition scheme with two test equations using an MPI+CUDA pattern on 40 processes over two nodes containing one GPU. The swept rule produces a factor of 1.9 to 23 speedup for the heat equation and a factor of 1.1 to 2.0 speedup for the Euler equations, using the same processors and work distribution, and with the best possible configurations. These results show the potential effectiveness of the swept rule for different equations and numerical schemes on massively parallel compute systems that incur substantial latency costs.
LQG Control and Sensing Co-Design

We investigate a linear-quadratic-Gaussian (LQG) control and sensing codesign problem, where one jointly designs sensing and control policies. We focus on the realistic case where the sensing design is selected among a finite set of available sensors, where each sensor is associated with a different cost (e.g., power consumption). We consider two dual problem instances: sensing-constrained LQG control, where one maximizes a control performance subject to a sensor cost budget, and minimum-sensing LQG control, where one minimizes a sensor cost subject to performance constraints. We prove that no polynomial time algorithm guarantees across all problem instances a constant approximation factor from the optimal. Nonetheless, we present the first polynomial time algorithms with per-instance suboptimality guarantees. To this end, we leverage a separation principle, which partially decouples the design of sensing and control. Then, we frame LQG codesign as the optimization of approximately supermodular set functions; we develop novel algorithms to solve the problems; and we prove original results on the performance of the algorithms and establish connections between their suboptimality and control-theoretic quantities. We conclude the article by discussing two applications, namely, sensing-constrained formation control and resource-constrained robot navigation .
Deconfusing the Confusogram: Getting New Insights from Zeeman Doppler Imaging

Zeeman Doppler Imaging (ZDI) is a powerful tool for characterizing the strength and topology of stellar magnetic fields. In this research note, we present a new way to visualize the typical results from ZDI for an ensemble of stars, addressing some of the concerns with the standard "confusogram" approach to illustrating the data. Our publicly available plotting methods further enable an accessible means to consider variability in the inferred magnetic field topologies from repeated observations, as we demonstrate with the literature ZDI data on M dwarfs.
Rational Design of a Protein Kinase A Nuclear-cytosol Translocation Reporter.

Protein Kinase A (PKA) exists as a tetrameric holoenzyme which activates with increase of cAMP and plays an important role in many physiological processes including cardiac physiology, neuronal development, and adipocyte function. Although this kinase has been the subject of numerous biosensor designs, a single-fluorophore reporter that performs comparably to Forster resonance energy transfer (FRET) has not yet been reported. Here, we have used basic observations of electrostatic interactions in PKA substrate recognition mechanism and nucleus localization sequence motif to design a phosphorylation switch that shuttles between the cytosol and the nucleus, a strategy that should be generalizable to all basophilic kinases. The resulting reporter yielded comparable kinetics and dynamic range to the PKA FRET reporter, AKAR3EV. We also performed basic characterization and demonstrated its potential use in monitoring multiple signaling molecules inside cells using basic fluorescence microscopy. Due to the single-fluorophore nature of this reporter, we envision that this could find broad applications in studies involving single cell analysis of PKA activity.
Towards Efficient Model Compression via Learned Global Ranking

Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g., model size or FLOP count) for the resulting architecture. However, determining a target model complexity can be difficult for optimizing various embodied AI applications such as autonomous robots, drones, and user-facing applications. First, both the accuracy and the speed of ConvNets can affect the performance of the application. Second, the performance of the application can be hard to assess without evaluating ConvNets during inference. As a consequence, finding a sweet-spot between the accuracy and speed via filter pruning, which needs to be done in a trial-and-error fashion, can be time-consuming. This work takes a first step toward making this process more efficient by altering the goal of model compression to producing a set of ConvNets with various accuracy and latency trade-offs instead of producing one ConvNet targeting some pre-defined latency constraint. To this end, we propose to learn a global ranking of the filters across different layers of the ConvNet, which is used to obtain a set of ConvNet architectures that have different accuracy/latency trade-offs by pruning the bottom-ranked filters. Our proposed algorithm, LeGR, is shown to be 2× to 3× faster than prior work while having comparable or better performance when targeting seven pruned ResNet-56 with different accuracy/FLOPs profiles on the CIFAR-100 dataset. Additionally, we have evaluated LeGR on ImageNet and Bird-200 with ResNet-50 and Mo- bileNetV2 to demonstrate its effectiveness. Code available at https://github.com/cmu-enyac/LeGR.
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects

Estimating the 3D pose of desktop objects is crucial for applications such as robotic manipulation. Many existing approaches to this problem require a depth map of the object for both training and prediction, which restricts them to opaque, lambertian objects that produce good returns in an RGBD sensor. In this paper we forgo using a depth sensor in favor of raw stereo input. We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera; and second, we develop a deep neural network, called KeyPose, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects. To evaluate the performance of our method, we create a dataset of 15 clear objects in five classes, with 48K 3D-keypoint labeled images. We train both instance and category models, and show generalization to new textures, poses, and objects. KeyPose surpasses state-of-the-art performance in 3D pose estimation on this dataset by factors of 1.5 to 3.5, even in cases where the competing method is provided with ground-truth depth. Stereo input is essential for this performance as it improves results compared to using monocular input by a factor of 2. We will release a public version of the data capture and labeling pipeline, the transparent object database, and the KeyPose models and evaluation code. Project website: https://sites.google.com/corp/view/keypose.
Exploring Content Selection in Summarization of Novel Chapters

