cssi/SciSciGPT-SciSciNet huggingface dataset:
The Empirical Case For 10 GeV Dark Matter

Abstract In this article, I summarize and discuss the body of evidence which has accumulated in favor of dark matter in the form of approximately 10-GeV particles. This evidence includes the spectrum and angular distribution of γ-rays from the Galactic Center, the synchrotron emission from the Milky Way’s radio filaments, the diffuse synchrotron emission from the Inner Galaxy (the “WMAP Haze”) and low-energy signals from the direct detection experiments DAMA/LIBRA, CoGeNT and CRESST-II. This collection of observations can be explained by a relatively light dark matter particle with an annihilation cross section consistent with that predicted for a simple thermal relic ( σv ∼ 10 −26 cm 3 /s) and with a distribution in the halo of the Milky Way consistent with that predicted from simulations. Astrophysical explanations for the γ-ray and synchrotron signals, in contrast, have not been successful in accommodating these observations. Similarly, the phase of the annual modulation observed by DAMA/LIBRA (and now supported by CoGeNT) is inconsistent with all known or postulated modulating backgrounds, but are in good agreement with expectations for dark matter scattering. This scenario is consistent with all existing indirect and collider constraints, as well as the constraints placed by CDMS. Consistency with xenon-based experiments can be achieved if the response of liquid xenon to very low-energy nuclear recoils is somewhat suppressed relative to previous evaluations, or if the dark matter possesses different couplings to protons and neutrons.
Differentially Private Matrix Completion Revisited

We provide the first provably joint differentially private algorithm with formal utility guarantees for the problem of user-level privacy-preserving collaborative filtering. Our algorithm is based on the Frank-Wolfe method, and it consistently estimates the underlying preference matrix as long as the number of users $m$ is $\omega(n^{5/4})$, where $n$ is the number of items, and each user provides her preference for at least $\sqrt{n}$ randomly selected items. Along the way, we provide an optimal differentially private algorithm for singular vector computation, based on the celebrated Oja's method, that provides significant savings in terms of space and time while operating on sparse matrices. We also empirically evaluate our algorithm on a suite of datasets, and show that it consistently outperforms the state-of-the-art private algorithms.
Maximal Ferrers Diagram Codes: Constructions and Genericity Considerations

This paper investigates the construction of rank-metric codes with specified Ferrers diagram shapes. These codes play a role in the multilevel construction for subspace codes. A conjecture from 2009 provides an upper bound for the dimension of a rank-metric code with given specified Ferrers diagram shape and rank distance. While the conjecture in its generality is wide open, several cases have been established in the literature. This paper contributes further cases of Ferrers diagrams and ranks for which the conjecture holds true. In addition, the proportion of maximal Ferrers diagram codes within the space of all rank-metric codes with the same shape and dimension is investigated. Special attention is being paid to MRD codes. It is shown that for growing field size the limiting proportion depends highly on the Ferrers diagram. For instance, for $[m\times 2]$ -MRD codes with rank 2 this limiting proportion is close to $1/e$ .
Boundedness, compactness, and invariant norms for Banach cocycles over hyperbolic systems

We consider group-valued cocycles over dynamical systems with hyperbolic behavior. The base system is either a hyperbolic diffeomorphism or a mixing subshift of finite type. The cocycle $A$ takes values in the group of invertible bounded linear operators on a Banach space and is Holder continuous. We consider the periodic data of $A$, i.e. the set of its return values along the periodic orbits in the base. We show that if the periodic data of $A$ is uniformly quasiconformal or bounded or contained in a compact set, then so is the cocycle. Moreover, in the latter case the cocycle is isometric with respect to a Holder continuous family of norms. We also obtain a general result on existence of a measurable family of norms invariant under a cocycle.
Homogeneous solutions to the 3D Euler system

We study stationary homogeneous solutions to the 3D Euler equation. The problem is motivated be recent exclusions of self-similar blowup for Euler and its relation to Onsager conjecture and intermittency. We reveal several new classes of solutions and prove rigidity properties in specific categories of genuinely 3D solutions. In particular, irrotational solutions are characterized by vanishing of the Bernoulli function; and tangential flows are necessarily 2D axisymmetric pure rotations. In several cases solutions are excluded altogether. The arguments reveal geodesic features of the Euler flow on the sphere. We further show that in the case when homogeneity corresponds to the Onsager-critical state, the anomalous energy flux at the singularity vanishes, which is suggestive of absence of extreme $0$-dimensional intermittencies in dissipative flows.
Shifts of a measurable function and criterion of p-integrability

Abstract It is shown that two conditions f ( a + ⋅ ) − f ( ⋅ ) ∈ L p ( R ) , and ( sin b ⋅ ) f ( ⋅ ) ∈ L p ( R ) guarantee f ∈ L p ( R ) , 1 ≤ p ∞ , if and only if a b is not in ( π Z ) .
COMPUTING WORD LENGTH IN ALTERNATE PRESENTATIONS OF THOMPSON'S GROUP F

