cssi/SciSciGPT-SciSciNet huggingface dataset:
RBM45 associates with nuclear stress bodies and forms nuclear inclusions during chronic cellular stress and in neurodegenerative diseases

The RNA binding protein (RBP) RBM45 forms nuclear and cytoplasmic inclusions in neurons and glia in amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP), and Alzheimer’s disease (AD). The normal functions of RBM45 are poorly understood, as are the mechanisms by which it forms inclusions in disease. To better understand the normal and pathological functions of RBM45, we evaluated whether the protein functions via association with several membraneless organelles and whether such an association could promote the formation of nuclear RBM45 inclusions. Under basal conditions, RBM45 is diffusely distributed throughout the nucleus and does not localize to membraneless organelles, including nuclear speckles, Cajal bodies, or nuclear gems. During cellular stress, however, nuclear RBM45 undergoes a reversible, RNA-binding dependent incorporation into nuclear stress bodies (NSBs). Chronic stress leads to the persistent association of RBM45 with NSBs and the irreversible accumulation of nuclear RBM45 inclusions. We also quantified the cell type- and disease-specific patterns of RBM45 pathology in ALS, FTLD-TDP, and AD. RBM45 nuclear and cytoplasmic inclusions are found in both neurons and glia in ALS, FTLD-TDP, and AD but are absent in non-neurologic disease controls. Across neurodegenerative diseases, RBM45 nuclear inclusion pathology occurs more frequently than cytoplasmic RBM45 inclusion pathology and exhibits cell type-specific variation. Collectively, our results define new stress-associated functions of RBM45, a mechanism for nuclear RBM45 inclusion formation, a role for NSBs in the pathogenesis of ALS, FTLD-TDP, and AD, and further underscore the importance of protein self-association to both the normal and pathological functions of RBPs in these diseases.
NASA SEXTANT Mission Operations Architecture

Abstract The Station Explorer for X-ray Timing and Navigation Technology (SEXTANT) mission is a technology demonstration enhancement to the Neutron Star Interior Composition Explorer (NICER) mission, a NASA Astrophysics Explorer Mission of Opportunity to the International Space Station (ISS) that was launched in June of 2017. The NICER instrument is a precision pointing X-ray telescope that tags pulsar sourced photon arrivals which the SEXTANT mission uses to perform autonomous on-board X-ray Pulsar Navigation (XNAV). By comparing the detected time of arrival of X-ray photons to a reference of expected pulsar timing models, one can infer a range and range rate measurement based on light time delay. Since the celestial source provides both timing and directional information, this technology could provide a GPS like navigation capability available throughout the Solar System and beyond. Applications that XNAV can support include outer planet and interstellar missions, manned missions, libration orbit missions, and current infrastructure such as the Deep Space Network (DSN). The SEXTANT team successfully completed a first demonstration of in-space and autonomous XNAV in November 2017. NICER and SEXTANT have separate teams, with NICER being the primary team with different science objectives. Operational modes for both missions must have concurrent and independent components as well as an integrated ground system. This paper describes the operational infrastructure and successful implementation of the SEXTANT demonstration. This paper first details the infrastructure implemented, the concept of operations, and finally the operations for the SEXTANT demonstration and lessons learned.
More Efficient Policy Learning via Optimal Retargeting

Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lac...
Defining what we study: The contribution of machine automation in archaeological research

Abstract In the 21st century, advances in computer science have impacted archaeology, most recently in the development of automated algorithms. Like most technology, these methods have been the source of ongoing debate, particularly in their utility for archaeology. Here, I focus on a contribution of automation and machine learning in archaeology that is often overlooked: the ability of computer algorithms to codify unambiguous, semantically consistent definitions. Archaeology has long struggled with establishing consistent characterizations of the phenomena it studies. As such, I argue that the procedures used for automated methods are useful for archaeologists – even outside of automated analyses – by allowing for the creation of consistent definitions which permit for reproducible research designs.
Antagonistic control of DDK binding to licensed replication origins by Mcm2 and Rad53

Eukaryotic replication origins are licensed by the loading of the replicative DNA helicase, Mcm2-7, in inactive double hexameric form around DNA. Subsequent origin activation is under control of multiple protein kinases that either promote or inhibit origin activation, which is important for genome maintenance. Using the reconstituted budding yeast DNA replication system, we find that the flexible N-terminal extension (NTE) of Mcm2 promotes the stable recruitment of Dbf4-dependent kinase (DDK) to Mcm2-7 double hexamers, which in turn promotes DDK phosphorylation of Mcm4 and -6 and subsequent origin activation. Conversely, we demonstrate that the checkpoint kinase, Rad53, inhibits DDK binding to Mcm2-7 double hexamers. Unexpectedly, this function is not dependent on Rad53 kinase activity, suggesting steric inhibition of DDK by activated Rad53. These findings identify critical determinants of the origin activation reaction and uncover a novel mechanism for checkpoint-dependent origin inhibition.
When Does A Minor’s Legal Competence To Make Health Care Decisions Matter?

