cssi/SciSciGPT-SciSciNet huggingface dataset:
Supervillin Binds the Rac/Rho‐GEF Trio and Increases Trio‐mediated Rac1 Activation

We investigated cross-talk between the membrane-associated, myosin II-regulatory protein supervillin and the actin-regulatory small GTPases Rac1, RhoA, and Cdc42. Supervillin knockdown reduced Rac1-GTP loading, but not the GTP loading of RhoA or Cdc42, in HeLa cells with normal levels of the Rac1-activating protein Trio. No reduction in Rac1-GTP loading was observed when supervillin levels were reduced in Trio-depleted cells. Conversely, overexpression of supervillin isoform 1 (SV1) or, especially, isoform 4 (SV4) increased Rac1 activation. Inhibition of the Trio-mediated Rac1 guanine nucleotide exchange (GEF) activity with ITX3 partially blocked the SV4-mediated increase in Rac1-GTP. Both SV4 and SV1 co-localized with Trio at or near the plasma membrane in ruffles and cell surface projections. Two sequences within supervillin bound directly to Trio spectrin repeats 4–7: SV1-171, which contains N-terminal residues found in both SV1 and SV4 and the SV4-specific differentially spliced coding exons 3, 4, and 5 within SV4 (SV4-E345; SV4 amino acids 276 – 669). In addition, SV4-E345 interacted with the homologous sequence in rat kalirin (repeats 4–7, amino acids 531 – 1101). Overexpressed SV1-174 and SV4-E345 affected Rac1-GTP loading, but only in cells with endogenous levels of Trio. Trio residues 771 – 1057, which contain both supervillin-interaction sites, exerted a dominant-negative effect on cell spreading. Supervillin and Trio knockdowns, separately or together, inhibited cell spreading, suggesting that supervillin regulates the Rac1 guanine nucleotide exchange activity of Trio, and potentially also kalirin, during cell spreading and lamellipodia extension.
Atlantic Hurricane Season of 1974

Abstract A general overview of the 1974 hurricane season in the North Atlantic is presented together with detailed accounts of individual storms.
UV-Net: Learning from Boundary Representations

We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models. The B-rep format is widely used in the design, simulation and manufacturing industries to enable sophisticated and precise CAD modeling operations. However, B-rep data presents some unique challenges when used with modern machine learning due to the complexity of the data structure and its support for both continuous non-Euclidean geometric entities and discrete topological entities. In this paper, we propose a unified representation for B-rep data that exploits the U and V parameter domain of curves and surfaces to model geometry, and an adjacency graph to explicitly model topology. This leads to a unique and efficient network architecture, UV-Net, that couples image and graph convolutional neural networks in a compute and memory-efficient manner To aid in future research we present a synthetic labelled B-rep dataset, SolidLetters, derived from human designed fonts with variations in both geometry and topology. Finally we demonstrate that UV-Net can generalize to supervised and unsupervised tasks on five datasets, while outperforming alternate 3D shape representations such as point clouds, voxels, and meshes.
Compression of volume-surface integral equation matrices via Tucker decomposition for magnetic resonance applications

In this work, we propose a method for the compression of the coupling matrix in volume-surface integral equation (VSIE) formulations. VSIE methods are used for electromagnetic analysis in magnetic resonance imaging (MRI) applications, for which the coupling matrix models the interactions between the coil and the body.We showed that these effects can be represented as independent interactions between remote elements in 3D tensor formats, and subsequently decomposed with the Tucker model. Our method can work in tandem with the adaptive cross approximation technique to provide fast solutions of VSIE problems. We demonstrated that our compression approaches can enable the use of VSIE matrices of prohibitive memory requirements, by allowing the effective use of modern graphical processing units (GPUs) to accelerate the arising matrix-vector products. This is critical to enable numerical MRI simulations at clinical voxel resolutions in a feasible computation time. In this paper, we demonstrate that the VSIE matrix-vector products needed to calculate the electromagnetic field produced by an MRI coil inside a numerical body model with 1 mm³ voxel resolution, could be performed in ~33 seconds in a GPU, after compressing the associated coupling matrix from ~80 TB to ~43 MB.
Quadratic Optimization-Based Nonlinear Control for Protein Conformation Prediction

Predicting the final folded structure of protein molecules and simulating their folding pathways is of crucial importance for designing viral drugs and studying diseases such as Alzheimer’s at the molecular level. To this end, this letter investigates the problem of protein conformation prediction under the constraint of avoiding high-entropy-loss routes during folding. Using the well-established kinetostatic compliance (KCM)-based nonlinear dynamics of a protein molecule, this letter formulates the protein conformation prediction as a pointwise optimal control synthesis problem cast as a quadratic program (QP). It is shown that the KCM torques in the protein folding literature can be utilized for defining a reference vector field for the QP-based control generation problem. The resulting kinetostatic control torque inputs will be close to the KCM-based reference vector field and guaranteed to be constrained by a predetermined bound; hence, high-entropy-loss routes during folding are avoided while the energy of the molecule is decreased.
Self-consistent electronic structure of (110) Ge-ZnSe

