cssi/SciSciGPT-SciSciNet huggingface dataset:
Is Spanish-Only Schooling Responsible for the Puerto Rican Language Gap?

Abstract Between 1898 and 1948, English was the language of instruction for most post-primary grades in Puerto Rican public schools. Since 1949, the language of instruction in all grades has been Spanish. We use these shifts in language of instruction policy to estimate the effect of English-intensive instruction on the English-language skills of Puerto Ricans. Although naive estimates suggest that English instruction increased English-speaking ability among Puerto Rican natives, estimates that allow for education-specific cohort trends show no effect. This result is surprising in light of the strong presumption by American policymakers at the time that instruction in English is the best way to raise English proficiency. This has implications for school language policies in former colonies as well as for U.S. education policy toward immigrant children.
FakeDetector: Effective Fake News Detection with Deep Diffusive Neural Network

In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. This paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance. This paper addresses the challenges introduced by the unknown characteristics of fake news and diverse connections among news articles, creators and subjects. This paper introduces a novel gated graph neural network, namely FAKEDETECTOR. Based on a set of explicit and latent features extracted from the textual information, FAKEDETECTOR builds a deep diffusive network model to learn the representations of news articles, creators and subjects simultaneously. Extensive experiments have been done on a real-world fake news dataset to compare FAKEDETECTOR with several state-of-the-art models, and the experimental results are provided in the full-version of this paper at [13].
Experiences with Improving the Transparency of AI Models and Services

AI models and services are used in a growing number of high-stakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality and more consistent documentation of AI data, models, and systems have emerged. Little is known, however, about the needs of those who would produce or consume these new forms of documentation. Through semi-structured developer interviews, and two document-creation exercises, we have assembled a clearer picture of these needs and the various challenges faced in creating accurate and useful AI documentation. Based on the observations from this work, supplemented by feedback received during multiple design explorations and stakeholder conversations, we make recommendations for easing the collection and flexible presentation of AI facts to promote transparency.
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization

We develop two new stochastic Gauss-Newton algorithms for solving a class of non-convex stochastic compositional optimization problems frequently arising in practice. We consider both the expectation and finite-sum settings under standard assumptions, and use both classical stochastic and SARAH estimators for approximating function values and Jacobians. In the expectation case, we establish $\mathcal{O}(\varepsilon^{-2})$ iteration-complexity to achieve a stationary point in expectation and estimate the total number of stochastic oracle calls for both function value and its Jacobian, where $\varepsilon$ is a desired accuracy. In the finite sum case, we also estimate $\mathcal{O}(\varepsilon^{-2})$ iteration-complexity and the total oracle calls with high probability. To our best knowledge, this is the first time such global stochastic oracle complexity is established for stochastic Gauss-Newton methods. Finally, we illustrate our theoretical results via two numerical examples on both synthetic and real datasets.
Banning information in hiring decisions

Over the past decade, federal, state and local governments have restricted the types and uses of information in hiring and promotion decisions. Examples include the banning of credit reports and criminal records. This paper presents a simple microeconomic model of a competitive labor market and studies the economic effects of information bans. The key trade off is between allocative efficiency and helping a labor type with an undesirable characteristic. We compare information bans to direct subsidies. Moreover, we discuss the case where the ban creates negative feedback on the perceptions of firms considering workers of the bad type.
Mobility Management for Cellular-Connected UAVs: A Learning-Based Approach

The pervasiveness of the wireless cellular network can be a key enabler for the deployment of autonomous unmanned aerial vehicles (UAVs) in beyond visual line of sight scenarios without human control. However, traditional cellular networks are optimized for ground user equipment (GUE) such as smartphones which makes providing connectivity to flying UAVs very challenging. Moreover, ensuring better connectivity to a moving cellular-connected UAV is notoriously difficult due to the complex air-to-ground path loss model. In this paper, a novel mechanism is proposed to ensure robust wireless connectivity and mobility support for cellular-connected UAVs by tuning the downtilt (DT) angles of all the ground base stations (GBSs). By leveraging tools from reinforcement learning (RL), DT angles are dynamically adjusted by using a model-free RL algorithm. The goal is to provide efficient mobility support in the sky by maximizing the received signal quality at the UAV while also maintaining good throughput performance of the ground users. Simulation results show that the proposed RL-based mobility management (MM) technique can reduce the number of handovers while maintaining the performance goals, compared to the baseline MM scheme in which the network always keeps the DT angle fixed.
Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China

As an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.
Synthesis of pentasubstituted 2-aryl pyrroles from boryl and stannyl alkynes via one-pot sequential Ti-catalyzed [2 + 2 + 1] pyrrole synthesis/cross coupling reactions.

