cssi/SciSciGPT-SciSciNet huggingface dataset:
Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes

Strongly Rayleigh distributions are natural generalizations of product and determinantal probability distributions and satisfy the strongest form of negative dependence properties. We show that the “natural” Monte Carlo Markov Chain (MCMC) algorithm mixes rapidly in the support of a homogeneous strongly Rayleigh distribution. As a byproduct, our proof implies Markov chains can be used to efficiently generate approximate samples of a k-determinantal point process. This answers an open question raised by Deshpande and Rademacher (2010) which was studied recently by Kang (2013); Li et al. (2015); Rebeschini and Karbasi (2015).
Global Stability Analysis Using the Eigenfunctions of the Koopman Operator

We propose a novel operator-theoretic framework to study global stability of nonlinear systems. Based on the spectral properties of the so-called Koopman operator, our approach can be regarded as a natural extension of classic linear stability analysis to nonlinear systems. The main results establish the (necessary and sufficient) relationship between the existence of specific eigenfunctions of the Koopman operator and the global stability property of hyperbolic fixed points and limit cycles. These results are complemented with numerical methods which are used to estimate the region of attraction of the fixed point or to prove in a systematic way global stability of the attractor within a given region of the state space.
Sums of Weighted Differentiation Composition Operators

We solve an interpolation problem in $$A^p_\alpha $$ involving specifying a set of (possibly not distinct) n points, where the $$k{\text {th}}$$ derivative at the $$k{\text {th}}$$ point is up to a constant as large as possible for functions of unit norm. The solution obtained has norm bounded by a constant independent of the points chosen. As a direct application, we obtain a characterization of the order-boundedness of a sum of products of weighted composition and differentiation operators acting between weighted Bergman spaces. We also characterize the compactness of such operators that map a weighted Bergman space into the space of bounded analytic functions.
Analysis of the Pólya-Gamma block Gibbs sampler for Bayesian logistic linear mixed models

Abstract In this article, we construct a two-block Gibbs sampler using Polson et al.’s (2013) data augmentation technique for Bayesian logistic linear mixed models under proper priors. Furthermore, we prove the uniform ergodicity of this Gibbs sampler.
Smart inverter impacts on california distribution feeders with increasing pv penetration: A case study

The impacts of high PV penetration on distribution feeders have been well documented within the last decade. To mitigate these impacts, interconnection standards have been amended to allow PV inverters to regulate voltage locally. However, there is a deficiency of literature discussing how these inverters will behave on real feeders under increasing PV penetration. In this paper, we simulate several deployment scenarios of these inverters on a real California distribution feeder. We show that minimum and maximum voltage, tap operations, and voltage variability are improved due to the inverters. Line losses were shown to increase at high PV penetrations as a side effect. Furthermore, we find inverter sizing was shown to be important as PV penetration increased. Finally we show that increasing the number of inverters and removing the deadband from the Volt/VAr control curve improves the effectiveness.
Random attractors for the stochastic Benjamin–Bona–Mahony equation on unbounded domains

Abstract We prove the existence of a compact random attractor for the stochastic Benjamin–Bona–Mahony equation defined on an unbounded domain. This random attractor is invariant and attracts every pulled-back tempered random set under the forward flow. The asymptotic compactness of the random dynamical system is established by a tail-estimates method, which shows that the solutions are uniformly asymptotically small when space and time variables approach infinity.
Cole's Parametric Search Technique Made Practical.

Parametric search has been widely used in geometric algorithms. Cole’s improvement provides a way of saving a logarithmic factor in the running time over what is achievable using the standard method. Unfortunately, this improvement comes at the expense of making an already complicated algorithm even more complex; hence, this technique has been mostly of theoretical interest. In this paper, we provide an algorithm engineering framework that allows for the same asymptotic complexity to be achieved probabilistically in a way that is both simple and practical (i.e., suitable for actual implementation). The main idea of our approach is to show that a variant of quicksort, known as boxsort, can be used to drive comparisons, instead of using a sorting network, like the complicated AKS network, or an EREW parallel sorting algorithm, like the fairly intricate parallel mergesort algorithm. This results in a randomized optimization algorithm with a running time matching that of using Cole’s method, with high probability, while also being practical. We show how this results in practical implementations of some geometric algorithms utilizing parametric searching and provide experimental results that prove practicality of the method.
Job placement advisor based on turnaround predictions for HPC hybrid clouds