We introduce a new method for computing the word length of an element of Thompson's group F with respect to a "consecutive" generating set of the form Xn = {x0,x1, …,xn}, which is a subset of the standard infinite generating set for F. We use this method to show that (F, Xn) is not almost convex, and has pockets of increasing, though bounded, depth dependent on n.
AGT and the Segal-Sugawara construction

The conjectures of Alday et al. [Lett. Math. Phys. 91, 167–197 (2010)] and their generalizations have been mathematically formulated as the existence of an action of a W-algebra on the cohomology or K-theory of the instanton moduli space, together with a Whitaker vector [A. Braverman et al., e-print arXiv:1406.2381 (2014); D. Maulik and A. Okounkov, e-print arXiv:1211.1287 (2012), pp. 1–276; O. Schiffmann and E. Vasserot, Publ. Math. Inst. Hautes Etud. Sci. 118, 213–342 (2013)]. However, the original conjectures also predict intertwining properties with the natural higher rank version of the “Ext1 operator” which was previously studied by Okounkov and the author in Carlsson and Okounkov [Duke Math. J. 161, 1797–1815 (2012)], a result which is now sometimes referred to as AGT in rank one [A. Alba et al., Lett. Math. Phys. 98, 33–64 (2011); M. Pedrini et al., J. Geom. Phys. 103, 43–89 (2016)]. Physically, this corresponds to incorporating matter in the Nekrasov partition functions, an obviously important fe...
Tensors of nonnegative rank two

Abstract A nonnegative tensor has nonnegative rank at most 2 if and only if it is supermodular and has flattening rank at most 2. We prove this result, then explore the semialgebraic geometry of the general Markov model on phylogenetic trees with binary states, and comment on possible extensions to tensors of higher rank.
Clockwork Convnets for Video Semantic Segmentation

Recent years have seen tremendous progress in still-image segmentation; however the naive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: (1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and (2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of “clockwork” convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets.
Theory of the GMM Kernel

In web search, data mining, and machine learning, two popular measures of data similarity are the cosine and the resemblance (the latter is for binary data). In this study, we develop theoretical results for both the cosine and the GMM (generalized min-max) kernel, which is a generalization of the resemblance. GMM has direct applications in machine learning as a positive definite kernel and can be efficiently linearized via probabilistic hashing to handle big data. Owing to its discrete nature, the hashed values can also be used to build hash tables for efficient near neighbor search. We prove the theoretical limit of GMM and the consistency result, assuming that the data follow an elliptical distribution, which is a general family of distributions and includes the multivariate normal and t-distribution as special cases. The consistency result holds as long as the data have bounded first moment (an assumption which typically holds for data commonly encountered in practice). Furthermore, we establish the asymptotic normality of GMM. We also prove the limit of cosine under elliptical distributions. In comparison, the consistency of GMM requires much weaker conditions. For example, when data follow a t-distribution with ν degrees of freedom, GMM typically provides a better estimate of similarity than cosine when ν
Learning granger causality for hawkes processes

Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes -- Hawkes process. According to the relationship between Hawkes process's impact function and its Granger causality graph, our model represents impact functions using a series of basis functions and recovers the Granger causality graph via group sparsity of the impact functions' coefficients. We propose an effective learning algorithm combining a maximum likelihood estimator (MLE) with a sparsegrouplasso (SGL) regularizer. Additionally, the flexibility of our model allows to incorporate the clustering structure event types into learning framework. We analyze our learning algorithm and propose an adaptive procedure to select basis functions. Experiments on both synthetic and real-world data show that our method can learn the Granger causality graph and the triggering patterns of the Hawkes processes simultaneously.
Two-term trace estimates for relativistic stable processes

We prove trace estimates for the relativistic α-stable process extending the result of Banuelos and Kulczycki (2008) in the stable case.
Sum-Networks From Incidence Structures: Construction and Capacity Analysis

A sum-network is an instance of a function computation problem over a directed acyclic network, in which each terminal node wants to compute the sum over a finite field of the information observed at all the source nodes. Many characteristics of the well-studied multiple unicast network communication problem also hold for sum-networks, due to a known reduction between the two problems. In this paper, we describe an algorithm to construct families of sum-network instances using incidence structures. The computation capacity of several of these sum-network families is evaluated. Unlike the coding capacity of a multiple unicast problem, the computation capacity of sum-networks depends on the characteristic of the finite field over which the sum is computed. This dependence is very strong; we show examples of sum-networks that have a rate-1 solution over one characteristic but a rate close to zero over a different characteristic. In addition, a sum-network can have arbitrarily different computation capacities for different alphabets.
Unitary equivalence of normal matrices over topological spaces

