cssi/SciSciGPT-SciSciNet huggingface dataset:
Raising expectations: automating expected cost analysis with types

This article presents a type-based analysis for deriving upper bounds on the expected execution cost of probabilistic programs. The analysis is naturally compositional, parametric in the cost model, and supports higher-order functions and inductive data types. The derived bounds are multivariate polynomials that are functions of data structures. Bound inference is enabled by local type rules that reduce type inference to linear constraint solving. The type system is based on the potential method of amortized analysis and extends automatic amortized resource analysis (AARA) for deterministic programs. A main innovation is that bounds can contain symbolic probabilities, which may appear in data structures and function arguments. Another contribution is a novel soundness proof that establishes the correctness of the derived bounds with respect to a distribution-based operational cost semantics that also includes nontrivial diverging behavior. For cost models like time, derived bounds imply termination with probability one. To highlight the novel ideas, the presentation focuses on linear potential and a core language. However, the analysis is implemented as an extension of Resource Aware ML and supports polynomial bounds and user defined data structures. The effectiveness of the technique is evaluated by analyzing the sample complexity of discrete distributions and with a novel average-case estimation for deterministic programs that combines expected cost analysis with statistical methods.
2-Dimensional palindromes with k mismatches

Abstract This paper extends the problem of 2-dimensional palindrome search into the area of approximate matching. Using the Hamming distance as the measure, we search for 2D palindromes that allow up to k mismatches. We consider two different definitions of 2D palindromes and describe efficient algorithms for both of them. The first definition implies a square, while the second definition (also known as a centrosymmetric factor), can be any rectangular shape. Given a text of size ( n × m ) , the time complexity of the first algorithm is O ( n m ( log ⁡ m + log ⁡ n + k ) ) and for the second algorithm it is O ( n m log ⁡ m + k log ⁡ k ( n m + o c c ) ) where occ is the size of the output.
Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones

Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes which drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine learning algorithms have limited applicability on this front due to their ``black box'' structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for over-ocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed ORB feature suite targets the global Organization, Radial structure, and Bulk morphology of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (versus absence) of such intensity change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine learning methods did not perform better than the linear logistic lasso model for current data.
A New Way to Measure Competition

This article introduces a new way to measure competition based on firms' profits. Within a general model, we derive conditions under which this measure is monotone in competition, where competition can be intensified both through a fall in entry barriers and through more aggressive interaction between players. The measure is shown to be more robust theoretically than the price cost margin. This allows for an empirical test of the problems associated with the price cost margin as a measure of competition.
The History of Workforce Concerns in Pediatric Pulmonary Medicine.

Children are affected by a broad spectrum of acute and chronic respiratory disorders. The number of children with respiratory disease is increasing, as are the complexity of disease pathophysiology and the management demands on pediatric pulmonologists. Despite slowly increasing numbers of board-certified pediatric pulmonologists, large areas of the country are underserved and there is a perception of an impending workforce crisis. There are multiple reasons for these concerns. A joint effort between the Pediatric Pulmonology Division Directors Association and Pediatric Pulmonary Training Directors Association was undertaken to address these issues.
Astroglial FMRP modulates synaptic signaling and behavior phenotypes in FXS mouse model

Fragile X syndrome (FXS) is one of the most common inherited intellectual disability (ID) disorders, in which the loss of FMRP protein induces a range of cellular signaling changes primarily through excess protein synthesis. Although neuron-centered molecular and cellular events underlying FXS have been characterized, how different CNS cell types are involved in typical FXS synaptic signaling changes and behavioral phenotypes is largely unknown. Recent evidence suggests that selective loss of astroglial FMRP is able to dysregulate glutamate uptake, increase spine density, and impair motor-skill learning. Here we investigated the effect of astroglial FMRP on synaptic signaling and FXS-related behavioral and learning phenotypes in astroglial Fmr1 cKO and cON mice in which FMRP expression is selectively diminished or restored in astroglia. We found that selective loss of astroglial FMRP contributes to cortical hyperexcitability by enhancing NMDAR-mediated evoked but not spontaneous miniEPSCs and elongating cortical UP state duration. Selective loss of astroglial FMRP is also sufficient to increase locomotor hyperactivity, significantly diminish social novelty preference, and induce memory acquisition and extinction deficits in astroglial Fmr1 cKO mice. Importantly, re-expression of astroglial FMRP is able to significantly rescue the hyperactivity (evoked NMDAR response, UP state duration, and open field test) and social novelty preference in astroglial Fmr1 cON mice. These results demonstrate a profound role of astroglial FMRP in the evoked synaptic signaling, spontaneously occurring cortical UP states, and FXS-related behavioral and learning phenotypes and provide important new insights in the cell type consideration for the FMRP reactivation strategy.
Bounding the Kreuzer‐Skarke Landscape