Abstract Several companies and research institutes are moving their CPU-intensive applications to hybrid High Performance Computing (HPC) cloud environments. Such a shift depends on the creation of software systems that help users decide where a job should be placed considering execution time and queue wait time to access on-premise clusters. Relying blindly on turnaround prediction techniques will affect negatively response times inside HPC cloud environments. This paper introduces a tool to make job placement decisions in HPC hybrid cloud environments taking into account the inaccuracy of execution and waiting time predictions. We used job traces from real supercomputing centers to run our experiments, and compared the performance between environments using real speedup curves. We also extended a state-of-the-art machine learning based predictor to work with data from the cluster scheduler. Our main findings are as follows: (i) depending on workload characteristics, there is a turning point where predictions should be disregarded in favor of a more conservative decision to minimize job turnaround times and (ii) scheduler data plays a key role in improving predictions generated with machine learning using job trace data—our experiments showed around 20% prediction accuracy improvements.
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits

The paper proposes a novel upper confidence bound (UCB) procedure for identifying the arm with the largest mean in a multi-armed bandit game in the fixed confidence setting using a small number of total samples. The procedure cannot be improved in the sense that the number of samples required to identify the best arm is within a constant factor of a lower bound based on the law of the iterated logarithm (LIL). Inspired by the LIL, we construct our confidence bounds to explicitly account for the infinite time horizon of the algorithm. In addition, by using a novel stopping time for the algorithm we avoid a union bound over the arms that has been observed in other UCBtype algorithms. We prove that the algorithm is optimal up to constants and also show through simulations that it provides superior performance with respect to the state-of-the-art.
Resonant interaction of trapped cold atoms with a magnetic cantilever tip

Magnetic resonance in an ensemble of laser-cooled trapped Rb atoms is excited using a microcantilever with a magnetic tip. The cantilever is mounted on a multilayer chip designed to capture, cool, and magnetically transport cold atoms. The coupling is observed by measuring the loss from a magnetic trap as the oscillating cantilever induces Zeeman-state transitions in the atoms. Interfacing cold atoms with mechanical devices could enable probing and manipulating atomic spins with nanometer spatial resolution and single-spin sensitivity, leading to new capabilities in quantum computation, quantum simulation, and precision sensing.
Rigid Ideals in Gorenstein Rings of Dimension One

Motivated by a conjecture of Huneke and Wiegand concerning torsion in tensor products of modules over local rings, we investigate the existence of ideals I in a one-dimensional Gorenstein local ring R satisfying $\text {Ext}^{1}_{R}(I,I)= 0$ .
Granular rheology: measuring boundary forces with laser-cut leaf springs

In granular physics experiments, it is a persistent challenge to obtain the boundary stress measurements necessary to provide full a rheological characterization of the dynamics. Here, we describe a new technique by which the outer boundary of a 2D Couette cell both confines the granular material and provides spatially- and temporally- resolved stress measurements. This key advance is enabled by desktop laser-cutting technology, which allows us to design and cut linearly-deformable walls with a specified spring constant. By tracking the position of each segment of the wall, we measure both the normal and tangential stress throughout the experiment. This permits us to calculate the amount of shear stress provided by basal friction, and thereby determine accurate values of μ (I).
Counting polynomials over finite fields with given root multiplicities

Abstract We give formulas for the number of polynomials over a finite field with given root multiplicities, in particular in cases when the formula is surprisingly simple (a power of q ). Besides this concrete interpretation, we also prove an analogous result on configuration spaces in the Grothendieck ring of varieties, suggesting new homological stabilization conjectures for configuration spaces of the plane.
Maximal vectors in Hilbert space and quantum entanglement

Let V be a norm-closed subset of the unit sphere of a Hilbert space H that is stable under multiplication by scalars of absolute value 1. A maximal vector (for V) is a unit vector ξ∈H whose distance to V is maximum d(ξ,V)=sup‖η‖=1d(η,V), d(ξ,V) denoting the distance from ξ to the set V. Maximal vectors generalize the maximally entangled unit vectors of quantum theory. In general, under a mild regularity hypothesis on V, there is a norm on H whose restriction to the unit sphere achieves its minimum precisely on V and its maximum precisely on the set of maximal vectors. This “entanglement-measuring norm” is unique. There is a corresponding “entanglement-measuring norm” on the predual of B(H) that faithfully detects entanglement of normal states. We apply these abstract results to the analysis of entanglement in multipartite tensor products H=H1⊗⋯⊗HN, and we calculate both entanglement-measuring norms. In cases for which dimHN is relatively large with respect to the others, we describe the set of maximal vectors in explicit terms and show that it does not depend on the number of factors of the Hilbert space H1⊗⋯⊗HN−1.
Adaptive smoothing algorithms for nonsmooth composite convex minimization