Multisubstituted pyrroles are commonly found in many bioactive small molecule scaffolds, yet the synthesis of highly-substituted pyrrole cores remains challenging. Herein, we report an efficient catalytic synthesis of 2-heteroatom-substituted (9-BBN or SnR3) pyrroles via Ti-catalyzed [2 + 2 + 1] heterocoupling of heteroatom-substituted alkynes. In particular, the 9-BBN-alkyne coupling reactions were found to be very sensitive to Lewis basic ligands in the reaction: exchange of pyridine ligands from Ti to B inhibited catalysis, as evidenced by in situ11B NMR studies. The resulting 2-boryl substituted pyrroles can then be used in Suzuki reactions in a one-pot sequential fashion, resulting in pentasubstituted 2-aryl pyrroles that are inaccessible via previous [2 + 2 + 1] heterocoupling strategies. This reaction provides a complementary approach to previous [2 + 2 + 1] heterocouplings of TMS-substituted alkynes, which could be further functionalized via electrophilic aromatic substitution.
Erythropoietin Regulates Transcription and YY1 Dynamics in a Pre-established Chromatin Architecture.

Summary The three-dimensional architecture of the genome plays an essential role in establishing and maintaining cell identity. However, the magnitude and temporal kinetics of changes in chromatin structure that arise during cell differentiation remain poorly understood. Here, we leverage a murine model of erythropoiesis to study the relationship between chromatin conformation, the epigenome, and transcription in erythroid cells. We discover that acute transcriptional responses induced by erythropoietin (EPO), the hormone necessary for erythroid differentiation, occur within an invariant chromatin topology. Within this pre-established landscape, Yin Yang 1 (YY1) occupancy dynamically redistributes to sites in proximity of EPO-regulated genes. Using HiChIP, we identify chromatin contacts mediated by H3K27ac and YY1 that are enriched for enhancer-promoter interactions of EPO-responsive genes. Taken together, these data are consistent with an emerging model that rapid, signal-dependent transcription occurs in the context of a pre-established chromatin architecture.
Frequency Specification Testing of Analog Filters Using Wavelet Transform of Dynamic Supply Current

Wavelet transform has the property of resolving signal in both time and frequency unlike Fourier transform. In this work, we show that time-domain information obtained from wavelet analysis of supply current can be used to test the frequency specification of analog filters efficiently. The pole/zero locations in the frequency response of analog filters shift due to change in component values with process variations. It is essential to test the filters for the shift in frequency response and fix it during production test. Wavelet analysis of supply current can be a promising alternative to test frequency specification of analog filters, since it needs only one AC stimulus and is virtually unaffected by transistor threshold variation. Simulation results on two test circuits demonstrate that we can estimate pole/zero shift with less than 3% error using only one measurement, which requires about 18 measurements in the conventional technique.
Decoding network-mediated retinal response to electrical stimulation: implications for fidelity of prosthetic vision

Objective. Patients with photovoltaic subretinal implant PRIMA demonstrated letter acuity ~0.1 logMAR worse than sampling limit for 100μm pixels (1.3 logMAR) and performed slower than healthy subjects tested with equivalently pixelated images. To explore the underlying differences between natural and prosthetic vision, we compare the fidelity of retinal response to visual and subretinal electrical stimulation through single-cell modeling and ensemble decoding. Approach. Responses of retinal ganglion cells (RGC) to optical or electrical white noise stimulation in healthy and degenerate rat retinas were recorded via MEA. Each RGC was fit with linear-nonlinear (LN) and convolutional neural network (CNN) models. To characterize RGC noise, we compared statistics of spike-triggered averages (STA) in RGCs responding to electrical or visual stimulation of healthy and degenerate retinas. At the population level, we constructed a linear decoder to determine the accuracy of the ensemble of RGCs on N-way discrimination tasks. Main results. Although computational models can match natural visual responses well (correlation ~0.6), they fit significantly worse to spike timings elicited by electrical stimulation of the healthy retina (correlation ~0.15). In the degenerate retina, response to electrical stimulation is equally bad. The signal-to-noise ratio of electrical STAs in degenerate retinas matched that of the natural responses when 78±6.5% of the spikes were replaced with random timing. However, the noise in RGC responses contributed minimally to errors in ensemble decoding. The determining factor in accuracy of decoding was the number of responding cells. To compensate for fewer responding cells under electrical stimulation than in natural vision, more presentations of the same stimulus are required to deliver sufficient information for image decoding. Significance. Slower-than-natural pattern identification by patients with the PRIMA implant may be explained by the lower number of electrically activated cells than in natural vision, which is compensated by a larger number of the stimulus presentations.
A Derivative Formula for the Free Energy Function