Self-consistent pseudopotential calculations of the electronic structure of the (110) Ge-ZnSe interface (IF) indicate a density of localized IF states that may be experimentally detectable. The full spectrum is presented and the character of the IF states is discussed. Evaluations of the bond charges indicate little charge transfer parallel to the IF. A proposed relaxation of atoms at the IF is also presented. The detailed electronic structure of Ge-ZnSe is found not to be a simple extension of that of Fe-GaAs.
Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion

The number of emergencies have increased over the years with the growth in urbanization. This pattern has overwhelmed the emergency services with limited resources and demands the optimization of response processes. It is partly due to traditional ‘reactive’ approach of emergency services to collect data about incidents, where a source initiates a call to the emergency number (e.g., 911 in U.S.), delaying and limiting the potentially optimal response. Crowdsourcing platforms such as Waze provides an opportunity to develop a rapid, ‘proactive’ approach to collect data about incidents through crowd-generated observational reports. However, the reliability of reporting sources and spatio-temporal uncertainty of the reported incidents challenge the design of such a proactive approach. Thus, this paper presents a novel method for emergency incident detection using noisy crowdsourced Waze data. We propose a principled computational framework based on Bayesian theory to model the uncertainty in the reliability of crowd-generated reports and their integration across space and time to detect incidents. Extensive experiments using data collected from Waze and the official reported incidents in Nashville, Tenessee in the U.S. show our method can outperform strong baselines for both Fl-score and AUC. The application of this work provides an extensible framework to incorporate different noisy data sources for proactive incident detection to improve and optimize emergency response operations in our communities.
Salt-Dependent Conformational Changes of Intrinsically Disordered Proteins.

The flexible structure of an intrinsically disordered protein (IDP) is known to be perturbed by salt concentrations, which can be understood by electrostatic screening on charged amino acids. However, an IDP usually contains more uncharged residues that are influenced by the salting-out effect. Here we have parametrized the salting-out effect into a coarse-grained model using a set of Forster resonance energy transfer data and verified with experimental salt-dependent liquid-liquid phase separation (LLPS) of 17 proteins. The new model can correctly capture the behavior of 6 more sequences, resulting in a total of 13 when varying salt concentrations. Together with a survey of more than 500 IDP sequences, we conclude that the salting-out effect, which was considered to be secondary to electrostatic screening, is important for IDP sequences with moderately charged residues at physiological salt concentrations. The presented scheme is generally applicable to other computational models for capturing salt-dependent IDP conformations.
The impact of local residential land use restrictions on land values across and within single family housing markets

Abstract We provide estimates of the impact of restrictive residential land use environments on the price of land across major American housing markets. Using micro data on vacant land purchased to develop single family housing, we implement a new empirical strategy for estimating so-called ‘zoning taxes’ – the amount by which land prices are bid up due to supply side regulations. Our results are broadly consistent with previous findings that zoning taxes are especially burdensome in large coastal markets. However, our more recent data indicates that price impacts in the big west coast markets now are the largest in the nation. In the San Francisco, Los Angeles, and Seattle metropolitan areas, the price of land everywhere within those three markets having been bid up by amounts that at least equal typical household income. Finally, we show that our zoning tax estimates are strongly positively correlated with a new measure of local housing market supply constraint (the Wharton Residential Land Use Regulatory Index of 2018). This relationship is not mechanically driven as the regulatory index is constructed from survey data that do not incorporate land or house price data in any way.
DIRT/3D: 3D root phenotyping for field-grown maize (Zea mays).

The development of crops with deeper roots holds substantial promise to mitigate the consequences of climate change. Deeper roots are an essential factor to improve water uptake as a way to enhance crop resilience to drought, to increase nitrogen capture, to reduce fertilizer inputs, and to increase carbon sequestration from the atmosphere to improve soil organic fertility. A major bottleneck to achieving these improvements is high-throughput phenotyping to quantify root phenotypes of field-grown roots. We address this bottleneck with Digital Imaging of Root Traits (DIRT)/3D, an image-based 3D root phenotyping platform, which measures 18 architecture traits from mature field-grown maize (Zea mays) root crowns (RCs) excavated with the Shovelomics technique. DIRT/3D reliably computed all 18 traits, including distance between whorls and the number, angles, and diameters of nodal roots, on a test panel of 12 contrasting maize genotypes. The computed results were validated through comparison with manual measurements. Overall, we observed a coefficient of determination of r2>0.84 and a high broad-sense heritability of Hmean2> 0.6 for all but one trait. The average values of the 18 traits and a developed descriptor to characterize complete root architecture distinguished all genotypes. DIRT/3D is a step toward automated quantification of highly occluded maize RCs. Therefore, DIRT/3D supports breeders and root biologists in improving carbon sequestration and food security in the face of the adverse effects of climate change.
Heterogeneity-stabilized homogeneous states in driven media.