We propose an adaptive smoothing algorithm based on Nesterov's smoothing technique in Nesterov (Math Prog 103(1):127---152, 2005) for solving "fully" nonsmooth composite convex optimization problems. Our method combines both Nesterov's accelerated proximal gradient scheme and a new homotopy strategy for smoothness parameter. By an appropriate choice of smoothing functions, we develop a new algorithm that has the $$\mathcal {O}\left( \frac{1}{\varepsilon }\right) $$O1?-worst-case iteration-complexity while preserves the same complexity-per-iteration as in Nesterov's method and allows one to automatically update the smoothness parameter at each iteration. Then, we customize our algorithm to solve four special cases that cover various applications. We also specify our algorithm to solve constrained convex optimization problems and show its convergence guarantee on a primal sequence of iterates. We demonstrate our algorithm through three numerical examples and compare it with other related algorithms.
Regularity of path ideals of gap free graphs

Abstract In this paper we study the Castelnuovo–Mumford regularity of the path ideals of finite simple graphs. We find new upper bounds for various path ideals of gap free graphs. In particular we prove that the t-path ideals of gap free, claw free and whiskered- K 4 free graphs have linear minimal free resolutions for all t ≥ 3 .
On the computational complexity of high-dimensional Bayesian variable selection

We study the computational complexity of Markov chain Monte Carlo (MCMC) methods for high-dimensional Bayesian linear regression under sparsity constraints. We rst show that a Bayesian approach can achieve variable-selection consistency under relatively mild conditions on the design matrix. We then demonstrate that the statistical criterion of posterior concentration need not imply the computational desideratum of rapid mixing of the MCMC algorithm. By introducing a truncated sparsity prior for variable selection, we provide a set of conditions that guarantee both variable-selection consistency and rapid mixing of a particular Metropolis-Hastings algorithm. The mixing time is linear in the number of covariates up to a logarithmic factor. Our proof controls the spectral gap of the Markov chain by constructing a canonical path ensemble that is inspired by the steps taken by greedy algorithms for variable selection.
Another pedagogy for pure-integer Gomory

We present a version of GMI (Gomory mixed-integer) cuts in a way so that they are derived with respect to a “dual form” mixed-integer optimization problem and applied on the standard-form primal side as columns, using the primal simplex algorithm. This follows the general scheme of He and Lee, who did the case of Gomory pure-integer cuts. Our input mixed-integer problem is not in standard form, and so our cuts are derived rather differently from how they are normally derived. A convenient way to develop GMI cuts is from MIR (mixed-integer rounding) cuts, which are developed from 2-dimensional BMI (basic mixed-integer) cuts, which involve a nonnegative continuous variable and an integer variable. The non-negativity of the continuous variable is not the right tool for us, as our starting point (the “dual form” mixed-integer optimization problem) has no non-negativity. So we work out a different 2-dimensional starting point, a pair of somewhat arbitrary inequalities in one continuous and one integer variable. In the end, we follow the approach of He and Lee, getting now a finitely converging primal simplex column-generation algorithm for mixed-integer optimization problems.
Numerical Approximation of Space-Time Fractional Parabolic Equations

In this paper, we develop a numerical scheme for the space-time fractional parabolic equation, i.e., an equation involving a fractional time derivative and a fractional spatial operator. Both the initial value problem and the non-homogeneous forcing problem (with zero initial data) are considered. The solution operator $E(t)$ for the initial value problem can be written as a Dunford-Taylor integral involving the Mittag-Leffler function $e_{\alpha,1}$ and the resolvent of the underlying (non-fractional) spatial operator over an appropriate integration path in the complex plane. Here $\alpha$ denotes the order of the fractional time derivative. The solution for the non-homogeneous problem can be written as a convolution involving an operator $W(t)$ and the forcing function $F(t)$. We develop and analyze semi-discrete methods based on finite element approximation to the underlying (non-fractional) spatial operator in terms of analogous Dunford-Taylor integrals applied to the discrete operator. The space error is of optimal order up to a logarithm of $1/h$. The fully discrete method for the initial value problem is developed from the semi-discrete approximation by applying an exponentially convergent sinc quadrature technique to approximate the Dunford-Taylor integral of the discrete operator and is free of any time stepping. To approximate the convolution appearing in the semi-discrete approximation to the non-homogeneous problem, we apply a pseudo midpoint quadrature. This involves the average of $W_h(s)$, (the semi-discrete approximation to $W(s)$) over the quadrature interval. This average can also be written as a Dunford-Taylor integral. We first analyze the error between this quadrature and the semi-discrete approximation. To develop a fully discrete method, we then introduce sinc quadrature approximations to the Dunford-Taylor integrals for computing the averages.
Information-theoretic limits of Bayesian network structure learning

