cssi/SciSciGPT-SciSciNet huggingface dataset:
Economics of Resilient Cloud Services

Today's computer systems must meet and maintain service availability, performance, and security requirements. Each of these demands requires redundancy and some form of isolation. When service requirements are implemented separately, the system architecture cannot easily share common components of redundancy and isolation. We will present these service traits collectively as cyber resilience with a system called Self-Cleansing Intrusion Tolerance (SCIT). Further, we will demonstrate that SCIT provides an effective resilient cloud implementation making cost effective utilization of cloud's excess capacity and economies of scale. Lastly, we will introduce the notion of serverless applications utilizing AWS Lambda and how a stateless architecture can drastically reduce operational costs by utilizing cloud function services.
Musings on Lorentz Violation Given the Recent Gravitational-Wave Observations of Coalescing Binary Black Holes

The recent observation of gravitational waves by the LIGO/Virgo collaboration provides a unique opportunity to probe the extreme gravity of coalescing binary black holes. In this regime, the gravitational interaction is not only strong, but the spacetime curvature is large, characteristic velocities are a non-negligible fraction of the speed of light, and the time scale on which the curvature and gravity change is small. This contribution discusses some consequences of these observations on modifications to General Relativity, with a special emphasis on Lorentz-violating theories.
20–80nm Channel length InGaAs gate-all-around nanowire MOSFETs with EOT=1.2nm and lowest SS=63mV/dec

In this paper, 20nm–80nm channel length (L ch ) InGaAs gate-all-around (GAA) nanowire MOSFETs with record high on-state and off-state performance have been demonstrated by equivalent oxide thickness (EOT) and nanowire width (W NW ) scaling down to 1.2nm and 20nm, respectively. SS and DIBL as low as 63mV/dec and 7mV/V have been demonstrated, indicating excellent interface quality and scalability. Highest I ON = 0.63mA/μm and g m = 1.74mS/μm have also been achieved at V DD =0.5V, showing great promise of InGaAs GAA technology for 10nm and beyond high-speed low-power logic applications.
A characterization of the behavior of the Anderson acceleration on linear problems

Abstract We give a complete characterization of the behavior of the Anderson acceleration (with arbitrary nonzero mixing parameters) on linear problems. Let ν be the grade of the residual at the starting point with respect to the matrix defining the linear problem. We show that if Anderson acceleration does not stagnate (that is, produces different iterates) up to ν , then the sequence of its iterates converges to the exact solution of the linear problem. Otherwise, the Anderson acceleration converges to a point that is not a solution. Anderson acceleration and GMRES are essentially equivalent up to the index where the iterates of Anderson acceleration begin to stagnate. This result holds also for an optimized version of Anderson acceleration, where at each step the mixing parameter is chosen so that it minimizes the residual of the current iterate.
On the Cauchy Problem for Integro-Differential Equations in the Scale of Spaces of Generalized Smoothness

Parabolic integro-differential model Cauchy problem is considered in the scale of Lp -spaces of functions whose regularity is defined by a scalable Levy measure. Existence and uniqueness of a solution is proved by deriving apriori estimates. Some rough probability density function estimates of the associated Levy process are used as well.
Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States

Establishing up-to-date large scale building maps is essential to understand the urban dynamics, such as estimating population, urban planning, and many other applications. Although many computer vision tasks have been successfully carried out with deep convolutional neural networks, there is a growing need to understand their large scale impact on building mapping with remote sensing imagery. Taking advantage of the scalability of convolutional neural networks (CNNs) and using only few areas with the abundance of building footprints, for the first time we conduct a comparative analysis of four state-of-the-art CNNs for extracting building footprints across the entire continental United States. The four CNN architectures namely: Branch-out CNN, fully convolutional network (FCN), conditional random field as recurrent neural network (CRFasRNN), and SegNet, support semantic pixelwise labeling and focus on capturing textural information at multiscale. We use 1-meter resolution aerial images from National Agriculture Imagery Program as the test-bed, and compare the extraction results across the four methods. In addition, we propose to combine signed-distance labels with SegNet, the preferred CNN architecture identified by our extensive evaluations, to advance building extraction results to instance level. We further demonstrate the usefulness of fusing additional near IR information into the building extraction framework. Large scale experimental evaluations are conducted and reported using metrics that include: Precision, recall rate, intersection over union, and the number of buildings extracted. With the improved CNN model and no requirement of further postprocessing, we have generated building maps for the United States with an average processing time less than one minute for an area of size $\sim {\text{56}}$ $\text{km}^2$ . The quality of extracted buildings and processing time demonstrated that the proposed CNN based framework fits the need of building extraction at scale.
Stability of hyperbolic manifolds with cusps under Ricci flow