Understanding the relationship between symmetry breaking, system properties, and instabilities has been a problem of longstanding scientific interest. Symmetry-breaking instabilities underlie the formation of important patterns in driven systems, but there are many instances in which such instabilities are undesirable. Using parametric resonance as a model process, here we show that a range of states that would be destabilized by symmetry-breaking instabilities can be preserved and stabilized by the introduction of suitable system asymmetry. Because symmetric states are spatially homogeneous and asymmetric systems are spatially heterogeneous, we refer to this effect as heterogeneity-stabilized homogeneity. We illustrate this effect theoretically using driven pendulum array models and demonstrate it experimentally using Faraday wave instabilities. Our results have potential implications for the mitigation of instabilities in engineered systems and the emergence of homogeneous states in natural systems with inherent heterogeneities. Spontaneous symmetry breaking can induce instabilities in natural and engineered systems. Nicolaou et al. show that such instabilities can be prevented by introducing suitable system asymmetry in the form of spatial heterogeneity, relevant for the development of novel control and design techniques.
Do short sellers anticipate late filings

Abstract Exploiting the setting of firms that are unable to disclose timely financial reports and thus must file with the U.S. Securities and Exchange Commission (SEC) the NT 10-K (Q) report, this study examines whether short sellers target firms with financial reporting weaknesses. We find that short interest increases in firms prior to the NT 10-K (Q) filing, suggesting that short sellers identify and target firms that cannot file their financial reports in a timely manner. Short selling is positively significantly related to subsequent late filing status, and is more pronounced in late filers with high newswire activity and with accelerated filing deadlines. Short selling of late filing firms is significantly negatively related to subsequent performance thereby suggesting that short sellers' trades pertinent to late filers are profitable. Overall, the results underscore a high information processing ability of short sellers in the setting of firms that exhibit financial reporting deficiencies.
Periodic magnetic geodesics on Heisenberg manifolds

We study the dynamics of magnetic flows on Heisenberg groups, investigating the extent to which properties of the underlying Riemannian geometry are reflected in the magnetic flow. Much of the analysis, including a calculation of the Mane critical value, is carried out for $$(2n+1)$$ -dimensional Heisenberg groups endowed with any left invariant metric and any exact, left-invariant magnetic field. In the three-dimensional Heisenberg case, we obtain a complete analysis of left-invariant, exact magnetic flows. This is interesting in and of itself, because of the difficulty of determining geodesic information on manifolds in general. We use this analysis to establish two primary results. We first show that the vectors tangent to periodic magnetic geodesics are dense for sufficiently large energy levels and that the lower bound for these energy levels coincides with the Mane critical value. We then show that the marked magnetic length spectrum of left-invariant magnetic systems on compact quotients of the Heisenberg group determines the Riemannian metric. Both results confirm that this class of magnetic flows carries significant information about the underlying geometry. Finally, we provide an example to show that extending this analysis of magnetic flows to the Heisenberg-type setting is considerably more difficult.
Storage Degradation Aware Economic Dispatch

In this paper, we formulate a cycling cost aware economic dispatch problem that co-optimizes generation and storage dispatch while taking into account cycle based storage degradation cost. Our approach exploits the Rainflow cycle counting algorithm to quantify storage degradation for each charging and discharging half-cycle based on its depth. We show that the dispatch is optimal for individual participants in the sense that it maximizes the profit of generators and storage units, under price taking assumptions. We further provide a condition under which the optimal storage response is unique for given market clearing prices. Simulations using data from the New York Independent System Operator (NYISO) illustrate the optimization framework. In particular, they show that the generation-centric dispatch that does not account for storage degradation is insufficient to guarantee storage profitability.
Comment on “Fast-slow mode coupling instability for coasting beams in the presence of detuning impedance”

