cssi/SciSciGPT-SciSciNet huggingface dataset:
Data-Driven Polymer Model for Mechanistic Exploration of Diploid Genome Organization

Chromosomes are positioned nonrandomly inside the nucleus to coordinate with their transcriptional activity. The molecular mechanisms that dictate the global genome organization and the nuclear localization of individual chromosomes are not fully understood. We introduce a polymer model to study the organization of the diploid human genome. It is data-driven because all parameters can be derived from Hi-C data; it is also a mechanistic model because the energy function is explicitly written out based on a few biologically motivated hypotheses. These two features distinguish the model from existing approaches and make it useful both for reconstructing genome structures and for exploring the principles of genome organization. We carried out extensive validations to show that simulated genome structures reproduce a wide variety of experimental measurements, including chromosome radial positions and spatial distances between homologous pairs. Detailed mechanistic investigations support the importance of both specific interchromosomal interactions and centromere clustering for chromosome positioning. We anticipate the polymer model, when combined with Hi-C experiments, to be a powerful tool for investigating large-scale rearrangements in genome structure upon cell differentiation and tumor progression.
Parameterizing uncertainty by deep invertible networks: An application to reservoir characterization

Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise. This analysis allows to assess the confidence in the candidate solution and how it is reflected in the tasks that are typically performed after imaging (e.g., stratigraphic segmentation following reservoir characterization). Classically, uncertainty comes in the form of a probability distribution formulated from Bayesian principles, from which we seek to obtain samples. A popular solution involves Monte Carlo sampling. Here, we propose instead an approach characterized by training a deep network that "pushes forward" Gaussian random inputs into the model space (representing, for example, density or velocity) as if they were sampled from the actual posterior distribution. Such network is designed to solve a variational optimization problem based on the Kullback-Leibler divergence between the posterior and the network output distributions. This work is fundamentally rooted in recent developments for invertible networks. Special invertible architectures, besides being computational advantageous with respect to traditional networks, do also enable analytic computation of the output density function. Therefore, after training, these networks can be readily used as a new prior for a related inversion problem. This stands in stark contrast with Monte-Carlo methods, which only produce samples. We validate these ideas with an application to angle-versus-ray parameter analysis for reservoir characterization.
Do online friends bring out the best in us? The effect of friend contributions on online review provision

Online reviews are a crucial source of information for consumer decision making. Many businesses, companies, and platforms are interested in encouraging more consumers to review their products but ...
Monodromy Charge in D7-brane Inflation

In axion monodromy inflation, traversing N axion periods corresponds to discharging N units of a quantized charge. In certain models with moving D7-branes, such as Higgs-otic inflation, this monodromy charge is D3-brane charge induced on the D7-branes. The stress-energy of the induced charge affects the internal space, changing the inflaton potential and potentially limiting the field range. We compute the backreaction of induced D3-brane charge in Higgs-otic inflation. The effect on the nonperturbative superpotential is dramatic even for N = 1, and may preclude large-field inflation in this model in the absence of a mechanism to control the backreaction.
Logarithmic enhancements in conformal perturbation theory and their real time interpretation

We study various corrections of correlation functions to leading order in conformal perturbation theory, both on the cylinder and on the plane. Many problems on the cylinder are mathematically equivalent to those in the plane if we give the perturbations a position dependent scaling profile. The integrals to be done are then similar to the study of correlation functions with one additional insertion at the center of the profile. We will be primarily interested in the divergence structure of these corrections when computed in dimensional regularization. In particular, we show that the logarithmic divergences (enhancements) that show up in the plane under these circumstances can be understood in terms of resonant behavior in time dependent perturbation theory, for a transition between states that is induced by an oscillatory perturbation on the cylinder.
A fully polynomial time approximation scheme for the Replenishment Storage problem

Abstract The Replenishment Storage problem (RSP) is to minimize the storage capacity requirement for a deterministic demand, multi-item inventory system with specified individual reorder cycle lengths. The reorders can only take place at integer time units. This problem was shown to be weakly NP-hard for constant joint cycle length (the least common multiple of all individual cycle lengths). We devise here the first known FPTAS for the RSP with different individual cycle lengths and constant joint cycle length.
Fluctuation Dynamo in a Collisionless, Weakly Magnetized Plasma