In this paper, we study the information-theoretic limits of learning the structure of Bayesian networks (BNs), on discrete as well as continuous random variables, from a finite number of samples. We show that the minimum number of samples required by any procedure to recover the correct structure grows as $\Omega(m)$ and $\Omega(k \log m + (k^2/m))$ for non-sparse and sparse BNs respectively, where $m$ is the number of variables and $k$ is the maximum number of parents per node. We provide a simple recipe, based on an extension of the Fano's inequality, to obtain information-theoretic limits of structure recovery for any exponential family BN. We instantiate our result for specific conditional distributions in the exponential family to characterize the fundamental limits of learning various commonly used BNs, such as conditional probability table based networks, gaussian BNs, noisy-OR networks, and logistic regression networks. En route to obtaining our main results, we obtain tight bounds on the number of sparse and non-sparse essential-DAGs. Finally, as a byproduct, we recover the information-theoretic limits of sparse variable selection for logistic regression.
Elastic Sheets, Phase Surfaces, and Pattern Universes

We connect the theories of the deformation of elastic surfaces and phase surfaces arising in the description of almost periodic patterns. In particular, we show parallels between asymptotic expansions for the energy of elastic surfaces in powers of the thickness h and the free energy for almost periodic patterns expanded in powers of e, the inverse aspect ratio of the pattern field. For sheets as well as patterns, the resulting energy can be expressed in terms of natural geometric invariants, the first and second fundamental forms of the elastic surface, respectively, the phase surface. We discuss various results for these energies and also address some of the outstanding questions. We extend previous work on point (in two dimensional) and loop (in three dimensional) disclinations and connect their topological indices with the condensation of Gaussian curvature of the phase surface. Motivated by this connection with the charge and spin of pattern quarks and leptons, we lay out an ambitious program to build a multiscale universe inspired by patterns in which the short (spatial and temporal) scales are given by a nearly periodic microstructure and whose macroscopic/slowly varying/averaged behaviors lead to a hierarchy of structures and features on much longer scales including analogs to quarks and leptons, dark matter, dark energy, and inflationary cosmology. One of our new findings is an interpretation of dark matter as the energy density in a pattern field. The associated gravitational forces naturally result in galactic rotation curves that are consistent with observations, while simultaneously avoiding some of the small-scale difficulties of the standard ΛCDM (cold dark matter) paradigm in cosmology.
Structured Sparse Regression via Greedy Hard-Thresholding

Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups. For very large datasets and under standard sparsity constraints, hard thresholding methods have proven to be extremely efficient, but such methods require NP hard projections when dealing with overlapping groups. In this paper, we show that such NP-hard projections can not only be avoided by appealing to submodular optimization, but such methods come with strong theoretical guarantees even in the presence of poorly conditioned data (i.e. say when two features have correlation $\geq 0.99$), which existing analyses cannot handle. These methods exhibit an interesting computation-accuracy trade-off and can be extended to significantly harder problems such as sparse overlapping groups. Experiments on both real and synthetic data validate our claims and demonstrate that the proposed methods are orders of magnitude faster than other greedy and convex relaxation techniques for learning with group-structured sparsity.
INTRINSICALLY LINKED GRAPHS WITH KNOTTED COMPONENTS

We construct a graph G such that any embedding of G into R3 contains a nonsplit link of two components, where at least one of the components is a nontrivial knot. Further, for any m < n we produce a graph H so that every embedding of H contains a nonsplit n component link, where at least m of the components are nontrivial knots. We then turn our attention to complete graphs and show that for any given n, every embedding of a large enough complete graph contains a 2-component link whose linking number is a nonzero multiple of n. Finally, we show that if a graph is a Cartesian product of the form G × K2, it is intrinsically linked if and only if G contains one of K5, K3,3 or K4,2 as a minor.
Memory Augmented Control Networks

Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory. But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to access memory and plan. To mitigate these challenges we introduce the Memory Augmented Control Network (MACN). The proposed network architecture consists of three main parts. The first part uses convolutions to extract features and the second part uses a neural network-based planning module to pre-plan in the environment. The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning. The performance of the network is evaluated in discrete grid world environments for path planning in the presence of simple and complex obstacles. We show that our network learns to plan and can generalize to new environments.
Trade Policy in American Economic History

This article reviews the broad changes in US trade policy over the course of the nation's history. Import tariffs have been the main instrument of trade policy and have had three main purposes: to ...
The impact of water scarcity on food, bioenergy and deforestation

We evaluate the impact of explicitly representing irrigated land and water scarcity in an economy‐wide model with and without a global carbon policy. The analysis develops supply functions of irrigable land from a water resource model for 282 river basins and applies them within a global economy‐wide model. The analysis reveals two key findings. First, explicitly representing irrigated land has a small impact on global food, bioenergy and deforestation outcomes. This is because this modification allows irrigated and rainfed land to expand in different proportions, which counters the effect of rising marginal costs for the expansion of irrigated land. Second, changes in water availability have small impacts on global food prices, bioenergy production, land use change and the overall economy, even with large‐scale (c. 150 exajoules) bioenergy production, due in part to endogenous irrigation and storage responses. However, representing water scarcity and changes in water availability can be important regionally, with relatively arid areas and/or areas with rapidly growing populations fully exhausting our estimated maximum irrigation capacity that allows for improved irrigation efficiency, lining of canals to limit water loss, and expanding storage to fully capture average annual water flows.
scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data

scRNA-seq dataset integration occurs in different contexts, such as the identification of cell type-specific differences in gene expression across conditions or species, or batch effect correction. We present scAlign, an unsupervised deep learning method for data integration that can incorporate partial, overlapping, or a complete set of cell labels, and estimate per-cell differences in gene expression across datasets. scAlign performance is state-of-the-art and robust to cross-dataset variation in cell type-specific expression and cell type composition. We demonstrate that scAlign reveals gene expression programs for rare populations of malaria parasites. Our framework is widely applicable to integration challenges in other domains.
The archaeal Dps nanocage targets kidney proximal tubules via glomerular filtration

Nature exploits cage-like proteins for a variety of biological purposes, from molecular packaging and cargo delivery to catalysis. These cage-like proteins are of immense importance in nanomedicine due to their propensity to self-assemble from simple identical building blocks to highly ordered architecture and the design flexibility afforded by protein engineering. However, delivery of protein nanocages to the renal tubules remains a major challenge because of the glomerular filtration barrier, which effectively excludes conventional size nanocages. Here, we show that DNA-binding protein from starved cells (Dps) - the extremely small archaeal antioxidant nanocage - is able to cross the glomerular filtration barrier and is endocytosed by the renal proximal tubules. Using a model of endotoxemia, we present an example of the way in which proximal tubule-selective Dps nanocages can limit the degree of endotoxin-induced kidney injury. This was accomplished by amplifying the endogenous antioxidant property of Dps with addition of a dinuclear manganese cluster. Dps is the first-in-class protein cage nanoparticle that can be targeted to renal proximal tubules through glomerular filtration. In addition to its therapeutic potential, chemical and genetic engineering of Dps will offer a nanoplatform to advance our understanding of the physiology and pathophysiology of glomerular filtration and tubular endocytosis.
On the Expressive Power of Deep Polynomial Neural Networks

We study deep neural networks with polynomial activations, particularly their expressive power. For a fixed architecture and activation degree, a polynomial neural network defines an algebraic map from weights to polynomials. The image of this map is the functional space associated to the network, and it is an irreducible algebraic variety upon taking closure. This paper proposes the dimension of this variety as a precise measure of the expressive power of polynomial neural networks. We obtain several theoretical results regarding this dimension as a function of architecture, including an exact formula for high activation degrees, as well as upper and lower bounds on layer widths in order for deep polynomials networks to fill the ambient functional space. We also present computational evidence that it is profitable in terms of expressiveness for layer widths to increase monotonically and then decrease monotonically. Finally, we link our study to favorable optimization properties when training weights, and we draw intriguing connections with tensor and polynomial decompositions.
Women and minorities in the computer professions