We consider bond percolation on the Zd lattice. Let Mn be the number of open clusters in B(n)=[−n,n]d. It is well known that EpMn/(2n+1)d converges to the free energy function κ(p) at the zero field. In this paper, we show that \(\sigma^{2}_{p}(M_{n})/(2n+1)^{d}\) converges to −p(1−p)κ′(p).
Polarization transfer from the twisted light to an atom

When polarized light is absorbed by an atom, the excited atomic system carries information about the initial polarization of light. For the light that carries an orbital angular momentum, or the twisted light, the polarization states are described by eight independent parameters, as opposed to three Stokes parameters for plane waves. We use a parameterization of the spin-density matrix of the twisted light in terms of vector and tensor polarization, in analogy with massive spin-1 particles, and derive formulae that define atom's response to specific polarization components of the twisted light. It is shown that for dipole ($S\to P$) atomic transitions, the atom's polarization is in one-to-one correspondence with polarization of the incident light; this relation is violated, however, for the transitions of higher multipolarity ($S\to D$, $S\to F$, etc.) We pay special attention to contributions of the longitudinal electric field into the matrix elements of atomic transitions.
Fluctuations in the Ionizing Background During and After Helium Reionization

The radiation background above the ionization edge of He II varies strongly during and after helium reionization, because the attenuation length of such photons is relatively short (40Mpc) and because the ionizing sources (quasars) are rare. Here we construct analytic and Monte Carlo models to examine these fluctuations, including, for the first time, those during the reionization era itself. In agreement with detailed numerical simulations, our analytic model for the post-reionization universe predicts order-of-magnitude fluctuations in the He II ionization rate Γ. Observations of the hardness ratio He II/H I show somewhat larger fluctuations, which may be due to more complicated radiative transfer effects. During reionization, the fluctuations are even stronger. In contrast to hydrogen reionization, our model predicts that regions with strong He II Lyα forest transmission should be reasonably common even during the beginning stages of reionization, because of strong illumination from nearby bright quasars. Partly because of this, the mean ionizing background does not evolve strongly during and after helium reionization; it is roughly proportional to the filling fraction of He III regions. On the other hand, regions full of He II and also fossil-ionized regions that contain no (or few) active sources appear as strong intergalactic absorbers. Their presence exaggerates the evolution of the hardness ratio, making it evolve more strongly than naively expected during the reionization era.
THE BINARY NATURE OF THE SUBGIANT CH STARS

Repeated radial velocities have been measured for a sample of 10 subgiant CH (sgCH) stars over a period of ~15 years. Long-term velocity variations are exhibited by all but one star, and spectroscopic orbits have been calculated for six of them. The periods are long, ranging from 876 to 4144 days. The distribution of eccentricities and mass functions for sgCH star orbits are similar to those for giant barium and CH stars, exhibiting significant orbital dissipation relative to normal late-type binaries. It is concluded that all sgCH stars are binaries, and like the barium and CH stars, they have had mass transferred from a former asymptotic giant-branch star.
On the Gaudin model associated to Lie algebras of classical types

We derive explicit formulas for solutions of the Bethe ansatz equations of the Gaudin model associated to the tensor product of one arbitrary finite-dimensional irreducible module and one vector representation for all simple Lie algebras of classical type. We use this result to show that the Bethe ansatz is complete in any tensor product where all but one factor are vector representations and the evaluation parameters are generic.
Hourly Variability in Q0957+561