Let A and B be normal matrices with coefficients that are continuous complex-valued functions on a topological space X that has the homotopy type of a CW complex, and suppose these matrices have the same distinct eigenvalues at each point of X. We use obstruction theory to establish a necessary and sufficient condition for A and B to be unitarily equivalent. We also determine bounds on the number of possible unitary equivalence classes in terms of cohomological invariants of X.
Nonrigidity of a class of two dimensional surfaces with positive curvature and planar points

The problem considered here deals with the bendings of an orientable, embedded surface S in R. We assume that S has a vanishing first homology group, that S is a C compact surface with boundary, that it has positive curvature except at finitely many planar points in S. The main result states that for any k ∈ Z, S has nontrivial infinitesimal bendings of class C. That is, there is a C function U : S −→ R such that the first fundamental form of the deformation surface Sσ = {p + σU(p), p ∈ S} satisfies dS σ = dS 2 + O(σ) as σ → 0, where σ is a real parameter. Furthermore, Sσ is not obtained from S through a rigid motion of R . A consequence of this result is the nonrigidity of S in the following sense. Any given ǫneighborhood of S (for the C topology) contains isometric surfaces that are not congruent. The study of bendings of surfaces in R has a rich history and many physical applications. In particular, it is used in the theory of elastic shells. We refer to the survey article of Sabitov ([14]) and the references therein. The results of this paper are also related to those contained in the following papers [2], [3], [4], [5], [6], [8], [10], [12], [19], [15] Our approach is through the study of the associated (complex) field of asymptotic directions on S. We prove that such a vector field generates an integrable structure on S. We reformulate the equations for the bending field U in terms of a Bers-Vekua type equation (with singularities). Then use recent results about the solvability of such equations to construct the bending fields.
Motivic infinite cyclic covers

Abstract We associate with an infinite cyclic cover of a punctured neighborhood of a simple normal crossing divisor on a complex quasi-projective manifold (under certain finiteness conditions) an element in the Grothendieck ring K 0 ( Var C μ ˆ ) , which we call motivic infinite cyclic cover, and show its birational invariance. Our construction provides a unifying approach for the Denef–Loeser motivic Milnor fiber of a complex hypersurface singularity germ, and the motivic Milnor fiber of a rational function, respectively.
Spatio-Temporal Change of Support with Application to American Community Survey Multi-Year Period Estimates

We present hierarchical Bayesian methodology to perform spatio-temporal change of support (COS) for survey data with Gaussian sampling errors. This methodology is motivated by the American Community Survey (ACS), which is an ongoing survey administered by the US Census Bureau that provides timely information on several key demographic variables. The ACS has published 1-year, 3-year, and 5-year period estimates, and margins of errors, for demographic and socio-economic variables recorded over predefined geographies. The spatio-temporal COS methodology considered here provides data users with a way to estimate ACS variables on customized geographies and time periods while accounting for sampling errors. Additionally, 3-year ACS period estimates are to be discontinued, and this methodology can provide predictions of ACS variables for 3-year periods given the available period estimates. The methodology is based on a spatio-temporal mixed-effects model with a low-dimensional spatio-temporal basis function representation, which provides multi-resolution estimates through basis function aggregation in space and time. This methodology includes a novel parameterization that uses a target dynamical process and recently proposed parsimonious Moran's I propagator structures. Our approach is demonstrated through two applications using public-use ACS estimates and is shown to produce good predictions on a hold-out set of 3-year period estimates. Copyright © 2015 John Wiley & Sons, Ltd.
A multiscale sub-linear time Fourier algorithm for noisy data

Abstract We extend the recent sparse Fourier transform algorithm of [1] to the noisy setting, in which a signal of bandwidth N is given as a superposition of k ≪ N frequencies and additive random noise. We present two such extensions, the second of which exhibits a form of error-correction in its frequency estimation not unlike that of the β-encoders in analog-to-digital conversion [2] . On k-sparse signals corrupted with additive complex Gaussian noise, the algorithm runs in time O ( k log ⁡ ( k ) log ⁡ ( N / k ) ) on average, provided the noise is not overwhelming. The error-correction property allows the algorithm to outperform FFTW [3] , a highly optimized software package for computing the full discrete Fourier transform, over a wide range of sparsity and noise values.
Effective conductivity of spiral and other radial symmetric assemblages

Abstract Assemblies of circular inclusions with spiraling laminate structure inside them are studied, such as spirals with inner inclusions, Spirals with Shells, assemblies of “wheels” – structures from laminates with radially dependent volume fractions, complex axisymmetric three-dimensional micro-geometries called Connected Hubs and Spiky Balls. The described assemblages model structures met in rock mechanics, biology, etc. The classical effective medium theory coupled with hierarchical homogenization is used. It is found that fields in spiral assemblages satisfy a coupled system of two second order differential equations, rather than a single differential equation; a homogeneous external field applied to the assembly is transformed into a rotated homogeneous field inside of the inclusions. The effective conductivity of the two-dimensional Star assembly is equivalent to that of Hashin–Shtrikman coated circles, but the conductivity of analogous three-dimensional Spiky Ball is different from the conductivity of coated sphere geometry.
Holonomic modules over Cherednik algebras, I