Abstract We show that every finite volume hyperbolic manifold of dimension greater than or equal to 3 is stable under rescaled Ricci flow, i.e. that every small perturbation of the hyperbolic metric flows back to the hyperbolic metric again. Note that we do not need to make any decay assumptions on this perturbation. It will turn out that the main difficulty in the proof comes from a weak stability of the cusps which has to do with infinitesimal cusp deformations. We will overcome this weak stability by using a new analytical method developed by Koch and Lamm.
The quantum content of the gluing equations

The gluing equations of a cusped hyperbolic 3‐manifold M are a system of polynomial equations in the shapes of an ideal triangulation T of M that describe the complete hyperbolic structure of M and its deformations. Given a Neumann‐Zagier datum (comprising the shapes together with the gluing equations in a particular canonical form) we define a formal power series with coefficients in the invariant trace field of M that should (a) agree with the asymptotic expansion of the Kashaev invariant to all orders, and (b) contain the nonabelian Reidemeister‐Ray‐Singer torsion of M as its first subleading “1‐loop” term. As a case study, we prove topological invariance of the 1‐loop part of the constructed series and extend it into a formal power series of rational functions on the PSL.2;C/ character variety of M . We provide a computer implementation of the first three terms of the series using the standard SnapPy toolbox and check numerically the agreement of our torsion with the Reidemeister‐Ray‐Singer for all 59924 hyperbolic knots with at most 14 crossings. Finally, we explain how the definition of our series follows from the quantization of 3‐dimensional hyperbolic geometry, using principles of topological quantum field theory. Our results have a straightforward extension to any 3‐manifold M with torus boundary components (not necessarily hyperbolic) that admits a regular ideal triangulation with respect to some PSL.2;C/ representation.
Hypergeometric functions and relations to Dwork hypersurfaces

We give an expression for number of points for the family of Dwork K3 surfaces over finite fields of order q ≡ 1 (mod 4) in terms of Greene’s finite field hypergeometric functions. We also develop hypergeometric point count formulas for all odd primes using McCarthy’s p-adic hypergeometric function. Furthermore, we investigate the relationship between certain period integrals of these surfaces and the trace of Frobenius over finite fields. We extend this work to higher dimensional Dwork hypersurfaces.
Metric Tannakian duality

Abstract We incorporate metric data into the framework of Tannaka–Krein duality. Thus, for any group with left invariant metric, we produce a dual metric on its category of unitary representations. We characterize the conditions under which a “double-dual” metric on the group may be recovered from the metric on representations, and provide conditions under which a metric agrees with its double-dual. We also explore a diverse class of possible applications of the theory, including applications to T -duality and to quantum Gromov–Hausdorff distance.
An involution based left ideal in the Hecke algebra

We define an imbedding of the Hecke algebra module carried by the involutions in a Weyl group W (defined by the author and Vogan) into a completion of the Hecke algebra. An analogous result is proved for any Coxeter group. A variant of the construction gives an imbedding of the biregular representation of a Hecke algebra of W in a completion of the Hecke algebra of W^2.
Smooth combs inside hedgehogs

We use techniques of tube-log Riemann surfaces due to R.Perez-Marco to construct a hedgehog containing smooth $C^\infty$ combs. The hedgehog is a common hedgehog for a family of commuting non-linearisable holomorphic maps with a common indifferent fixed point. The comb is made up of smooth curves, and is transversally bi-Holder regular.
Algorithms that Satisfy a Stopping Criterion, Probably

Iterative numerical algorithms are typically equipped with a stopping criterion, where the iteration process is terminated when some error or misfit measure is deemed to be below a given tolerance. This is a useful setting for comparing algorithm performance, among other purposes. However, in practical applications, a precise value for such a tolerance is rarely known; rather, only some possibly vague idea of the desired quality of the numerical approximation is at hand. In this review paper, we first discuss four case studies from different areas of numerical computation, where uncertainty in the error tolerance value and in the stopping criterion is revealed in different ways. This leads us to think of approaches to relax the notion of exactly satisfying a tolerance value. We then concentrate on a probabilistic relaxation of the given tolerance in the context of our fourth case study which allows, for instance, derivation of proven bounds on the sample size of certain Monte Carlo methods. We describe an algorithm that becomes more efficient in a controlled way as the uncertainty in the tolerance increases and demonstrate this in the context of some particular applications of inverse problems.
Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices

To effectively evaluate subjective visual quality in weakly-controlled environments, we propose an Adaptive Paired Comparison method based on particle filtering. As our approach requires each sample to be rated only once, the test time compared to regular paired comparison can be reduced. The method works with non-experts and improves reliability compared to MOS and DS-MOS methods.
Rigorous derivation of nonlinear Dirac equations for wave propagation in honeycomb structures

We consider a nonlinear Schrodinger equation in two spatial dimensions subject to a periodic honeycomb lattice potential. Using a multi-scale expansion together with rigorous error estimates, we derive an effective model of nonlinear Dirac type. The latter describes the propagation of slowly modulated, weakly nonlinear waves spectrally localized near a Dirac point.
Sketching for simultaneously sparse and low-rank covariance matrices

We introduce a technique for estimating a structured covariance matrix from observations of a random vector which have been sketched. Each observed random vector xt is reduced to a single number by taking its inner product against one of a number of pre-selected vector al. These observations are used to form estimates of linear observations of the covariance matrix Σ, which is assumed to be simultaneously sparse and low-rank. We show that if the sketching vectors al have a special structure, then we can use straightforward two-stage algorithm that exploits this structure. We show that the estimate is accurate when the number of sketches is proportional to the maximum of the rank times the number of significant rows/columns of Σ. Moreover, our algorithm takes direct advantage of the low-rank structure of Σ by only manipulating matrices that are far smaller than the original covariance matrix.
Variational multiscale modeling with discontinuous subscales: analysis and application to scalar transport

We examine a variational multiscale method in which the unresolved fine-scales are approximated element-wise using a discontinuous Galerkin method. We establish stability and convergence results for the methodology as applied to the scalar transport problem, and we prove that the method exhibits optimal convergence rates in the SUPG-norm and is robust with respect to the Peclet number if the discontinuous subscale approximation space is sufficiently rich. We apply the method to isogeometric NURBS discretizations of steady and unsteady transport problems, and the corresponding numerical results demonstrate that the method is stable and accurate in the advective limit even when low-order discontinuous subscale approximations are employed.
Nonuniform sampling and multiscale computation

In homogenization theory and multiscale modeling, typical functions satisfy the scaling law $f^{\epsilon}(x)=f(x,x/\epsilon)$, where $f$ is periodic in the second variable and $\epsilon$ is the smallest relevant wavelength, $0<\epsilon\ll1$. Our main result is a new $L^{2}$-stability estimate for the reconstruction of bandlimited multiscale functions $f^{\epsilon}$ from periodic nonuniform samples. The goal of this paper is to demonstrate the close relation between sampling strategies developed in information theory and computational grids in multiscale modeling. This connection is of much interest because numerical simulations often involve discretizations by means of sampling, and the proposed sampling sets are of optimal rate according to the minimal sampling requirements of Landau [Proc. IEEE, 55 (1967), pp. 1701--1706].
Dimer models on cylinders over Dynkin diagrams and cluster algebras

In this paper, we describe a general setting for dimer models on cylinders over Dynkin diagrams which in type A reduces to the well studied case of dimer models on a disc. We prove that all Berenstein--Fomin--Zelevinsky quivers for Schubert cells in a symmetric Kac--Moody algebra give rise to dimer models on the cylinder over the corresponding Dynkin diagram. We also give an independent proof of a result of Buan, Iyama, Reiten and Smith that the corresponding superpotentials are rigid using the dimer model structure of the quivers.
Distributed load management in anycast-based CDNs

Anycast is an internet addressing protocol where multiple hosts share the same IP-address. A popular architecture for modern Content Distribution Networks (CDNs) for geo-replicated HTTP-services consists of multiple layers of proxy nodes for service and co-located DNS-servers for load-balancing on different proxies. Both the proxies and the DNS-servers use anycast addressing, which offers simplicity of design and high availability of service at the cost of partial loss of routing control. Due to the very nature of anycast, load-management actions by a co-located DNS-server also affects loads at nearby proxies in the network. This makes the problem of distributed load management highly challenging. In this paper, we propose an analytical framework to formulate and solve the load-management problem in this context. We consider two distinct algorithms. In the first half of the paper, we pose the load-management problem as a convex optimization problem. Following a dual decomposition technique, we propose a fully-distributed load-management algorithm by introducing FastControl packets. This algorithm utilizes the underlying anycast mechanism itself to enable effective coordination among the nodes, thus obviating the need for any external control channel. In the second half of the paper, we consider an alternative greedy load-management heuristic, currently in production in a major commercial CDN. We study its dynamical characteristics and analytically identify its operational and stability properties. Finally, we critically evaluate both the algorithms and explore their optimality-vs-complexity trade-off using trace-driven simulations.
Control Inversion: A Clustering-Based Method for Distributed Wide-Area Control of Power Systems