In this article, I examine the role of minors' competence for medical decision-making in modern American law. The doctrine of parental consent remains the default legal and bioethical framework for health care decisions on behalf of children, complemented by a complex array of exceptions. Some of those exceptions vest decisional authority in the minors themselves. Yet, in American law, judgments of minors' competence do not typically trigger shifts in decision-making authority from adults to minors. Rather, minors' decisional capacity becomes relevant only after legislatures or courts determine that the default of parental discretion does not achieve important policy goals or protect implicated constitutional rights in a particular health care context and that those goals can best be achieved or rights best protected by authorizing capable minors to choose for themselves. It is at that point that psychological and neuroscientific evidence plays an important role in informing the legal inquiry as to whether minors whose health is at issue are legally competent to decide.
Flexible Coordinator and Switcher Hubs for Adaptive Task Control.

Functional connectivity studies have identified at least two large-scale neural systems that constitute cognitive control networks – the frontoparietal network (FPN) and cingulo-opercular network (CON). Control networks are thought to support goal-directed cognition and behavior. It was previously shown that the FPN flexibly shifts its global connectivity pattern according to task goal, consistent with a “flexible hub” mechanism for cognitive control. Our aim was to build on this finding to develop a functional cartography (a multi-metric profile) of control networks in terms of dynamic network properties. We quantified network properties in (male and female) humans using a high-control-demand cognitive paradigm involving switching among 64 task sets. We hypothesized that cognitive control is enacted by the FPN and CON via distinct but complementary roles reflected in network dynamics. Consistent with a flexible “coordinator” mechanism, FPN connections were varied across tasks, while maintaining within-network connectivity to aid cross-region coordination. Consistent with a flexible “switcher” mechanism, CON regions switched to other networks in a task-dependent manner, driven primarily by reduced within-network connections to other CON regions. This pattern of results suggests FPN acts as a dynamic, global coordinator of goal-relevant information, while CON transiently disbands to lend processing resources to other goal-relevant networks. This cartography of network dynamics reveals a dissociation between two prominent cognitive control networks, suggesting complementary mechanisms underlying goal-directed cognition. Significance statement Cognitive control supports a variety of behaviors requiring flexible cognition, such as rapidly switching between tasks. Furthermore, cognitive control is negatively impacted in a variety of mental illnesses. We used tools from network science to characterize the implementation of cognitive control by large-scale brain systems. This revealed that two systems – the frontoparietal (FPN) and cingulo-opercular (CON) networks – have distinct but complementary roles in controlling global network reconfigurations. The FPN exhibited properties of a flexible coordinator (orchestrating task changes), while CON acted as a flexible switcher (switching specific regions to other systems to lend processing resources). These findings reveal an underlying distinction in cognitive processes that may be applicable to clinical, educational, and machine learning work targeting cognitive flexibility.
Controlling for Corporate Governance in Nonprofit Research

Using a large sample of nonprofit organizations in the United States reporting governance information on their IRS Forms 990, we develop and evaluate several different composite measures of nonprofit governance. These measures can be used to control for governance broadly in a variety of settings, including research that examines nonprofit funding, reporting quality, and executive compensation. Our results suggest that relatively basic indices perform as well as, and in some cases better than, more complex indices. In fact, when controlling for governance in the standard donations model, the collective evidence indicates that an index computed using the simple sum of five binary indicators (audit committee, majority independent board, no outsourcing, CEO salary review, and information available on website) performs best.
Stochastic DC Optimal Power Flow With Reserve Saturation

Abstract We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that generator outputs respond to load imbalances according to an affine control policy, but introduces a model of saturation of generator reserves by assuming that when a generator’s target level hits its limit, it abandons the affine policy and produces at that limit. This is a particularly interesting feature in models where wind power plants, which have uncertain upper generation limits, are scheduled to provide reserves to balance load fluctuations. The resulting model is a nonsmooth nonconvex two-stage stochastic program, and we use a stochastic approximation method to find stationary solutions to a smooth approximation. Computational results on 6-bus and 118-bus test instances demonstrate that by considering the effects of saturation, our model can yield solutions with lower expected generation costs (at the same target line violation probability level) than those obtained from a model that enforces the affine policy to stay within generator limits with high probability.
Mean-field spin-oscillation dynamics beyond the single-mode approximation for a harmonically trapped spin-1 Bose-Einstein condensate