We study Calabi-Yau threefolds with large Hodge numbers by constructing and counting triangulations of reflexive polytopes. By counting points in the associated secondary polytopes, we show that the number of fine, regular, star triangulations of polytopes in the Kreuzer-Skarke list is bounded above by $\binom{14,111}{494} \approx 10^{928}$. Adapting a result of Anclin on triangulations of lattice polygons, we obtain a bound on the number of triangulations of each 2-face of each polytope in the list. In this way we prove that the number of topologically inequivalent Calabi-Yau hypersurfaces arising from the Kreuzer-Skarke list is bounded above by $10^{428}$. We introduce efficient algorithms for constructing representative ensembles of Calabi-Yau hypersurfaces, including the extremal case $h^{1,1}=491$, and we study the distributions of topological and physical data therein. Finally, we demonstrate that neural networks can accurately predict these data once the triangulation is encoded in terms of the secondary polytope.
CAGE: Context-Aware Grasping Engine

Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks. In order for robots to effectively perform object manipulation, a broad sense of contexts, including object and task constraints, needs to be accounted for. We introduce the Context-Aware Grasping Engine, which combines a novel semantic representation of grasp contexts with a neural network structure based on the Wide & Deep model, capable of capturing complex reasoning patterns. We quantitatively validate our approach against three prior methods on a novel dataset consisting of 14,000 semantic grasps for 44 objects, 7 tasks, and 6 different object states. Our approach outperformed all baselines by statistically significant margins, producing new insights into the importance of balancing memorization and generalization of contexts for semantic grasping. We further demonstrate the effectiveness of our approach on robot experiments in which the presented model successfully achieved 31 of 32 suitable grasps. The code and data are available at: https://github.com/wliu88/railsemanticgrasping
GISNet:Graph-Based Information Sharing Network For Vehicle Trajectory Prediction

The trajectory prediction is a critical and challenging problem in the design of an autonomous driving system. Many AI-oriented companies, such as Google Waymo, Uber and DiDi, are investigating more accurate vehicle trajectory prediction algorithms. However, the prediction performance is governed by lots of entangled factors, such as the stochastic behaviors of surrounding vehicles, historical information of self-trajectory, and relative positions of neighbors, etc. In this paper, we propose a novel graph-based information sharing network (GISNet) that allows the information sharing between the target vehicle and its surrounding vehicles. Meanwhile, the model encodes the historical trajectory information of all the vehicles in the scene. Experiments are carried out on the public NGSIM US-101 and I-80 Dataset and the prediction performance is measured by the Root Mean Square Error (RMSE). The quantitative and qualitative experimental results show that our model significantly improves the trajectory prediction accuracy, by up to 50.00%, compared to existing models.
Facilitating Access to a COVID-19 Vaccine through Global Health Law.

This column explores the global health law reforms necessary to progressively realize universal access to a future COVID-19 vaccine. We begin by explaining the importance of global health law in the distribution of essential vaccines as a determinant of public health. The column then turns to examine the human rights foundations of global health law, conceptualizing vaccination access as a universal human right. We find that it will be crucial to develop legal commitments to ensure vaccine access prior to a scientific breakthrough, analyzing the legal barriers that impede global access and the global health law reforms necessary to facilitate global solidarity.
Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data

Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for functional divergence and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the parameters driving duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built on a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents a major advancement in classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.
A Weak Fermi Gamma-ray Event Associated with a Halo CME and a Type II Radio Burst

We report on the 2015 June 25 sustained gamma-ray emission (SGRE) event associated with a halo coronal mass ejection and a type II radio burst in the decameter-hectometric (DH) wavelengths. The duration and ending frequency of the type II burst are linearly related to the SGRE duration as found in previous works involving intense gamma-ray events. This study confirms that the SGRE event is due to protons accelerated in the shock that produced the DH type II burst.
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Limited data access is a longstanding barrier to data-driven research and development in the networked systems community. In this work, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge. As a specific target, our focus in this paper is on time series datasets with metadata (e.g., packet loss rate measurements with corresponding ISPs). We identify key challenges of existing GAN approaches for such workloads with respect to fidelity (e.g., long-term dependencies, complex multidimensional relationships, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity). To improve fidelity, we design a custom workflow called DoppelGANger (DG) and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DG achieves up to 43% better fidelity than baseline models. Although we do not resolve the privacy problem in this work, we identify fundamental challenges with both classical notions of privacy and recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges. By shedding light on the promise and challenges, we hope our work can rekindle the conversation on workflows for data sharing.
Assessing COVID-19 risk, vulnerability and infection prevalence in communities.