Wide-area control (WAC) has been shown to be an effective tool for damping low-frequency oscillations in power systems. In the current state of art, WAC is challenged by two main factors, namely, scalability of design and complexity of implementation. In this paper, we present a control design called control inversion that bypasses both of these challenges using the idea of clustering. The basic philosophy behind this method is to project the original power system model into a lower-dimensional state space through clustering and aggregation of generator states, and then designing a linear-quadratic regulator (LQR) controller for the lower-dimensional model. This controller is finally projected back to the original coordinates for wide-area implementation. The main problem is, therefore, posed as finding the projection which best matches the closed-loop performance of the WAC controller with that of a reference LQR controller for damping low-frequency oscillations. We verify the effectiveness of the proposed design using the IEEE 48-machine power system model of the Northeastern Power Coordinating Council.
Prediction of galaxy ellipticities and reduction of shape noise in cosmic shear measurements

The intrinsic scatter in the ellipticities of galaxies about the mean shape, known as "shape noise," is the most important source of noise in weak lensing shear measurements. Several approaches to reducing shape noise have recently been put forward, using information beyond photometry, such as radio polarization and optical spectroscopy. Here we investigate how well the intrinsic ellipticities of galaxies can be predicted using other, exclusively photometric parameters. These parameters (such as galaxy colours) are already available in the data and do not necessitate additional, often expensive observations. We apply two regression techniques, generalized additive models (GAM) and projection pursuit regression (PPR) to the publicly released data catalog of galaxy properties from CFHTLenS. In our simple analysis we find that the individual galaxy ellipticities can indeed be predicted from other photometric parameters to better precision than the scatter about the mean ellipticity. This means that without additional observations beyond photometry the ellipticity contribution to the shear can be measured to higher precision, comparable to using a larger sample of galaxies. Our best-fit model, achieved using PPR, yields a gain equivalent to having 114.3% more galaxies. Using only parameters unaffected by lensing (e.g.~surface brightness, colour), the gain is only ~12%.
Positivity-Preserving Discontinuous Galerkin Methods with Lax---Wendroff Time Discretizations

This work introduces a single-stage, single-step method for the compressible Euler equations that is provably positivity-preserving and can be applied on both Cartesian and unstructured meshes. This method is the first case of a single-stage, single-step method that is simultaneously high-order, positivity-preserving, and operates on unstructured meshes. Time-stepping is accomplished via the Lax---Wendroff approach, which is also sometimes called the Cauchy---Kovalevskaya procedure, where temporal derivatives in a Taylor series in time are exchanged for spatial derivatives. The Lax---Wendroff discontinuous Galerkin (LxW-DG) method developed in this work is formulated so that it looks like a forward Euler update but with a high-order time-extrapolated flux. In particular, the numerical flux used in this work is a convex combination of a low-order positivity-preserving contribution and a high-order component that can be damped to enforce positivity of the cell averages for the density and pressure for each time step. In addition to this flux limiter, a moment limiter is applied that forces positivity of the solution at finitely many quadrature points within each cell. The combination of the flux limiter and the moment limiter guarantees positivity of the cell averages from one time-step to the next. Finally, a simple shock capturing limiter that uses the same basic technology as the moment limiter is introduced in order to obtain non-oscillatory results. The resulting scheme can be extended to arbitrary order without increasing the size of the effective stencil. We present numerical results in one and two space dimensions that demonstrate the robustness of the proposed scheme.
Tropical analytic geometry, Newton polygons, and tropical intersections

Abstract In this paper we use the connections between tropical algebraic geometry and rigid-analytic geometry in order to prove two main results. We use tropical methods to prove a theorem about the Newton polygon for convergent power series in several variables: if f 1 , … , f n are n convergent power series in n variables with coefficients in a non-Archimedean field K, we give a formula for the valuations and multiplicities of the common zeros of f 1 , … , f n . We use rigid-analytic methods to show that stable complete intersections of tropical hypersurfaces compute algebraic multiplicities even when the intersection is not tropically proper. These results are naturally formulated and proved using the theory of tropicalizations of rigid-analytic spaces, as introduced by Einsiedler, Kapranov, and Lind (2006) [14] and Gubler (2007) [20] . We have written this paper to be as readable as possible both to tropical and arithmetic geometers.
How to Start Training: The Effect of Initialization and Architecture