Results from a numerical study of fluctuation dynamo in a collisionless, weakly magnetized plasma are presented. The key difference between this dynamo and its magnetohydrodynamic (MHD) counterpart is the adiabatic production of magnetic-field-aligned pressure anisotropy by the amplification of a weak seed field. This in turn drives kinetic instabilities on the ion-Larmor scale---namely, firehose and mirror---which sever the adiabatic link between the thermal and magnetic pressures, thereby allowing the dynamo to proceed. After an initial phase of rapid growth driven by these instabilities, the magnetic energy grows exponentially and exhibits a $k^{3/2}$ spectrum that peaks near the resistive scale, similar to the large-magnetic-Prandtl-number ($\mathrm{Pm}\gg{1}$) MHD dynamo. The magnetic field self-organizes into a folded-sheet topology, with direction reversals at the resistive scale and field lines curved at the parallel scale of the flow. The effective $\mathrm{Pm}$ is determined by whether the ion-Larmor scale is above or below the field-reversing scale: in the former case, particles undergo Bohm-like diffusion; in the latter case, particles scatter primarily off firehose fluctuations residing at the ends of the magnetic folds, and the viscosity becomes anisotropic. The magnetic field ultimately saturates at dynamical strengths, with its spectral peak migrating towards larger scales. This feature, along with an anti-correlation of magnetic-field strength and field-line curvature and a gradual thinning of magnetic sheets into ribbons, resemble the saturated state of the large-$\mathrm{Pm}$ dynamo, the primary differences manifesting in firehose/mirror-unstable regions. These results have implications for magnetic-field growth in the weakly collisional intracluster medium of galaxy clusters.
Note on limit distribution of normalized return times and escape rate

In this note we discuss limit distribution of normalized return times for shrinking targets and draw a necessary and sufficient condition using sweep-out sequence in order for the limit distribution to be exponential with parameter 1. The normalizing coefficients are the same as the sizes of the targets. Moreover we study escape rate, namely the exponential decay rate of sweep-out sequence and prove that in ψ-mixing systems for a certain class of sets the escape rate is in limit proportional to the size of the set.
Fractional calculus ties the microscopic and macroscopic scales of complex network dynamics

A two-state, master equation-based decision-making model has been shown to generate phase transitions, to be topologically complex, and to manifest temporal complexity through an inverse power-law probability distribution function in the switching times between the two critical states of consensus. These properties are entailed by the fundamental assumption that the network elements in the decision-making model imperfectly imitate one another. The process of subordination establishes that a single network element can be described by a fractional master equation whose analytic solution yields the observed inverse power-law probability distribution obtained by numerical integration of the two-state master equation to a high degree of accuracy.
A Deep Test of Radial Differential Rotation in a Helium-atmosphere White Dwarf.I. Discovery of Pulsations in PG 0112+104

We present the detection of non-radial oscillations in a hot, helium-atmosphere white dwarf using 78.7 d of nearly uninterrupted photometry from the Kepler space telescope. With an effective temperature >30,000 K, PG 0112+104 becomes the hottest helium-atmosphere white dwarf known to pulsate. The rich oscillation spectrum of low-order g-modes includes clear patterns of rotational splittings from consecutive sequences of dipole and quadrupole modes, which can be used to probe the rotation rate with depth in this highly evolved stellar remnant. We also measure a surface rotation rate of 10.17404 hr from an apparent spot modulation in the K2 data. With two independent measures of rotation, PG 0112+104 provides a remarkable test of asteroseismic inference.
A Preliminary Jupiter Model

In anticipation of new observational results for Jupiter's axial moment of inertia and gravitational zonal harmonic coefficients from the forthcoming Juno orbiter, we present a number of preliminary Jupiter interior models. We combine results from ab initio computer simulations of hydrogen–helium mixtures, including immiscibility calculations, with a new nonperturbative calculation of Jupiter's zonal harmonic coefficients, to derive a self-consistent model for the planet's external gravity and moment of inertia. We assume helium rain modified the interior temperature and composition profiles. Our calculation predicts zonal harmonic values to which measurements can be compared. Although some models fit the observed (pre-Juno) second- and fourth-order zonal harmonics to within their error bars, our preferred reference model predicts a fourth-order zonal harmonic whose absolute value lies above the pre-Juno error bars. This model has a dense core of about 12 Earth masses and a hydrogen–helium-rich envelope with approximately three times solar metallicity.
Hamiltonian pseudo-rotations of projective spaces

The main theme of the paper is the dynamics of Hamiltonian diffeomorphisms of $$\mathbb {C}{\mathbb {P}}^n$$ with the minimal possible number of periodic points (equal to $$n+1$$ by Arnold’s conjecture), called here Hamiltonian pseudo-rotations. We prove several results on the dynamics of pseudo-rotations going beyond periodic orbits, using Floer theoretical methods. One of these results is the existence of invariant sets in arbitrarily small punctured neighborhoods of the fixed points, partially extending a theorem of Le Calvez and Yoccoz and of Franks and Misiurewicz to higher dimensions. The other is a strong variant of the Lagrangian Poincare recurrence conjecture for pseudo-rotations. We also prove the $$C^0$$ -rigidity of pseudo-rotations with exponentially Liouville mean index vector. This is a higher-dimensional counterpart of a theorem of Bramham establishing such rigidity for pseudo-rotations of the disk.
A Uniform Substitution Calculus for Differential Dynamic Logic