We have continued our effort to re-reduce archival Q0957+561 brightness monitoring data and present results for 1629 R-band images using the methods of galaxy subtraction and seeing correction that we reported previously. The new data set comes from four observing runs, several nights apiece, with sampling of typically 5 minutes, which allows the first measurement of the structure function for variations in the R band from timescales of hours to years. Comparison of our reductions to previous reductions of the same data and to r-band photometry produced at Apache Point Observatory shows good overall agreement. Two of the data runs, separated by 417 days, allow us for the first time to use intranight variations to estimate the time delay. However, our sharpened value of 417.4 days is valid only if the time delay is close to the now-fashionable 417 day value, and worse, it is highly subject to diurnal windowing effects: our statistical estimators have little discriminatory power where there is no overlap of data, namely, at half-day offsets. Results herein show no unambiguous signature of daily microlensing, though a suggestive feature is found in the data. Because both time delay measurement and microlensing searches suffer from the lack of sampling at half-day offsets, inevitable at a single observatory, we discuss the need for round-the-clock monitoring with participation by multiple observatories.
Thermodynamics and cosmological constant of non-local field theories from p-adic strings

We develop the thermodynamics of field theories characterized by non-local propagators. We analyze the partition function and main thermodynamic properties arising from perturbative thermal loops. We focus on the p-adic models associated with the tachyon phenomenology in string theories. We reproduce well known features of these theories, but also obtain many new results. In particular, we explain how to maintain consistency of such non-local theories by avoiding the appearance of ghosts at finite temperature. As a consequence of this fact, the vacuum energy in p-adic theories becomes positive. It is also hierarchically suppressed, and we explore the parameter space where it is consistent with the observed value of the cosmological constant.
Improving Correlation Function Fitting with Ridge Regression: Application to Cross-correlation Reconstruction

Cross-correlation techniques provide a promising avenue for calibrating photometric redshifts and determining redshift distributions using spectroscopy which is systematically incomplete (e.g., current deep spectroscopic surveys fail to obtain secure redshifts for 30%-50% or more of the galaxies targeted). In this paper, we improve on the redshift distribution reconstruction methods from our previous work by incorporating full covariance information into our correlation function fits. Correlation function measurements are strongly covariant between angular or spatial bins, and accounting for this in fitting can yield substantial reduction in errors. However, frequently the covariance matrices used in these calculations are determined from a relatively small set (dozens rather than hundreds) of subsamples or mock catalogs, resulting in noisy covariance matrices whose inversion is ill-conditioned and numerically unstable. We present here a method of conditioning the covariance matrix known as ridge regression which results in a more well behaved inversion than other techniques common in large-scale structure studies. We demonstrate that ridge regression significantly improves the determination of correlation function parameters. We then apply these improved techniques to the problem of reconstructing redshift distributions. By incorporating full covariance information, applying ridge regression, and changing the weighting of fields in obtaining average correlation functions, we obtain reductions in the mean redshift distribution reconstruction error of as much as ~40% compared to previous methods. We provide a description of POWERFIT, an IDL code for performing power-law fits to correlation functions with ridge regression conditioning that we are making publicly available.
REGULARIZATION OF TUNNELING RATES WITH QUANTUM CHAOS

We study tunneling in various shaped, closed, two-dimensional, flat-potential, double wells by calculating the energy splitting between symmetric and antisymmetric state pairs. For shapes that have regular or nearly regular classical behavior (e.g. rectangular or circular) the tunneling rates vary greatly over wide ranges often by several orders of magnitude. However, for well shapes that admit more classically chaotic behavior (e.g. the stadium, the Sinai billiard) the range of tunneling rates narrows, often by orders of magnitude. This dramatic narrowing appears to come from destabilization of periodic orbits in the regular wells that produce the largest and smallest tunneling rates and causes the splitting versus energy relation to take on a possibly universal shape. It is in this sense that we say the quantum chaos regularizes the tunneling rates.
Stopping quirks at the LHC

Quirks are exotic particles charged under a new confining gauge group that can give rise to unique collider signatures, depending on their vector-like mass, quantum numbers, and the confinement scale. In this work, we consider the possibility that quirks produced at the LHC lose all of their kinetic energy through ionization loss before escaping the detector, and annihilate at a time when there are no active pp collisions. We recast an existing CMS search for out-of-time decays of R-hadrons to place new limits on quirk parameter space. We propose several simple modifications to the existing out-of-time search strategy that can give these searches sensitivity in regions of quirk parameter space not covered by any existing or proposed search strategy.
ON CONFIGURATIONS WHERE THE LOOMIS–WHITNEY INEQUALITY IS NEARLY SHARP AND APPLICATIONS TO THE FURSTENBERG SET PROBLEM