Generally, while full equity may not have been achieved by women and minorities in computer occupations, the belief has been that their employment status was better and conditions were more favorable for full utilization of skills due to the relative youth of the computer field. The federal government, in particular, has been involved in computers since their beginnings, and in addition has taken a stance in support of both equal employment opportunity (EEO) and affirmative action. 19 This position has been backed by laws (e.g., Civil Rights Act of 1964) and Executive Orders (e.g., E.O. 10590 and E.O. 11375) prohibiting discrimination on the basis of race, color, religion, sex, national origin, or age in federal employment. Accordingly, it is reasonable to assume that women and minorities would have more opportunities within the federal government, especially within the federal computer-related professions.
Data-driven discovery of coordinates and governing equations

The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics, balancing model complexity with descriptive ability, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam's razor for model discovery. However, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work, we design a custom deep autoencoder network to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. This method places the discovery of coordinates and models on an equal footing.
Modulation of Voltage-Gated Sodium Channel Activity in Human Dorsal Root Ganglion Neurons by Herpesvirus Quiescent Infection

ABSTRACT The molecular mechanisms of pain associated with alphaherpesvirus latency are not clear. We hypothesize that the voltage-gated sodium channels (VGSC) on the dorsal root ganglion (DRG) neurons controlling electrical impulses may have abnormal activity during latent viral infection and reactivation. We used herpes simplex virus 1 (HSV-1) to infect the human DRG-derived neuronal cell line HD10.6 in order to study the establishment and maintenance of viral latency, viral reactivation, and changes in the functional expression of VGSCs. Differentiated cells exhibited robust tetrodotoxin (TTX)-sensitive sodium currents, and acute infection significantly reduced the functional expression of VGSCs within 24 h and completely abolished VGSC activity within 3 days. A quiescent state of infection mimicking latency can be achieved in the presence of acyclovir (ACV) for 7 days followed by 5 days of ACV washout, and then the viruses can remain dormant for another 3 weeks. It was noted that during the establishment of HSV-1 latency, the loss of VGSC activity caused by HSV-1 infection could not be blocked by ACV treatment. However, neurons with continued ACV treatment for another 4 days showed a gradual recovery of VGSC functional expression. Furthermore, the latently infected neurons exhibited higher VGSC activity than controls. The overall regulation of VGSCs by HSV-1 during quiescent infection was proved by increased transcription and possible translation of Nav1.7. Together, these observations demonstrated a very complex pattern of electrophysiological changes during HSV infection of DRG neurons, which may have implications for understanding of the mechanisms of virus-mediated pain linked to latency and reactivation. IMPORTANCE The reactivation of herpesviruses, most commonly varicella-zoster virus (VZV) and pseudorabies virus (PRV), may cause cranial nerve disorder and unbearable pain. Clinical studies have also reported that HSV-1 causes postherpetic neuralgia and chronic occipital neuralgia in humans. The current work meticulously studies the functional expression profile changes of VGSCs during the processes of HSV-1 latency establishment and reactivation using human dorsal root ganglion-derived neuronal HD10.6 cells as an in vitro model. Our results indicated that VGSC activity was eliminated upon infection but steadily recovered during latency establishment and that latent neurons exhibited even higher VGSC activity. This finding advances our knowledge of how ganglion neurons generate uncharacteristic electrical impulses due to abnormal VGSC functional expression influenced by the latent virus.
Movable intersection and bigness criterion

In this note, we give a Morse-type bigness criterion for the difference of two pseudo-effective (1, 1)-classes by using movable intersections. As an application, we give a Morse-type bigness criterion for the difference of two movable (n − 1, n − 1)-classes.
Handover Optimality in Heterogeneous Networks

This paper introduces a new theoretical framework for optimal h andover procedures in heterogeneous networks by devising the novel fractional Gittins indices, which are dynamical priorities whose values can be statically associated to the decision states of evolving processes representing handover alternatives. The simple policy of activating at any time the one process currently at highest priority optimizes the bandwidth of a handover, if all other inactive processes remain idle. However, numerical evidence shows that in practice this condition can be relaxed for a wide range of hand over models, because the bandwidth actually achieved by the policy never deviates for more than 12% from the optimally achievable bandwidth and remains in median within a deviation of 2% from this optimum.
Certum Quod Factum: How Formal Models Contribute to the Theoretical and Empirical Robustness of Organization Theory:

The aim of this commentary is to show how the use of formal models—both closed form and computational—can improve theory development and theory testing in organization theory. I also provide practi...
Defined Cell Types in Superior Colliculus Make Distinct Contributions to Prey Capture Behavior in the Mouse