This paper introduces a new proof calculus for differential dynamic logic (\(\mathsf {d}\mathcal {L}\)) that is entirely based on uniform substitution, a proof rule that substitutes a formula for a predicate symbol everywhere. Uniform substitutions make it possible to rely on axioms rather than axiom schemata, substantially simplifying implementations. Instead of subtle schema variables and soundness-critical side conditions on the occurrence patterns of variables, the resulting calculus adopts only a finite number of ordinary \(\mathsf {d}\mathcal {L}\) formulas as axioms. The static semantics of differential dynamic logic is captured exclusively in uniform substitutions and bound variable renamings as opposed to being spread in delicate ways across the prover implementation. In addition to sound uniform substitutions, this paper introduces differential forms for differential dynamic logic that make it possible to internalize differential invariants, differential substitutions, and derivations as first-class axioms in \(\mathsf {d}\mathcal {L}\).
Spin superfluidity in noncollinear antiferromagnets

We explore the spin superfluid transport in exchange interaction-dominated three-sublattice antiferromagnets. The system in the long-wavelength regime is described by an $\mathrm{S}\mathrm{O}(3)$ invariant field theory. Additional corrections from Dzyaloshinskii-Moriya interactions or anisotropies can break the symmetry; however, the system still approximately holds a $\mathrm{U}(1)$-rotation symmetry. Thus, the power-law spatial decay signature of spin superfluidity is identified in a nonlocal-measurement setup where the spin injection is described by the generalized spin-mixing conductance. We suggest iron jarosites as promising material candidates for realizing our proposal.
Simple Question Answering with Subgraph Ranking and Joint-Scoring.

Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the literature has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject–relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point we focus on two aspects: improving subgraph selection through a novel ranking method, and leveraging the subject–relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.
The Massive Disk around OH 231.8+4.2

We have obtained 11.7 and 17.9 μm images at the Keck I telescope of the circumstellar dust emission from OH 231.8+4.2, an evolved mass-losing red giant with a well-studied bipolar outflow. We detect both a central unresolved point source with a diameter of less than 05 producing Fν(17.9 μm) = 60 Jy and emission extended more than 1'' away from the star, which is aligned with the bipolar outflow seen on larger scales. We find that the unresolved central source can be explained by an opaque, flared disk with an outer radius of ~5 × 1015 cm and an outer temperature of ~130 K. One possible model to explain this flaring is that the material in the disk is orbiting the central star and not simply undergoing a radial expansion.
Symmetric products of the line: embeddings and retractions

The n-th symmetric product of a metric space is the set of its nonempty subsets with cardinality at most n, equipped with the Hausdorff metric. We prove that every symmetric product of the line is an absolute Lipschitz retract and admits a bi-Lipschitz embedding into a Euclidean space of sufficiently high dimension.
A fractional-spin phase in the power-law Kondo model

We consider a Kondo impurity coupled to a fermionic host with a power-law density of states near the Fermi level, ρ(e) ∼ |e|r, with exponent r < 0. Using both perturbative renormalization group (poor man's scaling) and numerical renormalization group methods, we analyze the phase diagram of this model for ferromagnetic and antiferromagnetic Kondo coupling. Both sectors display non-trivial behavior with several stable phases separated by continuous transitions. In particular, on the ferromagnetic side there is a stable intermediate-coupling fixed point with universal properties corresponding to a fractional ground-state spin.
A Novel Deep Learning Pipeline for Retinal Vessel Detection In Fluorescein Angiography

While recent advances in deep learning have significantly advanced the state of the art for vessel detection in color fundus (CF) images, the success for detecting vessels in fluorescein angiography (FA) has been stymied due to the lack of labeled ground truth datasets. We propose a novel pipeline to detect retinal vessels in FA images using deep neural networks (DNNs) that reduces the effort required for generating labeled ground truth data by combining two key components: cross-modality transfer and human-in-the-loop learning. The cross-modality transfer exploits concurrently captured CF and fundus FA images. Binary vessels maps are first detected from CF images with a pre-trained neural network and then are geometrically registered with and transferred to FA images via robust parametric chamfer alignment to a preliminary FA vessel detection obtained with an unsupervised technique. Using the transferred vessels as initial ground truth labels for deep learning, the human-in-the-loop approach progressively improves the quality of the ground truth labeling by iterating between deep-learning and labeling. The approach significantly reduces manual labeling effort while increasing engagement. We highlight several important considerations for the proposed methodology and validate the performance on three datasets. Experimental results demonstrate that the proposed pipeline significantly reduces the annotation effort and the resulting deep learning methods outperform prior existing FA vessel detection methods by a significant margin. A new public dataset, RECOVERY-FA19, is introduced that includes high-resolution ultra-widefield images and accurately labeled ground truth binary vessel maps.
Inference of Unresolved Point Sources at High Galactic Latitudes Using Probabilistic Catalogs