Background The spread of coronavirus in the United States with nearly five and half million confirmed cases and over 170,000 deaths has strained public health and health care systems. While many have focused on clinical outcomes, less attention has been paid to vulnerability and risk of infection. In this study, we developed a planning tool that examines factors that affect vulnerability to COVID-19. Methods Across 46 variables, we defined five broad categories: 1) access to medical services, 2) underlying health conditions, 3) environmental exposures, 4) vulnerability to natural disasters, and 5) sociodemographic, behavioral, and lifestyle factors. The developed tool was validated by comparing the estimated overall vulnerability with the real-time reported normalized confirmed cases of COVID-19. Analysis A principal component analysis was undertaken to reduce the dimensions. In order to identify vulnerable census tracts, we conducted rank-based exceedance and K-means cluster analyses. Results All of the 5 vulnerability categories, as well as the overall vulnerability, showed significant (P-values Conclusion Policymakers can use this planning tool to identify neighborhoods at high risk for becoming hot spots; efficiently match community resources with needs, and ensure that the most vulnerable have access to equipment, personnel, and medical interventions.
IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery

Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applying machine learning methods to create predictive models capable of mapping from composition and crystal structures to materials properties. Generally, these are regression problems with the input being a 1D vector composed of numerical attributes representing the material composition and/or crystal structure. While neural networks consisting of fully connected layers have been applied to such problems, their performance often suffers from the vanishing gradient problem when network depth is increased. Hence, predictive modeling for such tasks has been mainly limited to traditional machine learning techniques such as Random Forest. In this paper, we study and propose design principles for building deep regression networks composed of fully connected layers with numerical vectors as input. We introduce a novel deep regression network with individual residual learning, IRNet, that places shortcut connections after each layer so that each layer learns the residual mapping between its output and input. We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet's performance against that of other machine learning techniques. Using multiple datasets from the Open Quantum Materials Database (OQMD) and Materials Project for training and evaluation, we show that IRNet provides significantly better prediction performance than the state-of-the-art machine learning approaches currently used by domain scientists. We also show that IRNet's use of individual residual learning leads to better convergence during the training phase than when shortcut connections are between multi-layer stacks while maintaining the same number of parameters.
Transport and dispersion of active particles in periodic porous media

The long-time transport of active Brownian particles flowing through a porous lattice is studied using generalized Taylor dispersion theory and Langevin simulations. The effects of motility, lattice geometry, and fluid flow on the asymptotic spreading of a dilute cloud of microswimmers are unraveled.
Determination of the universality class of crystal plasticity

Although scaling phenomena have long been documented in crystalline plasticity, the universality class has been difficult to identify due to the rarity of avalanche events, which require large system sizes and long times in order to accurately measure scaling exponents and functions. Here we present comprehensive simulations of two-dimensional dislocation dynamics under shear, using finite-size scaling to extract scaling exponents and the avalanche profile scaling function from time-resolved measurements of slip avalanches. Our results provide compelling evidence that both the static and dynamic universality classes are consistent with the mean-field interface depinning model.
Enhancing the capabilities of LIGO time–frequency plane searches through clustering

One class of gravitational wave signals LIGO is searching for consists of short duration bursts of unknown waveforms. Potential sources include core collapse supernovae, gamma ray burst progenitors and mergers of binary black holes or neutron stars. We present a density-based clustering algorithm to improve the performance of time–frequency searches for such gravitational-wave bursts when they are extended in time and/or frequency, and not sufficiently well known to permit matched filtering. We have implemented this algorithm as an extension to the QPipeline, a gravitational-wave data analysis pipeline for the detection of bursts, which currently determines the statistical significance of events based solely on the peak significance observed in minimum uncertainty regions of the time–frequency plane. Density-based clustering improves the performance of such a search by considering the aggregate significance of arbitrarily shaped regions in the time–frequency plane and rejecting the isolated minimum uncertainty features expected from the background detector noise. In this paper, we present test results for simulated signals and demonstrate that density-based clustering improves the performance of the QPipeline for signals extended in time and/or frequency.
Topographic Deep Artificial Neural Networks (TDANNs) predict face selectivity topography in primate inferior temporal (IT) cortex