Abstract The goal of this paper is to generalize several basic results from the theory of D -modules to the representation theory of rational Cherednik algebras. We relate characterizations of holonomic modules in terms of singular support and Gelfand–Kirillov dimension. We study pullback, pushforward, and dual on the derived category of (holonomic) Cherednik modules for certain classes of maps between varieties. We prove, in the case of generic parameters for the rational Cherednik algebra, that pushforward with respect to an open affine inclusion preserves holonomicity.
Characterizing Co-located Datacenter Workloads: An Alibaba Case Study

Warehouse-scale cloud datacenters co-locate workloads with different and often complementary characteristics for improved resource utilization. To better understand the challenges in managing such intricate, heterogeneous workloads while providing quality-assured resource orchestration and user experience, we analyze Alibaba's co-located workload trace, the first publicly available dataset with precise information about the category of each job. Two types of workload---long-running, user-facing, containerized production jobs, and transient, highly dynamic, non-containerized, and non-production batch jobs---are running on a shared cluster of 1313 machines. Our multifaceted analysis reveals insights that we believe are useful for system designers and IT practitioners working on cluster management systems.
A realization for a $\mathbb{Q}$-Hermitian variation of Hodge structure of Calabi-Yau type with real multiplication

We show that the $\mathbb{Q}$-descents of the canonical $\mathbb{R}$-variation of Hodge structure of Calabi-Yau type over a tube domain of type $A$ can be realized as sub-variations of Hodge structure of certain $\mathbb{Q}$-variations of Hodge structure which are naturally associated to abelian varieties of (generalized) Weil type.
The Hilbert Scheme of the Diagonal in a Product of Projective Spaces

The diagonal in a product of projective spaces is cut out by the ideal of 2×2-minors of a matrix of unknowns. The multigraded Hilbert scheme which classifies its degenerations has a unique Borel-fixed ideal. This Hilbert scheme is generally reducible, and its main component is a compactification of PGL(d) n /PGL(d). For n = 2, we recover the manifold of complete collineations. For projective lines, we obtain a novel space of trees that is irreducible but singular. All ideals in our Hilbert scheme are radical. We also explore connections to affine buildings and Deligne schemes.
The fundamental class of smooth Kuranishi atlases with trivial isotropy

Kuranishi structures were introduced in the 1990s by Fukaya and Ono for the purpose of assigning a virtual cycle to moduli spaces of pseudoholomorphic curves that cannot be regularized by geometric methods. Their core idea was to build such a cycle by patching local finite dimensional reductions. The first sections of this paper discuss topological, algebraic and analytic challenges that arise in this program. We then develop a theory of Kuranishi atlases and cobordisms that transparently resolves these challenges, for simplicity concentrating on the case of trivial isotropy. In this case, we assign to a cobordism class of additive weak Kuranishi atlases both a virtual moduli cycle (VMC — a cobordism class of smooth manifolds) and a virtual fundamental class (VFC — a Cech homology class). We, moreover, show that such Kuranishi atlases exist on simple Gromov–Witten moduli spaces and develop the technical results in a manner that easily transfers to more general settings.
MCHM acts as a hydrotrope, altering the balance of metals in yeast

While drugs and other industrial chemicals are routinely studied to assess risks, many widely used chemicals have not been thoroughly evaluated. One such chemical, 4-methylcyclohexane methanol (MCHM), is an industrial coal-cleaning chemical that contaminated the drinking water supply in Charleston, WV, USA in 2014. While a wide range of ailments was reported following the spill, little is known about the molecular effects of MCHM exposure. We used the yeast model to explore the impacts of MCHM on cellular function. Exposure to MCHM dramatically altered the yeast transcriptome and the balance of metals in yeast. Underlying genetic variation in the response to MCHM, transcriptomics and, mutant analysis uncovered the role of the metal transporters, Arn2 and Yke4, to MCHM response. Expression of Arn2, which is involved in iron uptake, was lower in MCHM-tolerant yeast and loss of Arn2 further increased MCHM tolerance. Genetic variation within Yke4, an ER zinc transporter, also mediated response to MCHM, and loss of Yke4 decreased MCHM tolerance. The addition of zinc to MCHM-sensitive yeast rescued growth inhibition. In vitro assays demonstrated that MCHM acted as a hydrotrope and prevented protein interactions, while zinc induced the aggregation of proteins. We hypothesized that MCHM altered the structures of extracellular domains of proteins, and the addition of zinc stabilized the structure to maintain metal homeostasis in yeast exposed to MCHM.
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study