Compared to single-component Bose-Einstein condensates, spinor Bose-Einstein condensates display much richer dynamics. In addition to density oscillations, spinor Bose-Einstein condensates exhibit intriguing spin dynamics that is associated with population transfer between different hyperfine components. This paper analyzes the validity of the widely employed single-mode approximation when describing the spin dynamics in response to a quench of the system Hamiltonian. The single-mode approximation assumes that the different hyperfine states all share the same time-independent spatial mode. This implies that the resulting spin Hamiltonian only depends on the spin interaction strength and not on the density interaction strength. Taking the spinor sodium Bose-Einstein condensate in the $f=1$ hyperfine manifold as an example and working within the mean-field theory framework, it is found numerically that the single-mode approximation misses, in some parameter regimes, intricate details of the spin and spatial dynamics. We develop a physical picture that explains the observed phenomenon. Moreover, using that the population oscillations described by the single-mode approximation enter into the effective potential felt by the mean-field spinor, we derive a semiquantitative condition for when dynamical mean-field induced corrections to the single-mode approximation are relevant. Our mean-field results have implications for a variety of published and planned experimental studies.
Perfect quantum state transfer on diamond fractal graphs

In the quest for designing novel protocols for quantum information and quantum computation, an important goal is to achieve perfect quantum state transfer for systems beyond the well-known one- dimensional cases, such as 1D spin chains. We use methods from fractal analysis and probability to find a new class of quantum spin chains on fractal-like graphs (known as diamond fractals) which support perfect quantum state transfer and which have a wide range of different Hausdorff and spectral dimensions. The resulting systems are spin networks combining Dyson hierarchical model structure with transverse permutation symmetries of varying order.
The Adoption of Robotics by Government Agencies: Evidence from Crime Labs

The adoption and use of emerging and smart technologies like robotics is a key concern as the advanced economies undergo the latest technological revolution. While firms and factories often adopt technologies like robotics and advanced manufacturing techniques at a fast rate, government agencies are often seen as lagging in their adoption of such tools. We offer evidence about the adoption of robotics from the case of American crime laboratories. Using data from the census of crime labs, we show that the adoption of such technologies depends on traditional drivers of agency capacity and demand: budgets, the agency’s task environment, and its relative level of professionalism. Together these findings suggest that agencies can be early adopters of such technologies if they have the capacity (and need) to do so.
On the non-asymptotic and sharp lower tail bounds of random variables

The non-asymptotic tail bounds of random variables play crucial roles in probability, statistics, and machine learning. Despite much success in developing upper bounds on tail probability in literature, the lower bounds on tail probabilities are relatively fewer. In this paper, we introduce systematic and user-friendly schemes for developing non-asymptotic lower bounds of tail probabilities. In addition, we develop sharp lower tail bounds for the sum of independent sub-Gaussian and sub-exponential random variables, which match the classic Hoeffding-type and Bernstein-type concentration inequalities, respectively. We also provide non-asymptotic matching upper and lower tail bounds for a suite of distributions, including gamma, beta, (regular, weighted, and noncentral) chi-square, binomial, Poisson, Irwin-Hall, etc. We apply the result to establish the matching upper and lower bounds for extreme value expectation of the sum of independent sub-Gaussian and sub-exponential random variables. A statistical application of signal identification from sparse heterogeneous mixtures is finally considered.
The derived category of the abelian category of constructible sheaves

We show that the triangulated category of bounded constructible complexes on an algebraic variety X over an algebraically closed field is equivalent to the bounded derived category of the abelian category of constructible sheaves on X, extending a theorem of Nori to the case of positive characteristic.
A How-to Guide to Building a Robust SARS-CoV-2 Testing Program at a University-Based Health System

When South Florida became a hot spot for COVID-19 disease in March 2020, we faced an urgent need to develop test capability to detect SARS-CoV-2 infection. We assembled a transdisciplinary team of knowledgeable and dedicated physicians, scientists, technologists, and administrators who rapidly built a multiplatform, polymerase chain reaction- and serology-based detection program, established drive-through facilities, and drafted and implemented guidelines that enabled efficient testing of our patients and employees. This process was extremely complex, due to the limited availability of needed reagents, but outreach to our research scientists and multiple diagnostic laboratory companies, and government officials enabled us to implement both Food and Drug Administration authorized and laboratory-developed testing-based testing protocols. We analyzed our workforce needs and created teams of appropriately skilled and certified workers to safely process patient samples and conduct SARS-CoV-2 testing and contact tracing. We initiated smart test ordering, interfaced all testing platforms with our electronic medical record, and went from zero testing capacity to testing hundreds of health care workers and patients daily, within 3 weeks. We believe our experience can inform the efforts of others when faced with a crisis situation.
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks

Adversarial attacks have exposed serious vulnerabilities in deep neural networks (DNNs), causing misclassifications through human-imperceptible perturbations to DNN inputs. We explore a new direction in the field of adversarial attacks by suggesting attacks that aim to degrade the energy or latency of DNNs rather than their classification accuracy. As a specific embodiment of this new threat vector, we propose and demonstrate adversarial sparsity attacks, which modify a DNN’s inputs so as to reduce sparsity (or the incidence of zeros) in its internal activation values. Exploiting sparsity in hardware and software has emerged as a popular approach to improve DNN efficiency in resource-constrained systems. The proposed attack, therefore, increases the execution time and energy consumption of sparsity-optimized DNN implementations, raising concern over their deployment in latency and energy-critical applications. We propose a systematic methodology to generate adversarial inputs for sparsity attacks by formulating an objective function that quantifies the network’s activation sparsity and minimizing this function using iterative gradient-descent techniques. To prevent easy detection of the attack, we further ensure that the perturbation magnitude is within a specified constraint and that the perturbation does not affect classification accuracy. We launch both white-box and black-box versions of adversarial sparsity attacks on image recognition DNNs and demonstrate that they decrease activation sparsity by $1.16\times $ – $1.82\times $ . On a sparsity-optimized DNN accelerator, the attack results in degradations of $1.12\times $ – $1.59\times $ in latency and $1.18\times $ – $1.99\times $ in energy-delay product (EDP). Additionally, we analyze the impact of various hyperparameters and constraints on the attack’s efficacy. Finally, we evaluate defense techniques, such as activation thresholding and input quantization and demonstrate that the proposed attack is able to withstand them, highlighting the need for further efforts in this new direction within the field of adversarial machine learning.
Landmark navigation in a mantis shrimp

Mantis shrimp commonly occupy burrows in shallow, tropical waters. These habitats are often structurally complex where many potential landmarks are available. Mantis shrimp of the species Neogonodactylus oerstedii return to their burrows between foraging excursions using path integration, a vector-based navigational strategy that is prone to accumulated error. Here, we show that N. oerstedii can navigate using landmarks in parallel with their path integration system, correcting for positional uncertainty generated when navigating using solely path integration. We also report that when the path integration and landmark navigation systems are placed in conflict, N. oerstedii will orientate using either system or even switch systems enroute. How they make the decision to trust one navigational system over another is unclear. These findings add to our understanding of the refined navigational toolkit N. oerstedii relies upon to efficiently navigate back to its burrow, complementing its robust, yet error prone, path integration system with landmark guidance.
Horizon radiation reaction forces

Using Effective Field Theory (EFT) methods, we compute the effects of horizon dissipation on the gravitational interactions of relativistic binary black hole systems. We assume that the dynamics is perturbative, i.e it admits an expansion in powers of Newton’s constant (post-Minkowskian, or PM, approximation). As applications, we compute corrections to the scattering angle in a black hole collision due to dissipative effects to leading PM order, as well as the post-Newtonian (PN) corrections to the equations of motion of binary black holes in non-relativistic orbits, which represents the leading order finite size effect in the equations of motion. The methods developed here are also applicable to the case of more general compact objects, eg. neutron stars, where the magnitude of the dissipative effects depends on non-gravitational physics (e.g, the equation of state for nuclear matter).
A polynomial dimensional decomposition framework based on topology derivatives for stochastic topology sensitivity analysis of high-dimensional complex systems and a type of benchmark problems

Abstract In this paper, a new computational framework based on the topology derivative concept is presented for evaluating stochastic topological sensitivities of complex systems. The proposed framework, designed for dealing with high dimensional random inputs, dovetails a polynomial dimensional decomposition (PDD) of multivariate stochastic response functions and deterministic topology derivatives. On one hand, it provides analytical expressions to calculate topology sensitivities of the first three stochastic moments which are often required in robust topology optimization (RTO). On another hand, it offers embedded Monte Carlo Simulation (MCS) and finite difference formulations to estimate topology sensitivities of failure probability for reliability-based topology optimization (RBTO). For both cases, the quantification of uncertainties and their topology sensitivities are determined concurrently from a single stochastic analysis. Moreover, an original example of two random variables is developed for the first time to obtain analytical solutions for topology sensitivity of moments and failure probability. Another 53-dimension example is constructed for analytical solutions of topology sensitivity of moments and semi-analytical solutions of topology sensitivity of failure probabilities in order to verify the accuracy and efficiency of the proposed method for high-dimensional scenarios. Those examples are new and make it possible for researchers to benchmark stochastic topology sensitivities of existing or new algorithms. In addition, it is unveiled that under certain conditions the proposed method achieves better accuracies for stochastic topology sensitivities than for the stochastic quantities themselves.
Density functional theory-based electric field gradient database