Deep convolutional neural networks are biologically driven models that resemble the hierarchical structure of primate visual cortex and are the current best predictors of the neural responses measured along the ventral stream. However, the networks lack topographic properties that are present in the visual cortex, such as orientation maps in primary visual cortex and category-selective maps in inferior temporal (IT) cortex. In this work, the minimum wiring cost constraint was approximated as an additional learning rule in order to generate topographic maps of the networks. We found that our topographic deep artificial neural networks (ANNs) can reproduce the category selectivity maps of the primate IT cortex.
Equivalence of the Klein-Gordon random field and the complex Klein-Gordon quantum field

The difference between a Klein-Gordon random field and the complex Klein-Gordon quantum field is characterized, explicitly comparing the roles played by negative-frequency modes of test functions in creation and annihilation operator presentations of the two theories. The random field and the complex quantum field can both be constructed from the same creation and annihilation operator algebra, making them equivalent in that sense.
Detection of a Millimeter Flare from Proxima Centauri

We present new analyses of ALMA 12 m and Atacama Compact Array (ACA) observations at 233 GHz (1.3 mm) of the Proxima Centauri system with sensitivities of 9.5 and 47 μJy beam−1, respectively, taken from 2017 January 21 through April 25. These analyses reveal that the star underwent a significant flaring event during one of the ACA observations on 2017 March 24. The complete event lasted for approximately 1 minute and reached a peak flux density of 100 ± 4 mJy, nearly a factor of 1000 times brighter than the star's quiescent emission. At the flare peak, the continuum emission is characterized by a steeply falling spectral index with frequency F ν ∝ ν α with α = −1.77 ± 0.45, and a lower limit on the fractional linear polarization of . Because the ACA observations do not show any quiescent excess emission, we conclude that there is no need to invoke the presence of a dust belt at 1–4 au. We also posit that the slight excess flux density of 101 ± 9 μJy observed in the 12 m observations, compared to the photospheric flux density of 74 ± 4 μJy extrapolated from infrared wavelengths, may be due to coronal heating from continual smaller flares, as is seen for AU Mic, another nearby well-studied M dwarf flare star. If this is true, then the need for warm dust at ~0.4 au is also removed.
Multi-symplectic magnetohydrodynamics

A multi-symplectic formulation of ideal magnetohydrodynamics (MHD) is developed based on the Clebsch variable variational principle in which the Lagrangian consists of the kinetic minus the potential energy of the MHD fluid modified by constraints using Lagrange multipliers that ensure mass conservation, entropy advection with the flow, the Lin constraint, and Faraday's equation (i.e. the magnetic flux is Lie dragged with the flow). The analysis is also carried out using the magnetic vector potential A where α=A⋅dx is Lie dragged with the flow, and B=∇×A. The multi-symplectic conservation laws give rise to the Eulerian momentum and energy conservation laws. The symplecticity or structural conservation laws for the multi-symplectic system corresponds to the conservation of phase space. It corresponds to taking derivatives of the momentum and energy conservation laws and combining them to produce n(n−1)/2 extra conservation laws, where n is the number of independent variables. Noether's theorem for the multi-symplectic MHD system is derived, including the case of non-Cartesian space coordinates, where the metric plays a role in the equations.
Induced subgraphs of Johnson graphs

The Johnson graph J(n,N) is defined as the graph whose vertices are the n-subsets of the set {1,2,...,N}, where two vertices are adjacent if they share exactly n - 1 elements. Unlike Johnson graphs, induced subgraphs of Johnson graphs (JIS for short) do not seem to have been studied before. We give some necessary conditions and some sufficient conditions for a graph to be JIS, including: in a JIS graph, any two maximal cliques share at most two vertices; all trees, cycles, and complete graphs are JIS; disjoint unions and Cartesian products of JIS graphs are JIS; every JIS graph of order n is an induced subgraph of J(m,2n) for some m <= n. This last result gives an algorithm for deciding if a graph is JIS. We also show that all JIS graphs are edge move distance graphs, but not vice versa.
Convectively driven shear and decreased heat flux

We report on direct numerical simulations of two-dimensional, horizontally periodic Rayleigh–Benard convection between free-slip boundaries. We focus on the ability of the convection to drive large-scale horizontal flow that is vertically sheared. For the Prandtl numbers ( ) between 1 and 10 simulated here, this large-scale shear can be induced by raising the Rayleigh number ( ) sufficiently, and we explore the resulting convection for up to . When present in our simulations, the sheared mean flow accounts for a large fraction of the total kinetic energy, and this fraction tends towards unity as . The shear helps disperse convective structures, and it reduces vertical heat flux; in parameter regimes where one state with large-scale shear and one without are both stable, the Nusselt number of the state with shear is smaller and grows more slowly with . When the large-scale shear is present with , the convection undergoes strong global oscillations on long timescales, and heat transport occurs in bursts. Nusselt numbers, time-averaged over these bursts, vary non-monotonically with for . When the shear is present with , the flow does not burst, and convective heat transport is sustained at all times. Nusselt numbers then grow roughly as powers of , but the growth rates are slower than any previously reported for Rayleigh–Benard convection without large-scale shear. We find that the Nusselt numbers grow proportionally to when and to when . Analogies with tokamak plasmas are described.
Multi-time wave functions