The detection of point sources in images is a fundamental operation in astrophysics, and is crucial for constraining population models of the underlying point sources or characterizing the background emission. Standard techniques fall short in the crowded-field limit, losing sensitivity to faint sources and failing to track their covariance with close neighbors. We construct a Bayesian framework to perform inference of faint or overlapping point sources. The method involves probabilistic cataloging, where samples are taken from the posterior probability distribution of catalogs consistent with an observed photon count map. In order to validate our method, we sample random catalogs of the gamma-ray sky in the direction of the North Galactic Pole (NGP) by binning the data in energy and point-spread function classes. Using three energy bins spanning 0.3–1, 1–3, and 3–10 GeV, we identify point sources inside a region around the NGP above our point-source inclusion limit of cm−2 s−1 sr−1 GeV−1 at the 1–3 GeV energy bin. Modeling the flux distribution as a power law, we infer the slope to be and estimate the contribution of point sources to the total emission as %. These uncertainties in the flux distribution are fully marginalized over the number as well as the spatial and spectral properties of the unresolved point sources. This marginalization allows a robust test of whether the apparently isotropic emission in an image is due to unresolved point sources or of truly diffuse origin.
Decelerating flows in TeV blazars

Abstract The peak of the de-absorbed energy distribution of the TeV emitting blazars, all of the BL Lacertae (BL) class, can reach values up to ∼10 TeV. In the context of synchrotron-self Compton (SSC) models of relativistic uniformly moving blobs of plasma, such high energy peak emission can be reproduced only by assuming Doppler factors of δ∼50. However, such high values strongly disagree with the unification of FR I radio galaxies and BLs. Additionally, the recent detections of slow, possibly sub-luminal velocities in the sub-pc scale jets of the TeV BLs MKN 421 and MKN 501 suggest that the jets in these sources decelerate very early to mildly relativistic velocities ( Γ∼ a few ). In this work we examine the possibility that the relativistic flows in the TeV BLs are longitudinally decelerating. In this case, modest Lorentz factors (Γ∼15), decelerating down to values compatible with the recent radio interferometric observations, can reproduce the ∼few TeV peak energy of these sources. Furthermore, such decelerating flows are shown to reproduce the observed broadband BL-FR I luminosity ratios.
Near-optimal deviation-proof medium access control designs in wireless networks

Distributed medium access control (MAC) protocols are essential for the proliferation of low-cost, decentralized wireless local area networks (WLANs). Most MAC protocols are designed with the presumption that nodes comply with prescribed rules. However, selfish nodes have natural motives to manipulate protocols in order to improve their own performance. This often degrades the performance of other nodes as well as that of the overall system. In this paper, we propose a class of protocols that limit the performance gain from selfish manipulation while incurring only a small efficiency loss. The proposed protocols are based on the idea of a review strategy, with which nodes collect signals about the actions of other nodes over a period of time, use a statistical test to infer whether or not other nodes are following the prescribed behavior, and trigger a punishment if a deviation is inferred. We consider the cases of private and public signals and provide analytical and numerical results to demonstrate the properties of the proposed protocols.
A Dark Year for Tidal Disruption Events

Main-sequence disruptions of stars by supermassive black holes result in the production of an extended, geometrically thin debris stream winding repeatedly around the black hole. In the absence of black hole spin, in-plane relativistic precession causes this stream to intersect with itself after a single winding. In this paper we show that relativistic precessions arising from black hole spin can induce deflections out of the original orbital plane that prevent the stream from self-intersecting even after many windings. This naturally leads to a “dark period” in which the flare is not observable for some time, persisting for up to a dozen orbital periods of the most bound material, which translates to years for disruptions around black holes with masses . When the stream eventually self-intersects, the distance from the black hole and the angle at which this collision occurs determine the rate of energy dissipation. We find that more-massive black holes () tend to have more violent stream self-intersections, resulting in prompt accretion. For these tidal disruption events (TDEs), the accretion rate onto the black hole should still closely follow the original fallback rate after a fixed delay time , . For lower black hole masses (), we find that flares are typically slowed down by about an order of magnitude, resulting in the majority of TDEs being sub-Eddington at peak. This also implies that current searches for TDEs are biased toward prompt flares, with slowed flares likely having been unidentified.
Duality in Quantum Quenches and Classical Approximation Algorithms: Pretty Good or Very Bad