The deviation of the electron density around the nuclei from spherical symmetry determines the electric field gradient (EFG), which can be measured by various types of spectroscopy. Nuclear Quadrupole Resonance (NQR) is particularly sensitive to the EFG. The EFGs, and by implication NQR frequencies, vary dramatically across materials. Consequently, searching for NQR spectral lines in previously uninvestigated materials represents a major challenge. Calculated EFGs can significantly aid at the search's inception. To facilitate this task, we have applied high-throughput density functional theory calculations to predict EFGs for 15187 materials in the JARVIS-DFT database. This database, which will include EFG as a standard entry, is continuously increasing. Given the large scope of the database, it is impractical to verify each calculation. However, we assess accuracy by singling out cases for which reliable experimental information is readily available and compare them to the calculations. We further present a statistical analysis of the results. The database and tools associated with our work are made publicly available by JARVIS-DFT ( https://www.ctcms.nist.gov/~knc6/JVASP.html ) and NIST-JARVIS API ( http://jarvis.nist.gov/ ).
Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis".

Author(s): Rau, Christoph D; Gonzales, Natalia M; Bloom, Joshua S; Park, Danny; Ayroles, Julien; Palmer, Abraham A; Lusis, Aldons J; Zaitlen, Noah | Abstract: BackgroundThe majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.ResultsWe applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.ConclusionsUnlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.
An Eddy–Zonal Flow Feedback Model for Propagating Annular Modes

The variability of the zonal-mean large-scale extratropical circulation is often studied using individual modes obtained from empirical orthogonal function (EOF) analyses. The prevailing reduced-order model of the leading EOF (EOF1) of zonal-mean zonal wind, called the annular mode, consists of an eddy-mean flow interaction mechanism that results in a positive feedback of EOF1 onto itself. However, a few studies have pointed out that under some circumstances in observations and GCMs, strong couplings exist between EOF1 and EOF2 at some lag times, resulting in decaying-oscillatory, or propagating, annular modes. Here, we introduce a reduced-order model for coupled EOF1 and EOF2 that accounts for potential cross-EOF eddy-zonal flow feedbacks. Using the analytical solution of this model, we derive conditions for the existence of the propagating regime based on the feedback strengths. Using this model, and idealized GCMs and stochastic prototypes, we show that cross-EOF feedbacks play an important role in controlling the persistence of the annular modes by setting the frequency of the oscillation. We find that stronger cross-EOF feedbacks lead to less persistent annular modes. Applying the coupled-EOF model to the Southern Hemisphere reanalysis data shows the existence of strong cross-EOF feedbacks. The results highlight the importance of considering the coupling of EOFs and cross-EOF feedbacks to fully understand the natural and forced variability of the zonal-mean large-scale circulation.
DOES INFLATION PROVIDE NATURAL INITIAL CONDITIONS FOR THE UNIVERSE

If our universe underwent inflation, its entropy during the inflationary phase would have been substantially lower than it is today. Because a low-entropy state is less likely to be chosen randomly than a high-entropy one, inflation is unlikely to arise through randomly-chosen initial conditions. To resolve this puzzle, we examine the notion of a natural state for the universe, and argue that it is a nearly-empty space–time. If empty space has a small vacuum energy, however, inflation can begin spontaneously in this background. This scenario explains why a universe like ours is likely to have begun via a period of inflation, and also provides an origin for the cosmological arrow of time.
Computing the energy of a water molecule using multideterminants: A simple, efficient algorithm

Quantum Monte Carlo (QMC) methods such as variational Monte Carlo and fixed node diffusion Monte Carlo depend heavily on the quality of the trial wave function. Although Slater-Jastrow wave functions are the most commonly used variational ansatz in electronic structure, more sophisticated wave functions are critical to ascertaining new physics. One such wave function is the multi-Slater-Jastrow wave function which consists of a Jastrow function multiplied by the sum of Slater determinants. In this paper we describe a method for working with these wave functions in QMC codes that is easy to implement, efficient both in computational speed as well as memory, and easily parallelized. The computational cost scales quadratically with particle number making this scaling no worse than the single determinant case and linear with the total number of excitations. Additionally, we implement this method and use it to compute the ground state energy of a water molecule.
Abundances and Depletions of Neutron-capture Elements in the Interstellar Medium