In non-relativistic quantum mechanics of N particles in three spatial dimensions, the wave function ψ( q 1, ..., q N , t) is a function of 3N position coordinates and one time coordinate. It is an obvious idea that in a relativistic setting, such functions should be replaced by ((t 1, q 1), ..., (tN, q N )), a function of N space-time points called a multi-time wave function because it involves N time variables. Its evolution is determined by N Schrodinger equations, one for each time variable; to ensure that simultaneous solutions to these N equations exist, the N Hamiltonians need to satisfy a consistency condition. This condition is automatically satisfied for non-interacting particles, but it is not obvious how to set up consistent multi-time equations with interaction. For example, interaction potentials (such as the Coulomb potential) make the equations inconsistent, except in very special cases. However, there have been recent successes in setting up consistent multi-time equations involving interaction, in two ways: either involving zero-range (δ potential) interaction or involving particle creation and annihilation. The latter equations provide a multi-time formulation of a quantum field theory. The wave function in these equations is a multi-time Fock function, i.e., a family of functions consisting of, for every n = 0, 1, 2, ..., an n-particle wave function with n time variables. These wave functions are related to the Tomonaga–Schwinger approach and to quantum field operators, but, as we point out, they have several advantages.
Spherical geometry and the least symmetric triangle

We study the problem of determining the least symmetric triangle, which arises both from pure geometry and from the study of molecular chirality in chemistry. Using the correspondence between planar n-gons and points in the Grassmannian of 2-planes in real n-space introduced by Hausmann and Knutson, this corresponds to finding the point in the fundamental domain of the hyperoctahedral group action on the Grassmannian which is furthest from the boundary, which we compute exactly. We also determine the least symmetric obtuse and acute triangles. These calculations provide prototypes for computations on polygon and shape spaces.
Analytic formula for the geometric phase of an asymmetric top

The motion of a handle spinning in space has an odd behavior. It seems to unexpectedly flip back and forth in a periodic manner as seen in a popular YouTube video (“Plasma Ben, Dancing T-handle in zero-g, HD,” ). As an asymmetrical top, its motion is completely described by the Euler equations and the equations of motion have been known for more than a century. However, recent concepts of the geometric phase have allowed a new perspective on this classical problem. Here, we explicitly use the equations of motion to find a closed form expression for the total phase and hence the geometric phase of the force-free asymmetric top and we explore some consequences of this formula with the particular example of the spinning handle for demonstration purposes. As one of the simplest dynamical systems, the asymmetric top should be a canonical example to explore the classical analog of the Berry phase.
From classical to modern opinion dynamics

In this age of Facebook, Instagram, and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion p...
Bigeodesics in First-Passage Percolation

In first-passage percolation, we place i.i.d. continuous weights at the edges of \({\mathbb{Z}^2}\) and consider the weighted graph metric. A distance-minimizing path between points x and y is called a geodesic, and a bigeodesic is a doubly-infinite path whose segments are geodesics. It is a famous conjecture that almost surely, there are no bigeodesics. In the 1990s, Licea–Newman showed that, under a curvature assumption on the “asymptotic shape,” all infinite geodesics have an asymptotic direction, and there is a full measure set \({D \subset [0,2\pi)}\) such that for any \({\theta \in D}\), there are no bigeodesics with one end directed in direction \({\theta}\). In this paper, we show that there are no bigeodesics with one end directed in any deterministic direction, assuming the shape boundary is differentiable. This rules out existence of ground state pairs for the related disordered ferromagnet whose interface has a deterministic direction. Furthermore, it resolves the Benjamini–Kalai–Schramm “midpoint problem” (Benjamini et al. in Ann Probab 31, p. 1976, 2003). under the extra assumption that the limit shape boundary is differentiable.
FOUR FUNDAMENTAL FOREGROUND POWER SPECTRUM SHAPES FOR 21 cm COSMOLOGY OBSERVATIONS