In this paper, we consider the so-called “Furstenberg set problem” in high dimensions. First, following Wolff’s work on the two-dimensional real case, we provide “reasonable” upper bounds for the problem for . Our key tool in obtaining this lower bound is a theorem about how things behave when the Loomis–Whitney inequality is nearly sharp, as it helps us to reduce the problem to dimension two.
Quantum Causal Influence

We introduce a framework to study the emergence of time and causal structure in quantum many-body systems. In doing so, we consider quantum states which encode spacetime dynamics, and develop information theoretic tools to extract the causal relationships between putative spacetime subsystems. Our analysis reveals a quantum generalization of the thermodynamic arrow of time and begins to explore the roles of entanglement, scrambling and quantum error correction in the emergence of spacetime. For instance, exotic causal relationships can arise due to dynamically induced quantum error correction in spacetime: there can exist a spatial region in the past which does not causally influence any small spatial regions in the future, but yet it causally influences the union of several small spatial regions in the future. We provide examples of quantum causal influence in Hamiltonian evolution, quantum error correction codes, quantum teleportation, holographic tensor networks, the final state projection model of black holes, and many other systems. We find that the quantum causal influence provides a unifying perspective on spacetime correlations in these seemingly distinct settings. In addition, we prove a variety of general structural results and discuss the relation of quantum causal influence to spacetime quantum entropies.
Join-idle-queue with service elasticity: large-scale asymptotics of a non-monotone system

We consider the model of a token-based joint autoscaling and load-balancing strategy, proposed in a recent paper by Mukherjee et al. [Mukherjee D, Dhara S, Borst SC, Van Leeuwaarden JSH (2017) Opti...
Symplectic integrators with adaptive time steps

In recent decades, there have been many attempts to construct symplectic integrators with variable time steps, with rather disappointing results. In this paper, we identify the causes for this lack of performance, and find that they fall into two categories. In the first, the time step is considered a function of time alone, ? = ?(t). In this case, backward error analysis shows that while the algorithms remain symplectic, parametric instabilities may arise because of resonance between oscillations of ?(t) and the orbital motion. In the second category the time step is a function of phase space variables ? = ?(q, p). In this case, the system of equations to be solved is analyzed by introducing a new time variable ? with dt = ?(q, p)?d?. The transformed equations are no longer in Hamiltonian form, and thus do not benefit from integration methods which would be symplectic for Hamiltonian systems. We analyze two methods for integrating the transformed equations which do, however, preserve the structure of the original equations. The first is an extended phase space method, which has been successfully used in previous studies of adaptive time step symplectic integrators. The second, novel, method is based on a non-canonical mixed-variable generating function. Numerical trials for both of these methods show good results, without parametric instabilities or spurious growth or damping. It is then shown how to adapt the time step to an error estimate found by backward error analysis, in order to optimize the time-stepping scheme. Numerical results are obtained using this formulation and compared with other time-stepping schemes for the extended phase space symplectic method.
Optical Tomography on Graphs

We present an algorithm for solving inverse problems on graphs analogous to those arising in diffuse optical tomography for continuous media. In particular, we formulate and analyze a discrete version of the inverse Born series, proving estimates characterizing the domain of convergence, approximation errors, and stability of our approach. We also present a modification which allows additional information on the structure of the potential to be incorporated, facilitating recovery for a broader class of problems.
Applications of Stein's method for concentration inequalities

Stein’s method for concentration inequalities was introduced to prove concentration of measure in problems involving complex dependencies such as random permutations and Gibbs measures. In this paper, we provide some extensions of the theory and three applications: (1) We obtain a concentration inequality for the magnetization in the Curie–Weiss model at critical temperature (where it obeys a nonstandard normalization and super-Gaussian concentration). (2) We derive exact large deviation asymptotics for the number of triangles in the Erdős–Renyi random graph G(n, p) when p ≥ 0.31. Similar results are derived also for general subgraph counts. (3) We obtain some interesting concentration inequalities for the Ising model on lattices that hold at all temperatures.
LATTICES OF QUASI-EQUATIONAL THEORIES AS CONGRUENCE LATTICES OF SEMILATTICES WITH OPERATORS: PART I