We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization, saliency maps (the focus of attention), and a more recent explanation type, reward-decomposition bars (predictions of future types of rewards). We designed a 124 participant, four-treatment experiment to compare participants' mental models of an RL agent in a simple Real-Time Strategy (RTS) game. Our results show that the combination of both saliency and reward bars were needed to achieve a statistically significant improvement in mental model score over the control. In addition, our qualitative analysis of the data reveals a number of effects for further study.
Jordan operator algebras revisited

Jordan operator algebras are norm-closed spaces of operators on a Hilbert space with a^2 in A for all a in A. In two recent papers by the authors and Neal, a theory for these spaces was developed. It was shown there that much of the theory of associative operator algebras, in particularly surprisingly much of the associative theory from several recent papers of the first author and coauthors, generalizes to Jordan operator algebras. In the present paper we complete this task, giving several results which generalize the associative case in these papers, relating to unitizations, real positivity, hereditary subalgebras, and a couple of other topics. We also solve one of the three open problems stated at the end of our earlier joint paper on Jordan operator algebras.
The Role of Financial Reporting in Resolving Uncertainty about Corporate Investment Opportunities

Abstract This discussion of the survey of the literature on financial reporting and corporate investment by Roychowdhury et al. (2019) focuses on an area in this literature that has received less attention—how financial reporting helps managers allocate capital across investment opportunities within their firms in the presence of uncertainty. We discuss the theory on investment under uncertainty and offer suggestions for future research.
Decentralization of Multiagent Policies by Learning What to Communicate

Effective communication is required for teams of robots to solve sophisticated collaborative tasks. In practice it is typical for both the encoding and semantics of communication to be manually defined by an expert; this is true regardless of whether the behaviors themselves are bespoke, optimization based, or learned. We present an agent architecture and training methodology using neural networks to learn task-oriented communication semantics based on the example of a communication-unaware expert policy. A perimeter defense game illustrates the system’s ability to handle dynamically changing numbers of agents and its graceful degradation in performance as communication constraints are tightened or the expert’s observability assumptions are broken.
Activist investors and open market share repurchases

Abstract This study examines the role of activist investors in firms’ decisions to conduct open market share repurchases. Compared with firms making ordinary share repurchases, firms making activist-involved repurchases have more cash holdings, are more undervalued, experience better subsequent stock performance and similar improvements in operating performance, and eventually repurchase more shares. Moreover, repurchasing firms in which an activist investor claims to take a passive role exhibit no undervaluation, and repurchasing firms that make multiple repurchases exhibit share undervaluation only in repurchases where an activist is involved. In all, our findings suggest that activist-involvement is associated with improved corporate repurchase decisions.
Maternal Education and Infant Health Gradient: New Answers to Old Questions

By using data from the National Vital Statistics System, this study provides an in-depth investigation of the well-documented mother's education- infant health gradient. The study allows for differential relationship between mother's education and infant health outcomes across localities based on income status by using birthweight and low birthweight as health measures. The results show that mother's education- infant birthweight relationship is more concentrated at relatively poor geographic areas. This can partially be explained by increases in utilization of health services among educated mothers residing in poorer areas compared to mothers with lower levels of education. Although the magnitude of education-health gradient has decreased in recent years, the gradient is still more pronounced in poorer localities. Access to health care during pregnancy, measured by adequacy of care, has improved particularly among less educated mothers living in poorer areas. However, smoking participation during pregnancy has declined substantially among less educated mothers across all geographic localities in recent years. Additionally, mother's education-infant health gradient is similar across black and white race groups.
Real-time three-dimensional MRI for the assessment of dynamic carpal instability

Author(s): Shaw, Calvin B; Foster, Brent H; Borgese, Marissa; Boutin, Robert D; Bateni, Cyrus; Boonsri, Pattira; Bayne, Christopher O; Szabo, Robert M; Nayak, Krishna S; Chaudhari, Abhijit J | Abstract: BackgroundCarpal instability is defined as a condition where wrist motion and/or loading creates mechanical dysfunction, resulting in weakness, pain and decreased function. When conventional methods do not identify the instability patterns, yet clinical signs of instability exist, the diagnosis of dynamic instability is often suggested to describe carpal derangement manifested only during the wrist's active motion or stress. We addressed the question: can advanced MRI techniques provide quantitative means to evaluate dynamic carpal instability and supplement standard static MRI acquisition? Our objectives were to (i) develop a real-time, three-dimensional MRI method to image the carpal joints during their active, uninterrupted motion; and (ii) demonstrate feasibility of the method for assessing metrics relevant to dynamic carpal instability, thus overcoming limitations of standard MRI.MethodsTwenty wrists (bilateral wrists of ten healthy participants) were scanned during radial-ulnar deviation and clenched-fist maneuvers. Images resulting from two real-time MRI pulse sequences, four sparse data-acquisition schemes, and three constrained image reconstruction techniques were compared. Image quality was assessed via blinded scoring by three radiologists and quantitative imaging metrics.ResultsReal-time MRI data-acquisition employing sparse radial sampling with a gradient-recalled-echo acquisition and constrained iterative reconstruction appeared to provide a practical tradeoff between imaging speed (temporal resolution up to 135 ms per slice) and image quality. The method effectively reduced streaking artifacts arising from data undersampling and enabled the derivation of quantitative measures pertinent to evaluating dynamic carpal instability.ConclusionThis study demonstrates that real-time, three-dimensional MRI of the moving wrist is feasible and may be useful for the evaluation of dynamic carpal instability.
Galkin’s lower bound conjecture holds for the Grassmannian