We identify and study two common failure modes for early training in deep ReLU nets. For each, we give a rigorous proof of when it occurs and how to avoid it, for fully connected, convolutional, and residual architectures. We show that the first failure mode, exploding or vanishing mean activation length, can be avoided by initializing weights from a symmetric distribution with variance 2/fan-in and, for ResNets, by correctly scaling the residual modules. We prove that the second failure mode, exponentially large variance of activation length, never occurs in residual nets once the first failure mode is avoided. In contrast, for fully connected nets, we prove that this failure mode can happen and is avoided by keeping constant the sum of the reciprocals of layer widths. We demonstrate empirically the effectiveness of our theoretical results in predicting when networks are able to start training. In particular, we note that many popular initializations fail our criteria, whereas correct initialization and architecture allows much deeper networks to be trained.
The approachability ideal without a maximal set

Abstract We develop a forcing poset with finite conditions which adds a partial square sequence on a given stationary set, with adequate sets of models as side conditions. We then develop a kind of side condition product forcing for simultaneously adding partial square sequences on multiple stationary sets. We show that certain quotients of such forcings have the ω 1 -approximation property. We apply these ideas to prove, assuming the consistency of a greatly Mahlo cardinal, that it is consistent that the approachability ideal I [ ω 2 ] does not have a maximal set modulo clubs.
On the network reliability problem of the heterogeneous key predistribution scheme

We consider the network reliability problem in wireless sensor networks secured by the heterogeneous random key predistribution scheme. This scheme generalizes Eschenauer-Gligor scheme by considering the cases when the network comprises sensor nodes with varying level of resources; e.g., regular nodes vs. cluster heads. The scheme induces the inhomogeneous random key graph, denoted G(n; μ, K, P). We analyze the reliability of G(n; μ, K, P) against random link failures. Namely, we consider G(n; μ, K, P, α) formed by deleting each edge of G(n; μ, K, P) independently with probability 1−α, and study the probability that the resulting graph i) has no isolated node; and ii) is connected. We present scaling conditions onK, P, and α such that both events take place with probability zero or one, respectively, as the number of nodes gets large. We present numerical results to support these in the finite-node regime.
A New Model of the Lifetime of Wireless Sensor Networks in Sea Water Communications

Abstract—In this paper we present an a new model for thelifetime of wireless sensor networks used for underwatercommunications in pure water. The new model for powercommunications takes into consideration parameters such aspower consumption for the active mode, power consumption forthe sleep mode, power consumption for the transient mode,transmission period, transient mode duration, sleep modeduration, and active mode duration. The power communicationsmodel is incorporated in the life time model of wireless sensornetworks. The life time model takes into consideration severalparameters such as the total number of sensors, network size,percentage of sink nodes, location of sensors, the mobility ofsensors, power consumption when nodes move and the powerconsumption of communications. The new model for powerconsumption in communications shows more accurate resultsabout the lifetime of the sensor network in comparison withpreviously published results.
Variance reduction for irreversible Langevin samplers and diffusion on graphs

In recent papers it has been demonstrated that sampling a Gibbs distribution from an appropriate time-irreversible Langevin process is,from several points of view, advantageous when compared to sampling from a time-reversible one. Adding an appropriate irreversible drift to the overdamped Langevin equation results in a larger large deviations rate function for the empirical measure of the process, a smaller variance for the long time average of observables of the process, as well as a larger spectral gap. In this work, we concentrate on irreversible Langevin samplers with a drift of increasing intensity. The asymptotic variance is monotonically decreasing with respect to the growth of the drift and we characterize its limiting behavior. For a Gibbs measure whose potential has one or more critical points, adding a large irreversible drift results in a decomposition of the process in a slow and fast component with fast motion along the level sets of the potential and slow motion in the orthogonal direction. This result helps understanding the variance reduction, which can be explained at the process level by the induced fast motion of the process along the level sets of the potential. Correspondingly the limit of the asymptotic variance is the asymptotic variance of the limiting slow motion which is a diffusion process on a graph.
Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Kording, 2008), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005; Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better th...
Localization of Rota-Baxter algebras

Abstract A commutative Rota–Baxter algebra can be regarded as a commutative algebra that carries an abstraction of the integral operator. With the motivation of generalizing the study of algebraic geometry to Rota–Baxter algebras, we extend the central concept of localization for commutative algebras to commutative Rota–Baxter algebras. The existence of such a localization is proved and, under mild conditions, its explicit construction is obtained. The existence of tensor products of commutative Rota–Baxter algebras is also proved and the compatibility of localization and the tensor product of Rota–Baxter algebras is established. We further study Rota–Baxter coverings and show that they form a Grothendieck topology.
Robust bent line regression