We show untenability of key points of the recent article of N. Biancacci, E. Metral, and M. Migliorati [Phys. Rev. Accel. Beams 23, 124402 (2020)], hereafter the article and the authors. The main Eqs. (23) are shown to be incorrect even as an approximation. The article claims the solution of this pair of equations to be in ``excellent agreement'' with the pyheadtail simulations for CERN PS, which is purportedly demonstrated by Fig. 6. Were it so, it would be either a signal of a mistake in the code or a consequence of a nonsmoothness of the simulations.
Stage Magic as a Performative Design Principle for VR Storytelling

This article examines The VOID’s Star Wars: Secrets of the Empire (2017) VR arcade attraction, and analyzes the intermedial magic principles employed by co-founder and magician Curtis Hickman to create the illusion of a fictive world with impossible space and liveness. I argue that The VOID (Vision of Infinite Dimensions) functioned like nineteenth century magic theaters run by Georges Melies and others, by employing magic principles of misdirection that directed player attention towards the aesthetics of an illusion, and away from the mechanics of the effects generating technology. Narrative framing and performative role play transported multiple players into a believable Star Wars immersive experience, creating an aesthetics of the impossible that reflected the goal of many stage magic tricks, and was foundational to trick films in the cinema of attractions of the early twentieth century. Using game studies concepts like Huizinga’s magic circle and theatre arts concepts like Craig’s uber-marionette, this article suggests that The VOID and other stage magic approaches to VR, like Derren Brown’s Ghost Train (2017), are a new medium for participatory theatre that incorporate immersive features from both cinema and games.
Approximate Analytical Solution to the Zonal Harmonics Problem Using Koopman Operator Theory

This work introduces the use of the Koopman operator theory to generate approximate analytical solutions for the zonal harmonics problem of a satellite orbiting a nonspherical celestial body. Parti...
Prognostics-Informed Battery Reconfiguration in a Multi-Battery Small UAS Energy System

Batteries have been identified as one of the most likely small unmanned aircraft system (sUAS) components to fail in flight. sUAS safety will therefore be improved with redundant or backup batteries. This paper presents a prognostics-informed Markov Decision Process model for managing multi-battery reconfiguration for sUAS missions. Typical lithium polymer battery properties are experimentally characterized and used in Monte Carlo simulations to establish battery dynamics in sUAS flights of varying duration. Case studies illustrate the trade off between multi-battery system increased complexity/weight and resilience to non-ideal battery performance.
Human-AI Collaboration with Bandit Feedback.

Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic decision-making solely centers on the algorithm's performance, while recent work that explores human-machine collaboration has framed the decision-making problems as classification tasks. In this paper, we first propose and then develop a solution for a novel human-machine collaboration problem in a bandit feedback setting. Our solution aims to exploit the human-machine complementarity to maximize decision rewards. We then extend our approach to settings with multiple human decision makers. We demonstrate the effectiveness of our proposed methods using both synthetic and real human responses, and find that our methods outperform both the algorithm and the human when they each make decisions on their own. We also show how personalized routing in the presence of multiple human decision-makers can further improve the human-machine team performance.
Modern Dimension Reduction

Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github.
Wavelet invariants for statistically robust multi-reference alignment

We propose a nonlinear, wavelet based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference alignment problem and generalizations thereof, we analyze the statistical properties of this representation given a large number of independent corruptions of a target signal. We prove the nonlinear wavelet based representation uniquely defines the power spectrum but allows for an unbiasing procedure that cannot be directly applied to the power spectrum. After unbiasing the representation to remove the effects of the additive noise and random dilations, we recover an approximation of the power spectrum by solving a convex optimization problem, and thus reduce to a phase retrieval problem. Extensive numerical experiments demonstrate the statistical robustness of this approximation procedure.
One-Bit Direct Position Determination of Narrowband Gaussian Signals

One of the main drawbacks of the well-known Direct Position Determination (DPD) method is the requirement that raw data be transferred to a common processor. It would therefore be of high practical value if DPD—or a modified version thereof—could be successfully applied to a coarsely quantized version of the raw data, thus alleviating the communication requirements between the different base stations. Motivated by the above, and inspired by recent work in the rejuvenated one-bit array processing field, we present One-Bit DPD: a direct localization method based on one-bit quantized measurements. We show that despite the coarse quantization, the proposed method nonetheless yields a position estimate with appealing asymptotic properties. We further establish the identifiability conditions of this model, which rely only on second-order statistics. Simulation results corroborate our analytical derivations, demonstrating that much of the information regarding the emitter position is preserved under this crude form of quantization.
Information Theoretic Approaches to Deciphering the Neural Code with Functional Fluorescence Imaging.