AbstractLet Gr(k,n) be the Grassmannian. The quantum multiplication by the first Chern class c1(Gr(k,n)) induces an endomorphism ĉ1 of the finite-dimensional vector space QH*(Gr(k,n))|q=1 speciali...
The Linked Selection Signature of Rapid Adaptation in Temporal Genomic Data

The majority of empirical population genetic studies have tried to understand the evolutionary processes that have shaped genetic variation in a single sample taken from a present-day population. However, genomic data collected over tens of generations in both natural and laboratory populations are increasingly used to find selected loci underpinning adaptation over these short timescales. Although these studies have been quite successful in detecting selection on large-effect loci, the fitness differences between individuals are often polygenic, such that selection leads to allele frequency changes that are difficult to distinguish from genetic drift. However, one promising signal comes from polygenic selection’s effect on neutral sites that become stochastically associated with the genetic backgrounds that lead to fitness differences between individuals. Previous theoretical work has established that the random associations between a neutral allele and heritable fitness backgrounds act to reduce the effective population size experienced by this neutral allele. These associations perturb neutral allele frequency trajectories, creating autocovariance in the allele frequency changes across generations. Here, we show how temporal genomic data allow us to measure the temporal autocovariance in allele frequency changes and characterize the genome-wide impact of polygenic selection. We develop expressions for these temporal autocovariances, showing that their magnitude is determined by the level of additive genetic variation, recombination, and linkage disequilibria in a region. Furthermore, by using analytic expressions for the temporal variances and autocovariances in allele frequency, we demonstrate that one can estimate the additive genetic variation for fitness and the drift-effective population size from temporal genomic data. We also show how the proportion of total variation in allele frequency change due to linked selection can be estimated from temporal data. Overall, we demonstrate that temporal genomic data offer opportunities to identify the role of linked selection on genome-wide diversity over short timescales, and can help bridge population genetic and quantitative genetic studies of adaptation.
Highly heritable and functionally relevant breed differences in dog behaviour

Variation across dog breeds presents a unique opportunity to investigate the evolution and biological basis of complex behavioural traits. We integrated behavioural data from more than 14 000 dogs ...
Connecting dots: from local covariance to empirical intrinsic geometry and locally linear embedding

Local covariance structure under the manifold setup has been widely applied in the machine-learning community. Based on the established theoretical results, we provide an extensive study of two relevant manifold learning algorithms, empirical intrinsic geometry (EIG) and locally linear embedding (LLE) under the manifold setup. Particularly, we show that without an accurate dimension estimation, the geodesic distance estimation by EIG might be corrupted. Furthermore, we show that by taking the local covariance matrix into account, we can more accurately estimate the local geodesic distance. When understanding LLE based on the local covariance structure, its intimate relationship with the curvature suggests a variation of LLE depending on the “truncation scheme”. We provide a theoretical analysis of the variation.
Effective construction of covers of canonical Hom-diagrams for equations over torsion-free hyperbolic groups

We show that, given a finitely generated group $G$ as the coordinate group of a finite system of equations over a torsion-free hyperbolic group $\Gamma$, there is an algorithm which constructs a cover of a canonical solution diagram. The diagram encodes all homomorphisms from $G$ to $\Gamma$ as compositions of factorizations through $\Gamma$-NTQ groups and canonical automorphisms of the corresponding NTQ-subgroups. We also give another characterization of $\Gamma$-limit groups as iterated generalized doubles over $\Gamma$.
Generalized solitary waves in the gravity‐capillary Whitham equation

We study the existence of traveling wave solutions to a unidirectional shallow water model which incorporates the full linear dispersion relation for both gravitational and capillary restoring forces. Using functional analytic techniques, we show that for small surface tension (corresponding to Bond numbers between $0$ and ${1}/{3}$) there exists small amplitude solitary waves that decay to asymptotically small periodic waves at spatial infinity. The size of the oscillations in the far field are shown to be small beyond all algebraic orders in the amplitude of the wave. We also present numerical evidence, based on the recent analytical work of Hur \& Johnson, that the asymptotic end states are modulationally stable for all Bond numbers between $0$ and $1/3$.
Closed geodesics on doubled polygons