We introduce a rank-based bent linear regression with an unknown change point. Using a linear reparameterization technique, we propose a rank-based estimate that can make simultaneous inference on all model parameters, including the location of the change point, in a computationally efficient manner. We also develop a score-like test for the existence of a change point, based on a weighted CUSUM process. This test only requires fitting the model under the null hypothesis in absence of a change point, thus it is computationally more efficient than likelihood-ratio type tests. The asymptotic properties of the test are derived under both the null and the local alternative models. Simulation studies and two real data examples show that the proposed methods are robust against outliers and heavy-tailed errors in both parameter estimation and hypothesis testing.
Hawking radiation from acoustic black holes in two space dimensions

We study the Hawking radiation for the acoustic black hole. In the beginning, we follow the outline of Jacobson but then we use a different 2 + 1 vacuum state similar to the vacuum state constructed by Unruh. We also use a special form of the wave packets. The focus of the paper is to treat the 2-dimensional case, in particular, the case when the radial and angular velocity are variable.
Estimates for the square variation of partial sums of Fourier series and their rearrangements

We investigate the square variation operator V2 (which majorizes the partial sum maximal operator) on general orthonormal systems (ONS) of size N. We prove that the L2 norm of the V2 operator is bounded by O(ln(N)) on any ONS. This result is sharp and refines the classical Rademacher–Menshov theorem. We show that this can be improved to O(ln(N)) for the trigonometric system, which is also sharp. We show that for any choice of coefficients, this truncation of the trigonometric system can be rearranged so that the L2 norm of the associated V2 operator is O(lnln(N)). We also show that for p>2, a bounded ONS of size N can be rearranged so that the L2 norm of the Vp operator is at most Op(lnln(N)) uniformly for all choices of coefficients. This refines Bourgainʼs work on Garsiaʼs conjecture, which is equivalent to the V∞ case. Several other results on operators of this form are also obtained. The proofs rely on combinatorial and probabilistic methods.
A new NOMA approach for fair power allocation

A non-orthogonal multiple access (NOMA) approach to user signal power allocation called Fair-NOMA is introduced. Fair-NOMA is the application of NOMA in such a way that two mobile users have the opportunity to always achieve at least the information capacity they can achieve by using orthogonal multiple access (OMA), regardless of the user selection criteria, making it suitable for implementation using any current or future scheduling paradigms. Given this condition, the bounds of the power allocation coefficients are derived as functions of the channel gains of the two mobile users. The NOMA power allocation is analyzed for two scheduled users that are selected randomly with i.i.d. channel gains. The capacity improvements made by each user and the sum capacity improvement are derived.
Binary forms with three different relative ranks

Suppose $f(x,y)$ is a binary form of degree $d$ with coefficients in a field $K \subseteq \mathbb C$. The $K$-rank of $f$ is the smallest number of $d$-th powers of linear forms over $K$ of which $f$ is a $K$-linear combination. We prove that for $d \ge 5$, there always exists a form of degree $d$ with at least three different ranks over various fields. The $K$-rank of a form $f$ (such as $x^3y^2$) may depend on whether -1 is a sum of two squares in $K$.
Rigorous solution of strongly coupled $SO(N)$ lattice gauge theory in the large $N$ limit

The main result of this paper is a rigorous computation of Wilson loop expectations in strongly coupled SO(N) lattice gauge theory in the large N limit, in any dimension. The formula appears as an absolutely convergent sum over trajectories in a kind of string theory on the lattice, demonstrating an explicit gauge-string duality. The generality of the proof technique may allow it to be extended other gauge groups.
Convergence rate of Krasulina estimator

Abstract Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications. Consider the points X 1 , X 2 , … , X n are vectors drawn i.i.d. from a distribution with mean zero and covariance Σ , where Σ is unknown. Let A n = X n X n T , then E [ A n ] = Σ . This paper considers the problem of finding the smallest eigenvalue and eigenvector of matrix Σ . A classical estimator of this type is due to (Krasulina, 1969). We are going to state the convergence proof of Krasulina for the smallest eigenvalue and corresponding eigenvector, and then find their convergence rate.
Did the American Recovery and Reinvestment Act Help Counties Most Affected by the Great Recession