Information theoretic metrics have proven useful in quantifying the relationship between behaviorally relevant parameters and neuronal activity with relatively few assumptions. However, these metrics are typically applied to action potential recordings and were not designed for the slow timescales and variable amplitudes typical of functional fluorescence recordings (e.g. calcium imaging). The lack of research guidelines on how to apply and interpret these metrics with fluorescence traces means the neuroscience community has yet to realize the power of information theoretic metrics. Here, we used computational methods to create mock action potential traces with known amounts of information. From these, we generated fluorescence traces and examined the ability of different information metrics to recover the known information values. We provide guidelines for how to use information metrics when applying them to functional fluorescence and demonstrate their appropriate application to GCaMP6f population recordings from mouse hippocampal neurons imaged during virtual navigation. Significance Statement Functional fluorescence imaging and information theoretic quantification could provide a powerful new combination of tools to study neural correlates of behavior, but functional fluorescence signals represent altered versions of the underlying physiological events. Therefore, it is unclear if or how information metrics can be applied to functional fluorescence imaging data. Here, we performed an in-depth simulation study to examine the application of the widely used bits per second and bits per action potential metrics of mutual information to functional florescence recordings. We provide guidelines for how to use information metrics when applying them to functional fluorescence and demonstrate their appropriate application to GCaMP6f population recordings from mouse hippocampal neurons imaged during virtual navigation.
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles.

Virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a single density functional approximation (DFA). Nevertheless, properties evaluated with different DFAs can be expected to disagree for cases with challenging electronic structure (e.g., open-shell transition-metal complexes, TMCs) for which rapid screening is most needed and accurate benchmarks are often unavailable. To quantify the effect of DFA bias, we introduce an approach to rapidly obtain property predictions from 23 representative DFAs spanning multiple families, “rungs” (e.g., semi-local to double hybrid) and basis sets on over 2000 TMCs. Although computed property values (e.g., spin state splitting and frontier orbital gap) differ by DFA, high linear correlations persist across all DFAs. We train independent ML models for each DFA and observe convergent trends in feature importance, providing DFA-invariant, universal design rules. We devise a strategy to train artificial neural network (ANN) models informed by all 23 DFAs and use them to predict properties (e.g., spin-splitting energy) of over 187k TMCs. By requiring consensus of the ANN-predicted DFA properties, we improve correspondence of computational lead compounds with literature-mined, experimental compounds over the typically employed single-DFA approach.
Understanding protein-complex assembly through grand canonical maximum entropy modeling

Inside a cell, heterotypic proteins assemble in inhomogeneous, crowded systems where the abundance of these proteins vary with cell types. While some protein complexes form putative structures that can be visualized with imaging, there are far more protein complexes that are yet to be solved because of their dynamic associations with one another. Nevertheless, it is possible to infer these protein complexes through a physical model. However, it is often not clear to physicists what kind of data from biology is necessary for such a modeling endeavor. Here, we aim to model these clusters of coarse-grained protein assemblies from multiple subunits through the constraints of interactions among the subunits and the chemical potential of each subunit. We obtained the constraints on the interactions among subunits from the known protein structures. We inferred the chemical potential that dictates the particle number distribution of each protein subunit from the knowledge of protein abundance from experimental data. Guided by the maximum entropy principle, we formulated an inverse statistical mechanical method to infer the distribution of particle numbers from the data of protein abundance as chemical potentials for a grand canonical multicomponent mixture. Using grand canonical Monte Carlo simulations, we captured a distribution of high-order clusters in a protein complex of succinate dehydrogenase with four known subunits. The complexity of hierarchical clusters varies with the relative protein abundance of each subunit in distinctive cell types such as lung, heart, and brain. When the crowding content increases, we observed that crowding stabilizes emergent clusters that do not exist in dilute conditions. We, therefore, proposed a testable hypothesis that the hierarchical complexity of protein clusters on a molecular scale is a plausible biomarker of predicting the phenotypes of a cell.
ModelGuard: Runtime Validation of Lipschitz-continuous Models

Abstract This paper presents ModelGuard, a sampling-based approach to runtime model validation for Lipschitz-continuous models. Although techniques exist for the validation of many classes of models, the majority of these methods cannot be applied to the whole of Lipschitz-continuous models, which includes neural network models. Additionally, existing techniques generally consider only white-box models. By taking a sampling-based approach, we can address black-box models, represented only by an input-output relationship and a Lipschitz constant. We show that by randomly sampling from a parameter space and evaluating the model, it is possible to guarantee the correctness of traces labeled consistent and provide a confidence on the correctness of traces labeled inconsistent. We evaluate the applicability and scalability of ModelGuard in three case studies, including a physical platform.
Stochastic Gradient Langevin Dynamics with Variance Reduction