We present an extensive analysis of the gas-phase abundances and depletion behaviors of neutron-capture elements in the interstellar medium (ISM). Column densities (or upper limits to the column densities) of Ga II, Ge II, As II, Kr I, Cd II, Sn II, and Pb II are determined for a sample of 69 sight lines with high- and/or medium-resolution archival spectra obtained with the Space Telescope Imaging Spectrograph onboard the Hubble Space Telescope. An additional 59 sight lines with column density measurements reported in the literature are included in our analysis. Parameters that characterize the depletion trends of the elements are derived according to the methodology developed by Jenkins (2009; arXiv:0905.3173). (In an appendix, we present similar depletion results for the light element B.) The depletion patterns exhibited by Ga and Ge comport with expectations based on the depletion results obtained for many other elements. Arsenic exhibits much less depletion than expected, and its abundance in low-depletion sight lines may even be supersolar. We confirm a previous finding by Jenkins (2009; arXiv:0905.3173) that the depletion of Kr increases as the overall depletion level increases from one sight line to another. Cadmium shows no such evidence of increasing depletion. We find a significant amount of scatter in the gas-phase abundances of Sn and Pb. For Sn, at least, the scatter may be evidence of real intrinsic abundance variations due to s-process enrichment combined with inefficient mixing in the ISM.
Sporadic SICs and the Normed Division Algebras

Symmetric informationally complete quantum measurements, or SICs, are mathematically intriguing structures, which in practice have turned out to exhibit even more symmetry than their definition requires. Recently, Zhu classified all the SICs whose symmetry groups act doubly transitively. I show that lattices of integers in the complex numbers, the quaternions and the octonions yield the key parts of these symmetry groups.
Electromagnetic Field Theory Without Divergence Problems 1. The Born Legacy

Born's quest for the elusive divergence problem-free quantum theory of electromagnetism led to the important discovery of the nonlinear Maxwell–Born–Infeld equations for the classical electromagnetic fields, the sources of which are classical point charges in motion. The law of motion for these point charges has however been missing, because the Lorentz self-force in the relativistic Newtonian (formal) law of motion is ill-defined in magnitude and direction. In the present paper it is shown that a relativistic Hamilton–Jacobi type law of point charge motion can be consistently coupled with the nonlinear Maxwell–Born–Infeld field equations to obtain a well-defined relativistic classical electrodynamics with point charges. Curiously, while the point charges are spinless, the Pauli principle for bosons can be incorporated. Born's reasoning for calculating the value of his aether constant is re-assessed and found to be inconclusive.
Entanglement entropy and variational methods: Interacting scalar fields

Abstract We develop a variational approximation to the entanglement entropy for scalar ϕ 4 theory in 1 + 1 , 2 + 1 , and 3 + 1 dimensions, and then examine the entanglement entropy as a function of the coupling. We find that in 1 + 1 and 2 + 1 dimensions, the entanglement entropy of ϕ 4 theory as a function of coupling is monotonically decreasing and convex. While ϕ 4 theory with positive bare coupling in 3 + 1 dimensions is thought to lead to a trivial free theory, we analyze a version of ϕ 4 with infinitesimal negative bare coupling, an asymptotically free theory known as precarious ϕ 4 theory, and explore the monotonicity and convexity of its entanglement entropy as a function of coupling. Within the variational approximation, the stability of precarious ϕ 4 theory is related to the sign of the first and second derivatives of the entanglement entropy with respect to the coupling.
ATTRACTING DOMAINS OF MAPS TANGENT TO THE IDENTITY WHOSE ONLY CHARACTERISTIC DIRECTION IS NON-DEGENERATE

Let f be a holomorphic germ on ℂ2 that fixes the origin and is tangent to the identity. Assume that f has a non-degenerate characteristic direction [v]. Hakim gave conditions that guarantee the existence of attracting domains along [v], however, when f has only one characteristic direction, these conditions are not satisfied. We prove that when [v] is unique, the existence results still hold. In particular, there is a domain Ω whose points converge to the origin along [v] and, on Ω, f is conjugate to a translation. Furthermore, if f is a global automorphism, the corresponding domain of attraction is a Fatou–Bieberbach domain.
Sparse Tensor Transpositions