Abstract One of the statements of purpose of the American Recovery and Reinvestment Act (ARRA) was “to assist those most impacted by the recession.” To consider this facet, the grants-in-aid portion of the ARRA is assessed from the perspective of fiscal federalism. We estimate a trend-stationary autoregressive model of county-level wage income dynamics where each county is subject to a common shock (with county-specific factor loading) and an idiosyncratic shock. We then ask if counties that experienced larger negative wage income shocks during the Great Contraction subsequently received more transfers per capita in the form of grants-in-aid. The fact that the negative business cycle shocks pre-date the passage of the ARRA and subsequent disbursements allows identification of the risk-pooling channel of the grants before fiscal multiplier effects confound these two channels. We find statistically significant and economically large risk-pooling effects.
The effect of environmental enrichment on behavioral variability depends on genotype, behavior, and type of enrichment

Non-genetic individuality in behavior, also termed intragenotypic variability, has been observed across many different organisms. A potential cause of intragenotypic variability is sensitivity to minute environmental differences during development, which are present even when major environmental parameters are kept constant. Animal enrichment paradigms often include the addition of environmental diversity, whether in the form of social interaction, novel objects or exploratory opportunities. Enrichment could plausibly affect intragenotypic variability in opposing ways: it could cause an increase in variability due to the increase in microenvironmental variation, or a decrease in variability due to elimination of aberrant behavior as animals are taken out of impoverished laboratory conditions. In order to test these hypothesis, we assayed five isogenic Drosophila melanogaster lines raised in control and mild enrichment conditions, and one isogenic line under both mild and intense enrichment conditions. We compared the mean and variability of six behavioral metrics between our enriched fly populations and the laboratory housing control. We found that enrichment often caused a small increase in variability across most of our behaviors, but that the ultimate effect of enrichment on both behavioral means and variabilities was highly dependent on genotype and its interaction with the particular enrichment treatment. Our results support previous work on enrichment that presents a highly variable picture of its effects on both behavior and physiology.
Imperial Rule, the Imposition of Bureaucratic Institutions, and their Long-Term Legacies

Significant variation in the institutions and efficiency of public bureaucracies across countries and regions are observed. These differences could be partially responsible for divergence in the effectiveness of policy implementation, corruption levels, and economic development. Do imperial legacies contribute to the observed variation in the organization of public administrations? Historical foreign rule and colonization have been shown to have lasting effects on legal systems, political institutions, and trade in former controlled territories. Imperial legacies could also explain variations in the performance of public administrations. The author uses the case of Poland to investigate the long-term effects of foreign rule on bureaucratic systems. Historically, Poland was split between three imperial powers with very different public administrations: Prussia, Austria, and Russia. Statistical analyses of original data collected through a survey of more than 650 Polish public administrations suggest that some present-day differences in the organization and efficiency of bureaucracies are due to imperial legacies.
3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams

In this paper, we focus on obtaining 2D and 3D labels, as well as track IDs for objects on the road with the help of a novel 3D Bounding Box Annotation Toolbox (3D BAT). Our open source, web-based 3D BAT incorporates several smart features to improve usability and efficiency. For instance, this annotation toolbox supports semi-automatic labeling of tracks using interpolation, which is vital for downstream tasks like tracking, motion planning and motion prediction. Moreover, annotations for all camera images are automatically obtained by projecting annotations from 3D space into the image domain. In addition to the raw image and point cloud feeds, a Masterview consisting of the top view (bird's-eye-view), side view and front views is made available to observe objects of interest from different perspectives. Comparisons of our method with other publicly available annotation tools reveal that 3D annotations can be obtained faster and more efficiently by using our toolbox.
FOUR FACTS CONCERNING COMPETITION IN U.S. GENERIC PRESCRIPTION DRUG MARKETS.

We establish four facts concerning competition among US generic drug suppliers, using IQVIA’s National Sales Perspective™ 2004 Q4–2016 Q3 data. We define a unique product market (“molform”), consis...
Universality of automorphisms on the ball of bounded holomorphic functions on the polydisk

Abstract Given a sequence of automorphisms of the polydisk, we show that the associated composition semigroup homomorphisms on the ball of bounded holomorphic functions on the polydisk admit a universal inner function if a certain condition on the automorphisms are satisfied.
More than efficacy revealed by single-cell analysis of antiviral therapeutics