Stochastic gradient Langevin dynamics (SGLD) has gained the attention of optimization researchers due to its global optimization properties. This paper proves an improved convergence property to local minimizers of nonconvex objective functions using SGLD accelerated by variance reductions. Moreover, we prove an ergodicity property of the SGLD scheme, which gives insights on its potential to find global minimizers of nonconvex objectives.
IMEX Runge-Kutta Parareal for Non-diffusive Equations

Parareal is a widely studied parallel-in-time method that can achieve meaningful speedup on certain problems. However, it is well known that the method typically performs poorly on non-diffusive equations. This paper analyzes linear stability and convergence for IMEX Runge-Kutta Parareal methods on non-diffusive equations. By combining standard linear stability analysis with a simple convergence analysis, we find that certain Parareal configurations can achieve parallel speedup on non-diffusive equations. These stable configurations possess low iteration counts, large block sizes, and a large number of processors. Numerical examples using the nonlinear Schrodinger equation demonstrate the analytical conclusions.
Time Evolution of ML-MCTDH Wavefunctions I: Gauge Conditions, Basis Functions, and Singularities

We derive a family of equations-of-motion (EOMs) for evolving multi-layer multiconfiguration time-dependent Hartree (ML-MCTDH) wavefunctions that, unlike the standard ML-MCTDH EOMs, never require the evaluation of the inverse of singular matrices. All members of this family of EOMs make use of alternative static gauge conditions than those used for standard ML-MCTDH. These alternative conditions result in an expansion of the wavefunction in terms of a set of potentially arbitrary orthonormal functions, rather than in terms of a set of non-orthonormal and potentially linearly dependent functions, as is the case for standard ML-MCTDH. We show that the EOMs used in the projector splitting integrator (PSI) and the invariant EOM approaches are two special cases of this family obtained from different choices for the dynamic gauge condition, with the invariant EOMs making use of a choice that introduces potentially unbounded operators into the EOMs. As a consequence, all arguments for the existence of parallelizable integration schemes for the invariant EOMs can also be applied to the PSI EOMs.
Team Assignment for Heterogeneous Multi-Robot Sensor Coverage through Graph Representation Learning

Sensor coverage is the critical multi-robot problem of maximizing the detection of events in an environment through the deployment of multiple robots. Large multi-robot systems are often composed of simple robots that are typically not equipped with a complete set of sensors, so teams with comprehensive sensing abilities are required to properly cover an area. Robots also exhibit multiple forms of relationships (e.g., communication connections or spatial distribution) that need to be considered when assigning robot teams for sensor coverage. To address this problem, in this paper we introduce a novel formulation of sensor coverage by multi-robot systems with heterogeneous relationships as a graph representation learning problem. We propose a principled approach based on the mathematical framework of regularized optimization to learn a unified representation of the multi-robot system from the graphs describing the heterogeneous relationships and to identify the learned representation’s underlying structure in order to assign the robots to teams. To evaluate the proposed approach, we conduct extensive experiments on simulated multi-robot systems and a physical multi-robot system as a case study, demonstrating that our approach is able to effectively assign teams for heterogeneous multi-robot sensor coverage.
Time evolution of ML-MCTDH wavefunctions. II. Application of the projector splitting integrator.

The multi-layer multi-configuration time-dependent Hartree (ML-MCTDH) approach can suffer from numerical instabilities whenever the wavefunction is weakly entangled. These instabilities arise from singularities in the equations of motion (EOMs) and necessitate the use of regularization of the EOMs. The Projector Splitting Integrator (PSI) has previously been presented as an approach for evolving ML-MCTDH wavefunctions that is free of singularities. Here, we will discuss the implementation of the multi-layer PSI with a particular focus on how the steps required relate to those required to implement standard ML-MCTDH. We demonstrate the efficiency and stability of the PSI for large ML-MCTDH wavefunctions containing up to hundreds of thousands of nodes by considering a series of spin-boson models with up to 106 bath modes and find that for these problems, the PSI requires roughly 3–4 orders of magnitude fewer Hamiltonian evaluations and 2–3 orders of magnitude fewer Hamiltonian applications than standard ML-MCTDH and 2–3/1–2 orders of magnitude fewer evaluations/applications than approaches that use improved regularization schemes. Finally, we consider a series of significantly more challenging multi-spin-boson models that require much larger numbers of single-particle functions with wavefunctions containing up to ∼1.3×109 parameters to obtain accurate dynamics.
To map contentious politics

Different forms of contentious politics such as social movements, revolutions, ethnic mobilizations, and cycles of protest share a number of causal properties, but disciplinary fragmentation has obscured their similarities. Recent works and this new journal - Mobilization - provide opportunities for comparison and synthesis. A network of researchers is undertaking a broad survey of contentious politics in hopes of producing an intelligible map of the field, a synthesis of recent inquiries, a specification of scope conditions for the validity of available theories, and an exploration of worldwide changes in the character of contention. Discussions of 1) social movements, cycles and revolutions; 2) collective identities and social networks; 3) social movements and institutional politics; and 4) globalization and transnational contention illustrate the promise and perils of the enterprise.
Giant Spin Lifetime Anisotropy and Spin-Valley Locking in Silicene and Germanene from First-Principles Density-Matrix Dynamics.