The superior colliculus (SC) plays a highly conserved role in visual processing and mediates visual orienting behaviors across species, including both overt motor orienting [1, 2] and orienting of attention [3, 4]. To determine the specific circuits within the superficial superior colliculus (sSC) that drive orienting and approach behavior toward appetitive stimuli, we explored the role of three genetically defined cell types in mediating prey capture in mice. Chemogenetic inactivation of two classically defined cell types, the wide-field (WF) and narrow-field (NF) vertical neurons, revealed that they are involved in distinct aspects of prey capture. WF neurons were required for rapid prey detection and distant approach initiation, whereas NF neurons were required for accurate orienting during pursuit as well as approach initiation and continuity. In contrast, prey capture did not require parvalbumin-expressing (PV) neurons that have previously been implicated in fear responses. The visual coding and projection targets of WF and NF cells were consistent with their roles in prey detection versus pursuit, respectively. Thus, our studies link specific neural circuit connectivity and function with stimulus detection and orienting behavior, providing insight into visuomotor and attentional mechanisms mediated by superior colliculus.
Controllability governs the balance between Pavlovian and instrumental action selection.

A Pavlovian bias to approach reward-predictive cues and avoid punishment-predictive cues can conflict with instrumentally-optimal actions. Here, we propose that the brain arbitrates between Pavlovian and instrumental control by inferring which is a better predictor of reward. The instrumental predictor is more flexible; it can learn values that depend on both stimuli and actions, whereas the Pavlovian predictor learns values that depend only on stimuli. The arbitration theory predicts that the Pavlovian predictor will be favored when rewards are relatively uncontrollable, because the additional flexibility of the instrumental predictor is not useful. Consistent with this hypothesis, we find that the Pavlovian approach bias is stronger under low control compared to high control contexts. Pavlovian and instrumentally driven actions often conflict when determining the best outcome. Here, the authors present an arbitration theory supported by human behavioral data where Pavlovian predictors drive action selection in an uncontrollable environment, while more flexible instrumental prediction dominates under conditions of high controllability.
Financial Risk Management in Agriculture : Analyzing Data from a New Module of the Global Findex Database

The ability to manage financial risk is especially important for people earning their living through agriculture. Many farmers only get paid once or twice a year, and households need to stretch their earnings across the year by saving or borrowing money. Moreover, agricultural production faces a variety of risks related to both production and markets because of their exposure to weather and disease shocks. Households engaged in agriculture may thus especially benefit from financial inclusion—access to and use of formal financial services. This paper explores the topic of financial risk management in agriculture—how adults who rely on growing crops or raising livestock as their household's main source of income manage financial risk and use financial services. The paper summarizes new data based on a nationally representative survey of about 15,000 adults in 15 lower-middle- and low-income Sub-Saharan African economies collected as part of the World Bank's Global Findex database. The majority of these adults reported suffering a bad harvest or significant livestock loss in the past five years, and most bear the entire financial risk of such a loss. Most adults in agricultural households lack the financial tools -- such as insurance, accounts, savings, and credit -- that could help them manage financial risks.
Demonstration of a tunable antenna-coupled intersubband terahertz (TACIT) mixer

A fast, voltage-tunable terahertz mixer based on the intersubband transition of a high-mobility 2-dimensional electron gas has been fabricated from a single 40 nm GaAs-AlGaAs square quantum well heterostructure. The device is called a Tunable Antenna-Coupled Intersubband Terahertz mixer and shows tunability of the detection frequency from 2.52 to 3.11 THz with small (<1 V) top gate and bottom gate voltage biases. Mixing at 2.52 THz has been observed at 60 K with a −3dB intermediate frequency bandwidth exceeding 6 GHz.
Human CardioChimeras: Creation of a Novel “Next‐Generation” Cardiac Cell

Background CardioChimeras produced by fusion of murine c‐kit+ cardiac interstitial cells with mesenchymal stem cells promote superior structural and functional recovery in a mouse model of myocardi...
A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

During the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~ 850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis. We found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K. Using high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level.
Pattern-recognition program for analysis of colon myoelectric and pressure data.