We show that for every quasivariety &#x1d4a6; of structures (where both functions and relations are allowed) there is a semilattice S with operators such that the lattice of quasi-equational theories of &#x1d4a6; (the dual of the lattice of sub-quasivarieties of &#x1d4a6;) is isomorphic to Con(S, +, 0, &#x1d4a1;. As a consequence, new restrictions on the natural quasi-interior operator on lattices of quasi-equational theories are found.
Quantum annealing for combinatorial clustering

Clustering is a powerful machine learning technique that groups “similar” data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver “qbsolv.” The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.
Novikov-symplectic cohomology and exact Lagrangian embeddings

Let N be a closed manifold satisfying a mild homotopy assumption. Then for any exact Lagrangian L T N the map 2.L/! 2.N/ has finite index. The homotopy assumption is either that N is simply connected, or more generally that m.N/ is finitely generated for each m 2. The manifolds need not be orientable, and we make no assumption on the Maslov class of L. We construct the Novikov homology theory for symplectic cohomology, denoted SH .MIE /, and we show that Viterbo functoriality holds. We prove that the symplectic cohomology SH .T NIE / is isomorphic to the Novikov homology of the free loopspace. Given the homotopy assumption on N , we show that this Novikov homology vanishes when 2 H 1 .L0N/ is the transgression of a nonzero class in H 2 . z N/. Combining these results yields the above obstructions to the existence of L. 57R17; 57R58
The SUPERWIDE Catalog: A Catalog of 99,203 Wide Binaries Found in Gaia and Supplemented by the SUPERBLINK High Proper Motion Catalog

We present a catalog of 99,203 wide binary systems, initially identified as common proper motion (CPM) pairs from a subset of ~5.2 million stars with proper motions $\mu > 40$ mas/year, selected from Gaia data release 2 (DR2) and the SUPERBLINK high proper motion catalog. CPM pairs are found by searching for pairs of stars with angular separations $ 95\%$ of being real binaries. Of those 99,203 high probability pairs, we estimate that only about 364 pairs are most likely to be false positives. In addition, we identify 57,506 pairs which have probabilities $>10\%$ from the first pass but have high parallax errors and therefore were not vetted in the second pass. We examine the projected physical separation distribution of our highest probability pairs and note that the distribution is a simple exponential tail and shows no evidence of being bi-modal. Among pairs with lower probability, wide binaries are detected at larger separations ($>10^{4-5}$ AU) consistent with the very wide population suggested in previous studies, however our analysis suggests that these do not represent a distinct population. We examine the HR diagram of this set of high probability wide binaries and find evidence for 980 over-luminous components among 2,227 K+K wide binaries; assuming these represent unresolved sub-systems, we determine that the higher order multiplicity fraction for K+K wide systems is at least 39.6\%.
Ion acceleration in non-relativistic astrophysical shocks

We explore the physics of shock evolution and particle acceleration in non-relativistic collisionless shocks using hybrid simulations. We analyze a wide range of physical parameters relevant to the acceleration of cosmic rays (CRs) in astrophysical shock scenarios. We show that there are fundamental differences between high and low Mach number shocks in terms of the electromagnetic turbulence generated in the pre-shock zone; dominant modes are resonant with the streaming CRs in the low Mach number regime, while both resonant and non-resonant modes are present for high Mach numbers. Energetic power-law tails for ions in the downstream plasma account for up to 15% of the incoming upstream flow energy, distributed over ~5% of the particles in a power law with slope ?2 ? 0.2 in energy. Quasi-parallel shocks with ? ? 45? are good ion accelerators, while power laws are greatly suppressed for quasi-perpendicular shocks, ? > 45?. The efficiency of conversion of flow energy into the energy of accelerated particles peaks at ? = 15?-30? and MA = 6, and decreases for higher Mach numbers, down to ~2% for MA = 31. Accelerated particles are produced by diffusive shock acceleration (DSA) and by shock drift acceleration (SDA) mechanisms, with the SDA contribution to the overall energy gain increasing with magnetic inclination. We also present a direct comparison between hybrid and fully kinetic particle-in-cell results at early times. In supernova remnant (SNR) shocks, particle acceleration will be significant for low Mach number quasi-parallel flows (MA < 30, ? < 45). This finding underscores the need for an effective magnetic amplification mechanism in SNR shocks.
Multistep model for predicting upper-limb 3D isometric force application from pre-movement electrocorticographic features

Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use a three-stage model of jPCA reduced-rank hidden Markov model (jPCA-RR-HMM), regularized shrunken-centroid discriminant analysis (RDA), and LASSO regression to extract direction-sensitive planning information and movement onset in an upper-limb 3D isometric force task in a human subject. This mode achieves a relatively high true positive force-onset prediction rate of 60% within 250ms, and an above-chance 36% accuracy (17% chance) in predicting one of six planned 3D directions of isometric force using pre-movement signals. We also find direction-distinguishing information up to 400ms before force onset in the pre-movement signals, captured by electrodes placed over the limb-ipsilateral dorsal premotor regions. This approach can contribute to more accurate decoding of higher-level movement goals, at earlier timescales, and inform sensor placement. Our results also contribute to further understanding of the spatiotemporal features of human motor planning.
Excursions beyond the horizon: black hole singularities in Yang-Mills theories (I)

We study black hole singularities in the AdS/CFT correspondence. These singularities show up in CFT in the behavior of finite-temperature correlation functions. We first establish a direct relation between space-like geodesics in the bulk and momentum space Wightman functions of CFT operators of large dimensions. This allows us to probe the regions inside the horizon and near the singularity using the CFT. Information about the black hole singularity is encoded in the exponential falloff of finite-temperature correlators at large imaginary frequency. We construct new gauge invariant observables whose divergences reflect the presence of the singularity. We also find a UV/UV connection that governs physics inside the horizon. Additionally, we comment on the possible resolution of the singularity.
Decoherence and measurement in open quantum systems

We review results of a recently developed model of a microscopic quantum system interacting with the macroscopic world components which are modeled by collections of bosonic modes. The interaction is via a general operator (Lambda) of the system, coupled to the creation and annihilation operators of the environment modes. We assume that in the process of a nearly instantaneous quantum measurement, the function of the environment involves two distinct parts: the pointer and the bath. Interaction of the system with the bath leads to decoherence such that the system and the pointer both evolve into a statistical mixture state described by the density matrix such that the system is in one of the eigenstates of (Lambda) with the correct quantum mechanical probability, whereas the expectation values of pointer operators retain amplified information on that eigenstate. We argue that this process represents the initial step of a quantum measurement.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Testing electroweak baryogenesis with future colliders

Electroweak Baryogenesis (EWBG) is a compelling scenario for explaining the matter-antimatter asymmetry in the universe. Its connection to the electroweak phase transition makes it inherently testable. However, completely excluding this scenario can seem difficult in practice, due to the sheer number of proposed models. We investigate the possibility of postulating a “no-lose” theorem for testing EWBG in future e + e − or hadron colliders. As a first step we focus on a factorized picture of EWBG which separates the sources of a stronger phase transition from those that provide new sources of CP violation. We then construct a “nightmare scenario” that generates a strong first-order phase transition as required by EWBG, but is very difficult to test experimentally. We show that a 100 TeV hadron collider is both necessary and possibly sufficient for testing the parameter space of the nightmare scenario that is consistent with EWBG.
Detecting Medical Misinformation on Social Media Using Multimodal Deep Learning

In 2019, outbreaks of vaccine-preventable diseases reached the highest number in the US since 1992. Medical misinformation, such as antivaccine content propagating through social media, is associated with increases in vaccine delay and refusal. Our overall goal is to develop an automatic detector for antivaccine messages to counteract the negative impact that antivaccine messages have on the public health. Very few extant detection systems have considered multimodality of social media posts (images, texts, and hashtags), and instead focus on textual components, despite the rapid growth of photo-sharing applications (e.g., Instagram). As a result, existing systems are not sufficient for detecting antivaccine messages with heavy visual components (e.g., images) posted on these newer platforms. To solve this problem, we propose a deep learning network that leverages both visual and textual information. A new semantic- and task-level attention mechanism was created to help our model to focus on the essential contents of a post that signal antivaccine messages. The proposed model, which consists of three branches, can generate comprehensive fused features for predictions. Moreover, an ensemble method is proposed to further improve the final prediction accuracy. To evaluate the proposed model's performance, a real-world social media dataset that consists of more than 30,000 samples was collected from Instagram between January 2016 and October 2019. Our 30 experiment results demonstrate that the final network achieves above 97% testing accuracy and outperforms other relevant models, demonstrating that it can detect a large amount of antivaccine messages posted daily. The implementation code is available at https://github.com/wzhings/antivaccine_detection .
Scattering resonances on truncated cones