We present a new algorithm for transposing sparse tensors called Quesadilla. The algorithm converts the sparse tensor data structure to a list of coordinates and sorts it with a fast multi-pass radix algorithm that exploits knowledge of the requested transposition and the tensors input partial coordinate ordering to provably minimize the number of parallel partial sorting passes. We evaluate both a serial and a parallel implementation of Quesadilla on a set of 19 tensors from the FROSTT collection, a set of tensors taken from scientific and data analytic applications. We compare Quesadilla and a generalization, Top-2-sadilla to several state of the art approaches, including the tensor transposition routine used in the SPLATT tensor factorization library. In serial tests, Quesadilla was the best strategy for 60% of all tensor and transposition combinations and improved over SPLATT by at least 19% in half of the combinations. In parallel tests, at least one of Quesadilla or Top-2-sadilla was the best strategy for 52% of all tensor and transposition combinations.
Von Neumann rho invariants and torsion in the topological knot concordance group

We discuss an infinite class of metabelian Von Neumann rho-invariants. Each one is a homomorphism from the monoid of knots to the real line. In general they are not well defined on the concordance group. Nonetheless, we show that they pass to well defined homomorphisms from the subgroup of the concordance group generated by anisotropic knots. Thus, the computation of even one of these invariants can be used to conclude that a knot is of infinite order. We introduce a method to give a computable bound on these invariants. Finally we compute this bound to get a new and explicit infinite set of twist knots which is linearly independent in the concordance group and whose every member is of algebraic order 2 .
Weak Lensing for Precision Cosmology

Weak gravitational lensing, the deflection of light by mass, is one of the best tools to constrain the growth of cosmic structure with time and reveal the nature of dark energy. I discuss the sourc...
Testing the no-hair theorem with observations in the electromagnetic spectrum. I. Properties of a Quasi-Kerr spacetime

According to the no-hair theorem, an astrophysical black hole is uniquely described by only two quantities, the mass and the spin. In this series of papers, we investigate a framework for testing the no-hair theorem with observations of black holes in the electromagnetic spectrum. We formulate our approach in terms of a parametric spacetime which contains a quadrupole moment that is independent of both mass and spin. If the no-hair theorem is correct, then any deviation of the black hole quadrupole moment from its Kerr value has to be zero. We analyze in detail the properties of this quasi-Kerr spacetime that are critical to interpreting observations of black holes and demonstrate their dependence on the spin and quadrupole moment. In particular, we show that the location of the innermost stable circular orbit and the gravitational lensing experienced by photons are affected significantly at even modest deviations of the quadrupole moment from the value predicted by the no-hair theorem. We argue that observations of black hole images, of relativistically broadened iron lines, as well as of thermal X-ray spectra from accreting black holes will lead in the near future to an experimental test of the no-hair theorem.
How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions

How much is 131 million US dollars? To help readers put such numbers in context, we propose a new task of automatically generating short descriptions known as perspectives, e.g. "$131 million is about the cost to employ everyone in Texas over a lunch period". First, we collect a dataset of numeric mentions in news articles, where each mention is labeled with a set of rated perspectives. We then propose a system to generate these descriptions consisting of two steps: formula construction and description generation. In construction, we compose formulae from numeric facts in a knowledge base and rank the resulting formulas based on familiarity, numeric proximity and semantic compatibility. In generation, we convert a formula into natural language using a sequence-to-sequence recurrent neural network. Our system obtains a 15.2% F1 improvement over a non-compositional baseline at formula construction and a 12.5 BLEU point improvement over a baseline description generation.
Optimal disease outbreak detection in a community using network observability

Given a network, we would like to determine which subset of nodes should be measured by limited sensing facilities to maximize information about the entire network. The optimal choice corresponds to the configuration that returns the highest value of a measure of observability of the system. Here, the determinant of the inverse of the observability Gramian is used to evaluate the degree of observability. Additionally, the effects of changes in the topology of the corresponding graph of a network on the observability of the network are investigated. The theory is illustrated on the problem of detection of an epidemic disease in a community. The purpose here is to find the smallest number of people who must be examined to predict the number of infected people in an arbitrary community. Results are demonstrated in simulation.
On the statistical treatment of the Cabibbo angle anomaly

We point out that testing the equality of the Cabibbo angle as extracted from Γ(K → πlν), the ratio Γ(K → lν)/Γ(π → lν) and nuclear β decays is not identical to a test of first row unitarity of the Cabibbo-Kobayashi-Maskawa (CKM) matrix. The reason is that a CKM unitarity test involves only two parameters, while the degrees of freedom for the assessment of the goodness-of-fit of the universality of the Cabibbo angle entailed by the Standard Model (SM) is equal to the number of measurements minus one. Beyond the SM all different processes could in principle give different Cabibbo angles. Consequently, the difference between the two tests becomes relevant starting from three observables giving results for the Cabibbo angle that are in tension with each other. With current data, depending on the treatment of the nuclear β decays, we find that New Physics is favored over the SM at 5.1 σ or 3.6 σ while CKM unitarity is rejected at 4.8σ or 3.0σ, respectively. We argue that the best method to test the SM is to test the equality of the Cabibbo angle, because CKM unitarity is only one aspect of the SM.
Holographic entanglement in a noncommutative gauge theory