We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries. We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries. We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods. Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis.
Structural thinking about social categories: Evidence from formal explanations, generics, and generalization

Abstract Many theories of kind representation suggest that people posit internal, essence-like factors that underlie kind membership and explain properties of category members. Across three studies (N = 281), we document the characteristics of an alternative form of construal according to which the properties of social kinds are seen as products of structural factors: stable, external constraints that obtain due to the kind's social position. Internalist and structural construals are similar in that both support formal explanations (i.e., “category member has property P due to category membership C”), generic claims (“Cs have P”), and the generalization of category properties to individual category members when kind membership and social position are confounded. Our findings thus challenge these phenomena as signatures of internalist thinking. However, once category membership and structural position are unconfounded, different patterns of generalization emerge across internalist and structural construals, as do different judgments concerning category definitions and the dispensability of properties for category membership. We discuss the broader implications of these findings for accounts of formal explanation, generic language, and kind representation.
Genetic algorithms for redundancy in interaction testing

It is imperative for testing to determine if the components within large-scale software systems operate functionally. Interaction testing involves designing a suite of tests, which guarantees to detect a fault if one exists among a small number of components interacting together. The cost of this testing is typically modeled by the number of tests, and thus much effort has been taken in reducing this number. Here, we incorporate redundancy into the model, which allows for testing in non-deterministic environments. Existing algorithms for constructing these test suites usually involve one "fast" algorithm for generating most of the tests, and another "slower" algorithm to "complete" the test suite. We employ a genetic algorithm that generalizes these approaches that also incorporates redundancy by increasing the number of algorithms chosen, which we call "stages." By increasing the number of stages, we show that not only can the number of tests be reduced compared to existing techniques, but the computational time in generating them is also greatly reduced.
Growing a growth mindset: characterizing how and why undergraduate students’ mindsets change

The extent to which students view their intelligence as improvable (i.e., their “mindset”) influences students’ thoughts, behaviors, and ultimately their academic success. Thus, understanding the development of students’ mindsets is of great interest to education scholars working to understand and promote student success. Recent evidence suggests that students’ mindsets continue to develop and change during their first year of college. We built on this work by characterizing how mindsets change and identifying the factors that may be influencing this change among upper-level STEM students. We surveyed 875 students in an organic chemistry course at four points throughout the semester and interviewed a subset of students about their mindsets and academic experiences. Latent growth modeling revealed that students tended to shift towards viewing intelligence as a stable trait (i.e., shifted towards a stronger fixed mindset and a weaker growth mindset). This trend was particularly strong for students who persistently struggled in the course. From qualitative analysis of students’ written survey responses and interview transcripts, we determined that students attribute their beliefs about intelligence to five factors: academic experiences, observing peers, deducing logically, taking societal cues, and formal learning. Extensive prior research has focused on the influence of mindset on academic performance. Our results corroborate this relationship and further suggest that academic performance influences students’ mindsets. Thus, our results imply that mindset and academic performance constitute a positive feedback loop. Additionally, we identified factors that influence undergraduates’ mindset beliefs, which could be leveraged by researchers and practitioners to design more persuasive and effective mindset interventions to promote student success.
Liouville type theorems for 3D stationary Navier–Stokes equations in weighted mixed-norm Lebesgue spaces

This work studies the system of $3D$ stationary Navier-Stokes equations. Several Liouville type theorems are established for solutions in mixed-norm Lebesgue spaces and weighted mixed-norm Lebesgue spaces. In particular, we show that, under some sufficient conditions in mixed-norm Lebesgue spaces, solutions of the stationary Navier-Stokes equations are identically zero. This result covers the important case that solutions may decay to zero with different rates in different spatial directions, and some these rates could be significantly slow. In the un-mixed norm case, the result recovers available results. With some additional geometric assumptions on the supports of solutions, this work also provides several other important Liouville type theorems for solutions in weighted mixed-norm Lebesgue spaces. To prove the results, we establish some new results on mixed-norm and weighted mixed-norm estimates for Navier-Stokes equations. All of these results are new and could be useful in other studies.
Dynamical explorations of nuclear structures in barred galaxies

NGC 4314 (Benedict et al. 1996) has a complex nuclear morphology with recent star formation confined to a nuclear ring. HST observations resolve the nuclear ring into multiple sites of new star formation and resolve the associated dust lanes into discrete clouds. We construct dynamical models of this galaxy in order to provide plausible identification of the dynamical processes that led to the formation of the observed structure. We assume that the center of this galaxy contains a very massive double black-hole, surrounded by relatively low-mass particles — the star clusters as well as gas and dust complexes. Our previous work (Anosova & Anandarao 1994, Anosova & Tanikawa 1995) showed that the dynamical evolution of such a model produces many structures similar to those observed in galaxies of diverse types: spiral and elliptical galaxies, interacting galaxies, and various types of flows and jets. In the present work, we consider a number of such models with different initial parameters. We study their dynamical evolution of the gravitational N-body problem, taking into account strong interactions of bodies. Comparison of the evolution of our models with the observed structure, distributions and motions of stars, gas, and dust complexes in NGC 4314 shows good agreement. The model predicts the velocity fields observed in this galaxy.