We consider classical and quantum algorithms which have a duality property: roughly, either the algorithm provides some nontrivial improvement over random or there exist many solutions which are significantly worse than random. This enables one to give guarantees that the algorithm will find such a nontrivial improvement: if few solutions exist which are much worse than random, then a nontrivial improvement is guaranteed. The quantum algorithm is based on a sudden of a Hamiltonian; while the algorithm is general, we analyze it in the specific context of MAX-$K$-LIN$2$, for both even and odd $K$. The classical algorithm is a “dequantization of this algorithm”, obtaining the same guarantee (indeed, some results which are only conjectured in the quantum case can be proven here); however, the quantum point of view helps in analyzing the performance of the classical algorithm and might in some cases perform better.
Probing the Circular Polarization of Relativistic Jets on VLBI Scales

High resolution studies of circular polarization allow us see where it arises in a jet, study its local fractional level and spectrum, and compare these results to local measures of linear polarization and Faraday rotation. Here we not only review past results from Very Long Baseline Array (VLBA) circular polarization studies, but we also present preliminary new results on two quasars. In the core of PKS 0607–157, we find strong circular polarization at 8 GHz and much weaker levels at 15 GHz. Combined with the linear polarization data, we favor a simple model where the circular is produced by Faraday conversion driven by a small amount of Faradayrotation. In the core of 3C 345, we find strong circular polarization at 15 GHz in a component with distinct linear polarization. This core component is optically thick at 8 GHz, where we detect no circular polarization. With opposite trends in frequency for PKS 0607–157 and 3C 345, it seems clear that local conditions in a jet can have a strong effect on circular polarization and need to be taken into account when studying inhomogeneous objects with multi-frequency observations.
Provision of contraceptive implants in school-based health centers: A cost-effectiveness analysis.

Abstract Objective To evaluate the cost-effectiveness of providing contraceptive implants in school-based health centers (SBHCs) compared to the practice of referring adolescents to non-SBHCs in New York City. Study Design We developed a microsimulation model of teen pregnancy to estimate the cost-effectiveness of immediate provision of contraceptive implants at SBHCs over a 3-year time horizon. Model parameters were derived from both a retrospective chart review of patient data and published literature. The model projected the number of pregnancies as well as the total costs for each intervention scenario. The incremental cost-effectiveness ratio was calculated using the public payer perspective, using direct costs only. Results The health care cost of immediate provision of contraceptive implants at SBHCs was projected to be $13,719 per person compared to $13,567 per person for delayed provision at the referral appointment over 3 years. However, immediate provision would prevent 78 more pregnancies per 1000 adolescents over 3 years. The incremental cost-effectiveness ratio for implementing in-school provision was $1940 per additional pregnancy prevented, which was less than the $4206.41 willingness-to-pay threshold. Sensitivity analyses showed that the cost-effectiveness conclusion was robust over a wide range of key model inputs. Conclusion Provision of contraceptive implants in SBHCs compared to non-SBHCs is cost-effective for preventing unintended teen pregnancy. Health care providers and policymakers should consider expanding this model of patient-centered health care delivery to other locations.
Vanishing theorems for representation homology and the derived cotangent complex

Let G be a reductive affine algebraic group defined over a field k of characteristic zero. We study the cotangent complex of the derived G –representation scheme DRep G ( X ) of a pointed connected topological space X . We use an (algebraic version of) unstable Adams spectral sequence relating the cotangent homology of DRep G ( X ) to the representation homology HR ∗ ( X , G ) : = π ∗ O [ DRep G ( X ) ] to prove some vanishing theorems for groups and geometrically interesting spaces. Our examples include virtually free groups, Riemann surfaces, link complements in ℝ 3 and generalized lens spaces. In particular, for any finitely generated virtually free group Γ , we show that HR i ( B Γ , G ) = 0 for all i > 0 . For a closed Riemann surface Σ g of genus g ≥ 1 , we have HR i ( Σ g , G ) = 0 for all i > dim G . The sharp vanishing bounds for Σ g actually depend on the genus: we conjecture that if g = 1 , then HR i ( Σ g , G ) = 0 for i > r a n k G , and if g ≥ 2 , then HR i ( Σ g , G ) = 0 for i > dim Z ( G ) , where Z ( G ) is the center of G . We prove these bounds locally on the smooth locus of the representation scheme Rep G [ π 1 ( Σ g ) ] in the case of complex connected reductive groups. One important consequence of our results is the existence of a well-defined K –theoretic virtual fundamental class for DRep G ( X ) in the sense of Ciocan-Fontanine and Kapranov (Geom. Topol. 13 (2009) 1779–1804). We give a new “Tor formula” for this class in terms of functor homology.
The Return of Lexical Dependencies: Neural Lexicalized PCFGs