We study 1∕k-geodesics, those closed geodesics that minimize on any subinterval of length L∕k, where L is the length of the geodesic. We investigate the existence and behavior of these curves on doubled polygons and show that every doubled regular n-gon admits a 1∕(2n)-geodesic. For the doubled regular p-gons, with p an odd prime, we conjecture that k=2p is the minimum value for k such that the space admits a 1∕k-geodesic.
Uncertainty Estimates for Sea Surface Temperature and Land Surface Air Temperature in NOAAGlobalTemp Version 5

AbstractThis analysis estimates uncertainty in the NOAA global surface temperature (GST) version 5 (NOAAGlobalTemp v5) product, which consists of sea surface temperature (SST) from the Extended Rec...
Adenocarcinoma of the Rete Testis

Adenocarcinoma of the rete testis is very rare. It is highly malignant with potential of early metastasis. We are reporting such a case occurring in a 34-year-old man who remains tumor-free 7 years after orchiectomy. Another 20 reported cases of such a tumor from the English literature are reviewed.
Autonomous Highway Driving using Deep Reinforcement Learning

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. Due to this, formulating a rule based decision maker for selecting driving maneuvers may not be ideal. Similarly, it may not be efficient to solve optimal control problem in real-time for a predefined cost function. In order to address these issues and to avoid peculiar behaviors when encountering unforeseen scenario, we propose a reinforcement learning (RL) based method, where the ego car, i.e., an autonomous vehicle, learns to make decisions by directly interacting with the simulated traffic. Here the decision maker is a deep neural network that provides an action choice for a given system state. We demonstrate the performance of the developed algorithm in highway driving scenario where the trained AV encounters varying traffic density.
Analysis of an approximation to a fractional extension problem

The purpose of this article is to study an approximation to an abstract Bessel-type problem, which is a generalization of the extension problem associated with fractional powers of the Laplace operator. Motivated by the success of such approaches in the analysis of time-stepping methods for abstract Cauchy problems, we adopt a similar framework herein. The proposed method differs from many standard techniques, as we approximate the true solution to the abstract problem, rather than solve an associated discrete problem. The numerical method is shown to be consistent, stable, and convergent in an appropriate Banach space. These results are built upon well understood results from semigroup theory. Numerical experiments are provided to demonstrate the theoretical results.
Rethinking Production Under Uncertainty

Conventional models of production under uncertainty specify that output is produced in fixed proportions across states of nature. I investigate a representation of technology that allows firms to transform output from one state to another. I allow the firm to choose the distribution of its random productivity from a convex set of such distributions, described by a limit on a moment of productivity scaled by a natural productivity shock. The model produces a simple discount factor linked to productivity, which can be used to price any asset, without regard to preferences.
The sources of international investment income in emerging market economies

We investigate international investment income flows in 26 emerging market countries during the period of 1998-2015. Net investment income registered a deficit for this group of countries of between 2-3% of GDP during this period. This deficit has been dominated by payments on foreign direct investment liabilities, which is consistent with the change in the composition of the external liabilities of these countries. Our results indicate that both capital account and trade openness are associated with the deficits on direct investment income. In addition, there was a small deficit in portfolio investment income, which is affected by the development of domestic financial markets and investor protection. Other investments’ income and the income from foreign exchange reserves have a negligible role in total investment income.
Temporal and spatial limitations in global surveillance for bat filoviruses and henipaviruses.

Sampling reservoir hosts over time and space is critical to detect epizootics, predict spillover and design interventions. However, because sampling is logistically difficult and expensive, researc...
Stochastic and mixed flower graphs.

Stochasticity is introduced to a well studied class of recursively grown graphs: $(u,v)$-flower nets, which have power-law degree distributions as well as small-world properties (when $u=1)$. The stochastic variant interpolates between different (deterministic) flower graphs thus adding flexibility to the model. The random multiplicative growth process involved, however, leads to a spread ensemble of networks with finite variance for the number of links, nodes, and loops. Nevertheless, the degree exponent and loopiness exponent attain unique values in the thermodynamic limit of infinitely large graphs. We also study a class of mixed flower networks, closely related to the stochastic flowers, but which are grown recursively in a deterministic way. The deterministic growth of mixed flower-nets eliminates ensemble spreads, and their recursive growth allows for exact analysis of their (uniquely defined) mixed properties.
Can social influence be exploited to compromise security: an online experimental evaluation