Through first-principles real-time density-matrix (FPDM) dynamics simulations, we investigated spin relaxation due to electron-phonon and electron-impurity scatterings with spin-orbit coupling (SOC) in two-dimensional Dirac materials silicene and germanene at finite temperatures. We discussed the applicability of conventional descriptions of spin relaxation mechanisms by Elliott-Yafet (EY) and D'yakonov-Perel' (DP) compared to the FPDM method, which is determined by a complex interplay of intrinsic SOC, external fields, and scattering strength. For example, the electric field dependence of the spin lifetime by FPDM is close to the DP mechanism for silicene at room temperature but similar to the EY mechanism for germanene. Because of its stronger SOC strength and buckled structure in contrast to graphene, germanene has a giant spin lifetime anisotropy and spin-valley locking effect under nonzero Ez and low temperatures. More importantly, germanene has a long spin lifetime (∼100 ns at 50 K) and an ultrahigh carrier mobility, making it advantageous for spin-valleytronic applications.
Only a matter of time? The role of time in school on four-day school week achievement impacts

Abstract Previous evidence has shown disparate achievement impacts of the four-day school week within specific states. This paper examines the effects of the four-day school week on achievement across 12 states to contextualize these four-day school week impacts nationally. We estimate these effects using a difference-in-differences design with data from the Stanford Educational Data Archive and a proprietary longitudinal national database of four-day school week use from 2009 to 2018. We find reductions in both math and English/language arts achievement when examining four-day school weeks nationally, but these aggregate effects appear to be masking important heterogeneity due to differences in overall time in school across districts. When stratifying four-day week districts into districts with low, middle, and high levels of time in school, we find statistically significant negative effects on math achievement for four-day school week districts with low time in school, but no statistically significant effects for four-day school week districts with middle or high time in school. Our findings suggest that maintaining sufficient overall time in school should be a key consideration for school districts contemplating four-day school week adoption.
Measuring the impact and reach of informal educational videos on YouTube: The case of Scientific Animations Without Borders

ABSTRACT Given the popularity, reach, and variable accessibility of online platforms as channels for informal education by higher-education institutions (HEIs), it becomes practically and theoretically important to better understand the factors that affect the impact and reach of any such Internet-delivered ICT informal learning. Accordingly, this study analysed viewer data from one informal, science animation educational channel on the currently the most-watched online platform, YouTube, to measure characteristics affecting the videos’ impact and reach. Results from the study identified the most watched videos on the channel—including survival gardening using drip irrigation, charcoal water filtration, and tuberculosis prevention—and characteristic demand, time, location, and volume of video access. While the basic findings reaffirm prior research measuring statistically significant correlations between user-activity and YouTube engagement metrics (including watch time, view counts, likes, and subscribes), they also demonstrate the and ability to make such content broadly applicable across demographics and globally. Offering some measure of insights for boosting HEI participation in informal educational science animation and ICT learning practices using platforms like YouTube—especially around the topics of water, agriculture, food security, and improved health outcomes—the findings also point to where improved methods for reaching the intended recipients of informal education online are needed to best leverage the potential of such platforms.
SOME RANDOM BOUNDARY PROBLEMS IN TWO DIMENSIONS

Two dimensional random boundary problems are introduced through physical examples. The problem of scattering by a rough surface is singled out for the purpose of illustration. Perturbation solutions of the reduced wave equation in a random domain are briefly discussed. A probabilistic model is presented and analyzed in greater detail.
Bifurcations in nonlinear networks initialized with linear network weight solutions

It has been reported (Yang and Wu, 1993) that weight convergence of multilayer perceptron networks (MLP) can be improved by smoothly changing the node nonlinearity from a linear to a sigmoidal function during training. While this approach might provide a useful training heuristic, formally, this method depends on an underlying homotopy which transforms a linear to a nonlinear network of the same architecture. As a parameter /spl tau/ (the homotopy parameter) is varied from ten, to one, the linear network weights are mapped onto nonlinear network weight solutions. In this paper, a geometric interpretation of the optimization equations is used to construct and describe an example network that illustrates practical and theoretical difficulties resulting due to bifurcation of solution paths. Since the linear system is a generic high-order bifurcation point of the homotopy equations, solution paths are discontinuous at initialization. Bifurcations and infinite solutions for intermediate values /spl tau//spl isin/(0,1) also can occur for data sets which are not of measure zero. These results weaken the guarantees on global convergence and exhaustive behavior normally associated with homotopy methods. The geometric perspective further provides insight into the relationship between linear and nonlinear perceptron networks, and how weight solutions arise in each. >
A wearable standards-based point-of-care system for home use