In this paper we demonstrate that context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies . This contrasts to the most popular current methods for grammar induction, which focus on discovering either constituents or dependencies. Previous approaches to marry these two disparate syntactic formalisms (e.g. lexicalized PCFGs) have been plagued by sparsity, making them unsuitable for unsupervised grammar induction. However, in this work, we present novel neural models of lexicalized PCFGs which allow us to overcome sparsity problems and effectively induce both constituents and dependencies within a single model. Experiments demonstrate that this unified framework results in stronger results on both representations than achieved when modeling either formalism alone. Code is available at https://github.com/neulab/neural-lpcfg .
Spin Hurwitz numbers and topological quantum field theory

Spin Hurwitz numbers count ramified covers of a spin surface, weighted by the size of their automorphism group (like ordinary Hurwitz numbers), but signed ± 1 according to the parity of the covering surface. These numbers were first defined by Eskin, Okounkov and Pandharipande in order to study the moduli of holomorphic differentials on a Riemann surface. They have also been related to Gromov–Witten invariants of complex 2–folds by work of Lee and Parker and work of Maulik and Pandharipande. In this paper, we construct a (spin) TQFT which computes these numbers, and deduce a formula for any genus in terms of the combinatorics of the Sergeev algebra, generalizing the formula of Eskin, Okounkov and Pandharipande. During the construction, we describe a procedure for averaging any TQFT over finite covering spaces based on the finite path integrals of Freed, Hopkins, Lurie and Teleman.
Inversion of the star transform

We define the star transform as a generalization of the broken ray transform introduced by us in previous work. The advantages of using the star transform include the possibility to reconstruct the absorption and the scattering coefficients of the medium separately and simultaneously (from the same data) and the possibility to utilize scattered radiation which, in the case of conventional x-ray tomography, is discarded. In this paper, we derive the star transform from physical principles, discuss its mathematical properties and analyze numerical stability of inversion. In particular, it is shown that stable inversion of the star transform can be obtained only for configurations involving odd number of rays. Several computationally-efficient inversion algorithms are derived and tested numerically.
Linking Literature and Data: Status Report and Future Efforts

In the current era of data-intensive science, it is increasingly important for researchers to be able to have access to published results, the supporting data, and the processes used to produce them. Six years ago, recognizing this need, the American Astronomical Society and the Astrophysics Data Centers Executive Committee (ADEC) sponsored an effort to facilitate the annotation and linking of datasets during the publishing process, with limited success. I will review the status of this effort and describe a new, more general one now being considered in the context of the Virtual Astronomical Observatory.
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments

Randomized experiments ensure robust causal inference that is critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B tests, are limiting in large scale digital learning environments. While traditional experiments can accurately compare two treatment options, they are less able to inform how to adapt interventions to continually meet learners' diverse needs. In this work, we introduce a trial design for developing adaptive interventions in scaled digital learning environments -- the sequential randomized trial (SRT). With the goal of improving learner experience and developing interventions that benefit all learners at all times, SRTs inform how to sequence, time, and personalize interventions. In this paper, we provide an overview of SRTs, and we illustrate the advantages they hold compared to traditional experiments. We describe a novel SRT run in a large scale data science MOOC. The trial results contextualize how learner engagement can be addressed through culturally-targeted reminder emails. We also provide practical advice for researchers who aim to run their own SRTs to develop adaptive interventions in scaled digital learning environments.
Spontaneous emission control in high-extraction efficiency plasmonic crystals

We experimentally and theoretically investigate exciton-field coupling for the surface plasmon polariton (SPP) in waveguide-confined (WC) anti-symmetric modes of hexagonal plasmonic crystals in InP-TiO-Au-TiO-Si heterostructures. The radiative decay time of the InP-based transverse magnetic (TM)-strained multi-quantum well (MQW) coupled to the SPP modes is observed to be 2.9–3.7 times shorter than that of a bare MQW wafer. Theoretically we find that 80% of the enhanced photoluminescence (PL) is emitted into SPP modes, and 17% of the enhanced PL is redirected into WC-anti-symmetric modes. In addition to the direct coupling of the excitons to the plasmonic modes, this demonstration is also useful for the development of high-temperature SPP lasers, the development of highly integrated photo-electrical devices, or miniaturized biosensors.
Clustering of cosmic string loops

Observational effects of cosmic string loops depend on how loops are distributed in space. Chernoff [1] has argued that loops can be gravitationally captured in galaxies and that for sufficiently small values of Gμ their distribution follows that of dark matter, independently of the loop's length. We re-analyze this issue using the spherical model of galaxy formation with full account taken of the gravitational rocket effect—loop accelerated motion due to asymmetric emission of gravitational waves. We find that only loops greater than a certain size are captured and that the number of captured loops is orders of magnitude smaller than estimated by Chernoff.
A Data Science Approach to Understanding Residential Water Contamination in Flint