We consider the problem of finding the resonances of the Laplacian on truncated Riemannian cones. In a similar fashion to Cheeger--Taylor, we construct the resolvent and scattering matrix for the Laplacian on cones and truncated cones. Following Stefanov, we show that the resonances on the truncated cone are distributed asymptotically as Ar^n + o(r^n), where A is an explicit coefficient. We also conclude that the Laplacian on a non-truncated cone has no resonances away from zero.
DISCOVERY OF THE VERY RED NEAR-INFRARED AND OPTICAL AFTERGLOW OF THE SHORT-DURATION GRB 070724A

We report the discovery of the near-infrared and optical afterglow of the short-duration gamma-ray burst GRB 070724A. The afterglow is detected in iJHK{sub s} observations starting 2.3 hr after the burst with K{sub s} = 19.59 +- 0.16 mag and i = 23.79 +- 0.07 mag, but is absent in images obtained 1.3 yr later. Fading is also detected in the K{sub s} band between 2.8 and 3.7 hr at a 4sigma significance level. The optical/near-IR spectral index, beta{sub O,NIR} approx -2, is much redder than expected in the standard afterglow model, pointing to either significant dust extinction, A {sup host} {sub V} approx 2 mag, or a non-afterglow origin for the near-IR emission. The case for extinction is supported by a shallow optical to X-ray spectral index, consistent with the definition for 'dark bursts', and a normal near-IR to X-ray spectral index. Moreover, a comparison to the optical discovery magnitudes of all short GRBs with optical afterglows indicates that the near-IR counterpart of GRB 070724A is one of the brightest to date, while its observed optical emission is one of the faintest. In the context of a non-afterglow origin, the near-IR emission may be dominated by a mini-supernova (mini-SN),more » leading to an estimated ejected mass of M approx 10{sup -4} M {sub sun} and a radioactive energy release efficiency of f approx 5 x 10{sup -3} (for v approx 0.3c). However, the mini-SN model predicts a spectral peak in the UV rather than near-IR, suggesting that this is either not the correct interpretation or that the mini-SN models need to be revised. Finally, the afterglow coincides with a star-forming galaxy at z = 0.457, previously identified as the host based on its coincidence with the X-ray afterglow position (approx2'' radius). Our discovery of the optical/near-IR afterglow makes this association secure, and furthermore localizes the burst to the outskirts of the galaxy, with an offset of 4.8 +- 0.1 kpc relative to the host center. At such a large offset, the possible large extinction points to a dusty environment local to the burst and rules out a halo or intergalactic origin.« less
Fused kernel-spline smoothing for repeatedly measured outcomes in a generalized partially linear model with functional single index

We propose a generalized partially linear functional single index risk score model for repeatedly measured outcomes where the index itself is a function of time. We fuse the nonparametric kernel method and regression spline method, and modify the generalized estimating equation to facilitate estimation and inference. We use local smoothing kernel to estimate the unspecified coefficient functions of time, and use B-splines to estimate the unspecified function of the single index component. The covariance structure is taken into account via a working model, which provides valid estimation and inference procedure whether or not it captures the true covariance. The estimation method is applicable to both continuous and discrete outcomes. We derive large sample properties of the estimation procedure and show different convergence rate of each component of the model. The asymptotic properties when the kernel and regression spline methods are combined in a nested fashion has not been studied prior to this work even in the independent data case.
Supporting High-Performance and High-Throughput Computing for Experimental Science

The advent of experimental science facilities—instruments and observatories, such as the Large Hadron Collider, the Laser Interferometer Gravitational Wave Observatory, and the upcoming Large Synoptic Survey Telescope —has brought about challenging, large-scale computational and data processing requirements. Traditionally, the computing infrastructure to support these facility’s requirements were organized into separate infrastructure that supported their high-throughput needs and those that supported their high-performance computing needs. We argue that to enable and accelerate scientific discovery at the scale and sophistication that is now needed, this separation between high-performance computing and high-throughput computing must be bridged and an integrated, unified infrastructure provided. In this paper, we discuss several case studies where such infrastructure has been implemented. These case studies span different science domains, software systems, and application requirements as well as levels of sustainability. A further aim of this paper is to provide a basis to determine the common characteristics and requirements of such infrastructure, as well as to begin a discussion of how best to support the computing requirements of existing and future experimental science facilities.