Contamination from instrumental effects interacting with bright astrophysical sources is the primary impediment to measuring Epoch of Reionization (EoR) and Baryon Acoustic Oscillations (BAO) 21 cm power spectra—an effect called mode mixing. In this paper, we identify four fundamental power spectrum shapes produced by mode mixing that will affect all upcoming observations. We are able, for the first time, to explain the wedge-like structure seen in advanced simulations and to forecast the shape of an "EoR window" that is mostly free of contamination. Understanding the origins of these contaminations also enables us to identify calibration and foreground subtraction errors below the imaging limit, providing a powerful new tool for precision observations.
THE COULOMB INTERACTION AND THE INVERSE FADDEEV–POPOV OPERATOR IN QCD

We give a proof of a local relation between the inverse Faddeev–Popov operator and the non-Abelian Coulomb interaction between color charges.
Magnetic properties of a two-dimensional mixed-spin system

Abstract Using a Langmuir–Blodgett (LB) synthesis method, novel two-dimensional (2D) mixed-spin magnetic systems, in which each magnetic layer is both structurally and magnetically isolated, have been generated. Specifically, a 2D Fe–Ni cyanide-bridged network with a face-centered square grid structure has been magnetically and structurally characterized. The results indicate the presence of ferromagnetic exchange interactions between the Fe 3+ (S=1/2) and Ni 2+ (S=1) centers.
Extended Formulations in Mixed Integer Conic Quadratic Programming

In this paper we consider the use of extended formulations in LP-based algorithms for mixed integer conic quadratic programming (MICQP). Extended formulations have been used by Vielma et al. (INFORMS J Comput 20: 438–450, 2008) and Hijazi et al. (Comput Optim Appl 52: 537–558, 2012) to construct algorithms for MICQP that can provide a significant computational advantage. The first approach is based on an extended or lifted polyhedral relaxation of the Lorentz cone by Ben-Tal and Nemirovski (Math Oper Res 26(2): 193–205 2001) that is extremely economical, but whose approximation quality cannot be iteratively improved. The second is based on a lifted polyhedral relaxation of the euclidean ball that can be constructed using techniques introduced by Tawarmalani and Sahinidis (Math Programm 103(2): 225–249, 2005). This relaxation is less economical, but its approximation quality can be iteratively improved. Unfortunately, while the approach of Vielma, Ahmed and Nemhauser is applicable for general MICQP problems, the approach of Hijazi, Bonami and Ouorou can only be used for MICQP problems with convex quadratic constraints. In this paper we show how a homogenization procedure can be combined with the technique by Tawarmalani and Sahinidis to adapt the extended formulation used by Hijazi, Bonami and Ouorou to a class of conic mixed integer programming problems that include general MICQP problems. We then compare the effectiveness of this new extended formulation against traditional and extended formulation-based algorithms for MICQP. We find that this new formulation can be used to improve various LP-based algorithms. In particular, the formulation provides an easy-to-implement procedure that, in our benchmarks, significantly improved the performance of commercial MICQP solvers.
IDENTIFYING LOCALLY OPTIMAL DESIGNS FOR NONLINEAR MODELS: A SIMPLE EXTENSION WITH PROFOUND CONSEQUENCES

We extend the approach in [Ann. Statist. 38 (2010) 2499–2524] for identifying locally optimal designs for nonlinear models. Conceptually the extension is relatively simple, but the consequences in terms of applications are profound. As we will demonstrate, we can obtain results for locally optimal designs under many optimality criteria and for a larger class of models than has been done hitherto. In many cases the results lead to optimal designs with the minimal number of support points.
Classifying spaces for $1$–truncated compact Lie groups

A 1 –truncated compact Lie group is any extension of a finite group by a torus. In this note we compute the homotopy types of Map ∗ ( B G , B H ) , Map ( B G , B H ) , and Map ( E G , B G H ) for compact Lie groups G and H with H 1 –truncated, showing that they are computed entirely in terms of spaces of homomorphisms from G to H . These results generalize the well-known case when H is finite, and the case when H is compact abelian due to Lashof, May, and Segal.
On the ranks of the 2-Selmer groups of twists of a given elliptic curve

Swinnerton-Dyer considered the proportion of twists of an elliptic curve with full 2-torsion that have 2-Selmer group of a particular dimension. Swinnerton-Dyer obtained asymptotic results on the number of such twists using an unusual notion of asymptotic density. We build on this work to obtain similar results on the density of twists with particular rank of 2-Selmer group using the natural notion of density.
IceCube Sterile Neutrino Searches

Anomalies in short baseline experiments have been interpreted as evidence for additional neutrino mass states with large mass splittings from the known, active flavors. This explanation mandates a corresponding signature in the muon neutrino disappearance channel, which has yet to be observed. Searches for muon neutrino disappearance at the IceCube neutrino telescope presently provide the strongest limits in the space of mixing angles for eVscale sterile neutrinos. This proceeding for the Very Large Volume Neutrino Telescopes (VLVnT) Workshop summarizes the IceCube analyses that have searched for sterile neutrinos and describes ongoing work toward enhanced, high-statistics sterile neutrino searches.
Rigidity of graph products of groups