When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system. In this paper, we predict the lead contamination for each household's water supply, and we study several related aspects of Flint's water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by Google.org. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water.
RADIATION ENERGY BALANCE METHOD FOR CALCULATING THE TIME EVOLUTION OF TYPE Ia SUPERNOVAE DURING THE POST-EXPLOSION PHASE

A new method is presented for calculating the time evolution of spherically symmetric Type Ia Supernovae in the post-explosion phase, enabling light curves and spectra to be simulated in a physically self-consistent way. The commonly exploited radiative equilibrium (RE), which is in essence a gas energy balance condition, is unsuitable for this purpose for important physical and numerical reasons. First, the RE depends on the heating and cooling rates of the gas by the radiation field, two quantities that almost completely cancel out and are very difficult to calculate accurately. Second, the internal energy of the gas is only a tiny fraction of the total energy in the system (the vast majority of the energy resides in the radiation field), so that the vast majority of the energy is neglected in solving for the energy balance. The method presented in this paper, based on the radiation energy balance (REB), addresses the bulk of the energy, does not depend on the heating/cooling rates, and guarantees an accurate run of the bolometric luminosity over time while making the gas temperatures consistent with the radiation field. We have implemented the method in the stellar atmosphere code PHOENIX and applied it to the classical W7 model. The results illustrate the importance of each of the four physical contributions to the energy balance as a function of time. The simulated spectra and light curves for W7 show good resemblance to the observations, which demonstrates what can be done using PHOENIX with the REB method.
Bootstrapping Domain-Specific Content Discovery on the Web

The ability to continuously discover domain-specific content from the Web is critical for many applications. While focused crawling strategies have been shown to be effective for discovery, configuring a focused crawler is difficult and time-consuming. Given a domain of interest D, subject-matter experts (SMEs) must search for relevant websites and collect a set of representative Web pages to serve as training examples for creating a classifier that recognizes pages in D, as well as a set of pages to seed the crawl. In this paper, we propose DISCO, an approach designed to bootstrap domain-specific search. Given a small set of websites , DISCO aims to discover a large collection of relevant websites . DISCO uses a ranking-based framework that mimics the way users search for information on the Web: it iteratively discovers new pages, distills, and ranks them. It also applies multiple discovery strategies, including keyword-based and related queries issued to search engines, backward and forward crawling. By systematically combining these strategies, DISCO is able to attain high harvest rates and coverage for a variety of domains. We perform extensive experiments in four social-good domains, using data gathered by SMEs in the respective domains, and show that our approach is effective and outperforms state-of-the-art methods.
Learning Flexible and Reusable Locomotion Primitives for a Microrobot

The design of gaits for robot locomotion can be a daunting process, which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alternative to analytical gait design. In this letter, we propose a novel approach to efficiently learn a wide range of locomotion tasks with walking robots. This approach formalizes locomotion as a contextual policy search task to collect data, and subsequently uses that data to learn multiobjective locomotion primitives that can be used for planning. As a proof-of-concept we consider a simulated hexapod modeled after a recently developed microrobot, and we thoroughly evaluate the performance of this microrobot on different tasks and gaits. Our results validate the proposed controller and learning scheme on single and multiobjective locomotion tasks. Moreover, the experimental simulations show that without any prior knowledge about the robot used (e.g., dynamics model), our approach is capable of learning locomotion primitives within 250 trials and subsequently using them to successfully navigate through a maze.
Plithogenic Set, an Extension of Crisp, Fuzzy, Intuitionistic Fuzzy, and Neutrosophic Sets - Revisited

In this paper, we introduce the plithogenic set (as generalization of crisp, fuzzy, intuitionistic fuzzy, and neutrosophic sets), which is a set whose elements are characterized by many attributes’ values. An attribute value v has a corresponding(fuzzy, intuitionistic fuzzy, or neutrosophic) degree of appurtenance d(x,v) of the element x, to the set P, with respect to somegiven criteria. In order to obtain a better accuracy for the plithogenic aggregation operators in the plithogenic set, and for a moreexact inclusion (partial order), a (fuzzy, intuitionistic fuzzy, or neutrosophic) contradiction (dissimilarity) degree is defined between each attribute value and the dominant (most important) attribute value. The plithogenic intersection and union are linearcombinations of the fuzzy operators tnorm and tconorm, while the plithogenic complement, inclusion (inequality), equality are influenced by the attribute values contradiction (dissimilarity) degrees. This article offers some examples and applications of thesenew concepts in our everyday life.
Magnetic Sensitivity of AlMn TESes and Shielding Considerations for Next-Generation CMB Surveys