While social media enables users and organizations to obtain useful information about technology like software and security feature usage, it can also allow an adversary to exploit users by obtaining information from them or influencing them towards injurious decisions. Prior research indicates that security technology choices are subject to social influence and that these decisions are often influenced by the peer decisions and number of peers in a user's network. In this study we investigated whether peer influence dictates users' decisions by manipulating social signals from peers in an online, controlled experiment. Human participants recruited from Amazon Mechanical Turk played a multi-round game in which they selected a security technology from among six of differing utilities. We observe that at the end of the game, a strategy to expose users to high quantity of peer signals reflecting suboptimal choices, in the later stages of the game successfully influences users to deviate from the optimal security technology. This strategy influences almost 1.5 times the number of users with respect to the strategy where users receive constant low quantity of similar peer signals in all rounds of the game.
A Hierarchical Bayes Unit-Level Small Area Estimation Model for Normal Mixture Populations

National statistical agencies are regularly required to produce estimates about various subpopulations, formed by demographic and/or geographic classifications, based on a limited number of samples. Traditional direct estimates computed using only sampled data from individual subpopulations are usually unreliable due to small sample sizes. Subpopulations with small samples are termed small areas or small domains. To improve on the less reliable direct estimates, model-based estimates, which borrow information from suitable auxiliary variables, have been extensively proposed in the literature. However, standard model-based estimates rely on the normality assumptions of the error terms. In this research we propose a hierarchical Bayesian (HB) method for the unit-level nested error regression model based on a normal mixture for the unit-level error distribution. Our method proposed here is applicable to model cases with unit-level error outliers as well as cases where each small area population is comprised of two subgroups, neither of which can be treated as an outlier. Our proposed method is more robust than the normality based standard HB method (Datta and Ghosh, Annals Stat. 19, 1748–1770, 1991) to handle outliers or multiple subgroups in the population. Our proposal assumes two subgroups and the two-component mixture model that has been recently proposed by Chakraborty et al. (Int. Stat. Rev. 87, 158–176, 2019) to address outliers. To implement our proposal we use a uniform prior for the regression parameters, random effects variance parameter, and the mixing proportion, and we use a partially proper non-informative prior distribution for the two unit-level error variance components in the mixture. We apply our method to two examples to predict summary characteristics of farm products at the small area level. One of the examples is prediction of twelve county-level crop areas cultivated for corn in some Iowa counties. The other example involves total cash associated in farm operations in twenty-seven farming regions in Australia. We compare predictions of small area characteristics based on the proposed method with those obtained by applying the Datta and Ghosh (Annals Stat. 19, 1748–1770, 1991) and the Chakraborty et al. (Int. Stat. Rev. 87, 158–176, 2019) HB methods. Our simulation study comparing these three Bayesian methods, when the unit-level error distribution is normal, or t, or two-component normal mixture, showed the superiority of our proposed method, measured by prediction mean squared error, coverage probabilities and lengths of credible intervals for the small area means.
ETV4 Is Necessary for Estrogen Signaling and Growth in Endometrial Cancer Cells

Estrogen signaling through estrogen receptor alpha (ER) plays a major role in endometrial cancer risk and progression, however, the molecular mechanisms underlying ER's regulatory role in endometrial cancer are poorly understood. In breast cancer cells, ER genomic binding is enabled by FOXA1 and GATA3, but the transcription factors that control ER genomic binding in endometrial cancer cells remain unknown. We previously identified ETV4 as a candidate factor controlling ER genomic binding in endometrial cancer cells, and here we explore the functional importance of ETV4. Homozygous deletion of ETV4, using CRISPR/Cas9, led to greatly reduced ER binding at the majority of loci normally bound by ER. Consistent with the dramatic loss of ER binding, the gene expression response to estradiol was dampened for most genes. ETV4 contributes to estrogen signaling in two distinct ways. ETV4 loss affects chromatin accessibility at some ER bound loci and impairs ER nuclear translocation. The diminished estrogen signaling upon ETV4 deletion led to decreased growth, particularly in 3D culture, where hollow organoids were formed and in vivo in the context of estrogen-dependent growth. These results show that ETV4 plays an important role in estrogen signaling in endometrial cancer cells. SIGNIFICANCE: Estrogen receptor alpha (ER) is a key oncogene in endometrial cancer. This study uncovers ETV4 as an important factor in controlling the activity of ER and the growth of endometrial cancer cells. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/6/1234/F1.large.jpg.
An Asymptotically Tight Bound on the Number of Relevant Variables in a Bounded Degree Boolean function

We prove that there is a constant C ≤ 6.614 such that every Boolean function of degree at most d (as a polynomial over ℝ) is a C·2d-junta, i.e., it depends on at most C·2d variables. This improves the d·2d-1 upper bound of Nisan and Szegedy [Computational Complexity 4 (1994)]. The bound of C·2d is tight up to the constant C, since a read-once decision tree of depth d depends on all 2d - 1 variables. We slightly improve this lower bound by constructing, for each positive integer d, a function of degree d with 3·2d-1 - 2 relevant variables. A similar construction was independently observed by Shinkar and Tal.