We show that if a group can be represented as a graph product of nite directly indecomposable groups, then this representation is unique. AMS Classication 20E34; 20F65
Advection of methane in the hydrate zone: model, analysis and examples

A two-phase two-component model is formulated for the advective–diffusive transport of methane in liquid phase through sediment with the accompanying formation and dissolution of methane hydrate. This free-boundary problem has a unique generalized solution in L1; the proof combines analysis of the stationary semilinear elliptic Dirichlet problem with the nonlinear semigroup theory in Banach space for an m-accretive multi-valued operator. Additional estimates of maximum principle type are obtained, and these permit appropriate maximal extensions of the phase-change relations. An example with pure advection indicates the limitations of these estimates and of the model developed here. We also consider and analyze the coupled pressure equation that determines the advective flux in the transport model. Copyright © 2015 John Wiley & Sons, Ltd.
Massive Yang–Mills for vector and axial-vector spectral functions at finite temperature

Abstract The hadronic mechanism which leads to chiral symmetry restoration is explored in the context of the ρ π a 1 system using Massive Yang–Mills, a hadronic effective theory which governs their microscopic interactions. In this approach, vector and axial-vector mesons are implemented as gauge bosons of a local chiral gauge group. We have previously shown that this model can describe the experimentally measured vector and axial-vector spectral functions in vacuum. Here, we carry the analysis to finite temperatures by evaluating medium effects in a pion gas and calculating thermal spectral functions. We find that the spectral peaks in both channels broaden along with a noticeable downward mass shift in the a 1 spectral peak and negligible movement of the ρ peak. The approach toward spectral function degeneracy is accompanied by a reduction of chiral order parameters, i.e., the pion decay constant and scalar condensate. Our findings suggest a mechanism where the chiral mass splitting induced in vacuum is burned off. We explore this mechanism and identify future investigations which can further test it.
Probing massive scalar fields from a pulsar in a stellar triple system

Pulsar timing observations precisely test general relativity. Recently, the hierarchical triple system PSR J0337+1715 has placed new constraints on the existence of a fifth force from violation in the strong equivalence principle. Many alternative gravity theories exist with massive (pseudo-)scalar fields to explain a variety of phenomena from the accelerating expansion of the universe at large scales to the QCD strong CP problem at small scales. We here develop a generic formalism for the fifth force effect in theories involving massive scalar fields arising from e.g. string theory. With PSR J0337 measurements, we find the strongest bound on the simplest theory with a massive scalar field beyond general relativity and derive new constraints in other theories with axions, dark matter mediators, and higher-curvature corrections. These results show that the triple system J0337 provides a stringent test for massive scalar fields.
Banach spaces with polynomial numerical index 1

We characterize Banach spaces with polynomial numerical index 1 when they have the Radon-Nikodým property. The holomorphic numerical index is introduced and the characterization of the Banach space with holomorphic numerical index 1 is obtained when it has the Radon-Nikodým property.
A Paradifferential Approach for Well-Posedness of the Muskat Problem

We study the Muskat problem for one fluid or two fluids, with or without viscosity jump, with or without rigid boundaries, and in arbitrary space dimension d of the interface. The Muskat problem is scaling invariant in the Sobolev space $$H^{s_c}({\mathbb {R}}^d)$$ where $$s_c=1+\frac{d}{2}$$. Employing a paradifferential approach, we prove local well-posedness for large data in any subcritical Sobolev spaces $$H^s({\mathbb {R}}^d)$$, $$s>s_c$$. Moreover, the rigid boundaries are only required to be Lipschitz and can have arbitrarily large variation. The Rayleigh–Taylor stability condition is assumed for the case of two fluids with viscosity jump but is proved to be automatically satisfied for the case of one fluid. The starting point of this work is a reformulation solely in terms of the Drichlet–Neumann operator. The key elements of proofs are new paralinearization and contraction results for the Drichlet–Neumann operator in rough domains.
Localizing Coalescing Massive Black Hole Binaries with Gravitational Waves