In the next decade, new ground-based cosmic microwave background (CMB) experiments such as Simons Observatory, CCAT-prime, and CMB-S4 will increase the number of detectors observing the CMB by an order of magnitude or more, dramatically improving our understanding of cosmology and astrophysics. These projects will deploy receivers with as many as hundreds of thousands of transition edge sensor (TES) bolometers coupled to superconducting quantum interference device (SQUID)-based readout systems. It is well known that superconducting devices such as TESes and SQUIDs are sensitive to magnetic fields. However, the effects of magnetic fields on TESes are not easily predicted due to the complex behavior of the superconducting transition, which motivates direct measurements of the magnetic sensitivity of these devices. We present comparative four-lead measurements of the critical temperature versus applied magnetic field of AlMn TESes varying in geometry, doping, and leg length, including Advanced ACT and POLARBEAR-2/Simons Array bolometers. MoCu ACTPol TESes are also tested and are found to be more sensitive to magnetic fields than the AlMn devices. We present an observation of weak-link-like behavior in AlMn TESes at low critical currents. We also compare measurements of magnetic sensitivity for time division multiplexing SQUIDs and frequency division multiplexing microwave ( $$\mu $$ MUX) rf-SQUIDs. We discuss the implications of our measurements on the magnetic shielding required for future experiments that aim to map the CMB to near-fundamental limits.
Effect of the Cosmological Constant on Halo Size

In this work, we consider the effect of the cosmological constant on galactic halo size. As a model, we study the general relativistic derivation of orbits in the Schwarzschild–de Sitter metric. We...
Naive Bayesian Learning in Social Networks

The DeGroot model of naive social learning assumes that agents only communicate scalar opinions. In practice, agents communicate not only their opinions, but their confidence in such opinions. We propose a model that captures this aspect of communication by incorporating signal informativeness into the naive social learning scenario. Our proposed model captures aspects of both Bayesian and naive learning. Agents in our model combine their neighbors' beliefs using Bayes' rule, but the agents naively assume that their neighbors' beliefs are independent. Depending on the initial beliefs, agents in our model may not reach a consensus, but we show that the agents will reach a consensus under mild continuity and boundedness assumptions on initial beliefs. This eventual consensus can be explicitly computed in terms of each agent's centrality and signal informativeness, allowing joint effects to be precisely understood. We apply our theory to adoption of new technology. In contrast to Banerjee et al. [2018], we show that information about a new technology can be seeded initially in a tightly clustered group without information loss, but only if agents can expressively communicate their beliefs.
Path length distribution in two-dimensional causal sets

We study the distribution of maximal-chain lengths between two elements of a causal set and its relationship with the embeddability of the causal set in a region of flat spacetime. We start with causal sets obtained from uniformly distributed points in Minkowski space. After some general considerations we focus on the 2-dimensional case and derive a recursion relation for the expected number of maximal chains $$n_k$$ as a function of their length k and the total number of points N between the maximal and minimal elements. By studying these theoretical distributions as well as ones generated from simulated sprinklings in Minkowski space we identify two features, the most probable path length or peak of the distribution $$k_0$$ and its width $$\varDelta $$ , which can be used both to provide a measure of the embeddability of the causal set as a uniform distribution of points in Minkowski space and to determine its dimensionality, if the causal set is manifoldlike in that sense. We end with a few simple examples of $$n_k$$ distributions for non-manifoldlike causal sets.
A PRIMER ON UNIFYING DEBRIS DISK MORPHOLOGIES

A "minimum model" for debris disks consists of a narrow ring of parent bodies, secularly forced by a single planet on a possibly eccentric orbit, colliding to produce dust grains that are perturbed by stellar radiation pressure. We demonstrate how this minimum model can reproduce a wide variety of disk morphologies imaged in scattered starlight. Five broad categories of disk shape can be captured: "rings," "needles," "ships-and-wakes," "bars," and "moths (a.k.a. fans)," depending on the viewing geometry. Moths can also sport "double wings." We explain the origin of morphological features from first principles, exploring the dependence on planet eccentricity, disk inclination dispersion, and the parent body orbital phases at which dust grains are born. A key determinant in disk appearance is the degree to which dust grain orbits are apsidally aligned. Our study of a simple steady-state (secularly relaxed) disk should serve as a reference for more detailed models tailored to individual systems. We use the intuition gained from our guidebook of disk morphologies to interpret, informally, the images of a number of real-world debris disks. These interpretations suggest that the farthest reaches of planetary systems are perturbed by eccentric planets, possibly just a few Earth masses each.