A pattern-recognition program was developed which emulates visual scoring of colonic myoelectric and pressure recordings. It smoothes digitized data with a moving average filter, computes difference scores between successive groups of three data points, and uses the signs of these difference scores to detect the beginning and end of waves. Adjacent waves are merged if their means are closer than 1.67 times the sum of their standard deviations, and amplitude and duration criteria are used to exclude nonsignificant waves. When compared to four experienced human scorers on randomly selected records, the program agreed as well with the human scorers as they agreed with each other, and it approached the level of agreement of these observers with themselves when they were asked to rescore the same records blindly four to six weeks later. Human scorers agreed with themselves on 36–71% of myoelectric slow waves and on 42–88% of pressure waves, compared to 100% test-retest reliability for the pattern-recognition program. Frequency histograms of the duration of waves detected by the pattern-recognition program differed from the spectra generated by the fast Fourier transform (FFT) method. This pattern-recognition program provides an alternative to spectral analysis for the reliable and objective quantification of colonic myoelectric slow waves and pressure waves.
Sharp space-time regularity of the solution to stochastic heat equation driven by fractional-colored noise

In this article, we study the following stochastic heat equation ∂tu(t,x)=Lu(t,x)+Ḃ, u(0,x)=0, 0≤t≤T, x∈Rd, where L is the generator of a Levy process X in Rd, B is a fractional-colored Gaussian n...
In Search of Large Signals at the Cosmological Collider

We look for oscillating signals in the primordial bispectrum from new physics heavy particles which are visibly large for next generation large scale structures (LSS) survey. We show that in ordinary inflation scenarios where a slow-rolling inflaton generates density fluctuations and with no breaking of scale invariance or spacetime symmetry, there exist no naturally large signals unless the rolling inflaton generates a parity-odd chemical potential for the heavy particles. We estimate the accessibility of this signal through observations. While current CMB data are already sensitive in the most optimistic scenario, future probes, including LSS survey and 21 cm observation, can cover interesting regions of the model space.
Dielectric embedding GW for weakly coupled molecule-metal interfaces

Molecule-metal interfaces have a broad range of applications in nanoscale materials science. Accurate characterization of their electronic structures from first-principles is key in understanding material and device properties. The GW approach within many-body perturbation theory is the state-of-the-art and can in principle yield accurate quasiparticle energy levels and interfacial level alignments that are in quantitative agreement with experiments. However, the interfaces are large heterogeneous systems that are currently challenging for first-principles GW calculations. In this work, we develop a GW-based dielectric embedding approach for molecule-metal interfaces, significantly reducing the computational cost of direct GW without sacrificing the accuracy. To be specific, we perform explicit GW calculations only in the simulation cell of the molecular adsorbate, in which the dielectric effect of the metallic substrate is embedded. This is made possible via a real-space truncation of the substrate polarizability and the use of the interface plasma frequency in the adsorbate GW calculation. Here, we focus on the level alignment at weakly coupled molecule-metal interfaces, i.e., the energy difference between a molecular frontier orbital resonance and the substrate Fermi level. We demonstrate our method and assess a few GW-based approximations using two well-studied systems, benzene adsorbed on the Al (111) and on the graphite (0001) surfaces.
Comparison of Bayesian and Frequentist Multiplicity Correction for Testing Mutually Exclusive Hypotheses Under Data Dependence

The problem of testing mutually exclusive hypotheses with dependent test statistics is considered. Bayesian and frequentist approaches to multiplicity control are studied and compared to help gain understanding as to the effect of test statistic dependence on each approach. The Bayesian approach is shown to have excellent frequentist properties and is argued to be the most effective way of obtaining frequentist multiplicity control, without sacrificing power, when there is considerable test statistic dependence.
A Model of Scientific Communication

We propose a positive model of empirical science in which an analyst makes a report to an audience after observing some data. Agents in the audience may differ in their beliefs or objectives, and may therefore update or act differently following a given report. We contrast the proposed model with a classical model of statistics in which the report directly determines the payoff. We identify settings in which the predictions of the proposed model differ from those of the classical model, and seem to better match practice.
Minimizing a Sum of Clipped Convex Functions

We consider the problem of minimizing a sum of clipped convex functions. Applications of this problem include clipped empirical risk minimization and clipped control. While the problem of minimizing the sum of clipped convex functions is NP-hard, we present some heuristics for approximately solving instances of these problems. These heuristics can be used to find good, if not global, solutions, and appear to work well in practice. We also describe an alternative formulation, based on the perspective transformation, that makes the problem amenable to mixed-integer convex programming and yields computationally tractable lower bounds. We illustrate our heuristic methods by applying them to various examples and use the perspective transformation to certify that the solutions are relatively close to the global optimum. This paper is accompanied by an open-source implementation.