Massive black hole binary coalescences are prime targets for space-based gravitational wave (GW) observatories such as LISA. GW measurements can localize the position of a coalescing binary on the sky to an ellipse with a major axis of a few tens of arcminutes to a few degrees, depending on source redshift, and a minor axis which is 2-4 times smaller. Neglecting weak gravitational lensing, the GWs would also determine the source's luminosity distance to better than percent accuracy for close sources, degrading to several percent for more distant sources. Weak lensing cannot, in fact, be neglected and is expected to limit the accuracy with which distances can be fixed to errors no less than a few percent. Assuming a well-measured cosmology, the source's redshift could be inferred with similar accuracy. GWs alone can thus pinpoint a binary to a three-dimensional "pixel" which can help guide searches for the hosts of these events. We examine the time evolution of this pixel, studying it at merger and at several intervals before merger. One day before merger, the major axis of the error ellipse is typically larger than its final value by a factor of ~1.5-6. The minor axis is larger by a factor of ~2-9, and, neglecting lensing, the error in the luminosity distance is larger by a factor of ~1.5-7. This large change over a short period of time is due to spin-induced precession, which is strongest in the final days before merger. The evolution is slower as we go back further in time. For z = 1, we find that GWs will localize a coalescing binary to within ~10 deg2 as early as a month prior to merger and determine distance (and hence redshift) to several percent.
Flag Fault-Tolerant Error Correction for any Stabilizer Code

A qubit-efficient error correction scheme applicable to arbitrary codes is developed, opening up an opportunity to engineer machines that tackle problems of increased complexity.
Dark radiation and superheavy dark matter from black hole domination

If even a relatively small number of black holes were created in the early universe, they will constitute an increasingly large fraction of the total energy density as space expands. It is thus well-motivated to consider scenarios in which the early universe included an era in which primordial black holes dominated the total energy density. Within this context, we consider Hawking radiation as a mechanism to produce both dark radiation and dark matter. If the early universe included a black hole dominated era, we find that Hawking radiation will produce dark radiation at a level ΔNeff ∼ 0.03 − 0.2 for each light and decoupled species of spin 0, 1/2, or 1. This range is well suited to relax the tension between late and early-time Hubble determinations, and is within the reach of upcoming CMB experiments. The dark matter could also originate as Hawking radiation in a black hole dominated early universe, although such dark matter candidates must be very heavy (mDM ≳ 1011 GeV) if they are to avoid exceeding the measured abundance.
IDCS J1426.5+3508: THE MOST MASSIVE GALAXY CLUSTER AT z > 1.5

We present a deep (100 ks) Chandra observation of IDCS J1426.5+3508, a spectroscopically confirmed, infrared-selected galaxy cluster at $z = 1.75$. This cluster is the most massive galaxy cluster currently known at $z > 1.5$, based on existing Sunyaev-Zel'dovich (SZ) and gravitational lensing detections. We confirm this high mass via a variety of X-ray scaling relations, including $T_X$-M, $f_g$-M, $Y_X$-M and $L_X$-M, finding a tight distribution of masses from these different methods, spanning M$_{500}$ = 2.3-3.3 $\times 10^{14}$ M$_{\odot}$, with the low-scatter $Y_X$-based mass $M_{500,Y_X} = 2.6^{+1.5}_{-0.5} \times 10^{14}$ M$_\odot$. IDCS J1426.5+3508 is currently the only cluster at $z > 1.5$ for which X-ray, SZ and gravitational lensing mass estimates exist, and these are in remarkably good agreement. We find a relatively tight distribution of the gas-to-total mass ratio, employing total masses from all of the aforementioned indicators, with values ranging from $f_{gas,500}$ = 0.087-0.12. We do not detect metals in the intracluster medium (ICM) of this system, placing a 2$\sigma$ upper limit of $Z(r 1$, and the first known cool core cluster at $z > 1.2$. The offset of this core from the large-scale centroid suggests that this cluster has had a relatively recent ($\lesssim$500 Myr) merger/interaction with another massive system.
Using Video Game Development to Motivate Program Design and Algebra Among Inner-City High School Students

Introducing inner-city high school students to program design presents unique challenges. The typical assumptions of an introductory programming course, like students understand what variables and functions are, may not be safe. Therefore, asking students to define functions as part of the program design process may be an overwhelming task. Many students do not understand that a function is an abstraction over similar expressions and that parameters represent the differences among these expressions. This articles presents a novel approach to teaching program design to high school students while simultaneously reinforcing high school algebra. The approach is based on a design recipe to help students develop the abstractions that lead to functions. Using a bottom-up approach, students are taught how to abstract over similar expressions. They are then taught how to use high school algebra concepts, like compound functions and function composition, to also design functions. In addition, the article also presents empirical data collected from students to measure their reaction to the course. For the students in the course, the empirical data suggests that high school algebra concepts are successfully reinforced and that students feel they become better problem solvers, find programming intellectually stimulating, and walk away with an interest in programming.