Because many aspects of viral infection dynamics and inhibition are governed by stochastic processes, single-cell analysis should provide more information than approaches using population averaging. We have developed a microfluidic device composed of ~6000 wells, with each well containing a microstructure to capture single, infected cells replicating an enterovirus expressing a fluorescent reporter protein. We have used this system to characterize enterovirus inhibitors with distinct mechanisms of action. Single-cell analysis reveals that each class of inhibitor interferes with the viral infection cycle in a manner that can be distinguished by principal component analysis. Single-cell analysis of antiviral candidates not only reveals efficacy but also facilitates clustering of drugs with the same mechanism of action and provides some indication of the ease with which resistance will develop.
A Dataset of Multi-Illumination Images in the Wild

Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. The data simply does not contain the necessary supervisory signals. Multi-illumination datasets are notoriously hard to capture, so the data is typically collected at small scale, in controlled environments, either using multiple light sources, or robotic gantries. This leads to image collections that are not representative of the variety and complexity of real world scenes. We introduce a new multi-illumination dataset of more than 1000 real scenes, each captured in high dynamic range and high resolution, under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: single-image illumination estimation, image relighting, and mixed-illuminant white balance.
Confinement and activity regulate bacterial motion in porous media

Understanding how bacteria move in porous media is critical to applications in healthcare, agriculture, environmental remediation, and chemical sensing. Recent work has demonstrated that E. coli, which moves by run-and-tumble dynamics in a homogeneous medium, exhibits a new form of motility when confined in a disordered porous medium: hopping-and-trapping motility, in which cells perform rapid, directed hops punctuated by intervals of slow, undirected trapping. Here, we use direct visualization to shed light on how these processes depend on pore-scale confinement and cellular activity. We find that hopping is determined by pore-scale confinement, and is independent of cellular activity; by contrast, trapping is determined by the competition between pore-scale confinement and cellular activity, as predicted by an entropic trapping model. These results thus help to elucidate the factors that regulate bacterial motion in porous media, and could help aid the development of new models of motility in heterogeneous environments.
Direct numerical simulation of supersonic turbulent boundary layers

The talk will present some initial results from the direct numerical simulation (DNS) of compressible turbulent boundary layers. We solve numerically the compressible Navier-Stokes equations using a method based on Spalart's transformation for the incompressible turbulent boundary layer. This allows the spatially developing boundary layer to be transformed to a calculation with periodic boundary conditions in the streamwise and spanwise directions. The equations are solved using Fourier expansions in the horizontal directions and B-splines in the wall-normal direction. The first simulation is at Mach 2.5 with a momentum thickness Reynolds number based on wall viscosity of R(sub theta(sup 1)) = 825. We are examining the physics of the compressible boundary layer using turbulence statistics and budget equations. The turbulence statistics include: rms (root mean square) and mean profiles, energy spectra, and two-point correlations. It is found that there are large density gradients which require significantly more resolution than the incompressible case.
Automation, Research Technology, and Researchers’ Trajectories: Evidence from Computer Science and Electrical Engineering

We examine how the introduction of a technology that automates research tasks influences the rate and type of researchers’ knowledge production. To do this, we leverage the unanticipated arrival of...
An Evaluation of Nephrology Literature for Transparency and Reproducibility Indicators: Cross-Sectional Review.

Introduction Reproducibility is critical to diagnostic accuracy and treatment implementation. Concurrent with clinical reproducibility, research reproducibility establishes whether the use of identical study materials and methodologies in replication efforts permits researchers to arrive at similar results and conclusions. In this study, we address this gap by evaluating nephrology literature for common indicators of transparent and reproducible research. Methods We searched the National Library of Medicine catalog to identify 36 MEDLINE-indexed, English-language nephrology journals. We randomly sampled 300 publications published between January 1, 2014, and December 31, 2018. Results Our search yielded 28,835 publications, of which we randomly sampled 300 publications. Of the 300 publications, 152 (50.7%) were publicly available, whereas 143 (47.7%) were restricted through paywall and 5 (1.7%) were inaccessible. Of the remaining 295 publications, 123 were excluded because they lack empirical data necessary for reproducibility. Of the 172 publications with empirical data, 43 (25%) reported data availability statements and 4 (2.3%) analysis scripts. Of the 71 publications analyzed for preregistration and protocol availability, 0 (0.0%) provided links to a protocol and 8 (11.3%) were preregistered. Conclusion Our study found that reproducible and transparent research practices are infrequently used by the nephrology research community. Greater efforts should be made by both funders and journals. In doing so, an open science culture may eventually become the norm rather than the exception.
Effects of Climate Modes on Interannual Variability of Upwelling in the Tropical Indian Ocean

AbstractThis paper investigates interannual variability of the tropical Indian Ocean (IO) upwelling through analyzing satellite and in situ observations from 1993 to 2016 using the conventional Sta...