From a user's point of view, a point-of-care system for home use should be wearable and easy to use in addition to satisfying requirements for medical devices. In this paper, we address mobility, power consumption, data storage, data transmission, and device synchronization for wearable home care devices that comply with the IEEE 1073 (medical information bus) standard. A wearable, standards-based, plug-and-play system for home care that utilizes IEEE 1073 and off-the-shelf components is presented. This work will result in proposed enhancements to IEEE 1073.
Scaffolds for Social Meaning

The perception of social meanings traditionally deemed to be private is addressed by contrasting the perception of social affordances with the perception of the physical affordances of environmental objects. Assuming that (a) affordances are defined by relationships between properties of the environment and properties of an actor and that (b) information must exist to specify this relationship for the perception of affordances, the question is whether and how private social meanings can fulfill these criteria. The attack is twofold. First, one needs to take seriously the ontology of the social world by considering social environment properties and actors' social roles as real and embodied—existing in the world and not just in mental representations. Second and more problematically, one needs to understand how information exists that specifies these more abstract and temporally extended aspects of the environment and actor. I propose that these problems can be averted by taking seriously as conceptual scaf...
Chapter 19 DNA Transformation

Publisher Summary This chapter discusses DNA transformation. DNA transformation assays in a whole organism provide experimental links between molecular structure and phenotype. Experiments with transgenic Caenorhabditis elegans start in general with the injection of DNA into the adult gonad. Effects on phenotype or gene expression patterns can be analyzed either in F1 progeny derived from the injected animals or in derived transgenic lines. Germ-line transformation has been achieved by microinjection of DNA directly into oocyte nuclei or by microinjection of DNA into the cytoplasm of the hermaphrodite syncytial gonad. Three forms of heritable DNA transformation have been observed in C. elegans are: (1) extra chromosomal transformation; (2) non-homologous integration; and (3) homologous integration. Setting up microinjection in a laboratory already equipped for C. elegans genetics and molecular biology requires a modest investment in space and money. A separate easily scoreable marker gene to identify transformed animals can be extremely useful in a variety of injection experiments. The propensity for injected DNA molecules to recombine with each other generally allows one to coinject the selectable marker with a DNA segment to be tested for activity.
There Again, Common Sense: Rethinking Literacy Through Ethnography

This article revisits the debate between cultural and critical literacy through ethnography challenging popular academic views in education and literacy. Set in a preschool classroom at the inception of the “No Child Left Behind” initiative, this essay focuses on teaching assistant Marylou Anderson. Her experiences growing up in Appalachia inform a teaching philosophy that differs significantly from her colleagues. Her story invites us to reconsider how “the culture of power” functions as a formidable gatekeeper.
Disruptive effects of glucocorticoids on glutathione peroxidase biochemistry in hippocampal cultures

Glucocorticoids (GCs), the adrenal steroids secreted during stress, compromise the ability of hippocampal neurons to survive various necrotic insults. We have previously observed that GCs enhance the hippocampal neurotoxicity of reactive oxygen species and, as a potential contributor to this, decrease the activity of the antioxidant enzyme, glutathione peroxidase (GSPx). In this report, we have studied the possible mechanisms underlying this GC effect upon GSPx in primary hippocampal cultures and have observed several results. (i) Corticosterone (the GC of rats) decreased glutathione levels; this was predominately a result of a decrease in levels of reduced glutathione (GSH), the form of glutathione which facilitates GSPx activity. (ii) Corticosterone also decreased levels of NADPH; this may help explain the effect on GSH as NADPH is required for regeneration of GSH from oxidized glutathione. (iii) However, the corticosterone effect on total glutathione levels could not just be caused by the NADPH effect, as there were also reduced levels of oxidized glutathione. (iv) Corticosterone caused a small but significant decrease in GSPx activity over a range of glucose concentrations; this occurred under circumstances of an excess of glutathione as a substrate, suggesting a direct effect of corticosterone on GSPx activity. (v) This corticosterone effect was likely to have functional implications, in that enhancement of GSPx activity (to the same magnitude as activity was inhibited by corticosterone) by GSPx overexpression protected against an excitotoxin. Thus, GCs have various effects, both energetic and non-energetic in nature, upon steps in GSPx biochemistry that, collectively, may impair hippocampal antioxidant capacity.