In this article we investigate aspects of entanglement entropy and mutual information in a large-N strongly coupled noncommutative gauge theory, both at zero and at finite temperature. Using the gauge-gravity duality and the Ryu-Takayanagi (RT) prescription, we adopt a scheme for defining spatial regions on such noncommutative geometries and subsequently compute the corresponding entanglement entropy. We observe that for regions which do not lie entirely in the noncommutative plane, the RT-prescription yields sensible results. In order to make sense of the divergence structure of the corresponding entanglement entropy, it is essential to introduce an additional cut-off in the theory. For regions which lie entirely in the noncommutative plane, the corresponding minimal area surfaces can only be defined at this cut-off and they have distinctly peculiar properties.
Flavorful Supersymmetry from Higher Dimensions

We present models of flavorful supersymmetry in higher dimensions. The Higgs fields and the supersymmetry breaking field are localized in the same place in the extra dimension(s). The Yukawa couplings and operators generating the supersymmetry breaking parameters then receive the same suppression factors from the wavefunction profiles of the matter fields, leading to a specific correlation between these two classes of interactions. The resulting phenomenology is very rich, while stringent experimental constraints from the low-energy flavor and CP violating processes can all be satisfied. We construct both unified and non-unified models in this framework, which can be either strongly or weakly coupled at the cutoff scale. We analyze one version in detail, a strongly coupled unified model, which addresses various issues of supersymmetric grand unification. The models presented here provide an explicit example in which the supersymmetry breaking spectrum can be a direct window into the physics of flavor at a very high energy scale.
Particle-filter-enabled real-time sensor fault detection without a model of faults

We are experiencing an explosion in the amount of sensors measuring our activities and the world around us. These sensors are spread throughout the built environment and can help us perform state estimation and control of related systems, but they are often built and/or maintained by third parties or system users. As a result, by outsourcing system measurement to third parties, the controller must accept their measurements without being able to directly verify the sensors' correct operation. Instead, detection and rejection of measurements from faulty sensors must be done with the raw data only. Towards this goal, we present a method of detecting possibly faulty behavior of sensors. The method does not require that the control designer have any model of faulty sensor behavior. As we discuss, it turns out that the widely-used particle filter state estimation algorithm provides the ingredients necessary for a hypothesis test against all ranges of correct operating behavior, obviating the need for a fault model to compare measurements. We demonstrate the applicability of our method by showing its ability to reject faulty measurements and accuracy in state estimation of a nonlinear vehicle traffic model, without information of generated faulty measurements' characteristics. In our test, we correctly identify nearly 90% of measurements as faulty or non-faulty without having any fault model. This leads to only a 3% increase in state estimation error over a theoretical 100%-accurate fault detector.
Continued fraction expansions in connection with the metric Mahler measure

The metric Mahler measure was first studied by Dubickas and Smyth in 2001 as a means of phrasing Lehmer’s conjecture in topological language. More recent work of the author examined a parametrized family of generalized metric Mahler measures that gives rise to a series of new, and apparently difficult, problems. We establish a connection between these metric Mahler measures and the theory of continued fractions in a certain class of special cases. Our results enable us to calculate metric Mahler measures in several new examples.
Mapping photocathode quantum efficiency with ghost imaging

Measuring the quantum efficiency (QE) map of a photocathode injector typically requires laser scanning, an invasive operation that involves modifying the injector laser focus and rastering the focused laser spot across the photocathode surface. Raster scanning interrupts normal operation and takes considerable time to setup. In this paper, we demonstrate a novel method of measuring the QE map using a ghost imaging framework that correlates the injector laser spatial variation over time with the total charge yield. Ghost imaging enables passive, real-time monitoring of the QE map without manually modifying the injector laser or interrupting injector operation. We first demonstrate the method at the UCLA Pegasus photoinjector with the help of a digital micromirror device (DMD) and a piezoelectric mirror to increase our control of the overall transverse variance of the illumination profile. The reconstruction algorithm parameters are fine-tuned using simulations and the results are validated against the ground truth map acquired using the traditional rastering method. Finally, we apply the technique to data acquired parasitically from the LCLS photoinjector, showing the feasibility of this method to retrieve a QE map without interrupting normal operation.